{"gname":"Rutgers University","grp_id":"31","rels":[{"rel_title":"Markers of environmental enteric dysfunction are associated with changes in pharmacokinetics of praziquantel in preschool age children with Schistosoma mansoni infection in Albertine region of Uganda","rel_doi":"10.64898\/2026.09.02.26362031","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362031","rel_abs":"Introduction Praziquantel (PZQ) is the only widely available chemotherapy that is effective against all species of schistosomes. Environmental enteric dysfunction (EED) is an acquired intestinal disorder of altered gut function whose effect on drug pharmacokinetics has not been directly explored. Methods Preschool-age children infected with S. mansoni were randomized to receive 40mg\/kg or 80mg\/kg of crushed PZQ tablets. Plasma PZQ concentrations were quantified using ultra-high performance liquid chromatography mass spectrometry. Maximum concentration (Cmax), time to Cmax (Tmax) and area under the curve (AUC) of PZQ were calculated. Biomarkers of intestinal inflammation (stool calprotectin), epithelial damage (plasma Intestinal Fatty Acid Binding Protein (IFABP)), permeability (urine lactulose:mannitol (LM) ratio and alpha-1 antitrypsin (AAT)), microbial translocation (plasma Endotoxin core antibodies (EndoCAb), systemic inflammation (plasma C-reactive protein (CRP)), and presence of faecal occult blood (FOB) were measured. Using linear regression, we assessed association of AUC, Cmax, Tmax and R- to S-PZQ exposure with each biomarker, adjusting for dose, age, and sex. Results Of the 184 participants included in the final analysis, 91 received 40mg\/kg and 93 received 80mg\/kg of PZQ. The Tmax was associated with LM ratio ({beta}=0.06, 95% CI 0.02 - 0.11, p=0.003) and calprotectin ({beta}=0.001, 0.0002 - 0.002, p=0.013). CRP was associated with AUC ({beta}=0.13, 95% CI 0.06 - 0.21, p=0.001) and Cmax ({beta}=0.12, 95% CI 0.05 - 0.21, p=0.002). Calprotectin ({beta}=0.12, 95% CI 0.04 - 0.20, p=0.003) and AAT ({beta}=0.09, 95% CI 0.02 - 0.15, p=0.008) were associated with a higher R-PZQ\/S-PZQ AUC ratio, while CRP was not (p=0.38). Conclusion Elevated intestinal inflammatory markers were associated with increased Tmax, indicating reduced rate of absorption and relative increase in exposure to the active R-enantiomer. Systemic inflammation was associated with higher Cmax and AUC, implying increased exposure to PZQ. This is the first report linking alterations in praziquantel pharmacokinetics to EED markers and systemic inflammation.","rel_num_authors":16,"rel_authors":[{"author_name":"Andrew Edielu","author_inst":"MRC\/UVRI and LSHTM Uganda Research Unit"},{"author_name":"Bonniface Obura","author_inst":"University of Liverpool"},{"author_name":"Patrice  A. Mawa","author_inst":"MRC\/UVRI and LSHTM Uganda Research Unit"},{"author_name":"Martin  J. Holland","author_inst":"London School of Hygiene & Tropical Medicine"},{"author_name":"Emily  L. Webb","author_inst":"London School of Hygiene & Tropical Medicine"},{"author_name":"Alison  M. Elliott","author_inst":"MRC\/UVRI and LSHTM Uganda Research Unit"},{"author_name":"Gloria  Kakoba Ayebazibwe","author_inst":"MRC\/UVRI and LSHTM Uganda Research Unit"},{"author_name":"Hannah  Wei Wu","author_inst":"Rhode Island Hospital"},{"author_name":"Nicholas Mancini","author_inst":"University of Rhode Island"},{"author_name":"Fabian  C. Fischer","author_inst":"University of Rhode Island"},{"author_name":"Susannah Colt","author_inst":"Brown University Warren Alpert Medical School"},{"author_name":"Meagan  A. Barry","author_inst":"Brown University Warren Alpert Medical School"},{"author_name":"William Hope","author_inst":"University of Liverpool"},{"author_name":"Catriona Waitt","author_inst":"University of Liverpool"},{"author_name":"Jennifer  F. Friedman","author_inst":"Rhode Island Hospital"},{"author_name":"Amaya  L. Bustinduy","author_inst":"London School of Hygiene & Tropical Medicine"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Revision Behavior and Explainability in Adaptive LLM Swarms for ICU Mortality Risk Prediction: A Two-Dataset Evaluation","rel_doi":"10.64898\/2026.09.01.26361960","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361960","rel_abs":"Purpose: To evaluate how adaptive LLM swarm revision changes ICU mortality-risk outputs and to characterize evidence use, explanation indicators, auditability, and computational burden in final adaptive outputs relative to an independently executed fixed-voting (FV) architecture. Methods: We retrospectively analyzed 1,607 eICU encounters (1,500 stays) and 1,607 ICU-2012 encounters. Initial (ASI) and final (ASF) adaptive outputs were compared within runs for revision engagement and risk-score drift; final ASF and separately generated FV outputs were compared for evidence use, explanation indicators, auditability, and computation. Paired differences and 95% confidence intervals used 10,000 hospital-stay-clustered bootstrap replicates. Results: At least one specialist revision trace occurred in 84.32% of eICU and 56.44% of ICU-2012 encounters. Mean ASF-minus-ASI risk-score changes were +0.0810 and +0.0539; 757 of 761 0.50-threshold crossings moved toward mortality, without clear AUROC or AUPRC improvement. In the independent benchmark, ASF explanations contained 1.26 and 0.55 more supporting-evidence items than FV, but counterevidence acknowledgement was 21.59 and 7.47 percentage points lower and unsupported-claim flags were 1.43 and 0.68 points higher. All final records met the reconstruction-completeness criterion, although ASF generated more warnings and required 1.97 and 1.52 times the FV runtime. Conclusion: Adaptive revision materially changed swarm operating behaviour. Independently, final ASF outputs showed greater supporting-evidence use but less balanced evidence engagement, more process warnings, and greater computational burden than FV. These automated artifact-level findings do not establish superior explanation quality or isolate revision as their cause.","rel_num_authors":1,"rel_authors":[{"author_name":"Callum Anderson","author_inst":"University of Ottawa"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Revision Behavior and Explainability in Adaptive LLM Swarms for ICU Mortality Risk Prediction: A Two-Dataset Evaluation","rel_doi":"10.64898\/2026.09.01.26361960","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361960","rel_abs":"Purpose: To evaluate how adaptive LLM swarm revision changes ICU mortality-risk outputs and to characterize evidence use, explanation indicators, auditability, and computational burden in final adaptive outputs relative to an independently executed fixed-voting (FV) architecture. Methods: We retrospectively analyzed 1,607 eICU encounters (1,500 stays) and 1,607 ICU-2012 encounters. Initial (ASI) and final (ASF) adaptive outputs were compared within runs for revision engagement and risk-score drift; final ASF and separately generated FV outputs were compared for evidence use, explanation indicators, auditability, and computation. Paired differences and 95% confidence intervals used 10,000 hospital-stay-clustered bootstrap replicates. Results: At least one specialist revision trace occurred in 84.32% of eICU and 56.44% of ICU-2012 encounters. Mean ASF-minus-ASI risk-score changes were +0.0810 and +0.0539; 757 of 761 0.50-threshold crossings moved toward mortality, without clear AUROC or AUPRC improvement. In the independent benchmark, ASF explanations contained 1.26 and 0.55 more supporting-evidence items than FV, but counterevidence acknowledgement was 21.59 and 7.47 percentage points lower and unsupported-claim flags were 1.43 and 0.68 points higher. All final records met the reconstruction-completeness criterion, although ASF generated more warnings and required 1.97 and 1.52 times the FV runtime. Conclusion: Adaptive revision materially changed swarm operating behaviour. Independently, final ASF outputs showed greater supporting-evidence use but less balanced evidence engagement, more process warnings, and greater computational burden than FV. These automated artifact-level findings do not establish superior explanation quality or isolate revision as their cause.","rel_num_authors":1,"rel_authors":[{"author_name":"Callum Anderson","author_inst":"University of Ottawa"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Evaluating synthetic-data fidelity in two-group biomedical studies: a multidimensional validation framework","rel_doi":"10.64898\/2026.08.31.26361878","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361878","rel_abs":"Synthetic data increasingly support model development and privacy-conscious sharing in biomedicine. Two-group studies require synthetic data to reproduce within-group structure and between-group differences, yet marginal agreement or predictive performance may obscure multivariate and conditional-dependence changes. We present a multidimensional validation framework for class-conditional synthetic data and apply it to three datasets spanning sample-size and dimensionality regimes. Two controls and four generators spanning mixture, interpolation, hybrid, and latent-variable architectures (GMM, SMOTE, GMM-SMOTE, and CVAE, respectively) were assessed using predictive utility, real-synthetic distinguishability, marginal agreement, PCA and t-SNE geometry, pairwise dependence, and Graphical LASSO networks. Noise perturbation, within-class permutation, and reverse ablation probed the sources of real--synthetic distinguishability. Across 18 dataset--method comparisons, discriminator AUC ranged from 0.55 to 1.00, while mean feature-level KS statistics ranged from 0.027 to 0.282. Thus, strong performance under individual criteria coexisted with detectable differences and lost or synthetic-only dependencies. Rather than assigning a single fidelity score, the framework supports multidimensional fidelity reporting as a minimum standard for shared synthetic biomedical data.","rel_num_authors":3,"rel_authors":[{"author_name":"Tony Tran","author_inst":"University of Guelph"},{"author_name":"Mohammad Sajjad Ghaemi","author_inst":"National Research Council Canada"},{"author_name":"Chapin Stephen Korosec","author_inst":"University of Guelph"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Ejection fraction on a budget: mapping the accuracy-compute trade space for video-based ejection fraction estimation","rel_doi":"10.64898\/2026.09.02.26362055","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362055","rel_abs":"Deep video networks estimate left ventricular ejection fraction (EF) from echocardiograms with expert-level accuracy, but the compute cost of running them is rarely reported. This leaves anyone building a handheld or bedside tool without clear guidance on what to deploy. We measured the accuracy-versus-compute tradeoff for EF estimation on EchoNet-Dynamic by training 22 configurations that varied clip length (8 to 64 frames), frame sampling period (1 to 4), and backbone: R(2+1)D-18, R3D-18, MC3-18, X3D-S, X3D-M, and a 2D ResNet-18 with temporal pooling. All models used one fixed training recipe. Every configuration was evaluated for accuracy using mean absolute error, R-squared, and Bland-Altman agreement; clinical utility using sensitivity and specificity at the clinically relevant EF cutoffs of 40% and 50%, plus error stratified by EF band; and cost using floating-point operations, parameter count, GPU and CPU latency, and peak memory under a single frozen measurement protocol. We stress-tested the main findings with replicate training seeds. Sparse temporal sampling outperformed dense sampling at matched frame budgets. A sampling period of 4 outperformed a period of 1 at every tested frame count while also reducing per-video cost. In the seed-replicated 8-frame comparison, the advantage averaged one full point lower mean absolute error across all nine cross-seed pairings. A standard R3D-18 achieved the best accuracy in the study, with a mean absolute error of 3.99, while requiring 19% less CPU latency than the reference configuration. A 16-frame, period-4 R(2+1)D-18 cut reference cost in half with no statistically confirmed loss in accuracy. Removing temporal modeling entirely substantially reduced accuracy, with a mean absolute error of 5.65, setting a practical floor for how inexpensive this task can be. We release the code, cost-measurement protocol, and per-configuration results.","rel_num_authors":3,"rel_authors":[{"author_name":"Aryan Pandey","author_inst":"Vanderbilt University"},{"author_name":"Kushaan Sharma","author_inst":"The University of Texas at Austin"},{"author_name":"Aryan Shah","author_inst":"Texas A and M University"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Metabolomic profiling in treated mucopolysaccharidosis IH reveals candidate biomarkers and adjunctive therapeutic pathways","rel_doi":"10.64898\/2026.09.04.26362233","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.04.26362233","rel_abs":"Both severe (Hurler syndrome; MPS IH) and attenuated (Hurler-Scheie or Scheie syndrome; MPS IA) forms of mucopolysaccharidosis I (MPS I) arise from the same underlying enzyme deficiency; however, they differ in the onset, persistence, and severity of key clinical features. These include skeletal abnormalities, joint contractures, cardiac disease, and neurocognitive and neurobehavioral impairment, which is not fully alleviated with either enzyme replacement (ERT) or hematopoietic cell transplantation (HCT). The objective of this study was to identify metabolic differences between MPS IH and MPS IA that could result in meaningful biomarkers and targeted adjunctive therapies to address unmet clinical needs in MPS IH treated with HCT. We performed plasma metabolomics in patients with MPS IH treated with HCT (N=17) or MPS IA treated with ERT (N=8). Welchs two-sample t-test was used to identify metabolites that differed significantly between groups. Statistical significance was evaluated based on p<0.05. After controlling for multiple testing, we used a false discovery rate of q<0.05. We identified 125 compounds that were significantly different between MPS IH and MPS IA. Of those metabolites, 14 had a q<0.05.  Individuals with MPS IH had increased metabolites in the sphingolipid, beta-oxidation, amino acid catabolism, and glycosaminoglycan pathways compared to MPS IA. Persistent metabolic differences were observed in MPS IH treated with HCT compared to MPS IA treated with ERT, pointing towards additional biological processes that may contribute to disease progression in MPS IH after HCT. Although these results require further confirmation, they provide a foundation to guide future investigations towards potential biomarkers or targeted adjunctive therapies.","rel_num_authors":11,"rel_authors":[{"author_name":"Troy  C. Lund","author_inst":"University of Minnesota"},{"author_name":"Ryan  H. Peretz","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"Patricia  I. Dickson","author_inst":"Washington University in St Louis School of Medicine"},{"author_name":"Jennifer  K. Yee","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"Michelina Iacovino","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"Kent  D. Taylor","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"David Elashoff","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"Ellen Fung","author_inst":"UCSF Benioff Children's Hospital"},{"author_name":"Bradley  S. Miller","author_inst":"University of Minnesota"},{"author_name":"Paul  J. Orchard","author_inst":"University of Minnesota"},{"author_name":"Lynda  E Polgreen","author_inst":"The Lundquist Institute for Biomedical Innovation: The Lundquist Institute"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Metabolomic profiling in treated mucopolysaccharidosis IH reveals candidate biomarkers and adjunctive therapeutic pathways","rel_doi":"10.64898\/2026.09.04.26362233","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.04.26362233","rel_abs":"Both severe (Hurler syndrome; MPS IH) and attenuated (Hurler-Scheie or Scheie syndrome; MPS IA) forms of mucopolysaccharidosis I (MPS I) arise from the same underlying enzyme deficiency; however, they differ in the onset, persistence, and severity of key clinical features. These include skeletal abnormalities, joint contractures, cardiac disease, and neurocognitive and neurobehavioral impairment, which is not fully alleviated with either enzyme replacement (ERT) or hematopoietic cell transplantation (HCT). The objective of this study was to identify metabolic differences between MPS IH and MPS IA that could result in meaningful biomarkers and targeted adjunctive therapies to address unmet clinical needs in MPS IH treated with HCT. We performed plasma metabolomics in patients with MPS IH treated with HCT (N=17) or MPS IA treated with ERT (N=8). Welchs two-sample t-test was used to identify metabolites that differed significantly between groups. Statistical significance was evaluated based on p<0.05. After controlling for multiple testing, we used a false discovery rate of q<0.05. We identified 125 compounds that were significantly different between MPS IH and MPS IA. Of those metabolites, 14 had a q<0.05.  Individuals with MPS IH had increased metabolites in the sphingolipid, beta-oxidation, amino acid catabolism, and glycosaminoglycan pathways compared to MPS IA. Persistent metabolic differences were observed in MPS IH treated with HCT compared to MPS IA treated with ERT, pointing towards additional biological processes that may contribute to disease progression in MPS IH after HCT. Although these results require further confirmation, they provide a foundation to guide future investigations towards potential biomarkers or targeted adjunctive therapies.","rel_num_authors":11,"rel_authors":[{"author_name":"Troy  C. Lund","author_inst":"University of Minnesota"},{"author_name":"Ryan  H. Peretz","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"Patricia  I. Dickson","author_inst":"Washington University in St Louis School of Medicine"},{"author_name":"Jennifer  K. Yee","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"Michelina Iacovino","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"Kent  D. Taylor","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"David Elashoff","author_inst":"University of California Los Angeles David Geffen School of Medicine"},{"author_name":"Ellen Fung","author_inst":"UCSF Benioff Children's Hospital"},{"author_name":"Bradley  S. Miller","author_inst":"University of Minnesota"},{"author_name":"Paul  J. Orchard","author_inst":"University of Minnesota"},{"author_name":"Lynda  E Polgreen","author_inst":"The Lundquist Institute for Biomedical Innovation: The Lundquist Institute"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"When to return genomic newborn screening results: health care professional perspectives","rel_doi":"10.64898\/2026.09.02.26362059","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362059","rel_abs":"Background and Objectives: Dozens of projects around the world are sequencing the genomes of healthy babies near birth. Genomic information becomes actionable across infancy, childhood and adulthood, making timing of return a design choice for programs. We report the first study of health care professionals' views on timing. Methods: We conducted a qualitative interview study of US-based clinical geneticists, genetic counselors, laboratory personnel, pediatric primary care clinicians and genomic screening implementers. Participants responded to three strategies: staged throughout childhood when information becomes actionable, all at birth with adult-actionable results deferred, and all at birth. Transcripts were analyzed using framework analysis. Results: We interviewed 52 individuals; 39 were asked directly about timing. Giving parents a choice, raised by participants rather than presented, was the most endorsed position (18), ahead of staging across childhood (12). Many viewed staging as preferable in theory, but feasibility concerns weighed against it, including that 'actionability' was not a robust enough concept. A further concern was that parents would not grasp the distinction between data generated and data examined, on which staging depends. Where information is staged, participants saw a role for adolescent assent; where it is not, disclosure to the developing child becomes important, and participants identified a lack of support for parents. Conclusions: Tying the return of information to the age of actionability is intuitive but hard to operationalize. Parental choice was the most endorsed position but will only be viable with decision support and guidance for disclosure to children.","rel_num_authors":7,"rel_authors":[{"author_name":"Anna C F Lewis","author_inst":"Mass General Brigham; Harvard Medical School; Broad Institute"},{"author_name":"Adam H Buchanan","author_inst":"Geisinger"},{"author_name":"Aaron J Goldenberg","author_inst":"Case Western Reserve University"},{"author_name":"Bartha M Knoppers","author_inst":"McGill University"},{"author_name":"Amy L McGuire","author_inst":"Baylor College of Medicine"},{"author_name":"Robert C Green","author_inst":"Brigham and Women's Hospital, Broad Institute, Harvard Medical School"},{"author_name":"Ingrid A Holm","author_inst":"Harvard Medical School; Boston Children's Hospital"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Venetoclax-based Therapy Improves Outcomes across the Evolving Biology of t(11;14) Multiple Myeloma","rel_doi":"10.64898\/2026.09.02.26361956","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26361956","rel_abs":"Background: Translocation t(11;14) defines a biologically distinct subset of multiple myeloma (MM) enriched for BCL2 dependency. Treatment with venetoclax, selective BCL2 inhibitor, has shown varied responses in clinical trials and retrospective cohorts. Efficacy of venetoclax-based combination therapies and optimal timing of treatment in t(11;14) MM patients remain incompletely characterized. Methods: We have compared the overall survival of t(11;14) MM patients who received Venetoclax (N = 97) at any moment in time, to those who never did (N =284), in the largest retrospective cohort (N = 381) reported to date. We used longitudinal fluorescence in situ hybridization (FISH) to assess cytogenetic evolution and genomic complexity. We performed transcriptomic profiling of CD138 enriched tumors using RNA sequencing in a subset of samples and gene expression signatures (UAMS\/HALLMARKS) were used to define molecular subtypes. Ex vivo drug sensitivity assays integrated with paired RNA sequencing were used to identify subtype specific therapeutic vulnerabilities and rational venetoclax based combination strategies. Results: Venetoclax exposure was associated with an improvement of median overall survival by nearly four years compared to non VEN exposed patients (p = 0.0003). Longitudinal cytogenetic analysis demonstrated stability of the primary t(11;14) translocation over time, while secondary abnormalities tend to accumulate, including those harboring high-risk secondary cytogenetic abnormalities such as del13q, amp\/gain1q21, del17p, and del1p, resulting in increasing genomic complexity with disease progression. Transcriptomic analyses identified selective enrichment of CD1\/CD2 signature (associated with t(11;14) NDMM) by single sample gene set enrichment analysis as a marker of prolonged progression free survival. However, patients with more than five prior lines of therapy were enriched for transcriptionally complex biology characterized by CD1\/CD2 with either proliferative\/hypermetabolic or inflammatory transcriptional programming which were associated with inferior outcomes with VEN based treatment. Ex vivo drug sensitivity profiling revealed subtype specific vulnerabilities, identifying daratumumab, lenalidomide, ixazomib, and panobinostat as rational partners for venetoclax depending on transcriptional context. Conclusion: Venetoclax-based therapy significantly improved overall survival in t(11;14) MM. Clinical benefit, defined by improved progression free survival, was greatest when venetoclax was administered earlier, preceding the emergence of transcriptomic reprogramming that reduces BCL2 dependency. These findings support transcriptomic biomarker guided, subtype specific venetoclax based treatment strategies to optimize outcomes in patients with t(11;14) MM.","rel_num_authors":16,"rel_authors":[{"author_name":"Praneeth Reddy Sudalagunta","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Filip Ionescu","author_inst":"Mon Health Cancer Center"},{"author_name":"Rafael Renatino Canevarolo","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Maria Coehlo Siqueira Silva","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Daniel DeAvila","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Mark B. Meads","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Xiaohong Zhao","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Angel Perez","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Dimitrios Drekolias","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Ruxandra Irimia","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Shrinjaya Thapa","author_inst":"Swedish Cancer Institute"},{"author_name":"Parth Patel","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Rachid Baz","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Kenneth H. Shain","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Ariosto Siqueira Silva","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"},{"author_name":"Ariel Grajales-Cruz","author_inst":"H. Lee Moffitt Cancer Center & Research Institute"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Socioeconomic disparities in substance use disorder prevalence and severity across the life course","rel_doi":"10.64898\/2026.09.03.26362182","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.03.26362182","rel_abs":"Gradients across socioeconomic status (SES) exist across most health conditions. While there has been significant attention devoted to the relationship between SES and physical and mental health, less research has examined how these relate to substance use disorders (SUD). In the current study, we use data from the prospective study within the Collaborative Study on the Genetics of Alcoholism (COGA) to explore the relationship between SES (in early life and adulthood) with multiple substance use disorders (alcohol, tobacco, cannabis, opioid, cocaine, and other substances). We find that early life SES (parental education and income) are associated with lower odds of lifetime diagnoses and lower severity for tobacco (TUD) and cannabis (CUD) use disorders. Associations with TUD remain after adjusting for demographic and familial risk, but associations with CUD were null after including familial risk factors. In participants aged 25+, adult education was associated with lower odds of diagnosis and severity for each of the SUD considered, conditional on early life SES, adult income, sociodemographic characteristics, and familial risk for SUD. Exploratory analyses of changes in SES revealed that those who were upwardly or downwardly mobile were at the lowest or greatest risk for more severe SUD across multiple substances, respectively. Our results demonstrate the relevance of both early life and adult SES in SUD risk. Early life conditions seem particularly relevant for specific substances while greater adult education was associated with reduced risk across all forms of SUDs.","rel_num_authors":11,"rel_authors":[{"author_name":"Peter B. Barr","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Institute for Genomics in Health (IGH), SUNY Downstate"},{"author_name":"Sally I-Chun Kuo","author_inst":"Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ"},{"author_name":"Megan E. Cooke","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Institute for Genomics in Health (IGH), SUNY Downstate"},{"author_name":"Henri M. Garrison-Desany","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Institute for Genomics in Health (IGH), SUNY Downstate"},{"author_name":"Gayathri Pandey","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY"},{"author_name":"Kathleen K. Bucholz","author_inst":"Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO"},{"author_name":"Howard J. Edenberg","author_inst":"Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN Department of Biochemistry and Molecular Biology, School "},{"author_name":"Sivan Kinreich","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY"},{"author_name":"Bernice Porjesz","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Institute for Genomics in Health (IGH), SUNY Downstate"},{"author_name":"Jessica E. Salvatore","author_inst":"Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ"},{"author_name":"Jacquelyn L. Meyers","author_inst":"VA New York Harbor Healthcare System, Brooklyn, NY Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Ins"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Socioeconomic disparities in substance use disorder prevalence and severity across the life course","rel_doi":"10.64898\/2026.09.03.26362182","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.03.26362182","rel_abs":"Gradients across socioeconomic status (SES) exist across most health conditions. While there has been significant attention devoted to the relationship between SES and physical and mental health, less research has examined how these relate to substance use disorders (SUD). In the current study, we use data from the prospective study within the Collaborative Study on the Genetics of Alcoholism (COGA) to explore the relationship between SES (in early life and adulthood) with multiple substance use disorders (alcohol, tobacco, cannabis, opioid, cocaine, and other substances). We find that early life SES (parental education and income) are associated with lower odds of lifetime diagnoses and lower severity for tobacco (TUD) and cannabis (CUD) use disorders. Associations with TUD remain after adjusting for demographic and familial risk, but associations with CUD were null after including familial risk factors. In participants aged 25+, adult education was associated with lower odds of diagnosis and severity for each of the SUD considered, conditional on early life SES, adult income, sociodemographic characteristics, and familial risk for SUD. Exploratory analyses of changes in SES revealed that those who were upwardly or downwardly mobile were at the lowest or greatest risk for more severe SUD across multiple substances, respectively. Our results demonstrate the relevance of both early life and adult SES in SUD risk. Early life conditions seem particularly relevant for specific substances while greater adult education was associated with reduced risk across all forms of SUDs.","rel_num_authors":11,"rel_authors":[{"author_name":"Peter B. Barr","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Institute for Genomics in Health (IGH), SUNY Downstate"},{"author_name":"Sally I-Chun Kuo","author_inst":"Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ"},{"author_name":"Megan E. Cooke","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Institute for Genomics in Health (IGH), SUNY Downstate"},{"author_name":"Henri M. Garrison-Desany","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Institute for Genomics in Health (IGH), SUNY Downstate"},{"author_name":"Gayathri Pandey","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY"},{"author_name":"Kathleen K. Bucholz","author_inst":"Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO"},{"author_name":"Howard J. Edenberg","author_inst":"Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN Department of Biochemistry and Molecular Biology, School "},{"author_name":"Sivan Kinreich","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY"},{"author_name":"Bernice Porjesz","author_inst":"Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Institute for Genomics in Health (IGH), SUNY Downstate"},{"author_name":"Jessica E. Salvatore","author_inst":"Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ"},{"author_name":"Jacquelyn L. Meyers","author_inst":"VA New York Harbor Healthcare System, Brooklyn, NY Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY Ins"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Cumulative Burden of Prediabetes, Subclinical Myocardial Injury, and Myocardial Stress and Risk of Incident Atrial Fibrillation in Adults With Hypertension","rel_doi":"10.64898\/2026.09.02.26362108","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362108","rel_abs":"Background: Metabolic dysfunction, subclinical myocardial injury, and myocardial stress may contribute to atrial fibrillation (AF), but ther independent and cumulative associations with incident AF are unclear. Methods: We analyzed 7,261 Systolic Blood Pressure Intervention Trial (SPRINT) participants without prevalent AF, who had baseline fasting glucose, high-sensitivity cardiac troponin I (hs-cTnI), and N-terminal pro?B-type natriuretic peptide (NT-proBNP) measurements. By trial design, SPRINT excluded individuals with diabetes, prior stroke or recent symptomatic heart failure or left ventricular ejection fraction <35%. Prediabetes represented metabolic dysfunction, elevated hs-cTnI myocardial injury, and elevated NT-proBNP myocardial stress. Cox models assessed associations of individual domains and the number of abnormal domains (0?3) with incident AF. Secondary analyses examined the 8 mutually exclusive domain combinations. Results: During a median 3.76-year follow-up, 174 participants developed AF. In multivariable adjusted model, prediabetes, elevated hs-cTnI, and elevated NT-proBNP were associated with incident AF (HR, 1.48 [95% CI, 1.07?2.05], 1.84 [95% CI, 1.30?2.60], and 2.35 [95% CI, 1.56?3.55], respectively). AF risk increased progressively with increasing domain burden (P for trend <0.001); each additional abnormal domain was associated with an 82% higher AF risk (HR, 1.82 [95% CI, 1.50?2.22]). Participants with abnormalities in all 3 domains had the highest risk (HR, 6.11 [95% CI, 2.82?13.24]). Conclusions: Prediabetes, subclinical myocardial injury, and myocardial stress were independently associated with incident AF, with progressively greater risk as abnormalities accumulated. These findings support a multidomain framework in which complementary metabolic and cardiac abnormalities collectively characterize susceptibility to AF.","rel_num_authors":8,"rel_authors":[{"author_name":"Moustafa Elnewishy","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Asem M. Mohsen","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Tarek Zaho","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Richard Kazibwe","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Takeki Suzuki","author_inst":"Wake Forest University"},{"author_name":"M. Benjamin Shoemaker","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Prashant Bhave","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Elsayed Z. Soliman","author_inst":"Wake Forest University School of Medicine"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Who Is Reached by Supervised Psilocybin Services? Oregon Services Versus National Psilocybin Use","rel_doi":"10.64898\/2026.09.03.26362170","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.03.26362170","rel_abs":"Supervised psilocybin services are expanding across the United States, yet how individuals accessing these programs compare with those using psilocybin in the general population is unknown. We conducted a cross-sectional descriptive comparison of Oregon Psilocybin Services (OPS) data from licensed service centers in 2025 (5,935 encounters) and the 2024 National Survey on Drug Use and Health (NSDUH) public-use file (1,822 of 47,299 adults reporting past-year psilocybin use), examining demographics, reasons for use, self-reported disability, and past-year psychiatric conditions and treatment. OPS clients were predominantly female (58.2%) and aged 35 or older (82.1%), whereas adults reporting psilocybin use nationally were predominantly male (64.0%) and younger than 35 (56.7%). Racial and ethnic minority representation was narrower in Oregon (0.9% Black, 2.5% Hispanic vs. 5.4% and 14.3% nationally). OPS clients reported higher incomes (58.5% earning >$95,000) and primarily cited wellness; self-reported disability was uncommon. Adults using psilocybin nationally had high past-year rates of major depressive episode (22.7%), serious psychological distress (36.8%), substance use disorder (61.7%), and mental health treatment receipt (42.9%). Oregon's open-access model selects higher-income, clinically stable, wellness-oriented adults, while millions with psychiatric and substance-use burden use psilocybin outside supervised channels; its safety and utilization data should not be generalized.","rel_num_authors":4,"rel_authors":[{"author_name":"Gabriel P. A. Costa","author_inst":"Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Christina Riggione","author_inst":"Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Christopher Pittenger","author_inst":"Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Joao P. De Aquino","author_inst":"Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA"}],"rel_date":"2026-09-07","rel_site":"medrxiv"},{"rel_title":"Environment-driven active transport of influenza A virus","rel_doi":"10.64898\/2026.09.03.749308","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749308","rel_abs":"Biological media such as airway mucus and extracellular matrix are usually viewed as transport barriers that particles cross by passive diffusion or with internal engines. We show instead that a particle can move actively by modifying the landscape it traverses, creating environmental memory and directional cues in two and three dimensions. Influenza A virus (IAV) realizes this principle through its envelope proteins hemagglutinin (HA) and neuraminidase (NA), which bind and cleave sialylated glycan receptors, respectively. Combining theory, simulations and single-virus tracking, we connect bind--cleave kinetics and HA--NA organization to macroscopic transport. Cleavage dissipates chemical free energy, biases rebinding to the edited landscape and leaves a trail that shapes future encounters. In heterogeneous receptor landscapes, multivalent binding biases motion toward higher receptor density, while receptor destruction by NA can amplify this bias by sharpening the contrast sampled by HA. Experiments on reconstituted glycan membranes show that IAV steps are biased up local receptor gradients, as predicted. The theory suggests that virion-to-virion variability can distribute transport functions across a population, providing a physical hedge against complex receptor environments. Together, these results establish environment-driven active matter as a mechanism for motorless transport powered and guided by chemical modification of the environment.","rel_num_authors":5,"rel_authors":[{"author_name":"Siddhansh Agarwal","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Liya F. Oster","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Boris Veytsman","author_inst":"George Mason University, Fairfax, VA, USA"},{"author_name":"Greg Huber","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Daniel A Fletcher","author_inst":"University of California, Berkeley, Berkeley, CA, USA"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Environment-driven active transport of influenza A virus","rel_doi":"10.64898\/2026.09.03.749308","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749308","rel_abs":"Biological media such as airway mucus and extracellular matrix are usually viewed as transport barriers that particles cross by passive diffusion or with internal engines. We show instead that a particle can move actively by modifying the landscape it traverses, creating environmental memory and directional cues in two and three dimensions. Influenza A virus (IAV) realizes this principle through its envelope proteins hemagglutinin (HA) and neuraminidase (NA), which bind and cleave sialylated glycan receptors, respectively. Combining theory, simulations and single-virus tracking, we connect bind--cleave kinetics and HA--NA organization to macroscopic transport. Cleavage dissipates chemical free energy, biases rebinding to the edited landscape and leaves a trail that shapes future encounters. In heterogeneous receptor landscapes, multivalent binding biases motion toward higher receptor density, while receptor destruction by NA can amplify this bias by sharpening the contrast sampled by HA. Experiments on reconstituted glycan membranes show that IAV steps are biased up local receptor gradients, as predicted. The theory suggests that virion-to-virion variability can distribute transport functions across a population, providing a physical hedge against complex receptor environments. Together, these results establish environment-driven active matter as a mechanism for motorless transport powered and guided by chemical modification of the environment.","rel_num_authors":5,"rel_authors":[{"author_name":"Siddhansh Agarwal","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Liya F. Oster","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Boris Veytsman","author_inst":"George Mason University, Fairfax, VA, USA"},{"author_name":"Greg Huber","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Daniel A Fletcher","author_inst":"University of California, Berkeley, Berkeley, CA, USA"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Preservation and storage effects on river sediment microbiomes: Implications for community stability and ecological inference","rel_doi":"10.64898\/2026.09.05.749588","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.05.749588","rel_abs":"Sample preservation and storage can alter microbial communities between sampling and analysis, influencing the interpretation of environmental microbiome data. We investigated how preservation method, storage temperature, and storage duration affect river sediment microbiomes using 16S and 18S rRNA gene amplicon sequencing. Sediments were preserved in ethanol, nucleic acid preservation (NAP) buffer, or without a preservative, and stored at room temperature or frozen at -20 degrees Celsius or -80 degrees Celsius for up to eight weeks. We report that preservation method, storage conditions, and their interaction influenced estimates of microbial diversity and community composition. Differences between treatments were detectable after approximately 18 h and generally increased with storage time. Frozen samples remained closest to their treatment-specific baseline communities, whereas room-temperature storage, particularly without a preservative, produced the largest changes. Notably, ethanol and NAP buffer preservation reduced, but did not prevent, changes during room-temperature storage. Preservation also influenced taxonomic patterns and the outcome of phylogenetic null-model analyses. Frozen storage generally retained the sample's assembly-metric estimates, with stronger signatures of deterministic community assembly, whereas room-temperature storage shifted the inferred balance toward stochastic processes. Our results show that preservation and storage conditions can affect both the microbial community measured and the ecological conclusions drawn from it. Among the conditions tested, frozen storage provided the best preservation of sediment microbial communities and should be preferred when samples cannot be processed immediately.","rel_num_authors":2,"rel_authors":[{"author_name":"Joeselle Serrana","author_inst":"Department of Environmental Science (ACES), Stockholm University, 106 91 Stockholm, Sweden"},{"author_name":"Malte Posselt","author_inst":"Department of Environmental Science (ACES), Stockholm University, 106 91 Stockholm, Sweden"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"The ferredoxin\/flavodoxin-NADP+ oxidoreductase YumC is essential for isoprenoid and peptidoglycan biosynthesis in Bacillus subtilis","rel_doi":"10.64898\/2026.09.04.749439","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.04.749439","rel_abs":"Redox reactions mediated by ferredoxin\/flavodoxin-NADP+ oxidoreductases (FNRs) and their associated electron-carrier proteins, ferredoxins and flavodoxins, are essential in biology. Although the biochemical activities of these redox proteins are conserved, their precise physiological roles can differ among organisms and cannot be easily inferred. Here we have defined an essential role for Bacillus subtilis YumC, a member of a distinct group of bacterial FNRs that resemble thioredoxin reductase. We have used targeted protein degradation, cytological profiling, metabolomics, and genetic complementation to show that YumC catalyzes the transfer of electrons from NADPH, through ferredoxin (Fer) or through the flavodoxin YkuP, to the isoprenoid biosynthesis pathway, and specifically to the redox enzyme IspG. When YumC was degraded, isoprenoid biosynthesis was compromised, and the level of undecaprenyl phosphate, the isoprenoid lipid carrier for peptidoglycan building block translocation, was diminished. Degradation of YumC or of Fer in a {Delta}ykuP strain led to defective peptidoglycan biosynthesis, activation of the {sigma}M-dependent cell-wall stress response, and lethality. The introduction into B. subtilis of an alternative pathway for isoprenoid biosynthesis that does not require input from electron-carrier proteins could complement the degradation of Fer in a {Delta}ykuP strain, but not the degradation of YumC. This finding indicates that YumC is required for other essential processes that do not necessarily involve Fer and YkuP. This work provides an explanation for why YumC is essential, reveals how reducing power is delivered to isoprenoid biosynthesis in B. subtilis, and illustrates how the varied roles of redox systems among bacteria depend upon metabolic context.","rel_num_authors":7,"rel_authors":[{"author_name":"Deniz Akbulut","author_inst":"MPI for Evolutionary Biology"},{"author_name":"Marirene Chacon-Arnaude","author_inst":"MPI for Evolutionary Biology"},{"author_name":"Dillon P McBee","author_inst":"University of Tennessee, Knoxville"},{"author_name":"Anne Lamsa","author_inst":"University of California, San Diego"},{"author_name":"Alan I Derman","author_inst":"Max Planck Institute for Evolutionary Biology"},{"author_name":"Joshua A Baccile","author_inst":"University of Tennessee, Knoxville"},{"author_name":"Javier Lopez-Garrido","author_inst":"Max Planck Institute for Evolutionary Biology"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Pannexin 1 inhibition reduces tumorigenic properties of patient-derived glioblastoma cells through the HIPPO and Wnt signalling pathways","rel_doi":"10.64898\/2026.09.03.749222","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749222","rel_abs":"Glioblastoma (GBM) is the most common primary brain tumour, with a median survival of 12-18 months, highlighting a need for new treatment targets. We observed that pannexin 1 (PANX1), a channel-forming glycoprotein important in purinergic signalling, is upregulated in GBM compared to normal tissue and expressed throughout patient tumours. Western blot analysis of patient-derived GBM cell lines revealed significantly increased PANX1 expression in these primary lines compared to brain tissue and control glial cells. Bulk RNA-sequencing compared the gene expression of GBM cells devoid of PANX1 via CRISPR\/Cas9 deletion (PANX1-KO) compared to controls. Gene Ontology and KEGG gene set analyses revealed PANX1-KO in GBM cells affects cell surface and cell junction components, processes, and pathways, including the HIPPO pathway, in addition to critically downregulating {beta}-catenin mRNA and other components of the Wnt pathway. The deletion of PANX1 resulted in a disruption of the {beta}-catenin protein and a dramatic reduction in migration and cell growth. Pharmacological inhibition of PANX1 in GBM cells with Probenecid (PBN) and Spironolactone (SPIR) demonstrated a significant reduction in live cell numbers and migration via scratch assay. Both blockers dramatically decreased F-actin filament formation, and the cellular localization of beta-catenin became more intracellular compared to controls. Xenografted GBM tumours showed a reduction in tumour cell viability by bioluminescent imaging and reduced hemorrhaging incidence when treated with PBN. These new insights support further investigation of PANX1 as a potential GBM therapeutic target and its role in multiple cancer signaling pathways that regulate this devastating disease.","rel_num_authors":15,"rel_authors":[{"author_name":"Danielle Johnston","author_inst":"Western University"},{"author_name":"Matthew Huver","author_inst":"Western University"},{"author_name":"Rehanna Kanji","author_inst":"Western University"},{"author_name":"Carlijn Van Kessel","author_inst":"Western University"},{"author_name":"John Kelly","author_inst":"Western University"},{"author_name":"Rafael E Sanchez Pupo","author_inst":"Western University"},{"author_name":"Brooke O'Donnell","author_inst":"Western University"},{"author_name":"Norah Defamie","author_inst":"University of Poitiers"},{"author_name":"Rebecca Lau","author_inst":"Western University"},{"author_name":"Carolina Herrera","author_inst":"Western University"},{"author_name":"Andrew Deweyert","author_inst":"Western University"},{"author_name":"Marc Mesnil","author_inst":"University of Poitiers"},{"author_name":"John Ronald","author_inst":"The University of Western Ontario"},{"author_name":"Matthew Hebb","author_inst":"Western University"},{"author_name":"Silvia Penuela","author_inst":"University of Western Ontario"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Chemical Genetic Targeting of the LRRK2 GTPase Domain","rel_doi":"10.64898\/2026.09.03.749217","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749217","rel_abs":"Genetic variants throughout the multi-domain protein leucine-rich repeat kinase 2 (LRRK2) gene are the most common cause of autosomal dominant Parkinson's disease, and the most prevalent Parkinson's-associated LRRK2 variants enhance kinase activity. Hence, the kinase domain has been extensively targeted for therapeutic development. However, clinical progression of LRRK2 kinase inhibitors has been limited by on-target peripheral toxicities linked to strong kinase suppression, raising the question of whether targeting LRRK2 kinase activity through other means could offer a more tunable therapeutic window. LRRK2 is one of two proteins in the human proteome that possess a Roc-COR GTPase domain in tandem with a kinase domain, and the GTPase domain has been shown to intramolecularly regulate kinase output. Here, we ask whether the Roc-COR GTPase can be targeted as an alternative approach to downregulate kinase activity. We utilize a chemical genetic approach to sensitize LRRK2 to existing GTPase inhibitors and show that pharmacologically engaging the Roc-COR GTPase domain in cells decreases but does not fully inhibit LRRK2-mediated Rab10 T73 phosphorylation. This study lays the groundwork for future efforts directed at the development of pharmacological inhibitors for the LRRK2 GTPase, providing an alternative approach for therapies targeting LRRK2-driven Parkinson's disease.","rel_num_authors":7,"rel_authors":[{"author_name":"Rachel E Prorok","author_inst":"Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Lawrence Y Zhu","author_inst":"Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Victoria Bowcut","author_inst":"Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Harry Wu","author_inst":"Department of Otolaryngology, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Keelan Z Guiley","author_inst":"Rezo Therapeutics, San Francisco, CA 94158, USA"},{"author_name":"Johannes Morstein","author_inst":"Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA"},{"author_name":"Kevan Shokat","author_inst":"Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Chemical Genetic Targeting of the LRRK2 GTPase Domain","rel_doi":"10.64898\/2026.09.03.749217","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749217","rel_abs":"Genetic variants throughout the multi-domain protein leucine-rich repeat kinase 2 (LRRK2) gene are the most common cause of autosomal dominant Parkinson's disease, and the most prevalent Parkinson's-associated LRRK2 variants enhance kinase activity. Hence, the kinase domain has been extensively targeted for therapeutic development. However, clinical progression of LRRK2 kinase inhibitors has been limited by on-target peripheral toxicities linked to strong kinase suppression, raising the question of whether targeting LRRK2 kinase activity through other means could offer a more tunable therapeutic window. LRRK2 is one of two proteins in the human proteome that possess a Roc-COR GTPase domain in tandem with a kinase domain, and the GTPase domain has been shown to intramolecularly regulate kinase output. Here, we ask whether the Roc-COR GTPase can be targeted as an alternative approach to downregulate kinase activity. We utilize a chemical genetic approach to sensitize LRRK2 to existing GTPase inhibitors and show that pharmacologically engaging the Roc-COR GTPase domain in cells decreases but does not fully inhibit LRRK2-mediated Rab10 T73 phosphorylation. This study lays the groundwork for future efforts directed at the development of pharmacological inhibitors for the LRRK2 GTPase, providing an alternative approach for therapies targeting LRRK2-driven Parkinson's disease.","rel_num_authors":7,"rel_authors":[{"author_name":"Rachel E Prorok","author_inst":"Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Lawrence Y Zhu","author_inst":"Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Victoria Bowcut","author_inst":"Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Harry Wu","author_inst":"Department of Otolaryngology, University of California San Francisco, San Francisco, CA 94158, USA"},{"author_name":"Keelan Z Guiley","author_inst":"Rezo Therapeutics, San Francisco, CA 94158, USA"},{"author_name":"Johannes Morstein","author_inst":"Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA"},{"author_name":"Kevan Shokat","author_inst":"Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA 94158, USA"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"A positively selected microRNA controls a reversible aging program in striated muscle","rel_doi":"10.64898\/2026.09.03.749250","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749250","rel_abs":"Aging is the primary risk factor for most chronic diseases and is characterized in striated muscle by progressive functional decline, mitochondrial dysfunction, and chronic inflammation. The miR-128-1 locus resides within a positively selected haplotype on chromosome 2q21.3 associated with variation in grip strength, pulmonary function, and cardiometabolic traits in humans. Here, we show that antisense oligonucleotide-mediated inhibition of miR-128-3p restores muscle mass and function in aged mice, improves cardiac function while limiting adverse remodeling following myocardial infarction, and ameliorates skeletal and cardiac muscle pathology in mouse and pig models of Duchenne muscular dystrophy. Across these contexts, miR-128-3p inhibition induces a conserved transcriptional response characterized by activation of mitochondrial programs and suppression of inflammatory and fibrotic signaling, resembling the effects of established longevity interventions. These findings identify miR-128-3p as a regulator of a conserved aging-associated program and establish its inhibition as a strategy to restore tissue function across aging-related muscle pathologies.","rel_num_authors":37,"rel_authors":[{"author_name":"Melissa A. Boldridge","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Lei Xu","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Xiaoyin Wang","author_inst":"Division of Cardiology, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Michael Stirm","author_inst":"Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, Munich, Germany"},{"author_name":"Gracia Bonilla","author_inst":"Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Chi Zhu","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Justin Y. Lee","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Rachelle L. Stark","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Sneha Damal Villivalam","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Marianne Bengtson Loevendorf","author_inst":"LEO Foundation Skin Immunology Research Center, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark"},{"author_name":"Andreas Petri","author_inst":"Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Copenhagen, Denmark"},{"author_name":"Alexandre Wagschal","author_inst":"Vertex Pharmaceuticals, Boston, Massachusetts, USA"},{"author_name":"Caslin Gilroy","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Federico Gonzalez","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Lei Cai","author_inst":"Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY, USA"},{"author_name":"Peter I. Hecker","author_inst":"Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY, USA"},{"author_name":"Mogens Vyberg","author_inst":"Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Copenhagen, Denmark"},{"author_name":"Rahul Almeida","author_inst":"Division of Cardiology, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Chaitanya Punnati","author_inst":"Division of Cardiology, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Christopher Jin","author_inst":"Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Theresia M. Schnurr","author_inst":"Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Dominique O. Riddell","author_inst":"Comparative Neuromuscular Diseases Laboratory, Department of Clinical Science and Services, Royal Veterinary College, London, UK"},{"author_name":"John C. W. Hildyard","author_inst":"Comparative Neuromuscular Diseases Laboratory, Department of Clinical Science and Services, Royal Veterinary College, London, UK"},{"author_name":"Bachuki Shashikadze","author_inst":"Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Munich, Germany"},{"author_name":"Andreas Lange","author_inst":"Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, Munich, Germany"},{"author_name":"Nikolai Klymiuk","author_inst":"Center for Innovative Medical Models (CiMM), LMU Munich, Munich, Germany"},{"author_name":"Thomas Froehlich","author_inst":"Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Munich, Germany"},{"author_name":"Sakari Kauppinen","author_inst":"Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Copenhagen, Denmark"},{"author_name":"Richard J. Piercy","author_inst":"Comparative Neuromuscular Diseases Laboratory, Department of Clinical Science and Services, Royal Veterinary College, London, UK"},{"author_name":"Joshua W. Knowles","author_inst":"Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Alexander Fay","author_inst":"Department of Neurology, Division of Child Neurology, UCSF School of Medicine, San Francisco, CA, USA"},{"author_name":"Sona Kang","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Ryan E. Temel","author_inst":"Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY, USA"},{"author_name":"Ruslan I. Sadreyev","author_inst":"Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Eckhard Wolf","author_inst":"Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, Munich, Germany"},{"author_name":"Matthew L. Springer","author_inst":"Division of Cardiology, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Anders M. Naar","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"A positively selected microRNA controls a reversible aging program in striated muscle","rel_doi":"10.64898\/2026.09.03.749250","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749250","rel_abs":"Aging is the primary risk factor for most chronic diseases and is characterized in striated muscle by progressive functional decline, mitochondrial dysfunction, and chronic inflammation. The miR-128-1 locus resides within a positively selected haplotype on chromosome 2q21.3 associated with variation in grip strength, pulmonary function, and cardiometabolic traits in humans. Here, we show that antisense oligonucleotide-mediated inhibition of miR-128-3p restores muscle mass and function in aged mice, improves cardiac function while limiting adverse remodeling following myocardial infarction, and ameliorates skeletal and cardiac muscle pathology in mouse and pig models of Duchenne muscular dystrophy. Across these contexts, miR-128-3p inhibition induces a conserved transcriptional response characterized by activation of mitochondrial programs and suppression of inflammatory and fibrotic signaling, resembling the effects of established longevity interventions. These findings identify miR-128-3p as a regulator of a conserved aging-associated program and establish its inhibition as a strategy to restore tissue function across aging-related muscle pathologies.","rel_num_authors":37,"rel_authors":[{"author_name":"Melissa A. Boldridge","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Lei Xu","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Xiaoyin Wang","author_inst":"Division of Cardiology, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Michael Stirm","author_inst":"Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, Munich, Germany"},{"author_name":"Gracia Bonilla","author_inst":"Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Chi Zhu","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Justin Y. Lee","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Rachelle L. Stark","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Sneha Damal Villivalam","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Marianne Bengtson Loevendorf","author_inst":"LEO Foundation Skin Immunology Research Center, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark"},{"author_name":"Andreas Petri","author_inst":"Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Copenhagen, Denmark"},{"author_name":"Alexandre Wagschal","author_inst":"Vertex Pharmaceuticals, Boston, Massachusetts, USA"},{"author_name":"Caslin Gilroy","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Federico Gonzalez","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Lei Cai","author_inst":"Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY, USA"},{"author_name":"Peter I. Hecker","author_inst":"Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY, USA"},{"author_name":"Mogens Vyberg","author_inst":"Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Copenhagen, Denmark"},{"author_name":"Rahul Almeida","author_inst":"Division of Cardiology, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Chaitanya Punnati","author_inst":"Division of Cardiology, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Christopher Jin","author_inst":"Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Theresia M. Schnurr","author_inst":"Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Dominique O. Riddell","author_inst":"Comparative Neuromuscular Diseases Laboratory, Department of Clinical Science and Services, Royal Veterinary College, London, UK"},{"author_name":"John C. W. Hildyard","author_inst":"Comparative Neuromuscular Diseases Laboratory, Department of Clinical Science and Services, Royal Veterinary College, London, UK"},{"author_name":"Bachuki Shashikadze","author_inst":"Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Munich, Germany"},{"author_name":"Andreas Lange","author_inst":"Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, Munich, Germany"},{"author_name":"Nikolai Klymiuk","author_inst":"Center for Innovative Medical Models (CiMM), LMU Munich, Munich, Germany"},{"author_name":"Thomas Froehlich","author_inst":"Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Munich, Germany"},{"author_name":"Sakari Kauppinen","author_inst":"Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, Copenhagen, Denmark"},{"author_name":"Richard J. Piercy","author_inst":"Comparative Neuromuscular Diseases Laboratory, Department of Clinical Science and Services, Royal Veterinary College, London, UK"},{"author_name":"Joshua W. Knowles","author_inst":"Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Alexander Fay","author_inst":"Department of Neurology, Division of Child Neurology, UCSF School of Medicine, San Francisco, CA, USA"},{"author_name":"Sona Kang","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"},{"author_name":"Ryan E. Temel","author_inst":"Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY, USA"},{"author_name":"Ruslan I. Sadreyev","author_inst":"Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Eckhard Wolf","author_inst":"Chair for Molecular Animal Breeding and Biotechnology, Gene Center and Department of Veterinary Sciences, LMU Munich, Munich, Germany"},{"author_name":"Matthew L. Springer","author_inst":"Division of Cardiology, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Anders M. Naar","author_inst":"Department of Metabolic Biology and Nutrition, University of California, Berkeley, California, USA"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"A mammary-specific microfluidic device for studying post-radiotherapy vascular-immune cell interactions","rel_doi":"10.64898\/2026.09.03.749257","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749257","rel_abs":"Radiotherapy (RT) significantly improves outcomes and reduces the risk of recurrence in breast cancer. However, in patients that experience recurrence despite treatment, the irradiated breast tissue may create a pre-metastatic niche that promotes tumor cell infiltration. Because current models of metastasis lack key physiological and microenvironmental cues, we developed a mammary vasculature-on-chip (MVoC) to model how the vasculature responds to RT and impacts immune and tumor cell behavior in mammary tissue. Murine and human MVoCs incorporated endothelial cells (ECs) cultured under physiologic shear stress and mammary-specific fibroblasts to mimic the vascular-fibrous stroma interface. MVoCs exhibited a transient, acute response to RT that led to a persistent phenotypic shift. Neutrophils, which play key roles in pre-metastatic niche formation, adhered more to irradiated ECs and induced persistent damage to the endothelial barrier, preventing recovery. Neutrophils also significantly increased tumor cell adhesion to the endothelium in MVoCs. These findings demonstrate that MVoCs can be used to study RT-induced pre-metastatic niche formation and suggest a positive feedback loop between damaged ECs and immune cell activation that may facilitate tumor cell colonization. MVoCs provide a biologically relevant approach to probe molecular and cellular interactions, improving translation toward developing novel therapies that improve patient outcomes.","rel_num_authors":8,"rel_authors":[{"author_name":"Shannon E. Martello","author_inst":"Vanderbilt University"},{"author_name":"Sofia N. Luna","author_inst":"Vanderbilt University"},{"author_name":"Katelyn Derr","author_inst":"Vanderbilt University"},{"author_name":"Gladys Martinez Franco","author_inst":"California State Polytechnic University Pomona"},{"author_name":"Marissa Paul","author_inst":"Susquehanna University"},{"author_name":"Mark Mc Veigh","author_inst":"Vanderbilt University"},{"author_name":"Leon M. Bellan","author_inst":"Vanderbilt University"},{"author_name":"Marjan Rafat","author_inst":"Vanderbilt University"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions","rel_doi":"10.64898\/2026.09.03.749176","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749176","rel_abs":"Artificial Intelligence (AI) frameworks for automating scientific research have shown strong performance on benchmarks, but their utility for real-world industrial research remains insufficiently characterized. Extending the analysis presented in the first paper of this series, we evaluated the same five advanced AI research frameworks (Kosmos, K-Dense, ToolUniverse, BioAgents from bio.xyz, and the AI Scientist-v2 from Sakana AI) on two more projects of high practical importance for biopharmaceutical development: predicting antibody developability properties with the use of pretrained protein language model embeddings, and modeling non-covalent lipid-RNA interactions in lipid nanoparticles with all-atom molecular dynamics (MD) simulations. The AI frameworks again showed genuine strengths, including unprompted identification of subtle methodological issues, successful use of pretrained protein embeddings, and consistent reporting of p-values and confidence intervals often absent from the original papers. However, no framework approached the scope of the original studies, and severe failures and hallucinations were observed. Our results confirm and extend the conclusion of the first paper that real published research from pharmaceutical companies that we tried to reproduce proved to be considerably harder for current AI frameworks than standard benchmarks suggest.","rel_num_authors":12,"rel_authors":[{"author_name":"Priyanka Bhutada","author_inst":"Northeastern University"},{"author_name":"Nitin Goyal","author_inst":"Northeastern University"},{"author_name":"Tatsam K. Lakhankiya","author_inst":"Northeastern University"},{"author_name":"Sai D. Narahari","author_inst":"Northeastern University"},{"author_name":"Shrish S. N. Thangaraju","author_inst":"Northeastern University"},{"author_name":"Tushar S. Nayak","author_inst":"Northeastern University"},{"author_name":"Yudong Peng","author_inst":"Northeastern University"},{"author_name":"Gouri S. A. Thota","author_inst":"Northeastern University"},{"author_name":"Ratna S. D. Thota","author_inst":"Northeastern University"},{"author_name":"Zhaoyang Wang","author_inst":"Northeastern University"},{"author_name":"KuoHao Lee","author_inst":"Independent Researcher"},{"author_name":"Anton Sinitskiy","author_inst":"ML LC"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Method to Identify Liver-to-Brain Region-Specific Crosstalk Using TurboID","rel_doi":"10.64898\/2026.09.03.749208","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749208","rel_abs":"Endocrine crosstalk between the liver and the brain regulates systemic energy homeostasis. However, this communication has not been comprehensively evaluated because identification of liver-derived proteins, or hepatokines, is difficult to define using bulk serum proteomics alone. To overcome this problem, we applied hepatocyte-restricted endoplasmic-reticulum targeted TurboID (ER-TurboID) in vivo and recovered biotinylated, liver-secretion competent proteins directly from specific brain regions, including the hypothalamus and hindbrain (nucleus of the solitary tract (NTS) and area postrema (AP)). Compared with AAV8-Null transduced controls, TurboID-expressing animals showed 3- to 21-fold enrichment of liver-expressed and predicted liver-secreted proteins among brain regions, with 81 canonical liver-secretome proteins, including multiple carboxylesterase (CES) family members. These results establish a method to identify novel secreted factors from peripheral tissues that can potentially act on specific brain regions.","rel_num_authors":4,"rel_authors":[{"author_name":"Matthew C. Juber","author_inst":"University of Iowa Carver College of Medicine"},{"author_name":"Arvand Asghari","author_inst":"University of Iowa Carver College of Medicine"},{"author_name":"Kristin E. Claflin","author_inst":"University of Iowa Carver College of Medicine"},{"author_name":"Matthew J. Potthoff","author_inst":"University of Oklahoma Health Sciences"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Computer vision-aided locomotor behavioral analysis identifies therapeutic motor signatures in a mouse model of Huntington disease","rel_doi":"10.64898\/2026.09.03.749147","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749147","rel_abs":"Huntington disease (HD) is a neurodegenerative disorder characterized by progressive motor dysfunction. Traditional open-field tests quantify spontaneous locomotor parameters; however, fine mouse motor signatures, particularly disease stage-specific changes in HD motor symptoms and pharmacodynamic responses to therapeutic treatments. Here, we employed a computer vision-aided behavioral flow analysis designed to quantify fine, HD-relevant motor dysfunction in the zQ175DN HD mouse model, ranging from early HD-like motor signatures to well-defined motor deficits. Markerless pose estimation and Keypoint-MoSeq segmented standard top-view open-field recordings into recurrent behavioral syllables, which were then organized into higher-order clusters and transition networks. Disease stage-dependent changes in syllable occurrence, syllable duration, behavioral-state composition, and transition structure were identified. These analyses are not possible with traditional open-field assays. Syllable-duration features provided the strongest genotype discrimination, and HD-like motor features were also characterized by hub remodeling and transition-network disorganization. These features were integrated into an HD motor dysfunction (HDMD) score based on age- or HD progress-matched wild-type (WT) -standardized absolute deviations. The HDMD score distinguished HD mice from WT across multiple symptomatic stages and correlated with HD pathology and disease severity. Effect-size and power analyses suggested improved efficiency for detecting potential therapeutic effects. This framework requires only standard top-view recordings and may also support retrospective analysis of existing open-field video datasets. Overall, the HDMD framework provides a practical strategy for identifying fine motor changes in HD mice, aiding study design and preclinical efficacy assessment in HD drug development.","rel_num_authors":12,"rel_authors":[{"author_name":"Lida Du","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Zhenyu Wang","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Qian Wu","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Hongshuai Liu","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Aadhya Bavkar","author_inst":"Johns Hopkins University"},{"author_name":"Yuan Zhou","author_inst":"Johns Hopkins University"},{"author_name":"Yukuan Shi","author_inst":"Johns Hopkins University"},{"author_name":"Lauren A Lim","author_inst":"Johns Hopkins University"},{"author_name":"Jiaxin Guo","author_inst":"Johns Hopkins University"},{"author_name":"Ziqi Qin","author_inst":"Johns Hopkins University"},{"author_name":"Christopher A. Ross","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Wenzhen Duan","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Predicting Endometriosis Status and Menstrual Cycle Phase Using DNA Methylation","rel_doi":"10.64898\/2026.09.02.749016","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749016","rel_abs":"Endometriosis is a chronic inflammatory disease associated with pelvic pain, infertility, and delayed diagnosis. Growing evidence suggests that altered DNA methylation contributes to disease development and could serve as a biomarker for disease. We developed a leakage-safe machine learning pipeline to classify endometriosis case-control status and menstrual cycle phase using genome-wide DNA methylation data from eutopic endometrial tissue. The dataset consisted of 984 samples profiled using the Illumina Infinium MethylationEPIC array, with measurements across approximately 759,000 CpG sites. Technical variation was corrected using SmartSVA batch correction. Ridge logistic regression models were trained using stratified 80\/20 train-test splits, with regularization strength selected via stratified cross-validation. Feature selection approaches included ridge coefficient ranking, per-CpG t-tests, and univariate logistic regression with FDR correction. Model validity was evaluated using label-shuffling analyses. Menstrual cycle phase classification showed strong performance (mean cross-validation AUROC: 0.971, held-out test AUROC: 0.989), reflecting genome-wide hormonally driven methylation. Ridge regression produced lower but meaningful performance for endometriosis classification (mean cross-validation AUROC: 0.854, held-out test AUROC: 0.875). Ridge coefficient-based feature selection identified compact predictive CpG sets, supporting the hypothesis that endometriosis-associated methylation signal is distributed across many loci rather than a few highly predictive CpGs. Pathway enrichment analyses identified substantial enrichment for menstrual cycle phase but limited enrichment for disease status following FDR correction, consistent with a diffuse endometriosis-associated signal. These findings demonstrate that ridge regression can detect methylation patterns associated with both endometriosis and menstrual cycle phase, highlighting the importance of accounting for cycle-related epigenetic variation in endometrial DNA methylation studies.","rel_num_authors":19,"rel_authors":[{"author_name":"Amrita Nagasuri","author_inst":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"},{"author_name":"Umair Khan","author_inst":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"},{"author_name":"Parker Grosjean","author_inst":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"},{"author_name":"Adi Siddharth","author_inst":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"},{"author_name":"Idit Kosti","author_inst":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"},{"author_name":"Sally Mortlock","author_inst":"The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia; Australian Women and Girls' Health Research Centre, School"},{"author_name":"Sahar Houshdaran","author_inst":"Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, CA, USA"},{"author_name":"Nilufer Rahmioglu","author_inst":"Centre for Human Genetics, University of Oxford, Oxford, UK; Oxford Endometriosis CaRe Centre, Nuffield Department of Women's and Reproductive Health, John Radc"},{"author_name":"Stacey A. Missmer","author_inst":"Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Boston Center for Endometriosis, Boston Children's Hospital and Brigham "},{"author_name":"Krina T. Zondervan","author_inst":"Oxford Endometriosis CaRe Centre, Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK"},{"author_name":"Grant Montgomery","author_inst":"The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia"},{"author_name":"Christian M. Becker","author_inst":"Oxford Endometriosis CaRe Centre, Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK"},{"author_name":"Peter Rogers","author_inst":"University of Melbourne Department of Obstetrics and Gynaecology, Royal Women's Hospital, Melbourne, Australia"},{"author_name":"Juan Irwin","author_inst":"Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, CA, USA"},{"author_name":"Tomiko Oskotsky","author_inst":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"},{"author_name":"Karla Lindquist","author_inst":"Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, CA, USA"},{"author_name":"Christopher Seaman","author_inst":"Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA"},{"author_name":"Linda C. Giudice","author_inst":"Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, CA, USA"},{"author_name":"Marina Sirota","author_inst":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Pediatrics, University of California, San Francis"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Long read sequencing of retinal RNA improves killifish transcriptome annotation","rel_doi":"10.64898\/2026.09.02.748420","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748420","rel_abs":"Purpose The African Turquoise Killifish has recently emerged as a powerful model for aging and age-related disease research studies. However, molecular based investigations have been limited by preliminary genome and transcriptome builds with incomplete reference genome sequence, fragmented chromosome assembly, and missing gene annotations. These issues make primary (alignment and quantification) and secondary (Gene Ontology, Gene Set Enrichment Analysis, cross-species comparisons) analyses difficult to reliably implement and interpret. This study seeks to generate a complete retinal reference transcriptome to facilitate future killifish transcriptomic, epigenetic, and proteomic studies of the visual system. Methods We generated an enhanced retina transcriptome using long-read PacBio RNAseq data that was processed using a robust computational pipeline to merge reads, classify genes, and annotate with nearest orthologous gene names from other species. This new annotation was compared to available references and validated using bulk and single cell RNAseq datasets. Results Comparison of the widely used Nfu_20140520 and the newly released NfurGRZ-RIMD1 genome builds identified NfurGRZ-RIMD1 to be more contiguous and complete. However, we identified limitations with both transcriptomes, including the lack of annotation of certain retina specific genes and many uninformative gene names. Using long-read PacBio sequencing of RNA collected from young and old Killifish retinas, we annotated a deep retinal transcriptome onto the NfurGRZ-RIMD1 reference genome. This analysis identified thousands of previously unannotated transcripts from retinas of young and old killifish. By matching each translated protein sequence to its nearest ortholog, we increased the number and proportion of genes with meaningful gene names. Mapping of bulk and single-cell RNAseq data showed substantial increase in mapping rate and identified hundreds of genes and transcripts with age-dependent expression dynamics. Conclusions Assembly of an enhanced retinal transcriptome for the killifish improved both primary and secondary analyses of bulk and single cell RNAseq data. Improvements will benefit future studies investigating the mechanisms of aging in the killifish and to best utilize this powerful model to understand human disease.","rel_num_authors":6,"rel_authors":[{"author_name":"Sohini Rebba","author_inst":"Washington University School of Medicine"},{"author_name":"Lianri van Schalkwyk","author_inst":"University College London"},{"author_name":"Aleksandra M Krzywanska","author_inst":"University College London"},{"author_name":"Ryan B MacDonald","author_inst":"University College London"},{"author_name":"Brian B Clark","author_inst":"Washington University School of Medicine"},{"author_name":"Philip A Ruzycki","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"Manganese availability determines insulin sensitivity by enhancing Akt activity","rel_doi":"10.64898\/2026.09.02.746017","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.746017","rel_abs":"Insulin signaling is a critical determinant of metabolic health, and impairments in insulin action contribute to the development of type 2 diabetes. The kinase Akt is a central mediator of insulin signaling and is required for insulin's suppression of hepatic glucose output. Although the regulation of Akt by the insulin receptor-PI3K pathway is well understood, there are instances in which signaling downstream of Akt is dissociated from proximal insulin signaling, for example in insulin resistance. Nonetheless, little is known about PI3K-independent mechanisms of Akt regulation. Here, we discovered that hepatocyte manganese concentrations are a key determinant of PI3K-independent Akt function in vivo. We further demonstrated that manganese increases Akt's catalytic efficiency, and quantitative phosphoproteomics revealed that manganese and insulin act additively to enhance Akt activity. Moreover, we uncovered that hepatic manganese concentrations fluctuate during fasting and feeding via carbohydrate-dependent transcriptional regulation of the manganese efflux transporter Slc30a10. This dynamic metal-signaling axis provides a mechanistic link between nutrient status and Akt activation, and suggests a molecular explanation for the glucose-lowering effects of manganese observed in humans. Our findings establish manganese as a physiologically regulated cofactor for Akt and position metal bioavailability as a previously unrecognized layer of insulin signaling control.","rel_num_authors":15,"rel_authors":[{"author_name":"Jennifer R Gamarra","author_inst":"Columbia University"},{"author_name":"Sei Higuchi","author_inst":"St. John's University College of Pharmacy and Health Sciences"},{"author_name":"Timothy L. Yuan","author_inst":"Columbia University"},{"author_name":"Yuke Xie","author_inst":"Columbia University"},{"author_name":"Niroshan Shanmugarajah","author_inst":"Columbia University"},{"author_name":"Hang Yang","author_inst":"Columbia University"},{"author_name":"Meredith O. Kelly","author_inst":"Columbia University"},{"author_name":"Sarah A. Hannou","author_inst":"Duke University"},{"author_name":"Kathrin Schilling","author_inst":"Columbia University"},{"author_name":"Ana Navas-Acien","author_inst":"Columbia University"},{"author_name":"Eunhee Choi","author_inst":"Columbia University"},{"author_name":"Anum Glasgow","author_inst":"Columbia University"},{"author_name":"Inna I. Astapova","author_inst":"Baylor College of Medicine"},{"author_name":"Mark A. Herman","author_inst":"Baylor College of Medicine"},{"author_name":"Rebecca A Haeusler","author_inst":"Columbia University"}],"rel_date":"2026-09-07","rel_site":"biorxiv"},{"rel_title":"MAP3K7 Loss of Function Causes Dilated Cardiomyopathy","rel_doi":"10.64898\/2026.09.02.26361780","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26361780","rel_abs":"Background and Aims: Dilated cardiomyopathy (DCM) is a genetically heterogeneous cause of heart failure and sudden cardiac death. Many patients remain without a molecular diagnosis. MAP3K7 encodes TAK1, a serine\/threonine kinase important for cardiac homeostasis. MAP3K7 variants are an established cause of syndromic disease, including cardiospondylocarpofacial syndrome (CSCF), in which DCM has been occasionally reported. Here, we demonstrate that MAP3K7 variants can cause apparently isolated DCM, expanding the phenotypic spectrum of MAP3K7-related disorders. Methods: We compiled four orthogonal lines of human genetic evidence: de novo variation in paediatric cardiomyopathy; common variant association with adult DCM; familial segregation; and rare variant enrichment in DCM cases, together with functional categorisation of rare variants. Results: In 117 paediatric cardiomyopathy trios from the 100,000 Genomes Project, two probands harboured rare de novo MAP3K7 missense variants, significantly more than expected (Bonferroni-adjusted p=0.036). Independent GWAS implicated MAP3K7 as a susceptibility locus for adult DCM. Across global DCM cohorts, we identified families harbouring rare MAP3K7 variants, including one with segregation in nine affected relatives. Rare damaging non-truncating variants were enriched in DCM cases, while truncating variants were associated with DCM in the Genomics England cohort and increased left ventricular volumes in UK Biobank. DCM-associated variants reduced TAK1 kinase activity, supporting a loss-of-function mechanism consistent with CSCF-associated alleles. Conclusion: Multiple independent lines of evidence establish an association between MAP3K7 loss of function variants and DCM. Several affected individuals lacked overt syndromic features, demonstrating that MAP3K7-related disease may present as apparently isolated DCM across the lifespan and supporting inclusion of MAP3K7 in DCM diagnostic pipelines.","rel_num_authors":39,"rel_authors":[{"author_name":"Katherine S. Josephs","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Carlos C. Smith-Diaz","author_inst":"Garvan Institute of Medical Research and University of New South Wales, Sydney, NSW, Australia; School of Clinical Medicine, Faculty of Medicine and Health, Uni"},{"author_name":"Angela Woods","author_inst":"MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom"},{"author_name":"Claire Prince","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom"},{"author_name":"Sean L. Zheng","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; MRC Laboratory of Medical Sciences, Imperial College London, London, United "},{"author_name":"Rachel Buchan","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Esme Cavanagh","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Shezan Elahi","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Emma Jennings","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Henry Procter","author_inst":"Leeds Clinical Genomics Service, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom"},{"author_name":"James S. McTaggart","author_inst":"Leeds Clinical Genomics Service, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom"},{"author_name":"Riyad Janan","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom"},{"author_name":"Pantazis Theotokis","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom"},{"author_name":"Amy Baker","author_inst":"Garvan Institute of Medical Research and University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Natasha Henden","author_inst":"Garvan Institute of Medical Research and University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Kathryn A. McGurk","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; MRC Laboratory of Medical Sciences, Imperial College London, London, United "},{"author_name":"Katrina Prescott","author_inst":"Leeds Clinical Genomics Service, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom"},{"author_name":"- Newcastle Clinical Genetics Team","author_inst":"Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom"},{"author_name":"Vaidehi Jobanputra","author_inst":"New York Genome Center, New York, NY, USA"},{"author_name":"Paul James","author_inst":"Department of Genomic Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Medicine, University of Melbourne, Melbourne, VIC, Australia;"},{"author_name":"Tina Thompson","author_inst":"Department of Genomic Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Medicine, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Dominica Zentner","author_inst":"Department of Genomic Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Medicine, University of Melbourne, Melbourne, VIC, Australia;"},{"author_name":"Upasana Tayal","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Brian P. Halliday","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Sanjay K. Prasad","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Declan P. O'Regan","author_inst":"MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom"},{"author_name":"R. Thomas Lumbers","author_inst":"Institute of Health Informatics, University College London, London, United Kingdom"},{"author_name":"Paul J. R. Barton","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Kate Richardson","author_inst":"Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom"},{"author_name":"Sabine Klaassen","author_inst":"Charite-Universitatsmedizin Berlin and Max Delbruck Center for Molecular Medicine, Berlin, Germany; DZHK, Berlin, Germany"},{"author_name":"Andres Rico-Armada","author_inst":"Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom"},{"author_name":"Piers E. F. Daubeney","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"Verity L. Hartill","author_inst":"Leeds Clinical Genomics Service, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom"},{"author_name":"Stephen P. Page","author_inst":"Leeds Clinical Genomics Service, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom"},{"author_name":"Angharad M. Roberts","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom"},{"author_name":"David Carling","author_inst":"MRC Laboratory of Medical Sciences, Imperial College London, London, United Kingdom"},{"author_name":"Jodie Ingles","author_inst":"Garvan Institute of Medical Research and University of New South Wales, Sydney, NSW, Australia; School of Clinical Medicine, Faculty of Medicine and Health, Uni"},{"author_name":"James S. Ware","author_inst":"National Heart and Lung Institute, Imperial College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation "},{"author_name":"- GoDCM Consortium","author_inst":""}],"rel_date":"2026-09-06","rel_site":"medrxiv"},{"rel_title":"Performance of protein panels is inflated across many biomarker studies","rel_doi":"10.64898\/2026.09.02.26362037","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362037","rel_abs":"Data leakage is a prevalent yet underappreciated flaw in biomarker discovery studies. Through simulation and real-world proteomic data, we demonstrate that typical pipelines are broadly susceptible to this issue, producing inflated performance estimates, poor generalization, and excess false positives. We further introduce two tools to detect data leakage at the code and manuscript level, providing a practical path toward more rigorous and reproducible biomarker reporting.","rel_num_authors":5,"rel_authors":[{"author_name":"Lijun An","author_inst":"Lund University"},{"author_name":"Caitlin A Finney","author_inst":"The University of Sydney"},{"author_name":"Artur Shvetcov","author_inst":"The University of Sydney"},{"author_name":"- The Global Neurodegeneration Proteomics Consortium","author_inst":"-"},{"author_name":"Jacob Vogel","author_inst":"Lund University"}],"rel_date":"2026-09-06","rel_site":"medrxiv"},{"rel_title":"A dual proteomics analysis of paired cerebrospinal fluid and plasma from patients with neurodegenerative diseases","rel_doi":"10.64898\/2026.09.02.26361977","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26361977","rel_abs":"INTRODUCTION: Understanding concordance across biofluids and platforms is critical for understanding neurodegenerative biomarkers results and translating them into clinical use; yet systematic comparisons remain limited. To address this gap, we performed large-scale proteomic profiling of patient-paired plasma and CSF to characterize cross-modal relationships. METHODS: We profiled paired plasma and CSF from 67 individuals using SomaScan 11K and NULISAseq CNS panels. Disease severity was assessed with the CDR+NACC FTLD-M Global Score. RESULTS: We identified 269 SomaScan and 18 NULISA proteins with significant cross-biofluid correlation. Cross-platform concordance within biofluids was strong. NEFL, NPTX2, TREM2, and CHIT1 demonstrated consistent cross-platform agreement. Associations with disease severity were compartment-specific, with decreased NPTX2 in CSF, increased NEFL and GFAP in plasma, and decreased TREM2 across biofluids. DISCUSSION: Cross-platform consistency supports biomarker robustness, while limited cross-biofluid concordance highlights compartmental biology. Given additional clinical correlations despite varying underlying pathology, these findings may point to shared neurodegenerative disorder pathways.","rel_num_authors":18,"rel_authors":[{"author_name":"Isabelle Kowal","author_inst":"Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institute"},{"author_name":"Sonja W Scholz","author_inst":"National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Jacob Epstein","author_inst":"Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institute"},{"author_name":"Bryan J Traynor","author_inst":"Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA"},{"author_name":"Laura E Danielian","author_inst":"National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Ying Hao","author_inst":"Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institute"},{"author_name":"Jody Crook","author_inst":"National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Ziyi Li","author_inst":"Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institute"},{"author_name":"Katelyn Porter","author_inst":"National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Brian Sellers","author_inst":"NIH Center for Human Immunology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Thomas J Langowski","author_inst":"NIH Center for Human Immunology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Marisa N Denkinger","author_inst":"Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Molndal, Sweden"},{"author_name":"Nicholas J Ashton","author_inst":"Banner Sun Health Research Institute, Sun City, AZ, USA"},{"author_name":"Kendall Van Keuron-Jensen","author_inst":"Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institute"},{"author_name":"Mark R Cookson","author_inst":"Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institute"},{"author_name":"Justin Y Kwan","author_inst":"National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Yue Andy A Qi","author_inst":"Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institute"},{"author_name":"Allison Snyder","author_inst":"National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA"}],"rel_date":"2026-09-06","rel_site":"medrxiv"},{"rel_title":"Peer Facilitation as a Methodological Condition: What Participatory Photovoice Revealed About Transition to Adulthood Among Youth Living with HIV in India","rel_doi":"10.64898\/2026.09.02.26361557","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26361557","rel_abs":"Transition readiness instruments for youth living with HIV were developed in high-income settings and define readiness as clinical competence and individual autonomy. Few reflect youth in low- and middle-income settings, where family obligation and community stigma shape the passage to adulthood. In March 2026, sixteen youth living with HIV aged 13-22 years took part in a peer-facilitated photovoice study at two sites in Karnataka, India: Bengaluru and Belgaum. Participants photographed and collectively interpreted their experiences of growing up and their expectations of the future. Six returned in May 2026 for a reflective discussion of the method. Participants defined independence as the capacity to sustain others, located the principal risks of transition in relational and social rather than clinical domains, and recommended that transition preparation begin around age twelve. Reciprocal obligation was absent from the initial codebook and emerged through participants' visual metaphors. Peer facilitation was a condition of the inquiry rather than an enhancement of it, and photovoice reporting should specify who exercised interpretive authority. Participatory photovoice can broaden prevailing definitions of transition readiness and inform more youth-responsive assessments.","rel_num_authors":8,"rel_authors":[{"author_name":"Anusha Sulladmath","author_inst":"University of Washington"},{"author_name":"Siddha Sannigrahi","author_inst":"John Hopkins University"},{"author_name":"Suhas Reddy","author_inst":"RISHI Foundation"},{"author_name":"Meghana Gowda","author_inst":"RISHI Foundation"},{"author_name":"Michael Babu Raj","author_inst":"Y.R. Gaitonde Centre for AIDS Research and Education (YRGCARE), Chennai, Tamil Nadu, India"},{"author_name":"Satish Kumar SK","author_inst":"Y.R. Gaitonde Centre for AIDS Research and Education (YRGCARE), Chennai, Tamil Nadu, India."},{"author_name":"Lakshmi Ganapathi","author_inst":"Massachusetts General Hospital"},{"author_name":"Anita Shet","author_inst":"John Hopkins University"}],"rel_date":"2026-09-06","rel_site":"medrxiv"},{"rel_title":"HyperSketch: de Bruijn graph sketching for genomic similarity estimation with Hyperdimensional Computing","rel_doi":"10.64898\/2026.09.01.748726","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748726","rel_abs":"The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch, a novel sketching tool that encodes the de Bruijn graph structure of a genome into a fixed-size, topology-aware vector using Hyperdimensional Computing (HDC). Unlike set-based sketches, HyperSketch encodes the transitions between adjacent k-mers into a superposition of orthogonal hypervectors. To formalize parameter selection, we also propose an analytical framework proving that graph-based sketches fundamentally require a smaller k-mer size than set-based models due to their expanded k+1 biological footprint. We benchmarked HyperSketch against Mash and HyperGen using a dataset of ~26 thousand viral reference genomes from NCBI GenBank. Under optimal parameters, we demonstrate a strong linear correlation (>99%) between the graph-based similarity computed by HyperSketch and standard MinHash distance estimates. Crucially, we show that the mathematical formulation of HyperSketch introduces a distance scaling effect that expands the dynamic range of estimates for closely related strains, providing a higher-resolution metric for sub-lineage clustering than purely compositional estimators. HyperSketch provides a computationally efficient, structure-aware alternative to traditional sketching. By natively encoding genomic syntax, it offers a new dimension of genomic comparison that excels at both high-resolution strain differentiation and deep evolutionary scaling, complementing existing nucleotide identity metrics without requiring sequence alignment.","rel_num_authors":5,"rel_authors":[{"author_name":"Fabio Cumbo","author_inst":"Cleveland Clinic Foundation"},{"author_name":"Kabir Dhillon","author_inst":"Cleveland Clinic Foundation"},{"author_name":"M. Hassan Najafi","author_inst":"Case Western Reserve University"},{"author_name":"Sercan Aygun","author_inst":"University of Louisiana at Lafayette"},{"author_name":"Daniel Blankenberg","author_inst":"Cleveland Clinic Foundation"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"Widespread structural variations at human chromosome ends","rel_doi":"10.64898\/2026.09.03.748401","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.748401","rel_abs":"The highly repetitive regions of the human genome were long underrepresented from reference assemblies, limiting study of their biological function. Long-read sequencing and improved assembly algorithms have since resolved many of these regions, from centromeres to ribosomal DNA arrays, revealing structural variation increasingly linked to human disease. However, the subtelomeres, the repeat-rich regions adjacent to the telomeres at each chromosome end, have remained poorly characterized. Here we present a collection of complete subtelomeric sequences spanning all non-acrocentric chromosome arms, derived from 860 haploid assemblies across six ancestry groups. We find that while subtelomeres are mosaics of blocks shared between chromosome arms, individual arms diverge extensively, such that most non-acrocentric autosomal arms (54%, 21 of 39) carry multiple haplotypes differing by up to 100-200 kb. These blocks are broadly conserved across the great apes. In humans, their diversity is associated with chromosome arm rather than ancestry, suggesting that cross-arm paralogy block duplications predate human population divergence, although some haplotypes show ancestry-specific enrichment. Remarkably, these divergent haplotypes differ in gene content, driving gene copy-number variation between individuals among olfactory receptors and other genes. This study also revealed rare subtelomeric recombination. We further show that our subtelomere data set enables the accurate measurement of telomere length at individual chromosome ends from long-read data. Together, these assemblies reveal an unappreciated scale of variation at human chromosome ends and provide a resource for studying the roles of this variation in disease, telomere biology and genome evolution across diverse populations.","rel_num_authors":13,"rel_authors":[{"author_name":"Kar-Tong Tan","author_inst":"National University of Singapore"},{"author_name":"Ryan Jun Xiang Ong","author_inst":"National University of Singapore"},{"author_name":"Brandon Bing Rui Kee","author_inst":"National University of Singapore"},{"author_name":"Russell Ker Han Yap","author_inst":"National University of Singapore"},{"author_name":"Alicia Jun Ting Ng","author_inst":"National University of Singapore"},{"author_name":"Gihan Kaushalya Rajapaksha","author_inst":"National University of Singapore"},{"author_name":"Max Garrity-Janger","author_inst":"Columbia University"},{"author_name":"Qiyu Lin","author_inst":"National University of Singapore"},{"author_name":"Jerome Rui Chong","author_inst":"National University of Singapore"},{"author_name":"Cin Thet Kyi","author_inst":"National University of Singapore"},{"author_name":"- Human Pangenome Reference Consortium","author_inst":""},{"author_name":"Matthew Meyerson","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Heng Li","author_inst":"Dana-Farber Cancer Institute"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"Identification and characterization of a human antibody profile inversely associated with adverse cardiovascular events","rel_doi":"10.64898\/2026.09.01.748614","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748614","rel_abs":"Objective: The immune response is linked to the progression of atherosclerotic cardiovascular disease (CVD). We sought to characterize a predictive cardiovascular antibody biomarker using two independent cohorts. Approach and Results: A human IgG profile serendipitously identified by ELISA was quantified in a well characterized cohort of 359 patients with a history of coronary artery disease (CAD) who experienced a total of 71 incident adverse CVD events (death, myocardial infarction, and stroke) over a median 4.1-year follow-up. Using Cox proportional hazard regression analysis, low biomarker levels were an independent predictor of adverse cardiovascular outcomes after adjustment for age, sex, diabetes mellitus, estimated glomerular filtration rate, presence of obstructive CAD, heart failure, total cholesterol, and high-density lipoprotein (HDL) cholesterol (adjusted hazard ratio of 1.90 [95% CI: 1.03 to 3.49; p=0.038] between lowest and highest tertiles). Validation was then performed in a larger secondary cohort using 4356 baseline samples from the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of cardiovascular outcomes resulting in adjusted odds ratio of 50.5 per unit decrease in log-transformed biomarker [95% CI: 2.4 to 2773.1; p=0.030] over one year by logistic regression analysis. Conclusions: Low levels of human IgG antibodies targeting Bovidae IgG are independently associated with increased incidence of CVD events in patients with or without a history of CAD, indicating the potential clinical predictive power of this antibody profile as an inverse biomarker for CVD.","rel_num_authors":10,"rel_authors":[{"author_name":"David Henson","author_inst":"University of Kentucky"},{"author_name":"Katherine L Thompson","author_inst":"University of Kentucky"},{"author_name":"Ayman Samman Tahhan","author_inst":"Emory University"},{"author_name":"Ryan Marion","author_inst":"University of Kentucky"},{"author_name":"Tabarak Azawi","author_inst":"University of Kentucky"},{"author_name":"Peng Yeh","author_inst":"University of Kentucky"},{"author_name":"Gregory S Hawk","author_inst":"University of Kentucky"},{"author_name":"Andrew P DeFilippis","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Arshed Ali Quyyumi","author_inst":"Emory University"},{"author_name":"Vincent J Venditto","author_inst":"University of Kentucky"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"PIGSTI: a modular, reproducible pipeline for detecting species identity, pathogens, and microbes from animal palaeogenomic data","rel_doi":"10.64898\/2026.09.01.748539","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748539","rel_abs":"Ancient genomics has enabled discovery of diverse pathogens across various time periods, host species, and material types. However, existing palaeogenomic pipelines predominantly focus on screening data from human hosts, or do not incorporate microbial screening methodologies. We present PIGSTI (Pathogen anImal Genome Sequence ToolkIt), a bioinformatic pipeline specifically designed for both the initial screening and subsequent detection of pathogens in shotgun sequencing data from ancient animal remains. PIGSTI's integrated Snakemake workflow performs both host detection, genome mapping and pathogen identification, generating outputs suitable for population genetics and phylogenetic analyses. Testing on 952 newly sequenced and publicly available animal palaeogenomic datasets, we identified ~15 ancient zoonotic and animal pathogens with high confidence, including the first documented case of Rickettsia felis and Leptospira borgpetersenii in an ancient animal. Our results demonstrate PIGSTI's utility for screening pathogen diversity in ancient animal hosts and reconstructing historical host-pathogen relationships.","rel_num_authors":35,"rel_authors":[{"author_name":"Louis L'Hote","author_inst":"UCD School of Agriculture and Food Science, University College Dublin, Belfield, Ireland"},{"author_name":"Catherine Butt","author_inst":"Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Aine Halpin","author_inst":"Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Luisa Sacristan","author_inst":"UCD School of Agriculture and Food Science, University College Dublin, Belfield, Ireland"},{"author_name":"Valeria Mattiangeli","author_inst":"Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Pernille Bangsgaard","author_inst":"Globe Institute, University of Copenhagen, Copenhagen, Denmark"},{"author_name":"Lisa Yeomans","author_inst":"Globe, Section for Geobiology, University of Copenhagen, Denmark"},{"author_name":"Melinda Zeder","author_inst":"Department of Anthropology, Smithsonian National Museum of Natural History, Washington, DC 20560, USA"},{"author_name":"Marjan Mashkour","author_inst":"BioArch, BioArcheologie, Interactions Societes Environnements, UMR7209, CNRS & Museum National d'Histoire Naturelle, Paris, France"},{"author_name":"Hossein Davoudi","author_inst":"Bioarchaeology Laboratory, Central Laboratory, University of Tehran, Iran"},{"author_name":"Svend Hansen","author_inst":"Eurasia-Department, German Archaeological Institute, Berlin, Germany"},{"author_name":"Delphine Decruyenaere","author_inst":"BioArch, BioArcheologie, Interactions Societes Environnements, UMR7209, CNRS & Museum National d'Histoire Naturelle, Paris, France"},{"author_name":"Melissa Kennedy","author_inst":"Archaeology, The University of Sydney, Sydney, Australia"},{"author_name":"Adeline Vautrin","author_inst":"Ruhr-Universitat Bochum, Bochum, Germany"},{"author_name":"Alisher Begmatov","author_inst":"Berlin-Brandenburg Academy of Sciences and Humanities, Berlin, Germany"},{"author_name":"Andrej B. Belinskiy","author_inst":"'Nasledie' Cultural Heritage Unit, Stavropol, Russia"},{"author_name":"Amridin Berdimuradov","author_inst":"Y. Ghulomov Samarkand Institute of Archaeology under the Cultural Heritage Agency of the Republic of Uzbekistan"},{"author_name":"Gennadiy Bogomolov","author_inst":"Institute of Anthropology of the Academy of Sciences of the Republic of Uzbekistan, Uzbekistan"},{"author_name":"Jacopo Bruno","author_inst":"Institute for Iranian Studies, Austrian Academy of Sciences, Vienna, Austria"},{"author_name":"Alexey Kalmykov","author_inst":"Independent researcher, Stavropol, Russia"},{"author_name":"Jane McMahon","author_inst":"Archaeology, The University of Sydney, Sydney, Australia"},{"author_name":"Jamal Mirzaakhmedov","author_inst":"Y. Ghulomov Samarkand Institute of Archaeology under the Cultural Heritage Agency of the Republic of Uzbekistan"},{"author_name":"Susan Pollock","author_inst":"Institute for Near Eastern Archaeology, Free University of Berlin, Berlin, Germany"},{"author_name":"Rocco Rante","author_inst":"UMR 7041 - ArScAn; Sorbonne-Pantheon, Paris 1, France"},{"author_name":"Sabine Reinhold","author_inst":"Eurasia-Department, German Archaeological Institute, Berlin, Germany"},{"author_name":"Tobias Richter","author_inst":"School of Archaeology, University of Copenhagen, Denmark"},{"author_name":"Alisher Sandiboev","author_inst":"Y. Ghulomov Samarkand Institute of Archaeology under the Cultural Heritage Agency of the Republic of Uzbekistan"},{"author_name":"Eberhard Sauer","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Laura Strolin","author_inst":"Institute for Prehistoric and Protohistoric Archaeology, University of Kiel, Kiel, Germany"},{"author_name":"Hirofumi Teramura","author_inst":"National Museum of Ethnology, Japan"},{"author_name":"Hugh Thomas","author_inst":"Archaeology, The University of Sydney, Sydney, Australia"},{"author_name":"Jolijn A. M. Erven","author_inst":"UCD School of Agriculture and Food Science, University College Dublin, Belfield, Ireland"},{"author_name":"Shigeki Nakagome","author_inst":"School of Medicine, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Daniel G. Bradley","author_inst":"Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Kevin G. Daly","author_inst":"UCD School of Agriculture and Food Science, University College Dublin, Belfield, Ireland"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"A Single-Cell Framework for Classifying Human Th17 Pathogenicity Links Acylcarnitine Metabolism to Non-Pathogenic Inflammation in Type 2 Diabetes","rel_doi":"10.64898\/2026.09.01.748572","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748572","rel_abs":"Based on in vitro and animal studies, Th17 cells are classified as pathogenic (pTh17) or non-pathogenic (nTh17), but the inability to identify these subsets in primary human samples limits translation. We developed a single-cell ELISA to enrich human Th17s, enabling transcriptomic and flow-cytometric classification. nTh17 cells predominated in Type 2 diabetes and exhibited signatures of acylcarnitine synthesis, while knockdown of CPT1A demonstrated that acylcarnitine metabolism regulates Th17 pathogenicity.","rel_num_authors":22,"rel_authors":[{"author_name":"Naveena S. Ujagar","author_inst":"University of California, Irvine"},{"author_name":"Suresh Poudel","author_inst":"St. Jude Childrens Research Hospital"},{"author_name":"Shubh Saraswat","author_inst":"University of Kentucky"},{"author_name":"Suhas Sureshchandra","author_inst":"University of California, Irvine"},{"author_name":"Josh Kim","author_inst":"University of California, Irvine"},{"author_name":"Uyen-Vy Le","author_inst":"University of California, Irvine"},{"author_name":"Albert R. Jones IV","author_inst":"Boston University School of Medicine"},{"author_name":"Nicole Hopkins","author_inst":"University of California, Irvine"},{"author_name":"Trupti Sriram","author_inst":"University of California, Irvine"},{"author_name":"Matthew Matson","author_inst":"University of California, Irvine"},{"author_name":"Emely Henriquez Pilier","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marlyd Mejia","author_inst":"University of California, Irvine"},{"author_name":"Samuel S. Bailin","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Celestine N. Wanjalla","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Michael Y. Sy","author_inst":"University of California, Irvine"},{"author_name":"Dawn C. Newcomb","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Craig Walsh","author_inst":"University of California, Irvine"},{"author_name":"Lisa E. Wagar","author_inst":"University of California, Irvine"},{"author_name":"Barbara S. Nikolajczyk","author_inst":"University of Kentucky"},{"author_name":"Xiaohua D. Zhang","author_inst":"University of Kentucky"},{"author_name":"Douglas R. Green","author_inst":"St. Jude Childrens Research Hospital"},{"author_name":"Dequina A. Nicholas","author_inst":"University of California, Irvine"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"Role of gut epithelial-cell derived transglutaminase 2 in the formation of celiac disease autoantibodies","rel_doi":"10.64898\/2026.09.01.748464","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748464","rel_abs":"Formation of autoantibodies to transglutaminase 2 (TG2) in celiac disease likely involves TG2-gluten complexes that allow gluten-specific CD4+ T cells to provide help to TG2-specific B cells. To investigate whether TG2 derived from intestinal epithelial cells (IECs) contributes to this process, we generated mice with inducible IEC-specific expression of TG2 fused to a deamidated gluten peptide (DGP) containing a T-cell epitope. Upon induction, the TG2-DGP fusion protein was expressed in IECs and released into the intestinal lumen. In HLA-DQ2.5 transgenic mice with activated gluten-specific CD4+ T cells and naive TG2-specific B cells, expression of TG2-DGP was immunogenic and drove the production of intestinal and systemic anti-TG2 autoantibodies. TG2-specific B cells predominantly expanded in Peyers patches, suggesting that their priming and subsequent differentiation into lamina propria TG2-specific IgA+ plasma cells occurs in gut-associated lymphoid tissues. The findings demonstrate immunogenicity of IEC-derived TG2-DGP fusion antigen supporting the notion of a pathogenic role for luminal TG2 in driving anti-TG2 autoimmunity in celiac disease.","rel_num_authors":7,"rel_authors":[{"author_name":"Runa I. L\u00f8berg","author_inst":"Oslo University Hospital"},{"author_name":"Helena A. Abdi-Dezfuli","author_inst":"Oslo University Hospital"},{"author_name":"Liv Kleppa","author_inst":"Oslo University Hospital"},{"author_name":"Alisa E. Dewan","author_inst":"Oslo University Hospital"},{"author_name":"Maureen T. Meling","author_inst":"Oslo University Hospital"},{"author_name":"Ludvig M Sollid","author_inst":"University of Oslo"},{"author_name":"M. Fleur du Pre","author_inst":"Oslo University Hospital"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"The Adhesion GPCR Flamingo-Like 1 (FMIL-1) Directs Synapse Formation in a Nociceptive Circuit","rel_doi":"10.64898\/2026.09.01.748526","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748526","rel_abs":"The species-specific anatomy of nervous systems suggests that circuit architecture is largely encoded by genetic blueprints. In C. elegans, PVD nociceptive neurons synapse with PVC and AVA interneurons to drive escape responses to noxious stimuli. We used fluorescent markers for PVD synapses with PVC and AVA in a candidate screen to detect connectivity genes. This approach revealed that the LIM homeodomain transcription factor MEC-3 and its target, FMIL-1 (Flamingo-like), function in PVD to direct connectivity with PVC and AVA. FMIL-1 is an adhesion class G Protein-coupled receptor (aGPCR), a protein family with members also implicated in mammalian synapse formation. We show that FMIL-1 acts early in PVD and is also sufficient to induce ectopic synapses in another circuit, thus suggesting that FMIL-1 promotes synaptogenesis. Our work establishes a new experimental circuit in C. elegans for investigating neuronal connectivity and provides evidence that FMIL-1\/aGPCRs regulate synapse formation.","rel_num_authors":13,"rel_authors":[{"author_name":"Tyler Kennedy","author_inst":"University of California, Berkeley"},{"author_name":"Damilola Oje","author_inst":"Vanderbilt University"},{"author_name":"Kylie Howerter","author_inst":"Vanderbilt University"},{"author_name":"Susan Reese","author_inst":"University of California, Berkeley"},{"author_name":"Rebecca McWhirter","author_inst":"Vanderbilt University"},{"author_name":"Barbara M.J. O'Brien","author_inst":"Vanderbilt University"},{"author_name":"Jamie Stern","author_inst":"Vanderbilt University"},{"author_name":"Morgan Ottley","author_inst":"Vanderbilt University"},{"author_name":"Demet Arac","author_inst":"The University of Chicago"},{"author_name":"Engin Ozkan","author_inst":"The University of Chicago"},{"author_name":"Richard Sando","author_inst":"Vanderbilt University"},{"author_name":"Andrew Dillin","author_inst":"University of California, Berkeley"},{"author_name":"David M. Miller III","author_inst":"Vanderbilt University"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"The Adhesion GPCR Flamingo-Like 1 (FMIL-1) Directs Synapse Formation in a Nociceptive Circuit","rel_doi":"10.64898\/2026.09.01.748526","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748526","rel_abs":"The species-specific anatomy of nervous systems suggests that circuit architecture is largely encoded by genetic blueprints. In C. elegans, PVD nociceptive neurons synapse with PVC and AVA interneurons to drive escape responses to noxious stimuli. We used fluorescent markers for PVD synapses with PVC and AVA in a candidate screen to detect connectivity genes. This approach revealed that the LIM homeodomain transcription factor MEC-3 and its target, FMIL-1 (Flamingo-like), function in PVD to direct connectivity with PVC and AVA. FMIL-1 is an adhesion class G Protein-coupled receptor (aGPCR), a protein family with members also implicated in mammalian synapse formation. We show that FMIL-1 acts early in PVD and is also sufficient to induce ectopic synapses in another circuit, thus suggesting that FMIL-1 promotes synaptogenesis. Our work establishes a new experimental circuit in C. elegans for investigating neuronal connectivity and provides evidence that FMIL-1\/aGPCRs regulate synapse formation.","rel_num_authors":13,"rel_authors":[{"author_name":"Tyler Kennedy","author_inst":"University of California, Berkeley"},{"author_name":"Damilola Oje","author_inst":"Vanderbilt University"},{"author_name":"Kylie Howerter","author_inst":"Vanderbilt University"},{"author_name":"Susan Reese","author_inst":"University of California, Berkeley"},{"author_name":"Rebecca McWhirter","author_inst":"Vanderbilt University"},{"author_name":"Barbara M.J. O'Brien","author_inst":"Vanderbilt University"},{"author_name":"Jamie Stern","author_inst":"Vanderbilt University"},{"author_name":"Morgan Ottley","author_inst":"Vanderbilt University"},{"author_name":"Demet Arac","author_inst":"The University of Chicago"},{"author_name":"Engin Ozkan","author_inst":"The University of Chicago"},{"author_name":"Richard Sando","author_inst":"Vanderbilt University"},{"author_name":"Andrew Dillin","author_inst":"University of California, Berkeley"},{"author_name":"David M. Miller III","author_inst":"Vanderbilt University"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"The Adhesion GPCR Flamingo-Like 1 (FMIL-1) Directs Synapse Formation in a Nociceptive Circuit","rel_doi":"10.64898\/2026.09.01.748526","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748526","rel_abs":"The species-specific anatomy of nervous systems suggests that circuit architecture is largely encoded by genetic blueprints. In C. elegans, PVD nociceptive neurons synapse with PVC and AVA interneurons to drive escape responses to noxious stimuli. We used fluorescent markers for PVD synapses with PVC and AVA in a candidate screen to detect connectivity genes. This approach revealed that the LIM homeodomain transcription factor MEC-3 and its target, FMIL-1 (Flamingo-like), function in PVD to direct connectivity with PVC and AVA. FMIL-1 is an adhesion class G Protein-coupled receptor (aGPCR), a protein family with members also implicated in mammalian synapse formation. We show that FMIL-1 acts early in PVD and is also sufficient to induce ectopic synapses in another circuit, thus suggesting that FMIL-1 promotes synaptogenesis. Our work establishes a new experimental circuit in C. elegans for investigating neuronal connectivity and provides evidence that FMIL-1\/aGPCRs regulate synapse formation.","rel_num_authors":13,"rel_authors":[{"author_name":"Tyler Kennedy","author_inst":"University of California, Berkeley"},{"author_name":"Damilola Oje","author_inst":"Vanderbilt University"},{"author_name":"Kylie Howerter","author_inst":"Vanderbilt University"},{"author_name":"Susan Reese","author_inst":"University of California, Berkeley"},{"author_name":"Rebecca McWhirter","author_inst":"Vanderbilt University"},{"author_name":"Barbara M.J. O'Brien","author_inst":"Vanderbilt University"},{"author_name":"Jamie Stern","author_inst":"Vanderbilt University"},{"author_name":"Morgan Ottley","author_inst":"Vanderbilt University"},{"author_name":"Demet Arac","author_inst":"The University of Chicago"},{"author_name":"Engin Ozkan","author_inst":"The University of Chicago"},{"author_name":"Richard Sando","author_inst":"Vanderbilt University"},{"author_name":"Andrew Dillin","author_inst":"University of California, Berkeley"},{"author_name":"David M. Miller III","author_inst":"Vanderbilt University"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"Intravital single-cell behavior profiling reveals disrupted germinal center B cell motility and interactions by EZH2 gain-of-function mutation","rel_doi":"10.64898\/2026.09.02.748910","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748910","rel_abs":"Germinal center (GC) B-cells give rise to the majority of non-Hodgkin lymphomas, underscoring the need to pinpoint critical processes that initiate and drive lymphomagenesis. Lymphoma driver mutations can alter GC B cell functions and B cell fate decisions. Here, we studied how EZH2 oncogenic mutation in GC B cells alters cellular motility and interactions with T follicular helper (Tfh) cells and follicular dendritic cells (FDCs) to determine B cell fate. By combining intravital imaging, single-cell behavior analyses, and RNA sequencing, we uncover how lymphoma-associated EZH2 mutations reprogram the behaviors of GC B cells in vivo. We found that EZH2 mutations increased single-cell motility speeds and morphological plasticity of GC B cells, redirecting migration toward the FDC-rich light zone subregions rather than to the dark zone. Although mutant EZH2 GC B cells exhibited normal engagement quality with FDCs, they showed shorter interaction times and reduced surface engagement with Tfh cells. Notably, EZH2 mutant B cells required prior contact with FDC before engaging with Tfh cells, thus impairing DZ recycling. This motility phenotype scaled with local mutant clone abundance, suggesting a behavioral strategy underlying how mutant cells outcompete WT cells. Lastly, we developed scMOTIPh, a computational framework that integrates single-cell behavioral features with transcriptomic profiles. Applying scMOTIPh to mutant GC B cells within the FDC-rich zone revealed enhanced ATP production, metabolic and antigen-presentation programs, and suppression of cell-death pathways, which is consistent with a tendency for malignant transformation and survival fitness. These findings provide an in vivo, single-cell view of how an epigenetic lesion rewires the local microenvironment by modulating single-cell behaviors within native GCs, revealing a dynamic mechanism for early lymphomagenesis.","rel_num_authors":9,"rel_authors":[{"author_name":"Chanhong Min","author_inst":"Johns Hopkins University"},{"author_name":"Kibaek Choe","author_inst":"Michigan State University"},{"author_name":"Xi Chen","author_inst":"Weill Cornell Medicine"},{"author_name":"Ioannis Karagiannidis","author_inst":"NYU Langone"},{"author_name":"Nikita Sivakumar","author_inst":"Johns Hopkins University"},{"author_name":"Chris Xu","author_inst":"Cornell University"},{"author_name":"Ari Melnick","author_inst":"Institut de Recerca contra la Leucemia Josep Carreras"},{"author_name":"Jude M Phillip","author_inst":"Johns Hopkins University"},{"author_name":"Wendy B\u00e9guelin","author_inst":"NYU Langone"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"Intravital single-cell behavior profiling reveals disrupted germinal center B cell motility and interactions by EZH2 gain-of-function mutation","rel_doi":"10.64898\/2026.09.02.748910","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748910","rel_abs":"Germinal center (GC) B-cells give rise to the majority of non-Hodgkin lymphomas, underscoring the need to pinpoint critical processes that initiate and drive lymphomagenesis. Lymphoma driver mutations can alter GC B cell functions and B cell fate decisions. Here, we studied how EZH2 oncogenic mutation in GC B cells alters cellular motility and interactions with T follicular helper (Tfh) cells and follicular dendritic cells (FDCs) to determine B cell fate. By combining intravital imaging, single-cell behavior analyses, and RNA sequencing, we uncover how lymphoma-associated EZH2 mutations reprogram the behaviors of GC B cells in vivo. We found that EZH2 mutations increased single-cell motility speeds and morphological plasticity of GC B cells, redirecting migration toward the FDC-rich light zone subregions rather than to the dark zone. Although mutant EZH2 GC B cells exhibited normal engagement quality with FDCs, they showed shorter interaction times and reduced surface engagement with Tfh cells. Notably, EZH2 mutant B cells required prior contact with FDC before engaging with Tfh cells, thus impairing DZ recycling. This motility phenotype scaled with local mutant clone abundance, suggesting a behavioral strategy underlying how mutant cells outcompete WT cells. Lastly, we developed scMOTIPh, a computational framework that integrates single-cell behavioral features with transcriptomic profiles. Applying scMOTIPh to mutant GC B cells within the FDC-rich zone revealed enhanced ATP production, metabolic and antigen-presentation programs, and suppression of cell-death pathways, which is consistent with a tendency for malignant transformation and survival fitness. These findings provide an in vivo, single-cell view of how an epigenetic lesion rewires the local microenvironment by modulating single-cell behaviors within native GCs, revealing a dynamic mechanism for early lymphomagenesis.","rel_num_authors":9,"rel_authors":[{"author_name":"Chanhong Min","author_inst":"Johns Hopkins University"},{"author_name":"Kibaek Choe","author_inst":"Michigan State University"},{"author_name":"Xi Chen","author_inst":"Weill Cornell Medicine"},{"author_name":"Ioannis Karagiannidis","author_inst":"NYU Langone"},{"author_name":"Nikita Sivakumar","author_inst":"Johns Hopkins University"},{"author_name":"Chris Xu","author_inst":"Cornell University"},{"author_name":"Ari Melnick","author_inst":"Institut de Recerca contra la Leucemia Josep Carreras"},{"author_name":"Jude M Phillip","author_inst":"Johns Hopkins University"},{"author_name":"Wendy B\u00e9guelin","author_inst":"NYU Langone"}],"rel_date":"2026-09-06","rel_site":"biorxiv"},{"rel_title":"A Multidimensional Immune Signature Predicts Susceptibility to Omicron Infection in Vaccinated Individuals","rel_doi":"10.64898\/2026.08.31.26361844","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361844","rel_abs":"Substantial inter-individual variation in susceptibility to viral infection persists despite widespread vaccination, and its immunological basis remains poorly understood. We investigated how innate and adaptive immune responses contribute to susceptibility to SARS-CoV-2 infection during the early COVID-19 pandemic. We compared two groups of vaccinated individuals who either remained uninfected or became infected during the first Omicron wave. Blood samples were collected at baseline and 24 weeks later. Peripheral blood mononuclear cells (PBMCs) and polymorphonuclear neutrophils (PMNs) were isolated and stimulated with the TLR7\/8 agonist R848 to assess innate responses. PBMCs were stimulated with SARS-CoV-2 peptide pools and highly purified inactivated viruses (ancestral and Omicron BA.1) to evaluate adaptive immunity. Prior to infection, individuals in the infected group exhibited reduced CD4 and CD8 T cells proliferative responses, alongside with increased TNF production across all stimulation conditions, despite largely comparable immune phenotypes, indicating a pre-existing functional immune deficit. Following infection, T-cell proliferation and IFN-gamma production were partially restored in response to viral antigens, although responses to Omicron BA.1 remained suboptimal. This functional deficit was accompanied by heightened inflammatory activity, including increased TNF and IFN-gamma production, elevated anti-nucleocapsid IgG3 levels, higher frequencies of B cells and myeloid cells, reduced circulating interferon-inducible T-cell Alpha Chemoattractant (I-TAC) concentrations, and a modest impairment in PMN IL-8 responses. Notably, these alterations were detectable prior to infection and persisted thereafter, indicating that they represent determinants rather than consequences of viral infection. Importantly, beyond differences in the magnitude of immune responses, protection was associated with the degree of functional coordination within the humoral compartment, as reflected by the relationship between Spike-binding antibodies and neutralizing activity. Together, these results demonstrate that susceptibility to Omicron infection is linked to a pre-existing and persistent functional immune imbalance affecting both innate and adaptive arms of immunity.","rel_num_authors":19,"rel_authors":[{"author_name":"Hend Jarras","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Wilfried Wenceslas Bazie","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Programme de Recherche sur les Maladies In"},{"author_name":"Isalie Blais","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Benjamin Goyer","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Julien Boucher","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Arielle Pakenham","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Kimberly Dancause-Caron","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Henintsoa Rabezanahary","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Mathieu Theriault","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Kim Santerre","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Marc-Andre Langlois","author_inst":"Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada"},{"author_name":"Philippe A Tessier","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"},{"author_name":"Jean-Francois Masson","author_inst":"Department of Chemistry, Institut Courtois, Quebec Center for Advanced Materials, Regroupement quebecois sur les materiaux de pointe, and Centre interdisciplina"},{"author_name":"Joelle N Pelletier","author_inst":"Department of Chemistry, Department of Biochemistry, Universite de Montreal, Montreal, QC, Canada, PROTEO- The Quebec Network for Research on Protein Function, "},{"author_name":"Nicholas Brousseau","author_inst":"Direction des risques biologiques, Institut national de sante publique du Quebec, Quebec, QC, Canada"},{"author_name":"Denis Boudreau","author_inst":"Departement de chimie et Centre d optique, photonique et laser COPL, Universite Laval, Quebec, QC, Canada"},{"author_name":"Sylvie Trottier","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Centre de recherche en infectiologie de l "},{"author_name":"Mariana Baz","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"},{"author_name":"Caroline Gilbert","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"A Multidimensional Immune Signature Predicts Susceptibility to Omicron Infection in Vaccinated Individuals","rel_doi":"10.64898\/2026.08.31.26361844","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361844","rel_abs":"Substantial inter-individual variation in susceptibility to viral infection persists despite widespread vaccination, and its immunological basis remains poorly understood. We investigated how innate and adaptive immune responses contribute to susceptibility to SARS-CoV-2 infection during the early COVID-19 pandemic. We compared two groups of vaccinated individuals who either remained uninfected or became infected during the first Omicron wave. Blood samples were collected at baseline and 24 weeks later. Peripheral blood mononuclear cells (PBMCs) and polymorphonuclear neutrophils (PMNs) were isolated and stimulated with the TLR7\/8 agonist R848 to assess innate responses. PBMCs were stimulated with SARS-CoV-2 peptide pools and highly purified inactivated viruses (ancestral and Omicron BA.1) to evaluate adaptive immunity. Prior to infection, individuals in the infected group exhibited reduced CD4 and CD8 T cells proliferative responses, alongside with increased TNF production across all stimulation conditions, despite largely comparable immune phenotypes, indicating a pre-existing functional immune deficit. Following infection, T-cell proliferation and IFN-gamma production were partially restored in response to viral antigens, although responses to Omicron BA.1 remained suboptimal. This functional deficit was accompanied by heightened inflammatory activity, including increased TNF and IFN-gamma production, elevated anti-nucleocapsid IgG3 levels, higher frequencies of B cells and myeloid cells, reduced circulating interferon-inducible T-cell Alpha Chemoattractant (I-TAC) concentrations, and a modest impairment in PMN IL-8 responses. Notably, these alterations were detectable prior to infection and persisted thereafter, indicating that they represent determinants rather than consequences of viral infection. Importantly, beyond differences in the magnitude of immune responses, protection was associated with the degree of functional coordination within the humoral compartment, as reflected by the relationship between Spike-binding antibodies and neutralizing activity. Together, these results demonstrate that susceptibility to Omicron infection is linked to a pre-existing and persistent functional immune imbalance affecting both innate and adaptive arms of immunity.","rel_num_authors":19,"rel_authors":[{"author_name":"Hend Jarras","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Wilfried Wenceslas Bazie","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Programme de Recherche sur les Maladies In"},{"author_name":"Isalie Blais","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Benjamin Goyer","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Julien Boucher","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Arielle Pakenham","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Kimberly Dancause-Caron","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Henintsoa Rabezanahary","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Mathieu Theriault","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Kim Santerre","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Marc-Andre Langlois","author_inst":"Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada"},{"author_name":"Philippe A Tessier","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"},{"author_name":"Jean-Francois Masson","author_inst":"Department of Chemistry, Institut Courtois, Quebec Center for Advanced Materials, Regroupement quebecois sur les materiaux de pointe, and Centre interdisciplina"},{"author_name":"Joelle N Pelletier","author_inst":"Department of Chemistry, Department of Biochemistry, Universite de Montreal, Montreal, QC, Canada, PROTEO- The Quebec Network for Research on Protein Function, "},{"author_name":"Nicholas Brousseau","author_inst":"Direction des risques biologiques, Institut national de sante publique du Quebec, Quebec, QC, Canada"},{"author_name":"Denis Boudreau","author_inst":"Departement de chimie et Centre d optique, photonique et laser COPL, Universite Laval, Quebec, QC, Canada"},{"author_name":"Sylvie Trottier","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Centre de recherche en infectiologie de l "},{"author_name":"Mariana Baz","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"},{"author_name":"Caroline Gilbert","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Secondary causes among adult patients presenting with first-episode psychosis to acute medical settings in Hong Kong: A 10-Year retrospective study","rel_doi":"10.64898\/2026.09.02.26362044","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362044","rel_abs":"BackgroundThe prevalence and pattern of secondary causes of first-onset psychosis (FEP) in Asian populations are understudied. Our objectives are to investigate the prevalence and pattern of secondary causes of FEP presenting in an acute medical setting in a metropolitan, Chinese-predominant population, and to investigate the prevalence and pattern of undiagnosed conditions presenting as psychosis.\n\nMethodThis is a retrospective observational study. We reviewed medical records of patients referred to the consultation psychiatry team at a tertiary acute teaching hospital in Hong Kong from January 2015 to Apr 2025. The inclusion criteria of the study are 1) age 18-64 at the time of the assessment, and 2) FEP confirmed by the consultation liaison team. Patients diagnosed with delirium were excluded.\n\nResultAmong the 384 patients included in the study (mean age = 40.0 {+\/-}13.7 years, 69% female), secondary causes of psychosis were found in 9.6% (n=37) of the cohort. Substance use is the most common secondary cause found (n=11, 2.9% of all FEPs) overall. Among those with secondary psychoses, patients underlying conditions were revealed only by the workup in relation to the FEP in 16 of them, of which definitive or probable autoimmune encephalitis (n=5) and early-onset dementia (n=4) were the most common conditions uncovered.\n\nConclusionThe prevalence of secondary psychoses in our FEP cohort is lower than published international figures. Clinicians need to be aware of the suspicious clinical features suggestive of autoimmune encephalitis and early-onset dementia in FEP patients with otherwise unremarkable past history and toxicology test.","rel_num_authors":6,"rel_authors":[{"author_name":"Steven Wai Ho Chau","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Lam KW Lam","author_inst":"Department of Psychiatry, Prince of Wales Hospital, Hong Kong"},{"author_name":"Matthew PM Yu","author_inst":"Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China"},{"author_name":"Yuen Cheuk Wong","author_inst":"Department of General Adult Psychiatry, Castle Peak Hospital, Hong Kong, China"},{"author_name":"Joseph CC Choi","author_inst":"Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong, China"},{"author_name":"Howan HW Leung","author_inst":"5.\tDivision of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong, China"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Socio-demographic and environmental factors amplify typhoon-related excess mortality in Japan","rel_doi":"10.64898\/2026.09.01.26362002","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26362002","rel_abs":"Mechanisms shaping population vulnerability to typhoon-related mortality remain poorly understood. Constructing a Bayesian spatio-temporal model, we linked 12.9 million deaths across Japan from 2010 to 2019 to population-weighted typhoon wind exposure and assessed effect modification by income, natural hazard vulnerability and healthcare access. Typhoon exposure was associated with 2,426 cumulative excess deaths [95% credible interval (CrI): 139, 4,632] among adults [&ge;]70 years, with mortality increasing within 0-1 weeks of exposure and more strongly in areas with limited healthcare access and greater hazard vulnerability. Among individuals <70 years, cumulative excess mortality was uncertain [781 deaths; -309 to 1,900], but delayed mortality increases were concentrated in lower-income and landslide-prone areas. These distinct patterns suggest that typhoon mortality reflects an interaction between acute exposure, demographic ageing and geographically uneven adaptive capacity, highlighting the need to incorporate local vulnerability into climate-resilient health systems.","rel_num_authors":5,"rel_authors":[{"author_name":"Lisa Yamasaki","author_inst":"Harvard T.H. Chan School of Public Health"},{"author_name":"Hiroaki Murayama","author_inst":"International University of Health and Welfare"},{"author_name":"Paul LC Chua","author_inst":"The University of Tokyo"},{"author_name":"Masahiro Hashizume","author_inst":"The University of Tokyo"},{"author_name":"Robbie M Parks","author_inst":"Columbia University"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Very low-calorie diet reduces hepatic steatosis and remodels circulating metabolite-microRNAs networks in metabolic dysfunction-associated steatotic liver disease: A pilot study","rel_doi":"10.64898\/2026.09.01.26361664","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361664","rel_abs":"Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major cause of chronic liver disease, with weight loss as the pivotal therapeutic strategy. However, the metabolic and molecular adaptations underlying rapid weight loss remain incompletely defined. In this pilot study, women with obesity and MASLD but without diabetes consumed a very low-calorie diet (VLCD) for 8 weeks. Clinical parameters, hepatic steatosis measured by controlled attenuation parameter (CAP), circulating metabolites, and microRNAs (miRs) were assessed before and after the dietary intervention. Integrated correlation and hierarchical clustering analyses were performed to identify molecular networks associated with clinical improvement. VLCD was well tolerated, resulting in significant weight loss ([~]11%) with [~]80% adherence. Significant improvements in metabolic parameters were observed, including fat mass, waist circumference, blood pressure, insulinemia, HOMA-IR, HbA1c, and triglycerides, with unchanged liver enzymes. Hepatic steatosis decreased markedly, as indicated by a reduction in CAP, while stiffness remained unchanged. Metabolomic profiling revealed elevated ketone bodies and broad reductions in amino acid levels, consistent with enhanced fatty acid oxidation and a catabolic metabolic state. Correlation analysis identified distinct metabolite signatures associated with hepatic steatosis, with changes in CAP positively associated with changes in amino acids and inversely associated with changes in ketone bodies and tricarboxylic acid cycle intermediates. Circulating miRs underwent selective rather than global remodeling, with only a limited subset showing strong associations with clinical parameters, including CAP and HOMA-IR. Specifically, VLCD altered the circulating levels of miR-148a-3p, miR-140-3p, miR-10b-5p, and miR-345-5p. Integration of metabolomic and miR datasets identified coordinated metabolitemiR modules involving glucose metabolism, branched-chain amino acid catabolism, mitochondrial metabolism, purine metabolism, microbial metabolites, and cellular redox pathways. These findings demonstrate that improvement in hepatic steatosis during VLCD-induced weight loss is accompanied by coordinated remodeling of circulating metabolite-miR networks. Integrated multi-omics analysis identifies candidate molecular signatures associated with metabolic adaptation and highlights circulating miR-metabolite modules as potential biomarkers of therapeutic response in MASLD.","rel_num_authors":13,"rel_authors":[{"author_name":"Paroma Deb","author_inst":"University of Iowa"},{"author_name":"Darin Bagar","author_inst":"University of Iowa"},{"author_name":"Prashant Kumar","author_inst":"University of Iowa"},{"author_name":"Leon Sun","author_inst":"University of Iowa"},{"author_name":"Ethan Chen","author_inst":"University of Iowa"},{"author_name":"Ravinder Reddy Gaddam","author_inst":"University of Iowa"},{"author_name":"Lorrana F Ferretto","author_inst":"University of Iowa"},{"author_name":"Constance R Shelsky","author_inst":"University of Iowa"},{"author_name":"Antonio J Sanchez","author_inst":"University of Iowa"},{"author_name":"Himani Thakkar","author_inst":"University of Iowa"},{"author_name":"Bhagirath Chaurasia","author_inst":"University of Iowa"},{"author_name":"Ajit Vikram","author_inst":"University of Iowa"},{"author_name":"Marcelo Lima DG Correia","author_inst":"University of Iowa"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Nanopore sequencing panel for saliva-based host pharmacogenomic screening in anti-tubercular therapy","rel_doi":"10.64898\/2026.09.02.26362034","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362034","rel_abs":"RationaleHost genotypes can predict subtherapeutic anti-tubercular drug exposures and treatment-associated toxicities. Screening for these variants could enable personalized dosing, but scalable assays for second-line drugs are lacking.\n\nObjectivesWe developed a nanopore sequencing panel to detect host variants affecting anti-tuberculosis drug troughs and toxicities, and evaluated its performance as a saliva-based screening tool.\n\nMethodsWe designed a 16-plex panel targeting 23 variants (21 clinically validated, 2 predicted actionable) relevant to linezolid, bedaquiline, clofazimine, moxifloxacin, and ethambutol exposure. We first sequenced 50 Coriell DNA (1000 Genomes Project) to benchmark accuracy against Illumina, then sequenced saliva from 202 individuals treated for drug-resistant tuberculosis in India using MinION Mk1C (R10.4). Plasma trough concentrations and toxicity frequencies were stratified by genotype. Data were analyzed using in-house pipelines.\n\nMeasurements and Main ResultsThe panel showed high coverage in saliva (median 3,609X). Several suggestive genotype-phenotype trends reached nominal significance in distinct subsets. Among patients on high-dose moxifloxacin (800mg daily), UGT1A1 rs3755319 A>C was associated with higher troughs in heterozygotes (6\/14, p<0.01) and homozygous alternates (4\/14, p<0.05). Among patients with linezolid-associated toxicity dose-reduced to 300mg, ABCB1 rs2032582 A>C homozygous alternates (7\/98) had significantly lower Cmin versus wild-type (p<0.05) and heterozygotes (p<0.01); neither association held at standard dosing. Linezolid toxicity was more frequent among ABCB1 rs1128503 A>G heterozygotes versus homozygous reference (58.3% vs. 29.1%), and UGT1A1 rs4148323 G>A heterozygotes showed higher moxifloxacin toxicity rates than wild-type (42.9% vs. 14.3%).\n\nConclusionsPortable, saliva-based sequencing reliably detects pharmacogenetic variants and could inform pre-treatment screening for drug exposure or toxicity.\n\nAt a glance summaryO_ST_ABSScientific knowledge on the subjectC_ST_ABSInterindividual variability in anti-tuberculosis plasma drug concentrations may contribute to poor outcomes, relapse, and toxicity, particularly with second-line regimens. Variants in host pharmacogenes partly explain subtherapeutic exposure and adverse effects and can be leveraged to identify individuals at risk, enabling dose optimization. However, pharmacogenes for second-line drugs remain underexplored, often assessed using limited variants in small populations despite substantial ethnic diversity. Scalable assays evaluating multiple variants are needed to enable routine screening for pharmacogenomics-guided treatment in clinical practice.\n\nWhat this study adds to the fieldWe developed a custom Nanopore sequencing panel targeting pharmacogenes for five second-line anti-tuberculosis drugs, incorporating both validated and predicted pharmacogenomic variants. The panel showed 100% concordance with Illumina whole-genome data (n=50) and was clinically validated on saliva samples from 202 patients receiving guideline-concordant, susceptibility-guided multidrug therapy for rifampin-resistant tuberculosis. The panel generated high-quality data from low DNA input, supporting scalable and noninvasive screening. This allowed for 174 distinct assessments of the impact of genotype on drug concentration and 152 assessments of association between genotype and clinical toxicity, with 3 associations demonstrating significance in subpopulations of people treated for MDR-TB. Specifically, UGT1A1 and ABCB1 polymorphisms were associated with moxifloxacin and linezolid trough concentrations at the extremes of the doses prescribed (800mg and 300mg daily, respectively). Carriers of an ABCB1 variant suggested a higher frequency of high-grade linezolid-associated toxicity. Overall, the panel provides baseline evidence for further validation of these markers in large-scale pharmacokinetic studies, as well as first clinical data on predicted variants that warrant screening in larger cohorts, demonstrating suitability for use on low-cost, portable Nanopore sequencers with shorter turnaround times.","rel_num_authors":16,"rel_authors":[{"author_name":"Priyanka Yadav","author_inst":"Institute of Bioinformatics, International Tech Park, Bangalore, Karnataka, India"},{"author_name":"Swarup A. V. Shah","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Aishwarya S. Babu","author_inst":"1. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India 2. Institute of Bioinformatics, International Tech Park, Bangalore, Karnataka, India"},{"author_name":"Minal Paradkar","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Shruthi Vasanthaiah","author_inst":"1. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India 2. Institute of Bioinformatics, International Tech Park, Bangalore, Karnataka, India"},{"author_name":"Karthick Vasudevan","author_inst":"1. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India 2. Institute of Bioinformatics, International Tech Park, Bangalore, Karnataka, India"},{"author_name":"Prerna R. Arora","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Rohan V. Lokhande","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Heeral U.B. Pandya","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Paolo Denti","author_inst":"Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa"},{"author_name":"Camilla Rodrigues","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Jason  R. Andrews","author_inst":"Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, California, USA"},{"author_name":"Akhilesh Pandey","author_inst":"Department of Laboratory Medicine and Pathology, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA"},{"author_name":"Jeffrey A Tornheim","author_inst":"Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA"},{"author_name":"Tester F. Ashavaid","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Renu Verma","author_inst":"Institute of bioinformatics"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Cell-type-resolved somatic variant discovery from bulk long-read sequencing","rel_doi":"10.64898\/2026.09.01.26361966","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361966","rel_abs":"Somatic mutations arise throughout life, with functional consequences tied to the cell populations in which they occur. Genome-wide studies measure somatic variations in bulk tissue, whereas single-cell approaches resolve cell identity but provide limited sensitivity for complex alleles. Here we developed SniffCell, which uses DNA methylation carried on native long reads to assign somatic variant-supporting molecules to methylation-resolvable cell types. SniffCell builds cell-type-discriminatory methylation signatures across eight tissues, assigns long reads to cell types, and provides cell-type-specific variant calling. Across peripheral blood mononuclear cells and brain benchmarks, SniffCell recovered sorted cell identities and validated cell-type-specific variant assignments using purified immune-cell, neuronal, and oligodendrocyte fractions. In blood, SniffCell recovered lineage-restricted antigen receptor rearrangements and localized a somatic tandem-repeat expansion to T cells. In the frontal cortex, SniffCell identified recurrent neuron-specific tandem-repeat expansions in genes including FGF14, LRRC7 and SH3RF3. Across three brain cohorts comprising 172 donors, recurrent neuron-associated expansions were enriched for GAA-rich motifs. In donors with matched blood, and diverged more strongly from the inherited repeat length, whereas oligodendrocyte-associated alleles more often tracked it. SniffCell transforms native bulk long-read genomes into a cell-type-aware resource for somatic variant discovery and reveals recurrent somatic instability in human tissues at cell-type resolution.","rel_num_authors":24,"rel_authors":[{"author_name":"Yilei Fu","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Caoimhe Morley","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Lauren M. Masters","author_inst":"Department of Molecular and Human Genetics, Baylor College of Medicine, TX, USA"},{"author_name":"Adam C. English","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Yiming Zhu","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Abraham G. Moller","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Luis F. Paulin","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Ben Thompson","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Ester Kalef-Ezra","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"George Weissenberger","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Helen Shen","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Melissa Meridith","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Arianna Manini","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Dominic Horner","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Xylena Reed","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Donna Muzny","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Zane Jaunmuktane","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Ziad M. Khan","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Heer Mehta","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Winston Timp","author_inst":"Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"author_name":"Kimberley Billingsley","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Graham S. Erwin","author_inst":"Department of Molecular and Human Genetics, Baylor College of Medicine, TX, USA"},{"author_name":"Christos Proukakis","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Fritz J. Sedlazeck","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, TX, USA; D"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Rest-activity and circadian rhythm parameters relate to cognition and disability outcomes in multiple sclerosis","rel_doi":"10.64898\/2026.08.31.26361636","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361636","rel_abs":"OBJECTIVETo prioritize novel measures of disease progression, we examined whether actigraphy-derived rest-activity rhythm (RAR) parameters relate to cognitive performance as well as disability in a multiple sclerosis (MS) cohort enrolled in a prospective brain donation program.\n\nMETHODSRAR parameters were assessed using a wrist actigraphy device (AX3 Axivity Actiwatch, Axivity Ltd.) over two weeks. The primary outcome measure was the Symbol Digit Modalities Test (SDMT, N=222). Secondary outcomes included Brixton Spatial Anticipation Test and self-reported disability. In 76 participants, volumetric measures were derived from repurposed clinical magnetic resonance imaging (MRI) data. We applied linear and logistic regression models, adjusting for age, sex, education, time since MS diagnosis, and body mass index.\n\nRESULTSAfter correction for multiple comparisons, higher intradaily variability (IV) of RAR and lower relative amplitude were associated with worse SDMT performance; higher IV was also associated with greater odds of disability. A broader set of RAR parameters was associated with disability measure. No MRI parameters were related to RAR in the subset of individuals with available MRI data, although we note suggestive associations with hippocampal and choroid plexus volumes warranting further investigation.\n\nINTERPRETATIONMore robust circadian rhythms were related to better cognition. These results highlight the utility of actigraphy and its more nuanced measures beyond the simple summaries of activity levels that quantitate the extent of motor disability. Selected RAR features may be an effective non-invasive approach to capture clinically relevant quantitative measures of brain function for persons with MS.","rel_num_authors":12,"rel_authors":[{"author_name":"Katrin Wolfova","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Charles White","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Nasim Montazeri Ghahjaverestan","author_inst":"Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada"},{"author_name":"Tenzing Choeying","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Rodolfo Arevalo","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Kaho Onomichi","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Levi Davis","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Victoria M Leavitt","author_inst":"Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA."},{"author_name":"Korhan Buyukturkoglu","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Claire Riley","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Andrew Lim","author_inst":"Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre University of Toronto, ON, Canada"},{"author_name":"Philip De Jager","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Interpretable photoacoustic phenotyping of distal microcirculation for peripheral artery disease diagnosis with exploratory perioperative assessment","rel_doi":"10.64898\/2026.09.02.26361270","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26361270","rel_abs":"Peripheral artery disease (PAD) spans a continuum from large-vessel obstruction to distal microvascular dysfunction, yet routine non-invasive tests, including the ankle-brachial index (ABI), do not provide structurally resolved assessment of the foot microvascular bed and may be unreliable in the setting of medial arterial calcification or perioperative follow-up. Here we developed a clinic-oriented multispectral compound-scanning photoacoustic tomography system (MCPATS) for compression-free distal toe imaging, and an interpretable photoacoustic tomography distal microcirculation score, termed PACT-DMS, for phenotyping PAD-related distal vascular abnormalities. PACT-DMS was derived from anatomically standardized distal toe sections and integrated seven prespecified vascular features spanning trunk-vessel morphology, microvascular distribution and pulsation-related dynamics through a traceable linear support vector machine. In a prospective single-centre cohort of 45 participants, the bilateral fusion PACT-DMS model distinguished patients with PAD from healthy controls with an area under the receiver operating characteristic curve of 0.964 (95% CI, 0.907-1.000) and an accuracy of 91.1% (95% CI, 82.2%-97.8%) under subject-level leave-one-out cross-validation, supported by complementary robustness analyses. Exploratory analyses further showed that PACT-DMS identified abnormal distal vascular phenotypes in 6 of 9 clinically diagnosed PAD limbs with non-abnormal ABI and visualized distal vascular-bed changes before and after revascularization. These findings support MCPATS-enabled interpretable photoacoustic vascular phenotyping as a candidate adjunctive approach for distal microcirculatory assessment in PAD; larger multicentre studies with external validation and prespecified analysis protocols will be required to define its clinical role.","rel_num_authors":12,"rel_authors":[{"author_name":"Handi Deng","author_inst":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University; Institute for Intelligent He"},{"author_name":"Tianhao Yuwen","author_inst":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University; Institute for Intelligent He"},{"author_name":"Zipeng Li","author_inst":"Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital"},{"author_name":"Jiaxuan Xiang","author_inst":"Beijing Tsingpai Technology Co., Ltd."},{"author_name":"Yizhou Bai","author_inst":"Department of Thyroid and Breast Surgery, Beijing Tsinghua Changgung Hospital"},{"author_name":"Naiyue Zhang","author_inst":"Beijing Tsingpai Technology Co., Ltd."},{"author_name":"Wubing Fu","author_inst":"Beijing Tsingpai Technology Co., Ltd."},{"author_name":"Xiaojun Wang","author_inst":"Beijing Tsingpai Technology Co., Ltd."},{"author_name":"Jianming Guo","author_inst":"Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University"},{"author_name":"Weiwei Wu","author_inst":"Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital"},{"author_name":"Cheng Ma","author_inst":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University"},{"author_name":"Ming-Yuan Liu","author_inst":"Department of Vascular Surgery, Beijing Friendship Hospital, Capital Medical University; Beijing Institute of Vascular Surgery"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Defining use cases for biomarkers and tests across tuberculosis infection, disease and treatment: An international consensus and prioritisation exercise","rel_doi":"10.64898\/2026.09.01.26361598","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361598","rel_abs":"BackgroundTranslation of tuberculosis (TB) biomarker and diagnostic research into tools that improve patient and public health outcomes has been slow, partly because no internationally agreed framework exists defining the use cases that new biomarkers and tests should address. We aimed to identify, validate, and prioritise use cases for TB biomarkers and tests across Mycobacterium tuberculosis (Mtb) infection, TB disease, TB treatment, and post-TB care, through an international consensus process.\n\nMethods and FindingsWe conducted a scoping review of the literature, guidelines, and target product profiles (24 documents; 69 candidate use cases consolidated to 13), followed by a hybrid RAND\/UCLA modified Delphi consensus process involving 185 identified interest-holders, including clinicians, researchers, diagnostic developers, industry, funders, civil society, national TB programmes, and policymakers (including WHO representatives). Interest-holders completed an online survey rating agreement with each use case, and attended a consensus meeting to discuss use cases with less than 80% agreement, followed by a further validation meeting. Eleven use cases were retained across four TB care pathway stages: three for Mtb infection, four for disease detection, three for treatment optimisation, and one for post-TB care. Highest-priority use cases were detection of drug-resistant TB, prediction of progression from infection to disease, identification of current Mtb infection, and improved diagnosis of active TB disease.\n\nConclusionsThis consensus exercise provides the first comprehensive, prioritised framework of use cases for TB biomarkers and tests, spanning the full care pathway. These eleven priority use cases can guide investment, focus biomarker discovery, and inform future target product profiles, funding calls, and policy development. Applying the framework to the current biomarker pipeline is a key next step to address gaps between innovation and priority needs.","rel_num_authors":24,"rel_authors":[{"author_name":"Sacha Wright","author_inst":"University College London"},{"author_name":"Federico Fama","author_inst":"University College London"},{"author_name":"Angus de Wilton","author_inst":"University of Bristol"},{"author_name":"Ellen Steward","author_inst":"University College London"},{"author_name":"Francesca Saluzzo","author_inst":"Scientific Institute San Raffaele: IRCCS Ospedale San Raffaele"},{"author_name":"Chiara Sepulcri","author_inst":"Scientific Institute San Raffaele: IRCCS Ospedale San Raffaele"},{"author_name":"James Greenan-Barrett","author_inst":"University College London"},{"author_name":"Khay Mar Aung","author_inst":"The University of Sydney"},{"author_name":"Thi  Mai Nguyen","author_inst":"The University of Sydney"},{"author_name":"Nora Engel","author_inst":"Vrije Universiteit Amsterdam"},{"author_name":"Seda Yerlikaya","author_inst":"Heidelberg University"},{"author_name":"Hanif Esmail","author_inst":"University College London"},{"author_name":"Salome Charalambous","author_inst":"University of the Witwatersrand Johannesburg"},{"author_name":"Cecily Miller","author_inst":"World Health Organization"},{"author_name":"Ruvandhi Nathavitharana","author_inst":"Harvard Medical School"},{"author_name":"Luan  Nguyen Quang Vo","author_inst":"Karolinska Institutet"},{"author_name":"Mikashmi Kohli","author_inst":"Foundation for Innovative New Diagnostics: FIND"},{"author_name":"Delia Goletti","author_inst":"Istituto Nazionale per le Malattie Infettive: Istituto Nazionale Malattie Infettive Lazzaro Spallanzani"},{"author_name":"Daniela Cirillo","author_inst":"Scientific Institute San Raffaele: IRCCS Ospedale San Raffaele"},{"author_name":"Mahdad Noursadeghi","author_inst":"University College London"},{"author_name":"Claudia  Maria Denkinger","author_inst":"Heidelberg University"},{"author_name":"Emily  Lai-Ho MacLean","author_inst":"The University of Sydney"},{"author_name":"Rishi  K Gupta","author_inst":"University College London"},{"author_name":"Ankur Gupta-Wright","author_inst":"Imperial College London \/ North Bristol NHS Trust \/ University of Heidelberg"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Transcranial photobiomodulation rebalances cortical excitation\/inhibition in young adults with attention deficit\/hyperactivity disorder","rel_doi":"10.64898\/2026.09.01.26361884","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361884","rel_abs":"BackgroundAttention deficit\/hyperactivity disorder (ADHD) is a neurodevelopmental condition lacking mechanistically grounded interventions. Here, we tested whether transcranial photobiomodulation (tPBM) can restore neural homeostasis in ADHD patients.\n\nMethodsIn a randomized, double-blind, sham-controlled crossover design, 28 young adults with ADHD completed a two-week intervention, receiving active (150 mW) and sham (0 mW) stimulation over the right prefrontal cortex for 16 minutes with concurrent electroencephalography (EEG) recording, alongside 29 healthy controls providing a normative reference.\n\nResultsBehaviorally, tPBM improved working memory K scores in the ADHD group, with performance closer to typical levels. Across sensor and source levels, tPBM progressively increased relative alpha power, steepened the aperiodic exponent, and enhanced neural complexity, as indexed by multiscale entropy, with widespread effects spanning frontoparietal and attention systems, extending to sensory and default-mode regions, collectively indicating a shift toward normative neural dynamics. Notably, these changes--consistent with rebalanced excitation\/inhibition dynamics--predict behavioral improvements in working memory.\n\nConclusionsTogether, our findings identify tPBM as a candidate approach for restoring excitation\/inhibition balance and normalizing large-scale neural dynamics in ADHD patients, providing a mechanistic foundation for its therapeutic potential.","rel_num_authors":8,"rel_authors":[{"author_name":"Xuye Yuan","author_inst":"Beijing Normal University"},{"author_name":"Yiyang Wang","author_inst":"Beijing Normal University"},{"author_name":"Chen Dang","author_inst":"Shanghai Jiao Tong University"},{"author_name":"Hongyu Liu","author_inst":"Peking University"},{"author_name":"Lili Yang","author_inst":"Beijing Normal University"},{"author_name":"Dongwei Li","author_inst":"Beijing Normal University"},{"author_name":"Li Sun","author_inst":"Peking University Sixth Hospital"},{"author_name":"Yan Song","author_inst":"Beijing Normal University"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Drought and Syphilis Exposure in Zambia, Uganda, and Tanzania: Are There Urban\/Rural Disparities?","rel_doi":"10.64898\/2026.08.31.26361827","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361827","rel_abs":"Eastern and southern Africa are highly affected by drought, and projections indicate that droughts will become more common in the coming decades. Whilst there has been research on the impact of drought on HIV, attributable to its effect on food insecurity and increased risky sexual behaviour, the link between drought and other sexually transmitted infections (STIs), particularly syphilis, remains largely unexplored. This study therefore assesses the association between drought and active syphilis, history of syphilis, and recovery from syphilis in Zambia, Uganda, and Tanzania, while examining disparities between urban and rural settings. It uses data on 75,225 people from Population-based HIV Impact Assessment surveys (2016-17), which include biomarker information, combined with rainfall data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset to define drought in the two years prior to the survey. Multivariate logistic regression models with country-level fixed effects show that in urban areas, drought was associated with a significant increase in the probability of having active syphilis (average marginal effects (AME) = 0.5%, 95% confidence interval (CI): 0.2%-1.0%) and of having ever had syphilis (AME = 2%, 95% CI: 0.8%-4%). No significant association was found in rural areas. Exposure to drought was not associated with recovery from syphilis in either setting, though confidence intervals were wide. Our results underscore the importance of strengthening healthcare systems to make them more resilient to drought shocks in urban areas, and the need to reinforce syphilis prevention and treatment programmes in those areas when such shocks occur.","rel_num_authors":7,"rel_authors":[{"author_name":"Arlette Simo Fotso","author_inst":"Institut National d'Etudes Demograpiques: INED"},{"author_name":"Charline Maltat Charline Maltat","author_inst":"Haute Ecole de Demographie"},{"author_name":"Baurice Gbaguidi-Sekpon","author_inst":"Haute Ecole de Demographie"},{"author_name":"Hakim Marzouk","author_inst":"Haute Ecole de Demographie"},{"author_name":"Adam Trickey","author_inst":"Population Health Sciences, University of Bristol"},{"author_name":"Andrea Low","author_inst":"Columbia University, Mailman School of Public Health"},{"author_name":"Valentine Becquet","author_inst":"Institut National d'Etudes Demographiques"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Direct measurement of PET hydrolase interfacial kinetics reveals catalysis-independent surface remodelling","rel_doi":"10.64898\/2026.09.02.749009","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749009","rel_abs":"How PET hydrolases engage solid plastic has been inferred almost entirely from soluble analogs and surfactant-stabilised nanoparticle suspensions, and reported affinities span orders of magnitude. Here we measure it directly, depositing thin amorphous PET films onto gold surface plasmon resonance chips and following enzyme binding to authentic polymer in real time. Four PET hydrolases, LCC, LCC-ICCG, IsPETase-EHA and TfCut2, all bind with nanomolar apparent affinity and surface residence half-lives of tens of minutes. LCC-ICCG dissociates at 4.0 x 10^-1, within twofold of its kcat measured on authentic PET, indicating that turnover is limited by disengagement rather than by ester hydrolysis. During association, we observe non-monophasic responses for these enzymes, including, for several, a signal fall while enzyme is still flowing over the surface. This decline persists in a catalytically inactivated variant, is absent in control proteins and a structurally homologous non-plastic degrading cutinase, and cannot arise from movement of the bound enzyme alone. The polymer surface must therefore be altered non-catalytically by PET hydrolase binding. The process runs at different rates for different enzymes on an identical film, is suppressed by dilute Triton X-100, and is modulated biphasically by PET degradation products. Altogether, we conclude that enzymatic PET depolymerisation involves a catalysis-independent chain-mobilisation step that soluble assays cannot report.","rel_num_authors":5,"rel_authors":[{"author_name":"Kailey J Petz","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"},{"author_name":"Arnaud Boudigou","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"},{"author_name":"Laura E Dickson","author_inst":"University of Ottawa, Department of Chemical and Biological Engineering"},{"author_name":"Benoit H Lessard","author_inst":"University of Ottawa, Department of Chemical and Biological Engineering"},{"author_name":"Adam M Damry","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Direct measurement of PET hydrolase interfacial kinetics reveals catalysis-independent surface remodelling","rel_doi":"10.64898\/2026.09.02.749009","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749009","rel_abs":"How PET hydrolases engage solid plastic has been inferred almost entirely from soluble analogs and surfactant-stabilised nanoparticle suspensions, and reported affinities span orders of magnitude. Here we measure it directly, depositing thin amorphous PET films onto gold surface plasmon resonance chips and following enzyme binding to authentic polymer in real time. Four PET hydrolases, LCC, LCC-ICCG, IsPETase-EHA and TfCut2, all bind with nanomolar apparent affinity and surface residence half-lives of tens of minutes. LCC-ICCG dissociates at 4.0 x 10^-1, within twofold of its kcat measured on authentic PET, indicating that turnover is limited by disengagement rather than by ester hydrolysis. During association, we observe non-monophasic responses for these enzymes, including, for several, a signal fall while enzyme is still flowing over the surface. This decline persists in a catalytically inactivated variant, is absent in control proteins and a structurally homologous non-plastic degrading cutinase, and cannot arise from movement of the bound enzyme alone. The polymer surface must therefore be altered non-catalytically by PET hydrolase binding. The process runs at different rates for different enzymes on an identical film, is suppressed by dilute Triton X-100, and is modulated biphasically by PET degradation products. Altogether, we conclude that enzymatic PET depolymerisation involves a catalysis-independent chain-mobilisation step that soluble assays cannot report.","rel_num_authors":5,"rel_authors":[{"author_name":"Kailey J Petz","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"},{"author_name":"Arnaud Boudigou","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"},{"author_name":"Laura E Dickson","author_inst":"University of Ottawa, Department of Chemical and Biological Engineering"},{"author_name":"Benoit H Lessard","author_inst":"University of Ottawa, Department of Chemical and Biological Engineering"},{"author_name":"Adam M Damry","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Air Pollution, Early Adversity, and Amygdala: Environmental Correlates of Psychopathology in Preadolescents","rel_doi":"10.64898\/2026.08.31.748438","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748438","rel_abs":"Early life adversity (ELA) and ambient fine particulate matter (PM2.5) are hypothesized to be environmental risk factors for altered brain structure and psychopathology, but their unique and interactive effects in childhood remain unclear. This study examines the interactive associations of ELA and annual average residential PM2.5 exposure (total mass and 15 components) on total amygdala and basolateral amygdala subregion volumes and psychopathology symptoms in a subset of children (N=3,601, 45% assigned female at birth, 9-10 years) from the Adolescent Brain Cognitive Development Study. Linear mixed-effects models, adjusting for sociodemographic factors, co-pollutants, and neuroimaging covariates, showed that ELA was associated with greater bifactor model-defined general, specific internalizing, and specific externalizing symptoms of psychopathology. PM2.5 moderated ELA associations with specific externalizing symptoms, with greater symptoms in those exposed to higher ELA and higher PM2.5 exposures. Among youth with higher ELA, smaller basolateral paralaminar volumes were linked to greater general and specific externalizing symptoms. These findings underscore the importance of considering psychosocial and physical environmental co-exposures when identifying children at risk for psychopathology.","rel_num_authors":11,"rel_authors":[{"author_name":"Amanda C Del Giacco","author_inst":"University of Southern California"},{"author_name":"Michael A Rosario","author_inst":"University of Southern California"},{"author_name":"Carlos Cardenas-Iniguez","author_inst":"University of Southern California"},{"author_name":"Nitya Chawla","author_inst":"Scripps College"},{"author_name":"Alan Wen","author_inst":"University of Southern California"},{"author_name":"Kirthana Sukumaran","author_inst":"University of Southern California"},{"author_name":"L. Nate Overholtzer","author_inst":"University of Southern California"},{"author_name":"Jiu-Chiuan Chen","author_inst":"University of Southern California"},{"author_name":"Benjamin B Lahey","author_inst":"University of Chicago"},{"author_name":"Tyler M Moore","author_inst":"University of Pennsylvania"},{"author_name":"Megan Herting","author_inst":"University of Southern California"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Pangenome alignment reveals global diversity and evolution of human centromeric regions","rel_doi":"10.64898\/2026.09.03.749043","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749043","rel_abs":"Centromeres play essential roles in chromosome segregation and genome stability, yet they remain among the least characterized regions of the human genome. Despite advances in long-read sequencing and complete genome assembly, the extreme repetitiveness and structural complexity of these regions still challenge population-scale analysis, obscuring their mutational dynamics. The Human Pangenome Reference Consortium has now accurately assembled over 6,000 centromeres, providing an opportunity to catalog global centromere variation. However, centromeric regions have been systematically excluded from pangenome alignments due to the technical challenge of aligning their highly repetitive tandem arrays and extreme structural variability. Here we introduce Centrolign, a graph-based multiple sequence alignment tool that combines a uniqueness-driven objective function with partial-order partial-order alignment to accurately align alpha satellite higher-order repeats. By prioritizing rare matches within tandem arrays and leveraging extended centromere-spanning haplotypes formed by suppressed recombination, Centrolign produces progressive multiple sequence alignments that preserve ancestral repeat organization. Applied across human centromeres, these alignments reveal the phylogenetic structure of similar satellite array haplotypes and enable precise estimation of variation rates, structural variant frequencies, and spatial patterns of mutation within satellite arrays. Integrating Centrolign graphs with repeat annotation tools and pangenome mapping algorithms allows accurate variant calling and genotyping from long reads without prior assembly. Moreover, we show that centromere haplotypes can be accurately subtyped with k-mers alone. Together, these advances establish a robust framework for incorporating centromeres into broader pangenomes, and population genomics in general, advancing our understanding of human genome evolution and diversity.","rel_num_authors":19,"rel_authors":[{"author_name":"Jordan Eizenga","author_inst":"University of California Santa Cruz"},{"author_name":"Mira Mastoras","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Julian Keith Lucas","author_inst":"UC Santa Cruz"},{"author_name":"Julian Menendez","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Faith Okamoto","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Glenn Hickey","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Prajna Hebbar","author_inst":"University of California Santa Cruz"},{"author_name":"Sasha A. Langley","author_inst":"Department of Evolution and Ecology, University of California, Davis, CA, USA"},{"author_name":"Hailey Loucks","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Fedor Ryabov","author_inst":"Centre for Biomedical Research and Technology, HSE University, Moscow, Russia; The Center for Bio- and Medical Technologies, Moscow, Russia"},{"author_name":"Yulia Zybina","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Mobin Asri","author_inst":"UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA"},{"author_name":"J Matthew Franklin","author_inst":"Department of Genetics, Stanford University, Palo Alto, CA 94304, USA."},{"author_name":"Nicolas Altemose","author_inst":"Stanford University"},{"author_name":"- Human Pangenome Reference Consortium","author_inst":""},{"author_name":"Ivan A Alexandrov","author_inst":"Russian Academy of Medical Sciences"},{"author_name":"Charles H. Langley","author_inst":"University of California"},{"author_name":"Benedict Paten","author_inst":"UCSC"},{"author_name":"Karen H Miga","author_inst":"University of California, Santa Cruz"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Genome-wide evolutionary shifts shape the emergence of dominant circulating clones in Mycobacterium abscessus","rel_doi":"10.64898\/2026.09.03.749045","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749045","rel_abs":"Mycobacterium abscessus (Mab) infections are increasingly associated with dominant circulating clones (DCCs), yet the evolutionary processes underlying their emergence remain poorly understood. Here, we analyzed 11,314 globally collected Mab genomes to investigate genome-wide evolutionary changes during the emergence of seven DCCs. We established a conservative core-genome analytical framework that integrates population-wide gene conservation with read-level validation to minimize the influence of assembly-derived variation on downstream evolutionary inference. Applying this framework to the global Mab population, we defined a stable core genome of 3,001 genes, representing a conservative lower bound across currently sequenced Mab populations. Core-genome analysis revealed a substantial decline in recombination relative to mutation following DCC expansion, indicating a broad shift towards mutation-dominated clonal evolution. Selective pressures also changed across this transition: 29 genes shifted from purifying to positive selection, consistent with continued adaptation during DCC expansion, whereas 19 showed the opposite pattern, suggesting increased functional constraint after clonal establishment. Although the accessory genes acquired differed among DCCs, gains consistently exceeded losses during DCC formation and showed functional convergence in environmental sensing, metabolism, metal homeostasis and stress responses. Together, these findings reveal consistent evolutionary shifts across independently emerged DCCs, with convergence occurring primarily in evolutionary processes and adaptive functions rather than through a single shared genetic determinant.","rel_num_authors":6,"rel_authors":[{"author_name":"Chendi Zhu","author_inst":"Beijing chest hospital"},{"author_name":"Yu Zhou","author_inst":"Beijing chest hospital"},{"author_name":"Mingxing Ni","author_inst":"Beijing chest hospital"},{"author_name":"Zhuofan Huang","author_inst":"Beijing chest hospital"},{"author_name":"Zhenyu Wang","author_inst":"Beijing chest hospital"},{"author_name":"Weimin Li","author_inst":"Beijing chest hospital"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Evaluating performance bias in face-to-BMI vision transformer models across diverse human populations","rel_doi":"10.64898\/2026.09.02.748815","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748815","rel_abs":"Computer vision models that estimate body mass index (BMI) from facial features offer a non-invasive, low-cost alternative to physical measurement, with uses in telemedicine, emergency care where a scale or measuring tools arent available, automated self-monitoring, and large-scale epidemiological research. Most of these models, however, are trained on government records, social media images, and celebrity photographs, sources that introduce dataset biases and fail to represent the general public. This study tests how well a face-to-BMI machine learning model generalizes across populations, specifically how morphological diversity and population-specific training data affect cross-cultural accuracy. We trained and evaluated Vision Transformer (ViT-H\/14) models on paired BMI measurements and facial photographs from four Indigenous populations: the Orang Asli of Malaysia, the Ju\/hoansi of Southern Africa, the Sama residing in the Philippines, and the Tsimane of Bolivia. To evaluate how training data composition affects predictions, we compared four training strategies, from single-population models (focal models) to models trained on the full combined global dataset (global models). In-distribution training always produced the best performance. Models exposed to a target populations morphology, whether focal or global, consistently predicted BMI most accurately for that population. But when a target population differed from the training sample, adding more cross-cultural variation to training improved out-of-distribution predictions. Therefore, training on a populations own data works best when that data exists, and training on data spanning a wide range of human morphology is the strongest fallback when it doesnt. These findings suggest that while target population training data produces the most accurate results, training on datasets that capture global morphological variation substantially improves performance in unrepresented populations. Broader diversity in training data is essential for developing machine learning health tools that generalize reliably across human populations.","rel_num_authors":17,"rel_authors":[{"author_name":"Jordie Hoffman","author_inst":"U of U: University of Utah"},{"author_name":"Michael Gurven","author_inst":"UC Santa Barbara: University of California Santa Barbara"},{"author_name":"Hillard Kaplan","author_inst":"Chapman University"},{"author_name":"Jonathan Stieglitz","author_inst":"Toulouse School of Economics Recherche"},{"author_name":"Benjamin  C. Trumble","author_inst":"Arizona State University"},{"author_name":"Bret Beheim","author_inst":"Max-Planck-Institute for Evolutionary Anthropology: Max-Planck-Institut fur evolutionare Anthropologie"},{"author_name":"Paul  L. Hooper","author_inst":"University of New Mexico - Albuquerque: The University of New Mexico"},{"author_name":"Richard  B. Lee","author_inst":"University of Toronto"},{"author_name":"Julia  R. Phelps","author_inst":"Arizona State University"},{"author_name":"Kim Hill","author_inst":"Arizona State University"},{"author_name":"Brian  F. Codding","author_inst":"University of California Santa Barbara"},{"author_name":"Simon Brewer","author_inst":"U of U: University of Utah"},{"author_name":"Yvonne  A. L. Lim","author_inst":"Universiti Malaya"},{"author_name":"Amanda  J. Lea","author_inst":"Vanderbilt University"},{"author_name":"Ian  J. Wallace","author_inst":"University of New Mexico - Albuquerque: The University of New Mexico"},{"author_name":"Vivek  V. Venkataraman","author_inst":"University of Calgary"},{"author_name":"Thomas  S. Kraft","author_inst":"University of Utah"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Long-range linkage maintained male-specific loci on a young Y chromosome prior to recombination shutdown","rel_doi":"10.64898\/2026.08.31.748364","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748364","rel_abs":"Suppression of recombination on the Y chromosome maintains linkage between male-specific loci and is commonly established by inversions. Here, we describe a young, inversion-free Y chromosome (neo-Y) in Drosophila albomicans with a unique history and paradoxical signature of exchange. Prior to recombination shutdown in males, it repeatedly recombined with the X-linked counterpart (neo-X) but at the same time preserved complete long-range linkage of the chromosome ends. By assembling multiple neo-Ys chromosomes and QTL-mapping, we show that double crossovers maintained linkage between the male-sex determining region at one end to sexually antagonistic alleles and a locus essential for spermatogenesis at the other. We argue that such long-range linkage of distal sex-specific loci disfavors inversions but instead encourages the emergence of achiasmy.","rel_num_authors":5,"rel_authors":[{"author_name":"May Wang","author_inst":"University of British Columbia"},{"author_name":"Mohammadebrahim Akhavizadegan","author_inst":"University of British Columbia"},{"author_name":"Jen-Yu Wang","author_inst":"University of California - Irvine"},{"author_name":"Ching-Ho Chang","author_inst":"Academia Sinica"},{"author_name":"Kevin H-C Wei","author_inst":"University of British Columbia"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Unmasking the large and highly repetitive genome of Phlox reveals a complex evolutionary history of speciation with gene flow","rel_doi":"10.64898\/2026.09.01.748350","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748350","rel_abs":"The large size and high repetitive content of many plant genomes have hindered elucidation of how the fundamental processes of evolution give rise to speciation. Here we present chromosome-level assemblies of 6 gigabase genomes for four closely related Phlox wildflower species. We generate extensive population genetic data for the three well-studied annual species P. drummondii, P. cuspidata, and P. roemeriana, including structural variation from whole-genome long-read resequencing data. The whole-genome assemblies reveal extensive differences in amount and distribution of genetic variation within and between species, reflective of differences in life-history strategies, mating systems, and edaphic specialization. The population genetic data exposes a history of widespread and consistent gene flow between all three annual Phlox species throughout their divergence and speciation. Our unmasking of structural variants and repetitive elements exposes rich and dynamic forms of genetic variation that show strong phylogenetic signal and pervasive patterns of gene flow. Our results lay a foundation for untangling the highly complex genomes of plants to make advancements in our understanding of the processes of speciation and divergence.","rel_num_authors":7,"rel_authors":[{"author_name":"Felix L Wu","author_inst":"Harvard University"},{"author_name":"Danielle E Khost","author_inst":"Harvard University"},{"author_name":"Patrick F McKenzie","author_inst":"Harvard University"},{"author_name":"Samridhi Chaturvedi","author_inst":"Tulane University"},{"author_name":"Grace A Burgin","author_inst":"Colgate University"},{"author_name":"Timothy B Sackton","author_inst":"Harvard University"},{"author_name":"Robin Hopkins","author_inst":"Harvard University"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Overlapping flower and pollen production in two imperiled pitcher plants raises conservation challenges","rel_doi":"10.64898\/2026.09.02.748929","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748929","rel_abs":"Balancing sexual reproduction with vegetative growth poses challenges for many plants. Additionally, reproductive effort, such as production of pollen or flowers, is not always correlated with numbers of viable and germinable seeds. Energetic tradeoffs like these can be particularly fraught for imperiled species and might be especially complicated in landscapes with a possibility of interspecific hybridization. In this multi-year study, we track the reproductive effort, flowering phenology, and reproductive output of two interfertile Sarracenia (pitcher plants). We show that reproductive effort varies among years and sites, and that production of pollen and flowers does not always result in higher seed output. We demonstrate that phenological overlap in the timing of flower production and pollen viability could enable hybridization, and that the more imperiled taxon produces fewer seeds per flower. Results have implications for conservation of these pitcher plants, management of hybridization in these and other systems, and general principles of reproductive allocation.","rel_num_authors":13,"rel_authors":[{"author_name":"Rebecca  E. Hale","author_inst":"University of North Carolina at Asheville"},{"author_name":"Caroline Kennedy","author_inst":"University of North Carolina Asheville"},{"author_name":"Wayne Morgan","author_inst":"NCSU: North Carolina State University"},{"author_name":"Todd Brasseur","author_inst":"University of North Carolina Asheville"},{"author_name":"Elizabeth Companion","author_inst":"University of North Carolina Asheville"},{"author_name":"William Gay","author_inst":"University of North Carolina Asheville"},{"author_name":"Kristen Hillegass","author_inst":"University of Kentucky"},{"author_name":"Alyssa Lynch","author_inst":"NCSU CALS: NC State University College of Agriculture and Life Sciences"},{"author_name":"Michelle Paredes","author_inst":"University of North Carolina Asheville"},{"author_name":"Gabi Parker","author_inst":"University of North Carolina Asheville"},{"author_name":"Lila Uzell","author_inst":"Friends of Virgin Islands National Park"},{"author_name":"Mars Zappia","author_inst":"University of North Carolina Asheville"},{"author_name":"Jennifer Rhode Ward","author_inst":"University of North Carolina Asheville"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"\u03b12\u03b4-2 mediates coupling of presynaptic calcium entry to vesicle release in hippocampal parvalbumin-expressing interneurons","rel_doi":"10.64898\/2026.08.31.748347","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748347","rel_abs":"The 2{delta} family of auxiliary voltage-gated calcium channel (VGCC) subunits have critical but incompletely understood roles in brain function. Parvalbumin-positive (PV+) interneurons in the hippocampus highly express the 2{delta}-2 isoform, and mice lacking 2{delta}-2 exhibit spontaneous seizures. Thus, we examined PV+ neuron-mediated synaptic inhibition in acutely prepared brain slices from 2{delta}-2 knockout (KO) mice. In the inner molecular layer of the dentate gyrus, 2{delta}-2 KO mice demonstrated an increase in the excitation\/inhibition ratio of synaptic inputs onto granule cells. We then used optogenetics to activate PV+ interneurons, which produced dramatically smaller inhibitory synaptic currents in granule cells from 2{delta}-2 KO mice. There was a reduction in PV+ inputs onto granule cells as determined by immunostaining. Functionally, these inputs had a lower probability of GABA release and a decreased readily releasable pool of vesicles compared to littermate controls. VGCC coupling to presynaptic vesicle release was also reduced in dentate gyrus PV+ cells in 2{delta}-2 KO mice, based on manipulations of intracellular and extracellular calcium. Together, our data indicate that 2{delta}-2 plays a critical role in PV+ interneuron-mediated synaptic inhibition, which may contribute to seizures in 2{delta}-2 mutant mice.","rel_num_authors":4,"rel_authors":[{"author_name":"Allison J Ellingson","author_inst":"Oregon Health & Science University"},{"author_name":"Emma C Jerome","author_inst":"Oregon Health& Science University"},{"author_name":"Ashlynn A Gallagher","author_inst":"Oregon Health & Science University"},{"author_name":"Eric Schnell","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation","rel_doi":"10.64898\/2026.08.31.748303","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748303","rel_abs":"Small interfering RNAs (siRNAs) are short guide RNAs that recruit the RNA-induced silencing complex (RISC) to complementary target sites on messenger RNAs (mRNAs), triggering Ago2-mediated cleavage and gene silencing. siRNA design requires compact candidate sets that cover a target while preserving efficacy, specificity, and practical sequence constraints. Existing pipelines usually enumerate candidate windows, assign a canonical guide to each window, and then rank preconstructed siRNA--mRNA pairs. This has produced strong pairwise efficacy predictors, but leaves a candidate-construction gap: candidate positions and guide sequences are fixed before the model begins to rank them. We address this gap by decomposing siRNA candidate construction into two generative decisions: where to place candidates within an mRNA segment, and what constrained guide variants to consider at a candidate position. We instantiate this framework as siProGenA, using a Discrete Denoising Diffusion Probabilistic Model (D3PM) for mRNA-conditioned position proposal and a Bayesian Flow Network (BFN) for temperature-controlled guide generation. On 62 positive test segments, the diversity-aware final library reaches Hit@1 = 0.790 and Hit@5 = 0.903. In a measured-site controlled Stage~2 evaluation, seed- and cleavage-preserving variants outscore the canonical complement for 89.8% of measured sites, with supporting gains across additional computational scorers, random-mismatch controls, and biophysical diagnostics. Together, the results support a modular proposal--generation view of siRNA candidate construction for prioritizing compact candidate sets.","rel_num_authors":6,"rel_authors":[{"author_name":"Zhiqi Ma","author_inst":"The Chinese University of Hong Kong\uff0cShenzhen"},{"author_name":"Jiale Zhou","author_inst":"School of Engineering, Westlake University"},{"author_name":"Rubo Wang","author_inst":"Shanghai Artificial Intelligence Laboratory"},{"author_name":"Zhipeng Deng","author_inst":"School of Engineering, Westlake University"},{"author_name":"Zhijian Wu","author_inst":"School of Engineering, Westlake University"},{"author_name":"Yefeng Zheng","author_inst":"School of Engineering, Westlake University"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"BROOQS: Spectral Methods Resolve Level-1 Hybridization Cycles without Tests of Symmetry","rel_doi":"10.64898\/2026.08.31.748319","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748319","rel_abs":"Modern phylogenomic analyses often seek to reconstruct both vertical and reticulate evolutionary histories. While the prevalence of non-vertical evolution is increasingly appreciated, inferring networks remains conceptually challenging and computationally demanding. Following the success of quartet-based methods for handling gene tree discordance, several quartet-based network inference methods have been developed. A key insight of these methods is that level-1 networks can be constructed by first building a multifurcating tree called tree-of-blobs and then resolving each polytomy into a cycle. This two-step approach makes the problem easier both conceptually and computationally. However, these quartet-based methods often rely on noisy statistical tests of asymmetry in quartet frequencies. Moreover, they either enumerate all quartets, losing some scalability, or subsample them, losing information. We introduce BROOQS, a quartet-based method for resolving trees of blobs into a level-1 phylogenetic network. BROOQS efficiently aggregates information from all quartets around a blob without enumerating them, builds a pairwise similarity matrix, and uses robust spectral ordering algorithms to recover the cyclic ordering without relying on individual quartet symmetry tests. We prove theoretically that our spectral method is consistent under the network multi-species coalescent (NMSC) model. Across simulated and empirical datasets, BROOQS consistently improves accuracy and scalability compared to existing methods and extends to thousands of taxa.","rel_num_authors":2,"rel_authors":[{"author_name":"Shayesteh Arasti","author_inst":"University of California San Diego"},{"author_name":"Siavash Mirarab","author_inst":"University of California San Diego"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Alpha-linolenic acid and dietary protein minimally but differentially regulate white adipose tissue lipolysis in female mice fed moderate-fat diets","rel_doi":"10.64898\/2026.08.31.748352","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748352","rel_abs":"Omega-3 polyunsaturated fatty acids (n-3 PUFA) influence white adipose tissue (WAT) lipid buffering capacity; however, sex-specific regulation remains understudied. We recently reported that male mice fed a diet containing high alpha-linolenic acid (ALA) had increased WAT mass, reduced serum triglycerides and elevated lipolysis compared to mice fed a diet containing recommended levels of ALA, independent of background dietary protein. The current study examined whether subcutaneous and visceral WAT (scWAT, vWAT) lipolytic activity was altered in female C57BL\/6N mice (n=16\/group) fed low-ALA (1% energy) or high-ALA (3% energy) diets containing skim milk protein (SMP) or soy protein isolate (SPI) for 8 weeks. Body weight, WAT depot weights and serum triglycerides were unchanged in response to ALA content. Lipolytic markers were mostly unchanged by ALA content except for an increase in adipose triglyceride lipase (ATGL) in vWAT, while diets containing SPI modestly reduced serum cholesterol levels and increased total hormone-sensitive lipase (HSL) content in vWAT. Collectively, WAT lipolytic markers in female mice showed minimal response to diets containing high ALA content, unlike that previously reported in male mice. These results highlight the importance of considering both sexes to ensure generalizability of findings when investigating diet regulation of WAT lipid metabolism.","rel_num_authors":7,"rel_authors":[{"author_name":"Mathieu J Clavet","author_inst":"University of Guelph"},{"author_name":"Siobhan E Woods","author_inst":"University of Guelph"},{"author_name":"Melissa Gonzalez-Soto","author_inst":"University of Guelph"},{"author_name":"Alexa N King","author_inst":"University of Guelph"},{"author_name":"Frederic Capel","author_inst":"INRAE-UCA"},{"author_name":"David C Wright","author_inst":"University of British Columbia"},{"author_name":"David M Mutch","author_inst":"University of Guelph"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Recurrent beta-gamma interactions between olfactory bulb and piriform cortex support cross-sniff perceptual continuity in humans","rel_doi":"10.64898\/2026.08.31.747807","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.747807","rel_abs":"Olfactory perception relies on active sampling, with successive inhalations providing discrete sensory inputs that in humans can be separated by several seconds. Yet odors are perceived as continuous and stable, raising the question of how the brain integrates sensory information across these temporal gaps. Here, we used electrobulbogram (EBG) recordings in 48 participants to demonstrate that successive sniffs are not processed independently but are linked through recurrent oscillatory dynamics between the olfactory bulb (OB) and piriform cortex (PC). Specifically, first-sniff alpha\/beta activity in the OB and OB-PC functional connectivity predicted second-sniff gamma power, while transfer entropy indicated a directional temporal dependence from first-sniff alpha\/beta to second-sniff gamma dynamics. In parallel, second-sniff gamma activity selectively tracked subjective odor valence prior to inhalation onset. At the network level, the PC exhibited stronger alpha\/beta-band connectivity with orbitofrontal, insular, and prefrontal regions during the first sniff than during the second, suggesting that early evaluative processing provides a contextual signal that is carried forward to shape subsequent sensory representations. These results demonstrate that the human OB-PC circuit carries evaluative information across inhalations through directed alpha\/beta-to-gamma interactions, providing a mechanism for maintaining perceptual continuity in a sensory system defined by temporally discrete sampling.","rel_num_authors":6,"rel_authors":[{"author_name":"Frans Nord\u00e9n","author_inst":"Karolinska Institutet"},{"author_name":"Anja L Winter","author_inst":"Karolinska Institutet"},{"author_name":"Leslie M Kay","author_inst":"The University of Chicago"},{"author_name":"Artin Arshamian","author_inst":"Karolinska Institutet"},{"author_name":"Mikael Lundqvist","author_inst":"Karolinska Institutet"},{"author_name":"Johan N Lundstr\u00f6m","author_inst":"Karolinska Institutet"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Linking coronary microvascular structure and function in preclinical models of coronary microvascular disease","rel_doi":"10.64898\/2026.08.31.748256","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748256","rel_abs":"Despite growing awareness of the importance of the coronary microvasculature in cardiac health and disease, Coronary Microvascular Disease (CMVD) remains poorly understood, underdiagnosed, and without targeted therapies. Preclinical models and quantitative tools to measure CMVD are needed. Here, we use two novel quantitative methods to assess coronary microvascular structure (multi-fractal spectrum analysis) and function (Single Photon Emission Computed Tomography (SPECT)-based intramyocardial blood volume (IMBV) imaging) in mouse models of CMVD. Change in IMBV ({bigtriangleup}IMBV), which serves as a quantitative measure of vasodilatory capacity or microvascular function, was reduced with aging, driven by a significant decrease in female mice. Male mice on ApoE-\/- background, fed a high-fat diet (HFD) for 6 months, or both had significantly reduced {bigtriangleup}IMBV. We used immunofluorescence to assess both traditional capillary density and global branching structure and vessel heterogeneity using multifractal spectrum analysis. Both were significantly reduced in all groups, and linear regression modeling showed that they were independently associated with {bigtriangleup}IMBV. Finally, we used {bigtriangleup}IMBV to assess the effects of widely used control Adeno-associated viral (AAV) vectors on coronary microvascular function. AAV-overexpression of GFP did not affect function, but Cre-recombinase compromised coronary microvascular structure and function by 16 weeks. In summary, quantitative assessments of coronary microvascular structure and function highlight changes consistent with CMVD seen with aging, female sex, and metabolic insults. Functional changes are partially driven, but not fully defined, by changes in the underlying structure, highlighting the important and incomplete link between structure and function.","rel_num_authors":10,"rel_authors":[{"author_name":"Mansi B. Kumar","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Varun Kanangat","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Matthew Woods","author_inst":"San Diego State University"},{"author_name":"Octavio Lopez","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Li Li","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Eric Blankemeyer","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Donna M. Conlon","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Uduak George","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Scott D. Metzler","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Marie A. Guerraty","author_inst":"University of Pennsylvania Perelman School of Medicine"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"CAMSAP3 loss of function models suggest causative role in generalized genetic epilepsy","rel_doi":"10.64898\/2026.09.01.744686","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.744686","rel_abs":"Advancements in next generation sequencing have led to the discovery of hundreds of human epilepsy gene associations. Newly associated genes require functional validation to establish causation and to inform patient treatment in the clinic. A recent exome trio analysis identified predicted pathogenic variants in two patients with generalized epilepsy in the gene CAMSAP3. CAMSAP3 regulates non-centrosomal microtubule dynamics, and the acetylation necessary for normal axonal differentiation and migration. We show that overexpression of patient variants leads to protein degradation and dysregulation of microtubule acetylation in cultured HEK cells. Camsap3 knockout zebrafish also exhibit increased axonal microtubule acetylation as well as epileptic features such as seizure-like swimming behaviors, aberrant inhibitory interneuron development and epileptiform via local field potential. Together these data suggest that CAMSAP3 plays an important role in generalized genetic epilepsy.","rel_num_authors":10,"rel_authors":[{"author_name":"Christopher Mark LaCoursiere","author_inst":"Boston Childrens Hospital"},{"author_name":"Zachary Stayn","author_inst":"Harvard University"},{"author_name":"Hannah Hepner","author_inst":"Harvard University"},{"author_name":"Sneham Tiwari","author_inst":"Boston Children's Hospital"},{"author_name":"Joseph Pascucci","author_inst":"Boston Children's Hospital"},{"author_name":"Chariton Moschopoulos","author_inst":"Boston Children's Hospital"},{"author_name":"Lacey Smith","author_inst":"Boston Children's Hospital"},{"author_name":"Hyun Yong Koh","author_inst":"Baylor College of Medicine"},{"author_name":"Parul Chaudhary","author_inst":"Boston Children's Hospital"},{"author_name":"Annapurna Poduri","author_inst":"Boston Children's Hospital"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs","rel_doi":"10.64898\/2026.08.31.748296","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748296","rel_abs":"Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2 enriched, and two basal associated clusters. The basal clusters separated into an immune rich region marked by T cell-inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer.","rel_num_authors":11,"rel_authors":[{"author_name":"Sonali Arora","author_inst":"FHCRC"},{"author_name":"Ramya Suresh","author_inst":"fred hutch cancer center"},{"author_name":"Nik Holland","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Gregory Glatzer","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Matt Jensen","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Eric Q Konnick","author_inst":"University of Washington"},{"author_name":"Colin Pritchard","author_inst":"Univ. Washington"},{"author_name":"Yi Li","author_inst":"Baylor College of Medicine"},{"author_name":"Heather A Parsons","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Sara A Hurvitz","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Eric C Holland","author_inst":"Fred Hutch Cancer Center"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs","rel_doi":"10.64898\/2026.08.31.748296","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748296","rel_abs":"Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2 enriched, and two basal associated clusters. The basal clusters separated into an immune rich region marked by T cell-inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer.","rel_num_authors":11,"rel_authors":[{"author_name":"Sonali Arora","author_inst":"FHCRC"},{"author_name":"Ramya Suresh","author_inst":"fred hutch cancer center"},{"author_name":"Nik Holland","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Gregory Glatzer","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Matt Jensen","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Eric Q Konnick","author_inst":"University of Washington"},{"author_name":"Colin Pritchard","author_inst":"Univ. Washington"},{"author_name":"Yi Li","author_inst":"Baylor College of Medicine"},{"author_name":"Heather A Parsons","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Sara A Hurvitz","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Eric C Holland","author_inst":"Fred Hutch Cancer Center"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"A Compendium of 49 Experimental SBS Signatures for Decoding Human Cancer Mutational Processes","rel_doi":"10.64898\/2026.08.31.748400","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748400","rel_abs":"Human cancer genomes harbor distinct mutational patterns that reflect past processes of DNA damage and repair. However, the precise attribution of these signatures to specific chemical carcinogens lacks a standardized experimental reference framework. To address this gap, we curated 4,282 genome-wide sequencing datasets from 42 model systems across five species exposed to 146 cancer-risk agents. This platform yielded 49 robust experimental single-base substitution signatures (eSS), with 28 matching 19 established COSMIC signatures and 21 defining novel mutational processes. We reconstructed 24 COSMIC signatures, assigning candidate etiologies to five signatures of unknown origin and revising two contested assignments. Pan-cancer decomposition detected four eSS-like mutational processes enriched in smokers across 4,951 tumors. Lastly, independent single-molecule sequencing of primary human organoids reproduced these profiles with high fidelity, confirming true platform-independent biological reproducibility across complex human models. This eSS repertoire provides a reference that links human mutational processes to mechanistic classes of DNA damage.","rel_num_authors":8,"rel_authors":[{"author_name":"Maria Zhivagui","author_inst":"University of Nevada Las Vegas"},{"author_name":"Jessica N Au","author_inst":"UC San Diego"},{"author_name":"Sanskruti Sharma","author_inst":"University of Nevada, Las Vegas"},{"author_name":"Peter T. Nguyen","author_inst":"University of Nevada, Las Vegas"},{"author_name":"Shams Al-Azzam","author_inst":"UC San Diego"},{"author_name":"Jiang Zhang","author_inst":"UC San Diego"},{"author_name":"Mark Barnes","author_inst":"UC San Diego"},{"author_name":"Ludmil B Alexandrov","author_inst":"UC San Diego"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Effects of collaborative clinical visit agenda-setting interventions: A systematic review and meta-analysis","rel_doi":"10.64898\/2026.08.30.26361729","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.30.26361729","rel_abs":"BackgroundAgenda-setting is a fundamental patient-centered communication practice in which a clinician works with a patient to elicit, propose, and organize topics for discussion during a clinical encounter. Various agenda-setting interventions have been developed, including patient-facing tools and clinician training, but their effects have not been systematically evaluated. We aimed to determine the effects of these interventions on encounter, patient, care partner, and clinician outcomes.\n\nMethodsWe searched grey literature and seven databases, including PubMed, from inception through July 2025 for randomized and non-randomized comparative studies of interventions designed to promote or improve clinical visit agenda-setting. Two reviewers independently screened articles and extracted data, with a third reviewer resolving conflicts. We assessed risk of bias using RoB 2 for randomized studies and ROBINS-I for non-randomized studies. We conducted random effects meta-analyses when outcomes were sufficiently comparable, assessed heterogeneity using I2, and rated certainty of evidence using GRADE. Post hoc exploratory subgroup analyses examined study design, adjustment status, and intervention structure.\n\nResultsTwenty-nine articles describing 22 unique studies met the inclusion criteria, including 13 randomized and nine non-randomized studies. Agenda-setting interventions increased the occurrence of agenda-setting (risk ratio 5.43, 95% confidence interval (CI) 2.06 to 14.28, I2=34.6%) and favored the intervention for concerns addressed when measured as a continuous outcome (standardized mean difference (SMD) 0.37, 95% CI 0.16 to 0.57, I2=65.3%) and overall clinician satisfaction (SMD 0.50, 95% CI 0.23 to 0.78, I2=0.0%). There were no clear differences in the number of concerns raised (mean difference (MD) 0.21, 95% CI -0.19 to 0.61, I2=59.6%), visit duration (MD 0.64 minutes, 95% CI -0.83 to 2.12, I2=51.4%), or overall patient satisfaction (SMD 0.05, 95% CI -0.05 to 0.15, I2=47.0%). Potentially important heterogeneity was present for four of these six outcomes. Post hoc exploratory subgroup analyses did not provide clear evidence that effects varied by study design, adjustment status, or intervention structure. Risk of bias was often high, serious, or critical, and certainty of evidence was low or very low for all pooled outcomes.\n\nConclusionsTo our knowledge, this is the first comprehensive synthesis of clinical visit agenda-setting interventions. Such interventions may increase the occurrence of agenda-setting and the extent to which patient concerns are addressed without increasing visit length. However, the certainty of evidence was low or very low, and the available evidence does not establish a superior intervention structure.","rel_num_authors":14,"rel_authors":[{"author_name":"Ailyn Sierpe","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Renata W. Yen","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Annika Milliman","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Elizabeth Cady","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Boyoung Ahn","author_inst":"The Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD 21205, United States"},{"author_name":"Anne E. Dade","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Anna Marie Devito","author_inst":"Hartford HealthCare Cancer Institute, Hartford HealthCare, 195 Retreat Ave, Hartford, CT 06103, United States"},{"author_name":"Bradley A. Eckert","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Vismaya V. Gopalan","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Stephanie C. Krasinski","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Meredith A. MacMartin","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Sophia G. Musacchio","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Jingyi Zhang","author_inst":"University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States"},{"author_name":"Catherine H. Saunders","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Relation of Self-Reported Race and Genetic Ancestry to Hypertension Prevalence Among Hispanics\/Latinos: The Hispanic Community Health Study\/Study of Latinos","rel_doi":"10.64898\/2026.09.01.26361995","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361995","rel_abs":"BackgroundThe imprecision of current metrics to capture the complex genetic admixture and racial identity among Hispanic\/Latino individuals in the United States (US) is a concern. We examined the relationship of self-reported race and genetic ancestry with hypertension (HTN) among Hispanics\/Latinos.\n\nMethodsCross-sectional study of the Hispanic Community Health Study\/Study of Latinos (HCHS\/SOL), including 10,586 Hispanic\/Latino unrelated adults. Genetic ancestry: West African (AA), Amerindian (AI), and European (EA). Self-reported race: White, Black, Native American, or Multiple\/Missing (More than one race or Unknown\/Not reported\/Refused). HTN: systolic (SBP) [&ge;]130 mmHg, diastolic blood pressure (DBP) [&ge;]80 mmHg, and\/or use of HTN medications. Age- and sex adjusted models were used.\n\nResultsSelf-reported race was White (38{middle dot}6%), Black (3{middle dot}6%), Native American (4{middle dot}1%), and Multiple\/Missing (53{middle dot}7%), with Unknown\/Not reported\/Refused representing 32{middle dot}7%. Black and White Hispanics\/Latinos had the greatest AA (55{middle dot}7%) and EA (69{middle dot}3%) ancestries, respectively. Each 10% AA increase was associated with OR 1{middle dot}15, SBP beta +0.9 mmHg, and DBP beta +0.7 mmHg. Conversely, each 10% AI increase was associated with OR 0{middle dot}83, SBP beta -0{middle dot}4 mmHg, and DBP beta -0{middle dot}6 mmHg. HTN prevalence was highest among those with Black race or in the highest AA quantile (45{middle dot}6% and 48{middle dot}0%, respectively), and lowest among those with Native American race or in the highest AI quantile (37{middle dot}6% and 26{middle dot}7%, respectively).\n\nConclusionOne-third of Hispanics\/Latinos did not self-report race. Black or White self-reporting race did somewhat relate to AA or EA ancestry, respectively. HTN profiles were related to self-reported race and genetic ancestry in this admixed population.","rel_num_authors":16,"rel_authors":[{"author_name":"Raul Antonio Montanez-Valverde","author_inst":"Montefiore Medical Group"},{"author_name":"Vivian Kim","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Priscilla Duran-Luciano","author_inst":"Albert Einstein College of Medicine Department of Medicine"},{"author_name":"Yawen Yuan","author_inst":"Albert Einstein College of Medicine\/Montefiore Medical Center"},{"author_name":"Tamar Sofer","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Robert C. Kaplan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Linda C Gallo","author_inst":"San Diego State University"},{"author_name":"Gregory A. Talavera","author_inst":"San Diego State University KPBS"},{"author_name":"Krista M Perreira","author_inst":"The University of North Carolina at Chapel Hill Innovate Carolina"},{"author_name":"Martha L. Daviglus","author_inst":"Institute for Minority Health Research, University of Illinois-Chicago"},{"author_name":"Sylvia E. Rosas","author_inst":"Joslin Diabetes Center"},{"author_name":"Maria M. Llabre","author_inst":"University of Miami"},{"author_name":"Tali Elfassy","author_inst":"University of Miami Miller School of Medicine"},{"author_name":"Xihao Li","author_inst":"UNC Gillings School of Global Public Health"},{"author_name":"Carmen R. Isasi","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Carlos Jose Rodriguez","author_inst":"Albert Einstein College of Medicine and Montefiore Medical Center"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Relation of Self-Reported Race and Genetic Ancestry to Hypertension Prevalence Among Hispanics\/Latinos: The Hispanic Community Health Study\/Study of Latinos","rel_doi":"10.64898\/2026.09.01.26361995","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361995","rel_abs":"BackgroundThe imprecision of current metrics to capture the complex genetic admixture and racial identity among Hispanic\/Latino individuals in the United States (US) is a concern. We examined the relationship of self-reported race and genetic ancestry with hypertension (HTN) among Hispanics\/Latinos.\n\nMethodsCross-sectional study of the Hispanic Community Health Study\/Study of Latinos (HCHS\/SOL), including 10,586 Hispanic\/Latino unrelated adults. Genetic ancestry: West African (AA), Amerindian (AI), and European (EA). Self-reported race: White, Black, Native American, or Multiple\/Missing (More than one race or Unknown\/Not reported\/Refused). HTN: systolic (SBP) [&ge;]130 mmHg, diastolic blood pressure (DBP) [&ge;]80 mmHg, and\/or use of HTN medications. Age- and sex adjusted models were used.\n\nResultsSelf-reported race was White (38{middle dot}6%), Black (3{middle dot}6%), Native American (4{middle dot}1%), and Multiple\/Missing (53{middle dot}7%), with Unknown\/Not reported\/Refused representing 32{middle dot}7%. Black and White Hispanics\/Latinos had the greatest AA (55{middle dot}7%) and EA (69{middle dot}3%) ancestries, respectively. Each 10% AA increase was associated with OR 1{middle dot}15, SBP beta +0.9 mmHg, and DBP beta +0.7 mmHg. Conversely, each 10% AI increase was associated with OR 0{middle dot}83, SBP beta -0{middle dot}4 mmHg, and DBP beta -0{middle dot}6 mmHg. HTN prevalence was highest among those with Black race or in the highest AA quantile (45{middle dot}6% and 48{middle dot}0%, respectively), and lowest among those with Native American race or in the highest AI quantile (37{middle dot}6% and 26{middle dot}7%, respectively).\n\nConclusionOne-third of Hispanics\/Latinos did not self-report race. Black or White self-reporting race did somewhat relate to AA or EA ancestry, respectively. HTN profiles were related to self-reported race and genetic ancestry in this admixed population.","rel_num_authors":16,"rel_authors":[{"author_name":"Raul Antonio Montanez-Valverde","author_inst":"Montefiore Medical Group"},{"author_name":"Vivian Kim","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Priscilla Duran-Luciano","author_inst":"Albert Einstein College of Medicine Department of Medicine"},{"author_name":"Yawen Yuan","author_inst":"Albert Einstein College of Medicine\/Montefiore Medical Center"},{"author_name":"Tamar Sofer","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Robert C. Kaplan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Linda C Gallo","author_inst":"San Diego State University"},{"author_name":"Gregory A. Talavera","author_inst":"San Diego State University KPBS"},{"author_name":"Krista M Perreira","author_inst":"The University of North Carolina at Chapel Hill Innovate Carolina"},{"author_name":"Martha L. Daviglus","author_inst":"Institute for Minority Health Research, University of Illinois-Chicago"},{"author_name":"Sylvia E. Rosas","author_inst":"Joslin Diabetes Center"},{"author_name":"Maria M. Llabre","author_inst":"University of Miami"},{"author_name":"Tali Elfassy","author_inst":"University of Miami Miller School of Medicine"},{"author_name":"Xihao Li","author_inst":"UNC Gillings School of Global Public Health"},{"author_name":"Carmen R. Isasi","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Carlos Jose Rodriguez","author_inst":"Albert Einstein College of Medicine and Montefiore Medical Center"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Proteoform-resolved neoGFAP as a diagnostic and prognostic biomarker across the TBI--MCI--AD continuum in Veterans","rel_doi":"10.64898\/2026.09.01.26361845","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361845","rel_abs":"Service members with traumatic brain injury are at approximately two- to four-fold higher risk of Alzheimers disease or related dementias than those without such an injury, with risk increasing with injury severity. The amyloid\/tau\/neurodegeneration biomarker framework treats amyloid, tau, and neurodegeneration as independent axes but omits astroglial injury, despite evidence that reactive astrogliosis (indexed by glial fibrillary acidic protein, GFAP) must be elevated for cognitive decline to occur in amyloid-positive individuals. Total GFAP immunoassays aggregate intact protein with multiple calpain- and caspase-cleaved proteoforms, blurring the biological signal. We compared a calpain-cleaved GFAP neoepitope, the glial fibrillary acidic protein neoepitope (neoGFAP), against total GFAP across the full traumatic-brain-injury-mild-cognitive-impairment-Alzheimers-disease continuum in Veterans using a two-stage plasma-to-cerebrospinal-fluid biomarker approach. A plasma triage gate combining phosphorylated tau 217 and amyloid beta 42 was applied to 367 unique subjects; a cerebrospinal-fluid benchmarking cohort of 57 subjects (controls, chronic blast traumatic brain injury, mild cognitive impairment, and Alzheimers disease) received head-to-head neoGFAP and total GFAP measurement. In the whole benchmarking cohort, neoGFAP discriminated mild cognitive impairment plus Alzheimers disease from non-Alzheimer subjects with an area under the receiver-operating-characteristic curve of 0.81 versus 0.73 for total GFAP, a trend-level advantage that did not reach nominal significance. Within the gate-positive, amyloid-committed subset of 23 subjects, neoGFAP dominance became significant by McNemars exact test (six discordant subjects favoured neoGFAP, none the reverse). Across diagnostic contrasts, neoGFAP outperformed total GFAP for Alzheimers disease versus control and, importantly for Veterans, for mild cognitive impairment versus chronic blast-exposed Veterans without cognitive impairment. In chronic blast injury, neoGFAP was paradoxically depleted relative to controls, consistent with tissue sequestration of aggregated proteoform fragments. Unbiased proteomic profiling confirmed coordinated elevation across astrocytic, neuronal, mitochondrial, and microglial compartments. An exploratory subject-level reclassification improved accuracy from 71.1 percent using plasma alone to 79.5 percent with added cerebrospinal-fluid markers and age. In a same-cohort ProQuantum replication (n=57), CSF neoGFAP preserved its discrimination advantage over total GFAP for MCI+AD versus non-AD (AUROC 0.76 vs 0.72; cross-platform Spearman {rho}=0.84), while plasma neoGFAP achieved AUROC 0.90, comparable to pTau217 (0.92) and exceeding A{beta}42\/40 (0.84). In this small sample, neoGFAP is a superior proteoform-resolved diagnostic and prognostic biomarker across the continuum and supports adding an astroglial-proteoform axis to amyloid\/tau\/neurodegeneration biomarker frameworks in high-risk populations.\n\nAbbreviated summaryHaskins and colleagues report that a calpain-cleaved GFAP neoepitope (neoGFAP) outperforms total GFAP for diagnostic and prognostic classification across the traumatic-brain-injury-mild-cognitive-impairment-Alzheimers-disease continuum in Veterans, supporting the addition of an astroglial-proteoform axis to amyloid\/tau\/neurodegeneration biomarker frameworks in high-risk populations.","rel_num_authors":24,"rel_authors":[{"author_name":"William E. Haskins","author_inst":"Gryphon Bio, South San Francisco, CA, USA; Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Kevin K. Wang","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA; Foundation for Applied Molecular Evolution, Alachua, FL, USA"},{"author_name":"Guangzheng Cai","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA; Foundation for Applied Molecular Evolution, Alachua, FL, USA"},{"author_name":"Khadija Boukholda","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA"},{"author_name":"Eman Elbayoumi","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA"},{"author_name":"Ruchi Bajpai","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Devin Jackson","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Katie Tehas","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Kristy Radeker","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Anthony DeLizza","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Caroline Popper","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Martin Kiendl","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Sigrun Badrnya","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Markus Miholits","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Stefan Jellbauer","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Todd Kilbaugh","author_inst":"Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Franklin Okumu","author_inst":"Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Ava Puccio","author_inst":"University of Pittsburgh, Pittsburgh, PA, USA"},{"author_name":"Raquel C. Gardner","author_inst":"Sheba Medical Center, Tel HaShomer, Israel; University of California San Francisco, San Francisco, CA, USA"},{"author_name":"Geoff Manley","author_inst":"University of California San Francisco, San Francisco, CA, USA"},{"author_name":"John B. Williamson","author_inst":"Brain Rehabilitation Research Center, Gainesville, FL, USA"},{"author_name":"Abigail B. Waters","author_inst":"Brain Rehabilitation Research Center, Gainesville, FL, USA"},{"author_name":"Gail Ge Li","author_inst":"VA Northwest Mental Illness Research, Education, and Clinical Center (VA NW MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA; Department of Psychiat"},{"author_name":"Elaine R. Peskind","author_inst":"VA Northwest Mental Illness Research, Education, and Clinical Center (VA NW MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA; Department of Psychiat"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Clinical deep sequencing to diagnose pathogenic mosaic variants in malformations of cortical development and epilepsy","rel_doi":"10.64898\/2026.09.01.26361943","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361943","rel_abs":"Background and ObjectivesDeep sequencing of brain tissue in the research setting has established that mosaic variants are a major cause of malformations of cortical development (MCDs) and epilepsy. However, genetic testing in the clinical setting primarily detects germline variants using clinically accessible samples. We aimed to determine the diagnostic yield and clinical utility of deep sequencing in the clinical setting to identify pathogenic mosaic variants for this population.\n\nMethodsWe performed a retrospective cohort analysis of individuals at Boston Childrens Hospital with MCDs with or without epilepsy who received clinical deep sequencing between September 2017 and February 2026. Demographic, clinical, and genetic testing data were abstracted from the medical record. For individuals without systemic features, we classified brain tissue as an affected tissue sample. For individuals with systemic features, we classified brain or relevant non-brain tissue as affected. The primary outcome was the diagnostic yield of clinical deep sequencing performed using affected vs unaffected tissue samples. The secondary outcome was the clinical utility of genetic diagnoses.\n\nResultsOur cohort included 37 individuals (19\/37 (51%) female, 18\/37 (49%) male) with MCDs, of whom 35\/37 (95%) had epilepsy (25 with brain tissue samples available from epilepsy surgery) and 8\/37 (22%) had systemic features. Most (35\/37 (95%)) had dysplasia phenotypes on MRI and 12\/27 (44%) with pathology available had Focal Cortical Dysplasia Type I or II. The diagnostic yield was 53% (17\/32; 16 mosaic and 1 germline variant) when clinical deep sequencing was performed using an affected tissue sample vs 0% (0\/6) using an unaffected tissue sample (p=0.016). Of the diagnosed cases, 13\/17 (76%) had testing performed on brain tissue (1 with systemic features) and 4\/17 (24%) on non-brain tissue (3 buccal and 1 duodenal tissue, all with systemic features). All but one diagnosis involved the mTOR pathway. All diagnoses had clinical utility.\n\nDiscussionClinical deep sequencing, when performed using an affected tissue sample, has high diagnostic yield and clinical utility for individuals with MCDs, especially dysplasia phenotypes, and epilepsy. Our findings support implementation of clinical deep sequencing for this population, especially as the genetic diagnoses have implications for emerging precision therapies.","rel_num_authors":14,"rel_authors":[{"author_name":"Katelyn Stone","author_inst":"Boston Children's Hospital"},{"author_name":"Gillian Prinzing","author_inst":"Boston Children's Hospital"},{"author_name":"Abbe Lai","author_inst":"Boston Children's Hospital"},{"author_name":"Lacey Smith","author_inst":"Boston Children's Hospital"},{"author_name":"Beth R Sheidley","author_inst":"Boston Children's Hospital"},{"author_name":"Meagan M Corliss","author_inst":"Washington University School of Medicine"},{"author_name":"Kevin Bowling","author_inst":"Washington University in St. Louis"},{"author_name":"Yang Cao","author_inst":"Washington University School of Medicine"},{"author_name":"Kimberly Wiltrout","author_inst":"Boston Children's Hospital"},{"author_name":"Scellig S.D. Stone","author_inst":"Boston Children's Hospital"},{"author_name":"Hart Lidov","author_inst":"Boston Children's Hospital"},{"author_name":"Edward Yang","author_inst":"Boston Children's Hospital"},{"author_name":"Annapurna Poduri","author_inst":"Boston Children's Hospital"},{"author_name":"Alissa M D'Gama","author_inst":"Boston Children's Hospital"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Hybrid risk scores integrating polygenic and clinical variables for endometriosis prediction","rel_doi":"10.64898\/2026.08.31.26361798","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361798","rel_abs":"BackgroundEndometriosis affects approximately 10% of reproductive-age women and is associated with substantial diagnostic delay and heterogeneous symptom presentation. Prior machine-learning prediction models have relied on comorbidity data alone or on small candidatevariant genetic scores, with inconsistent or incompletely reported performance. No study has combined a well-powered, multi-ancestry polygenic risk score (PRS) with environmental, reproductive, and symptom data in a single hybrid model. We developed and evaluated hybrid risk-prediction models integrating a genome-wide, multi-ancestry PRS with clinical and symptom data for endometriosis in the US-based All of Us Research Program.\n\nMethodsAmong 69,376 participants (15,382 endometriosis cases, 53,994 controls) across six genetically inferred ancestry groups, we computed individual-level PRS values using PRS-CS weights derived from an independent, multi-ancestry GWAS. Five nested logistic regression, random forest, and XGBoost models progressively added age, ancestry, and within-ancestry genetic principal components (Model 1), environmental and reproductive factors (Model 2), symptom and comorbidity indicators (Model 3), all covariates combined (Model 4), and PRS x environment interactions (Model 5). Performance was assessed by AUROC in a held-out test set and 5-fold cross-validation, with class-weighted, Youden-optimized thresholds used for sensitivity, specificity, and predictive values; permutation importance identified top contributors. Pairwise AUROC differences were tested with a Holm-corrected DeLong-type test.\n\nResultsDiscrimination improved from AUROC 0.63 (PRS, age, ancestry, principal components) to 0.72 for the full model, driven mainly by symptom and comorbidity data. XGBoost consistently outperformed logistic regression and random forest. The PRS ranked among the top individual predictors by permutation importance in nearly every model, alongside age, while genetic and demographic information alone gave only modest discrimination, and PRS x environment interactions did not improve on environmental factors alone. Threshold optimization yielded balanced sensitivity and specificity (~0.67\/0.65) versus near-zero sensitivity at a default threshold.\n\nConclusionsCombining the PRS with symptom and comorbidity data gave the best discrimination compared to solely a well-powered, multi-ancestry PRS as a predictor of endometriosis. This study clarifies both the promise and current limits of hybrid genetic-clinical prediction for endometriosis and points to symptom-based phenotyping, molecular subtyping, and external validation as priorities.","rel_num_authors":2,"rel_authors":[{"author_name":"Oksana Goroshchuk","author_inst":"Yale University School of Medicine"},{"author_name":"Dora Koller","author_inst":"Institut de Recerca Sant Pau"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"A target trial emulation study to estimate the causal effect of intravenous iron use during pregnancy and its effect on haematological and birth outcomes in Pakistan","rel_doi":"10.64898\/2026.08.29.26361700","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.29.26361700","rel_abs":"BackgroundDespite several trials on the hematological outcomes of intravenous (IV) iron in pregnancy, only few have examined its effect on birth outcomes. We estimated the causal effect of IV-iron on moderate or severe anaemia and birth outcomes.\n\nMethodsWomen presenting to routine antenatal care in Pakistan with haemoglobin [&le;]10 g\/dL were eligible for treatment. We used target trial emulation (TTE) methodology to estimate the effect of IV-iron treatment within 14 days of anaemia identification, compared to no treatment, on anaemia status at follow-up. A modified TTE analysis examined birth outcomes at delivery for singleton pregnancies, including birthweight, size-for-gestational-age, and mortality. We conducted a separate TTE for each of five gestational-age periods and pooled the results of each TTE.\n\nResultsWe screened 3115 pregnancies of which 1715 were eligible for IV-iron; 1043 participants were treated during pregnancy. Those who received IV-iron had half the risk of moderate or severe anaemia in pregnancy compared with no treatment (pooled relative risk (RR) 0.40; 95% confidence interval (CI): 0.27, 0.59). The pooled effect of IV-iron on stillbirth suggested an 83% risk reduction (95% CI 55-94%), and trends were similar for perinatal and neonatal mortality.\n\nConclusionIV-iron treatment improved haematological status in pregnant women and was associated with a large reduction in stillbirth. Given limited data from randomised trials regarding fetal death and treatment earlier in pregnancy, this study contributes important information to the potential benefit of IV-iron in contexts where anaemia and its sequelae are a major public health problem.\n\nKey MessagesO_LIOur research question was to assess if intravenous (IV) iron, compared with continued prophylactic oral iron alone, improves haematological and birth outcomes for those diagnosed with moderate or severe anaemia during pregnancy.\nC_LIO_LIThe use of IV-iron for treatment of anemia in pregnancy during routine antenatal care settings may be more effective than continuing prophylactic dose of oral iron supplementation for improving hematological outcomes and reducing the risk of stillbirth, although there was no difference in the risk of preterm or low birth weight babies.\nC_LIO_LIOur study assessed the effect of treatment across the gestational age continuum, measured a comprehensive set of maternal and newborn health outcomes which were not studied in many clinical trials, and used data that reflects real world management of anemia where compliance to oral iron may be poor.\nC_LI","rel_num_authors":17,"rel_authors":[{"author_name":"Nida Salman Yazdani Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Erin Oakley Ms.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Amna Khan Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Muhammad Farrukh Qazi Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Shayan Khakwani Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Asad Sheikh Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Azqa Mazhar Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Uzma Muhammad Iqbal Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Jaime Marquis Ms.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Bushra Liaqat Dr.","author_inst":"Department of Obstetrics & Gynaecology, Koohi Goth Women's Hospital Karachi, Karachi, Sindh, Pakistan"},{"author_name":"Kaveeta Kumari Dr.","author_inst":"Department of Obstetrics & Gynaecology, Creek General Hospital, Karachi, Sindh, Pakistan"},{"author_name":"Ellen C. Caniglia Dr.","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA"},{"author_name":"Aneeta Hotwani Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Imran Nisar Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Fyezah Jehan Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Emily R. Smith Dr.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Zahra Hoodbhoy Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Evaluating Clinical Foundation Models for Early Alzheimer's Disease and Related Dementia Prediction from Longitudinal EHRs","rel_doi":"10.64898\/2026.09.01.26361933","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361933","rel_abs":"Early identification of Alzheimers disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.","rel_num_authors":5,"rel_authors":[{"author_name":"Shahla Farzana","author_inst":"Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA"},{"author_name":"Ash Arian","author_inst":"Pritzker School of Medicine, University of Chicago, Chicago, IL, USA"},{"author_name":"Tatjana Rundek","author_inst":"University of Miami"},{"author_name":"Moise Desvarieux","author_inst":"Mailman School of Public Health, Columbia University"},{"author_name":"Habibul Ahsan","author_inst":"Department of Family Medicine, Biological Sciences Division, University of Chicago Medicine, Chicago, IL, USA"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Evaluating Clinical Foundation Models for Early Alzheimer's Disease and Related Dementia Prediction from Longitudinal EHRs","rel_doi":"10.64898\/2026.09.01.26361933","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361933","rel_abs":"Early identification of Alzheimers disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.","rel_num_authors":5,"rel_authors":[{"author_name":"Shahla Farzana","author_inst":"Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA"},{"author_name":"Ash Arian","author_inst":"Pritzker School of Medicine, University of Chicago, Chicago, IL, USA"},{"author_name":"Tatjana Rundek","author_inst":"University of Miami"},{"author_name":"Moise Desvarieux","author_inst":"Mailman School of Public Health, Columbia University"},{"author_name":"Habibul Ahsan","author_inst":"Department of Family Medicine, Biological Sciences Division, University of Chicago Medicine, Chicago, IL, USA"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"The human metabolite - protein interactome reveals a global layer of cellular coordination","rel_doi":"10.64898\/2026.08.28.747909","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.28.747909","rel_abs":"Metabolites are substrates, products, cofactors, and regulators, but protein-protein interaction networks do not represent their potential to organize proteins across conventional pathway boundaries. Using the LIGMAP virtual-screening algorithm, we mapped 308 human metabolite codes to pockets in monomers, dimer interfaces, and non-interface sites in dimers and represented attractive or repulsive COLIG states involving pairs of metabolites in the same pocket. On a fixed cohort of 3,938 proteins, the mean coverage of 104 strict non-enzyme pathways was 44.6% for LIGMAP, 70.1% for STRING, and 82.5% for STRING+LIGMAP; the union placed 86.3% of eligible proteins in the largest connected component and 95.1% in the two largest components. STRING+LIGMAP protein coverage was 88.0% for 68 enzyme-only pathways and 88.9% for 1,283 mixed pathways. In pathway-held-out, degree-matched prediction, adding LIGMAP to degree plus STRING increased the mean area under the precision-recall curve from 0.651 to 0.660 (paired P = 0.024); adding BioLiP2 increased it to 0.663 (paired P = 0.005). Ancient-only and non-ancient-only subnetworks were each globally connected; ancient features were denser, whereas non-ancient features covered more proteins and pathways. At the full 5,426-protein scale, retaining only features assigned to 2-100 proteins recovered 698 of 1,691 strict non-enzyme reference edges (41.3%) and exceeded both protein-label and exact bipartite degree-preserving nulls. Uncapped recovery approached saturation and lost identity-selective enrichment. Experimentally established metabolite-dependent complexes validate the local mechanism independently of LIGMAP; LIGMAP fully recovered two of seven stringent direct mechanisms and all three broader serial axes examined. Our findings reveal a global metabolite-mediated architecture with the capacity to coordinate proteins across otherwise distinct cellular systems.","rel_num_authors":2,"rel_authors":[{"author_name":"Jeffrey Skolnick","author_inst":"Georgia Institute of Technology"},{"author_name":"Bharath Srinivasan","author_inst":"Cancer Research Horizons"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Sea stickleback genome reveals repeated chromosomal rearrangements in sticklebacks","rel_doi":"10.64898\/2026.08.30.747834","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747834","rel_abs":"Sticklebacks (Gasterosteidae) encompass model organisms which are of particular interest for evolutionary and ecological genomics. Within Gasterosteidae, chromosome number is variable (2n=40-46) and independent fusions of homologous chromosomes have been proposed. The sea stickleback (or fifteen-spined stickleback, Spinachia spinachia ) has the lowest known number of chromosomes (2n=40) and hence is crucial in understanding chromosome evolution among sticklebacks, but is so far missing in genomic datasets. Here, we present a high-quality diploid genome assembly of S. spinachia. PacBio HiFi and Hi-C reads were assembled into a genome of 407.5 Mb in size, consisting of 20 chromosomes, with an N50 of 6.6 Mb and 98.96% complete single-copy BUSCO genes. A phylogenetic tree inferred across five stickleback species and four outgroup genomes from 19,156 genes, alongside synteny analyses and ancestral chromosome reconstructions, confirms S. spinachia as the sister species to the four-spined stickleback (Apeltes quadracus) and not as the sister group to all other sticklebacks as once thought. It has one species-specific chromosome fusion and shares two fusions with the three-spined stickleback (Gasterosteus aculeatus), none of which are present in its sister species. One of these fusions is also present in Pungitius, leading to reinterpretion of this fusion as ancestral to Gasterosteidae, with subsequent fission in Apeltes. This implies a lower ancestral chromosome number in Gasterosteidae (2n=44) than previously thought. The other fusion shared with G. aculeatus presents a case of convergence. Our results suggest that karyotype evolution in Gasterosteidae has been shaped by ancestral chromosome fusion, convergent fusion, and secondary fission.","rel_num_authors":25,"rel_authors":[{"author_name":"Jule Drewalowski","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Sergei Kliver","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Leon Hilgers","author_inst":"Senckenberg, Leibniz Institution for Biodiversity and Earth System Research, Senckenberganlage 25, 60325 Frankfurt, Germany"},{"author_name":"Peter Rask M\u00f8ller","author_inst":"Natural History Museum of Denmark, University of Copenhagen, Denmark"},{"author_name":"Sarah ST Mak","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Iva Kova\u010di\u0107","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Bent Petersen","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Joseph Nesme","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Ann M Mc Cartney","author_inst":"Institute of Clinical and Translational Sciences, University of California, Irvine, CA, USA"},{"author_name":"Alice Mouton","author_inst":"CARE, Laboratoire des Transitions, University of Liege, Belgium"},{"author_name":"Giulio Formenti","author_inst":"Vertebrate Genome Lab, Rockefeller University, New York City, USA"},{"author_name":"Hannes Svardal","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Genevieve Diedericks","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Henrique G Leit\u00e3o","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Rosa Fern\u00e1ndez","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Nuria Escudero","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Judit Salces-Ortiz","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Claudio Ciofi","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Chiara Natali","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Maria Angela Diroma","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Alessio Iannucci","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Marco Sollitto","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Michael Hiller","author_inst":"Senckenberg, Leibniz Institution for Biodiversity and Earth System Research, Senckenberganlage 25, 60325 Frankfurt, Germany"},{"author_name":"M Thomas P Gilbert","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Josefin Stiller","author_inst":"Department of Biology, University of Copenhagen, Denmark"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multi-kingdom microbial diversity and interaction landscapes in mosquitoes revealed by 5,163 individual meta-transcriptomes","rel_doi":"10.64898\/2026.09.02.748197","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748197","rel_abs":"Mosquitoes are pathogen vectors embedded within diverse microbial ecosystems. However, the nature and interactions among their multi-kingdom microbiome remain poorly understood. We conducted a nationwide single-mosquito meta-transcriptomic survey of 5,163 mosquitoes representing 100 species across China, integrating viral discovery with marker-gene profiling of bacteria, archaea, fungi, and other eukaryotic microbes. From this, we identified 1,606 microbial species-level taxa, including extensive novel diversity, and revealed pronounced host species-specific organization of mosquito-associated communities. We detected 34 pathogens or potential pathogens of human or animal relevance, whose prevalence, abundance, host range, and geographic distribution defined distinct epidemiological patterns. Network analysis uncovered pervasive cross-kingdom microbial associations, including candidate antiviral relationships involving Wolbachia and other microbial taxa. Our study establishes a detailed view of the full-spectrum microbiome and provides a resource and conceptual framework for studying vector competence, pathogen emergence, and microbiome-informed mosquito-borne disease control.","rel_num_authors":40,"rel_authors":[{"author_name":"Qin-Yu Gou","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Wei-Chen Wu","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Pei-Bo Shi","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Geng-Yan Luo","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yuan-Fei Pan","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jing Wang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yan Gao","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Kai-Jie Liu","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Hai-Long Zhao","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Yun Feng","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"Kun Li","author_inst":"National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, "},{"author_name":"Wei-Hong Yang","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"De Wu","author_inst":"Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China."},{"author_name":"Shi-Jia Le","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Gen-Yang Xin","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Min-Wu Peng","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yu-Qi Liao","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Chun-Hui Yang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Shi-Qiang Mei","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jia-Ming Huang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jin-Xia Cheng","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Xin Hou","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jian-Bin Kong","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Xin-Xin Chen","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Bing Zhang","author_inst":"Xinjiang Key Laboratory of Molecular Biology for Endemic Diseases, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China."},{"author_name":"Zi-Rui Ren","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Jun-Hua Li","author_inst":"State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China."},{"author_name":"Xin Jin","author_inst":"State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China."},{"author_name":"Juan Wang","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"Tong-Qing An","author_inst":"State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China."},{"author_name":"Xin-Yi Huang","author_inst":"State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China."},{"author_name":"Jie Cui","author_inst":"Department of Infectious Diseases, National Medical Center for Infectious Diseases, Huashan Hospital, Institute of Infection and Health Research, Fudan Universi"},{"author_name":"John-Sebastian Eden","author_inst":"Centre for Virus Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.;School of Medical Sciences, The University of Sydney, "},{"author_name":"Gong Cheng","author_inst":"New Cornerstone Science Laboratory, Tsinghua University-Peking University Joint Center for Life Sciences, School of Basic Medical Sciences, Tsinghua University,"},{"author_name":"De-Yin Guo","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Guo-Dong Liang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Ce"},{"author_name":"Edward C. Holmes","author_inst":"School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia."},{"author_name":"Zi-Qing Deng","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Mang Shi","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Da-Xi Wang","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Rebuilding microbiome diversity theory on the closed simplex","rel_doi":"10.64898\/2026.09.02.748976","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748976","rel_abs":"Ecological diversity theory links diversity within local communities to diversity of higher-level ensembles, but this scale structure is largely absent from microbiome analysis. Alpha diversity is usually treated as a within-sample summary, whereas \"beta diversity\" often denotes pairwise dissimilarity and gamma diversity is rarely explicit. We restore the local-regional architecture for environmental, host-associated and longitudinal microbiomes and distinguish regional beta diversity from pairwise dissimilarity and predictor-associated compositional variation. To quantify these objects for sparse compositions, we introduce Hellinger-Riemann intrinsic coordinates (HRIC), a one-to-one, bounded normal-coordinate representation of the closed simplex that retains exact zeros. The same coordinates yield Simplex Hellinger alpha and gamma diversity, additive regional beta diversity, taxon contributions, pairwise dissimilarity and model-explained dispersion. Simulations established the correspondence between HRIC dispersion and the between-condition component of PERMANOVA. Across Arctic and North Atlantic communities, local diversity relative to each regional benchmark covaried similarly with vertical environmental gradients despite partly different taxon-level associations. In a randomized autologous faecal microbiota transplantation trial, recipients returned earlier towards their personal pre-transplant compositions, whereas the alpha-diversity difference was smaller and less precise. Explicit local and regional referents therefore connect diversity partitioning with compositional analysis across microbial systems.","rel_num_authors":2,"rel_authors":[{"author_name":"Yiqian Zhang","author_inst":"The Ohio State University"},{"author_name":"Zihan Zhu","author_inst":"Yale University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"From Housing to Hotspots: Integrating a Housing-Based Measure of Individual Socioeconomic Status with Geospatial Analysis to Target Colorectal Cancer Screening in Rural Communities","rel_doi":"10.64898\/2026.08.28.26361444","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361444","rel_abs":"PurposeThis study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions.\n\nMethodsWe used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race\/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1).\n\nFindingsAmong 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81).\n\nConclusionLower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.","rel_num_authors":30,"rel_authors":[{"author_name":"Rebecca Yao","author_inst":"University of Chicago"},{"author_name":"Chung-Il Wi","author_inst":"Mayo Clinic"},{"author_name":"Madison J Beenken","author_inst":"Mayo Clinic"},{"author_name":"Dave Watson","author_inst":"Mayo Clinic"},{"author_name":"Philip H Wheeler","author_inst":"Mayo Clinic"},{"author_name":"Mike Finch","author_inst":"Children's Minnesota Research Institute"},{"author_name":"Dan P Kelleher","author_inst":"Mayo Clinic"},{"author_name":"Gokhan Anil","author_inst":"Mayo Clinic"},{"author_name":"Trent Anderson","author_inst":"Mayo Clinic Health System"},{"author_name":"Kathy Madden","author_inst":"Mayo Clinic Health System"},{"author_name":"Scott H Okuno","author_inst":"Mayo Clinic"},{"author_name":"Folakemi T Odedina","author_inst":"Mayo Clinic"},{"author_name":"Erin C Westfall","author_inst":"Mayo Clinic Health System"},{"author_name":"Eunice Y Park","author_inst":"Mayo Clinic"},{"author_name":"Pravesh Sharma","author_inst":"Mayo Clinic Health System"},{"author_name":"Sagar Dugani","author_inst":"Mayo Clinic"},{"author_name":"Randy M Foss","author_inst":"Mayo Clinic Health System"},{"author_name":"Brandon H Hidaka","author_inst":"Mayo Clinic Health System"},{"author_name":"Jessica L Sosso","author_inst":"Mayo Clinic Health System"},{"author_name":"Shivani Sabarish","author_inst":"Mayo Clinic"},{"author_name":"Gurpreet Singh","author_inst":"Mayo Clinic"},{"author_name":"Nahyr Lugo-Fagundo","author_inst":"Mayo Clinic"},{"author_name":"James Howick","author_inst":"Mayo Clinic"},{"author_name":"W. Ray Kim","author_inst":"Mayo Clinic"},{"author_name":"Andrew D Calvin","author_inst":"Mayo Clinic"},{"author_name":"Cheryl L. Walker-Mcgill","author_inst":"Carolina Complete Health"},{"author_name":"Lior Rennert","author_inst":"Clemson University"},{"author_name":"Young J Juhn","author_inst":"Mayo Clinic"},{"author_name":"James R Cerhan","author_inst":"Mayo Clinic"},{"author_name":"Brian A Lynch","author_inst":"Mayo Clinic"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Immune Checkpoint Blockade Modifies Drug-Associated Toxicity Across Phenotypes and Time","rel_doi":"10.64898\/2026.08.31.26361880","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361880","rel_abs":"ImportanceImmune checkpoint inhibitors (ICIs) produce diverse immune toxicities, but whether checkpoint blockade also modifies associations between other drugs and adverse events is poorly understood.\n\nObjectiveTo define ICI-associated toxicity organization and determine whether drug-associated adverse events and onset vary with ICI exposure and checkpoint pathway.\n\nDesign and SettingCross-sectional analysis of deduplicated FAERS reports from 2016 through 2025; analyses performed in 2026.\n\nParticipantsAmong 13,701,106 deduplicated reports, 2,365,269 were cancer associated and 256,940 contained an ICI. Median age among cancer reports with observed age was 66 years (IQR, 56-75 years); 1,031,999 (43.6%) were female and 1,003,154 (42.4%) were male.\n\nExposuresICI exposure in any reported drug role, individual primary-suspect drugs, and checkpoint-pathway exposure.\n\nMain Outcomes and MeasuresReporting odds ratios (ORs), cross-organ adverse-event communities, adjusted primary-suspect drug x ICI interaction ORs for Stevens-Johnson syndrome\/toxic epidermal necrolysis (SJS\/TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), interstitial nephritis, drug-induced liver injury (DILI), and vomiting (VOM), and accelerated failure-time model time ratios for documented onset.\n\nResultsOf 3001 eligible Preferred Terms in cancer-associated reports, 2091 differed at a false discovery rate (FDR) less than .05. Four cross-organ toxicity communities were identified. Of 138 eligible drug-phenotype pairs, 65 had FDR-significant interactions, including moxifloxacin-SJS\/TEN amplification (interaction OR, 101.72; 95% CI, 39.11-264.55), enfortumab vedotin-SJS\/TEN attenuation (interaction OR, 0.17; 95% CI, 0.13-0.23), and omeprazole-interstitial nephritis amplification (interaction OR, 10.35; 95% CI, 7.62-14.05). Among 60,324 reports contributing to temporal analyses, ICI exposure was associated with longer adjusted documented time to onset for 5 of 6 phenotypes (time ratios, 1.37-1.59) but not AGEP (time ratio, 0.99; 95% CI, 0.67-1.46). Temporal associations also differed across checkpoint pathways.\n\nConclusions and RelevanceICIs were associated with a structured cross-organ toxicity landscape, phenotype-specific modification of drug-associated adverse events, and distinct temporal patterns across checkpoint pathways. These findings support checkpoint blockade as a modifier of drug-associated toxicity and motivate longitudinal and mechanistic validation.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSWhat is the structure of toxicity associated with immune checkpoint blockade?\n\nFindingsIn this cross-sectional study of 13,701,106 deduplicated adverse-event reports, ICI exposure defined a broad toxicity landscape containing 4 cross-organ communities. Sixty-five drug x ICI interactions were identified across 5 immune-mediated phenotypes and a control (vomiting), and documented time to onset differed by phenotype and checkpoint pathway.\n\nMeaningCheckpoint blockade may create an altered immune state in which the phenotype and timing of drug-associated toxicity depend on both the accompanying drug and the checkpoint pathway inhibited.","rel_num_authors":7,"rel_authors":[{"author_name":"Eric M Mukherjee","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Amir Asiaee","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Dodi Park","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Matthew S Krantz","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Cosby A Stone Jr.","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Michelle Martin-Pozo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Elizabeth Jane Phillips","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"The accuracy of urine-based mycobacterial antigens to detect childhood tuberculosis using an ultrasensitive immunoassay","rel_doi":"10.64898\/2026.08.28.26361530","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361530","rel_abs":"BackgroundUrine-based testing offers a promising non-sputum approach for diagnosing paediatric tuberculosis. However, the currently available lipoarabinomannan (LAM) assay shows limited sensitivity in children and is primarily indicated for those living with HIV. Co-detection of LAM with Mycobacterium tuberculosis (Mtb) proteins in urine could provide complementary pathogen-derived biomarkers that improve diagnostic performance.\n\nMethodsWe developed an ultrasensitive multiplex electrochemiluminescence (ECL) immunoassay to measure Ag85B, CFP-10, ESAT-6, MPT32, and MPT64 in urine. We determined the analytical limits of detection and evaluated the diagnostic performance of individual proteins and LAM using urine samples from children with Confirmed, Unconfirmed, and Unlikely pulmonary tuberculosis enrolled across five high-burden countries (The Gambia, India, Peru, South Africa, and Uganda). Performance was assessed overall, by HIV and nutritional status, and across biomarker combinations.\n\nFindingsUrine samples from 630 children were analysed (median age was 4 years [IQR 2-8]; 44% female, 15% living with HIV, 19% underweight, 24% with Confirmed tuberculosis). The ECL assay achieved femtomolar limits of detection (1{middle dot}5 to 4{middle dot}0 fM). The sensitivity and specificity of individual Mtb proteins were 12-33% and 98-100%, respectively. Ag85B had the highest sensitivity (33%, 95% CI 26-41) for Confirmed tuberculosis and was similar to LAM. A four-antigen signature (Ag85B, MPT64, MPT32, LAM) was 50% sensitive (95% CI 42-58) and 94% specific (95% CI 90-96), and was significantly more sensitive than LAM alone, in particular among those without HIV. An additional sixteen (10%) of children with Unconfirmed TB had at least one Mtb protein or LAM detected.\n\nInterpretationMultiple Mtb proteins are detectable in paediatric urine with high specificity, and multi-antigen signatures can augment sensitivity versus LAM alone. These findings demonstrate the potential of multi-antigen urine detection for childhood TB and define analytical targets for the development of future point-of-care diagnostics.\n\nFundingNational Institutes of Health.\n\nRESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe examined the literature for peer-reviewed research articles on the accuracy of biomarker- based urine tests for pulmonary tuberculosis in children <15 years old. We used PubMed and Google Scholar, with the search terms \"child\", \"tuberculosis\", \"urine\", and \"diagnosis\" regardless of language from July 2016 to July 2026. We excluded articles on host-based markers and extrapulmonary tuberculosis. Molecular urine assays, including Xpert MTB\/RIF, have limited sensitivity to detect childhood pulmonary tuberculosis. Most of the research on urine-based diagnostics has focused on detection of lipoarabinomannan (LAM), which has had variable sensitivity and specificity in children against a microbiological reference standard. Accuracy is higher in those with HIV, and current guidelines only recommend LAM for adults and children with HIV.\n\nAdded value of this studyWe developed an ultrasensitive multiplex immunoassay to detect and measure Mtb-specific proteins in urine samples from children with presumptive tuberculosis in five high-burden countries. We found that Mtb proteins could be detected in paediatric urine samples with high specificity, and Ag85B had similar sensitivity as LAM. A four-marker panel (Ag85B, MPT32, MPT64, and LAM) improved sensitivity over LAM alone without a significant loss of specificity, in particular among children without HIV.\n\nImplications of all the available evidenceMulti-antigen urine tests can improve sensitivity over single marker assays, and have the potential to provide non-sputum, point-of-care tuberculosis detection in children regardless of HIV status.","rel_num_authors":24,"rel_authors":[{"author_name":"Esin Nkereuwem","author_inst":"London School of Hygiene and Tropical Medicine"},{"author_name":"Salvia Misaghian","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Devan Jaganath","author_inst":"University of California San Francisco"},{"author_name":"Roger I Calderon","author_inst":"Advanced Research and Health"},{"author_name":"Juaneta Luiz","author_inst":"University of Cape Town"},{"author_name":"Mandar Paradkar","author_inst":"Byramjee Jeejeebhoy Government Medical College"},{"author_name":"Peter Wambi","author_inst":"WALIMU"},{"author_name":"Robert Castro","author_inst":"University of California San Francisco"},{"author_name":"Rutuja Nerurkar","author_inst":"University of California San Francisco"},{"author_name":"Mingyue Wang","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Jacob Wohlstadter","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Molly F Franke","author_inst":"Harvard Medical School"},{"author_name":"Beate Kampmann","author_inst":"Charit\u00e9 Universit\u00e4tsmedizin Berlin"},{"author_name":"Aarti Kinikar","author_inst":"Byramjee Jeejeebhoy Government Medical College"},{"author_name":"Heather J Zar","author_inst":"University of Cape Town"},{"author_name":"Mark Segal","author_inst":"University of California San Francisco"},{"author_name":"Midori Kato-Maeda","author_inst":"University of California San Francisco"},{"author_name":"Jeffrey M Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Danielle Swaney","author_inst":"University of California San Francisco"},{"author_name":"Adithya Cattamanchi","author_inst":"University of California Irvine"},{"author_name":"Joel D Ernst","author_inst":"University of California San Francisco"},{"author_name":"Eric Wobudeya","author_inst":"WALIMU"},{"author_name":"George Sigal","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"- The Combo Study","author_inst":""}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Social Determinants of Health in HIV\/HBV Coinfection Compared with HIV and HBV Monoinfection: A Framework for Dynamic Social Vulnerability","rel_doi":"10.64898\/2026.08.31.26361856","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361856","rel_abs":"Human immunodeficiency virus (HIV) and hepatitis B virus (HBV) coinfection is associated with accelerated liver disease, but whether coinfection is associated with newly documented social determinants of health (SDoH) is unclear. We conducted a retrospective cohort study using TriNetX across 110 U.S. healthcare organizations (2010-2026). We propensity score matched adults with HIV\/HBV to adults with HIV or HBV monoinfection. We organized newly documented SDoH indicators using a dynamic individual-level framework with four clinically recognized domains of social disadvantage: material vulnerability, healthcare access and engagement, interpersonal adversity, and psychosocial vulnerability. Matched cohorts included 10,071 HIV\/HBV-HIV pairs and 9,659 HIV\/HBV-HBV pairs (mean age, 47 years; 79% male; 66% non-White; median follow-up, 3.3 years). Over 178,900 person-years, HIV\/HBV was associated with higher risk of the primary SDoH composite compared with HIV (11.5% vs 9.7%; incidence rate, 2.50 vs 1.97 per 100 person-years; hazard ratio [HR], 1.25; 95% confidence interval [CI], 1.15-1.37) and HBV (11.0% vs 6.4%; incidence rate, 2.39 vs 1.67; HR, 1.50; 95% CI, 1.35-1.67). HIV\/HBV was also associated with higher material vulnerability and healthcare access and engagement composites in both comparisons, including housing instability, food insecurity, financial insecurity, insurance instability, and care disengagement\/nonadherence (HR range, 1.22-3.33 vs HIV; 1.31-1.94 vs HBV). In the HBV comparison, HIV\/HBV was additionally associated with interpersonal adversity, primary support stressors, and violence or victimization (HR range, 1.36-2.16). Findings were robust across sensitivity analyses. HIV\/HBV was associated with more newly documented SDoH than monoinfection, supporting dynamic SDoH assessment.","rel_num_authors":9,"rel_authors":[{"author_name":"George Yendewa","author_inst":"Case Western Reserve University"},{"author_name":"Tayoot Chengsupanimit","author_inst":"Case Western Reserve University"},{"author_name":"Ali Dehghani","author_inst":"Case Western Reserve University"},{"author_name":"Ali Ahmed","author_inst":"University of Pennsylvania"},{"author_name":"Amir Mohareb","author_inst":"Harvard Medical School"},{"author_name":"Michael Freeman","author_inst":"Case Western Reserve University"},{"author_name":"Chari Cohen","author_inst":"Hepatitis B Foundation"},{"author_name":"Ighovwerha Ofotokun","author_inst":"Case Western Reserve University"},{"author_name":"Karine Dube","author_inst":"University of Pennsylvania"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Cross-System Meta-Analysis of Machine Learning Predictors Identifies Value-Specific Risk Drivers and Interactions Underlying Acute Kidney Injury","rel_doi":"10.64898\/2026.08.31.26361849","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361849","rel_abs":"BackgroundCurrent understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific biomarker values or physiologic thresholds influence susceptibility. We aimed to synthesize knowledge from machine learning models trained across multiple health systems to identify generalizable, value-specific risk drivers and biomarker interactions contributing to AKI risk.\n\nMethodsWe analyzed electronic health records (EHRs) from 785,497 adult inpatients between 2010 and 2019 across nine U.S. academic medical centers within PCORnet. Interpretable gradient boosting machine models were independently developed at each health system to quantify predictor-outcome associations. Meta-regression was applied to integrate these site-level results, characterize nonlinear value-risk relationships, and identify bivariate interactions between predictors.\n\nResultMeta-analysis revealed consistent, value-specific risk drivers across health systems. An increase in glucose from 100 mg\/dL to 140 mg\/dL was associated with a 1.46-fold higher risk of AKI. Chloride and anion gap also demonstrated elevated AKI risk with risk increases overlapping portions of their reference ranges, with anion gap showing a 1.14-fold increase across 4-12 mmol\/L and chloride a 1.28-fold increase across 96-100 mEq\/L. Electrolytes including potassium, calcium, and sodium showed quadratic associations with AKI risk. Bivariate meta-regression identified interactions between key predictors, highlighting pathways that jointly modulate AKI risk.\n\nConclusionThis cross-system meta-analysis synthesizes machine learning-derived evidence into clinically interpretable knowledge, revealing how specific biomarker ranges and interactions modulate AKI risk. By moving beyond surface-level associations to quantitative, generalizable physiologic thresholds, these findings provide actionable insights to enhance risk stratification and personalized prevention in hospital care.\n\nHighlightsO_LICross-system meta-analysis uncovered generalizable, value-specific AKI risk drivers\nC_LIO_LIGlucose, chloride, and anion gap within reference ranges linked to higher AKI risk\nC_LIO_LIKey predictor interactions suggest coordinated pathways jointly modulating AKI risk\nC_LI","rel_num_authors":18,"rel_authors":[{"author_name":"Ho Yin Chan","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Deyi Li","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Alan S.L. Yu","author_inst":"Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"John A. Kellum","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Dana Y. Fuhrman","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Qi Xu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Elizabeth A Chrischilles","author_inst":"Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, Iowa, USA"},{"author_name":"Lindsay G. Cowell","author_inst":"Department of Health Data Science and Biostatistics, Peter O Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Sravani Chandaka","author_inst":"Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"Alfred Jerrod Anzalone","author_inst":"Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA"},{"author_name":"Jacob Kean","author_inst":"Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA"},{"author_name":"Kathleen M. McTigue","author_inst":"Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Abu Saleh Mohammad Mosa","author_inst":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA"},{"author_name":"Bradley Taylor","author_inst":"CTSI Center for Biomedical Informatics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA"},{"author_name":"Mahanaz Syed","author_inst":"Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA"},{"author_name":"Lemuel R. Waitman","author_inst":"Department of Biomedical and Health Informatics, University of Missouri-Kansas City, Kansas City, Missouri, USA"},{"author_name":"Yong Hu","author_inst":"Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China"},{"author_name":"Mei Liu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Cross-System Meta-Analysis of Machine Learning Predictors Identifies Value-Specific Risk Drivers and Interactions Underlying Acute Kidney Injury","rel_doi":"10.64898\/2026.08.31.26361849","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361849","rel_abs":"BackgroundCurrent understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific biomarker values or physiologic thresholds influence susceptibility. We aimed to synthesize knowledge from machine learning models trained across multiple health systems to identify generalizable, value-specific risk drivers and biomarker interactions contributing to AKI risk.\n\nMethodsWe analyzed electronic health records (EHRs) from 785,497 adult inpatients between 2010 and 2019 across nine U.S. academic medical centers within PCORnet. Interpretable gradient boosting machine models were independently developed at each health system to quantify predictor-outcome associations. Meta-regression was applied to integrate these site-level results, characterize nonlinear value-risk relationships, and identify bivariate interactions between predictors.\n\nResultMeta-analysis revealed consistent, value-specific risk drivers across health systems. An increase in glucose from 100 mg\/dL to 140 mg\/dL was associated with a 1.46-fold higher risk of AKI. Chloride and anion gap also demonstrated elevated AKI risk with risk increases overlapping portions of their reference ranges, with anion gap showing a 1.14-fold increase across 4-12 mmol\/L and chloride a 1.28-fold increase across 96-100 mEq\/L. Electrolytes including potassium, calcium, and sodium showed quadratic associations with AKI risk. Bivariate meta-regression identified interactions between key predictors, highlighting pathways that jointly modulate AKI risk.\n\nConclusionThis cross-system meta-analysis synthesizes machine learning-derived evidence into clinically interpretable knowledge, revealing how specific biomarker ranges and interactions modulate AKI risk. By moving beyond surface-level associations to quantitative, generalizable physiologic thresholds, these findings provide actionable insights to enhance risk stratification and personalized prevention in hospital care.\n\nHighlightsO_LICross-system meta-analysis uncovered generalizable, value-specific AKI risk drivers\nC_LIO_LIGlucose, chloride, and anion gap within reference ranges linked to higher AKI risk\nC_LIO_LIKey predictor interactions suggest coordinated pathways jointly modulating AKI risk\nC_LI","rel_num_authors":18,"rel_authors":[{"author_name":"Ho Yin Chan","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Deyi Li","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Alan S.L. Yu","author_inst":"Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"John A. Kellum","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Dana Y. Fuhrman","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Qi Xu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Elizabeth A Chrischilles","author_inst":"Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, Iowa, USA"},{"author_name":"Lindsay G. Cowell","author_inst":"Department of Health Data Science and Biostatistics, Peter O Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Sravani Chandaka","author_inst":"Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"Alfred Jerrod Anzalone","author_inst":"Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA"},{"author_name":"Jacob Kean","author_inst":"Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA"},{"author_name":"Kathleen M. McTigue","author_inst":"Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Abu Saleh Mohammad Mosa","author_inst":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA"},{"author_name":"Bradley Taylor","author_inst":"CTSI Center for Biomedical Informatics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA"},{"author_name":"Mahanaz Syed","author_inst":"Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA"},{"author_name":"Lemuel R. Waitman","author_inst":"Department of Biomedical and Health Informatics, University of Missouri-Kansas City, Kansas City, Missouri, USA"},{"author_name":"Yong Hu","author_inst":"Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China"},{"author_name":"Mei Liu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Secondhand Cannabis Smoke Exposure: Prevalence, Personal Use, and Neurocognitive Trajectories Over Time in Adolescents in the United States","rel_doi":"10.64898\/2026.08.31.26361835","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361835","rel_abs":"BackgroundAs cannabis policy changes in the United States, secondhand cannabis smoke (SCS) is increasingly common, including within families. However, prevalence of exposure and clinical correlates over time in adolescents are not fully understood.\n\nObjectives(1) To estimate the prevalence of SCS and personal cannabis use in US-based teens exposed to SCS, and (2) examine the cognitive trajectories of adolescents exposed to SCS compared to non- exposed peers.\n\nMethodsData from the Adolescent Brain Cognitive Development (ABCD) Study was used. Participants (n=11,316 of full cohort with follow-up data; n=776 with self-reported family SCS exposure) attended yearly visits from ages 11-17, completing substance use interviews, toxicological testing, and the NIH Toolbox Cognitive battery. Youth with SCS but no personal cannabis use (n=419; 47% female) were matched on prenatal substance exposure, family substance use history, and sociodemographics to non- SCS exposed and non-cannabis-using youth with a 1:2 ratio (Controls n=838). Linear mixed-effects models assessed cognitive performance by SCS*age interactions, accounting for random effects of subject and family. Covariates included sex and alcohol, nicotine, and other substance use. Secondary models analyzed performance by cumulative waves of reported SCS exposure interacting with age.\n\nResultsOf the full cohort, 6.9% (n=776) reported exposure to SCS. Of these individuals, 46% endorsed lifetime personal cannabis use by age 17, relative to 20% of non-SCS exposed youth (OR=3.83[95%CI:3.29,4.44]). Within matched participants, SCS*age demonstrated a significant interaction on attention and inhibitory control ({beta}=-0.32, p=.028), with SCS demonstrating reduced improvement over time. More waves of exposure were also associated with worse performance over time ({beta}=-0.39, p=.057).\n\nDiscussionAlmost half of those who had been exposed to SCS endorsed personal cannabis use. Cognitive findings were domain specific, similar to findings in secondhand tobacco: SCS exposed youth showed restricted improvement in attention and inhibitory control by age 17. Public health and policymakers should make efforts to curb youth SCS exposure, given the potential for risk which has not been fully explored to date.","rel_num_authors":6,"rel_authors":[{"author_name":"Jenicca Bastien","author_inst":"University of California, San Diego"},{"author_name":"Kimberly Garcia","author_inst":"University of California, San Diego"},{"author_name":"Alexander L. Wallace","author_inst":"University of California, San Diego"},{"author_name":"Ryan M Sullivan","author_inst":"University of California, San Diego"},{"author_name":"Eunha Hoh","author_inst":"San Diego State University"},{"author_name":"Natasha E Wade","author_inst":"University of California, San Diego"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Towards Electronic Health Records-Based Paediatric Growth References: Results from the SwissPedGrowth Project","rel_doi":"10.64898\/2026.08.28.26361619","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361619","rel_abs":"BACKGROUNDWe used anthropometric data from electronic health records (EHRs) of Swiss childrens hospitals to evaluate growth references and estimate centile curves.\n\nMETHODSWe received EHRs extracted from seven Swiss childrens hospitals and analysed two samples: all children with a height, weight, body mass index (BMI), or head circumference recording, and a subsample restricted to children without diseases potentially affecting growth, weighted to represent the general population. We calculated mean z-scores based on the World Health Organization growth references adopted for Switzerland in 2011 (CH-WHO 2011) and current Swiss growth references (Swiss 2026). We estimated sex-specific centile curves in the subsample using generalised additive models for location, scale, and shape.\n\nRESULTSWe included 213,868 children with height, 448,002 with weight, 209,244 with BMI, and 67,397 with head circumference recordings. Mean z-scores in the  all children sample were (CH- WHO 2011; Swiss 2026): height (0.10; -0.19), weight (0.16; -0.09), BMI (0.04; -0.07), head circumference (-0.28, -0.28); and in the subsample: height (0.34; 0.00), weight (0.27; 0.01), BMI (0.18; 0.05), and head circumference (0.04; 0.01). The 50th height, weight, BMI, and head circumference centiles of girls and boys in the subsample closely followed those of Swiss 2026, with slightly wider 3rd and 97th centiles in infancy and adolescence.\n\nCONCLUSIONHeight, weight, BMI, and head circumference centiles aligned well with the Swiss 2026 growth references in Switzerland, demonstrating that hospital EHRs could contribute to future growth references.","rel_num_authors":20,"rel_authors":[{"author_name":"Lorenz Manuel Leuenberger","author_inst":"University of Bern"},{"author_name":"Yara Shoman","author_inst":"University of Bern"},{"author_name":"Franco Romero","author_inst":"University of Bern"},{"author_name":"Mari Sasaki","author_inst":"University of Bern"},{"author_name":"Xeni Deligianni","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Nicole Goebel","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Rebeca Mozun","author_inst":"University Children's Hospital Zurich"},{"author_name":"Julia Anna Bielicki","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Marie-Anne Burckhardt","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Christoph Saner","author_inst":"Department of Pediatrics, Inselspital, Bern University Hospital"},{"author_name":"Valerie Schwitzgebel","author_inst":"University of Geneva"},{"author_name":"Michael Hauschild","author_inst":"Lausanne University Hospital (CHUV)"},{"author_name":"Franziska Righini Grunder","author_inst":"Children's Hospital of Central Switzerland"},{"author_name":"Pascal Mueller","author_inst":"Children's Hospital of Eastern Switzerland"},{"author_name":"Luregn J Schlapbach","author_inst":"University Children`s Hospital Zurich"},{"author_name":"Oskar Jenni","author_inst":"University Children's Hospital Zurich"},{"author_name":"Ben Daniel Spycher","author_inst":"University of Bern"},{"author_name":"Claudia Elisabeth Kuehni","author_inst":"University of Bern"},{"author_name":"Fabien N. Belle","author_inst":"University of Bern"},{"author_name":"- SwissPedHealth consotrium","author_inst":""}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"}]}