{"gname":"University of Iowa","grp_id":"24","rels":[{"rel_title":"REM sleep infraslow rhythms correlate with neuropsychiatric symptom severity","rel_doi":"10.64898\/2026.10.06.26364874","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.06.26364874","rel_abs":"Study objectives: Rapid eye movement (REM) sleep is commonly divided into phasic and tonic substates based on higher and lower oculomotor activity, respectively. Our previous study described an alternative framework in which REM temporal structure forms a continuum of infraslow rhythms (~0.01 Hz) across brain and body physiology. We now ask whether these infraslow rhythms vary with neuropsychiatric symptoms, given the long-standing but still inconsistent links between REM and mood- or trauma-related disorders. Methods: Using a cross-sectional polysomnographic dataset from male veterans, we examined 59 participants for correlations between REM infraslow rhythms and neuropsychiatric symptom scores, excluding participants with moderate\/severe apnea. Results: Oscillations in REM respiratory rate, but not cardiac rate or electroencephalogram beta power, showed higher infraslow frequencies in participants with multiple symptoms, including posttraumatic stress, pain, anxiety, depression, and cognitive difficulties. No significant correlations were found between these symptoms and conventional REM metrics (e.g., REM density, latency and duration). To further examine REM beyond phasic\/tonic binarization, we analyzed the timing between oculomotor activity and infraslow oscillations. Eye movements occurred preferentially during periods of high respiratory rate, low beta power and intermediate cardiac rate, with no changes in these temporal relationships across symptom severity. Conclusions: Neuropsychiatric associations with REM infraslow rhythms spanned multiple symptom domains but were physiologically specific, with respiratory rate oscillations representing the strongest readout of symptom burden. Furthermore, REM infraslow cycles across brain and body were differentially aligned with eye movements: a temporal pattern that may serve as a physiological target for neuropsychiatric and mechanistic research.","rel_num_authors":5,"rel_authors":[{"author_name":"Lezio S. Bueno-Junior","author_inst":"Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI 48109, USA"},{"author_name":"Peyton T. Wickham","author_inst":"Portland VA Research Foundation; Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA"},{"author_name":"Carolyn E. Jones-Tinsley","author_inst":"Portland VA Research Foundation; Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA"},{"author_name":"Miranda M. Lim","author_inst":"Portland VA Research Foundation; Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA"},{"author_name":"Brendon O. Watson","author_inst":"Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"rel_date":"2026-10-08","rel_site":"medrxiv"},{"rel_title":"Testing Bidirectional Genetic Links Between ADHD and the Big Five Personality Traits","rel_doi":"10.64898\/2026.10.05.26363336","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.05.26363336","rel_abs":"Attention Deficit Hyperactivity Disorder (ADHD) frequently co-occurs with distinct personality profiles, but whether ADHD shapes personality (scar model) or personality influences the development of ADHD (vulnerability model) remains unclear. Leveraging genome-wide association study (GWAS) data, we examined directional associations between ADHD and the Big Five personality traits (Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness). We estimated genetic correlations and conducted bidirectional two-sample Mendelian Randomization (MR) analyses between ADHD and the Big Five. Robustness was evaluated using multiple sensitivity tests, including MR-CAUSE, which accounts for correlated and uncorrelated pleiotropy, and colocalization analyses on fine-mapped variants. Higher genetic liability to Neuroticism and Extraversion, alongside lower liability to Conscientiousness, increased ADHD risk, consistent with the vulnerability model. Conversely, ADHD genetic liability was associated with lower Agreeableness, supporting the scar model. Associations were generally robust across sensitivity analyses, with MR-CAUSE supporting the Neuroticism and Agreeableness associations and colocalization providing variant-level support for associations between ADHD and Neuroticism, Conscientiousness, and Agreeableness. These findings provide early, population-level insight into personality-related vulnerability to ADHD and motivate further work on the role of personality traits in ADHD risk.","rel_num_authors":8,"rel_authors":[{"author_name":"Hugo Peyre","author_inst":"Autism Reference Centre of Languedoc-Roussillon CRA-LR, Excellence Centre for Autism and Neurodevelopmental disorders CeAND, Montpellier University Hospital, MU"},{"author_name":"Nicolas Hoertel","author_inst":"Universite Paris Cite, Inserm U1266, Institut de Psychiatrie et Neuroscience de Paris, Paris, France; Service de Psychiatrie et Addictologie, DMU Psychiatrie et"},{"author_name":"Baptiste Pignon","author_inst":"Departement Medico Universitaire (DMU) - Innovation en sante Mentale, Psychiatrie et AddiCTologie, Hopitaux Universitaires \"H. Mondor\", Assistance Publique hopi"},{"author_name":"Sebastien Weibel","author_inst":"Psychiatry, Mental Health and Addictology Department, University Hospitals of Strasbourg, 1 place de l'Hopital, Strasbourg, 67000, France; INSERM UMR-S 1329, St"},{"author_name":"Mario Speranza","author_inst":"CESP Centre de recherche en Epidemiologie et Sante des Populations, INSERM U1178, Villejuif, France; Versailles Hospital Center, University Department of Child "},{"author_name":"Boris Chaumette","author_inst":"Department of Psychiatry, McGill University, Montreal, Canada; Universite Paris Cite, NeuroDiderot (INSERM U1141), Institut Pasteur (Human Genetics and Cognitiv"},{"author_name":"Ted Schwaba","author_inst":"Department of Psychology, Michigan State University, East Lansing, United States"},{"author_name":"Camille M. Williams","author_inst":"Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Superieure, PSL University, Paris, France"}],"rel_date":"2026-10-08","rel_site":"medrxiv"},{"rel_title":"Delay in antiretroviral therapy initiation is associated with worse mood symptoms in adults with chronic HIV disease","rel_doi":"10.64898\/2026.10.07.26364968","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.07.26364968","rel_abs":"Background. In the era of successful antiretroviral therapy (ART), mood disturbance remains highly prevalent in people with HIV (PWH). In the present study, we investigated the long-term legacy effects of long delay in ART initiation and low nadir CD4 count on mood symptoms in adults with HIV. The impacts of demographic and other HIV clinical factors were also examined. Methods. A total of 109 PWH (disease duration: 24.6{+\/-}9.3 years) and 42 demographically matched healthy participants (people without HIV, or PWoH) participated in a cross-sectional study (41-70 years old (55.6{+\/-}6.8), 62.9% African Americans, 27.2% female, 14.3{+\/-}2.9 years of education). Mood symptoms were assessed with the Beck Depression Inventory-II (BDI-II) and the Profile of Mood States (POMS). A comprehensive neuropsychological test battery was administered to assess neurocognitive function. Results. Despite largely comparable neurocognitive performance between PWH and PWoH , PWH had worse mood symptoms than PWoH, including higher total BDI-II (p<.001) and total POMS scores (p=.002), as well as higher score in all POMS sub-scores (p<.007), except Vigor (p=.724). Within the PWH group, longer delays in ART initiation, lower nadir CD4 count, and lower educational attainment were associated with worse mood symptoms. By contrast, current plasma CD4+ cell count, plasma detectable viral load, and HIV disease duration were not associated with either BDI-II or POMS score. Conclusion. Mood disturbance is highly prevalent in PWH on ART. The long-term legacy effects of delay in ART initiation and low nadir CD4 count on mood management reinforce the necessity of early ART initiation in clinical practice.","rel_num_authors":8,"rel_authors":[{"author_name":"Hyunchan Lee","author_inst":"Georgetown University Medical Center"},{"author_name":"Claire O'Connor","author_inst":"Georgetown University Medical Center"},{"author_name":"Grace Hanly","author_inst":"Georgetown University Medical Center"},{"author_name":"Linxi Chen","author_inst":"Georgetown University Medical Center"},{"author_name":"Danial Mahmood","author_inst":"Georgetown University Medical Center"},{"author_name":"David J. Moore","author_inst":"University of California San Diego"},{"author_name":"Ronald J. Ellis","author_inst":"University of California, San Diego"},{"author_name":"Xiong Jiang","author_inst":"Georgetown University Medical Center"}],"rel_date":"2026-10-08","rel_site":"medrxiv"},{"rel_title":"Carrying Bangladesh with Them: How Pre-Migration Healthcare Norms Shape Bangladeshi Immigrants' Health-Seeking in the United States","rel_doi":"10.64898\/2026.10.07.26364503","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.07.26364503","rel_abs":"Bangladeshi immigrants are a rapidly growing U.S. immigrant population, yet little is known about how pre-migration healthcare experiences shape healthcare use after migration. This qualitative study explored how healthcare norms from Bangladesh influence the health-seeking behaviors of Bangladeshi immigrants in the U.S., the barriers and facilitators they encounter, and their perceptions of care quality. Semi-structured interviews were conducted with 29 Bangladeshi-born adults living in the U.S. from September 2020 to January 2021 and analyzed using the constant comparative method. Participants described healthcare expectations shaped by the healthcare system of Bangladesh, which is mostly out-of-pocket and resource-limited, including symptom-driven rather than preventive care, self-treatment, and family-based caregiving. In the U.S., they faced barriers such as insurance confusion, long waits, difficulty establishing care, coverage disruptions, financial concerns, and language challenges. While participants valued culturally and linguistically concordant care, preferences for Bangla-speaking or co-ethnic providers varied, especially when perceived quality was low. Community networks, online information, self-education, and self-advocacy helped facilitate care. Provider communication strongly shaped perceptions of U.S. healthcare: feeling heard, respected, and well-informed promoted trust, while dismissal or delayed diagnosis led to skepticism and avoidance. These findings suggest that healthcare engagement of Bangldeshi immigrants is shaped by both structural barriers and pre-migration norms. Improving access will require attention to subgroup-specific histories, insurance navigation, language access, and the relational quality of care.","rel_num_authors":2,"rel_authors":[{"author_name":"Aantaki Raisa","author_inst":"Washington University in St. Louis"},{"author_name":"Andrew H. Vu","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-10-08","rel_site":"medrxiv"},{"rel_title":"Dynamic near-term risk prediction of clinically significant immune-related adverse events using longitudinal electronic health records","rel_doi":"10.64898\/2026.10.06.26364907","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.06.26364907","rel_abs":"Objective: To develop and evaluate a dynamic model that repeatedly estimates the 30-day risk of a first grade [&ge;]2 immune-related adverse event (irAE) during immune checkpoint inhibitor (ICI) therapy. Materials and Methods: Adults initiating ICI therapy during 2015-2022 comprised the development cohort; 2023 initiators formed a nonoverlapping internal temporal evaluation cohort. Every 21 days during the first treatment year, models used available EHR information to predict a first structured phenotype-defined above Grade 2 irAE during the next 30 days. Evaluation included pooled and within-landmark discrimination, precision-recall performance, calibration, and patient-clustered bootstrap confidence intervals. Feature-state ablations and alternative update schedules examined the contribution of dynamic information. Results: Development included 1,522 patients and 18,744 evaluable landmarks; temporal evaluation included 526 patients and 6,263 landmarks, of which 184 were positive (2.94%). The multiscale XGBoost model achieved pooled AUROC 0.716 (95% CI, 0.676-0.755), AUPRC 0.067, within-landmark AUROC 0.672 (95% CI, 0.631-0.716), Brier score 0.0281, and log loss 0.1245. The time-aware baseline achieved AUROC 0.631 and within-landmark AUROC 0.536. Updated treatment exposure and laboratory trajectories accounted for most of the gain. A complete-state extension achieved AUROC 0.719 and AUPRC 0.073. The 21-day schedule had the highest discrimination among tested cadences. Conclusion: Longitudinal EHR updating improved near-term irAE prediction beyond pretreatment characteristics and treatment time. Phenotype adjudication and independent evaluation of calibration, transportability, and clinical utility are needed before implementation","rel_num_authors":5,"rel_authors":[{"author_name":"Liyuan Gong","author_inst":"Columbia University"},{"author_name":"Alice Agyekum","author_inst":"Columbia University"},{"author_name":"Nitish Aswani","author_inst":"Columbia University"},{"author_name":"Harry Reyes Nieva","author_inst":"Columbia University"},{"author_name":"Chin Hur","author_inst":"Columbia University"}],"rel_date":"2026-10-08","rel_site":"medrxiv"},{"rel_title":"National Prevalence and Determinants of Concurrent Wasting and Stunting among Under-Five Children in Yemen: Evidence from the 2022-2023 Multiple Indicator Cluster Survey","rel_doi":"10.64898\/2026.10.06.26364923","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.06.26364923","rel_abs":"Background Concurrent wasting and stunting (WaSt) is a severe form of child undernutrition, linked to higher mortality and long-term negative health outcomes. Malnutrition is a significant challenge in Yemen, where the country is experiencing a long-standing conflict, food insecurity, and socioeconomic deprivation. But there is still limited evidence on the burden and determinants of WaSt in Yemen on a national level. This study aimed to estimate the prevalence and identify factors associated with WaSt among children under 5 years in Yemen. Methods We conducted a cross-sectional analysis using nationally representative data from the 2022-2023 Yemen Multiple Indicator Cluster Survey (MICS). A significantly large dataset of 17,734 children aged 0-59 months from all the 22 governorates of Yemen was analyzed. Concurrent wasting and stunting (WaSt) was defined as the coexistence of a weight-for-height z-score (WHZ) below -2 standard deviations and a height-for-age z-score (HAZ) below -2 standard deviations from the WHO Child Growth Standards in the same child. Survey-weighted logistic regression was performed to analyze the factors associated with WaSt among the under 5 children in Yemen. Results The national prevalence of WaSt was 8.4% (95% CI: 7.6-9.2). The highest prevalence of WaSt was found in children aged 12-23 months (12.0%) and in the Western Coastal region (11.6%). After adjustment, male sex (AOR 1.52, 95% CI: 1.30-1.80), residence in the Western Coastal (AOR 2.00, 95% CI: 1.60-2.40), Eastern (AOR 2.00, 95% CI: 1.50-2.70), and Southern regions (AOR 1.60, 95% CI: 1.20-2.10), poorest household wealth (AOR 1.96, 95% CI: 1.30-2.90), moderate (AOR 1.51, 95% CI: 1.13-1.94) and severe food insecurity (AOR 1.94, 95% CI: 1.40-2.60), and recent diarrhoea (AOR 1.22, 95% CI: 1.02-1.44) were associated with higher odds of WaSt. Having a handwashing facility and improved sanitation were protective. Conclusion At least 1 in every 12 under-five children in Yemen was affected by WaSt, and there were significant socioeconomic, geographic, food-security and WASH-related factors influencing this devastating outcome. Instead of simple, one-dimensional approaches, multi-sectoral interventions are needed to address this adversity. And those efforts must be integrated into the system, interconnected with each other and specifically targeted to vulnerable children and households.","rel_num_authors":4,"rel_authors":[{"author_name":"Abdullah Salman","author_inst":"Ministry of Health & Family Welfare , Bangladesh"},{"author_name":"Rifa Sunzida","author_inst":"Chittagong Medical College"},{"author_name":"Bushra Kabir","author_inst":"Dhaka Medical College and Hospital"},{"author_name":"Rajat Das Gupta","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-10-08","rel_site":"medrxiv"},{"rel_title":"Evaluating Behavioral Parallelism and Cost Incentives in Voluntary Quarantine: A Randomized App-Based Epidemic Game at VinUniversity in Hanoi, Vietnam","rel_doi":"10.64898\/2026.10.06.26364443","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.06.26364443","rel_abs":"Background: Quantifying behavioral drivers of protective action during infectious disease outbreaks remains challenging. App-based experimental epidemic games (epigames) provide a framework for studying these decisions in naturalistic settings, but correspondence between in-game measures and real-life beliefs or behaviors requires evaluation. We examined whether an activity-based opportunity cost influenced voluntary quarantine decisions within an epigame and whether real-life health beliefs transferred into the game and predicted observed choices. Methods: We conducted a two-week randomized controlled trial from May 4 to May 17, 2026, at VinUniversity (VinUni) in Hanoi, Vietnam. Participants using the Epigames app installed in their smartphone were randomized to a low-barrier group, earning 1 point per qualifying contact with other participant when remaining active, or a high-barrier group, earning 2 points per contact. Of 439 enrolled participants, 283 were successfully randomized to one of the two groups, made at least one valid daily quarantine decision, and their data was used in the analysis. Real-life (S1) and in-game (S2) beliefs measured susceptibility, severity, quarantine self-efficacy, and perceived benefits. Belief correspondence was assessed using Holm-adjusted Spearman correlations and prospective cumulative-logit models. Quarantine choices were analyzed using participant-day binomial generalized estimating equations (GEE) and participant-level quasi-binomial models. Results: Participants made 1,823 valid daily decisions and chose quarantine on 458 participant-days (25.1%). Quarantine rates were 30.1% in the low-barrier group and 20.2% in the high-barrier group. The high barrier was associated with 41% lower population-averaged odds of quarantine (GEE odds ratio [OR] 0.59, 95% confidence interval [CI] 0.40-0.86,  = 0.0059); the quasibinomial estimate was identical (OR 0.59, 95% CI 0.40-0.86,  = 0.0055). Among 251 participants with matched S1-S2 data, all four corresponding beliefs were positively associated (Spearman's  = 0.259-0.418; all Holm-adjusted  < 0.001). Prospective ordinal models confirmed positive matched-domain associations (ORs 1.39-2.18 per one-point S1 increase). Beliefs showed little robust association with quarantine behavior, and neither S1 nor S2 beliefs jointly modified the randomized effect. Self-efficacy, network degree, and the preregistered gender main effect were also unsupported. Conclusion: A higher activity-based opportunity cost reduced voluntary quarantine within the VinUni epigame, while participants carried relative differences in stated health beliefs from real-life to in-game framing. These beliefs did not reliably predict observed quarantine choices within the game framing. Epigames can support controlled study of incentives and belief correspondence in naturalistic settings such as college campuses, but behavioral validity or real-world generalizability requires further work.","rel_num_authors":10,"rel_authors":[{"author_name":"Andres Colubri","author_inst":"UMass Chan Medical School, Worcester, Massachusetts, United States"},{"author_name":"Thu Le Anh","author_inst":"College of Health Sciences, VinUniversity, Hanoi, Vietnam"},{"author_name":"Ngoc Le Thi Minh","author_inst":"College of Health Sciences, VinUniversity, Hanoi, Vietnam"},{"author_name":"Huy Vu Nhat Ho","author_inst":"College of Health Sciences, VinUniversity, Hanoi, Vietnam"},{"author_name":"Anh Dang Ngoc Minh","author_inst":"College of Health Sciences, VinUniversity, Hanoi, Vietnam"},{"author_name":"Duong Le Huong","author_inst":"College of Health Sciences, VinUniversity, Hanoi, Vietnam"},{"author_name":"Minh Doan Hoang","author_inst":"College of Health Sciences, VinUniversity, Hanoi, Vietnam"},{"author_name":"Dale King","author_inst":"American University of Iraq-Baghdad, Baghdad, Iraq"},{"author_name":"Jasmina Panovska-Griffiths","author_inst":"Queen's College, University of Oxford, Oxford, United Kingdom"},{"author_name":"Pei Yee Woh","author_inst":"Innovations in Health Sciences, VinUniversity, Hanoi, Vietnam"}],"rel_date":"2026-10-08","rel_site":"medrxiv"},{"rel_title":"Plasma metabolomic profiling of frailty in older adults at risk of dementia","rel_doi":"10.64898\/2026.10.06.26364906","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.06.26364906","rel_abs":"Background: Frailty is a prominent risk factor for dementia. Early detection of at-risk individuals may enable intervention to prevent or slow dementia pathology. Blood-based biomarker discovery could offer a path for early detection of frailty and cognitive decline. Methods: Older adults at risk of dementia underwent comprehensive multi-disciplinary assessment at the Healthy Brain Ageing (HBA) clinic, including geriatric, neurophysiological and mood evaluations, in addition to providing a fasted blood sample. Clinical data were used to calculate a deficit accumulation frailty index (FI) score. Additional cohorts attended either one (n=118) or two (n=73) subsequent visits over an average period of 4 years. Untargeted metabolomics was performed on plasma samples, and metabolites were clustered by similarity. Linear regression was used to assess association of clusters with FI. Finally, change in plasma abundance over time was assessed. Results: At assessment, participants had a mean (SD) age of 68.10 (8.07) years; 72.2% were female. Hierarchical clustering identified 10 clusters of metabolites, of which 6 were significantly associated with FI (p<0.05) at baseline and contained similar classes of metabolite. Differential abundance analysis found unique temporal regulation of metabolite abundance in each cluster, but across clusters severity of frailty impacted direction of change in plasma abundance over time. Conclusions: Frailty index was associated with carboxylic acids and derivatives, fatty acyls and organooxygen compounds across multiple clusters, and a large number of lipids were linked to frailty score, potentially implicating dysregulation of energy metabolism in the pathophysiology of frailty and forming a panel of predictive biomarkers of at risk patients.","rel_num_authors":7,"rel_authors":[{"author_name":"Isabelle Alldritt","author_inst":"University of Technology Sydney"},{"author_name":"Johannes C Michaelian","author_inst":"The University of Sydney"},{"author_name":"Rachael Yu","author_inst":"The University of Sydney"},{"author_name":"Sarah E Deemer","author_inst":"University of North Texas"},{"author_name":"Richard J Mills","author_inst":"Murdoch Children's Research Institute"},{"author_name":"Sharon L Naismith","author_inst":"The University of Sydney"},{"author_name":"Andrew Philp","author_inst":"University of Technology Sydney"}],"rel_date":"2026-10-08","rel_site":"medrxiv"},{"rel_title":"The Dilated Cardiomyopathy E525K Mutation Stabilizes the Cardiac Myosin Interacting-Heads Motif While Activating the Isolated Motor Domain","rel_doi":"10.64898\/2026.10.02.756299","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756299","rel_abs":"Mutations in {beta}-cardiac myosin are a common cause of inherited cardiomyopathies. The dilated cardiomyopathy E525K mutation alters both thick filament regulation and motor activity, yet the structural basis for these effects has remained unclear. Here, we combined cryo-EM and MD simulations to determine how E525K affects the conformational landscape of cardiac myosin in both its autoinhibited interacting-heads motif (IHM) and isolated myosin head states. E525K-mutated myosin exclusively adopted a single S2 conformation in IHM, in contrast to the conformational heterogeneity observed in wild-type myosin. Structural and electrostatic analyses revealed that the mutation increased positive charge density at the blocked-head (BH) - S2 (subfragment-2) interface, strengthening interactions with negatively charged residues within S2. 3D variability analysis and MD simulations of the E525K IHM demonstrated reduced S2 mobility, consistent with strengthened BH - S2 interactions, and decreased conformational flexibility, providing a structural mechanism for stabilization of the IHM state. To investigate the isolated myosin head, we determined cryo-EM structures of wild-type and E525K subfragment-1 (S1). The mutation induced local conformational rearrangements in the activation-loop and Loop 3 region, the SH3-like domain, and the essential light chain of S1, revealing structural changes that support the enhanced motor activity previously reported for isolated E525K myosin motors. These findings show that E525K stabilizes the autoinhibited IHM through electrostatic stabilization of the BH - S2 interface while inducing structural changes consistent with activation of the isolated motor domain, providing structural insights into how a single DCM mutation differentially regulates cardiac myosin S1 and IHM structure and function.","rel_num_authors":9,"rel_authors":[{"author_name":"Ruchi Gautam","author_inst":"University of Massachusetts Chan Medical School, Worcester, MA, USA"},{"author_name":"Arun Kumar Somavarapu","author_inst":"University of Massachusetts Chan Medical School, Worcester, MA, USA"},{"author_name":"Kalen Z. Robeson","author_inst":"University of Washington, Seattle, WA, USA"},{"author_name":"Matthew C. Childers","author_inst":"University of Washington, Seattle, WA, USA"},{"author_name":"Michael Regnier","author_inst":"University of Washington, Seattle, WA, USA"},{"author_name":"Jinghua Ge","author_inst":"Pennsylvania State University, College of Medicine; Hershey, PA, USA"},{"author_name":"Christopher M. Yengo","author_inst":"Pennsylvania State University, College of Medicine; Hershey, PA, USA"},{"author_name":"Roger Craig","author_inst":"University of Massachusetts Chan Medical School; Worcester, MA, USA"},{"author_name":"Raul Padron","author_inst":"University of Massachusetts Chan Medical School; Worcester, MA, USA"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"mzQC: a versatile way to communicate quality information for biological mass spectrometry","rel_doi":"10.64898\/2026.10.02.756144","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756144","rel_abs":"Reliable quality control (QC) is essential for reproducible biological mass spectrometry, yet QC information is still typically stored in tool-specific, poorly interoperable formats that hinder comparison, reuse, and transparent reporting. We present mzQC, a community-developed standard from the HUPO Proteomics Standards Initiative for the compact, machine-readable representation of QC metrics and their associated metadata across mass spectrometry workflows. mzQC is implemented in JSON, organized to support both individual mass spectrometry acquisitions and collections thereof, with formal QC metrics definitions supported by controlled vocabularies to ensure unambiguous metric interpretation and semantic consistency. The format is supported by validation tools and reference libraries in Python, R, and Java, facilitating integration into existing software ecosystems. We demonstrate the utility of mzQC across diverse analytical modalities and QC settings, spanning longitudinal instrument monitoring, acquisition-method evaluation, study-level quality assessment, repository-scale data characterization, and spatially resolved mass spectrometry imaging. Together, these use cases show that mzQC provides not merely a file format, but a shared foundation for interoperable QC reporting, large-scale data assessment, and more rigorous, reproducible, and reusable mass spectrometry-based research.","rel_num_authors":24,"rel_authors":[{"author_name":"Chris Bielow","author_inst":"Bioinformatics Solution Center, Institut fuer Informatik, Freie Universitaet Berlin, Takustr. 9, 14195 Berlin, Germany"},{"author_name":"Mathias Walzer","author_inst":"European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD, Cambridge, United Kingdom"},{"author_name":"Nils Hoffmann","author_inst":"Forschungszentrum Juelich GmbH, Institute for Bio- and Geosciences (IBG-5), Wilhelm-Johnen-Strasse 1, 52428 Juelich, Germany"},{"author_name":"Julian Uszkoreit","author_inst":"Ruhr University Bochum, Medical Faculty, Medical Bioinformatics, Universitaetsstr. 150, D-44801 Bochum, Germany"},{"author_name":"Fatemeh Mirzadeh Sarcheshmeh","author_inst":"University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium"},{"author_name":"Franziska Nicolaus","author_inst":"Forschungszentrum Juelich GmbH, Institute for Bio- and Geosciences (IBG-5), Wilhelm-Johnen-Strasse 1, 52428 Juelich, Germany"},{"author_name":"Lars Andernach","author_inst":"Leibniz Institute of Plant Biochemistry, Program Center MetaCom, Halle, Germany"},{"author_name":"Patrick Boschmann","author_inst":"Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tuebingen, Germany"},{"author_name":"Bo Burla","author_inst":"Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, 28 Medical Dr, Singapore 117456"},{"author_name":"Eralp Dogu","author_inst":"Department of Statistics, Mugla Sitki Kocman University, 48000, Mu?la, Turkiye"},{"author_name":"Martin Eisenacher","author_inst":"Ruhr University Bochum, Medical Faculty, Medical Bioinformatics, Universitaetsstr. 150, D-44801 Bochum, Germany"},{"author_name":"Yasin El Abiead","author_inst":"BOKU University, Institute of Analytical Chemistry, Department of Natural Sciences and Sustainable Resources, 1190, Vienna, Austria"},{"author_name":"David Jimenez-Morales","author_inst":"Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Steffen Neumann","author_inst":"Leibniz Institute of Plant Biochemistry, Program Center MetaCom, Halle, Germany"},{"author_name":"Yasset Perez-Riverol","author_inst":"European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD, Cambridge, United Kingdom"},{"author_name":"Timo Sachsenberg","author_inst":"Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tuebingen, Germany"},{"author_name":"Reza M Salek","author_inst":"School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0SP, United Kingdom"},{"author_name":"Michael Strobel","author_inst":"Department of Computer Science and Engineering, University of California Riverside. 900 University Ave. Riverside, CA 92521"},{"author_name":"Stefan Tenzer","author_inst":"Institute of Immunology, University Medical Center of the Johannes-Gutenberg University, 55131, Mainz, Germany"},{"author_name":"Tim Van Den Bossche","author_inst":"VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium"},{"author_name":"Olga Vitek","author_inst":"Khoury College of Computer Science and Barnett Institute for Chemical and Biological Analysis, Northeastern University, Boston, MA 02115, USA"},{"author_name":"Mingxun Wang","author_inst":"Department of Computer Science and Engineering, University of California Riverside, Riverside, CA 92521, USA"},{"author_name":"David L. Tabb","author_inst":"Centre for Bioinformatics and Computational Biology, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch Uni"},{"author_name":"Wout Bittremieux","author_inst":"University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Distinct metabolic states of yeast and pseudohyphae differentially prime innate immune responsiveness","rel_doi":"10.64898\/2026.10.08.757614","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.08.757614","rel_abs":"Pseudohyphal growth in skin-associated fungi is often considered a morphological response to environmental stress, but whether it represents a coordinated metabolic state that influences host immunity remains unclear. Using Malassezia furfur, we combined synchronized morphogenesis, temporal metabolomics, stable-isotope tracing, targeted metabolite measurements, transcriptional profiling, and functional immune assays to compare yeast and pseudohyphal states. Yeast cells maintained an active tryptophan-aldehyde metabolic program that partitioned carbon between oxidative and reductive branches associated with redox balance and methylation potential. Pseudohyphal cells attenuated this program and instead underwent sequential metabolic remodeling, characterized by early changes in one-carbon and amino acid metabolism followed by sulfur-pathway remodeling and increased glutathione biosynthesis. These distinct fungal states generated extracellular environments that altered THP-1 innate immune responsiveness without directly inducing canonical macrophage polarization. Yeast-conditioned cells responded preferentially to tissue-repair signals, whereas pseudohypha-conditioned cells displayed enhanced responses to inflammatory stimuli. Individual metabolites did not reproduce these effects, suggesting that immune conditioning depends on the integrated extracellular milieu. Together, these findings identify pseudohyphal growth as an organized adaptive metabolic state and establish a framework linking fungal morphogenetic potential to context-dependent host immune responses.","rel_num_authors":9,"rel_authors":[{"author_name":"Hyuga Kato","author_inst":"Case Western Reserve University"},{"author_name":"Monali NandyMazumdar","author_inst":"Case Western Reserve University"},{"author_name":"Zhara Rahmani","author_inst":"Case Western Reserve University"},{"author_name":"Teagan Kukhta","author_inst":"Case Western Reserve University"},{"author_name":"Claire Wolford","author_inst":"Case Western Reserve University"},{"author_name":"Sahishnu Vallabhajoysul","author_inst":"Case Western Reserve University"},{"author_name":"Masaru Miyagi","author_inst":"Case Western Reserve University"},{"author_name":"Susumu Kajiwara","author_inst":"Tokyo Kogyo Daigaku"},{"author_name":"Mei Zhang","author_inst":"Case Western Reserve University"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Fu Ling (Wolfiporia cocos) Fungal \u03b2-Glucan in Combination with Bacillus coagulans Improve Colitis-Related Outcomes and Cecal Fermentation in DSS-Treated Mice","rel_doi":"10.64898\/2026.10.07.757334","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.07.757334","rel_abs":"Inflammatory bowel disease challenges modern gastroenterology due to high relapse rates. This study evaluated the effects of medicinal fungal sclerotium Fu Ling (Wolfiporia cocos), its derived {beta}-1,3-1,6-Glucan, Bacillus coagulans GBI-30, 6086, and their combinations in dextran sulfate sodium-induced colitis in male BALB\/c mice. The {beta}-glucan-B. coagulans combination significantly improved body weight, disease activity index, colon length and architecture, mucus secretion, and cecal fermentation over single ingredient. Consistently, this combination increased total short-chain fatty acids production, particularly acetate, improved gut tight-junction organization and reduced myeloperoxidase accumulation. Shotgun metagenomic sequencing indicated that this combination reshaped the gut microbiota, notably enriching Anaeromassilibacillus sp., which associated with improved clinical feature. These findings highlight the potential of fungal-derived {beta}-glucan and probiotic combinations as functional food ingredients for mitigating intestinal inflammation and supporting barrier repair.","rel_num_authors":5,"rel_authors":[{"author_name":"Ka Lee Ma","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Nelson Kei","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Sui Shan Chan","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Hoi Shan Kwan","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Peter C.K. Cheung","author_inst":"The Chinese University of Hong Kong"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Rv2061c, a Putative F420-Dependent Oxidoreductase, is Required for Tolerance to Malachite Green and Other Antimicrobials and Oxidative Stress Protection in Mycobacterium tuberculosis","rel_doi":"10.64898\/2026.10.08.757578","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.08.757578","rel_abs":"The pathogenicity of Mycobacterium tuberculosis relies on its ability to withstand hostile host environments. Coenzyme F420 is critical for redox metabolism in Mycobacterium tuberculosis an organism distinguished by its abundant use of this low-redox-potential cofactor and a repertoire of F420-dependent enzymes. While F420-dependent mechanisms are known to confer protection against antimicrobials and oxidative stress, the specific enzymes responsible remain largely unidentified. In this study, using the F420-dependent detoxification of malachite green as a phenotypic marker, we performed a genome-wide screen in Mycobacterium tuberculosis using transposon mutagenesis and whole genome sequencing. The results of transposon mutagenesis and whole genome sequencing revealed that, among the 28 genes encoding F420-dependent enzymes, only mutations in fgd (essential for F420H2 re-generation) and Rv2061c, a gene encoding a putative F420-dependent oxidoreductase, were highly susceptible to malachite green. Subsequent experiments confirmed that Rv2061c and its homolog MT2120 are required for detoxifying malachite green and crystal violet. Furthermore, the Rv2061c mutant exhibited increased susceptibility to isoniazid and pretomanid; as well as menadione-induced oxidative stress. We also identified mutations in the transcriptional repressor Rv0678 as a key determinant of resistance to malachite green and crystal violet. This study establishes Rv2061c as a pivotal F420-dependent oxidoreductase that protects Mycobacterium tuberculosis from malachite green and other antimicrobial compounds as well as oxidative stress and further supports Rv0678 as a master regulator of multidrug resistance. These findings expand our knowledge of Mycobacterium tuberculosis defense mechanisms and highlight F420-dependent pathways as a potential target for new drug development.","rel_num_authors":3,"rel_authors":[{"author_name":"Dalin Rifat","author_inst":"Johns Hopkins University School of Medicine Center for Tuberculosis Research"},{"author_name":"Thomas R Ioerger","author_inst":"Texas A&M University"},{"author_name":"Eric L. Nuermberger","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Genome-wide within-host evolution during prolonged SARS-CoV-2 infection in a person with advanced HIV infection","rel_doi":"10.64898\/2026.10.07.756802","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.07.756802","rel_abs":"Within-host evolution during prolonged SARS-CoV-2 infection can be extensive in immunocompromised individuals, but the consequences of mutations that emerge and change in frequency over time remain difficult to predict. We characterized an XBB.1.5.10 infection lasting nearly 90 days in a person with advanced human immunodeficiency virus (HIV) infection. Genome-wide tracking of allele frequencies in samples collected on study days 43, 48, and 70 revealed a Spike N-terminal domain (NTD) deletion spanning residues 138-145 at frequencies of 33%, 80%, and 96%, respectively. A receptor-binding domain (RBD) deletion-insertion spanning residues 373-378 was detected on days 43 and 48 but not on day 70, while nsp12 C464Y was detected at approximately 8% on day 48 and 86% on day 70. We reconstructed representative Spike and non-Spike mutation profiles from the longitudinal samples and tested their effects on cell entry and viral RNA replication. The representative Spike construct for days 43 and 48 increased entry relative to XBB.1.5. Comparison with a matched construct lacking the receptor-binding domain deletion-insertion showed that the deletion-insertion enhanced entry, although it was not detected in the day 70 sample. The day 48 non-Spike profile supported the highest RNA replication among the tested replicons. Together, allele-frequency tracking revealed both transient mutations and mutations detected at progressively higher frequencies across the sampled time points. Functional testing showed that increased entry or RNA replication in vitro did not necessarily predict persistence in vivo.","rel_num_authors":12,"rel_authors":[{"author_name":"Karina Perlaza","author_inst":"Biohub San Francisco, San Francisco, California, USA | Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, Califo"},{"author_name":"Ishaan Dureja","author_inst":"Biohub San Francisco, San Francisco, California, USA"},{"author_name":"Diane Havlir","author_inst":"Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, Zuckerberg San Francisco General Hos"},{"author_name":"Vivek Jain","author_inst":"Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, Zuckerberg San Francisco General Hos"},{"author_name":"Jeffrey Whitman","author_inst":"Department of Laboratory Medicine, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Monica Dayao","author_inst":"Biohub San Francisco, San Francisco, California, USA | Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, Califo"},{"author_name":"Bryan Castillo-Rojas","author_inst":"Biohub San Francisco, San Francisco, California, USA"},{"author_name":"Julia Rosecrans","author_inst":"J. David Gladstone Institutes, San Francisco, California, USA"},{"author_name":"Melanie Ott","author_inst":"Biohub San Francisco, San Francisco, California, USA | J. David Gladstone Institutes, San Francisco, California, USA | Department of Medicine, University of Cal"},{"author_name":"Carina Marquez","author_inst":"Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco, Zuckerberg San Francisco General Hos"},{"author_name":"Joseph L. DeRisi","author_inst":"Biohub San Francisco, San Francisco, California, USA | Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, Califo"},{"author_name":"Taha Y. Taha","author_inst":"J. David Gladstone Institutes, San Francisco, California, USA | Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, "}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Mutational fitness landscapes of diverse human influenza H1N1 neuraminidases","rel_doi":"10.64898\/2026.10.07.757144","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.07.757144","rel_abs":"While neuraminidase (NA) is emerging as a promising influenza vaccine target, it evolves rapidly and undergoes antigenic drift. However, our understanding of its evolutionary constraints remains limited. Here, we systematically quantified the replication fitness effects of >6,000 mutations for each of three antigenically diverse H1N1 NAs using deep mutational scanning. Moderate correlations among H1N1 NA fitness landscapes revealed mutations with strain-dependent replication fitness effects. Additionally, cross-subtype comparison between N1 and N2 NAs uncovered similar epistatic effects that were enriched in the C-terminal interprotomer interface. We also showed that NA mutational fitness strongly correlated between in vivo and in vitro. Characterization of 40 individually constructed NA mutants further demonstrated that NA mutational fitness depended more on its surface protein expression than on its total protein expression or endoplasmic reticulum stress induction. Overall, the large dataset generated in this work provides critical insights into the evolutionary constraints of NA.","rel_num_authors":13,"rel_authors":[{"author_name":"Qi Wen Teo","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Zongjun Mou","author_inst":"Case Western Reserve University"},{"author_name":"Wenkan Liu","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Huibin Lv","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Kevin J. Mao","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Jessica J. Huang","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Yang Wei Huan","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Ruipeng Lei","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Akshita B. Gopal","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Arjun Mehta","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Emily X. Ma","author_inst":"University of Illinois Urbana-Champaign"},{"author_name":"Xinghong Dai","author_inst":"Case Western Reserve University"},{"author_name":"Nicholas C. Wu","author_inst":"University of Illinois Urbana-Champaign"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Immune determinants of leuprolide-associated cardiovascular risk in prostate cancer","rel_doi":"10.64898\/2026.10.01.756122","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.756122","rel_abs":"Leuprolide, a widely used androgen-deprivation therapy (ADT) for prostate cancer (PC), has been associated with increased cardiovascular events (CVE), although the underlying mechanisms remain unclear. Through integrated spatial transcriptomic analyses of murine heart and immune-stromal profiling in patient blood, we identified immune programs linked to leuprolide-associated CVE risk. Spatial analyses revealed the distinct organization of lymphoid (T+B cells) aggregates and inflammatory macrophages in the plaque following leuprolide exposure that accompanied by ventricular hypertrophy, relative to normal vasculature. Parallel profiling of circulatory soluble and cellular components in patients demonstrated CD8+T cell enriched pattern in ~44% of patients, including memory and type 1 cytotoxic subsets that linked to treatment-induced CVE, such as ventricular dysfunctions, but no contribution of adipocytokines. These translational findings highlight the stratification of leuprolide-associated CVE risk in PC patients based on circulating T cell profiling and could guide the risk mitigation with alternative ADT agents.","rel_num_authors":22,"rel_authors":[{"author_name":"Tuisha Gupta","author_inst":"Emory University School of Medicine"},{"author_name":"Fanyuan Zeng","author_inst":"Emory University School of Medicine"},{"author_name":"Marguerite Li","author_inst":"Emory University School of Medicine"},{"author_name":"Ian Moore","author_inst":"Emory University"},{"author_name":"Vaunita Cohen Parihar","author_inst":"Emory University School fo Medicine"},{"author_name":"Adithya K. Yadalam","author_inst":"Emory University School of Medicine"},{"author_name":"Amandeep Ahluwalia","author_inst":"Columbia University Medical Center\/New York-Presbyterian Hospital"},{"author_name":"Rendy Javier Chaparro Reyes","author_inst":"Emory University School of Medicine"},{"author_name":"Yujie Chen","author_inst":"Emory University School of Medicine"},{"author_name":"Jacklyn Hammons","author_inst":"Emory University School fo Medicine"},{"author_name":"Emily Chung","author_inst":"Emory University School of Medicine"},{"author_name":"Haitong Xu","author_inst":"Emory University School of Medicine"},{"author_name":"Sonia Mecorapaj","author_inst":"Emory University School of Medicine"},{"author_name":"Michelle Lu","author_inst":"Emory University School fo Medicine"},{"author_name":"Ryan Han","author_inst":"Emory University School of Medicine"},{"author_name":"Arshed A. Quyyumi","author_inst":"Emory University School of Medicine"},{"author_name":"Gregory B. Lesinski","author_inst":"Emory University Winship Cancer Institute"},{"author_name":"Edmund K. Waller","author_inst":"Emory University School of Medicine"},{"author_name":"Brian Olsen","author_inst":"Emory University Winship Cancer Institute"},{"author_name":"Sagar A. Patel","author_inst":"Emory University School of Medicine"},{"author_name":"Anant Mandawat","author_inst":"Emory University School of Medicine"},{"author_name":"Kiranj Chaudagar","author_inst":"Emory University School of Medicine"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"CBFA2T3-GLIS2 establishes transcriptionally permissive enhancer hypermethylation associated with venetoclax response in pediatric AML","rel_doi":"10.64898\/2026.10.01.756000","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.756000","rel_abs":"CBFA2T3-GLIS2 (C\/G) defines a high-risk pediatric AML subtype in which de novo enhancer rewiring sustains an aggressive leukemic transcriptional state. The early epigenomic mechanisms reinforcing this program and linking it to leukemic survival remain incompletely defined. Here, we identify enhancer-proximal hypermethylation as a noncanonical, transcription-permissive feature of C\/G AML. Using primary C\/G-positive AML samples and a developmentally relevant C\/G-transformed cord blood hematopoietic stem\/progenitor cell model, we integrated DNA methylation, chromatin state, fusion occupancy, and gene expression across early and leukemia-stage contexts. Early C\/G-associated hypermethylated regions localized near H3K27ac\/H3K4me1-marked active or primed cis-regulatory elements, were enriched for ERG\/ETS-like motifs, and showed related architecture in leukemia-stage contexts. DNMT3B was prominently increased in C\/G contexts, and its loss selectively altered methylation-associated gene expression without measurably changing global 5mC. Genetic DNMT3B loss increased venetoclax sensitivity in vitro, while pharmacologic DNMT3B inhibition with Nanaomycin-A similarly enhanced venetoclax response. During venetoclax treatment, DNMT3B-deficient xenografts showed reduced disease burden and prolonged survival. To assess whether this interaction extended to clinically used hypomethylating agents, azacitidine or decitabine plus venetoclax induced apoptosis-related transcriptional programs and mitochondrial depolarization. These findings identify hypermethylated active cis-regulatory elements as C\/G-associated states linked to transcriptional dysregulation and venetoclax response.","rel_num_authors":6,"rel_authors":[{"author_name":"Samrat Roy Choudhury","author_inst":"University of Arkansas for Medical Sciences"},{"author_name":"Arundhati Chavan","author_inst":"University of Arkansas for Medical Sciences"},{"author_name":"Rhonda Ries","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Pritam Biswas","author_inst":"University of Arkansas for Medical Sciences"},{"author_name":"Jason Farrar","author_inst":"University of Arkansas for Medical Sciences"},{"author_name":"Soheil Meshinchi","author_inst":"Fred Hutchinson Cancer Center"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Cross-species convergence of surface polysaccharides shapes the virulence of an atypical carbapenem resistant uropathogenic Escherichia coli clone","rel_doi":"10.64898\/2026.10.05.755231","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.05.755231","rel_abs":"Carbapenem-resistant Escherichia coli (CREC) is an emerging global health threat associated with limited treatment options and increased mortality. Here, analysis of 20,331 E. coli genomes from urinary tract infection (UTI) and urine samples identifies phylogroup A sequence type 361 (ST361) as an emerging CREC clone associated with UTI. Phylogenetic and phylodynamic analyses reveal two dominant ST361 clades that emerged in Asia around 2006 and subsequently disseminated intercontinentally. ST361 exhibits near pan-drug resistance, with blaNDM-5 conferring carbapenem resistance. In silico capsule and O-antigen typing reveals a highly conserved Klebsiella-like surface polysaccharide serotype in 95.9% of ST361 genomes, which we show contributes to virulence in UTI and catheter-associated UTI mouse models. Analysis of the mobile genetic elements harbouring blaNDM-5 in ST361 and other CREC further reveals a dynamic interplay between plasmid transfer and insertion elements that contributes to blaNDM-5 dissemination. Together, these findings identify ST361 as an emerging UTI-associated CREC clone and reveal the convergence of clonal expansion, virulence-associated surface architecture and mobile genetic elements in its spread.","rel_num_authors":23,"rel_authors":[{"author_name":"Lachlan L. Walker","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Zheng Jie Lian","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Minh-Duy Phan","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Nguyen Thi Khanh Nhu","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Yvette Yu Ting Ong","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Vitus Brix","author_inst":"University at Buffalo Jacobs School of Medicine and Biomedical Sciences"},{"author_name":"Adam N. Marin","author_inst":"University at Buffalo Jacobs School of Medicine and Biomedical Sciences"},{"author_name":"Brian S. Learman","author_inst":"University of Buffalo Jacobs School of Medicine and Biomedical Sciences"},{"author_name":"Budi Permana","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Irene Martinez-Roman","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Samuel A.S. Morris","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Kate M. Peters","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Chelsea Stewart","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Kay A. Ramsay","author_inst":"Frazer Institute, The University of Queensland"},{"author_name":"David M.P. De Oliveira","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Niels Pfennigwerth","author_inst":"Department of Medical Microbiology, Ruhr-University Bochum"},{"author_name":"Patrick N A Harris","author_inst":"Frazer Institute, The University of Queensland"},{"author_name":"David L. Paterson","author_inst":"ADVANCE-ID, Saw Swee Hock School of Public Health, National University of Singapore"},{"author_name":"Matthew J. Sweet","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Leo A. Featherstone","author_inst":"The Kirby Institute, Faculty of Medicine, University of New South Wales"},{"author_name":"Chelsie E. Armbruster","author_inst":"University of Buffalo Jacobs School of Medicine and Biomedical Sciences"},{"author_name":"Brian M. Forde","author_inst":"Institute for Molecular Bioscience, The University of Queensland"},{"author_name":"Mark A. Schembri","author_inst":"Institute for Molecular Bioscience, The University of Queensland"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"A Bispecific Biologic That Robustly Reverses HIV-1 and SIV Latency","rel_doi":"10.64898\/2026.10.06.757104","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.06.757104","rel_abs":"Antiretroviral therapy suppresses HIV-1 replication but cannot eliminate the latent viral reservoir persisting in memory CD4 T cells. Existing latency reversal candidates have yet to achieve robust viral reactivation at tolerable doses. We engineered T4IL15{triangleup}, a bispecific biologic that selectively delivers IL-15 signaling to memory CD4 T cells, with minimal off-target stimulation. T4IL15{triangleup} reactivated latent virus in 94% (48\/51) of clinical samples ex vivo, 75% (15\/20) of administrations in HIV-1-infected and ART-suppressed humanized mice, and 100% (6\/6) of SIV-infected and ART-suppressed rhesus macaques at doses well tolerated. We also found that off-target stimulation of non-CD4 cells hindered HIV-1 reactivation ex vivo. T4IL15{triangleup} provides a robust reactivation platform that could complement other reservoir-elimination strategies toward an HIV-1 cure.","rel_num_authors":17,"rel_authors":[{"author_name":"Yaoxing Huang","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Manoj S. Nair","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Jian Yu","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Sue Chong","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Hsiang Hong","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Hiroshi Mohri","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Hiroshi Takata","author_inst":"Oregon Health & Science University"},{"author_name":"Benjamin D. Varco-Merth","author_inst":"Oregon Health & Science University"},{"author_name":"Alejandra Marenco","author_inst":"Oregon Health & Science University"},{"author_name":"Jeffrey D. Lifson","author_inst":"Frederick National Laboratory for Cancer Research"},{"author_name":"Lishomwa Ndhlovu","author_inst":"Weill Cornell Medicine"},{"author_name":"Anthony Bowen","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Lydie Trautmann","author_inst":"Henry M Jackson Foundation for the Advancement of Military Medicine Inc"},{"author_name":"Sandhya Vasan","author_inst":"Henry M Jackson Foundation for the Advancement of Military Medicine Inc"},{"author_name":"Tae-Wook Chun","author_inst":"National Institute of Allergy and Infectious Diseases"},{"author_name":"Afam A. Okoye","author_inst":"Oregon Health & Science University"},{"author_name":"David D. Ho","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"A Bispecific Biologic That Robustly Reverses HIV-1 and SIV Latency","rel_doi":"10.64898\/2026.10.06.757104","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.06.757104","rel_abs":"Antiretroviral therapy suppresses HIV-1 replication but cannot eliminate the latent viral reservoir persisting in memory CD4 T cells. Existing latency reversal candidates have yet to achieve robust viral reactivation at tolerable doses. We engineered T4IL15{triangleup}, a bispecific biologic that selectively delivers IL-15 signaling to memory CD4 T cells, with minimal off-target stimulation. T4IL15{triangleup} reactivated latent virus in 94% (48\/51) of clinical samples ex vivo, 75% (15\/20) of administrations in HIV-1-infected and ART-suppressed humanized mice, and 100% (6\/6) of SIV-infected and ART-suppressed rhesus macaques at doses well tolerated. We also found that off-target stimulation of non-CD4 cells hindered HIV-1 reactivation ex vivo. T4IL15{triangleup} provides a robust reactivation platform that could complement other reservoir-elimination strategies toward an HIV-1 cure.","rel_num_authors":17,"rel_authors":[{"author_name":"Yaoxing Huang","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Manoj S. Nair","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Jian Yu","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Sue Chong","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Hsiang Hong","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Hiroshi Mohri","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Hiroshi Takata","author_inst":"Oregon Health & Science University"},{"author_name":"Benjamin D. Varco-Merth","author_inst":"Oregon Health & Science University"},{"author_name":"Alejandra Marenco","author_inst":"Oregon Health & Science University"},{"author_name":"Jeffrey D. Lifson","author_inst":"Frederick National Laboratory for Cancer Research"},{"author_name":"Lishomwa Ndhlovu","author_inst":"Weill Cornell Medicine"},{"author_name":"Anthony Bowen","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"},{"author_name":"Lydie Trautmann","author_inst":"Henry M Jackson Foundation for the Advancement of Military Medicine Inc"},{"author_name":"Sandhya Vasan","author_inst":"Henry M Jackson Foundation for the Advancement of Military Medicine Inc"},{"author_name":"Tae-Wook Chun","author_inst":"National Institute of Allergy and Infectious Diseases"},{"author_name":"Afam A. Okoye","author_inst":"Oregon Health & Science University"},{"author_name":"David D. Ho","author_inst":"Columbia University Vagelos College of Physicians and Surgeons"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Self-Exciting Population Event Models Reveal Abnormal Temporal Amplification in MAPT-Mutant Human Brain Assembloids","rel_doi":"10.64898\/2026.10.01.756068","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.756068","rel_abs":"Human brain assembloids provide a tractable platform for studying mutation-specific network dysfunction, but most studies do not distinguish whether abnormal activity arises from increased spontaneous initiation or stronger history-dependent amplification. We introduce a low-dimensional discrete-time self-exciting population model for calcium-imaging event sequences that separates baseline initiation (%[mu]), integrated history-dependent gain (%[eta]), and memory decay (%[tau]). Across 175 recordings from 13 MAPT p.R406W or CRISPR-corrected isogenic assembloids, biological-unit inference identified a 4.31-fold increase in %[eta] in mutants (95% CI 3.20- 5.84; exact p=7.8x10-4), without increased baseline activity. Parametric predictive checks exposed residual Poisson overdispersion; fixed-decay and beta-binomial sensitivity analyses nevertheless preserved the mutant history effect. Median %[eta] separated all 13 held-out assembloids, while a multifeature recording-level RBF-SVM achieved AUC 0.816 under nested whole-assembloid holdout. These results identify robust history-dependent temporal amplification as a functional MAPT-mutant network phenotype.","rel_num_authors":8,"rel_authors":[{"author_name":"Tonmoy Monsoor","author_inst":"University of California, Los Angeles"},{"author_name":"Colin M. McCrimmon","author_inst":"University of California, Los Angeles"},{"author_name":"Prateik Sinha","author_inst":"Carnegie Mellon University"},{"author_name":"Lina Zhang","author_inst":"University of California, Los Angeles"},{"author_name":"Manoj Reddy Dareddy","author_inst":"University of California, Los Angeles"},{"author_name":"Mehmet Efe Lorasdagi","author_inst":"University of California, Los Angeles"},{"author_name":"Ranmal A. Samarasinghe","author_inst":"University of California, Los Angeles"},{"author_name":"Vwani Roychowdhury","author_inst":"University of California, Los Angeles"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Programming N-linked glycan composition through de novo protein design","rel_doi":"10.64898\/2026.10.07.757460","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.07.757460","rel_abs":"N-linked glycans are ubiquitous post-translational modifications that mediate diverse biological processes, including cell signaling and adhesion, molecular recognition, and host-pathogen interactions. Despite their importance, N-linked glycans have largely remained beyond the reach of biomolecular design due to a lack of methods capable of jointly modeling glycans and protein structure. Here, we develop a general all-atom computational pipeline for creating de novo glycoproteins in which the amino acid sequence encodes both the location and composition of N-linked glycans. The method uses three-dimensional protein structure to tune glycan accessibility to processing enzymes in the eukaryotic secretory pathway. Systematic increases in steric restriction progressively reduced processing, shifting glycans from heterogeneous complex-type structures toward increasingly underprocessed oligomannose-type species resembling those found on many viral glycoproteins. Despite this programmed restriction, the resulting oligomannose glycans remained accessible for biological recognition, inhibiting uropathogenic Escherichia coli adhesion to human bladder cells and binding mannose-binding lectin. These results establish steric control of N-glycan processing as a genetically encodable design principle and extend computational protein design to functional glycoproteins with programmed composition.","rel_num_authors":19,"rel_authors":[{"author_name":"Margaret C Lunn-Halbert","author_inst":"University of Washington"},{"author_name":"Joel D Allen","author_inst":"University Of Southampton"},{"author_name":"Rohith Krishna","author_inst":"University of Washington"},{"author_name":"Anna Manchenko","author_inst":"University of Washington"},{"author_name":"Xinting Li","author_inst":"University of Washington"},{"author_name":"Edward D Lopatto","author_inst":"Washington University in Saint Louis"},{"author_name":"Jerome Pinkner","author_inst":"Washington University in Saint Louis"},{"author_name":"Carolina Moller","author_inst":"University of Washington"},{"author_name":"Woody Ahern","author_inst":"University of Washington"},{"author_name":"Johnny Mendoza","author_inst":"University of Washington"},{"author_name":"Alex Kang","author_inst":"University of Washington"},{"author_name":"Hannah Nguyen","author_inst":"University of Washington"},{"author_name":"Emily Joyce","author_inst":"University of Washington"},{"author_name":"Asim Bera","author_inst":"University of Washington"},{"author_name":"Scott  J Hultgren","author_inst":"Washington University in Saint Louis"},{"author_name":"Evgeni Sokurenko","author_inst":"University of Washington"},{"author_name":"Max Crispin","author_inst":"University of Southampton"},{"author_name":"David Baker","author_inst":"University of Washington"},{"author_name":"Neil P King","author_inst":"University of Washington"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Modulation of gp130 Cytokine Receptor Family by Herpes Simplex Virus 1 as a Strategy for Immune Evasion?","rel_doi":"10.64898\/2026.10.06.757054","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.06.757054","rel_abs":"HSV-1 is a neurotropic human pathogen that establishes lifelong latency in sensory neurons. Periodic reactivation causes mucocutaneous lesions and, in some cases, severe corneal and neurological disease. While HSV-1 utilizes numerous viral proteins to extensively reprogram host signaling pathways and evade antiviral responses, how it alters host cytokine receptor networks remains poorly understood. Here, we demonstrate that HSV-1 infection selectively downmodulates multiple members of the gp130 (glycoprotein 130) cytokine receptor family, including gp130, Oncostatin M receptor {beta} (OSMR-{beta}), and leukemia inhibitory factor receptor (LIFR), while sparing IL-6Ra and CNTFRa (Ciliary Neurotrophic Factor Receptor). Because gp130 is the central signaling component of the IL-6 cytokine family and mediates JAK\/STAT3 activation, its elimination likely represents a viral strategy to suppress inflammatory and antiviral signaling. Consistent with this, HSV-1 does not induce IL-6 mRNA expression in productively infected cells, suggesting coordinated inhibition of both cytokine production and receptor availability. Mechanistically, we found that the viral RNase VHS is at least partially responsible for gp130 family receptor downmodulation, likely in combination with virus-induced endocytosis\/degradation pathways. Functionally, activation of gp130 signaling significantly impairs HSV-1 infection, demonstrating that suppression of antiviral cytokine responses is critical for virus replication. Finally, we determined that CNTF causes reactivation of latent HSV-1 using the LUHMES in vitro latency model, illustrating CNTFRa signaling contribution to latency and reactivation. A similar effect has been reported for IL-6. Together, these findings reveal a previously unrecognized immune evasion mechanism by which HSV-1 remodels gp130 cytokine signaling to promote infection.","rel_num_authors":4,"rel_authors":[{"author_name":"Kimberly Foster-Lemieur","author_inst":"The University of Kansas Medical Center"},{"author_name":"Sreenath Muraleedharan Suma","author_inst":"The University of Kansas Medical Center"},{"author_name":"Meenu Ajith","author_inst":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Em"},{"author_name":"Maria Kalamvoki","author_inst":"The University of Kansas Medical Center"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Modulation of gp130 Cytokine Receptor Family by Herpes Simplex Virus 1 as a Strategy for Immune Evasion?","rel_doi":"10.64898\/2026.10.06.757054","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.06.757054","rel_abs":"HSV-1 is a neurotropic human pathogen that establishes lifelong latency in sensory neurons. Periodic reactivation causes mucocutaneous lesions and, in some cases, severe corneal and neurological disease. While HSV-1 utilizes numerous viral proteins to extensively reprogram host signaling pathways and evade antiviral responses, how it alters host cytokine receptor networks remains poorly understood. Here, we demonstrate that HSV-1 infection selectively downmodulates multiple members of the gp130 (glycoprotein 130) cytokine receptor family, including gp130, Oncostatin M receptor {beta} (OSMR-{beta}), and leukemia inhibitory factor receptor (LIFR), while sparing IL-6Ra and CNTFRa (Ciliary Neurotrophic Factor Receptor). Because gp130 is the central signaling component of the IL-6 cytokine family and mediates JAK\/STAT3 activation, its elimination likely represents a viral strategy to suppress inflammatory and antiviral signaling. Consistent with this, HSV-1 does not induce IL-6 mRNA expression in productively infected cells, suggesting coordinated inhibition of both cytokine production and receptor availability. Mechanistically, we found that the viral RNase VHS is at least partially responsible for gp130 family receptor downmodulation, likely in combination with virus-induced endocytosis\/degradation pathways. Functionally, activation of gp130 signaling significantly impairs HSV-1 infection, demonstrating that suppression of antiviral cytokine responses is critical for virus replication. Finally, we determined that CNTF causes reactivation of latent HSV-1 using the LUHMES in vitro latency model, illustrating CNTFRa signaling contribution to latency and reactivation. A similar effect has been reported for IL-6. Together, these findings reveal a previously unrecognized immune evasion mechanism by which HSV-1 remodels gp130 cytokine signaling to promote infection.","rel_num_authors":4,"rel_authors":[{"author_name":"Kimberly Foster-Lemieur","author_inst":"The University of Kansas Medical Center"},{"author_name":"Sreenath Muraleedharan Suma","author_inst":"The University of Kansas Medical Center"},{"author_name":"Meenu Ajith","author_inst":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Em"},{"author_name":"Maria Kalamvoki","author_inst":"The University of Kansas Medical Center"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Calcium-associated Pro367 ring puckering supports conformational selection in a thermophilic lipase","rel_doi":"10.64898\/2026.10.06.756949","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.06.756949","rel_abs":"Thermophilic bacterial lipases must combine structural stability with the conformational flexibility required for interfacial activation, yet the local organization of their metal-binding sites remains incompletely understood. Here, we determined two X-ray crystal structures of the family I.5 lipase from Geobacillus kaustophilus (GkLip). The 2.47-[A] orthorhombic model contains a Ca2+ ion at the conserved metal-binding site, whereas the 2.30-[A] anisotropic triclinic model contains four non-equivalent protein chains in which no Ca2+ ion was modeled. GkLip retains the canonical \/{beta}-hydrolase fold, the Ser114-Asp318-His359 catalytic triad, the Phe17\/Gln115 oxyanion-hole region, an extended lid, and a conserved Zn2+-binding site. In the orthorhombic structure, Gly287, Glu361, Asp366, and Pro367 contribute to the Ca2+ coordination environment. Pro367 adopts a C{gamma}-endo pucker in the Ca2+-containing structure; three Ca2+-free chains also adopt the endo pucker, whereas one adopts a C{gamma}-exo pucker. The presence of both Pro367 pucker states in the Ca2+-free structure is consistent with calcium preferentially stabilizing a pre-existing, coordination-compatible conformation. This local organization is further supported by an Arg35-centered network of direct and water-mediated contacts. Comparison with the 94%-identical open G. thermocatenulatus lipase reveals substantial lid rearrangement despite strong conservation of the protein core. Together, these structures highlight localized conformational variability at the calcium-binding region within an otherwise highly conserved thermophilic lipase scaffold.","rel_num_authors":5,"rel_authors":[{"author_name":"M. Sait Eyerci","author_inst":"Bogazici University"},{"author_name":"Ahmet Tulek","author_inst":"Kutahya Health Sciences University"},{"author_name":"F. Inci Ozdemir","author_inst":"Gebze Technical University"},{"author_name":"Gozde Sukur","author_inst":"Gebze Technical University"},{"author_name":"Hasan Demirci","author_inst":"Koc University"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Flexible 3D multimodal organoid interface reveals functional subnetworks and coordinated circuit activity in cortical organoids","rel_doi":"10.64898\/2026.10.02.756317","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756317","rel_abs":"The balance between excitatory and inhibitory (E\/I) neurons is fundamental to cortical function, and its disruption underlies a wide range of neurological disorders, including epilepsy, Alzheimers disease and autism spectrum disorder. Cortical organoids offer a promising model to study this balance, as they recapitulate key features of brain development, including the emergence of E\/I circuitry. However, no existing technology can simultaneously resolve neuronal identity, spatial organization, and dynamic network activity within intact organoids, limiting precise interrogation of E\/I circuit function. Here, we report INFORM, an INtegrated Flexible Organoid Recording Multimodal neural interface that conformally wraps organoids of any size and shape, enabling long-term recordings over 371 days with above 90% of channels detecting spikes. INFORM interface combines four distinct modalities; electrophysiology, two-photon calcium imaging, targeted electrical microstimulation, and chemical intervention to monitor and modulate neural activity across multiple spatial and temporal scales spanning from action potentials from single neurons to synchronized bursts from large-scale populations. Such high-resolution interrogation enables probing of emergence of functional connectivity in fusing assembloids and resolves spatially discrete functional subnetworks that can be selectively recruited and modulated by INFORM. Neurotransmission blockade confirms that stimulus-evoked suppression of neuronal spiking is mediated by GABAergic inputs providing direct functional evidence of the presence of inhibitory signaling in organoids. This work allows single-cell dissection of functional networks in brain organoids, providing a powerful platform for modelling and studying cortical development, neurological diseases, and the E\/I imbalances underlying circuit dysfunction.","rel_num_authors":17,"rel_authors":[{"author_name":"Madison N Wilson","author_inst":"University of California San Diego"},{"author_name":"Elizabeth K Kharitonova","author_inst":"Boston University"},{"author_name":"Joseph Yeh","author_inst":"University of California San Diego"},{"author_name":"Joann Lee","author_inst":"University of California San Diego"},{"author_name":"Shadee Hemaidan","author_inst":"University of California San Diego"},{"author_name":"Samuel Essig","author_inst":"Boston University"},{"author_name":"Marie Shi","author_inst":"Boston University"},{"author_name":"Colt W Crain","author_inst":"University of California San Diego"},{"author_name":"Mehrdad Ramezani","author_inst":"University of California San Diego"},{"author_name":"Fengyi Sun","author_inst":"University of California San Diego"},{"author_name":"Teng Zhou","author_inst":"University of California San Diego"},{"author_name":"Ikbal Surucu","author_inst":"University of California San Diego"},{"author_name":"Farzad Mortazavi","author_inst":"Boston University"},{"author_name":"Ertugrul Cubukcu","author_inst":"University of California San Diego"},{"author_name":"Martin Thunemann","author_inst":"Boston University"},{"author_name":"Ella Zeldich","author_inst":"Boston University"},{"author_name":"Duygu Kuzum","author_inst":"University of California San Diego"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Neural circuit mechanisms for multi-tasking","rel_doi":"10.64898\/2026.10.01.755960","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755960","rel_abs":"Neural activity during performance of a cognitive or motor task is often described as lying on a low-dimensional manifold in the space of all possible activity patterns. When multiple tasks are performed, an individual brain region's activity may generate multiple such manifolds. Several models have been introduced to explain this capability, each with different assumptions concerning architecture and inputs. Here, we develop a theory of multi-tasking according to which models and experimental paradigms can be organized. We introduce a distinction between sequential and simultaneous multi-tasking, the performance of one task at a time or multiple in parallel. We show that interference between tasks often prevents multi-tasking and introduce a model of ``task barcodes\" that circumvents this problem and parameterizes a neuronal population's task selectivity. Our theory reveals categorical distinctions between neural circuit mechanisms supporting multi-tasking that can be probed using physiological recordings.","rel_num_authors":6,"rel_authors":[{"author_name":"Manuel Beiran","author_inst":"Columbia University"},{"author_name":"Owen Marschall","author_inst":"Columbia University"},{"author_name":"Klavdia Zemlianova","author_inst":"Columbia University"},{"author_name":"Arianna Di Bernardo","author_inst":"Ecole Normale Superieure"},{"author_name":"Srdjan Ostojic","author_inst":"Ecole Normale Superieure"},{"author_name":"Ashok Litwin-Kumar","author_inst":"Columbia University"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"LLM-powered automatic scoring tools for memory recall","rel_doi":"10.64898\/2026.10.01.755994","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755994","rel_abs":"Free recall of naturalistic stimuli, such as films and stories, reveals what people remember and how they organize it. Scoring such recall requires matching each recalled utterance to the encoded stimulus, and doing this manually often makes large studies impractical. Existing automated methods mostly return similarity or aggregate scores rather than explicit matches between recalled and stimulus content. Here we introduce an open-source, model-agnostic pipeline that uses a large language model (LLM) to divide the stimulus and the recall into information units and to match units between the two texts. The output is a table of unit-to-unit matches with accuracy labels, from which standard analysis scripts compute recall measures. We validated the pipeline with four LLMs (Claude Sonnet 4.5, GPT 5.4, Gemini 2.5 Pro, and Gemini 2.5 Flash) on three datasets. On typed recall of four short stories, agreement between each model and two trained raters ({phi} = .73-.79) was close to the agreement between the raters themselves ({phi} = .77). A second run reproduced each model's matches ({phi} = .91-.97) more closely than the raters agreed with each other. On spoken recall of 10 films, every model attributed recall to the correct film with high agreement. On uncorrected speech-to-text recall of a television episode, agreement varied more across models. The median cost was under $1 per participant for every model. These results suggest that the pipeline can serve as a reliable, low-cost alternative to manual scoring of free recall.","rel_num_authors":6,"rel_authors":[{"author_name":"Rayna S. Tang","author_inst":"Department of Psychological and Brain Sciences, Washington University in St. Louis"},{"author_name":"Savannah J. Born","author_inst":"Department of Psychological and Brain Sciences, Washington University in St. Louis"},{"author_name":"Angelique I. Delarazan","author_inst":"Department of Psychology, University of Arizona"},{"author_name":"Phillip L. Demarest","author_inst":"Department of Neurosurgery, Washington University in St. Louis"},{"author_name":"Ata B. Karagoz","author_inst":"Institute for Mind and Biology, University of Chicago"},{"author_name":"Zachariah Meadows Reagh","author_inst":"Department of Psychological and Brain Sciences, Washington University in St. Louis"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"LLM-powered automatic scoring tools for memory recall","rel_doi":"10.64898\/2026.10.01.755994","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755994","rel_abs":"Free recall of naturalistic stimuli, such as films and stories, reveals what people remember and how they organize it. Scoring such recall requires matching each recalled utterance to the encoded stimulus, and doing this manually often makes large studies impractical. Existing automated methods mostly return similarity or aggregate scores rather than explicit matches between recalled and stimulus content. Here we introduce an open-source, model-agnostic pipeline that uses a large language model (LLM) to divide the stimulus and the recall into information units and to match units between the two texts. The output is a table of unit-to-unit matches with accuracy labels, from which standard analysis scripts compute recall measures. We validated the pipeline with four LLMs (Claude Sonnet 4.5, GPT 5.4, Gemini 2.5 Pro, and Gemini 2.5 Flash) on three datasets. On typed recall of four short stories, agreement between each model and two trained raters ({phi} = .73-.79) was close to the agreement between the raters themselves ({phi} = .77). A second run reproduced each model's matches ({phi} = .91-.97) more closely than the raters agreed with each other. On spoken recall of 10 films, every model attributed recall to the correct film with high agreement. On uncorrected speech-to-text recall of a television episode, agreement varied more across models. The median cost was under $1 per participant for every model. These results suggest that the pipeline can serve as a reliable, low-cost alternative to manual scoring of free recall.","rel_num_authors":6,"rel_authors":[{"author_name":"Rayna S. Tang","author_inst":"Department of Psychological and Brain Sciences, Washington University in St. Louis"},{"author_name":"Savannah J. Born","author_inst":"Department of Psychological and Brain Sciences, Washington University in St. Louis"},{"author_name":"Angelique I. Delarazan","author_inst":"Department of Psychology, University of Arizona"},{"author_name":"Phillip L. Demarest","author_inst":"Department of Neurosurgery, Washington University in St. Louis"},{"author_name":"Ata B. Karagoz","author_inst":"Institute for Mind and Biology, University of Chicago"},{"author_name":"Zachariah Meadows Reagh","author_inst":"Department of Psychological and Brain Sciences, Washington University in St. Louis"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Impaired value estimation over space explains turn-induced freezing of gait in Parkinson's disease: A cortico-basal ganglia-inspired reinforcement learning model","rel_doi":"10.64898\/2026.10.01.755546","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755546","rel_abs":"Parkinson's disease (PD) gait is characterized by reduced walking speed, short shuffling steps, increased double stance time, and increased step time variability. The most debilitating symptom in PD is freezing of gait (FOG), a context-dependent locomotion arrest, mostly occurring while encountering turns, obstacles, and narrow pathways. FOG is more pronounced during turning, a complex task that challenges motor planning and execution. Here, we simulate this phenomenon using a computational model inspired by the cortico-basal ganglia circuitry, based on reinforcement learning (RL) principles. In the model, the agent was trained to learn a value profile while navigating a path that involved turning. To simulate PD conditions, we clamped the temporal difference (TD) error, typically associated with dopaminergic signaling. Our model results show that compared to healthy controls (HC), PD non-freezers and PD freezers exhibit a reduction in turning velocity and step length, alongside an attenuated compensatory step-width widening at the sharpest turn, more pronounced in PD freezers, who additionally show increased step time variability. These kinematic impairments worsen during tighter turns. Ultimately, this model implicates FOG as a problem of impaired estimation of value over space and could potentially represent a generalized deficit in gait impairments in PD.","rel_num_authors":4,"rel_authors":[{"author_name":"Neermita Bhattacharya","author_inst":"Indian Institute of Technology Jodhpur"},{"author_name":"Sandeep Sathyanandan Nair","author_inst":"Case Western Reserve University"},{"author_name":"V. Srinivasa Chakravarthy","author_inst":"Indian Institute of Technology Madras"},{"author_name":"Vignesh Muralidharan","author_inst":"Indian Institute of Technology Jodhpur"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"APOE4 reduction in vascular-associated fibroblasts decreases cerebral amyloid angiopathy and associated vascular dysfunction","rel_doi":"10.64898\/2026.10.02.756327","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756327","rel_abs":"Cerebral amyloid angiopathy (CAA), characterized by cerebrovascular amyloid deposition, is strongly associated with Alzheimers disease, ischemic injury, and lobar hemorrhages. APOE4 is the strongest known genetic risk factor for CAA, yet the cellular sources of APOE4 that drive pathogenic vascular amyloid deposition remain poorly defined. Here, we identify vascular-associated fibroblasts (VAFs) as a key source of APOE4 that mediates CAA and cerebrovascular dysfunction. In post-mortem brain tissue from APOE4\/E4 patients with confirmed CAA, APOE colocalizes with COL6 VAFs around A{beta}-laden vessels. Therefore, to study the contribution of APOE4 derived from VAFs in CAA, independently of parenchymal sources, we used 5xFAD mice in which the endogenous mouse Apoe coding exons are replaced with floxed human APOE4 sequences. This enables selective suppression of APOE4 expression in VAFs upon tamoxifen induction of Col1a2-CreERT+. APOE4 reduction in VAFs markedly reduced CAA, with the strongest effects observed in leptomeningeal vessels. Additionally, A{beta}40, which preferentially associates with vascular amyloid, was significantly reduced in the vascular fraction. VAF APOE4 depletion resulted in fewer small microbleeds and an approximately 50% reduction in parenchymal border macrophages within the leptomeningeal compartment, whereas astrocytic reactivity remained unchanged. Functional assessment using in vivo multi-parametric photoacoustic microscopy revealed improved arterial dilation and blood flow in amyloid-laden leptomeningeal arteries. Together, these findings establish VAF-derived APOE4 as a compartment-specific driver of CAA and cerebrovascular dysfunction.\n\nOne Sentence SummaryVascular-associated fibroblasts are a major source of pathogenic APOE4 that drives vascular dysfunction in cerebral amyloid angiopathy.","rel_num_authors":18,"rel_authors":[{"author_name":"Fareeha Saadi","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Yoonho Cho","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Ilia V Katritch","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Brisa Lugo","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Yue Wu","author_inst":"Washington University in St louis"},{"author_name":"Leon Smyth","author_inst":"Monash University Monash Biomedicine Discovery Institute"},{"author_name":"Megan E Bosch","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Kevin A Telfer","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Krzysztof Hyrc","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Samira Parhizkar","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Xin Bao","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Aishwarya Nambiar","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Bernd Zinselmeyer","author_inst":"Washington University in St Louis School of Medicine"},{"author_name":"Song Hu","author_inst":"Washington University in St. Louis"},{"author_name":"Jonathan Kipnis","author_inst":"Washington University in St. Louis"},{"author_name":"Gwendalyn Randolph","author_inst":"Washington University in St louis, School of Medicine"},{"author_name":"Jason D Ulrich","author_inst":"Washington University in St Louis School of Medicine"},{"author_name":"David M Holtzman","author_inst":"Washington University in St louis, School of Medicine"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Large-scale cerebrospinal fluid proteomics identifies diagnostic biomarker candidates across the clinico-pathological continuum of frontotemporal lobar degeneration","rel_doi":"10.64898\/2026.09.29.755483","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.755483","rel_abs":"Frontotemporal lobar degeneration (FTLD), predominantly comprised of tau and TDP-43 proteinopathies, is a common cause of early-onset dementia. Biomarkers that reliably classify FTLD subtypes are limited, thereby hindering in-vivo diagnosis and clinical trial stratification. We conducted large-scale, multi-platform cerebrospinal fluid proteomics in 566 individuals, including 274 FTLD-spectrum (48 autopsy-confirmed FTLD-TDP, 90 autopsy-confirmed FTLD-tau), 171 Alzheimers disease, and 95 controls. In neuropathological subtype contrasts, elevated matrix metalloproteinase MMP10 classified Picks disease (PiD) versus other subtypes with near-perfect accuracy across proteomic platforms. We further validated CSF MMP10 as a potential PiD diagnostic biomarker via a targeted immunoassay in an independent, autopsy-confirmed FTLD cohort. Post-synaptic proteins HOMER1 and DLG4 exhibited elevations across FTLD-TDP subtypes (A, B, and C) versus FTLD-tau. Network analysis identified RNA metabolism and pro-inflammatory signatures in FTLD-TDP, and cytoskeletal and heat shock signatures in progressive supranuclear palsy. These findings reveal candidate diagnostic biomarkers and proteomic pathways underlying FTLD clinico-pathological heterogeneity.","rel_num_authors":35,"rel_authors":[{"author_name":"Rowan Saloner","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Louisa Cornelis","author_inst":"Department of Physics, University of California, Santa Barbara, CA, USA"},{"author_name":"Joshua Downer","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Connor Dietz","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Brandon Chan","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Michelle Hughes","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Molly Olzinski","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Yann Cobigo","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Argentina Lario-Lago","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hilary Heuer","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Karly Cody","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Madeline Wood","author_inst":"Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada"},{"author_name":"Katheryn A.Q. Cousins","author_inst":"Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"David Wolk","author_inst":"Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Edward B. Lee","author_inst":"Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"David Irwin","author_inst":"Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Ahmed Abdelhak","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Bradley F. Boeve","author_inst":"Department of Neurology, Mayo Clinic, Rochester, MN, USA"},{"author_name":"Gil D. Rabinovici","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Renaud La Joie","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Lea T. Grinberg","author_inst":"Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA"},{"author_name":"Salvatore Spina","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Emily W. Paolillo","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Adam Staffaroni","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Lawren VandeVrede","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Peter A. Ljubenkov","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Maria Luisa Gorno-Tempini","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Howard J. Rosen","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Jennifer S. Yokoyama","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Joel H. Kramer","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Kaitlin B. Casaletto","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Bruce Miller","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"William W. Seeley","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Julio C. Rojas","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Adam L. Boxer","author_inst":"Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Vibrotactile transcutaneous auricular vagus nerve stimulation (taVNS) during working memory: convergent electrophysiological evidence for cortical efficiency","rel_doi":"10.64898\/2026.10.07.756069","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.07.756069","rel_abs":"Background: Working memory, the cognitive ability to maintain and manipulate a finite amount of information, is fundamental in reasoning, learning, and decision-making. Working memory erodes in conditions from Alzheimer's disease to stroke to healthy aging. Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising neuromodulation method for improving working memory. By stimulating the auricular branch of the vagus nerve, taVNS is thought to engage ascending arousal systems such as the locus coeruleus-norepinephrine (LC-NE) system. However, the physiological link between LC-NE system modulation via taVNS and improved working memory remains largely unknown. This mechanistic gap limits the rational optimization of the stimulation protocol and the identification of the cognitive functions and patient populations that are likely to benefit. Objective: We test two competing hypotheses for how taVNS may improve working memory: sensory amplification or enhancement of cortical efficiency. The sensory-amplification hypothesis predicts increased cortical responses to stimuli, whereas the cortical-efficiency hypothesis predicts reduced cortical responses. Methods: Twenty healthy adults completed N-back working memory tasks during three counterbalanced sessions: baseline, active vibrotactile taVNS (vtaVNS), and sham stimulation, while we simultaneously recorded electroencephalography, electrocardiography, eye-tracking, and behavioral responses. To study the effect of vtaVNS on neuronal activity, we also recruited 20 patients with epilepsy undergoing invasive monitoring. Of these, 19 fully completed the spatial working-memory task. We quantified cortical responses to visual stimuli using event-related potentials (ERPs) in scalp EEG and high-gamma activity in intracranial local field potentials. To examine prediction of the cortical-efficiency hypothesis, we further explored the effect of vtaVNS on distance to criticality and heartbeat-evoked potentials. Results: Active vtaVNS reduced both scalp-recorded visual ERP and high-gamma response in temporal cortices, relative to sham stimulation. Moreover, smaller visual event-related potentials were associated with successful encoding during the most demanding working-memory condition (i.e., 4-back task). Exploratory analyses showed that vtaVNS shifted cortical dynamics closer to criticality. Conclusion: Our results converge on the conclusion that vtaVNS does not uniformly amplify cortical responses to external stimuli. Instead, vtaVNS enhances cortical efficiency, supporting more effective allocation of cognitive resources between exteroceptive and mnemonic processes. These findings provide a framework for predicting when taVNS will be most beneficial and for optimizing stimulation protocols across cognitive and clinical applications.","rel_num_authors":9,"rel_authors":[{"author_name":"Gansheng Tan","author_inst":"Washington University in St. Louis"},{"author_name":"Phillip Demarest","author_inst":"Washington University in St Louis"},{"author_name":"Tatiana Kardashina","author_inst":"Washington University in St. Louis"},{"author_name":"Josh Adams","author_inst":"Saint Louis University"},{"author_name":"Jon T. Willie","author_inst":"University of Texas in Austin"},{"author_name":"Jarod Roland","author_inst":"Washington University in St. Louis"},{"author_name":"Jenna  L. Gorlewicz","author_inst":"Saint Louis University"},{"author_name":"Peter Brunner","author_inst":"Washington University School of Medicine in St. Louis"},{"author_name":"Eric C. Leuthardt","author_inst":"Washington University in St Louis School of Medicine"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Metabolite Relaxation Times at 3T From Birth to 7 Years","rel_doi":"10.64898\/2026.10.02.754450","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.754450","rel_abs":"PurposeWith growing interest in pediatric MRS to understand the neurochemistry of brain development, it is essential to establish pediatric-specific metabolite T1 and T2 relaxation times for reliable quantification of metabolites. Furthermore, characterization of age-related changes in relaxation times may provide insight into developmental differences in tissue microstructure and cellular composition. Therefore, the goals of this study were two-fold: (1) to estimate metabolite T1 and T2 relaxation times in a pediatric cohort; and (2) to assess linear relationships between age and relaxation times.\n\nMethodsTI- and TE-series data were acquired from the bilateral thalamus in 20 and 25 participants aged between 0 and 7 years, respectively. Data were processed and modelled in Osprey 2.5.0. Metabolite T1 and T2 relaxation times of total N-acetylaspartate at 2.0 ppm (tNAA2.0), total creatine at 3.0 ppm (tCr3.0), total creatine at 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI) and the combined glutamate and glutamine (Glx) were estimated by fitting the modelled signal amplitudes to TI-series inversion recovery signal and TE-series exponential decay equations, respectively. Linear relationships with age were tested for metabolite T1 and T2 relaxation times using Spearman correlations.\n\nResultstNAA2.0 and Glx showed significant changes in T1 with age. tNAA2.0, tCr3.0, tCho and mI showed significant decrease in T2 with age.\n\nConclusionSeveral metabolites showed significant changes in relaxation times with age in the thalamus region. Therefore, use of age-specific relaxation correction for pediatric studies using the linear equations provided is encouraged for reliable quantification of metabolites.","rel_num_authors":18,"rel_authors":[{"author_name":"Saipavitra Murali-Manohar","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Abdelrahman Gad","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Zahra Shams","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Gizeaddis Lamesgin Simegn","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Yulu Song","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Aaron T. Gudmundson","author_inst":"Johns Hopkins University"},{"author_name":"Helge J. Zoellner","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Christopher W. Davies-Jenkins","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Dunja Simicic","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Vivek Yedavalli","author_inst":"Johns Hopkins University Schoo of Medicine"},{"author_name":"Borjan Gagoski","author_inst":"Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Childrens Hospital, Boston, MA, United States"},{"author_name":"Ellen P. Grant","author_inst":"Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Childrens Hospital, Boston, MA, United States"},{"author_name":"Douglas C. Dean III","author_inst":"Department of Pediatrics, Division of Neonatology and Newborn Medicine, University of Wisconsin Madison, Madison, WI, United States"},{"author_name":"Ralph Noeske","author_inst":"GE HealthCare, Munich, Germany"},{"author_name":"Jessica L. Wisnowski","author_inst":"The Saban Research Institute, Childrens Hospital Los Angeles, CA, United States"},{"author_name":"Mary Beth Nebel","author_inst":"Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA"},{"author_name":"Georg Oeltzschner","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Richard A. E. Edden","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Psychedelics restructure information in the human brain","rel_doi":"10.64898\/2026.10.01.755915","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755915","rel_abs":"How do psychedelic drugs alter human brain function? Answering this question relies on our ability to understand how information is stored, transferred, and transformed across the cortex. Here, we leverage a precise mathematical framework for multivariate information decomposition to construct the first comprehensive picture of these processes across five compounds: LSD, psilocybin, ketamine, DMT and 5-MeO-DMT (N=80). We provide a formal and empirical explanation of major prior results, revealing sets of competing underlying phenomena masked by extant approaches. We extend this analysis to novel higher-order phenomena, and we demonstrate how all of these observations can emerge from a minimal generative model in which neural populations operate with reduced self-coupling. In sum, our results suggest that the psychedelic brain is one in which cortical populations become alike, but not necessarily together. This has important implications for models of how psychedelics reorganize brain dynamics to alter subjective experience and alleviate mental health conditions.","rel_num_authors":9,"rel_authors":[{"author_name":"George Blackburne","author_inst":"University College London"},{"author_name":"Alberto Liardi","author_inst":"Imperial College London"},{"author_name":"Pablo Mallaroni","author_inst":"Imperial College London"},{"author_name":"Rosalind McAlpine","author_inst":"University College London"},{"author_name":"Robin Carhart-Harris","author_inst":"UCSF"},{"author_name":"Christopher Timmermann","author_inst":"University College London"},{"author_name":"Suresh Daniel Muthukumaraswamy","author_inst":"University of Auckland"},{"author_name":"Jeremy I Skipper","author_inst":"University College London"},{"author_name":"Pedro A.M. Mediano","author_inst":"Imperial College London"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Exacerbation of neurodegeneration by a metabolically engineered bacterium in a mouse model of tauopathy","rel_doi":"10.64898\/2026.09.28.755025","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.28.755025","rel_abs":"BackgroundTherapeutics that modulate peripheral and central immune responses are emerging as promising approaches for neurodegenerative disease. However, the biological context in which these interventions are deployed may critically influence therapeutic outcomes. An attenuated Brucella melitensis 16M strain and a similar strain engineered to express the metabolite indole have previously demonstrated therapeutic efficacy in preclinical models of cancer and autoimmunity through immune system modulation. Here, we tested whether repeated administration of these bacterial strains could modify pathological outcomes in a mouse model of tauopathy.\n\nMethodsAttenuated Brucella strains were administered to transgenic mice overexpressing mutant tau protein on a human APOE4 knock-in background via intraperitoneal injection every 5 weeks starting at 6 months of age. Mice were analyzed at 9.5 months of age for tau pathology, neurodegeneration, and changes in innate and adaptive immune responses.\n\nResultsBrucella strains distributed predominantly in the periphery following repeated intraperitoneal treatment in transgenic mice, with minimal detection in the brain and no detection in the dural meninges. Repeated administration of either bacterial strain was well tolerated, with no evidence of adverse safety events. Surprisingly, repeated treatment with the engineered Brucella strain exacerbated neurodegenerative pathology, resulting in reduced hippocampal volume and increased phosphorylated tau pathology compared with control- and non-engineered Brucella strain -treated animals. Immunohistochemical analyses revealed increased numbers of hippocampal CD4+ T cells following treatment with either Brucella strain relative to PBS controls. However, only treatment with the engineered Brucella increased microglial reactivity within the hippocampus.\n\nConclusionThese findings demonstrate that peripheral administration of an engineered bacterial therapeutic can differentially influence neuroimmune responses and disease progression. These results underscore the importance of disease context when developing bacteria-based immunomodulatory therapies for neurodegenerative disorders.","rel_num_authors":11,"rel_authors":[{"author_name":"Wade K. Self","author_inst":"Washington University School of Medicine"},{"author_name":"Prabal Sharma","author_inst":"Washington University School of Medicine"},{"author_name":"Grace Gent","author_inst":"Washington University School of Medicine"},{"author_name":"Megan E. Bosch","author_inst":"Washington University School of Medicine"},{"author_name":"Yiyang Zhu","author_inst":"Washington University School of Medicine"},{"author_name":"Noah Powell","author_inst":"University of Missouri School of Medicine"},{"author_name":"Cansu Agca","author_inst":"University of Missouri School of Medicine"},{"author_name":"Jason D Ulrich","author_inst":"Washington University School of Medicine"},{"author_name":"Jianxun J Song","author_inst":"Texas AM University Health Science Center"},{"author_name":"Paul de Figueiredo","author_inst":"University of Missouri School of Medicine"},{"author_name":"David M. Holtzman","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Volitional control of a recurrent brain computer interface via covert reinforcement of endogenous reward networks","rel_doi":"10.64898\/2026.10.01.755922","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755922","rel_abs":"Recurrent brain-computer interfaces (rBCIs) deliver targeted stimulation to one brain region based on neural activity recorded and processed in real-time at another. In a volitionally controlled rBCI, the user must also learn to intentionally produce the triggering neural activity, a process typically facilitated by an external reward. However, reliance on an external reward introduces a delay between the triggering event and reinforcement that can slow association between the two and, ultimately, control of the rBCI (particularly for neural activity not directly related to an observable behavior such as a goal-directed movement). The constraint to an external reward also limits studies of neural dynamics during natural, free behavior. In contrast, delivering activity-dependent stimulation directly to the brain's own reward centers - i.e., covert reinforcement - would bypass the need for external feedback, enabling precise temporal coupling between triggering events and reward and allowing for unrestrained rBCI use. Here, we show that rats can gain volitional control of an rBCI using activity-dependent electrical stimulation of the medial forebrain bundle as a sole reinforcer. Animals successfully learned to modulate motor unit discharge rates as well as specific electrocorticographic frequency bands (beta and gamma) to self-trigger stimulation. Furthermore, rats demonstrated flexible control by independently modulating distinct spectral features from the same cortical site. These findings suggest that covert reinforcement of endogenous reward networks can drive operant conditioning of specific neural signals during unrestrained behavior, opening new avenues for adaptive BCIs and closed-loop psychiatric therapies.","rel_num_authors":4,"rel_authors":[{"author_name":"Jacob Graves McPherson","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Robert R Miller","author_inst":"University of Washington"},{"author_name":"Eberhard E Fetz","author_inst":"University of Washington"},{"author_name":"Steve I Perlmutter","author_inst":"University of Washington"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Careers in Scientific Editing: Results from the 2024 Scientific Editors Network (ScENe) Survey","rel_doi":"10.64898\/2026.10.05.756087","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.05.756087","rel_abs":"Funding and publishing scientific research has become increasingly competitive, highlighting a growing need for clear and accurate scientific communication. Scientific editors are professionals who help ensure that grant proposals and research manuscripts meet the standards of funding agencies and peer-reviewed journals while effectively communicating research significance to diverse audiences. However, detailed information characterizing the scientific editing profession is scarce. While some organizations have conducted salary surveys, to our knowledge, no comprehensive survey of the scientific editing field has been conducted. Here, we report findings from a 2024 comprehensive survey of 132 scientific editors. We collected data on demographics and education, career experience and current employment, skills and types of editing, and outcomes and satisfaction. Our data reveal that scientific editors have homogenous demographic characteristics but considerable variability in job titles, employment mechanisms, and career pathways. As the responsibilities of many scientific editors, including supporting proposal development, applying best practices in grantsmanship, and identifying strategies to enhance competitiveness for research funding, overlap with those of research development professionals, our findings provide considerations for further incorporating or defining the role of scientific editors within research development offices. Our results also suggest avenues for scientific editors to start or advance their careers. We anticipate that our results will serve as a foundation for discussions on the future directions of the scientific editing profession as well as areas for future research and professional development.","rel_num_authors":6,"rel_authors":[{"author_name":"Heather Aldrich","author_inst":"One Health Institute, Colorado State University, Fort Collins, CO"},{"author_name":"Jennifer Barr","author_inst":"Scientific Editing and Research Communication Core (SERCC), University of Iowa Carver College of Medicine, Iowa City, IA"},{"author_name":"Becky James","author_inst":"College of Arts and Sciences, Washington State University, Pullman, WA"},{"author_name":"Robert Lawrence","author_inst":"Department of Medicine, Baylor College of Medicine, Houston, TX"},{"author_name":"Megan Mayerle","author_inst":"Beckman Center for Molecular and Genetic Medicine, Stanford University, Stanford, CA"},{"author_name":"Ann Sutton","author_inst":"Research Medical Library, The University of Texas MD Anderson Cancer Center, Houston, TX"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"A strategy for ionic current analysis of nanopore protein readouts","rel_doi":"10.64898\/2026.09.28.755023","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.28.755023","rel_abs":"BackgroundNanopore-based protein reading holds promise for single-molecule proteomics, but analyzing the resulting ionic current signals presents substantial computational challenges. Unlike nucleic acids, peptide-associated ionic signals exhibit complex patterns with subtle transitions between amino acids that can be obscured by noise. We developed an integrated computational strategy for processing nanopore protein ionic current signals from synthetic protein constructs containing all 20 amino acids in controlled sequence contexts. We started with denoising to remove artifacts, followed by change point detection using variable-number and fixed-number segmentation methods.\n\nResultsWe employed dynamic time warping barycenter averaging to generate amino acid-specific reference templates. In parallel, we performed classification of amino acid regions using bidirectional LSTM networks trained on segment-level statistical features. Accuracy for 20-way amino acid classification remained limited, while charge-based binary classification reached 93.3% accuracy. Pairwise classification revealed that certain amino acids, particularly negatively charged residues, generate distinctive signatures. We used a profile hidden Markov model to capture sequential structure of protein translocation.\n\nConclusionThis approach provides computational strategies for automated segmentation, classification, and modeling of nanopore peptide translocation ionic current data.","rel_num_authors":7,"rel_authors":[{"author_name":"Andrew J Stein","author_inst":"Northeastern University"},{"author_name":"Neda Ghohabi Esfahani","author_inst":"Northeastern University"},{"author_name":"stuart Akeson","author_inst":"Northeastern University"},{"author_name":"Pooria Kakhaki","author_inst":"Northeastern University"},{"author_name":"Daphne Kontogiorgos-Heintz","author_inst":"University of Washington"},{"author_name":"Jeff Nivala","author_inst":"University of Washington"},{"author_name":"Miten Jain","author_inst":"Northeastern University"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Benchmarking longitudinal local centiles for radiographic knee osteoarthritis prognosis","rel_doi":"10.64898\/2026.10.06.757088","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.06.757088","rel_abs":"Cartilage thickness on MRI is a structural biomarker of osteoarthritis (OA), yet anatomical variation and aging can mask early disease, including changes occurring 12-36 months before incident OA. We propose longitudinal local centile maps that use normative growth charts as prior information and integrate a knee's earlier scans to identify cartilage that is thinner or thicker than expected. We built the reference charts from 5,045 scans of 957 persistently radiographically normal knees in the Osteoarthritis Initiative. Under participant-separated nested cross-validation, longitudinal centile profiles achieved higher 12- and 24-month AUCs for predicting incident radiographic OA than regional PCA, radiomics, cross-sectional centile features, and raw-map deep features across most classifiers. These conventional descriptors added little when combined with centile profiles, and cross-sectional centiles added little beyond longitudinal centiles. Deep centile features achieved the best AUCs (0.954 [95% CI 0.931-0.974] at 12 months and 0.969 [0.949-0.985] at 24 months; balanced accuracy 0.917 and 0.931) and outperformed raw-map deep features in all 12 classifiers after correction for multiple comparisons. No representation consistently improved 36-month prediction. Longitudinal centiles transform cartilage thickness maps into reference-based deviation maps: deep longitudinal centile features provide the highest predictive accuracy, while regional longitudinal centile profiles provide interpretable prognosis with the second-highest accuracy.","rel_num_authors":12,"rel_authors":[{"author_name":"Tengfei Li","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Tianyou Luo","author_inst":"Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599"},{"author_name":"Boqi Chen","author_inst":"Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599"},{"author_name":"Chao Huang","author_inst":"Department of Epidemiology & Biostatistics, University of Georgia, Athens, GA, USA, 30606"},{"author_name":"Zhengyang Shen","author_inst":"Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599"},{"author_name":"Zhenlin Xu","author_inst":"Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599"},{"author_name":"Daniel Nissman","author_inst":"Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599"},{"author_name":"Yvonne M. Golightly","author_inst":"Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599; Thurston Arthritis Research Center, University of North Ca"},{"author_name":"Amanda E. Nelson","author_inst":"Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599; Thurston Arthritis Research Center, University of North Ca"},{"author_name":"Girish Gandikota","author_inst":"Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599"},{"author_name":"Marc Niethammer","author_inst":"Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599; Department of Computer Science and Engineering and Dep"},{"author_name":"Hongtu Zhu","author_inst":"Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 27599"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Ancient Mitogenomes from Belize Provide Insights into the Mobility and Migration of Ancient Communities in Mesoamerica and the Caribbean.","rel_doi":"10.64898\/2026.10.06.756460","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.06.756460","rel_abs":"Maya peoples have undergone several largescale demographic transformations over the past 3000 years, including the formation and depopulation of large urban centers, climatic events, and multiple colonial regimes. Archaeological and stable isotope evidence from across the Maya lowlands throughout these periods paints a picture of a demographically mobile population with complex and dynamic relationships among some of the largest urban polities in antiquity. The nature of the genomic relationship between ancient Maya urban centers, as well as their relationship with present-day Maya communities, however, remains under-characterized. Here, we present mitogenomic data of 16 ancient Maya Ancestors recovered from interments in the ancient Maya cities of Caracol and Santa Rita Corozal located in Western and Northern Belize. These ancient data were combined with 110 newly collected contemporary individuals from Northern Belize, along with nearly 600 previously published ancient and contemporary comparative mitogenomes recovered from sites spanning Mesoamerica and the Caribbean to reconstruct paleodemographic and mobility patterns of maternal lineages through time. Our results are consistent with broadly interconnected Maya populations that have maintained maternal genetic continuity across much of the Holocene, and which are not structured by linguistic family.","rel_num_authors":15,"rel_authors":[{"author_name":"Celia D Cleary","author_inst":"Baylor University"},{"author_name":"Lauren C Springs","author_inst":"Texas State University"},{"author_name":"Angelina J Locker","author_inst":"Northwestern University"},{"author_name":"Diane Z Chase","author_inst":"University of Houston"},{"author_name":"Arlen F Chase","author_inst":"University of Houston"},{"author_name":"Adrian SZ Chase","author_inst":"Tulane University"},{"author_name":"Melissa M Badillo","author_inst":"Belize Institute of Archaeology"},{"author_name":"Adela Pederson Vallejos","author_inst":"To'one Masehualoon NGO"},{"author_name":"Genara Cano","author_inst":"To'one Masehualoon NGO"},{"author_name":"Roy Rodriguez","author_inst":"To'one Masehualoon NGO"},{"author_name":"Humberto Garcia-Ortiz","author_inst":"Instituto Nacional de Medicina Genomica"},{"author_name":"Lorena Orozco","author_inst":"Instituto Nacional de Medicina Genomica"},{"author_name":"Jada Benn Torres","author_inst":"Vanderbilt University"},{"author_name":"Rick WA Smith","author_inst":"George Mason University"},{"author_name":"Austin W Reynolds","author_inst":"University of North Texas Health Science Center"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Ethmoid bone marrow region modulates lymphoid activation and differentiation with distinct myeloid profile in neuroinflammation","rel_doi":"10.64898\/2026.10.03.755016","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.03.755016","rel_abs":"The skull bone marrow axis has recently emerged as an important source of immune cells during neuroinflammation, including in the experimental autoimmune encephalomyelitis (EAE) , the mouse model of multiple sclerosis. The cribriform plate bone marrow (cpBM), is a portion of the ethmoid bone of the skull of mice, and is positioned next to nasal regions. At the cribriform plate olfactory nerves pass through the skull and recent evidence supports that it represents an important immune niche with links to CSF outflow and connections to cpBM. Previous data supports cellular composition of the ethmoid bone marrow in mice is altered in neuroinflammation suggesting it senses inflammatory cues, but the exact nature of these changes is still unclear. Here using scRNAseq and flow cytometry of the ethmoid bone marrow region we test how the region responds to EAE. In addition we also compare the cpBM region with systemic bone marrow in the femur to understand region specific differences. We report that neuroinflammation creates a regulatory response at these sites and implicates the cpBM in controlling these effects. A better understanding of the cribriform niche may offer new treatments for neuroinflammatory disease","rel_num_authors":11,"rel_authors":[{"author_name":"Collin J Laaker","author_inst":"University of Wisconsin-Madison"},{"author_name":"Kristof G Kovacs","author_inst":"University of Wisconsin-Madison"},{"author_name":"Melinda Herbath","author_inst":"University of Wisconsin-Madison"},{"author_name":"Kylee Vi","author_inst":"University of Wisconsin-Madison"},{"author_name":"Jenna Port","author_inst":"University of Wisconsin-Madison"},{"author_name":"Sophia M Vrba","author_inst":"University of Wisconsin-Madison"},{"author_name":"Simon Federico Woen Ordonez","author_inst":"University of Wisconsin-Madison"},{"author_name":"Skyla Coogan","author_inst":"University of Wisconsin-Madison"},{"author_name":"Thanthrige Thiunuwan Priyathilaka","author_inst":"University of Wisconsin-Madison"},{"author_name":"Matyas Sandor","author_inst":"University of Wisconsin-Madison"},{"author_name":"Zsuzsanna Fabry","author_inst":"University of Wisconsin"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Palmitoylation of p75 neurotrophin receptor is required for retrograde regressive signaling","rel_doi":"10.64898\/2026.10.02.756216","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756216","rel_abs":"Target-derived neurotrophins regulate neuronal survival and pruning via retrograde signaling. Unlike Trk receptors, which are transported as transmembrane proteins, the p75 neurotrophin receptor (p75NTR) undergoes regulated intramembrane proteolysis to release a soluble intracellular domain (ICD) that must somehow associate with axonal endosomes for retrograde transport. Here, we identify palmitoylation of a conserved juxta membrane cysteine (C281) as a critical requirement for this process. In sympathetic neurons, we demonstrate that p75NTR palmitoylation is dispensable for surface trafficking and proteolytic cleavage but is strictly required for ligand-induced internalization and the biogenesis of p75ICD-positive signaling endosomes. Consequently, blocking palmitoylation, either pharmacologically or via a palmitoylation-deficient C281A mutation, prevents the accumulation of p75ICD in axonal endosomes and blocks axon-initiated apoptosis. In vivo, C281A knock-in mice exhibit reduced developmental apoptosis of sympathetic neurons, similar to p75NTR deletion. These findings define palmitoylation as an essential checkpoint that couples p75NTR cleavage to endosomal segregation, thereby enabling the long-distance transmission of regressive signals.","rel_num_authors":6,"rel_authors":[{"author_name":"Amrita Pathak","author_inst":"Amity University Noida"},{"author_name":"Hrishita Das","author_inst":"Vanderbilt University School of Medicine Basic Sciences"},{"author_name":"Pooja Shukla","author_inst":"Amity University Noida"},{"author_name":"Austin B Keeler","author_inst":"University of Delaware"},{"author_name":"Christopher Deppmann","author_inst":"University of Virginia"},{"author_name":"Bruce D. Carter","author_inst":"Vanderbilt University"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Cell-intrinsic regulation of presynaptic release site patterning","rel_doi":"10.64898\/2026.10.02.756300","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756300","rel_abs":"Functional neuronal connectivity depends not only on synaptic partner choice but also on the number, strength, and spatial distribution of release sites between partners. How these features are regulated remains poorly understood. Here, using the C. elegans DA9 motor neuron, we identify the immunoglobulin superfamily cell adhesion molecules SYG-1 and SYG-2 as cell-autonomous regulators of presynaptic release-site patterning. SYG-2 regulates presynapse number, size, and spacing, whereas SYG-1 primarily regulates spacing. Endogenous localization and cell-specific knockout analyses reveal that SYG-2 recruits SYG-1 to the presynaptic domain through previously unrecognized cis interactions. Endogenous structure-function studies further demonstrate that the SYG-2 intracellular domain is required for presynaptic organization and can promote presynapse formation independently of its extracellular domain. Genetic and targeted actin-disruption experiments implicate the adaptor protein NCK-1 and local actin remodeling in SYG-2 intracellular signaling. Together, these findings reveal how cell adhesion molecules can act cell-intrinsically to pattern presynaptic release sites after synaptic partners have been established.","rel_num_authors":5,"rel_authors":[{"author_name":"Jada D Summerville","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Elisa B Frankel","author_inst":"Albert Einstein College of Medicine and University of Puget Sound"},{"author_name":"Yongming Dong","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Jihong Bai","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Peri Kurshan","author_inst":"Albert Einstein College of Medicine"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Cell-intrinsic regulation of presynaptic release site patterning","rel_doi":"10.64898\/2026.10.02.756300","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756300","rel_abs":"Functional neuronal connectivity depends not only on synaptic partner choice but also on the number, strength, and spatial distribution of release sites between partners. How these features are regulated remains poorly understood. Here, using the C. elegans DA9 motor neuron, we identify the immunoglobulin superfamily cell adhesion molecules SYG-1 and SYG-2 as cell-autonomous regulators of presynaptic release-site patterning. SYG-2 regulates presynapse number, size, and spacing, whereas SYG-1 primarily regulates spacing. Endogenous localization and cell-specific knockout analyses reveal that SYG-2 recruits SYG-1 to the presynaptic domain through previously unrecognized cis interactions. Endogenous structure-function studies further demonstrate that the SYG-2 intracellular domain is required for presynaptic organization and can promote presynapse formation independently of its extracellular domain. Genetic and targeted actin-disruption experiments implicate the adaptor protein NCK-1 and local actin remodeling in SYG-2 intracellular signaling. Together, these findings reveal how cell adhesion molecules can act cell-intrinsically to pattern presynaptic release sites after synaptic partners have been established.","rel_num_authors":5,"rel_authors":[{"author_name":"Jada D Summerville","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Elisa B Frankel","author_inst":"Albert Einstein College of Medicine and University of Puget Sound"},{"author_name":"Yongming Dong","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Jihong Bai","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Peri Kurshan","author_inst":"Albert Einstein College of Medicine"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation","rel_doi":"10.64898\/2026.10.01.755876","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755876","rel_abs":"Single-cell RNA sequencing (scRNA-seq) studies often profile only one or a few biological samples, limiting assessment of discovery stability under between-sample variation. We present scStable, a framework for assessing scRNA-seq analysis stability by generating synthetic samples that preserve cell-level heterogeneity from a single-sample reference while injecting bulk-derived between-sample variation. In gastric cancer data comprising 21 matched scRNA-seq and bulk RNA-seq profiles, scStable better recapitulates observed between-sample variation than ZINB-WaVE, subsampling, and Gaussian noise injection. Using synthetic sam-ples, scStable enables stability-aware analysis, including prioritization of stable differentially expressed genes and selection of clustering methods and hyperparameters.","rel_num_authors":2,"rel_authors":[{"author_name":"Chengfeng Jiang","author_inst":"University of California, Los Angeles"},{"author_name":"Jingyi Jessica Li","author_inst":"Fred Hutchinson Cancer Center"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"SpiE is a distinct gut microbial 4-cholesten-3-one 5\u03b2-reductase that acts with SpiR to convert cholesterol to coprostanol","rel_doi":"10.64898\/2026.10.06.757028","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.06.757028","rel_abs":"Gut microbes convert cholesterol into coprostanol, a poorly absorbed sterol that is largely excreted in feces and is linked to lower serum cholesterol levels. However, the enzymes responsible for this process remain poorly characterized. The second step of the pathway, cholestenone reduction into coprostanone, has long been established, yet the enzyme(s) responsible were unknown. Here, we use comparative genomics to identify SpiE, a cholestenone (4-cholesten-3-one) reductase that is part of the steroid processing (spi) pathway with SpiR. We confirm that SpiE binds to and reduces both cholestenone and progesterone into 5{beta}-reduced forms. SpiR and SpiE together convert cholesterol into coprostanol, indicating that both enzymes are sufficient for coprostanol formation. SpiE belongs to the short-chain dehydrogenase (SDR) family and represents an evolutionarily distinct lineage of SDR ene-reductases. Metagenomic analyses reveal that spiE relative abundance is strongly correlated with stool coprostanol (Spearman's {rho}=0.81, p=5.99e-65). In longitudinal patient cohorts, antibiotic treatment coincided with loss of SpiE and coprostanol production. Post antibiotics, the emergence of distinct SpiE-encoding genera rescued coprostanol production in some individuals. Our findings provide the biochemical basis of the cholesterol to coprostanol pathway and lay the groundwork for future studies linking it to host physiology.","rel_num_authors":17,"rel_authors":[{"author_name":"Angela K Jiang","author_inst":"National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, Colle"},{"author_name":"Gabriela Arp","author_inst":"National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, Colle"},{"author_name":"Margaret Kato","author_inst":"Department of Biology, University of Maryland, College Park, College Park, MD, USA; National Library of Medicine, National Institutes of Health, Bethesda, MD, U"},{"author_name":"Tess Brunner","author_inst":"Duchossois Family Institute, University of Chicago, Chicago, IL, USA; Department of Microbiology, University of Chicago, Chicago, IL, USA"},{"author_name":"Maggie R Grant","author_inst":"Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, College Park, MD, USA"},{"author_name":"Alexandra M Clarke","author_inst":"Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, College Park, MD, USA"},{"author_name":"Ashley M Sidebottom","author_inst":"Duchossois Family Institute, University of Chicago, Chicago, IL, USA"},{"author_name":"Mary McMillin","author_inst":"Duchossois Family Institute, University of Chicago, Chicago, IL, USA"},{"author_name":"Christopher J Lehmann","author_inst":"Department of Medicine, Section of Infectious Disease and Global Health, University of Chicago Medicine, Chicago, IL, USA; Department of Pediatrics, Section of "},{"author_name":"Matthew A Odenwald","author_inst":"Inflammatory Bowel Disease Center, Section of Gastroenterology, Hepatology, and Nutrition, University of Chicago, Chicago, IL, USA"},{"author_name":"Krysta S Wolfe","author_inst":"Department of Medicine, Section of Pulmonary and Critical Care Medicine, University of Chicago Medicine, Chicago, IL, USA"},{"author_name":"Bhakti Patel","author_inst":"Department of Medicine, Section of Pulmonary and Critical Care Medicine, University of Chicago Medicine, Chicago, IL 60637, USA"},{"author_name":"Olatoyosi Odenike","author_inst":"Section of Hematology\/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA"},{"author_name":"Yue Li","author_inst":"Department of Chemistry & Biochemistry, University of Maryland, College Park, MD, USA"},{"author_name":"Samuel H Light","author_inst":"Duchossois Family Institute, University of Chicago, Chicago, IL, USA; Department of Microbiology, University of Chicago, Chicago, IL, USA"},{"author_name":"Brantley Hall","author_inst":"Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, College Park, MD, USA; Center for Bioinformatics and Computational Biol"},{"author_name":"Xiaofang Jiang","author_inst":"National Library of Medicine, National Institutes of Health, Bethesda, MD, USA"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"The effects of temporal smoothing and interpolation on EEG microstate parameters, syntax, and our view of resting-state brain dynamics","rel_doi":"10.64898\/2026.09.30.755764","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755764","rel_abs":"Microstate analysis studies dynamic brain activity through a discrete representation of electroencephalographic (EEG) signals and their topography. While the body of literature describing findings in diverse experimental and clinical settings is growing rapidly, the effects of several core components of the microstate algorithm are still insufficiently understood. This study systematically investigates how two common temporal smoothing algorithms, parametric smoothing and GFP peak interpolation, influence a wide range of microstate sequence parameters, and how they shape the integrated picture of brain dynamics inferred from these metrics. We study eyes-closed resting-state EEG recordings from N=201 healthy subjects from a public EEG database. For the elementary temporal metrics, we find that widening smoothing windows markedly increase microstate duration, reduce occurrence rates, but leave coverage mostly unaffected. In terms of simple syntax properties, smoothing windows above 30 ms yield an inconsistent picture with a memoryless, first-order Markov structure at short time scales, but a positive Hurst phenomenon suggestive of long-range correlations at long time scales. Evidence of asymmetry and non-equilibrium dynamics are suppressed by smoothing. At intermediate time scales, microstate periodicity was only observed up to a window size of 28 ms. Smoothing had a strong impact on the complexity metrics entropy rate and excess entropy, decreasing the former and increasing the latter. For most metrics, the effects of GFP peak interpolation were comparable to those of smoothing windows in the range between 28 ms and 44 ms. In conclusion, elementary and higher-order microstate parameters must be interpreted in the context of preprocessing, as many parameters change continuously with smoothing intensity. This limits the comparability of microstate parameters obtained with different preprocessing settings. Strong smoothing with a window size above 30 ms or GFP peak interpolation yields a fundamentally different picture of resting state brain dynamics, i.e. that of a memoryless, aperiodic, time-reversible first-order Markov chain. Since insights from other neurophysiological modalities suggest that time series memory and time-irreversibility are markers of normal brain activity, our recommendation is to avoid intense smoothing to minimize alterations of the initial sequences.","rel_num_authors":5,"rel_authors":[{"author_name":"Frederic von Wegner","author_inst":"University of New South Wales"},{"author_name":"Gesine Hermann","author_inst":"Department of Neurology, Christian-Albrechts University, Kiel, Germany"},{"author_name":"Inken Toedt","author_inst":"Institute of Sexual Medicine and Forensic Psychiatry, Christian-Albrechts University, Kiel, Germany"},{"author_name":"Inga Karin Todtenhaupt","author_inst":"Department of Neurology, Christian-Albrechts University, Kiel, Germany"},{"author_name":"Helmut Laufs","author_inst":"Department of Neurology, Christian-Albrechts University, Kiel, Germany"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Neuropeptide NLP-40 and Cognate G- Protein Coupled Receptor AEX-2 Regulate Behavioral Responses During Anoxia in Caenorhabditis elegans","rel_doi":"10.64898\/2026.09.30.755124","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755124","rel_abs":"Background: Insufficient oxygen (hypoxia) or complete lack of oxygen (anoxia) causes cellular dysfunction and death in terrestrial animals. As a result, animals have developed homeostatic strategies, including changes in movement strategies to escape and survive stressful environments. Caenorhabditis elegans respond to changes in oxygen by increasing the frequency of reversals (backward movement) and increasing locomotor speed; these behaviors enable them to escape unfavorable environments and move to more suitable ones. The molecular and neural mechanisms underlying behavioral responses to low-oxygen environments are incomplete. Neuropeptides regulate C. elegans behaviors by modifying neural circuit function in response to hypoxia. Loss of function in C. elegans neuropeptide nlp-40 and its receptor aex-2 increased survival to anoxia [1]. However, the cells in which NLP-40 and AEX-2 function for anoxia, as well as the mechanisms underlying responses to anoxia, remain elusive. We hypothesized that NLP-40 and AEX-2 regulate anoxia by altering escape behaviors. Results: We found that neuropeptide NLP-40 functions in cholinergic neurons and AEX-2 functions in cholinergic or GLR-1 interneurons or motor neurons to regulate susceptibility to anoxia. We found that loss of either nlp-40 or aex-2 decreased locomotor activity and reversal frequency early during 48 hours of anoxia, suggesting that nlp-40 and aex-2 are required for the proper execution of these behaviors. We found that neuronal knockdown of nlp-40 recapitulated the behaviors observed in nlp-40 mutant animals and increased survival; however, restoring aex-2 function in glr-1 interneurons or motor neurons was sufficient to rescue behaviors to wild-type and restore vulnerability to anoxia. Conclusions: Overall, our results suggest that NLP-40 and AEX-2 likely function in cholinergic or glr-1 interneurons or motor neurons to regulate escape behaviors during anoxia. Escape behaviors are part of the homeostatic response and are typically protective in the wild; however, in our paradigm, we show that loss of either nlp-40 or aex-2 reduces escape responses and confers protection against 48 hours of anoxia. Collectively, these results highlight a new role for NLP-40 and AEX-2 in anoxia and shed light on mechanisms underlying the adaptive responses to anoxia.","rel_num_authors":2,"rel_authors":[{"author_name":"Ginger Z Watzinger","author_inst":"Trinity College"},{"author_name":"Heather L Bennett","author_inst":"Trinity College"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Transfer Matrix and Tensor Approaches to Nucleic Acid Secondary Structure Thermodynamics","rel_doi":"10.64898\/2026.09.30.755805","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755805","rel_abs":"Nucleic acid secondary structure thermodynamic models have great utility for the analysis and design of nucleic acid structures, devices, and systems, yet their complexity and piecemeal development over decades pose challenges to further innovation. Employing different functional forms for each loop type within a structure, existing nearest neighbor (NN) models necessitate numerous loop-specific dynamic programming recursions in order to calculate physical quantities of interest. Here, we derive and implement a new class of models based on transfer matrices, yielding a single functional form for the free energy of all loop types (as the trace of a matrix product) and a single dynamic programming recursion for calculating physical quantities of interest. This new class of models has additional attractive properties: 1) incorporating a subensemble of states describing proximal interactions within each loop, 2) approximating logarithmic entropic scaling for all closed loops, 3) offering the opportunity to model correlations between the subensembles of neighboring loops by generalizing transfer matrices to tensors, and 4) providing a basis for systematically adjusting the number of model parameters to enhance accuracy as new empirical data become available for parameter regression. We use literature experimental data to regress parameters for representative new transfer matrix models, observing accuracy as good as or better than that of existing NN models, yet using dramatically fewer parameters (e.g., dozens vs hundreds or thousands). For complex ensembles containing an arbitrary number of interacting nucleic acid strands totaling N nucleotides, the new single recursion leads to exact dynamic programs for calculating diverse physical quantities (including the partition function, equilibrium base-pairing probabilities, minimum free energy and suboptimal proxy structures, Boltzmann-sampled secondary structures, and gradient of the partition function) with O(N3) time complexity. The simplicity of the new partition function dynamic program enables overflow-safe partition function calculations using renormalization to achieve scalability for large complexes (e.g., 50,000 nt) as well as formulation using a global matrix multiplication primitive that facilitates GPU acceleration, yielding speedups of an order of magnitude relative to NN partition function algorithms for large complexes. The new class of transfer matrix and tensor models provides an elegant framework for future efforts to advance the accuracy, speed, and scope of computational nucleic acid analysis and design.","rel_num_authors":2,"rel_authors":[{"author_name":"Mark E. Fornace","author_inst":"California Institute of Technology, Lawrence Berkeley National Laboratory"},{"author_name":"Niles A. Pierce","author_inst":"California Institute of Technology"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"AI agent supervision of structural model building and refinement","rel_doi":"10.64898\/2026.10.05.756902","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.05.756902","rel_abs":"Structural model building and refinement need many decisions between different programs. These decisions often depend on an experienced researcher who knows how the programs work together and how to read experimental data. We used Claude Code with Claude Opus 5.5 to supervise this work for 24 cryo-EM and X-ray cases. The work finished in about 36 hours on one workstation. The agent prepared the inputs, ran established programs, read the validation results, compared candidate models and chose the next step. For 15 deposited cryo-EM models with weak starting geometry, the median MolProbity score improved from 2.62 to 1.76. For three crystal structures, the agent models came close to the published Rfree values. Chemical information such as ligands, ions and modifications must be provided by the researcher, in the same way as the sequence. With this information, the agent can place and check these components. These results show that agent supervision is acceptable when every decision can be inspected. The researcher makes the final scientific judgment. We suggest practical requirements for this kind of use. Future AI agents may help researchers obtain high quality and reliable structures of biological macromolecules.","rel_num_authors":2,"rel_authors":[{"author_name":"Kuen-Phon Wu","author_inst":"Academia Sinica"},{"author_name":"Chuan-Kai Chueh","author_inst":"Academia Sinica"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Naturalistic face-directed attention predicts face recognition ability","rel_doi":"10.64898\/2026.09.30.755511","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755511","rel_abs":"Recognising people precedes and informs most social interactions, yet it is not clear why individuals differ so markedly in face recognition ability. Exposure-based accounts propose that it is shaped by visual experience with faces, known as 'face diet', but most prior work treats exposure as passively acquired, thereby masking the active and dynamic nature of visual attention in natural settings. Here we examined whether spontaneous attention to faces in everyday environments predicts face recognition ability. Eighty participants wore mobile eye-tracking glasses while walking through a busy university campus and during a face-to-face interaction, before completing standard measures of face recognition ability. Fixations directed to faces showed stable individual differences in both settings. People who looked more at faces scored higher on our composite measure of face recognition ability, and gaze in the two settings predicted ability independently, together accounting for 22% of its variance. These findings suggest that differences in how people actively sample visual information from their environments may be an overlooked source of variance in perceptual expertise.","rel_num_authors":7,"rel_authors":[{"author_name":"David White","author_inst":"UNSW Sydney"},{"author_name":"Victor P. L. Varela","author_inst":"Centro Universitario FEI, Sao Bernardo do Campo, Brazil"},{"author_name":"Monique Piggott","author_inst":"University of Sydney"},{"author_name":"James D Dunn","author_inst":"UNSW Sydney"},{"author_name":"Sebastien Miellet","author_inst":"University of Wollongong"},{"author_name":"Alice Towler","author_inst":"University of Queensland"},{"author_name":"Bojana Popovic","author_inst":"UNSW Sydney"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Naturalistic face-directed attention predicts face recognition ability","rel_doi":"10.64898\/2026.09.30.755511","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755511","rel_abs":"Recognising people precedes and informs most social interactions, yet it is not clear why individuals differ so markedly in face recognition ability. Exposure-based accounts propose that it is shaped by visual experience with faces, known as 'face diet', but most prior work treats exposure as passively acquired, thereby masking the active and dynamic nature of visual attention in natural settings. Here we examined whether spontaneous attention to faces in everyday environments predicts face recognition ability. Eighty participants wore mobile eye-tracking glasses while walking through a busy university campus and during a face-to-face interaction, before completing standard measures of face recognition ability. Fixations directed to faces showed stable individual differences in both settings. People who looked more at faces scored higher on our composite measure of face recognition ability, and gaze in the two settings predicted ability independently, together accounting for 22% of its variance. These findings suggest that differences in how people actively sample visual information from their environments may be an overlooked source of variance in perceptual expertise.","rel_num_authors":7,"rel_authors":[{"author_name":"David White","author_inst":"UNSW Sydney"},{"author_name":"Victor P. L. Varela","author_inst":"Centro Universitario FEI, Sao Bernardo do Campo, Brazil"},{"author_name":"Monique Piggott","author_inst":"University of Sydney"},{"author_name":"James D Dunn","author_inst":"UNSW Sydney"},{"author_name":"Sebastien Miellet","author_inst":"University of Wollongong"},{"author_name":"Alice Towler","author_inst":"University of Queensland"},{"author_name":"Bojana Popovic","author_inst":"UNSW Sydney"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Bone regulates locomotion by sustaining motoneuronal mitochondrial function","rel_doi":"10.64898\/2026.10.07.750453","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.07.750453","rel_abs":"Body-brain communication is crucial for optimal neuronal performance across the lifespan, yet whether peripheral organs regulate spinal cord motoneuron (MN) function through circulating molecules remains unknown. The expression of hormone receptors by spinal MNs raises the possibility that circulating cues allow the spinal cord to sense the body's energetic state and sustain motor activity. Here, we show that the bone-derived hormones osteocalcin (OCN) signals on spinal MNs via its receptor GPR158. GPR158 is selectively enriched in spinal cholinergic MNs, and its loss, globally or specifically in spinal MNs, or the one of OCN, impairs locomotor function and reduces choline acetyltransferase (ChAT) levels. Mechanistically, OCN signaling through GPR158 fine-tunes autophagy, mitophagy and mitochondrial activity in spinal MNs. Restoring any of these processes, by pharmacologically inducing mitophagy or chemogenetically activating mitochondria, is sufficient to rescue the locomotor deficits of Gpr158-deficient mice. Finally, OCN restoration in aged mice reverses age-related locomotor decline through a mechanism that requires MN autophagy and mitophagy. This study reveals that through OCN, the skeleton influences motor functions by fine-tuning motoneuronal mitochondria, and suggests that age-related motor decline is, at least in part, reversible.","rel_num_authors":12,"rel_authors":[{"author_name":"David Romeo Guitart","author_inst":"Institut Necker Enfants Malades"},{"author_name":"Tala Tirani","author_inst":"Institut Necker Enfants-Malades, INSERM U1151, Universite Paris Descartes, Paris, France."},{"author_name":"Dimitrije Milunov","author_inst":"Medinsights SAS, 24 Boulevard Saint-Jacques 75014 Paris France"},{"author_name":"Angie K. Torres-Juacida","author_inst":"Institut Necker Enfants-Malades, INSERM U1151, Universite Paris Descartes, Paris, France."},{"author_name":"Boris Lamotte","author_inst":"Universite de Paris Cite, SPPIN - Saints-Peres Paris Institute for the Neurosciences, CNRS, UMR 8003, Paris, France"},{"author_name":"Carolina Arenas-Plascencia","author_inst":"Institut Necker Enfants-Malades, INSERM U1151, Universite Paris Descartes, Paris, France."},{"author_name":"Sylvere Durand","author_inst":"Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France"},{"author_name":"Ivan Nemazanyy","author_inst":"Platform for Metabolic Analyses, Structure Federative de Recherche Necker, INSERM US24\/CNRS UAR 3633, Paris, France"},{"author_name":"Stephanie Moriceau","author_inst":"Platform for Metabolic Analyses, Structure Federative de Recherche Necker, INSERM US24\/CNRS UAR 3633, Paris, France"},{"author_name":"Soham Saha","author_inst":"Medinsights"},{"author_name":"Gerard Karsenty","author_inst":"Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University, 701 West 168th Street, New York, NY, USA"},{"author_name":"Franck Oury","author_inst":"Institut Necker Enfants-Malades, INSERM U1151, Universite Paris Descartes, Paris, France."}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Design and Test Novel EEG Electrode Attachments for Type 4 Afro-textured Hairstyles","rel_doi":"10.64898\/2026.10.01.755885","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755885","rel_abs":"Black participants remain underrepresented in electroencephalography (EEG) research, partly because current EEG devices work poorly with dense, coily Type 4 Afro-textured hair. Research-grade EEG typically wires electrodes on a cap, and therefore the electrodes often fail to reach the scalp through dense, Afro-textured hair. Protocols that ask Black participants to unbraid or restyle protective hairstyles add burdens that discourage enrollment in research studies. To address this, we designed and tested 3D-printed attachments that fit onto an EEG electrode and anchored it directly in the hair, using the hairstyle itself as the mounting structure. We fabricated three design configurations for natural Afro-textured hair, cornrows\/braids, and twists\/microlocs. Seventeen healthy adults with Type 4a-4c hair used electrode attachments that matched their hairstyle. During a single visit, EEG was recorded with 16 conventional cap electrodes defined by the international 10-20 system and with 16 electrodes using novel electrode attachments anchored directly to their hair. Participants completed resting and walking trials with each system. Electrode-to-scalp distance and gel volume were measured during setup. Electrode disconnection, impedance, signal quality (i.e. peak alpha power and aperiodic (1\/f) component), and comfort level were measured throughout the data collection. Compared with the conventional cap, the novel attachments reduced electrode-to-scalp distance, gel use, and impedance. Twenty percent of the conventional cap electrodes failed to connect to the scalp compared with 0.1% of novel electrodes. Impedance rose across trials for the conventional cap, whereas the novel attachments showed a smaller increase and maintained lower impedance throughout. Signal quality was significantly better using the novel attachments, while comfort level did not differ between systems. In conclusion, hair-anchored electrode attachment may present a feasible alternative to conventional cap-based EEG for people with Type 4 hair, yielding more usable channels and better signal quality, and supporting more inclusive EEG acquisition.","rel_num_authors":4,"rel_authors":[{"author_name":"Mishti Broor","author_inst":"University of Illinois Chicago"},{"author_name":"Kenneth Ogunniyi","author_inst":"University of Illinois Chicago"},{"author_name":"Lucinda Williamson","author_inst":"University of Illinois Chicago"},{"author_name":"Chang Liu","author_inst":"University of Illinois Chicago"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Retinotopic organization of head-orienting vectors in the pigeon ventral lateral geniculate nucleus","rel_doi":"10.64898\/2026.10.03.756353","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.03.756353","rel_abs":"The ventral lateral geniculate nucleus (GLv) is a conserved retinorecipient structure positioned between visual and premotor systems, yet its role in visuomotor transformations remains poorly understood. Here, we examined whether the pigeon GLv contains a spatially organized motor representation linking visual-field locations to orienting movements. Anatomical tracing identified descending projections from neurons in the GLv inner lamina (GLv-li) through pretectal and tegmental regions toward pontine targets. GLv-li projection neurons are GABAergic, yet electrical microstimulation of GLv-li in awake pigeons evoked rapid head movements followed by smaller eye movements. Each stimulation locus produced a reproducible characteristic motor vector whose direction and duration were largely insensitive to stimulation parameters, whereas movement amplitude and velocity approached site-specific plateaus. Movements initiated from different head positions were better explained by repetition of a fixed displacement than by convergence toward a common spatial goal. Critically, motor-vector direction was systematically related to the direction of the visual receptive field represented at the same GLv locus, while motor-vector amplitude covaried with receptive-field eccentricity. These defining features persisted after optic tectum ablation. Together, these findings reveal a retinotopically-organized map of fixed-vector head movements in the avian GLv and identify a tectum-independent route through which visual spatial representations can access descending orienting circuitry.","rel_num_authors":7,"rel_authors":[{"author_name":"Daniel Severin","author_inst":"Johns Hopkins University"},{"author_name":"Bryan Reynaert","author_inst":"Universidad de Chile"},{"author_name":"Tomas Vega-Zuniga","author_inst":"Universidad de Chile"},{"author_name":"Juan Carlos Letelier","author_inst":"Universidad de Chile"},{"author_name":"Gonzalo Marin","author_inst":"Universidad de Chile"},{"author_name":"Harvey J. Karten","author_inst":"University of California San Diego"},{"author_name":"Jorge Mpodozis","author_inst":"Universidad de Chile"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Eyes on the prize: a neural basis of perceptual choking under pressure","rel_doi":"10.64898\/2026.09.30.755534","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755534","rel_abs":"Even professional athletes sometimes fail to perform when it matters most. This phenomenon, known as choking under pressure, has been observed in a variety of motor and cognitive tasks. Can inordinately large rewards influence even sensory processing, and if so, what might be its neural basis? To probe this, we studied rhesus monkeys performing a challenging color change detection task and recorded from populations of neurons in visual area V4. We found that monkeys underperformed when offered rare \"jackpot\" rewards and uncovered associated detrimental alterations in V4 population activity. These findings were consistent with misallocated spatial attention, providing a potential neural basis for perceptual choking under pressure.","rel_num_authors":9,"rel_authors":[{"author_name":"Megan E McDonnell","author_inst":"Carnegie Mellon University"},{"author_name":"Adithya Narayan Chandrasekaran","author_inst":"University of Pittsburgh"},{"author_name":"Chris Sungho Ki","author_inst":"Carnegie Mellon University"},{"author_name":"Adam L Smoulder","author_inst":"Boston University"},{"author_name":"Hiroo Miyata","author_inst":"Carnegie Mellon University"},{"author_name":"Byron Yu","author_inst":"Carnegie Mellon University"},{"author_name":"Aaron P Batista","author_inst":"Carnegie Mellon University"},{"author_name":"Matthew A Smith","author_inst":"Carnegie Mellon University"},{"author_name":"Steven M Chase","author_inst":"Carnegie Mellon University"}],"rel_date":"2026-10-08","rel_site":"biorxiv"},{"rel_title":"Learning the electronic health record at the minute-scale","rel_doi":"10.64898\/2026.10.04.26364562","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.04.26364562","rel_abs":"Electronic health record foundation models are traditionally trained on the scale of years, days, or hours. These timescales, however, lack the minute-scale resolution required to directly guide clinical decisions at the bedside. We introduced minute-scale learning, a new paradigm for training and evaluating models, and developed MINT, a minute-scale foundation model for pediatric emergencies. MINT was pretrained and validated on 766,733 pediatric emergency department visits at five health systems, comprising 16 years of data from 10 hospitals. On minute-scale forecasting tasks, MINT outperformed and generalized to external health systems better than task-specific models (superior in 21 of 25 comparisons). MINT outperformed physicians in forecasting escalations of respiratory support. MINT demonstrated uniquely minute-scale capabilities including department-scale monitoring, dynamic risk explanations, individualized physiologic response forecasts, and hypothesis generation. Learning at the minute-scale improves performance, strengthens generalizability, and provides insights, actionability, and scientific capabilities that are not accessible at other timescales.","rel_num_authors":12,"rel_authors":[{"author_name":"Kush Narang","author_inst":"Stanford University"},{"author_name":"Newton Addo","author_inst":"University of California, San Francisco"},{"author_name":"Jaskaran Bains","author_inst":"University of California, San Francisco"},{"author_name":"Maytal Firnberg","author_inst":"University of California, San Francisco"},{"author_name":"Sonny Tat","author_inst":"University of California, San Francisco"},{"author_name":"Anneka Hooft","author_inst":"University of California, San Francisco"},{"author_name":"Daniela Chanci Arrubla","author_inst":"Duke University"},{"author_name":"Baraa Abed","author_inst":"Duke University"},{"author_name":"Chandan Singh","author_inst":"Microsoft Research"},{"author_name":"Rishikesan Kamaleswaran","author_inst":"Duke University"},{"author_name":"Jean Feng","author_inst":"University of California, San Francisco"},{"author_name":"Aaron E Kornblith","author_inst":"University of California San Francisco"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Memory and executive functioning show differential associations with scam susceptibility in middle to older adulthood","rel_doi":"10.64898\/2026.10.04.26364617","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.04.26364617","rel_abs":"Objectives: The rising prevalence of financial exploitation among older adults has prompted research on the cognitive mechanisms underlying financial decision making in later life. Prior work has demonstrated a link between cognitive impairment and financial vulnerability. The present study examined the unique contributions of composite measures of memory and executive functioning to scam susceptibility in middle-aged to older adults without dementia. Methods: One hundred and fifty-one participants (age= 68.2 {+\/-} 8.0 years, 72.2% female, years of education= 16.4 {+\/-} 2.2) completed standard neuropsychological measures from the Uniform Data Set (UDS), Version 3, and the California Verbal Learning Test, Second Edition, and a self-report scam susceptibility measure. Psychometrically robust composite scores, the UDS-M+ and UDS3-EF, were computed to represent memory and executive functioning domains. Linear regression models examined independent associations between UDS-M+ and UDS3-EF performance and scam susceptibility. Nested model comparisons evaluated unique contributions of the UDS-M+ and UDS3-EF when included in a joint model. Results: Lower UDS-M+ (b= -0.25, 95% CI [-0.45, -0.04], p= .018) and lower UDS3-EF (b= -0.33, 95% CI [-0.54, -0.12], p= .002) scores were independently associated with greater scam susceptibility after adjusting for age, sex, years of education, and income. However, only UDS3-EF remained significant in a joint model. Post hoc analyses revealed that only memory was associated with scam susceptibility in middle-aged adults whereas only executive functioning was significant in older adults. Conclusions: These findings highlight the importance of monitoring memory and executive functioning across middle to older adulthood when considering susceptibility to scams.","rel_num_authors":12,"rel_authors":[{"author_name":"Cassidy P. Molinare","author_inst":"University of Southern California"},{"author_name":"Belinda Y. Zhang","author_inst":"University of Southern California"},{"author_name":"Mark Sanderson-Cimino","author_inst":"UC San Francisco Memory and Aging Center"},{"author_name":"Melanie Leguizamon","author_inst":"University of Southern California"},{"author_name":"Emma Oyen","author_inst":"University of Southern California"},{"author_name":"Daisy T. Noriega-Makarskyy","author_inst":"University of Southern California"},{"author_name":"Jenna Axelrod","author_inst":"University of Southern California"},{"author_name":"Aaron C Lim","author_inst":"University of Southern California"},{"author_name":"Camdyn Wu","author_inst":"University of Southern California"},{"author_name":"Maanya Agarwal","author_inst":"University of Southern California"},{"author_name":"Laura Mosqueda","author_inst":"University of Southern California"},{"author_name":"S. Duke Han","author_inst":"University of Southern California"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Dietary sodium intake and kidney tubular protein abundance in urinary extracellular vesicles","rel_doi":"10.64898\/2026.10.04.26364680","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.04.26364680","rel_abs":"Background: Dietary sodium intake is a modifiable determinant of blood pressure, and impaired renal sodium handling is implicated in the pathogenesis of hypertension. Whether dietary sodium intake modulates renal tubular sodium transporter abundance among normotensive adults at risk for hypertension is unknown. Methods: Forty-six normotensive adults with normal kidney function at risk of developing hypertension were prospectively enrolled to complete a very low sodium (LS) diet followed by a high sodium (HS) diet. Urinary extracellular vesicle (uEV) proteins were quantified from a 24-hour urine collection at the end of each diet. We compared normalized uEV protein concentrations between diets and tested whether diet modified uEV protein associations with serum aldosterone, urinary aldosterone, and serum cortisol-cortisone ratio under either diet using Wilcoxon rank sums and linear mixed effects models, respectively. Results: Participants achieved median [IQR] urine sodium 13.2 [7.7-19.9] mEq\/24h on the LS diet and 244.0 [197.1-297.0] mEq\/24h on the HS diet. LS intake induced greater renin activity, greater plasma and urinary aldosterone, and lower urinary cortisol, when compared to HS intake. In parallel, NCC, ENaC, SGLT2, OXSR1, and Pendrin uEV concentrations were significantly higher during LS intake than HS intake (p<0.05 for all), whereas TRPV5 and AQP2 uEV concentrations were lower during LS intake than HS intake (p<0.001 for both). Conclusions: Controlled dietary sodium modulation induced nephron-segment specific changes in uEV protein abundance. These findings demonstrate the ability of uEVs to serve as a non-invasive tool to probe in vivo renal sodium, water, and electrolyte handling.","rel_num_authors":13,"rel_authors":[{"author_name":"Sophie E Claudel","author_inst":"Boston Medical Center"},{"author_name":"Sanan Mahrokhian","author_inst":"Brigham and Women's Hospital, Harvard Medical School"},{"author_name":"Isabelle Hanna","author_inst":"Brigham and Women's Hospital, Harvard Medical School"},{"author_name":"Andrew J Newman","author_inst":"Brigham and Women's Hospital, Harvard Medical School"},{"author_name":"Jenifer Brown","author_inst":"Brigham and Women's Hospital"},{"author_name":"Ashish Verma","author_inst":"Boston University School of Medicine, Boston, MA"},{"author_name":"Sushrut Waikar","author_inst":"Boston Medical Center"},{"author_name":"Richard J. Auchus","author_inst":"University of Michigan"},{"author_name":"Hayes McDonald","author_inst":"Vanderbilt University"},{"author_name":"Kevin Schey","author_inst":"Vanderbilt University"},{"author_name":"Dungeng Peng","author_inst":"Vanderbilt University"},{"author_name":"James M Luther","author_inst":"Vanderbilt University"},{"author_name":"Anand Vaidya","author_inst":"Brigham and Women's Hospital, Harvard Medical School"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"A Modular Architecture for Dynamic Evaluation of Best-Practice Care","rel_doi":"10.64898\/2026.10.03.26364324","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.03.26364324","rel_abs":"ObjectiveRetrospective evaluation of clinical pathway adherence using electronic medical record (eMR) data is limited by heterogeneous data structures and statistical measures that do not account for patient-specific context, care sequence, or timing. We designed a modular event-based architecture to dynamically evaluate the New South Wales Adult Sepsis Pathway over time.\n\nMethodData from eMRs were collected from adult emergency department encounters across four hospitals between July 2022 and June 2024 and processed through three sequential layers: event-stream transformation, pathway-relevance filtering, and state-based pathway evaluation. Heterogeneous records were standardized and organized into a chronological timeline for each encounter. The framework then updated the pathways state as new clinical information became available and assessed whether recommended care was delivered within the relevant timeframe and sequence.\n\nResultThe architecture processed 451,124 encounters. This included 57,631,084 clinical events extracted and transformed into encounter-level event streams. After filtering for pathway relevance, 43,951,146 events remained (76.3%). A total of 129,907 (34.6%) encounters entered the sepsis pathway, 50,118 (38.6%) entered YELLOW state, indicating progression to suspected sepsis, and 3,349 (2.6%) encounters progressed to RED state from GREEN, indicating the initial shock. Of 50,118 encounters entering YELLOW state, 2,426 (4.8%) progressed to shock, 8,700 (17.4%) encounters were recognized with sepsis through a blood culture order, and 4,287 (8.6%) encounters were recognized with sepsis through an antibiotic order. This demonstrates the architectures ability to prioritize higher-acuity states during evaluation rather than going through an intermediate state.\n\nConclusionThe architecture developed shows the feasibility of transforming fragmented routine eMR data into a reusable longitudinal representation that supports context-dependent pathway evaluation. Separating data preparation from pathway rules improves transparency, maintainability, and adaptability and provides a foundation for future interoperable, near-real-time pathway monitoring","rel_num_authors":6,"rel_authors":[{"author_name":"Minh Trang Hoang","author_inst":"Faculty of Medicine and Health, The Univeristy of Sydney"},{"author_name":"Tim Shaw","author_inst":"Faculty of Medicine and Health, The Univeristy of Sydney"},{"author_name":"Christina Igasto","author_inst":"Digital Canberra"},{"author_name":"Candice Donnelly","author_inst":"Faculty of Medicine and Health, The Univeristy of Sydney"},{"author_name":"Amith Shetty","author_inst":"NSW Ministry of Heatlh"},{"author_name":"Malcolm Pradhan","author_inst":"Faculty of Medicine and Health, The Univeristy of Sydney"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Identifying Message \"Recipes\" for High Perceived Effectiveness: Cognitive, Social, and Emotional Profiles of Vaping Public Education Messages Among Young Adults Who Vape","rel_doi":"10.64898\/2026.10.04.26364669","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.04.26364669","rel_abs":"Introduction: Vaping public education campaigns are a central prevention strategy for young adults (YAs), but message effectiveness varies widely and formative testing typically evaluates one message attribute at a time. This study examined how four perceptual dimensions, including cognitive engagement, social relevance, emotional arousal, and emotional valence, relate to perceived message effectiveness (PME), and whether multiple combinations of these dimensions correspond to similarly high PME. Methods: In a cross-sectional online message-testing study conducted from March to April 2025, 540 U.S. YAs who vape each evaluated 15 messages randomly sampled from a corpus of 229 text-image vaping public education messages, yielding 8,100 message evaluations. Cross-classified multilevel models estimated associations between each perceptual dimension and PME. An Explainable Boosting Machine probed nonlinear associations, and k-means clustering of high-predicted-PME perceptual configurations identified distinct profiles. Analyses were conducted in 2026. Results: Cognitive engagement (b = 0.20, 95% CI [0.18, 0.22]) and social relevance (b = 0.15, 95% CI [0.13, 0.17]) showed the strongest associations with PME, followed by emotional arousal (b = 0.06, 95% CI [0.04, 0.08]); emotional valence showed no linear association (b = 0.00, 95% CI [-0.02, 0.02]). The Explainable Boosting Machine revealed a U-shaped valence pattern, with higher predicted PME at both the negative and positive ends of the scale. Clustering yielded five distinct perceptual profiles with similarly high predicted PME (4.10-4.25). Conclusions: Multiple perceptual configurations correspond to similarly high PME, supporting a configurational approach to formative message testing rather than a single optimal message formula.","rel_num_authors":9,"rel_authors":[{"author_name":"Jiaying Liu","author_inst":"University of California, Santa Barbara"},{"author_name":"Qiyao Peng","author_inst":"University of California, Santa Barbara"},{"author_name":"Musa Malik","author_inst":"University of Oklahoma"},{"author_name":"Yidi Wang","author_inst":"University of California, San Diego"},{"author_name":"Emily Norton","author_inst":"University of Georgia"},{"author_name":"Tianlai Ye","author_inst":"University of California, Santa Barbara"},{"author_name":"Colleen Markey","author_inst":"University of Georgia"},{"author_name":"Allison Worsdale","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Lawrence H. Sweet","author_inst":"University of Georgia"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Rural-urban differences in gut microbiome among Vietnamese women","rel_doi":"10.64898\/2026.10.04.26364707","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.04.26364707","rel_abs":"Gut microbiome plays an important role in human health. Urbanization has been suggested to alter the gut microbiome. We comprehensively evaluated differences in gut microbiome profiles of 76 urban and 81 rural female residents of Vietnam using shotgun metagenomic data. We evaluated rural-urban differences in gut microbial diversity, individual microbial taxa, and metabolic pathways. Rural residents had higher alpha and beta diversities in age-adjusted models (p<0.05). Additionally, significant rural-urban differences were observed for 35 microbial taxa in age-adjusted models (FDR<0.1). Rural residents had higher relative abundance of classes, orders, and families of FGB2982 and FGB38642 and strain SGB15332 of the species Faecalibacterium prausnitzii, whereas urban residents had higher relative abundances of species Bacteroides thetaiotaomicron, Coprobacter fastidiosus, Bilophila wadsworthia, Enterobacter cloacae, Enterobacter hormaechei, Klebsiella aerogenes, Dielma fastidiosa, and Holdemania massiliensis. Notably, these associations were attenuated after further adjustment for socioeconomic characteristics, lifestyle, dietary factors, and comorbidity (FDR>0.1). In the fully adjusted model, significant rural-urban differences were observed in the abundance of 17 microbial taxa, including Ruminococcus sp AF13 28 and Prevotella pectinovora, as well as in three metabolic pathways (FDR<0.1). This study suggests that urbanization-related factors may contribute to differences in the gut microbiome between urban and rural Vietnamese women.","rel_num_authors":6,"rel_authors":[{"author_name":"Justin Y Guo","author_inst":"Montgomery Bell Academy"},{"author_name":"Huong T.T. Tran","author_inst":"Vietnam National Cancer Institute, National Cancer Hospital, Hanoi, Vietnam"},{"author_name":"Qiuyin Y Cai","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Minh  Van Hoang","author_inst":"Hanoi University of Public Health"},{"author_name":"Xiao-ou Shu","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Sang Minh Nguyen","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Post-Initiation Care Patterns and Weight Loss With Semaglutide and Tirzepatide for Obesity in Clinical Practice","rel_doi":"10.64898\/2026.10.04.26364715","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.04.26364715","rel_abs":"Abstract Background: Randomized trials show substantial weight loss with semaglutide and tirzepatide under structured care, but little is known about whether patients in routine practice continue therapy, reach a maintenance dose, and have their weight measured. Objectives: To describe the first year after initiation of semaglutide or tirzepatide for obesity, including care patterns, maintenance-dose attainment, weight documentation, and total body weight loss (TBWL). Methods: This retrospective cohort study used Epic Cosmos, an electronic health record network of >350 US health systems, to identify adults with obesity and without diabetes who initiated injectable semaglutide or tirzepatide in 2024. Outcomes over 12 months included prescribing persistence, discontinuation, switching, add-on therapy, and reinitiation; attainment of Food and Drug Administration (FDA)-labeled maintenance doses; availability of follow-up weight; and TBWL across progressively restricted populations. Results: Among 743,153 adults (60.1% semaglutide; mean age, 48.5 years; 72.5% female), 12-month prescribing persistence was 29.9% for semaglutide and 35.9% for tirzepatide. FDA-labeled maintenance-dose attainment was 28.3% and 55.0%, respectively, and only 8.8% and 16.4% remained prescribing persistent at a maintenance dose with a recorded weight at 12 months. About half of initiators lacked a 12-month weight. Among all initiators, [&ge;]5% TBWL was documented in 25.6% (semaglutide) and 30.4% (tirzepatide), rising to 51.9% and 61.9% among those with recorded weight, 66.1% and 79.5% among those also prescribing persistent, and 82.9% and 87.3% among those further at a maintenance dose. Conclusions: In routine practice, most patients initiating semaglutide or tirzepatide did not remain on therapy, reach a maintenance dose, and have weight documented through the first year. Documented weight loss was greatest among those completing each step of care, suggesting continuation, dose escalation, and monitoring as targets for improvement.","rel_num_authors":14,"rel_authors":[{"author_name":"Huanhuan Yang","author_inst":"Yale University"},{"author_name":"Chungsoo Kim","author_inst":"Yale School of Medicine"},{"author_name":"Joseph S. Ross","author_inst":"Yale University"},{"author_name":"Chenxi Huang","author_inst":"Yale University"},{"author_name":"Kyungseon Choi","author_inst":"Yale University"},{"author_name":"Bo Kang","author_inst":"Yale University"},{"author_name":"Adith Arun","author_inst":"Yale School of Medicine"},{"author_name":"Huilin Tang","author_inst":"The University of Pennsylvania"},{"author_name":"Yong Chen","author_inst":"The University of Pennsylvania"},{"author_name":"Hua Xu","author_inst":"Yale University"},{"author_name":"Mona Sharifi","author_inst":"Yale School of Medicine"},{"author_name":"Zhihui Li","author_inst":"Tsinghua University"},{"author_name":"Harlan Krumholz","author_inst":"Yale University"},{"author_name":"Yuan Lu","author_inst":"Yale University"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Emergency Department Utilization After 60-Day Peripheral Nerve Stimulation Versus Usual Care for Refractory Occipital Neuralgia: A Prospective Propensity-Matched Cohort, Difference-in-Differences Analysis","rel_doi":"10.64898\/2026.10.02.26364550","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364550","rel_abs":"Background: Temporary 60-day percutaneous peripheral nerve stimulation (PNS) can relieve pain in refractory occipital neuralgia (ON), but whether it alters acute healthcare utilization relative to the natural trajectory of the disease is unknown, because prior studies have lacked a concurrent comparator. Methods: We performed a secondary analysis of prospectively collected data from a tertiary interventional pain center. Adults with medically refractory ON (primary diagnosis occipital neuralgia, ICD-10-CM M54.81) who underwent 60-day occipital PNS (n = 42) were compared with concurrent patients who were clinically eligible for PNS but were denied insurance authorization and continued optimized non-PNS management (usual care; n = 63). Headache-related emergency department (ED) encounters - index (occipital neuralgia) and non-index (other headache\/facial-pain diagnoses) - were ascertained over the 12 months before and after the treatment window via the electronic record and the statewide health information exchange. Patients were 1:1 propensity-matched on a priori confounders (age, sex, baseline total headache-related ED utilization, HIT-6, and baseline headache-medication use). The primary analysis was a difference-in-differences (DiD) comparison of the change in headache-related ED utilization, reported as the additive difference (Hodges-Lehmann estimate, distribution-free 95% CI) and, alongside, as a multiplicative rate ratio (RR). Results: Thirty-six matched pairs were well balanced. Baseline headache-related ED utilization was nearly identical between matched groups (6.3 vs. 6.2 visits\/year). Over the following year, PNS patients' utilization fell to 2.5 while usual-care patients' rose to 6.9. The DiD was 5.0 fewer headache-related ED visits per patient (95% CI 3.0-6.0; p < 0.001), corresponding to a rate ratio of 0.36 (95% CI 0.27-0.48; p < 0.001). Results were consistent in the full sample, in covariate-adjusted models, and after excluding controls denied for incomplete conservative-therapy trials. The effect was driven by non-index headache visits. Conclusions: Relative to a concurrent, matched usual-care cohort whose headache related ED utilization rose over time, 60-day occipital PNS was associated with a substantial reduction in acute-care use, strengthening the case for a role of PNS on healthcare utilization in refractory ON.","rel_num_authors":6,"rel_authors":[{"author_name":"Cameron William Harris","author_inst":"Valley Health System GME"},{"author_name":"Eric T Nguyen","author_inst":"Department of Physical Medicine and Rehabilitation, Charles R. Drew University of Medicine and Science"},{"author_name":"Sergio Mosquera Limas","author_inst":"Department of Orthopaedics and Rehabilitation, Division of Physiatry, Yale New Haven Hospital"},{"author_name":"Samir J Sheth","author_inst":"Division of Pain Management, Sutter Health System"},{"author_name":"Robert William Chow","author_inst":"Department of Anesthesiology, Yale School of Medicine"},{"author_name":"Charles A. Odonkor","author_inst":"Division of Physiatry, Interventional Pain Medicine, Department of Orthopaedics and Rehabilitation, Yale School of Medicine"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Power Calculation for Noninferiority Stepped-Wedge Cluster Randomized Trials","rel_doi":"10.64898\/2026.10.04.26364705","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.04.26364705","rel_abs":"Background: Stepped-wedge cluster randomized trials (SW-CRTs) are increasingly used when interventions need to be implemented sequentially across clusters. Despite extensive developments in the design and analysis of superiority SW-CRTs, methods for planning noninferiority SW-CRTs remain limited. Methods: We propose swNIsim, a simulation-based framework for power calculation in noninferiority SW-CRTs using generalized linear mixed-effects models (GLMMs). The proposed approach accommodates both continuous and binary outcomes and supports practical design features, including unequal numbers of clusters across sequences, varying cluster-period sizes, delayed intervention effects, and analyses with or without adjustment for secular time effects. Noninferiority margins can be specified using several commonly applied strategies. Results: Through extensive simulation studies, we examined the impact of key design parameters on statistical power, including the number of periods, the number of clusters per sequence, the number of individuals per cluster-period, and the degree of between-cluster variability. For a fixed total sample size, designs with more time periods consistently achieved higher power. Increasing the number of clusters per sequence produced modest gains in power, whereas greater between-cluster variability reduced power. Type I error rates were well controlled across the scenarios examined. Balanced allocation of clusters across sequences was more efficient than unbalanced allocation schemes. Two real-world SW-CRT examples were used to illustrate application of the method. Conclusions: The proposed swNIsim provides a flexible and practical approach for power calculation in noninferiority SW-CRTs. By accommodating a wide range of design configurations and outcome types, and through implementation as a freely available R package, swNIsim may facilitate the planning and design of noninferiority stepped-wedge trials.","rel_num_authors":3,"rel_authors":[{"author_name":"Justin Guo","author_inst":"Montgomery Bell Academy"},{"author_name":"Chih-Yuan Hsu","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Yu Shyr","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Acquisition Speed versus Spatial Resolution in Ultra-High-Resolution Photon-Counting CT: Phantom Study to Guide Protocol Development","rel_doi":"10.64898\/2026.10.04.26363713","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.04.26363713","rel_abs":"Faster ultrahigh-resolution (UHR) photon-counting CT acquisitions facilitate breath-hold imaging, but identical reconstruction settings may conceal differences in spatial resolution. To assess spatial resolution and depiction of small airways and venous stents across helical UHR acquisition configurations. Stationary wire, airway, and venous-stent phantoms underwent one acquisition per configuration using five configurations on a dual-source scanner at 120 kVp. Reconstructions used 0.2- and 0.4-mm thicknesses, the same sharp kernel, and iterative reconstruction strength 1. We measured the full-width at half-maximum of the slice sensitivity profile (SSP) and spatial frequency at 20% of the modulation transfer function (MTF20). Rank-based F tests with Holm adjustment assessed configuration differences in resolution and variability across slices. Airway detection used a profile contrast-to-noise criterion; stent assessment was qualitative. Calculated acquisition time for 400-mm coverage decreased from 9.80 to 1.30 seconds between the slowest and fastest configurations. At 0.2-mm reconstruction thickness, median SSP increased from 0.316 to 0.496 mm, and median MTF20 decreased from 2.870 to 2.059 lp\/mm (both P<.001). Interquartile ranges increased from 0.047 to 0.197 mm for SSP and from 0.256 to 0.654 lp\/mm for MTF20 (both variability P<.001). Increasing reconstruction thickness to 0.4 mm increased SSP by a median 0.221 mm across 2459 matched slice-wire measurements (P<.001). At 0.2-mm reconstruction thickness, 0.4- and 0.6-mm lumens with 0.2-mm walls were detected only in the slowest configuration. Faster configurations showed less distinct stent struts and greater adjacent-wall distortion. Faster helical UHR photon-counting CT configurations had poorer spatial resolution despite identical nominal slice thickness. Consistent acquisition and reconstruction settings are important for longitudinal comparisons.","rel_num_authors":3,"rel_authors":[{"author_name":"Navid Azimi","author_inst":"Emory University"},{"author_name":"Joao A.C. Lima","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Amir Pourmorteza","author_inst":"Emory University"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Overdose-related communication in Connecticut: reported receipt, perceived utility, and responses among overdose prevention and response professionals","rel_doi":"10.64898\/2026.10.01.26363949","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26363949","rel_abs":"Background Timely overdose response depends not only on surveillance systems that detect overdose clusters, but also on communication pathways that deliver actionable information to the professionals positioned to respond. Understanding which overdose-related communications are received, perceived as useful, and translated into action is essential for improving overdose prevention and response infrastructure. Objective We assessed which overdose-related communication channels professionals reported receiving, which they perceived as most helpful, how organizations reported responding to overdoses or overdose clusters, and whether these patterns differed by professional role, county, and funding source. Methods We conducted an exploratory descriptive survey in 2024 using a convenience sample of professionals across Connecticut whose jobs involved overdose prevention or response (N=149). Using this survey data, we summarized reported receipt and perceived utility of overdose-related communication channels, organizational response actions, and internal receipt of Connecticut Department of Public Health overdose alerts. Results were stratified descriptively by professional role, county, and funding source. We interpreted findings using a public health communication-to-action cascade focused on signal detection, communication receipt, internal dissemination, perceived utility, and response action. Results Reported communication receipt and perceived utility differed most clearly by professional role. Peer information sharing was the most commonly reported communication source and was frequently perceived as helpful, but respondents who reported receiving formal channels such as state alerts, local alerts, or platform-based information often identified those channels as most helpful. Internal receipt of Connecticut Department of Public Health overdose alerts also varied by role. SSPs\/substance use treatment programs and first responders more often reported outreach to people who use drugs, whereas government and health district respondents reported a more even mix of public communication and outreach activities. Conclusions Overdose-related communication in Connecticut appears role-dependent and unevenly distributed. Informal peer networks are widely used and perceived as helpful, while formal alert systems may require improvements to reach and actionability. Practical improvements include opt-in communication pathways tailored to professional roles, clearer access points for state and local alerts, internal dissemination protocols that specify who should receive which alerts, and alert-to-action playbooks that connect specific alert types to recommended response steps.","rel_num_authors":2,"rel_authors":[{"author_name":"A Ram","author_inst":"Yale University"},{"author_name":"Frederick  L Altice","author_inst":"Yale University School of Medicine"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Diabetes Polygenic Scores Predict Glycemic Indices and Insulin Use in Individuals with Atypical Diabetes","rel_doi":"10.64898\/2026.10.01.26364299","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364299","rel_abs":"Aims\/Hypothesis: Atypical forms of diabetes often provide diagnostic challenges and are suboptimally treated. We sought to determine whether type 1 and 2 diabetes polygenic scores (PS) would help improve classification of islet autoantibody-negative (IAb-) atypical diabetes cases. Methods: We implemented type 1 diabetes PS (T1D PS) and type 2 diabetes PS (T2D PS) in 309 IAb- individuals' genome sequencing from the Rare and Atypical Diabetes Network (RADIANT), the largest and most comprehensively phenotyped collection of unsolved and atypical diabetes cases. We performed regression analyses to assess associations between PS, clinical characteristics, and glycemic measures. Results: Both T1D and T2D PS were significantly higher in RADIANT than in ancestry-matched controls from UK Biobank. Both PS were associated with C-peptide measures throughout participant oral glucose tolerance tests (OGTT), although in opposite directions; T1D PS was associated with lower C-peptide measures, while T2D PS was associated with higher measures. Participants with T1D PS above a previously established optimal PS value for differentiating cases and controls (T1D PS GRS2>12.88) had 13-fold higher odds of both insulin deficiency on OGTT and use of both basal and bolus insulin therapy. Conclusions\/Interpretation: Individuals in RADIANT with ambiguous diabetes subtypes displayed genetic enrichment for both type 1 and type 2 diabetes, despite ascertainment to remove standard type 1 and type 2 diabetes. T1D PS identified individuals with an unrecognized \"type 1 diabetes-like\" phenotype with insulin deficiency and need for intensive insulin in the absence of islet autoantibodies. PS may help identify individuals with atypical diabetes who will ultimately benefit from insulin therapy.","rel_num_authors":21,"rel_authors":[{"author_name":"Steven D. Gage","author_inst":"Center for Genomic Medicine and Diabetes Unit, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Mass General Brigham, Boston, MA, USA"},{"author_name":"Raymond J. Kreienkamp","author_inst":"Boston Children's Hospital"},{"author_name":"Aaron J. Deutsch","author_inst":"Center for Genomic Medicine and Diabetes Unit, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Mass General Brigham, Boston, MA, USA"},{"author_name":"Eric J. Richards","author_inst":"Diabetes Genetics Initiative and Programs in Metabolism and Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Boyce T"},{"author_name":"Liana K. Billings","author_inst":"Division of Endocrinology, Endeavor Health, Skokie, Illinois; Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, Illinois, USA"},{"author_name":"Sara J. Cromer","author_inst":"Center for Genomic Medicine and Diabetes Unit, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Mass General Brigham, Boston, MA, USA"},{"author_name":"Siri Atma W. Greeley","author_inst":"Department of Medicine and Pediatrics, Section of Endocrinology, Diabetes, and Metabolism, the Kovler Diabetes Center, University of Chicago, Chicago, IL, USA"},{"author_name":"Irl B. Hirsch","author_inst":"Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA"},{"author_name":"Alicia Huerta-Chagoya","author_inst":"Center for Genomic Medicine and Diabetes Unit, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Mass General Brigham, Boston, MA, USA"},{"author_name":"Steven E. Kahn","author_inst":"Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, WA, USA"},{"author_name":"Klara R. Klein","author_inst":"Division of Endocrinology and Metabolism, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA"},{"author_name":"Josep M. Mercader","author_inst":"Center for Genomic Medicine and Diabetes Unit, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Mass General Brigham, Boston, MA, USA"},{"author_name":"Neda Rasouli","author_inst":"Division of Endocrinology, Metabolism and Diabetes, University of Colorado, School of Medicine and VA Eastern Colorado Heath Care System, Aurora, CO, USA"},{"author_name":"Marjan Rezaei","author_inst":"Division of Endocrinology, Metabolism and Diabetes, University of Colorado, School of Medicine, Aurora, CO, USA"},{"author_name":"Maria J. Redondo","author_inst":"Department of Pediatrics, Diabetes and Endocrinology, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Ashok Balasubramanyam","author_inst":"Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Jose C. Florez","author_inst":"Center for Genomic Medicine and Diabetes Unit, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Mass General Brigham, Boston, MA, USA"},{"author_name":"Louis H. Philipson","author_inst":"Department of Medicine and Pediatrics, Section of Endocrinology, Diabetes, and Metabolism, the Kovler Diabetes Center, University of Chicago, Chicago, IL, USA"},{"author_name":"Jason Flannick","author_inst":"Diabetes Genetics Initiative and Programs in Metabolism and Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Divisio"},{"author_name":"Miriam S. Udler","author_inst":"Center for Genomic Medicine and Diabetes Unit, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Mass General Brigham, Boston, MA, USA"},{"author_name":"- RADIANT Study Group","author_inst":""}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"When Is Amyloid Really Cleared? Substantial Discordance Between Centiloid Quantification and Visual Reads of Amyloid PET after Amyloid-Targeting Therapy","rel_doi":"10.64898\/2026.10.03.26364642","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.03.26364642","rel_abs":"Amyloid-targeting therapies (ATTs) substantially reduce amyloid PET signal and slow clinical decline in early symptomatic Alzheimer disease. As ATTs enter clinical practice, PET may assess target engagement and guide treatment management, but there is no consensus on how to measure treatment-related amyloid clearance (TRAC). We compared Centiloid quantification across pipelines and visual reads for interpreting pre- and post-ATT amyloid PET. Methods: We analyzed 18F-florbetapir or 18F-florbetaben PET from 101 patients treated with lecanemab or donanemab: 88 pre-ATT and 54 post-ATT scans, including 41 paired scans. Centiloids were derived using FDA-cleared MIMneuro and two research pipelines (rPOP, CapAIBL) and compared using intraclass correlation coefficients (ICC). Clearance rates were estimated using linear mixed-effects models. Full TRAC was defined on post-ATT scans quantitatively (<11 Centiloids) or based on visual interpretation by three expert readers. Results: Treated patients were 74+\/-7 years old; 57% were female, 59% were apolipoprotein E4 carriers, and 78% had mild cognitive impairment (22% mild dementia). Centiloid agreement between pipelines was equally high pre- and post-ATT (both ICCs=0.90). Among paired scans (mean treatment duration, 16.7 +\/- 4.6 months), clearance estimates were similar across pipelines and greater with donanemab (n=15 patients; between -62 and -60 Centiloids\/year depending on the pipeline) than lecanemab (n=26; -38 to -36 Centiloids\/year; all Ps<0.005). Among 54 post-ATT scans, between 44% and 57% were <11 Centiloids depending on pipeline, with 20 (37%) meeting this criterion across all three pipelines. In contrast, only 11% to 19% were visually negative across readers, with 4 (7%) unanimously read as negative. Of the 20 post-ATT scans <11 Centiloids across all pipelines, 13 (65%) were considered positive by all readers, commonly with residual occipital binding. Conclusion: Centiloid quantification was robust across pipelines before and after ATT initiation, yielding clearance rates consistent with clinical trials. However, quantitative and visual assessments were markedly discordant after ATT, with an unusually high proportion of scans <11 Centiloids remaining visually positive, warranting cautious interpretation of post-ATT Centiloid values. Larger studies should assess the clinical relevance of these findings and further develop robust approaches to establish full TRAC.","rel_num_authors":19,"rel_authors":[{"author_name":"Renaud La Joie","author_inst":"UCSF"},{"author_name":"Marlene Lin","author_inst":"UCSF"},{"author_name":"Yingbing Wang","author_inst":"UCSF"},{"author_name":"David N Soleimani-Meigooni","author_inst":"UCSF"},{"author_name":"Ganna Blazhenets","author_inst":"UCSF"},{"author_name":"Corrina S Fonseca","author_inst":"UCSF"},{"author_name":"Tara Ellingson","author_inst":"UCSF"},{"author_name":"Hong Nguyen","author_inst":"UCSF"},{"author_name":"Kambiz Nael","author_inst":"UCSF"},{"author_name":"Salil Soman","author_inst":"UCSF"},{"author_name":"Pierrick Bourgeat","author_inst":"CSIRO"},{"author_name":"Jake P Levy","author_inst":"UCSF"},{"author_name":"Gina Rhee","author_inst":"UCSF"},{"author_name":"Julio C Rojas","author_inst":"UCSF"},{"author_name":"Melanie Stephens","author_inst":"UCSF"},{"author_name":"Nhat Bui","author_inst":"UCSF"},{"author_name":"Peter A Ljubenkov","author_inst":"UCSF"},{"author_name":"Lawren VandeVrede","author_inst":"UCSF"},{"author_name":"Gil D Rabinovici","author_inst":"UCSF"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"An adaptive technical and behavioral approach for biomarker acquisition in autism with intellectual disability","rel_doi":"10.64898\/2026.10.01.26364439","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364439","rel_abs":"Autistic individuals with intellectual disability (ASD+ID) and profound autism are rarely studied in neuroscience research due to barriers including communication challenges, sensory sensitivities, anxiety, and elevated behavioral support needs. Consequently, biomarker development in autism fails to include the portion of the autistic population with highest support needs and greatest potential to benefit from treatments derived from imminent advances in personalized medicine. Here we present a multimodal, participant-centered protocol for simultaneous electroencephalography (EEG) and eye-tracking (ET) acquisition to advance clinical trial readiness research in this underserved population. The protocol combines individualized behavioral supports and an adaptive EEG-ET acquisition platform. Pre-visit caregiver interviews informed participant-specific research plans, including customized visual supports, reinforcement strategies, and EEG net desensitization procedures. A gaze-contingent stimulus presentation system used participant-preferred videos to support attention to experimental stimuli and behavior consistent with data acquisition (e.g., refraining from movement) while dynamically adapting stimulus delivery based on visual engagement with the assay and real-time assessment of data quality. By quantifying attended trials in real time and prioritizing unattended stimulus classes, the paradigm maximizes acquisition efficiency to minimize participant burden. This approach was utilized to deploy candidate biomarkers with demonstrated replicability in the Autism Biomarkers Consortium for Clinical Trials (ABC-CT): the N170 event-related potential to faces, resting-state EEG, and visual attention to human faces (measured with ET). Feasibility was evaluated in 76 children with ASD+ID (M age = 8.76 years; IQ = 32.4) and a small comparison sample of 8 children with intellectual disability without autism (M age = 8.91 years; IQ = 45.9). Usable EEG data (>7 minutes) were acquired in 61% (ASD+ID) and 88% (ID) of participants, respectively. Sufficient ET data were obtained from 67% (ASD+ID) and 63% (ID) of participants, respectively. Findings demonstrate the feasibility of inclusive biomarker acquisition and provide a framework for expanding participation of individuals with ASD+ID and profound autism in neuroscience research. Ongoing research seeks to evaluate this approach in larger samples and to demonstrate feasibility in multisite research studies.","rel_num_authors":8,"rel_authors":[{"author_name":"Cassandra J Stevens","author_inst":"Schiefelbusch Institute for Life Span Studies, University of Kansas"},{"author_name":"Sara Eberle","author_inst":"Yale Child Study Center"},{"author_name":"Casey Carrow","author_inst":"Yale Child Study Center"},{"author_name":"Sherab Tsheringla","author_inst":"Yale Child Study Center"},{"author_name":"Christine Cukar-Capizzi","author_inst":"Yale Child Study Center"},{"author_name":"Julie  M. Wolf","author_inst":"Yale Child Study Center"},{"author_name":"Adam Naples","author_inst":"Yale Child Study Center"},{"author_name":"James McPartland","author_inst":"Yale Child Study Center"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"An adaptive technical and behavioral approach for biomarker acquisition in autism with intellectual disability","rel_doi":"10.64898\/2026.10.01.26364439","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364439","rel_abs":"Autistic individuals with intellectual disability (ASD+ID) and profound autism are rarely studied in neuroscience research due to barriers including communication challenges, sensory sensitivities, anxiety, and elevated behavioral support needs. Consequently, biomarker development in autism fails to include the portion of the autistic population with highest support needs and greatest potential to benefit from treatments derived from imminent advances in personalized medicine. Here we present a multimodal, participant-centered protocol for simultaneous electroencephalography (EEG) and eye-tracking (ET) acquisition to advance clinical trial readiness research in this underserved population. The protocol combines individualized behavioral supports and an adaptive EEG-ET acquisition platform. Pre-visit caregiver interviews informed participant-specific research plans, including customized visual supports, reinforcement strategies, and EEG net desensitization procedures. A gaze-contingent stimulus presentation system used participant-preferred videos to support attention to experimental stimuli and behavior consistent with data acquisition (e.g., refraining from movement) while dynamically adapting stimulus delivery based on visual engagement with the assay and real-time assessment of data quality. By quantifying attended trials in real time and prioritizing unattended stimulus classes, the paradigm maximizes acquisition efficiency to minimize participant burden. This approach was utilized to deploy candidate biomarkers with demonstrated replicability in the Autism Biomarkers Consortium for Clinical Trials (ABC-CT): the N170 event-related potential to faces, resting-state EEG, and visual attention to human faces (measured with ET). Feasibility was evaluated in 76 children with ASD+ID (M age = 8.76 years; IQ = 32.4) and a small comparison sample of 8 children with intellectual disability without autism (M age = 8.91 years; IQ = 45.9). Usable EEG data (>7 minutes) were acquired in 61% (ASD+ID) and 88% (ID) of participants, respectively. Sufficient ET data were obtained from 67% (ASD+ID) and 63% (ID) of participants, respectively. Findings demonstrate the feasibility of inclusive biomarker acquisition and provide a framework for expanding participation of individuals with ASD+ID and profound autism in neuroscience research. Ongoing research seeks to evaluate this approach in larger samples and to demonstrate feasibility in multisite research studies.","rel_num_authors":8,"rel_authors":[{"author_name":"Cassandra J Stevens","author_inst":"Schiefelbusch Institute for Life Span Studies, University of Kansas"},{"author_name":"Sara Eberle","author_inst":"Yale Child Study Center"},{"author_name":"Casey Carrow","author_inst":"Yale Child Study Center"},{"author_name":"Sherab Tsheringla","author_inst":"Yale Child Study Center"},{"author_name":"Christine Cukar-Capizzi","author_inst":"Yale Child Study Center"},{"author_name":"Julie  M. Wolf","author_inst":"Yale Child Study Center"},{"author_name":"Adam Naples","author_inst":"Yale Child Study Center"},{"author_name":"James McPartland","author_inst":"Yale Child Study Center"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Social and behavioral risk factors are associated with age-related differences in sustained attention: A cross-sectional GradCPT analysis of the All of Us dataset","rel_doi":"10.64898\/2026.10.01.26364102","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364102","rel_abs":"Background Attentional control declines with normal aging, yet the role of modifiable lifestyle factors in accelerating this decline remains incompletely characterised in large, diverse populations. We examined associations between four modifiable risk factors (smoking intensity, alcohol binge drinking, social satisfaction and blood pressure) and sustained attention performance in 13,866 adults drawn from the All of Us Research Program. Methods Cognitive performance was assessed using the Gradual-Onset Continuous Performance Task (GradCPT). A composite cognitive efficiency score was derived via principal component analysis of four GradCPT metrics: d' (perceptual sensitivity), response criterion, median reaction time and reaction time variability. Associations between cognitive efficiency and each risk factor were examined using multiple linear regression adjusted for age, sex and race\/ethnicity. Effect sizes were expressed as age-equivalent years of cognitive aging (age-equivalent years = regression coefficient for the risk factor divided by the regression coefficient for age) to aid clinical interpretation. Results GradCPT performance declined progressively with age (p < 0.001). Social satisfaction emerged as the strongest lifestyle predictor: low social satisfaction was associated with approximately 5.8 additional age-equivalent years of cognitive aging relative to high social satisfaction. Smoking showed a dose-response pattern, with heavy smokers (> 20 cigarettes\/day) exhibiting an approximately 5-year age-equivalent deficit versus never-smokers. Stage 2 hypertension showed a modest, non-significant negative association. Binge drinking showed limited significant associations, likely complicated by survivor and abstainer biases. Bootstrapped analyses of combined exposures showed that combinations of risk factors, particularly those involving low social satisfaction, produced larger cognitive deficits than expected under an additive model. Conclusions These findings support an interactive rather than a purely additive model of lifestyle-related cognitive vulnerability and highlight the GradCPT as a subclinical measure of attention-based cognitive risk in non-clinical populations. Keywords Cognitive aging, Sustained attention, GradCPT, Modifiable risk factors, Social isolation, Smoking, Hypertension, All of Us Research Program","rel_num_authors":4,"rel_authors":[{"author_name":"Natan M Coresh-Chiappori","author_inst":"Morgan State University"},{"author_name":"Theresa Boyer","author_inst":"Johns Hopkins University"},{"author_name":"A Richey Sharrett","author_inst":"Johns Hopkins University"},{"author_name":"Ingrid K Tulloch","author_inst":"Morgan State University"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Sex differences in genetic liability to modifiable Alzheimer's disease risk factors","rel_doi":"10.64898\/2026.10.01.26364536","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364536","rel_abs":"NTRODUCTION: Alzheimer's disease (AD) shows sex differences in risk, clinical progression, and biomarker profiles, but whether inherited genetic liability to modifiable risk factors contributes differently to AD susceptibility in females and males remains unclear. We evaluated whether genetic relationships between several modifiable dementia risk factors and AD differ between females and males. METHODS: We integrated polygenic risk scores (PRSs) for modifiable risk factors, genome-wide genetic correlation, and two-sample Mendelian randomization (MR) analyses. PRS associations with AD diagnosis were evaluated in the Alzheimer's Disease Genetics Consortium (n=27,751) and with AD plasma biomarkers in the Health and Aging Brain Study-Health Disparities (n=2,307-2,737, depending on the biomarker). Primary analyses evaluated female- and male-specific associations and compared sex-specific estimates. RESULTS: Educational attainment and physical activity PRSs were associated with AD in both sexes. PRS sex differences were nominal for educational attainment, total cholesterol, and LDL cholesterol, but none remained significant after FDR correction. Genetic correlation showed FDR-significant sex differences for social isolation, physical activity, LDL cholesterol, and pulse pressure. MR identified FDR-significant associations within sex strata; higher HDL cholesterol was associated with lower AD risk in females, whereas higher total cholesterol and type 2 diabetes were associated with lower and higher AD risk, respectively, in males. Sensitivity analyses provided inconsistent support, and no sex difference in MR estimates remained significant after FDR correction. In HABS-HD, type 2 diabetes PRS was associated with higher NfL, BMI PRS with higher pTau181, and the composite PRS with higher NfL among males after FDR correction. DISCUSSION: Evidence that modifiable dementia risk factors relate differently to AD by sex was limited and depended on the genetic measure examined. Sex differences in genome-wide genetic overlap were not consistently reflected in PRS or MR, indicating that shared genetic architecture does not necessarily translate into sex-specific inherited liability or causal effects","rel_num_authors":10,"rel_authors":[{"author_name":"Rakshya U Sharma","author_inst":"University of California, San Francisco"},{"author_name":"Paulina Tolosa-Tort","author_inst":"University of California, San Francisco"},{"author_name":"Meri Okorie","author_inst":"University of California, San Francisco"},{"author_name":"Aadrita Chatterjee","author_inst":"University of California, San Francisco"},{"author_name":"Caroline Jonson","author_inst":"DataTecnica LLC"},{"author_name":"Kristine Yaffe","author_inst":"University of California San Francisco"},{"author_name":"Michael E Belloy","author_inst":"Washington University in St. Louis"},{"author_name":"Shea J Andrews","author_inst":"University of California San Francisco"},{"author_name":"- ADGC","author_inst":""},{"author_name":"- HABS-HD Study team","author_inst":""}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Sex differences in genetic liability to modifiable Alzheimer's disease risk factors","rel_doi":"10.64898\/2026.10.01.26364536","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364536","rel_abs":"NTRODUCTION: Alzheimer's disease (AD) shows sex differences in risk, clinical progression, and biomarker profiles, but whether inherited genetic liability to modifiable risk factors contributes differently to AD susceptibility in females and males remains unclear. We evaluated whether genetic relationships between several modifiable dementia risk factors and AD differ between females and males. METHODS: We integrated polygenic risk scores (PRSs) for modifiable risk factors, genome-wide genetic correlation, and two-sample Mendelian randomization (MR) analyses. PRS associations with AD diagnosis were evaluated in the Alzheimer's Disease Genetics Consortium (n=27,751) and with AD plasma biomarkers in the Health and Aging Brain Study-Health Disparities (n=2,307-2,737, depending on the biomarker). Primary analyses evaluated female- and male-specific associations and compared sex-specific estimates. RESULTS: Educational attainment and physical activity PRSs were associated with AD in both sexes. PRS sex differences were nominal for educational attainment, total cholesterol, and LDL cholesterol, but none remained significant after FDR correction. Genetic correlation showed FDR-significant sex differences for social isolation, physical activity, LDL cholesterol, and pulse pressure. MR identified FDR-significant associations within sex strata; higher HDL cholesterol was associated with lower AD risk in females, whereas higher total cholesterol and type 2 diabetes were associated with lower and higher AD risk, respectively, in males. Sensitivity analyses provided inconsistent support, and no sex difference in MR estimates remained significant after FDR correction. In HABS-HD, type 2 diabetes PRS was associated with higher NfL, BMI PRS with higher pTau181, and the composite PRS with higher NfL among males after FDR correction. DISCUSSION: Evidence that modifiable dementia risk factors relate differently to AD by sex was limited and depended on the genetic measure examined. Sex differences in genome-wide genetic overlap were not consistently reflected in PRS or MR, indicating that shared genetic architecture does not necessarily translate into sex-specific inherited liability or causal effects","rel_num_authors":10,"rel_authors":[{"author_name":"Rakshya U Sharma","author_inst":"University of California, San Francisco"},{"author_name":"Paulina Tolosa-Tort","author_inst":"University of California, San Francisco"},{"author_name":"Meri Okorie","author_inst":"University of California, San Francisco"},{"author_name":"Aadrita Chatterjee","author_inst":"University of California, San Francisco"},{"author_name":"Caroline Jonson","author_inst":"DataTecnica LLC"},{"author_name":"Kristine Yaffe","author_inst":"University of California San Francisco"},{"author_name":"Michael E Belloy","author_inst":"Washington University in St. Louis"},{"author_name":"Shea J Andrews","author_inst":"University of California San Francisco"},{"author_name":"- ADGC","author_inst":""},{"author_name":"- HABS-HD Study team","author_inst":""}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"A Modality-Invariant Measure of Neural Health","rel_doi":"10.64898\/2026.10.02.26364203","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364203","rel_abs":"Quantifying neural health with a single measure remains a major unmet challenge. Here, we report a physiological marker based on the spatial distribution of signal-derived energy correlates in the resting brain. We hypothesize that energy organization in the healthy brain approaches an entropy-maximizing, homeostatically regulated state characterized by five properties: lognormal energy distributions, balanced spatial allocation, statistical independence between regions, low energy variance, and temporal stability. Together, these properties define an energetic profile that the Neural Health Index (NHI) summarizes as a single score, computable from (scalp\/intracranial) electroencephalography, magnetoencephalography, or functional magnetic resonance imaging. Across 808 participants spanning neurological, psychiatric, neurodevelopmental, and neurodegenerative disorders, NHI distinguished healthy from clinical populations with sensitivity and specificity exceeding 95% in internal and external validation, scaled with disease severity, tracked pharmacological treatment exposure, and generalized to unseen diagnoses. These findings support NHI as a disease-agnostic, modality-invariant candidate biomarker for disease stratification and treatment monitoring.","rel_num_authors":5,"rel_authors":[{"author_name":"Luis A Sanchez Colon","author_inst":"Johns Hopkins University"},{"author_name":"Ernst Niebur","author_inst":"Johns Hopkins University"},{"author_name":"Joon Kang","author_inst":"Johns Hopkins University"},{"author_name":"Chiadi Onyike","author_inst":"Johns Hopkins University"},{"author_name":"Sridevi Sarma","author_inst":"Johns Hopkins University"}],"rel_date":"2026-10-06","rel_site":"medrxiv"},{"rel_title":"Learning fly pose from vision-language annotations","rel_doi":"10.64898\/2026.09.30.755712","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755712","rel_abs":"Animal pose estimation typically requires manual annotation of body parts to train neural networks for each new recording system or environmental condition. Here we use a general-purpose vision-language model (VLM) to generate annotations in multi-animal recordings of fruit flies (Drosophila melanogaster). Initial annotations follow written anatomical descriptions and are reviewed by an independent VLM session using neighboring video frames. On 120 held-out fly images, AI annotations had a median distance of 2.28 pixels from human labels, with 84.0% agreement within eight pixels, averaged across landmark types. The comparison included 10 landmarks, with head and eyes analyzed separately because of systematic differences in placement. Review improved agreement primarily at the feet, with the largest remaining discrepancies at positions with limited visual evidence. Visual inspection revealed cases in which the AI annotations were more anatomically accurate than those of an experienced human annotator, particularly at the feet. A convolutional network trained using SLEAP on 300 AI-annotated fly images showed similar agreement with human labels and closely reproduced the AI annotations on held-out images, with a median distance of 1.56 pixels. We also applied the procedure to recordings from another laboratory, using new AI annotations to train a separate pose network. Although VLM annotation required minutes per fly, the trained networks enabled pose estimation throughout more than 700,000 frames. These results demonstrate that VLM annotations can support fast animal pose estimation without manually labeled training images.","rel_num_authors":4,"rel_authors":[{"author_name":"Joshua W. Shaevitz","author_inst":"Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA"},{"author_name":"Matthew G. Sierra","author_inst":"Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA"},{"author_name":"Sarah Kuo Kim","author_inst":"Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA"},{"author_name":"Scott D. Pletcher","author_inst":"Department of Molecular and Integrative Physiology and Institute of Gerontology, University of Michigan, Ann Arbor, MI 48109, USA"}],"rel_date":"2026-10-06","rel_site":"biorxiv"},{"rel_title":"Estimating Hepatitis C Virus Prevalence in US States and the District of Columbia, 2017-2020","rel_doi":"10.64898\/2026.09.29.26364309","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364309","rel_abs":"BackgroundState-level estimates of hepatitis C virus (HCV) prevalence are needed to guide resource allocation and to provide a baseline against which to measure progress toward elimination goals. Population prevalence is not directly observable, requiring estimation from indirect data sources.\n\nMethodsWe adapted a Bayesian spatial integrated abundance model to estimate state-level HCV prevalence during 2017-2020 across 48 states and the District of Columbia. The model integrated six HCV-related outcomes: acute and chronic surveillance cases, HCV-related deaths, observations of HCV in [MarketScan] administrative claims data, diagnoses of HCV in Medicaid recipients, and treatment with direct-acting antivirals in Medicaid recipients. Estimates were anchored to a national prevalence estimate, and the model accounted for data source-specific selection, heterogeneity in HCV risk factors across states, and geospatial correlation.\n\nResultsEstimated average prevalence was 1.32% (95% credible interval [CrI]: 0.94%-1.80%), corresponding to 3.33 million (95% CrI: 2.37-4.65 million) adults with HCV infection across 48 states and DC. State estimates ranged from 0.74% in North Dakota to 2.22% in Oklahoma (median state-specific prevalence, 1.27%), with higher prevalence concentrated in South Central states, Appalachia, and the West, and the lower prevalence in the upper Midwest, Southeast, and New England. Ten states accounted for 56% of estimated infections. Estimates were stable in sensitivity analyses (most differences <1 percentage point).\n\nConclusionsThese estimates quantify state-level HCV burden prior to the 2021 federal expansion of HCV surveillance funding, providing a baseline for monitoring elimination progress. Geographic variation indicates that resource needs will differ by jurisdiction.","rel_num_authors":6,"rel_authors":[{"author_name":"Heather Bradley","author_inst":"Emory University"},{"author_name":"Ya-Hui Yu","author_inst":"Emory University"},{"author_name":"Lanxin Li","author_inst":"University of Edinburgh"},{"author_name":"Shashi N Kapadia","author_inst":"Weill Cornell Medicine"},{"author_name":"Patrick S Sullivan","author_inst":"Emory University"},{"author_name":"Eric William Hall","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Measuring enteric pathogen force of infection through antibody responses in children","rel_doi":"10.64898\/2026.09.29.26364344","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364344","rel_abs":"Enteric pathogens account for a substantial global disease burden, yet population-based surveillance remains limited by the transient nature of pathogen shedding, which constrains the sensitivity of stool-based molecular testing to a narrow detection window. Detection of IgG responses in serological surveys could enable new insights into enteric pathogen transmission, but benchmarking serological measures relative to stool-based measures of infection remains a critical evidence gap. We compared measures of disease transmission in longitudinal birth cohort samples using multiplex IgG (1,601 dried blood spots, 370 children) and PCR assays (2,231 stool samples, 370 children) that overlapped for nine pathogens (norovirus GI, GII, Shigella\/enteroinvasive Escherichia coli (EIEC), Campylobacter spp., enterotoxigenic Escherichia coli (LT-ETEC), Salmonella enterica, Giardia spp., Cryptosporidium spp., Entamoeba histolytica). LT-ETEC and Campylobacter ranked highest while E. histolytica and S. enterica ranked lowest by both IgG and PCR measures of infection. Measures of infection were less aligned for norovirus GI, norovirus GII, Cryptosporidium, Shigella\/EIEC, and Giardia. Despite this, for most pathogens, and across both measures, force of infection was lowest in the urban city of Esmeraldas and substantially higher in more rural populations, with relative risks in assay measures (seroconversion rate and PCR detected prevalence) over 2.0 for pathogens with the largest differences (Shigella\/EIEC and norovirus GI). Together, our results suggest that although absolute levels of infection differed between assays, IgG and PCR captured consistent relative risk of infection across geographic strata, suggesting either method can identify high-versus low-transmission settings.","rel_num_authors":25,"rel_authors":[{"author_name":"Nikolina Walas","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Lesly Simba\u00f1a Vivanco","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Stuart Torres Ayala","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Chabier Coleman","author_inst":"Independent consultant"},{"author_name":"E. Brook Goodhew","author_inst":"Independent consultant"},{"author_name":"Kelsey J. Jesser","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Nicolette A. Zhou","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Christine S. Fagnant-Sperati","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jesse Contreras","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Caitlin Hemlock","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jeffrey W. Priest","author_inst":"Retired"},{"author_name":"Richelle C. Charles","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Edward T. Ryan","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Claire Munroe","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Robert L. Atmar","author_inst":"Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Julianna Colado","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Hadley Burroughs","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Manuel Calvopi\u00f1a","author_inst":"Universidad de las Am\u00e9ricas, Facultad de Medicina, Carrera de Medicina, Quito, Ecuador"},{"author_name":"William Cevallos","author_inst":"Universidad Central del Ecuador, Instituto de Biomedicina, Quito, Ecuador"},{"author_name":"Josefina Coloma","author_inst":"School of Public Health, University of California, Berkeley, CA, USA"},{"author_name":"Gwenyth O. Lee","author_inst":"Rutgers Global Health Institute and Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, New Brunswick, NJ, USA"},{"author_name":"Gabriel Trueba","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Joseph N.S. Eisenberg","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Karen Levy","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Benjamin F. Arnold","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Measuring enteric pathogen force of infection through antibody responses in children","rel_doi":"10.64898\/2026.09.29.26364344","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364344","rel_abs":"Enteric pathogens account for a substantial global disease burden, yet population-based surveillance remains limited by the transient nature of pathogen shedding, which constrains the sensitivity of stool-based molecular testing to a narrow detection window. Detection of IgG responses in serological surveys could enable new insights into enteric pathogen transmission, but benchmarking serological measures relative to stool-based measures of infection remains a critical evidence gap. We compared measures of disease transmission in longitudinal birth cohort samples using multiplex IgG (1,601 dried blood spots, 370 children) and PCR assays (2,231 stool samples, 370 children) that overlapped for nine pathogens (norovirus GI, GII, Shigella\/enteroinvasive Escherichia coli (EIEC), Campylobacter spp., enterotoxigenic Escherichia coli (LT-ETEC), Salmonella enterica, Giardia spp., Cryptosporidium spp., Entamoeba histolytica). LT-ETEC and Campylobacter ranked highest while E. histolytica and S. enterica ranked lowest by both IgG and PCR measures of infection. Measures of infection were less aligned for norovirus GI, norovirus GII, Cryptosporidium, Shigella\/EIEC, and Giardia. Despite this, for most pathogens, and across both measures, force of infection was lowest in the urban city of Esmeraldas and substantially higher in more rural populations, with relative risks in assay measures (seroconversion rate and PCR detected prevalence) over 2.0 for pathogens with the largest differences (Shigella\/EIEC and norovirus GI). Together, our results suggest that although absolute levels of infection differed between assays, IgG and PCR captured consistent relative risk of infection across geographic strata, suggesting either method can identify high-versus low-transmission settings.","rel_num_authors":25,"rel_authors":[{"author_name":"Nikolina Walas","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Lesly Simba\u00f1a Vivanco","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Stuart Torres Ayala","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Chabier Coleman","author_inst":"Independent consultant"},{"author_name":"E. Brook Goodhew","author_inst":"Independent consultant"},{"author_name":"Kelsey J. Jesser","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Nicolette A. Zhou","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Christine S. Fagnant-Sperati","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jesse Contreras","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Caitlin Hemlock","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jeffrey W. Priest","author_inst":"Retired"},{"author_name":"Richelle C. Charles","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Edward T. Ryan","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Claire Munroe","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Robert L. Atmar","author_inst":"Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Julianna Colado","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Hadley Burroughs","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Manuel Calvopi\u00f1a","author_inst":"Universidad de las Am\u00e9ricas, Facultad de Medicina, Carrera de Medicina, Quito, Ecuador"},{"author_name":"William Cevallos","author_inst":"Universidad Central del Ecuador, Instituto de Biomedicina, Quito, Ecuador"},{"author_name":"Josefina Coloma","author_inst":"School of Public Health, University of California, Berkeley, CA, USA"},{"author_name":"Gwenyth O. Lee","author_inst":"Rutgers Global Health Institute and Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, New Brunswick, NJ, USA"},{"author_name":"Gabriel Trueba","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Joseph N.S. Eisenberg","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Karen Levy","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Benjamin F. Arnold","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Measuring enteric pathogen force of infection through antibody responses in children","rel_doi":"10.64898\/2026.09.29.26364344","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364344","rel_abs":"Enteric pathogens account for a substantial global disease burden, yet population-based surveillance remains limited by the transient nature of pathogen shedding, which constrains the sensitivity of stool-based molecular testing to a narrow detection window. Detection of IgG responses in serological surveys could enable new insights into enteric pathogen transmission, but benchmarking serological measures relative to stool-based measures of infection remains a critical evidence gap. We compared measures of disease transmission in longitudinal birth cohort samples using multiplex IgG (1,601 dried blood spots, 370 children) and PCR assays (2,231 stool samples, 370 children) that overlapped for nine pathogens (norovirus GI, GII, Shigella\/enteroinvasive Escherichia coli (EIEC), Campylobacter spp., enterotoxigenic Escherichia coli (LT-ETEC), Salmonella enterica, Giardia spp., Cryptosporidium spp., Entamoeba histolytica). LT-ETEC and Campylobacter ranked highest while E. histolytica and S. enterica ranked lowest by both IgG and PCR measures of infection. Measures of infection were less aligned for norovirus GI, norovirus GII, Cryptosporidium, Shigella\/EIEC, and Giardia. Despite this, for most pathogens, and across both measures, force of infection was lowest in the urban city of Esmeraldas and substantially higher in more rural populations, with relative risks in assay measures (seroconversion rate and PCR detected prevalence) over 2.0 for pathogens with the largest differences (Shigella\/EIEC and norovirus GI). Together, our results suggest that although absolute levels of infection differed between assays, IgG and PCR captured consistent relative risk of infection across geographic strata, suggesting either method can identify high-versus low-transmission settings.","rel_num_authors":25,"rel_authors":[{"author_name":"Nikolina Walas","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Lesly Simba\u00f1a Vivanco","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Stuart Torres Ayala","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Chabier Coleman","author_inst":"Independent consultant"},{"author_name":"E. Brook Goodhew","author_inst":"Independent consultant"},{"author_name":"Kelsey J. Jesser","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Nicolette A. Zhou","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Christine S. Fagnant-Sperati","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jesse Contreras","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Caitlin Hemlock","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jeffrey W. Priest","author_inst":"Retired"},{"author_name":"Richelle C. Charles","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Edward T. Ryan","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Claire Munroe","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Robert L. Atmar","author_inst":"Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Julianna Colado","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Hadley Burroughs","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Manuel Calvopi\u00f1a","author_inst":"Universidad de las Am\u00e9ricas, Facultad de Medicina, Carrera de Medicina, Quito, Ecuador"},{"author_name":"William Cevallos","author_inst":"Universidad Central del Ecuador, Instituto de Biomedicina, Quito, Ecuador"},{"author_name":"Josefina Coloma","author_inst":"School of Public Health, University of California, Berkeley, CA, USA"},{"author_name":"Gwenyth O. Lee","author_inst":"Rutgers Global Health Institute and Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, New Brunswick, NJ, USA"},{"author_name":"Gabriel Trueba","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Joseph N.S. Eisenberg","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Karen Levy","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Benjamin F. Arnold","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Escaping the Negative Attentional Bias in Depression with Real-Time Neurofeedback","rel_doi":"10.64898\/2026.09.30.26363896","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26363896","rel_abs":"Individuals with major depressive disorder (MDD) show an attentional bias toward negatively valenced stimuli and thoughts. In this study, we applied a closed-loop neurofeedback procedure designed to reduce this bias. Participants were shown composite negative faces and neutral scenes with variable opacity and were instructed to attend to the neutral scene while ignoring the negative face. Internal attentional states were decoded in real time from functional magnetic resonance imaging (fMRI) data. When a participants decoded attentional state indicated a failure to ignore the negative faces, the faces became more visible (higher opacity), thus externalizing the brains attentional lapse in that moment. Forty-eight individuals with MDD were randomly assigned to a real neurofeedback training group (N = 24) or a sham control group (N = 24); the control group received feedback yoked to a participant in the real group. All participants completed three fMRI neurofeedback sessions. The main outcome quantified the extent to which participants got \"stuck\" in the most negative attentional state of focusing strongly on the negative faces. We hypothesized that the real neurofeedback group would learn to escape that state by the end of training, relative to the sham control group. Consistent with our hypothesis, neurofeedback training reduced the probability of getting stuck in the most negative attentional state. In offline analyses, training reduced fMRI activity in the precuneus\/posterior cingulate and the medial prefrontal cortex when participants successfully attended to scenes and ignored faces. These results demonstrate the efficacy of remediating the negative attentional bias in depression with closed-loop neurofeedback from real-time fMRI.","rel_num_authors":9,"rel_authors":[{"author_name":"Nitzan Lubianiker","author_inst":"Department of Psychology, Yale University, New Haven, CT, USA"},{"author_name":"Brendan Woods","author_inst":"Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Frederick Nitchie","author_inst":"Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Alexandra Batzdorf","author_inst":"Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Anne Mennen","author_inst":"Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA"},{"author_name":"Qi Lin","author_inst":"Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea"},{"author_name":"Kenneth A. Norman","author_inst":"Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA"},{"author_name":"Nicholas B. Turk-Browne","author_inst":"Department of Psychology, Yale University, New Haven, CT, USA"},{"author_name":"Yvette I. Sheline","author_inst":"Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Metabolic Signatures of Resistance to Mycobacterium Tuberculosis Infection: Insights from a Multi-Country Plasma Metabolomics Study","rel_doi":"10.64898\/2026.09.29.26364337","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364337","rel_abs":"BackgroundTuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST\/IGRA-negative despite sustained exposure, termed \"TB resisters\". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts.\n\nMethodsWe compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST\/IGRA-negative. Mtb-infected close contacts were those who were TST\/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests.\n\nResultsThere were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 {micro}M, whereas hydroxyproline was lower by 0.90 {micro}M, butyrobetaine was lower by 0.06 {micro}M, and homogentisate was lower by 0.0036 {micro}M at false discovery rate of 0.2.\n\nConclusionsPlasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.","rel_num_authors":22,"rel_authors":[{"author_name":"Chang Liu","author_inst":"Emory University School of Public Health"},{"author_name":"Jeffrey  M. Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Matheus Fernandes Gyorfy","author_inst":"Emory University"},{"author_name":"Mariana Araujo Pereira","author_inst":"Fundacao Oswaldo Cruz"},{"author_name":"Vidya Mave","author_inst":"Johns Hopkins University"},{"author_name":"Senbagavalli Prakash","author_inst":"Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India"},{"author_name":"Kamakshi Prudhula Devalraju","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Neil A. Martinson","author_inst":"Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa"},{"author_name":"Fay Willis","author_inst":"Emory University"},{"author_name":"Marina  C Figueiredo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Universidade do Estado do Amazonas, Manaus, Brazil"},{"author_name":"Artur Trancoso Lopo de Queiroz","author_inst":"Laboratorio de Pesquisa Clinica e Translacional, Instituto Goncalo Moniz, Fundacao Oswaldo Cruz, Salvador, Brazil"},{"author_name":"Venkata Sanjeev Kumar Neela","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Rajesh Karyakarte","author_inst":"BJ Government Medical College, Pune"},{"author_name":"Timothy R Sterling","author_inst":"Vanderbilt University"},{"author_name":"Jerrold J. Ellner","author_inst":"Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Rutgers Health, Newark, New Jersey, USA"},{"author_name":"James C.M. Brust","author_inst":"Division of General Internal Medicine, Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Amita Gupta","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bruno  B Andrade","author_inst":"FIOCRUZ Bahia: Instituto Goncalo Moniz"},{"author_name":"Yan V. Sun","author_inst":"Emory University"},{"author_name":"Neel  R. Gandhi","author_inst":"Emory University School of Public Health"},{"author_name":"- TB GWAS collaboration","author_inst":""}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Metabolic Signatures of Resistance to Mycobacterium Tuberculosis Infection: Insights from a Multi-Country Plasma Metabolomics Study","rel_doi":"10.64898\/2026.09.29.26364337","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364337","rel_abs":"BackgroundTuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST\/IGRA-negative despite sustained exposure, termed \"TB resisters\". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts.\n\nMethodsWe compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST\/IGRA-negative. Mtb-infected close contacts were those who were TST\/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests.\n\nResultsThere were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 {micro}M, whereas hydroxyproline was lower by 0.90 {micro}M, butyrobetaine was lower by 0.06 {micro}M, and homogentisate was lower by 0.0036 {micro}M at false discovery rate of 0.2.\n\nConclusionsPlasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.","rel_num_authors":22,"rel_authors":[{"author_name":"Chang Liu","author_inst":"Emory University School of Public Health"},{"author_name":"Jeffrey  M. Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Matheus Fernandes Gyorfy","author_inst":"Emory University"},{"author_name":"Mariana Araujo Pereira","author_inst":"Fundacao Oswaldo Cruz"},{"author_name":"Vidya Mave","author_inst":"Johns Hopkins University"},{"author_name":"Senbagavalli Prakash","author_inst":"Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India"},{"author_name":"Kamakshi Prudhula Devalraju","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Neil A. Martinson","author_inst":"Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa"},{"author_name":"Fay Willis","author_inst":"Emory University"},{"author_name":"Marina  C Figueiredo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Universidade do Estado do Amazonas, Manaus, Brazil"},{"author_name":"Artur Trancoso Lopo de Queiroz","author_inst":"Laboratorio de Pesquisa Clinica e Translacional, Instituto Goncalo Moniz, Fundacao Oswaldo Cruz, Salvador, Brazil"},{"author_name":"Venkata Sanjeev Kumar Neela","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Rajesh Karyakarte","author_inst":"BJ Government Medical College, Pune"},{"author_name":"Timothy R Sterling","author_inst":"Vanderbilt University"},{"author_name":"Jerrold J. Ellner","author_inst":"Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Rutgers Health, Newark, New Jersey, USA"},{"author_name":"James C.M. Brust","author_inst":"Division of General Internal Medicine, Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Amita Gupta","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bruno  B Andrade","author_inst":"FIOCRUZ Bahia: Instituto Goncalo Moniz"},{"author_name":"Yan V. Sun","author_inst":"Emory University"},{"author_name":"Neel  R. Gandhi","author_inst":"Emory University School of Public Health"},{"author_name":"- TB GWAS collaboration","author_inst":""}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Metabolic Signatures of Resistance to Mycobacterium Tuberculosis Infection: Insights from a Multi-Country Plasma Metabolomics Study","rel_doi":"10.64898\/2026.09.29.26364337","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364337","rel_abs":"BackgroundTuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST\/IGRA-negative despite sustained exposure, termed \"TB resisters\". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts.\n\nMethodsWe compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST\/IGRA-negative. Mtb-infected close contacts were those who were TST\/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests.\n\nResultsThere were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 {micro}M, whereas hydroxyproline was lower by 0.90 {micro}M, butyrobetaine was lower by 0.06 {micro}M, and homogentisate was lower by 0.0036 {micro}M at false discovery rate of 0.2.\n\nConclusionsPlasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.","rel_num_authors":22,"rel_authors":[{"author_name":"Chang Liu","author_inst":"Emory University School of Public Health"},{"author_name":"Jeffrey  M. Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Matheus Fernandes Gyorfy","author_inst":"Emory University"},{"author_name":"Mariana Araujo Pereira","author_inst":"Fundacao Oswaldo Cruz"},{"author_name":"Vidya Mave","author_inst":"Johns Hopkins University"},{"author_name":"Senbagavalli Prakash","author_inst":"Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India"},{"author_name":"Kamakshi Prudhula Devalraju","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Neil A. Martinson","author_inst":"Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa"},{"author_name":"Fay Willis","author_inst":"Emory University"},{"author_name":"Marina  C Figueiredo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Universidade do Estado do Amazonas, Manaus, Brazil"},{"author_name":"Artur Trancoso Lopo de Queiroz","author_inst":"Laboratorio de Pesquisa Clinica e Translacional, Instituto Goncalo Moniz, Fundacao Oswaldo Cruz, Salvador, Brazil"},{"author_name":"Venkata Sanjeev Kumar Neela","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Rajesh Karyakarte","author_inst":"BJ Government Medical College, Pune"},{"author_name":"Timothy R Sterling","author_inst":"Vanderbilt University"},{"author_name":"Jerrold J. Ellner","author_inst":"Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Rutgers Health, Newark, New Jersey, USA"},{"author_name":"James C.M. Brust","author_inst":"Division of General Internal Medicine, Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Amita Gupta","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bruno  B Andrade","author_inst":"FIOCRUZ Bahia: Instituto Goncalo Moniz"},{"author_name":"Yan V. Sun","author_inst":"Emory University"},{"author_name":"Neel  R. Gandhi","author_inst":"Emory University School of Public Health"},{"author_name":"- TB GWAS collaboration","author_inst":""}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Metabolic Signatures of Resistance to Mycobacterium Tuberculosis Infection: Insights from a Multi-Country Plasma Metabolomics Study","rel_doi":"10.64898\/2026.09.29.26364337","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364337","rel_abs":"BackgroundTuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST\/IGRA-negative despite sustained exposure, termed \"TB resisters\". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts.\n\nMethodsWe compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST\/IGRA-negative. Mtb-infected close contacts were those who were TST\/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests.\n\nResultsThere were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 {micro}M, whereas hydroxyproline was lower by 0.90 {micro}M, butyrobetaine was lower by 0.06 {micro}M, and homogentisate was lower by 0.0036 {micro}M at false discovery rate of 0.2.\n\nConclusionsPlasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.","rel_num_authors":22,"rel_authors":[{"author_name":"Chang Liu","author_inst":"Emory University School of Public Health"},{"author_name":"Jeffrey  M. Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Matheus Fernandes Gyorfy","author_inst":"Emory University"},{"author_name":"Mariana Araujo Pereira","author_inst":"Fundacao Oswaldo Cruz"},{"author_name":"Vidya Mave","author_inst":"Johns Hopkins University"},{"author_name":"Senbagavalli Prakash","author_inst":"Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India"},{"author_name":"Kamakshi Prudhula Devalraju","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Neil A. Martinson","author_inst":"Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa"},{"author_name":"Fay Willis","author_inst":"Emory University"},{"author_name":"Marina  C Figueiredo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Universidade do Estado do Amazonas, Manaus, Brazil"},{"author_name":"Artur Trancoso Lopo de Queiroz","author_inst":"Laboratorio de Pesquisa Clinica e Translacional, Instituto Goncalo Moniz, Fundacao Oswaldo Cruz, Salvador, Brazil"},{"author_name":"Venkata Sanjeev Kumar Neela","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Rajesh Karyakarte","author_inst":"BJ Government Medical College, Pune"},{"author_name":"Timothy R Sterling","author_inst":"Vanderbilt University"},{"author_name":"Jerrold J. Ellner","author_inst":"Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Rutgers Health, Newark, New Jersey, USA"},{"author_name":"James C.M. Brust","author_inst":"Division of General Internal Medicine, Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Amita Gupta","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bruno  B Andrade","author_inst":"FIOCRUZ Bahia: Instituto Goncalo Moniz"},{"author_name":"Yan V. Sun","author_inst":"Emory University"},{"author_name":"Neel  R. Gandhi","author_inst":"Emory University School of Public Health"},{"author_name":"- TB GWAS collaboration","author_inst":""}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"A Framework to Monitor Editing of Artificial Intelligence-Generated Medical Documentation","rel_doi":"10.64898\/2026.09.30.26364427","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364427","rel_abs":"ObjectiveTo develop and evaluate a framework for characterizing clinician editing of Artificial Intelligence (AI)-generated documentation and assess its feasibility for health system-level monitoring of AI scribes.\n\nMaterials and MethodsWe analyzed outpatient encounters in which an AI scribe was used at a single academic health system, examining the History of Present Illness (HPI) and Assessment and Plan (A&P) sections of notes. We characterized edits on three dimensions: lexical edit intensity based on Levenshtein distance, embedding edit intensity BERTScore, and clinical edit intensity based on removed and added UMLS concepts. We analyzed the Positive Predictive Value (PPV) of clinical edit intensity as a measure of clinically meaningful editing using clinicians as the gold standard, examined correlations among dimensions, and designed exponentially weighted moving-average control charts to monitor longitudinal changes in clinician editing behavior.\n\nResults268,379 encounters were included (267,654 HPI, 267,594 A&P). Clinical edit intensity [&ge;]1 had an 88.9% PPV for clinically meaningful editing. Lexical and embedding edit intensity were highly correlated (Spearman {rho} 0.95), while clinical edit intensity was less strongly correlated with both ({rho} 0.77-0.81). Longitudinal monitoring detected changes coinciding with system-wide rollout.\n\nDiscussionClinicians edited A&Ps more heavily than HPIs, potentially reflecting greater attention to content involving clinical decision-making. Over one-third of sections involved clinical concept changes, and clinical edit intensity identified clinically meaningful edits while providing information complementary to lexical editing measures.\n\nConclusionClinician editing can be characterized at scale using complementary editing dimensions, providing a scalable signal for post-deployment surveillance of the human-AI documentation process.","rel_num_authors":17,"rel_authors":[{"author_name":"Augusto Garcia-Agundez","author_inst":"University of California San Francisco"},{"author_name":"Siyu Zhou","author_inst":"University of California San Francisco"},{"author_name":"Jessica Pourian","author_inst":"University of California San Francisco"},{"author_name":"Catherine Blebea","author_inst":"University of California San Francisco"},{"author_name":"Parnaz Daneshpajouhnejad","author_inst":"University of California San Francisco"},{"author_name":"Elizabeth Dente","author_inst":"University of California San Francisco"},{"author_name":"Kevin Shi","author_inst":"University of California San Francisco"},{"author_name":"Sarah Pollet","author_inst":"University of California San Francisco"},{"author_name":"Fan Xia","author_inst":"University of California San Francisco"},{"author_name":"Xu Shi","author_inst":"University of Michigan"},{"author_name":"Robert Thombley","author_inst":"University of California San Francisco"},{"author_name":"Cynthia Fenton","author_inst":"University of California San Francisco"},{"author_name":"Sara G Murray","author_inst":"University of California San Francisco"},{"author_name":"Julia Adler-Milstein","author_inst":"University of California San Francisco"},{"author_name":"Gabriela Schmajuk","author_inst":"University of California San Francisco"},{"author_name":"Jean Feng","author_inst":"University of California San Francisco"},{"author_name":"Jinoos Yazdany","author_inst":"University of California San Francisco"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Cohort-scale Spatial Host-Microbiome Predicts Post-Resection Recurrence in Colorectal Cancer","rel_doi":"10.64898\/2026.09.29.26363734","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26363734","rel_abs":"The tumor microenvironment in colorectal cancer (CRC) is a heterogeneous ecosystem in which host cells and microbial communities interact dynamically, influencing disease progression. However, the clinical utility is limited by the lack of a scalable spatial host-microbiome technique and by insufficient integration of artificial intelligence for the interpretation of high-dimensional multi-omics data. To overcome the barriers, we present AlphaFISH, a platform technology integrating both technical and computational innovations for multi-omics spatial analysis of clinical biopsies at subcellular resolution. The system uses a sequencing-free, high-throughput, spatial profiling technique to construct, to date, the largest clinical spatial transcriptomics and spatial microbiome datasets acquired from 149 colorectal biopsies from 68 human subjects, supported by a comprehensive scRNA-seq atlas covering 4.27 million cells across 650 patients for robust cell annotation. Deep learning of the cohort-scale dual-omics data, consisting of more than 10 million subcellular sampling vectors, enables the development of a transformer model with joint embeddings of gene expression, spatial architecture, and the microbial microenvironment in colon tissues, achieving nearly 90% accuracy in predicting CRC-associated pathological features using unseen spatial omics inputs. The AI interrogation further predicts tumour recurrence at 81% accuracy in 28 patients followed within 1 year post tumor resection period. AlphaFISH reveals that spatial interactions between Fusobacterium and cellular niche consisting of tumor and T cells serve as key markers of CRC malignancy, progression, and recurrence, indicating the critical role of spatial bacterial-immune crosstalks.","rel_num_authors":13,"rel_authors":[{"author_name":"Feng Guo","author_inst":"City University of Hong Kong"},{"author_name":"Hailiang Sun","author_inst":"City University of Hong Kong"},{"author_name":"Chenxi Hu","author_inst":"Tsinghua University; Institute for AI Industry Research, Tsinghua University"},{"author_name":"Minsheng Hao","author_inst":"Tsinghua University"},{"author_name":"Youyang Wan","author_inst":"City University of Hong Kong"},{"author_name":"Chuxiao Xiong","author_inst":"City University of Hong Kong"},{"author_name":"Feng Gao","author_inst":"The Sixth Affiliated Hospital, Sun Yat-sen University"},{"author_name":"Lung-Yi Mak","author_inst":"University of Hong Kong"},{"author_name":"Xin Deng","author_inst":"City University of Hong Kong"},{"author_name":"Ajay Goel","author_inst":"City of Hope Comprehensive Cancer Center"},{"author_name":"Jia Ke","author_inst":"The Sixth Affiliated Hospital, Sun Yat-sen University"},{"author_name":"Jianzhu Ma","author_inst":"Institute for AI Industry Research, Tsinghua University; Department of Electronic Engineering, Tsinghua University"},{"author_name":"Peng Shi","author_inst":"City University of Hong Kong; Hong Kong Centre for Cerebro-Cardiovascular Health Engineering; COSDAF, City University of Hong Kong; Shenzhen Research Institute,"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Functional near infrared spectroscopy-based dual-stream adaptive language mapping in adults with and without post-stroke aphasia","rel_doi":"10.64898\/2026.09.28.26364106","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.28.26364106","rel_abs":"In aphasia, variability in lesion location and post-stroke network reorganization complicates efforts to identify the regions that support semantic versus phonological processing. In prior work, researchers have often examined these domains in separate patient cohorts, further limiting direct comparison of their neural substrates within aphasia. To address this gap, we investigated the degree of overlap and specialization within semantic and phonological networks in adults with and without aphasia and examined how neural activity relates to task performance and lesion profiles. Sixteen neurologically healthy adults and 15 participants with aphasia following left hemisphere stroke completed adaptive Semantic Matching and Rhyme Judgment tasks during functional near-infrared spectroscopy (fNIRS) recording. Changes in oxyhemoglobin and deoxyhemoglobin were measured within regions of interest, and activation was related to standardized task performance and damage to left ventral and dorsal language pathways in participants with aphasia. Controls showed largely distinct activation patterns consistent with prior fMRI findings: Semantic Matching preferentially recruited ventral stream regions, particularly left temporal cortex, whereas Rhyme Judgment more strongly recruited dorsal regions, including left inferior frontal and inferior parietal cortex. Participants with aphasia showed greater inter-individual variability and no clear group-level segregation of dorsal and ventral activity. Better Semantic Matching performance was associated with less left ventral stream damage and greater left dorsal activation, whereas better Rhyme Judgment performance was associated with lower right hemisphere ventral and dorsal activation. These findings suggest that post-stroke language performance reflects residual specialization, lesion-dependent constraints, and flexible recruitment of surviving tissue. Overall, fNIRS shows promise as an alternative to fMRI for language mapping in post-stroke aphasia, although replication in larger samples with more extensive cortical coverage is warranted.","rel_num_authors":8,"rel_authors":[{"author_name":"Erin L. Meier","author_inst":"Northeastern University"},{"author_name":"Veronica Fletcher","author_inst":"Northeastern University"},{"author_name":"Caela Hung","author_inst":"Northeastern University"},{"author_name":"Esprit Ange Andraos","author_inst":"Northeastern University"},{"author_name":"Leanna Ugent","author_inst":"Northeastern University"},{"author_name":"Gengchen Wei","author_inst":"Northeastern University"},{"author_name":"David J. Lin","author_inst":"Massachusetts General Hospital"},{"author_name":"Meryem A. Y\u00fccel","author_inst":"Boston University"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Diurnal Heart Rate Range and Heart Rate Excursions: Novel Characterizations of Wearable Heart Rate Variability With an Application to Aging","rel_doi":"10.64898\/2026.09.29.26364190","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364190","rel_abs":"AbstractO_ST_ABSBackgroundC_ST_ABSWearable heart rate (HR) monitors collect minute-level HR continuously across days, yet these data are commonly reduced to scalars such as resting HR (RHR) and heart rate reserve (HRR), defined as the difference between maximum HR and RHR. This discards two dimensions of within-day HR dynamics: how HR range varies across the day and the magnitude and duration of individual HR rises and declines. We introduce diurnal heart rate range (dHRR), a time-of-day-specific measure of HR range across repeated days, and heart rate excursions (HREs), individual HR rises and declines characterized by amplitude and duration.\n\nMethodsWe analyzed minute-level HR from 750 participants in the Baltimore Longitudinal Study of Aging (mean [SD] age, 66 [12] years). We estimated the 5th, 50th, and 95th HR percentiles at each minute of the 24-hour cycle across repeated days, defining diurnal HR (dHR) as the median curve and dHRR as the 95th minus 5th percentile curve. A moving-average algorithm segmented HR into HREs. Function-on-scalar regression modeled dHR and dHRR by age, sex,and BMI across the day; local polynomial regression examined age and sex differences in HRR and HREs.\n\nResultsConventional HRR decreased with age, indicating a smaller overall observed HR range. Age-related differences in dHR varied substantially across the day: between ages 40 and 80, fitted dHR was approximately 2 bpm lower at 3:00 AM but 8 bpm lower at 6:00 PM. dHRR showed that this contraction was not uniform across the day: the evening peak near 6:00 PM at younger ages was largely absent by age 70, while the morning peak shifted from approximately 9:00 AM toward noon. At the event level, HREs showed two parallel age-related changes: between ages 40 and 80, mean excursion amplitude decreased from approximately 40 to 32 bpm, while mean excursion duration increased from approximately 90 to 104 minutes.\n\nConclusionsAging was characterized by a smaller global HR range, compression and reorganization of HR range across the day, and smaller, longer-lasting HREs. dHRR and HREs provide complementary views of the diurnal organization and event-level dynamics of wearable HR and are implemented in the open-source ihr R package.","rel_num_authors":8,"rel_authors":[{"author_name":"Samuel D Fansler","author_inst":"Johns Hopkins University Bloomberg School of Public Health"},{"author_name":"Owen Yoo","author_inst":"University of Michigan College of Literature, Science, and the Arts"},{"author_name":"Shuiqing Han","author_inst":"University of Michigan College of Literature, Science, and the Arts"},{"author_name":"Luigi Ferrucci","author_inst":"NIH-NIA"},{"author_name":"Eleanor Simonsick","author_inst":"NIH-NIA"},{"author_name":"Jennifer A Schrack","author_inst":"Johns Hopkins University Bloomberg School of Public Health"},{"author_name":"Irina Gaynanova","author_inst":"University of Michigan School of Public Health"},{"author_name":"Vadim Zipunnikov","author_inst":"Johns Hopkins University Bloomberg School of Public Health"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Release-aware, SAS-equivalent Elixhauser comorbidity scoring in R: cross-implementation validation and application to Texas inpatient discharges","rel_doi":"10.64898\/2026.10.01.26364551","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364551","rel_abs":"The Elixhauser comorbidity measures are among the most widely used risk adjusters in administrative health data. Their reference implementation, published by the Agency for Healthcare Research and Quality (AHRQ) as SAS programs, has eleven annual releases in two families, one screening pre-existing conditions on the present-on-admission (POA) indicator and the earlier one on Medicare Severity Diagnosis-Related Group, and no implementation of every release existed outside SAS. We developed ecsr10, an open-source R package covering all eleven releases of both families, with a browser-based application over the same functions, and validated it against AHRQs own SAS programs over 40,027,114 value-level comparisons with zero disagreements. Two existing open-source reimplementations scored on the same dataset showed reproducible defects. We then used the package to quantify two choices AHRQs software leaves to the analyst and studies seldom report, the release and the handling of diagnoses not present on admission, on the Texas Inpatient Public Use Data File, 2016 Q1-2019 Q4 (8,585,244 adult discharges from 726 hospitals), scoring one predefined cohort under every release and pre-existing-condition setting. POA handling dominated: disabling POA changed the comorbidity profile of 56.7% of admissions, and a POA-naive mortality index scored higher on discrimination (area under the curve 0.807 versus 0.789; difference 0.0183, 95% confidence interval 0.0161-0.0204) by counting in-hospital complications as pre-existing disease. POA reporting was bimodal across hospitals, so pooled hospital comparisons on such a file partly compare documentation practice. Because the cohort predates every release compared, the release contrast is a lower bound: the release chosen between v2022.1 and v2026.1 changed at least one comorbidity flag for 0.0036% of admissions, yet a single revision of ten mortality weights changed the comorbidity index for 42.4% of them. An exactly validated implementation makes such choices measurable; release, POA handling and the implementation used should be reported as study characteristics.\n\nAuthor summaryHospital discharge records are routinely used to compare hospitals, adjust for how sick patients were on arrival, and predict outcomes. Much of that work rests on comorbidity measures maintained by a United States federal agency, whose reference software exists only in SAS and has changed eleven times. We built a free package that reproduces the agencys programs exactly in every version, checked it against the agencys own programs with no disagreement, and found that two existing free reimplementations do not. We then used it to score 8.6 million Texas hospital admissions every way the agencys software allows, to ask which of the choices studies rarely report change conclusions. Far more consequential than the annual version was whether the software is told which diagnoses patients had on arrival. Ignoring that information counts hospital complications as pre-existing illness. It changed the comorbidity profile of more than half of admissions, and made a mortality model look more accurate while measuring something else. Hospitals also differed sharply in whether they recorded that information. The annual version barely moved the comorbidity flags, yet one revision of the scoring weights changed the risk score for 42% of admissions.","rel_num_authors":2,"rel_authors":[{"author_name":"Minh Tran","author_inst":"University of Kansas Medical Center"},{"author_name":"Dong Pei","author_inst":"University of Kansas Medical Center"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Safety & Effectiveness of Mifepristone for Medication Abortion in a National Claims Cohort, 2017-2023","rel_doi":"10.64898\/2026.10.02.26364611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364611","rel_abs":"BACKGROUNDExtensive research has documented medication abortion safety. Large-scale insurance claims data offer an important complement to clinical studies by assessing real-world outcomes in routine practice. In claims data, researchers must infer mifepristone use, clinical outcomes, and event severity from diagnosis, treatment, and utilization codes. Understanding how these analytic choices influence safety and effectiveness estimates is important for interpreting evidence used to inform care delivery and medication policy.\n\nMETHODSWe conducted a retrospective cohort study of mifepristone use with national multipayer claims data, both medical and pharmacy, from 2017 through 2023. We identified both a broad cohort and a more specific medication-abortion cohort after excluding evidence of other indications. We classified potential safety events, effectiveness outcomes, and health care utilization within 45 days of mifepristone use. Serious adverse events required claims-observable consequences consistent with FDA seriousness criteria.\n\nRESULTSThe broad cohort included 869,409 mifepristone uses among 698,780 unique patients for medication abortion or other indications. After restriction to uses with additional evidence of medication abortion, 775,931 uses among 626,443 unique patients remained. In the medication-abortion cohort, 3,306 uses were followed by at least one serious adverse event, resulting in an absolute 45-day risk of 0.43%. Serious bleeding or hemorrhage occurred after 0.24% of uses, serious infection or sepsis after 0.05%, serious hypersensitivity or anaphylaxis after 0.01%, and serious ectopic pregnancy outcomes after 0.05%.\n\nCONCLUSIONSSerious adverse events after mifepristone use were rare in this national study. Applying established, clinically grounded criteria markedly reduced the number of events classified as serious.","rel_num_authors":6,"rel_authors":[{"author_name":"Liana R Woskie","author_inst":"Department of Community Health, Tufts University, Medford, Massachusetts, USA"},{"author_name":"Julia Strasser","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Risa Griffin","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Qian (Eric) Luo","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Maria I. Rodriguez","author_inst":"Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA"},{"author_name":"Ushma Upadhyay","author_inst":"Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Safety & Effectiveness of Mifepristone for Medication Abortion in a National Claims Cohort, 2017-2023","rel_doi":"10.64898\/2026.10.02.26364611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364611","rel_abs":"BACKGROUNDExtensive research has documented medication abortion safety. Large-scale insurance claims data offer an important complement to clinical studies by assessing real-world outcomes in routine practice. In claims data, researchers must infer mifepristone use, clinical outcomes, and event severity from diagnosis, treatment, and utilization codes. Understanding how these analytic choices influence safety and effectiveness estimates is important for interpreting evidence used to inform care delivery and medication policy.\n\nMETHODSWe conducted a retrospective cohort study of mifepristone use with national multipayer claims data, both medical and pharmacy, from 2017 through 2023. We identified both a broad cohort and a more specific medication-abortion cohort after excluding evidence of other indications. We classified potential safety events, effectiveness outcomes, and health care utilization within 45 days of mifepristone use. Serious adverse events required claims-observable consequences consistent with FDA seriousness criteria.\n\nRESULTSThe broad cohort included 869,409 mifepristone uses among 698,780 unique patients for medication abortion or other indications. After restriction to uses with additional evidence of medication abortion, 775,931 uses among 626,443 unique patients remained. In the medication-abortion cohort, 3,306 uses were followed by at least one serious adverse event, resulting in an absolute 45-day risk of 0.43%. Serious bleeding or hemorrhage occurred after 0.24% of uses, serious infection or sepsis after 0.05%, serious hypersensitivity or anaphylaxis after 0.01%, and serious ectopic pregnancy outcomes after 0.05%.\n\nCONCLUSIONSSerious adverse events after mifepristone use were rare in this national study. Applying established, clinically grounded criteria markedly reduced the number of events classified as serious.","rel_num_authors":6,"rel_authors":[{"author_name":"Liana R Woskie","author_inst":"Department of Community Health, Tufts University, Medford, Massachusetts, USA"},{"author_name":"Julia Strasser","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Risa Griffin","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Qian (Eric) Luo","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Maria I. Rodriguez","author_inst":"Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA"},{"author_name":"Ushma Upadhyay","author_inst":"Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Three Cerebellar Imaging Subtypes in Schizophrenia with Distinct Spatiotemporal Trajectories and Biological Characteristics","rel_doi":"10.64898\/2026.10.03.26364634","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.03.26364634","rel_abs":"The potential role of the cerebellum in the pathophysiology of schizophrenia (SCZ) has received relatively insufficient attention. Prior studies characterized cerebellar abnormalities mainly at the group level, without fully considering the heterogeneity of the disease. Here, we applied a machine learning approach (Subtype and Stage Inference, SuStaIn) to cross-sectional 3D volumetric MRIs of the cerebellum, including 1,588 individuals with SCZ (638 females; mean age: 31.9 {+\/-} 12.0 years) and 2,341 healthy controls (HC) (1,041 females; mean age: 33.3 {+\/-} 13.6 years), from 17 sites worldwide. SuStaIn identified three distinct spatiotemporal trajectories in cerebellar gray matter volume (GMV) reduction, respectively originating in lobule X (subtype 1), lobule III (subtype 2) and lobule VIIb (subtype 3), with subtypes 1 and 3 corresponding to the posterior lobe and subtype 2 to the anterior lobe. These cross-sectionally inferred trajectories were replicated in two independent samples of 1,334 and 530 patients, respectively. Multimodal analyses using neuroimaging, transcriptomic and behavioral data revealed subtype-specific biological characteristics in brain morphological patterns, cerebellar-cortical connectivity, gene expression and clinical symptoms. Specifically, subtype-related genes were enriched in metabolism-related processes and immunity-related processes, respectively, in subtype 1 and subtype 2; subtype 3 showed more severe brain abnormalities and worse cognitive symptoms. Treatment data from 381 patients, with up to 12 months of follow-up, revealed poorer response to antipsychotic medications (APM) in subtype 2 and subtype 3, but better response to transcranial magnetic stimulation (TMS) in subtype 1, which also had worse emotion-related symptoms. Together, our findings offer a comprehensive characterization of the heterogeneity of cerebellar pathophysiological progresses in SCZ, which may help in developing future clinical stratification and targeted intervention strategies.","rel_num_authors":69,"rel_authors":[{"author_name":"Zhaoyun Liu","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"},{"author_name":"Zhenyu Huang","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"},{"author_name":"Xinjia Lin","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"},{"author_name":"Jingyu Zhou","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"},{"author_name":"Hao Hu","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Qian Guo","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Yingying Tang","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Tianhong Zhang","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Jijun Wang","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Weihua Yue","author_inst":"Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, PR China."},{"author_name":"Yuyanan Zhang","author_inst":"Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, PR China."},{"author_name":"Xin Yu","author_inst":"Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, PR China."},{"author_name":"Long-Biao Cui","author_inst":"Schizophrenia Imaging Lab, Xijing 986 Hospital, Fourth Military Medical University, Xi'an, PR China"},{"author_name":"Xiao Chang","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China."},{"author_name":"Huan Huang","author_inst":"huan_huang17@163.com"},{"author_name":"Cheng Luo","author_inst":"The Clinical Hospital of Chengdu Brain Science Institute, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chen"},{"author_name":"Dezhong Yao","author_inst":"The Clinical Hospital of Chengdu Brain Science Institute, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chen"},{"author_name":"Ryota Hashimoto","author_inst":"Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan."},{"author_name":"Junya Matsumoto","author_inst":"Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan."},{"author_name":"Kiyotaka Nemoto","author_inst":"Department of Medical Informatics and Management and Psychiatry, Institute of Medicine, University of Tsukuba, Tsukuba, Japan."},{"author_name":"Tilo Kircher","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Florian Thomas-Odenthal","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany"},{"author_name":"Paula Usemann","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Lea Teutenberg","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Benjamin Straube","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Igor Nenadi\u0107","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Frederike Stein","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany"},{"author_name":"Nina Alexander","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Andreas Jansen","author_inst":"Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany."},{"author_name":"Hamidreza Jamalabadi","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Nooshin Javaheripour","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Jannik Lepper","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Udo Dannlowski","author_inst":"Department of Psychiatry, Medical School and University Medical Center OWL, Protestant Hospital of the Bethel Foundation, Bielefeld University."},{"author_name":"Dominik Grotegerd","author_inst":"Institute for Translational Psychiatry, University of Munster, Munster, Germany."},{"author_name":"Susanne Meinert","author_inst":"Institute for Translational Psychiatry, University of Munster, Munster, Germany."},{"author_name":"Kira Flinkenflugel","author_inst":"Institute for Translational Psychiatry, University of Munster, Munster, Germany"},{"author_name":"Rebekka Lencer","author_inst":"Institute for Translational Psychiatry, University of Munster, Munster, Germany"},{"author_name":"Michael Ziller","author_inst":"Department of Psychiatry, University of Munster, 48149 Munster, Germany."},{"author_name":"Ali Saffet Gonul","author_inst":"Ege University school of medicine SoCAT Lab, Izmir, Turkey."},{"author_name":"Asli Ceren Hinc","author_inst":"Ege University school of medicine SoCAT Lab, Izmir, Turkey."},{"author_name":"Kang Sim","author_inst":"West Region, Institute of Mental Health, Singapore, Singapore."},{"author_name":"Qian Hui Chew","author_inst":"West Region, Institute of Mental Health, Singapore, Singapore."},{"author_name":"Yann Quid\u00e9","author_inst":"NeuroRecovery Research Hub, School of Psychology, The Unversity of New South Wales (UNSW) Sydney, Sydney, NSW, Australia; Centre for Pain IMPACT, Neuroscience R"},{"author_name":"Melissa J. Green","author_inst":"School of Clinical Medicine, Discipline of Psychiatry and Mental Health, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia."},{"author_name":"Young-Chul chung","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Republic of Korea."},{"author_name":"Woo-Sung Kim","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Republic of Korea."},{"author_name":"Soyolsaikhan Odkhuu","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Republic of Korea."},{"author_name":"Felice Iasevoli","author_inst":"Section of Psychiatry - Department of Neuroscience and Reproductive Science and Dentistry - University \"Federico II\", Naples, Italy."},{"author_name":"Giuseppe Pontillo","author_inst":"Department of Advanced Biomedical Sciences - University \"Federico II\", Naples, Italy."},{"author_name":"Andrea de Bartolomeis","author_inst":"Section of Psychiatry - Department of Neuroscience and Reproductive Science and Dentistry - University \"Federico II\", Naples, Italy."},{"author_name":"Sirio Cocozza","author_inst":"Department of Advanced Biomedical Sciences - University \"Federico II\", Naples, Italy"},{"author_name":"Annarita Barone","author_inst":"Section of Psychiatry - Department of Neuroscience and Reproductive Science and Dentistry - University \"Federico II\", Naples, Italy."},{"author_name":"Arturo Brunetti","author_inst":"Department of Advanced Biomedical Sciences - University \"Federico II\", Naples, Italy"},{"author_name":"Mariateresa Ciccarelli","author_inst":"Section of Psychiatry - Department of Neuroscience and Reproductive Science and Dentistry - University \"Federico II\", Naples, Italy."},{"author_name":"Mario Tranfa","author_inst":"Department of Advanced Biomedical Sciences - University \"Federico II\", Naples, Italy"},{"author_name":"Tamsyn E.Van Rheenen","author_inst":"Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, MEL, Australia."},{"author_name":"Susan L Rossell","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Matthew Hughes","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Will Woods","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Sean Carruthers","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Philip J. Sumner","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Elysha Ringin","author_inst":"Department of Psychiatry, University of Melbourne, Parkville, Australia."},{"author_name":"Georgia Caruana","author_inst":"Department of Psychiatry, University of Melbourne, Parkville, Australia."},{"author_name":"Jessica A. Turner","author_inst":"Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States."},{"author_name":"Theo G.M. van Erp","author_inst":"Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine Hall, room 109, Irvine, CA,"},{"author_name":"Lena Palaniyappan","author_inst":"Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada."},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA"},{"author_name":"Jianfeng Feng","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China."},{"author_name":"Yuchao Jiang","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"}],"rel_date":"2026-10-05","rel_site":"medrxiv"}]}