{"gname":"Oregon Health & Science University","grp_id":"22","rels":[{"rel_title":"SNAC-DB: An ML-Ready Database for Antibody and NANOBODY(R) VHH-Antigen Complexes with Expanded Structural Diversity and Real-World Benchmarking","rel_doi":"10.64898\/2026.04.22.720253","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720253","rel_abs":"Predicting antibody and NANOBODY(R) VHH-antigen complexes remains a critical challenge for state-of-the-art structure prediction models, limiting their impact in therapeutic discovery pipelines. We introduce SNAC-DB, an ML-ready database and curation pipeline enriched with structural biology expertise, designed to accelerate model accuracy and generalization by providing 31-37% expanded structural diversity over existing resources like SAbDab through comprehensive re-curation that extracts maximum value from available experimental structures. SNAC-DB expands coverage by capturing often-overlooked complexes and accurately identifying complete multi-chain epitopes through improved biological-assembly-based logic. Built for ML practitioners, SNAC-DB provides standardized formats with multi-threshold structure-based clustering to enable principled sample weighting during training. Using a rigorous benchmark of public PDB entries deposited post-May 2024 plus confidential therapeutic structures, we evaluate seven leading models (Protenix-v1, OpenFold-3p2, RosettaFold-3, Boltz-2, Boltz-1x, Chai-1, and AlphaFold2.3-multimer) with evaluation methodology tailored to antibody\/NANOBODY(R) VHH-antigen complexes to ensure correct handling of multi-chain epitopes, revealing systematic performance gaps: success rates rarely exceed 25%, confidence-based ranking fails to identify best predictions even when accurate structures exist in ensembles, and all models consistently struggle with therapeutically relevant NANOBODY(R) VHHs. Systematic evaluation of sampling strategies demonstrates that while generating 1000 samples per target substantially increases the likelihood of producing accurate structures (oracle selection improves from 11.9% to 50.5%), confidence-based ranking remains nearly flat (between 10.9% and 14.9%), revealing that improved ranking mechanisms represent a more tractable path to performance gains. Finally, fine-tuning GeoDock on SNAC-DB yields higher success rates than training on SAbDab (11.0% vs. 7.1% for antibodies; 7.0% vs. 4.0% for NANOBODY(R) VHHs), suggesting that SNAC-DB's expanded structural diversity translates to improved model generalization.","rel_num_authors":8,"rel_authors":[{"author_name":"Abhinav Gupta","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Bryan Munoz Rivero","author_inst":"Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"author_name":"Ruijiang Li","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Jorge Roel-Touris","author_inst":"Large Molecule Research, Sanofi, Barcelona, Spain"},{"author_name":"Yves Fomekong Nanfack","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Maria Wendt","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Yu Qiu","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Norbert Furtmann","author_inst":"Large Molecule Research, Sanofi, Frankfurt, Germany"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"A Self-Sustaining Mechanism for Endothelial Tension Maintenance Through GqGPCR Signaling","rel_doi":"10.64898\/2026.04.22.720219","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720219","rel_abs":"The vascular endothelium maintains homeostasis by acting as a selective barrier, permitting the exchange of nutrients, immune cells, and signaling molecules while restricting pathogens. It further regulates vascular function by generating and sustaining mechanical tension. Aging and disease alter the vascular environment and disrupt the regulation of endothelial tension, contributing to vascular diseases such as hypertension and atherosclerosis. Although endothelial mechanics are influenced by the cellular environment, the mechanisms that enable endothelial cells (ECs) to maintain tension over time remain poorly understood. Here, we demonstrate that confluent human umbilical vein endothelial cells (HUVECs) sustain stable tension for at least three days in the absence of external chemical or mechanical stimuli, indicating the presence of an intrinsic, active mechanism for long-term tension maintenance. Imaging of an EC multicellular ensemble shows a collective phenomenon where diacylglycerol release consistently precedes a rise in intracellular contractility. This contractility propagates to neighboring cells, wherein we identify a Gq-G-protein-coupled receptor (GqGPCR) signaling pathway as a key regulator driving force generation in ECs. The persistence of this signaling sequence in the absence of exogenous agonists suggests a 'force-induced-force-generation' mechanism that coordinates tension maintenance across the monolayer. Together, these findings demonstrate that ECs actively regulate tension through continuous GqGPCR signaling, revealing tension maintenance as a dynamic, collective process. This work provides new insight into how vascular tissues preserve mechanical homeostasis and suggests potential therapeutic targets for vascular endothelial dysfunction and age-related vascular stiffening.","rel_num_authors":3,"rel_authors":[{"author_name":"Benjamin M Goykadosh","author_inst":"Northeastern University"},{"author_name":"Vasuretha Chander","author_inst":"Northeastern University"},{"author_name":"Hari M Parameswaran","author_inst":"Northeastern University"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Dysregulated tissue-resident lymphocytes drive age-associated emphysema by impairing alveolar regeneration","rel_doi":"10.64898\/2026.04.22.720146","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720146","rel_abs":"Aging is often associated with progressive tissue degeneration and chronic inflammation, yet the role of immune cells in mediating structural and functional decline in organs remains poorly defined. Here, we investigated immune-tissue interactions in the aged lung and identified emphysematous remodeling characterized by alveolar loss. Notably, aged lungs exhibited a marked expansion of tissue-resident lymphocytes (TRLs) with senescent features, accompanied by a significant reduction in alveolar stem\/progenitor cell (AT2) abundance. In vivo adoptive T cell transfer and 3D immune-stem cell organoid assays revealed that these expanded TRLs suppressed AT2 growth via secretion of oncostatin M and interferon gamma. In vivo blockade of IL-7 receptor (IL-7R) reduced TRL accumulation in the lungs and ameliorated age-related emphysematous changes, including restoration of alveolar density. Our findings identify TRLs as key drivers of alveolar degeneration in aging and propose IL-7R inhibition as a therapeutic strategy to mitigate pulmonary decline.","rel_num_authors":14,"rel_authors":[{"author_name":"Yanjing Su","author_inst":"Nanjing University; Zhongshan Institute for Drug Discovery"},{"author_name":"Xinguo Yang","author_inst":"Zhongshan Institute for Drug Discovery"},{"author_name":"Ziyi Ren","author_inst":"Nanjing University of Chinese Medicine"},{"author_name":"Yuxuan Guan","author_inst":"Nanjing University of Chinese Medicine"},{"author_name":"Xuelu Zhou","author_inst":"Southern Medical University"},{"author_name":"Shuilian Chi","author_inst":"Nanjing University of Chinese Medicine"},{"author_name":"Yanjun Huang","author_inst":"Guangzhou College of Applied Science and Technology"},{"author_name":"Tao Yan","author_inst":"The Second Affiliated Hospital Zhejiang University School of Medicine"},{"author_name":"Jialong Liang","author_inst":"The Affiliated Wuxi People's Hospital of Nanjing Medical University"},{"author_name":"Fei Gao","author_inst":"The Affiliated Wuxi People's Hospital of Nanjing Medical University"},{"author_name":"Dijun Chen","author_inst":"Nanjing University"},{"author_name":"Jingyu Chen","author_inst":"The Second Affiliated Hospital Zhejiang University School of Medicine; The Affiliated Wuxi People's Hospital of Nanjing Medical University"},{"author_name":"Zimu Deng","author_inst":"Zhongshan Institute for Drug Discovery; Southern Medical University; University of Chinese Academy of Sciences"},{"author_name":"Chaoqun Wang","author_inst":"Chinese Academy of Sciences"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Environmental Enrichment Remodels Brain Structural and Behavioral Plasticity in Restricted and Repetitive C58 Mouse Models","rel_doi":"10.64898\/2026.04.22.720233","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720233","rel_abs":"Restricted and repetitive behaviors are characteristic of several neurodevelopmental disorders. While environmental enrichment has been shown to affect these behaviors, the underlying neural mechanisms remain poorly understood. In this study, we systematically explored the effects of environmental enrichment on brain structure and microstructure in C58 mice, a model of restricted and repetitive behaviors, compared to C57 control mice. Using structural magnetic resonance imaging and diffusion-weighted imaging, we assessed regional brain volumes and microstructural properties and examined their association with behavioral outcomes. Our results revealed significant reductions in total brain volume in C58 mice, with region-specific volumetric changes following environmental enrichment exposure. Importantly, environmental enrichment promoted microstructural plasticity in both strains, with significant alterations in fractional anisotropy and fiber density. These neuroanatomical changes were linked to reductions in restricted and repetitive behaviors, with strain- and sex-dependent effects. Overall, our findings suggest that environmental enrichment remodels brain plasticity at both structural and microstructural levels, as well as behavior, providing insights into potential therapeutic approaches through environmental enrichment for neurodevelopmental disorders.","rel_num_authors":4,"rel_authors":[{"author_name":"Qiang Li","author_inst":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)"},{"author_name":"Anna L. Farmer","author_inst":"Department of Psychology, University of Florida"},{"author_name":"Pearlson D. Godfrey","author_inst":"Yale University"},{"author_name":"Vince D. Calhoun","author_inst":"Georgia State, Georgia Tech, and Emory"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"C. elegans models of Alternating Hemiplegia of Childhood have dominant neuromuscular junction defects","rel_doi":"10.64898\/2026.04.22.720250","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720250","rel_abs":"Dominant missense mutations in ATP1A3, encoding a Na+, K+ ATPase alpha-3 subunit, can cause Alternating Hemiplegia of Childhood (AHC), but how these mutations lead to AHC remains unclear. Here, we establish the first C. elegans AHC models by introducing AHC-causing ATP1A3 patient mutations (D801N, E815K, L839P, and G947R) into the orthologous gene, eat-6, using CRISPR\/Cas9. Homozygous C. elegans AHC model animals have recessive developmental defects. Heterozygous AHC model animals have dominant defects in neuromuscular junction (NMJ) function that are inconsistent with haploinsufficiency and dominant sleep or arousal defects. Previous work in a Drosophila G755S AHC model found that loss of a K+-dependent, Na+\/Ca2+ exchanger exacerbated neuronal defects. We introduced a loss-of-function allele of the orthologous C. elegans gene, ncx-4, into C. elegans AHC models; loss of ncx-4 function did not consistently alter C. elegans AHC model defects across alleles. Our results establish novel C. elegans models of AHC with robust phenotypes, demonstrate that AHC mutations disrupt NMJ function, and provide proof-of-concept for discovering cross-species modifiers of AHC-related phenotypes.","rel_num_authors":6,"rel_authors":[{"author_name":"Diana Wall","author_inst":"Brown University"},{"author_name":"Adam Friedberg","author_inst":"Brown University"},{"author_name":"Jeremy Lins","author_inst":"Brown University"},{"author_name":"Roza Khalifa","author_inst":"Brown University"},{"author_name":"Sienna Partipilo","author_inst":"Brown University"},{"author_name":"Anne C. Hart","author_inst":"Brown University"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Origins of reactivity in SAM-utilizing ribozyme SAMURI-catalyzed RNA alkylation","rel_doi":"10.64898\/2026.04.24.720726","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720726","rel_abs":"Unlocking the design principles of programmable RNA catalysts capable of site-specific chemical modification is critical for expanding the functional and therapeutic potential of RNA. The SAM analogue-utilizing ribozyme (SAMURI) enables site-specific RNA alkylation using either S-adenosylmethionine (SAM) or the synthetic cofactor propargylic Se-2,6-diaminopurinribosyl-selenomethionineamide (ProSeDMA), yet the molecular determinants of its reactivity remain incompletely understood. Here, we combined molecular dynamics, 3D-RISM solvation analysis, alchemical free-energy calculations, quantum pKa shift predictions, and ab initio QM\/MM free-energy simulations to characterize the conformational and electronic factors that govern catalysis. Simulations show that, although the global fold of SAMURI remains stable in solution, the formation of catalytically competent near-attack configurations is rare, indicating that the observed rate depends on access to a minor fraction of these reactive conformations (freact). A putative Mg2+ binding site between the SAM carboxylate and the G30 phosphate, together with a hydrogen bond between the cofactor -amine and U8:O2, enriches freact. QM\/MM simulations support an SN2-like alkyl transfer mechanism and show that ProSeDMA reacts more readily than SAM primarily due to its more favorable electronic leaving-group properties that enhance the intrinsic rate (kint). Atomic substitutions at A52 that tune the N3 pKa enhance nucleophilicity, further lower the activation barrier, and increase kint. Together, these results show that SAMURI catalysis is governed by a combination of conformational preorganization and electronic effects, providing a framework to guide the design of new programmable RNA alkyltransferases.","rel_num_authors":5,"rel_authors":[{"author_name":"Julie Puyo-Fourtine","author_inst":"Rutgers University"},{"author_name":"Yanan Du","author_inst":"Rutgers University"},{"author_name":"Erika McCarthy","author_inst":"Rutgers University"},{"author_name":"\u015e\u00f6len Ekesan","author_inst":"Rutgers University"},{"author_name":"Darrin M York","author_inst":"Rutgers University"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Adding iPSC donor lines does not adequately control for genetic heterogeneity","rel_doi":"10.64898\/2026.04.22.720258","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720258","rel_abs":"Human induced pluripotent stem cell (iPSC)-based disease modelling studies are widely expected to include three to five independent donor lines to control for the contribution of donor genetic background to phenotypic variance. This convention has been formalized into major guidelines, yet no power analysis has evaluated whether these sample sizes can detect, estimate, or control for donor-level genetic effects. Here, we provide that evaluation. Using Monte Carlo simulation, closed-form confidence intervals, population genetics, and empirical resampling of transcriptomic data from iPSC lines, we show that studies with three to five donors cannot reliably detect donor-level variance, cannot estimate its magnitude with useful precision, and cannot determine whether a treatment effect generalizes across genetic backgrounds. The sample sizes required to reliably detect, estimate, or control for donor-level variance exceed 20 donors and, for many phenotypes, exceed 50, well beyond what any standard disease modelling experiment can deliver. Adding two or three donor lines to a study does not meaningfully increase statistical power, narrow confidence intervals, or establish whether a treatment effect generalizes across genetic backgrounds. The inability to control for genetic background is not a limitation of individual study design but a structural property of iPSC-based modelling. We propose that the field adopt isogenic controls for variant-specific questions and orthogonal validation against clinical datasets for generalizability, rather than treating donor number as a proxy for rigour.","rel_num_authors":3,"rel_authors":[{"author_name":"Artur Shvetcov","author_inst":"Westmead Institute for Medical Research"},{"author_name":"Shannon Thomson","author_inst":"Westmead Institute for Medical Research"},{"author_name":"Caitlin A Finney","author_inst":"Westmead Institute for Medical Research"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"De novo design of a macrocycle induced dimerization system for cellular control","rel_doi":"10.64898\/2026.04.24.720480","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720480","rel_abs":"Investigating and manipulating cellular events requires precise control of protein function. To enable control over cellular processes, we set out to design a chemically induced dimerization (CID) system consisting of a de novo designed ligand and protein pair. Here we describe the design of a C2 symmetric membrane permeable macrocyclic peptide and a cognate protein homodimer which binds the macrocycle through a large interface with both chains. The designed homodimer binds the macrocycle with a KD of 36 nM, and the x-ray crystal structure of the protein homodimer-macrocycle complex is very close to the computational design model, with the C2 axis of the macrocycle aligned with the homodimer C2 axis. Transcriptional and split luciferase assays in mammalian cells demonstrates conditional control over both a reporter gene expression and luciferase reconstitution.","rel_num_authors":15,"rel_authors":[{"author_name":"David Baker","author_inst":"University of Washington"},{"author_name":"Stephanie Hanna","author_inst":"University of Washington"},{"author_name":"Patrick Salveson","author_inst":"University of Washington"},{"author_name":"Basile Wicky","author_inst":"University of Washington"},{"author_name":"Madison Kennedy","author_inst":"University of Washington"},{"author_name":"Derrick Hicks","author_inst":"University of Washington"},{"author_name":"Carolina Moller","author_inst":"University of Washington"},{"author_name":"Suna Cheng","author_inst":"University of Washington"},{"author_name":"Xinting Li","author_inst":"University of Washington"},{"author_name":"Mohamad Abedi","author_inst":"University of Washington"},{"author_name":"Brian Coventry","author_inst":"University of Washington"},{"author_name":"Meerit Said","author_inst":"University of Washington"},{"author_name":"Asim K. Bera","author_inst":"University of Washington"},{"author_name":"Alex Kang","author_inst":"University of Washington"},{"author_name":"Barry L Stoddard","author_inst":"Fred Hutchinson Cancer Research Center"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Opposing BOLD signals and oxygen metabolism largely arise from statistical uncertainty in metabolic estimates","rel_doi":"10.64898\/2026.04.21.719913","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719913","rel_abs":"Recent work by Epp et al. (2025) reported widespread voxel-wise sign discordance between task-evoked blood-oxygenation-level-dependent (BOLD) responses and estimated changes in cerebral metabolic rate of oxygen ({Delta}CMRO2), raising important questions about the interpretability of BOLD functional magnetic resonance imaging. Reanalysing the dataset, we found that {Delta}CMRO2 estimates showed substantial voxel-wise variability across participants, consistent with the noise sensitivity of model-based metabolic estimates. When this variability was taken into account, 77.2% of voxels could not be robustly classified, as {Delta}CMRO2 effects lacked sufficient statistical support to determine concordance or discordance. Where classification was possible, positive BOLD responses were predominantly concordant with metabolism, whereas discordance was considerably higher for negative BOLD responses. These findings suggest that the observed BOLD-metabolism discordance reported previously largely reflects statistical uncertainty in CMRO2 estimates rather than widespread physiological sign reversal.","rel_num_authors":3,"rel_authors":[{"author_name":"Ole Goltermann","author_inst":"University Medical Center Hamburg-Eppendorf"},{"author_name":"Alex R Huth","author_inst":"University of California, Berkeley"},{"author_name":"Christian B\u00fcchel","author_inst":"University Medical Center Hamburg-Eppendorf"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Integrative Clinical-Molecular Modeling Identifies LRRN4CL as a Determinant of Structural and Functional Myocardial Improvement","rel_doi":"10.64898\/2026.04.21.720029","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720029","rel_abs":"Background: Mechanical ventricular unloading and systemic circulatory support with left ventricular assist devices (LVADs) enable myocardial recovery in a subset of advanced heart failure (HF) patients, but predictors and mechanisms of recovery are not well understood. Integrating clinical and molecular data may improve identification of patients most likely to recover and uncover biologically relevant targets in HF. Methods: We collected and analyzed left ventricular apical myocardial tissue and clinical data from 208 patients undergoing LVAD implantation across five centers. Pre-implant transcriptomic profiles (22,373 mRNA transcripts) were integrated with 59 clinical variables using supervised machine learning with repeated cross-validation to identify and prioritize features associated with myocardial recovery, defined as a binary outcome based on improvement in left ventricular ejection fraction (LVEF [&ge;]40%) and left ventricular end-diastolic diameter (LVEDD [&le;]5.9 cm). We also modeled functional (LVEF) and structural (LVEDD) improvement as a continuous outcome without any predefined LVEF and LVEDD pathological thresholds. Feature prioritization was followed by validation in human myocardial tissue and mechanistic interrogation in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). Results: Integrative models achieved modest discrimination for myocardial recovery as a binary categorical outcome (maximum mean cross-validated area under the curve 0.73{+\/-}0.15), identifying clinical features such as HF duration, LVEDD, HF pharmacologic therapy, and device configuration. Leucine-rich repeat neuronal 4C-like (LRRN4CL), measured in human myocardium, consistently emerged as a top transcriptomic predictor across both binary and continuous metric models (functional and structural). Higher pre-LVAD LRRN4CL expression was associated with reduced likelihood of myocardial recovery and localized primarily to cardiomyocytes. In iPSC-CMs, LRRN4CL overexpression localized to the sarcoplasmic reticulum, induced transcriptional remodeling characterized by suppression of contractile pathways and activation of stress programs, impaired calcium handling, impaired contraction?relaxation kinetics, and diminished mitochondrial respiratory reserve capacity. Conclusions: Integration of clinical and myocardial transcriptomic data identifies LRRN4CL as a novel marker associated with impaired myocardial recovery following LVAD-mediated ventricular unloading and systemic circulatory support. These findings move beyond predictive modeling, linking integrative computational discovery to cardiomyocyte dysfunction and providing a translational framework for biologically informed risk stratification and therapeutic targeting for myocardial recovery.","rel_num_authors":36,"rel_authors":[{"author_name":"Ezra Johnson","author_inst":"The University of Utah"},{"author_name":"Joseph R Visker","author_inst":"University of Utah"},{"author_name":"Ben J Brintz","author_inst":"University of Utah"},{"author_name":"Christos P. Kyriakopoulos","author_inst":"The University of Utah School of Medicine"},{"author_name":"James Jeong","author_inst":"The University of Utah"},{"author_name":"Yuyu Zhang","author_inst":"The University of Utah"},{"author_name":"Thirupura S. Shankar","author_inst":"The University of Utah"},{"author_name":"Yanni Hillas","author_inst":"University of Utah"},{"author_name":"Iosif Taleb","author_inst":"University of California San Diego"},{"author_name":"Rachit Badolia","author_inst":"University of Utah, CVRTI"},{"author_name":"Junedh M Amrute","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Chris J Stubben","author_inst":"University of Utah"},{"author_name":"Luis Cedeno-Rosario","author_inst":"The University of Utah Department of Biochemistry"},{"author_name":"Ioannis Kyriakoulis","author_inst":"U of Utah"},{"author_name":"Konstantinos Sideris","author_inst":"University of Utah"},{"author_name":"Jing Ling","author_inst":"University of Utah"},{"author_name":"Rana Hamouche","author_inst":"university of utah"},{"author_name":"Eleni Tseliou","author_inst":"University of Utah Health"},{"author_name":"Sutip Navankasattusas","author_inst":"University of Utah"},{"author_name":"Gregory S. Ducker","author_inst":"University of Utah"},{"author_name":"Jared Rutter","author_inst":"University of Utah"},{"author_name":"William L Holland","author_inst":"University of Utah Health"},{"author_name":"Scott A Summers","author_inst":"The University of Utah"},{"author_name":"TingTing Hong","author_inst":"The University of Utah"},{"author_name":"Steven C Koenig","author_inst":"University of Louisville"},{"author_name":"Thomas C Hanff","author_inst":"University of Utah Health"},{"author_name":"Kory J. Lavine","author_inst":"Washington University School of Medicine"},{"author_name":"Tom Greene","author_inst":"University of Utah"},{"author_name":"Stephen Bailey","author_inst":"Allegheny General Hosptial"},{"author_name":"Rami Alharethi","author_inst":"Intermountain Medical Center"},{"author_name":"Craig H. Selzman","author_inst":"University of Utah"},{"author_name":"Palak Shah","author_inst":"Inova Schar Heart and Vascular"},{"author_name":"Hongchao Guo","author_inst":"University of Utah Health"},{"author_name":"Mark S. Slaughter","author_inst":"University of Louisville"},{"author_name":"Manreet K Kanwar","author_inst":"The University of Chicago"},{"author_name":"Stavros G Drakos","author_inst":"The University of Utah School of Medicine"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Integrative Clinical-Molecular Modeling Identifies LRRN4CL as a Determinant of Structural and Functional Myocardial Improvement","rel_doi":"10.64898\/2026.04.21.720029","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720029","rel_abs":"Background: Mechanical ventricular unloading and systemic circulatory support with left ventricular assist devices (LVADs) enable myocardial recovery in a subset of advanced heart failure (HF) patients, but predictors and mechanisms of recovery are not well understood. Integrating clinical and molecular data may improve identification of patients most likely to recover and uncover biologically relevant targets in HF. Methods: We collected and analyzed left ventricular apical myocardial tissue and clinical data from 208 patients undergoing LVAD implantation across five centers. Pre-implant transcriptomic profiles (22,373 mRNA transcripts) were integrated with 59 clinical variables using supervised machine learning with repeated cross-validation to identify and prioritize features associated with myocardial recovery, defined as a binary outcome based on improvement in left ventricular ejection fraction (LVEF [&ge;]40%) and left ventricular end-diastolic diameter (LVEDD [&le;]5.9 cm). We also modeled functional (LVEF) and structural (LVEDD) improvement as a continuous outcome without any predefined LVEF and LVEDD pathological thresholds. Feature prioritization was followed by validation in human myocardial tissue and mechanistic interrogation in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). Results: Integrative models achieved modest discrimination for myocardial recovery as a binary categorical outcome (maximum mean cross-validated area under the curve 0.73{+\/-}0.15), identifying clinical features such as HF duration, LVEDD, HF pharmacologic therapy, and device configuration. Leucine-rich repeat neuronal 4C-like (LRRN4CL), measured in human myocardium, consistently emerged as a top transcriptomic predictor across both binary and continuous metric models (functional and structural). Higher pre-LVAD LRRN4CL expression was associated with reduced likelihood of myocardial recovery and localized primarily to cardiomyocytes. In iPSC-CMs, LRRN4CL overexpression localized to the sarcoplasmic reticulum, induced transcriptional remodeling characterized by suppression of contractile pathways and activation of stress programs, impaired calcium handling, impaired contraction?relaxation kinetics, and diminished mitochondrial respiratory reserve capacity. Conclusions: Integration of clinical and myocardial transcriptomic data identifies LRRN4CL as a novel marker associated with impaired myocardial recovery following LVAD-mediated ventricular unloading and systemic circulatory support. These findings move beyond predictive modeling, linking integrative computational discovery to cardiomyocyte dysfunction and providing a translational framework for biologically informed risk stratification and therapeutic targeting for myocardial recovery.","rel_num_authors":36,"rel_authors":[{"author_name":"Ezra Johnson","author_inst":"The University of Utah"},{"author_name":"Joseph R Visker","author_inst":"University of Utah"},{"author_name":"Ben J Brintz","author_inst":"University of Utah"},{"author_name":"Christos P. Kyriakopoulos","author_inst":"The University of Utah School of Medicine"},{"author_name":"James Jeong","author_inst":"The University of Utah"},{"author_name":"Yuyu Zhang","author_inst":"The University of Utah"},{"author_name":"Thirupura S. Shankar","author_inst":"The University of Utah"},{"author_name":"Yanni Hillas","author_inst":"University of Utah"},{"author_name":"Iosif Taleb","author_inst":"University of California San Diego"},{"author_name":"Rachit Badolia","author_inst":"University of Utah, CVRTI"},{"author_name":"Junedh M Amrute","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Chris J Stubben","author_inst":"University of Utah"},{"author_name":"Luis Cedeno-Rosario","author_inst":"The University of Utah Department of Biochemistry"},{"author_name":"Ioannis Kyriakoulis","author_inst":"U of Utah"},{"author_name":"Konstantinos Sideris","author_inst":"University of Utah"},{"author_name":"Jing Ling","author_inst":"University of Utah"},{"author_name":"Rana Hamouche","author_inst":"university of utah"},{"author_name":"Eleni Tseliou","author_inst":"University of Utah Health"},{"author_name":"Sutip Navankasattusas","author_inst":"University of Utah"},{"author_name":"Gregory S. Ducker","author_inst":"University of Utah"},{"author_name":"Jared Rutter","author_inst":"University of Utah"},{"author_name":"William L Holland","author_inst":"University of Utah Health"},{"author_name":"Scott A Summers","author_inst":"The University of Utah"},{"author_name":"TingTing Hong","author_inst":"The University of Utah"},{"author_name":"Steven C Koenig","author_inst":"University of Louisville"},{"author_name":"Thomas C Hanff","author_inst":"University of Utah Health"},{"author_name":"Kory J. Lavine","author_inst":"Washington University School of Medicine"},{"author_name":"Tom Greene","author_inst":"University of Utah"},{"author_name":"Stephen Bailey","author_inst":"Allegheny General Hosptial"},{"author_name":"Rami Alharethi","author_inst":"Intermountain Medical Center"},{"author_name":"Craig H. Selzman","author_inst":"University of Utah"},{"author_name":"Palak Shah","author_inst":"Inova Schar Heart and Vascular"},{"author_name":"Hongchao Guo","author_inst":"University of Utah Health"},{"author_name":"Mark S. Slaughter","author_inst":"University of Louisville"},{"author_name":"Manreet K Kanwar","author_inst":"The University of Chicago"},{"author_name":"Stavros G Drakos","author_inst":"The University of Utah School of Medicine"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Tongue swab Xpert MTB\/RIF Ultra testing for tuberculosis in adolescents: a cross-sectional study of diagnostic accuracy and acceptability","rel_doi":"10.64898\/2026.04.17.26351119","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351119","rel_abs":"Introduction Improved diagnostics are needed for people at risk of tuberculosis, especially adolescents. Tongue swab (TS) molecular testing has emerged as a promising strategy for tuberculosis diagnosis. We evaluated diagnostic accuracy and acceptability of Xpert MTB\/RIF Ultra (Xpert) using TS samples for tuberculosis detection among adolescents. Methods We conducted a cross-sectional diagnostic accuracy study with consecutive recruitment in Vietnam. Adolescents aged 10-19 who were recommended to undergo investigation for tuberculosis and had not received tuberculosis treatment in the past years were eligible. Participants provided TS and sputum samples and completed a structured survey regarding sampling experiences. TS was tested on Xpert, with sputum tested on Xpert and liquid culture. We utilised a composite reference standard of a positive result on sputum Xpert or sputum culture to define disease status. Sensitivity, specificity, and diagnostic yield were calculated for TS Xpert. Results From July to December 2025, we enrolled 225 adolescents from Can Tho and An Giang provinces in southern Vietnam. Fewer than half (96\/225, 43%) the participants exhibited a tuberculosis -like symptom, and the majority (157\/225, 70%) were close contacts of a person recently diagnosed with tuberculosis. TS were collected from all adolescents, while 116 (52%) could provide mucopurulent sputum. Tuberculosis prevalence was relatively low (12\/225, 5.3%). TS Xpert sensitivity (90% CI) and specificity (90% CI) were 58.3% (35.6, 78.0) and 99.5% (97.9, 99.9), respectively. Diagnostic yield among all diagnosed was 58.3% (7\/12). TS sampling was highly acceptable to adolescents; the short time and simplicity of collecting TS were considered favourably. Conclusions The sensitivity and diagnostic yield of TS Xpert was relatively low among adolescents recommended for tuberculosis investigation, which includes asymptomatic individuals who may not provide high quality sputum. Specificity was excellent, and everyone could provide a TS. TS high acceptability indicates it remains a promising sample for diagnostic algorithms.","rel_num_authors":10,"rel_authors":[{"author_name":"Emily L MacLean","author_inst":"The University of Sydney"},{"author_name":"Thu Thuy Ma","author_inst":"The University of Sydney Vietnam Institute"},{"author_name":"Long Huynh Chuong","author_inst":"The University of Sydney Vietnam Institute"},{"author_name":"Khanh Huynh Minh","author_inst":"The University of Sydnet Vietnam Institute"},{"author_name":"Graeme Hoddinott","author_inst":"The University of Sydney"},{"author_name":"Yen Ngoc Pham","author_inst":"The University of Sydney Vietnam Institute"},{"author_name":"Hua Trung Tiep","author_inst":"Can Tho Lung Hospital"},{"author_name":"Thu-Anh Nguyen","author_inst":"The University of Sydney Vietnam Institute"},{"author_name":"Greg Fox","author_inst":"The University of Sydney"},{"author_name":"Ngoc Thuy Nguyen","author_inst":"Can Tho Lung Hospital"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"Effect mechanisms of different malaria chemoprevention regimens in pregnancy on infant growth outcomes: causal mediation analysis of a randomized controlled trial","rel_doi":"10.64898\/2026.04.17.26351121","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351121","rel_abs":"Introduction: Intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine (SP) has become less effective at preventing malaria due to rising parasite resistance. IPTp with dihydroartemisinin-piperaquine (DP) alone or in combination with SP (DP+SP) dramatically lowers the risk of malaria in pregnancy compared to SP but is associated with lower birthweight and early life wasting. We estimated the effect of IPTp-DP, DP+SP, and SP on infant growth outcomes and assessed possible treatment mechanisms through a causal mediation analysis. Methods: We used infant follow-up data (N=761) from a trial (NCT04336189) that randomized pregnant women to receive monthly IPTp-DP, SP, or DP+SP. We compared weight-for-length (WLZ) and length-for-age (LAZ) z-scores between treatment arms. We assessed possible mediation through pregnancy, birth, and infancy factors using interventional indirect effect models. Results: Compared to IPTp-SP, IPTp-DP+SP decreased mean WLZ by 0.18 [95% confidence interval (CI) -0.03, 0.39] between 1-3 months and 0.28 (95% CI 0.07, 0.49) between 4-6 months, with the largest differences among primigravidae. Lower risk of active placental malaria in IPTp-DP+SP helped reduce differences in mean WLZ vs IPTp-SP (+0.06, 95% CI 0.02, 0.10). The IPTp-DP+SP arm had up to 0.28 lower mean LAZ between 7-13 months compared to IPTp-DP, particularly among children who were wasted between 0-6 months; low birthweight had a persistent, mediating effect on linear growth. Conclusion: Adverse birth outcomes contributed to early growth faltering among children born to mothers receiving IPTp-DP+SP vs IPTp-SP, but the prevention of placental malaria partially counteracted the negative effects of IPTp-DP+SP on ponderal growth.","rel_num_authors":12,"rel_authors":[{"author_name":"Anna T Nguyen","author_inst":"Stanford University"},{"author_name":"Joaniter I Nankabirwa","author_inst":"Infectious Diseases Research Collaboration (IDRC); Makerere University College of Health Sciences"},{"author_name":"Abel Kakuru","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Michelle E Roh","author_inst":"Oregon Health & Science University; University of California San Francisco"},{"author_name":"Miriam Aguti","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Harriet Adrama","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Jimmy Kizza","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Peter Olwoch","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Moses R Kamya","author_inst":"Infectious Diseases Research Collaboration (IDRC); Makerere University College of Health Sciences"},{"author_name":"Grant Dorsey","author_inst":"University of California San Francisco"},{"author_name":"Prasanna Jagannathan","author_inst":"Stanford University"},{"author_name":"Jade Benjamin-Chung","author_inst":"Stanford University; Chan Zuckerberg Biohub"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"Effect mechanisms of different malaria chemoprevention regimens in pregnancy on infant growth outcomes: causal mediation analysis of a randomized controlled trial","rel_doi":"10.64898\/2026.04.17.26351121","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351121","rel_abs":"Introduction: Intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine (SP) has become less effective at preventing malaria due to rising parasite resistance. IPTp with dihydroartemisinin-piperaquine (DP) alone or in combination with SP (DP+SP) dramatically lowers the risk of malaria in pregnancy compared to SP but is associated with lower birthweight and early life wasting. We estimated the effect of IPTp-DP, DP+SP, and SP on infant growth outcomes and assessed possible treatment mechanisms through a causal mediation analysis. Methods: We used infant follow-up data (N=761) from a trial (NCT04336189) that randomized pregnant women to receive monthly IPTp-DP, SP, or DP+SP. We compared weight-for-length (WLZ) and length-for-age (LAZ) z-scores between treatment arms. We assessed possible mediation through pregnancy, birth, and infancy factors using interventional indirect effect models. Results: Compared to IPTp-SP, IPTp-DP+SP decreased mean WLZ by 0.18 [95% confidence interval (CI) -0.03, 0.39] between 1-3 months and 0.28 (95% CI 0.07, 0.49) between 4-6 months, with the largest differences among primigravidae. Lower risk of active placental malaria in IPTp-DP+SP helped reduce differences in mean WLZ vs IPTp-SP (+0.06, 95% CI 0.02, 0.10). The IPTp-DP+SP arm had up to 0.28 lower mean LAZ between 7-13 months compared to IPTp-DP, particularly among children who were wasted between 0-6 months; low birthweight had a persistent, mediating effect on linear growth. Conclusion: Adverse birth outcomes contributed to early growth faltering among children born to mothers receiving IPTp-DP+SP vs IPTp-SP, but the prevention of placental malaria partially counteracted the negative effects of IPTp-DP+SP on ponderal growth.","rel_num_authors":12,"rel_authors":[{"author_name":"Anna T Nguyen","author_inst":"Stanford University"},{"author_name":"Joaniter I Nankabirwa","author_inst":"Infectious Diseases Research Collaboration (IDRC); Makerere University College of Health Sciences"},{"author_name":"Abel Kakuru","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Michelle E Roh","author_inst":"Oregon Health & Science University; University of California San Francisco"},{"author_name":"Miriam Aguti","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Harriet Adrama","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Jimmy Kizza","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Peter Olwoch","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Moses R Kamya","author_inst":"Infectious Diseases Research Collaboration (IDRC); Makerere University College of Health Sciences"},{"author_name":"Grant Dorsey","author_inst":"University of California San Francisco"},{"author_name":"Prasanna Jagannathan","author_inst":"Stanford University"},{"author_name":"Jade Benjamin-Chung","author_inst":"Stanford University; Chan Zuckerberg Biohub"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"Patient preferences for portable versus table-mounted visual field devices in rural Alabama: a mixed methods study within a telemedicine setting","rel_doi":"10.64898\/2026.04.23.26351565","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351565","rel_abs":"Purpose To evaluate patient satisfaction and preferences for portable versus table-mounted visual field (VF) devices in a rural telemedicine setting and identify influencing factors. Methods We conducted a sequential explanatory mixed methods study at three Federally Qualified Health Centers (FQHCs) within the Alabama Screening and Intervention for Glaucoma and eye Health through Telemedicine (AL-SIGHT) study. Participants completed VF testing with table-mounted Humphrey Field Analyzer (HFA), tablet-based Melbourne Rapid Fields (MRF), and virtual reality (VR)-based VisuALL perimeters. Participants rated satisfaction, comfort, ease of use, and future testing preference. Chi-square tests assessed differences in device preferences. Twelve participants completed semi-structured interviews to explore reasons underlying preferences. Qualitative data were analyzed in NVivo 14 using reflexive thematic analysis. Results Among 271 respondents (mean age 60.4 years; 62.4% women), 50.6% preferred VR-based, 35.1% tablet-based, and 14.4% table-mounted for future testing ({chi}2 (2) = 53.52, p<0.001, Cramers V = 0.31). Satisfaction was highest for VR-based (56.9% very satisfied), followed by tablet-based (49.4%), and HFA (38.0%). VR-based perimeter was most frequently selected as the most comfortable (55.7%; {chi}2 (2) = 63.33, p<0.001, V = 0.34) and easiest to use (54.6%; {chi}2 (2) = 71.96, p<0.001, V = 0.36). Preferences did not vary significantly across demographic variables (all p>0.05). Qualitative themes identified four key drivers: comfort and physical experience, visual experience, ease of use and interaction, and psychological and motivational factors. Portability and community suitability were valued. Conclusion Rural underserved patients strongly preferred portable visual field devices, particularly VR-based, over table-mounted HFA. Comfort, ergonomic flexibility, immersive visual experience, and simplicity of interaction were central determinants of preference. Portable perimetry may enhance patient-centered glaucoma monitoring within telemedicine programs and access in resource-limited settings.","rel_num_authors":7,"rel_authors":[{"author_name":"Ellen  Konadu Antwi-Adjei","author_inst":"University of Alabama at Birmingham"},{"author_name":"Sourav Datta","author_inst":"University of Alabama at Birmingham"},{"author_name":"Christopher  A. Girkin","author_inst":"University of California San Diego"},{"author_name":"Cynthia Owsley","author_inst":"University of Alabama at Birmingham"},{"author_name":"Lindsay  A. Rhodes","author_inst":"University of Alabama at Birmingham"},{"author_name":"Matthew Fifolt","author_inst":"University of Alabama at Birmingham"},{"author_name":"Lyne Racette","author_inst":"University of Alabama at Birmingham"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"Multi-omic signatures of genetic mechanisms inform on type 2 diabetes biology and patient heterogeneity","rel_doi":"10.64898\/2026.04.17.26351136","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351136","rel_abs":"Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and beta cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for up to 45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.","rel_num_authors":22,"rel_authors":[{"author_name":"Magdalena Sevilla-Gonzalez","author_inst":"Massachusetts General Hospital"},{"author_name":"Alan Magno Martinez-Munoz","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Paul A. Hanson","author_inst":"Massachusetts General Hospital"},{"author_name":"Sarah Hsu","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Xingyang Wang","author_inst":"Harvard School of Public Health"},{"author_name":"Kirk Smith","author_inst":"Massachusetts General Hospital"},{"author_name":"Zsu-Zsu Chen","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Lukasz Szczerbinski","author_inst":"Massachusetts General Hospital"},{"author_name":"Varinderpal Kaur","author_inst":"Massachusetts General Hospital"},{"author_name":"Kent D. Taylor","author_inst":"The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center"},{"author_name":"Alexis C. Wood","author_inst":"Baylor College of Medicine"},{"author_name":"Michael Y. Mi","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Hui Li","author_inst":"Chalmers University of Technology"},{"author_name":"Clemens Wittenbecher","author_inst":"Chalmers University of Technology"},{"author_name":"Robert E. Gerszten","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Steve Rich","author_inst":"University of Virginia School of Medicine"},{"author_name":"Jerome Rotter","author_inst":"The Lundquist Institute"},{"author_name":"Jun Li","author_inst":"Brigham and Women's Hospital"},{"author_name":"Josep M Mercader","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Alisa K Manning","author_inst":"Massachusetts General Hospital"},{"author_name":"Ravi V K Shah","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Miriam Udler","author_inst":"Massachusetts General Hospital"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"A branching cell-fate decision in biofilm dispersal enables long-term surface persistence","rel_doi":"10.64898\/2026.04.24.720661","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720661","rel_abs":"Biofilms are the most ancient multicellular communities on Earth, representing a primitive developmental system that protects microbes from threats. Biofilm dispersal, whereby bacteria exit biofilms, is critical for the spread of pathogens to new infection sites. Here, using Vibrio cholerae, we show that dispersal events are accompanied by a branching cell-fate decision. While ~90% of cells disperse, a viable subpopulation remains within a residual matrix. This post-dispersal biofilm community (PDBC) is established by the matrix protein RbmA and adopts a specialized anabolic program that enhances tolerance to antibiotics and bacteriophages. Our findings reveal that PDBCs act as a resilient \"seed-bank\" capable of rapidly re-populating the niche without requiring de novo matrix biosynthesis, providing a mechanistic basis for the recurrence and spread of chronic infections.","rel_num_authors":3,"rel_authors":[{"author_name":"Sandhya Kasivisweswaran","author_inst":"Carnegie Mellon University"},{"author_name":"Jojo A Prentice","author_inst":"Yale University"},{"author_name":"Andrew  A. Bridges","author_inst":"Carnegie Mellon University"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"A branching cell-fate decision in biofilm dispersal enables long-term surface persistence","rel_doi":"10.64898\/2026.04.24.720661","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720661","rel_abs":"Biofilms are the most ancient multicellular communities on Earth, representing a primitive developmental system that protects microbes from threats. Biofilm dispersal, whereby bacteria exit biofilms, is critical for the spread of pathogens to new infection sites. Here, using Vibrio cholerae, we show that dispersal events are accompanied by a branching cell-fate decision. While ~90% of cells disperse, a viable subpopulation remains within a residual matrix. This post-dispersal biofilm community (PDBC) is established by the matrix protein RbmA and adopts a specialized anabolic program that enhances tolerance to antibiotics and bacteriophages. Our findings reveal that PDBCs act as a resilient \"seed-bank\" capable of rapidly re-populating the niche without requiring de novo matrix biosynthesis, providing a mechanistic basis for the recurrence and spread of chronic infections.","rel_num_authors":3,"rel_authors":[{"author_name":"Sandhya Kasivisweswaran","author_inst":"Carnegie Mellon University"},{"author_name":"Jojo A Prentice","author_inst":"Yale University"},{"author_name":"Andrew  A. Bridges","author_inst":"Carnegie Mellon University"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"Mitochondrial mechanics nucleates axonal jamming and swelling","rel_doi":"10.64898\/2026.04.23.720276","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720276","rel_abs":"Neuronal function requires precise spatial organization of mitochondria to meet localized energetic demand. However, the physical constraints governing mitochondrial transport in axons remain poorly defined. Bidirectional motor-driven trafficking inherently introduces the potential for collisions, but the implications of these interactions for transport failure and structural damage are not understood. Here, we develop an agent-based model that couples mitochondrial motility, morphology, and lifecycle dynamics to a deformable axonal boundary. We show that mitochondrial traffic jams emerge from a force balance between active propulsion and steric interactions, and that their severity is governed by organelle shape and mechanical properties. Elongated, mechanically rigid mitochondria remain aligned and are transported rapidly, whereas flexible, low-aspect-ratio mitochondria are prone to jamming and accumulation. Incorporating fission and fusion dynamics reveals that fission amplifies transport disruption by generating collision-prone populations, while fusion restores transport by producing anisotropic structures that navigate crowded environments more efficiently. Importantly, we find that sustained jamming generates mechanical stress on the axonal membrane, leading to deformation and swelling. Together, these results establish a physical framework linking mitochondrial dynamics to axonal integrity and provide testable predictions for how dysregulated fission-fusion balance can drive transport failure and structural pathology in neurons.","rel_num_authors":4,"rel_authors":[{"author_name":"Patrick S Noerr","author_inst":"University of California San Diego"},{"author_name":"Ahmed A Abushawish","author_inst":"Department of Neurobiology, School of Biological Sciences, University of California San Diego"},{"author_name":"Gulcin Pekkurnaz","author_inst":"Department of Neurobiology, School of Biological Sciences, University of California San Diego"},{"author_name":"Padmini Rangamani","author_inst":"Department of Pharmacology, School of Medicine, University of California San Diego; Department of Mechanical and Aerospace Engineering, University of California"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"Targeting WNK1 Releases Differentiation Block in Acute Myeloid Leukemia","rel_doi":"10.64898\/2026.04.22.720037","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720037","rel_abs":"Impaired differentiation is a hallmark of Acute Myeloid Leukemia (AML). Current differentiation therapies benefit only a small subset of AML patients, leaving a substantial gap in care for other subtypes. Identifying novel molecular drivers of maturation arrest is critical to expand differentiation induction to a broader range of AML patients. This study addresses this unmet clinical need, by identifying With-no-Lysine(K) kinase 1 (WNK1) as a novel regulator of AML differentiation arrest. We show that WNK1 expression and activity are elevated in AML patients. WNK1 inhibition induced differentiation accompanied by decreased growth and survival of AML cell lines and AML patient cells. It also inhibited self-renewal of AML patient cells in vitro and elicited significant anti-tumor activity in vivo in mouse models. Mechanistically, WNK1 inhibition derepressed the MEK-ERK-C\/EBP{beta} signaling axis and increased the expression of myeloid differentiation genes. Our findings reveal a novel role of WNK1 in promoting AML through differentiation arrest, posing WNK1 inhibition as a potential approach for AML differentiation therapy.","rel_num_authors":3,"rel_authors":[{"author_name":"Jordan Cress","author_inst":"Case Western Reserve University"},{"author_name":"Emily Katoni","author_inst":"Case Western Reserve University"},{"author_name":"Parameswaran Ramakrishnan","author_inst":"Case Western Reserve University"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"PIEZOs regulate oligodendrocyte sheath formation, expansion, and myelination potential","rel_doi":"10.64898\/2026.04.23.720488","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720488","rel_abs":"Myelination requires precise integration of physical cues by oligodendrocyte lineage cells (OLCs), but the molecular sensors that detect these cues remain incompletely understood. Here, we demonstrate that oligodendrocyte progenitor cells (OPCs) are sensitive to sub-micron changes in membrane displacement. Based on channel properties, RNA expression, and protein abundance, we find that the mechanosensitive ion channel PIEZO1 contributes to OPC mechanosensitivity. In vivo, zebrafish with oligodendrocyte (OL)-specific disruption of piezo1 have fewer sheaths per OL. Zebrafish with OL-specific piezo2 disruption also have fewer sheaths as well as decreased total myelin capacity over time. OL-specific disruption of both piezo1 and piezo2 caused more severe phenotypes, with reduced OPC volume, and in myelinating OLs, reduced sheath number, sheath length, and total myelin output. Furthermore, piezo1\/piezo2 disruption leads to sporadic sheath formation outside the normal developmental window. Our findings indicate that OLs use Piezo channels in vivo to influence sheath formation, expansion, and retractions.","rel_num_authors":8,"rel_authors":[{"author_name":"Adam M Coombs","author_inst":"Oregon Health & Science University"},{"author_name":"Dongeun Heo","author_inst":"Oregon Health & Science University"},{"author_name":"Daniel J Orlin","author_inst":"Oregon Health & Science University"},{"author_name":"Cody L Call","author_inst":"Oregon Health & Science University"},{"author_name":"Marie E Bechler","author_inst":"SUNY Upstate Medical University"},{"author_name":"Swetha E Murthy","author_inst":"Oregon Health & Science University"},{"author_name":"Ben Emery","author_inst":"Oregon Health & Science University"},{"author_name":"Kelly R Monk","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"Behavioral and psychological symptoms of dementia: insights from a multivariate and network-based brain proteome-wide study","rel_doi":"10.64898\/2026.04.23.26351110","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351110","rel_abs":"Behavioral and psychological symptoms of dementia (BPSD) are common, profoundly troubling to patients and caregivers, and difficult to treat, yet their molecular underpinnings remain poorly understood. Here, we generated the first brain proteomic dataset with BPSD phenotyping, profiling the dorsolateral prefrontal cortex of 376 donors from three cohorts spanning nine BPSD domains assessed in life. Protein associations with BPSD were examined using complementary approaches - domain-specific BPSD, multi-domain BPSD, and latent factor modeling - and integrated via cross-cohort meta-analysis. Four proteins (NMT1, DCAKD, DNPH1, and HIBADH) were associated with anxiety in dementia and five proteins (ABL1, SAP18, PLXND1, CTRB2, and LDHD) with multi-domain BPSD or BPSD latent factors after adjusting for sex, age, and other covariates (FDR < 0.05). Additionally, eight protein co-expression networks were associated with BPSD across cohorts. These results link BPSD to dysregulation of synaptic signaling, protein folding, and humoral immune response, providing a molecular framework for therapeutic discovery.","rel_num_authors":9,"rel_authors":[{"author_name":"Selina M Vattathil","author_inst":"University of California, Davis"},{"author_name":"Duc M Duong","author_inst":"Emory University School of Medicine"},{"author_name":"Marla Gearing","author_inst":"Emory University School of Medicine"},{"author_name":"Nicholas T Seyfried","author_inst":"Emory University School of Medicine"},{"author_name":"Robert S. Wilson","author_inst":"Rush University Medical Center"},{"author_name":"David A. Bennett","author_inst":"Rush University Medical Center"},{"author_name":"Randall L. Woltjer","author_inst":"Oregon Health and Science University"},{"author_name":"Thomas S. Wingo","author_inst":"University of California, Davis"},{"author_name":"Aliza P. Wingo","author_inst":"University of California, Davis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Preconception metabolic-bariatric surgery and child health outcomes: Identification and cohort profile of the POSIT study protocol","rel_doi":"10.64898\/2026.04.22.26351521","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351521","rel_abs":"Preconception weight loss by metabolic-bariatric surgery (MBS) improves maternal-fetal outcomes, but little is known about its impact on offspring growth and health. The preconception bariatric surgery and child health outcomes (POSIT) study aims to estimate the effects of maternal MBS-induced preconception weight loss on infant and childhood body size, growth, and related outcomes. This report presents the methods used to construct the POSIT cohort and its baseline characteristics. This retrospective cohort study sampled members from a United States healthcare system aged 18 and older with a singleton, live birth to create three study groups: 1) a treatment group including women who underwent preconception MBS and subsequently became pregnant (n=1,374); 2)  a control group matched to the MBS pre-surgery body mass index (BMI) (pre-surgery controls, n=13,740); and 3) a second control group matched to the MBS post-surgical, pre-pregnancy BMI (pre-pregnancy controls, n=13,740). MBS and pre-surgery BMI controls showed slight imbalances in that pre-surgery BMI controls were on average ~6 months younger, had 0.6 lower BMI (44.5 kg\/m2) at the time of their pregnancy and were more likely to have become pregnant in earlier years than the MBS group prior to surgery. MBS and pre-pregnancy controls had comparable age (mean {+\/-} SD 33 {+\/-} 5 years), pre-pregnancy BMI (33 {+\/-} 6 kg\/m2), and year of delivery. Following matching, the MBS group had similar socioeconomic and health disparities as the pre-surgery control group, and both were worse than pre-pregnancy control group. Pregestational maternal comorbidity index improved after MBS and matched the pre-pregnancy controls. Upon extraction of offspring growth patterns and mediation analyses of maternal weight loss and metabolic responses to MBS, study findings will investigate effects of preconception weight loss by MBS on short- and long-term child health outcomes. Results will guide future studies focusing on improving maternal preconception weight and maternal-fetal outcomes.","rel_num_authors":10,"rel_authors":[{"author_name":"Jonathan  Q Purnell","author_inst":"Oregon Health & Science University"},{"author_name":"Darios Getahun","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Kimberly  K Vesco","author_inst":"Kaiser Permanente Northwest"},{"author_name":"Sijia Qiu","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Jiaxiao  M Shi","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Carmen  P Wong","author_inst":"Kaiser Permanente"},{"author_name":"Padma Koppolu","author_inst":"Kaiser Permanente Northwest"},{"author_name":"Theresa  M Im","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Caryn  E Oshiro","author_inst":"Kaiser Permanente"},{"author_name":"Janne Boone-Heinonen","author_inst":"University of California San Francisco"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Preconception metabolic-bariatric surgery and child health outcomes: Identification and cohort profile of the POSIT study protocol","rel_doi":"10.64898\/2026.04.22.26351521","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351521","rel_abs":"Preconception weight loss by metabolic-bariatric surgery (MBS) improves maternal-fetal outcomes, but little is known about its impact on offspring growth and health. The preconception bariatric surgery and child health outcomes (POSIT) study aims to estimate the effects of maternal MBS-induced preconception weight loss on infant and childhood body size, growth, and related outcomes. This report presents the methods used to construct the POSIT cohort and its baseline characteristics. This retrospective cohort study sampled members from a United States healthcare system aged 18 and older with a singleton, live birth to create three study groups: 1) a treatment group including women who underwent preconception MBS and subsequently became pregnant (n=1,374); 2)  a control group matched to the MBS pre-surgery body mass index (BMI) (pre-surgery controls, n=13,740); and 3) a second control group matched to the MBS post-surgical, pre-pregnancy BMI (pre-pregnancy controls, n=13,740). MBS and pre-surgery BMI controls showed slight imbalances in that pre-surgery BMI controls were on average ~6 months younger, had 0.6 lower BMI (44.5 kg\/m2) at the time of their pregnancy and were more likely to have become pregnant in earlier years than the MBS group prior to surgery. MBS and pre-pregnancy controls had comparable age (mean {+\/-} SD 33 {+\/-} 5 years), pre-pregnancy BMI (33 {+\/-} 6 kg\/m2), and year of delivery. Following matching, the MBS group had similar socioeconomic and health disparities as the pre-surgery control group, and both were worse than pre-pregnancy control group. Pregestational maternal comorbidity index improved after MBS and matched the pre-pregnancy controls. Upon extraction of offspring growth patterns and mediation analyses of maternal weight loss and metabolic responses to MBS, study findings will investigate effects of preconception weight loss by MBS on short- and long-term child health outcomes. Results will guide future studies focusing on improving maternal preconception weight and maternal-fetal outcomes.","rel_num_authors":10,"rel_authors":[{"author_name":"Jonathan  Q Purnell","author_inst":"Oregon Health & Science University"},{"author_name":"Darios Getahun","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Kimberly  K Vesco","author_inst":"Kaiser Permanente Northwest"},{"author_name":"Sijia Qiu","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Jiaxiao  M Shi","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Carmen  P Wong","author_inst":"Kaiser Permanente"},{"author_name":"Padma Koppolu","author_inst":"Kaiser Permanente Northwest"},{"author_name":"Theresa  M Im","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Caryn  E Oshiro","author_inst":"Kaiser Permanente"},{"author_name":"Janne Boone-Heinonen","author_inst":"University of California San Francisco"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Influenza vaccine effectiveness against influenza-associated hospitalizations and emergency department or urgent care encounters among children and adults - United States, 2024-25 season","rel_doi":"10.64898\/2026.04.22.26350853","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26350853","rel_abs":"Background: The 2024-25 influenza season was the most severe in the United States (US) since 2017-18, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics. Methods: Using electronic healthcare encounter data from eight US states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated hospitalizations and emergency department or urgent care (ED\/UC) encounters from October 2024-April 2025 among children aged 6 months-17 years and adults aged 18+ years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness (ARI) encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders. Results: Analyses included 108,618 encounters (5,764 hospitalizations and 102,854 ED\/UC encounters) among children and 309,483 encounters (76,072 hospitalizations and 233,411 ED\/UC encounters) among adults. Among children across care settings, 17.0% (6,097\/35,765) of cases versus 29.4% (21,449\/72,853) of controls were vaccinated. Among adults, 28.2% (21,832\/77,477) of cases versus 44.2% (102,560\/232,006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41-60%) against influenza-associated hospitalizations and 54% (95% CI: 52-55%) against influenza-associated ED\/UC encounters among children. VE was 43% (95% CI: 41-46%) against influenza-associated hospitalizations and 49% (95% CI: 47-50%) against influenza-associated ED\/UC encounters among adults. Conclusions: Influenza vaccination provided protection against influenza-associated hospitalizations and ED\/UC encounters among children and adults in the US during the severe 2024-25 influenza season. These findings support influenza vaccination as an important tool to reduce influenza-associated disease.","rel_num_authors":44,"rel_authors":[{"author_name":"Jennifer DeCuir","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Emily L. Reeves","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Zachary A. Weber","author_inst":"Westat Inc., Bethesda, Maryland, United States"},{"author_name":"Duck-Hye Yang","author_inst":"Westat Inc., Bethesda, Maryland, United States"},{"author_name":"Stephanie A. Irving","author_inst":"Kaiser Permanente Center for Health Research, Portland, Oregon, United States"},{"author_name":"Sara Y. Tartof","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States; Kaiser Permanente Bernard J. Tyson School of Me"},{"author_name":"Nicola P. Klein","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"Shaun J. Grannis","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; School of Medicine, Indiana University, Indianapolis, Indiana, U"},{"author_name":"Toan C. Ong","author_inst":"School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States"},{"author_name":"Sarah W. Ball","author_inst":"Westat, Inc. Bethesda, Maryland, United States"},{"author_name":"Malini B. DeSilva","author_inst":"HealthPartners Institute, Minneapolis, Minnesota, United States"},{"author_name":"Kristin Dascomb","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States"},{"author_name":"Allison L. Naleway","author_inst":"Kaiser Permanente Center for Health Research, Portland, Oregon, United States"},{"author_name":"Padma Koppolu","author_inst":"Kaiser Permanente Center for Health Research, Portland, Oregon, United States"},{"author_name":"S. Bianca Salas","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States"},{"author_name":"Lina S. Sy","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States"},{"author_name":"Bruno Lewin","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States; Kaiser Permanente Bernard J. Tyson School of Me"},{"author_name":"Richard Contreras","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States"},{"author_name":"Ousseny Zerbo","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"John R. Hansen","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"Lawrence Block","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"Karen B. Jacobson","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"Brian E. Dixon","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; Fairbanks School of Public Health, Indiana University Indianapol"},{"author_name":"Colin Rogerson","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; School of Medicine, Indiana University, Indianapolis, Indiana, U"},{"author_name":"Thomas Duszynski","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; Fairbanks School of Public Health, Indiana University, Indianapo"},{"author_name":"William F. Fadel","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; Fairbanks School of Public Health, Indiana University, Indianapo"},{"author_name":"Michelle A. Barron","author_inst":"School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States"},{"author_name":"David Mayer","author_inst":"School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States"},{"author_name":"Catia Chavez","author_inst":"School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States"},{"author_name":"Adam Yates","author_inst":"Westat Inc., Bethesda, Maryland, United States"},{"author_name":"Lindsey Kirshner","author_inst":"Westat Inc., Bethesda, Maryland, United States"},{"author_name":"Charlene E. McEvoy","author_inst":"HealthPartners Institute, Minneapolis, Minnesota, United States"},{"author_name":"Omobosola O. Akinsete","author_inst":"HealthPartners Institute, Minneapolis, Minnesota, United States"},{"author_name":"Inih J. Essien","author_inst":"HealthPartners Institute, Minneapolis, Minnesota, United States"},{"author_name":"Tamara Sheffield","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States; Immunization Programs, Intermountain Healt"},{"author_name":"Daniel Bride","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States; Enterprise Analytics, Intermountain Health"},{"author_name":"Julie Arndorfer","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States"},{"author_name":"Josh Van Otterloo","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States"},{"author_name":"Karthik Natarajan","author_inst":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States; Medical Informatics Services, NewYork-Presby"},{"author_name":"Caitlin S. Ray","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Amanda B. Payne","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Katherine Adams","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Brendan Flannery","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Shikha Garg","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Racioethnic Disparities in Risk of Cardiometabolic Risk Factors and Cardiovascular Disease among Women Treated for Breast Cancer: The Pathways Heart Study","rel_doi":"10.64898\/2026.04.23.26351612","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351612","rel_abs":"Background: Few studies have examined racioethnic disparities in cardiovascular disease (CVD) in women after breast cancer treatment, who are at higher risk due to cardiotoxic cancer treatment. Methods: Based on the Pathways Heart Study of women with a history of breast cancer, this analysis examines the association between cardiometabolic risk factors (hypertension, diabetes, and dyslipidemia) and CVD events with self-reported race and ethnicity, as well as genetic similarity. Multivariable logistic and Cox proportional hazards regression models were used to test race and ethnicity and genetic similarity with prevalent and incident cardiometabolic risk factors and CVD events. Results: Of the 4,071 patients in this analysis, non-Hispanic Black (NHB), Asian, and Hispanic women were more likely to have prevalent and incident diabetes than non-Hispanic White (NHW) women. Analysis of genetic similarity revealed results consistent with self-reported race and ethnicity. For CVD risk, NHB women were more likely to develop heart failure and cardiomyopathy than NHW women. In contrast, Hispanic women were at lower risk of any incident CVD, serious CVD, arrhythmia, heart failure or cardiomyopathy, and ischemic heart disease, which was consistent with the associations found with Native American ancestry. Conclusions: This is the largest multi-ethnic study of disparities in CVD health in breast cancer survivors, demonstrating corroborating findings between self-reported race and ethnicity and genetic similarity. The results highlight disparities in cardiometabolic risk factors and CVD among breast cancer survivors that warrant more research and clinical attention in these distinct, high-risk populations.","rel_num_authors":21,"rel_authors":[{"author_name":"Song Yao","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Alexa Zimbalist","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Haiyang Sheng","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Peter Fiorica","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Richard Cheng","author_inst":"University of Washington School of Medicine"},{"author_name":"Lucas Medicino","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Angela Omilian","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Qianqian Zhu","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Janise Roh","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Cecile Laurent","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Valerie Lee","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Isaac Ergas","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Carlos Iribarren","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Jamal Rana","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Mai Nguyen-Huynh","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Eileen Rillamas-Sun","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Dawn Hershman","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Christine Ambrosone","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Lawrence Kushi","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Heather Greenlee","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Marilyn Kwan","author_inst":"Kaiser Permanente Northern California"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Racioethnic Disparities in Risk of Cardiometabolic Risk Factors and Cardiovascular Disease among Women Treated for Breast Cancer: The Pathways Heart Study","rel_doi":"10.64898\/2026.04.23.26351612","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351612","rel_abs":"Background: Few studies have examined racioethnic disparities in cardiovascular disease (CVD) in women after breast cancer treatment, who are at higher risk due to cardiotoxic cancer treatment. Methods: Based on the Pathways Heart Study of women with a history of breast cancer, this analysis examines the association between cardiometabolic risk factors (hypertension, diabetes, and dyslipidemia) and CVD events with self-reported race and ethnicity, as well as genetic similarity. Multivariable logistic and Cox proportional hazards regression models were used to test race and ethnicity and genetic similarity with prevalent and incident cardiometabolic risk factors and CVD events. Results: Of the 4,071 patients in this analysis, non-Hispanic Black (NHB), Asian, and Hispanic women were more likely to have prevalent and incident diabetes than non-Hispanic White (NHW) women. Analysis of genetic similarity revealed results consistent with self-reported race and ethnicity. For CVD risk, NHB women were more likely to develop heart failure and cardiomyopathy than NHW women. In contrast, Hispanic women were at lower risk of any incident CVD, serious CVD, arrhythmia, heart failure or cardiomyopathy, and ischemic heart disease, which was consistent with the associations found with Native American ancestry. Conclusions: This is the largest multi-ethnic study of disparities in CVD health in breast cancer survivors, demonstrating corroborating findings between self-reported race and ethnicity and genetic similarity. The results highlight disparities in cardiometabolic risk factors and CVD among breast cancer survivors that warrant more research and clinical attention in these distinct, high-risk populations.","rel_num_authors":21,"rel_authors":[{"author_name":"Song Yao","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Alexa Zimbalist","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Haiyang Sheng","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Peter Fiorica","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Richard Cheng","author_inst":"University of Washington School of Medicine"},{"author_name":"Lucas Medicino","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Angela Omilian","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Qianqian Zhu","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Janise Roh","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Cecile Laurent","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Valerie Lee","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Isaac Ergas","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Carlos Iribarren","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Jamal Rana","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Mai Nguyen-Huynh","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Eileen Rillamas-Sun","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Dawn Hershman","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Christine Ambrosone","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Lawrence Kushi","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Heather Greenlee","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Marilyn Kwan","author_inst":"Kaiser Permanente Northern California"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Comparison of the Expert Guidelines With Artificial Intelligence-Driven Echocardiographic Assessment of Diastolic Function","rel_doi":"10.64898\/2026.04.23.26350072","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26350072","rel_abs":"Backgound: Accurate assessment of diastolic function and left ventricular (LV) filling pressure is central to heart failure diagnosis and risk stratification. Contemporary guideline algorithms rely on complex parameters that are not consistently available in routine clinical practice. Objective: To compare the diagnostic and prognostic performance of the 2016 American Society of Echocardiography\/European Association of Cardiovascular Imaging (ASE\/EACVI) and 2025 ASE guidelines with a deep learning model based on routinely acquired echocardiographic variables. Methods: This study evaluated the guideline-based algorithms and a deep learning model in participants from the Atherosclerosis Risk in Communities (ARIC) cohort (n=5450) for prognostication and two invasive hemodynamic validation cohorts from the United States (n=83) and Japan (n=130) for detection of elevated left ventricular filling pressure. Results: In the ARIC cohort, the deep learning model demonstrated superior prognostic performance compared with the 2016 and 2025 guidelines (C-index: 0.676 vs. 0.638 and 0.602, respectively; both p<0.001). Similar findings were observed among participants with preserved ejection fraction (C-index: 0.660 vs. 0.628 and 0.590; both p<0.001), with improved performance compared with the H2FPEF score (C-index: 0.660 vs. 0.607; p<0.001). In the US hemodynamic validation cohort, the deep learning model showed higher diagnostic performance than the 2025 guidelines (AUC: 0.879 vs. 0.822; p=0.041) and similar performance compared with the 2016 guidelines (AUC: 0.879 vs. 0.812; p=0.138). In the Japanese hemodynamic validation cohort, the deep learning model outperformed both guidelines (AUC: 0.816 vs. 0.634 and 0.694; both p<0.05). Conclusions: A deep learning model leveraging routinely available echocardiographic parameters demonstrated improved diagnostic and prognostic performance compared with contemporary guideline-based approaches, potentially offering a scalable alternative for assessing diastolic function and left ventricular filling pressures.","rel_num_authors":12,"rel_authors":[{"author_name":"Marton Tokodi","author_inst":"Heart and Vascular Center, Semmelweis University and Department of Experimental Cardiology and Surgical Techniques, Semmelweis University, Budapest, Hungary"},{"author_name":"Nobuyuki Kagiyama","author_inst":"Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan"},{"author_name":"Ambarish Pandey","author_inst":"Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA"},{"author_name":"Yutaka Nakamura","author_inst":"Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan"},{"author_name":"Yuka Akama","author_inst":"Juntendo University Graduate School of Medicine"},{"author_name":"Sachiko Takamatsu","author_inst":"Department of Nursing, The Sakakibara Heart Institute of Okayama, Okayama, Japan"},{"author_name":"Misako Toki","author_inst":"Department of Clinical Laboratory, The Sakakibara Heart Institute of Okayama, Okayama, Japan"},{"author_name":"Takeshi Kitai","author_inst":"National Cerebral and Cardiovascular Center"},{"author_name":"Taiji Okada","author_inst":"Shimane University Faculty of Medicine"},{"author_name":"Carolyn SP Lam","author_inst":"Duke-National University of Singapore"},{"author_name":"Naveena Yanamala","author_inst":"Rutgers University, NJ, USA"},{"author_name":"Partho Sengupta","author_inst":"Rutgers, The State University of New Jersey"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Deep Learning Reveals the Modular Genetic Architecture of Cardiovascular Aging","rel_doi":"10.64898\/2026.04.22.26351478","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351478","rel_abs":"Chronological age is a potent determinant of clinical events, but it is conventionally treated as a linear function of time rather than a dynamic process shaped by genetics and tissue-specific senescence. Deep learning models derived from cardiovascular imaging offer an opportunity to quantify biological age across multiple domains and to examine the extent to which these measures capture shared or distinct vulnerabilities. Here, we applied deep learning to estimate biological age from electrocardiograms, cardiac MRI, carotid ultrasound, and retinal imaging, capturing electrical, structural, macrovascular, and microvascular domains in more than 100,000 UK Biobank participants. Genome-wide association and cross-trait heritability analyses showed that cardiovascular aging is not a singular process but a modular phenotype with distinct genetic determinants across modalities. Polygenic risk scores supported these distinct trajectories, showing that different biological age measures capture partly divergent biological processes with corresponding differences in clinical associations. Modality-specific genes also showcased distinct cell-type enrichment patterns. By deconvoluting aging into electrical, structural, macrovascular, and microvascular components, our results demonstrate that AI-derived age metrics capture distinct, disease-specific aging pathways. Ultimately, this modular framework positions deep learning-derived aging models not as holistic measures of health, but as domain-specific biomarkers of cardiovascular vulnerability.","rel_num_authors":5,"rel_authors":[{"author_name":"Ryan B Choi","author_inst":"Yale School of Medicine"},{"author_name":"Philip M Croon","author_inst":"Yale School of Medicine"},{"author_name":"Sudheesha Perera","author_inst":"Yale School of Medicine"},{"author_name":"Evangelos Oikonomou","author_inst":"Yale School of Medicine"},{"author_name":"Rohan Khera","author_inst":"Yale School of Medicine"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Genetic and Environmental Predictors of Seasonality and Seasonal Affective Disorder in Individuals with Depression","rel_doi":"10.64898\/2026.04.22.26351539","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351539","rel_abs":"Background: The etiology and nosological status of seasonal affective disorder (SAD) as a specifier of depressive episodes versus a transdiagnostic disorder are the subject of debate. In this study, we investigated the underlying etiology of SAD and dimensional seasonality by examining their association with latitude and genetic risk for a range of traits, and investigated gene-environment interactions. Methods: This study included 12,460 adults aged 18-90 with a history of depression from the Australian Genetics of Depression Study. Regression models included predictors for latitude (distance from equator) and polygenic scores for eight traits; major depressive disorder, bipolar disorder, anxiety disorders, chronotype, sleep duration, body mass index, vitamin D levels, and educational attainment. Outcomes were SAD status and general seasonality score. Results: SAD was positively associated with latitude (OR[95%CI] = 1.05[1.03-1.06], padjusted<0.001), and there was nominal evidence of additive and multiplicative interactions between chronotype genetic risk and latitude (OR = 0.99[0.99-0.99], padjusted=0.381; OR=0.98[0.97-0.99], padjusted=0.489). General seasonality score was associated with latitude (IRR=1.01[1.01-1.01], padjusted 0.001) and genetic risk for major depressive disorder (IRR =1.02[1.01-1.03], padjusted<0.001), bipolar disorder (IRR=1.02[1.01-1.03], padjusted=0.001), anxiety disorders (IRR=1.03[1.01-1.04], padjusted<0.001), vitamin D levels (OR=0.89[0.80-0.95], padjusted=0.048), and educational attainment (IRR=0.97[0.96-0.99], padjusted<0.001). Conclusions: These findings enhance understanding of SAD etiology, highlighting contributions of psychiatric genetic risk and geographic measures on seasonal behavior, and support examining seasonality as a continuous dimension.","rel_num_authors":7,"rel_authors":[{"author_name":"Floris Huider","author_inst":"Vrije Universiteit Amsterdam"},{"author_name":"Jacob Crouse","author_inst":"Brain and Mind Centre, The University of Sydney"},{"author_name":"Sarah Medland","author_inst":"QIMR Berghofer"},{"author_name":"Ian Hickie","author_inst":"Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, Australia."},{"author_name":"Nick Martin","author_inst":"QIMR Berghofer"},{"author_name":"Jodi Thea Thomas","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Brittany L Mitchell","author_inst":"QIMR Berghofer Medical Research Institute"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Phase 1a Evaluation of LP-184 in Recurrent Glioblastoma: Safety, Pharmacokinetics, and Translational Optimization of CNS Exposure","rel_doi":"10.64898\/2026.04.21.26351406","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351406","rel_abs":"Purpose: Limited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and\/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER. Patients: LP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment. Results: Patients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data (~20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by ~ 80% and increases tumor LP-184 cytotoxicity 2-fold. Conclusions: LP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial.","rel_num_authors":9,"rel_authors":[{"author_name":"Karisa Schreck","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bachchu Lal","author_inst":"Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland"},{"author_name":"Jianli Zhou","author_inst":"Lantern Pharma Inc"},{"author_name":"Hernando Lopez Bertoni","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Matthias Holdhoff","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Reginald Ewesudo","author_inst":"Lantern Pharma Inc"},{"author_name":"Kishor Bhatia","author_inst":"Lantern Pharma Inc"},{"author_name":"Marc Chamberlain","author_inst":"Lantern Pharma Inc"},{"author_name":"John Laterra","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Shared Risk Genes and Casual Relationships across Sex Hormone Related Traits and Alzheimer's Disease","rel_doi":"10.64898\/2026.04.23.26351626","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351626","rel_abs":"Background: Alzheimer's disease (AD) exhibits marked sex differences. While sex hormone levels across the lifespan likely contribute to this, little remains known about their causal impact and their relation to sex-biased genetic risk for AD. We therefore sought to identify potential shared genetic architectures, as well as causal genes and relationships, between sex hormone-related traits and AD risk. Methods: Large-scale AD sex-stratified genome-wide association study (GWAS) results were available from case-control, proxy-based, and population-based cohorts, including the Alzheimer's Disease Genetics Consortium, Alzheimer's Disease Sequencing Project, UK Biobank, and FinnGen. Sex hormone-related trait GWAS were available for age at menarche, menopause, and voice breaking, as well as testosterone, sex hormone-binding globulin (SHBG), progesterone, follicle stimulating hormone, luteinizing hormone, and estradiol levels. Cross-trait conjunctional analyses were conducted to identify pleiotropic overlap between sex-hormone traits and AD, followed by prioritization of candidate causal sex-biased AD genes through quantitative trait locus genetic colocalization analyses. The potential regulatory impact of sex hormones on these genes was assessed through transcription factor motif analyses. Finally, sex-stratified mendelian randomization analyses were used to infer causal effects of sex hormones on AD risk. Results: Genome-wide pleiotropy analyses demonstrated enrichment of AD with testosterone, SHBG, and age-at-menarche traits in women. We identified 12 high-confidence pleiotropic loci, 9 of which showed stronger AD effect sizes in women (3 in men) and 8 that were novel. Genes at these loci were often causally implicated in brain tissues and enriched for promoter-associated androgen receptor transcription factor binding motifs. Mendelian randomization indicated higher bioavailable testosterone in women (OR:0.88; 95%-CI:0.82-0.96) and higher SHBG levels in men (OR:0.86; 95%-CI:0.77-0.96) were associated with lower AD risk. Conclusions: Our findings reveal sex-specific shared genetic architectures between AD and sex hormone-related traits and nominate related genes that may drive sex-biases in AD risk. Several of the implicated female-biased genes are relevant to phosphatidylinositol and lipid metabolism, including Fatty Acid Desaturase 2 (FADS2). While we observed no causal effect of estradiol-related traits on AD risk, the protective effects of bioavailable testosterone in women and SHBG in men provide targets for sex-informed AD risk stratification and prevention strategies.","rel_num_authors":8,"rel_authors":[{"author_name":"chenyu yang","author_inst":"Washington University in St Louis"},{"author_name":"Noah Cook","author_inst":"Washington University in St. Louis"},{"author_name":"Youjie Zeng","author_inst":"Washington University in Saint Louis"},{"author_name":"Sathesh Kumar Sivasankaran","author_inst":"Washington University in St Louis"},{"author_name":"- FinnGen","author_inst":""},{"author_name":"Alex Decasien","author_inst":"National Institutes of Health"},{"author_name":"Shea J Andrews","author_inst":"University of California San Francisco"},{"author_name":"Michael E Belloy","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Shared Risk Genes and Casual Relationships across Sex Hormone Related Traits and Alzheimer's Disease","rel_doi":"10.64898\/2026.04.23.26351626","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351626","rel_abs":"Background: Alzheimer's disease (AD) exhibits marked sex differences. While sex hormone levels across the lifespan likely contribute to this, little remains known about their causal impact and their relation to sex-biased genetic risk for AD. We therefore sought to identify potential shared genetic architectures, as well as causal genes and relationships, between sex hormone-related traits and AD risk. Methods: Large-scale AD sex-stratified genome-wide association study (GWAS) results were available from case-control, proxy-based, and population-based cohorts, including the Alzheimer's Disease Genetics Consortium, Alzheimer's Disease Sequencing Project, UK Biobank, and FinnGen. Sex hormone-related trait GWAS were available for age at menarche, menopause, and voice breaking, as well as testosterone, sex hormone-binding globulin (SHBG), progesterone, follicle stimulating hormone, luteinizing hormone, and estradiol levels. Cross-trait conjunctional analyses were conducted to identify pleiotropic overlap between sex-hormone traits and AD, followed by prioritization of candidate causal sex-biased AD genes through quantitative trait locus genetic colocalization analyses. The potential regulatory impact of sex hormones on these genes was assessed through transcription factor motif analyses. Finally, sex-stratified mendelian randomization analyses were used to infer causal effects of sex hormones on AD risk. Results: Genome-wide pleiotropy analyses demonstrated enrichment of AD with testosterone, SHBG, and age-at-menarche traits in women. We identified 12 high-confidence pleiotropic loci, 9 of which showed stronger AD effect sizes in women (3 in men) and 8 that were novel. Genes at these loci were often causally implicated in brain tissues and enriched for promoter-associated androgen receptor transcription factor binding motifs. Mendelian randomization indicated higher bioavailable testosterone in women (OR:0.88; 95%-CI:0.82-0.96) and higher SHBG levels in men (OR:0.86; 95%-CI:0.77-0.96) were associated with lower AD risk. Conclusions: Our findings reveal sex-specific shared genetic architectures between AD and sex hormone-related traits and nominate related genes that may drive sex-biases in AD risk. Several of the implicated female-biased genes are relevant to phosphatidylinositol and lipid metabolism, including Fatty Acid Desaturase 2 (FADS2). While we observed no causal effect of estradiol-related traits on AD risk, the protective effects of bioavailable testosterone in women and SHBG in men provide targets for sex-informed AD risk stratification and prevention strategies.","rel_num_authors":8,"rel_authors":[{"author_name":"chenyu yang","author_inst":"Washington University in St Louis"},{"author_name":"Noah Cook","author_inst":"Washington University in St. Louis"},{"author_name":"Youjie Zeng","author_inst":"Washington University in Saint Louis"},{"author_name":"Sathesh Kumar Sivasankaran","author_inst":"Washington University in St Louis"},{"author_name":"- FinnGen","author_inst":""},{"author_name":"Alex Decasien","author_inst":"National Institutes of Health"},{"author_name":"Shea J Andrews","author_inst":"University of California San Francisco"},{"author_name":"Michael E Belloy","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"Background: Patients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients. Methods: We analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide. Results: In CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004). Conclusions: SVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"Background: Patients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients. Methods: We analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide. Results: In CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004). Conclusions: SVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"Background: Patients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients. Methods: We analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide. Results: In CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004). Conclusions: SVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"Background: Patients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients. Methods: We analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide. Results: In CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004). Conclusions: SVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"Background: Patients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients. Methods: We analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide. Results: In CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004). Conclusions: SVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images","rel_doi":"10.64898\/2026.04.23.26351616","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351616","rel_abs":"Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging beyond structured electronic health record (EHR) data remains unclear in real-world settings. We developed a multimodal survival modeling framework integrating optical coherence tomography (OCT) and EHR data to predict time to visual improvement in patients with diabetic macular edema (DME), and evaluated how different ophthalmic foundation model representations contribute to prognostic performance. In a retrospective cohort of 973 patients (1,450 eyes) receiving anti-vascular endothelial growth factor therapy, we compared multimodal models combining 22,227 EHR variables with 196,402 OCT images, with OCT embeddings derived from three ophthalmic foundation models (RETFound, EyeCLIP, and VisionFM). The EHR-only model showed minimal prognostic discrimination (C-index 0.50 [95% CI, 0.45-0.55]). Incorporating OCT improved performance, with the magnitude of improvement depending on the representation. EHR+RETFound achieved the strongest performance (C-index 0.59 [0.54-0.65]), followed by EHR+EyeCLIP (0.57 [0.52-0.62]) and EHR+VisionFM (0.56 [0.51-0.61]). Multimodal models, particularly EHR+RETFound, demonstrated improved risk stratification with clearer separation of Kaplan-Meier curves. Partial information decomposition revealed that prognostic information was dominated by modality-specific contributions, with OCT and EHR providing largely distinct signals and minimal shared information. The magnitude of OCT-specific contribution varied across foundation models and aligned with observed performance differences. These findings indicate that OCT provides complementary prognostic value beyond structured clinical data, but gains are modest and depend strongly on representation choice. Our results highlight both the promise of multimodal modeling for personalized prognosis and the need for rigorous, context-specific evaluation of foundation models in real-world clinical settings.","rel_num_authors":11,"rel_authors":[{"author_name":"Siqi Sun","author_inst":"Washington University in St. Louis"},{"author_name":"Cindy X. Cai","author_inst":"Wilmer Eye Institute, . Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine"},{"author_name":"Ruochong Fan","author_inst":"Washington University in St. Louis"},{"author_name":"Saiyu You","author_inst":"Washington University in St. Louis"},{"author_name":"Diep Tran","author_inst":"Wilmer Eye Institute"},{"author_name":"P. Kumar Rao","author_inst":"Washington University in St. Louis"},{"author_name":"Marc A Suchard","author_inst":"University of California, Los Angeles"},{"author_name":"Yixin Wang","author_inst":"University of Michigan, Ann Arbor"},{"author_name":"Cecilia S Lee","author_inst":"Washington University In St Louis: Washington University in St Louis"},{"author_name":"Aaron Y Lee","author_inst":"Washington University in St Louis"},{"author_name":"Linying Zhang","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images","rel_doi":"10.64898\/2026.04.23.26351616","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351616","rel_abs":"Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging beyond structured electronic health record (EHR) data remains unclear in real-world settings. We developed a multimodal survival modeling framework integrating optical coherence tomography (OCT) and EHR data to predict time to visual improvement in patients with diabetic macular edema (DME), and evaluated how different ophthalmic foundation model representations contribute to prognostic performance. In a retrospective cohort of 973 patients (1,450 eyes) receiving anti-vascular endothelial growth factor therapy, we compared multimodal models combining 22,227 EHR variables with 196,402 OCT images, with OCT embeddings derived from three ophthalmic foundation models (RETFound, EyeCLIP, and VisionFM). The EHR-only model showed minimal prognostic discrimination (C-index 0.50 [95% CI, 0.45-0.55]). Incorporating OCT improved performance, with the magnitude of improvement depending on the representation. EHR+RETFound achieved the strongest performance (C-index 0.59 [0.54-0.65]), followed by EHR+EyeCLIP (0.57 [0.52-0.62]) and EHR+VisionFM (0.56 [0.51-0.61]). Multimodal models, particularly EHR+RETFound, demonstrated improved risk stratification with clearer separation of Kaplan-Meier curves. Partial information decomposition revealed that prognostic information was dominated by modality-specific contributions, with OCT and EHR providing largely distinct signals and minimal shared information. The magnitude of OCT-specific contribution varied across foundation models and aligned with observed performance differences. These findings indicate that OCT provides complementary prognostic value beyond structured clinical data, but gains are modest and depend strongly on representation choice. Our results highlight both the promise of multimodal modeling for personalized prognosis and the need for rigorous, context-specific evaluation of foundation models in real-world clinical settings.","rel_num_authors":11,"rel_authors":[{"author_name":"Siqi Sun","author_inst":"Washington University in St. Louis"},{"author_name":"Cindy X. Cai","author_inst":"Wilmer Eye Institute, . Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine"},{"author_name":"Ruochong Fan","author_inst":"Washington University in St. Louis"},{"author_name":"Saiyu You","author_inst":"Washington University in St. Louis"},{"author_name":"Diep Tran","author_inst":"Wilmer Eye Institute"},{"author_name":"P. Kumar Rao","author_inst":"Washington University in St. Louis"},{"author_name":"Marc A Suchard","author_inst":"University of California, Los Angeles"},{"author_name":"Yixin Wang","author_inst":"University of Michigan, Ann Arbor"},{"author_name":"Cecilia S Lee","author_inst":"Washington University In St Louis: Washington University in St Louis"},{"author_name":"Aaron Y Lee","author_inst":"Washington University in St Louis"},{"author_name":"Linying Zhang","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer","rel_doi":"10.64898\/2026.04.23.26351499","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351499","rel_abs":"Purpose: To test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. Methods: We conducted a multi-cohort study including CALGB 9344 (anthracycline +\/- paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrell's C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen\/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. Results: A total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. Conclusion: Histology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit.","rel_num_authors":25,"rel_authors":[{"author_name":"Frederick Matthew Howard","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Anran Li","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sara Kochanny","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Megan Sullivan","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Elbio Martin Flores","author_inst":"Department of Pathology, Ingalls Memorial Hospital, Harvey, IL, USA"},{"author_name":"James Dolezal","author_inst":"Geisinger Cancer Institute, Danville, PA, USA"},{"author_name":"Galina Khramtsova","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sasha Hassan","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Riley Medenwald","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Poornima Saha","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Cheng Fan","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Linda McCart","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Mark Watson","author_inst":"Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Lauren R Teras","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"Clara Bodelon","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"Alpa V Patel","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"W Fraser Symmans","author_inst":"Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Ann Partridge","author_inst":"Dana-Farber Cancer Institute, Boston, MA, USA"},{"author_name":"Lisa Carey","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Olofunmilayo I. Olopade","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Daniel Stover","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Charles Perou","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Katharine Yao","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Alexander T Pearson","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Dezheng Huo","author_inst":"Department of Public Health Sciences, University of Chicago, Chicago, IL, USA"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer","rel_doi":"10.64898\/2026.04.23.26351499","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351499","rel_abs":"Purpose: To test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. Methods: We conducted a multi-cohort study including CALGB 9344 (anthracycline +\/- paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrell's C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen\/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. Results: A total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. Conclusion: Histology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit.","rel_num_authors":25,"rel_authors":[{"author_name":"Frederick Matthew Howard","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Anran Li","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sara Kochanny","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Megan Sullivan","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Elbio Martin Flores","author_inst":"Department of Pathology, Ingalls Memorial Hospital, Harvey, IL, USA"},{"author_name":"James Dolezal","author_inst":"Geisinger Cancer Institute, Danville, PA, USA"},{"author_name":"Galina Khramtsova","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sasha Hassan","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Riley Medenwald","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Poornima Saha","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Cheng Fan","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Linda McCart","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Mark Watson","author_inst":"Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Lauren R Teras","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"Clara Bodelon","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"Alpa V Patel","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"W Fraser Symmans","author_inst":"Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Ann Partridge","author_inst":"Dana-Farber Cancer Institute, Boston, MA, USA"},{"author_name":"Lisa Carey","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Olofunmilayo I. Olopade","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Daniel Stover","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Charles Perou","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Katharine Yao","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Alexander T Pearson","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Dezheng Huo","author_inst":"Department of Public Health Sciences, University of Chicago, Chicago, IL, USA"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Plasma proteomics link menopause timing to brain aging and dementia risk","rel_doi":"10.64898\/2026.04.23.26351500","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351500","rel_abs":"Earlier menopause is a risk factor for several age-related diseases, including dementia. The biological pathways linking menopause timing to later-life brain aging are not understood. Leveraging large-scale plasma proteomics in postmenopausal women from the UK Biobank (N=15,012), earlier menopause was associated with upregulation of pro-inflammatory and extracellular matrix degradation pathways, plus accelerated aging across proteomic clocks of organ and cellular aging, including brain and oligodendrocyte aging. Elevated GDF15, a canonical aging marker, was the top protein correlate of earlier menopause. We observed robust replication of menopause timing proteomic shifts in the Women's Health Initiative Long Life Study (N=1,210). In UKB, proteins associated with earlier menopause, including GDF15, exhibited concordant associations with incident dementia risk and brain atrophy, cerebral small vessel disease burden, and white matter microstructural integrity. Collectively, our findings identify proteomic signatures linking ovarian aging to brain aging, providing a framework to inform interventions to reduce dementia risk.","rel_num_authors":16,"rel_authors":[{"author_name":"Madeline Wood Alexander","author_inst":"University of Toronto"},{"author_name":"Brendan Wood","author_inst":"University of Toronto"},{"author_name":"Hamilton See-Hwee Oh","author_inst":"Mount Sinai"},{"author_name":"Veronica Augustina Bot","author_inst":"Stanford University"},{"author_name":"Julia Borger","author_inst":"UCSF"},{"author_name":"Francesca Galbiati","author_inst":"UCSF"},{"author_name":"Keenan  A. Walker","author_inst":"National Institute on Aging"},{"author_name":"Susan M. Resnick","author_inst":"NIA"},{"author_name":"Heather M Ochs-Balcom","author_inst":"SUNY"},{"author_name":"Tony Wyss-Coray","author_inst":"Stanford University"},{"author_name":"Charles Kooperberg","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Alexander P. Reiner","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Emily G Jacobs","author_inst":"UCSB"},{"author_name":"Jennifer S Rabin","author_inst":"Sunnybrook Health Sciences Centre"},{"author_name":"Kaitlin B Casaletto","author_inst":"UCSF"},{"author_name":"Rowan Saloner","author_inst":"University of California, San Francisco"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Plasma proteomics link menopause timing to brain aging and dementia risk","rel_doi":"10.64898\/2026.04.23.26351500","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351500","rel_abs":"Earlier menopause is a risk factor for several age-related diseases, including dementia. The biological pathways linking menopause timing to later-life brain aging are not understood. Leveraging large-scale plasma proteomics in postmenopausal women from the UK Biobank (N=15,012), earlier menopause was associated with upregulation of pro-inflammatory and extracellular matrix degradation pathways, plus accelerated aging across proteomic clocks of organ and cellular aging, including brain and oligodendrocyte aging. Elevated GDF15, a canonical aging marker, was the top protein correlate of earlier menopause. We observed robust replication of menopause timing proteomic shifts in the Women's Health Initiative Long Life Study (N=1,210). In UKB, proteins associated with earlier menopause, including GDF15, exhibited concordant associations with incident dementia risk and brain atrophy, cerebral small vessel disease burden, and white matter microstructural integrity. Collectively, our findings identify proteomic signatures linking ovarian aging to brain aging, providing a framework to inform interventions to reduce dementia risk.","rel_num_authors":16,"rel_authors":[{"author_name":"Madeline Wood Alexander","author_inst":"University of Toronto"},{"author_name":"Brendan Wood","author_inst":"University of Toronto"},{"author_name":"Hamilton See-Hwee Oh","author_inst":"Mount Sinai"},{"author_name":"Veronica Augustina Bot","author_inst":"Stanford University"},{"author_name":"Julia Borger","author_inst":"UCSF"},{"author_name":"Francesca Galbiati","author_inst":"UCSF"},{"author_name":"Keenan  A. Walker","author_inst":"National Institute on Aging"},{"author_name":"Susan M. Resnick","author_inst":"NIA"},{"author_name":"Heather M Ochs-Balcom","author_inst":"SUNY"},{"author_name":"Tony Wyss-Coray","author_inst":"Stanford University"},{"author_name":"Charles Kooperberg","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Alexander P. Reiner","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Emily G Jacobs","author_inst":"UCSB"},{"author_name":"Jennifer S Rabin","author_inst":"Sunnybrook Health Sciences Centre"},{"author_name":"Kaitlin B Casaletto","author_inst":"UCSF"},{"author_name":"Rowan Saloner","author_inst":"University of California, San Francisco"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Culture of preimplantation embryos in media containing L-proline increases intracellular GSH concentration throughout development","rel_doi":"10.64898\/2026.04.23.720483","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720483","rel_abs":"Careful balance of the redox status of the embryo and reduction of oxidative stress is crucial in early development. Here we show that the culture of preimplantation mouse embryos in the conditionally non-essential amino acid L-proline (Pro) increases the intracellular concentration of the potent antioxidant glutathione as shown by staining of 2-cell, 4-cell and 8-cell embryos with tetrafluoroterephthalonitrile (4F-2CN). Further, liquid-chromatography\/mass spectrometry showed increased GSH levels in all Pro-treated preimplantation stages of development compared to controls. The GSH:GSSG ratio also showed a Pro-dependent increase. Overall, our results indicate that the beneficial effect of Pro in preimplantation embryo culture is due to the reduction in oxidative stress mediated through an increase in cellular GSH concentration.","rel_num_authors":3,"rel_authors":[{"author_name":"Madeleine LM Hardy","author_inst":"Australian National University"},{"author_name":"Michael B. Morris","author_inst":"University of Sydney"},{"author_name":"Margot L Day","author_inst":"The university of sydney"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Single cell eQTL mapping reveals convergent glial-neuronal risk architecture in Parkinson's disease","rel_doi":"10.64898\/2026.04.24.720642","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720642","rel_abs":"Synucleinopathies affect ~15 million people and are classically divided into neuronal (Parkinson's disease(PD), dementia with Lewy bodies) and glial (multiple system atrophy) disorders. Here we challenge this dichotomy. We functionally fine-map 90 PD GWAS signals across nine cell types in cortex and substantia nigra using disease-context, population-scale single-nucleus eQTL meta-analysis (N = 1,197), bulk brain eQTL analysis (N = 1,182), and Mendelian randomization. A stringent causal framework integrates single-nucleus allelic imbalance (snASE) with orthogonal validation. We identify 125 functional risk genes for 50 loci--nearly doubling supported genes--and assign genes and cell types to over half of GWAS signals. Unexpectedly, 51% of risk genes are regulated in glia, particularly oligodendrocytes and their precursors. Across cell types, risk converges on a shared glial--neuronal vesiculopathy network. These findings uncover a convergent glial-neuronal risk architecture and establish a single cell atlas for context-aware gene discovery and precision therapeutics for PD.","rel_num_authors":22,"rel_authors":[{"author_name":"Zechuan Lin","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Jacob Parker","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Vanitha Nithianandam","author_inst":"Department of Pathology, Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts, USA"},{"author_name":"Sakthikumar Mathivanan","author_inst":"Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA 92037"},{"author_name":"Tao Wang","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Zhixiang Liao","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Sean K. Simmons","author_inst":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA  02142, USA"},{"author_name":"Idil Tuncali","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Xian Adiconis","author_inst":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA  02142, USA"},{"author_name":"Nathan Haywood","author_inst":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA  02142, USA"},{"author_name":"Beatrice Weykopf","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Xufei Teng","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Monika Sharma","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Jie Yuan","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Clare Baecher-Allan","author_inst":"Department of Neurology, Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts, USA"},{"author_name":"Xianjun Dong","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Thomas G. Beach","author_inst":"Banner Sun Health Research, Sun City, AZ  85351, USA"},{"author_name":"Geidy E. Serrano","author_inst":"Banner Sun Health Research, Sun City, AZ  85351, USA"},{"author_name":"Joshua Z. Levin","author_inst":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA  02142, USA"},{"author_name":"Suchun Zhang","author_inst":"Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA 92037"},{"author_name":"Mel B. Feany","author_inst":"Department of Pathology, Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts, USA"},{"author_name":"Clemens R. Scherzer","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"EMPIRE: The Ellipse Model for Phylogenetic Inference of Range Evolution","rel_doi":"10.64898\/2026.04.23.720387","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720387","rel_abs":"Many phylogenetic models of historical biogeography exist for describing how lineages move and evolve over time. Here, we present the Ellipse Model for Phylogenetic Inference of Range Evolution (empire), which models the movement and splitting of species range ellipses in continuous space, summarizing important attributes of each range, such as its position, size, and orientation. The framework allows us to reconstruct ancestral range ellipses, investigate rates governing important processes like movement, expansion, and elongation, and examine the spatial context of speciation, including asymmetric range inheritance at cladogenesis. We apply empire to the Australian Sphenomorphinae, a group of skinks whose diversification has coincided with substantial climatic change over the past [~]36 million years. We find that speciation events are positively associated with aridification, while daughter lineages post-speciation do not tend to show evidence of ecological partitioning.","rel_num_authors":3,"rel_authors":[{"author_name":"Sarah Kathryn Swiston","author_inst":"Washington University in Saint Louis"},{"author_name":"Sean W McHugh","author_inst":"Washington University in Saint Louis"},{"author_name":"Michael J Landis","author_inst":"Washington University in Saint Louis"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Additive baselines furnish no evidence for epistasis learning by MULTI-evolve","rel_doi":"10.64898\/2026.04.23.719915","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.719915","rel_abs":"Recent work from Tran et al. (Science, 2026) introduced MULTI-evolve, a framework for protein engineering that combines single-mutant nomination via a protein language model (PLM) or a deep mutational scan (DMS), experimental single- and double-mutant characterization, and neural networks to engineer hyperactive multimutant proteins. The authors attribute the framework's performance to \"epistasis-aware modeling\" and claim that their neural networks \"learn the epistatic landscape\" and \"identify synergistic interactions\" from limited double-mutant training data. Here we show that MULTI-evolve's multimutant predictions are almost perfectly correlated with an additive model across all three engineering applications (APEX, dCasRx, and HuABC2), such that the engineering of multimutants reduces to combining beneficial mutations with the largest additive effects--a standard protein engineering strategy for over four decades. We also find that MULTI-evolve's neural networks do not outperform an additive model in held-out test set predictions. Finally, we revisit a DMS benchmark finding presented as evidence of epistasis learning and show that it is expected even under a null additive model due to an elementary statistical phenomenon. Indeed, we fit an additive model to the benchmark data and reproduce the pattern purported to demonstrate epistasis learning.","rel_num_authors":3,"rel_authors":[{"author_name":"Gian Marco Visani","author_inst":"University of Washington, Seattle"},{"author_name":"Aayush Verma","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"William S. DeWitt","author_inst":"University of Washington, Seattle"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Life history stage-dependent nuclear selection in chimeric fungi","rel_doi":"10.64898\/2026.04.23.718558","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.718558","rel_abs":"A single hyphal compartment of the filamentous fungus \\textit{Neurospora crassa} may contain tens or hundreds of nuclei, sharing macromolecules with each other, and, via a continuous cytoplasm, with the nuclei in other compartments. Nuclear lineages acquire mutations with each mitosis, which, combined with the autonomous mitosis of nuclei, has fueled speculation that multilevel selection may occur, both upon the mycelium, and upon individual nuclear populations. Here, we combine experiments on fungal chimera formed from two auxotrophically and epitopically labeled nuclear populations, with specially created microscopy toolkit for extracting the proportions of the two nucleotypes to analyze the strength of nuclear-level selective forces at different stages in the fungal life history. We find strong nucleotype-selective forces during spore-germination and establishment of the mycelium, and no evidence of selection on nuclei inside a growing mycelium. The kinetics of mycelial initiation from individual spores therefore allow for the selection of nuclear compositions best adapted to the fungus' environment, in accordance with the hypothesized function of unicellular life history stages for purging deleterious mutations.","rel_num_authors":7,"rel_authors":[{"author_name":"Jiayu Li","author_inst":"Princeton University"},{"author_name":"Ariel Fitzmorris","author_inst":"University of California, Los Angeles"},{"author_name":"Justina Martelli","author_inst":"University of California, Los Angeles"},{"author_name":"Alex Mela","author_inst":"University of California, Berkeley"},{"author_name":"Louise Glass","author_inst":"University of California, Berkeley"},{"author_name":"Amy S. Gladfelter","author_inst":"Duke University"},{"author_name":"Marcus Roper","author_inst":"University of California Los Angeles"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Adaptive molecular convergence is pervasive across deep time and largely decoupled from phenotypic convergence","rel_doi":"10.64898\/2026.04.23.718300","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.718300","rel_abs":"Researchers often infer evolutionary repeatability when selection scans implicate homologous genes in repeatedly evolved traits or ecologies. However, the causes and frequency of genome-scale molecular convergence remain unresolved, particularly over deep time. We show that adaptive molecular convergence--excess convergence of nonsynonymous substitutions, consistent with positive selection--is pervasive across Medusozoa. Molecular convergence declines over time but persists among lineages separated by >600 million years, exceeding null expectations based on random overlap. However, lineages sharing repeatedly evolved phenotypes (eyes, medusa loss, upright colonies) do not exhibit elevated molecular convergence relative to other comparisons. Instead, convergence is non-randomly distributed across genes and enriched for environment-facing functions, including metabolism, immunity, and xenobiotic processing, suggesting that widespread reuse of genes reflects multifaceted organism-environment interactions.","rel_num_authors":7,"rel_authors":[{"author_name":"Cory A Berger","author_inst":"University of California, Santa Barbara"},{"author_name":"Marina I. Stoilova","author_inst":"University of Kansas"},{"author_name":"Rebecca M Varney","author_inst":"University of Nebraska-Lincoln"},{"author_name":"Sam C. Abrams","author_inst":"UC Santa Barbara"},{"author_name":"Maria Pia Miglietta","author_inst":"Texas A and M"},{"author_name":"Paulyn Cartwright","author_inst":"The University of Kansas"},{"author_name":"Todd H. Oakley","author_inst":"UC Santa Barbara"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Virus-like antigen display delivers a stand-alone danger signal through the BCR that circumvents tolerance","rel_doi":"10.64898\/2026.04.23.720482","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720482","rel_abs":"How B cells discriminate self from foreign antigens remains a central question, given inherent autoreactivity of the mature B cell receptor (BCR) repertoire. Soluble antigen (sAg) induces tolerance, whereas patterned antigen display on virus-like particles (pAg) triggers robust B cell responses that can proceed without T cell help. Here, we show how this divergence arises early in BCR signaling. Unlike sAg, pAg can bypass a Lyn-dependent negative feedback loop to trigger digital signaling, such that ultra-low concentrations of pAg produce strong and sustained Ca2+ responses. Surprisingly, pAg drives maximal nuclear NF-{kappa}B but limited NFAT, whereas sAg does the opposite, reflecting differential production of diacylglycerol. Consequently, sAg induced an NFAT-dependent anergy program, whereas pAg evaded this state and instead engaged a cMyc-driven program that partially resembles a TLR-dependent danger response. Our findings reveal how proximal signaling directs distinct transcriptional fate to enable immunogenic B cell responses to virus-like antigen display.","rel_num_authors":12,"rel_authors":[{"author_name":"Julianne Riggs","author_inst":"UCSF"},{"author_name":"Alexander J. Ritter","author_inst":"UCSF"},{"author_name":"Francois X. P. Bourassa","author_inst":"Princeton University"},{"author_name":"Alexander R. Meyer","author_inst":"University of Michigan"},{"author_name":"Erika Kay-Tsumagari","author_inst":"University of Michigan"},{"author_name":"Wei-Yun Wholey","author_inst":"University of Michigan"},{"author_name":"Jeremy Libang","author_inst":"UCSF"},{"author_name":"James L. Mueller","author_inst":"UCSF"},{"author_name":"Wen Lu","author_inst":"UCSF"},{"author_name":"Ned S. Wingreen","author_inst":"Princeton University"},{"author_name":"Wei Cheng","author_inst":"University of Michigan"},{"author_name":"Julie Zikherman","author_inst":"UCSF"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Turnip mosaic virus-based gRNA delivery system for plant genome editing","rel_doi":"10.64898\/2026.04.22.720221","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720221","rel_abs":"Plant virus-based gRNA delivery systems offer a rapid alternative to stable transformation for CRISPR-mediated genome editing, but potyvirus-based platforms in Cas9-expressing plants are still underexplored. Here, we developed a turnip mosaic virus (TuMV)-based system for gRNA delivery in Cas9-expressing Nicotiana benthamiana and tested whether Csy4-mediated gRNA processing could improve editing efficiency. A TuMV construct carrying a gRNA targeting PHYTOENE DESATURASE (NbPDS) induced detectable editing in both infiltrated and systemic tissues, although editing frequencies were low. Incorporation of the bacterial endoribonuclease Csy4 increased editing efficiencies in the two NbPDS genes, raising editing in infiltrated leaves to 7.1-13.8% for NbPDSa and 7.6-23.0% for NbPDSb, while lower but reproducible editing was detectable in systemic leaves. The TuMV-Csy4 platform also supported editing of a second endogenous target, MAGNESIUM CHELATASE SUBUNIT H (NbChlH), and enabled multiplex editing of NbPDS and NbChlH regardless of guide order. Editing efficiencies were consistently higher in infiltrated leaves than in systemic leaves, and no visible photobleaching or chlorosis was observed in systemic tissues despite confirmed molecular editing. To assess the potential for heritable editing, a tRNA<S>Ile<\/S> mobility element was fused to the NbPDS gRNA. Although this construct increased somatic editing, no albino progeny were recovered after screening approximately 20,000 seedlings, indicating that heritable editing was not achieved under these conditions. Together, these results establish TuMV as a platform for Cas9-based gRNA delivery and show that Csy4-mediated processing improves editing efficiency, supports multiplex targeting, and demonstrates the feasibility of potyvirus-based genome editing systems in plants.","rel_num_authors":4,"rel_authors":[{"author_name":"Ekkachai Khwanbua","author_inst":"Iowa State University"},{"author_name":"Ryan R. Lappe","author_inst":"Iowa State University"},{"author_name":"Austin A. Bierl","author_inst":"Iowa State University"},{"author_name":"Steven Whitham","author_inst":"Iowa State University"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"CCZ1 is a modulator of TPC2 activity and melanoma cell migration","rel_doi":"10.64898\/2026.04.22.718428","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.718428","rel_abs":"The small GTPase RAB7a is a key regulator of melanoma progression by enhancing the activity of the endolysosomal two-pore cation channel TPC2. In this study, we demonstrate that CCZ1- a core component of the RAB7a guanine nucleotide exchange factor (GEF) complex- is essential for mediating this RAB7a-dependent enhancement of TPC2. Unexpectedly, we find that constitutively active (GTP-locked) RAB7a fails to bind and regulate TPC2 in the absence of CCZ1, indicating that CCZ1 contributes to the RAB7a-TPC2 interaction through mechanisms beyond its GEF activity. Furthermore, the CCZ1 facilitated GTPase-activating function on RAB5 is dispensable for modulating TPC2. Notably, in the absence of CCZ1, TPC2 exhibits increased affinity for its agonist, PI(3,5)P2, along with markedly upregulated channel activity. In melanoma cell lines, this upregulation enhances migratory capacity. Our findings identify CCZ1 as a functional inhibitor of TPC2 and highlight its critical role in regulating cancer cell migration.","rel_num_authors":12,"rel_authors":[{"author_name":"Zhuo Yang","author_inst":"Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians-University, Munich, Germany"},{"author_name":"Colin Feldmann","author_inst":"Institute of Cardiovascular Physiology and Pathophysiology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany"},{"author_name":"Lina Ouologuem","author_inst":"Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians-University, Munich, Germany"},{"author_name":"Alice Lin","author_inst":"Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan. Department of Laboratory "},{"author_name":"Stefanie Fenske","author_inst":"Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians-University, Munich, Germany"},{"author_name":"Stylianos Michalakis","author_inst":"Department of Ophthalmology, LMU Hospital, LMU Munich, Munich, Germany"},{"author_name":"Karin Bartel","author_inst":"Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians-University, Munich, Germany"},{"author_name":"Michael Schaenzler","author_inst":"Institute of Neurophysiology, Hannover Medical School, Germany"},{"author_name":"Christian Grimm","author_inst":"Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany. Immunology, Infection and Pandemic"},{"author_name":"Cheng-Chang Chen","author_inst":"Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan. Department of Laboratory "},{"author_name":"Christian Wahl-Schott","author_inst":"Institute of Cardiovascular Physiology and Pathophysiology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany."},{"author_name":"Martin Biel","author_inst":"Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians-University, Munich, Germany"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"PanVariants: Best Practice for Pangenome-based Variant Calling Pipeline and Framework","rel_doi":"10.64898\/2026.04.22.720142","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720142","rel_abs":"Background: Although pangenome references offer richer population diversity compared to linear references, current mainstream pangenome-based variant callers are limited to detecting only known variants stored in the graph. To address this limitation, we developed PanVariants, a novel pipeline designed to improve the detection of both known and novel variants accurately. We systematically evaluated its performance against the traditional linear alignment solution (BWA+GATK\/Manta) and the existing pangenome-aware solution (DRAGEN\/PanGenie) in three contexts: small variants (SNVs\/indels) and structural variants (SVs) accuracy in Genome in a Bottle samples, clinical detection on positive samples, and application in cohort-based joint calling. Results: By integrating k-mer-based and mapping-based methods, PanVariants significantly reduced variant errors (FPs + FNs), achieving a 73% reduction compared to BWA+GATK and a 45% reduction compared to DRAGEN for SNVs. Retraining the DeepVariant model with high-quality DNBSEQ data further decreased errors by 15%. For SVs detection, PanVariants attained an F1-score of 89.39%, markedly outperforming DRAGEN (68.18%) and BWA+Manta (58.33%), approaching long-read sequencing performance (95.22%). In validation using clinical positive samples, PanVariants successfully detected all expected pathogenic variants while PanGenie failed. In the cohort joint-calling analysis, PanVariants detected more variants, made fewer Mendelian inheritance errors, and gave better per-sample accuracy than GATK. Conclusions: PanVariants establishes a robust framework and best-practice pipeline for pangenome-based variant detection, achieving both sensitive novel variant discovery and high accuracy for SNVs, indels and SVs. Our systematic evaluation of optional processing steps and input variables offers practical guidance for users. Validated across diagnostic and population-based applications, our findings strongly support the transition from linear to pangenome references in future genomics.","rel_num_authors":14,"rel_authors":[{"author_name":"Heng Yi","author_inst":"College of Life Sciences, University of Chinese Academy of Sciences, Beijing 101408, China"},{"author_name":"Linqi Wang","author_inst":"MGI Tech, Shenzhen 518083, China"},{"author_name":"Xinrui Chen","author_inst":"MGI Tech, Shenzhen 518083, China"},{"author_name":"Yi Ding","author_inst":"MGI Tech, Shenzhen 518083, China"},{"author_name":"Andrew Carroll","author_inst":"Google LLC, Mountain View, CA, USA"},{"author_name":"Pi-Chuan Chang","author_inst":"Google LLC, Mountain View, CA, USA"},{"author_name":"Kishwar Shafin","author_inst":"Google LLC, Mountain View, CA, USA"},{"author_name":"Lingyun Xu","author_inst":"MGI Tech, Shenzhen 518083, China"},{"author_name":"Xiaojie Zeng","author_inst":"MGI Tech, Shenzhen 518083, China"},{"author_name":"Xia Zhao","author_inst":"MGI Tech, Shenzhen 518083, China"},{"author_name":"Meihua Gong","author_inst":"MGI Tech, Shenzhen 518083, China"},{"author_name":"Xiaofang Wei","author_inst":"MGI Tech, Shenzhen 518083, China"},{"author_name":"Yong Hou","author_inst":"College of Life Sciences, University of Chinese Academy of Sciences, Beijing 101408, China"},{"author_name":"Ming Ni","author_inst":"MGI Tech, Shenzhen 518083, China"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"PKA\/CIP4 SIGNALING REGULATES CIP4 RELOCATION IN ACTIVATED NATURAL KILLER CELLS","rel_doi":"10.64898\/2026.04.22.720117","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720117","rel_abs":"Natural killer (NK) cells are cytotoxic lymphocytes of the innate immune system that eliminate virus infected and transformed cells through the formation of a specialized immune synapse. Effective target cell killing requires coordinated plasma membrane remodeling and dynamic reorganization of the actin and microtubule cytoskeletons, enabling centrosome polarization and directed secretion of lytic granules. The scaffold protein CIP4 has emerged as an important regulator of cytoskeletal coordination in NK cells, yet how its subcellular localization is controlled during NK cell activation is unknown. CIP4 contains a unique protein kinase A (PKA) phosphorylation site (threonine 225, T225) within its F BAR domain, a domain that mediates interactions with microtubules and the plasma membrane. We hypothesized that localized PKA signaling controls CIP4 redistribution during immune synapse assembly. To test this hypothesis, we analyzed CIP4 localization and phosphorylation in NK cells engaged with sensitive target cells using biochemical and imaging approaches. We show that NK target cell interaction enhances PKA activity and promotes phosphorylation of CIP4, coinciding with its delocalization from microtubules and accumulation at the immune synapse. Importantly, this relocalization process requires the PKA anchoring protein AKAP350, which positions PKA and CIP4 within the same protein complex, thereby facilitating CIP4 phosphorylation. Consistently, pharmacological inhibition of PKA prevented CIP4 delocalization from microtubules and reduced its accumulation at the immune synapse. The non phosphorylatable CIP4 mutant T225A displayed increased association with microtubules compared with a phosphomimetic mutant, identifying phosphorylation at T225 as a key determinant of CIP4 spatial regulation. Together, these findings identify a signaling mechanism that links compartmentalized PKA activity to the spatial control of CIP4 during immune synapse formation, providing new insight into the molecular mechanisms governing immune synapse maturation.","rel_num_authors":11,"rel_authors":[{"author_name":"Alejandro Pedro Pariani","author_inst":"Instituto de FisiologIa Experimental, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)"},{"author_name":"Victoria Huhn","author_inst":"Instituto de Fisiologia Experimental, CONICET"},{"author_name":"Leandra Marin","author_inst":"Facultad de Ciencias Bioquimicas y Farmaceuticas, Universidad Nacional de Rosario"},{"author_name":"Evangelina Almada","author_inst":"Instituto Universitario Italiano de Rosario"},{"author_name":"Tomas Rivabella Maknis","author_inst":"Instituto de Fisiologia Experimental, CONICET"},{"author_name":"Felipe Zecchinati","author_inst":"Instituto de Fisiologia Experimental, CONICET"},{"author_name":"Rodrigo Vena","author_inst":"Instituto de Biologia Molecular y Celular de Rosario, CONICET"},{"author_name":"Esteban Serra","author_inst":"Instituto de Biologia Molecular y Celular de Rosario, CONICET"},{"author_name":"James R Goldenring","author_inst":"Cell & Developmental Biology, Vanderbilt University School of Medicine"},{"author_name":"Cristian Favre","author_inst":"Instituto de Fisiologia Experimental, CONICET"},{"author_name":"Maria C Larocca","author_inst":"Instituto de Fisiologia Experimental, CONICET"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"MSAgent: An Evidence Grounded Agentic Framework for LLM-driven Scientific Exploration in Mass Spectrometry-based Metabolomics","rel_doi":"10.64898\/2026.04.22.720103","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720103","rel_abs":"Mass spectrometry (MS) is a cornerstone high-throughput technology for molecular discovery, yet the reliable elucidation of chemical structures remains a formidable, expert-dependent bottleneck. Currently, achieving a reliable molecular identification from raw mass spectra necessitates a manual assembly, a labor-intensive ordeal of heuristic reasoning and the tedious integration of siloed computational tools, perpetuating a profound throughput gap between rapid data acquisition and the glacial pace of structural annotation. Here we present MSAgent, an autonomous agentic framework that bridges the gap between computational automation and expert intuition by emulating the cognitive logic of human specialists. By orchestrating a MSToolbox of over 50 domain-specific tools via Large Language Models (LLMs), MSAgent dynamically unifies the analytical pipeline into a scalable, evidence-grounded workflow, allowing for intent-aware planning, cross-resources outputs synthesis, and visual mechanistic interpretation within traceable reasoning chains and evidence-backed analytical reports. We evaluated MSAgent across multiple open benchmarks, including the established community challenges - Critical Assessment of Small Molecule Identification (CASMI) 2016\/2022, CANOPUS, and LLM-oriented test cases. On CASMI, MSAgent consistently boosts retrieval performance by over 10% MRR across diverse benchmarks while ensuring high reliability, improving or preserving ranks in 95% of cases. For more challenging molecular de novo tasks on CANOPUS, MSAgent builds upon the outputs of baseline models with consistent refinement, yielding over a 40% average gain in Tanimoto similarity for ground-truth recovery. In addition, MSAgent demonstrates remarkable advantages in eliminating the hallucination phenomenon over LLMs without domain tool support, producing better-calibrated confidence (Pearson r = 0.438 vs -0.219 for gpt-4o). It improves exact-match rate by 38.8% over gpt-4o in candidate discrimination tasks, and achieved a 64% success rate in recommending high-quality candidate structures with Tanimoto similarity more than 0.7, where gpt-4o predominantly selected candidates with similarity below 0.3. Our work enables high-throughput mass spectrometry data to be analyzed in an intent-driven and automated manner, lowering the analysis barrier for no-expert to deliver molecular identification result with transparent analytical process, and accelerating discovery in metabolism and related fields by bridging the gap between experimental data acquisition and computational interpretation.","rel_num_authors":6,"rel_authors":[{"author_name":"Yifan Li","author_inst":"The Hong Kong University of Science and Technology (Guangzhou)"},{"author_name":"Yunhua Zhong","author_inst":"The University of Hong Kong"},{"author_name":"Pan Liu","author_inst":"The Hong Kong University of Science and Technology (Guangzhou)"},{"author_name":"Tan Yusheng","author_inst":"The Hong Kong University of Science and Technology (Guangzhou)"},{"author_name":"Hongyu Zhan","author_inst":"The Hong Kong University of Science and Technology (Guangzhou)"},{"author_name":"Jun Xia","author_inst":"The Hong Kong University of Science and Technology (Guangzhou)"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Dolichol biosynthesis in yeast traces the expanded reaction pathway of higher eukaryotes","rel_doi":"10.64898\/2026.04.22.720056","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720056","rel_abs":"The recent reassignment of Saccharomyces cerevisiae Dfg10 as a polyprenal reductase left two unresolved steps in yeast dolichol biosynthesis: the conversion of polyprenol to polyprenal and dolichal to dolichol. In humans, both of these steps are carried out by DHRSX, an enzyme with a unique dual specificity. We found that yeast Env9 catalyzes an NADPH-dependent reduction of dolichal to dolichol. However, in contrast to DHRSX, Env9 does not catalyze the conversion of polyprenol to polyprenal. Instead, our data indicates that this reaction is catalyzed by another yeast short chain oxidoreductase, Tda5. Thus, we provide evidence that the dual role of DHRSX in human dolichol synthesis is fulfilled by two dedicated yeast enzymes, Env9 and Tda5. Accordingly, deletion of ENV9 and TDA5 led to the accumulation of polyisoprenoid intermediates, transfer of immature lipid-linked oligosaccharides onto nascent proteins, and defective N-glycosylation. This is similar to what had been observed in DHRSX-deficient mammalian cells and yeast cells with DFG10 deleted. Furthermore, we discovered that loss of Dfg10 results in a deficiency of cell wall -mannan, revealing a critical sensitivity of yeast mannan biosynthesis to the quality of nascent N-linked glycans.","rel_num_authors":19,"rel_authors":[{"author_name":"Matthew P Wilson","author_inst":"Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, Leuven, Belgium"},{"author_name":"Takfarinas Kentache","author_inst":"Metabolic Research Group, de Duve Institute, Universite Catholique de Louvain, Brussels, Belgium"},{"author_name":"Charlotte R Althoff","author_inst":"Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, Leuven, Belgium"},{"author_name":"Isabelle Gerin","author_inst":"Metabolic Research Group, de Duve Institute, Universite Catholique de Louvain, Brussels, Belgium"},{"author_name":"Shin-Yi Yu","author_inst":"Univ. Lille, CNRS, UMR 8576 UGSF Unite de Glycobiologie Structurale et Fonctionnelle, Lille, France"},{"author_name":"Marie Morel","author_inst":"Univ. Lille, CNRS, UMR 8576 UGSF Unite de Glycobiologie Structurale et Fonctionnelle, Lille, France"},{"author_name":"Celine Schulz","author_inst":"Univ. Lille, CNRS, UMR 8576 UGSF Unite de Glycobiologie Structurale et Fonctionnelle, Lille, France"},{"author_name":"Sahar Sabry","author_inst":"Biochemical Genetics Department, Human Genetics and Genome Research Institute, National Research Centre (NRC), Cairo, Egypt."},{"author_name":"Jean Jacobs","author_inst":"Metabolic Research Group, de Duve Institute, Universite Catholique de Louvain, Brussels, Belgium"},{"author_name":"Julie Graff","author_inst":"Metabolic Research Group, de Duve Institute, Universite Catholique de Louvain, Brussels, Belgium"},{"author_name":"Geoffroy de Bettignies","author_inst":"Univ. Lille, CNRS, UMR 8576 UGSF Unite de Glycobiologie Structurale et Fonctionnelle, Lille, France"},{"author_name":"Liliana Surmacz","author_inst":"Institute of Biochemistry and Biophysics Polish Academy of Sciences"},{"author_name":"William C Sessa","author_inst":"Yale School of Medicine"},{"author_name":"Emile Van Schaftingen","author_inst":"Metabolic Research Group, de Duve Institute, Universite Catholique de Louvain, Brussels, Belgium"},{"author_name":"Gert Matthijs","author_inst":"Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, Leuven, Belgium"},{"author_name":"Charlie Boone","author_inst":"University of Toronto"},{"author_name":"Kariona A Grabinska","author_inst":"Yale"},{"author_name":"Guido T Bommer","author_inst":"Metabolic Research Group, de Duve Institute, Universite Catholique de Louvain, Brussels, Belgium"},{"author_name":"Fran\u00e7ois Foulquier","author_inst":"Univ. Lille, CNRS, UMR 8576, UGSF, Unite de Glycobiologie Structurale et Fonctionnelle, Lille, France"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"A Patient-derived Organoid Platform for Uterine Carcinosarcoma that Emulates Disease Characteristics","rel_doi":"10.64898\/2026.04.21.720027","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720027","rel_abs":"Uterine carcinosarcoma (UCS) is a rare but highly lethal endometrial malignancy characterized by early dissemination, marked lineage plasticity, and limited therapeutic options. Although genomic studies have established UCS as a copy-number-high, carcinoma-like tumor with strong epithelial-mesenchymal transition (EMT) features, mechanistic and translational progress has been hindered by the lack of physiologically relevant patient-derived models, particularly models representing patients from African ancestry who are disproportionately affected by UCS. Here, we establish an ancestrally diverse cohort of UCS patient-derived organoids (PDOs) with matched normal endometrial PDOs that preserve the histological, genomic and transcriptional features of the tumors from which they were derived. Across the cohort, UCS PDOs retain somatic mutations, copy number alterations and recapitulate biphasic epithelial and mesenchymal cell states at single-cell resolution, and model dynamic transitions along an epithelial-to-mesenchymal continuum. Integrated bulk and single-cell analyses identify mesenchymal, proliferative, and metabolic transcriptional programs in UCS, with prominent enrichment of CREB-family motifs. Functionally, UCS PDOs reproduce heterogeneous responses to carboplatin and paclitaxel, reveal sensitivity to CREB inhibition, and suggest a potential cooperative vulnerability to combined FGFR and YAP pathway inhibition. Together, these data establish a genomically faithful and ancestrally inclusive UCS PDOs platform for studying tumor plasticity, lineage-state regulation, and therapy response in an understudied and clinically aggressive gynecologic cancer.","rel_num_authors":23,"rel_authors":[{"author_name":"Santhilal Subhash","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Marie-Th\u00e9r\u00e8se Bammert","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Brian Yueh","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Kadir A Ozler","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Timothy Chu","author_inst":"New York Genome Center, New York, NY, USA"},{"author_name":"Melissa Kramer","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Pascal Belleau","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Astrid Desch\u00eanes","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Onur Eskiocak","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Charlie Chung","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Ali Oku","author_inst":"New York Genome Center, New York, NY, USA"},{"author_name":"Mali Barbi","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Northwell Health Cancer Institute, New Hyde Park, NY, USA"},{"author_name":"Aybuke Alici","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Megan Gorman","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Arielle Katcher","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Aaron Nizam","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Ariel Kredentser","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Divya Bhana","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Jonathan Werner","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Nicolas Robine","author_inst":"New York Genome Center, New York, NY, USA"},{"author_name":"Marina Frimer","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Gary L Goldberg","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker "},{"author_name":"Semir Beyaz","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"CellPulse: A Foundation Model of Coordinated Gene Dynamics Simulating Viral Infectious Diseases","rel_doi":"10.64898\/2026.04.22.720078","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720078","rel_abs":"Understanding how cells respond to perturbations like viral infections requires models capturing coordinated gene dynamics. However, current gene expression foundation models are predominantly reliant on single-cell data and static gene expression, limiting their applicability in real clinical scenarios. We present CellPulse, a direction-aware foundation model trained on the Virus Stimulated Atlas (VISTA), a newly curated atlas of over 23 million bulk RNA-sequencing differential expression profiles from viral infections. CellPulse models the direction and magnitude of gene expression changes via a structured representation of differential expression and a direction-aware attention mechanism, enabling the learning of coherent regulatory programs. It shows powerful diagnosing capability by accurately classifying 31 distinct virus types across diverse clinical and laboratory samples, solely from host transcriptional signatures. Crucially, without prior knowledge injection, CellPulse's interpretability reveals virus-associated host factors that mediate infection. Using a selection of host factors for in silico drug screening yielded numerous compounds with confirmed efficacies in wet-lab assays, while cell-based and animal experiments further verified the causal relationship between host targets and viral infections. Overall, CellPulse represents a generalizable foundation model for deciphering coordinated gene dynamics from bulk transcriptomics, bridging host response modeling with clinical relevance and therapeutic discovery for infectious diseases and beyond.","rel_num_authors":9,"rel_authors":[{"author_name":"Dan Liu","author_inst":"Institute of Software, CAS"},{"author_name":"Xiaoxu Zhu","author_inst":"Wuhan Institute of Virology, CAS"},{"author_name":"Libo Zhang","author_inst":"Institute of Software, CAS, Beijing, China"},{"author_name":"Deyu Xu","author_inst":"Wuhan Institute of Virology, CAS"},{"author_name":"Jing Lou","author_inst":"Wuhan Institute of Virology, CAS"},{"author_name":"Xiaobei Xiong","author_inst":"Wuhan Institute of Virology, CAS"},{"author_name":"Yujie Ren","author_inst":"State Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, CAS, Wuhan, China"},{"author_name":"Yanjun Wu","author_inst":"Institute of Software, Chinese Academy of Sciences (CAS), Beijing, China"},{"author_name":"Xi Zhou","author_inst":"State Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, CAS"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"The NMR Exchange Format (NEF): Specification and Applications","rel_doi":"10.64898\/2026.04.22.715536","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.715536","rel_abs":"The NMR Exchange Format (NEF) is a community-driven standard for representing NMR experimental data in a consistent, interoperable, and machine-readable form. Built on the STAR syntax, NEF provides a structured framework for storing and exchanging chemical shifts, peak lists, various types of structural restraints, and related metadata, thus allowing for data exchange across software platforms. By enabling direct, lossless transfer of information, NEF simplifies multi-software workflows, improves reproducibility, and supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles. We describe the NEF specification, its current implementation across commonly used NMR software packages, and its application in areas including biomolecular structure determination, metabolomics, and ligand screening. Testing demonstrates that NEF can be used to exchange complete datasets between programs without loss of information or functionality. We also outline recent developments and future directions, such as inclusion of NMR relaxation data and support for non-standard residue topologies. NEF growing adoption highlights its potential as a unifying standard for NMR data, enabling more efficient, transparent and collaborative research.","rel_num_authors":42,"rel_authors":[{"author_name":"Eliza Ploskon Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Kumaran Baskaran Dr","author_inst":"Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, UCONN Health, Farmington, CT-06030, USA."},{"author_name":"Roberto Tejero Prof","author_inst":"Departamento Quimica Fisica. Universidad de Valencia. Avda Dr. Moliner, 50. Burjassot (Valencia). Spain."},{"author_name":"Charles D. Schwieters Dr","author_inst":"Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA."},{"author_name":"Benjamin Bardiaux Dr","author_inst":"acterial Transmembrane Systems Unit, Institut Pasteur, Universite Paris-Cite, CNRS UMR3528, 75015, Paris, France."},{"author_name":"Peter Guentert Prof. Dr.","author_inst":"Institute of Molecular Physical Science, ETH Zurich, Zurich, Switzerland."},{"author_name":"Rasmus H. Fogh Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Aleksandras Gutmanas","author_inst":"Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB"},{"author_name":"Edward J. Brooksbank Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Masashi Yokochi","author_inst":"Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan."},{"author_name":"David S. Wishart Prof","author_inst":"Department of Biological Sciences, CW 405, Biological Sciences Building, University of Alberta, Edmonton, Alberta, Canada T6G 2E9"},{"author_name":"Jonathan R. Wedell","author_inst":"Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, UCONN Health, Farmington, CT-06030, USA."},{"author_name":"Wim F. Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, VUB\/ULB, Brussels, 1050, Belgium."},{"author_name":"Daniel Thompson","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Gary Thompson Dr","author_inst":"Wellcome Biomolecular NMR Facility, School of Natural Sciences, University of Kent,  Canterbury, CT2 7NZ, United Kingdom."},{"author_name":"Brian O. Smith Dr","author_inst":"School of Molecular Biosciences, College of Medical, Veterinary and Life Sciences, Joseph Black Building, University of Glasgow, G12 8QQ, United Kingdom."},{"author_name":"Saima Rehman","author_inst":"Centre for Host-Microbiome Interactions, Kings College London, London, SE1 9RT, United Kingdom."},{"author_name":"Theresa A. Ramelot","author_inst":"Chemistry and Chemical Biology \/ Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute,Troy, New York, USA."},{"author_name":"Timothy J. Ragan Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Alberto Perez Prof","author_inst":"Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida, 32607, USA."},{"author_name":"Binod L. Perera","author_inst":"Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida, 32607, USA."},{"author_name":"Ezra Peisach Dr","author_inst":"RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ, USA."},{"author_name":"Michael Nilges","author_inst":"Structural Bioinformatics Unit, Institut Pasteur, Universite Paris-Cite, CNRS UMR3528, 75015, Paris, France."},{"author_name":"Luca G. Mureddu Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Arup Mondal","author_inst":"Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida, 32607, USA."},{"author_name":"Emilia A. Lubicka Prof","author_inst":"Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics, G. Narutowicza 11, 80-233 Gdansk, Poland."},{"author_name":"Adam Liwo Prof","author_inst":"Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland."},{"author_name":"Genji Kurisu","author_inst":"Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan."},{"author_name":"Naohiro Kobayashi Dr","author_inst":"Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan."},{"author_name":"Piotr Klukowski","author_inst":"Institute of Biophysical Chemistry, Goethe University Frankfurt, Frankfurt am Main, Germany."},{"author_name":"Bruce A. Johnston","author_inst":"Structural Biology Initiative, Advanced Science Research Center at the CUNY Graduate Center, New York, NY, USA."},{"author_name":"Yuanpeng J. Huang Dr","author_inst":"Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180 USA."},{"author_name":"Jeffrey C. Hoch Prof","author_inst":"Molecular, Microbial and Structural Biology, UConn Health, 263 Farmington Avenue, Farmington, CT 06030 USA."},{"author_name":"Victoria A. Higman Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Torsten Herrmann","author_inst":"University Grenoble Alpes, CNRS, CEA, IBS, F-38000, Grenoble, France."},{"author_name":"Morgan W. Hayward Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"James A. Garnet Dr","author_inst":"Centre for Host-Microbiome Interactions, Kings College London, London, SE1 9RT, United Kingdom."},{"author_name":"David A. Case Prof","author_inst":"Department of Chemistry & Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA."},{"author_name":"Stephen K. Burley","author_inst":"RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ, USA."},{"author_name":"Paul D. Adams","author_inst":"Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA."},{"author_name":"Gaetano T. Montelione Prof","author_inst":"Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180 USA."},{"author_name":"Geerten W. Vuister Prof","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"The NMR Exchange Format (NEF): Specification and Applications","rel_doi":"10.64898\/2026.04.22.715536","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.715536","rel_abs":"The NMR Exchange Format (NEF) is a community-driven standard for representing NMR experimental data in a consistent, interoperable, and machine-readable form. Built on the STAR syntax, NEF provides a structured framework for storing and exchanging chemical shifts, peak lists, various types of structural restraints, and related metadata, thus allowing for data exchange across software platforms. By enabling direct, lossless transfer of information, NEF simplifies multi-software workflows, improves reproducibility, and supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles. We describe the NEF specification, its current implementation across commonly used NMR software packages, and its application in areas including biomolecular structure determination, metabolomics, and ligand screening. Testing demonstrates that NEF can be used to exchange complete datasets between programs without loss of information or functionality. We also outline recent developments and future directions, such as inclusion of NMR relaxation data and support for non-standard residue topologies. NEF growing adoption highlights its potential as a unifying standard for NMR data, enabling more efficient, transparent and collaborative research.","rel_num_authors":42,"rel_authors":[{"author_name":"Eliza Ploskon Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Kumaran Baskaran Dr","author_inst":"Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, UCONN Health, Farmington, CT-06030, USA."},{"author_name":"Roberto Tejero Prof","author_inst":"Departamento Quimica Fisica. Universidad de Valencia. Avda Dr. Moliner, 50. Burjassot (Valencia). Spain."},{"author_name":"Charles D. Schwieters Dr","author_inst":"Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA."},{"author_name":"Benjamin Bardiaux Dr","author_inst":"acterial Transmembrane Systems Unit, Institut Pasteur, Universite Paris-Cite, CNRS UMR3528, 75015, Paris, France."},{"author_name":"Peter Guentert Prof. Dr.","author_inst":"Institute of Molecular Physical Science, ETH Zurich, Zurich, Switzerland."},{"author_name":"Rasmus H. Fogh Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Aleksandras Gutmanas","author_inst":"Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB"},{"author_name":"Edward J. Brooksbank Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Masashi Yokochi","author_inst":"Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan."},{"author_name":"David S. Wishart Prof","author_inst":"Department of Biological Sciences, CW 405, Biological Sciences Building, University of Alberta, Edmonton, Alberta, Canada T6G 2E9"},{"author_name":"Jonathan R. Wedell","author_inst":"Biological Magnetic Resonance Data Bank, Department of Molecular Biology and Biophysics, UCONN Health, Farmington, CT-06030, USA."},{"author_name":"Wim F. Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, VUB\/ULB, Brussels, 1050, Belgium."},{"author_name":"Daniel Thompson","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Gary Thompson Dr","author_inst":"Wellcome Biomolecular NMR Facility, School of Natural Sciences, University of Kent,  Canterbury, CT2 7NZ, United Kingdom."},{"author_name":"Brian O. Smith Dr","author_inst":"School of Molecular Biosciences, College of Medical, Veterinary and Life Sciences, Joseph Black Building, University of Glasgow, G12 8QQ, United Kingdom."},{"author_name":"Saima Rehman","author_inst":"Centre for Host-Microbiome Interactions, Kings College London, London, SE1 9RT, United Kingdom."},{"author_name":"Theresa A. Ramelot","author_inst":"Chemistry and Chemical Biology \/ Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute,Troy, New York, USA."},{"author_name":"Timothy J. Ragan Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Alberto Perez Prof","author_inst":"Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida, 32607, USA."},{"author_name":"Binod L. Perera","author_inst":"Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida, 32607, USA."},{"author_name":"Ezra Peisach Dr","author_inst":"RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ, USA."},{"author_name":"Michael Nilges","author_inst":"Structural Bioinformatics Unit, Institut Pasteur, Universite Paris-Cite, CNRS UMR3528, 75015, Paris, France."},{"author_name":"Luca G. Mureddu Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Arup Mondal","author_inst":"Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida, 32607, USA."},{"author_name":"Emilia A. Lubicka Prof","author_inst":"Gdansk University of Technology, Faculty of Electronics, Telecommunications and Informatics, G. Narutowicza 11, 80-233 Gdansk, Poland."},{"author_name":"Adam Liwo Prof","author_inst":"Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland."},{"author_name":"Genji Kurisu","author_inst":"Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan."},{"author_name":"Naohiro Kobayashi Dr","author_inst":"Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan."},{"author_name":"Piotr Klukowski","author_inst":"Institute of Biophysical Chemistry, Goethe University Frankfurt, Frankfurt am Main, Germany."},{"author_name":"Bruce A. Johnston","author_inst":"Structural Biology Initiative, Advanced Science Research Center at the CUNY Graduate Center, New York, NY, USA."},{"author_name":"Yuanpeng J. Huang Dr","author_inst":"Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180 USA."},{"author_name":"Jeffrey C. Hoch Prof","author_inst":"Molecular, Microbial and Structural Biology, UConn Health, 263 Farmington Avenue, Farmington, CT 06030 USA."},{"author_name":"Victoria A. Higman Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"Torsten Herrmann","author_inst":"University Grenoble Alpes, CNRS, CEA, IBS, F-38000, Grenoble, France."},{"author_name":"Morgan W. Hayward Dr","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"},{"author_name":"James A. Garnet Dr","author_inst":"Centre for Host-Microbiome Interactions, Kings College London, London, SE1 9RT, United Kingdom."},{"author_name":"David A. Case Prof","author_inst":"Department of Chemistry & Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA."},{"author_name":"Stephen K. Burley","author_inst":"RCSB Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ, USA."},{"author_name":"Paul D. Adams","author_inst":"Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA."},{"author_name":"Gaetano T. Montelione Prof","author_inst":"Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180 USA."},{"author_name":"Geerten W. Vuister Prof","author_inst":"Division of Molecular and Cellular Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Lancaster Road, Leicester, LE1 9HN,"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"CellChem: Cellular transcriptional responses reshape molecular representation space for efficient and multi-scale drug discovery","rel_doi":"10.64898\/2026.04.22.719826","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.719826","rel_abs":"Despite decades of progress in computational drug discovery, deep learning-based molecular representation models remain largely structure-centric, assuming that chemical similarity approximates functional similarity. However, drug effects in cells are shaped not only by chemical similarity but also by molecular interactions in the cellular context. To capture this complexity, we introduce CellChem, a cellular-chemical representation-learning framework that learns cell-guided molecular representations of small-molecule actions within cells. By incorporating large-scale cellular transcriptional profiles during pretraining, CellChem reshapes molecular representation space from structure-centric to a balanced integration of structure and function. The learned CellChem molecular representations exhibit biologically meaningful geometric organization, such that distances between molecules encode not only structural similarity but also similarity in the cellular responses they elicit, independent of downstream tasks. Using downstream derivative models such as cell-guided compound-protein interaction prediction and drug-induced transcriptional response profile generation, CellChem supports highly efficient, multi-scale drug discovery, achieving significantly better performance than traditional models.","rel_num_authors":17,"rel_authors":[{"author_name":"Jiaxiao Chen","author_inst":"Peking University"},{"author_name":"Letian Lin","author_inst":"Peking University"},{"author_name":"Yishen Wang","author_inst":"Peking University"},{"author_name":"Yifan Lin","author_inst":"Infinite Intelligence Pharma"},{"author_name":"Yu Li","author_inst":"Peking University"},{"author_name":"Weilin Zhang","author_inst":"Infinite Intelligence Pharma"},{"author_name":"Yiwei Fu","author_inst":"Peking University"},{"author_name":"Jin Xie","author_inst":"Peking University"},{"author_name":"Jintao Zhu","author_inst":"Peking University"},{"author_name":"Chao Sun","author_inst":"Shanghai Qilu Pharmaceutical RD Center"},{"author_name":"Guqin Shi","author_inst":"Shanghai Qilu Pharmaceutical RD Center"},{"author_name":"Zheng Wang","author_inst":"Shanghai Qilu Pharmaceutical RD Center"},{"author_name":"Haoyu Lin","author_inst":"Peking University"},{"author_name":"Liying Wang","author_inst":"Peking University"},{"author_name":"Minghua Deng","author_inst":"Peking University"},{"author_name":"Luhua Lai","author_inst":"Peking University"},{"author_name":"Jianfeng Pei","author_inst":"Peking University"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Sex differences in cardiomyocyte proteomic responses to the cardiotoxic chemotherapy drug doxorubicin","rel_doi":"10.64898\/2026.04.21.715711","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.715711","rel_abs":"Anthracyclines, including doxorubicin, are widely used chemotherapeutic agents, but dose-dependent cardiotoxicity limits their clinical utility and increases the risk of heart failure in cancer survivors. In paediatric patients, female sex is a significant risk factor for anthracycline-associated cardiotoxicity, yet pre-clinical studies rarely investigate sex differences in immature hearts. Here, we provide a proteomic dataset from primary cardiomyocytes, isolated from postnatal day 2 rat hearts and treated with a clinically relevant concentration of doxorubicin. Analysis of proteins present in all samples identified candidates previously shown to be regulated by doxorubicin in adult hearts, as well as candidates that may be specifically regulated in young hearts. This dataset provides a resource for generating hypotheses on molecular mechanisms contributing to sex differences in juvenile doxorubicin-induced cardiotoxicity.","rel_num_authors":8,"rel_authors":[{"author_name":"Sanela Dozic","author_inst":"Monash University"},{"author_name":"Michael G. Leeming","author_inst":"Bio21 Molecular Science & Biotechnology Institute"},{"author_name":"Keshava K Datta","author_inst":"MRC Laboratory of Medical Sciences"},{"author_name":"Hitesh Kore","author_inst":"The University of Melbourne"},{"author_name":"Erin J Howden","author_inst":"University of New South Wales"},{"author_name":"Benjamin L Parker","author_inst":"The University of Melbourne"},{"author_name":"Lea M. D. Delbridge","author_inst":"The University of Melbourne"},{"author_name":"Kate L Weeks","author_inst":"The University of Melbourne"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Protein structure-informed deep learning enables species-specific codon optimization","rel_doi":"10.64898\/2026.04.21.720047","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720047","rel_abs":"Codon usage bias is highly species-specific, posing a major challenge for heterologous protein expression. Existing deep learning approaches to codon optimization rely primarily on DNA or protein sequence information and largely neglect constraints imposed by protein structure and folding. Here, we present Protein structure-Informed Species-specific Codon Optimization (PISCO), a Geometric Vector Perceptron (GVP)-based model that integrates protein sequence, three-dimensional protein structure, and host codon usage statistics to generate optimal, host-specific codon sequences. Compared with protein-structure-agnostic models, PISCO improves codon recovery by 6% and substantially increases similarity to natural coding sequences, reducing divergence by at least 42% in Codon Similarity Index (CSI), 50% in Codon Frequency Distribution (CFD), and 14% in Dynamic Time Warping (DTW) metrics. Ablation analyses demonstrate that incorporating protein folding kinetics and host-specific information is critical to these gains. Moreover, by leveraging host codon usage statistics, PISCO generalizes to optimize codon sequences for species absent from the training data. An autoregressive variant of PISCO further enhances concordance with natural codon usage patterns, at the cost of a modest reduction in codon recovery rate. Wet-lab validation confirms that PISCO-optimized sequences significantly enhance protein solubility and functional expression. Together, these results establish protein structure as a key determinant of species-specific codon optimization and provide a transferable framework for structure-aware gene design.","rel_num_authors":7,"rel_authors":[{"author_name":"Wenzhe Jin","author_inst":"University of Chinese Academy of Science"},{"author_name":"Wuwei Tan","author_inst":"MoleculeMind Ltd"},{"author_name":"Hui Li","author_inst":"MoleculeMind Ltd"},{"author_name":"Xiangyu Ji","author_inst":"MoleculeMind Ltd"},{"author_name":"Ming Li","author_inst":"MoleculeMind Ltd"},{"author_name":"Deqiang Zhang","author_inst":"MoleculeMind"},{"author_name":"Jinbo Xu","author_inst":"MoleculeMind Ltd"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"A Single-Cell Atlas of Uterine Carcinosarcoma from Diverse Ancestries","rel_doi":"10.64898\/2026.04.21.720013","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720013","rel_abs":"Uterine carcinosarcoma (UCS) is an aggressive endometrial malignancy characterized by epithelial-mesenchymal plasticity, early metastasis, and poor therapeutic response. However, its single-cell organization and microenvironmental interactions remain incompletely defined, particularly in patients of African ancestry who are underrepresented in genomic datasets despite a disproportionate disease burden. Here, we generate a single-cell transcriptomic atlas of UCS from a diverse cohort enriched for patients of African ancestry, integrated with whole-genome sequencing. We identify pronounced inter-patient heterogeneity and resolve epithelial-like, mesenchymal-like, transitional, and stem-like malignant states. Copy-number and trajectory analyses reveal multiple subclones linked along epithelial, progenitor-like, and mesenchymal programs, consistent with metaplastic transitions. Compared to normal endometrium, primary tumors exhibit epithelial-mesenchymal-transition (EMT), mTORC1, and glycolytic programs, whereas metastases show enhanced TNF-NF{kappa}B signaling linked to invasion. The tumor microenvironment comprises diverse immunosuppressive myeloid states and heterogeneous cancer-associated fibroblast populations, including pericyte-like and matrix-remodeling subsets that act as communication hubs via chemokine and immune-checkpoint signaling. These data define the UCS cellular ecosystem in which malignant plasticity is coupled to stromal-immune cell remodeling in a patient cohort of diverse ancestries.","rel_num_authors":17,"rel_authors":[{"author_name":"Santhilal Subhash","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Marie-Th\u00e9r\u00e8se Bammert","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Brian Yueh","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Timothy Chu","author_inst":"New York Genome Center, New York, NY, USA"},{"author_name":"Mali Barbi","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Northwell Health Cancer Institute, New Hyde Park, NY, USA"},{"author_name":"Aybuke Alici","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Onur Eskiocak","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Graduate Program in Genetics, Stony Brook University, Stony Brook, NY, USA"},{"author_name":"Kadir A Ozler","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Aaron Nizam","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Arielle Katcher","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Charlie Chung","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Vyom Shah","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Elif Ozcelik","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Nicolas Robine","author_inst":"New York Genome Center"},{"author_name":"Marina Frimer","author_inst":"Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker School of Medicine at Hofstra\/Northwell, New Hyde Park, NY, "},{"author_name":"Gary L Goldberg","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Northwell Health, Zucker "},{"author_name":"Semir Beyaz","author_inst":"Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Cooling fast and slow: Characterising the effects of vitrification in cryo-EM and the subsequent recovery of equilibrium populations","rel_doi":"10.64898\/2026.04.21.720011","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720011","rel_abs":"Single-particle cryogenic electron microscopy (cryo-EM) has enabled near-atomic resolution structure determination of diverse biomolecules. Because the high vacuum required for electron microscopy prevents the imaging of liquid-phase samples, cryo-EM samples are prepared by plunging the sample into a cryogen, rapidly cooling the sample and suspending the ensemble of biomolecules in a matrix of water glass. However, the effects of this vitrification on the biomolecular ensemble are unknown, complicating efforts to use cryo-EM to derive conformational ensembles of biomolecules. To study these effects, we carried out extensive molecular dynamics simulations (over 50 milliseconds) of the Trp-cage miniprotein at equilibrium and undergoing rapid cooling. We simulated seven cooling rates spanning three orders of magnitude, with the slowest coolings matching experimental rates. By inspecting molecular mobility and density-temperature equations of state for water with and without protein, we found that water vitrification is unaltered by the protein. To track protein conformation changes, and to relate them to conformational kinetics, we made a Markov State Model (MSM) of Trp-cage from 5.4 milliseconds of equilibrium sampling at 277 K. We observed that MSM states with a characteristic time longer than the duration of the non-equilibrium cooling, tend to be more robust to artefacts induced by such cooling. Critically, although we observe that some states vanish in the equilibrium ensemble at 230 K, none do in our nonequilibrium cooled ensembles. However, to account for perturbations induced by nonequilibrium cooling for more labile states, we developed a thermodynamic inference framework for recovering equilibrium populations from the measured vitrified ensembles. These results indicate that cryo-EM has the capacity to be a reliable and accurate biophysical technique for the study of biomolecular ensembles.","rel_num_authors":9,"rel_authors":[{"author_name":"Robert Clark","author_inst":"University of Oxford"},{"author_name":"Louis G. Smith","author_inst":"Flatiron Institute"},{"author_name":"Matthew P. Leighton","author_inst":"Yale University"},{"author_name":"Ryan J. Szukalo","author_inst":"Princeton University"},{"author_name":"Syma Khalid","author_inst":"University of Oxford"},{"author_name":"Pablo G. Debenedetti","author_inst":"Princeton University"},{"author_name":"Pilar Cossio","author_inst":"Flatiron Institute"},{"author_name":"Miro A. Astore","author_inst":"Flatiron Institute"},{"author_name":"Sonya M. Hanson","author_inst":"Flatiron Institute"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Conformational Ensembles of the Disordered 4E-BP2:eIF4E Complex Restrained by smFRET Experiments","rel_doi":"10.64898\/2026.04.21.719986","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719986","rel_abs":"Eukaryotic cap-dependent translation initiation is regulated by binding of the predominantly folded eukaryotic initiation factor 4E (eIF4E) to the intrinsically disordered eIF4E binding proteins (4E-BPs). Here, we report full-length atomistic conformational ensembles generated by IDPConformerGenerator and optimized by X-EISDv2 workflow for both apo 4E-BP2, the neuronal 4E-BP, and 4E-BP2 in complex with eIF4E, using data from single-molecule fluorescence and nuclear magnetic resonance (NMR), together with select coordinates from a 4E-BP1:eIF4E crystal structure. Structural sampling within dynamic complexes is often under-appreciated, with NMR and crystal structure data for 4E-BP:eIF4E suggesting different degrees of structural heterogeneity. Our ensemble models validated by solution spectroscopy data enable comparison of free 4E-BP2 and its complex with eIF4E. This shows a delocalization of contacts around canonical regions, which supports previous findings of unidirectional conditional occupancy of the binding sites. Two new contact regions emerged: one between the disordered N-termini of eIF4E and 4E-BP2, which may play an allosteric role in tuning the binding affinity, and the other between the C-terminus of 4E-BP2 and an extended region of eIF4E, which is consistent with the extended, dynamic binding interface that we reported previously. These results support a model of translation regulation in which the dynamic 4E-BP2:eIF4E complex facilitates accessibility of regulatory sites of 4E-BP2 when bound.","rel_num_authors":6,"rel_authors":[{"author_name":"Spencer Smyth","author_inst":"University of Toronto"},{"author_name":"Zi Hao Liu","author_inst":"University of Toronto"},{"author_name":"Thomas Tsangaris","author_inst":"University of Toronto"},{"author_name":"Teresa Head-Gordon","author_inst":"UC Berkeley"},{"author_name":"Julie Deborah Forman-Kay","author_inst":"Hospital for Sick Children"},{"author_name":"Claudiu C Gradinaru","author_inst":"University of Toronto"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Transcriptional regulators predicted to drive macrophage dysregulation during impaired wound healing in diabetic mice","rel_doi":"10.64898\/2026.04.21.719960","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719960","rel_abs":"Dysregulation of Mo\/M{varphi} activity is known to contribute to impaired healing in diabetes; however, the mechanisms underlying this dysregulation are not well understood. In this study, we used a variety of bioinformatics approaches along with our time series scRNA-seq data on wound Mo\/M{varphi} from non-diabetic and diabetic mice to identify transcriptional regulators (TRs) that drive Mo\/M{varphi} state transitions during normal and impaired healing. First, we used the Lamian framework and our newly developed Pseudotime Graph Diffusion method to show that state transitions from early stage phenotypes to later stage reparative and antigen presenting phenotypes characteristic of normally healing wounds are impaired and that transitions to inflammatory, foam cell-like, and Lyve-1 M{varphi} phenotypes are enhanced during impaired healing of diabetic mice. Using our BITFAM model, we identified a broad range of TRs predicted to be preferentially active in each cell state and using CellOracle, we performed in silico perturbation to identify groups of TRs predicted to drive cell state transitions along multiple trajectories (e.g. CEBPA, IRF8), whereas other TRs were predicted to drive cell state transition towards reparative phenotypes (e.g. NR1H3, NR3C1) or towards an antigen-presenting phenotype (e.g. IRF4, OGT). Selected findings were validated using existing experimental data, confirming the usefulness of this approach. In conclusion, we identified TRs that likely drive Mo\/M{varphi} state transitions towards desirable and undesirable phenotypes for wound healing. These findings provide insight into novel targets for altering Mo\/M{varphi} phenotypes to promote healing of diabetic wounds.","rel_num_authors":4,"rel_authors":[{"author_name":"Brandon E Lukas","author_inst":"University of Illinois Chicago"},{"author_name":"Jingbo Pang","author_inst":"University of Illinois Chicago"},{"author_name":"Yang Dai","author_inst":"University of Illinois Chicago"},{"author_name":"Timothy J Koh","author_inst":"University of Illinois Chicago"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"A human specific CCG repeat in the RBFOX1 promoter is implicated in speech and autism","rel_doi":"10.64898\/2026.04.21.719679","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719679","rel_abs":"Human speech likely arose from regulatory changes for speech-related brain regions, yet causal variants and mechanisms remain unclear. RBFOX1 is a prime candidate, showing specialized expression in vocal learning circuits of human and zebra finch brains and carrying a promoter deletion linked to autism spectrum disorder (ASD) with language dysfunction. Here, we perform integrative analyses with cross-species brain single-cell multi-omic data and the more complete genomes of the Vertebrate Genomes Project. We identify a human-specific CCG insertion in the RBFOX1 promoter, creating a human-unique CCG-repeated motif. This motif is fixed in both archaic and modern humans but is disrupted by rare clinical variants that exhibit language-related phenotypes and autism. Binding motif models predicted, and reporter assays reveal that this human allele drives stronger EGR1-dependent transcription than its chimpanzee allele. Genome-wide, 107 other genes have core promoters with the identical motif; enriched for postsynapse and implicated in ASD, including PTCHD1. At the PTCHD1 promoter, an ASD-causative CCG-repeated variant enhances EGR1-dependent promoter activity, and its activating effects are predicted in human brain regions using AlphaGenome. Our findings suggest that small variations in the number of CCG repeats in promoters can exert a large regulatory effect on complex traits and their associated disorders.","rel_num_authors":3,"rel_authors":[{"author_name":"Chul Lee","author_inst":"The Rockefeller University"},{"author_name":"Matthew H Davenport","author_inst":"The Rockefeller University"},{"author_name":"Erich D Jarvis","author_inst":"The Rockefeller University"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"A SuperSelective primer-based real-time PCR Platform for hypersensitive detection of azole heteroresistance in Cryptococcus neoformans","rel_doi":"10.64898\/2026.04.23.720376","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720376","rel_abs":"Cryptococcus neoformans is the leading cause of fungal meningitis with limited treatment options, making early and accurate diagnosis critical for improved patient outcome. Current diagnostic methods for cryptococcosis rely largely on capsule antigen detection and fungal culture, which are time-consuming and unable to identify mutation-driven antifungal heteroresistance. In this study, we have developed a SuperSelective primer-based PCR (SSP-PCR) platform for the rapid and specific detection of azole resistance-associated single-nucleotide polymorphisms (SNPs). We demonstrate that SSP-PCR reliably detected a single copy mutant allele in the presence of excess wild-type (WT) DNA, with sensitivity reaching a 1:104 mutant-to-WT ratio. Incorporating molecular beacon (MB) probes with our SSP-PCR platform further enhanced amplification specificity, enabling selective detection of the ERG11 Y145F (A434T) mutation that is known to cause high azole resistance. Using genomic DNA from in vitro cultures and mouse lung tissues infected with either WT strain H99 strain or a fluconazole-hyper-resistant mrl1 clinical isolate that carries the ERG11(A434T) mutation, or both strains, we successfully detected the A434T mutant allele in both settings. Moreover, our SSP-PCR simultaneously identified ERG11(A434T) and the multi-azole resistance-associated ERG11(G1885A) mutant alleles in a single-tube duplex reaction. Collectively, the SSP-PCR platform provides a robust and ultrasensitive molecular approach for the detection of azole resistance and heteroresistance in C. neoformans, with strong potential for high-throughput clinical screening applications. Key words: Cryptococcus neoformans, heteroresistance, fluconazole, molecular beacon, SuperSelective Primer","rel_num_authors":5,"rel_authors":[{"author_name":"Siddhi Pawar","author_inst":"Rutgers, The State University Of New Jersey"},{"author_name":"Honglin H. Xue","author_inst":"Rutgers, The State University of NJ"},{"author_name":"Sophia Wang","author_inst":"Rutgers, The State University Of New Jersey"},{"author_name":"Salvatore Marras","author_inst":"Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, New Jersey, USA"},{"author_name":"Chaoyang Xue","author_inst":"Rutgers The State University of New Jersey"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Synovial transcriptional clusters link cartilage degeneration to cell-type-specific gene expression in knee osteoarthritis","rel_doi":"10.64898\/2026.04.21.719697","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719697","rel_abs":"Objectives: To identify synovial transcriptional clusters in human knee osteoarthritis (OA) and determine how these relate to synovial histologic features, cell-type-associated gene expression, and cartilage degeneration severity. Methods: Bulk RNA sequencing (RNA-seq) of synovial tissue from n = 135 patients with knee OA was analyzed using consensus clustering. Clusters were compared by clinical and histologic features, including cartilage degeneration severity (OARSI score). Single-cell RNA-seq (n = 18) and spatial transcriptomics were used to relate cartilage degeneration-associated gene expression patterns to synovial cell populations. Results: Four synovial transcriptional clusters that differed in synovial histologic features and cartilage degeneration severity were identified. Greater cartilage degeneration was associated with enrichment of lining fibroblast- and inflammatory myeloid-associated gene expression, whereas lesser cartilage degeneration was associated with enrichment of sublining fibroblast, endothelial, mural cell, and adipocyte-associated gene expression. Conclusions: Human knee OA synovium segregates into transcriptional clusters associated with cartilage degeneration severity. Synovial transcriptional heterogeneity corresponds to cell-type-associated gene expression.","rel_num_authors":27,"rel_authors":[{"author_name":"Michael R. Mazzucco","author_inst":"The Rockefeller University"},{"author_name":"Bella Mehta","author_inst":"Hospital for Special Surgery"},{"author_name":"Jenelys Ruiz-Ortiz","author_inst":"The Rockefeller University"},{"author_name":"Caryn Hale","author_inst":"The Rockefeller University"},{"author_name":"Fuadur Omi","author_inst":"The Rockefeller University"},{"author_name":"Purva Singh","author_inst":"Hospital for Special Surgery"},{"author_name":"Ruoxi Yuan","author_inst":"Hospital for Special Surgery"},{"author_name":"Samantha Lessard","author_inst":"Hospital for Special Surgery"},{"author_name":"Eun Kyung Song","author_inst":"Stanford University"},{"author_name":"Mengrui Zhang","author_inst":"Stanford University"},{"author_name":"Shady Younis","author_inst":"Stanford University"},{"author_name":"William H. Robinson","author_inst":"Stanford University"},{"author_name":"Daniel Ramirez","author_inst":"Hospital for Special Surgery"},{"author_name":"Edward DiCarlo","author_inst":"Hospital for Special Surgery"},{"author_name":"Wei Wang","author_inst":"The Rockefeller University"},{"author_name":"Thomas Carroll","author_inst":"The Rockefeller University"},{"author_name":"Jose Rodriguez","author_inst":"Hospital for Special Surgery"},{"author_name":"Peter Sculco","author_inst":"Hospital for Special Surgery"},{"author_name":"Xiaoshun Li","author_inst":"Hospital for Special Surgery"},{"author_name":"YiYuan Wu","author_inst":"Weill Cornell Medicine"},{"author_name":"Robert B. Darnell","author_inst":"The Rockefeller University"},{"author_name":"Martin Lotz","author_inst":"Scripps Research"},{"author_name":"Rachel E. Miller","author_inst":"Rush University Medical Center"},{"author_name":"Tristan Maerz","author_inst":"University of Michigan"},{"author_name":"Anne-Marie Malfait","author_inst":"Rush University Medical Center"},{"author_name":"Miguel Otero","author_inst":"Hospital for Special Surgery"},{"author_name":"Dana E. Orange","author_inst":"The Rockefeller University"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Targeting Gi\/o-coupled GPCRs to inhibit nociceptors: insights from the serotonin receptor Htr1b and triptans","rel_doi":"10.64898\/2026.04.22.719367","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.719367","rel_abs":"Pain perception is initiated upon activation of nociceptors of the dorsal root ganglia (DRG) and trigeminal ganglia. We identified G protein-coupled receptors (GPCRs) expressed in CGRP+ mouse and human nociceptors and found that agonists of several identified Gi\/o-coupled and orphan GPCRs attenuated neuronal excitability. Experiments focusing on the Gi\/o-coupled serotonin receptor Htr1b, which is expressed in mouse and human CGRP+ DRG neurons, revealed that Htr1b\/1d agonists, the triptans sumatriptan and zolmitriptan, attenuated CGRP+ neuron excitability in vitro and exhibited analgesia across several pain models, including neuropathic pain. Conditional genetic deletion experiments showed that triptan-induced analgesia is mediated by Htr1b expressed in A-fiber mechanonociceptors. Also, triptan-associated adverse effects are partially mediated by Htr1b-independent targets. Further testing identified the GPCR Gpr19 as an additional promising target for treating pain. These findings establish a preclinical screening platform for identifying novel analgesics and reveal nociceptor GPCRs that may be targeted to treat pain.","rel_num_authors":12,"rel_authors":[{"author_name":"Jing Peng","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Brianna T Sanchez","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Anda M Chirila","author_inst":"Brown University"},{"author_name":"Xiangsunze Zeng","author_inst":"BOSTON CHILDREN'S HOSPITAL"},{"author_name":"Michelle M DeLisle","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Lijun Qi","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Jiayin Xiao","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Karina Lezgiyeva","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Sarah A Low","author_inst":"Boston Children's Hospital"},{"author_name":"Clifford J Woolf","author_inst":"Boston Children's Hospital"},{"author_name":"Nikhil Sharma","author_inst":"Columbia University"},{"author_name":"David D Ginty","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Targeting Gi\/o-coupled GPCRs to inhibit nociceptors: insights from the serotonin receptor Htr1b and triptans","rel_doi":"10.64898\/2026.04.22.719367","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.719367","rel_abs":"Pain perception is initiated upon activation of nociceptors of the dorsal root ganglia (DRG) and trigeminal ganglia. We identified G protein-coupled receptors (GPCRs) expressed in CGRP+ mouse and human nociceptors and found that agonists of several identified Gi\/o-coupled and orphan GPCRs attenuated neuronal excitability. Experiments focusing on the Gi\/o-coupled serotonin receptor Htr1b, which is expressed in mouse and human CGRP+ DRG neurons, revealed that Htr1b\/1d agonists, the triptans sumatriptan and zolmitriptan, attenuated CGRP+ neuron excitability in vitro and exhibited analgesia across several pain models, including neuropathic pain. Conditional genetic deletion experiments showed that triptan-induced analgesia is mediated by Htr1b expressed in A-fiber mechanonociceptors. Also, triptan-associated adverse effects are partially mediated by Htr1b-independent targets. Further testing identified the GPCR Gpr19 as an additional promising target for treating pain. These findings establish a preclinical screening platform for identifying novel analgesics and reveal nociceptor GPCRs that may be targeted to treat pain.","rel_num_authors":12,"rel_authors":[{"author_name":"Jing Peng","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Brianna T Sanchez","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Anda M Chirila","author_inst":"Brown University"},{"author_name":"Xiangsunze Zeng","author_inst":"BOSTON CHILDREN'S HOSPITAL"},{"author_name":"Michelle M DeLisle","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Lijun Qi","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Jiayin Xiao","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Karina Lezgiyeva","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"},{"author_name":"Sarah A Low","author_inst":"Boston Children's Hospital"},{"author_name":"Clifford J Woolf","author_inst":"Boston Children's Hospital"},{"author_name":"Nikhil Sharma","author_inst":"Columbia University"},{"author_name":"David D Ginty","author_inst":"Harvard Medical School, Howard Hughes Medical Institute"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Selective conservation of symbiont cell-surface glycans across generations in a vertically transmitting coral","rel_doi":"10.64898\/2026.04.21.719984","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719984","rel_abs":"Coral resilience under climate change depends on the stability of coral-Symbiodiniaceae symbioses. While vertically transmitting corals inherit symbionts directly from parental colonies, the extent to which symbiont cellular traits are conserved across life stages remains unclear. Here, we examined cell-surface glycan profiles of Symbiodiniaceae in parental colonies and eggs of the coral Montipora capitata. Glycan signatures were structured by symbiont genus and differed between life stages, with mannose\/glucose- and galactose-containing glycoproteins as primary drivers of variation. Despite life-stage differences, parent-offspring comparisons revealed significant conservation of glycan profiles, indicating intergenerational transmission of symbiont cellular traits that differed between Cladocopium and Durusdinium and were driven by distinct glycan classes. These results suggest that vertical transmission preserves key recognition-relevant glycans while allowing flexibility in other symbionts' surface traits, providing a mechanistic basis for symbiosis stability.","rel_num_authors":9,"rel_authors":[{"author_name":"Giada Tortorelli","author_inst":"University of Hawaii"},{"author_name":"Nerissa Fisher","author_inst":"University of Hawaii"},{"author_name":"Alyssa Varela","author_inst":"University of Hawaii"},{"author_name":"Sabrina Rosset","author_inst":"University of Hawaii, Michigan State University"},{"author_name":"Immy Ashley","author_inst":"University of Hawaii, University of California Los Angeles"},{"author_name":"Eva Majerova","author_inst":"University of Hawaii"},{"author_name":"Khalil Smith","author_inst":"University of Hawaii"},{"author_name":"Kira Hughes","author_inst":"State of Hawaii Division of Aquatic Resources"},{"author_name":"Crawford Drury","author_inst":"University of Hawaii"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Rewiring Mitochondrial Phosphatidylethanolamine Metabolism Identifies New and Unaccounted Trafficking Steps","rel_doi":"10.64898\/2026.04.22.720193","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720193","rel_abs":"The distinct compositions of the two mitochondrial membranes are generated through a combination of phospholipids that mitochondria can make and those they take; both processes depend on a series of distinct lipid trafficking steps. Mitochondria make phosphatidylethanolamine (PE) through the action of the phosphatidylserine decarboxylase Psd1, an intermembrane space (IMS)-facing integral inner membrane (IM) protein. Psd1 has been proposed to act on its endoplasmic reticulum-derived substrate, phosphatidylserine (PS), after its transport to the mitochondrial outer membrane (OM) and either following its Ups2\/Mdm35-mediated transport across the IMS to the IM or instead, on the IMS-side of the OM in a process enabled by the mitochondrial contact site and cristae organizing system (MICOS). Here, we implement a two-pronged Psd1 rewiring-based strategy predicted to either 1) circumvent the need for Ups2\/Mdm35 and\/or MICOS; or 2) selectively ablate the ability of Psd1 to work in trans. Our results with yeast harboring Psd1 targeted to the OM demonstrate that, with respect to mitochondrial PE production, Ups2\/Mdm35 and MICOS indeed function within the IMS. Using yeast expressing a topologically inverted Psd1 chimera that faces the matrix, we identify previously unappreciated transbilayer lipid trafficking steps within the IM and show that Psd1 does not operate via a MICOS-organized in trans mechanism. Further, retained flux through inverted Psd1 when both Ups2\/Mdm35 and MICOS are absent strongly implicates the existence of a major, yet presently unknown, mediator(s) of lipid movement across the IMS. Collectively, these data suggest a new model of how mitochondrial membrane diversity is established and maintained.","rel_num_authors":5,"rel_authors":[{"author_name":"Rashima Prem","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"author_name":"Erica Avery","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"author_name":"Juliana M. Marquez","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"author_name":"Chi Xie","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"author_name":"Steven M Claypool","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins School of Medicine, Baltimore, MD 21205, USA"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Rewiring Mitochondrial Phosphatidylethanolamine Metabolism Identifies New and Unaccounted Trafficking Steps","rel_doi":"10.64898\/2026.04.22.720193","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720193","rel_abs":"The distinct compositions of the two mitochondrial membranes are generated through a combination of phospholipids that mitochondria can make and those they take; both processes depend on a series of distinct lipid trafficking steps. Mitochondria make phosphatidylethanolamine (PE) through the action of the phosphatidylserine decarboxylase Psd1, an intermembrane space (IMS)-facing integral inner membrane (IM) protein. Psd1 has been proposed to act on its endoplasmic reticulum-derived substrate, phosphatidylserine (PS), after its transport to the mitochondrial outer membrane (OM) and either following its Ups2\/Mdm35-mediated transport across the IMS to the IM or instead, on the IMS-side of the OM in a process enabled by the mitochondrial contact site and cristae organizing system (MICOS). Here, we implement a two-pronged Psd1 rewiring-based strategy predicted to either 1) circumvent the need for Ups2\/Mdm35 and\/or MICOS; or 2) selectively ablate the ability of Psd1 to work in trans. Our results with yeast harboring Psd1 targeted to the OM demonstrate that, with respect to mitochondrial PE production, Ups2\/Mdm35 and MICOS indeed function within the IMS. Using yeast expressing a topologically inverted Psd1 chimera that faces the matrix, we identify previously unappreciated transbilayer lipid trafficking steps within the IM and show that Psd1 does not operate via a MICOS-organized in trans mechanism. Further, retained flux through inverted Psd1 when both Ups2\/Mdm35 and MICOS are absent strongly implicates the existence of a major, yet presently unknown, mediator(s) of lipid movement across the IMS. Collectively, these data suggest a new model of how mitochondrial membrane diversity is established and maintained.","rel_num_authors":5,"rel_authors":[{"author_name":"Rashima Prem","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"author_name":"Erica Avery","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"author_name":"Juliana M. Marquez","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"author_name":"Chi Xie","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA"},{"author_name":"Steven M Claypool","author_inst":"Department of Physiology, Pharmacology and Therapeutics, The Johns Hopkins School of Medicine, Baltimore, MD 21205, USA"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"A Cross-Cohort Validated Plasma Lipid Biomarker Assay for Early Breast Cancer Detection Using Machine Learning","rel_doi":"10.64898\/2026.04.23.26351564","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351564","rel_abs":"Early detection of breast cancer remains essential for improving clinical outcomes, and complementary non-invasive approaches are needed to support existing screening methods, particularly for women with dense breast tissue. We have previously reported plasma lipid biomarker discovery using untargeted high-resolution liquid chromatography tandem mass spectrometry (LC-MS\/MS). In this study, we performed biomarker confirmation and developed machine-learning models applied to targeted plasma lipid measurements for the non-invasive detection of early-stage breast cancer across international cohorts with independent external validation. Targeted LC-MS\/MS was used to quantify candidate lipid panels in plasma samples from European discovery cohorts (n = 554) and an independent Australian cohort (n = 266) used for external validation. Data-driven feature selection identified a 15-lipid panel with strong performance in European cohorts (AUC [&ge;] 0.94). External validation prior to confidence stratification yielded 76% sensitivity, 64% specificity, and an AUC of 0.81 in the Australian validation cohort. Clinical assay development requires iterative panel and model testing to support translational feasibility and performance in the intended-use population. An analytically viable panel, excluding lipids requiring complex and costly synthesis, achieved comparable accuracy with improved assay robustness. Confidence-based analysis showed enhanced performance for predictions made with moderate to high confidence, with sensitivity up to 89% and AUC up to 0.85, suggesting that ongoing research should focus on strategies to enhance diagnostic model confidence. Importantly, model predictions were independent of breast density, tumour size, grade, subtype, and morphology, indicating biological specificity of the lipid signature. These results demonstrate that calibrated machine-learning models applied to plasma lipid biomarkers can support non-invasive breast cancer detection. Expanding training datasets to include greater diversity will further improve performance in the ongoing development of this lipid-based detection approach.","rel_num_authors":12,"rel_authors":[{"author_name":"Tim Huang","author_inst":"OmniOmics.AI Pty Ltd"},{"author_name":"Forrest C Koch","author_inst":"OmniOmics.AI Pty Ltd"},{"author_name":"David A Peake","author_inst":"BCAL Diagnostics Ltd"},{"author_name":"Klaus-Peter Adam","author_inst":"BCAL Diagnostics Inc"},{"author_name":"Mark David","author_inst":"BCAL Diagnostics Ltd"},{"author_name":"Desmond Li","author_inst":"BCAL Diagnostics Ltd"},{"author_name":"Kerry Heffernan","author_inst":"BCAL Diagnostics Ltd"},{"author_name":"Ameline Lim","author_inst":"BCAL Diagnostics Inc"},{"author_name":"John G Hurrell","author_inst":"BCAL Diagnostics Ltd"},{"author_name":"Simon Preston","author_inst":"BCAL Diagnostics Ltd"},{"author_name":"Amani Baterseh","author_inst":"BCAL Diagnostics Ltd"},{"author_name":"Fatemeh Vafaee","author_inst":"University of New South Wales (UNSW Sydney)"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Latent Class Analysis Identifies Pulmonary Function Trajectory Phenotypes in Lung Transplant Recipients with Chronic Allograft Dysfunction","rel_doi":"10.64898\/2026.04.22.26351501","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351501","rel_abs":"BackgroundWe aimed to identify data-driven FEV1 trajectory phenotypes post-chronic lung allograft dysfunction (CLAD), relate these phenotypes to patient factors and future graft loss, and develop a classification approach for prospective patients.\n\nMethodsWe studied adult first lung recipients with probable CLAD from two prospective multicenter cohorts: CTOT-20 (n=206) and LTOG (n=1418). FEV1 trajectories over the first nine months post-CLAD were characterized using joint latent class mixed models, jointly modelling time-to-graft loss to account for informative censoring. Models were fit independently in both cohorts and also only among LTOG bilateral recipients. A classification and regression tree (CART) model was derived in LTOG bilateral recipients and applied to CTOT-20 bilateral recipients.\n\nFindingsFour distinct early FEV1 trajectory classes were identified in CTOT-20, with large differences in nine-month graft loss (72{middle dot}3%, 31{middle dot}1%, 2{middle dot}2%, 0%). In LTOG, similar trajectory patterns were reproduced, with an additional class demonstrating early post-CLAD FEV1 improvement. Among bilateral recipients, trajectory classes showed a clear risk gradient, including a high-risk class with 100% graft loss and a low-risk class with no early graft loss. A CART model incorporating clinical and spirometric variables demonstrated good discrimination in LTOG bilateral recipients (multiclass AUC 0{middle dot}85) and consistent class assignment and trajectory patterns when applied to CTOT-20.\n\nInterpretationWe identified reproducible, clinically meaningful early post-CLAD FEV1 trajectory phenotypes with differential graft loss risk. These phenotypes and a pragmatic classification tool may support risk stratification, trial enrichment, and improved prognostication for patients and clinicians.\n\nFundingNational Institutes of Health, Cystic Fibrosis Foundation","rel_num_authors":29,"rel_authors":[{"author_name":"Megan Neely","author_inst":"Duke Clinical Research institute"},{"author_name":"Daniel M Wojdyla","author_inst":"Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA"},{"author_name":"Hwanhee Hong","author_inst":"Duke University"},{"author_name":"Peijin Wang","author_inst":"Duke University"},{"author_name":"Michaela R Anderson","author_inst":"University of Pennsylvania"},{"author_name":"Katelyn Arroyo","author_inst":"Duke University"},{"author_name":"John Belperio","author_inst":"UCLA"},{"author_name":"Luke Benvenuto","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Marie Budev","author_inst":"Cleveland Clinic"},{"author_name":"Michael Combs","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Gundeep Dhillon","author_inst":"Stanford University"},{"author_name":"Jesse Y Hsu","author_inst":"University of Pennsylvania"},{"author_name":"Laurel Kalman","author_inst":"University of Pennsylvania"},{"author_name":"Tereza Martinu","author_inst":"University of Toronto"},{"author_name":"John McDyer","author_inst":"University of Pittsburgh"},{"author_name":"Michelle Oyster","author_inst":"University of Pennsylvania"},{"author_name":"Krishna Pandya","author_inst":"University of Pennsylvania"},{"author_name":"John M Reynolds","author_inst":"Duke University"},{"author_name":"Jeeyon G Rim","author_inst":"Duke University"},{"author_name":"David W Roe","author_inst":"Indiana University"},{"author_name":"Pali D Shah","author_inst":"Johns Hopkins University"},{"author_name":"Jonathan P Singer","author_inst":"UC San Francisco"},{"author_name":"Lianne Singer","author_inst":"University of Toronto"},{"author_name":"Laurie P Snyder","author_inst":"Duke University Health System"},{"author_name":"Wayne Tsuang","author_inst":"Cleveland Clinic"},{"author_name":"S. Samuel Weigt","author_inst":"UCLA"},{"author_name":"Jason D Christie","author_inst":"University of Pennsylvania"},{"author_name":"Scott M Palmer","author_inst":"Duke University School of Medicine"},{"author_name":"Jamie Todd","author_inst":"Duke University"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Latent Class Analysis Identifies Pulmonary Function Trajectory Phenotypes in Lung Transplant Recipients with Chronic Allograft Dysfunction","rel_doi":"10.64898\/2026.04.22.26351501","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351501","rel_abs":"BackgroundWe aimed to identify data-driven FEV1 trajectory phenotypes post-chronic lung allograft dysfunction (CLAD), relate these phenotypes to patient factors and future graft loss, and develop a classification approach for prospective patients.\n\nMethodsWe studied adult first lung recipients with probable CLAD from two prospective multicenter cohorts: CTOT-20 (n=206) and LTOG (n=1418). FEV1 trajectories over the first nine months post-CLAD were characterized using joint latent class mixed models, jointly modelling time-to-graft loss to account for informative censoring. Models were fit independently in both cohorts and also only among LTOG bilateral recipients. A classification and regression tree (CART) model was derived in LTOG bilateral recipients and applied to CTOT-20 bilateral recipients.\n\nFindingsFour distinct early FEV1 trajectory classes were identified in CTOT-20, with large differences in nine-month graft loss (72{middle dot}3%, 31{middle dot}1%, 2{middle dot}2%, 0%). In LTOG, similar trajectory patterns were reproduced, with an additional class demonstrating early post-CLAD FEV1 improvement. Among bilateral recipients, trajectory classes showed a clear risk gradient, including a high-risk class with 100% graft loss and a low-risk class with no early graft loss. A CART model incorporating clinical and spirometric variables demonstrated good discrimination in LTOG bilateral recipients (multiclass AUC 0{middle dot}85) and consistent class assignment and trajectory patterns when applied to CTOT-20.\n\nInterpretationWe identified reproducible, clinically meaningful early post-CLAD FEV1 trajectory phenotypes with differential graft loss risk. These phenotypes and a pragmatic classification tool may support risk stratification, trial enrichment, and improved prognostication for patients and clinicians.\n\nFundingNational Institutes of Health, Cystic Fibrosis Foundation","rel_num_authors":29,"rel_authors":[{"author_name":"Megan Neely","author_inst":"Duke Clinical Research institute"},{"author_name":"Daniel M Wojdyla","author_inst":"Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA"},{"author_name":"Hwanhee Hong","author_inst":"Duke University"},{"author_name":"Peijin Wang","author_inst":"Duke University"},{"author_name":"Michaela R Anderson","author_inst":"University of Pennsylvania"},{"author_name":"Katelyn Arroyo","author_inst":"Duke University"},{"author_name":"John Belperio","author_inst":"UCLA"},{"author_name":"Luke Benvenuto","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Marie Budev","author_inst":"Cleveland Clinic"},{"author_name":"Michael Combs","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Gundeep Dhillon","author_inst":"Stanford University"},{"author_name":"Jesse Y Hsu","author_inst":"University of Pennsylvania"},{"author_name":"Laurel Kalman","author_inst":"University of Pennsylvania"},{"author_name":"Tereza Martinu","author_inst":"University of Toronto"},{"author_name":"John McDyer","author_inst":"University of Pittsburgh"},{"author_name":"Michelle Oyster","author_inst":"University of Pennsylvania"},{"author_name":"Krishna Pandya","author_inst":"University of Pennsylvania"},{"author_name":"John M Reynolds","author_inst":"Duke University"},{"author_name":"Jeeyon G Rim","author_inst":"Duke University"},{"author_name":"David W Roe","author_inst":"Indiana University"},{"author_name":"Pali D Shah","author_inst":"Johns Hopkins University"},{"author_name":"Jonathan P Singer","author_inst":"UC San Francisco"},{"author_name":"Lianne Singer","author_inst":"University of Toronto"},{"author_name":"Laurie P Snyder","author_inst":"Duke University Health System"},{"author_name":"Wayne Tsuang","author_inst":"Cleveland Clinic"},{"author_name":"S. Samuel Weigt","author_inst":"UCLA"},{"author_name":"Jason D Christie","author_inst":"University of Pennsylvania"},{"author_name":"Scott M Palmer","author_inst":"Duke University School of Medicine"},{"author_name":"Jamie Todd","author_inst":"Duke University"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Latent Class Analysis Identifies Pulmonary Function Trajectory Phenotypes in Lung Transplant Recipients with Chronic Allograft Dysfunction","rel_doi":"10.64898\/2026.04.22.26351501","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351501","rel_abs":"BackgroundWe aimed to identify data-driven FEV1 trajectory phenotypes post-chronic lung allograft dysfunction (CLAD), relate these phenotypes to patient factors and future graft loss, and develop a classification approach for prospective patients.\n\nMethodsWe studied adult first lung recipients with probable CLAD from two prospective multicenter cohorts: CTOT-20 (n=206) and LTOG (n=1418). FEV1 trajectories over the first nine months post-CLAD were characterized using joint latent class mixed models, jointly modelling time-to-graft loss to account for informative censoring. Models were fit independently in both cohorts and also only among LTOG bilateral recipients. A classification and regression tree (CART) model was derived in LTOG bilateral recipients and applied to CTOT-20 bilateral recipients.\n\nFindingsFour distinct early FEV1 trajectory classes were identified in CTOT-20, with large differences in nine-month graft loss (72{middle dot}3%, 31{middle dot}1%, 2{middle dot}2%, 0%). In LTOG, similar trajectory patterns were reproduced, with an additional class demonstrating early post-CLAD FEV1 improvement. Among bilateral recipients, trajectory classes showed a clear risk gradient, including a high-risk class with 100% graft loss and a low-risk class with no early graft loss. A CART model incorporating clinical and spirometric variables demonstrated good discrimination in LTOG bilateral recipients (multiclass AUC 0{middle dot}85) and consistent class assignment and trajectory patterns when applied to CTOT-20.\n\nInterpretationWe identified reproducible, clinically meaningful early post-CLAD FEV1 trajectory phenotypes with differential graft loss risk. These phenotypes and a pragmatic classification tool may support risk stratification, trial enrichment, and improved prognostication for patients and clinicians.\n\nFundingNational Institutes of Health, Cystic Fibrosis Foundation","rel_num_authors":29,"rel_authors":[{"author_name":"Megan Neely","author_inst":"Duke Clinical Research institute"},{"author_name":"Daniel M Wojdyla","author_inst":"Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA"},{"author_name":"Hwanhee Hong","author_inst":"Duke University"},{"author_name":"Peijin Wang","author_inst":"Duke University"},{"author_name":"Michaela R Anderson","author_inst":"University of Pennsylvania"},{"author_name":"Katelyn Arroyo","author_inst":"Duke University"},{"author_name":"John Belperio","author_inst":"UCLA"},{"author_name":"Luke Benvenuto","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Marie Budev","author_inst":"Cleveland Clinic"},{"author_name":"Michael Combs","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Gundeep Dhillon","author_inst":"Stanford University"},{"author_name":"Jesse Y Hsu","author_inst":"University of Pennsylvania"},{"author_name":"Laurel Kalman","author_inst":"University of Pennsylvania"},{"author_name":"Tereza Martinu","author_inst":"University of Toronto"},{"author_name":"John McDyer","author_inst":"University of Pittsburgh"},{"author_name":"Michelle Oyster","author_inst":"University of Pennsylvania"},{"author_name":"Krishna Pandya","author_inst":"University of Pennsylvania"},{"author_name":"John M Reynolds","author_inst":"Duke University"},{"author_name":"Jeeyon G Rim","author_inst":"Duke University"},{"author_name":"David W Roe","author_inst":"Indiana University"},{"author_name":"Pali D Shah","author_inst":"Johns Hopkins University"},{"author_name":"Jonathan P Singer","author_inst":"UC San Francisco"},{"author_name":"Lianne Singer","author_inst":"University of Toronto"},{"author_name":"Laurie P Snyder","author_inst":"Duke University Health System"},{"author_name":"Wayne Tsuang","author_inst":"Cleveland Clinic"},{"author_name":"S. Samuel Weigt","author_inst":"UCLA"},{"author_name":"Jason D Christie","author_inst":"University of Pennsylvania"},{"author_name":"Scott M Palmer","author_inst":"Duke University School of Medicine"},{"author_name":"Jamie Todd","author_inst":"Duke University"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Latent Class Analysis Identifies Pulmonary Function Trajectory Phenotypes in Lung Transplant Recipients with Chronic Allograft Dysfunction","rel_doi":"10.64898\/2026.04.22.26351501","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351501","rel_abs":"BackgroundWe aimed to identify data-driven FEV1 trajectory phenotypes post-chronic lung allograft dysfunction (CLAD), relate these phenotypes to patient factors and future graft loss, and develop a classification approach for prospective patients.\n\nMethodsWe studied adult first lung recipients with probable CLAD from two prospective multicenter cohorts: CTOT-20 (n=206) and LTOG (n=1418). FEV1 trajectories over the first nine months post-CLAD were characterized using joint latent class mixed models, jointly modelling time-to-graft loss to account for informative censoring. Models were fit independently in both cohorts and also only among LTOG bilateral recipients. A classification and regression tree (CART) model was derived in LTOG bilateral recipients and applied to CTOT-20 bilateral recipients.\n\nFindingsFour distinct early FEV1 trajectory classes were identified in CTOT-20, with large differences in nine-month graft loss (72{middle dot}3%, 31{middle dot}1%, 2{middle dot}2%, 0%). In LTOG, similar trajectory patterns were reproduced, with an additional class demonstrating early post-CLAD FEV1 improvement. Among bilateral recipients, trajectory classes showed a clear risk gradient, including a high-risk class with 100% graft loss and a low-risk class with no early graft loss. A CART model incorporating clinical and spirometric variables demonstrated good discrimination in LTOG bilateral recipients (multiclass AUC 0{middle dot}85) and consistent class assignment and trajectory patterns when applied to CTOT-20.\n\nInterpretationWe identified reproducible, clinically meaningful early post-CLAD FEV1 trajectory phenotypes with differential graft loss risk. These phenotypes and a pragmatic classification tool may support risk stratification, trial enrichment, and improved prognostication for patients and clinicians.\n\nFundingNational Institutes of Health, Cystic Fibrosis Foundation","rel_num_authors":29,"rel_authors":[{"author_name":"Megan Neely","author_inst":"Duke Clinical Research institute"},{"author_name":"Daniel M Wojdyla","author_inst":"Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA"},{"author_name":"Hwanhee Hong","author_inst":"Duke University"},{"author_name":"Peijin Wang","author_inst":"Duke University"},{"author_name":"Michaela R Anderson","author_inst":"University of Pennsylvania"},{"author_name":"Katelyn Arroyo","author_inst":"Duke University"},{"author_name":"John Belperio","author_inst":"UCLA"},{"author_name":"Luke Benvenuto","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Marie Budev","author_inst":"Cleveland Clinic"},{"author_name":"Michael Combs","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Gundeep Dhillon","author_inst":"Stanford University"},{"author_name":"Jesse Y Hsu","author_inst":"University of Pennsylvania"},{"author_name":"Laurel Kalman","author_inst":"University of Pennsylvania"},{"author_name":"Tereza Martinu","author_inst":"University of Toronto"},{"author_name":"John McDyer","author_inst":"University of Pittsburgh"},{"author_name":"Michelle Oyster","author_inst":"University of Pennsylvania"},{"author_name":"Krishna Pandya","author_inst":"University of Pennsylvania"},{"author_name":"John M Reynolds","author_inst":"Duke University"},{"author_name":"Jeeyon G Rim","author_inst":"Duke University"},{"author_name":"David W Roe","author_inst":"Indiana University"},{"author_name":"Pali D Shah","author_inst":"Johns Hopkins University"},{"author_name":"Jonathan P Singer","author_inst":"UC San Francisco"},{"author_name":"Lianne Singer","author_inst":"University of Toronto"},{"author_name":"Laurie P Snyder","author_inst":"Duke University Health System"},{"author_name":"Wayne Tsuang","author_inst":"Cleveland Clinic"},{"author_name":"S. Samuel Weigt","author_inst":"UCLA"},{"author_name":"Jason D Christie","author_inst":"University of Pennsylvania"},{"author_name":"Scott M Palmer","author_inst":"Duke University School of Medicine"},{"author_name":"Jamie Todd","author_inst":"Duke University"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"CT-Based Deep Foundation Model for Predicting Immune Checkpoint Inhibitor-Induced Pneumonitis Risk in Lung Cancer","rel_doi":"10.64898\/2026.04.21.26351428","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351428","rel_abs":"BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical to close monitoring, enable timely intervention, and optimize outcomes.\n\nPurposeTo develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer.\n\nMethodsWe designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning-powered foundation model combining contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in lung cancer patients. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients, to understand heterogeneous lung parenchyma. Following pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, with images from 254 patients used for model development and from 93 patients for internal validation. We compared CIPHER with classical radiomic models. We also validated CIPHER on an external NSCLC cohort of 116 patients.\n\nResultsIn our internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming radiomic model. In the external validation cohort, CIPHER maintained high performance (AUC=0.83; balanced accuracy=81.7%), exceeding the radiomic models (Delong p=0.0318) and demonstrating superior specificity without sacrificing sensitivity. By contrast, radiomic model, despite high sensitivity (85.0%), showed markedly lower specificity (45.8%). Confusion matrix analyses confirmed CIPHERs robust classification, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases.\n\nConclusionsWe developed and externally validated CIPHER for predicting future risk of developing ICI-P from pre-treatment CT scans. With prospective validation, CIPHER can be incorporated into routine patient management to improve outcomes.\n\nHighlightsO_LIThe first chest CT AI foundation model for immune toxicity - We introduce CIPHER (Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR), a transformer-based masked autoencoder trained through self-supervised contrastive learning on 590,284 CT slices from 4,242 NSCLC patients scans. This large-scale pretraining enables CIPHER to learn intrinsic lung parenchymal representations linked to immune toxicity risk.\nC_LIO_LIEarly risk prediction prior to therapy initiation - CIPHER predicts the likelihood of ICI-induced pneumonitis directly from baseline CT scans, offering the first non-invasive foundation model for early risk assessment before ICI.\nC_LIO_LIRobust validation and benchmarking - We fine-tuned and evaluated CIPHER across independent internal and external NSCLC immunotherapy cohorts, achieving AUCs of 0.77-\nC_LIO_LI0.85 internal cross validation and 0.83 external testing, surpassing conventional radiomic models in both performance and generalizability.\nC_LIO_LIInterpretability and clinical readiness - We demonstrate how model-derived attention maps align with clinically relevant pulmonary patterns, enhancing interpretability and enabling seamless integration into radiology workflows.\nC_LIO_LITranslational potential - CIPHERs performance and scalability underscore its potential as decision-support tool to guide treatment planning, pre-emptive monitoring, and toxicity mitigation in immunotherapy practice.\nC_LI","rel_num_authors":26,"rel_authors":[{"author_name":"Amgad Muneer","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Eman Showkatian","author_inst":"Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Yuliya Kitsel","author_inst":"Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic\/Head and Neck Medical Oncology, The U"},{"author_name":"Maliazurina B. Saad","author_inst":"Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Sheeba J Sujit","author_inst":"Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Felipe Soto","author_inst":"Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Girish S. Shroff","author_inst":"Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Saadia A. Faiz","author_inst":"Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Mohammad I. Ghanbar","author_inst":"Department of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Sherif M. Ismail","author_inst":"Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Natalie I. Vokes","author_inst":"Department of Thoracic\/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Tina Cascone","author_inst":"Department of Thoracic\/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Xiuning Le","author_inst":"Department of Thoracic\/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Jianjun Zhang","author_inst":"Department of Thoracic\/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Lauren A. Byers","author_inst":"Department of Thoracic\/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"David Jaffray","author_inst":"Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Radiation Physics, The University of "},{"author_name":"Joe Y. Chang","author_inst":"Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Zhongxing Liao","author_inst":"Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Aung Naing","author_inst":"Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Don L. Gibbons","author_inst":"Department of Thoracic\/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Ara A. Vaporciyan","author_inst":"Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"John V. Heymach","author_inst":"Department of Thoracic\/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Karthik S. Suresh","author_inst":"Department of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Mehmet Altan","author_inst":"Department of Thoracic\/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Ajay Sheshadri","author_inst":"Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Jia Wu","author_inst":"Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Thoracic\/Head and Neck Medical Oncology, The U"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Improving Care by FAster risk-STratification through use of high sensitivity point-of-care troponin in patients presenting with possible acute coronary syndrome in the EmeRgency department (ICare-FASTER): a stepped-wedge cluster randomized trial","rel_doi":"10.64898\/2026.04.21.26351433","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351433","rel_abs":"BACKGROUNDPoint-of-care (POC) high-sensitivity cardiac troponin (hs-cTn) testing has the potential to expedite decision-making and reduce emergency department (ED) length of stay for patients presenting with possible myocardial infarction (MI) by ensuring that results are consistently available when looked for by clinicians. We assessed the real-life effectiveness and safety of implementing POC hs-cTn testing in the ED.\n\nMETHODSWe conducted a pragmatic, stepped-wedge cluster randomized trial. The control arm was usual care with an accelerated diagnostic pathway utilizing a single-sample rule-out step with a central laboratory hs-cTn assay. The intervention arm used the same pathway with a POC hs-cTnI. The primary effectiveness outcome was ED length of stay assessed using a generalized linear mixed model, and the safety outcome was 30-day MI or cardiac death.\n\nRESULTSSix sites participated with 59,980 ED presentations (44,747 individuals, 61{+\/-}19 years, 49.5% female) from February 2023 to January 2025, in which 31,392 presentations were during the intervention arm. After adjustment for co-variates associated with length of stay, the intervention reduced length of stay by 13% (95% confidence intervals [CI], 9 to 16%. P<0.001), corresponding to a reduction of 47 minutes (95%CI, 33 to 61 minutes) from a mean length of stay in the control arm of 376 minutes. The 30-day MI or cardiac death rate was similar in the control and intervention arms (0.39% and 0.39% respectively, P=0.54).\n\nCONCLUSIONSImplementation of whole-blood hs-cTnI testing at the POC into an accelerated diagnostic pathway was safe and reduced length of stay in the ED compared with laboratory testing. (Australia New Zealand Clinical Trials Registry ACTRN12619001189112)\n\nClinical PerspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIPoint-of-care troponin assays with good precision have become available and received regulatory approval as high-sensitivity assays.\nC_LIO_LIA pragmatic stepped-wedge cluster randomized trial implementing the validated Siemens VTLi high-sensitivity point-of-care assay into 6 emergency departments investigating [~]60,000 patients, was conducted to ascertain if the rapid turnaround time reduced lengths of stay.\nC_LIO_LIIntroduction of the VTLi high-sensitivity point-of-care assay into clinical pathways reduced emergency department lengths of stay by an average 13% (47 minutes) without compromising safety.\nC_LI\n\nWhat are the clinical implications?O_LIEarly decision-making using a point-of-care high-sensitivity troponin assay within a structured clinical pathway was safe.\nC_LIO_LIFaster turnaround means troponin results are consistently available when clinicians first seek them. This can facilitate earlier decisions and reduce patient length of stay.\nC_LI","rel_num_authors":31,"rel_authors":[{"author_name":"Martin Than","author_inst":"Christchurch Hospital"},{"author_name":"John W. Pickering","author_inst":"University of Otago"},{"author_name":"Laura R Joyce","author_inst":"University of Otago Christchurch and Christchurch Hospital"},{"author_name":"Vanessa A Buchan","author_inst":"Canterbury Health Laboratories"},{"author_name":"Christopher M Florkowski","author_inst":"Christchurch Hospital"},{"author_name":"Nicholas L Mills","author_inst":"The University of Edinburgh"},{"author_name":"Laura Hamill","author_inst":"Christchurch Hospital"},{"author_name":"Jason Prystowsky","author_inst":"Hauora Tairawhiti Gisborne Hospital"},{"author_name":"Simon Harger","author_inst":"Health New Zealand Hawke's Bay"},{"author_name":"Mary Reed","author_inst":"Health New Zealand Rotorua and Taupo"},{"author_name":"Jared Bayless","author_inst":"Health New Zealand Rotorua and Taupo"},{"author_name":"Alexander Feberwee","author_inst":"Health New Zealand Canterbury Health Laboratories"},{"author_name":"Tamsin Attenburrow","author_inst":"Christchurch Hospital"},{"author_name":"Timothy Norman","author_inst":"Observation Consultancy"},{"author_name":"Oliver Welfare","author_inst":"Queensland Health"},{"author_name":"Tamika Heiden","author_inst":"Research Impact Academy"},{"author_name":"Peter Kavsak","author_inst":"McMaster University"},{"author_name":"Allan S. Jaffe","author_inst":"Mayo Clinic Minnesota"},{"author_name":"fred apple","author_inst":"Hennepin Heathcare\/Hennepin County Medical Center"},{"author_name":"W. Frank Peacock","author_inst":"Baylor College of Medicine"},{"author_name":"Louise Cullen","author_inst":"Royal Brisbane and Women's Hospital"},{"author_name":"Sally Aldous","author_inst":"Christchurch Hospital"},{"author_name":"A. Mark Richards","author_inst":"National University of Singapore"},{"author_name":"Cameron Lacey","author_inst":"Christchurch Hospital"},{"author_name":"Richard Troughton","author_inst":"Christchurch Heart Institute"},{"author_name":"Christopher Frampton","author_inst":"University of Otago"},{"author_name":"Richard Body","author_inst":"Central Manchester University Hospitals NHS Foundation Trust"},{"author_name":"Christian Mueller","author_inst":"Universitatsspital Basel Kardiologie"},{"author_name":"Sarah Jane Lord","author_inst":"The University of Sydney School of Life and Environmental Sciences"},{"author_name":"Peter Myles George","author_inst":"Austin Hospital"},{"author_name":"Gerard Devlin","author_inst":"Health New Zealand Tairawhiti"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Stakeholder perspectives on the use of enhanced mobile phone capabilities for public health surveillance for non-communicable disease risk factors: A qualitative study","rel_doi":"10.64898\/2026.04.22.26351443","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351443","rel_abs":"Background: Mobile phone-based tools are increasingly used to collect data on non-communicable disease (NCD) risk factors, particularly in low-resource settings where traditional data collection systems face operational and infrastructural constraints. This study examined stakeholder perspectives on the use of enhanced mobile phone-based capabilities to support the collection of public health surveillance data on NCD risk factors in low-resource settings. Methods: An exploratory qualitative study was conducted between November 2022 and July 2023. Twenty in-depth interviews were conducted with public health specialists, ethicists, NCD researchers, health informaticians, and policy makers in Uganda. Thematic analysis was used to interpret the results. Results: Four themes emerged from the data, including benefits of using mobile phone capabilities for NCD risk factor data collection; ethical, legal, and social implications; perceived challenges of using such mobile phone capabilities; and proposed solutions to improve the utility of phone-based capabilities in data collection on NCD risk factors. Participants recognized the potential of mobile technologies to improve data collection efficiency and expand access to hard-to-reach populations. However, concerns emerged regarding inadequate informed consent, risks to privacy and confidentiality, unclear data ownership, and vulnerabilities created by inconsistent enforcement of data protection laws. Social concerns included low digital literacy, unequal access to mobile devices, and fear of stigmatization. Participants emphasized the need for transparent communication, robust data governance, and community engagement. Conclusion: Mobile phone-based systems can strengthen the collection of NCD risk factor data in low-resource settings; however, their benefits depend on addressing key ethical, legal, and social challenges. To ensure responsible deployment, digital health initiatives must prioritize participant autonomy, data protection, equity, and trust building. Integrating contextualized ethical, legal, and social considerations into design and policy frameworks will be essential to leveraging mobile technologies in ways that support inclusive and effective NCD prevention and control.","rel_num_authors":7,"rel_authors":[{"author_name":"Erisa  Sabakaki Mwaka","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Sylvia Nabukenya","author_inst":"Makerere University College of Computing and Information Sciences"},{"author_name":"Vicent Kasiita","author_inst":"Makerere University College of Humanities and Social Sciences"},{"author_name":"Godfrey Bagenda","author_inst":"Makerere University CHS: Makerere University College of Health Sciences"},{"author_name":"Elizeus Rutebemberwa","author_inst":"Makerere University CHS: Makerere University College of Health Sciences"},{"author_name":"Joseph Ali","author_inst":"Johns Hopkins University Bloomberg School of Public Health"},{"author_name":"Dustin Gibson","author_inst":"Johns Hopkins University Bloomberg School of Public Health"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"X-Chromosome-Wide Association Study Identifies Novel Genetic Signals for Heart Failure and Subtypes","rel_doi":"10.64898\/2026.04.21.26351435","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351435","rel_abs":"BackgroundHeart failure (HF) is a major and growing public health problem, and prior studies support a meaningful genetic contribution to HF susceptibility. Clinically, HF is commonly categorized into the major clinical sub-types of HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), which differ in pathophysiology and clinical profiles. However, previous genome-wide association studies have focused on autosomal variation and have routinely excluded the X chromosome, leaving X-linked genetic contributions to HF and its subtypes under-characterized.\n\nMethodsWe performed X-chromosome wide association study (XWAS) utilizing directly genotyped data from 590,568 Million Veteran Program participants, including 90,694 HF cases across European, African, Hispanic, and Asian Americans. Sex- and ancestry-stratified logistic regression was used with XWAS quality control measures, adjusting for age and population structure, followed by fixed-effects multi-ancestry meta-analysis. Functional annotation, gene-based testing, fine-mapping, and colocalization were performed. We replicated genetic associations with all-cause HF in the UK Biobank.\n\nResultsIn the multi-ancestry meta-analysis, we identified five X-chromosome-wide significant loci for all-cause HF, five for HFrEF, and one locus for HFpEF in males. No loci reached significance in female-specific analyses. In sex-combined analyses, we identified six loci for all-cause HF and four for HFrEF. The strongest and most emphasized signals mapped to genes were BRWD3, FHL1, and CHRDL1. Ancestry-specific analyses revealed additional loci, including NDP and WDR44 in African ancestry and PHF8 in Hispanic ancestry. One locus, BRWD3, was replicated in UK Biobank HF cohort. Integrated post-GWAS analyses (fine-mapping, colocalization and pleiotropy trait association studies) reinforced the biological plausibility of the X-linked signals.\n\nConclusionsThis multi-ancestry, sex-stratified XWAS identifies X-linked genetic contributions to HF and its subtypes and highlights the role of X-chromosome in heart failure pathogenesis.","rel_num_authors":17,"rel_authors":[{"author_name":"Junling Ren","author_inst":"Providence VA Medical Center"},{"author_name":"- VA Million Veteran Program","author_inst":"-"},{"author_name":"Chang Liu","author_inst":"Emory University Rollins School of Public Health"},{"author_name":"Qin Hui","author_inst":"Emory University Rollins School of Public Health"},{"author_name":"Maryam Rahafrooz","author_inst":"Brown University and VA Providence Healthcare"},{"author_name":"Nicole M Kosik","author_inst":"Veterans Affairs Healthcare System"},{"author_name":"Kevin Urak","author_inst":"US Department of Veterans Affairs"},{"author_name":"Jennifer Moser","author_inst":"US Department of Veterans Affairs"},{"author_name":"Sumitra Muralidhar","author_inst":"Office of Research and Development, Veterans Health Administration, Washington"},{"author_name":"Alexandre Pereira","author_inst":"Harvard Medical School"},{"author_name":"Kelly Cho","author_inst":"Harvard Medical School Department of Medicine"},{"author_name":"J. Michael Gaziano","author_inst":"Brigham and Women's Hospital and Harvard Medical School"},{"author_name":"Peter W.F. Wilson","author_inst":"Emory University School of Medicine"},{"author_name":"VA Million Veteran Program","author_inst":"US Department of Veterans Affairs"},{"author_name":"Lawrence S Phillips","author_inst":"Atlanta VA Medical Center & Emory University School of Medicine"},{"author_name":"Yan Sun","author_inst":"Emory University Rollins School of Public Health"},{"author_name":"Jacob Joseph","author_inst":"Brown University and VA Providence Healthcare"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Recovering Clinical Detail in AI-Generated Responses for Low Back Pain Through Prompt Design","rel_doi":"10.64898\/2026.04.21.26351437","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351437","rel_abs":"IntroductionLarge language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for procedural planning. Prior studies show that simplified prompts reduce clinical detail in AI-generated responses. It remains unclear whether this reflects knowledge loss or simply prompt-driven suppression of information.\n\nMethodsWe performed a controlled comparative study using 15 standardized low back pain questions representing common interventional pain questions. Each question was submitted to ChatGPT under three conditions, professional-level prompt (DP), fourth-grade reading-level prompt (D4), and clinician-directed rewriting of the D4 response to a medical level (U4[-&gt;]MD). No follow-up prompting was allowed. Three physicians independently rated responses for accuracy using a 0-2 ordinal scale. Clinical completeness was determined by consensus. Word count and Flesch-Kincaid Grade Level (FKGL) were also measured. Paired t-tests compared conditions.\n\nResultsAccuracy was highest with professional prompting (1.76). Accuracy declined with the fourth-grade prompt (1.33; p = 0.00086). When simplified responses were rewritten for clinicians, accuracy returned to baseline (1.76; p {approx} 1.00 vs DP). Clinical completeness followed the same pattern showing DP 80.0%, D4 6.7%, U4[-&gt;]MD 73.3%. Fourth-grade responses were shorter and less complex. Upscaled responses were more complex and similar in length to professional responses. Inter-rater reliability was low (Fleiss {kappa} = 0.17), but trends were consistent across conditions.\n\nConclusionsReduced clinical detail under simplified prompts appears to reflect constrained output rather than loss of knowledge. Clinician-directed reframing restores omitted content. LLM performance in interventional pain depends strongly on prompt design and intended audience.","rel_num_authors":5,"rel_authors":[{"author_name":"Ahmed Basharat","author_inst":"Yale University"},{"author_name":"Omar Hamza","author_inst":"Cambridge Health Alliance"},{"author_name":"Paragi Rana","author_inst":"Yale New Haven Hospital"},{"author_name":"Charles  A. Odonkor","author_inst":"Yale New Haven Hospital"},{"author_name":"Robert Chow","author_inst":"Yale New Haven Hospital"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Factors influencing repeated decisions to decline cervical cancer screening among women living with HIV in Jos, Nigeria: a qualitative study","rel_doi":"10.64898\/2026.04.22.26351475","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351475","rel_abs":"Women living with HIV face about a six-fold higher risk of cervical cancer, yet screening uptake remains low in many sub-Saharan African settings. We explored factors influencing repeated decisions to decline cervical cancer screening during routine HIV care among women living with HIV at a large HIV clinic in Jos, Nigeria. Between September and December 2024, we conducted an exploratory qualitative study at the AIDS Prevention Initiative in Nigeria Clinic in Jos, Nigeria. We purposively recruited 27 women living with HIV aged 21 to 65 years who had never undergone cervical cancer screening and had repeatedly declined screening offers during routine HIV care, including at the current clinic visit. Semi-structured in-depth interviews were conducted in English or Hausa, audio-recorded, transcribed verbatim, and translated into English where needed. Data were analyzed thematically using theory-informed coding based on the Health Belief Model and Social Ecological Model. Among 27 women living with HIV who had repeatedly declined screening, perceived susceptibility was often low or uncertain despite recognition of cervical cancer severity. Perceived benefits were acknowledged but were frequently outweighed by overlapping barriers, including knowledge gaps and misinformation, indirect and downstream costs, emotional barriers, logistical constraints, clinic-flow and service-delivery barriers, and anticipated stigma. Education, reminders, and supportive clinic processes acted as cues to action, and most participants expressed willingness to screen in future. Among women living with HIV at this clinic who repeatedly declined screening when it was offered, perceived benefits were often outweighed by multilevel barriers. Screening programs may integrate fear-reduction and stigma-sensitive counseling with practical service delivery improvements, including shorter waiting times, reduced indirect costs, predictable and streamlined clinic flow, and consistent provider invitations and reminders, while addressing misinformation through community-embedded, culturally tailored messaging. These strategies may improve screening uptake and support more equitable cervical cancer prevention for women living with HIV in similar HIV-care settings.","rel_num_authors":8,"rel_authors":[{"author_name":"Auwal Abubakar","author_inst":"The University of Arizona"},{"author_name":"Suraj Musa Inuwa","author_inst":"Department of Community Medicine, Aminu Kano Teaching Hospital, Kano"},{"author_name":"Maryam Jamila Ali","author_inst":"University of Jos"},{"author_name":"Kabir Mohammed Abdullahi","author_inst":"ATBUTH"},{"author_name":"Amenyo Doe","author_inst":"University of California, Berkeley, Tamale Teaching Hospital"},{"author_name":"Maiya G Block Ngaybe","author_inst":"School of Landscape Architecture and Planning, College of Architecture, Planning and Landscape Architecture, University of Arizona"},{"author_name":"Purnima Madhivanan","author_inst":"The University of Arizona"},{"author_name":"Jonah Musa","author_inst":"University of Jos Faculty of Medical Sciences"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"A Return-on-Investment Analysis of a Community-Based Diabetes Self-Management Program In New York City","rel_doi":"10.64898\/2026.04.22.26351481","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351481","rel_abs":"OBJECTIVETo evaluate the return on investment (ROI) of a community-based Diabetes Self-Management Program (DSMP) enhanced with health-related social needs (HRSN) screening and referrals, implemented by the New York City (NYC) Department of Health and Mental Hygiene with three community-based organizations in highly-impacted, under-resourced neighborhoods.\n\nRESEARCH DESIGN AND METHODSA retrospective cost-benefit analysis from a public-sector payer perspective was conducted among 171 adults with type 2 diabetes who completed a six-week, peer-led DSMP delivered by community health workers (CHWs) in English, Spanish, and Korean during 2018-2019. A time-driven, activity-based costing model captured direct implementation costs, CHW workforce turnover, and administrative overhead. Monetized benefits included avoided diabetes-related complications, reductions in self-reported emergency department (ED) visits and hospitalizations, and quality-adjusted life year (QALY) gains from improved medication adherence. Univariate sensitivity analyses tested robustness under conservative assumptions.\n\nRESULTSTotal program costs were $179,224; monetized benefits totaled $1,824,213, yielding a net benefit of $1,644,989 and an ROI of 918%--approximately $10 returned per $1 invested. Excluding QALY gains, ROI remained 551%. Self-reported ED visits declined from 149 to 82 and hospitalizations from 93 to 24 in the six months following intervention. Over 80% of participants reported housing instability; 72% were Medicaid-covered and 16% uninsured. Sensitivity analyses confirmed a positive ROI under all conservative scenarios.\n\nCONCLUSIONSA CHW-led, community-based DSMP integrated with HRSN screening and referrals delivered substantial economic and public health value among adults facing housing instability and structural barriers to care. Findings support inclusion of DSMP as a covered benefit in Medicaid managed care, value-based payment arrangements, and housing access initiatives to advance equitable diabetes outcomes.","rel_num_authors":10,"rel_authors":[{"author_name":"Jason C Goldwater","author_inst":"Laurel Health Advisors, LLC"},{"author_name":"Yael Harris","author_inst":"Laurel Health Advisors, LLC"},{"author_name":"Sonali K Das","author_inst":"NYC Department of Health and Mental Hygiene"},{"author_name":"Maria A Fernandez Galvis","author_inst":"NYC Department of Health and Mental Hygiene"},{"author_name":"Duncan Maru","author_inst":"NYC Department of Health and Mental Hygiene"},{"author_name":"William B Jordan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Crystal Sacaridiz","author_inst":"NYC Department of Health and Mental Hygiene"},{"author_name":"Chris Norwood","author_inst":"Health People"},{"author_name":"Sara Soonsik Kim","author_inst":"Korean Community Services of Metropolitan NY, Inc"},{"author_name":"Kyle Neustrom","author_inst":"NYC Department of Health and Mental Hygiene"}],"rel_date":"2026-04-23","rel_site":"medrxiv"},{"rel_title":"Machine Learning Approach for Enumeration of Circulating Cells with Diffuse in vivo Flow Cytometry","rel_doi":"10.64898\/2026.04.21.719882","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719882","rel_abs":"Significance: Diffuse in vivo flow cytometry (DiFC) is an emerging technique for enumerating rare, fluorescently-labeled circulating tumor cells (CTCs) in small animals without drawing blood samples. DiFC uses detection of transient fluorescent peaks in time-series data. Previously, we used a simple amplitude threshold-based method for identifying peak candidates, but it ignores potentially useful information in peak shape that could reduce false-positive detections from instrument noise and increase detection efficiency of lower-amplitude peaks. Aim: To develop a machine learning (ML)-integrated signal processing approach for improved CTC enumeration using DiFC by distinguishing CTC peaks from artifacts. Approach: We developed an ML-integrated approach that incorporates a convolutional neural network (CNN) classifier. The CNN was trained to distinguish CTC peaks from artifacts by analyzing peak amplitude and temporal shape characteristics. Performance was validated on in-silico, control, and CTC-bearing mouse datasets. Results: The CNN classifier achieved accuracy, precision, sensitivity, and specificity exceeding 98% on test data. Compared with our previously published threshold-based approach, the ML-integrated method increased the number of correctly identified CTCs and their flow direction while reducing false detections across validation datasets. Conclusions: The ML-integrated approach significantly improves DiFC CTC enumeration, enabling robustness against artifacts in noisy conditions.","rel_num_authors":5,"rel_authors":[{"author_name":"Mehrnoosh Emamifar","author_inst":"Northeastern University"},{"author_name":"Jane Lee","author_inst":"Northeastern University"},{"author_name":"Joshua S Pace","author_inst":"Northeastern University"},{"author_name":"Chiara Bellini","author_inst":"Northeastern University"},{"author_name":"Mark Niedre","author_inst":"Northeastern University"}],"rel_date":"2026-04-23","rel_site":"biorxiv"},{"rel_title":"Profiling and Targeting of Regulatory RNAs to Upregulate Gene Expression","rel_doi":"10.64898\/2026.04.21.719874","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719874","rel_abs":"Transcription of long noncoding RNAs (lncRNAs), including enhancer RNAs (eRNAs) and promoter-associated RNAs (paRNAs), collectively termed regulatory RNAs (regRNAs), is a hallmark of active gene expression, yet it remains unknown whether regRNAs can be targeted to selectively enhance transcription in cis. We developed regRNA Capture-seq, a high-throughput method to profile regRNAs, and applied it to primary human hepatocytes, annotating thousands of regRNAs at ~2,000 enhancers and promoters. Using this approach, we interrogated a genetically validated enhancer of the ornithine transcarbamylase (OTC) gene, mutations of which cause OTC deficiency (OTCD), the most common urea cycle disorder. Antisense oligonucleotides (ASOs) targeting enhancer-derived regRNAs led to dose-dependent upregulation of OTC in hepatocytes. Mechanistically, ASOs altered regRNA structure, elevated regRNA levels, displaced transcriptional repressors, and increased H3K27 acetylation at the targeted enhancer. This work establishes a potential therapeutic strategy for addressing haploinsufficiency and highlights regRNAs as actionable targets for ASO-mediated upregulation of gene expression.","rel_num_authors":28,"rel_authors":[{"author_name":"Brynn N Akerberg","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Bryan J Matthews","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Yuting Liu","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Cecile Mathieu","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Jenna Williams","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Salome Manska","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Jiaqi Huang","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Yuichi Nishi","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Abeer Almutairy","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Eric Coughlin","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Gabriel Golczer","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Scott Waldron","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Isabella Pellegrino","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Yuchun Guo","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Evan Cohick","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Harpreet Turna","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Kevin Xiong","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Gavin Whissell","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Rachana S Kelker","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Mario Gamboa","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Evan Lenz","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Christopher J Jurcisin","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Rutuja Pai","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Justin A Caravella","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Alfica Sehgal","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Daniel F Tardiff","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"Alla A Sigova","author_inst":"CAMP4 Therapeutics Corporation"},{"author_name":"David A Bumcrot","author_inst":"CAMP4 Therapeutics Corporation"}],"rel_date":"2026-04-23","rel_site":"biorxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors.\n\nStatement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors.\n\nStatement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors.\n\nStatement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors.\n\nStatement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors.\n\nStatement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors.\n\nStatement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Generalizable Deep Learning Framework for Radiotherapy Dose Prediction Across Cancer Sites, Prescriptions and Treatment Modalities","rel_doi":"10.64898\/2026.04.17.26350770","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26350770","rel_abs":"Optimizing radiotherapy dose distributions remain a resource-intensive bottleneck. Existing AI-based dose prediction methods often have limited generalizability because they rely on small, heterogeneous datasets. We present nnDoseNetv2, an auto-configured, end-to-end framework for dose prediction across diverse disease sites (head and neck, prostate, breast, and lung), prescription levels (1.5-84 Gy), and treatment modalities (IMRT, VMAT, and 3D-CRT). By integrating machine-specific beam geometry with 3D structural information, the framework is designed to generalize across varied clinical scenarios.\n\nA single multi-site model was trained on 1,000 clinical plans. On sites seen during training, performance was comparable to specialized site-specific models. On unseen sites (liver and whole brain), the model outperformed site-specific models, with mean absolute errors of 2.46% and 6.97% of prescription, respectively.\n\nThese results suggest that geometric awareness can bridge disparate anatomical domains while eliminating the need for site-specific model maintenance, providing a scalable and high-fidelity approach for personalized radiotherapy planning.","rel_num_authors":9,"rel_authors":[{"author_name":"Ho-hsin Chang","author_inst":"University of Alabama at Birmingham"},{"author_name":"Rex Cardan","author_inst":"University of Alabama at Birmingham"},{"author_name":"Ritish Nedunoori","author_inst":"University of Alabama at Birmingham"},{"author_name":"John Fiveash","author_inst":"University of Alabama at Birmingham"},{"author_name":"Richard Popple","author_inst":"University of Alabama at Birmingham"},{"author_name":"Sandeep Bodduluri","author_inst":"University of Alabama at Birmingham"},{"author_name":"Dennis N Stanley","author_inst":"University of Alabama at Birmingham"},{"author_name":"Joseph Harms","author_inst":"Washington University"},{"author_name":"Carlos Cardenas","author_inst":"University of Alabama at Birmingham"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Artificial Intelligence Agents in Mental Health: A Systematic Review and Meta Analysis","rel_doi":"10.64898\/2026.04.21.26351365","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351365","rel_abs":"The rapid rise of large language models (LLMs) and foundation models has accelerated efforts to build artificial intelligence (AI) agents for mental health assessment, triage, psychotherapy support and clinical decision assistance. Yet a gap persists between healthcare and AI-focused work: while both communities use the language of \"agents,\" clinical research largely describes monolithic chatbots, whereas AI studies emphasize agentic properties such as autonomous planning, multiagent coordination, tool and database use and integration with multimodal mental health data streams.\n\nIn this Review, we conduct a systematic analysis of mental health AI agent systems from 2023 to 2025 using a six-dimensional audit framework: (i) system type (base model lineage, interface modality and workflow composition, from rule-based tools to role-aware multi-agent foundation-model systems), (ii) data scope (modalities and provenance, from elicited self-report and chatbot dialogues to electronic health records, biosensing and synthetic corpora), (iii) mental health focus (mapped to ICD-11 diagnostic groupings), (iv) demographics (age strata, geography and sex representation), (v) downstream tasks (screening\/triage, clinical decision support, therapeutic interventions, documentation, ethical-legal support and education\/simulation) and (vi) evaluation types (automated metrics, language quality benchmarks, safety stress tests, expert review and clinician or patient involvement).\n\nAcross this corpus, we find that most systems (1) concentrate on depression, anxiety and suicidality, with sparse coverage of severe mental illness, neurocognitive disorders, substance use and complex comorbidity; (2) rely heavily on text-based self-report rather than clinically verified longitudinal data or genuinely multimodal inputs; (3) are implemented as single-agent chatbots powered by general-purpose LLMs rather than role-structured, workflow-integrated pipelines; and (4) are evaluated primarily via offline metrics or vignette-based scenarios, with few prospective, clinician- or patient-in-the-loop studies. At the same time, an emerging class of agentic systems assigns foundation models explicit roles as planners, retrieval agents, safety auditors or supervisors coordinating other models and tools. These multiagent, tool-augmented workflows promise personalization, safety monitoring and greater transparency, but they also introduce new risks around reliability, bias amplification, privacy, regulatory accountability and the blurring of clinical versus non-clinical roles.\n\nWe conclude by outlining priorities for the next generation of mental health AI agents: clinically grounded, role-aware multi-agent architectures; transparent and privacy-preserving use of clinical and elicited data; demographic and cultural broadening beyond predominantly Western adult samples; and evaluation pipelines that progress from offline benchmarks to longitudinal, real-world studies with routine safety auditing and clear governance of responsibilities between agents and human clinicians.","rel_num_authors":20,"rel_authors":[{"author_name":"Lexuan Zhu","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Wenkong Wang","author_inst":"Shandong University"},{"author_name":"Zhiying Liang","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Wenjia Tan","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Bingyi Chen","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Xinxin Lin","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Zhengdong Wu","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Huizi Yu","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Xiang Li","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Jiyuan Jiao","author_inst":"University of Maryland"},{"author_name":"Sijia He","author_inst":"University of Michigan"},{"author_name":"Guangxin Dai","author_inst":"Shandong University"},{"author_name":"Jiahui Niu","author_inst":"Shandong University"},{"author_name":"Yi Zhong","author_inst":"Peking University Sixth Hospital"},{"author_name":"Wenyue Hua","author_inst":"Microsoft Research"},{"author_name":"Ngan Yin Chan","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Lin Lu","author_inst":"Peking University Third Hospital"},{"author_name":"Yun Kwok Wing","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Xin Ma","author_inst":"Shandong University"},{"author_name":"Lizhou Fan","author_inst":"The Chinese University of Hong Kong"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Missed Opportunities for Stroke Prevention in Hypertensive Patients: A Retrospective Case-Control Study","rel_doi":"10.64898\/2026.04.21.26351407","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351407","rel_abs":"BackgroundHypertension is the leading modifiable risk factor for ischemic stroke, yet the adequacy of preventative hypertension care in routine clinical practice remains suboptimal. Whether gaps in hypertension management represent missed opportunities for stroke prevention remains unclear.\n\nObjectiveTo evaluate the association between hypertension care delivery and the risk of incident ischemic stroke.\n\nMethodsWe conducted a retrospective, matched, nested case-control study among adults with hypertension using electronic health record data from a large regional health system (2010-2024). Patients with a first-ever ischemic stroke were matched 1:2 to controls on age, sex, race and ethnicity, and calendar time. Three care metrics were assessed during follow-up: (1) outpatient visits with blood pressure (BP) measurement per year; (2) number of antihypertensive medication ingredients; and (3) medication intensification score. Conditional logistic regression estimated adjusted odds ratios (aORs).\n\nResultsThe study included 13,476 cases and 26,952 matched controls (N = 40,428). Mean (SD) age was 64.8 (12.2) years, 54.1% were female, and mean follow-up was 2,497 (1,308) days. Cases had fewer BP visits per year (median, 2.50 vs. 3.01; p < 0.001), similar number of medication ingredients (2.00 vs 2.00), and lower treatment intensification scores (-0.211 vs - 0.125). In adjusted models, >5 BP visits per year was associated with lower stroke odds (aOR, 0.55; 95% CI, 0.51-0.59) compared with [&le;]1 visit. Use of 2-3 medication ingredients (vs 0) was also associated with reduced stroke odds (aOR, 0.80; 95% CI, 0.75-0.86), whereas >3 ingredients was not significant. The highest quartile of treatment intensification showed the strongest association (aOR, 0.47; 95% CI, 0.44-0.51). Findings were consistent across subgroup and sensitivity analyses, including strata defined by baseline SBP and follow-up SBP.\n\nConclusionsGreater engagement in hypertension care was associated with lower odds of ischemic stroke, suggesting that gaps in routine management may represent missed opportunities for prevention.","rel_num_authors":12,"rel_authors":[{"author_name":"Huanhuan Yang","author_inst":"Yale University"},{"author_name":"Yuntian Liu","author_inst":"Yale University"},{"author_name":"Chungsoo Kim","author_inst":"Yale School of Medicine"},{"author_name":"Chenxi Huang","author_inst":"Yale University"},{"author_name":"Mitsuaki Sawano","author_inst":"Yale School of Medicine"},{"author_name":"Patrick Young","author_inst":"Yale School of Medicine"},{"author_name":"Jacob McPadden","author_inst":"Yale New Haven Hospital"},{"author_name":"Mark Anderson","author_inst":"Sentara Health"},{"author_name":"John S Burrows","author_inst":"Sentara Health"},{"author_name":"Harlan M Krumholz","author_inst":"Yale University"},{"author_name":"John E Brush","author_inst":"Sentara Health"},{"author_name":"Yuan Lu","author_inst":"Yale University"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Validation of 3D-DXA-Derived Proximal Femur Measurements Against QCT Across International Clinical Cohorts","rel_doi":"10.64898\/2026.04.22.26351450","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351450","rel_abs":"Three-dimensional dual-energy X-ray absorptiometry (3D-DXA) reconstructs proximal femur models from standard scans to estimate cortical and trabecular bone parameters. The aim of this study was to evaluate 3D-DXA against quantitative computed tomography (QCT) across independent international cohorts. The study included 537 subjects from four cohorts: an adult population from Spain, a postmenopausal female population from the United States, an osteoarthrosis population and a young population, both from Japan. Subjects underwent both 3D-DXA and QCT imaging. Accuracy was assessed using linear regression and Bland-Altman analysis to evaluate systematic and random errors. 3D-DXA parameters strongly correlated with QCT across all datasets, with correlation coefficients between 0.82 and 0.97. Random errors were consistent across cohorts and ranged between 16.55 and 19.91 mg\/cm3 for integral volumetric bone mineral density (vBMD), between 13.52 and 18.47 mg\/cm3 for trabecular vBMD, and between 9.13 and 11.37 mg\/cm2 for cortical surface bone mineral density (sBMD). Systematic errors ranged between -14.84 and 4.50 mg\/cm3 for integral vBMD, between -8.31 and 14.41 mg\/cm3 for trabecular vBMD, and between -5.58 and 3.21 mg\/cm2 for cortical sBMD. The variations in systematic errors were likely attributable to differences in QCT acquisition protocols. Overall, these results demonstrate consistent agreement between 3D-DXA and QCT across sex, age, ethnicity, geographic regions, and clinical profiles. Taken together, these findings support the use of 3D-DXA as an accurate, non-invasive, and clinically accessible technology for advanced assessment of the cortical and trabecular compartments of the proximal femur.","rel_num_authors":7,"rel_authors":[{"author_name":"Marta I. Bracco","author_inst":"3D-Shaper Medical, Barcelona, Spain"},{"author_name":"Dennis M. Black","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Teruki Sone","author_inst":"Kawasaki Medical School, Kurashiki, Japan"},{"author_name":"Luis del Rio","author_inst":"CETIR ASCIRES, Barcelona, Spain"},{"author_name":"Silvana Di Gregorio","author_inst":"CETIR ASCIRES, Barcelona, Spain"},{"author_name":"Jorge Malouf","author_inst":"Mineral Metabolism Unit, Grup Creu Groga, Mataro, Spain."},{"author_name":"Ludovic Humbert","author_inst":"3D-Shaper Medical, Barcelona, Spain"}],"rel_date":"2026-04-22","rel_site":"medrxiv"}]}