{"gname":"University of Sydney","grp_id":"50","rels":[{"rel_title":"Renin-Guided Risk Stratification and Therapy in Hypertension to Reduce Major Adverse Cardiovascular Outcomes","rel_doi":"10.64898\/2026.05.11.26352940","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.11.26352940","rel_abs":"Background: Risk stratification in hypertension remains challenging. The prognostic value of plasma renin in guiding therapy for hypertension is not well established. Methods: In this multicenter retrospective cohort of 16,600 people with hypertension, we evaluated the association between plasma renin activity and major adverse cardiovascular events (MACE) defined as stroke, myocardial infarction, and all-cause death. Plasma renin was analyzed as a continuous variable using restricted cubic splines. A 6-month landmark analysis assessed treatment effects of mineralocorticoid receptor antagonists (MRA) as opposed to baseline renin-angiotensin system inhibitors. Results: Continuous renin level showed a U-shaped association with MACE, with the lowest risk at 1.17ng\/mL\/h. In categorical analyses, low renin (<0.3 ng\/mL\/h; adjusted hazard ratio [HR]=1.29, 95% CI 1.15-1.45) and high renin (>3.0ng\/mL\/h; HR=1.19, 95% CI 1.06-1.33) were both associated with higher MACE risk. Initiation of MRA therapy after renin measurement was associated with a graded reduction in MACE risk where patients with low renin had the lowest risk (HR=0.75, 95%CI 0.60-0.92), and patients with high-renin had the highest risk (HR=1.41, 95%CI 1.03-1.94). In contrast, baseline use of renin-angiotensin system inhibitors was associated with a graded reduction in MACE risk where patients with high-renin had the lowest risk (HR=0.76, 95%CI 0.63-0.92) but those with low renin did not benefit (HR=0.87, 95%CI 0.72-1.04). Conclusions: Plasma renin is a prognostic biomarker for MACE and may serve as a guide for treatment selection. A renin-guided strategy that favors MRAs in patients with low renin may reduce MACE and support individualized hypertension care.","rel_num_authors":8,"rel_authors":[{"author_name":"Cheng-Hsuan Tsai","author_inst":"National Taiwan University Hospital"},{"author_name":"Yu-Ching Chang","author_inst":"National Taiwan University Hospital"},{"author_name":"Chin-Chen Chang","author_inst":"National Taiwan University Hospital"},{"author_name":"Andrew J Newman","author_inst":"Mass General Brigham"},{"author_name":"Jenifer Brown","author_inst":"Mass General Brigham"},{"author_name":"Vin-Cent Wu","author_inst":"National Taiwan University Hospital"},{"author_name":"Yen-Hung Lin","author_inst":"National Taiwan University College of Medicine and Hospital"},{"author_name":"Anand Vaidya","author_inst":"Mass General Brigham"}],"rel_date":"2026-05-14","rel_site":"medrxiv"},{"rel_title":"Higher Neighborhood Social Vulnerability is Associated with Lower Life's Essential 8 Cardiovascular Health Scores: the Coronary Artery Risk Development in Young Adults (CARDIA) Study","rel_doi":"10.64898\/2026.05.11.26352953","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.11.26352953","rel_abs":"Background: Neighborhood social vulnerability may shape cardiovascular health (CVH), but its association with Life's Essential 8 (LE8) and whether changes in vulnerability track with changes in CVH during midlife are unclear. We examined cross-sectional and longitudinal associations of the Social Vulnerability Index (SVI) with LE8 and assessed differences by SVI domain, LE8 component, race, and sex. Methods: We analyzed CARDIA participants at Year 15 (Y15; 2000-2001; n = 3,168; mean age 40 years) and Year 30 (Y30; 2015-2016; n = 2,267; mean age 55 years). Residential addresses were geocoded and linked to 2000 and 2016 SVI. SVI was divided into quartiles. CVH scores were calculated from LE8 metrics (range 0-100; higher is better CVH), excluding sleep. Using multivariable linear regression adjusted for age, sex, race, and educational attainment, we estimated LE8 differences across SVI quartiles and associations of 15-year SVI change\/residential mobility with change in LE8. Cox models estimated incident CVD associations. Results: Higher SVI was associated with lower LE8 at both exams. Adjusted Q4 vs Q1 differences in overall LE8 were -5.34 points (95% CI, -6.90 to -3.78) at Y15 and -4.60 points (95% CI, -6.51 to -2.69) at Y30. SES and household domains showed the largest LE8 differences. Among the four SVI domains, SES and household characteristics drove most of the disparity in LE8 scores (Y30 Q4 vs. Q1: SES {Delta} = -6.98, household {Delta} = -6.56 points). Component-level differences were largest for nicotine exposure at Y15 (-13.09 points) and physical activity at Y30 (-13.09 points). Changes in SVI and residential mobility were not significantly associated with change in LE8. Conclusion: Higher social vulnerability was associated with significantly lower CVH. Socioeconomic and household factors, along with behavioral gaps in nicotine exposure and physical activity, may be key targets for community-level interventions to improve cardiovascular health equity.","rel_num_authors":9,"rel_authors":[{"author_name":"James Michael Walker","author_inst":"Northwestern University Feinberg School of Medicine"},{"author_name":"Emily Lam","author_inst":"Northwestern University Feinberg School of Medicine"},{"author_name":"Daniel J Won","author_inst":"Northwestern University Feinberg School of Medicine"},{"author_name":"Cyanna McGowan","author_inst":"Northwestern University Feinberg School of Medicine"},{"author_name":"Lucia Juarez","author_inst":"University of Alabama at Birmingham"},{"author_name":"Catarina I. Kiefe","author_inst":"University of Massachusetts Medical School"},{"author_name":"Kiarri N Kershaw","author_inst":"Feinberg School of Medicine, Northwestern University"},{"author_name":"Hongyan Ning","author_inst":"Northwestern University Feinberg School of Medicine"},{"author_name":"Donald M Lloyd-Jones","author_inst":"Boston University Chobanian & Avedisian School of Medicine"}],"rel_date":"2026-05-14","rel_site":"medrxiv"},{"rel_title":"Asymmetry between warmth and clinical substance in multilingual consumer health AI","rel_doi":"10.64898\/2026.05.09.26352813","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.09.26352813","rel_abs":"The same patient question can yield different clinical quality across languages. Across 504 forum-derived patient queries in six languages and four chatbots, language-matched clinicians rated responses on five clinical dimensions (1,008 ratings; 5,040 dimension scores). Patient language outweighed chatbot identity across the four clinical-substance dimensions (composite language partial eta-squared 0.275 vs chatbot 0.035; robust to investigator-rating exclusion: eta-squared 0.260) but not for empathy (eta-squared 0.029): clinical substance was language-associated; warmth was relatively preserved. Catastrophic safety ratings ranged 4.3-fold by language (3.6% English, 15.5% Thai and Hebrew); 62% of catastrophic ratings exceeded the English baseline (descriptive disparity). Failures were systematic and silent: none of 24 stroke responses conveyed time-criticality framing, none of 24 CO-poisoning responses challenged the family's stress framing, and 120 sentinel responses contained no confident errors. Warmth did not discriminate clinical danger (response-level empathy AUC = 0.49): consumer health AI can deliver fluent, caring tone with degraded clinical substance.","rel_num_authors":11,"rel_authors":[{"author_name":"Dean Ariel","author_inst":"Clalit Health Services"},{"author_name":"Lyel Romina Grumberg","author_inst":"Department of Radiology, Charite Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Sopak Supakul","author_inst":"Fujita Health University, Toyoake, Aichi, Japan"},{"author_name":"Sirawit Wannasri","author_inst":"Don Tan Hospital, Mukdahan, Thailand"},{"author_name":"Ilan Y. Mitchnik","author_inst":"Orthopaedic Surgery Department, Shamir (Assaf Harofeh) Medical Center, Zerifin, Israel; Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv"},{"author_name":"Anna Lev","author_inst":"Department of Family Medicine, Clalit Health Services, Tel Aviv, Israel; Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Weerawat Ariyamethanon","author_inst":"Independent practice, Thailand"},{"author_name":"Muhammad Agbarieh","author_inst":"Department of Family Medicine, Clalit Health Services, Tel Aviv, Israel"},{"author_name":"Shafiq Miari","author_inst":"Department of Family Medicine, Clalit Health Services, Tel Aviv, Israel"},{"author_name":"Guy Laban","author_inst":"Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel; The Azrieli National Center for Autism and Neurodev"},{"author_name":"Boaz Hasid","author_inst":"Department of Family Medicine, Clalit Health Services, Tel Aviv, Israel"}],"rel_date":"2026-05-14","rel_site":"medrxiv"},{"rel_title":"Design and methodology of a randomized clinical trial of prolonged daily antibiotic suppression with and without fulguration for uncomplicated recurrent urinary tract infections in women","rel_doi":"10.64898\/2026.05.11.26352945","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.11.26352945","rel_abs":"Objective: Recurrent urinary tract infections (rUTIs) significantly decrease quality of life and antibiotics are becoming increasingly less effective due to antimicrobial resistance. Alternative effective treatment strategies are urgently needed for rUTIs. Prior studies have indicated that women can experience resolved or improved rUTI following electrofulguration (EF). To further investigate these findings, we report on the design and methodology behind a randomized trial examining two treatment arms: standard prolonged antibiotic treatment with nitrofurantoin (NF) alone or in combination with EF. Patients and Methods: The aim of this randomized trial is to determine, at two institutions, the efficacy of two interventions for rUTI associated with early stages of chronic cystitis (stages 1 and 2): conventional 6 months low-dose (100mg) NF daily antibiotic suppression alone (NF) or conventional NF with EF (EF + NF). The study is also designed to analyze changes in the urinary microbiomes in the two different treatment arms and to determine the durability of clinical outcomes in both treatment arms at 2 years after the end of each intervention. The primary outcomes will be obtained from 6 to 18 months, as well as 18 to 30 months following completion of the original 6-month intervention. Failure is defined based on UTI symptoms documented by a validated questionnaire with a documented urine culture confirming a bacterial strain at each UTI episode following the end of the 6-month intervention. Conclusions: This randomized trial is designed to examine the efficacy and durability of treating women with rUTIs using the standard of care of NF alone, or an EF procedure with NF.","rel_num_authors":5,"rel_authors":[{"author_name":"Philippe E. Zimmern","author_inst":"UT Southwestern Medical Center"},{"author_name":"Colby Souders","author_inst":"University of Kansas Medical Center"},{"author_name":"Bonnie Chase Prokesch","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Kevin Lutz","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Nicole J. De Nisco","author_inst":"The University of Texas at Dallas"}],"rel_date":"2026-05-14","rel_site":"medrxiv"},{"rel_title":"Structure tokens sharpen the feature vocabulary of protein language models","rel_doi":"10.64898\/2026.05.12.724593","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.12.724593","rel_abs":"Protein language models predict structure and function from amino acid sequences, but the internal computations that produce these predictions remain opaque. We applied sparse autoencoders to ESM-2 (650M parameters, sequence-only) and ESM-3 (1.4B parameters, multimodal) and found that 78% of learned features converge between the two architectures (permutation null: 14.2%, p < 0.001). These convergent features account for nearly all functional knowledge encoded by the models (functional site AUROC 0.925 versus 0.661 for architecture-unique features). Structure tokens in ESM-3 do not create a new feature vocabulary. Instead, the 15.2% of features most activated by structure tokens are more convergent with sequence-only ESM-2 than structure-invariant features are (r = 0.54 versus 0.45) and carry richer biological annotation (134 versus 29 enriched GO terms). Attention analysis identified a single geometric head (L0H7) as the bottleneck through which structural information enters the network; ablating this head alone changed secondary structure predictions at 40% of residues, while ablating random layer-0 heads altered fewer than 17%. Steering vectors, attribution patching, and sparse feature circuits confirmed that these features sit within the model's causal pathway. Two architecturally distinct models, trained on different objectives and input modalities, converge on a shared biological vocabulary - and explicit structure tokens sharpen that vocabulary rather than rewriting it.","rel_num_authors":1,"rel_authors":[{"author_name":"Jacob L Steenwyk","author_inst":"University of California, Berkeley"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"BRIDGE-GRN: Role-Aware Bi-Tower Graph Learning with Cross-View Contrast for Directed Gene Regulatory Network Inference","rel_doi":"10.64898\/2026.05.12.724562","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.12.724562","rel_abs":"Inferring directed gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data remains difficult because expression profiles are sparse, regulatory priors are incomplete, and experimentally supported TF-target labels are limited. To address these challenges, we propose BRIDGE-GRN, a role-aware graph learning framework that separates shared graph-context encoding from directional edge decoding. BRIDGE-GRN constructs an undirected support graph from training positive regulatory evidence, learns shared gene representations with an attention-based graph encoder, and projects them into transcription factor-role and target-role embedding spaces for asymmetric TF-to-target scoring. To improve robustness under noisy and incomplete supervision, the model aligns identity and edge-perturbed graph views through cross-view contrastive regularization. We evaluated BRIDGE-GRN across mouse benchmark settings spanning five cell types, three prior-network families, and two gene-scale settings, and further examined low-supervision transfer to target domains, architectural ablations, and biological interpretability. BRIDGE-GRN achieved consistently strong performance, outperforming or matching the strongest competing baseline in most benchmark configurations. Transfer initialization improved low-shot target-domain adaptation, while ablation analyses confirmed the importance of both role-specific bi-tower projections and contrastive regularization. Biological interpretation analyses further showed role-structured embeddings, enrichment of top-ranked predictions for external regulatory support, and coherent driver-centered regulatory modules. These results support BRIDGE-GRN as a robust, transferable, and interpretable framework for directed GRN inference from single-cell transcriptomic data.","rel_num_authors":2,"rel_authors":[{"author_name":"Hao Chen","author_inst":"University of Sydney"},{"author_name":"Wenze Ding","author_inst":"University of Sydney"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Phage-based microbiome manipulation reveals ecological interactions within gut communities","rel_doi":"10.64898\/2026.05.13.724931","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.13.724931","rel_abs":"Mechanistic understanding of gut ecology is limited by the availability of tools for precise manipulation of microbiome composition. Here, we isolate lytic phages to enable targeted removal of gut commensal Escherichia fergusonii (Ef) from complex, undefined stool-derived in vitro communities. A single phage drove resistance without fitness cost in monoculture, but resistant Ef exhibited reduced fitness in communities, enabling expansion of closely related Proteobacteria. Resistance arose via reversible promoter inversion linked to outer-membrane function. A phage cocktail overcame resistance to achieve Ef knockout across communities with minimal collateral effects. Using knockout communities, we show that Ef is necessary and sufficient for preventing Salmonella invasion. Replacement with an Ef transposon-mutant library revealed that community-specific fitness defects are enriched in genes involved in outer-membrane assembly. Disruption of these genes sensitized Ef to antagonistic community members, highlighting interspecies warfare as a key driver of microbiome ecology. These results establish phage-mediated perturbation as a framework for linking species to community-level function and for enabling precision microbiome engineering.","rel_num_authors":15,"rel_authors":[{"author_name":"Taylor H. Nguyen","author_inst":"Stanford University"},{"author_name":"Morgan Su","author_inst":"Stanford University"},{"author_name":"Nhien T. Lu","author_inst":"Stanford University"},{"author_name":"Valentine Trotter","author_inst":"Lawrence Berkeley National Laboratory"},{"author_name":"Saria A. McKeithen-Mead","author_inst":"Stanford University"},{"author_name":"Jamie Alcira Lopez","author_inst":"Stanford University"},{"author_name":"Jiawei Sun","author_inst":"Stanford University"},{"author_name":"Zachary Hallberg","author_inst":"University of California, Berkeley"},{"author_name":"Handuo Shi","author_inst":"Stanford University"},{"author_name":"Po-Yi Ho","author_inst":"Westlake University"},{"author_name":"Brian C. DeFelice","author_inst":"Chan Zuckerberg Biohub"},{"author_name":"Michiko E. Taga","author_inst":"University of California, Berkeley"},{"author_name":"Adam M. Deutschbauer","author_inst":"Lawrence Berkeley National Laboratory"},{"author_name":"Andrew J. Hryckowian","author_inst":"University of Wisconsin-Madison"},{"author_name":"Kerwyn Casey Huang","author_inst":"Stanford University"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Pathogen-specific host responses define distinct pneumonia endotypes in the human lung","rel_doi":"10.64898\/2026.05.12.724509","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.12.724509","rel_abs":"Pneumonia is the leading cause of death from infectious disease worldwide. The diagnosis and treatment of patients with pneumonia lag behind other major conditions, relying on syndromic definitions that lack molecular resolution and ignore underlying endotypes. We sought to test the hypothesis that dynamic pathogen-specific host responses in the alveolar space represent distinct pneumonia endotypes linked to different clinical features and outcomes. We prospectively enrolled a cohort of 690 patients (including immunocompromised patients) with known or suspected pneumonia receiving mechanical ventilation in whom the etiology of pneumonia was determined by gold-standard analysis of distal lung fluid obtained by bronchoalveolar lavage (BAL) combined with clinical adjudication. From these patients, we analyzed 792 BAL fluid samples, including 310 serial samples, using flow cytometry (482 patients) and single-cell RNA-sequencing (170 patients; 263 samples, complemented by 9 healthy controls and 25 post-COVID-19 patients, yielding ~2.4 million single cells across 28 cell types), and extracted daily clinical data from the electronic health record (>15,000 patient-days). We used machine learning models to identify pathogen-specific host responses in the transcriptome of alveolar immune cells that were associated with changes in alveolar cell abundance and clinical features. Our results suggest that therapeutic strategies for pneumonia should be individualized to specific host-pathogen interactions.","rel_num_authors":35,"rel_authors":[{"author_name":"Nikolay S Markov","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Marcin Mo\u017cejko","author_inst":"Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland"},{"author_name":"Vijeeth Guggilla","author_inst":"Division of Biostatistics and Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Ma\u0142gorzata \u0141az\u0119cka","author_inst":"Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland"},{"author_name":"Helen K Donnelly","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Alvaro Donayre","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Samuel Fenske","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Alec Peltekian","author_inst":"Department of Computer Science, Northwestern University McCormick School of Engineering and Applied Science, Chicago, IL, USA"},{"author_name":"Bruno Puczko-Szyma\u0144ski","author_inst":"Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland"},{"author_name":"Paulina Szymczak","author_inst":"Institute of AI for Health, Helmholtz Zentrum Munchen, Munich, Germany"},{"author_name":"Adam Izdebski","author_inst":"Institute of AI for Health, Helmholtz Zentrum Munchen, Munich, Germany"},{"author_name":"Lucy Luo","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Karolina J Senkow","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Luisa Cusick","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Zhan Yu","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Suchitra Swaminathan","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Ziyan Lu","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Hiam Abdala-Valencia","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Duc Phan","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Rebecca K Clepp","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Luke V Rasmussen","author_inst":"Division of Biostatistics and Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Anna Pawlowski","author_inst":"Northwestern Medicine Enterprise Data Warehouse, Chicago, IL, USA"},{"author_name":"Chiagozie O Pickens","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Nandita R Nadig","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Theresa Walunas","author_inst":"Division of General Internal Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Robert Tighe","author_inst":"Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Duke University, Durham, NC, USA"},{"author_name":"- The Neu-Lung Investigators","author_inst":""},{"author_name":"Richard G Wunderink","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"GR Scott Budinger","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Luisa Morales-Nebreda","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Catherine A Gao","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Benjamin D Singer","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Alexander V Misharin","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"},{"author_name":"Ewa Szczurek","author_inst":"Institute of AI for Health, Helmholtz Zentrum Munchen, Munich, Germany; Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland"},{"author_name":"- The NU SCRIPT Study Investigators","author_inst":"Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Covalent tumor anchoring spatially orchestrates antitumor immunity","rel_doi":"10.64898\/2026.05.13.724746","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.13.724746","rel_abs":"Protein immunotherapies can elicit potent tumor rejection, but reversible target engagement, incomplete tumor retention, and systemic leakage often erode spatial control. Here, we develop covalently anchored tumor immunotherapeutic proteins (CATIPs), a modular platform that uses proximity-enabled covalent chemistry to immobilize immune cues on tumor-cell surfaces after intratumoral administration. CATIPs combine tumor-targeting nanobodies with payloads for T cell engagement, co-stimulation, and cytokine support. In human PBMC-reconstituted NSG mice, CATIPs completely eradicated treated EGFR-positive tumors, outperforming matched non-covalent proteins while limiting redistribution, systemic T cell activation, cytokine release, xGVHD-associated morbidity, and on-target, off tumor toxicity. In immunocompetent melanoma models, CATIPs remodeled the tumor microenvironment, expanded antigen-specific CD8+ T cells, induced antigen-restricted abscopal control, and generated durable protection against local and metastatic rechallenge. CATIP-engineered tumor cells further functioned as whole-cell vaccines. Thus, covalent tumor anchoring converts local protein delivery into tumor-surface immune programming, enabling systemic, tumor-specific, durable antitumor immunity while limiting systemic immunopathology.","rel_num_authors":6,"rel_authors":[{"author_name":"Qingke Li","author_inst":"University of California San Francisco"},{"author_name":"Hongfei Chen","author_inst":"University of California San Francisco"},{"author_name":"Pan Zhang","author_inst":"University of California San Francisco"},{"author_name":"Li Cao","author_inst":"University of California San Francisco"},{"author_name":"Bingchen Yu","author_inst":"University of California San Francisco"},{"author_name":"Lei Wang","author_inst":"University of California San Francisco"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Explainable prediction and simulation of complex system dynamics through networks of manifolds","rel_doi":"10.64898\/2026.05.12.724527","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.12.724527","rel_abs":"Complex systems such as brains and other interacting biological and physical processes are difficult to represent because they evolve across many variables, scales, and nonlinear interactions. To capture these multivariate, multiscale interactions we have developed Generative Manifold Networks (GMNs) a machine learning framework consisting of a network of linked dynamical systems. The network is discovered by an interaction function which can focus on causality, shared information, nonlinearity or other metric. Network nodes are low--dimensional data--driven state--space manifolds with generator functions accommodating multiscale dynamics. In contrast to many machine learning approaches GMNs have no latent or randomly initialized variables providing transparent explainability. GMNs generate short term dynamics of chaos on par with echo state networks while outperforming them in long term generation of chaos and neural dynamics, but with a markedly reduced number of dimensions and without sensitive dependence on reservoir parameters or random states. As a result of their holistic, multiscale representation GMNs can learn the complete dynamics of a complex system. We further show that GMNs are universal approximators. GMNs are demonstrated on chaotic dynamics, neural and behavioral recordings of the fruit fly and domestic rat with comparisons to echo state networks and crossformer - a time series transformer.","rel_num_authors":8,"rel_authors":[{"author_name":"Joseph Park","author_inst":"Okinawa Institute of Science and Technology graduate university"},{"author_name":"Cameron Smith","author_inst":"University of Southern California"},{"author_name":"Shih Yi Tseng","author_inst":"Kavli Institute for Fundamental Neuroscience, HHMI, UCSF"},{"author_name":"Jennifer Guidera","author_inst":"UCSF-UC Berkeley Graduate Program in Bioengineering"},{"author_name":"Andrei V Semenov","author_inst":"Saint Petersburg State University"},{"author_name":"Stanislav Smirnov","author_inst":"University of Geneva, Section de mathematiques"},{"author_name":"Loren M Frank","author_inst":"Kavli Institute for Fundamental Neuroscience, HHMI, UCSF"},{"author_name":"Gerald M Pao","author_inst":"Okinawa Institute of Science and Technology graduate university"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Explainable prediction and simulation of complex system dynamics through networks of manifolds","rel_doi":"10.64898\/2026.05.12.724527","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.12.724527","rel_abs":"Complex systems such as brains and other interacting biological and physical processes are difficult to represent because they evolve across many variables, scales, and nonlinear interactions. To capture these multivariate, multiscale interactions we have developed Generative Manifold Networks (GMNs) a machine learning framework consisting of a network of linked dynamical systems. The network is discovered by an interaction function which can focus on causality, shared information, nonlinearity or other metric. Network nodes are low--dimensional data--driven state--space manifolds with generator functions accommodating multiscale dynamics. In contrast to many machine learning approaches GMNs have no latent or randomly initialized variables providing transparent explainability. GMNs generate short term dynamics of chaos on par with echo state networks while outperforming them in long term generation of chaos and neural dynamics, but with a markedly reduced number of dimensions and without sensitive dependence on reservoir parameters or random states. As a result of their holistic, multiscale representation GMNs can learn the complete dynamics of a complex system. We further show that GMNs are universal approximators. GMNs are demonstrated on chaotic dynamics, neural and behavioral recordings of the fruit fly and domestic rat with comparisons to echo state networks and crossformer - a time series transformer.","rel_num_authors":8,"rel_authors":[{"author_name":"Joseph Park","author_inst":"Okinawa Institute of Science and Technology graduate university"},{"author_name":"Cameron Smith","author_inst":"University of Southern California"},{"author_name":"Shih Yi Tseng","author_inst":"Kavli Institute for Fundamental Neuroscience, HHMI, UCSF"},{"author_name":"Jennifer Guidera","author_inst":"UCSF-UC Berkeley Graduate Program in Bioengineering"},{"author_name":"Andrei V Semenov","author_inst":"Saint Petersburg State University"},{"author_name":"Stanislav Smirnov","author_inst":"University of Geneva, Section de mathematiques"},{"author_name":"Loren M Frank","author_inst":"Kavli Institute for Fundamental Neuroscience, HHMI, UCSF"},{"author_name":"Gerald M Pao","author_inst":"Okinawa Institute of Science and Technology graduate university"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Nmur1 and Cckar fail to support functional genetic access in adult dopamine neurons and challenge GPCR atlas assignments","rel_doi":"10.64898\/2026.05.11.724447","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724447","rel_abs":"Apuschkin et al. (2024) proposed a GPCR-based transcriptomic atlas for midbrain dopamine (DA) neuron subpopulations, including candidates such as Nmur1, Cckar, and Ffar4. To guide genetic targeting, these markers must reflect functional expression in adult DA neurons. Using in situ hybridization, Cre-dependent reporter lines, and both intracranial and systemic viral approaches, we find no evidence of adult Nmur1-mediated recombination in DA neurons, while Cckar-driven recombination is consistent with developmental expression only. Notably, Ffar4 expression overlaps extensively with Ntsr1 midbrain populations, indicating that it does not define a distinct DA neuron class. Furthermore, analysis of independent spatial transcriptomic datasets together with our MERFISH data shows that many proposed GPCR markers are not detectably expressed in adult DA neurons. These findings demonstrate that transcriptomic enrichment does not always yield reliable adult markers and highlight the need for functional validation prior to use in circuit targeting.","rel_num_authors":11,"rel_authors":[{"author_name":"Moueez Shah","author_inst":"Cal Poly Pomona"},{"author_name":"Renqi Wu","author_inst":"Cal Poly Pomona"},{"author_name":"Qiao Ye","author_inst":"University of California Irvine"},{"author_name":"Raluca Bugescur","author_inst":"Michigan State University"},{"author_name":"Andrew Villa","author_inst":"California State Polytechnic University Pomona"},{"author_name":"Justine Wong","author_inst":"Cal Poly Pomona"},{"author_name":"Fernando Garcia","author_inst":"Cal Poly Pomona"},{"author_name":"Zhiqun Tan","author_inst":"University of California Irvine"},{"author_name":"Xiangmin Xu","author_inst":"University of California Irvine"},{"author_name":"Gina Leinninger","author_inst":"Michigan State University"},{"author_name":"Andrew Steele","author_inst":"Cal Poly Pomona"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Integrated spatial and single-cell transcriptomic analysis of aggressive glioblastoma growth dynamics.","rel_doi":"10.64898\/2026.05.11.724432","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724432","rel_abs":"Glioblastoma (GBM) develops within a complex tumor ecosystem whose temporal dynamics remain poorly understood. Here, we performed longitudinal single-cell RNA sequencing and spatial transcriptomics across multiple timepoints in two widely used murine GBM models - CT2A and GL261 - which differ markedly in aggressiveness and response to immune checkpoint blockade. Tumor cell transcriptomes revealed model-specific programs: CT2A cells progressively upregulated epithelial-mesenchymal transition (EMT), non-classical MHC Class I, and progressively, hypoxia response pathways, resembling the human mesenchymal GBM cell state, while GL261 cells exhibited MHC Class II expression and developmental signatures resembling oligodendrocyte progenitor and astrocytic states. Ligand-receptor interaction analyses identified thrombospondins (Thbs1, Thbs2) and osteopontin (Spp1) as CT2A-specific tumor ligands mediating tumorigenic interactions with immune cells, with downstream targets enriched for EMT and TGF-{beta} pathways. Conversely, the GL261 model presented a differential potential to engage neuronal and perivascular guidance networks, with Glutamate and L1 cell adhesion molecule (L1cam) as lead signaling partners. The CT2A immune compartment exhibited progressive microglia-to-macrophage phenotypic conversion, enhanced macrophage infiltration driven by Spp1, and elevated T cell exhaustion, while GL261 maintained a distinct adaptive immune communication hub via MHC class II-CD4 signaling. Elevated THBS1, THBS2, and SPP1 expression correlated with poor survival in human GBM datasets. Together, these findings reveal divergent tumor-immune ecosystems in CT2A and GL261 that recapitulate distinct aspects of human GBM, with implications for therapeutic targeting.","rel_num_authors":13,"rel_authors":[{"author_name":"Clara F. Alves-Pereira","author_inst":"Massachusetts General Hospital"},{"author_name":"Gyeong Dae Kim","author_inst":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA."},{"author_name":"Ngima Sherpa","author_inst":"Biological and Biomedical Sciences Graduate Program, Harvard University, Cambridge, MA, USA."},{"author_name":"Kayla Colvin","author_inst":"Biological and Biomedical Sciences Graduate Program, Harvard University, Cambridge, MA, USA."},{"author_name":"Saad M Khan","author_inst":"Massachusetts General Hospital"},{"author_name":"Khanh P Phan","author_inst":"Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA."},{"author_name":"Anthony Z Wang","author_inst":"Department of Pathology and Immunology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA."},{"author_name":"Ian F Dunn","author_inst":"Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA."},{"author_name":"Tanner Johanns","author_inst":"The Brain Tumor Center at Siteman Cancer Center, Division of Medical Oncology, Washington University School of Medicine, St. Louis, MO, USA."},{"author_name":"Erdyni Tsitsykov","author_inst":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Rupen Desai","author_inst":"Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin"},{"author_name":"Gavin P Dunn","author_inst":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA."},{"author_name":"Allegra A Petti","author_inst":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA."}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"FASN Inhibition Resensitizes Chordoma to Radiotherapy by Targeting Adaptive Unsaturated Fatty Acid Metabolism","rel_doi":"10.64898\/2026.05.11.724415","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724415","rel_abs":"Chordoma, a rare malignant notochordal tumor of the skull base and spine, is typically resistant to chemotherapy and radiotherapy and exhibits aggressive local recurrence. Here we show that chordoma recurrence correlates with a coordinated upregulation of monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), a low PFA\/MUFA ratio and an adaptive, lipid peroxidation-resistant state that protects against DNA damage and cell death. Single-cell metabolic profiling identified a tumor subpopulation marked by a fatty acid biosynthesis-high state coupled to stemness. RT-tolerance was directly linked to elevated FASN and lipid droplet (LD) expansion, and MUFA-loading phenocopied RT-tolerance in chordoma cells. Mechanistically, LDs accumulated in response to RT via generation of ROS, and subsequent activation of ER-stress, SREBP1 and Fatty Acid Synthetase (FASN). DESI-MS showed that low-dose irradiation was sufficient to increase MUFAs early and build peroxidation resistant MUFA-LDs, whereas PUFA induction required a higher radiation dose. In a spatially defined manner in a patient-derived xenograft. Finally, in silico knockout and pharmacologic FASN blockade restored radiosensitivity and apoptosis in vitro and in vivo. Collectively, our result support a unifying model in which RT resistance in chordoma is shaped by an adaptive fatty acid metabolic program that buffers oxidative injury and increases survival of RT-resistant, stem-like tumor subpopulations. These findings further support FASN inhibition as a practical radiosensitization strategy for chordoma particulary where RT dose escalation is constrained by anatomy.","rel_num_authors":11,"rel_authors":[{"author_name":"RUOLUN WEI","author_inst":"Stanford University"},{"author_name":"Yifan Meng","author_inst":"Nankai University"},{"author_name":"Emon Nasajpour","author_inst":"Stanford University"},{"author_name":"Dena Panovska","author_inst":"Stanford University"},{"author_name":"Helena C.M. Oft","author_inst":"University of Pittsburgh"},{"author_name":"Yao Lulu Xing","author_inst":"Stanford University"},{"author_name":"Christine K Lee","author_inst":"Brown University"},{"author_name":"Juan Carlos Fernandez-Miranda","author_inst":"Stanford University"},{"author_name":"Matei A. Banu","author_inst":"Stanford University"},{"author_name":"Richard N. Zare","author_inst":"Stanford University"},{"author_name":"Claudia Katharina Petritsch","author_inst":"Stanford Univeristy"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"ERR\u03b3 deletion in podocytes accelerates aging related kidney disease","rel_doi":"10.64898\/2026.05.11.724391","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724391","rel_abs":"We have recently demonstrated that treatment of aged mice with a pan-ERR agonist reverses age-related increase in urinary albumin, decrease in podocyte density, impaired mitochondrial function, and inflammation. The contribution of individual isoforms of ERRs however has not been determined. Since the aging kidney showed a possible compensatory increased expression of ERR{gamma} in the podocytes, in the face of decreased ERR expression, in the present study we aimed to determine the role of ERR{gamma} in aging podocyte. To this end, we cross bred ERR{gamma} floxed mice with podocin-Cre mice to achieve a podocyte-specific ERR{gamma} deletion. While these mice at 3 months of age showed no effect on albuminuria compared to the wild type, when the mice were aged to 21 months of age, there was a significant increase in albuminuria and decrease in podocyte density. Furthermore, we found that the podocyte deletion of ERR{gamma} primarily targeted the expression of mitochondrial biogenesis regulator PGC-1, and mitochondrial fatty acid oxidation enzymes CPT1a and MCAD in the kidney. Electron Microscopy (EM) revealed thickened glomerular basement membrane and diffuse podocyte foot process effacement, as well as severe mitochondrial damage including cristae abnormalities, fragmentation, and changes indicative of altered fusion and fission dynamics. Fluorescence Lifetime Imaging Microscopy (FLIM) to determine NADH and FAD lifetimes indicate a metabolic shift from mitochondrial oxidative phosphorylation towards glycolysis, and decrease in mitochondrial redox capacity. Considering a significantly decreased expression of ERR in aging podocytes plus its traditional role in mitochondrial function, these studies using podocyte ERR{gamma} deletion suggested an overlapping mechanism for ERR\/ERR{gamma} to act as modulators of age-related mitochondrial dysfunction and age-related kidney disease.","rel_num_authors":15,"rel_authors":[{"author_name":"Xiaoxin x. Wang","author_inst":"Georgetown University"},{"author_name":"Komuraiah Myakala","author_inst":"Georgetown University Medical Center"},{"author_name":"Nataliia  V. Shults","author_inst":"Georgetown University Medical Center"},{"author_name":"Rozhin Penjweini","author_inst":"nih"},{"author_name":"Cheryl Clarkson-Paredes","author_inst":"gwu"},{"author_name":"Ewa Krawczyk","author_inst":"georgetown univ"},{"author_name":"Sujit Hegde","author_inst":"gu"},{"author_name":"Anastas Popratiloff","author_inst":"George Washington University"},{"author_name":"Julia Panov","author_inst":"univ haifa"},{"author_name":"Ruzong Fan","author_inst":"gu"},{"author_name":"Garret Guthrie","author_inst":"gu"},{"author_name":"Xiao Ping Yang","author_inst":"Johns Hopkins University"},{"author_name":"Avi Z. Rosenberg","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Jay Knutson","author_inst":"nih"},{"author_name":"Moshe Levi","author_inst":"gu"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Pyridoxine supplementation confers protection against SGPL1R222Q variant sphingosine phosphate lyase insufficiency syndrome","rel_doi":"10.64898\/2026.05.11.724358","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724358","rel_abs":"Sphingosine-1-phosphate lyase insufficiency syndrome (SPLIS) is a rare condition causing nephrotic syndrome, neuropathy, and other manifestations. SPLIS is caused by mutations in SGPL1, which encodes sphingosine-1-phosphate lyase (SPL), a pyridoxal 5-phosphate (PLP)-dependent enzyme needed to degrade the bioactive sphingolipid sphingosine-1-phosphate (S1P). Supplementation with the PLP precursor pyridoxine benefits some individuals with PLP-dependent enzymopathies. We sought to establish whether pyridoxine has therapeutic activity in SPLIS. Neurological improvement, plasma S1P normalization, and increased SPL activity in patient-derived fibroblasts were observed after pyridoxine supplementation in a patient with R222Q-variant SPLIS. Additionally, PLP dose-dependently augmented recombinant R222Q-variant SPL activity. To further explore pyridoxine effects, gene editing was employed to create an R222Q-variant SPLIS mouse model. SPLR222Q mice fed pyridoxine-enriched chow lacked obvious phenotypes. However, SPL inactivation, S1P accumulation, wasting, anemia, proteinuria, and glomerulosclerosis developed in SPLR222Q but not WT mice fed chow with reduced pyridoxine. Ultrastructural analysis and super-resolution microscopy showed podocyte loss and foot process effacement. Transcriptional profiling revealed a pattern of cytokine upregulation and extracellular matrix remodeling. Inhibiting S1P production prevented nephrosis in SPLR222Q mice fed chow lacking pyridoxine. Our findings establish a novel SPLIS mouse model that recapitulates R222Q-variant SPLIS, demonstrates its responsiveness to pyridoxine, and implicates S1P in its pathophysiology","rel_num_authors":17,"rel_authors":[{"author_name":"Ranjha Khan","author_inst":"University of California, San Francisco (UCSF)"},{"author_name":"Maria L Allende","author_inst":"Genetics and Biochemistry Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD, USA"},{"author_name":"Ehtesham Khalid","author_inst":"Ochsner Clinical School \/ University of Queensland (Australia) and Ochsner Health, New Orleans, Louisiana, USA"},{"author_name":"Joanna Y Lee","author_inst":"Department of Pediatrics, UCSF School of Medicine, San Francisco, CA, USA"},{"author_name":"Everett Stone","author_inst":"The University of Texas at Austin"},{"author_name":"Max Rodnick Smith","author_inst":", University of Texas, Austin, TX, USA"},{"author_name":"Audrey Izuhara","author_inst":"Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California"},{"author_name":"Vadym Buncha","author_inst":"Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California"},{"author_name":"Georgina Gyarmati","author_inst":"Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California"},{"author_name":"Janos Peti-Peterdi","author_inst":"University of Southern California"},{"author_name":"Ranya  Noura Al-Khaledy","author_inst":"The University of Texas at Austin Cockrell School of Engineering"},{"author_name":"Jeffrey B Hodgin","author_inst":"University of Michigan School of Medicine, Ann Arbor, MI, USA"},{"author_name":"Gizachew Tassew","author_inst":"University of California, San Francisco, CA, USA"},{"author_name":"Babak Oskouian","author_inst":"UCSF School of Medicine, San Francisco, CA, USA"},{"author_name":"Rachel Zhang","author_inst":"UCSF School of Medicine, San Francisco, CA, USA"},{"author_name":"Richard L Proia","author_inst":"Genetics and Biochemistry Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD, USA"},{"author_name":"Julie D. Saba","author_inst":"Department of Pediatrics, UCSF School of Medicine, San Francisco, CA, USA"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Pyridoxine supplementation confers protection against SGPL1R222Q variant sphingosine phosphate lyase insufficiency syndrome","rel_doi":"10.64898\/2026.05.11.724358","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724358","rel_abs":"Sphingosine-1-phosphate lyase insufficiency syndrome (SPLIS) is a rare condition causing nephrotic syndrome, neuropathy, and other manifestations. SPLIS is caused by mutations in SGPL1, which encodes sphingosine-1-phosphate lyase (SPL), a pyridoxal 5-phosphate (PLP)-dependent enzyme needed to degrade the bioactive sphingolipid sphingosine-1-phosphate (S1P). Supplementation with the PLP precursor pyridoxine benefits some individuals with PLP-dependent enzymopathies. We sought to establish whether pyridoxine has therapeutic activity in SPLIS. Neurological improvement, plasma S1P normalization, and increased SPL activity in patient-derived fibroblasts were observed after pyridoxine supplementation in a patient with R222Q-variant SPLIS. Additionally, PLP dose-dependently augmented recombinant R222Q-variant SPL activity. To further explore pyridoxine effects, gene editing was employed to create an R222Q-variant SPLIS mouse model. SPLR222Q mice fed pyridoxine-enriched chow lacked obvious phenotypes. However, SPL inactivation, S1P accumulation, wasting, anemia, proteinuria, and glomerulosclerosis developed in SPLR222Q but not WT mice fed chow with reduced pyridoxine. Ultrastructural analysis and super-resolution microscopy showed podocyte loss and foot process effacement. Transcriptional profiling revealed a pattern of cytokine upregulation and extracellular matrix remodeling. Inhibiting S1P production prevented nephrosis in SPLR222Q mice fed chow lacking pyridoxine. Our findings establish a novel SPLIS mouse model that recapitulates R222Q-variant SPLIS, demonstrates its responsiveness to pyridoxine, and implicates S1P in its pathophysiology","rel_num_authors":17,"rel_authors":[{"author_name":"Ranjha Khan","author_inst":"University of California, San Francisco (UCSF)"},{"author_name":"Maria L Allende","author_inst":"Genetics and Biochemistry Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD, USA"},{"author_name":"Ehtesham Khalid","author_inst":"Ochsner Clinical School \/ University of Queensland (Australia) and Ochsner Health, New Orleans, Louisiana, USA"},{"author_name":"Joanna Y Lee","author_inst":"Department of Pediatrics, UCSF School of Medicine, San Francisco, CA, USA"},{"author_name":"Everett Stone","author_inst":"The University of Texas at Austin"},{"author_name":"Max Rodnick Smith","author_inst":", University of Texas, Austin, TX, USA"},{"author_name":"Audrey Izuhara","author_inst":"Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California"},{"author_name":"Vadym Buncha","author_inst":"Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California"},{"author_name":"Georgina Gyarmati","author_inst":"Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California"},{"author_name":"Janos Peti-Peterdi","author_inst":"University of Southern California"},{"author_name":"Ranya  Noura Al-Khaledy","author_inst":"The University of Texas at Austin Cockrell School of Engineering"},{"author_name":"Jeffrey B Hodgin","author_inst":"University of Michigan School of Medicine, Ann Arbor, MI, USA"},{"author_name":"Gizachew Tassew","author_inst":"University of California, San Francisco, CA, USA"},{"author_name":"Babak Oskouian","author_inst":"UCSF School of Medicine, San Francisco, CA, USA"},{"author_name":"Rachel Zhang","author_inst":"UCSF School of Medicine, San Francisco, CA, USA"},{"author_name":"Richard L Proia","author_inst":"Genetics and Biochemistry Branch, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD, USA"},{"author_name":"Julie D. Saba","author_inst":"Department of Pediatrics, UCSF School of Medicine, San Francisco, CA, USA"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"The Central Nucleus of the Amygdala Encodes the Motivation to Pursue Ethanol","rel_doi":"10.64898\/2026.05.11.724330","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724330","rel_abs":"Prior studies implicate the central nucleus of the amygdala (CeA) in reward motivation, yet how this region encodes motivation for ethanol (EtOH) seeking and consumption--and how this compares to encoding of natural rewards--remains poorly understood. We recorded single-unit neural activity from rats during a cued instrumental task in which lever insertion on each trial indicates the opportunity to lever press for ethanol and post-press lever retraction signals ethanol delivery. We found neurons responsive to multiple trial events (lever insertion and retraction cues, lever press, port entry, and reward licks), including neurons with rhythmic activity entrained to licking during EtOH consumption. Notably, CeA neural responses to the lever insertion cue at trial start encoded both the likelihood and speed of engagement during reward seeking. Using a supervised classifier, we found that trial engagement and motivation level could be decoded from pre-trial and lever insertion response periods. Finally, we assessed whether these effects generalized to natural reward seeking. Sucrose-rewarded rats (both ethanol-exposed and ethanol-naive) showed higher motivation (more rewards earned and shorter first-press latencies) and stronger CeA recruitment and responses at lever insertion than ethanol-rewarded rats, in agreement with the notion that CeA circuits amplify reward-predictive cue signals to facilitate rapid action initiation under high motivational states. Together, these findings indicate that CeA neurons dynamically encode motivational states, with tonic activity and reward-predictive cue responses predicting both engagement in reward seeking and the vigor of action initiation. We suggest these patterns of neural activity drive ethanol and sucrose pursuit.","rel_num_authors":6,"rel_authors":[{"author_name":"Matilde Castro","author_inst":"The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205"},{"author_name":"Jingren Gu","author_inst":"Johns Hopkins University"},{"author_name":"Teresa Dong","author_inst":"Johns Hopkins University"},{"author_name":"David J. Ottenheimer","author_inst":"Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Seattle, Washington, 98195"},{"author_name":"C\u00e9line Drieu","author_inst":"The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205; Department of Psychological and Brain"},{"author_name":"Patricia H. Janak","author_inst":"The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205; Department of Psychological and Brain"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Tauopathy primes co-filament assembly and dysfunction of TDP-43","rel_doi":"10.64898\/2026.05.11.723888","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.723888","rel_abs":"While most Alzheimers disease (AD) which is associated with Limbic Predominant Age-related TDP-43 Encephalopathy (LATE) exhibits accelerated brain atrophy, the pathogenic mechanism remains elusive. We show here, in mice harboring depositions of amyloid beta and tau, the age-dependent emergence of TDP-43 proteinopathy. We demonstrate that TDP-43 dysfunction facilitates caspase 3-mediated endoproteolysis of tau, accelerates tauopathy and exacerbates neuron loss. Unexpectedly, we found that the emergence and spread of TDP-43 proteinopathy is associated with the spread of tauopathy and correlated with co-filament assembly of tau and TDP-43. Importantly, TDP-43 dysfunction precedes such co-filament assembly and TDP-43 cytoplasmic aggregates. Consistent with the idea that tauopathy could prime co-filament assembly and proteinopathy of TDP-43 to exacerbate neurodegeneration, we found tau co-filament assembly with TDP-43 in AD and AD-LATE cases. These findings suggest that TDP-43 dysfunction accelerates tauopathy, which, in turn, primes co-filament assembly and dysfunction of TDP-43 to exacerbate neuron loss in AD-LATE, a pathogenic mechanism disclosing novel targets and therapeutic strategies.","rel_num_authors":9,"rel_authors":[{"author_name":"Meghraj S Baghel","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Grace D. Burns","author_inst":"The Johns Hopkins University School of Medicine"},{"author_name":"Aswathy Peethambaran Mallika","author_inst":"The Johns Hopkins University School of Medicine"},{"author_name":"Xiaoke K. Chen","author_inst":"The Johns Hopkins University School of Medicine"},{"author_name":"Fangbing Liu","author_inst":"The Johns Hopkins University School of Medicine"},{"author_name":"Shruti Renganathan","author_inst":"The Johns Hopkins University School of Medicine"},{"author_name":"Tong Li","author_inst":"Division of Neuroscience, National Institute on Aging, National Institutes of Health"},{"author_name":"Juan Troncoso","author_inst":"Johns Hopkins University"},{"author_name":"Philip C Wong","author_inst":"Johns Hopkins Medicine"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"The AvianMetaNetwork: biotic interactions among birds of the continental United States and Canada","rel_doi":"10.64898\/2026.05.11.723238","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.723238","rel_abs":"All organisms interact with other organisms, directly, and indirectly through different ecological relationships involving multiple types of interactions. Yet at broad continental scales, we lack comprehensive information on biotic interactions, which has hindered our ability to answer macroecological and eco-evolutionary questions across scales and to fully quantify the diversity of biotic interactions as an important dimension of biodiversity. Here, we help fill these gaps with an open and comprehensive dataset and data workflow of 25,907 pairwise, directional interspecific interactions among birds spanning a continental scale. All data are empirically documented and comprise bird-bird interactions across both breeding and non-breeding ranges of 731 focal avian taxa, covering all birds in the focal region of Canada and the continental United States, including Alaska. These data also include 1,258 additional avian taxa interacting with the focal taxa outside the focal region, resulting in 1,989 avian taxa altogether. The continental scale and breadth of interspecific interactions within these data fill fundamental knowledge gaps and enable scientists and practitioners to address a myriad of questions at broader scales than were previously possible.","rel_num_authors":9,"rel_authors":[{"author_name":"Phoebe L Zarnetske","author_inst":"Michigan State University"},{"author_name":"Patrick S. Bills","author_inst":"Michigan State University"},{"author_name":"Kelly E Kapsar","author_inst":"Michigan State University"},{"author_name":"Lucas Mansfield","author_inst":"Michigan State University"},{"author_name":"Emily Parker","author_inst":"Michigan State University"},{"author_name":"Caroline Roche","author_inst":"Michigan State University"},{"author_name":"India Hirschowitz","author_inst":"Michigan State University"},{"author_name":"Giovanni DePasquale","author_inst":"Michigan State University"},{"author_name":"Sara Zonneveld","author_inst":"Independent Researcher"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Ethylene-Gibberellin Crosstalk Drives Phenotypic Sex Changes in Cannabis sativa","rel_doi":"10.64898\/2026.05.11.724340","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724340","rel_abs":"Sex expression in Cannabis sativa is determined by XX\/XY sex chromosomes but remains plastic, with ethylene inhibition inducing male flowers on XX plants and ethylene release inducing female flowers on XY plants. Although ethylene is a central regulator of this process, the contribution of gibberellin signaling to cannabis sex reversal remains poorly defined. Here, we reconstructed the GA biosynthesis, regulation, and signaling pathway in C. sativa and profiled GA-related gene expression during chemically induced sex reversal. Orthology-based searches identified 50 putative C. sativa GA-related genes, widely distributed across the genome, with the X chromosome harboring 11 genes, including six within the non-recombining region. Transcriptomic analyses across vegetative baseline, early post-treatment leaves, and developing flowers showed that expression profiles were broadly similar between XX and XY plants at day 0, weakly perturbed at day 1, and strongly structured by floral phenotype at day 14. Early responses were limited to downregulation of CsGA3ox1 in ethephon-treated XY plants and CsGASA1 in STS-treated XX plants. By day 14, sex reversal was associated with differential expression of key genes, including CsGA1, multiple GA20ox orthologs, CsGID1B, CsSLY2, and several GASA genes, indicating broad remodeling of GA regulation. Our findings position the GA pathway as a downstream module of ethylene-driven sex reversal in C. sativa, with GA activity tracking floral sexual identity, extending the framework of sexual plasticity beyond ethylene, and identifying candidate genes for functional validation and the development of sex-stable cultivars.","rel_num_authors":3,"rel_authors":[{"author_name":"Julien Roy","author_inst":"Universite Laval"},{"author_name":"Davoud Torkamaneh","author_inst":"University of Guelph and Universite Laval"},{"author_name":"Adrian S Monthony","author_inst":"Universite Laval"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Hierarchical Interplay between H3K27ac and H3K4me3 in Transcriptional Regulation","rel_doi":"10.64898\/2026.05.11.724317","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724317","rel_abs":"H3K27ac and H3K4me3 are enriched at transcriptional start sites and have been implicated in transcription. However, how these marks concertedly regulate transcription is not fully understood. Here, we developed a dual chemically inducible CRISPR\/dCas9-based epigenome editing system that enables independent, temporal and transcription stage-specific modulation of H3K27ac and H3K4me3 at a specific gene locus. Stage-specific removal of H3K4me3 impaired RNA polymerase II recruitment, increased promoter-proximal pausing, reduced productive elongation, and accelerates mRNA decay via increased mA deposition. Losing both H3K27ac and H3K4me3 rapidly abolished transcriptional activity, while preserving H3K4me3 without H3K27ac can partially sustain transcription. These findings revealed a functional hierarchy and interdependence between H3K27ac and H3K4me3 in different transcription stages and the established versatile tool will contribute to the functional dissection of the temporal dynamics of chromatin modifications in gene regulation.","rel_num_authors":4,"rel_authors":[{"author_name":"Chenwei Zhou","author_inst":"Case Western Reserve University"},{"author_name":"Chanjuan Dong","author_inst":"Case Western Reserve University"},{"author_name":"Weiye Zhao","author_inst":"Case Western Reserve University"},{"author_name":"Fu-Sen Liang","author_inst":"Case Western Reserve University"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"ppGpp regulates transcription elongation via direct and indirect inputs to RNA polymerase pausing and nucleotide addition","rel_doi":"10.64898\/2026.05.13.724835","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.13.724835","rel_abs":"The signaling molecules guanosine 5'-tri\/diphosphate 3'-diphosphate, (p)ppGpp, control bacterial protein synthesis rates and cell growth by targeting transcription, translation, NTP synthesis, and other functions. In lineages like E. coli, (p)ppGpp produced in response to charged-tRNA deficiency directly targets transcribing RNAP polymerase (RNAP) to match its pace to the pioneering ribosome on the nascent RNA (transcription-translation coupling). However, the mechanism by which (p)ppGpp slows RNAP is poorly defined. (p)ppGpp may allosterically stimulate RNAP pausing, inhibit catalysis, promote backtracking, compete for substrate GTP, inhibit GTP synthesis, or uncouple transcription-translation by inhibiting translation. Using a combination of cryo-EM, biochemical assays, and quantitative nascent elongating transcript sequencing (qNET-seq), we establish that (p)ppGpp allosterically regulates pausing and nucleotide addition via distinct motions of the RNAP swivel module and both competes with and lowers GTP in vivo. (p)ppGpp stimulates swiveling at pause sites to delay escape but may also inhibit counter-swiveling required in every round of nucleotide addition.","rel_num_authors":11,"rel_authors":[{"author_name":"Andreas U Mueller","author_inst":"The Rockefeller University"},{"author_name":"Rachel Anne Mooney","author_inst":"University of Wisconsin-Madison"},{"author_name":"Michael D Engstrom","author_inst":"University of Wisconsin-Madison"},{"author_name":"Yu Bao","author_inst":"Cambridge University"},{"author_name":"Michael B Wolfe","author_inst":"Florida State University"},{"author_name":"Balendra Sah","author_inst":"University of Wisconsin-Madison"},{"author_name":"Jonathan Buscher","author_inst":"University of Wisconsin-Madison"},{"author_name":"Jason Saba","author_inst":"University of Wisconsin-Madison"},{"author_name":"James Liu","author_inst":"Columbia University"},{"author_name":"Seth A Darst","author_inst":"The Rockefeller University"},{"author_name":"Robert Landick","author_inst":"University of Wisconsin-Madison"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"ppGpp regulates transcription elongation via direct and indirect inputs to RNA polymerase pausing and nucleotide addition","rel_doi":"10.64898\/2026.05.13.724835","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.13.724835","rel_abs":"The signaling molecules guanosine 5'-tri\/diphosphate 3'-diphosphate, (p)ppGpp, control bacterial protein synthesis rates and cell growth by targeting transcription, translation, NTP synthesis, and other functions. In lineages like E. coli, (p)ppGpp produced in response to charged-tRNA deficiency directly targets transcribing RNAP polymerase (RNAP) to match its pace to the pioneering ribosome on the nascent RNA (transcription-translation coupling). However, the mechanism by which (p)ppGpp slows RNAP is poorly defined. (p)ppGpp may allosterically stimulate RNAP pausing, inhibit catalysis, promote backtracking, compete for substrate GTP, inhibit GTP synthesis, or uncouple transcription-translation by inhibiting translation. Using a combination of cryo-EM, biochemical assays, and quantitative nascent elongating transcript sequencing (qNET-seq), we establish that (p)ppGpp allosterically regulates pausing and nucleotide addition via distinct motions of the RNAP swivel module and both competes with and lowers GTP in vivo. (p)ppGpp stimulates swiveling at pause sites to delay escape but may also inhibit counter-swiveling required in every round of nucleotide addition.","rel_num_authors":11,"rel_authors":[{"author_name":"Andreas U Mueller","author_inst":"The Rockefeller University"},{"author_name":"Rachel Anne Mooney","author_inst":"University of Wisconsin-Madison"},{"author_name":"Michael D Engstrom","author_inst":"University of Wisconsin-Madison"},{"author_name":"Yu Bao","author_inst":"Cambridge University"},{"author_name":"Michael B Wolfe","author_inst":"Florida State University"},{"author_name":"Balendra Sah","author_inst":"University of Wisconsin-Madison"},{"author_name":"Jonathan Buscher","author_inst":"University of Wisconsin-Madison"},{"author_name":"Jason Saba","author_inst":"University of Wisconsin-Madison"},{"author_name":"James Liu","author_inst":"Columbia University"},{"author_name":"Seth A Darst","author_inst":"The Rockefeller University"},{"author_name":"Robert Landick","author_inst":"University of Wisconsin-Madison"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Structural basis for the intestinal protocadherin-based intermicrovillar adhesion complex","rel_doi":"10.64898\/2026.05.11.724279","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724279","rel_abs":"The intestinal brush border (BB), composed of densely packed microvilli on enterocytes, is essential for nutrient absorption and host defense. Its organization relies on the intermicrovillar adhesion complex (IMAC), mediated by protocadherins CDHR2 and CDHR5. Despite their clinical relevance in inflammatory bowel disease and several carcinomas, structural details of IMAC assemblies have remained elusive. Herein, we report the Cryo-EM structure of the adhesive complex at 3.4 [A] resolution, revealing a heterotetrameric ensemble composed of a dimer of CDHR2 and a dimer of CDHR5. This assembly ensures uniform adhesive strength between neighboring microvilli, and facilitates hexagonal packing of microvilli. Biophysical analyses and molecular dynamics simulations revealed a kinked, Ca2+-free linker between domains EC3 and EC4 of CDHR5 conferring the necessary flexibility to withstand the shear stress caused during intestinal peristalsis. Collectively, these findings provide a structural framework for understanding BB organization and suggest strategies for therapeutics targeting IMAC in intestinal disorders.","rel_num_authors":17,"rel_authors":[{"author_name":"Akinobu Senoo","author_inst":"Kyushu University"},{"author_name":"Pablo Guillen-Poza","author_inst":"The University of Hong Kong"},{"author_name":"Karin Fujishima","author_inst":"Kyushu University"},{"author_name":"Hirofumi Kosuge","author_inst":"The University of Tokyo"},{"author_name":"Takamasa Doumoto","author_inst":"Kyushu University"},{"author_name":"Keisuke Kasahara","author_inst":"Kyushu University"},{"author_name":"Tomohito Tanihara","author_inst":"Kyushu University"},{"author_name":"Yuya Yoshida","author_inst":"Kyushu University"},{"author_name":"Saeko Yanaka","author_inst":"Institute of Science Tokyo"},{"author_name":"Makoto Nakakido","author_inst":"The University of Tokyo"},{"author_name":"Satoru Nagatoishi","author_inst":"The University of Tokyo"},{"author_name":"Katsumi Maenaka","author_inst":"Hokkaido University"},{"author_name":"Shigehiro Ohdo","author_inst":"Kyushu University"},{"author_name":"Naoya Matsunaga","author_inst":"Kyushu University"},{"author_name":"Ruben Hervas","author_inst":"The University of Hong Kong"},{"author_name":"Kouhei Tsumoto","author_inst":"The University of Tokyo"},{"author_name":"Jose Caaveiro","author_inst":"Kyushu University"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Functional screening of ZIP8 naturally occurring variants identifies pathogenic mutations and trafficking defects","rel_doi":"10.64898\/2026.05.11.724455","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724455","rel_abs":"The rapid expansion of human genomic data has revealed a large number of naturally occurring variants, creating a major challenge for functional annotation. The human metal transporter SLC39A8 (ZIP8) is a clinically important, promiscuous divalent metal transporter, yet most of its documented variants remain uncharacterized. Here, we developed a workflow to functionally evaluate ZIP8 variants by integrating laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA -ICP-TOF-MS) with scaled-up cell-based transport assays. Using this method, we systematically analyzed 33 naturally occurring missense variants located in the extracellular domain (ECD) of ZIP8. The assay enables direct quantification of intracellular metal accumulation with substantially improved throughput (~150 samples per hour). Functional screening identified 14 potential pathogenic variants with significantly reduced transport activity. Comparison with computational predictions revealed a moderate correlation between activity and AlphaMissense pathogenicity scores (R2 = 0.423), while an error rate of ~20% underscores the need for experimental validation. Flow cytometry analysis showed that most loss-of-function variants exhibit impaired trafficking of the protein to the cell surface possibly due to mutation-caused protein misfolding or instability. Structural mapping of activity-compromised variants, together with functional assessment of the ZIP8-ECD, highlights the importance of this domain in ZIP8 expression and intracellular trafficking. Together, this work establishes a scalable approach for functional screening of metal transporter variants and provides new insights into the structure-function relationships of ZIP8.","rel_num_authors":7,"rel_authors":[{"author_name":"Michael Nikolovski","author_inst":"Michigan State University"},{"author_name":"Tianqi Wang","author_inst":"Michigan State University"},{"author_name":"Aaron Sue","author_inst":"Michigan State University"},{"author_name":"Keith MacRenaris","author_inst":"Michigan State University"},{"author_name":"Hongyan Zhao","author_inst":"Michigan State University"},{"author_name":"Thomas O'Halloran","author_inst":"Michigan State University"},{"author_name":"Jian Hu","author_inst":"Michigan State University"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"CD11c+ myeloid cells are the predominant CD4+CCR5+ immune population in the foreskin and are increased in men with HIV-associated penile anaerobes","rel_doi":"10.64898\/2026.05.11.724468","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724468","rel_abs":"Specific anaerobic species within the penile microbiome - Bacteria Associated with Seroconversion, Inflammation and Immune Cells (BASIC) - have been linked to increased HIV-1 susceptibility. These bacteria can directly disrupt epithelial integrity and are believed to increase local inflammation, resulting in an increased density of HIV-susceptible T cells in the inner foreskin. It is currently unknown whether other immune cells bearing the HIV entry receptors, CD4 and CCR5, are also elevated in individuals with a high abundance of BASIC species. Using inner foreskin tissues and penile swabs from males undergoing voluntary medical male circumcision, we performed a retrospective cross-sectional study to assess the relationship between BASIC species and the tissue density of such immune cells, including CD68+ macrophages, CD11c+ dendritic cells, and CD207+ Langerhans cells. The most abundant cells in the inner foreskin expressing the HIV co-receptors were CD11c+ dendritic cells (48.6% of CD4+\/CCR5+ cells), followed by CD68+ macrophages (28.6%), CD3+ T cells (18.8%), and CD207+ Langerhans-like (8.8%) cells. The absolute abundance of BASIC species was associated with elevated tissue densities of both CD4+\/CCR5+ T cells (as previously reported) and a heterogeneous population of CD3-\/CD4+\/CCR5+ cells of myeloid origin. In the dermis, BASIC species abundance was linked to elevated densities of cells expressing CD11c, CD68, and CD207, as well as those co-expressing CD11c and CD207; furthermore, CD11c+ and CD207+ cells were farther from the basement membrane in participants with a high abundance of BASIC species. Myeloid cells were not elevated in participants with a high abundance of control taxa. In an integrated analysis including previously published data from this same cohort, myeloid-cell densities clustered tightly together, positively correlated with BASIC species and pro-inflammatory cytokines, and had trends to negative correlations with control taxa (significant for CD207+ cell density). Overall, our findings suggest that BASIC species are associated with a broader foreskin immune phenotype marked by increased densities of HIV-susceptible myeloid and T cells, alongside epithelial disruption.","rel_num_authors":14,"rel_authors":[{"author_name":"Lane  B Buchanan","author_inst":"Western University"},{"author_name":"Yazan Khan","author_inst":"Western University"},{"author_name":"Jorge  R Vargas","author_inst":"Western University"},{"author_name":"Zhongtian Shao","author_inst":"Western University"},{"author_name":"Victoria  Menya Biribawa","author_inst":"Uganda Virus Research Institute"},{"author_name":"Henry  Rogers Ssemunywa","author_inst":"Uganda Virus Research Institute"},{"author_name":"Annemarie Namuniina","author_inst":"Uganda Virus Research Institute"},{"author_name":"Brenda Okech","author_inst":"Uganda Virus Research Institute"},{"author_name":"Aaron  AR Tobian","author_inst":"Johns Hopkins School of Medicine: The Johns Hopkins University School of Medicine"},{"author_name":"Daniel  E Park","author_inst":"The George Washington University Milken Institute of Public Health"},{"author_name":"Cindy  M Liu","author_inst":"The George Washington University Milken Institute of Public Health"},{"author_name":"Rupert Kaul","author_inst":"University of Toronto"},{"author_name":"Ronald  M Galiwango","author_inst":"Rakai Health Sciences Program"},{"author_name":"Jessica  L Prodger","author_inst":"Western Univeristy"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"CD11c+ myeloid cells are the predominant CD4+CCR5+ immune population in the foreskin and are increased in men with HIV-associated penile anaerobes","rel_doi":"10.64898\/2026.05.11.724468","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724468","rel_abs":"Specific anaerobic species within the penile microbiome - Bacteria Associated with Seroconversion, Inflammation and Immune Cells (BASIC) - have been linked to increased HIV-1 susceptibility. These bacteria can directly disrupt epithelial integrity and are believed to increase local inflammation, resulting in an increased density of HIV-susceptible T cells in the inner foreskin. It is currently unknown whether other immune cells bearing the HIV entry receptors, CD4 and CCR5, are also elevated in individuals with a high abundance of BASIC species. Using inner foreskin tissues and penile swabs from males undergoing voluntary medical male circumcision, we performed a retrospective cross-sectional study to assess the relationship between BASIC species and the tissue density of such immune cells, including CD68+ macrophages, CD11c+ dendritic cells, and CD207+ Langerhans cells. The most abundant cells in the inner foreskin expressing the HIV co-receptors were CD11c+ dendritic cells (48.6% of CD4+\/CCR5+ cells), followed by CD68+ macrophages (28.6%), CD3+ T cells (18.8%), and CD207+ Langerhans-like (8.8%) cells. The absolute abundance of BASIC species was associated with elevated tissue densities of both CD4+\/CCR5+ T cells (as previously reported) and a heterogeneous population of CD3-\/CD4+\/CCR5+ cells of myeloid origin. In the dermis, BASIC species abundance was linked to elevated densities of cells expressing CD11c, CD68, and CD207, as well as those co-expressing CD11c and CD207; furthermore, CD11c+ and CD207+ cells were farther from the basement membrane in participants with a high abundance of BASIC species. Myeloid cells were not elevated in participants with a high abundance of control taxa. In an integrated analysis including previously published data from this same cohort, myeloid-cell densities clustered tightly together, positively correlated with BASIC species and pro-inflammatory cytokines, and had trends to negative correlations with control taxa (significant for CD207+ cell density). Overall, our findings suggest that BASIC species are associated with a broader foreskin immune phenotype marked by increased densities of HIV-susceptible myeloid and T cells, alongside epithelial disruption.","rel_num_authors":14,"rel_authors":[{"author_name":"Lane  B Buchanan","author_inst":"Western University"},{"author_name":"Yazan Khan","author_inst":"Western University"},{"author_name":"Jorge  R Vargas","author_inst":"Western University"},{"author_name":"Zhongtian Shao","author_inst":"Western University"},{"author_name":"Victoria  Menya Biribawa","author_inst":"Uganda Virus Research Institute"},{"author_name":"Henry  Rogers Ssemunywa","author_inst":"Uganda Virus Research Institute"},{"author_name":"Annemarie Namuniina","author_inst":"Uganda Virus Research Institute"},{"author_name":"Brenda Okech","author_inst":"Uganda Virus Research Institute"},{"author_name":"Aaron  AR Tobian","author_inst":"Johns Hopkins School of Medicine: The Johns Hopkins University School of Medicine"},{"author_name":"Daniel  E Park","author_inst":"The George Washington University Milken Institute of Public Health"},{"author_name":"Cindy  M Liu","author_inst":"The George Washington University Milken Institute of Public Health"},{"author_name":"Rupert Kaul","author_inst":"University of Toronto"},{"author_name":"Ronald  M Galiwango","author_inst":"Rakai Health Sciences Program"},{"author_name":"Jessica  L Prodger","author_inst":"Western Univeristy"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Whisker-based pre-neuronal and peripheral encoding of surface stickiness","rel_doi":"10.64898\/2026.05.11.724292","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724292","rel_abs":"Texture is a multidimensional perceptual feature of touch, with coarseness, stickiness, and compliance as its major axes of variability. Of these, coarseness is the best understood in the rodent whisker system. However, variation in surface stickiness is also a common feature of natural scenes, and is likely to alter the mechanical interactions between whiskers and surfaces that drive neuronal responses and are the basis for perceptual experience. In this study, we asked whether and how stickiness information could be extracted from whisker-surface interactions and represented in the activity of whisker follicle innervating mechanosensory neurons. We developed a 3D whisker tracking system applicable to texture sensing, and used it to characterize the whisker-surface interactions occurring during whisking against surfaces of jointly varying stickiness, coarseness, and position, as well as the responses of whisker follicle innervating neurons in the trigeminal ganglion. The bending, twisting, and roll of the whisker shaft, the rates and amplitudes of stick-slip events at the whisker tip, and the firing rates of a subset of mechanosensory neurons could all be used to distinguish between surfaces of high and low stickiness. These results demonstrate that stickiness information is available to the whisker system.","rel_num_authors":3,"rel_authors":[{"author_name":"Isis S Wyche","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Michaela A O'Neil","author_inst":"Johns Hopkins University"},{"author_name":"Daniel H O'Connor","author_inst":"Johns Hopkins University"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"CYP4F2-mediated \u03c9-hydroxylation of 1-deoxysphingolipids reveals a new hepatic detoxification pathway","rel_doi":"10.64898\/2026.05.11.724297","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724297","rel_abs":"1-deoxysphingolipids (1-deoxySLs) are atypical, cytotoxic sphingolipids (SL) formed by the serine palmitoyltransferase through the alternative use of L-Alanine over its canonical substrate L-Serine. Elevated plasma levels of 1-deoxySLs have been implicated in metabolic and neurodegenerative diseases. Due to the missing C1 hydroxyl group, 1-deoxySLs cannot be converted into complex sphingolipids nor degraded via the canonical SL catabolic pathways. However, previous reports suggested a cytochrome P450 mediated {omega}-hydroxylation of 1-deoxySLs as a potential detoxification mechanism although the exacts downstream metabolism of these lipids remained unclear. We combined genome-wide association analysis with targeted lipid analysis to identify genes involved in 1-deoxySL metabolism. Functional validation was performed in cell culture models, enzyme assays, and through quantitative high-resolution mass spectrometry using isotope labelled synthetic standards.We identified a strong association between the CYP4F2 rs2108622 variant and plasma 1-deoxySL, implicating CYP4F2 is involved in 1-deoxySL metabolism. We demonstrated that CYP4F2 catalyzes the {omega}-hydroxylation of 1-deoxysphinganine, forming a previously uncharacterized hydroxylated sphingoid base. In liver cells, this metabolite was further metabolized via three distinct pathways: one forming the N-acyl, a second involving omega acylation and third resulting in omega carboxylation. All reactions generated a new spectrum of 1-deoxysphingolipids that are based on {omega}-hydroxylated 1-deoxySA as a precursor. The metabolic steps were confirmed by structural validation using synthetically prepared external standards. Importantly, {omega}-hydroxylation significantly attenuated the acute cytotoxicity of 1-deoxySLs in liver cells, indicating that this modification is the initiating step of a multi-branched metabolic clearance pathway. This study identifies CYP4F2 as a key enzyme initiating the hepatic clearance of atypical 1-deoxySLs, mitigating their cellular toxicity and revealing multiple downstream metabolic fates. Our findings highlight a previously unrecognized clearance mechanism for atypical sphingolipids with relevance to metabolic disease.","rel_num_authors":10,"rel_authors":[{"author_name":"Adam Majcher","author_inst":"University and University Hospital Zurich"},{"author_name":"Essa M Saied","author_inst":"Humboldt University Berlin"},{"author_name":"Zoltan Kutalik","author_inst":"University of Lausanne"},{"author_name":"Maftuna Shamshiddinova","author_inst":"University and University Hospital Zurich"},{"author_name":"Andreas J. Hulsmeier","author_inst":"University and University Hospital Zurich"},{"author_name":"Par Bjorklund","author_inst":"Karolinska Institutet, Stockholm"},{"author_name":"Elkhan Yusifov","author_inst":"University and University Hospital Zurich"},{"author_name":"Irina Alecu","author_inst":"University and University Hospital Zurich"},{"author_name":"Christoph Arenz","author_inst":"Humboldt University"},{"author_name":"Thorsten Hornemann","author_inst":"University Zurich and University Hospital Zurich"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Membrane Phase, Charge, and Curvature Regulate \u03b1-Synuclein Binding Dynamics","rel_doi":"10.64898\/2026.05.12.724662","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.12.724662","rel_abs":"-Synuclein (Syn) is an intrinsically disordered protein whose interactions with lipid membranes are central to both its physiological function and its role in synucleopathies. While membrane charge, phase, and curvature are each known to influence Syn binding, these properties are typically examined independently, leaving their combined effects on both equilibrium and dynamic membrane association unresolved. Here, we systematically investigate how membrane phase and charge jointly regulate Syn binding, curvature sensitivity, and exchange dynamics using fluorescence microscopy, circular dichroism spectroscopy, and fluorescence recovery after photobleaching (FRAP), complemented by coarse-grained molecular dynamics simulations. Under zwitterionic conditions, Syn preferentially binds highly curved gel-phase membranes, driven by curvature-dependent enrichment of packing defects arising from faceted vesicle morphologies. Incorporation of anionic lipids selectively enhances binding in liquid-phase membranes while attenuating curvature-dependent partitioning in gel-phase membranes. Dynamic measurements reveal that membrane phase and charge also govern the stability of membrane-associated Syn, with gel-phase membranes and anionic lipids promoting kinetically stabilized states. Simulations show that curvature-induced defect formation is strongly amplified in gel-phase membranes but largely insensitive to charge. These findings establish that Syn-membrane interactions are governed by a cooperative interplay between membrane phase, curvature, and charge and highlight the importance of resolving both thermodynamic and kinetic contributions to protein-membrane binding.","rel_num_authors":8,"rel_authors":[{"author_name":"Orianna H. Kou","author_inst":"University of Southern California"},{"author_name":"Cailyn M. Sakurai","author_inst":"University of California San Diego"},{"author_name":"Stephanie Y. Ramirez","author_inst":"University of Southern California"},{"author_name":"Brian H. Kim","author_inst":"University of Southern California"},{"author_name":"David H. Johnson","author_inst":"University of Southern California"},{"author_name":"Zixin Zhang","author_inst":"University of Southern California"},{"author_name":"Christopher T Lee","author_inst":"University of California San Diego"},{"author_name":"Wade F. Zeno","author_inst":"University of Southern California"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"GlyComboCLI enables command line-based FAIR workflows for glycan composition assignment in mass spectrometry data","rel_doi":"10.64898\/2026.05.13.725058","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.13.725058","rel_abs":"Glycans are integral biomolecules whose presence cannot be predicted from genomic data alone, necessitating experimental characterisation through approaches including mass spectrometry. Assignment of glycan compositions to observed mass to charge ratios is computationally challenging due to the potential monosaccharide diversity and existing tools lack the required flexibility for integration into automated bioinformatic workflows. Here, we present GlyComboCLI, an open-source command-line application for the assignment of glycan compositions to mass spectrometry data which expands upon our previous GUI application, GlyCombo. GlyComboCLI accepts mass lists and vendor-neutral mzML files, supports an extensive range of monosaccharides, derivatisation states, reducing-end modifications, and adducts to ensure compatibility with a breadth of glycomics approaches. Outputs are compatible with downstream tools including Skyline and GlycoWorkBench. This software is deployable as a standalone executable, a Docker container, and a Galaxy tool, adhering to FAIR principles. When applied to 52 raw files from a published mouse glycomics dataset, a local instance completed composition assignment and downstream quality control in under three hours, recovering biologically consistent findings. Furthermore, an integrated Galaxy workflow demonstrated reproducible detection of sialidase treatment effects. GlyComboCLI substantially reduces the pool of spectra requiring manual structural interpretation, offering a flexible and scalable solution for glycomics bioinformatic workflows.","rel_num_authors":5,"rel_authors":[{"author_name":"Maia I Kelly","author_inst":"Protea Glycosciences, Wollongong, 2500 Australia"},{"author_name":"W. C. Mike Thang","author_inst":"Queensland Cyber Infrastructure Foundation (QCIF), Australia and Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia"},{"author_name":"C. N. Ignatius Pang","author_inst":"Australian Proteome Analysis Facility, Macquarie University, Sydney, Australia"},{"author_name":"Ove Johan Ragnar Gustafsson","author_inst":"Australian BioCommons, The University of Melbourne, Parkville, Vic. 3010, Australia"},{"author_name":"Christopher Ashwood","author_inst":"Protea Glycosciences, Wollongong, 2500 Australia"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Substrate-derived peptides for selective covalent inhibition of protein tyrosine kinases","rel_doi":"10.64898\/2026.05.11.724146","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724146","rel_abs":"Protein tyrosine kinases are important regulators of cell signaling, and aberrant kinase activity contributes to many human diseases, including cancers. All protein tyrosine kinases share a highly-conserved ATP binding pocket but diverge in their substrate binding sites in order to mediate distinct signaling events. Many potent and efficacious ATP-competitive tyrosine kinase inhibitors have been developed, however it remains challenging to achieve on-target selectivity across different kinases and target specific disease mutants, given the high degree of conservation in the ATP-binding pocket. By contrast, the variable substrate-binding site offers an opportunity for selective inhibition, provided molecules can be targeted to this site. Here, we present a modular strategy to design selective, peptide-based covalent inhibitors of tyrosine kinases with a distinct binding mode from existing ATP-competitive inhibitors. Using Src kinase as a model system, we demonstrate that Src-selective reactivity can be achieved by first designing an optimized substrate peptide and then strategically positioning an electrophile on the peptide to target a non-conserved cysteine on the kinase. We show that substrate-derived covalent peptides can inhibit kinase activity, bind simultaneously with an ATP-competitive inhibitor, and even inhibit the activity of kinases bearing a common drug resistance mutation. We further explore the application of this approach to develop an inhibitor of the cancer-relevant fibroblast growth factor receptor 1 kinase that shows selectivity for an oncogenic mutant over the wild-type enzyme. Our modular strategy to generate selective covalent peptides targeting protein tyrosine kinases provides a promising framework for future chemical probe and drug development efforts.","rel_num_authors":4,"rel_authors":[{"author_name":"Minhee Lee","author_inst":"Columbia University"},{"author_name":"Zijing Wang","author_inst":"Columbia University"},{"author_name":"Andrew C Johns","author_inst":"Columbia University"},{"author_name":"Neel H Shah","author_inst":"Columbia University"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Mutational and bioinformatic analysis of the binding site for the ribonucleotide reductase-specific transcriptional repressor NrdR","rel_doi":"10.64898\/2026.05.11.724285","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724285","rel_abs":"The prevalent transcriptional repressor NrdR binds to highly conserved prokaryotic sequences in the promoter regions of operons encoding the essential enzyme ribonucleotide reductase. The NrdR binding sites consist of two partially palindromic 16 bp sequences (NrdR boxes) separated by a 15-16 bp linker sequence. We have assessed the requirement of both boxes for binding, the propensity of different NrdRs to bind to heterologous binding sites, and that the linker sequence is only limited to length and not sequence conservation. As we have observed several deviations from the conserved sequences of the NrdR boxes, we here test the conservation requirements of individual basepairs in the NrdR boxes using a synthetic DNA fragment (Synt DNA) to which the NrdR proteins from the actinomycete Streptomyces coelicolor and the gammaproteobacterium Escherichia coli bind equally well as to their homologous binding sites. By introducing isolated mutations to Synt DNA and testing the binding capacity of NrdR from S. coelicolor and E. coli we expand our understanding of what criteria are needed to build a functional binding site for the NrdR repressor.","rel_num_authors":4,"rel_authors":[{"author_name":"Saher Shahid","author_inst":"Stockholm University"},{"author_name":"Daniel Lundin","author_inst":"Stockholm University"},{"author_name":"Inna Rozman Grinberg","author_inst":"Stockholm University"},{"author_name":"Britt-Marie Sj\u00f6berg","author_inst":"Stockholm University"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Molecular and Structural Characterization Reveals Divergent Extracellular Vesicle Profiles Between Wild Type and Alzheimer's Disease Cerebrocortical Organoids","rel_doi":"10.64898\/2026.05.13.724352","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.13.724352","rel_abs":"Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions of patients globally. Despite significant efforts from researchers in recent decades, there are still many unanswered questions about AD pathogenesis. AD patient brains manifest changes in extracellular vesicles (EVs) secreted from diseased neurons, and the effect of this phenomenon remains poorly understood. EVs contain a variety of biomolecules and play a critical role in cell-to-cell communication in all eukaryotic organisms. Here, we report a thorough characterization of small EVs purified from cultures of human cerebrocortical organoids. These organoids are differentiated from human patient-derived stem cells that bear a familial AD mutation in the presenilin 1 (PSEN1) gene, or from an isogenic wildtype (WT) control. The organoid conditioned media was aspirated from cultures and processed for EV enrichment using a non-invasive technique that requires no cellular disruption. EVs purified from AD organoid conditioned media have a wider size distribution and show differential expression of tetraspanins CD63, CD9, and CD81 when compared to WT organoid-derived EVs. AD organoid-derived EVs can have single, double, and even triple membranes and display luminal fibrillar material. A deep proteomic profiling of the EVs reveals several statistically significant differences, including evidence for modifications in secretory autophagy. EV isolates from both WT and AD organoids show strong binding to amyloid detecting dyes, both in bulk fluorescence and fluorescence microscopy assays. After a 1-week co-culture of AD organoids with WT organoids, there is evidence of endosomal membrane transfer between the isogenic cultures with an increase in amyloid-{beta} peptides in the WT organoids. These observations support the notion that non-cell-autonomous spread of amyloid-containing EVs in human AD brains can be modeled in a cerebral organoid system.","rel_num_authors":19,"rel_authors":[{"author_name":"Anthony Balistreri","author_inst":"The Scripps Research Institute"},{"author_name":"Natalie Turner","author_inst":"The Scripps Research Institute"},{"author_name":"Jadon Compher","author_inst":"The Scripps Research Institute"},{"author_name":"Mireya Almaraz","author_inst":"The Scripps Research Institute"},{"author_name":"Akhil Prabhavalkar","author_inst":"The Scripps Research Institute"},{"author_name":"Sachita Chittal","author_inst":"The Scripps Research Institute"},{"author_name":"Sergio R. Labra","author_inst":"The Scripps Research Institute"},{"author_name":"Kehinde Ezekiel","author_inst":"The Scripps Research Institute"},{"author_name":"Christine Baal","author_inst":"The Scripps Research Institute"},{"author_name":"Claudia Cedeno Kwong","author_inst":"The Scripps Research Institute"},{"author_name":"Swagata Ghatak","author_inst":"The Scripps Research Institute"},{"author_name":"Jan-Hannes Schaefer","author_inst":"The Scripps Research Institute"},{"author_name":"Kimberly Vanderpool","author_inst":"The Scripps Research Institute"},{"author_name":"Kathryn Spencer","author_inst":"The Scripps Research Institute"},{"author_name":"John R. Yates III","author_inst":"The Scripps Research Institute"},{"author_name":"John P. Nolan","author_inst":"The Scintillon Institute"},{"author_name":"Scott Henderson","author_inst":"The Scripps Research Institute"},{"author_name":"Stuart A. Lipton","author_inst":"The Scripps Research Institute; University of California at San Diego"},{"author_name":"Jeffery W. Kelly","author_inst":"The Scripps Research Institute"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"A TSST 1 structural motif disrupts endothelial programs required for vascular regeneration.","rel_doi":"10.64898\/2026.05.12.724633","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.12.724633","rel_abs":"Staphylococcus aureus causes profound vascular damage during infection, where it inflicts vascular injury across organs and generates lesions that fail to heal. Superantigens are major virulence factors in S. aureus infections, yet their direct effects on vascular repair remain unclear. We provide evidence that TSST 1 disrupts endothelial regeneration through coordinated mechanisms. TSST 1 interferes with collective directed migration, impairing endothelial cell directional persistence and preventing re-endothelialization in vitro. These defects stem from cytoskeletal disorganization, characterized by stress fiber accumulation and loss of lamellipodia, and broad suppression of motility associated secreted factors. In an ex vivo aortic ring assay, TSST 1 suppresses angiogenic sprouting and generates dysmorphic vascular networks. Proteomic profiling reveals a shift toward matrix rigidity, adhesion stabilization, and overall suppression of angiogenesis. These activities map to a conserved dodecapeptide motif. Hence, TSST 1 suppression of vascular repair may convert sites of tissue injury into persistently non healing niches suited for S. aureus persistence.","rel_num_authors":9,"rel_authors":[{"author_name":"Sharon S. Tang","author_inst":"University of Wisconsin-Madison"},{"author_name":"Oluwadamilola F. Babatunde","author_inst":"University of Wiscosin-Madison"},{"author_name":"Phuong M. Tran","author_inst":"University of Wisconsin-Madison"},{"author_name":"Xiao-Jun Wu","author_inst":"University of Wisconsin-Madison"},{"author_name":"Annabella Castiglione","author_inst":"University of Wisconsin-Madison"},{"author_name":"Kyle J. Kinney","author_inst":"University of Iowa"},{"author_name":"Dhruthi Suresh","author_inst":"University of Wisconsin - Madison"},{"author_name":"Kavi  PM J Mehta","author_inst":"University of Wisconsin-Madison"},{"author_name":"Wilmara Salgado-Pab\u00f3n","author_inst":"University of Wisconsin-Madison"}],"rel_date":"2026-05-14","rel_site":"biorxiv"},{"rel_title":"Video-based Detection of Delirium in Hospitalized Adults","rel_doi":"10.64898\/2026.05.11.26352902","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.11.26352902","rel_abs":"Delirium, a dynamic neuropsychiatric condition associated with morbidity and mortality, remains underdiagnosed due to reliance on subjective, intermittent screening tools. Objective and potentially continuous identification is needed to improve clinical care. We developed and validated an analytic framework for delirium classification based on automatically extracted video features. In this prospective cohort study, patients (>=18 years) admitted to the inpatient medical or neurological ward of a tertiary academic center between August 2020 and March 2022 with an expected stay longer than one night were enrolled. Daily structured delirium assessments and brief video recordings were performed in consenting patients. Videos were analyzed using deep learning pose estimation to extract keypoints and calculate behavioral features based on eye, face, and limb postures and movements. Four machine learning models (logistic regression, gradient boosting, support vector machines, and random forests) were trained to predict delirium status from extracted features. Model performance was evaluated on 20 repetitions of three-fold cross-validation using the area under the curve of the receiver operating characteristics curve (AUC ROC). The cohort included 109 videos from 25 male and 25 female participants (median age: 72, IQR: 63.25-78). Twenty videos (18%) were from patients with delirium. Keypoints for this dataset were more accurately extracted using a customized ResNet-101 model developed with DeepLabCut (sensitivity 0.94, specificity 0.89, compared to human-labeled gold standards) than using off-the-shelf models. Keypoints were then used to generate behavioral features summarizing movement and postures throughout the video. A support vector machine model achieved an average delirium classification AUC ROC of 0.79 (SD +\/- 0.09), sensitivity of 0.71 (SD +\/- 0.16), and specificity of 0.78 (SD +\/- 0.07). This study demonstrates the feasibility of identifying delirium using brief videos in clinically heterogeneous cohorts and reveals novel features for objective identification.","rel_num_authors":12,"rel_authors":[{"author_name":"Maanasa Mendu","author_inst":"Massachusetts General Hospital"},{"author_name":"Ryan A. Tesh","author_inst":"Massachusetts General Hospital"},{"author_name":"Kyle Pellerin","author_inst":"Massachusetts General Hospital"},{"author_name":"Grace E. Steward","author_inst":"Northwestern University"},{"author_name":"Ivo H. Cerda","author_inst":"Massachusetts General Hospital"},{"author_name":"Marta Williams","author_inst":"Massachusetts General Hospital"},{"author_name":"Mia Colman","author_inst":"Massachusetts General Hospital"},{"author_name":"Simran Shah","author_inst":"Massachusetts General Hospital"},{"author_name":"Alice D Lam","author_inst":"Massachusetts General Hospital"},{"author_name":"Sydney S. Cash","author_inst":"Massachusetts General Hospital"},{"author_name":"Michael Brandon Westover","author_inst":"Beth Israel Lahey Health Medical System"},{"author_name":"Eyal Y Kimchi","author_inst":"Northwestern University"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Video-based Detection of Delirium in Hospitalized Adults","rel_doi":"10.64898\/2026.05.11.26352902","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.11.26352902","rel_abs":"Delirium, a dynamic neuropsychiatric condition associated with morbidity and mortality, remains underdiagnosed due to reliance on subjective, intermittent screening tools. Objective and potentially continuous identification is needed to improve clinical care. We developed and validated an analytic framework for delirium classification based on automatically extracted video features. In this prospective cohort study, patients (>=18 years) admitted to the inpatient medical or neurological ward of a tertiary academic center between August 2020 and March 2022 with an expected stay longer than one night were enrolled. Daily structured delirium assessments and brief video recordings were performed in consenting patients. Videos were analyzed using deep learning pose estimation to extract keypoints and calculate behavioral features based on eye, face, and limb postures and movements. Four machine learning models (logistic regression, gradient boosting, support vector machines, and random forests) were trained to predict delirium status from extracted features. Model performance was evaluated on 20 repetitions of three-fold cross-validation using the area under the curve of the receiver operating characteristics curve (AUC ROC). The cohort included 109 videos from 25 male and 25 female participants (median age: 72, IQR: 63.25-78). Twenty videos (18%) were from patients with delirium. Keypoints for this dataset were more accurately extracted using a customized ResNet-101 model developed with DeepLabCut (sensitivity 0.94, specificity 0.89, compared to human-labeled gold standards) than using off-the-shelf models. Keypoints were then used to generate behavioral features summarizing movement and postures throughout the video. A support vector machine model achieved an average delirium classification AUC ROC of 0.79 (SD +\/- 0.09), sensitivity of 0.71 (SD +\/- 0.16), and specificity of 0.78 (SD +\/- 0.07). This study demonstrates the feasibility of identifying delirium using brief videos in clinically heterogeneous cohorts and reveals novel features for objective identification.","rel_num_authors":12,"rel_authors":[{"author_name":"Maanasa Mendu","author_inst":"Massachusetts General Hospital"},{"author_name":"Ryan A. Tesh","author_inst":"Massachusetts General Hospital"},{"author_name":"Kyle Pellerin","author_inst":"Massachusetts General Hospital"},{"author_name":"Grace E. Steward","author_inst":"Northwestern University"},{"author_name":"Ivo H. Cerda","author_inst":"Massachusetts General Hospital"},{"author_name":"Marta Williams","author_inst":"Massachusetts General Hospital"},{"author_name":"Mia Colman","author_inst":"Massachusetts General Hospital"},{"author_name":"Simran Shah","author_inst":"Massachusetts General Hospital"},{"author_name":"Alice D Lam","author_inst":"Massachusetts General Hospital"},{"author_name":"Sydney S. Cash","author_inst":"Massachusetts General Hospital"},{"author_name":"Michael Brandon Westover","author_inst":"Beth Israel Lahey Health Medical System"},{"author_name":"Eyal Y Kimchi","author_inst":"Northwestern University"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Racial and Socioeconomic Disparities in Blood Pressure Control Before and After Intracerebral Hemorrhage","rel_doi":"10.64898\/2026.05.11.26352899","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.11.26352899","rel_abs":"Background: Hypertension is the most potent modifiable risk factor for recurrent intracerebral hemorrhage (ICH), yet blood pressure (BP) control after ICH remains suboptimal, particularly among disadvantaged racial and socioeconomic groups. To what extent post-ICH BP disparities reflect pre-existing hypertension inequities versus differences in post-ICH management is unknown. We examined disparities in BP control before and after ICH, assessed whether post-ICH care differentially improves BP across groups and whether post-ICH disparities persist after accounting for pre-existing BP differences. Methods: We performed a case-only study in the All of Us Research Program, identifying ICH survivors using electronic health record diagnosis codes. Mean systolic BP was calculated for pre-ICH (1-365 days before) and post-ICH (30-365 days after) windows. Neighborhood deprivation tertiles were calculated using 3-digit ZIP codes. The primary outcome was uncontrolled BP (>=140 mmHg). Logistic regression estimated odds of uncontrolled BP, and mediation analysis estimated the proportion of post-ICH disparities explained by pre-ICH BP. Results: Among 2,226 ICH survivors (mean age 60; 50.6% female), 1,760 had pre-ICH and 1,852 had post-ICH BP data. Uncontrolled BP was more common in Black than White survivors both pre-ICH (38.9% vs 21.4%; p<0.001) and post-ICH (34.3% vs 16.3%; p<0.001), and in Deprived versus Privileged neighborhoods post-ICH (23.7% vs 15.8%; p<0.001). In adjusted models, Black race (OR 3.51; 95% CI 2.55-4.83; p<0.001) and Deprived neighborhoods (OR 1.38; 95% CI 1.00-1.91; p=0.048) were associated with uncontrolled post-ICH BP. Among survivors uncontrolled before ICH, 67% of White but only 45% of Black survivors achieved control afterward (p=0.001). Adjusting for pre-ICH BP control status only modestly attenuated the Black-White disparity (OR 4.05 to 2.95; P<0.001). In mediation analyses, pre-ICH BP explained only 27% of the racial (P<0.001) and 26% of the deprivation (P=0.014) disparity. Conclusions: Racial and socioeconomic disparities in BP control persist after ICH, but most post-ICH disparities are not explained by pre-existing inequalities. More advantaged populations achieve greater BP improvement, suggesting effective post-ICH management exists but does not reach all patients equitably. Targeted interventions addressing barriers to post-ICH BP control in disadvantaged populations may substantially reduce persistent disparities.","rel_num_authors":17,"rel_authors":[{"author_name":"Samuel Namian","author_inst":"Yale University School of Medicine"},{"author_name":"Joel Smith","author_inst":"Yale University School of Medicine"},{"author_name":"Sofia Constantinescu","author_inst":"Yale University School of Medicine"},{"author_name":"Yome Tawaldermedhen","author_inst":"Yale University School of Medicine"},{"author_name":"Santiago Clocchiatti-Tuozzo","author_inst":"Yale University School of Medicine"},{"author_name":"Cyprien A Rivier","author_inst":"Yale University School of Medicine"},{"author_name":"Shufan Huo","author_inst":"Yale University School of Medicine"},{"author_name":"Kane Wu","author_inst":"Yale University School of Medicine"},{"author_name":"Victor Torres Lopez","author_inst":"Yale University School of Medicine"},{"author_name":"Sanjula Dhillon Singh","author_inst":"Brain Care Labs, Department of Neurology, Mass General Brigham"},{"author_name":"Christopher Anderson","author_inst":"Brain Care Labs, Department of Neurology, Mass General Brigham"},{"author_name":"Jonathan Rosand","author_inst":"Brain Care Labs, Department of Neurology, Mass General Brigham"},{"author_name":"Seyedmehdi Payabvash","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Santosh B. Murthy","author_inst":"Weill Cornell Medicine"},{"author_name":"Kevin N Sheth","author_inst":"Yale University School of Medicine"},{"author_name":"Adam de Havenon","author_inst":"Yale University School of Medicine"},{"author_name":"Guido J. Falcone","author_inst":"Yale University School of Medicine"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Racial and Socioeconomic Disparities in Blood Pressure Control Before and After Intracerebral Hemorrhage","rel_doi":"10.64898\/2026.05.11.26352899","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.11.26352899","rel_abs":"Background: Hypertension is the most potent modifiable risk factor for recurrent intracerebral hemorrhage (ICH), yet blood pressure (BP) control after ICH remains suboptimal, particularly among disadvantaged racial and socioeconomic groups. To what extent post-ICH BP disparities reflect pre-existing hypertension inequities versus differences in post-ICH management is unknown. We examined disparities in BP control before and after ICH, assessed whether post-ICH care differentially improves BP across groups and whether post-ICH disparities persist after accounting for pre-existing BP differences. Methods: We performed a case-only study in the All of Us Research Program, identifying ICH survivors using electronic health record diagnosis codes. Mean systolic BP was calculated for pre-ICH (1-365 days before) and post-ICH (30-365 days after) windows. Neighborhood deprivation tertiles were calculated using 3-digit ZIP codes. The primary outcome was uncontrolled BP (>=140 mmHg). Logistic regression estimated odds of uncontrolled BP, and mediation analysis estimated the proportion of post-ICH disparities explained by pre-ICH BP. Results: Among 2,226 ICH survivors (mean age 60; 50.6% female), 1,760 had pre-ICH and 1,852 had post-ICH BP data. Uncontrolled BP was more common in Black than White survivors both pre-ICH (38.9% vs 21.4%; p<0.001) and post-ICH (34.3% vs 16.3%; p<0.001), and in Deprived versus Privileged neighborhoods post-ICH (23.7% vs 15.8%; p<0.001). In adjusted models, Black race (OR 3.51; 95% CI 2.55-4.83; p<0.001) and Deprived neighborhoods (OR 1.38; 95% CI 1.00-1.91; p=0.048) were associated with uncontrolled post-ICH BP. Among survivors uncontrolled before ICH, 67% of White but only 45% of Black survivors achieved control afterward (p=0.001). Adjusting for pre-ICH BP control status only modestly attenuated the Black-White disparity (OR 4.05 to 2.95; P<0.001). In mediation analyses, pre-ICH BP explained only 27% of the racial (P<0.001) and 26% of the deprivation (P=0.014) disparity. Conclusions: Racial and socioeconomic disparities in BP control persist after ICH, but most post-ICH disparities are not explained by pre-existing inequalities. More advantaged populations achieve greater BP improvement, suggesting effective post-ICH management exists but does not reach all patients equitably. Targeted interventions addressing barriers to post-ICH BP control in disadvantaged populations may substantially reduce persistent disparities.","rel_num_authors":17,"rel_authors":[{"author_name":"Samuel Namian","author_inst":"Yale University School of Medicine"},{"author_name":"Joel Smith","author_inst":"Yale University School of Medicine"},{"author_name":"Sofia Constantinescu","author_inst":"Yale University School of Medicine"},{"author_name":"Yome Tawaldermedhen","author_inst":"Yale University School of Medicine"},{"author_name":"Santiago Clocchiatti-Tuozzo","author_inst":"Yale University School of Medicine"},{"author_name":"Cyprien A Rivier","author_inst":"Yale University School of Medicine"},{"author_name":"Shufan Huo","author_inst":"Yale University School of Medicine"},{"author_name":"Kane Wu","author_inst":"Yale University School of Medicine"},{"author_name":"Victor Torres Lopez","author_inst":"Yale University School of Medicine"},{"author_name":"Sanjula Dhillon Singh","author_inst":"Brain Care Labs, Department of Neurology, Mass General Brigham"},{"author_name":"Christopher Anderson","author_inst":"Brain Care Labs, Department of Neurology, Mass General Brigham"},{"author_name":"Jonathan Rosand","author_inst":"Brain Care Labs, Department of Neurology, Mass General Brigham"},{"author_name":"Seyedmehdi Payabvash","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Santosh B. Murthy","author_inst":"Weill Cornell Medicine"},{"author_name":"Kevin N Sheth","author_inst":"Yale University School of Medicine"},{"author_name":"Adam de Havenon","author_inst":"Yale University School of Medicine"},{"author_name":"Guido J. Falcone","author_inst":"Yale University School of Medicine"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Retention and loss to follow-up among patients with hypertension in primary care: a multi-practice cohort study","rel_doi":"10.64898\/2026.05.10.26352856","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.10.26352856","rel_abs":"Effective hypertension management depends on sustained engagement with primary care, and there is a need to understand the magnitude and determinants of follow-up loss in real-world primary care. We analyzed electronic health record data from 26,541 patients with hypertension across primary care practices participating in the EvidenceNOW quality improvement initiative. We characterized retention in care, longitudinal blood pressure control, and predictors of loss to follow-up using descriptive statistics, cumulative retention curves, and multivariable Cox proportional hazards regression. At baseline, mean systolic and diastolic blood pressure were 140.0 plus-minus 20.6 and 84.7 plus-minus 13.0 mmHg, respectively; only 10.7 percent of patients had controlled blood pressure and 18.1 percent never returned for any follow-up visit. Among the 21,729 patients who had at least one follow-up encounter, retention declined steeply over time from 59.9 percent at 6 months to 16.3 percent at 36 months. Patients identifying as Black or African American, Hispanic or Latino, or Other race or ethnicity had significantly higher hazards of being lost to follow-up than White patients, whereas older age, female sex, comorbid diabetes, heart failure, chronic kidney disease, stroke, and baseline blood pressure control were each independently protective. Among patients retained for at least 12 months, blood pressure control rose to 63.7 percent and remained near 64 to 66 percent through 36 months. These findings reveal a substantial and inequitable longitudinal care engagement gap that is likely a principal driver of suboptimal hypertension control in the United States and identify actionable demographic and clinical targets for primary care retention interventions.","rel_num_authors":2,"rel_authors":[{"author_name":"Jiancheng Ye","author_inst":"Weill Cornell Medicine"},{"author_name":"Alan Song","author_inst":"University of Hong Kong"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"The Great Recanalization Debate in Acute Ischemic Stroke-Direct Thrombectomy versus Bridging Therapy A Meta-analysis of Randomized Controlled Trials","rel_doi":"10.64898\/2026.05.10.26352784","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.10.26352784","rel_abs":"Background: Endovascular thrombectomy (EVT) is the standard of care for acute ischemic stroke caused by large-vessel occlusion. However, the additional benefit of intravenous thrombolysis (IVT) before EVT remains controversial. This systematic review and meta-analysis evaluated the efficacy and safety of bridging therapy (EVT plus IVT) compared with EVT alone. Methods: This systematic review and meta-analysis was conducted according to PRISMA 2020 and Cochrane Handbook recommendations and prospectively registered in PROSPERO. PubMed, EMbase, Scopus, and the Cochrane Library were searched for randomized controlled trials published between 1st January 2015 and 30th April 2026 comparing EVT plus IVT versus EVT alone in acute ischemic stroke. Random-effects meta-analysis was performed to estimate pooled odds ratios (ORs) with 95% confidence intervals (CIs). Primary outcomes included functional independence at 90 days and successful recanalization. Secondary outcomes included symptomatic intracranial hemorrhage (sICH) and all-cause mortality. Results: Eleven randomized controlled trials involving 4,419 patients were included in the meta-analysis. Compared with EVT alone, bridging therapy was associated with significantly better functional independence at 90 days (OR=1.25; 95% CI: 1.02 - 1.53). Patients receiving EVT plus IVT also demonstrated a trend toward higher rates of successful recanalization (OR=1.25; 95% CI: 0.95- 1.64) and lower 90-day mortality (OR=0.84; 95% CI: 0.67-1.04). The risk of sICH was comparable between the two treatment strategies (OR=1.07; 95% CI: 0.81-1.40). Overall, the certainty of evidence was rated as moderate. Conclusions: Bridging therapy before EVT may improve functional outcomes and recanalization without increasing sICH, supporting its use as a reasonable treatment strategy in eligible patients with acute ischemic stroke.","rel_num_authors":5,"rel_authors":[{"author_name":"Adila Jawaid","author_inst":"All India Institute of Medical Sciences, New Delhi"},{"author_name":"Manabesh Nath","author_inst":"All India Institute of Medical Sciences, New Delhi"},{"author_name":"Shubham Misra","author_inst":"Yale University"},{"author_name":"Deepti Vibha","author_inst":"All India Institute of Medical Sciences"},{"author_name":"Pradeep Kumar","author_inst":"All India Institute of Medical Sciences, New Delhi"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Time until symptoms and design-related associations in Alzheimer's disease clinical progression analyses","rel_doi":"10.64898\/2026.05.10.26352825","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.10.26352825","rel_abs":"IntroductionStudies of the risk and timing of symptomatic Alzheimers disease (AD) in cognitively unimpaired individuals are challenging due to the relatively small number of clinical progressors and limited clinical follow-up, which can lead to design-related associations. Clock models can be used to anchor the timing of events to biological events such as biomarker positivity. We hypothesized that estimated age at plasma %p-tau217 positivity based on clock models is less affected by design-related associations as compared to baseline age.\n\nMethodsData from the Knight Alzheimer Disease Research Center (Knight ADRC) and Alzheimers Disease Neuroimaging Initiative (ADNI) were analyzed. Age at %p-tau217 positivity was estimated using two clock model approaches, TIRA and SILA. The C-index of estimated age at plasma %p-tau217 positivity and age at the baseline plasma sample (baseline age) for ranking age of AD symptom onset was evaluated in initially cognitively unimpaired individuals, including progressors and non-progressors. In progressor sub-cohorts, baseline age and time from %p-tau217 positivity to baseline were associated with time from baseline until symptom onset; baseline age and estimated age at %p-tau217 positivity were associated with age at symptom onset. Commonality analyses partitioned the variance unique to each predictor and shared between predictors. Randomization analyses evaluated whether observed associations exceeded those expected by chance.\n\nResultsEstimated age at %p-tau217 positivity enabled analyses of a greater number of progressors in the research cohorts, which did not have plasma %p-tau217 data from every clinical assessment. The estimated age at %p-tau217 positivity had a higher C-index than baseline age for ordering the likelihood of AD symptom onset when all follow-up was considered; when follow-up was truncated, the C-index for estimated age at %p-tau217 positivity remained stable while the C-index for baseline age became inflated. In progressors, estimated age at %p-tau217 positivity contributed unique variance beyond baseline age in associations with age at symptom onset. Randomization analyses in the larger Knight ADRC found that associations between clock-derived measures and time from baseline until symptom onset and age at symptom onset exceeded the permuted null distribution, with some mixed results in the smaller ADNI cohort.\n\nConclusionsCompared to baseline age, the biologically-anchored estimated age at %p-tau217 positivity is less susceptible to design-related associations and incrementally improves prediction of age at symptom onset in analyses conditional on progression.","rel_num_authors":3,"rel_authors":[{"author_name":"Suzanne E. Schindler","author_inst":"Washington University"},{"author_name":"Yan Li","author_inst":"Washington University"},{"author_name":"Kellen K. Petersen","author_inst":"Washington University"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Biopsychosocial risk factors for Alzheimer's disease and related dementias in UK immigrants from the Middle East and North Africa (MENA)","rel_doi":"10.64898\/2026.05.08.26352762","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352762","rel_abs":"The Middle East and North Africa (MENA) region represents the area of greatest projected growth in instances of Alzheimer's disease and related dementias (ADRDs) globally, yet, it remains virtually uncharacterized in health studies of aging and ADRDs. The UK Biobank is one of the largest and well characterized datasets of aging immigrants in the UK, offering an unprecedented opportunity to identify risk factors for ADRDs in individuals from MENA regions. Here we used the UK Biobank to compare sociodemographic, disease, lifestyle, genetic, and neuroimaging risk factors for ADRDs among UK immigrants from MENA countries (N=3,552) with two other large immigrant populations from Germany (N=1,097) and India (N=2,935), as well as a genetically British white control group born outside the UK (N=1,925). MENA immigrants exhibited a distinct and adverse risk profile characterized by greater socioeconomic deprivation, higher exposure to air pollution, poorer diet quality, lower physical activity, worse sleep, and higher smoking prevalence compared to European immigrant groups. The same trends were observed when comparing MENA to Indian immigrants, though these differences were less pronounced. These behavioral and environmental risk factors were accompanied by markedly higher rates of obesity, diabetes, hypertension, and other cardiometabolic conditions. Despite this substantial phenotypic burden, MENA participants carried a lower frequency of established AD genetic risk variants, including ApoE4, highlighting a discordance between genetic risk and observed disease related vulnerability. Neuroimaging analyses revealed lower hippocampal volume in MENA and Indian participants relative to European groups despite younger average age, consistent with early limbic vulnerability associated with metabolic and inflammatory stress. Overall, our results indicate that dementia risk in MENA populations is driven by a multidimensional framework of metabolic, systemic, and social environmental exposures that may shape vulnerability independently of canonical European-derived genetic risk factors. These findings highlight the urgent need for ancestry- and context-specific frameworks to support equitable dementia prevention and avoid under-predicting risk in underrepresented populations.","rel_num_authors":10,"rel_authors":[{"author_name":"Elizabeth Haddad","author_inst":"University of Southern California"},{"author_name":"Ravi R Bhatt","author_inst":"University of Southern California"},{"author_name":"Akum Dhillon","author_inst":"University of Southern California"},{"author_name":"Talia M Nir","author_inst":"University of Southern California"},{"author_name":"Lauren Salminen","author_inst":"University of Southern California"},{"author_name":"Tala Al-Rousan","author_inst":"University of California San Diego"},{"author_name":"Kristine Ajrouch","author_inst":"University of Michigan, Ann Arbor"},{"author_name":"Arpana Church","author_inst":"University of California, Los Angeles"},{"author_name":"Nasim Sheikh Bahaei","author_inst":"University of Southern California"},{"author_name":"Neda Jahanshad","author_inst":"University of Southern California"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Two-Year Outcomes from the PRESERVE Trial: Durable Oncologic Control Following Focal Irreversible Electroporation Ablation for Intermediate-Risk Prostate Cancer","rel_doi":"10.64898\/2026.05.08.26352470","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352470","rel_abs":"The PRESERVE trial (NCT04972097) is a prospective, single-arm pivotal IDE study evaluating focal irreversible electroporation (IRE) using the NanoKnife System for intermediate-risk prostate cancer. Men with Gleason Grade Group 2-3 disease underwent focal IRE and were followed for durability of oncologic control and safety. At 24 months, 68 patients completed follow-up with no new treatment failures identified. PSA levels were below baseline in 97% of patients, and one clinically triggered biopsy was negative for cancer. No new device- or procedure-related adverse events occurred beyond 12 months. These findings demonstrate durable efficacy and sustained safety of focal IRE.","rel_num_authors":2,"rel_authors":[{"author_name":"Jonathan A Coleman","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Arvin K George","author_inst":"Brady Urological Institute, Johns Hopkins University"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Reproducibility of Apparent Diffusion Coefficient and Restriction Spectrum Imaging Restriction Score in the Prostate Across MRI Sessions, Vendors, and Acquisition Settings: a Prospective Study","rel_doi":"10.64898\/2026.05.10.26352843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.10.26352843","rel_abs":"Abstract Background: Diffusion-weighted MRI is central to prostate cancer detection, but apparent diffusion coefficient (ADC) has limited reproducibility across scanners and sites. Restriction Spectrum Imaging restriction score maximum value (RSIrs-max) may provide a more reproducible biomarker. Purpose: To evaluate cross-session reproducibility of within-lesion mean ADC and RSIrs-max on prostate MRI, including same-vendor and cross-vendor comparisons, and in unfavorable-histology prostate cancer (uhPC) and different interpolation settings. Materials and Methods: In this prospective study, participants with suspected or known prostate cancer enrolled from August 2022 to January 2026 underwent two MRI examinations including an RSI protocol. MRI-visible lesions were contoured on T2-weighted MRI; in participants with multiple lesions, the index lesion was selected. Mean ADC and RSIrs-max were measured within MRI-visible lesions. Analyses included all visible lesions, same-vendor and cross-vendor subgroups, participants with uhPC, and 20 participants with scans reconstructed with and without zero-filled interpolation (a setting with different defaults across vendors). Pearson correlation coefficients with 10,000 bootstrap resamples were used to estimate 95% confidence intervals. Results: Sixty-one male participants (median age, 69 years [IQR, 63-74]) were evaluated; 58 of 61 (95%) had MRI-visible lesions, and 26 of 58 (45%) had uhPC. For all MRI-visible lesions, correlations were 0.55 (95% CI: 0.23-0.76) for mean ADC and 0.83 (95% CI: 0.72-0.90) for RSIrs-max. In same-vendor scans, correlations were 0.76 (95% CI: 0.27-0.95) and 0.88 (95% CI: 0.72-0.96); in cross-vendor scans, they were 0.31 (95% CI: -0.07-0.62) and 0.79 (95% CI: 0.65-0.89), respectively. In uhPC, correlations were 0.42 (95% CI: -0.02-0.83) for mean ADC and 0.90 (95% CI: 0.77-0.96) for RSIrs-max. With inconsistent versus consistent interpolation, RSIrs-max correlation increased from 0.73 (95% CI: 0.48-0.89) to 0.89 (95% CI: 0.78-0.96). Conclusion: ADC showed limited reproducibility, particularly across vendors. RSIrs-max has stronger between-session reproducibility across same-vendor, cross-vendor, uhPC, and interpolation analyses.","rel_num_authors":12,"rel_authors":[{"author_name":"yuze song","author_inst":"University of California, San Diego"},{"author_name":"Christopher Charles Conlin","author_inst":"UC San Diego"},{"author_name":"Kang-Lung Lee","author_inst":"University of Cambridge"},{"author_name":"Anna Dornisch","author_inst":"UCSD"},{"author_name":"Tristan Barrett","author_inst":"University of Cambridge"},{"author_name":"Son Do","author_inst":"UCSD"},{"author_name":"Deondre D Do","author_inst":"University of California, San Diego"},{"author_name":"Daniel JA Margolis","author_inst":"Cornell University"},{"author_name":"Rebecca Rakow-Penner","author_inst":"UCSD"},{"author_name":"Anders Dale","author_inst":"University of California San Diego"},{"author_name":"Michael A Liss","author_inst":"UCSD"},{"author_name":"Tyler M Seibert","author_inst":"UCSD"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Stimulant Craving and Drug Use Dynamics: A Cross-Lagged Residual Dynamic Structural Equation Modeling Study","rel_doi":"10.64898\/2026.05.09.26352809","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.09.26352809","rel_abs":"Aims: To examine the longitudinal dynamic interactions of craving and drug use in the course of treatment of stimulant use disorders. Design: Cross-lagged residual dynamic structural equation modeling (R-DSEM) was used to examine the reciprocal (bidirectional) longitudinal associations between craving and drug use. Setting: Pooled data from 11 randomized controlled trials of pharmacotherapies for methamphetamine and cocaine use disorders in the United States sponsored by the National Institute on Drug Abuse. Participants: 1,936 adults with cocaine or methamphetamine use disorder. Measurements: Craving was measured using Brief Substance Craving Scale (BSCS), drug use was measured using Timeline Followback and urine drug screen (UDS). Findings: Craving and stimulant drug use were dynamically associated over time (within-person association). Daily craving significantly predicted drug use in subsequent days (estimate=0.092, 95% credible interval [CrI]=0.081, 0.103 for self-reported drug use and estimate=0.081, 95% CrI=0.069, 0.095 for UDS-ascertained drug use). In turn, drug use predicted subsequent craving (estimate=0.361, 95% CrI=0.325, 0.398 and estimate=0.060, 95% CrI=0.028, 0.094, respectively). There was substantial between-person heterogeneity in these cross-lagged effects, as reflected in the coefficients of variation ranging from 0.78 to 2.88. Conclusions: There is a bidirectional interaction between stimulant drug craving and drug use. The heterogeneity in the interaction of craving with stimulant drug use may partly explain between-person variability in responses to anti-craving medications in treatment of stimulant use disorders.","rel_num_authors":9,"rel_authors":[{"author_name":"Ramin Mojtabai","author_inst":"Tulane University School of Medicine"},{"author_name":"Ryoko Susukida","author_inst":"Johns Hookins Bloomberg School of Public Health"},{"author_name":"Trang Nguyen","author_inst":"Johns Hopkins Bloomberg School of Public Health"},{"author_name":"Mehdi Farokhnia","author_inst":"NIDA: National Institute on Drug Abuse"},{"author_name":"Lorenzo Leggio","author_inst":"Clinical Psychoneuroendocrinology and Neuropsychopharmacology Section, Translational Addiction Medicine Branch, National Institute on Drug Abuse Intramural Rese"},{"author_name":"Cecilia Bergeria","author_inst":"Kahlert Institute for Addiction Medicine, University of Maryland School of Medicine"},{"author_name":"Smita Prasad","author_inst":"Tulane University School of Medicine"},{"author_name":"Kelly Dunn","author_inst":"Kahlert Institute for Addiction Medicine, University of Maryland School of Medicine"},{"author_name":"Masoumeh Amin-Esmaeili","author_inst":"Johns Hopkins Bloomberg School of Public Health"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Mediators of Treatment Response in Clinical Trial of Naltrexone and Bupropion for Methamphetamine Use Disorder: A Longitudinal Mediation Analysis","rel_doi":"10.64898\/2026.05.09.26352807","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.09.26352807","rel_abs":"Background: The mechanisms underlying pharmacological treatments for stimulant use disorders are poorly understood. This study examined whether changes in craving, depressive symptoms, and\/or impulsivity mediate treatment effect in pharmacotherapy with combined naltrexone and bupropion for methamphetamine use disorder. Methods: The study was based on secondary analysis of data from the Accelerated Development of Additive Pharmacotherapy Treatment for methamphetamine disorder (ADAPT-2) trial which randomized adults with methamphetamine use disorder to combined treatment with injectable naltrexone (380 mg every three weeks) plus oral bupropion (450 mg daily) versus placebo. A total of 403 adults with methamphetamine use disorder participated in the first Stage; 225 of first Stage participants in the placebo arm who did not respond to treatment were re-randomized in the second Stage. Mediation effects were examined using longitudinal multi-level structural equation modeling. Results: Naltrexone-bupropion treatment was associated with decreases in drug use, craving, depressive symptoms, and impulsivity. The indirect effect of treatment through change in craving was significant (self-reported use=-0.21, 95% Credible Interval [CrI]=-0.35, -0.09; drug screen-ascertained use=-0.36, 95% CrI=-0.63, -0.16). Change in craving mediated 56% of the treatment effect on self-reported use and 45% of the effect on drug screen-ascertained use. Estimates for mediated effects for depressive symptoms and impulsivity were smaller in magnitude and non-significant. Conclusion: Reduction in craving mediates the effect of naltrexone-bupropion pharmacotherapy in methamphetamine use disorder. Craving may serve as a surrogate measure of treatment efficacy in short-term trials and help identify promising candidate medications to be tested in larger and longer-term trials.","rel_num_authors":9,"rel_authors":[{"author_name":"Ramin Mojtabai","author_inst":"Tulane University School of Medicine"},{"author_name":"Ryoko Susukida","author_inst":"Johns Hookins University"},{"author_name":"Mehdi Farokhnia","author_inst":"NIDA: National Institute on Drug Abuse"},{"author_name":"Trang Quynh Nguyen","author_inst":"Johns Hopkins Bloomberg School of Public Health"},{"author_name":"Lorenzo Leggio","author_inst":"Clinical Psychoneuroendocrinology and Neuropsychopharmacology Section, Translational Addiction Medicine Branch, National Institute on Drug Abuse Intramural Rese"},{"author_name":"Cecilia Bergeria","author_inst":"Kahlert Institute for Addiction Medicine, University of Maryland School of Medicine"},{"author_name":"Smita Prasad","author_inst":"Tulane University School of Medicine"},{"author_name":"Kelly Dunn","author_inst":"Kahlert Institute for Addiction Medicine, University of Maryland School of Medicine"},{"author_name":"Masoumeh Amin-Esmaeili","author_inst":"Johns Hopkins School of Public Health"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Cohort profile: The Australian Children of the Digital Age (ACODA) longitudinal cohort study measuring the digital lives of Australians during early childhood","rel_doi":"10.64898\/2026.05.09.26352795","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.09.26352795","rel_abs":"<strong>Objectives<\/strong> The Australian Children of the Digital Age (ACODA) study is a longitudinal cohort study investigating the digital lives of Australians during early childhood. This paper presents a comprehensive description of the study protocol and overview of childrens digital technology use in the home at the first wave of data collection. <strong>Methods<\/strong> Caregivers of children aged 6-months to 5-years completed a survey that captured the availability and use of digital technology within the home, and child- and caregiver-related factors that may influence childrens digital technology use. <strong>Results<\/strong> A total of 3,388 caregivers from across all Australian states and territories completed the survey. Majority (98%) of children had digital technology and internet access within their homes. Most children (93%) used at least one device in the last year, with televisions, tablets, and mobile phones most frequently used (89%, 47%, 42%, respectively). Digital technology use started early, with 61% of children aged <1-year having used a television. A greater proportion of older children used devices, and for longer durations than younger children. Across all ages, daily time was longest on televisions (M = 1:20, SD = 1:14), tablets (M = 1:06, SD = 1:36), and mobile phones (M = 0:30, SD = 1:05). Digital technology was used most for entertainment and learning activities, and was used typically with a caregiver and in lounge\/living rooms. <strong>Conclusions<\/strong> The ACODA study is the first longitudinal study to describe the digital technology use of Australians during early childhood and the context of this use. Data indicated that Australian children frequently used digital technology for entertainment and with their caregivers. Also, older children used digital technology more than younger children. Future waves allow for exploration of changes in childrens digital technology use over time, and associations with factors that may influence childrens digital technology use.","rel_num_authors":11,"rel_authors":[{"author_name":"Janelle MacKenzie","author_inst":"Queensland University of Technology - QUT: Queensland University of Technology"},{"author_name":"Daniel Johnson","author_inst":"Queensland University of Technology - QUT: Queensland University of Technology"},{"author_name":"Grace Sarra","author_inst":"Queensland University of Technology - QUT: Queensland University of Technology"},{"author_name":"Julian  R Matthews","author_inst":"Queensland University of Technology - QUT: Queensland University of Technology"},{"author_name":"Laura Martinez-Buelvas","author_inst":"Queensland University of Technology - QUT: Queensland University of Technology"},{"author_name":"Dana Trenaman","author_inst":"Queensland University of Technology - QUT: Queensland University of Technology"},{"author_name":"Julian Sefton-Green","author_inst":"Deakin University"},{"author_name":"Steven  J Howard","author_inst":"University of Oxford"},{"author_name":"Simon  S Smith","author_inst":"The University of Queensland"},{"author_name":"Susan Danby","author_inst":"Queensland University of Technology - QUT: Queensland University of Technology"},{"author_name":"Juliana Zabatiero","author_inst":"Curtin University"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"A Blood-Based Transcriptomic Signature for PTSD Classification Using Machine Learning","rel_doi":"10.64898\/2026.05.10.26352854","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.10.26352854","rel_abs":"Post-traumatic stress disorder (PTSD) remains a significant psychiatric burden; despite growing biomarker research, no blood-based molecular diagnostic tool has been clinically validated for routine use. In this study, we developed a machine learning classifier for PTSD using peripheral blood leukocyte RNA-seq data from combat-exposed U.S. Marines (GSE64813), diagnosed via the Clinician-Administered PTSD Scale (CAPS) under DSM-IV criteria. Differentially expressed genes (DEGs) were identified and further refined through additional filtering criteria, yielding a 90-gene feature set used to train and compare multiple machine learning models. The support vector machine (SVM) classifier achieved the best performance, with an accuracy of 89\\% and an AUC of 0.95, outperforming logistic regression and random forest approaches. Furthermore we evaluated our model on independent external datasets to assess generalizability. These findings highlight the promise of transcriptomic signatures as a foundation for objective, blood-based PTSD diagnostics, while emphasizing the critical need for robust cross-dataset generalizability.","rel_num_authors":5,"rel_authors":[{"author_name":"Shawheen Alipour","author_inst":"University of Houston"},{"author_name":"Rishika Pamanji","author_inst":"University of Houston"},{"author_name":"Emina Jamil","author_inst":"Rutgers University"},{"author_name":"Suneetha Yeguvapalli","author_inst":"University of Houston"},{"author_name":"Kumaraswamy Naidu Chitrala","author_inst":"University of Houston"}],"rel_date":"2026-05-13","rel_site":"medrxiv"},{"rel_title":"Activation-dependent lentiviruses enable antigen-specific T cell expansion and transduction","rel_doi":"10.64898\/2026.05.11.724165","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724165","rel_abs":"Cancer immunotherapies rely on tumor-specific T cells, which arise endogenously in most patients with cancer, but can be low frequency and poorly functional. Methods to specifically identify, expand, and manipulate tumor-specific T cells at the rare frequencies found in peripheral blood would enable new immunotherapeutic strategies. Here, we demonstrate an approach to virally transduce polyclonal tumor-reactive T cells across any MHC haplotype and in the absence of knowing the cognate antigen. By generating lentiviral vectors that selectively transduce cells expressing 4-1BB (CD137), a marker of T cell activation, we can transduce antigen-specific T cells with user-defined genetic cargoes that can selectively expand and track individual clonotypes via single-cell sequencing. Anti-4-1BB lentiviruses (4-1BB LVs) encoding therapeutic cargoes can also enhance antigen-specific T cells to extend survival in a xenograft model of human melanoma and transduce tumor-infiltrating T cells from patients with ovarian cancer. Overall, the 4-1BB LV platform targets antigen-specific T cells in a manner agnostic to both the antigen and presenting MHC, with potential applications in adoptive cell therapy manufacturing and TCR identification.","rel_num_authors":16,"rel_authors":[{"author_name":"Blake E. Smith","author_inst":"Harvard Medical School"},{"author_name":"Lindsey M. Draper","author_inst":"UCSF"},{"author_name":"Andrea Garmilla","author_inst":"Harvard Medical School"},{"author_name":"Caleb R. Perez","author_inst":"MIT"},{"author_name":"Nishant Singh","author_inst":"Ragon Institute"},{"author_name":"Lucia T Padilla","author_inst":"MIT"},{"author_name":"Ellen J.K. Xu","author_inst":"MIT"},{"author_name":"Stephanie A. Gaglione","author_inst":"MIT"},{"author_name":"Jiao Shen","author_inst":"DFCI"},{"author_name":"Winiffer D. Conce Alberto","author_inst":"Harvard Medical School"},{"author_name":"Qingyang Henry Zhao","author_inst":"MIT"},{"author_name":"Connor S. Dobson","author_inst":"MIT"},{"author_name":"Kole T. Roybal","author_inst":"UCSF"},{"author_name":"Michael Dougan","author_inst":"Harvard Medical School"},{"author_name":"Michael E. Birnbaum","author_inst":"MIT"},{"author_name":"Stephanie K. Dougan","author_inst":"DFCI"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"Kaiso reads methylated CpGs at nucleosome entry\/exit and displaces the H3 tail","rel_doi":"10.64898\/2026.05.10.724166","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724166","rel_abs":"The zinc finger transcription factor Kaiso recognizes methylated CpG dinucleotides at silenced promoters and imprinted loci, but how it engages methylated DNA within the nucleosome remains unclear. To address this, we developed a DNMT1-based strategy for preparing site-specifically methylated nucleosomes with defined position and methylation state of the Kaiso recognition motif. Electrophoretic mobility shift assays show that Kaiso binds methylated nucleosomes with strong positional preference, with high-affinity engagement at the entry\/exit site (SHL 6.5; Kd {approx} 100 nM), reduced affinity at SHL 5.5 (Kd {approx} 170 nM), and no methylation-dependent enhancement at dyad-proximal positions. Hemi- and fully methylated substrates bind Kaiso comparably at SHL 6.5, and the E535A mutation, which disrupts a key methyl-CpG contact, reduces binding in a methylation- and position-dependent manner. Solution NMR titrations of 15N-labeled H3 nucleosomes reveal that Kaiso binding perturbs a discrete set of H3 N-terminal tail residues, with chemical shifts trending toward free-peptide values, indicating release of the tail from its nucleosomal DNA contacts. This pattern closely resembles that produced by the pioneer factor Sox2 at the same nucleosomal region, suggesting H3 tail displacement is a general consequence of factor engagement at the nucleosome edge, independent of DNA-recognition mode. These results establish Kaiso as an active reader of methylated nucleosomal DNA that may prime local chromatin by exposing the H3 tail.","rel_num_authors":2,"rel_authors":[{"author_name":"Fabiana C Malaga Gadea","author_inst":"Johns Hopkins University"},{"author_name":"Evgenia N Nikolova","author_inst":"Johns Hopkins University"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"Phylogenomic coupling of F1 chemosensory and archaellum systems across archaea and monoderm bacteria","rel_doi":"10.64898\/2026.05.11.724246","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724246","rel_abs":"Archaellum-associated motility has been viewed as solely archaeal, yet new findings in Chloroflexota prompt a broader perspective. By analysing a curated ~22,000 NCBI reference genomes alongside 2,397 archaeal and 226 archaellum-encoding Chloroflexota genomes, this study systematically characterises the co-distribution of archaellum loci with chemosensory system (CSS) classes. Maximum-likelihood phylogeny of 3,727 F1-type CheA proteins reveals three major clades, with Clade 1 comprising ~80% monoderm representation, uniting archaeal and monoderm bacterial lineages in a shared evolutionary grouping. Overall, this work shows that not only archaeal-type motility, but also F1-CSS based sensing system, might have been gained from Archaea to Chloroflexota via horizontal gene transfer and both systems shared an evolutionary trajectory altogether.","rel_num_authors":3,"rel_authors":[{"author_name":"Utkarsha Mahanta","author_inst":"Department of Biotechnology, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India"},{"author_name":"Matthew Baker","author_inst":"School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Gaurav Sharma","author_inst":"Department of Biotechnology, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"NovaClone: A Network-Based Algorithm for Clonal and Subclonal Genotyping of Barcoded Transgene Integrations","rel_doi":"10.64898\/2026.05.11.724244","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724244","rel_abs":"Single-cell lineage tracing technologies are providing new and powerful ways to interrogate the evolution and divergence of cell populations in cancer, development, and other contexts. A key initial step in any such analysis is the grouping of cells into clonal populations, based on clone-level marks. Unfortunately, clone calling is prone to technical effects due to sequencing errors, missing data, multiplets, background noise, and accidental sharing of clonal barcodes between unrelated clones (homoplasy). We present NovaClone, a principled algorithm for hierarchical clone calling that is broadly applicable to all current tracing technologies, including both static barcoding and the more recent evolving tracers. We benchmark NovaClone on simulated and real data to show that it outperforms the current solutions in terms of both quality and speed, thereby helping to mitigate one of the most prevalent problems with single-cell lineage tracing. To complement NovaClone, we introduce a suite of algorithm-agnostic quality control metrics to evaluate clone calls when ground truth is not available. NovaClone and the associated QCs are available through the open source Python package nova-clone.","rel_num_authors":5,"rel_authors":[{"author_name":"Sebastian Prillo","author_inst":"University of California Berkeley"},{"author_name":"Dana Rimini","author_inst":"Weizmann Institute of Science"},{"author_name":"Pedro Olivares-Chauvet","author_inst":"Weizmann Institute of Science"},{"author_name":"Yun S. Song","author_inst":"University of California Berkeley"},{"author_name":"Nir Yosef","author_inst":"Weizmann Institute of Science"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"NovaClone: A Network-Based Algorithm for Clonal and Subclonal Genotyping of Barcoded Transgene Integrations","rel_doi":"10.64898\/2026.05.11.724244","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724244","rel_abs":"Single-cell lineage tracing technologies are providing new and powerful ways to interrogate the evolution and divergence of cell populations in cancer, development, and other contexts. A key initial step in any such analysis is the grouping of cells into clonal populations, based on clone-level marks. Unfortunately, clone calling is prone to technical effects due to sequencing errors, missing data, multiplets, background noise, and accidental sharing of clonal barcodes between unrelated clones (homoplasy). We present NovaClone, a principled algorithm for hierarchical clone calling that is broadly applicable to all current tracing technologies, including both static barcoding and the more recent evolving tracers. We benchmark NovaClone on simulated and real data to show that it outperforms the current solutions in terms of both quality and speed, thereby helping to mitigate one of the most prevalent problems with single-cell lineage tracing. To complement NovaClone, we introduce a suite of algorithm-agnostic quality control metrics to evaluate clone calls when ground truth is not available. NovaClone and the associated QCs are available through the open source Python package nova-clone.","rel_num_authors":5,"rel_authors":[{"author_name":"Sebastian Prillo","author_inst":"University of California Berkeley"},{"author_name":"Dana Rimini","author_inst":"Weizmann Institute of Science"},{"author_name":"Pedro Olivares-Chauvet","author_inst":"Weizmann Institute of Science"},{"author_name":"Yun S. Song","author_inst":"University of California Berkeley"},{"author_name":"Nir Yosef","author_inst":"Weizmann Institute of Science"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"Proposed core role for cytosolic and transmembrane calpain cysteine proteases in mitotic cell divisions","rel_doi":"10.64898\/2026.05.11.724225","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.724225","rel_abs":"Abstract Calpains constitute an ancient, extensive family of calcium-dependent cysteine proteases found in some bacteria and most eukaryotes. They are involved in a wide variety of developmental and cellular processes and are implicated in major human diseases, yet it remains to be seen if they have a common core function explaining their widespread and varied presence across taxa. Beyond their core CysPc catalytic domain, calpains contain diverse domain combinations and can be either cytosolic or membrane bound. Here we hypothesize a general role for both cytosolic and transmembrane calpains in cellular cytokinesis through positional anchoring and organization of microtubules (MTs). We propose that during plant cell division, the singular transmembrane calpain DEK1 localizes and organizes the array of cortical MTs from the microtubule organizing center (MTOC) to establish the location of the preprophase band and\/or the site of cell plate formation according to the positional activation of DEK1 proteins in the nuclear membrane. Similarly, during cell division in animals, their calpains may be involved in setting the point of membrane invagination via their association with membrane-bound proteins. This proposition adds to the current picture of animal MTOC\/centrosome function and suggests how a calcium peak during the initial cytokinetic furrowing might be transmitted. We discuss this novel mechanistic model for calpain activity in the context of data from the animal and plant literature, as well as of our novel discovery here of calpain sequences in both brown and red algal genomes. Finally, we speculate that the ancestral role of calpains in early eukaryotes, before the split into the major eukaryotic supergroups, may have been to facilitate the formation and function of MT arrays in flagella and cilia. From this origin, calpains may have developed new functions in eukaryote cell division processes by anchoring centrosomes\/MTOC to set the cell division orientations that are especially important for complex multicellularity.","rel_num_authors":3,"rel_authors":[{"author_name":"Jenn C Fletcher","author_inst":"United State Department of Agriculture-Agricultural Research Service, Plant Gene Expression Center, Albany, CA, USA & UC-Berkeley"},{"author_name":"Mary A Biggs","author_inst":"United State Department of Agriculture-Agricultural Research Service, Plant Gene Expression Center, Albany, CA, USA & UC-Berkeley"},{"author_name":"Hilde-Gunn Opsahl Sorteberg","author_inst":"NMBU"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"Drug Proarrhythmic Evaluation in a High Throughput Cardiac New Approach Methodology","rel_doi":"10.64898\/2026.05.11.722965","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.11.722965","rel_abs":"Background and Purpose: Cardiotoxicity is a major cause for drug failure throughout the drug development process, with particular concern for action potential prolongation and arrhythmia. Hence, such liabilities are heavily considered during the early phases of drug design to prevent dangerous compounds from progressing. New approach methodologies (NAMs) that efficiently examine this risk early in the discovery pipeline should help streamline drug development programs. We developed a cardiac NAM, a 384-well open bath platform consisting of cardiac tissue derived from human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes, enabling high-throughput drug screening while maintaining the structural and functional complexity of 3D cardiac micromuscles. Methods: We dramatically increased throughput without compromising physiological relevance provided by the 3D micromuscle structure. Our 384-well open bath high-throughput platform allowed evaluation of multiple compounds at a time, enabling us to study the CiPA (comprehensive in vitro proarrhythmia assay) drug panel for proarrhythmia screening. We obtained phenotypic fingerprints of all 28 compounds (9 low, 11 intermediate, and 8 high arrhythmia risk; https:\/\/cipaproject.org ) in dose-escalation studies around their respective clinical concentrations. The analysis was augmented with an in silico pipeline that used phenotypic biomarkers to invert data into a mathematical model of cellular currents to infer which ion channels were affected upon drug exposure, and then trained a ML model to predict channel block. Results and Conclusions: We found accurate detection of arrhythmic potential for most of the compounds, and the in silico model inversions were consistent with published values of compound channel block. All the high risk compounds showed action potential duration (APD) prolongation coupled with either action potential abnormalities, early afterdepolarizations (EADs), or beat cessation. For the intermediate risk group, 9 out of 11 compounds caused APD prolongation alone or in combination with EADs while 2 others showed either beat cessation or beat rate change. Augmentation of APD analysis with detailed biophysical modeling and ML tools provided meaningful insight into the mechanisms involved in APD changes. Overall, our cardiac NAM allowed for fast and relevant screening for mechanistic understanding of APD prolongation and proarrhythmic activity, at massively increased throughput compared to other 3D micromuscle models.","rel_num_authors":9,"rel_authors":[{"author_name":"Verena Charwat","author_inst":"Johannes Kepler University"},{"author_name":"Adrian Ramirez","author_inst":"Organos Inc, Berkeley, CA, USA"},{"author_name":"Karoline  Horgmo Jaeger","author_inst":"Simula Research Laboratory AS"},{"author_name":"Brennan Kandalaft","author_inst":"Organos Inc, Berkeley, CA, USA"},{"author_name":"Henrik Finsberg","author_inst":"Simula Research Laboratory"},{"author_name":"Brian Siemons","author_inst":"Organos Inc, Berkeley, CA, USA"},{"author_name":"Aslak Tveito","author_inst":"Simula Research Laboratory"},{"author_name":"Kevin Healy","author_inst":"UC Berkeley"},{"author_name":"Samuel T Wall","author_inst":"Simula Research Laboratory, Oslo, Norway"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"An explainable machine learning consensus framework for robust estimations of environmental effects on population dynamics","rel_doi":"10.64898\/2026.05.10.724190","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724190","rel_abs":"Explainable machine learning (ML) methods are gaining increasing attention in environmental and ecological research for their ability to reveal relationships between environmental drivers and population dynamics. However, there remain questions on the reliability of these tools, especially given recent research shows that these explanations can be highly sensitive to model architecture. In ecology, it is typical to use a single ML model, and a comparative evaluation of sensitivity of explainability for different ML approaches is overlooked. In this paper, we develop a novel framework that quantifies explanation consistency between multiple ML model architectures. This framework provides a discrepancy measure for each model prediction, with high discrepancy indicating substantive explanation disagreement across models and low discrepancy indicating strong consensus in explanations across models. We then demonstrate that low explanation discrepancy aligns well with ground truth mechanism. Furthermore, high explanation discrepancy provide a mechanism to identify areas for model refinement and further investigation by domain experts. We do this by using a simulation study based on synthetic coral cover data that incorporate spatio-temporal variability driven by known disturbance effects. Our method provides a quantitative approach to assess the sensitivity of explainable ML in the absence of ground truth. As a result, this enhances the utility of ML approaches in conservation and ecological management. While we focus primarily on ecological modelling for coral reefs, our methods are generally applicable to other ecological and environmental modelling settings.","rel_num_authors":4,"rel_authors":[{"author_name":"Anuradha Dhananjanie","author_inst":"Queensland University of Technology"},{"author_name":"Helen Thompson","author_inst":"Queensland University of Technology"},{"author_name":"Julie Vercelloni","author_inst":"Australian Institute for Marine Sciences"},{"author_name":"David J Warne","author_inst":"Queensland University of Technology"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"MASCAF: a Cable Model Fitting Pipeline for Topologically Complex Surface Meshes","rel_doi":"10.64898\/2026.05.10.721501","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.721501","rel_abs":"We present a free and open-source, semi-automated, topologically robust pipeline for fitting cable models to 3D surface mesh morphology data of neuronal membranes, particularly suited to structures with complex shapes and topological holes. The motivation for this work is the discovery of morphologically complex neural spines on the auditory space-specific neurons of the barn owl (Tyto alba, Tyto furcata), dubbed toric spines, notable for their high curvature, branching density, and holes\/loops. Multicompartmental simulation software requires morphology to be represented as cable models (e.g., SWC format), yet existing software tools for fitting cable models to complex 3D surface meshes have not produced satisfactory results for toric spines, and loops are generally unsupported. We present the Mesh and Skeleton Cable Fitting (MASCAF) pipeline and software, which fits a cable model (e.g., SWC format) to a surface mesh using mean-curvature flow skeletonization. In this paper, we demonstrate how MASCAF is applied to fit cable models, how loops can be reconstructed in simulations with the Arbor and NEURON simulation software, and how the results can be validated using geometry and simulator-based methods. While non-tree morphologies such as toric spines are neuroanatomically special, our software pipeline provides a cable-model fitting approach for surface mesh data that is topologically robust, deterministic, open-source, and applicable to general morphologies, thereby closing a crucial gap between neuronal imaging and high-resolution simulation.n simulation.","rel_num_authors":4,"rel_authors":[{"author_name":"Jordan M. R. Fox","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Brian J Fischer","author_inst":"Seattle University"},{"author_name":"William M DeBello","author_inst":"UC Davis"},{"author_name":"Jose L Pena","author_inst":"Albert Einstein College of Medicine"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"From criticality to cognitive effort: scale-invariant EEG dynamics supporting cognitive flexibility are suppressed by effort","rel_doi":"10.64898\/2026.05.10.724092","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724092","rel_abs":"The critical brain hypothesis contends that brains operate near a phase transition where excitation and inhibition are balanced, enabling neural dynamics to rapidly adapt and reorganize for cognitive demands. Allocating control resources to maintain stable task representations likely shifts brains away from criticality. Here, we test whether proximity to criticality indexes the balance between flexible adaptation and effortful task engagement. To do so, we adapt a time-resolved measure of scale invariance in EEG amplitude fluctuations (d2), capable of quantifying distance-to-criticality under non-stationary conditions - as during cognitive tasks. We benchmark our measure using ground-truth simulations of a neural mass model and show that d2 is lowest when excitation and inhibition are balanced. Next, we apply d2 to data collected during a task-switching paradigm and find that more demanding trials increased deviation from criticality, whereas greater flexibility, faster responses, and higher accuracy occurred closer to criticality. These effects were region-specific: deviation at posterior electrodes predicted worse performance, while deviations at frontal midline electrodes predicted better performance. Together, these results suggest that deviations from criticality reflect both cognitive load and effort exertion, highlighting EEG amplitude scale-invariance as a sensitive marker of adaptive neural dynamics under cognitive demand.","rel_num_authors":8,"rel_authors":[{"author_name":"Li Xin Lim","author_inst":"Rutgers University"},{"author_name":"Abhimanyu Bhardwaj","author_inst":"Rutgers University"},{"author_name":"Arthur-Ervin Avramiea","author_inst":"Vrije Universiteit Amsterdam"},{"author_name":"Klaus Linkenkaer-Hansen","author_inst":"Vrije universiteit Amsterdam"},{"author_name":"Asako Mitsuto","author_inst":"Kyoto University"},{"author_name":"Lily tran","author_inst":"Brown University"},{"author_name":"Woodrow Shew","author_inst":"University of Arkansas Fayetteville"},{"author_name":"Andrew Westbrook","author_inst":"Rutgers University"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"From criticality to cognitive effort: scale-invariant EEG dynamics supporting cognitive flexibility are suppressed by effort","rel_doi":"10.64898\/2026.05.10.724092","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724092","rel_abs":"The critical brain hypothesis contends that brains operate near a phase transition where excitation and inhibition are balanced, enabling neural dynamics to rapidly adapt and reorganize for cognitive demands. Allocating control resources to maintain stable task representations likely shifts brains away from criticality. Here, we test whether proximity to criticality indexes the balance between flexible adaptation and effortful task engagement. To do so, we adapt a time-resolved measure of scale invariance in EEG amplitude fluctuations (d2), capable of quantifying distance-to-criticality under non-stationary conditions - as during cognitive tasks. We benchmark our measure using ground-truth simulations of a neural mass model and show that d2 is lowest when excitation and inhibition are balanced. Next, we apply d2 to data collected during a task-switching paradigm and find that more demanding trials increased deviation from criticality, whereas greater flexibility, faster responses, and higher accuracy occurred closer to criticality. These effects were region-specific: deviation at posterior electrodes predicted worse performance, while deviations at frontal midline electrodes predicted better performance. Together, these results suggest that deviations from criticality reflect both cognitive load and effort exertion, highlighting EEG amplitude scale-invariance as a sensitive marker of adaptive neural dynamics under cognitive demand.","rel_num_authors":8,"rel_authors":[{"author_name":"Li Xin Lim","author_inst":"Rutgers University"},{"author_name":"Abhimanyu Bhardwaj","author_inst":"Rutgers University"},{"author_name":"Arthur-Ervin Avramiea","author_inst":"Vrije Universiteit Amsterdam"},{"author_name":"Klaus Linkenkaer-Hansen","author_inst":"Vrije universiteit Amsterdam"},{"author_name":"Asako Mitsuto","author_inst":"Kyoto University"},{"author_name":"Lily tran","author_inst":"Brown University"},{"author_name":"Woodrow Shew","author_inst":"University of Arkansas Fayetteville"},{"author_name":"Andrew Westbrook","author_inst":"Rutgers University"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"From criticality to cognitive effort: scale-invariant EEG dynamics supporting cognitive flexibility are suppressed by effort","rel_doi":"10.64898\/2026.05.10.724092","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724092","rel_abs":"The critical brain hypothesis contends that brains operate near a phase transition where excitation and inhibition are balanced, enabling neural dynamics to rapidly adapt and reorganize for cognitive demands. Allocating control resources to maintain stable task representations likely shifts brains away from criticality. Here, we test whether proximity to criticality indexes the balance between flexible adaptation and effortful task engagement. To do so, we adapt a time-resolved measure of scale invariance in EEG amplitude fluctuations (d2), capable of quantifying distance-to-criticality under non-stationary conditions - as during cognitive tasks. We benchmark our measure using ground-truth simulations of a neural mass model and show that d2 is lowest when excitation and inhibition are balanced. Next, we apply d2 to data collected during a task-switching paradigm and find that more demanding trials increased deviation from criticality, whereas greater flexibility, faster responses, and higher accuracy occurred closer to criticality. These effects were region-specific: deviation at posterior electrodes predicted worse performance, while deviations at frontal midline electrodes predicted better performance. Together, these results suggest that deviations from criticality reflect both cognitive load and effort exertion, highlighting EEG amplitude scale-invariance as a sensitive marker of adaptive neural dynamics under cognitive demand.","rel_num_authors":8,"rel_authors":[{"author_name":"Li Xin Lim","author_inst":"Rutgers University"},{"author_name":"Abhimanyu Bhardwaj","author_inst":"Rutgers University"},{"author_name":"Arthur-Ervin Avramiea","author_inst":"Vrije Universiteit Amsterdam"},{"author_name":"Klaus Linkenkaer-Hansen","author_inst":"Vrije universiteit Amsterdam"},{"author_name":"Asako Mitsuto","author_inst":"Kyoto University"},{"author_name":"Lily tran","author_inst":"Brown University"},{"author_name":"Woodrow Shew","author_inst":"University of Arkansas Fayetteville"},{"author_name":"Andrew Westbrook","author_inst":"Rutgers University"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"Toward a Random Background for Ligand Optimization","rel_doi":"10.64898\/2026.05.10.724162","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724162","rel_abs":"Ligand optimization is central to drug discovery as hundreds of analogs might be designed and synthesized between an initial hit and a therapeutic candidate. The efficiency of this process is unclear, at least partly because there is no random background for optimization against which to compare. Such a random background might emerge from synthetically accessible but otherwise systematic random small substitutions across starting ligands, measuring likelihood of achieving a substantial improvement in affinity\/potency or other property by any single perturbation. Recent literature and ligand-affinity\/potency databases suggest that perhaps 10% of analogs with minor modifications improve upon a parent's potency substantially, by more than 10-fold, but this number is clouded by reporting bias, intentional improvement, and inter-group reproducibility. To begin to establish a background expectation for ligand optimization, we comprehensively and systematically modified 18 lead molecules across six targets with single atom changes; 257 compounds were synthesized. Unexpectedly, 11.2% of these random small perturbation analogs improved potency by more than 10-fold over their parents. Conversely, these more potent analogs typically had worse in vitro pharmacokinetics, for example reduced metabolic stability and lower plasma free fraction. While it was possible to find analogs where the potency increase compensated for inferior exposure and half-life, resulting in more potent compounds in vivo, overall a frustrated landscape for ligand optimization is revealed. This study begins to establish a background expectation for ligand potency optimization and offers a simple strategy to do so. It also begins to quantify the challenges confronting the field in moving beyond in vitro potency.","rel_num_authors":32,"rel_authors":[{"author_name":"Xinyu Xu","author_inst":"UCSF"},{"author_name":"Olivier Mailhot","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Galen J. Correy","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"XP Huang","author_inst":"Department of Pharmacology, NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, US"},{"author_name":"Joao Braz","author_inst":"Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Da Shi","author_inst":"Schrodinger Inc, San Diego, CA, USA; Schrodinger Inc, New York, NY, USA"},{"author_name":"Karthik Srinivasan","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Kara Zielinski","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Yuliia Holota","author_inst":"Bienta; Winston Churchill St. 78, 02094 Kyiv, Ukraine"},{"author_name":"Yuliia Kuziv","author_inst":"Bienta; Winston Churchill St. 78, 02094 Kyiv, Ukraine"},{"author_name":"Christos Tsoutsouvas","author_inst":"Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Nathan Levinzon","author_inst":"Department of Medicinal Chemistry, College of Pharmacy, University of Utah, 2000 East 30 South Skaggs 306, Salt Lake City, Utah 84112, United States"},{"author_name":"Yagmur U. Doruk","author_inst":"Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA"},{"author_name":"Moira Rachman","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Morgan Diolaiti","author_inst":"Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA"},{"author_name":"Maisie Stevens","author_inst":"Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA"},{"author_name":"Fangyu Liu","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Katie Holland","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Harald Hubner","author_inst":"Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany"},{"author_name":"Jing Wang","author_inst":"Department of Pharmacology, NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, US"},{"author_name":"Yujin Wu","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Alan Ashworth","author_inst":"Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA"},{"author_name":"Alexander Makriyannis","author_inst":"Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Yuqi Zhang","author_inst":"Schrodinger Inc, San Diego, CA, USA; Schrodinger Inc, New York, NY, USA"},{"author_name":"Yurii Moroz","author_inst":"Chemspace LLC, Kyiv 02094, Ukraine. Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine. Enamine Ltd., Kyiv 02094, Ukraine"},{"author_name":"Peter Gmeiner","author_inst":"Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany"},{"author_name":"Robert Abel","author_inst":"Schrodinger Inc, San Diego, CA, USA; Schrodinger Inc, New York, NY, USA"},{"author_name":"Aashish Manglik","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Allan I. Basbaum","author_inst":"Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Bryan L. Roth","author_inst":"Department of Pharmacology, NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, US"},{"author_name":"James S. Fraser","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Brian K. Shoichet","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"Toward a Random Background for Ligand Optimization","rel_doi":"10.64898\/2026.05.10.724162","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724162","rel_abs":"Ligand optimization is central to drug discovery as hundreds of analogs might be designed and synthesized between an initial hit and a therapeutic candidate. The efficiency of this process is unclear, at least partly because there is no random background for optimization against which to compare. Such a random background might emerge from synthetically accessible but otherwise systematic random small substitutions across starting ligands, measuring likelihood of achieving a substantial improvement in affinity\/potency or other property by any single perturbation. Recent literature and ligand-affinity\/potency databases suggest that perhaps 10% of analogs with minor modifications improve upon a parent's potency substantially, by more than 10-fold, but this number is clouded by reporting bias, intentional improvement, and inter-group reproducibility. To begin to establish a background expectation for ligand optimization, we comprehensively and systematically modified 18 lead molecules across six targets with single atom changes; 257 compounds were synthesized. Unexpectedly, 11.2% of these random small perturbation analogs improved potency by more than 10-fold over their parents. Conversely, these more potent analogs typically had worse in vitro pharmacokinetics, for example reduced metabolic stability and lower plasma free fraction. While it was possible to find analogs where the potency increase compensated for inferior exposure and half-life, resulting in more potent compounds in vivo, overall a frustrated landscape for ligand optimization is revealed. This study begins to establish a background expectation for ligand potency optimization and offers a simple strategy to do so. It also begins to quantify the challenges confronting the field in moving beyond in vitro potency.","rel_num_authors":32,"rel_authors":[{"author_name":"Xinyu Xu","author_inst":"UCSF"},{"author_name":"Olivier Mailhot","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Galen J. Correy","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"XP Huang","author_inst":"Department of Pharmacology, NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, US"},{"author_name":"Joao Braz","author_inst":"Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Da Shi","author_inst":"Schrodinger Inc, San Diego, CA, USA; Schrodinger Inc, New York, NY, USA"},{"author_name":"Karthik Srinivasan","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Kara Zielinski","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Yuliia Holota","author_inst":"Bienta; Winston Churchill St. 78, 02094 Kyiv, Ukraine"},{"author_name":"Yuliia Kuziv","author_inst":"Bienta; Winston Churchill St. 78, 02094 Kyiv, Ukraine"},{"author_name":"Christos Tsoutsouvas","author_inst":"Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Nathan Levinzon","author_inst":"Department of Medicinal Chemistry, College of Pharmacy, University of Utah, 2000 East 30 South Skaggs 306, Salt Lake City, Utah 84112, United States"},{"author_name":"Yagmur U. Doruk","author_inst":"Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA"},{"author_name":"Moira Rachman","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Morgan Diolaiti","author_inst":"Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA"},{"author_name":"Maisie Stevens","author_inst":"Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA"},{"author_name":"Fangyu Liu","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Katie Holland","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Harald Hubner","author_inst":"Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany"},{"author_name":"Jing Wang","author_inst":"Department of Pharmacology, NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, US"},{"author_name":"Yujin Wu","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Alan Ashworth","author_inst":"Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA"},{"author_name":"Alexander Makriyannis","author_inst":"Center for Drug Discovery, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Yuqi Zhang","author_inst":"Schrodinger Inc, San Diego, CA, USA; Schrodinger Inc, New York, NY, USA"},{"author_name":"Yurii Moroz","author_inst":"Chemspace LLC, Kyiv 02094, Ukraine. Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine. Enamine Ltd., Kyiv 02094, Ukraine"},{"author_name":"Peter Gmeiner","author_inst":"Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany"},{"author_name":"Robert Abel","author_inst":"Schrodinger Inc, San Diego, CA, USA; Schrodinger Inc, New York, NY, USA"},{"author_name":"Aashish Manglik","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Allan I. Basbaum","author_inst":"Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA"},{"author_name":"Bryan L. Roth","author_inst":"Department of Pharmacology, NIMH Psychoactive Drug Screening Program, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, US"},{"author_name":"James S. Fraser","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Brian K. Shoichet","author_inst":"Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"Spatial distribution of blood-brain barrier membrane proteins is controlled by sorting motifs and physiological signals in vivo","rel_doi":"10.64898\/2026.05.10.724157","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724157","rel_abs":"The blood-brain barrier (BBB), formed by brain endothelial cells (BECs), creates a safe and homeostatic environment for proper brain function. Together with pericytes and astrocytes, the BBB controls substance influx and efflux into and out of the brain. While BECs are extraordinarily thin, their luminal and abluminal plasma membranes face markedly different environments: the blood and brain parenchyma, respectively. How BBB membrane proteins are spatially distributed between these membranes and the factors regulating their localization are not clear. Here, we establish a method for measuring polarized protein sorting at the BBB in vivo. We characterized the distribution of transporters and receptors on the luminal and abluminal plasma membranes and identified protein-intrinsic motifs that control polarized sorting. Finally, we observed a change in membrane protein distribution that aligns with circadian rhythms, revealing an under-appreciated dimension of BBB dynamics. This method can be broadly used for evaluating membrane protein landscape changes during development and disease and for choosing optimal molecular targets for BBB-crossing therapeutics.","rel_num_authors":5,"rel_authors":[{"author_name":"Joseph Amick","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School"},{"author_name":"Shamika Bhandarkar","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School"},{"author_name":"Douglas R Wilcox","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School; Division of Neuroimmunology, Brigham and Women's Hospital, Massachusetts Ge"},{"author_name":"Solenn Boloker","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School"},{"author_name":"Chenghua Gu","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"Spatial distribution of blood-brain barrier membrane proteins is controlled by sorting motifs and physiological signals in vivo","rel_doi":"10.64898\/2026.05.10.724157","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724157","rel_abs":"The blood-brain barrier (BBB), formed by brain endothelial cells (BECs), creates a safe and homeostatic environment for proper brain function. Together with pericytes and astrocytes, the BBB controls substance influx and efflux into and out of the brain. While BECs are extraordinarily thin, their luminal and abluminal plasma membranes face markedly different environments: the blood and brain parenchyma, respectively. How BBB membrane proteins are spatially distributed between these membranes and the factors regulating their localization are not clear. Here, we establish a method for measuring polarized protein sorting at the BBB in vivo. We characterized the distribution of transporters and receptors on the luminal and abluminal plasma membranes and identified protein-intrinsic motifs that control polarized sorting. Finally, we observed a change in membrane protein distribution that aligns with circadian rhythms, revealing an under-appreciated dimension of BBB dynamics. This method can be broadly used for evaluating membrane protein landscape changes during development and disease and for choosing optimal molecular targets for BBB-crossing therapeutics.","rel_num_authors":5,"rel_authors":[{"author_name":"Joseph Amick","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School"},{"author_name":"Shamika Bhandarkar","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School"},{"author_name":"Douglas R Wilcox","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School; Division of Neuroimmunology, Brigham and Women's Hospital, Massachusetts Ge"},{"author_name":"Solenn Boloker","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School"},{"author_name":"Chenghua Gu","author_inst":"Department of Neurobiology, Howard Hughes Medical Institute, Harvard Medical School"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"CXCR4 antagonism restores dendritic cell migration and activation in a WHIM syndrome mouse model","rel_doi":"10.64898\/2026.05.10.724115","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.724115","rel_abs":"WHIM (warts, hypogammaglobulinemia, infections, and myelokathexis) syndrome is a primary immunodeficiency caused by gain-of-function in CXCR4 chemokine receptor (CXCR4GOF) in response to its chemokine ligand CXCL12. The patients suffering from this syndrome display lymphopenia and neutropenia, and most of them show exacerbated susceptibility to human papillomavirus pathogenesis. In a mouse model harboring a WHIM-associated CXCR4 mutation and expressing HPV16 oncoproteins in keratinocytes, we previously reported reduced circulating plasmacytoid dendritic cells (pDCs), mirroring patients' blood, and impaired dendritic cell (DC) trafficking from the skin to lymphoid organs, with the few migrating DCs displaying an overactivated phenotype. Given the promising results of CXCR4-targeted therapies in WHIM patients, we investigated whether and how the orally available CXCR4-specific antagonist, X4-136, affects DC localization, activation, and trafficking at the subset level, as well as skin immune landscape. CXCR4GOF inhibition corrected defects in circulating myeloid cells and pDCs, as well as in lymph node-resident DCs. Furthermore, it rescued skin DC migration to lymph nodes in WHIM mice, in a context- and subset-dependent manner, by promoting their activation and relocation within the dermis. Taken together, these findings indicate that inhibiting CXCR4GOF may restore skin immunity in WHIM syndrome by rescuing DC counts and functions.","rel_num_authors":23,"rel_authors":[{"author_name":"Alexie OUCHAKOFF","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Mathilde PUEL","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France; Laboratory Department, Pa"},{"author_name":"Agnieszka JARACZ-ROS","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Molene DOCQ","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Morgan OCIMEK","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Francoise MERCIER-NOME","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France; UMS-IPSIT, UFR de Pharmac"},{"author_name":"Yuna DELARUE","author_inst":"LBAI, UMR1227, Univ Brest, Inserm, Brest, France"},{"author_name":"Sophie SERVAIN-VIEL","author_inst":"UMS-IPSIT, UFR de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Gabriela CUESTA-MARGOLLES","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Anvi Laetitia NGUYEN","author_inst":"6Universite Paris-Saclay, CEA, INRAE, Departement Medicaments et Technologies pour La Sante (DMTS), MetaboHUB, F-91191, Gif-sur-Yvette, France"},{"author_name":"Aurelie MESSAGER","author_inst":"Universite Paris-Saclay, CEA, INRAE, Departement Medicaments et Technologies pour La Sante (DMTS), MetaboHUB, F-91191, Gif-sur-Yvette, France"},{"author_name":"Alain PRUVOST","author_inst":"Universite Paris-Saclay, CEA, INRAE, Departement Medicaments et Technologies pour La Sante (DMTS), MetaboHUB, F-91191, Gif-sur-Yvette, France"},{"author_name":"Khadidiatou KOUYATE","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Katarina ZMAJKOVICOVA","author_inst":"X4 Pharmaceuticals, Boston, Massachusetts, USA"},{"author_name":"Lukas DILLINGER","author_inst":"X4 Pharmaceuticals (Austria) GmbH, Vienna, Austria"},{"author_name":"Sandra ZEHENTMEIER","author_inst":"X4 Pharmaceuticals (Austria) GmbH, Vienna, Austria"},{"author_name":"Chi Huu NGUYEN","author_inst":"X4 Pharmaceuticals, Boston, Massachusetts, USA"},{"author_name":"Robert JOHNSON","author_inst":"X4 Pharmaceuticals, Boston, Massachusetts, USA"},{"author_name":"Art TAVERAS","author_inst":"X4 Pharmaceuticals, Boston, Massachusetts, USA"},{"author_name":"Claire DEBACK","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Patrice HEMON","author_inst":"LBAI, UMR1227, Univ Brest, Inserm, Brest, France"},{"author_name":"Francoise BACHELERIE","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"},{"author_name":"Geraldine SCHLECHT-LOUF","author_inst":"INSERM UMR-996, Inserm, Inflammation, Microbiome and Immunosurveillance, Faculte de Pharmacie, Universite Paris-Saclay, Orsay, France"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"GluDs are ionotropic dopamine receptors tuned by G-proteins","rel_doi":"10.64898\/2026.05.10.723887","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.10.723887","rel_abs":"Dopamine is a neurotransmitter essential for cognition, and its dysregulation is associated with neurological diseases. Historically, dopamine has been understood to signal exclusively through metabotropic receptors. Delta-type ionotropic glutamate receptors (GluDs), which have recently been established as ligand-gated ion channels, are fundamental for synaptic maintenance, are implicated in neurological disorders, and co-localize with dopaminergic machinery. Here, we report that dopamine is a direct agonist of GluDs, eliciting ionotropic activity, as visualized by cryo-electron microscopy (cryo-EM), bilayer recordings, mutagenesis, and patch clamp recordings. Dopamine binds to the GluD ligand binding domain, inducing clamshell closure and channel activation through a distinct molecular interface. GluD channel activity is tightly regulated by G-proteins, which act as molecular switches to tune GluD activity: free G{beta}{gamma} inhibits ligand-gating, while G or inactive G-protein heterotrimers enable dopamine-induced GluD currents. This tuning of GluD activity by G-proteins is uncoupled in a point mutation associated with neurodegeneration. These findings expand mechanisms of neuronal dopaminergic signaling, uncover how G-proteins tune GluD channel activity, and provide a framework for targeting GluDs in neurological diseases.","rel_num_authors":11,"rel_authors":[{"author_name":"Haobo Wang","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Mae G Weaver","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Elisa Carrillo","author_inst":"University of Texas Health Science Center"},{"author_name":"Iris Zheng","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Weiqi Gu","author_inst":"University of Texas Health Science Center"},{"author_name":"Jeffrey Khau","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Anish Kumar Mondal","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Anthony Yanez","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Evan S. O'Brien","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Vasanthi Jayaraman","author_inst":"University of Texas Health Science Center"},{"author_name":"Edward C Twomey","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-05-13","rel_site":"biorxiv"},{"rel_title":"An Automated CT-derived Marker of Renal Tumor Complexity: The CLARITY Score","rel_doi":"10.64898\/2026.05.08.26352647","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352647","rel_abs":"Background and ObjectiveSurgical complexity for renal tumors has traditionally been assessed using manual nephrometry scores, which require unreimbursed physician effort and are subject to interobserver variability. This study introduces an objective, fully automated alternative derived from decades of experience at a large academic center.\n\nMethodsWe trained a CT classification model to predict whether a patient would ultimately undergo Partial or Radical Nephrectomy (PN or RN). We hypothesized that the models confidence in RN (termed the CLARITY score) would serve as a surrogate for the difficulty of nephron-sparing approaches and thus for tumor complexity. This hypothesis was tested using multivariate logistic regression for failure to achieve trifecta, estimated blood loss (EBL) [&ge;] 500 mL, and length of stay [&ge;] 3 d. CLARITY was compared with tumor size and R.E.N.A.L. score. External validation in a geographically distinct cohort was performed.\n\nKey Findings and LimitationsFor predicting RN, CLARITY achieved an AUROC of 0.899 internally and 0.898 externally. In the external PN subgroup, it outperformed tumor size and R.E.N.A.L. score in predicting failure to achieve trifecta (AUROC 0.613), EBL [&ge;] 500 mL (0.727), and length of stay [&ge;] 3 d (0.673). In multivariable analysis, CLARITY remained associated with each outcome, whereas R.E.N.A.L. and size were not. This study is limited by its retrospective design.\n\nConclusions and Clinical ImplicationsCLARITY is an automated CT-derived marker that quantifies renal tumor complexity more effectively than tumor size and R.E.N.A.L. score and may support scalable, objective preoperative complexity assessment. To support reproducibility and external validation, we have released a public inference pipeline and web-based DICOM upload portal for research use.","rel_num_authors":16,"rel_authors":[{"author_name":"Rishi Jonnalagadda","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Sahil H Patel","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Haya T Abusafieh","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Rikhil Seshadri","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Daniel Jevnikar","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Salim Younis","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Abdulrahman Al-Bayati","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Nicolas Saputro","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Jacob Knorr","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Betty Wang","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Michal Ozery-Flato","author_inst":"IBM Research"},{"author_name":"Michal Rosen-Zvi","author_inst":"IBM"},{"author_name":"Robert Abouassaly","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Erick Remer","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Nicholas Heller","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"},{"author_name":"Christopher Weight","author_inst":"Cleveland Clinic Glickman Urological and Kidney Institute"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Monocyte Oxidative Stress Underlies Persistent Immune Activation in Long-COVID Postural Orthostatic Tachycardia Syndrome","rel_doi":"10.64898\/2026.05.08.26352776","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352776","rel_abs":"BackgroundLong COVID postural orthostatic tachycardia syndrome (LCPOTS) is characterized by persistent orthostatic tachycardia and multiple constitutional symptoms, many of which suggest persistent inflammation. We sought to define mechanisms responsible for ongoing immune activation in LCPOTs and to determine if this is related to autonomic dysregulation.\n\nMethodsWe performed a case-control study of 25 patients with LCPOTS and 15 controls who recovered from COVID-19 without persistent autonomic sequelae. Peripheral blood mononuclear cells were analyzed by flow cytometry to quantify circulating CD3CD14 T cell-monocyte doublets, cytokine production, memory phenotype, mitochondrial ROS, and isolevuglandin (IsoLG)-adduct formation. Forster resonance energy transfer was used to assess T-cell receptor-HLA interactions within doublets. Single-cell RNA sequencing (scRNA-seq) was performed on a subset of participants, and autonomic phenotyping included orthostatic heart rate responses, heart rate variability, baroreflex sensitivity, and blood volume measurements.\n\nResultsLCPOTS was linked to impaired cardiovagal function and greater autonomic symptom burden. It was also associated with roughly a threefold rise in circulating CD3CD14 doublets and enhanced T cell-monocyte interactions. These complexes demonstrated signs of genuine immune synapse formation and were enriched with effector-memory and TEMRA T-cell types. T cells in doublets produced higher levels of IFN-{gamma} and IL-17A, and the proportion of cytokine-producing doublets correlated with the severity of orthostatic tachycardia and total COMPASS-31 score. Monocytes from LCPOTS showed increased mitochondrial content, superoxide generation, and IsoLG-adduct accumulation, along with decreased expression of antioxidant genes, including those related to NFE2L2.\n\nConclusionsOur findings suggest that ongoing immune activation contributes to LCPOTS pathogenesis. We propose that impaired cardiovagal regulation stimulates monocyte ROS production, promotes neoantigen formation, and T cell activation. This persistent immune response, together with disrupted mitochondrial function, likely contributes to the diverse symptoms linked to LCPOTS.\n\nNovelty and SignificanceO_ST_ABSWhat Is Known?C_ST_ABSO_LILong COVID postural orthostatic tachycardia syndrome is associated with persistent orthostatic tachycardia and disabling orthostatic intolerance symptoms after SARS-CoV-2 infection.\nC_LIO_LIImmune dysregulation and oxidative stress have been implicated in long COVID, but the cellular mechanisms linking inflammation to autonomic dysfunction are not well defined.\nC_LIO_LICirculating T cell: monocyte doublets are a recently recognized marker of ongoing immune activation.\nC_LI\n\nWhat New Information Does This Article Contribute?O_LIPatients with LCPOTS exhibit a marked increase in circulating CD3CD14 T cell-monocyte doublets.\nC_LIO_LIDoublet-associated T cells are enriched for inflammatory effector-memory\/TEMRA phenotypes and produce IFN-{gamma} and IL-17A in proportion to orthostatic tachycardia and autonomic symptoms severity.\nC_LIO_LIImpaired cardiovagal activity, monocyte mitochondrial ROS, IsoLG-adduct formation, and suppression of antioxidant pathways identify a mechanistic axis linking oxidative injury to persistent immune activation in LCPOTS.\nC_LI\n\nSummary of Novelty and SignificanceThis study identifies a mechanistic link between impaired cardiovagal function, mitochondrial oxidative stress, and persistent immune activation in LCPOTS. We show that circulating CD3CD14 T cell-monocyte doublets are expanded in LCPOTS and form true immune synapses, as demonstrated by T-cell receptor-HLA proximity. These are enriched in inflammatory effector-memory\/TEMRA T cells and are associated with increased IFN-{gamma} and IL-17A production that correlate with orthostatic tachycardia severity and symptom burden. We further identified increased mitochondrial ROS, accumulation of IsoLG adducts, and reduced antioxidant gene expression in monocytes, suggesting that oxidation-induced neoantigen formation sustains pathogenic T-cell engagement. Together, these findings move LCPOTS beyond a descriptive post-viral syndrome and define a biologically plausible immune mechanism with diagnostic and therapeutic implications.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC=\"FIGDIR\/small\/26352776v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (68K):\norg.highwire.dtl.DTLVardef@1e5770forg.highwire.dtl.DTLVardef@1eb80e9org.highwire.dtl.DTLVardef@5c6c0aorg.highwire.dtl.DTLVardef@1b909d6_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":14,"rel_authors":[{"author_name":"Marwa Ahmed Mohamed","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Meenakshi Golchha","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Yullya A. Vance","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Sachin Y. Paranjape","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Celestine Wanjalla","author_inst":"Vanderbilt Health Services LLC"},{"author_name":"Kuniko C Hunter","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Sergey Dikalov","author_inst":"Vanderbilt University School of Medicine"},{"author_name":"Andr\u00e9 Diedrich","author_inst":"Vanderbilt University"},{"author_name":"Surat Kulapatana","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Pouya E Mehr","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Tatoama X Solis Montegegro","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Joshua D Simmons","author_inst":"Vanderbilt University Medical Center"},{"author_name":"David G. Harrison","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Cyndya A Shibao","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Quantifying the contributions of asymptomatic and symptomatic colonized patients to Clostridioides difficile acquisition in oncological units","rel_doi":"10.64898\/2026.05.08.26352751","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352751","rel_abs":"ObjectiveLeukemic and hematopoietic cell transplant patients have one of the highest incidences of C. difficile infection (CDI). While CDI patients are considered the primary source of transmission, asymptomatic colonized patients (AC) can progress to CDI or contribute to in-unit transmission. We aim to quantify the roles of CDI and AC patients in C. difficile importation and transmission within oncological units.\n\nDesignProspective cohort study\n\nSettingTwo leukemia and HCT transplant units in a large tertiary care hospital in the US\n\nMethodsWe developed a stochastic, individual-based network model to simulate C. difficile acquisition and transmission. Data from cultures and nucleic acid amplification testing (NAAT) obtained at admission and weekly, and toxin enzyme immunoassay (EIA) tests used for CDI diagnosis were used to calibrate the model. Healthcare worker room assignments informed the network structure. Key parameters were estimated via particle filtering.\n\nResultsThe model reproduced observed weekly test counts and transmission pairs. AC patients were the primary source of new colonizations: 51% were due to importation (of those, 88% were admitted as AC), and 49% were due to transmission (AC was the source in 92% of transmissions). Sensitivity analysis showed that these findings were most influenced by the colonization rate and rates of environmental contamination and cleaning.\n\nConclusionsThese findings reinforce the role of AC, particularly via admission importation, in sustaining C. difficile transmission in high-risk hospital settings. Infection control focused on CDI effectively reduced onward transmission, as indicated by CDIs low contribution to new colonizations.","rel_num_authors":13,"rel_authors":[{"author_name":"Curtis Savannah","author_inst":"Department of Mathematics, North Carolina State University, Raleigh, NC, USA"},{"author_name":"Mary M. Lee","author_inst":"Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA"},{"author_name":"Tiffany Hink","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Kimberly A. Reske","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Emily Struttmann","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Zainab Hassan Iqbal","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Candice Cass","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Margaret A. Olsen","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Sankalp Arya","author_inst":"Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA"},{"author_name":"Carey-Ann Burnham","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Suzanne Lenhart","author_inst":"Department of Mathematics, University of Tennessee, Knoxville, TN, USA"},{"author_name":"Erik R. Dubberke","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Cristina Lanzas","author_inst":"Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Quantifying the contributions of asymptomatic and symptomatic colonized patients to Clostridioides difficile acquisition in oncological units","rel_doi":"10.64898\/2026.05.08.26352751","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352751","rel_abs":"ObjectiveLeukemic and hematopoietic cell transplant patients have one of the highest incidences of C. difficile infection (CDI). While CDI patients are considered the primary source of transmission, asymptomatic colonized patients (AC) can progress to CDI or contribute to in-unit transmission. We aim to quantify the roles of CDI and AC patients in C. difficile importation and transmission within oncological units.\n\nDesignProspective cohort study\n\nSettingTwo leukemia and HCT transplant units in a large tertiary care hospital in the US\n\nMethodsWe developed a stochastic, individual-based network model to simulate C. difficile acquisition and transmission. Data from cultures and nucleic acid amplification testing (NAAT) obtained at admission and weekly, and toxin enzyme immunoassay (EIA) tests used for CDI diagnosis were used to calibrate the model. Healthcare worker room assignments informed the network structure. Key parameters were estimated via particle filtering.\n\nResultsThe model reproduced observed weekly test counts and transmission pairs. AC patients were the primary source of new colonizations: 51% were due to importation (of those, 88% were admitted as AC), and 49% were due to transmission (AC was the source in 92% of transmissions). Sensitivity analysis showed that these findings were most influenced by the colonization rate and rates of environmental contamination and cleaning.\n\nConclusionsThese findings reinforce the role of AC, particularly via admission importation, in sustaining C. difficile transmission in high-risk hospital settings. Infection control focused on CDI effectively reduced onward transmission, as indicated by CDIs low contribution to new colonizations.","rel_num_authors":13,"rel_authors":[{"author_name":"Curtis Savannah","author_inst":"Department of Mathematics, North Carolina State University, Raleigh, NC, USA"},{"author_name":"Mary M. Lee","author_inst":"Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA"},{"author_name":"Tiffany Hink","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Kimberly A. Reske","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Emily Struttmann","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Zainab Hassan Iqbal","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Candice Cass","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Margaret A. Olsen","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Sankalp Arya","author_inst":"Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA"},{"author_name":"Carey-Ann Burnham","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Suzanne Lenhart","author_inst":"Department of Mathematics, University of Tennessee, Knoxville, TN, USA"},{"author_name":"Erik R. Dubberke","author_inst":"Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Cristina Lanzas","author_inst":"Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Exome sequencing directly implicates 68 genes in inflammatory bowel disease","rel_doi":"10.64898\/2026.05.08.26352648","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352648","rel_abs":"Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide association studies (GWAS) lie in non-coding regions, limiting direct gene assignment and biological interpretation1,2. Here we analysed whole-exome and whole-genome sequencing data from 86,213 cases and 478,363 controls to define the contribution of protein-altering variation to IBD susceptibility. We identify 68 genes directly implicated by coding variation, including genes supported by single-variant associations and ultra-rare mutational burden. 57 of these genes lie within regions previously highlighted by GWAS, indicating convergence of regulatory and protein-altering evidence in IBD. The implicated genes point to coherent biological themes, including post-transcriptional control of inflammatory programmes, epithelial restitution, and calibrated immune pathway signalling, and nominate targets with therapeutic relevance. These results show that large-scale sequencing can resolve disease genes and pathways that remain ambiguous from non-coding association alone, providing a more direct route from human genetics to biological insight and therapeutic hypotheses.","rel_num_authors":80,"rel_authors":[{"author_name":"Ruifei Zhu","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Qian Zhang","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"},{"author_name":"Kai Yuan","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Rui Zhang","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Alexandra K Turvey","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Christine R Stevens","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Laura Fachal","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"},{"author_name":"- IIBDGC Sequencing Group","author_inst":""},{"author_name":"Tariq Ahmad","author_inst":"Royal Devon & Exeter NHS Foundation Trust Royal Devon and Exeter Hospital, UK; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Con"},{"author_name":"Klaartje Bel Kok","author_inst":"Queen Mary University, Innovation Centre, London , E1 2EF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Charles N Bernstein","author_inst":"University of Manitoba, Winnipeg, Manitoba, Canada; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Bernd Bokemeyer","author_inst":"Department of Internal Medicine, University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Steve R Brant","author_inst":"Crohns Colitis Center of New Jersey, Department of Medicine, Rutgers Robert Wood Johnson Medical School and Department of Genetics and the Human Genetics Instit"},{"author_name":"Johanne Brooks","author_inst":"Lister Hospital, Stevenage SG1 4AB, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Jeffrey Butterworth","author_inst":"ROYAL SHREWSBURY HOSPITAL, SHREWSBURY, SHROPSHIRE, SY3 8XQ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Judy H Cho","author_inst":"Pathology, Molecular & Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; NIDDK IBD Genetics Consortium; International Inflammator"},{"author_name":"Katie Clark","author_inst":"Whistion Hospital, Prescot L35 5DR, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Fraser Cummings","author_inst":"Southampton General Hospital, Southampton, SO166YD, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Richard H Duerr","author_inst":"University of Pittsburgh, Pittsburgh, PA, USA; NIDDK IBD Genetics Consortium; SPARC IBD Network; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Sarah Ennis","author_inst":"Human Genetics and Genomic Medicine, University of Southampton, UK; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Martti Farkkila","author_inst":"Helsinki University Central Hospital, Helsinki, Finland; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"William A Faubion","author_inst":"Mayo Clinic in Arizona, Phoenix, AZ, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Stephen Foley","author_inst":"Kings Mill Hospital, Sutton in Ashfield, NG174JL, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Denis Franchimont","author_inst":"Erasme Hospital, ULB, Brussels, Belgium; Belgium IBD Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Andre Franke","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Laura Hancock","author_inst":"Wythenshawe Hospital, Manchester, M23 9LT, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Ailsa Hart","author_inst":"St Marks Hospital, Watford Road, Harrow, Middlesex, UK; NIHR IBD BioResource; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Cons"},{"author_name":"Patricia Hooper","author_inst":"Leicester General Hospital, Leicester LE5 4PW, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Peter Irving","author_inst":"Department of Gastroenterology, Guy's & St. Thomas' Hospitals NHS Foundation Trust, London SE1 7EH, UK; NIHR IBD BioResource; International Inflammatory Bowel D"},{"author_name":"Mark Jarvis","author_inst":"Basildon Hospital, Nethermayne , Basildon , Essex, SS16 5NL, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Emma Johnston","author_inst":"West Middlesex University Hospital, Isleworth, TW7 6AF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Elizabeth W Karlson","author_inst":"Mass General Brigham Personalized Medicine, Mass General Brigham HealthCare, Boston, MA, USA; Department of Medicines, Brigham and Women's Hospital and Beth Isr"},{"author_name":"Cheryl Kemp","author_inst":"Watford General Hospital, Watford WD18 0HB, UK"},{"author_name":"Nick Kennedy","author_inst":"Royal Devon & Exeter NHS Foundation Trust Royal Devon and Exeter Hospital, UK; UK IBD Genetics Consortium; NIHR IBD BioResource; International Inflammatory Bowe"},{"author_name":"Juozas Kupcinskas","author_inst":"Department of Gastroenterology and Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, Lithuania; International Inflammatory Bow"},{"author_name":"Chris Lamb","author_inst":"Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK; Department of Gastroenterology, Newcastle upon Tyne Hospi"},{"author_name":"Charlie Lees","author_inst":"Great Western Hospitals NHS Foundation Trust Western General Hospital, Edinburgh, UK; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genet"},{"author_name":"James Lewis","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Crohns and Colitis Foundation, New York, NY, USA; SPARC IBD Network; Internation"},{"author_name":"Andy Li","author_inst":"Worthing Hospital, Western Sussex Hospitals NHS Foundation Trust, Worthing Bn11 2DH, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics"},{"author_name":"Jimmy Limdi","author_inst":"Fairfield General Hospital, Bury, BL9 7TD, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Britt-Sabina Loescher","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Edouard Louis","author_inst":"Department of Gastroenterology, Liege University Hospital, CHU Liege, Belgium; University of Liege, ULiege, Liege, Belgium; Belgium IBD Consortium; Internationa"},{"author_name":"Jacob L McCauley","author_inst":"John P. Hussman Institute for Human Genomics, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, USA; The Dr. John T. Macdonald Foundation De"},{"author_name":"Dermot McGovern","author_inst":"F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA; NIDDK IBD Genetics Consortium;"},{"author_name":"John McLaughlin","author_inst":"Department of Gastroenterology, Salford Royal NHS Foundation Trust Brooke Building, Salford Royal Hospital, Salford, M6 8HD, UK; NIHR IBD BioResource; Internati"},{"author_name":"Paul Moayyedi","author_inst":"McMaster University, Hamilton, Ontario, Canada; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Gordon Moran","author_inst":"Queen's Medical Centre Campus, Nottingham NG7 2UH, UK; NIHR Nottingham Biomedical Research Centre, Nottingham, UK; Translational Medical Sciences, School of Med"},{"author_name":"Rodney Newberry","author_inst":"Washington University School of Medicine, St. Louis, MO, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Arabis Oglesby","author_inst":"Dorset County Hospital, Dorset DT1 2JY, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Joel Pekow","author_inst":"Section of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Chicago, Chicago, Illinois, USA; Department of Gastroenterology, Un"},{"author_name":"Kate J Perez","author_inst":"Cambridge University Hospitals NHS Foundation Trust, Patient Representative, Cambridge, UK"},{"author_name":"Richard Pollok","author_inst":"Dept Gastroenterology, St Georges Foundation Trust NHS Hospitals, 2nd Floor Gosvenor Wing, London SW17 0QT, UK; NIHR IBD BioResource; International Inflammatory"},{"author_name":"Natalie Prescott","author_inst":"Department of Medical and Molecular Genetics, Guys Hospital, London, SE1 9RT; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Cons"},{"author_name":"Tim Raine","author_inst":"Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Arvind Ramadas","author_inst":"James Cook University Hospital, Middlesbrough TS4 3BW, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Subramaniam Ramakrishnan","author_inst":"Warrington & Halton hospitals NHS Foundation Trust, Warrington, WA5 1QG, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Ksenija Sabic","author_inst":"Pathology, Molecular & Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; NIDDK IBD Genetics Consortium; International Inflammator"},{"author_name":"Bruce Sands","author_inst":"Icahn School of Medicine at Mount Sinai, New York, NY, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Jack Satsangi","author_inst":"Translational Gastroenterology Unit, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DS; NIHR IBD BioResource; UK IBD Genetics Consortium; Interna"},{"author_name":"Aleksejs Sazonovs","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK"},{"author_name":"Stefan Schreiber","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Christian Selinger","author_inst":"St. Jamess University Hospital, Leeds LS9 7TF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Sophy Shedwell","author_inst":"Queen Elizabeth Hospital, King's Lynn PE30 4ET, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Mark Silverberg","author_inst":"Mount Sinai Hospital, Toronto, Ontario, Canada; NIDDK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Salil Singh","author_inst":"Royal Bolton Hospital, Bolton, BL4 0JR, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Harry Sokol","author_inst":"Department of Gastroenterology, Sorbonne Universite, INSERM, Centre de Recherche Saint-Antoine, CRSA, AP-HP, Saint-Antoine Hospital, Paris, France.; Internation"},{"author_name":"Helen Steed","author_inst":"New Cross Hospital of The Royal Wolverhampton NHS Trust, Wolverhampton, WV10 0QP, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Co"},{"author_name":"Alan Steel","author_inst":"The Royal Liverpool University Hospital, Liverpool L7 8YE, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Holm Uhlig","author_inst":"Translational Gastroenterology Unit and Biomedical Research Centre, Nuffield Department of Clinical Medicine, Experimental Medicine Division, University of Oxfo"},{"author_name":"Ajay Verma","author_inst":"Kettering General Hospital NHS Foundation Trust, Kettering, NN16 8UZ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Severine Vermeire","author_inst":"University Hospitals Leuven, Leuven, Belgium; Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium; Belgium IBD Consortium; International I"},{"author_name":"Rinse Weersma","author_inst":"Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands; International Inflam"},{"author_name":"Ramnik Xavier","author_inst":"Kurt Isselbacher Professor of Medicine at Harvard Medical School, Cambridge, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Ho"},{"author_name":"Mingrui Yu","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Miles Parkes","author_inst":"Department of Gastroenterology, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK; UK IBD Genetics Consortium; NIHR IBD BioResource; In"},{"author_name":"John D Rioux","author_inst":"Faculty of Medicine, Universite de Montreal, Montreal, Canada; Research Center Montreal Heart Institute, Montreal, Canada; NIDDK IBD Genetics Consortium; Intern"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, The "},{"author_name":"Carl A Anderson","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Exome sequencing directly implicates 68 genes in inflammatory bowel disease","rel_doi":"10.64898\/2026.05.08.26352648","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352648","rel_abs":"Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide association studies (GWAS) lie in non-coding regions, limiting direct gene assignment and biological interpretation1,2. Here we analysed whole-exome and whole-genome sequencing data from 86,213 cases and 478,363 controls to define the contribution of protein-altering variation to IBD susceptibility. We identify 68 genes directly implicated by coding variation, including genes supported by single-variant associations and ultra-rare mutational burden. 57 of these genes lie within regions previously highlighted by GWAS, indicating convergence of regulatory and protein-altering evidence in IBD. The implicated genes point to coherent biological themes, including post-transcriptional control of inflammatory programmes, epithelial restitution, and calibrated immune pathway signalling, and nominate targets with therapeutic relevance. These results show that large-scale sequencing can resolve disease genes and pathways that remain ambiguous from non-coding association alone, providing a more direct route from human genetics to biological insight and therapeutic hypotheses.","rel_num_authors":80,"rel_authors":[{"author_name":"Ruifei Zhu","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Qian Zhang","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"},{"author_name":"Kai Yuan","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Rui Zhang","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Alexandra K Turvey","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Christine R Stevens","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Laura Fachal","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"},{"author_name":"- IIBDGC Sequencing Group","author_inst":""},{"author_name":"Tariq Ahmad","author_inst":"Royal Devon & Exeter NHS Foundation Trust Royal Devon and Exeter Hospital, UK; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Con"},{"author_name":"Klaartje Bel Kok","author_inst":"Queen Mary University, Innovation Centre, London , E1 2EF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Charles N Bernstein","author_inst":"University of Manitoba, Winnipeg, Manitoba, Canada; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Bernd Bokemeyer","author_inst":"Department of Internal Medicine, University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Steve R Brant","author_inst":"Crohns Colitis Center of New Jersey, Department of Medicine, Rutgers Robert Wood Johnson Medical School and Department of Genetics and the Human Genetics Instit"},{"author_name":"Johanne Brooks","author_inst":"Lister Hospital, Stevenage SG1 4AB, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Jeffrey Butterworth","author_inst":"ROYAL SHREWSBURY HOSPITAL, SHREWSBURY, SHROPSHIRE, SY3 8XQ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Judy H Cho","author_inst":"Pathology, Molecular & Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; NIDDK IBD Genetics Consortium; International Inflammator"},{"author_name":"Katie Clark","author_inst":"Whistion Hospital, Prescot L35 5DR, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Fraser Cummings","author_inst":"Southampton General Hospital, Southampton, SO166YD, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Richard H Duerr","author_inst":"University of Pittsburgh, Pittsburgh, PA, USA; NIDDK IBD Genetics Consortium; SPARC IBD Network; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Sarah Ennis","author_inst":"Human Genetics and Genomic Medicine, University of Southampton, UK; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Martti Farkkila","author_inst":"Helsinki University Central Hospital, Helsinki, Finland; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"William A Faubion","author_inst":"Mayo Clinic in Arizona, Phoenix, AZ, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Stephen Foley","author_inst":"Kings Mill Hospital, Sutton in Ashfield, NG174JL, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Denis Franchimont","author_inst":"Erasme Hospital, ULB, Brussels, Belgium; Belgium IBD Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Andre Franke","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Laura Hancock","author_inst":"Wythenshawe Hospital, Manchester, M23 9LT, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Ailsa Hart","author_inst":"St Marks Hospital, Watford Road, Harrow, Middlesex, UK; NIHR IBD BioResource; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Cons"},{"author_name":"Patricia Hooper","author_inst":"Leicester General Hospital, Leicester LE5 4PW, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Peter Irving","author_inst":"Department of Gastroenterology, Guy's & St. Thomas' Hospitals NHS Foundation Trust, London SE1 7EH, UK; NIHR IBD BioResource; International Inflammatory Bowel D"},{"author_name":"Mark Jarvis","author_inst":"Basildon Hospital, Nethermayne , Basildon , Essex, SS16 5NL, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Emma Johnston","author_inst":"West Middlesex University Hospital, Isleworth, TW7 6AF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Elizabeth W Karlson","author_inst":"Mass General Brigham Personalized Medicine, Mass General Brigham HealthCare, Boston, MA, USA; Department of Medicines, Brigham and Women's Hospital and Beth Isr"},{"author_name":"Cheryl Kemp","author_inst":"Watford General Hospital, Watford WD18 0HB, UK"},{"author_name":"Nick Kennedy","author_inst":"Royal Devon & Exeter NHS Foundation Trust Royal Devon and Exeter Hospital, UK; UK IBD Genetics Consortium; NIHR IBD BioResource; International Inflammatory Bowe"},{"author_name":"Juozas Kupcinskas","author_inst":"Department of Gastroenterology and Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, Lithuania; International Inflammatory Bow"},{"author_name":"Chris Lamb","author_inst":"Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK; Department of Gastroenterology, Newcastle upon Tyne Hospi"},{"author_name":"Charlie Lees","author_inst":"Great Western Hospitals NHS Foundation Trust Western General Hospital, Edinburgh, UK; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genet"},{"author_name":"James Lewis","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Crohns and Colitis Foundation, New York, NY, USA; SPARC IBD Network; Internation"},{"author_name":"Andy Li","author_inst":"Worthing Hospital, Western Sussex Hospitals NHS Foundation Trust, Worthing Bn11 2DH, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics"},{"author_name":"Jimmy Limdi","author_inst":"Fairfield General Hospital, Bury, BL9 7TD, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Britt-Sabina Loescher","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Edouard Louis","author_inst":"Department of Gastroenterology, Liege University Hospital, CHU Liege, Belgium; University of Liege, ULiege, Liege, Belgium; Belgium IBD Consortium; Internationa"},{"author_name":"Jacob L McCauley","author_inst":"John P. Hussman Institute for Human Genomics, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, USA; The Dr. John T. Macdonald Foundation De"},{"author_name":"Dermot McGovern","author_inst":"F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA; NIDDK IBD Genetics Consortium;"},{"author_name":"John McLaughlin","author_inst":"Department of Gastroenterology, Salford Royal NHS Foundation Trust Brooke Building, Salford Royal Hospital, Salford, M6 8HD, UK; NIHR IBD BioResource; Internati"},{"author_name":"Paul Moayyedi","author_inst":"McMaster University, Hamilton, Ontario, Canada; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Gordon Moran","author_inst":"Queen's Medical Centre Campus, Nottingham NG7 2UH, UK; NIHR Nottingham Biomedical Research Centre, Nottingham, UK; Translational Medical Sciences, School of Med"},{"author_name":"Rodney Newberry","author_inst":"Washington University School of Medicine, St. Louis, MO, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Arabis Oglesby","author_inst":"Dorset County Hospital, Dorset DT1 2JY, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Joel Pekow","author_inst":"Section of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Chicago, Chicago, Illinois, USA; Department of Gastroenterology, Un"},{"author_name":"Kate J Perez","author_inst":"Cambridge University Hospitals NHS Foundation Trust, Patient Representative, Cambridge, UK"},{"author_name":"Richard Pollok","author_inst":"Dept Gastroenterology, St Georges Foundation Trust NHS Hospitals, 2nd Floor Gosvenor Wing, London SW17 0QT, UK; NIHR IBD BioResource; International Inflammatory"},{"author_name":"Natalie Prescott","author_inst":"Department of Medical and Molecular Genetics, Guys Hospital, London, SE1 9RT; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Cons"},{"author_name":"Tim Raine","author_inst":"Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Arvind Ramadas","author_inst":"James Cook University Hospital, Middlesbrough TS4 3BW, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Subramaniam Ramakrishnan","author_inst":"Warrington & Halton hospitals NHS Foundation Trust, Warrington, WA5 1QG, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Ksenija Sabic","author_inst":"Pathology, Molecular & Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; NIDDK IBD Genetics Consortium; International Inflammator"},{"author_name":"Bruce Sands","author_inst":"Icahn School of Medicine at Mount Sinai, New York, NY, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Jack Satsangi","author_inst":"Translational Gastroenterology Unit, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DS; NIHR IBD BioResource; UK IBD Genetics Consortium; Interna"},{"author_name":"Aleksejs Sazonovs","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK"},{"author_name":"Stefan Schreiber","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Christian Selinger","author_inst":"St. Jamess University Hospital, Leeds LS9 7TF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Sophy Shedwell","author_inst":"Queen Elizabeth Hospital, King's Lynn PE30 4ET, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Mark Silverberg","author_inst":"Mount Sinai Hospital, Toronto, Ontario, Canada; NIDDK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Salil Singh","author_inst":"Royal Bolton Hospital, Bolton, BL4 0JR, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Harry Sokol","author_inst":"Department of Gastroenterology, Sorbonne Universite, INSERM, Centre de Recherche Saint-Antoine, CRSA, AP-HP, Saint-Antoine Hospital, Paris, France.; Internation"},{"author_name":"Helen Steed","author_inst":"New Cross Hospital of The Royal Wolverhampton NHS Trust, Wolverhampton, WV10 0QP, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Co"},{"author_name":"Alan Steel","author_inst":"The Royal Liverpool University Hospital, Liverpool L7 8YE, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Holm Uhlig","author_inst":"Translational Gastroenterology Unit and Biomedical Research Centre, Nuffield Department of Clinical Medicine, Experimental Medicine Division, University of Oxfo"},{"author_name":"Ajay Verma","author_inst":"Kettering General Hospital NHS Foundation Trust, Kettering, NN16 8UZ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Severine Vermeire","author_inst":"University Hospitals Leuven, Leuven, Belgium; Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium; Belgium IBD Consortium; International I"},{"author_name":"Rinse Weersma","author_inst":"Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands; International Inflam"},{"author_name":"Ramnik Xavier","author_inst":"Kurt Isselbacher Professor of Medicine at Harvard Medical School, Cambridge, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Ho"},{"author_name":"Mingrui Yu","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Miles Parkes","author_inst":"Department of Gastroenterology, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK; UK IBD Genetics Consortium; NIHR IBD BioResource; In"},{"author_name":"John D Rioux","author_inst":"Faculty of Medicine, Universite de Montreal, Montreal, Canada; Research Center Montreal Heart Institute, Montreal, Canada; NIDDK IBD Genetics Consortium; Intern"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, The "},{"author_name":"Carl A Anderson","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Exome sequencing directly implicates 68 genes in inflammatory bowel disease","rel_doi":"10.64898\/2026.05.08.26352648","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352648","rel_abs":"Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide association studies (GWAS) lie in non-coding regions, limiting direct gene assignment and biological interpretation1,2. Here we analysed whole-exome and whole-genome sequencing data from 86,213 cases and 478,363 controls to define the contribution of protein-altering variation to IBD susceptibility. We identify 68 genes directly implicated by coding variation, including genes supported by single-variant associations and ultra-rare mutational burden. 57 of these genes lie within regions previously highlighted by GWAS, indicating convergence of regulatory and protein-altering evidence in IBD. The implicated genes point to coherent biological themes, including post-transcriptional control of inflammatory programmes, epithelial restitution, and calibrated immune pathway signalling, and nominate targets with therapeutic relevance. These results show that large-scale sequencing can resolve disease genes and pathways that remain ambiguous from non-coding association alone, providing a more direct route from human genetics to biological insight and therapeutic hypotheses.","rel_num_authors":80,"rel_authors":[{"author_name":"Ruifei Zhu","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Qian Zhang","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"},{"author_name":"Kai Yuan","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Rui Zhang","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Alexandra K Turvey","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Christine R Stevens","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Laura Fachal","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"},{"author_name":"- IIBDGC Sequencing Group","author_inst":""},{"author_name":"Tariq Ahmad","author_inst":"Royal Devon & Exeter NHS Foundation Trust Royal Devon and Exeter Hospital, UK; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Con"},{"author_name":"Klaartje Bel Kok","author_inst":"Queen Mary University, Innovation Centre, London , E1 2EF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Charles N Bernstein","author_inst":"University of Manitoba, Winnipeg, Manitoba, Canada; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Bernd Bokemeyer","author_inst":"Department of Internal Medicine, University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Steve R Brant","author_inst":"Crohns Colitis Center of New Jersey, Department of Medicine, Rutgers Robert Wood Johnson Medical School and Department of Genetics and the Human Genetics Instit"},{"author_name":"Johanne Brooks","author_inst":"Lister Hospital, Stevenage SG1 4AB, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Jeffrey Butterworth","author_inst":"ROYAL SHREWSBURY HOSPITAL, SHREWSBURY, SHROPSHIRE, SY3 8XQ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Judy H Cho","author_inst":"Pathology, Molecular & Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; NIDDK IBD Genetics Consortium; International Inflammator"},{"author_name":"Katie Clark","author_inst":"Whistion Hospital, Prescot L35 5DR, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Fraser Cummings","author_inst":"Southampton General Hospital, Southampton, SO166YD, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Richard H Duerr","author_inst":"University of Pittsburgh, Pittsburgh, PA, USA; NIDDK IBD Genetics Consortium; SPARC IBD Network; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Sarah Ennis","author_inst":"Human Genetics and Genomic Medicine, University of Southampton, UK; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Martti Farkkila","author_inst":"Helsinki University Central Hospital, Helsinki, Finland; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"William A Faubion","author_inst":"Mayo Clinic in Arizona, Phoenix, AZ, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Stephen Foley","author_inst":"Kings Mill Hospital, Sutton in Ashfield, NG174JL, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Denis Franchimont","author_inst":"Erasme Hospital, ULB, Brussels, Belgium; Belgium IBD Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Andre Franke","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Laura Hancock","author_inst":"Wythenshawe Hospital, Manchester, M23 9LT, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Ailsa Hart","author_inst":"St Marks Hospital, Watford Road, Harrow, Middlesex, UK; NIHR IBD BioResource; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Cons"},{"author_name":"Patricia Hooper","author_inst":"Leicester General Hospital, Leicester LE5 4PW, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Peter Irving","author_inst":"Department of Gastroenterology, Guy's & St. Thomas' Hospitals NHS Foundation Trust, London SE1 7EH, UK; NIHR IBD BioResource; International Inflammatory Bowel D"},{"author_name":"Mark Jarvis","author_inst":"Basildon Hospital, Nethermayne , Basildon , Essex, SS16 5NL, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Emma Johnston","author_inst":"West Middlesex University Hospital, Isleworth, TW7 6AF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Elizabeth W Karlson","author_inst":"Mass General Brigham Personalized Medicine, Mass General Brigham HealthCare, Boston, MA, USA; Department of Medicines, Brigham and Women's Hospital and Beth Isr"},{"author_name":"Cheryl Kemp","author_inst":"Watford General Hospital, Watford WD18 0HB, UK"},{"author_name":"Nick Kennedy","author_inst":"Royal Devon & Exeter NHS Foundation Trust Royal Devon and Exeter Hospital, UK; UK IBD Genetics Consortium; NIHR IBD BioResource; International Inflammatory Bowe"},{"author_name":"Juozas Kupcinskas","author_inst":"Department of Gastroenterology and Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, Lithuania; International Inflammatory Bow"},{"author_name":"Chris Lamb","author_inst":"Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK; Department of Gastroenterology, Newcastle upon Tyne Hospi"},{"author_name":"Charlie Lees","author_inst":"Great Western Hospitals NHS Foundation Trust Western General Hospital, Edinburgh, UK; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genet"},{"author_name":"James Lewis","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Crohns and Colitis Foundation, New York, NY, USA; SPARC IBD Network; Internation"},{"author_name":"Andy Li","author_inst":"Worthing Hospital, Western Sussex Hospitals NHS Foundation Trust, Worthing Bn11 2DH, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics"},{"author_name":"Jimmy Limdi","author_inst":"Fairfield General Hospital, Bury, BL9 7TD, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Britt-Sabina Loescher","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Edouard Louis","author_inst":"Department of Gastroenterology, Liege University Hospital, CHU Liege, Belgium; University of Liege, ULiege, Liege, Belgium; Belgium IBD Consortium; Internationa"},{"author_name":"Jacob L McCauley","author_inst":"John P. Hussman Institute for Human Genomics, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, USA; The Dr. John T. Macdonald Foundation De"},{"author_name":"Dermot McGovern","author_inst":"F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA; NIDDK IBD Genetics Consortium;"},{"author_name":"John McLaughlin","author_inst":"Department of Gastroenterology, Salford Royal NHS Foundation Trust Brooke Building, Salford Royal Hospital, Salford, M6 8HD, UK; NIHR IBD BioResource; Internati"},{"author_name":"Paul Moayyedi","author_inst":"McMaster University, Hamilton, Ontario, Canada; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Gordon Moran","author_inst":"Queen's Medical Centre Campus, Nottingham NG7 2UH, UK; NIHR Nottingham Biomedical Research Centre, Nottingham, UK; Translational Medical Sciences, School of Med"},{"author_name":"Rodney Newberry","author_inst":"Washington University School of Medicine, St. Louis, MO, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Arabis Oglesby","author_inst":"Dorset County Hospital, Dorset DT1 2JY, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Joel Pekow","author_inst":"Section of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Chicago, Chicago, Illinois, USA; Department of Gastroenterology, Un"},{"author_name":"Kate J Perez","author_inst":"Cambridge University Hospitals NHS Foundation Trust, Patient Representative, Cambridge, UK"},{"author_name":"Richard Pollok","author_inst":"Dept Gastroenterology, St Georges Foundation Trust NHS Hospitals, 2nd Floor Gosvenor Wing, London SW17 0QT, UK; NIHR IBD BioResource; International Inflammatory"},{"author_name":"Natalie Prescott","author_inst":"Department of Medical and Molecular Genetics, Guys Hospital, London, SE1 9RT; UK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Cons"},{"author_name":"Tim Raine","author_inst":"Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Arvind Ramadas","author_inst":"James Cook University Hospital, Middlesbrough TS4 3BW, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Subramaniam Ramakrishnan","author_inst":"Warrington & Halton hospitals NHS Foundation Trust, Warrington, WA5 1QG, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Ksenija Sabic","author_inst":"Pathology, Molecular & Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; NIDDK IBD Genetics Consortium; International Inflammator"},{"author_name":"Bruce Sands","author_inst":"Icahn School of Medicine at Mount Sinai, New York, NY, USA; SHARE Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Jack Satsangi","author_inst":"Translational Gastroenterology Unit, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DS; NIHR IBD BioResource; UK IBD Genetics Consortium; Interna"},{"author_name":"Aleksejs Sazonovs","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK"},{"author_name":"Stefan Schreiber","author_inst":"Christian-Albrechts-University of Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany; International Inflammatory Bowel Disease Genetics Consor"},{"author_name":"Christian Selinger","author_inst":"St. Jamess University Hospital, Leeds LS9 7TF, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Sophy Shedwell","author_inst":"Queen Elizabeth Hospital, King's Lynn PE30 4ET, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Mark Silverberg","author_inst":"Mount Sinai Hospital, Toronto, Ontario, Canada; NIDDK IBD Genetics Consortium; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Salil Singh","author_inst":"Royal Bolton Hospital, Bolton, BL4 0JR, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Harry Sokol","author_inst":"Department of Gastroenterology, Sorbonne Universite, INSERM, Centre de Recherche Saint-Antoine, CRSA, AP-HP, Saint-Antoine Hospital, Paris, France.; Internation"},{"author_name":"Helen Steed","author_inst":"New Cross Hospital of The Royal Wolverhampton NHS Trust, Wolverhampton, WV10 0QP, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Co"},{"author_name":"Alan Steel","author_inst":"The Royal Liverpool University Hospital, Liverpool L7 8YE, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Holm Uhlig","author_inst":"Translational Gastroenterology Unit and Biomedical Research Centre, Nuffield Department of Clinical Medicine, Experimental Medicine Division, University of Oxfo"},{"author_name":"Ajay Verma","author_inst":"Kettering General Hospital NHS Foundation Trust, Kettering, NN16 8UZ, UK; NIHR IBD BioResource; International Inflammatory Bowel Disease Genetics Consortium"},{"author_name":"Severine Vermeire","author_inst":"University Hospitals Leuven, Leuven, Belgium; Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium; Belgium IBD Consortium; International I"},{"author_name":"Rinse Weersma","author_inst":"Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands; International Inflam"},{"author_name":"Ramnik Xavier","author_inst":"Kurt Isselbacher Professor of Medicine at Harvard Medical School, Cambridge, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Ho"},{"author_name":"Mingrui Yu","author_inst":"Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanley Center for Psychiatric Research, The Broad Insti"},{"author_name":"Miles Parkes","author_inst":"Department of Gastroenterology, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK; UK IBD Genetics Consortium; NIHR IBD BioResource; In"},{"author_name":"John D Rioux","author_inst":"Faculty of Medicine, Universite de Montreal, Montreal, Canada; Research Center Montreal Heart Institute, Montreal, Canada; NIDDK IBD Genetics Consortium; Intern"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, The "},{"author_name":"Carl A Anderson","author_inst":"Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK; UK IBD Ge"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"A portable molecular laboratory for rapid genotyping in the field: application to sickle cell disease","rel_doi":"10.64898\/2026.05.05.26352080","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352080","rel_abs":"BackgroundSickle cell disease (SCD) is the most common recessive genetic disorder, caused by pathogenic variants of the HBB gene. SCD is associated with a range of clinical manifestations, including vaso-occlusive crises, infections, and severe anaemia, which contribute to increased morbidity and mortality. The frequency of pathogenic alleles is high in Sub-Saharan African countries, with heterozygous carriers reaching up to 25% of the population. Several methods can be employed for molecular diagnostics, with HBB gene sequencing being the most precise. However, access to DNA analyses and sequencing in Low- and Middle-Income Countries (LMICs), where SCD prevalence is high, is limited. Understanding genetic profiles is crucial at both individual and population levels, as it can guide public health strategies and facilitate accurate genetic counselling.\n\nAimThis feasibility study aimed to demonstrate that a portable medical genetic laboratory (in suitcases) can be used to genotype individuals for the HBB A, S, and C alleles and their combinations within a few hours outside of a laboratory setting.\n\nMethods and resultsWe established a portable medical genetics laboratory capable of DNA extraction and isothermal DNA amplification using a commercially available kit for the A, S, and C alleles of the HBB gene. During one single study day, this portable lab was set up in a room where the Swiss Association of Patients with SCD was holding its annual meeting. We analysed the samples of 27 participants who were aware of their A, S, or C status. We collected buccal swabs and dried blood samples for genotyping. Genotype results for all participants were obtained within five hours after sample collection. In four cases, we observed discrepancies between the buccal swab and blood genotypes; three were resolved upon repeat testing, and one reflected donor chimerism following hematopoietic stem-cell transplantation.\n\nConclusionsThis study demonstrates the feasibility and efficiency of using a portable medical genetics laboratory for rapid genotyping of HBB SCD alleles in community settings.This approach can improve access to molecular diagnostics in resource-limited environments. Such tools have the potential to significantly enhance local capabilities for genetic screening, counselling, and public health planning in regions heavily affected by SCD.","rel_num_authors":9,"rel_authors":[{"author_name":"Fabienne Grunder","author_inst":"University of Bern: Universitat Bern"},{"author_name":"Anne-Flore Haemmerli","author_inst":"University of Bern: Universitat Bern"},{"author_name":"Claude  Isofa Nkanga Bokembya","author_inst":"Association Suisse Dr\u00e9pano"},{"author_name":"St\u00e9phen Hennart","author_inst":"Xpedite Diagnostics GmBH"},{"author_name":"Muriel Helmers","author_inst":"University of Bern: Universitat Bern"},{"author_name":"Naomi  Azur Porret","author_inst":"Inselspital University Hospital Bern: Inselspital Universitatsspital Bern"},{"author_name":"Bertrand Graz","author_inst":"University of Geneva: Universite de Geneve"},{"author_name":"C\u00e9cile Choudja Ouabo","author_inst":"Independent"},{"author_name":"Hugues Abriel","author_inst":"University of Bern: Universitat Bern"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence","rel_doi":"10.64898\/2026.05.08.26352562","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352562","rel_abs":"PurposeTo develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables.\n\nMethodsWe developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrells concordance index (C-index) and Kaplan-Meier analyses.\n\nResultsA total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria.\n\nConclusionDigital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.","rel_num_authors":33,"rel_authors":[{"author_name":"Ngoc-Kim Nguyen","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":"James Dolezal","author_inst":"Geisinger Cancer Institute, Danville, PA, USA"},{"author_name":"Siddhi Ramesh","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Gil Shamai","author_inst":"Technion-Israel Institute of Technology, Haifa, Israel"},{"author_name":"Junhan Zhao","author_inst":"Department of Pediatrics, University of Chicago, Chicago, IL"},{"author_name":"Rita Nanda","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Nan Chen","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Olufunmilayo I. Olopade","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":"Galina Khramtsova","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sasha Jain-Liu","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":"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":"W Fraser Symmans","author_inst":"Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Kevin Kalinsky","author_inst":"Emory University Department of Medicine Section of Hematology\/Oncology, Atlanta GA, USA"},{"author_name":"Lajos Pusztai","author_inst":"Yale University Department of Medicine Section of Hematology\/Oncology, New Haven CT, USA"},{"author_name":"Michal Gala","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Evan D Paul","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Barbora Huraiova","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Pavol Cekan","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Ann H 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":"Daniel Stover","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Katharine Yao","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Joseph A Sparano","author_inst":"Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York NY, USA"},{"author_name":"Dezheng Huo","author_inst":"Department of Public Health Sciences, University of Chicago, Chicago, IL, USA"},{"author_name":"Alexander T Pearson","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Frederick Matthew Howard","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence","rel_doi":"10.64898\/2026.05.08.26352562","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352562","rel_abs":"PurposeTo develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables.\n\nMethodsWe developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrells concordance index (C-index) and Kaplan-Meier analyses.\n\nResultsA total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria.\n\nConclusionDigital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.","rel_num_authors":33,"rel_authors":[{"author_name":"Ngoc-Kim Nguyen","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":"James Dolezal","author_inst":"Geisinger Cancer Institute, Danville, PA, USA"},{"author_name":"Siddhi Ramesh","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Gil Shamai","author_inst":"Technion-Israel Institute of Technology, Haifa, Israel"},{"author_name":"Junhan Zhao","author_inst":"Department of Pediatrics, University of Chicago, Chicago, IL"},{"author_name":"Rita Nanda","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Nan Chen","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Olufunmilayo I. Olopade","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":"Galina Khramtsova","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sasha Jain-Liu","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":"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":"W Fraser Symmans","author_inst":"Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Kevin Kalinsky","author_inst":"Emory University Department of Medicine Section of Hematology\/Oncology, Atlanta GA, USA"},{"author_name":"Lajos Pusztai","author_inst":"Yale University Department of Medicine Section of Hematology\/Oncology, New Haven CT, USA"},{"author_name":"Michal Gala","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Evan D Paul","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Barbora Huraiova","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Pavol Cekan","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Ann H 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":"Daniel Stover","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Katharine Yao","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Joseph A Sparano","author_inst":"Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York NY, USA"},{"author_name":"Dezheng Huo","author_inst":"Department of Public Health Sciences, University of Chicago, Chicago, IL, USA"},{"author_name":"Alexander T Pearson","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Frederick Matthew Howard","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence","rel_doi":"10.64898\/2026.05.08.26352562","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352562","rel_abs":"PurposeTo develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables.\n\nMethodsWe developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrells concordance index (C-index) and Kaplan-Meier analyses.\n\nResultsA total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria.\n\nConclusionDigital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.","rel_num_authors":33,"rel_authors":[{"author_name":"Ngoc-Kim Nguyen","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":"James Dolezal","author_inst":"Geisinger Cancer Institute, Danville, PA, USA"},{"author_name":"Siddhi Ramesh","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Gil Shamai","author_inst":"Technion-Israel Institute of Technology, Haifa, Israel"},{"author_name":"Junhan Zhao","author_inst":"Department of Pediatrics, University of Chicago, Chicago, IL"},{"author_name":"Rita Nanda","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Nan Chen","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Olufunmilayo I. Olopade","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":"Galina Khramtsova","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sasha Jain-Liu","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":"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":"W Fraser Symmans","author_inst":"Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Kevin Kalinsky","author_inst":"Emory University Department of Medicine Section of Hematology\/Oncology, Atlanta GA, USA"},{"author_name":"Lajos Pusztai","author_inst":"Yale University Department of Medicine Section of Hematology\/Oncology, New Haven CT, USA"},{"author_name":"Michal Gala","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Evan D Paul","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Barbora Huraiova","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Pavol Cekan","author_inst":"MultiplexDX, s.r.o., Comenius University Science Park, Bratislava, Slovakia"},{"author_name":"Ann H 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":"Daniel Stover","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Katharine Yao","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Joseph A Sparano","author_inst":"Icahn School of Medicine at Mount Sinai, Tisch Cancer Institute, New York NY, USA"},{"author_name":"Dezheng Huo","author_inst":"Department of Public Health Sciences, University of Chicago, Chicago, IL, USA"},{"author_name":"Alexander T Pearson","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Frederick Matthew Howard","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Characterization of menopause onset and associated disease risks using large-scale electronic health records","rel_doi":"10.64898\/2026.05.08.26352769","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352769","rel_abs":"Menopause affects over one billion women worldwide, yet remains poorly characterized at scale. We apply an ICD-10-based phenotyping algorithm to electronic health records (EHR) from an academic medical center (n=33,444 women aged 35-64) and a safety-net hospital system (n=7,041), yielding one of the most racially and socioeconomically diverse menopause cohorts in the literature. Structured EHR fields underrepresent symptom burden: only 38.8% of patients had any documented symptom via natural language processing, despite an estimated prevalence of 90%. Adverse pregnancy outcomes were associated with earlier menopause onset after adjustment ({beta}=-1.21 years, p=8.7x10-45). Menopausal women showed elevated risk for osteoporosis (hazard ratio of 12.40), rheumatoid arthritis (HR of 2.43), and mental and behavioral disorders (HR 2.38) relative to age-matched men, with divergence at menopause onset. We show that large-scale EHR can characterize menopause at a scale and diversity that prospective enrollment has not achieved.","rel_num_authors":7,"rel_authors":[{"author_name":"Nitya Thakkar","author_inst":"Department of Computer Science, Stanford University, Stanford, CA, USA"},{"author_name":"Rajita Patil","author_inst":"Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA"},{"author_name":"Rebecca Levy-Gantt","author_inst":"Premier Women's Health Consulting, Napa, CA, USA"},{"author_name":"Yulin Hswen","author_inst":"The Artificial Intelligence Interdisciplinary Institute at Maryland, University of Maryland, College Park, MD, USA"},{"author_name":"Monica Agrawal","author_inst":"Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA"},{"author_name":"James Zou","author_inst":"Department of Biomedical Data Science, Stanford University, Stanford, CA, USA"},{"author_name":"Irene Y Chen","author_inst":"Computational Precision Health, University of California, San Francisco, CA, USA"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Characterization of menopause onset and associated disease risks using large-scale electronic health records","rel_doi":"10.64898\/2026.05.08.26352769","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352769","rel_abs":"Menopause affects over one billion women worldwide, yet remains poorly characterized at scale. We apply an ICD-10-based phenotyping algorithm to electronic health records (EHR) from an academic medical center (n=33,444 women aged 35-64) and a safety-net hospital system (n=7,041), yielding one of the most racially and socioeconomically diverse menopause cohorts in the literature. Structured EHR fields underrepresent symptom burden: only 38.8% of patients had any documented symptom via natural language processing, despite an estimated prevalence of 90%. Adverse pregnancy outcomes were associated with earlier menopause onset after adjustment ({beta}=-1.21 years, p=8.7x10-45). Menopausal women showed elevated risk for osteoporosis (hazard ratio of 12.40), rheumatoid arthritis (HR of 2.43), and mental and behavioral disorders (HR 2.38) relative to age-matched men, with divergence at menopause onset. We show that large-scale EHR can characterize menopause at a scale and diversity that prospective enrollment has not achieved.","rel_num_authors":7,"rel_authors":[{"author_name":"Nitya Thakkar","author_inst":"Department of Computer Science, Stanford University, Stanford, CA, USA"},{"author_name":"Rajita Patil","author_inst":"Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA"},{"author_name":"Rebecca Levy-Gantt","author_inst":"Premier Women's Health Consulting, Napa, CA, USA"},{"author_name":"Yulin Hswen","author_inst":"The Artificial Intelligence Interdisciplinary Institute at Maryland, University of Maryland, College Park, MD, USA"},{"author_name":"Monica Agrawal","author_inst":"Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA"},{"author_name":"James Zou","author_inst":"Department of Biomedical Data Science, Stanford University, Stanford, CA, USA"},{"author_name":"Irene Y Chen","author_inst":"Computational Precision Health, University of California, San Francisco, CA, USA"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Declining but increasingly concentrated HIV stigma in rural Uganda: population-based cohort study, 2014-2024","rel_doi":"10.64898\/2026.05.08.26352137","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352137","rel_abs":"BackgroundHIV-related stigma remains a primary barrier to the elimination of the HIV epidemic worldwide. No studies have examined long-term changes in the distribution of stigmatizing attitudes within populations.\n\nMethodsWe conducted a whole-population, open cohort study of adults in 8 villages in rural southwestern Uganda, with 5 biennial surveys spanning 2014-2024 (N=1,776 at baseline; 869 participated in all waves). We measured individual negative attitudes toward people with HIV (\"public stigma\") and perceptions of negative attitudes among others (\"perceived stigma\") using parallel 15-item scales. We estimated mean stigma scores, computed inequality measures at each wave, and decomposed inequality by sociodemographic characteristics. Leveraging the cohort design, we estimated intraclass correlation coefficients and rank-order stability over time.\n\nResultsBoth public and perceived stigma declined substantially from baseline to endline and became concentrated in an increasingly smaller subgroup of the population. Theil decomposition failed to identify any stratifying variables that explained more than 3% of this variation: nearly all the inequality in HIV stigma occurred within population subgroups rather than between them. In longitudinal analyses, public stigma showed trait-like properties (intraclass correlation coefficient=0.35; 95% CI, 0.31-0.38) and meaningful rank-order stability (baseline-to-endline r=0.41). Perceived stigma showed no rank-order stability, no appreciable between-person variance, and universal convergence to low levels regardless of baseline.\n\nConclusionsBoth public and perceived HIV stigma declined substantially in this rural Ugandan population, but remaining public stigma has become concentrated within a persistent minority. Sociodemographic profiling to target individuals who carry persistently negative attitudes toward people with HIV is unlikely to succeed.","rel_num_authors":21,"rel_authors":[{"author_name":"Alexander C. Tsai","author_inst":"Center for Global Health, Massachusetts General Hospital, Boston, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; De"},{"author_name":"Charles Baguma","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Phionah Ahereza","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Scholastic Ashaba","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Patience Ayebare","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"David R. Bangsberg","author_inst":"Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada"},{"author_name":"Alison B. Comfort","author_inst":"Department of Obstetrics and Gynecology, University of California at San Francisco, San Francisco, California, United States"},{"author_name":"Patrick Gumisiriza","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Mercy Juliet","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Justus Kananura","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Allen Kiconco","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Viola Kyokunda","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Patrick Lukwago","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Rumbidzai S. Mushavi","author_inst":"Harvard Medical School, Boston, Massachusetts, United States; Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, Massachusetts, Un"},{"author_name":"Elizabeth B. Namara","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Jessica M. Perkins","author_inst":"Peabody College, Department of Human and Organizational Development, Vanderbilt University, Nashville, Tennessee, United States"},{"author_name":"Justin M. Rasmussen","author_inst":"Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, United States"},{"author_name":"Emily N. Satinsky","author_inst":"Center for Global Health, Massachusetts General Hospital, Boston, Massachusetts, United States; Department of Psychology, Dana and David Dornsife College of Let"},{"author_name":"Mark J. Siedner","author_inst":"Harvard Medical School, Boston, Massachusetts, United States; Mbarara University of Science and Technology, Mbarara, Uganda; Medical Practice Evaluation Center,"},{"author_name":"Benjamin M. Tweheyo","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Bernard Kakuhikire","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Declining but increasingly concentrated HIV stigma in rural Uganda: population-based cohort study, 2014-2024","rel_doi":"10.64898\/2026.05.08.26352137","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352137","rel_abs":"BackgroundHIV-related stigma remains a primary barrier to the elimination of the HIV epidemic worldwide. No studies have examined long-term changes in the distribution of stigmatizing attitudes within populations.\n\nMethodsWe conducted a whole-population, open cohort study of adults in 8 villages in rural southwestern Uganda, with 5 biennial surveys spanning 2014-2024 (N=1,776 at baseline; 869 participated in all waves). We measured individual negative attitudes toward people with HIV (\"public stigma\") and perceptions of negative attitudes among others (\"perceived stigma\") using parallel 15-item scales. We estimated mean stigma scores, computed inequality measures at each wave, and decomposed inequality by sociodemographic characteristics. Leveraging the cohort design, we estimated intraclass correlation coefficients and rank-order stability over time.\n\nResultsBoth public and perceived stigma declined substantially from baseline to endline and became concentrated in an increasingly smaller subgroup of the population. Theil decomposition failed to identify any stratifying variables that explained more than 3% of this variation: nearly all the inequality in HIV stigma occurred within population subgroups rather than between them. In longitudinal analyses, public stigma showed trait-like properties (intraclass correlation coefficient=0.35; 95% CI, 0.31-0.38) and meaningful rank-order stability (baseline-to-endline r=0.41). Perceived stigma showed no rank-order stability, no appreciable between-person variance, and universal convergence to low levels regardless of baseline.\n\nConclusionsBoth public and perceived HIV stigma declined substantially in this rural Ugandan population, but remaining public stigma has become concentrated within a persistent minority. Sociodemographic profiling to target individuals who carry persistently negative attitudes toward people with HIV is unlikely to succeed.","rel_num_authors":21,"rel_authors":[{"author_name":"Alexander C. Tsai","author_inst":"Center for Global Health, Massachusetts General Hospital, Boston, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; De"},{"author_name":"Charles Baguma","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Phionah Ahereza","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Scholastic Ashaba","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Patience Ayebare","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"David R. Bangsberg","author_inst":"Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada"},{"author_name":"Alison B. Comfort","author_inst":"Department of Obstetrics and Gynecology, University of California at San Francisco, San Francisco, California, United States"},{"author_name":"Patrick Gumisiriza","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Mercy Juliet","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Justus Kananura","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Allen Kiconco","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Viola Kyokunda","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Patrick Lukwago","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Rumbidzai S. Mushavi","author_inst":"Harvard Medical School, Boston, Massachusetts, United States; Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, Massachusetts, Un"},{"author_name":"Elizabeth B. Namara","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Jessica M. Perkins","author_inst":"Peabody College, Department of Human and Organizational Development, Vanderbilt University, Nashville, Tennessee, United States"},{"author_name":"Justin M. Rasmussen","author_inst":"Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, United States"},{"author_name":"Emily N. Satinsky","author_inst":"Center for Global Health, Massachusetts General Hospital, Boston, Massachusetts, United States; Department of Psychology, Dana and David Dornsife College of Let"},{"author_name":"Mark J. Siedner","author_inst":"Harvard Medical School, Boston, Massachusetts, United States; Mbarara University of Science and Technology, Mbarara, Uganda; Medical Practice Evaluation Center,"},{"author_name":"Benjamin M. Tweheyo","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"},{"author_name":"Bernard Kakuhikire","author_inst":"Mbarara University of Science and Technology, Mbarara, Uganda"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Source Matters: An Examination of Drug Checking Samples from Police Departments and Community Based Programs in Massachusetts","rel_doi":"10.64898\/2026.05.08.26352755","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352755","rel_abs":"Aims and SettingIn the U.S., the emergence of new adulterants and novel psychoactive substances continues to complicate approaches to overdose, treatment, and public safety. Information about this changing drug supply is often gleaned from police drug seizures, but community drug checking services, which test the contents of a persons drug supply and share that data, provide another means to understand local drug supplies. However, it is unclear how seized drugs differ from those collected in the community, whether one approach is potentially more instructive, and what can be learned about local drug supplies from each source. We therefore compared drug samples tested from police departments (PDs) and community partner (CP) drug checking programs to examine what, if any, differences existed in sample content, form, submitter characteristics, and emerging substance presence.\n\nDesignWe conducted a retrospective cohort analysis of drug samples collected and tested between April 2018 and December 2025 by the Massachusetts Drug Supply DataStream derived from CPs and PDs operating in the same geographic area across eight locations. Bivariate analyses (Chi-square, Fishers exact) tested for differences in sample and submitter characteristics by source.\n\nFindingsThere were 2,430 unique samples submitted by CPs (68.1%) and PDs (31.9%) from the same location. Compared to CP samples, proportionally more PD samples showed fentanyl as primary substance (74.2% PD vs. 64% CP, p<.001) and less often contained additives (xylazine 15.0% PD vs. 27.4% CP; medetomidine 0.6% PD vs. 2.2% CP, both p<.001). PD samples were typically powders (73.2% vs. 37.9%) and pills (13.6% vs. 3.6%) while CP samples were more often residue (51.9% vs. 2.1%, p<.001). Submitter characteristics, when reported, differed by source: gender (n=528, male: 78.6% PD vs. 50.1% CP, p<.001), race\/ethnicity (n=468, Black: 15.8% PD vs. 7.8% CP; Hispanic: 6.7% PD vs. 13.2% CP, p<.05), and associated overdose (n=242, fatal: 62.9% vs. 10.9%, p<.001). Emergent substances were detected a median of 249 days sooner in CP than co-located PD samples, though drugs exhibiting concerning patterns (e.g., unexpected fentanyl in stimulants) had similar, swift detection times.\n\nConclusionDrug samples differ based on PD vs. CP source in significant ways that may introduce bias when drawing conclusions about drug supply trends but also offer unique insights for public health and responses to emerging drugs. Modern drug monitoring should include a broad range of sources to best prepare for changes the illicit supply may bring to overdose prevention, public safety, and health systems.","rel_num_authors":8,"rel_authors":[{"author_name":"Joseph Silcox","author_inst":"Brandeis University"},{"author_name":"Sabrina Rapisarda","author_inst":"University of Massachusetts, Lowell"},{"author_name":"Ella Chase","author_inst":"Brandeis University"},{"author_name":"Nick Huntington","author_inst":"Brandeis University"},{"author_name":"Shannon Raeke","author_inst":"Brandeis University"},{"author_name":"Amanda Consigli","author_inst":"National Foundation of the Centers for Disease Control and Prevention"},{"author_name":"Brandon Del Pozo","author_inst":"The Warren Alpert Medical School of Brown University, COBRE on Opioids and Overdose at Rhode Island Hospital"},{"author_name":"Traci C Green","author_inst":"Brandeis University\/Brown University"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Epidemiology-Informed Graph Neural Networks for Predicting and Interpreting Transmissible Hospital-Acquired Infections: A Retrospective Cohort and Simulation Study","rel_doi":"10.64898\/2026.05.08.26352740","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352740","rel_abs":"Transmissible hospital-acquired infections (HAIs) arise from complex, time-varying interactions among patients, healthcare workers, and clinical environments. Although data-driven approaches like graph neural networks (GNNs) effectively model these contacts, they often function as black boxes that over-look established epidemiological principles, limiting interpretability and clinical trust. Inspired by physics-informed neural networks, we propose a epidemiology-informed GNN (EIGNN) framework for patient-level state transitions prediction in dynamic hospital settings, integrating mechanistic epidemiological models into GNNs in a principled manner. Patient-level risk factors learned from dynamic contact networks are jointly leveraged to infer latent epidemiological states, predict state transitions across multiple horizons, and estimate key epidemiological parameters, including transmission and recovery rates. We evaluate the approach on a real-world hospital-onset COVID-19 cohort and two public datasets simulating viral and bacterial HAIs. Across multiple architectures and horizons, EIGNNs achieves AUC-ROC up to 98.46% while providing interpretable, mechanistically consistent insights, offering a transparent tool for infection prevention and control.","rel_num_authors":6,"rel_authors":[{"author_name":"Yamil E Vindas Yassine","author_inst":"University of Geneva"},{"author_name":"Alban Bornet","author_inst":"University of Geneva"},{"author_name":"Mohamed Abbas","author_inst":"Geneva University Hospitals and University of Geneva"},{"author_name":"Damien Geissbuehler","author_inst":"University of Geneva and Max Planck Institute for the Science of Light"},{"author_name":"Jose F Rodrigues-Jr","author_inst":"University of Sao Paulo"},{"author_name":"Douglas Teodoro","author_inst":"University of Geneva"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Systematic common and rare variant association testing in 392,030 whole genomes in All of Us","rel_doi":"10.64898\/2026.05.08.26350964","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26350964","rel_abs":"Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We present an analysis of the All of Us Research Program v8 release, which includes whole genome sequencing data and harmonized phenotypic information of 392,030 participants after quality control, enabling a unified investigation of rare and common variants across a spectrum of human traits and diseases. We build an extensive phenome- and genome-wide (\"All by All\") computational framework to perform GWAS and RVAS on 3,602 phenotypes and identify 49,863 approximately independent, high-quality single-variant and gene-level associations. Meta-analyses of All of Us and UK Biobank, with sample sizes as large as 786,871 participants, further enhance statistical power and find 193 pLoF gene-phenotype associations that are not significant in either cohort alone, including 22 associations not highlighted by previous studies. We also present a public interactive browser that integrates association results for common and rare variants to facilitate interpretation and rapid querying of summary statistics, along with supporting documentation, and a Featured Workspace in the All of Us Researcher Workbench. Our framework will apply to iterative data releases as All of Us grows, empowering researchers worldwide to uncover insights into the functional effects of genetic components on complex traits and diseases.","rel_num_authors":50,"rel_authors":[{"author_name":"Wenhan Lu","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Robert J. Carroll","author_inst":"Department of Biomedical Informatics,Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"Matthew Solomonson","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Jeremy Guez","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Megan K. He","author_inst":"Vanderbilt Institute for Clinical and Translational Research; Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"Daniel J. Marten","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA"},{"author_name":"Alejandro Martinez-Carrosco","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Ying Wang","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Connor S. Dowd","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Masahiro Kanai","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Bram L. Gorissen","author_inst":"Broad Institute, Program in Medical and Population Genetics, Cambridge, MA, USA"},{"author_name":"Aymone Jeanne S. Kouame","author_inst":"Vanderbilt Institute for Clinical and Translational Research; Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"James Brogan","author_inst":"Division of Hospital Medicine, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD"},{"author_name":"Bennett J. Waxse","author_inst":"National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD"},{"author_name":"Ryan Samarakoon","author_inst":"Vanderbilt Institute for Clinical and Translational Research Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"Justin A. Cook","author_inst":"Vanderbilt Institute for Clinical and Translational Research; Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"Jun Qian","author_inst":"Department of Biomedical Informatics,Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"Yu Zhou","author_inst":"Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA"},{"author_name":"Karmel W. Choi","author_inst":"Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA"},{"author_name":"Melissa Basford","author_inst":"Vanderbilt Institute for Clinical and Translational Research Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"Michael Lyons","author_inst":"Vanderbilt Institute for Clinical and Translational Research Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"Jodell E. Linder","author_inst":"Vanderbilt Institute for Clinical and Translational Research; Vanderbilt University Medical Center, Nashville TN"},{"author_name":"Samantha Stewart","author_inst":"Vanderbilt Institute for Clinical and Translational Research; Vanderbilt University Medical Center, Nashville TN"},{"author_name":"Namrata Gupta","author_inst":"Broad Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Patrick Schultz","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Daniel Goldstein","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Christopher Llanwarne","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Jacqueline I. Goldstein","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Edmund G. C. Higham","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Daniel C. King","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Duncan S. Palmer","author_inst":"Department of Statistics, University of Oxford, Oxford, United Kingdom"},{"author_name":"Jared S. Elenbaas","author_inst":"Department of Pathology, Mass General Brigham, Boston, MA, 02115, USA"},{"author_name":"Gregory K. Rohlicek","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Qin He","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Julia K. Goodrich","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"- The All of Us Research Program Genomics Investigators","author_inst":""},{"author_name":"Jordan W. Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA"},{"author_name":"Lee Lichtenstein","author_inst":"Broad Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Stacey B. Gabriel","author_inst":"Broad Genomics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Alicia R. Martin","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Jason H. Karnes","author_inst":"Department of Pharmacy Practice and Science, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ, USA"},{"author_name":"Scott J. Hebbring","author_inst":"Marshfield Clinic Health System, Marshfield, WI 54449, USA"},{"author_name":"Mark J. Daly","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Tara Dutka","author_inst":"All of Us Research Program, Office of the Director, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Anjene Musick","author_inst":"All of Us Research Program, Office of the Director, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Joshua C. Denny","author_inst":"All of Us Research Program, Office of the Director, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Wei Zhou","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"},{"author_name":"Dan M. Roden","author_inst":"Departments of Medicine, Pharmacology, and Biomedical Informatics,Vanderbilt University Medical Center, Nashville, TN"},{"author_name":"Benjamin M. Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA"},{"author_name":"Konrad J. Karczewski","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Huntingtin CAG repeat is a continuous modifier of brain structure and health vulnerability","rel_doi":"10.64898\/2026.05.08.26352223","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352223","rel_abs":"Huntingtons disease is caused by a CAG repeat expansion in the Huntingtin gene (HTT) above a pathogenic threshold; however, the biological consequences of repeat-length variation below this threshold remain poorly understood. Using whole-genome sequencing and linked phenotypic data from UK Biobank participants, we show that repeat-length variation within the normal and intermediate range is associated with measurable differences in brain volume, neuropsychiatric risk, and cognitive processing, and that only one third of pathogenic allele carriers have a recorded clinical diagnosis.\n\nAnalyses were performed in 474,446 UK Biobank participants, including 30,052 with intermediate repeats (27-35), 873 with reduced-penetrance repeats (36-39), and 155 with pathogenic repeats ([&ge;]40); 48,378 individuals had structural MRI. For quantitative phenotypes (brain volumes and cognition), associations with continuous repeat length were modelled using linear regression within the normal and intermediate range ([&le;]35 repeats); deviation at [&ge;]36 repeats was defined as departure from the extrapolated linear trend. For clinical outcomes (depression, anxiety, dementia, and delirium), repeat length was analysed categorically using Kaplan-Meier and Cox proportional hazards models with age as the timescale.\n\nWithin the normal and intermediate range, longer HTT CAG repeat length was associated with smaller subcortical and global brain volumes, including the accumbens, putamen, thalamus, hippocampus, and total grey and white matter, with effects amplified in older individuals. Intermediate alleles were associated with an increase in age-dependent depression risk (HR = 1.05, 95% CI 1.02-1.10) and longer repeat length within the normal and intermediate range predicted faster reaction time, a pattern that reversed sharply at pathogenic lengths. Among carriers of 40-41 CAG repeats, only 42% (95% CI 19-59%) had received a recorded Huntingtons disease diagnosis by age 84; however, the majority of pathogenic allele carriers who underwent neuroimaging met biomarker criteria for Stage 1 disease, indicating that early neurodegeneration is present in these individuals.\n\nThis work challenges the current understanding of the HTT CAG repeat length as a purely categorical determinant of monogenic disease and shows that repeat length acts as a quantitative modifier of brain structure and neuropsychiatric vulnerability across the population. These findings have implications for risk prediction, penetrance estimation, and the interpretation of repeat variation in population genomics.","rel_num_authors":9,"rel_authors":[{"author_name":"Harriet Cullen","author_inst":"Kings College London"},{"author_name":"Christopher Clarkson","author_inst":"Queen Mary University of London"},{"author_name":"Henrique Nascimento","author_inst":"University College London"},{"author_name":"Matteo Zanovello","author_inst":"University College London"},{"author_name":"Jeffrey Long","author_inst":"University of Iowa"},{"author_name":"Mark Caulfield","author_inst":"Queen Mary University of London"},{"author_name":"Michael Simpson","author_inst":"King's College London"},{"author_name":"Sarah Tabrizi","author_inst":"University College London"},{"author_name":"Arianna Tucci","author_inst":"Queen Mary University of London"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"Multimodal Wearable System for Objective Assessment of Dynamic Rotational Knee Biomechanics Following ACL Injury and Reconstruction: A Clinical Validation Study Using Ensemble Deep Learning","rel_doi":"10.64898\/2026.05.08.26352706","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.08.26352706","rel_abs":"BackgroundThe clinical assessment of knee stability after an Anterior Cruciate Ligament (ACL) injury is routinely conducted via operator-dependent physical examination tests (i.e. pivot shift) and standardized patient-reported outcomes. Unfortunately, both are unable to perceive and quantify the subtle rotational biomechanical deficiencies from an ACL tear. Although specialized laboratory-based motion capture systems may provide objective measurements, they are found in research institutions and thus, are not suitable for clinical use. In contrast, GATOR PRO is a clinic-based multimodal wearable sensor system that uses a machine learning (ML) model (ensemble deep learning) to differentiate and classify its data outputs for assessing in-vivo dynamic rotational knee stability.\n\nObjectiveThe purpose of this study is to validate the deep machine learning model and its performance used in GATOR PRO, which integrates knee-mounted Inertial Measurement Units (IMUs) with ultrasound images to derive high-fidelity in-vivo biomechanical rotational data. Based on this data collected by the GATOR PRO, it is hypothesized that the model can effectively classify knee stability after ACL injury and reconstruction.\n\nMethodsThis prospective clinical study at Singapore General Hospital (SGH) (CIRB 2019\/2766, PDPA-compliant) aimed to enroll 60 patients (30 ACL-deficient, 30 ACL-reconstructed [&ge;]6 months post-surgery). At the halfway point of the clinical trial, 29 patients (8 ACL-deficient, 21 ACL-reconstructed [&ge;]6 months post-surgery) were recruited through physician referral at SGH outpatient clinics to perform standardized chair-stand tests. An ensemble deep learning model that combines convolutional (EfficientNet) and time-series (InceptionTime) classifiers is used to output binary stability classifications (ACL-deficient\/ACL-reconstructed). The models performance was evaluated using 10-fold stratified cross-validation with patient-wise splitting, repeated across 100 random seeds to assess variability.\n\nResultsAt the halfway point of the trial, the ensemble model performance with regard to the Receiver Operating Characteristic area under the curve (ROC-AUC) was 0.8365 (SD: 0.042, p-value < 0.001), and the classification accuracy was 75.9% (SD: 3.2%) when the model was tested on the 29 CIRB-approved patients. For the ACL-reconstructed class, the performance indicators were as follows: precision 71.4%, recall 93.8%, F1-score 81.1%. For the ACL-deficient class, the indicators were: precision 87.5%, recall 53.8%, F1-score 66.7%.Against the clinical pivot shift tests low sensitivity (24-32%), the model delivers an almost 2X better sensitivity (53.8%)[2, 3], with a comparable specificity (93.8% vs. 90-98%)\n\nConclusionThe multimodal machine learning model was able to perform at a level that was relevant to clinical classification (AUC-ROC 0.8365, accuracy 75.9%) in differentiating between ACL-deficient and ACL-reconstructed knees. Moreover, the model demonstrated far superior sensitivity than previously published estimates for manual pivot shift testing (53.8% vs. 24-32%). These findings demonstrate that rotational knee instability can be reliably differentiated in clinical settings with a ML model deployed on GATOR PRO data.","rel_num_authors":5,"rel_authors":[{"author_name":"Jiya Dutta","author_inst":"PreciX Pte Ltd"},{"author_name":"Kah Weng Lai","author_inst":"PreciX Pte Ltd"},{"author_name":"Zi Yang Chia","author_inst":"Singapore General Hospital"},{"author_name":"David Tan Yuan Yu","author_inst":"DxD Hub"},{"author_name":"Jeffrey Zhu","author_inst":"University of California, Berkeley"}],"rel_date":"2026-05-12","rel_site":"medrxiv"},{"rel_title":"The TBVaxRepository: A living database of projects supporting the preparedness for adult and adolescent TB vaccine rollout","rel_doi":"10.64898\/2026.05.06.26352615","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352615","rel_abs":"BackgroundTuberculosis (TB) continues to cause substantial morbidity and mortality, with adults and adolescents carrying the largest burden of disease. Multiple promising novel vaccine candidates are in clinical trials, and their eventual impact will depend on effective implementation strategies. Information on TB vaccine preparedness efforts that could inform coordination remains fragmented.\n\nMethodsWe developed the first living and interactive online repository (https:\/\/tbvaxrepository.org\/) collating completed, ongoing, and planned adult and adolescent TB vaccine preparedness initiatives. Data were obtained through a prior scoping review, direct stakeholder engagement, international conferences, and open calls via social media and partner networks between March 2023-November 2024. Projects were categorized using the World Health Organizations (WHO) framework for TB vaccine preparedness across three thematic areas: availability, accessibility, and acceptability.\n\nFindingsBy December 2024, the repository included 90 projects from 119 countries. Most projects focused on health- (47%) and economic modelling (21%), demand and acceptability studies (19%) or implementation feasibility (14%). Most of the projects were situated in India (n=36), South Africa (n=34), China (n=19), Indonesia, (n=17), Kenya (n=17), Brazil (n=14), and Pakistan (n=14). Few initiatives targeted key populations such as people living with HIV, pregnant or lactating individuals, or socially marginalized and occupational high-risk groups. Research on communication strategies for facilitating uptake as part of rollout were absent.\n\nConclusionsThe repository reveals both progress and gaps in global TB vaccine preparedness across WHOs three thematic areas, with particular attention to geographic coverage, and the inclusion of key populations. As novel vaccines for adults and adolescents approach potential licensure, coordinated and inclusive preparedness efforts will be critical to ensure equitable and effective rollout. This repository offers a transparent platform to strengthen collaboration, reduce duplication, and guide strategic planning in a historically underfunded field.","rel_num_authors":4,"rel_authors":[{"author_name":"Joeri  Sumina Buis","author_inst":"KNCV Tuberculosis Foundation: KNCV Tuberculosefonds"},{"author_name":"Andrew  D Kerkhoff","author_inst":"UCSF: University of California San Francisco"},{"author_name":"Christiaan Mulder","author_inst":"KNCV Tuberculosis Foundation: KNCV Tuberculosefonds"},{"author_name":"Degu Jerene","author_inst":"KNCV Tuberculosis Foundation: KNCV Tuberculosefonds"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Germline polygenic score for prostate cancer aggressiveness","rel_doi":"10.64898\/2026.05.07.26352488","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352488","rel_abs":"BackgroundRisk stratification for prostate cancer (PCa) progression or aggressiveness is often based on clinicopathologic features, some of which may be influenced by genetic factors. We developed a novel, germline polygenic risk score (PRSagg) to predict likelihood of developing aggressive PCa.\n\nMethodsPRSagg was developed using data from 38,688 patients with PCa (case-only analysis) from the Million Veteran Program (MVP) through a genome-wide search for variants associated with PCa grade group at diagnosis. We tested associations of PRSagg with grade group using the entire MVP dataset using the .632 bootstrap method. In an MVP cohort with localized PCa that was initially monitored without treatment, we tested PRSagg for association with unfavorable outcomes (subsequent development of grade group 4-5, metastasis, and\/or biochemical recurrence after definitive treatment). We performed external validation in data from patients in the PRACTICAL Consortium (n=45,214) and from participants in the ProtecT randomized trial who underwent active monitoring (n=316). Odds ratios (ORs) were calculated per standard deviation (SD) increase with 95% confidence intervals, while adjusting for age, genetic ancestry, a previously developed polygenic score for risk of PCa (PHS601), and a polygenic score for benign elevated prostate-specific antigen (PRSPSA). For the outcome of metastasis, we additionally adjusted for PSA at diagnosis.\n\nResultsIn the MVP training dataset, PRSagg (172 variants) was associated with higher grade group at diagnosis (OR = 1.53 [1.51-1.56]) and with increased risk of unfavorable outcomes during monitoring (OR = 1.13 [1.09-1.18]). These findings were confirmed in the external datasets. PRSagg was associated with greater odds of higher grade group at diagnosis (OR = 1.09 [1.06-1.11]). Among ProtecT participants undergoing active monitoring, PRSagg was associated with higher risk of metastasis (OR = 2.15 [1.02-3.88]). Among MVP participants with high polygenic risk of developing any PCa, the risk of aggressive disease was highest in men with high PRSagg and low genetic risk of PSA elevation.\n\nConclusionsAmong men who develop PCa, a weighted sum of common germline variants (PRSagg) is independently associated with PCa aggressiveness. These findings may inform future study of germline influence on tumor evolution and risk-stratified intensity of active surveillance.","rel_num_authors":82,"rel_authors":[{"author_name":"George Jiajie Xu","author_inst":"VA San Diego Healthcare System"},{"author_name":"Roshan Karunamuni","author_inst":"VA San Diego Healthcare System"},{"author_name":"Anna M Dornisch","author_inst":"University of California San Diego"},{"author_name":"Charles A Brunette","author_inst":"VA Boston Healthcare System"},{"author_name":"Morgan E Danowski","author_inst":"VA Boston Healthcare System"},{"author_name":"Heena Desai","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Daniel Dochtermann","author_inst":"VA Boston Healthcare System"},{"author_name":"Isla P Garraway","author_inst":"VA Greater Los Angeles Healthcare System"},{"author_name":"Richard L Hauger","author_inst":"VA San Diego Healthcare System"},{"author_name":"Adam S Kibel","author_inst":"Harvard Medical School"},{"author_name":"Julie A Lynch","author_inst":"VA Salt Lake City Healthcare System"},{"author_name":"Saiju Pyarajan","author_inst":"VA Boston Healthcare System"},{"author_name":"Brent S Rose","author_inst":"VA San Diego Healthcare System"},{"author_name":"Craig C Teerlink","author_inst":"VA Salt Lake City Healthcare System"},{"author_name":"Ole A Andreassen","author_inst":"Oslo University Hospital and University of Oslo"},{"author_name":"Anders M Dale","author_inst":"University of California San Diego"},{"author_name":"Jenny L Donovan","author_inst":"University of Bristol"},{"author_name":"Freddie Hamdy","author_inst":"University of Oxford"},{"author_name":"Linda Kachuri","author_inst":"Stanford University"},{"author_name":"Athene Lane","author_inst":"University of Bristol"},{"author_name":"Richard M Martin","author_inst":"University of Bristol"},{"author_name":"Ian G Mills","author_inst":"University of Oxford"},{"author_name":"David E Neal","author_inst":"University of Oxford"},{"author_name":"Emma L Turner","author_inst":"University of Bristol"},{"author_name":"John S Witte","author_inst":"Stanford University"},{"author_name":"Johanna Schleutker","author_inst":"University of Turku"},{"author_name":"Nora Pashayan","author_inst":"University of Cambridge"},{"author_name":"Jyotsna Batra","author_inst":"Bond University"},{"author_name":"- Australian Prostate Cancer BioResource (APCB)","author_inst":"-"},{"author_name":"B\u00f8rge G Nordestgaard","author_inst":"University of Copenhagen"},{"author_name":"Robert J Hamilton","author_inst":"Princess Margaret Cancer Centre"},{"author_name":"Alicja Wolk","author_inst":"Karolinska Institutet"},{"author_name":"Demetrius Albanes","author_inst":"National Cancer Institute"},{"author_name":"Joshua Atkins","author_inst":"University of Oxford"},{"author_name":"William J Blot","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Lorelei A Mucci","author_inst":"Harvard T.H. Chan School of Public Health"},{"author_name":"Sune F Nielsen","author_inst":"Copenhagen University Hospital"},{"author_name":"Olivier Cussenot","author_inst":"Sorbonne Universite"},{"author_name":"Sonja I Berndt","author_inst":"National Cancer Institute"},{"author_name":"Stella Koutros","author_inst":"National Cancer Institute"},{"author_name":"Karina Dalsgaard S\u00f8rensen","author_inst":"Aarhus University Hospital"},{"author_name":"Cezary Cybulski","author_inst":"Pomeranian Medical University"},{"author_name":"Florence Menegaux","author_inst":"Universit\u00e9 Paris-Saclay"},{"author_name":"Jong Y Park","author_inst":"Moffitt Cancer Center"},{"author_name":"Robert J MacInnis","author_inst":"Cancer Council Victoria"},{"author_name":"Barry S Rosenstein","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Yong-Jie Lu","author_inst":"Queen Mary University of London"},{"author_name":"Stephen Watya","author_inst":"Uro Care"},{"author_name":"Ana Vega","author_inst":"Santiago de Compostela"},{"author_name":"- NC-LA PCaP Investigators","author_inst":"-"},{"author_name":"- The IMPACT Study Steering Committee and Collaborators","author_inst":"-"},{"author_name":"Manolis Kogevinas","author_inst":"ISGLOBAL: Instituto de Salud Global de Barcelona"},{"author_name":"Fredrik Wiklund","author_inst":"Karolinska Institutet"},{"author_name":"Anna Plym","author_inst":"Karolinska Institutet"},{"author_name":"Manuel R Teixeira","author_inst":"Porto Comprehensive Cancer Center"},{"author_name":"Luc Multigner","author_inst":"Institut de recherche en sant\u00e9, environnement et travail"},{"author_name":"Robin J Leach","author_inst":"University of Texas Health Science Center at San Antonio"},{"author_name":"Hermann Brenner","author_inst":"German Cancer Research Centre: Deutsches Krebsforschungszentrum"},{"author_name":"Esther M John","author_inst":"Stanford University"},{"author_name":"Radka Kaneva","author_inst":"Medical University of Sofia"},{"author_name":"Christopher J Logothetis","author_inst":"The University of Texas M. D. Anderson Cancer Center"},{"author_name":"Susan L Neuhausen","author_inst":"Beckman Research Institute of City of Hope"},{"author_name":"Piet Ost","author_inst":"Ghent University"},{"author_name":"Azad Razack","author_inst":"University of Malaya"},{"author_name":"Jay H Fowke","author_inst":"University of Tennessee Health Science Center"},{"author_name":"Marija Gamulin","author_inst":"University of Zagreb School of Medicine"},{"author_name":"Nawaid Usmani","author_inst":"University of Alberta"},{"author_name":"Frank Claessens","author_inst":"KU Leuven"},{"author_name":"Jose Esteban Castelao","author_inst":"Instituto de Investigaci\u00f3n Biom\u00e9dica Galicia Sur"},{"author_name":"Gyorgy Petrovics","author_inst":"Uniformed Services University"},{"author_name":"Marie-\u00c9lise Parent","author_inst":"Institut national de la recherche scientifique"},{"author_name":"Jennifer J Hu","author_inst":"The University of Miami School of Medicine"},{"author_name":"Wei Zheng","author_inst":"Vanderbilt University Medical Center"},{"author_name":"- The Profile Study Steering Committee","author_inst":"-"},{"author_name":"- UKGPCS collaborators","author_inst":"-"},{"author_name":"Zsofia Kote-Jarai","author_inst":"The Institute of Cancer Research"},{"author_name":"Rosalind A Eeles","author_inst":"The Institute of Cancer Research"},{"author_name":"- The PRACTICAL Consortium","author_inst":"-"},{"author_name":"- VA Million Veteran Program","author_inst":"-"},{"author_name":"Kara N Maxwell","author_inst":"Corporal Michael Crescenz Veterans Affairs Medical Center"},{"author_name":"Jason L Vassy","author_inst":"VA Boston Healthcare System"},{"author_name":"Tyler M Seibert","author_inst":"VA San Diego Healthcare System"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Germline polygenic score for prostate cancer aggressiveness","rel_doi":"10.64898\/2026.05.07.26352488","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352488","rel_abs":"BackgroundRisk stratification for prostate cancer (PCa) progression or aggressiveness is often based on clinicopathologic features, some of which may be influenced by genetic factors. We developed a novel, germline polygenic risk score (PRSagg) to predict likelihood of developing aggressive PCa.\n\nMethodsPRSagg was developed using data from 38,688 patients with PCa (case-only analysis) from the Million Veteran Program (MVP) through a genome-wide search for variants associated with PCa grade group at diagnosis. We tested associations of PRSagg with grade group using the entire MVP dataset using the .632 bootstrap method. In an MVP cohort with localized PCa that was initially monitored without treatment, we tested PRSagg for association with unfavorable outcomes (subsequent development of grade group 4-5, metastasis, and\/or biochemical recurrence after definitive treatment). We performed external validation in data from patients in the PRACTICAL Consortium (n=45,214) and from participants in the ProtecT randomized trial who underwent active monitoring (n=316). Odds ratios (ORs) were calculated per standard deviation (SD) increase with 95% confidence intervals, while adjusting for age, genetic ancestry, a previously developed polygenic score for risk of PCa (PHS601), and a polygenic score for benign elevated prostate-specific antigen (PRSPSA). For the outcome of metastasis, we additionally adjusted for PSA at diagnosis.\n\nResultsIn the MVP training dataset, PRSagg (172 variants) was associated with higher grade group at diagnosis (OR = 1.53 [1.51-1.56]) and with increased risk of unfavorable outcomes during monitoring (OR = 1.13 [1.09-1.18]). These findings were confirmed in the external datasets. PRSagg was associated with greater odds of higher grade group at diagnosis (OR = 1.09 [1.06-1.11]). Among ProtecT participants undergoing active monitoring, PRSagg was associated with higher risk of metastasis (OR = 2.15 [1.02-3.88]). Among MVP participants with high polygenic risk of developing any PCa, the risk of aggressive disease was highest in men with high PRSagg and low genetic risk of PSA elevation.\n\nConclusionsAmong men who develop PCa, a weighted sum of common germline variants (PRSagg) is independently associated with PCa aggressiveness. These findings may inform future study of germline influence on tumor evolution and risk-stratified intensity of active surveillance.","rel_num_authors":82,"rel_authors":[{"author_name":"George Jiajie Xu","author_inst":"VA San Diego Healthcare System"},{"author_name":"Roshan Karunamuni","author_inst":"VA San Diego Healthcare System"},{"author_name":"Anna M Dornisch","author_inst":"University of California San Diego"},{"author_name":"Charles A Brunette","author_inst":"VA Boston Healthcare System"},{"author_name":"Morgan E Danowski","author_inst":"VA Boston Healthcare System"},{"author_name":"Heena Desai","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Daniel Dochtermann","author_inst":"VA Boston Healthcare System"},{"author_name":"Isla P Garraway","author_inst":"VA Greater Los Angeles Healthcare System"},{"author_name":"Richard L Hauger","author_inst":"VA San Diego Healthcare System"},{"author_name":"Adam S Kibel","author_inst":"Harvard Medical School"},{"author_name":"Julie A Lynch","author_inst":"VA Salt Lake City Healthcare System"},{"author_name":"Saiju Pyarajan","author_inst":"VA Boston Healthcare System"},{"author_name":"Brent S Rose","author_inst":"VA San Diego Healthcare System"},{"author_name":"Craig C Teerlink","author_inst":"VA Salt Lake City Healthcare System"},{"author_name":"Ole A Andreassen","author_inst":"Oslo University Hospital and University of Oslo"},{"author_name":"Anders M Dale","author_inst":"University of California San Diego"},{"author_name":"Jenny L Donovan","author_inst":"University of Bristol"},{"author_name":"Freddie Hamdy","author_inst":"University of Oxford"},{"author_name":"Linda Kachuri","author_inst":"Stanford University"},{"author_name":"Athene Lane","author_inst":"University of Bristol"},{"author_name":"Richard M Martin","author_inst":"University of Bristol"},{"author_name":"Ian G Mills","author_inst":"University of Oxford"},{"author_name":"David E Neal","author_inst":"University of Oxford"},{"author_name":"Emma L Turner","author_inst":"University of Bristol"},{"author_name":"John S Witte","author_inst":"Stanford University"},{"author_name":"Johanna Schleutker","author_inst":"University of Turku"},{"author_name":"Nora Pashayan","author_inst":"University of Cambridge"},{"author_name":"Jyotsna Batra","author_inst":"Bond University"},{"author_name":"- Australian Prostate Cancer BioResource (APCB)","author_inst":"-"},{"author_name":"B\u00f8rge G Nordestgaard","author_inst":"University of Copenhagen"},{"author_name":"Robert J Hamilton","author_inst":"Princess Margaret Cancer Centre"},{"author_name":"Alicja Wolk","author_inst":"Karolinska Institutet"},{"author_name":"Demetrius Albanes","author_inst":"National Cancer Institute"},{"author_name":"Joshua Atkins","author_inst":"University of Oxford"},{"author_name":"William J Blot","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Lorelei A Mucci","author_inst":"Harvard T.H. Chan School of Public Health"},{"author_name":"Sune F Nielsen","author_inst":"Copenhagen University Hospital"},{"author_name":"Olivier Cussenot","author_inst":"Sorbonne Universite"},{"author_name":"Sonja I Berndt","author_inst":"National Cancer Institute"},{"author_name":"Stella Koutros","author_inst":"National Cancer Institute"},{"author_name":"Karina Dalsgaard S\u00f8rensen","author_inst":"Aarhus University Hospital"},{"author_name":"Cezary Cybulski","author_inst":"Pomeranian Medical University"},{"author_name":"Florence Menegaux","author_inst":"Universit\u00e9 Paris-Saclay"},{"author_name":"Jong Y Park","author_inst":"Moffitt Cancer Center"},{"author_name":"Robert J MacInnis","author_inst":"Cancer Council Victoria"},{"author_name":"Barry S Rosenstein","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Yong-Jie Lu","author_inst":"Queen Mary University of London"},{"author_name":"Stephen Watya","author_inst":"Uro Care"},{"author_name":"Ana Vega","author_inst":"Santiago de Compostela"},{"author_name":"- NC-LA PCaP Investigators","author_inst":"-"},{"author_name":"- The IMPACT Study Steering Committee and Collaborators","author_inst":"-"},{"author_name":"Manolis Kogevinas","author_inst":"ISGLOBAL: Instituto de Salud Global de Barcelona"},{"author_name":"Fredrik Wiklund","author_inst":"Karolinska Institutet"},{"author_name":"Anna Plym","author_inst":"Karolinska Institutet"},{"author_name":"Manuel R Teixeira","author_inst":"Porto Comprehensive Cancer Center"},{"author_name":"Luc Multigner","author_inst":"Institut de recherche en sant\u00e9, environnement et travail"},{"author_name":"Robin J Leach","author_inst":"University of Texas Health Science Center at San Antonio"},{"author_name":"Hermann Brenner","author_inst":"German Cancer Research Centre: Deutsches Krebsforschungszentrum"},{"author_name":"Esther M John","author_inst":"Stanford University"},{"author_name":"Radka Kaneva","author_inst":"Medical University of Sofia"},{"author_name":"Christopher J Logothetis","author_inst":"The University of Texas M. D. Anderson Cancer Center"},{"author_name":"Susan L Neuhausen","author_inst":"Beckman Research Institute of City of Hope"},{"author_name":"Piet Ost","author_inst":"Ghent University"},{"author_name":"Azad Razack","author_inst":"University of Malaya"},{"author_name":"Jay H Fowke","author_inst":"University of Tennessee Health Science Center"},{"author_name":"Marija Gamulin","author_inst":"University of Zagreb School of Medicine"},{"author_name":"Nawaid Usmani","author_inst":"University of Alberta"},{"author_name":"Frank Claessens","author_inst":"KU Leuven"},{"author_name":"Jose Esteban Castelao","author_inst":"Instituto de Investigaci\u00f3n Biom\u00e9dica Galicia Sur"},{"author_name":"Gyorgy Petrovics","author_inst":"Uniformed Services University"},{"author_name":"Marie-\u00c9lise Parent","author_inst":"Institut national de la recherche scientifique"},{"author_name":"Jennifer J Hu","author_inst":"The University of Miami School of Medicine"},{"author_name":"Wei Zheng","author_inst":"Vanderbilt University Medical Center"},{"author_name":"- The Profile Study Steering Committee","author_inst":"-"},{"author_name":"- UKGPCS collaborators","author_inst":"-"},{"author_name":"Zsofia Kote-Jarai","author_inst":"The Institute of Cancer Research"},{"author_name":"Rosalind A Eeles","author_inst":"The Institute of Cancer Research"},{"author_name":"- The PRACTICAL Consortium","author_inst":"-"},{"author_name":"- VA Million Veteran Program","author_inst":"-"},{"author_name":"Kara N Maxwell","author_inst":"Corporal Michael Crescenz Veterans Affairs Medical Center"},{"author_name":"Jason L Vassy","author_inst":"VA Boston Healthcare System"},{"author_name":"Tyler M Seibert","author_inst":"VA San Diego Healthcare System"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Psychosocial mediators for the impact of personal genomic risk information on melanoma prevention and early detection behaviors","rel_doi":"10.64898\/2026.05.07.26352695","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352695","rel_abs":"BackgroundIn the Melanoma Genomics Managing Your Risk Study, access to personal genomic risk testing led to improvements in some melanoma prevention and early detection behaviors.\n\nPurposeWe aimed to examine the hypothesized psychosocial mediators of the effects observed in the trial.\n\nMethodsAustralians of European ancestry without melanoma and aged 18-69 years were recruited via the national Medicare database and randomized to receive personal genomic risk information or usual care (N=1,025). Questionnaires were administered at baseline, 1-month post-intervention, and 12-months post-baseline to assess self-reported prevention and early detection behaviors and psychosocial measures. To identify potential mediators, we first evaluated the interventions effect on psychosocial measures and the associations between psychosocial measures and behavioral outcomes. We then estimated the natural indirect effects (NIEs) and their 95% confidence intervals (CIs) to quantify the effects mediated by potential mediators identified.\n\nResultsAmong participants with high traditional melanoma risk, the interventions effect on increased sun protection at 1-month was partially mediated by changes in perceived importance [NIE mean difference (95% CI): 0.02 (0.00, 0.04)] and perceived effectiveness [0.01 (0.00, 0.03)] of sun protection strategies. Among women, the interventions effect on increased whole-body skin examinations at 1-month was partially mediated by perceived capability to engage in skin examinations [NIE odds ratio (95% CI): 1.08 (1.00, 1.29)] and perceived control over detecting a future melanoma [1.13 (1.03, 1.32)].\n\nConclusionsThe effectiveness of precision prevention and early detection interventions may be enhanced by targeting key psychosocial mediators through tailored communication of personal melanoma risk.","rel_num_authors":5,"rel_authors":[{"author_name":"Sabrina E Wang","author_inst":"The Daffodil Centre, The University of Sydney, and Cancer Council NSW"},{"author_name":"David Espinoza","author_inst":"NHMRC Clinical Trials Centre, The University of Sydney"},{"author_name":"Serigne Lo","author_inst":"Melanoma Institute Australia, The University of Sydney"},{"author_name":"Amelia K Smit","author_inst":"The Daffodil Centre, The University of Sydney, and Cancer Council NSW"},{"author_name":"Anne  E. Cust","author_inst":"The Daffodil Centre, The University of Sydney, and Cancer Council NSW"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Decoding the diet-gut-liver axis: links between dietary pattern adherence, gut microbiome, and hepatic health","rel_doi":"10.64898\/2026.05.04.26352208","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.04.26352208","rel_abs":"Metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly becoming the leading cause of chronic liver disease and confers substantial cardiometabolic burden. Diet quality and gut microbiota composition have been implicated in MASLD development; however, the interplay among diet, gut microbiota, and hepatic health remains insufficiently characterized. Here, in 9,616 deeply phenotyped middle-aged participants (mean age 52 years) from the Human Phenotype Project, we investigated how five dietary quality indices capturing complementary dimensions of healthy eating, including plant-based (hPDI), Mediterranean-style (AMED), anti-inflammatory (rDII), anti-hyperinsulinemic (rEDIH), and overall quality (AHEI), relate to gut microbial composition and liver steatosis. Dietary pattern scores were derived from two-week continuous diet logs, gut microbiota was characterized by shotgun metagenomic sequencing, and hepatic health was assessed by both ultrasound-derived metrics and prevalent MASLD status. Adherence to each of the five healthy dietary patterns was inversely associated with MASLD prevalence and positively associated with liver speed of sound (SoS), an ultrasound-derived metric that correlates inversely with hepatic fat content. Across all five dietary patterns, greater adherence was consistently associated with 138 gut microbial species, including inverse associations with Flavonifractor plautii, Dysosmobacter welbionis, Ruthenibacterium lactatiformans, Bilophila wadsworthia, and Phocea massiliensis. These five species were also associated with lower liver SoS and higher odds of prevalent MASLD, emerging as potential mediators of the diet-liver relationship in cross-sectional mediation analyses after adjustment for body mass index (BMI). This study identifies candidate microbial targets for future interventional studies investigating dietary strategies for MASLD prevention.","rel_num_authors":10,"rel_authors":[{"author_name":"Keyong Deng","author_inst":"Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Quinten R. Ducarmon","author_inst":"Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Anastasia Godneva","author_inst":"Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. Department of Molecular Cell Biology, Weizmann Institute"},{"author_name":"Zheqing Zhang","author_inst":"Department of Nutrition and Food Hygiene, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University"},{"author_name":"Astrid van Hylckama Vlieg","author_inst":"Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Frits R. Rosendaal","author_inst":"Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Georg Zeller","author_inst":"Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Eran Segal","author_inst":"Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE"},{"author_name":"Ruifang Li-Gao","author_inst":"Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"- DIYUFOOD consortium","author_inst":""}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Scalable deep-learning-based inference of time-varying transmission dynamics from outbreak phylogenies","rel_doi":"10.64898\/2026.05.07.26352673","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352673","rel_abs":"Infectious disease dynamics can be inferred from pathogen genomic data using phylodynamic methods, but the applicability of many such approaches to large data sets is constrained by computational cost. Recent deep-learning approaches to phylodynamics have improved scalability, yet challenges remain when genetic divergence is limited during fast spreading outbreaks. To address this, we use pathogen-specific models to show that deep-learning models trained on outbreak-like phylogenies can accurately estimate the reproductive number (R) when both the birth-death model and the expected phylogenetic resolution are matched to the target pathogen, highlighting the importance of realistic training conditions. Focusing on three major respiratory pathogens of public health importance (SARS-CoV-2, seasonal human influenza virus, and respiratory syncytial virus (RSV)), we introduce PhyloRt, a scalable framework for estimating the time-varying reproductive number (Rt) from large outbreak phylogenies. PhyloRt decomposes large trees into overlapping subtrees and applies a hierarchical deep-learning-based inference strategy to classify subtrees as exhibiting constant or time-varying reproduction numbers, enabling identifiable and computationally efficient estimation of Rt as a piecewise-constant trajectory through time. Applications to SARS-CoV-2 and influenza outbreaks show that PhyloRt recovers transmission dynamics consistent with estimates derived from mathematical epidemiological and Bayesian phylodynamic analyses. Our work enables scalable and rapid estimation of time-varying transmission dynamics from very large-scale outbreak genomic data sets, supporting real-time genomic epidemiology of emerging pathogens.\n\nSignificanceEstimating changes in transmission dynamics over time is important for responding to infectious disease outbreaks. Current methods mostly rely on reported case data from epidemiological surveillance, which can be biased or incomplete due to variable testing capabilities, particularly in resource-limited settings. A complementary approach is to use viral genomes as an alternative data source. However, inferences from genomic data can be computationally intensive and have mainly been applied retrospectively. We present PhyloRt, a scalable deep-learning-based phylodynamic framework that enables fast inference of the time-varying reproductive number (Rt) from large outbreak phylogenies. Our approach is widely applicable and provides a practical approach to monitoring epidemic dynamics, complementing traditional surveillance and supporting timely public health decision-making.","rel_num_authors":11,"rel_authors":[{"author_name":"RUOPENG XIE","author_inst":"University of Oxford"},{"author_name":"Anna Zhukova","author_inst":"Institut Pasteur"},{"author_name":"Pablo G. Pena","author_inst":"Real Jardin Botanico CSIC"},{"author_name":"Guillermo Iglesias","author_inst":"ETSI de Sistemas Informaticos"},{"author_name":"Shu Hu","author_inst":"Fudan University"},{"author_name":"Jiawei Wang","author_inst":"University of Bath"},{"author_name":"Tim K Tsang","author_inst":"University of Hong Kong"},{"author_name":"Vijaykrishna Dhanasekaran","author_inst":"Univesity of Hong Kong"},{"author_name":"Moritz U. G. Kraemer","author_inst":"University of Oxford"},{"author_name":"Oliver G. Pybus","author_inst":"University of Oxford"},{"author_name":"Olivier Gascuel","author_inst":"Museum National dHistoire Naturelle"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Patterns of emergency department use among young people with bipolar disorder: A data linkage cohort study","rel_doi":"10.64898\/2026.05.07.26352617","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352617","rel_abs":"ObjectiveTo charactertise emergency department (ED) use among young people with bipolar disorder (BD) and compare patterns to those observed in anxiety, depressive, and psychotic disorders.\n\nDesign, setting and participantsData linkage study using administrative ED presentation records (January 2020 to October 2020) and a transdiagnostic youth mental health cohort of 2243 individuals aged 12-30 years in New South Wales, Australia.\n\nMain outcome measuresED presentation patterns (any presentation, frequency, and rates) and reasons for presentation (mental health-related and non-mental health-related).\n\nResultsOf the 354 young people with BD, 309 (87.3%) presented to an ED at least once. ED presentation rates were higher for BD than for anxiety (incidence rate ratio [IRR]=1.82, p<.001) and depressive disorders (IRR=1.32, p<.001), but similar to psychotic disorders (IRR=0.91, p=.379). Differences were primarily driven by mental health-related presentations. Recurrent mental health presentations were associated with illness progression (clinical stage and functional impairment) rather than diagnosis. However, the likelihood of mental health-related presentations remained higher in BD compared with anxiety and depressive disorders after adjustment.\n\nConclusionsYoung people with BD have high rates of ED use, comparable to those with psychotic disorders. Although mental health-related presentations are more common in BD than in anxiety and depressive disorders, recurrence is largely explained by markers of illness progression. These findings highlight the need for community-based services that provide continuous and coordinated care for young people with complex mental health needs.","rel_num_authors":12,"rel_authors":[{"author_name":"Ashlee Turner Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Ian B Hickie Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Mathew Varidel Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Nicholas Ho Mr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Catherine M McHugh Dr","author_inst":"Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney NSW Australia"},{"author_name":"Jacob J Crouse Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Joanne S Carpenter Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Alissa Nichles Ms","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Natalia Zmicerevska Ms","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Yun Ju (Christine) Song Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Elizabeth M Scott A\/Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Frank Iorfino A\/Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Patterns of emergency department use among young people with bipolar disorder: A data linkage cohort study","rel_doi":"10.64898\/2026.05.07.26352617","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352617","rel_abs":"ObjectiveTo charactertise emergency department (ED) use among young people with bipolar disorder (BD) and compare patterns to those observed in anxiety, depressive, and psychotic disorders.\n\nDesign, setting and participantsData linkage study using administrative ED presentation records (January 2020 to October 2020) and a transdiagnostic youth mental health cohort of 2243 individuals aged 12-30 years in New South Wales, Australia.\n\nMain outcome measuresED presentation patterns (any presentation, frequency, and rates) and reasons for presentation (mental health-related and non-mental health-related).\n\nResultsOf the 354 young people with BD, 309 (87.3%) presented to an ED at least once. ED presentation rates were higher for BD than for anxiety (incidence rate ratio [IRR]=1.82, p<.001) and depressive disorders (IRR=1.32, p<.001), but similar to psychotic disorders (IRR=0.91, p=.379). Differences were primarily driven by mental health-related presentations. Recurrent mental health presentations were associated with illness progression (clinical stage and functional impairment) rather than diagnosis. However, the likelihood of mental health-related presentations remained higher in BD compared with anxiety and depressive disorders after adjustment.\n\nConclusionsYoung people with BD have high rates of ED use, comparable to those with psychotic disorders. Although mental health-related presentations are more common in BD than in anxiety and depressive disorders, recurrence is largely explained by markers of illness progression. These findings highlight the need for community-based services that provide continuous and coordinated care for young people with complex mental health needs.","rel_num_authors":12,"rel_authors":[{"author_name":"Ashlee Turner Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Ian B Hickie Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Mathew Varidel Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Nicholas Ho Mr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Catherine M McHugh Dr","author_inst":"Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney NSW Australia"},{"author_name":"Jacob J Crouse Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Joanne S Carpenter Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Alissa Nichles Ms","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Natalia Zmicerevska Ms","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Yun Ju (Christine) Song Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Elizabeth M Scott A\/Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Frank Iorfino A\/Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Self and Caregiver Reported Choice Making in Autistic Adults: Development and Validation of the AASPIRE Choices and Decisions Scale","rel_doi":"10.64898\/2026.05.07.26352693","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352693","rel_abs":"Self-determination has been assessed as an internal psychological construct. External factors may also affect self-determination, but opportunities to make choices and decisions remain understudied. We developed and evaluated the AASPIRE-Choices and Decisions Scale (AASPIRE-CDS), a new measure of autistic adults opportunities to make choices and decisions, using a community-based participatory approach. We created and refined the AASPIRE-CDS through an iterative process. Data, from the AASPIRE Outcomes Project, included 839 autistic adults participating through direct report, supported direct report, and caregiver report (CR).\n\nExploratory and confirmatory analyses supported a unidimensional structure. Measurement invariance analyses supported configural, metric, and partial scalar invariance across report type without CR, and across living status, with and without CR. The AASPIRE-CDS showed high internal consistency, test-retest reliability, and responsiveness to change over time. Convergent validity analyses showed that higher AASPIRE-CDS scores were associated with greater self-determination and communication fluency, more independent living, and fewer support needs. The AASPIRE-CDS showed weaker (albeit significant) associations with quality of life, overall health, and employment satisfaction than the self-determination measure showed with these variables. This pattern suggests that opportunities for choice-making are related to, but distinct from, commonly used measures of self-determination. Findings support the AASPIRE-CDS as a valid and reliable measure of choice-making opportunities in autistic adults across support needs but suggest caution interpreting CR. They underscore the importance of supporting autistic adults choice-making and evaluating opportunities for choice alongside internal self-determination. Future research should use larger CR samples to examine the validity of caregiver-reported choice-making opportunities.","rel_num_authors":15,"rel_authors":[{"author_name":"So Yoon Kim","author_inst":"Korea University"},{"author_name":"Kristen Gillespie-Lynch","author_inst":"City University of New York"},{"author_name":"Steven Kapp","author_inst":"University of Portsmouth"},{"author_name":"Liu-Qin Yang","author_inst":"Portland State University"},{"author_name":"Anna Furra Wallington","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Dora Raymaker","author_inst":"Portland State University"},{"author_name":"Ian Moura","author_inst":"Brandeis University"},{"author_name":"Katherine McDonald","author_inst":"Syracuse University"},{"author_name":"Joelle Maslak","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Rachel Kripke-Ludwig","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Andrea Joyce","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Willi Horner-Johnson","author_inst":"Oregon Health & Science University"},{"author_name":"Emanuel Frowner","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Mary Baker-Ericzen","author_inst":"San Diego State University"},{"author_name":"Christina Nicolaidis","author_inst":"Portland State University"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@c4b67borg.highwire.dtl.DTLVardef@a84ac2org.highwire.dtl.DTLVardef@c44470org.highwire.dtl.DTLVardef@1694ee6_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@c4b67borg.highwire.dtl.DTLVardef@a84ac2org.highwire.dtl.DTLVardef@c44470org.highwire.dtl.DTLVardef@1694ee6_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@c4b67borg.highwire.dtl.DTLVardef@a84ac2org.highwire.dtl.DTLVardef@c44470org.highwire.dtl.DTLVardef@1694ee6_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"}]}