{"gname":"Institute of Science and Technology Austria","grp_id":"12","rels":[{"rel_title":"Individualized cortical gradient and network topology reveal symptom-linked disruptions and neurobiological subtypes in schizophrenia","rel_doi":"10.64898\/2026.04.25.26351736","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.25.26351736","rel_abs":"Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small-world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior-superior and frontal-temporal axes. Alterations in small-world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain-symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.","rel_num_authors":111,"rel_authors":[{"author_name":"Bin Wan","author_inst":"Department of Psychiatry, University Hospitals of Gen\u00e8ve, Thonex, Switzerland; Synapsy Center for Neuroscience and Mental Health Research, University of Gen\u00e8ve,"},{"author_name":"Sara Larivi\u00e8re","author_inst":"Department of Nuclear Medicine and Radiobiology, Universite de Sherbrooke, Montreal, Canada."},{"author_name":"Clara A. Moreau","author_inst":"Sainte Justine Hospital Azrieli Research Center, Department of Psychiatry and Addictology, University of Montr\u00e9al, Montr\u00e9al, QC, Canada."},{"author_name":"Varun Warrier","author_inst":"Department of Psychiatry, University of Cambridge, Cambridge, UK."},{"author_name":"Richard A.I. Bethlehem","author_inst":"Department of Psychology, University of Cambridge, Cambridge, UK."},{"author_name":"Yun-Shuang Fan","author_inst":"The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Cheng"},{"author_name":"Yuankai He","author_inst":"Department of Psychiatry, University of Cambridge, Cambridge, UK."},{"author_name":"Ingrid Agartz","author_inst":"Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway."},{"author_name":"Stener Nerland","author_inst":"Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway."},{"author_name":"Erik G. J\u00f6nsson","author_inst":"Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Swede"},{"author_name":"Derin Cobia","author_inst":"Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Canada."},{"author_name":"Lei Wang","author_inst":"Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Canada."},{"author_name":"Benedicto Crespo Facorro","author_inst":"Instituto de Biomedicina de Sevilla (IBiS) HUVR\/CSIC, CIBERSAM, University of Seville, Seville, Spain."},{"author_name":"Rafael Romero-Garcia","author_inst":"Instituto de Biomedicina de Sevilla (IBiS) HUVR\/CSIC, CIBERSAM, University of Seville, Seville, Spain."},{"author_name":"Patricia Segura","author_inst":"Instituto de Biomedicina de Sevilla (IBiS) HUVR\/CSIC, CIBERSAM, University of Seville, Seville, Spain."},{"author_name":"Nerisa Banaj","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy."},{"author_name":"Daniela Vecchio","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy."},{"author_name":"Tamsyn Van Rheenen","author_inst":"Department of Psychiatry, University of Melbourne, Melbourne, Australia; Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawtho"},{"author_name":"Philip James Sumner","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Elysha Ringin","author_inst":"Department of Psychiatry, University of Melbourne, Melbourne, Australia."},{"author_name":"Susan Rossell","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Sean Carruthers","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Philip J. Sumner","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Will Woods","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Matthew Hughes","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Gary Donohoe","author_inst":"School of Psychology, Centre for Neuroimaging, Cognition and Genomics (NICOG), & Galway Neuroscience Centre, University of Galway, Galway, Ireland."},{"author_name":"Emma Corley","author_inst":"School of Psychology, Centre for Neuroimaging, Cognition and Genomics (NICOG), & Galway Neuroscience Centre, University of Galway, Galway, Ireland."},{"author_name":"Ulrich Schall","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Frans Henskens","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Rodney Scott","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Patricia Michie","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Carmel Loughland","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Paul Rasser","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Murray Cairns","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Bryan Mowry","author_inst":"Faculty of Health, Medicine and Behavioural Sciences, University of Queensland, Brisbane, Australia."},{"author_name":"Stanley Catts","author_inst":"Faculty of Health, Medicine and Behavioural Sciences, University of Queensland, Brisbane, Australia."},{"author_name":"Christos Pantelis","author_inst":"Department of Psychiatry, University of Melbourne, Carlton South, VIC, Australia."},{"author_name":"Aristotle Voineskos","author_inst":"Campbell Family Mental Health Research Institute, CAMH, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, "},{"author_name":"Erin Dickie","author_inst":"Campbell Family Mental Health Research Institute, CAMH, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, "},{"author_name":"Henk Temmingh","author_inst":"Neuroscience Institute, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South "},{"author_name":"Freda Scheffler","author_inst":"Neuroscience Institute, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South "},{"author_name":"Oliver Gruber","author_inst":"Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Baden-W\u00fcrttemberg, Germany."},{"author_name":"Rosanne Picotin","author_inst":"Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Baden-W\u00fcrttemberg, Germany."},{"author_name":"Vince D. Calhoun","author_inst":"The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Em"},{"author_name":"Kyle M. Jensen","author_inst":"The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Em"},{"author_name":"Filip _paniel","author_inst":"National Institute of Mental Health, Klecany, Czech Republic."},{"author_name":"David Tomecek","author_inst":"National Institute of Mental Health, Klecany, Czech Republic."},{"author_name":"Raymond Salvador","author_inst":"FIDMAG Germanes Hospital\u00e0ries Research Foundation, Barcelona, Spain; CIBERSAM, ISCIII, Barcelona, Spain."},{"author_name":"Andriana Karuk","author_inst":"FIDMAG Germanes Hospital\u00e0ries Research Foundation, Barcelona, Spain; CIBERSAM, ISCIII, Barcelona, Spain."},{"author_name":"Raymond Salvador","author_inst":"FIDMAG Germanes Hospital\u00e0ries Research Foundation, Barcelona, Spain; CIBERSAM, ISCIII, Barcelona, Spain."},{"author_name":"Edith Pomarol-Clotet","author_inst":"FIDMAG Germanes Hospital\u00e0ries Research Foundation, Barcelona, Spain; CIBERSAM, ISCIII, Barcelona, Spain."},{"author_name":"Tilo Kircher","author_inst":"Department of Psychiatry, Marburg University, Marburg, Germany."},{"author_name":"Lea Teutenberg","author_inst":"Department of Psychiatry, Marburg University, Marburg, Germany."},{"author_name":"Frederike Stein","author_inst":"Department of Psychiatry, Marburg University, Marburg, Germany."},{"author_name":"Udo Dannlowski","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany; Department of Psychiatry, Medical School and University Medical Center OWL, Pro"},{"author_name":"Dominik Grotegerd","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany."},{"author_name":"Tiana Borgers","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany."},{"author_name":"Tim Hahn","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany."},{"author_name":"Rebekka Lencer Lencer","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany."},{"author_name":"Carlos L\u00f3pez-Jaramillo","author_inst":"Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia."},{"author_name":"Melissa Green","author_inst":"School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia."},{"author_name":"Yann Quide","author_inst":"School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia."},{"author_name":"Vaughan Carr","author_inst":"School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia."},{"author_name":"Stefan Ehrlich","author_inst":"Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden"},{"author_name":"Peter Kochunov","author_inst":"Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA."},{"author_name":"Christian Sorg","author_inst":"Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical Universtiy of Munich, Munich,"},{"author_name":"Melissa Thalhammer","author_inst":"Department of Psychiatry, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical Universtiy of Munich, Munich, Germany."},{"author_name":"David Glahn","author_inst":"Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA."},{"author_name":"Amanda Rodrigue","author_inst":"Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA."},{"author_name":"Kang Sim","author_inst":"West Region, Institute of Mental Health, Singapore, Singapore."},{"author_name":"Ali Saffet Gonul","author_inst":"Ege University Hospital, Psychiatry Department, Izmir, Turkiye."},{"author_name":"Aslihan Uyar Demir","author_inst":"Ege University Hospital, Psychiatry Department, Izmir, Turkiye."},{"author_name":"Nicolas Crossley","author_inst":"Department of Psychiatry, Pontificia Universidad Cat\u00f3lica de Chile, Chile."},{"author_name":"Alfonso Gonzalez-Valderrama","author_inst":"School of Medicine, Finis Terrae University, Chile; and Early Intervention Service (PROITP), Dr Jos\u00e9 Horwitz Barak Psychiatric Institute, Santiago, Chile."},{"author_name":"Philipp Homan","author_inst":"Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland."},{"author_name":"Wolfgang Omlor","author_inst":"Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland."},{"author_name":"Giacomo Cecere","author_inst":"Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland."},{"author_name":"Felice Iasevoli","author_inst":"Department of Neuroscience, University of Naples \"Federico II\", Naples, Italy."},{"author_name":"Giuseppe Pontillo","author_inst":"Department of Advanced Biomedical Sciences, University of Naples \"Federico II\", Naples, Italy."},{"author_name":"Raquel Gur","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."},{"author_name":"Ruben C. Gur","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."},{"author_name":"Kosha Ruparel","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."},{"author_name":"Theodore D. Satterthwaite","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."},{"author_name":"Scott Sponheim","author_inst":"Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA."},{"author_name":"Caroline Demro","author_inst":"Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA."},{"author_name":"Young Chul Chung","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea."},{"author_name":"Soyolsaikhan Odkhuu","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea."},{"author_name":"Albert Yang","author_inst":"Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Brain Science, National Yang Ming "},{"author_name":"I-Jou Chi","author_inst":"Department of Occupational Therapy, Kaohsiung Medical University, Kaohsiung City, Taiwan; Biomedical Artificial Intelligence Academy, Kaohsiung Medical Universi"},{"author_name":"Ole Andreassen","author_inst":"Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & University of Oslo, Oslo, Norway; KG Jebsen Centre for Neur"},{"author_name":"Lars T. Westlye","author_inst":"Department of Psychology, University of Oslo, Oslo, Norway; Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital &"},{"author_name":"Unn K.H. Haukvik","author_inst":"Adult Psychiatry Department, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Research and "},{"author_name":"Nadine Parker","author_inst":"Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & University of Oslo, Oslo, Norway."},{"author_name":"Jakub Kopal","author_inst":"Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & University of Oslo, Oslo, Norway."},{"author_name":"Dag Alnaes","author_inst":"Department of Psychology, University of Oslo, Oslo, Norway; Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital &"},{"author_name":"Jaroslav Rokicki","author_inst":"Centre of Research and Education in Forensic Psychiatry (SIFER), Oslo University Hospital, Oslo, Norway; Department of Electronic Systems, Vilnius Tech, Vilnius"},{"author_name":"Carl M Sellgren","author_inst":"Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden; Centre for Psychiatric Research, Department of Clinical Neuroscience, Karol"},{"author_name":"Maria Lee","author_inst":"Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Swede"},{"author_name":"Stefan Borgwardt","author_inst":"Department of Psychiatry and Psychotherapy, Center of Brain, Behavior and Metabolism (CBBM), University of L\u00fcbeck, L\u00fcbeck, Germany."},{"author_name":"Mihai Avram","author_inst":"Department of Psychiatry and Psychotherapy, Center of Brain, Behavior and Metabolism (CBBM), University of L\u00fcbeck, L\u00fcbeck, Germany."},{"author_name":"Taeyoung Lee","author_inst":"Department of Psychiatry, Kyungpook National University, Daegu, South Korea."},{"author_name":"Hang Joon Jo","author_inst":"Department of Physiology, College of Medicine, Hanyang University, Seoul, South Korea."},{"author_name":"Irina Lebedeva","author_inst":"Mental Health Research Center, Moscow, Russian Federation."},{"author_name":"Alexander Tomyshev","author_inst":"Mental Health Research Center, Moscow, Russian Federation."},{"author_name":"Stefan Kaiser","author_inst":"Department of Psychiatry, University Hospitals of Gen\u00e8ve, Thonex, Switzerland; Synapsy Center for Neuroscience and Mental Health Research, University of Gen\u00e8ve,"},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA."},{"author_name":"Theo G.M. van Erp","author_inst":"Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA."},{"author_name":"Jessica A. Turner","author_inst":"Department of Psychiatry and Behavioral Health, the Ohio State University, Columbus, OH, USA."},{"author_name":"Boris C. Bernhardt","author_inst":"McConnell Brain Imaging Centre, Montr\u00e9al Neurological Institute-Hospital, McGill University, Montr\u00e9al, QC, Canada."},{"author_name":"Sofie L. Valk","author_inst":"Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Cent"},{"author_name":"Matthias Kirschner","author_inst":"Department of Psychiatry, University Hospitals of Gen\u00e8ve, Thonex, Switzerland; Synapsy Center for Neuroscience and Mental Health Research, University of Gen\u00e8ve,"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Individualized cortical gradient and network topology reveal symptom-linked disruptions and neurobiological subtypes in schizophrenia","rel_doi":"10.64898\/2026.04.25.26351736","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.25.26351736","rel_abs":"Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small-world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior-superior and frontal-temporal axes. Alterations in small-world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain-symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.","rel_num_authors":111,"rel_authors":[{"author_name":"Bin Wan","author_inst":"Department of Psychiatry, University Hospitals of Gen\u00e8ve, Thonex, Switzerland; Synapsy Center for Neuroscience and Mental Health Research, University of Gen\u00e8ve,"},{"author_name":"Sara Larivi\u00e8re","author_inst":"Department of Nuclear Medicine and Radiobiology, Universite de Sherbrooke, Montreal, Canada."},{"author_name":"Clara A. Moreau","author_inst":"Sainte Justine Hospital Azrieli Research Center, Department of Psychiatry and Addictology, University of Montr\u00e9al, Montr\u00e9al, QC, Canada."},{"author_name":"Varun Warrier","author_inst":"Department of Psychiatry, University of Cambridge, Cambridge, UK."},{"author_name":"Richard A.I. Bethlehem","author_inst":"Department of Psychology, University of Cambridge, Cambridge, UK."},{"author_name":"Yun-Shuang Fan","author_inst":"The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Cheng"},{"author_name":"Yuankai He","author_inst":"Department of Psychiatry, University of Cambridge, Cambridge, UK."},{"author_name":"Ingrid Agartz","author_inst":"Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway."},{"author_name":"Stener Nerland","author_inst":"Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway."},{"author_name":"Erik G. J\u00f6nsson","author_inst":"Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Swede"},{"author_name":"Derin Cobia","author_inst":"Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Canada."},{"author_name":"Lei Wang","author_inst":"Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Canada."},{"author_name":"Benedicto Crespo Facorro","author_inst":"Instituto de Biomedicina de Sevilla (IBiS) HUVR\/CSIC, CIBERSAM, University of Seville, Seville, Spain."},{"author_name":"Rafael Romero-Garcia","author_inst":"Instituto de Biomedicina de Sevilla (IBiS) HUVR\/CSIC, CIBERSAM, University of Seville, Seville, Spain."},{"author_name":"Patricia Segura","author_inst":"Instituto de Biomedicina de Sevilla (IBiS) HUVR\/CSIC, CIBERSAM, University of Seville, Seville, Spain."},{"author_name":"Nerisa Banaj","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy."},{"author_name":"Daniela Vecchio","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy."},{"author_name":"Tamsyn Van Rheenen","author_inst":"Department of Psychiatry, University of Melbourne, Melbourne, Australia; Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawtho"},{"author_name":"Philip James Sumner","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Elysha Ringin","author_inst":"Department of Psychiatry, University of Melbourne, Melbourne, Australia."},{"author_name":"Susan Rossell","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Sean Carruthers","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Philip J. Sumner","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Will Woods","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Matthew Hughes","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia."},{"author_name":"Gary Donohoe","author_inst":"School of Psychology, Centre for Neuroimaging, Cognition and Genomics (NICOG), & Galway Neuroscience Centre, University of Galway, Galway, Ireland."},{"author_name":"Emma Corley","author_inst":"School of Psychology, Centre for Neuroimaging, Cognition and Genomics (NICOG), & Galway Neuroscience Centre, University of Galway, Galway, Ireland."},{"author_name":"Ulrich Schall","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Frans Henskens","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Rodney Scott","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Patricia Michie","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Carmel Loughland","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Paul Rasser","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Murray Cairns","author_inst":"Hunter Medical Research Institute, Newcastle, Australia."},{"author_name":"Bryan Mowry","author_inst":"Faculty of Health, Medicine and Behavioural Sciences, University of Queensland, Brisbane, Australia."},{"author_name":"Stanley Catts","author_inst":"Faculty of Health, Medicine and Behavioural Sciences, University of Queensland, Brisbane, Australia."},{"author_name":"Christos Pantelis","author_inst":"Department of Psychiatry, University of Melbourne, Carlton South, VIC, Australia."},{"author_name":"Aristotle Voineskos","author_inst":"Campbell Family Mental Health Research Institute, CAMH, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, "},{"author_name":"Erin Dickie","author_inst":"Campbell Family Mental Health Research Institute, CAMH, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, "},{"author_name":"Henk Temmingh","author_inst":"Neuroscience Institute, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South "},{"author_name":"Freda Scheffler","author_inst":"Neuroscience Institute, University of Cape Town, Cape Town, South Africa; Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South "},{"author_name":"Oliver Gruber","author_inst":"Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Baden-W\u00fcrttemberg, Germany."},{"author_name":"Rosanne Picotin","author_inst":"Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Baden-W\u00fcrttemberg, Germany."},{"author_name":"Vince D. Calhoun","author_inst":"The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Em"},{"author_name":"Kyle M. Jensen","author_inst":"The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Em"},{"author_name":"Filip _paniel","author_inst":"National Institute of Mental Health, Klecany, Czech Republic."},{"author_name":"David Tomecek","author_inst":"National Institute of Mental Health, Klecany, Czech Republic."},{"author_name":"Raymond Salvador","author_inst":"FIDMAG Germanes Hospital\u00e0ries Research Foundation, Barcelona, Spain; CIBERSAM, ISCIII, Barcelona, Spain."},{"author_name":"Andriana Karuk","author_inst":"FIDMAG Germanes Hospital\u00e0ries Research Foundation, Barcelona, Spain; CIBERSAM, ISCIII, Barcelona, Spain."},{"author_name":"Raymond Salvador","author_inst":"FIDMAG Germanes Hospital\u00e0ries Research Foundation, Barcelona, Spain; CIBERSAM, ISCIII, Barcelona, Spain."},{"author_name":"Edith Pomarol-Clotet","author_inst":"FIDMAG Germanes Hospital\u00e0ries Research Foundation, Barcelona, Spain; CIBERSAM, ISCIII, Barcelona, Spain."},{"author_name":"Tilo Kircher","author_inst":"Department of Psychiatry, Marburg University, Marburg, Germany."},{"author_name":"Lea Teutenberg","author_inst":"Department of Psychiatry, Marburg University, Marburg, Germany."},{"author_name":"Frederike Stein","author_inst":"Department of Psychiatry, Marburg University, Marburg, Germany."},{"author_name":"Udo Dannlowski","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany; Department of Psychiatry, Medical School and University Medical Center OWL, Pro"},{"author_name":"Dominik Grotegerd","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany."},{"author_name":"Tiana Borgers","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany."},{"author_name":"Tim Hahn","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany."},{"author_name":"Rebekka Lencer Lencer","author_inst":"Institute for Translational Psychiatry, University of M\u00fcnster, M\u00fcnster, Germany."},{"author_name":"Carlos L\u00f3pez-Jaramillo","author_inst":"Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia."},{"author_name":"Melissa Green","author_inst":"School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia."},{"author_name":"Yann Quide","author_inst":"School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia."},{"author_name":"Vaughan Carr","author_inst":"School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia."},{"author_name":"Stefan Ehrlich","author_inst":"Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden"},{"author_name":"Peter Kochunov","author_inst":"Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA."},{"author_name":"Christian Sorg","author_inst":"Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical Universtiy of Munich, Munich,"},{"author_name":"Melissa Thalhammer","author_inst":"Department of Psychiatry, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical Universtiy of Munich, Munich, Germany."},{"author_name":"David Glahn","author_inst":"Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA."},{"author_name":"Amanda Rodrigue","author_inst":"Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA."},{"author_name":"Kang Sim","author_inst":"West Region, Institute of Mental Health, Singapore, Singapore."},{"author_name":"Ali Saffet Gonul","author_inst":"Ege University Hospital, Psychiatry Department, Izmir, Turkiye."},{"author_name":"Aslihan Uyar Demir","author_inst":"Ege University Hospital, Psychiatry Department, Izmir, Turkiye."},{"author_name":"Nicolas Crossley","author_inst":"Department of Psychiatry, Pontificia Universidad Cat\u00f3lica de Chile, Chile."},{"author_name":"Alfonso Gonzalez-Valderrama","author_inst":"School of Medicine, Finis Terrae University, Chile; and Early Intervention Service (PROITP), Dr Jos\u00e9 Horwitz Barak Psychiatric Institute, Santiago, Chile."},{"author_name":"Philipp Homan","author_inst":"Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland."},{"author_name":"Wolfgang Omlor","author_inst":"Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland."},{"author_name":"Giacomo Cecere","author_inst":"Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland."},{"author_name":"Felice Iasevoli","author_inst":"Department of Neuroscience, University of Naples \"Federico II\", Naples, Italy."},{"author_name":"Giuseppe Pontillo","author_inst":"Department of Advanced Biomedical Sciences, University of Naples \"Federico II\", Naples, Italy."},{"author_name":"Raquel Gur","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."},{"author_name":"Ruben C. Gur","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."},{"author_name":"Kosha Ruparel","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."},{"author_name":"Theodore D. Satterthwaite","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA."},{"author_name":"Scott Sponheim","author_inst":"Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA."},{"author_name":"Caroline Demro","author_inst":"Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA."},{"author_name":"Young Chul Chung","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea."},{"author_name":"Soyolsaikhan Odkhuu","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea."},{"author_name":"Albert Yang","author_inst":"Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Brain Science, National Yang Ming "},{"author_name":"I-Jou Chi","author_inst":"Department of Occupational Therapy, Kaohsiung Medical University, Kaohsiung City, Taiwan; Biomedical Artificial Intelligence Academy, Kaohsiung Medical Universi"},{"author_name":"Ole Andreassen","author_inst":"Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & University of Oslo, Oslo, Norway; KG Jebsen Centre for Neur"},{"author_name":"Lars T. Westlye","author_inst":"Department of Psychology, University of Oslo, Oslo, Norway; Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital &"},{"author_name":"Unn K.H. Haukvik","author_inst":"Adult Psychiatry Department, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Research and "},{"author_name":"Nadine Parker","author_inst":"Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & University of Oslo, Oslo, Norway."},{"author_name":"Jakub Kopal","author_inst":"Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & University of Oslo, Oslo, Norway."},{"author_name":"Dag Alnaes","author_inst":"Department of Psychology, University of Oslo, Oslo, Norway; Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital &"},{"author_name":"Jaroslav Rokicki","author_inst":"Centre of Research and Education in Forensic Psychiatry (SIFER), Oslo University Hospital, Oslo, Norway; Department of Electronic Systems, Vilnius Tech, Vilnius"},{"author_name":"Carl M Sellgren","author_inst":"Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden; Centre for Psychiatric Research, Department of Clinical Neuroscience, Karol"},{"author_name":"Maria Lee","author_inst":"Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Swede"},{"author_name":"Stefan Borgwardt","author_inst":"Department of Psychiatry and Psychotherapy, Center of Brain, Behavior and Metabolism (CBBM), University of L\u00fcbeck, L\u00fcbeck, Germany."},{"author_name":"Mihai Avram","author_inst":"Department of Psychiatry and Psychotherapy, Center of Brain, Behavior and Metabolism (CBBM), University of L\u00fcbeck, L\u00fcbeck, Germany."},{"author_name":"Taeyoung Lee","author_inst":"Department of Psychiatry, Kyungpook National University, Daegu, South Korea."},{"author_name":"Hang Joon Jo","author_inst":"Department of Physiology, College of Medicine, Hanyang University, Seoul, South Korea."},{"author_name":"Irina Lebedeva","author_inst":"Mental Health Research Center, Moscow, Russian Federation."},{"author_name":"Alexander Tomyshev","author_inst":"Mental Health Research Center, Moscow, Russian Federation."},{"author_name":"Stefan Kaiser","author_inst":"Department of Psychiatry, University Hospitals of Gen\u00e8ve, Thonex, Switzerland; Synapsy Center for Neuroscience and Mental Health Research, University of Gen\u00e8ve,"},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA."},{"author_name":"Theo G.M. van Erp","author_inst":"Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA."},{"author_name":"Jessica A. Turner","author_inst":"Department of Psychiatry and Behavioral Health, the Ohio State University, Columbus, OH, USA."},{"author_name":"Boris C. Bernhardt","author_inst":"McConnell Brain Imaging Centre, Montr\u00e9al Neurological Institute-Hospital, McGill University, Montr\u00e9al, QC, Canada."},{"author_name":"Sofie L. Valk","author_inst":"Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Cent"},{"author_name":"Matthias Kirschner","author_inst":"Department of Psychiatry, University Hospitals of Gen\u00e8ve, Thonex, Switzerland; Synapsy Center for Neuroscience and Mental Health Research, University of Gen\u00e8ve,"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Molecular epidemiology of rifampicin resistant Mycobacterium tuberculosis in Vietnam","rel_doi":"10.64898\/2026.04.20.26351312","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351312","rel_abs":"Background: Vietnam is a top 20 burden country for multi-drug resistant\/rifampicin-resistant tuberculosis (MDR\/RR-TB), with nearly 10,000 cases a year. With the emergence of new diagnostic assays for M. tuberculosis and resistance, along with new drugs for both treatment and prevention, we sought to better understand the molecular epidemiology of RR-TB in this high-burden setting, through the study of clinical trial isolates from the VQUIN MDR trial. Methods: We assembled a sample of cultured isolates, collected from patients with confirmed RR-M. tuberculosis within 10 provinces, enriching for isolates from outside of the 2 major cities, Hanoi and Ho Chi Minh City. We subjected these isolates whole genome sequencing (WGS) and bioinformatic analysis, with a subset subject to phenotypic drug susceptibility testing to evaluate phenotypic\/genotypic concordance. New genome sequences were phylogenetically contextualised to publicly-available M. tuberculosis genome sequences sampled in Vietnam from National Center for Biotechnology Information (NCBI) Sequence Read Archives (SRA). Results: Isolates from 252 RR-TB cases passed quality controls and were available for analysis. Xpert MTB\/RIF had a high concordance with WGS-based rifampicin-resistance prediction (PPV=96.8%). Of the 244 isolates confirmed to be rifampicin resistant, a high proportion (235\/244 = 96.3%) had mutations associated with resistance to at least one other first- or second-line antibiotic. Phenotypic drug susceptibility testing (DST) for rifampicin, isoniazid, and levofloxacin was completed for 77 isolates with a high concordance demonstrated between DST and genomic-based resistance predictions (67\/77, 87.0% RIF; 76\/77, 98.7% INH; 73\/77, 94.8%LFX). High concordance was also observed with new and repurposed antibiotics linezolid (100%, 60\/60), pretomanid (100%, 60\/60), and bedaquiline (56\/60, 93.3%). Rifampicin-resistant strains were more likely to be lineage 2.2.1, compared to rifampicin-susceptible M. tuberculosis strains in Vietnam, particularly in the major cities. Conclusions: The high prevalence of secondary drug-resistance beyond RIF and INH, along with the dominance of one major lineage across geographic regions, provides insights on the spread of MDR\/RR-TB in Vietnam and reinforces the importance of prompt and broad detection of drug-resistance to inform the timely initiation of effective drug regimens.","rel_num_authors":7,"rel_authors":[{"author_name":"Ori E Solomon","author_inst":"Research Institute of the McGill University Health Centre"},{"author_name":"Viet Nhung Nguyen","author_inst":"Vietnam National University"},{"author_name":"Hoa Binh Nguyen","author_inst":"National Lung Hospital"},{"author_name":"Thu Anh Nguyen","author_inst":"Sydney Vietnam Institute"},{"author_name":"Emily Lai-Ho MacLean","author_inst":"University of Sydney"},{"author_name":"Greg J Fox","author_inst":"University of Sydney"},{"author_name":"Marcel A Behr","author_inst":"Research Institute of the McGill University Health Centre"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"LANTERN: Leveraging Local Ancestry Tracts to Enhance Rare-Variant Aggregate Association Testing","rel_doi":"10.64898\/2026.04.24.26351693","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351693","rel_abs":"Individuals with admixed ancestry comprise a significant proportion of populations of the Americas. Statistical methods have been developed to specifically leverage local ancestry inference to enhance the power and interpretability of genome-wide association studies in admixed populations. However, no such methods currently exist to test for rare-variant aggregate associations. Here we present LANTERN (Leveraging local ANcestry Tracts to Enhance Rare variaNt aggregate associations), a method that infers the alleles that lie on each ancestral haplotype and conducts rare-variant aggregate association testing in a generalized linear mixed model framework. Through simulation studies we demonstrated that LANTERN achieves proper control of Type 1 error while boosting power to detect associations when causal alleles predominately lie on one ancestral haplotype. Using data from a cohort of African American participants from the Jackson Heart Study, LANTERN identified two genes known to be involved in red-blood cell (RBC) biology when local ancestry information was incorporated. Specifically, a burden of rare alleles on European ancestral haplotypes in EPO was associated with both hemoglobin levels (HGB) and RBC counts, whereas a burden of rare alleles on African ancestral haplotypes in EPB42 was associated with HGB and RBC. In summary, LANTERN (i) allows for the identification of ancestry-specific rare-variant associations; and (ii) enhances rare-variant association signals compared to an analysis that ignores local ancestry. LANTERN is implemented in R and is freely available on GitHub.","rel_num_authors":8,"rel_authors":[{"author_name":"Yu Wang","author_inst":"Medical College of Wisconsin"},{"author_name":"Bjoernar Tuftin","author_inst":"University of North Carolina, Chapel Hill"},{"author_name":"Laura M. Raffield","author_inst":"University of North Carolina, Chapel Hill"},{"author_name":"Bertha Hidalgo","author_inst":"University of Alabama at Birmingham"},{"author_name":"Sarah L. Kerns","author_inst":"Medical College of Wisconsin"},{"author_name":"Andrew T DeWan","author_inst":"Yale University"},{"author_name":"Suzanne M. Leal","author_inst":"Columbia University"},{"author_name":"Paul Auer","author_inst":"Medical College of Wisconsin"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"LANTERN: Leveraging Local Ancestry Tracts to Enhance Rare-Variant Aggregate Association Testing","rel_doi":"10.64898\/2026.04.24.26351693","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351693","rel_abs":"Individuals with admixed ancestry comprise a significant proportion of populations of the Americas. Statistical methods have been developed to specifically leverage local ancestry inference to enhance the power and interpretability of genome-wide association studies in admixed populations. However, no such methods currently exist to test for rare-variant aggregate associations. Here we present LANTERN (Leveraging local ANcestry Tracts to Enhance Rare variaNt aggregate associations), a method that infers the alleles that lie on each ancestral haplotype and conducts rare-variant aggregate association testing in a generalized linear mixed model framework. Through simulation studies we demonstrated that LANTERN achieves proper control of Type 1 error while boosting power to detect associations when causal alleles predominately lie on one ancestral haplotype. Using data from a cohort of African American participants from the Jackson Heart Study, LANTERN identified two genes known to be involved in red-blood cell (RBC) biology when local ancestry information was incorporated. Specifically, a burden of rare alleles on European ancestral haplotypes in EPO was associated with both hemoglobin levels (HGB) and RBC counts, whereas a burden of rare alleles on African ancestral haplotypes in EPB42 was associated with HGB and RBC. In summary, LANTERN (i) allows for the identification of ancestry-specific rare-variant associations; and (ii) enhances rare-variant association signals compared to an analysis that ignores local ancestry. LANTERN is implemented in R and is freely available on GitHub.","rel_num_authors":8,"rel_authors":[{"author_name":"Yu Wang","author_inst":"Medical College of Wisconsin"},{"author_name":"Bjoernar Tuftin","author_inst":"University of North Carolina, Chapel Hill"},{"author_name":"Laura M. Raffield","author_inst":"University of North Carolina, Chapel Hill"},{"author_name":"Bertha Hidalgo","author_inst":"University of Alabama at Birmingham"},{"author_name":"Sarah L. Kerns","author_inst":"Medical College of Wisconsin"},{"author_name":"Andrew T DeWan","author_inst":"Yale University"},{"author_name":"Suzanne M. Leal","author_inst":"Columbia University"},{"author_name":"Paul Auer","author_inst":"Medical College of Wisconsin"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Demographic Factors Moderate the Effectiveness of Obesity Prevention Interventions: A Secondary Analysis of College Intervention Trials","rel_doi":"10.64898\/2026.04.22.26351238","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351238","rel_abs":"Background: College-attending young adults frequently experience declines in diet quality, physical activity, and psychological well-being during the transition to independent living, contributing to weight gain during the first year of college. Although multicomponent lifestyle interventions have been developed to address these behaviors, the responsiveness to such programs could differ across demographic factors associated with health behaviors, such as sex, race, and ethnicity. Hence, this secondary analysis of large-scale college health trials evaluated whether the effectiveness of such interventions differed by these demographic factors. Methods: Data were combined from two multi-site randomized controlled trials: Young Adults Eating and Active for Health (YEAH) trial and the Get FRUVED trial. Both interventions used theory-based approaches to promote healthy weight management through improvements in diet quality, physical activity, and stress management. Baseline-adjusted linear regression models evaluated the effects of group (intervention, control) and its interactions with sex, race (White, Black, Other), or Hispanic ethnicity. Models were adjusted for baseline outcome values, baseline BMI, study (YEAH vs. FRUVED), and state of data collection. Results: Intervention participants reported higher fruit and vegetable intake, lower processed meat intake, and longer sleep duration compared with controls. However, there was significant heterogeneity in these dietary outcomes by ethnicity, race, and sex. Non-Hispanic participants in the intervention group had higher fruit and vegetable intake compared to controls (p < 0.05). And, within the intervention group, Hispanic females had lower bacon\/sausage intake than Hispanic males and non-Hispanic females (p < 0.05). With respect to race, Black participants reported higher total processed meat intake than White and Other race participants (p <0.05). These demographic factors did not moderate the intervention's impact on physical activity, sleep duration, and perceived stress. Overall, the intervention appeared to be the least effective for Hispanic males who exhibited higher body weight and waist circumference compared with Hispanic females and non-Hispanic males (p < 0.05). Conclusions: Multicomponent lifestyle interventions can improve selected dietary outcomes among college students, but effectiveness may differ across demographic subgroups. Culturally and sex-tailored strategies that consider the intersecting influences of sex, race, and ethnicity may enhance intervention effectiveness during the transition to college.","rel_num_authors":13,"rel_authors":[{"author_name":"Caitlyn Winn","author_inst":"Division of Food, Nutrition, and Exercise Sciences, College of Agriculture, Food and Natural Resources (CAFNR), University of Missouri-Columbia"},{"author_name":"Leah Groene","author_inst":"Division of Food, Nutrition, and Exercise Sciences, College of Agriculture, Food and Natural Resources (CAFNR), University of Missouri-Columbia"},{"author_name":"Sarah Colby","author_inst":"University of Tennessee Knoxville"},{"author_name":"Lilian Ademu","author_inst":"Texas AM AgriLife Research at El Paso"},{"author_name":"Melissa D. Olfert","author_inst":"School of Agriculture and Food Systems, Davis College of Agriculture and Natural Resources, Division of Land Grant Engagement, West Virginia University"},{"author_name":"Carol Byrd-Bredbenner","author_inst":"Rutgers University"},{"author_name":"Anne Mathews","author_inst":"University of Florida, Food Science and Human Nutrition Department, Gainesville, FL"},{"author_name":"Jesse Stabile Morrell","author_inst":"University of New Hampshire, Department of Agriculture, Nutrition, and Food Systems, Durham NH"},{"author_name":"Priscilla Brenes","author_inst":"Kansas State University"},{"author_name":"Onikia Brown","author_inst":"Auburn University, Department of Nutritional Sciences, 102A Poultry Science Bldg, Auburn, AL"},{"author_name":"Makenzie Barr-Porter","author_inst":"University of Kentucky, Martin-Gatton College of Agriculture, Food, and Environment, Department of Dietetics and Human Nutrition, Lexington, KY"},{"author_name":"Geoffrey Greene","author_inst":"University of Rhode Island, Department of Nutrition, 125 Fogarty Hall, Kingston, RI"},{"author_name":"Jaapna Dhillon","author_inst":"Division of Food, Nutrition, and Exercise Sciences, College of Agriculture, Food and Natural Resources (CAFNR), School of Medicine, University of Missouri-Colum"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"AT(N) Framework in Older Adults with Epilepsy: Plasma Biomarkers and Associations with Demographic, Clinical, and Cognitive Features","rel_doi":"10.64898\/2026.04.24.26351489","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351489","rel_abs":"Background and Objectives: Older adults with epilepsy have a 2- to 4-fold increased risk of dementia, including Alzheimer's disease (AD), yet underlying mechanisms remain poorly defined. The NIA-AA classifies AD using amyloid (A), tau (T), and neurodegeneration [(N)] biomarkers. We applied this framework to characterize AT(N) profiles and clinical correlates in epilepsy. Methods: Eighty-four older adults with focal epilepsy (mean age=66.3 years) from the Brain Aging and Cognition in Epilepsy (BrACE) study were classified as A+, T+, and\/or (N)+ using plasma {beta}-amyloid (A{beta}) 42\/40 ratio, phosphorylated tau 181 (p-tau181), and neurofilament light chain (NfL) levels, and grouped into normal, AD-continuum, and non-AD pathologic change. Demographic, clinical, and cognitive characteristics were compared. Cognition was assessed using the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) and the Montreal Cognitive Assessment (MoCA). Memory was examined using IC-CoDE memory domain classification, with word-list delayed recall analyzed separately. Associations with cognition were modeled using logistic and linear regression. Secondary analyses examined biomarkers continuously, including p-tau217, and substituted hippocampal volume for NfL. Results: Only 32% of participants had normal biomarkers, while 37% were on the AD-continuum and 31% showed non-AD pathologic change. Participants with normal biomarkers were younger with shorter epilepsy duration, whereas APOE-{epsilon}4 carriers were enriched in the AD-continuum group. Early-onset compared to late-onset epilepsy (cutoff: [&ge;]55 years) showed higher odds of biomarker abnormality (aOR=8.84, 95% CI [2.35, 41.89], P=0.003), driven by elevated p-tau217, NfL, and greater amyloid burden. While categorical AT(N) profiles were not associated with cognition, higher p-tau181 levels were independently associated with lower word-list delayed recall (95% CI [-10.31, -0.86], P=0.021). Substituting hippocampal volume for NfL shifted more participants to normal profiles (48% vs. 32%) and fewer to non-AD pathologic change (15% vs. 31%). Discussion: AT(N) biomarker profiles showed substantial heterogeneity, with higher abnormality rates than in aging populations, particularly among those with early-onset epilepsy. Continuous p-tau181 was associated with memory performance while categorical AT(N) profiles were not, and NfL and hippocampal volume showed discordant classifications, highlighting divergence across neurodegeneration markers. These findings underscore the complexity of applying AD-centric frameworks to epilepsy and support multimodal, epilepsy-adapted biomarker approaches to characterize neurodegenerative risk.","rel_num_authors":16,"rel_authors":[{"author_name":"Kayela Arrotta","author_inst":"Cleveland Clinic"},{"author_name":"McKenna Williams","author_inst":"University of California, San Diego"},{"author_name":"Nicolas R Thompson","author_inst":"Cleveland Clinic"},{"author_name":"Katherine J Bangen","author_inst":"University of California, San Diego"},{"author_name":"Anny Reyes","author_inst":"Cleveland Clinic"},{"author_name":"Ifrah Zawar","author_inst":"University of Virginia School of Medicine"},{"author_name":"Vineet Punia","author_inst":"Cleveland Clinic"},{"author_name":"Irene Wang","author_inst":"Cleveland Clinic"},{"author_name":"Jerry J Shih","author_inst":"University of California- San Diego"},{"author_name":"Lynn M Bekris","author_inst":"University of Washington"},{"author_name":"Lisa Ferguson","author_inst":"Cleveland Clinic"},{"author_name":"Dace N Almane","author_inst":"University of Wisconsin School Madison"},{"author_name":"Jana E Jones","author_inst":"University of Wisconsin Madison"},{"author_name":"Bruce  P. Hermann","author_inst":"University of Wisconsin Madison"},{"author_name":"Robyn M Busch","author_inst":"Cleveland Clinic"},{"author_name":"Carrie R McDonald","author_inst":"University of California San Diego"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Cutaneous Microvascular Reserve and Kidney Function and Histopathologic Injury in CKD","rel_doi":"10.64898\/2026.04.24.26351712","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351712","rel_abs":"Background: Microvascular dysfunction contributes to chronic kidney disease (CKD), but reproducible clinical measures are limited. Laser Doppler flowmetry (LDF) provides a noninvasive assessment of cutaneous microvascular blood flow and may reflect systemic microvascular health. Its relationship with kidney function and histopathology in CKD remains unclear. Methods: We assessed cutaneous microvascular function in 150 participants with CKD (eGFR <90 mL\/min\/1.73 m2) using a standardized forearm LDF protocol. Baseline perfusion was recorded at ~30{degrees}C, followed by local heating to 44{degrees}C to induce hyperemia. Percent change in perfusion units (PU) defined microvascular functional reserve. Associations of LDF measures with eGFR and urine protein-to-creatinine ratio (uPCR) were evaluated using multivariable linear regression. K-means clustering identified microvascular phenotypes. In a subset (n=20), associations with glomerulosclerosis (GS) and interstitial fibrosis\/tubular atrophy (IFTA) were examined. Results: The mean (SD) age was 64 (14) years, 46% were female. The mean eGFR was 42 (21) mL\/min\/1.73m2 and median uPCR was 0.21 (interquartile range (IQR) 0.11 to 1.20) mg\/mg. Higher baseline PU ({beta} = -12; 95% CI, -24 to -1) and reduced percentage change in PU ({beta} = 7; 95% CI, 2 to 13) were associated with lower eGFR, independent of covariates. Neither measure was associated with uPCR. Clustering identified four phenotypes with graded differences in perfusion and reserve. In biopsy participants, higher baseline PU and lower percent change were associated with greater GS and IFTA severity. Conclusion: CKD is characterized by elevated resting perfusion and impaired microvascular reserve, which are associated with lower eGFR and histopathologic injury.","rel_num_authors":11,"rel_authors":[{"author_name":"Armin Ahmadi","author_inst":"University of California San Diego"},{"author_name":"Masfiqur Rahaman","author_inst":"University of California San Diego"},{"author_name":"Amol Harsh","author_inst":"Mohamed bin Zayed University of Artificial Intelligence"},{"author_name":"Jason Yang","author_inst":"University of California San Diego"},{"author_name":"Basma Ghanim","author_inst":"University of California San Diego"},{"author_name":"Subhasis Dasgupta","author_inst":"University of California San Diego"},{"author_name":"Robert N. Weinreb","author_inst":"University of California San Diego"},{"author_name":"Tauhidur Rahman","author_inst":"University of California San Diego"},{"author_name":"Alfons J.H.M. Houben","author_inst":"Maastricht University Medical Centre"},{"author_name":"Joachim H. Ix","author_inst":"University of California San Diego"},{"author_name":"Rakesh Malhotra","author_inst":"University of California, San Diego"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Feature-Based Parametric Response Mapping on Thoracic Computed Tomography for Robust Disease Classification in COPD","rel_doi":"10.64898\/2026.04.24.26351675","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351675","rel_abs":"Purpose: To develop an interpretable feature-based Deep Parametric Response Mapping (PRMD) method that combines wavelet scattering convolution networks and machine learning to spatially detect and quantify functional small airways disease (fSAD) and emphysema on paired inspiratory-expiratory CT scans, with enhanced noise robustness. Materials and Methods: In this retrospective analysis of prospectively acquired data (2007-2017), we developed and validated a deep learning-based PRM approach using paired CT scans from 8,972 tobacco-exposed COPDGene participants ([&ge;]10 pack-years; mean age 60.1 {+\/-} 8.8 years; 46.5% women), including controls with normal spirometry (n = 3,872; controls), PRISm (n = 1,089), GOLD 1-4 COPD (n = 4,011). Data were stratified into training, validation, and testing sets (24:6:70). PRMD extracts translation-invariant image features using a wavelet scattering network and applies a subspace learning classifier to classify voxels as emphysema or non-emphysematous air trapping (fSAD). PRMD was compared with conventional density-based PRM for voxel-wise agreement, correlation with pulmonary function, robustness to noise, and sensitivity to misregistration using Pearson correlation, Bland-Altman analysis, and paired t tests. Results: PRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV1; (emphysema: r = - 0.54; fSAD: r = - 0.51; P < 0.0001) than standard PRM (r = - 0.42 for both; P < 0.0001). Under simulated high-noise conditions, standard PRM overestimated disease by ~15%, whereas PRMD limited error to < 5% (P < 0.001). Conclusion: PRMD provides an interpretable, feature-driven and noise-resilient alternative to traditional PRM for emphysema and fSAD classification, enhancing the reliability of CT-based COPD phenotyping for multi-center studies and low-dose imaging applications.","rel_num_authors":13,"rel_authors":[{"author_name":"Ali Namvar","author_inst":"University of Michigan"},{"author_name":"Bingzhao Shan","author_inst":"University of Michigan"},{"author_name":"Benjamin Hoff","author_inst":"University of Michigan"},{"author_name":"Wassim W. Labaki","author_inst":"University of Michigan"},{"author_name":"Susan Murray","author_inst":"University of Michigan"},{"author_name":"Alexander J. Bell","author_inst":"University of Michigan"},{"author_name":"Stefanie Galban","author_inst":"University of Michigan"},{"author_name":"Ella A. Kazerooni","author_inst":"University of Michigan"},{"author_name":"Fernando J. Martinez","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Charles R. Hatt","author_inst":"4D Medical"},{"author_name":"MeiLan K. Han","author_inst":"University of Michigan"},{"author_name":"Craig J Galban","author_inst":"University of Michigan"},{"author_name":"Sundaresh Ram","author_inst":"Emory University & Georgia Institute of Technology"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Feature-Based Parametric Response Mapping on Thoracic Computed Tomography for Robust Disease Classification in COPD","rel_doi":"10.64898\/2026.04.24.26351675","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351675","rel_abs":"Purpose: To develop an interpretable feature-based Deep Parametric Response Mapping (PRMD) method that combines wavelet scattering convolution networks and machine learning to spatially detect and quantify functional small airways disease (fSAD) and emphysema on paired inspiratory-expiratory CT scans, with enhanced noise robustness. Materials and Methods: In this retrospective analysis of prospectively acquired data (2007-2017), we developed and validated a deep learning-based PRM approach using paired CT scans from 8,972 tobacco-exposed COPDGene participants ([&ge;]10 pack-years; mean age 60.1 {+\/-} 8.8 years; 46.5% women), including controls with normal spirometry (n = 3,872; controls), PRISm (n = 1,089), GOLD 1-4 COPD (n = 4,011). Data were stratified into training, validation, and testing sets (24:6:70). PRMD extracts translation-invariant image features using a wavelet scattering network and applies a subspace learning classifier to classify voxels as emphysema or non-emphysematous air trapping (fSAD). PRMD was compared with conventional density-based PRM for voxel-wise agreement, correlation with pulmonary function, robustness to noise, and sensitivity to misregistration using Pearson correlation, Bland-Altman analysis, and paired t tests. Results: PRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV1; (emphysema: r = - 0.54; fSAD: r = - 0.51; P < 0.0001) than standard PRM (r = - 0.42 for both; P < 0.0001). Under simulated high-noise conditions, standard PRM overestimated disease by ~15%, whereas PRMD limited error to < 5% (P < 0.001). Conclusion: PRMD provides an interpretable, feature-driven and noise-resilient alternative to traditional PRM for emphysema and fSAD classification, enhancing the reliability of CT-based COPD phenotyping for multi-center studies and low-dose imaging applications.","rel_num_authors":13,"rel_authors":[{"author_name":"Ali Namvar","author_inst":"University of Michigan"},{"author_name":"Bingzhao Shan","author_inst":"University of Michigan"},{"author_name":"Benjamin Hoff","author_inst":"University of Michigan"},{"author_name":"Wassim W. Labaki","author_inst":"University of Michigan"},{"author_name":"Susan Murray","author_inst":"University of Michigan"},{"author_name":"Alexander J. Bell","author_inst":"University of Michigan"},{"author_name":"Stefanie Galban","author_inst":"University of Michigan"},{"author_name":"Ella A. Kazerooni","author_inst":"University of Michigan"},{"author_name":"Fernando J. Martinez","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Charles R. Hatt","author_inst":"4D Medical"},{"author_name":"MeiLan K. Han","author_inst":"University of Michigan"},{"author_name":"Craig J Galban","author_inst":"University of Michigan"},{"author_name":"Sundaresh Ram","author_inst":"Emory University & Georgia Institute of Technology"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Genetics of cannabis ever-use and frequency across ancestries implicate novel loci and brain-specific biology","rel_doi":"10.64898\/2026.04.25.26351611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.25.26351611","rel_abs":"Cannabis use is widespread, with genetic differences partly explaining variation in individual patterns of use. We performed the largest-to-date genome-wide association study (GWAS) meta-analysis of cannabis ever-use (N=736,322, 76% European ancestry) and various measures of frequency of use (N=269,160 cannabis users, 84% European ancestry). We identified 54 independent genome-wide significant loci for ever-use and 6 for frequency and show that the genetic architecture of ever-use, frequency, and cannabis use disorder (CUD) are overlapping but distinguishable. We identified 63 loci that were associated with common liability (All-cannabis) to different cannabis use traits in European-ancestry individuals. Across analyses, we identified 75 unique loci that had not previously been implicated in cannabis use. Gene prioritization analyses identified 349 genes for ever-use, 5 genes for frequency of use, and 429 for All-cannabis, including previously identified and novel genes. We found enrichment of genetic signals for cannabis use in biologically meaningful categories and relevant human brain cell types, including excitatory neuronal populations. There were substantial genetic correlations between cannabis use and a range of psychiatric disorders and substance use traits, while cannabis polygenic scores were associated with increased risk of psychiatric disorders. Mendelian Randomization showed evidence for (bidirectional) causal associations between cannabis use and ADHD, bipolar disorder, schizophrenia and PTSD.","rel_num_authors":85,"rel_authors":[{"author_name":"Joelle A Pasman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Zachary F Gerring","author_inst":"Walter and Eliza Hall Institute of Medical Research"},{"author_name":"Jackson G Thorp","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Abdel Abdellaoui","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Pierre Youssef","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Anil Ori","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Muhannad Smadi","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Anais B Thijssen","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Damian Woodward","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Briar Wormington","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Daniel E Adkins","author_inst":"University of Utah"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Chris Chatzinakos","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Sarah L Elson","author_inst":"23andMe"},{"author_name":"Pierre Fontanillas","author_inst":"23andMe"},{"author_name":"Ian R Gizer","author_inst":"University of Missouri"},{"author_name":"Haixia Gu","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Lindsey A Hines","author_inst":"University of Bath"},{"author_name":"Emma C Johnson","author_inst":"Washington University School of Medicine"},{"author_name":"Kadri Koiv","author_inst":"University of Tartu"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer"},{"author_name":"Penelope A Lind","author_inst":"Queensland University of Technology"},{"author_name":"Penelope A Lind","author_inst":"University of Queensland"},{"author_name":"Miriam A Mosing","author_inst":"Karolinska Institutet"},{"author_name":"Ilja M Nolte","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Jue-Sheng Ong","author_inst":"QIMR Berghofer"},{"author_name":"Jackie M Otto","author_inst":"Regeneron Pharmaceuticals, Inc."},{"author_name":"Teemu Palviainen","author_inst":"University of Helsinki"},{"author_name":"Roseann E Peterson","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Hannah M Sallis","author_inst":"University of Bristol"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Jean Shin","author_inst":"CHU Sainte-Justine Research Centre"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Camiel M van der Laan","author_inst":"Vrije Universiteit"},{"author_name":"Peter J van der Most","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Saskia van Dorsselaer","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Kristel R van Eijk","author_inst":"UMC Utrecht"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"Lovisenberg Diaconal Hospital"},{"author_name":"Robyn E Wootton","author_inst":"Norwegian Institute of Public Health"},{"author_name":"Stephanie Zellers","author_inst":"University of Minnesota"},{"author_name":"Catharina Hartman","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Georgi Hudjashov","author_inst":"University of Tartu"},{"author_name":"Marco P Boks","author_inst":"Amsterdam UMC"},{"author_name":"Dorret I Boomsma","author_inst":"Vrije Universiteit"},{"author_name":"Enda M Byrne","author_inst":"The University of Queensland"},{"author_name":"William E Copeland","author_inst":"University of Vermont"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Bart Ferweda","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Andrew C Heath","author_inst":"Washington University School of Medicine"},{"author_name":"Ian B Hickie","author_inst":"The University of Sydney"},{"author_name":"William G Iacono","author_inst":"University of Minnesota"},{"author_name":"Martin A Kennedy","author_inst":"University of Otago Christchurch"},{"author_name":"Kelli Lehto","author_inst":"University of Tartu"},{"author_name":"Anja Lok","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Stuart MacGregor","author_inst":"QIMR Berghofer"},{"author_name":"Pamela AF Madden","author_inst":"Washington University School of Medicine"},{"author_name":"Hermine HM Maes","author_inst":"Virginia Commonwealth University"},{"author_name":"Nicholas G Martin","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Matt McGue","author_inst":"University of Minnesota"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Marcus R Munafo","author_inst":"University of Bath"},{"author_name":"Max Nieuwdorp","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Tineke J Oldehinkel","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Miina Ollikainen","author_inst":"University of Helsinki"},{"author_name":"Abraham A Palmer","author_inst":"University of California San Diego"},{"author_name":"Tomas Paus","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"Zdenka Pausova","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"John F Pearson","author_inst":"University of Otago Christchurch"},{"author_name":"Sandra Sanchez-Roige","author_inst":"University of California San Diego"},{"author_name":"Harold Snieder","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Margreet ten Have","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Jorien L Treur","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Scott Vrieze","author_inst":"University of Minnesota"},{"author_name":"Kirk C Wilhelmsen","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Aelko H Zwinderman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Jacqueline M Vink","author_inst":"Radboud University"},{"author_name":"Nathan A Gillespie","author_inst":"Virginia Commonwealth University"},{"author_name":"Eske M Derks","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Karin JH Verweij","author_inst":"Amsterdam UMC, location University of Amsterdam"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Genetics of cannabis ever-use and frequency across ancestries implicate novel loci and brain-specific biology","rel_doi":"10.64898\/2026.04.25.26351611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.25.26351611","rel_abs":"Cannabis use is widespread, with genetic differences partly explaining variation in individual patterns of use. We performed the largest-to-date genome-wide association study (GWAS) meta-analysis of cannabis ever-use (N=736,322, 76% European ancestry) and various measures of frequency of use (N=269,160 cannabis users, 84% European ancestry). We identified 54 independent genome-wide significant loci for ever-use and 6 for frequency and show that the genetic architecture of ever-use, frequency, and cannabis use disorder (CUD) are overlapping but distinguishable. We identified 63 loci that were associated with common liability (All-cannabis) to different cannabis use traits in European-ancestry individuals. Across analyses, we identified 75 unique loci that had not previously been implicated in cannabis use. Gene prioritization analyses identified 349 genes for ever-use, 5 genes for frequency of use, and 429 for All-cannabis, including previously identified and novel genes. We found enrichment of genetic signals for cannabis use in biologically meaningful categories and relevant human brain cell types, including excitatory neuronal populations. There were substantial genetic correlations between cannabis use and a range of psychiatric disorders and substance use traits, while cannabis polygenic scores were associated with increased risk of psychiatric disorders. Mendelian Randomization showed evidence for (bidirectional) causal associations between cannabis use and ADHD, bipolar disorder, schizophrenia and PTSD.","rel_num_authors":85,"rel_authors":[{"author_name":"Joelle A Pasman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Zachary F Gerring","author_inst":"Walter and Eliza Hall Institute of Medical Research"},{"author_name":"Jackson G Thorp","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Abdel Abdellaoui","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Pierre Youssef","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Anil Ori","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Muhannad Smadi","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Anais B Thijssen","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Damian Woodward","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Briar Wormington","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Daniel E Adkins","author_inst":"University of Utah"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Chris Chatzinakos","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Sarah L Elson","author_inst":"23andMe"},{"author_name":"Pierre Fontanillas","author_inst":"23andMe"},{"author_name":"Ian R Gizer","author_inst":"University of Missouri"},{"author_name":"Haixia Gu","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Lindsey A Hines","author_inst":"University of Bath"},{"author_name":"Emma C Johnson","author_inst":"Washington University School of Medicine"},{"author_name":"Kadri Koiv","author_inst":"University of Tartu"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer"},{"author_name":"Penelope A Lind","author_inst":"Queensland University of Technology"},{"author_name":"Penelope A Lind","author_inst":"University of Queensland"},{"author_name":"Miriam A Mosing","author_inst":"Karolinska Institutet"},{"author_name":"Ilja M Nolte","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Jue-Sheng Ong","author_inst":"QIMR Berghofer"},{"author_name":"Jackie M Otto","author_inst":"Regeneron Pharmaceuticals, Inc."},{"author_name":"Teemu Palviainen","author_inst":"University of Helsinki"},{"author_name":"Roseann E Peterson","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Hannah M Sallis","author_inst":"University of Bristol"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Jean Shin","author_inst":"CHU Sainte-Justine Research Centre"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Camiel M van der Laan","author_inst":"Vrije Universiteit"},{"author_name":"Peter J van der Most","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Saskia van Dorsselaer","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Kristel R van Eijk","author_inst":"UMC Utrecht"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"Lovisenberg Diaconal Hospital"},{"author_name":"Robyn E Wootton","author_inst":"Norwegian Institute of Public Health"},{"author_name":"Stephanie Zellers","author_inst":"University of Minnesota"},{"author_name":"Catharina Hartman","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Georgi Hudjashov","author_inst":"University of Tartu"},{"author_name":"Marco P Boks","author_inst":"Amsterdam UMC"},{"author_name":"Dorret I Boomsma","author_inst":"Vrije Universiteit"},{"author_name":"Enda M Byrne","author_inst":"The University of Queensland"},{"author_name":"William E Copeland","author_inst":"University of Vermont"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Bart Ferweda","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Andrew C Heath","author_inst":"Washington University School of Medicine"},{"author_name":"Ian B Hickie","author_inst":"The University of Sydney"},{"author_name":"William G Iacono","author_inst":"University of Minnesota"},{"author_name":"Martin A Kennedy","author_inst":"University of Otago Christchurch"},{"author_name":"Kelli Lehto","author_inst":"University of Tartu"},{"author_name":"Anja Lok","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Stuart MacGregor","author_inst":"QIMR Berghofer"},{"author_name":"Pamela AF Madden","author_inst":"Washington University School of Medicine"},{"author_name":"Hermine HM Maes","author_inst":"Virginia Commonwealth University"},{"author_name":"Nicholas G Martin","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Matt McGue","author_inst":"University of Minnesota"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Marcus R Munafo","author_inst":"University of Bath"},{"author_name":"Max Nieuwdorp","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Tineke J Oldehinkel","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Miina Ollikainen","author_inst":"University of Helsinki"},{"author_name":"Abraham A Palmer","author_inst":"University of California San Diego"},{"author_name":"Tomas Paus","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"Zdenka Pausova","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"John F Pearson","author_inst":"University of Otago Christchurch"},{"author_name":"Sandra Sanchez-Roige","author_inst":"University of California San Diego"},{"author_name":"Harold Snieder","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Margreet ten Have","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Jorien L Treur","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Scott Vrieze","author_inst":"University of Minnesota"},{"author_name":"Kirk C Wilhelmsen","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Aelko H Zwinderman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Jacqueline M Vink","author_inst":"Radboud University"},{"author_name":"Nathan A Gillespie","author_inst":"Virginia Commonwealth University"},{"author_name":"Eske M Derks","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Karin JH Verweij","author_inst":"Amsterdam UMC, location University of Amsterdam"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Genetics of cannabis ever-use and frequency across ancestries implicate novel loci and brain-specific biology","rel_doi":"10.64898\/2026.04.25.26351611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.25.26351611","rel_abs":"Cannabis use is widespread, with genetic differences partly explaining variation in individual patterns of use. We performed the largest-to-date genome-wide association study (GWAS) meta-analysis of cannabis ever-use (N=736,322, 76% European ancestry) and various measures of frequency of use (N=269,160 cannabis users, 84% European ancestry). We identified 54 independent genome-wide significant loci for ever-use and 6 for frequency and show that the genetic architecture of ever-use, frequency, and cannabis use disorder (CUD) are overlapping but distinguishable. We identified 63 loci that were associated with common liability (All-cannabis) to different cannabis use traits in European-ancestry individuals. Across analyses, we identified 75 unique loci that had not previously been implicated in cannabis use. Gene prioritization analyses identified 349 genes for ever-use, 5 genes for frequency of use, and 429 for All-cannabis, including previously identified and novel genes. We found enrichment of genetic signals for cannabis use in biologically meaningful categories and relevant human brain cell types, including excitatory neuronal populations. There were substantial genetic correlations between cannabis use and a range of psychiatric disorders and substance use traits, while cannabis polygenic scores were associated with increased risk of psychiatric disorders. Mendelian Randomization showed evidence for (bidirectional) causal associations between cannabis use and ADHD, bipolar disorder, schizophrenia and PTSD.","rel_num_authors":85,"rel_authors":[{"author_name":"Joelle A Pasman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Zachary F Gerring","author_inst":"Walter and Eliza Hall Institute of Medical Research"},{"author_name":"Jackson G Thorp","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Abdel Abdellaoui","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Pierre Youssef","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Anil Ori","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Muhannad Smadi","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Anais B Thijssen","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Damian Woodward","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Briar Wormington","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Daniel E Adkins","author_inst":"University of Utah"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Chris Chatzinakos","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Sarah L Elson","author_inst":"23andMe"},{"author_name":"Pierre Fontanillas","author_inst":"23andMe"},{"author_name":"Ian R Gizer","author_inst":"University of Missouri"},{"author_name":"Haixia Gu","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Lindsey A Hines","author_inst":"University of Bath"},{"author_name":"Emma C Johnson","author_inst":"Washington University School of Medicine"},{"author_name":"Kadri Koiv","author_inst":"University of Tartu"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer"},{"author_name":"Penelope A Lind","author_inst":"Queensland University of Technology"},{"author_name":"Penelope A Lind","author_inst":"University of Queensland"},{"author_name":"Miriam A Mosing","author_inst":"Karolinska Institutet"},{"author_name":"Ilja M Nolte","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Jue-Sheng Ong","author_inst":"QIMR Berghofer"},{"author_name":"Jackie M Otto","author_inst":"Regeneron Pharmaceuticals, Inc."},{"author_name":"Teemu Palviainen","author_inst":"University of Helsinki"},{"author_name":"Roseann E Peterson","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Hannah M Sallis","author_inst":"University of Bristol"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Jean Shin","author_inst":"CHU Sainte-Justine Research Centre"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Camiel M van der Laan","author_inst":"Vrije Universiteit"},{"author_name":"Peter J van der Most","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Saskia van Dorsselaer","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Kristel R van Eijk","author_inst":"UMC Utrecht"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"Lovisenberg Diaconal Hospital"},{"author_name":"Robyn E Wootton","author_inst":"Norwegian Institute of Public Health"},{"author_name":"Stephanie Zellers","author_inst":"University of Minnesota"},{"author_name":"Catharina Hartman","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Georgi Hudjashov","author_inst":"University of Tartu"},{"author_name":"Marco P Boks","author_inst":"Amsterdam UMC"},{"author_name":"Dorret I Boomsma","author_inst":"Vrije Universiteit"},{"author_name":"Enda M Byrne","author_inst":"The University of Queensland"},{"author_name":"William E Copeland","author_inst":"University of Vermont"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Bart Ferweda","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Andrew C Heath","author_inst":"Washington University School of Medicine"},{"author_name":"Ian B Hickie","author_inst":"The University of Sydney"},{"author_name":"William G Iacono","author_inst":"University of Minnesota"},{"author_name":"Martin A Kennedy","author_inst":"University of Otago Christchurch"},{"author_name":"Kelli Lehto","author_inst":"University of Tartu"},{"author_name":"Anja Lok","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Stuart MacGregor","author_inst":"QIMR Berghofer"},{"author_name":"Pamela AF Madden","author_inst":"Washington University School of Medicine"},{"author_name":"Hermine HM Maes","author_inst":"Virginia Commonwealth University"},{"author_name":"Nicholas G Martin","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Matt McGue","author_inst":"University of Minnesota"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Marcus R Munafo","author_inst":"University of Bath"},{"author_name":"Max Nieuwdorp","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Tineke J Oldehinkel","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Miina Ollikainen","author_inst":"University of Helsinki"},{"author_name":"Abraham A Palmer","author_inst":"University of California San Diego"},{"author_name":"Tomas Paus","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"Zdenka Pausova","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"John F Pearson","author_inst":"University of Otago Christchurch"},{"author_name":"Sandra Sanchez-Roige","author_inst":"University of California San Diego"},{"author_name":"Harold Snieder","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Margreet ten Have","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Jorien L Treur","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Scott Vrieze","author_inst":"University of Minnesota"},{"author_name":"Kirk C Wilhelmsen","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Aelko H Zwinderman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Jacqueline M Vink","author_inst":"Radboud University"},{"author_name":"Nathan A Gillespie","author_inst":"Virginia Commonwealth University"},{"author_name":"Eske M Derks","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Karin JH Verweij","author_inst":"Amsterdam UMC, location University of Amsterdam"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Genetics of cannabis ever-use and frequency across ancestries implicate novel loci and brain-specific biology","rel_doi":"10.64898\/2026.04.25.26351611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.25.26351611","rel_abs":"Cannabis use is widespread, with genetic differences partly explaining variation in individual patterns of use. We performed the largest-to-date genome-wide association study (GWAS) meta-analysis of cannabis ever-use (N=736,322, 76% European ancestry) and various measures of frequency of use (N=269,160 cannabis users, 84% European ancestry). We identified 54 independent genome-wide significant loci for ever-use and 6 for frequency and show that the genetic architecture of ever-use, frequency, and cannabis use disorder (CUD) are overlapping but distinguishable. We identified 63 loci that were associated with common liability (All-cannabis) to different cannabis use traits in European-ancestry individuals. Across analyses, we identified 75 unique loci that had not previously been implicated in cannabis use. Gene prioritization analyses identified 349 genes for ever-use, 5 genes for frequency of use, and 429 for All-cannabis, including previously identified and novel genes. We found enrichment of genetic signals for cannabis use in biologically meaningful categories and relevant human brain cell types, including excitatory neuronal populations. There were substantial genetic correlations between cannabis use and a range of psychiatric disorders and substance use traits, while cannabis polygenic scores were associated with increased risk of psychiatric disorders. Mendelian Randomization showed evidence for (bidirectional) causal associations between cannabis use and ADHD, bipolar disorder, schizophrenia and PTSD.","rel_num_authors":85,"rel_authors":[{"author_name":"Joelle A Pasman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Zachary F Gerring","author_inst":"Walter and Eliza Hall Institute of Medical Research"},{"author_name":"Jackson G Thorp","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Abdel Abdellaoui","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Pierre Youssef","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Anil Ori","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Muhannad Smadi","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Anais B Thijssen","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Damian Woodward","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Briar Wormington","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Daniel E Adkins","author_inst":"University of Utah"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Chris Chatzinakos","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Sarah L Elson","author_inst":"23andMe"},{"author_name":"Pierre Fontanillas","author_inst":"23andMe"},{"author_name":"Ian R Gizer","author_inst":"University of Missouri"},{"author_name":"Haixia Gu","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Lindsey A Hines","author_inst":"University of Bath"},{"author_name":"Emma C Johnson","author_inst":"Washington University School of Medicine"},{"author_name":"Kadri Koiv","author_inst":"University of Tartu"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer"},{"author_name":"Penelope A Lind","author_inst":"Queensland University of Technology"},{"author_name":"Penelope A Lind","author_inst":"University of Queensland"},{"author_name":"Miriam A Mosing","author_inst":"Karolinska Institutet"},{"author_name":"Ilja M Nolte","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Jue-Sheng Ong","author_inst":"QIMR Berghofer"},{"author_name":"Jackie M Otto","author_inst":"Regeneron Pharmaceuticals, Inc."},{"author_name":"Teemu Palviainen","author_inst":"University of Helsinki"},{"author_name":"Roseann E Peterson","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Hannah M Sallis","author_inst":"University of Bristol"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Jean Shin","author_inst":"CHU Sainte-Justine Research Centre"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Camiel M van der Laan","author_inst":"Vrije Universiteit"},{"author_name":"Peter J van der Most","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Saskia van Dorsselaer","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Kristel R van Eijk","author_inst":"UMC Utrecht"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"Lovisenberg Diaconal Hospital"},{"author_name":"Robyn E Wootton","author_inst":"Norwegian Institute of Public Health"},{"author_name":"Stephanie Zellers","author_inst":"University of Minnesota"},{"author_name":"Catharina Hartman","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Georgi Hudjashov","author_inst":"University of Tartu"},{"author_name":"Marco P Boks","author_inst":"Amsterdam UMC"},{"author_name":"Dorret I Boomsma","author_inst":"Vrije Universiteit"},{"author_name":"Enda M Byrne","author_inst":"The University of Queensland"},{"author_name":"William E Copeland","author_inst":"University of Vermont"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Bart Ferweda","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Andrew C Heath","author_inst":"Washington University School of Medicine"},{"author_name":"Ian B Hickie","author_inst":"The University of Sydney"},{"author_name":"William G Iacono","author_inst":"University of Minnesota"},{"author_name":"Martin A Kennedy","author_inst":"University of Otago Christchurch"},{"author_name":"Kelli Lehto","author_inst":"University of Tartu"},{"author_name":"Anja Lok","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Stuart MacGregor","author_inst":"QIMR Berghofer"},{"author_name":"Pamela AF Madden","author_inst":"Washington University School of Medicine"},{"author_name":"Hermine HM Maes","author_inst":"Virginia Commonwealth University"},{"author_name":"Nicholas G Martin","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Matt McGue","author_inst":"University of Minnesota"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Marcus R Munafo","author_inst":"University of Bath"},{"author_name":"Max Nieuwdorp","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Tineke J Oldehinkel","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Miina Ollikainen","author_inst":"University of Helsinki"},{"author_name":"Abraham A Palmer","author_inst":"University of California San Diego"},{"author_name":"Tomas Paus","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"Zdenka Pausova","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"John F Pearson","author_inst":"University of Otago Christchurch"},{"author_name":"Sandra Sanchez-Roige","author_inst":"University of California San Diego"},{"author_name":"Harold Snieder","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Margreet ten Have","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Jorien L Treur","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Scott Vrieze","author_inst":"University of Minnesota"},{"author_name":"Kirk C Wilhelmsen","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Aelko H Zwinderman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Jacqueline M Vink","author_inst":"Radboud University"},{"author_name":"Nathan A Gillespie","author_inst":"Virginia Commonwealth University"},{"author_name":"Eske M Derks","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Karin JH Verweij","author_inst":"Amsterdam UMC, location University of Amsterdam"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Genetics of cannabis ever-use and frequency across ancestries implicate novel loci and brain-specific biology","rel_doi":"10.64898\/2026.04.25.26351611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.25.26351611","rel_abs":"Cannabis use is widespread, with genetic differences partly explaining variation in individual patterns of use. We performed the largest-to-date genome-wide association study (GWAS) meta-analysis of cannabis ever-use (N=736,322, 76% European ancestry) and various measures of frequency of use (N=269,160 cannabis users, 84% European ancestry). We identified 54 independent genome-wide significant loci for ever-use and 6 for frequency and show that the genetic architecture of ever-use, frequency, and cannabis use disorder (CUD) are overlapping but distinguishable. We identified 63 loci that were associated with common liability (All-cannabis) to different cannabis use traits in European-ancestry individuals. Across analyses, we identified 75 unique loci that had not previously been implicated in cannabis use. Gene prioritization analyses identified 349 genes for ever-use, 5 genes for frequency of use, and 429 for All-cannabis, including previously identified and novel genes. We found enrichment of genetic signals for cannabis use in biologically meaningful categories and relevant human brain cell types, including excitatory neuronal populations. There were substantial genetic correlations between cannabis use and a range of psychiatric disorders and substance use traits, while cannabis polygenic scores were associated with increased risk of psychiatric disorders. Mendelian Randomization showed evidence for (bidirectional) causal associations between cannabis use and ADHD, bipolar disorder, schizophrenia and PTSD.","rel_num_authors":85,"rel_authors":[{"author_name":"Joelle A Pasman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Zachary F Gerring","author_inst":"Walter and Eliza Hall Institute of Medical Research"},{"author_name":"Jackson G Thorp","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Abdel Abdellaoui","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Pierre Youssef","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Anil Ori","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Muhannad Smadi","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Anais B Thijssen","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Damian Woodward","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Briar Wormington","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Daniel E Adkins","author_inst":"University of Utah"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Fazil Aliev","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Chris Chatzinakos","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Sarah L Elson","author_inst":"23andMe"},{"author_name":"Pierre Fontanillas","author_inst":"23andMe"},{"author_name":"Ian R Gizer","author_inst":"University of Missouri"},{"author_name":"Haixia Gu","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Lindsey A Hines","author_inst":"University of Bath"},{"author_name":"Emma C Johnson","author_inst":"Washington University School of Medicine"},{"author_name":"Kadri Koiv","author_inst":"University of Tartu"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer"},{"author_name":"Penelope A Lind","author_inst":"Queensland University of Technology"},{"author_name":"Penelope A Lind","author_inst":"University of Queensland"},{"author_name":"Miriam A Mosing","author_inst":"Karolinska Institutet"},{"author_name":"Ilja M Nolte","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Jue-Sheng Ong","author_inst":"QIMR Berghofer"},{"author_name":"Jackie M Otto","author_inst":"Regeneron Pharmaceuticals, Inc."},{"author_name":"Teemu Palviainen","author_inst":"University of Helsinki"},{"author_name":"Roseann E Peterson","author_inst":"SUNY Downstate Health Sciences University"},{"author_name":"Hannah M Sallis","author_inst":"University of Bristol"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Andrey A Shabalin","author_inst":"University of Utah"},{"author_name":"Jean Shin","author_inst":"CHU Sainte-Justine Research Centre"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Nathaniel S Thomas","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Camiel M van der Laan","author_inst":"Vrije Universiteit"},{"author_name":"Peter J van der Most","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Saskia van Dorsselaer","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Kristel R van Eijk","author_inst":"UMC Utrecht"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"University of Bristol"},{"author_name":"Robyn E Wootton","author_inst":"Lovisenberg Diaconal Hospital"},{"author_name":"Robyn E Wootton","author_inst":"Norwegian Institute of Public Health"},{"author_name":"Stephanie Zellers","author_inst":"University of Minnesota"},{"author_name":"Catharina Hartman","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Georgi Hudjashov","author_inst":"University of Tartu"},{"author_name":"Marco P Boks","author_inst":"Amsterdam UMC"},{"author_name":"Dorret I Boomsma","author_inst":"Vrije Universiteit"},{"author_name":"Enda M Byrne","author_inst":"The University of Queensland"},{"author_name":"William E Copeland","author_inst":"University of Vermont"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Danielle M Dick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Bart Ferweda","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Andrew C Heath","author_inst":"Washington University School of Medicine"},{"author_name":"Ian B Hickie","author_inst":"The University of Sydney"},{"author_name":"William G Iacono","author_inst":"University of Minnesota"},{"author_name":"Martin A Kennedy","author_inst":"University of Otago Christchurch"},{"author_name":"Kelli Lehto","author_inst":"University of Tartu"},{"author_name":"Anja Lok","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Stuart MacGregor","author_inst":"QIMR Berghofer"},{"author_name":"Pamela AF Madden","author_inst":"Washington University School of Medicine"},{"author_name":"Hermine HM Maes","author_inst":"Virginia Commonwealth University"},{"author_name":"Nicholas G Martin","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Matt McGue","author_inst":"University of Minnesota"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Marcus R Munafo","author_inst":"University of Bath"},{"author_name":"Max Nieuwdorp","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Tineke J Oldehinkel","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Miina Ollikainen","author_inst":"University of Helsinki"},{"author_name":"Abraham A Palmer","author_inst":"University of California San Diego"},{"author_name":"Tomas Paus","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"Zdenka Pausova","author_inst":"University of Montreal, CHU Sainte-Justine Research Centre"},{"author_name":"John F Pearson","author_inst":"University of Otago Christchurch"},{"author_name":"Sandra Sanchez-Roige","author_inst":"University of California San Diego"},{"author_name":"Harold Snieder","author_inst":"University of Groningen, University Medical Center Groningen"},{"author_name":"Margreet ten Have","author_inst":"Netherlands Institute of Mental Health and Addiction"},{"author_name":"Jorien L Treur","author_inst":"Amsterdam UMC,  University of Amsterdam"},{"author_name":"Scott Vrieze","author_inst":"University of Minnesota"},{"author_name":"Kirk C Wilhelmsen","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Aelko H Zwinderman","author_inst":"Amsterdam UMC, location University of Amsterdam"},{"author_name":"Jacqueline M Vink","author_inst":"Radboud University"},{"author_name":"Nathan A Gillespie","author_inst":"Virginia Commonwealth University"},{"author_name":"Eske M Derks","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Karin JH Verweij","author_inst":"Amsterdam UMC, location University of Amsterdam"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Interpretable Machine Learning Reveals Integrated Water Chemistry and Parameter-Specific Nonlinear Responses Shaping Legionella spp. and Mycobacterium spp. in Drinking Water","rel_doi":"10.64898\/2026.04.23.26351579","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351579","rel_abs":"Traditionally, studies have explored the impacts of individual water chemistry parameters on the persistence of Mycobacterium spp. and Legionella spp. in isolation with the underlying assumption that these associations are likely monotonic in nature. Yet chemical and microbiological changes are complex, and associations are likely highly combinatorial. In this study, we use interpretable machine learning models to disentangle the integrative and nonlinear associations between water chemistry and occurrence\/abundance of Mycobacterium spp. and Legionella spp. Seasonal data from source water, point-of-entry and distribution systems of eight full-scale drinking water systems demonstrated that shifts in overall water chemistry were associated with the changes in microbial abundance during treatment and distribution. Machine learning models indicated moderate predictive ability of integrated water chemistry towards Legionella spp. abundance and towards the occurrence of both Legionella spp. and Mycobacterium spp., whereas predictive performance for Mycobacterium spp. abundance was limited. The association between nitrate and Legionella spp. abundance was disinfectant regimes dependent, while dissolved organic carbon exhibited a concentration dependent response type (i.e., positive and negative association). In chloraminated systems, Legionella spp. abundance was positively associated with ammonia and nitrate, highlighting the critical role of nitrification. Here, it appears that pH likely influences the initial colonization of Legionella spp. while ammonia governs its abundance in drinking water. Overall, this study demonstrates that integrated water chemistry and parameter-specific nonlinear effects collectively explain persistence of Mycobacterium spp. and Legionella spp. in drinking water systems.","rel_num_authors":12,"rel_authors":[{"author_name":"Jinhao Yang","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Huanqi He","author_inst":"School of Science and Engineering, Benedict College, Columbia, South Carolina, USA; School of Civil and Environmental Engineering, Georgia Institute of Technolo"},{"author_name":"Samantha DiLoreto","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Kaiqin Bian","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Jacob R. Phaneuf","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Patrick Milne","author_inst":"Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA"},{"author_name":"Kelsey Pieper","author_inst":"Department of Civil and Environmental Engineering, University of North Carolina Charlotte, North Carolina, USA"},{"author_name":"Aron Stubbins","author_inst":"Department of Chemistry and Chemical Biology; Department of Marine and Environmental Sciences; Department of Civil and Environmental Engineering, Northeastern U"},{"author_name":"Ching-Hua Huang","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Katherine E. Graham","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Christopher A. Impellitteri","author_inst":"The Water Tower Institute Inc., Buford, Georgia, USA"},{"author_name":"Ameet Pinto","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA; School of Earth and Atmospheric Sciences, Georgia Institu"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Interpretable Machine Learning Reveals Integrated Water Chemistry and Parameter-Specific Nonlinear Responses Shaping Legionella spp. and Mycobacterium spp. in Drinking Water","rel_doi":"10.64898\/2026.04.23.26351579","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351579","rel_abs":"Traditionally, studies have explored the impacts of individual water chemistry parameters on the persistence of Mycobacterium spp. and Legionella spp. in isolation with the underlying assumption that these associations are likely monotonic in nature. Yet chemical and microbiological changes are complex, and associations are likely highly combinatorial. In this study, we use interpretable machine learning models to disentangle the integrative and nonlinear associations between water chemistry and occurrence\/abundance of Mycobacterium spp. and Legionella spp. Seasonal data from source water, point-of-entry and distribution systems of eight full-scale drinking water systems demonstrated that shifts in overall water chemistry were associated with the changes in microbial abundance during treatment and distribution. Machine learning models indicated moderate predictive ability of integrated water chemistry towards Legionella spp. abundance and towards the occurrence of both Legionella spp. and Mycobacterium spp., whereas predictive performance for Mycobacterium spp. abundance was limited. The association between nitrate and Legionella spp. abundance was disinfectant regimes dependent, while dissolved organic carbon exhibited a concentration dependent response type (i.e., positive and negative association). In chloraminated systems, Legionella spp. abundance was positively associated with ammonia and nitrate, highlighting the critical role of nitrification. Here, it appears that pH likely influences the initial colonization of Legionella spp. while ammonia governs its abundance in drinking water. Overall, this study demonstrates that integrated water chemistry and parameter-specific nonlinear effects collectively explain persistence of Mycobacterium spp. and Legionella spp. in drinking water systems.","rel_num_authors":12,"rel_authors":[{"author_name":"Jinhao Yang","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Huanqi He","author_inst":"School of Science and Engineering, Benedict College, Columbia, South Carolina, USA; School of Civil and Environmental Engineering, Georgia Institute of Technolo"},{"author_name":"Samantha DiLoreto","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Kaiqin Bian","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Jacob R. Phaneuf","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Patrick Milne","author_inst":"Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA"},{"author_name":"Kelsey Pieper","author_inst":"Department of Civil and Environmental Engineering, University of North Carolina Charlotte, North Carolina, USA"},{"author_name":"Aron Stubbins","author_inst":"Department of Chemistry and Chemical Biology; Department of Marine and Environmental Sciences; Department of Civil and Environmental Engineering, Northeastern U"},{"author_name":"Ching-Hua Huang","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Katherine E. Graham","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA"},{"author_name":"Christopher A. Impellitteri","author_inst":"The Water Tower Institute Inc., Buford, Georgia, USA"},{"author_name":"Ameet Pinto","author_inst":"School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA; School of Earth and Atmospheric Sciences, Georgia Institu"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Neighborhood Deprivation Is Associated with Accelerated Epigenetic Aging Via Greater Individual Adversity","rel_doi":"10.64898\/2026.04.24.26351669","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351669","rel_abs":"Importance: Adverse neighborhood conditions can lead to poorer health outcomes, potentially through accelerated biological aging. However, whether these relationships are explained by individual- or neighborhood-level factors remains unclear. Objective: To examine the association between neighborhood deprivation, measured by the Area Deprivation Index (ADI), and epigenetic age acceleration and assess whether individual- and neighborhood-level characteristics mediate or modify these associations. Design: Cross-sectional study using data from a Yale Stress Center study between 2008 and 2012. Data analysis was conducted from July 2025 to January 2026. Setting: Community-based sample from the greater New Haven, CT area. Participants: A total of 370 healthy adults aged 18 to 50 years without major psychiatric, medical, or cognitive disorders who provided blood samples for DNA methylation analysis. Main Outcomes and Measures: Epigenetic age acceleration measured from DNA methylation using four second-generation epigenetic clocks, with associations assessed among aging, neighborhood deprivation, and individual- and neighborhood-level factors. Results: Data were analyzed from 370 participants (212 women [57.3%], 158 men [42.7%]; mean [SEM] age, 29.3 [0.46] years). Greater neighborhood deprivation was associated with greater lifetime adversity ({beta}=0.112, p<.001) and lower educational attainment ({beta}=-0.019, p=.012), and accelerated epigenetic aging as measured by GrimAge ({beta}=0.037, p<.001), PCGrimAge ({beta}=0.019, p<.001), and PCPhenoAge ({beta}=0.041, p<.001), but not PhenoAge (p=.23). In multivariable models accounting for individual factors, neighborhood deprivation remained associated with these three clocks. Lifetime adversity partially mediated the association between ADI and accelerated GrimAge (20.3% of total effect) and PCGrimAge (23.3%). Race moderated the direct association between ADI and epigenetic aging, with stronger associations between neighborhood deprivation and accelerated GrimAge ({beta}=0.061, p=.004) and PCPhenoAge ({beta}=0.057, p=.02) observed among Black participants compared to White. Conclusions: Greater neighborhood deprivation was associated with accelerated epigenetic aging across multiple second-generation clocks, with lifetime adversity partially mediating these associations. Stronger effects were observed among Black participants. These findings suggest that neighborhood environments and cumulative stress may contribute to biological aging and racial disparities in aging trajectories.","rel_num_authors":8,"rel_authors":[{"author_name":"Alija S Koirala","author_inst":"Yale University"},{"author_name":"Justin R Shields","author_inst":"Yale University"},{"author_name":"Anjali S Vijan","author_inst":"Yale University"},{"author_name":"Stephanie Wemm","author_inst":"Yale University"},{"author_name":"Ke Xu","author_inst":"Yale University"},{"author_name":"Benson S Ku","author_inst":"Emory University"},{"author_name":"Rajita Sinha","author_inst":"Yale University"},{"author_name":"Zachary M Harvanek","author_inst":"Yale University"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Toward trustworthy clinical AI for obsessive-compulsive disorder: reliability, generalizability, and interpretability of a transformer model across the ENIGMA-OCD consortium","rel_doi":"10.64898\/2026.04.24.26351711","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351711","rel_abs":"Background. Studies applying machine learning to obsessive-compulsive disorder (OCD) typically report accuracy in homogeneous samples but rarely assess model reliability, generalizability, and interpretability needed for clinical use. Methods. We applied a transformer-based deep learning model, the Multi-Band Brain Net, to the ENIGMA-OCD cohort - the largest available resting-state functional magnetic resonance imaging (rs-fMRI) dataset in OCD with 1,706 participants (869 cases with OCD, 837 controls) across 23 sites worldwide. We evaluated model reliability by calculating calibration - the model's ability to \"know what it doesn't know\". We assessed generalizability using leave-one-site-out validation to test performance on unseen sites with different scanners, acquisition protocols, and patient populations. Finally, we examined interpretability by analyzing model attention weights to identify the neural connectivity patterns that influence model predictions. Results. The model achieved modest but competitive classification performance (AUROC = .653, SD = .039). Crucially, while large-scale pretraining on the UK Biobank (N = 40,783) did not boost accuracy, it significantly enhanced model calibration by reducing overconfident predictions. Leave-one-site-out validation showed a generalization gap across sites (AUROC = .427-.819). Pretraining did not close this gap but removed scanner manufacturer bias. Finally, attention-based mapping identified biologically plausible patterns of widespread hypoconnectivity in OCD relative to healthy controls, particularly in low-frequency bands involving the default mode, salience, and somatomotor networks. These findings aligned with known OCD neurobiology. Conclusions. This study provides a framework for developing more reliable and trustworthy clinical artificial intelligence for OCD.","rel_num_authors":87,"rel_authors":[{"author_name":"Maria Pak","author_inst":"Department of Psychology, Seoul National University, Republic of Korea"},{"author_name":"Youngchan Ryu","author_inst":"Department of Electrical and Computer Engineering, Seoul National University, Republic of Korea"},{"author_name":"Sangyoon Bae","author_inst":"Graduate School of Artificial Intelligence, Seoul National University, Republic of Korea"},{"author_name":"Alan Anticevic","author_inst":"Johnson & Johnson, Neuroscience Therapeutic Area"},{"author_name":"Ana Daniela Costa","author_inst":"Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS\/3B's, PT Government Associate Laboratory, Bra"},{"author_name":"Anders L. Thorsen","author_inst":"Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway; Centre for Crisis Psychology, University of Bergen, Bergen, Norway"},{"author_name":"Anouk L. van der Straten","author_inst":"Levvel, Academic Center for Child and Adolescent Psychiatry, Amsterdam, The Netherlands"},{"author_name":"Beatriz Couto","author_inst":"Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS\/3B's, PT Government Associate Laboratory, Bra"},{"author_name":"Benedetta Vai","author_inst":"Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy ; Vita-Salute San Raffaele University, Milan, Italy"},{"author_name":"Bjarne Hansen","author_inst":"Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway; Centre for Crisis Psychology, University of Bergen, Bergen, Norway"},{"author_name":"Carles Soriano-Mas","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; CIBERSAM, Barcelona, Spain; Department of Social Psychology an"},{"author_name":"Chiang-shan R. Li","author_inst":"Departments of Psychiatry and of Neuroscience, Yale University, New Haven, CT"},{"author_name":"Chris Vriend","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Department of  Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterd"},{"author_name":"Christine Lochner","author_inst":"SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa"},{"author_name":"Christopher Pittenger","author_inst":"Department of Psychiatry, Neuroscience, Psychology, and Yale Child Study Center, Yale University, New Haven, CT; Center for Brain and Mind Health, Yale Universi"},{"author_name":"Clara A. Moreau","author_inst":"Sainte Justine Hospital Azrieli Research Center, Department of Psychiatry and Addictology, University of Montreal, Canada"},{"author_name":"Daniela Rodriguez-Manrique","author_inst":"Department of Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; School of Medicine "},{"author_name":"Daniela Vecchio","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy"},{"author_name":"Eiji Shimizu","author_inst":"Research Center for Child Mental Development, Chiba University, Chiba, Japan; United Graduate School of Child Development, The University of Osaka, Suita, Japan"},{"author_name":"Emily R. Stern","author_inst":"Department of Psychiatry, New York University School of Medicine, New York, NY; Neuroscience Institute, New York University School of Medicine, New York, NY; Cl"},{"author_name":"Emma Munoz-Moreno","author_inst":"MRI Core Facility, IDIBAPS, Barcelona, Spain"},{"author_name":"Erika L. Nurmi","author_inst":"Division of Child and Adolescent Psychiatry, Jane & Terry Semel Institute For Neurosciences, University of California, Los Angeles, CA, USA"},{"author_name":"Fabrizio Piras","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy"},{"author_name":"Federica Colombo","author_inst":"Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy"},{"author_name":"Federica Piras","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy"},{"author_name":"Fern Jaspers-Fayer","author_inst":"Department of Psychiatry, University of British Columbia, Vancouver, Canada"},{"author_name":"Francesco Benedetti","author_inst":"Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy ; Vita-Salute San Raffaele University, Milan, Italy"},{"author_name":"Ganesan Venkatasubramanian","author_inst":"OCD clinic, Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India"},{"author_name":"Goi Khia Eng","author_inst":"Department of Psychiatry, New York University School of Medicine, New York, NY; Clinical Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, "},{"author_name":"H. Blair Simpson","author_inst":"Columbia University Irving Medical College, Columbia University, New York, NY, U.S.A.; New York State Psychiatric Institute, New York, NY, U.S.A."},{"author_name":"Hanyang Ruan","author_inst":"Department of Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; School of Medicine "},{"author_name":"Hao Hu","author_inst":"Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R.China"},{"author_name":"Hein J.F. van Marle","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Dept. Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands"},{"author_name":"Hirofumi Tomiyama","author_inst":"Graduate School of Medical Sciences, Kyushu University, Fukuoka-shi, Japan"},{"author_name":"Ignacio Martinez-Zalacain","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; Department of Radiology, Bellvitge University Hospital, Barcel"},{"author_name":"Jamie Feusner","author_inst":"Department of Psychiatry, Division of Neurosciences and Clinical Translation, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health"},{"author_name":"Janardhanan C. Narayanaswamy","author_inst":"Department of Psychiatry, School of Clinical Sciences, Monash University, Melbourne, Australia; Monash Health, Melbourne, Australia; OCD clinic, Department of P"},{"author_name":"Je-Yeon Yun","author_inst":"Seoul National University Hospital, Seoul, Republic of Korea; Yeongeon Student Support Center, Seoul National University College of Medicine"},{"author_name":"Joao R. Sato","author_inst":"Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo Andre, Brazil"},{"author_name":"Jonathan Ipser","author_inst":"Department of Psychiatry and Mental Health and Neuroscience Institute, University of Cape Town, Cape Town, South Africa"},{"author_name":"Jose C. Pariente","author_inst":"MRI Core Facility, IDIBAPS, Barcelona, Spain"},{"author_name":"Jose M. Menchon","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; CIBERSAM, Barcelona, Spain; Department of Clinical Sciences, U"},{"author_name":"Joseph O'Neill","author_inst":"Division of Child and Adolescent Psychiatry, Jane & Terry Semel Institute For Neurosciences, University of California, Los Angeles, CA, USA"},{"author_name":"Jun Soo Kwon","author_inst":"Department of Psychiatry, Hanyang University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Hanyang University Hospital, Seoul, Republ"},{"author_name":"Kathrin Koch","author_inst":"Department of Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; School of Medicine "},{"author_name":"Kristen Hagen","author_inst":"Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway; Psychiatric Department, More og Romsdal Hospital Trust, Molde, Norway; Depart"},{"author_name":"Lea Backhausen","author_inst":"Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitat Dresden, Dresden, Germany"},{"author_name":"Lea Waller","author_inst":"Department of Psychiatry and Neurosciences CCM, Charite Universitatsmedizin Berlin, corporate member of Freie Universitat Berlin and Humboldt-Universitat zu Ber"},{"author_name":"Luisa Lazaro","author_inst":"Department of Child and Adolescent Psychiatry and Psychology, Hospital Clinic, IDIBAPS, Barcelona, Spain; Department of Medicine, University of Barcelona, Spain"},{"author_name":"Marcelo C. Batistuzzo","author_inst":"Departamento de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Departament of Methods and "},{"author_name":"Marcelo Q. Hoexter","author_inst":"Departamento de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil"},{"author_name":"Maria Pico-Perez","author_inst":"Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; Departamento de Psicologia Basica, Clinica y Psico"},{"author_name":"Minah Kim","author_inst":"Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medi"},{"author_name":"Nadza Dzinalija","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Department of  Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterd"},{"author_name":"Nicole Beyer","author_inst":"Department of Child and Adolescent Psychiatry and Psychotherapy, Faculty of Medicine, Technische Universitat Dresden, Dresden; Germany"},{"author_name":"Nora C. Vetter","author_inst":"Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitat Dresden, Dresden, Germany; Faculty of Natural Sciences, Departm"},{"author_name":"Patricia Gruner","author_inst":"Department of Psychiatry, Yale University, New Haven, CT"},{"author_name":"Pedro Morgado","author_inst":"Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS\/3B's, PT Government Associate Laboratory, Bra"},{"author_name":"Philip R. Szeszko","author_inst":"Department of Psychiatry Icahn School of Medicine at Mount Sinai, New York, NY; James J. Peters VA Medical Center, Mental Illness Research, Education and Clinic"},{"author_name":"Pino Alonso","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; CIBERSAM, Barcelona, Spain; Department of Clinical Sciences, U"},{"author_name":"Qing Zhao","author_inst":"Department of Clinical Psychology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R.China"},{"author_name":"Rachel Marsh","author_inst":"Columbia University Irving Medical College, Columbia University, New York, NY, U.S.A.; New York State Psychiatric Institute, New York, NY, U.S.A."},{"author_name":"S. Evelyn Stewart","author_inst":"Department of Psychiatry, University of British Columbia, Vancouver, Canada"},{"author_name":"Sara Bertolin","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; CIBERSAM, Barcelona, Spain; Department of Clinical Sciences, U"},{"author_name":"Silvia Brem","author_inst":"Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Cente"},{"author_name":"Sophia I. Thomopoulos","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Srinivas Balachander","author_inst":"OCD clinic, Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India"},{"author_name":"Susanne Walitza","author_inst":"Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Cente"},{"author_name":"Tokiko Yoshida","author_inst":"Research Center for Child Mental Development, Chiba University, Chiba, Japan; United Graduate School of Child Development, The University of Osaka, Suita, Japan"},{"author_name":"Tomohiro Nakao","author_inst":"Graduate School of Medical Sciences, Kyushu University, Fukuoka-shi, Japan"},{"author_name":"Venkataram Shivakumar","author_inst":"Department of Integrative Medicine, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India"},{"author_name":"Wieke van Leeuwen","author_inst":"Arkin Institute for Mental Health, Department of Psychiatry, Amsterdam, The Netherlands"},{"author_name":"Y.C. Janardhan Reddy","author_inst":"OCD clinic, Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India"},{"author_name":"Yoshinari Abe","author_inst":"Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine; Sugimoto Psychiatric Clinic"},{"author_name":"Yoshiyuki Hirano","author_inst":"Research Center for Child Mental Development, Chiba University, Chiba, Japan; United Graduate School of Child Development, The University of Osaka, Suita, Japan"},{"author_name":"Youngsun Cho","author_inst":"Department of Psychiatry, Yale University, New Haven, CT; Child Study Center, Yale University, New Haven, CT"},{"author_name":"Ysbrand D. van der Werf","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amste"},{"author_name":"Yuki Ikemizu","author_inst":"Research Center for Child Mental Development, Chiba University, Chiba, Japan; United Graduate School of Child Development, The University of Osaka, Suita, Japan"},{"author_name":"Yuki Sakai","author_inst":"ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan; Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural Uni"},{"author_name":"Zhen Wang","author_inst":"Department of Clinical Psychology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R.China"},{"author_name":"- ENIGMA-OCD Working Group","author_inst":""},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Willem Bruin","author_inst":"Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands; Sectio"},{"author_name":"Guido van Wingen","author_inst":"Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands"},{"author_name":"Dan J. Stein","author_inst":"SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa"},{"author_name":"Odile A. van den Heuvel","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Department of  Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterd"},{"author_name":"Jiook Cha","author_inst":"Department of Psychology, Seoul National University, Republic of Korea; Graduate School of Artificial Intelligence, Seoul National University, Republic of Korea"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Toward trustworthy clinical AI for obsessive-compulsive disorder: reliability, generalizability, and interpretability of a transformer model across the ENIGMA-OCD consortium","rel_doi":"10.64898\/2026.04.24.26351711","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351711","rel_abs":"Background. Studies applying machine learning to obsessive-compulsive disorder (OCD) typically report accuracy in homogeneous samples but rarely assess model reliability, generalizability, and interpretability needed for clinical use. Methods. We applied a transformer-based deep learning model, the Multi-Band Brain Net, to the ENIGMA-OCD cohort - the largest available resting-state functional magnetic resonance imaging (rs-fMRI) dataset in OCD with 1,706 participants (869 cases with OCD, 837 controls) across 23 sites worldwide. We evaluated model reliability by calculating calibration - the model's ability to \"know what it doesn't know\". We assessed generalizability using leave-one-site-out validation to test performance on unseen sites with different scanners, acquisition protocols, and patient populations. Finally, we examined interpretability by analyzing model attention weights to identify the neural connectivity patterns that influence model predictions. Results. The model achieved modest but competitive classification performance (AUROC = .653, SD = .039). Crucially, while large-scale pretraining on the UK Biobank (N = 40,783) did not boost accuracy, it significantly enhanced model calibration by reducing overconfident predictions. Leave-one-site-out validation showed a generalization gap across sites (AUROC = .427-.819). Pretraining did not close this gap but removed scanner manufacturer bias. Finally, attention-based mapping identified biologically plausible patterns of widespread hypoconnectivity in OCD relative to healthy controls, particularly in low-frequency bands involving the default mode, salience, and somatomotor networks. These findings aligned with known OCD neurobiology. Conclusions. This study provides a framework for developing more reliable and trustworthy clinical artificial intelligence for OCD.","rel_num_authors":87,"rel_authors":[{"author_name":"Maria Pak","author_inst":"Department of Psychology, Seoul National University, Republic of Korea"},{"author_name":"Youngchan Ryu","author_inst":"Department of Electrical and Computer Engineering, Seoul National University, Republic of Korea"},{"author_name":"Sangyoon Bae","author_inst":"Graduate School of Artificial Intelligence, Seoul National University, Republic of Korea"},{"author_name":"Alan Anticevic","author_inst":"Johnson & Johnson, Neuroscience Therapeutic Area"},{"author_name":"Ana Daniela Costa","author_inst":"Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS\/3B's, PT Government Associate Laboratory, Bra"},{"author_name":"Anders L. Thorsen","author_inst":"Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway; Centre for Crisis Psychology, University of Bergen, Bergen, Norway"},{"author_name":"Anouk L. van der Straten","author_inst":"Levvel, Academic Center for Child and Adolescent Psychiatry, Amsterdam, The Netherlands"},{"author_name":"Beatriz Couto","author_inst":"Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS\/3B's, PT Government Associate Laboratory, Bra"},{"author_name":"Benedetta Vai","author_inst":"Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy ; Vita-Salute San Raffaele University, Milan, Italy"},{"author_name":"Bjarne Hansen","author_inst":"Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway; Centre for Crisis Psychology, University of Bergen, Bergen, Norway"},{"author_name":"Carles Soriano-Mas","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; CIBERSAM, Barcelona, Spain; Department of Social Psychology an"},{"author_name":"Chiang-shan R. Li","author_inst":"Departments of Psychiatry and of Neuroscience, Yale University, New Haven, CT"},{"author_name":"Chris Vriend","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Department of  Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterd"},{"author_name":"Christine Lochner","author_inst":"SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa"},{"author_name":"Christopher Pittenger","author_inst":"Department of Psychiatry, Neuroscience, Psychology, and Yale Child Study Center, Yale University, New Haven, CT; Center for Brain and Mind Health, Yale Universi"},{"author_name":"Clara A. Moreau","author_inst":"Sainte Justine Hospital Azrieli Research Center, Department of Psychiatry and Addictology, University of Montreal, Canada"},{"author_name":"Daniela Rodriguez-Manrique","author_inst":"Department of Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; School of Medicine "},{"author_name":"Daniela Vecchio","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy"},{"author_name":"Eiji Shimizu","author_inst":"Research Center for Child Mental Development, Chiba University, Chiba, Japan; United Graduate School of Child Development, The University of Osaka, Suita, Japan"},{"author_name":"Emily R. Stern","author_inst":"Department of Psychiatry, New York University School of Medicine, New York, NY; Neuroscience Institute, New York University School of Medicine, New York, NY; Cl"},{"author_name":"Emma Munoz-Moreno","author_inst":"MRI Core Facility, IDIBAPS, Barcelona, Spain"},{"author_name":"Erika L. Nurmi","author_inst":"Division of Child and Adolescent Psychiatry, Jane & Terry Semel Institute For Neurosciences, University of California, Los Angeles, CA, USA"},{"author_name":"Fabrizio Piras","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy"},{"author_name":"Federica Colombo","author_inst":"Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy"},{"author_name":"Federica Piras","author_inst":"Laboratory of Neuropsychiatry, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy"},{"author_name":"Fern Jaspers-Fayer","author_inst":"Department of Psychiatry, University of British Columbia, Vancouver, Canada"},{"author_name":"Francesco Benedetti","author_inst":"Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy ; Vita-Salute San Raffaele University, Milan, Italy"},{"author_name":"Ganesan Venkatasubramanian","author_inst":"OCD clinic, Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India"},{"author_name":"Goi Khia Eng","author_inst":"Department of Psychiatry, New York University School of Medicine, New York, NY; Clinical Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, "},{"author_name":"H. Blair Simpson","author_inst":"Columbia University Irving Medical College, Columbia University, New York, NY, U.S.A.; New York State Psychiatric Institute, New York, NY, U.S.A."},{"author_name":"Hanyang Ruan","author_inst":"Department of Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; School of Medicine "},{"author_name":"Hao Hu","author_inst":"Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R.China"},{"author_name":"Hein J.F. van Marle","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Dept. Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands"},{"author_name":"Hirofumi Tomiyama","author_inst":"Graduate School of Medical Sciences, Kyushu University, Fukuoka-shi, Japan"},{"author_name":"Ignacio Martinez-Zalacain","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; Department of Radiology, Bellvitge University Hospital, Barcel"},{"author_name":"Jamie Feusner","author_inst":"Department of Psychiatry, Division of Neurosciences and Clinical Translation, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health"},{"author_name":"Janardhanan C. Narayanaswamy","author_inst":"Department of Psychiatry, School of Clinical Sciences, Monash University, Melbourne, Australia; Monash Health, Melbourne, Australia; OCD clinic, Department of P"},{"author_name":"Je-Yeon Yun","author_inst":"Seoul National University Hospital, Seoul, Republic of Korea; Yeongeon Student Support Center, Seoul National University College of Medicine"},{"author_name":"Joao R. Sato","author_inst":"Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo Andre, Brazil"},{"author_name":"Jonathan Ipser","author_inst":"Department of Psychiatry and Mental Health and Neuroscience Institute, University of Cape Town, Cape Town, South Africa"},{"author_name":"Jose C. Pariente","author_inst":"MRI Core Facility, IDIBAPS, Barcelona, Spain"},{"author_name":"Jose M. Menchon","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; CIBERSAM, Barcelona, Spain; Department of Clinical Sciences, U"},{"author_name":"Joseph O'Neill","author_inst":"Division of Child and Adolescent Psychiatry, Jane & Terry Semel Institute For Neurosciences, University of California, Los Angeles, CA, USA"},{"author_name":"Jun Soo Kwon","author_inst":"Department of Psychiatry, Hanyang University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Hanyang University Hospital, Seoul, Republ"},{"author_name":"Kathrin Koch","author_inst":"Department of Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; School of Medicine "},{"author_name":"Kristen Hagen","author_inst":"Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway; Psychiatric Department, More og Romsdal Hospital Trust, Molde, Norway; Depart"},{"author_name":"Lea Backhausen","author_inst":"Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitat Dresden, Dresden, Germany"},{"author_name":"Lea Waller","author_inst":"Department of Psychiatry and Neurosciences CCM, Charite Universitatsmedizin Berlin, corporate member of Freie Universitat Berlin and Humboldt-Universitat zu Ber"},{"author_name":"Luisa Lazaro","author_inst":"Department of Child and Adolescent Psychiatry and Psychology, Hospital Clinic, IDIBAPS, Barcelona, Spain; Department of Medicine, University of Barcelona, Spain"},{"author_name":"Marcelo C. Batistuzzo","author_inst":"Departamento de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Departament of Methods and "},{"author_name":"Marcelo Q. Hoexter","author_inst":"Departamento de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil"},{"author_name":"Maria Pico-Perez","author_inst":"Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; Departamento de Psicologia Basica, Clinica y Psico"},{"author_name":"Minah Kim","author_inst":"Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medi"},{"author_name":"Nadza Dzinalija","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Department of  Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterd"},{"author_name":"Nicole Beyer","author_inst":"Department of Child and Adolescent Psychiatry and Psychotherapy, Faculty of Medicine, Technische Universitat Dresden, Dresden; Germany"},{"author_name":"Nora C. Vetter","author_inst":"Department of Child and Adolescent Psychiatry, Faculty of Medicine of the Technische Universitat Dresden, Dresden, Germany; Faculty of Natural Sciences, Departm"},{"author_name":"Patricia Gruner","author_inst":"Department of Psychiatry, Yale University, New Haven, CT"},{"author_name":"Pedro Morgado","author_inst":"Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS\/3B's, PT Government Associate Laboratory, Bra"},{"author_name":"Philip R. Szeszko","author_inst":"Department of Psychiatry Icahn School of Medicine at Mount Sinai, New York, NY; James J. Peters VA Medical Center, Mental Illness Research, Education and Clinic"},{"author_name":"Pino Alonso","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; CIBERSAM, Barcelona, Spain; Department of Clinical Sciences, U"},{"author_name":"Qing Zhao","author_inst":"Department of Clinical Psychology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R.China"},{"author_name":"Rachel Marsh","author_inst":"Columbia University Irving Medical College, Columbia University, New York, NY, U.S.A.; New York State Psychiatric Institute, New York, NY, U.S.A."},{"author_name":"S. Evelyn Stewart","author_inst":"Department of Psychiatry, University of British Columbia, Vancouver, Canada"},{"author_name":"Sara Bertolin","author_inst":"Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain; CIBERSAM, Barcelona, Spain; Department of Clinical Sciences, U"},{"author_name":"Silvia Brem","author_inst":"Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Cente"},{"author_name":"Sophia I. Thomopoulos","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Srinivas Balachander","author_inst":"OCD clinic, Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India"},{"author_name":"Susanne Walitza","author_inst":"Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Cente"},{"author_name":"Tokiko Yoshida","author_inst":"Research Center for Child Mental Development, Chiba University, Chiba, Japan; United Graduate School of Child Development, The University of Osaka, Suita, Japan"},{"author_name":"Tomohiro Nakao","author_inst":"Graduate School of Medical Sciences, Kyushu University, Fukuoka-shi, Japan"},{"author_name":"Venkataram Shivakumar","author_inst":"Department of Integrative Medicine, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India"},{"author_name":"Wieke van Leeuwen","author_inst":"Arkin Institute for Mental Health, Department of Psychiatry, Amsterdam, The Netherlands"},{"author_name":"Y.C. Janardhan Reddy","author_inst":"OCD clinic, Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India"},{"author_name":"Yoshinari Abe","author_inst":"Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine; Sugimoto Psychiatric Clinic"},{"author_name":"Yoshiyuki Hirano","author_inst":"Research Center for Child Mental Development, Chiba University, Chiba, Japan; United Graduate School of Child Development, The University of Osaka, Suita, Japan"},{"author_name":"Youngsun Cho","author_inst":"Department of Psychiatry, Yale University, New Haven, CT; Child Study Center, Yale University, New Haven, CT"},{"author_name":"Ysbrand D. van der Werf","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amste"},{"author_name":"Yuki Ikemizu","author_inst":"Research Center for Child Mental Development, Chiba University, Chiba, Japan; United Graduate School of Child Development, The University of Osaka, Suita, Japan"},{"author_name":"Yuki Sakai","author_inst":"ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan; Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural Uni"},{"author_name":"Zhen Wang","author_inst":"Department of Clinical Psychology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R.China"},{"author_name":"- ENIGMA-OCD Working Group","author_inst":""},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Willem Bruin","author_inst":"Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands; Sectio"},{"author_name":"Guido van Wingen","author_inst":"Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands"},{"author_name":"Dan J. Stein","author_inst":"SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa"},{"author_name":"Odile A. van den Heuvel","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam, Department of  Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterd"},{"author_name":"Jiook Cha","author_inst":"Department of Psychology, Seoul National University, Republic of Korea; Graduate School of Artificial Intelligence, Seoul National University, Republic of Korea"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Disentangling Fatigue from Depression among Survivors of Severe COVID-19","rel_doi":"10.64898\/2026.04.24.26351694","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351694","rel_abs":"ABSTRACT Purpose: Survivors of severe COVID-19 commonly experience post-intensive care syndrome (PICS), which includes depression and fatigue. Fatigue is far more common and may inflate depression severity given overlapping symptoms. We sought to disentangle fatigue from depression in PICS. Methods: We conducted a cross-sectional analysis of the RAFT COVID study, a national multicenter longitudinal cohort of severe prolonged COVID-19 survivors. We included participants who completed validated surveys at 1-year from hospitalization for depression (PHQ-9) and fatigue (FACIT-Fatigue). We described correlation of FACIT-fatigue with the PHQ9, and separately with PHQ-2 and PHQ-7, which both omit the two items we hypothesized are influenced by fatigue: tiredness and sleeping. Using a MIMIC model, we performed differential item functioning to evaluate the impact of fatigue on depression directly through these two questions and indirectly with the latent depression construct. We then compared PHQ-7 to PHQ-9 scores by fatigue status. Results: Among 82 participants, 61.0% reported fatigue (reverse-scored FACIT-Fatigue[&ge;]9), and 15.9% moderately severe depression (PHQ-9[&ge;]10). FACIT-fatigue was strongly correlated with PHQ-9 (r=.87, p<.001), but less so for PHQ-2 (r=.76, p<.001) and PHQ-7 (r=.82, p<.001). The MIMIC model identified significant direct effects on tiredness ({lambda}=.89, p<.001) and sleep ({lambda}=.52, p<.001). Among fatigued participants, the rescaled PHQ-7 was lower than the PHQ-9 (median of 4.5, IQR 1.50-9.75, vs 7, IQR 4-9.75). Conclusions: Fatigue significantly inflated depression symptoms in severe COVID-19 survivors through tiredness and sleeping PHQ-9 items. PHQ-2 may better screen for true depressive symptoms in PICS, minimizing the risk of misdiagnosis and overtreatment.","rel_num_authors":4,"rel_authors":[{"author_name":"Juan R Cabrera","author_inst":"University of California, Berkeley"},{"author_name":"Peter Pham","author_inst":"University of California,Berkeley"},{"author_name":"W John Boscardin","author_inst":"University of California, San Francisco"},{"author_name":"Anil N Makam","author_inst":"University of California, San Francisco"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Disentangling Fatigue from Depression among Survivors of Severe COVID-19","rel_doi":"10.64898\/2026.04.24.26351694","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351694","rel_abs":"ABSTRACT Purpose: Survivors of severe COVID-19 commonly experience post-intensive care syndrome (PICS), which includes depression and fatigue. Fatigue is far more common and may inflate depression severity given overlapping symptoms. We sought to disentangle fatigue from depression in PICS. Methods: We conducted a cross-sectional analysis of the RAFT COVID study, a national multicenter longitudinal cohort of severe prolonged COVID-19 survivors. We included participants who completed validated surveys at 1-year from hospitalization for depression (PHQ-9) and fatigue (FACIT-Fatigue). We described correlation of FACIT-fatigue with the PHQ9, and separately with PHQ-2 and PHQ-7, which both omit the two items we hypothesized are influenced by fatigue: tiredness and sleeping. Using a MIMIC model, we performed differential item functioning to evaluate the impact of fatigue on depression directly through these two questions and indirectly with the latent depression construct. We then compared PHQ-7 to PHQ-9 scores by fatigue status. Results: Among 82 participants, 61.0% reported fatigue (reverse-scored FACIT-Fatigue[&ge;]9), and 15.9% moderately severe depression (PHQ-9[&ge;]10). FACIT-fatigue was strongly correlated with PHQ-9 (r=.87, p<.001), but less so for PHQ-2 (r=.76, p<.001) and PHQ-7 (r=.82, p<.001). The MIMIC model identified significant direct effects on tiredness ({lambda}=.89, p<.001) and sleep ({lambda}=.52, p<.001). Among fatigued participants, the rescaled PHQ-7 was lower than the PHQ-9 (median of 4.5, IQR 1.50-9.75, vs 7, IQR 4-9.75). Conclusions: Fatigue significantly inflated depression symptoms in severe COVID-19 survivors through tiredness and sleeping PHQ-9 items. PHQ-2 may better screen for true depressive symptoms in PICS, minimizing the risk of misdiagnosis and overtreatment.","rel_num_authors":4,"rel_authors":[{"author_name":"Juan R Cabrera","author_inst":"University of California, Berkeley"},{"author_name":"Peter Pham","author_inst":"University of California,Berkeley"},{"author_name":"W John Boscardin","author_inst":"University of California, San Francisco"},{"author_name":"Anil N Makam","author_inst":"University of California, San Francisco"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"Multicohort development and validation of a machine learning model to predict six-month functional traumatic brain injury outcomes in a large national registry","rel_doi":"10.64898\/2026.04.23.26351622","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351622","rel_abs":"Background: Prognostication after moderate-to-severe traumatic brain injury (TBI) rarely captures long-term functional recovery, despite its importance to patients, families, and clinicians. Large trauma registries such as the Trauma Quality Improvement Program (TQIP) dataset contain detailed clinical data but lack systematic follow-up, limiting their ability to study longer-term functional outcomes. Methods: We developed and externally validated a machine learning model to predict favorable six-month functional outcome (GOS MD\/GR or GOSE >=5) using harmonized data from two randomized clinical trials: CRASH (training) and ROC-TBI (validation). Five candidate classifiers (random forest [RF], linear discriminant analysis, k-nearest neighbors, naive Bayes, and support vector machine) were trained using seven shared clinical predictors. Models were evaluated using ROC-AUC, calibration metrics, and performance at the Youden optimal threshold and a high-sensitivity secondary threshold. The final model was applied to patients with moderate-to-severe TBI in the national TQIP registry (2017-2022) to estimate population-level recovery patterns. Results: The RF model demonstrated the highest overall performance after recalibration, achieving strong discrimination (AUC internal and external, 0.887 and 0.784), good calibration, and high sensitivity (0.890) and negative predictive value (0.909). Applied to 63,289 patients from TQIP, the model estimated that 45% would achieve favorable six-month outcomes at the Youden optimal threshold and 57% at the high-sensitivity threshold, with predicted recovery aligning with established clinical correlates such as younger age, higher admission GCS, and lower rates of penetrating or brainstem injuries. Conclusion: A machine learning model trained on high-quality trial data can generate clinically plausible estimates of long-term functional recovery when applied at scale to national trauma registries that lack systematic follow-up. This approach enables imputation of functional outcomes in datasets lacking follow-up, supports benchmarking and quality improvement across trauma systems, and provides a foundation for future models incorporating physiologic time-series, imaging, and biomarker data.","rel_num_authors":12,"rel_authors":[{"author_name":"Vikas N. Vattipally","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Ritvik R Jillala","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Patrick Kramer","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Mazin Elshareif","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Shivam Singh","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Jacob Jo","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Jose I Suarez","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Joseph V Sakran","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Elliott R Haut","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Judy Huang","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Chetan Bettegowda","author_inst":"Johns Hopkins University"},{"author_name":"Tej D Azad","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-04-27","rel_site":"medrxiv"},{"rel_title":"The Phenotypic Landscape of a Circadian Clock","rel_doi":"10.64898\/2026.04.23.720472","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720472","rel_abs":"Circadian clocks produce near-24-hour oscillations through biochemical feedback loops. To study their architecture, we developed a deep sequencing assay that measures the phenotypes of thousands of mutant clocks in parallel. We reveal a landscape where oscillator properties are factorized: mutations change period without decreasing amplitude and while maintaining a balanced waveform. Mutations that either shorten or lengthen period localize to specific protein-protein interaction surfaces, while a particularly sensitive region near the KaiC interdomain linker can cause extreme effects. After entrainment, high amplitude mutant oscillators form a tunable low-dimensional manifold in the period-phase plane, suggesting that most period mutations leave the coupling to the environment unchanged. In contrast, mutations that reduce amplitude are concentrated in a specific long period phenotype. This correlation structure may support the evolvability of this dynamical molecular system and is a powerful constraint on underlying mechanism.","rel_num_authors":5,"rel_authors":[{"author_name":"Soo Ji Kim","author_inst":"University of Chicago"},{"author_name":"Diane Schnitkey","author_inst":"University of Chicago"},{"author_name":"Bryan Andrews","author_inst":"The University of Chicago"},{"author_name":"Rama Ranganathan","author_inst":"University of Chicago"},{"author_name":"Michael Rust","author_inst":"University of Chicago"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Designed Minibinders Rewire Receptor Signaling to Enable Functional Human Myogenic Reprogramming","rel_doi":"10.64898\/2026.04.26.720818","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720818","rel_abs":"Sarcopenia, loss of muscle mass is a considerable health burden that demands immediate societal attention. Direct myogenic somatic cell reprogramming, a potential muscle regeneration method is constrained by an inability to control the signaling logic that governs cell fate. Here, we show that this barrier can be overcome using AI-designed receptor modulators. Screening de novo minibinders, we identify a synthetic protein cocktail, C6-DPC, that drives efficient human fibroblast-to-muscle transdifferentiation with robust structural and metabolic maturation. C6-DPC reprograms extracellular signaling by activating pro-myogenic FGFR1\/2c pathways while suppressing anti-myogenic inputs through ALK1 and TGFBR2; targeted depletion of ALK1 is sufficient to lower the reprogramming barrier. Inflammatory signaling via gp130 emerges as a dominant checkpoint, and its inhibition further enhances conversion. Engineered tissues generate high twitch and tetanic forces in both wild-type and dystrophindeficient human cells. These findings demonstrate that programmable synthetic ligands can rewrite receptor-level signaling to direct cell fate and enable functional tissue regeneration.","rel_num_authors":21,"rel_authors":[{"author_name":"Riya Keshri","author_inst":"University of Washington"},{"author_name":"Zachary Foreman","author_inst":"University of Washington"},{"author_name":"Philip Barrett","author_inst":"University of Washington"},{"author_name":"Alexander J. Robinson","author_inst":"University of Washington"},{"author_name":"Gabriela Reyes","author_inst":"University if Washington"},{"author_name":"Ashish A. Phal","author_inst":"University oof Washington"},{"author_name":"Aditya Krishnakumar","author_inst":"University of Washington"},{"author_name":"Ethan Narog","author_inst":"University of Washington"},{"author_name":"Melodie Chiu","author_inst":"University of Washington"},{"author_name":"Shruti Jain","author_inst":"University of Washington"},{"author_name":"Xinru Wang","author_inst":"University of Washington"},{"author_name":"David Lee","author_inst":"University of Washington"},{"author_name":"Marc Exposit","author_inst":"University of Washington"},{"author_name":"Mohamad Abedi","author_inst":"University of Washington"},{"author_name":"Alec Simon Tulloch Smith","author_inst":"University of Washington"},{"author_name":"Sanjay R Srivatsan","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Jay Shendure","author_inst":"University of Washington"},{"author_name":"Julie Mathieu","author_inst":"UW"},{"author_name":"David L Mack","author_inst":"University of Washington Medicine"},{"author_name":"David Baker","author_inst":"University of Washington"},{"author_name":"Hannele Ruohola-Baker","author_inst":"University of Washington"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Single-nucleus multiome sequencing identifies candidate regulators of mouse gastric epithelial homeostasis","rel_doi":"10.64898\/2026.04.23.720450","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720450","rel_abs":"Background & Aims: Gastric epithelial cells maintain homeostasis through dynamic self-renewal mechanisms involving stem and progenitor cells. However, identifying them has been challenging. This study aims to identify stem cells of healthy gastric epithelium and cell type-specific regulators defining gastric epithelial homeostasis via single-nucleus multiome analysis. Methods: Ten unique gastric samples were collected from 8-12 week old wildtype mice. Isolated nuclei were subjected to simultaneous profiling of gene expression and chromatin accessibility. After quality control, 31,598 cells were analyzed with Seurat and Signac using weighted-nearest neighbors analysis for joint RNA and ATAC clustering. Furthermore, SCENIC+, MultiVelo, EpiCHAOS and Cell plasticity score were used to uncover gene regulatory networks, cell state dynamics and lineage trajectories. Results: Our analyses were validated by the identification of known regulators of stem-cell differentiation into mature cell types. More importantly, it revealed previously uncharacterized regulatory networks comprising novel transcription factor combinations that define cell identities, including Ppara, Pparg, Arid5b and Sox5 as candidate regulators of parietal, foveolar, chief and neck cells, respectively. Further, our data support the identity of isthmus cells as stem-like cells of healthy gastric epithelium, as evidenced by epigenetic plasticity that simultaneously contains open chromatin states of all differentiated cell types in the absence of transcriptional reprogramming. Conclusion: Consistent with Waddington's epigenetic landscape hypothesis, gastric epithelial homeostasis is controlled by orchestrated epigenetic and transcriptional programs. Contrary to the prevailing hypothesis, stem cells can be defined not by a separate epigenetic state but by epigenetic superposition of differentiated cell states. Future work is needed to define the universality of these results.","rel_num_authors":16,"rel_authors":[{"author_name":"Maith\u00ea Rocha Monteiro de Barros","author_inst":"Herbert and Florence Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA. New York Genome Center, New York, NY, USA."},{"author_name":"Katharina Bosch","author_inst":"Tumorigenesis and Molecular Cancer Prevention Group, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany."},{"author_name":"Salima Soualhi","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Shirin Issa Bhaloo","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Thomas Chu","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Tanya Hemrajani","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Jin Cho","author_inst":"Division of Surgical Science, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA."},{"author_name":"Kurtay Ozuner","author_inst":"Division of Surgical Science, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA."},{"author_name":"Rui Fu","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Heather Geiger","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Nicolas Robine","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Jade E.B. Carter","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Silas Maniatis","author_inst":"New York Genome Center, New York, NY, USA."},{"author_name":"Sandra Ryeom","author_inst":"Division of Surgical Science, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA."},{"author_name":"Simon Tavar\u00e9","author_inst":"Herbert and Florence Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA. New York Genome Center, New York, NY, USA."},{"author_name":"Karol Nowicki-Osuch","author_inst":"Tumorigenesis and Molecular Cancer Prevention Group, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany."}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Mechanism of nucleolytic degradation of human ribosomes","rel_doi":"10.64898\/2026.04.26.720784","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720784","rel_abs":"Stresses like starvation trigger degradation of mature 40S ribosomes, requiring the coordinated breakdown of large and stable RNA-protein complexes. The atypical kinase RIOK3 orchestrates degradation by binding ubiquitylated 40S ribosomes and promoting rRNA decay. However, the mechanisms and factors that mediate rRNA decay remain unknown. Here we find that in response to starvation, RIOK3 recruits the terminal uridylyl- transferase TUT7 and the exonuclease DIS3L2 to 40S ribosomes. Sequencing analyses show that TUT7 adds oligo(uridine) tails to the 3' end of the 18S rRNA in these ribosomes. DIS3L2 subsequently recognizes uridylated 18S rRNA and carries out 3'-5' decay. We identify major decay intermediates that undergo further uridylation in a process of iterative uridylation and decay. Loss of DIS3L2 impairs 18S rRNA decay during starvation and leads to accumulation of uridylated 18S rRNA. Together these findings define a mechanism for ribosome degradation in which 3' oligo(uridine) tailing drives decay of rRNA from ribosomes.","rel_num_authors":3,"rel_authors":[{"author_name":"Frances F Diehl","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Allen R Buskirk","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Rachel Green","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Iron-responsive phosphorylation of TolQ modulates cell envelope integrity and antibiotic susceptibility in Klebsiella pneumoniae","rel_doi":"10.64898\/2026.04.25.720785","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.25.720785","rel_abs":"Klebsiella pneumoniae is an opportunistic bacterial pathogen associated with high morbidity and mortality, exacerbated by the rapid emergence of resistance to last-resort antibiotics, such as carbapenems. Adaptation to nutrient limitation, particularly fluctuations in metal availability, is critical for bacterial survival and virulence, yet the regulatory mechanisms coordinating these responses remain incompletely understood. Protein phosphorylation represents a key post-translational modification governing bacterial physiology and offers a promising avenue for identifying novel antimicrobial targets. Here, we applied mass spectrometry-based phosphoproteomics to define nutrient-responsive signaling networks in K. pneumoniae under varying iron and zinc conditions. This analysis identified iron-dependent phosphorylation of TolQ, a conserved inner membrane component of the Tol-Pal system that maintains cell envelope integrity. Structural modeling predicted that phosphorylation modulates TolQ-TolR conformation, suggesting a mechanism by which iron availability regulates Tol-Pal function. Functional characterization demonstrated that deletion of tolQ results in reduced bacterial viability, increased susceptibility to host immune clearance, and heightened sensitivity to antibiotic treatment. To further explore the therapeutic potential of this pathway, we integrated high-throughput compound screening with computational modeling and identified small molecules that phenocopy {delta}tolQ. Collectively, these findings reveal a previously unrecognized link between iron availability and phosphoregulation of the Tol-Pal system and establish TolQ as a critical mediator of bacterial survival. This work highlights phosphoproteomics as a powerful strategy to uncover regulatory vulnerabilities and identify targets for antimicrobial development in drug-resistant pathogens.","rel_num_authors":6,"rel_authors":[{"author_name":"Chelsea Reitzel","author_inst":"University of Guelph"},{"author_name":"Jonathan Sayewich","author_inst":"SPARC Drug Discovery at The Hospital for Sick Children"},{"author_name":"Stevan Cucic","author_inst":"University of Guelph"},{"author_name":"Oscar Romero","author_inst":"University of Guelph"},{"author_name":"Norris Chan","author_inst":"University of Guelph"},{"author_name":"Jennifer Geddes-McAlister","author_inst":"University of Guelph"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"High-depth whole genome sequencing of blood culture plates reveals evolutionary dynamics in cases of persistent bacteremia due to methicillin-resistant Staphylococcus aureus","rel_doi":"10.64898\/2026.04.25.720776","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.25.720776","rel_abs":"Within a single bacterial strain, DNA sequence variation is expected between individual clones. Whole genome sequencing (WGS) can be applied to clinical cultures to detect this polyclonal variation, enabling tracking of within-host evolution and transmission. Culture isolates from infected patients are often sequenced as individual colonies (c-seq). To increase the sensitivity of variant detection, cultures can also be sequenced to a high depth of coverage as a pool (p-seq), but the utility of this approach is not clear for most clinical specimens. To understand the performance of high-depth WGS in bacteremia, we applied p-seq to blood culture plates for 10 patients with persistent bacteremia due to methicillin-resistant Staphylococcus aureus. As a comparison, for six patients, we also applied c-seq to five colonies (c5-seq) from the same plates. p-seq was more sensitive than c5-seq for detecting low frequency variant alleles; however, the most important factor for new variant detection was the number of culture plates analyzed rather than the sequencing method used. We also used these data to construct Muller plots for three patients with especially diverse infecting populations,evolutionary dynamics in response to antibiotic exposures. We identified 204 unique variant alleles, and our analysis provides additional evidence for parallel evolution of several different genes during S. aureus bacteremia. Overall, these data provide a detailed view of evolutionary dynamics during clinical cases of MRSA bacteremia and describe the merits and limitations of a c-seq versus p-seq strategy for analyzing blood culture plates using WGS.","rel_num_authors":7,"rel_authors":[{"author_name":"Emma Mills","author_inst":"University of Pittsburgh"},{"author_name":"Leah M. Grady","author_inst":"University of Pittsburgh School of Medicine"},{"author_name":"Edwin Chen","author_inst":"University of Pittsburgh School of Medicine"},{"author_name":"Marla G. Shaffer","author_inst":"The University of Iowa Department of Microbiology and Immunology"},{"author_name":"Ryan K Shields","author_inst":"University of Pittsburgh School of Medicine"},{"author_name":"Daria Van Tyne","author_inst":"University of Pittsburgh School of Medicine"},{"author_name":"Matthew J. Culyba","author_inst":"University of Pittsburgh"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"SbmA coordinates iron homeostasis and antimicrobial susceptibility in Klebsiella pneumoniae through phosphorylation-dependent regulation","rel_doi":"10.64898\/2026.04.25.720692","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.25.720692","rel_abs":"Iron is an essential nutrient that underpins fundamental biological processes, yet its bioavailability is severely restricted during infection due to oxidation and host-mediated sequestration. In Gram-negative pathogens, such as Klebsiella pneumoniae, iron limitation imposes a critical selective pressure, necessitating tightly regulated acquisition systems to support growth, virulence, and survival. While canonical pathways for iron uptake are well characterized, regulatory mechanisms coordinating these processes remain incompletely understood. Here, we applied mass spectrometry-based phosphoproteomics to identify iron-responsive regulatory events associated with bacterial iron homeostasis. This approach revealed iron-dependent phosphorylation of SbmA, a conserved inner membrane transporter previously implicated in the uptake of antimicrobial peptides and related substrates. Functional characterization demonstrated that deletion of sbmA results in reduced intracellular iron levels and altered cellular morphology, supporting a role in iron acquisition. Complementary proteome mapping of {delta}sbmA revealed compensatory production of siderophore receptors and TonB-dependent transport systems, further implicating SbmA in maintaining iron balance. Leveraging these findings, integration with high-throughput drug screening identified a compound that exploits SbmA-mediated transport to inhibit bacterial growth, highlighting its potential as a therapeutic entry point. Collectively, this work uncovers a previously unrecognized role for SbmA in iron homeostasis and demonstrates the power of phosphoproteomics to identify condition-specific regulators of essential bacterial pathways. These findings position SbmA as a promising target for antimicrobial development in K. pneumoniae.","rel_num_authors":6,"rel_authors":[{"author_name":"Chelsea Reitzel","author_inst":"University of Guelph"},{"author_name":"Jonathan Sayewich","author_inst":"SPARC Drug Discovery at The Hospital for Sick Children"},{"author_name":"Stevan Cucic","author_inst":"University of Guelph"},{"author_name":"Oscar Romero","author_inst":"University of Guelph"},{"author_name":"Norris Chan","author_inst":"University of Guelph"},{"author_name":"Jennifer Geddes-McAlister","author_inst":"University of Guelph"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Human lung \u03b3\u03b4 T cells maintain functionality during inflammatory lung disease","rel_doi":"10.64898\/2026.04.23.720435","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720435","rel_abs":"{gamma}{delta} T cells provide mucosal defense against infection while also contributing to tissue repair. However, data regarding the effect of the human lung environment on {gamma}{delta} T cell functionality remains limited. To address whether lung inflammation impacts {gamma}{delta} T cell functionality, we analyzed lung and matched hilar lymph node (LN) tissue from deceased donors and patients with interstitial lung disease (ILD). We performed high-parameter spectral flow cytometry to examine the expression pattern of phenotypic biomarkers and assess ex vivo function. We identified lung-specific enrichment of {gamma}{delta} T cells with an effector memory phenotype relative to matched regional LN. We then used an ex vivo stimulation approach to interrogate the capacity to protect against infection (granzyme B [GzmB], interferon-{gamma} [IFN{gamma}] and tumor necrosis factor [TNF]) and promote epithelial cell proliferation (amphiregulin [AREG]). We found that {gamma}{delta} T cells in lung and LN from deceased donors had similar functional properties. While {gamma}{delta} T cell populations from ILD lungs largely maintained cytokine production capacity, expression was diminished relative to LN counterparts. Importantly, lung {gamma}{delta} T cells maintained polyfunctional GzmB, IFN{gamma} and TNF expression across cohorts. Overall, we report human lung {gamma}{delta} T cells are regionally distinct with conserved functionality in a fibrotic environment.","rel_num_authors":8,"rel_authors":[{"author_name":"Alexis Taber","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Marie Frutoso","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Nicole Potchen","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Amanda L Koehne","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Chelsea Schmitz","author_inst":"University of Washington"},{"author_name":"Eric D Morrell","author_inst":"University of Washington"},{"author_name":"Martin Prlic","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Shelton W Wright","author_inst":"University of Washington"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Electric signal polymorphism predicts dietary niche partitioning in a weakly electric fish","rel_doi":"10.64898\/2026.04.23.720378","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720378","rel_abs":"Electric organ discharge (EOD) waveform diversity in African elephantfish is often attributed to sexual selection, yet EODs also mediate active electrolocation during prey detection, raising the possibility that natural selection on foraging ecology contributes to waveform divergence. Paramormyrops kingsleyae exhibits an intraspecific polymorphism where certain populations emit biphasic EODs whereas other populations emit triphasic waveforms. The genes underlying this polymorphism show signatures of selection; the polymorphism persists despite gene flow and is behaviorally discriminable by the fish themselves. If waveform differences influence prey detection during active electrolocation, biphasic and triphasic fish should consume systematically different prey. We tested this prediction using DNA metabarcoding of gut contents from 186 mormyrids representing 16 species across eight sites in Gabon, employing two independent COI primer sets for cross-validation and pairing dietary data with environmental invertebrate sampling to distinguish active prey preference from passive availability. At the community level in the diverse Bale Creek mormyrid assemblage, species identity was the dominant predictor of diet composition (R2; 24%), consistent with phylogenetic signal in foraging ecology. Within P. kingsleyae, waveform type was the strongest independent predictor of dietary composition (R2 = 5-6%), explaining variance independently of geographic region, sex, body size, and parasitism status, a result concordant across both primer sets. Dietary differences were driven by prey species turnover rather than differential abundance of shared prey, and prey selectivity analyses confirmed that waveform types differ in which prey they actively prefer, not merely in what is locally available. These findings are consistent with natural selection on foraging ecology contributing to the maintenance of EOD waveform polymorphism, though the sensory mechanisms linking subtle waveform differences to prey detection remain an open question.","rel_num_authors":12,"rel_authors":[{"author_name":"Sophie Picq","author_inst":"Michigan State University Department of Integrative Biology, East Lansing, MI USA; Field Museum of Natural History, Chicago, IL USA"},{"author_name":"Rita Gorsuch","author_inst":"Michigan State University Department of Integrative Biology, East Lansing, MI USA"},{"author_name":"Rosella Bills","author_inst":"Michigan State University Department of Integrative Biology, East Lansing, MI USA"},{"author_name":"Lauren Koenig","author_inst":"Michigan State University Department of Integrative Biology, East Lansing, MI USA; Michigan State University Graduate Program in Ecology Evolution and Behavior,"},{"author_name":"Nestor Ngoua Aba'a","author_inst":"Centre National de la Recherche Scientifique et Technologique, Laboratoire d'Hydrobiologie et d'Ichtyologie, Libreville, Gabon"},{"author_name":"Franck Nzigou","author_inst":"Centre National de la Recherche Scientifique et Technologique, Laboratoire d'Hydrobiologie et d'Ichtyologie, Libreville, Gabon"},{"author_name":"Hans Kevin Mipounga","author_inst":"Centre National de la Recherche Scientifique et Technologique, Laboratoire d'Hydrobiologie et d'Ichtyologie, Libreville, Gabon"},{"author_name":"Elise C. Knobloch","author_inst":"Randolph-Macon College, Ashland, Virginia USA"},{"author_name":"Ray C. Schmidt","author_inst":"Randolph-Macon College, Ashland, Virginia USA"},{"author_name":"Emilie Parkanzky","author_inst":"Michigan State University Department of Entomology, East Lansing, MI USA"},{"author_name":"M. Eric Benbow","author_inst":"Michigan State University Graduate Program in Ecology Evolution and Behavior, East Lansing, MI USA; Michigan State University Department of Entomology, East Lan"},{"author_name":"Jason R. Gallant","author_inst":"Michigan State University Department of Integrative Biology, East Lansing, MI USA; Michigan State University Graduate Program in Ecology Evolution and Behavior,"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"GIRAF 1.0: A unified global framework to anticipate plant pest invasions","rel_doi":"10.64898\/2026.04.23.720440","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720440","rel_abs":"Plant pests threaten 10-40% of global food production, resulting in $55-220 billion in annual economic losses. Despite these escalating risks, biosecurity remains largely reactive, lacking anticipatory frameworks that integrate pest-specific drivers governing transboundary spread. We present GIRAF 1.0 (Global Invasion Risk Assessment Framework), the first quantitative, data-driven system that unifies pest-specific multi-host landscapes, abiotic suitability, and global trade networks with international phytosanitary policies. We applied GIRAF to four globally devastating pests - ranging from viral to insect taxa - to reconstruct a century of transcontinental spread and generate the first multiscale atlases of future invasion potential. GIRAF reveals that 22-37% of Earth's land surface can contain host communities that largely overlap with environmentally suitable hotspots. Over 115 countries are highly vulnerable to trade-mediated pest introductions despite adopted phytosanitary policies. GIRAF provides a foundation for proactive surveillance and pandemic preparedness, offering a scalable path for transnational biosecurity agencies and global food industries.","rel_num_authors":16,"rel_authors":[{"author_name":"Aaron I Plex Sula","author_inst":"University of Florida"},{"author_name":"Ozgur Batuman","author_inst":"University of Florida"},{"author_name":"Gilles Cellier","author_inst":"ANSES, Plant Health Laboratory"},{"author_name":"Nicholas S Dufault","author_inst":"University of Florida"},{"author_name":"Berea A Etherton","author_inst":"University of Florida"},{"author_name":"Amanda Hodges","author_inst":"University of Florida"},{"author_name":"Tiffany Lowe-Power","author_inst":"UC Davis"},{"author_name":"John D McVay","author_inst":"Florida Department of Agriculture and Consumer Services (FDACS) Division of Plant Industry"},{"author_name":"Cory Penca","author_inst":"USDA APHIS PPQ"},{"author_name":"Kyle Schroeder","author_inst":"University of Florida"},{"author_name":"Eleni Stilian","author_inst":"University of Florida"},{"author_name":"Piotr Suder","author_inst":"Duke University"},{"author_name":"Yu Takeuchi","author_inst":"North Carolina State University"},{"author_name":"Henri E. Z. Tonnang","author_inst":"University of KwaZulu-Natal"},{"author_name":"Ying Wang","author_inst":"University of Florida"},{"author_name":"Karen A Garrett","author_inst":"University of Florida"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"All Models are Wrong, Some are Annotated: Automating Metadata in Biomedical Repositories","rel_doi":"10.64898\/2026.04.23.720371","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720371","rel_abs":"Objective: High-quality metadata is essential for scientific discovery, yet sparse annotations in rapidly growing repositories leave many biologically relevant details uncaptured. We evaluated whether large language models (LLMs) can accurately infer ion channel and receptor subtype metadata from source code in a neuroscience repository. Materials and Methods: We extracted 5,133 model files from ModelDB. A subset of 1,100 was manually annotated; 253 were held out for testing, and the remainder split into training (80%) and validation (20%) sets. LLM-based approaches (GPT-5.2 and GPT-mini) were evaluated under zero-shot and heuristic-augmented prompting. Performance was assessed at type and subtype levels using weighted metrics (accuracy, precision, recall, and F1 score). A feature-engineered XGBoost model using text- and simulation-derived features served as a baseline. Results: LLMs outperformed the XGBoost baseline. At the type level, GPT-mini with heuristic augmentation achieved the highest performance (accuracy 96.0%, F1 0.962). At the subtype level, both GPT-5.2+heuristics and GPT-mini+heuristics achieved identical accuracy (88.1%), with GPT-5.2+heuristics achieving the highest F1(0.878). Model outputs were consistent across runs and errors confined to related mechanistic families. Discussion and Conclusion: LLMs demonstrate strong potential for metadata annotation directly from source code, outperforming feature-engineering approaches with minimal tuning. However, performance varied across subtypes, and errors often reflected ambiguity or bias toward more common labels. These findings suggest LLMs may serve as practical tools for scalable metadata generation in biomedical repositories, although careful evaluation and domain-specific validation remain important. While demonstrated in computational neuroscience, this approach may generalize to repository-agnostic metadata annotation in other scientific code repositories.","rel_num_authors":3,"rel_authors":[{"author_name":"Inessa Cohen","author_inst":"Yale University"},{"author_name":"Hongyi Yu","author_inst":"Yale University"},{"author_name":"Robert A McDougal","author_inst":"Yale University"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Macronutrient Composition and Genetic Background Determine the Response to a Ketogenic Diet","rel_doi":"10.64898\/2026.04.23.720368","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720368","rel_abs":"While standard high fat diets cause hyperphagia and obesity in mice, high fat-low carbohydrate ketogenic diets (KDs) reduce food intake and body weight. Because the basis for this difference is still unclear, we systematically altered the macronutrient content of a standard KD and found that feeding C57BL\/6J (B6J) mice a KD with 5% protein resulted in hypophagia, weight loss, and hypoglycemia, whereas the same diet with 10% protein led to increased adiposity and glucose intolerance. However, these effects were strain-dependent as C57BL\/6NJ (B6NJ) weighed similar amounts on the two diets leading us to investigate the molecular mechanisms. When fed the KD-5% diet, B6J but not B6NJ mice showed increased levels of two anorexigenic factors, GDF15 and LCN2, and loss of function of either blunted the weight loss of B6J mice fed the diet. B6J mice harbor mutations in Nnt (Nicotinamide nucleotide transhydrogenase) and Nlrp12 (NLR family pyrin domain containing 12), both of which are wildtype in B6NJ mice. B6J mice fed the KD-5% diet showed the RNA signature of oxidative and integrated stress responses (ISR) and restoring NNT function in liver reduced the levels of GDF15. RNA-seq also revealed that B6J but not B6NJ mice had the RNA signature for hepatic inflammation and a knockout of Nlrp12 led B6NJ mice to lose weight on the KD-5% diet with increased levels of LCN2. Suppression of oxidative stress with N-acetylcysteine (NAC) reduced expression of both GDF15 and LCN2 and prevented the weight loss associated with the KD-5% protein diet in B6J mice, whereas inhibition of the integrated stress response with ISRIB only attenuated the GDF15 axis. Collectively, these findings explain why B6J mice lose weight on a ketogenic diet and reveal a critical interplay between macronutrient composition and genetic background leading to increased levels of GDF15 and LCN2 to induce hypophagia. Finally, these data suggest that the response to different diets among humans might be similarly variable based on genetic variation and macronutrient composition, suggesting the possible need for personalized dietary interventions.","rel_num_authors":11,"rel_authors":[{"author_name":"Zhaoyue Zhang","author_inst":"The Rockefeller University"},{"author_name":"Alexandre Moura-Assis","author_inst":"The Rockefeller University"},{"author_name":"Shanshan Liu","author_inst":"The Rockefeller University"},{"author_name":"Alon Millet","author_inst":"The Rockefeller University"},{"author_name":"Jordan Shaked","author_inst":"Yale School of Medicine"},{"author_name":"Divya Rajan","author_inst":"The Rockefeller University"},{"author_name":"Hanan Alwaseem","author_inst":"Michigan Medicine"},{"author_name":"Michael Isay-Del Viscio","author_inst":"The Rockefeller University"},{"author_name":"Henrik Molina","author_inst":"The Rockefeller University"},{"author_name":"Kivanc Birsoy","author_inst":"The Rockefeller University"},{"author_name":"Jeffrey M. Friedman","author_inst":"The Rockefeller University"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Inhibiting the interaction between the mitochondrial receptor Tom70 and SARS CoV 2 Orf9b with small molecules","rel_doi":"10.64898\/2026.04.27.721040","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.27.721040","rel_abs":"The SARS CoV 2 accessory protein Orf9b is in a complex monomer-dimer equilibrium that influences its interactions with the host mitochondrial receptor Tom70. This interaction is critical for viral suppression of a Type-1 interferon response during infection. Modulating this equilibrium with a small molecule, either by stabilizing the Orf9b dimer or blocking its interaction with Tom70, represents a promising strategy for restoring interferon signaling and the antiviral response. To build tool molecules that could test this concept, we performed two screens: a crystallographic fragment screen against the Orf9b homodimer and a high-throughput fluorescence polarization screen for competitors of an Orf9b-derived peptide binding to Tom70. Fragment screening revealed two binding sites with potential to be developed into an inhibitor: one located at the peripheral dimer interface and the other just outside the lipid-binding channel that defines the central dimer interface. Functionalization of the fragments outside of the lipid-binding channel with hydrophobic moieties stabilized the Orf9b dimer thereby indirectly inhibiting association with Tom70. In parallel, the high throughput screen for competitive inhibitors of the Tom70:Orf9b interaction discovered a separate series of molecules. These molecules display dynamic structure activity relationship (SAR) and could be improved in the future to modulate the interaction between Tom70 and potentially a wide range of substrates. Collectively, these results demonstrate the feasibility of two distinct strategies to manipulate the Orf9b-Tom70 equilibrium, which is critical to the host response to SARS CoV 2 infection.","rel_num_authors":5,"rel_authors":[{"author_name":"CJ San Felipe","author_inst":"University of California, San Francisco"},{"author_name":"Kliment A Verba","author_inst":"University of California, San Francisco"},{"author_name":"Nevan J Krogan","author_inst":"University of California, San Francisco"},{"author_name":"Michael Grabe","author_inst":"University of California, San Francisco"},{"author_name":"James S Fraser","author_inst":"University of California, San Francisco"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"HuR Regulates GATA3-Driven Type 2 Inflammation in CD4\u207a T cells and ILC2 in Airway Inflammation","rel_doi":"10.64898\/2026.04.23.720195","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720195","rel_abs":"Type 2 high asthma is driven by coordinated GATA3 dependent programs in CD4+ T cells and group 2 innate lymphoid cells (ILC2). Although biologics targeting IL4, IL5, or IL13 benefit subsets of patients, many remain symptomatic, suggesting that upstream regulatory mechanisms may sustain type 2 inflammation. We investigated whether HuR (ELAVL1), an RNA-binding protein that stabilizes GATA3 and Th2 cytokines mRNA, regulates type 2 inflammatory programs in allergic asthma. Using a house dust mite (HDM) model in vivo, HuR inhibition with the small molecule KH3 reduced lung inflammation, suppressed Th2 cytokine expression, accelerated Gata3 mRNA decay in lung CD4+ T cells, and attenuated airway hyperresponsiveness toward control levels. In ex vivo activated human lung CD4+ T cells, KH3 accelerated GATA3 mRNA decay with minimal effects on RORC or TBX21 and selectively reduced Th2 cytokine secretion, while IL10 and IL2 were unchanged. Similarly, ILC2s isolated from peripheral blood mononuclear cells (PBMCs) of type 2 high asthmatic donors showed reduced GATA3 mRNA stability and diminished Th2 cytokine production following KH3 treatment. Single-cell transcriptomic analysis of bronchoalveolar lavage fluid after allergen challenge demonstrated co-enrichment of ELAVL1 and GATA3 within Th2 clusters in human airways. Together, these findings identify HuR as a post-transcriptional regulator of GATA3 driven type 2 inflammation in allergic asthma.","rel_num_authors":13,"rel_authors":[{"author_name":"Ulus Atasoy","author_inst":"University of Michigan Medical School and Ann Arbor Veterans Affairs Healthcare System"},{"author_name":"Fatemeh Fattahi","author_inst":"University of Michigan Medical School"},{"author_name":"Laura Yaekle","author_inst":"University of Michigan Medical School"},{"author_name":"Julia Holden","author_inst":"University of Michigan Medical School"},{"author_name":"Brandon Tepper","author_inst":"University of Michigan Medical School"},{"author_name":"Kareem Hussein","author_inst":"University of Michigan Medical School"},{"author_name":"Joshua Meier","author_inst":"University of Michigan Medical School"},{"author_name":"Liang Xu","author_inst":"University of Kansas"},{"author_name":"Srilaxmi Nerella","author_inst":"University of California, San Francisco"},{"author_name":"Jing Lei","author_inst":"University of Michigan Medical School"},{"author_name":"Kelley Bentley","author_inst":"University of Michigan Medical School"},{"author_name":"Marc Hershenson","author_inst":"University of Michigan Medical School"},{"author_name":"Steven K Huang","author_inst":"University of Michigan Medical School"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"HuR Regulates GATA3-Driven Type 2 Inflammation in CD4\u207a T cells and ILC2 in Airway Inflammation","rel_doi":"10.64898\/2026.04.23.720195","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720195","rel_abs":"Type 2 high asthma is driven by coordinated GATA3 dependent programs in CD4+ T cells and group 2 innate lymphoid cells (ILC2). Although biologics targeting IL4, IL5, or IL13 benefit subsets of patients, many remain symptomatic, suggesting that upstream regulatory mechanisms may sustain type 2 inflammation. We investigated whether HuR (ELAVL1), an RNA-binding protein that stabilizes GATA3 and Th2 cytokines mRNA, regulates type 2 inflammatory programs in allergic asthma. Using a house dust mite (HDM) model in vivo, HuR inhibition with the small molecule KH3 reduced lung inflammation, suppressed Th2 cytokine expression, accelerated Gata3 mRNA decay in lung CD4+ T cells, and attenuated airway hyperresponsiveness toward control levels. In ex vivo activated human lung CD4+ T cells, KH3 accelerated GATA3 mRNA decay with minimal effects on RORC or TBX21 and selectively reduced Th2 cytokine secretion, while IL10 and IL2 were unchanged. Similarly, ILC2s isolated from peripheral blood mononuclear cells (PBMCs) of type 2 high asthmatic donors showed reduced GATA3 mRNA stability and diminished Th2 cytokine production following KH3 treatment. Single-cell transcriptomic analysis of bronchoalveolar lavage fluid after allergen challenge demonstrated co-enrichment of ELAVL1 and GATA3 within Th2 clusters in human airways. Together, these findings identify HuR as a post-transcriptional regulator of GATA3 driven type 2 inflammation in allergic asthma.","rel_num_authors":13,"rel_authors":[{"author_name":"Ulus Atasoy","author_inst":"University of Michigan Medical School and Ann Arbor Veterans Affairs Healthcare System"},{"author_name":"Fatemeh Fattahi","author_inst":"University of Michigan Medical School"},{"author_name":"Laura Yaekle","author_inst":"University of Michigan Medical School"},{"author_name":"Julia Holden","author_inst":"University of Michigan Medical School"},{"author_name":"Brandon Tepper","author_inst":"University of Michigan Medical School"},{"author_name":"Kareem Hussein","author_inst":"University of Michigan Medical School"},{"author_name":"Joshua Meier","author_inst":"University of Michigan Medical School"},{"author_name":"Liang Xu","author_inst":"University of Kansas"},{"author_name":"Srilaxmi Nerella","author_inst":"University of California, San Francisco"},{"author_name":"Jing Lei","author_inst":"University of Michigan Medical School"},{"author_name":"Kelley Bentley","author_inst":"University of Michigan Medical School"},{"author_name":"Marc Hershenson","author_inst":"University of Michigan Medical School"},{"author_name":"Steven K Huang","author_inst":"University of Michigan Medical School"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Exposure to naturalistic occlusion promotes generalized, human-like robustness in deep neural networks","rel_doi":"10.64898\/2026.04.23.720370","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720370","rel_abs":"Human object recognition is robust to challenging conditions, such as when one's view of an object is fragmented due to an occluding foreground object. In comparison, deep neural networks (DNNs) are typically more susceptible to occlusion, suggesting that human vision relies on distinct mechanisms. Here, we investigated the role of visual diet in the emergence of these mechanisms by asking whether human-like robustness might arise in DNNs when trained with image datasets that better reflect the properties of occlusion in natural vision. We trained convolutional and transformer DNNs to classify clear images only, images augmented with artificial occluders (i.e., geometric shapes) or natural occluders (objects segmented from photographs). We then evaluated DNN occlusion robustness and compared their performance profiles with 30 human participants. We found that DNNs trained with artificial occluders remained vulnerable to natural occlusion and exhibited less human-like performance than those trained with natural occlusion. Our findings suggest that human robustness to visual occlusion arises from learning to disentangle natural objects from each other rather than simply learning to recognize objects from partial views. They also imply that commonly used forms of artificial occlusion are unsuitable for the evaluation or promotion of robustness to real-world occlusion in DNNs.","rel_num_authors":2,"rel_authors":[{"author_name":"David D Coggan","author_inst":"Vanderbilt University, Department of Psychology, 111 21st Ave S, Nashville, TN, USA, 37240"},{"author_name":"Frank Tong","author_inst":"Vanderbilt University, Department of Psychology, 111 21st Ave S, Nashville, TN, USA, 37240"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"HyperMap: An Efficient Framework for Transferring Perturbation Responses Across Diverse Biological Contexts","rel_doi":"10.64898\/2026.04.23.720505","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720505","rel_abs":"Recent perturbation atlases profile transcriptional responses to thousands of targeted perturbations in a reference cell type. Generalising these datasets across lineages and individuals has been problematic, however, as similar baseline transcriptomes can yield highly divergent responses. To address this challenge, we present HyperMap, a meta-learning framework that translates existing atlases to predict perturbation responses in new biological contexts using a small number of perturbation \"seeds.\" Applied to CRISPR gene knockdowns in induced pluripotent stem cells, HyperMap accurately captures responses of new iPSC donors. It generalises to additional cell lines, perturbations by small-molecule drugs, and knockdowns not yet performed in any context. HyperMap is highly efficient, obtaining best-in-class predictions with one-eighth the parameters of typical foundation models. Integrating across atlases yields HyperMapDB, a complete 1819,036 (cell-line perturbation) matrix expanding current data by 27-fold. HyperMap enables predictive maps spanning the combinatorial space of biological contexts, gene knockdowns and drugs.","rel_num_authors":3,"rel_authors":[{"author_name":"Bhavya Dhaka","author_inst":"University College Dublin"},{"author_name":"Jiahao Gao","author_inst":"University of California San Diego"},{"author_name":"Trey Ideker","author_inst":"University of California San Diego"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"PPM1B utilizes a trinuclear metal architecture for phosphatase activity","rel_doi":"10.64898\/2026.04.23.720145","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720145","rel_abs":"The metal-dependent protein phosphatase (PPM\/PP2C) family regulates innate immune and cell death pathways through reversible phosphorylation. Although these enzymes contain a conserved third Mg2+\/Mn2+ ion (M3) that is essential for activity, its chemical role in phosphate hydrolysis has remained unclear. Here, we report studies that reveal PPM1B promotes cell death during Pseudomonas aeruginosa infection and utilizes a trinuclear metal center in which M3 directly coordinates the substrate phosphate, positioning it for in-line SN2 hydrolysis. In addition to substrate orientation, M3 positions a water molecule to protonate the departing alkoxide, stabilizing the leaving-group. Functionally, M3 substitutes for the arginine clamp in phosphoprotein phosphatases (PPP), revealing that these evolutionarily distinct phosphatase families have converged on the same chemical strategy through fundamentally different catalytic architectures. Together, these findings define a three-metal mechanism in PPM phosphatases and identify the M3 site as a rare and potentially druggable feature for immune and infectious diseases.","rel_num_authors":17,"rel_authors":[{"author_name":"Reece P Stevens","author_inst":"Johns Hopkins University"},{"author_name":"Viktoriya Solodushko","author_inst":"University of South Alabama"},{"author_name":"Andrzej Wierzbicki","author_inst":"University of South Alabama"},{"author_name":"Thomas C Rich","author_inst":"University of South Alabama"},{"author_name":"Mikael F Alexeyev","author_inst":"University of South Alabama"},{"author_name":"Marlo K Thompson","author_inst":"University of South Alabama"},{"author_name":"Madeline Stone","author_inst":"University of South Alabama"},{"author_name":"Camryn Hall","author_inst":"University of South Alabama"},{"author_name":"Althea deWeever","author_inst":"University of South Alabama"},{"author_name":"Sarah L Sayner","author_inst":"University of South Alabama"},{"author_name":"Troy Stevens","author_inst":"University of South Alabama"},{"author_name":"Joel Andrews","author_inst":"University of South Alabama"},{"author_name":"Aishwarya Prakash","author_inst":"University of South Alabama"},{"author_name":"Richard E Honkanen","author_inst":"University of South Alabama"},{"author_name":"Ji Young Lee","author_inst":"University of South Alabama"},{"author_name":"Edward Alan Salter","author_inst":"University of South Alabama"},{"author_name":"Mark R Swingle","author_inst":"University of South Alabama"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Transdiagnostic Neurobiological Biotypes of Trauma Timing: Data-driven approach of Childhood and Adulthood-Onset Trauma","rel_doi":"10.64898\/2026.04.22.720234","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720234","rel_abs":"Background: The developmental timing of trauma exposure may critically shape neurobiological outcomes, yet distinctions between childhood-onset trauma (CT) and adulthood-onset trauma (AT) remain poorly understood. Aim: This study explores whether trauma onset timing is associated with distinct resting-state functional connectivity (rsFC) pattern using data driven approach. Methods: Seventy-seven trauma-exposed individuals (Mage=36.74 years) with post-traumatic stress disorder (PTSD), PTSD with major depressive disorder (MDD), and trauma-exposed healthy controls (TEHC) underwent resting-state fMRI. Of these participants, 15 with CT only, 17 with both CT and AT, and 47 with AT only. RsFC was calculated across the amygdala, hippocampus, nucleus accumbens (NAcc), the salience (SN), default mode (DMN), and frontoparietal networks (FPN). K-means clustering identified subgroups based on rsFC, with robustness assessed via bootstrapping, cross-validation, and replication using Gaussian Mixture Modeling. The identified clusters were compared on trauma timing, type, cumulative exposure, and clinical measures. Results: A two-cluster solution provided the most stable fit. The two generated clusters were significantly different in CT-only prevalence (p < 0.05; Cramer's V = 0.26, 95% CI). The CT cluster was marked by hyperconnectivity between amygdala-FPN, DMN-SN, NAcc-SN, and hippocampus-FPN relative to the AT cluster. Individuals with both CT and AT were evenly distributed across clusters. Clusters did not differ in PTSD or comorbid diagnoses, trauma type, or cumulative exposure. Conclusion: Data-driven clustering revealed distinct neurobiological profiles differentiating CT and AT. CT was associated with hyperconnectivity across salience, reward, and regulatory circuits, supporting developmental timing as a determinant of brain network organization in trauma-exposed populations.","rel_num_authors":6,"rel_authors":[{"author_name":"Shilat Haim-Nachum","author_inst":"School of Social Work, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Chen Zhang","author_inst":"Department of Bioengineering, University of Texas at Arlington, Texas, USA"},{"author_name":"Kangyi Peng","author_inst":"Department of Bioengineering, University of Texas at Arlington, Texas, USA"},{"author_name":"Yuval Neria","author_inst":"Columbia University Department of Psychiatry & New York State Psychiatric Institute, New York, NY, USA"},{"author_name":"Sigal Zilcha-Mano","author_inst":"Department of Psychology, University of Haifa, Mount Carmel, Haifa, Israel"},{"author_name":"Xi Zhu","author_inst":"Department of Bioengineering, University of Texas at Arlington, Texas, USA"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Transdiagnostic Neurobiological Biotypes of Trauma Timing: Data-driven approach of Childhood and Adulthood-Onset Trauma","rel_doi":"10.64898\/2026.04.22.720234","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720234","rel_abs":"Background: The developmental timing of trauma exposure may critically shape neurobiological outcomes, yet distinctions between childhood-onset trauma (CT) and adulthood-onset trauma (AT) remain poorly understood. Aim: This study explores whether trauma onset timing is associated with distinct resting-state functional connectivity (rsFC) pattern using data driven approach. Methods: Seventy-seven trauma-exposed individuals (Mage=36.74 years) with post-traumatic stress disorder (PTSD), PTSD with major depressive disorder (MDD), and trauma-exposed healthy controls (TEHC) underwent resting-state fMRI. Of these participants, 15 with CT only, 17 with both CT and AT, and 47 with AT only. RsFC was calculated across the amygdala, hippocampus, nucleus accumbens (NAcc), the salience (SN), default mode (DMN), and frontoparietal networks (FPN). K-means clustering identified subgroups based on rsFC, with robustness assessed via bootstrapping, cross-validation, and replication using Gaussian Mixture Modeling. The identified clusters were compared on trauma timing, type, cumulative exposure, and clinical measures. Results: A two-cluster solution provided the most stable fit. The two generated clusters were significantly different in CT-only prevalence (p < 0.05; Cramer's V = 0.26, 95% CI). The CT cluster was marked by hyperconnectivity between amygdala-FPN, DMN-SN, NAcc-SN, and hippocampus-FPN relative to the AT cluster. Individuals with both CT and AT were evenly distributed across clusters. Clusters did not differ in PTSD or comorbid diagnoses, trauma type, or cumulative exposure. Conclusion: Data-driven clustering revealed distinct neurobiological profiles differentiating CT and AT. CT was associated with hyperconnectivity across salience, reward, and regulatory circuits, supporting developmental timing as a determinant of brain network organization in trauma-exposed populations.","rel_num_authors":6,"rel_authors":[{"author_name":"Shilat Haim-Nachum","author_inst":"School of Social Work, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Chen Zhang","author_inst":"Department of Bioengineering, University of Texas at Arlington, Texas, USA"},{"author_name":"Kangyi Peng","author_inst":"Department of Bioengineering, University of Texas at Arlington, Texas, USA"},{"author_name":"Yuval Neria","author_inst":"Columbia University Department of Psychiatry & New York State Psychiatric Institute, New York, NY, USA"},{"author_name":"Sigal Zilcha-Mano","author_inst":"Department of Psychology, University of Haifa, Mount Carmel, Haifa, Israel"},{"author_name":"Xi Zhu","author_inst":"Department of Bioengineering, University of Texas at Arlington, Texas, USA"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Reciprocal repulsions enforce heterotypic dendrite segregation in an olfactory circuit","rel_doi":"10.64898\/2026.04.22.719985","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.719985","rel_abs":"How dendrites of different neurons segregate into discrete spatial domains during neural circuit assembly is poorly understood. Here, using the Drosophila olfactory system, we found that heterophilic interactions between two cell-surface proteins Teneurin-m (Ten-m) and Capricious (Caps) drive dendrite segregation. Ten-m and Caps are expressed in largely inverse patterns across projection neuron (PN) types when PNs are establishing their dendritic territories. Loss of Ten-m in Ten-m+ PNs causes their dendrites to invade Caps+ territories, whereas loss of Caps in Caps+ PNs causes dendrite invasion into Ten-m+ territories. Structure-guided mutations that abolish Ten-m-Caps binding disrupt dendrite segregation, whereas the same mutation on Ten-m preserves its homophilic attraction in a synaptic partner matching assay. These results support a model in which mutual repulsion between two inversely expressed cell-surface proteins drive dendrite segregation into discrete glomerular territories.","rel_num_authors":14,"rel_authors":[{"author_name":"Hui Ji","author_inst":"Stanford University, Howard Hughes Medical Institute"},{"author_name":"Jingxian Li","author_inst":"University of Chicago"},{"author_name":"Yizhen Xu","author_inst":"Stanford University, Howard Hughes Medical Institute"},{"author_name":"Kenneth Kin Lam Wong","author_inst":"Stanford University"},{"author_name":"Yunming Wu","author_inst":"Stanford University, Howard Hughes Medical Institute"},{"author_name":"David J Luginbuhl","author_inst":"Stanford University, Howard Hughes Medical Institute"},{"author_name":"Yanbo Zhang","author_inst":"Stanford University, Howard Hughes Medical Institute"},{"author_name":"Zhuoran Li","author_inst":"Stanford University, Howard Hughes Medical Institute"},{"author_name":"Jaeyoon Lee","author_inst":"Stanford University"},{"author_name":"Robert C Jones","author_inst":"Stanford University"},{"author_name":"Stephen R. Quake","author_inst":"Stanford University, The Chan Zuckerberg Initiative"},{"author_name":"Demet Ara\u00e7","author_inst":"University of Chicago"},{"author_name":"Engin \u00d6zkan","author_inst":"University of Chicago"},{"author_name":"Liqun Luo","author_inst":"Stanford University, Howard Hughes Medical Institute"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Maternal Inflammation in Late Gestation Alters Vaccine-Induced Immune Responses in Adult Murine Offspring","rel_doi":"10.64898\/2026.04.23.719749","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.719749","rel_abs":"Background: Intrauterine inflammation, commonly presenting as chorioamnionitis, is variably linked to preterm birth, neonatal infections and postnatal chronic inflammatory disorders. However, the effects of systemic maternal inflammation on exposed fetuses and offspring are less clear. We previously reported inflammatory responses in murine pups born after brief gestational exposure to experimental maternal inflammation. These findings led us to hypothesize that fetal exposure to maternal inflammation could lead to persistent alterations in postnatal immunity. Objective: To test our hypothesis, we examined immune responses to vaccination, a useful measure of immune status, in adult offspring following gestational exposure to LPS-induced maternal inflammation. Design\/Methods: Late-gestation pregnant dams were treated with LPS or saline. Offspring (LPS-exposed or saline controls) were either immunized with the Tdap vaccine or remained unimmunized (naive mice), and were subsequently infected with Bordetella pertussis. Lung and spleen immune responses were assessed by multi-parameter flow cytometry, protein microarray and RT-PCR. Results: We observed that young adult (7 week old) mice exposed to maternal LPS during gestation, vaccinated with TDaP, and subsequently infected with pertussis exhibited lower lung neutrophil but higher CD4+ lymphocyte proportions relative to unexposed controls. In splenic studies, LPS-exposed mice had lower frequencies of CD4+IFNgamma+ (Th1) and CD4+IL-17+ (Th17) cell populations. In vitro studies of post-vaccination responses to heat-killed B. pertussis showed variable levels of IL-2 and IL-4 in splenic cultures from LPS-exposed vs. control mice. Vaccinated, LPS-exposed mice showed variable splenic Stat3 and NFkb gene expression levels relative to those of naive LPS-exposed mice. Conclusion: Our present murine studies show that experimental maternal inflammation during late gestation can alter immune response patterns to secondary challenge in young adult offspring. However, whether such intrauterine inflammatory exposure might also influence protective immune function remains to be determined. Our findings lead us to speculate that fetal exposure to systemic maternal inflammation in humans could potentially have long-term implications for protective immunity.","rel_num_authors":4,"rel_authors":[{"author_name":"Casey M Nichols","author_inst":"Vanderbilt University"},{"author_name":"Dajana Sabic","author_inst":"Baylor College of Medicine"},{"author_name":"Jay J McQuillan","author_inst":"Saint Louis University"},{"author_name":"Joyce M Koenig","author_inst":"Saint Louis University"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Triplex formation drives noncontiguous VIPR RNA-guided DNA recognition","rel_doi":"10.64898\/2026.04.26.720927","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720927","rel_abs":"Viral Interference Programmable Repeat (VIPR) systems use a noncontiguous code for RNA-guided transcriptional silencing. How the Vipr protein and a vrRNA comprising alternating GGY and NN segments achieve precise DNA targeting is unknown. Here we present 21 cryo-electron microscopy structures that span the VIPR assembly pathway. Vipr protomers oligomerize along the vrRNA to form a right-handed helical filament, sequestering each GGY motif and positioning the adjacent NN bases for target base pairing. DNA binding, in which every third nucleotide is skipped, results in target strand rotation to form a gapped vrRNA-DNA hybrid helix that wraps around the non-target DNA strand to form a structural triplex. These findings provide the structural basis of noncontiguous RNA-guided DNA binding in VIPR, establishing triplex-driven target-strand handoff as an elegant mechanism of programmable nucleic acid recognition.","rel_num_authors":7,"rel_authors":[{"author_name":"Peter H. Yoon","author_inst":"University of California, Berkeley"},{"author_name":"Trevor A. Docter PhD","author_inst":"University of California, Berkeley"},{"author_name":"Zeyuan Zhang","author_inst":"University of California, Berkeley"},{"author_name":"Kenneth J. Loi","author_inst":"University of California, Berkeley"},{"author_name":"Luis E. Valentin-Alvarado PhD","author_inst":"University of California, Berkeley"},{"author_name":"Stephen G. Brohawn PhD","author_inst":"University of California, Berkeley"},{"author_name":"Jennifer A. Doudna PhD","author_inst":"University of California, Berkeley"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"A Noncontiguous Code for RNA-Guided DNA Recognition Preceded CRISPR","rel_doi":"10.64898\/2026.04.26.720920","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720920","rel_abs":"CRISPR-Cas systems use RNA-guided proteins for adaptive immunity through a mechanism whose origin is unknown. Here we report the discovery of Viral Interference Programmable Repeat (VIPR) systems consisting of a Vipr protein more ancient than CRISPR-Cas and vrRNAs comprising alternating GGY\/NN motifs. Unlike canonical guide RNAs that base pair with nucleic acid targets using an uninterrupted sequence, vrRNAs recognize double-stranded DNA through a noncontiguous code in which the variable NNs of each repeat collectively specify a target that itself contains a gapped recognition sequence. Analysis of natural vrRNA targets suggests VIPR acts against competing phages. We demonstrate programmable phage defense by redirecting the complex for transcriptional repression. These results suggest that the roots of adaptive immunity lie in ancient warfare between viruses, and reveal a new logic for programmable genetic control.","rel_num_authors":15,"rel_authors":[{"author_name":"Peter H. Yoon","author_inst":"University of California, Berkeley"},{"author_name":"Kenneth J. Loi","author_inst":"University of California, Berkeley"},{"author_name":"Zeyuan Zhang","author_inst":"University of California, Berkeley"},{"author_name":"Trevor A. Docter PhD","author_inst":"University of California, Berkeley"},{"author_name":"Santiago C. Lopez PhD","author_inst":"University of California, Berkeley"},{"author_name":"Conner J. Langeberg PhD","author_inst":"University of California, Berkeley"},{"author_name":"Muhammad Moez ur-Rehman","author_inst":"University of California, Berkeley"},{"author_name":"Kamakshi Vohra","author_inst":"Yale University"},{"author_name":"Zehan Zhou","author_inst":"University of California, Berkeley"},{"author_name":"Honglue Shi PhD","author_inst":"University of California, Berkeley"},{"author_name":"Ron Boger","author_inst":"University of California, Berkeley"},{"author_name":"Peter Y. Wang PhD","author_inst":"University of California, Berkeley"},{"author_name":"Benjamin A. Adler PhD","author_inst":"University of California, Berkeley"},{"author_name":"Stephen G. Brohawn PhD","author_inst":"University of California, Berkeley"},{"author_name":"Jennifer A. Doudna PhD","author_inst":"University of California, Berkeley"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"A Noncontiguous Code for RNA-Guided DNA Recognition Preceded CRISPR","rel_doi":"10.64898\/2026.04.26.720920","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720920","rel_abs":"CRISPR-Cas systems use RNA-guided proteins for adaptive immunity through a mechanism whose origin is unknown. Here we report the discovery of Viral Interference Programmable Repeat (VIPR) systems consisting of a Vipr protein more ancient than CRISPR-Cas and vrRNAs comprising alternating GGY\/NN motifs. Unlike canonical guide RNAs that base pair with nucleic acid targets using an uninterrupted sequence, vrRNAs recognize double-stranded DNA through a noncontiguous code in which the variable NNs of each repeat collectively specify a target that itself contains a gapped recognition sequence. Analysis of natural vrRNA targets suggests VIPR acts against competing phages. We demonstrate programmable phage defense by redirecting the complex for transcriptional repression. These results suggest that the roots of adaptive immunity lie in ancient warfare between viruses, and reveal a new logic for programmable genetic control.","rel_num_authors":15,"rel_authors":[{"author_name":"Peter H. Yoon","author_inst":"University of California, Berkeley"},{"author_name":"Kenneth J. Loi","author_inst":"University of California, Berkeley"},{"author_name":"Zeyuan Zhang","author_inst":"University of California, Berkeley"},{"author_name":"Trevor A. Docter PhD","author_inst":"University of California, Berkeley"},{"author_name":"Santiago C. Lopez PhD","author_inst":"University of California, Berkeley"},{"author_name":"Conner J. Langeberg PhD","author_inst":"University of California, Berkeley"},{"author_name":"Muhammad Moez ur-Rehman","author_inst":"University of California, Berkeley"},{"author_name":"Kamakshi Vohra","author_inst":"Yale University"},{"author_name":"Zehan Zhou","author_inst":"University of California, Berkeley"},{"author_name":"Honglue Shi PhD","author_inst":"University of California, Berkeley"},{"author_name":"Ron Boger","author_inst":"University of California, Berkeley"},{"author_name":"Peter Y. Wang PhD","author_inst":"University of California, Berkeley"},{"author_name":"Benjamin A. Adler PhD","author_inst":"University of California, Berkeley"},{"author_name":"Stephen G. Brohawn PhD","author_inst":"University of California, Berkeley"},{"author_name":"Jennifer A. Doudna PhD","author_inst":"University of California, Berkeley"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"Selective Editing and Functionalization of the Mammalian Lipidome","rel_doi":"10.64898\/2026.04.24.720406","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720406","rel_abs":"Lipids exhibit extraordinary molecular diversity, yet tools to selectively manipulate defined lipid classes in living cells are lacking. Here we show that lipid tail structure biases metabolic fate, enabling the design of synthetic lipid analogs with programmable metabolic selectivity. This approach enables selective cellular production of distinct lipid species or subclasses, including types of neutral lipids, phospholipids, sphingolipids, and ether lipids, without genetic or enzymatic perturbation. We further couple metabolic selectivity to chemical functionalization using bifunctional lipids, in which one modification directs metabolic flux and a second enables bioorthogonal tagging. Using this strategy, we achieve selective in situ labeling of different lipid pools in living cells. Together, our work establishes a chemical biology strategy that enables unprecedented precision in modulating, functionalizing, and rewiring the mammalian lipidome.","rel_num_authors":7,"rel_authors":[{"author_name":"Binyou Wang","author_inst":"Caltech"},{"author_name":"Lukas Luethy","author_inst":"Caltech"},{"author_name":"Logan Tenney","author_inst":"Caltech"},{"author_name":"Leo Qi","author_inst":"Caltech"},{"author_name":"Takeshi Harayama","author_inst":"Institut de Pharmacologie Moleculaire et Cellulaire"},{"author_name":"Kim Ekroos","author_inst":"Lipidomics Consulting"},{"author_name":"Johannes Morstein","author_inst":"Caltech"}],"rel_date":"2026-04-27","rel_site":"biorxiv"},{"rel_title":"SNAC-DB: An ML-Ready Database for Antibody and NANOBODY(R) VHH-Antigen Complexes with Expanded Structural Diversity and Real-World Benchmarking","rel_doi":"10.64898\/2026.04.22.720253","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720253","rel_abs":"Predicting antibody and NANOBODY(R) VHH-antigen complexes remains a critical challenge for state-of-the-art structure prediction models, limiting their impact in therapeutic discovery pipelines. We introduce SNAC-DB, an ML-ready database and curation pipeline enriched with structural biology expertise, designed to accelerate model accuracy and generalization by providing 31-37% expanded structural diversity over existing resources like SAbDab through comprehensive re-curation that extracts maximum value from available experimental structures. SNAC-DB expands coverage by capturing often-overlooked complexes and accurately identifying complete multi-chain epitopes through improved biological-assembly-based logic. Built for ML practitioners, SNAC-DB provides standardized formats with multi-threshold structure-based clustering to enable principled sample weighting during training. Using a rigorous benchmark of public PDB entries deposited post-May 2024 plus confidential therapeutic structures, we evaluate seven leading models (Protenix-v1, OpenFold-3p2, RosettaFold-3, Boltz-2, Boltz-1x, Chai-1, and AlphaFold2.3-multimer) with evaluation methodology tailored to antibody\/NANOBODY(R) VHH-antigen complexes to ensure correct handling of multi-chain epitopes, revealing systematic performance gaps: success rates rarely exceed 25%, confidence-based ranking fails to identify best predictions even when accurate structures exist in ensembles, and all models consistently struggle with therapeutically relevant NANOBODY(R) VHHs. Systematic evaluation of sampling strategies demonstrates that while generating 1000 samples per target substantially increases the likelihood of producing accurate structures (oracle selection improves from 11.9% to 50.5%), confidence-based ranking remains nearly flat (between 10.9% and 14.9%), revealing that improved ranking mechanisms represent a more tractable path to performance gains. Finally, fine-tuning GeoDock on SNAC-DB yields higher success rates than training on SAbDab (11.0% vs. 7.1% for antibodies; 7.0% vs. 4.0% for NANOBODY(R) VHHs), suggesting that SNAC-DB's expanded structural diversity translates to improved model generalization.","rel_num_authors":8,"rel_authors":[{"author_name":"Abhinav Gupta","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Bryan Munoz Rivero","author_inst":"Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"author_name":"Ruijiang Li","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Jorge Roel-Touris","author_inst":"Large Molecule Research, Sanofi, Barcelona, Spain"},{"author_name":"Yves Fomekong Nanfack","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Maria Wendt","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Yu Qiu","author_inst":"Large Molecule Research, Sanofi, Cambridge, MA, USA"},{"author_name":"Norbert Furtmann","author_inst":"Large Molecule Research, Sanofi, Frankfurt, Germany"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"A Self-Sustaining Mechanism for Endothelial Tension Maintenance Through GqGPCR Signaling","rel_doi":"10.64898\/2026.04.22.720219","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720219","rel_abs":"The vascular endothelium maintains homeostasis by acting as a selective barrier, permitting the exchange of nutrients, immune cells, and signaling molecules while restricting pathogens. It further regulates vascular function by generating and sustaining mechanical tension. Aging and disease alter the vascular environment and disrupt the regulation of endothelial tension, contributing to vascular diseases such as hypertension and atherosclerosis. Although endothelial mechanics are influenced by the cellular environment, the mechanisms that enable endothelial cells (ECs) to maintain tension over time remain poorly understood. Here, we demonstrate that confluent human umbilical vein endothelial cells (HUVECs) sustain stable tension for at least three days in the absence of external chemical or mechanical stimuli, indicating the presence of an intrinsic, active mechanism for long-term tension maintenance. Imaging of an EC multicellular ensemble shows a collective phenomenon where diacylglycerol release consistently precedes a rise in intracellular contractility. This contractility propagates to neighboring cells, wherein we identify a Gq-G-protein-coupled receptor (GqGPCR) signaling pathway as a key regulator driving force generation in ECs. The persistence of this signaling sequence in the absence of exogenous agonists suggests a 'force-induced-force-generation' mechanism that coordinates tension maintenance across the monolayer. Together, these findings demonstrate that ECs actively regulate tension through continuous GqGPCR signaling, revealing tension maintenance as a dynamic, collective process. This work provides new insight into how vascular tissues preserve mechanical homeostasis and suggests potential therapeutic targets for vascular endothelial dysfunction and age-related vascular stiffening.","rel_num_authors":3,"rel_authors":[{"author_name":"Benjamin M Goykadosh","author_inst":"Northeastern University"},{"author_name":"Vasuretha Chander","author_inst":"Northeastern University"},{"author_name":"Hari M Parameswaran","author_inst":"Northeastern University"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Dysregulated tissue-resident lymphocytes drive age-associated emphysema by impairing alveolar regeneration","rel_doi":"10.64898\/2026.04.22.720146","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720146","rel_abs":"Aging is often associated with progressive tissue degeneration and chronic inflammation, yet the role of immune cells in mediating structural and functional decline in organs remains poorly defined. Here, we investigated immune-tissue interactions in the aged lung and identified emphysematous remodeling characterized by alveolar loss. Notably, aged lungs exhibited a marked expansion of tissue-resident lymphocytes (TRLs) with senescent features, accompanied by a significant reduction in alveolar stem\/progenitor cell (AT2) abundance. In vivo adoptive T cell transfer and 3D immune-stem cell organoid assays revealed that these expanded TRLs suppressed AT2 growth via secretion of oncostatin M and interferon gamma. In vivo blockade of IL-7 receptor (IL-7R) reduced TRL accumulation in the lungs and ameliorated age-related emphysematous changes, including restoration of alveolar density. Our findings identify TRLs as key drivers of alveolar degeneration in aging and propose IL-7R inhibition as a therapeutic strategy to mitigate pulmonary decline.","rel_num_authors":14,"rel_authors":[{"author_name":"Yanjing Su","author_inst":"Nanjing University; Zhongshan Institute for Drug Discovery"},{"author_name":"Xinguo Yang","author_inst":"Zhongshan Institute for Drug Discovery"},{"author_name":"Ziyi Ren","author_inst":"Nanjing University of Chinese Medicine"},{"author_name":"Yuxuan Guan","author_inst":"Nanjing University of Chinese Medicine"},{"author_name":"Xuelu Zhou","author_inst":"Southern Medical University"},{"author_name":"Shuilian Chi","author_inst":"Nanjing University of Chinese Medicine"},{"author_name":"Yanjun Huang","author_inst":"Guangzhou College of Applied Science and Technology"},{"author_name":"Tao Yan","author_inst":"The Second Affiliated Hospital Zhejiang University School of Medicine"},{"author_name":"Jialong Liang","author_inst":"The Affiliated Wuxi People's Hospital of Nanjing Medical University"},{"author_name":"Fei Gao","author_inst":"The Affiliated Wuxi People's Hospital of Nanjing Medical University"},{"author_name":"Dijun Chen","author_inst":"Nanjing University"},{"author_name":"Jingyu Chen","author_inst":"The Second Affiliated Hospital Zhejiang University School of Medicine; The Affiliated Wuxi People's Hospital of Nanjing Medical University"},{"author_name":"Zimu Deng","author_inst":"Zhongshan Institute for Drug Discovery; Southern Medical University; University of Chinese Academy of Sciences"},{"author_name":"Chaoqun Wang","author_inst":"Chinese Academy of Sciences"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Environmental Enrichment Remodels Brain Structural and Behavioral Plasticity in Restricted and Repetitive C58 Mouse Models","rel_doi":"10.64898\/2026.04.22.720233","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720233","rel_abs":"Restricted and repetitive behaviors are characteristic of several neurodevelopmental disorders. While environmental enrichment has been shown to affect these behaviors, the underlying neural mechanisms remain poorly understood. In this study, we systematically explored the effects of environmental enrichment on brain structure and microstructure in C58 mice, a model of restricted and repetitive behaviors, compared to C57 control mice. Using structural magnetic resonance imaging and diffusion-weighted imaging, we assessed regional brain volumes and microstructural properties and examined their association with behavioral outcomes. Our results revealed significant reductions in total brain volume in C58 mice, with region-specific volumetric changes following environmental enrichment exposure. Importantly, environmental enrichment promoted microstructural plasticity in both strains, with significant alterations in fractional anisotropy and fiber density. These neuroanatomical changes were linked to reductions in restricted and repetitive behaviors, with strain- and sex-dependent effects. Overall, our findings suggest that environmental enrichment remodels brain plasticity at both structural and microstructural levels, as well as behavior, providing insights into potential therapeutic approaches through environmental enrichment for neurodevelopmental disorders.","rel_num_authors":4,"rel_authors":[{"author_name":"Qiang Li","author_inst":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)"},{"author_name":"Anna L. Farmer","author_inst":"Department of Psychology, University of Florida"},{"author_name":"Pearlson D. Godfrey","author_inst":"Yale University"},{"author_name":"Vince D. Calhoun","author_inst":"Georgia State, Georgia Tech, and Emory"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"C. elegans models of Alternating Hemiplegia of Childhood have dominant neuromuscular junction defects","rel_doi":"10.64898\/2026.04.22.720250","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720250","rel_abs":"Dominant missense mutations in ATP1A3, encoding a Na+, K+ ATPase alpha-3 subunit, can cause Alternating Hemiplegia of Childhood (AHC), but how these mutations lead to AHC remains unclear. Here, we establish the first C. elegans AHC models by introducing AHC-causing ATP1A3 patient mutations (D801N, E815K, L839P, and G947R) into the orthologous gene, eat-6, using CRISPR\/Cas9. Homozygous C. elegans AHC model animals have recessive developmental defects. Heterozygous AHC model animals have dominant defects in neuromuscular junction (NMJ) function that are inconsistent with haploinsufficiency and dominant sleep or arousal defects. Previous work in a Drosophila G755S AHC model found that loss of a K+-dependent, Na+\/Ca2+ exchanger exacerbated neuronal defects. We introduced a loss-of-function allele of the orthologous C. elegans gene, ncx-4, into C. elegans AHC models; loss of ncx-4 function did not consistently alter C. elegans AHC model defects across alleles. Our results establish novel C. elegans models of AHC with robust phenotypes, demonstrate that AHC mutations disrupt NMJ function, and provide proof-of-concept for discovering cross-species modifiers of AHC-related phenotypes.","rel_num_authors":6,"rel_authors":[{"author_name":"Diana Wall","author_inst":"Brown University"},{"author_name":"Adam Friedberg","author_inst":"Brown University"},{"author_name":"Jeremy Lins","author_inst":"Brown University"},{"author_name":"Roza Khalifa","author_inst":"Brown University"},{"author_name":"Sienna Partipilo","author_inst":"Brown University"},{"author_name":"Anne C. Hart","author_inst":"Brown University"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Origins of reactivity in SAM-utilizing ribozyme SAMURI-catalyzed RNA alkylation","rel_doi":"10.64898\/2026.04.24.720726","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720726","rel_abs":"Unlocking the design principles of programmable RNA catalysts capable of site-specific chemical modification is critical for expanding the functional and therapeutic potential of RNA. The SAM analogue-utilizing ribozyme (SAMURI) enables site-specific RNA alkylation using either S-adenosylmethionine (SAM) or the synthetic cofactor propargylic Se-2,6-diaminopurinribosyl-selenomethionineamide (ProSeDMA), yet the molecular determinants of its reactivity remain incompletely understood. Here, we combined molecular dynamics, 3D-RISM solvation analysis, alchemical free-energy calculations, quantum pKa shift predictions, and ab initio QM\/MM free-energy simulations to characterize the conformational and electronic factors that govern catalysis. Simulations show that, although the global fold of SAMURI remains stable in solution, the formation of catalytically competent near-attack configurations is rare, indicating that the observed rate depends on access to a minor fraction of these reactive conformations (freact). A putative Mg2+ binding site between the SAM carboxylate and the G30 phosphate, together with a hydrogen bond between the cofactor -amine and U8:O2, enriches freact. QM\/MM simulations support an SN2-like alkyl transfer mechanism and show that ProSeDMA reacts more readily than SAM primarily due to its more favorable electronic leaving-group properties that enhance the intrinsic rate (kint). Atomic substitutions at A52 that tune the N3 pKa enhance nucleophilicity, further lower the activation barrier, and increase kint. Together, these results show that SAMURI catalysis is governed by a combination of conformational preorganization and electronic effects, providing a framework to guide the design of new programmable RNA alkyltransferases.","rel_num_authors":5,"rel_authors":[{"author_name":"Julie Puyo-Fourtine","author_inst":"Rutgers University"},{"author_name":"Yanan Du","author_inst":"Rutgers University"},{"author_name":"Erika McCarthy","author_inst":"Rutgers University"},{"author_name":"\u015e\u00f6len Ekesan","author_inst":"Rutgers University"},{"author_name":"Darrin M York","author_inst":"Rutgers University"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Adding iPSC donor lines does not adequately control for genetic heterogeneity","rel_doi":"10.64898\/2026.04.22.720258","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720258","rel_abs":"Human induced pluripotent stem cell (iPSC)-based disease modelling studies are widely expected to include three to five independent donor lines to control for the contribution of donor genetic background to phenotypic variance. This convention has been formalized into major guidelines, yet no power analysis has evaluated whether these sample sizes can detect, estimate, or control for donor-level genetic effects. Here, we provide that evaluation. Using Monte Carlo simulation, closed-form confidence intervals, population genetics, and empirical resampling of transcriptomic data from iPSC lines, we show that studies with three to five donors cannot reliably detect donor-level variance, cannot estimate its magnitude with useful precision, and cannot determine whether a treatment effect generalizes across genetic backgrounds. The sample sizes required to reliably detect, estimate, or control for donor-level variance exceed 20 donors and, for many phenotypes, exceed 50, well beyond what any standard disease modelling experiment can deliver. Adding two or three donor lines to a study does not meaningfully increase statistical power, narrow confidence intervals, or establish whether a treatment effect generalizes across genetic backgrounds. The inability to control for genetic background is not a limitation of individual study design but a structural property of iPSC-based modelling. We propose that the field adopt isogenic controls for variant-specific questions and orthogonal validation against clinical datasets for generalizability, rather than treating donor number as a proxy for rigour.","rel_num_authors":3,"rel_authors":[{"author_name":"Artur Shvetcov","author_inst":"Westmead Institute for Medical Research"},{"author_name":"Shannon Thomson","author_inst":"Westmead Institute for Medical Research"},{"author_name":"Caitlin A Finney","author_inst":"Westmead Institute for Medical Research"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"De novo design of a macrocycle induced dimerization system for cellular control","rel_doi":"10.64898\/2026.04.24.720480","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720480","rel_abs":"Investigating and manipulating cellular events requires precise control of protein function. To enable control over cellular processes, we set out to design a chemically induced dimerization (CID) system consisting of a de novo designed ligand and protein pair. Here we describe the design of a C2 symmetric membrane permeable macrocyclic peptide and a cognate protein homodimer which binds the macrocycle through a large interface with both chains. The designed homodimer binds the macrocycle with a KD of 36 nM, and the x-ray crystal structure of the protein homodimer-macrocycle complex is very close to the computational design model, with the C2 axis of the macrocycle aligned with the homodimer C2 axis. Transcriptional and split luciferase assays in mammalian cells demonstrates conditional control over both a reporter gene expression and luciferase reconstitution.","rel_num_authors":15,"rel_authors":[{"author_name":"David Baker","author_inst":"University of Washington"},{"author_name":"Stephanie Hanna","author_inst":"University of Washington"},{"author_name":"Patrick Salveson","author_inst":"University of Washington"},{"author_name":"Basile Wicky","author_inst":"University of Washington"},{"author_name":"Madison Kennedy","author_inst":"University of Washington"},{"author_name":"Derrick Hicks","author_inst":"University of Washington"},{"author_name":"Carolina Moller","author_inst":"University of Washington"},{"author_name":"Suna Cheng","author_inst":"University of Washington"},{"author_name":"Xinting Li","author_inst":"University of Washington"},{"author_name":"Mohamad Abedi","author_inst":"University of Washington"},{"author_name":"Brian Coventry","author_inst":"University of Washington"},{"author_name":"Meerit Said","author_inst":"University of Washington"},{"author_name":"Asim K. Bera","author_inst":"University of Washington"},{"author_name":"Alex Kang","author_inst":"University of Washington"},{"author_name":"Barry L Stoddard","author_inst":"Fred Hutchinson Cancer Research Center"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Opposing BOLD signals and oxygen metabolism largely arise from statistical uncertainty in metabolic estimates","rel_doi":"10.64898\/2026.04.21.719913","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719913","rel_abs":"Recent work by Epp et al. (2025) reported widespread voxel-wise sign discordance between task-evoked blood-oxygenation-level-dependent (BOLD) responses and estimated changes in cerebral metabolic rate of oxygen ({Delta}CMRO2), raising important questions about the interpretability of BOLD functional magnetic resonance imaging. Reanalysing the dataset, we found that {Delta}CMRO2 estimates showed substantial voxel-wise variability across participants, consistent with the noise sensitivity of model-based metabolic estimates. When this variability was taken into account, 77.2% of voxels could not be robustly classified, as {Delta}CMRO2 effects lacked sufficient statistical support to determine concordance or discordance. Where classification was possible, positive BOLD responses were predominantly concordant with metabolism, whereas discordance was considerably higher for negative BOLD responses. These findings suggest that the observed BOLD-metabolism discordance reported previously largely reflects statistical uncertainty in CMRO2 estimates rather than widespread physiological sign reversal.","rel_num_authors":3,"rel_authors":[{"author_name":"Ole Goltermann","author_inst":"University Medical Center Hamburg-Eppendorf"},{"author_name":"Alex R Huth","author_inst":"University of California, Berkeley"},{"author_name":"Christian B\u00fcchel","author_inst":"University Medical Center Hamburg-Eppendorf"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Integrative Clinical-Molecular Modeling Identifies LRRN4CL as a Determinant of Structural and Functional Myocardial Improvement","rel_doi":"10.64898\/2026.04.21.720029","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720029","rel_abs":"Background: Mechanical ventricular unloading and systemic circulatory support with left ventricular assist devices (LVADs) enable myocardial recovery in a subset of advanced heart failure (HF) patients, but predictors and mechanisms of recovery are not well understood. Integrating clinical and molecular data may improve identification of patients most likely to recover and uncover biologically relevant targets in HF. Methods: We collected and analyzed left ventricular apical myocardial tissue and clinical data from 208 patients undergoing LVAD implantation across five centers. Pre-implant transcriptomic profiles (22,373 mRNA transcripts) were integrated with 59 clinical variables using supervised machine learning with repeated cross-validation to identify and prioritize features associated with myocardial recovery, defined as a binary outcome based on improvement in left ventricular ejection fraction (LVEF [&ge;]40%) and left ventricular end-diastolic diameter (LVEDD [&le;]5.9 cm). We also modeled functional (LVEF) and structural (LVEDD) improvement as a continuous outcome without any predefined LVEF and LVEDD pathological thresholds. Feature prioritization was followed by validation in human myocardial tissue and mechanistic interrogation in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). Results: Integrative models achieved modest discrimination for myocardial recovery as a binary categorical outcome (maximum mean cross-validated area under the curve 0.73{+\/-}0.15), identifying clinical features such as HF duration, LVEDD, HF pharmacologic therapy, and device configuration. Leucine-rich repeat neuronal 4C-like (LRRN4CL), measured in human myocardium, consistently emerged as a top transcriptomic predictor across both binary and continuous metric models (functional and structural). Higher pre-LVAD LRRN4CL expression was associated with reduced likelihood of myocardial recovery and localized primarily to cardiomyocytes. In iPSC-CMs, LRRN4CL overexpression localized to the sarcoplasmic reticulum, induced transcriptional remodeling characterized by suppression of contractile pathways and activation of stress programs, impaired calcium handling, impaired contraction?relaxation kinetics, and diminished mitochondrial respiratory reserve capacity. Conclusions: Integration of clinical and myocardial transcriptomic data identifies LRRN4CL as a novel marker associated with impaired myocardial recovery following LVAD-mediated ventricular unloading and systemic circulatory support. These findings move beyond predictive modeling, linking integrative computational discovery to cardiomyocyte dysfunction and providing a translational framework for biologically informed risk stratification and therapeutic targeting for myocardial recovery.","rel_num_authors":36,"rel_authors":[{"author_name":"Ezra Johnson","author_inst":"The University of Utah"},{"author_name":"Joseph R Visker","author_inst":"University of Utah"},{"author_name":"Ben J Brintz","author_inst":"University of Utah"},{"author_name":"Christos P. Kyriakopoulos","author_inst":"The University of Utah School of Medicine"},{"author_name":"James Jeong","author_inst":"The University of Utah"},{"author_name":"Yuyu Zhang","author_inst":"The University of Utah"},{"author_name":"Thirupura S. Shankar","author_inst":"The University of Utah"},{"author_name":"Yanni Hillas","author_inst":"University of Utah"},{"author_name":"Iosif Taleb","author_inst":"University of California San Diego"},{"author_name":"Rachit Badolia","author_inst":"University of Utah, CVRTI"},{"author_name":"Junedh M Amrute","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Chris J Stubben","author_inst":"University of Utah"},{"author_name":"Luis Cedeno-Rosario","author_inst":"The University of Utah Department of Biochemistry"},{"author_name":"Ioannis Kyriakoulis","author_inst":"U of Utah"},{"author_name":"Konstantinos Sideris","author_inst":"University of Utah"},{"author_name":"Jing Ling","author_inst":"University of Utah"},{"author_name":"Rana Hamouche","author_inst":"university of utah"},{"author_name":"Eleni Tseliou","author_inst":"University of Utah Health"},{"author_name":"Sutip Navankasattusas","author_inst":"University of Utah"},{"author_name":"Gregory S. Ducker","author_inst":"University of Utah"},{"author_name":"Jared Rutter","author_inst":"University of Utah"},{"author_name":"William L Holland","author_inst":"University of Utah Health"},{"author_name":"Scott A Summers","author_inst":"The University of Utah"},{"author_name":"TingTing Hong","author_inst":"The University of Utah"},{"author_name":"Steven C Koenig","author_inst":"University of Louisville"},{"author_name":"Thomas C Hanff","author_inst":"University of Utah Health"},{"author_name":"Kory J. Lavine","author_inst":"Washington University School of Medicine"},{"author_name":"Tom Greene","author_inst":"University of Utah"},{"author_name":"Stephen Bailey","author_inst":"Allegheny General Hosptial"},{"author_name":"Rami Alharethi","author_inst":"Intermountain Medical Center"},{"author_name":"Craig H. Selzman","author_inst":"University of Utah"},{"author_name":"Palak Shah","author_inst":"Inova Schar Heart and Vascular"},{"author_name":"Hongchao Guo","author_inst":"University of Utah Health"},{"author_name":"Mark S. Slaughter","author_inst":"University of Louisville"},{"author_name":"Manreet K Kanwar","author_inst":"The University of Chicago"},{"author_name":"Stavros G Drakos","author_inst":"The University of Utah School of Medicine"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Integrative Clinical-Molecular Modeling Identifies LRRN4CL as a Determinant of Structural and Functional Myocardial Improvement","rel_doi":"10.64898\/2026.04.21.720029","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720029","rel_abs":"Background: Mechanical ventricular unloading and systemic circulatory support with left ventricular assist devices (LVADs) enable myocardial recovery in a subset of advanced heart failure (HF) patients, but predictors and mechanisms of recovery are not well understood. Integrating clinical and molecular data may improve identification of patients most likely to recover and uncover biologically relevant targets in HF. Methods: We collected and analyzed left ventricular apical myocardial tissue and clinical data from 208 patients undergoing LVAD implantation across five centers. Pre-implant transcriptomic profiles (22,373 mRNA transcripts) were integrated with 59 clinical variables using supervised machine learning with repeated cross-validation to identify and prioritize features associated with myocardial recovery, defined as a binary outcome based on improvement in left ventricular ejection fraction (LVEF [&ge;]40%) and left ventricular end-diastolic diameter (LVEDD [&le;]5.9 cm). We also modeled functional (LVEF) and structural (LVEDD) improvement as a continuous outcome without any predefined LVEF and LVEDD pathological thresholds. Feature prioritization was followed by validation in human myocardial tissue and mechanistic interrogation in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). Results: Integrative models achieved modest discrimination for myocardial recovery as a binary categorical outcome (maximum mean cross-validated area under the curve 0.73{+\/-}0.15), identifying clinical features such as HF duration, LVEDD, HF pharmacologic therapy, and device configuration. Leucine-rich repeat neuronal 4C-like (LRRN4CL), measured in human myocardium, consistently emerged as a top transcriptomic predictor across both binary and continuous metric models (functional and structural). Higher pre-LVAD LRRN4CL expression was associated with reduced likelihood of myocardial recovery and localized primarily to cardiomyocytes. In iPSC-CMs, LRRN4CL overexpression localized to the sarcoplasmic reticulum, induced transcriptional remodeling characterized by suppression of contractile pathways and activation of stress programs, impaired calcium handling, impaired contraction?relaxation kinetics, and diminished mitochondrial respiratory reserve capacity. Conclusions: Integration of clinical and myocardial transcriptomic data identifies LRRN4CL as a novel marker associated with impaired myocardial recovery following LVAD-mediated ventricular unloading and systemic circulatory support. These findings move beyond predictive modeling, linking integrative computational discovery to cardiomyocyte dysfunction and providing a translational framework for biologically informed risk stratification and therapeutic targeting for myocardial recovery.","rel_num_authors":36,"rel_authors":[{"author_name":"Ezra Johnson","author_inst":"The University of Utah"},{"author_name":"Joseph R Visker","author_inst":"University of Utah"},{"author_name":"Ben J Brintz","author_inst":"University of Utah"},{"author_name":"Christos P. Kyriakopoulos","author_inst":"The University of Utah School of Medicine"},{"author_name":"James Jeong","author_inst":"The University of Utah"},{"author_name":"Yuyu Zhang","author_inst":"The University of Utah"},{"author_name":"Thirupura S. Shankar","author_inst":"The University of Utah"},{"author_name":"Yanni Hillas","author_inst":"University of Utah"},{"author_name":"Iosif Taleb","author_inst":"University of California San Diego"},{"author_name":"Rachit Badolia","author_inst":"University of Utah, CVRTI"},{"author_name":"Junedh M Amrute","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Chris J Stubben","author_inst":"University of Utah"},{"author_name":"Luis Cedeno-Rosario","author_inst":"The University of Utah Department of Biochemistry"},{"author_name":"Ioannis Kyriakoulis","author_inst":"U of Utah"},{"author_name":"Konstantinos Sideris","author_inst":"University of Utah"},{"author_name":"Jing Ling","author_inst":"University of Utah"},{"author_name":"Rana Hamouche","author_inst":"university of utah"},{"author_name":"Eleni Tseliou","author_inst":"University of Utah Health"},{"author_name":"Sutip Navankasattusas","author_inst":"University of Utah"},{"author_name":"Gregory S. Ducker","author_inst":"University of Utah"},{"author_name":"Jared Rutter","author_inst":"University of Utah"},{"author_name":"William L Holland","author_inst":"University of Utah Health"},{"author_name":"Scott A Summers","author_inst":"The University of Utah"},{"author_name":"TingTing Hong","author_inst":"The University of Utah"},{"author_name":"Steven C Koenig","author_inst":"University of Louisville"},{"author_name":"Thomas C Hanff","author_inst":"University of Utah Health"},{"author_name":"Kory J. Lavine","author_inst":"Washington University School of Medicine"},{"author_name":"Tom Greene","author_inst":"University of Utah"},{"author_name":"Stephen Bailey","author_inst":"Allegheny General Hosptial"},{"author_name":"Rami Alharethi","author_inst":"Intermountain Medical Center"},{"author_name":"Craig H. Selzman","author_inst":"University of Utah"},{"author_name":"Palak Shah","author_inst":"Inova Schar Heart and Vascular"},{"author_name":"Hongchao Guo","author_inst":"University of Utah Health"},{"author_name":"Mark S. Slaughter","author_inst":"University of Louisville"},{"author_name":"Manreet K Kanwar","author_inst":"The University of Chicago"},{"author_name":"Stavros G Drakos","author_inst":"The University of Utah School of Medicine"}],"rel_date":"2026-04-26","rel_site":"biorxiv"},{"rel_title":"Tongue swab Xpert MTB\/RIF Ultra testing for tuberculosis in adolescents: a cross-sectional study of diagnostic accuracy and acceptability","rel_doi":"10.64898\/2026.04.17.26351119","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351119","rel_abs":"Introduction Improved diagnostics are needed for people at risk of tuberculosis, especially adolescents. Tongue swab (TS) molecular testing has emerged as a promising strategy for tuberculosis diagnosis. We evaluated diagnostic accuracy and acceptability of Xpert MTB\/RIF Ultra (Xpert) using TS samples for tuberculosis detection among adolescents. Methods We conducted a cross-sectional diagnostic accuracy study with consecutive recruitment in Vietnam. Adolescents aged 10-19 who were recommended to undergo investigation for tuberculosis and had not received tuberculosis treatment in the past years were eligible. Participants provided TS and sputum samples and completed a structured survey regarding sampling experiences. TS was tested on Xpert, with sputum tested on Xpert and liquid culture. We utilised a composite reference standard of a positive result on sputum Xpert or sputum culture to define disease status. Sensitivity, specificity, and diagnostic yield were calculated for TS Xpert. Results From July to December 2025, we enrolled 225 adolescents from Can Tho and An Giang provinces in southern Vietnam. Fewer than half (96\/225, 43%) the participants exhibited a tuberculosis -like symptom, and the majority (157\/225, 70%) were close contacts of a person recently diagnosed with tuberculosis. TS were collected from all adolescents, while 116 (52%) could provide mucopurulent sputum. Tuberculosis prevalence was relatively low (12\/225, 5.3%). TS Xpert sensitivity (90% CI) and specificity (90% CI) were 58.3% (35.6, 78.0) and 99.5% (97.9, 99.9), respectively. Diagnostic yield among all diagnosed was 58.3% (7\/12). TS sampling was highly acceptable to adolescents; the short time and simplicity of collecting TS were considered favourably. Conclusions The sensitivity and diagnostic yield of TS Xpert was relatively low among adolescents recommended for tuberculosis investigation, which includes asymptomatic individuals who may not provide high quality sputum. Specificity was excellent, and everyone could provide a TS. TS high acceptability indicates it remains a promising sample for diagnostic algorithms.","rel_num_authors":10,"rel_authors":[{"author_name":"Emily L MacLean","author_inst":"The University of Sydney"},{"author_name":"Thu Thuy Ma","author_inst":"The University of Sydney Vietnam Institute"},{"author_name":"Long Huynh Chuong","author_inst":"The University of Sydney Vietnam Institute"},{"author_name":"Khanh Huynh Minh","author_inst":"The University of Sydnet Vietnam Institute"},{"author_name":"Graeme Hoddinott","author_inst":"The University of Sydney"},{"author_name":"Yen Ngoc Pham","author_inst":"The University of Sydney Vietnam Institute"},{"author_name":"Hua Trung Tiep","author_inst":"Can Tho Lung Hospital"},{"author_name":"Thu-Anh Nguyen","author_inst":"The University of Sydney Vietnam Institute"},{"author_name":"Greg Fox","author_inst":"The University of Sydney"},{"author_name":"Ngoc Thuy Nguyen","author_inst":"Can Tho Lung Hospital"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"Effect mechanisms of different malaria chemoprevention regimens in pregnancy on infant growth outcomes: causal mediation analysis of a randomized controlled trial","rel_doi":"10.64898\/2026.04.17.26351121","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351121","rel_abs":"Introduction: Intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine (SP) has become less effective at preventing malaria due to rising parasite resistance. IPTp with dihydroartemisinin-piperaquine (DP) alone or in combination with SP (DP+SP) dramatically lowers the risk of malaria in pregnancy compared to SP but is associated with lower birthweight and early life wasting. We estimated the effect of IPTp-DP, DP+SP, and SP on infant growth outcomes and assessed possible treatment mechanisms through a causal mediation analysis. Methods: We used infant follow-up data (N=761) from a trial (NCT04336189) that randomized pregnant women to receive monthly IPTp-DP, SP, or DP+SP. We compared weight-for-length (WLZ) and length-for-age (LAZ) z-scores between treatment arms. We assessed possible mediation through pregnancy, birth, and infancy factors using interventional indirect effect models. Results: Compared to IPTp-SP, IPTp-DP+SP decreased mean WLZ by 0.18 [95% confidence interval (CI) -0.03, 0.39] between 1-3 months and 0.28 (95% CI 0.07, 0.49) between 4-6 months, with the largest differences among primigravidae. Lower risk of active placental malaria in IPTp-DP+SP helped reduce differences in mean WLZ vs IPTp-SP (+0.06, 95% CI 0.02, 0.10). The IPTp-DP+SP arm had up to 0.28 lower mean LAZ between 7-13 months compared to IPTp-DP, particularly among children who were wasted between 0-6 months; low birthweight had a persistent, mediating effect on linear growth. Conclusion: Adverse birth outcomes contributed to early growth faltering among children born to mothers receiving IPTp-DP+SP vs IPTp-SP, but the prevention of placental malaria partially counteracted the negative effects of IPTp-DP+SP on ponderal growth.","rel_num_authors":12,"rel_authors":[{"author_name":"Anna T Nguyen","author_inst":"Stanford University"},{"author_name":"Joaniter I Nankabirwa","author_inst":"Infectious Diseases Research Collaboration (IDRC); Makerere University College of Health Sciences"},{"author_name":"Abel Kakuru","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Michelle E Roh","author_inst":"Oregon Health & Science University; University of California San Francisco"},{"author_name":"Miriam Aguti","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Harriet Adrama","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Jimmy Kizza","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Peter Olwoch","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Moses R Kamya","author_inst":"Infectious Diseases Research Collaboration (IDRC); Makerere University College of Health Sciences"},{"author_name":"Grant Dorsey","author_inst":"University of California San Francisco"},{"author_name":"Prasanna Jagannathan","author_inst":"Stanford University"},{"author_name":"Jade Benjamin-Chung","author_inst":"Stanford University; Chan Zuckerberg Biohub"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"Effect mechanisms of different malaria chemoprevention regimens in pregnancy on infant growth outcomes: causal mediation analysis of a randomized controlled trial","rel_doi":"10.64898\/2026.04.17.26351121","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351121","rel_abs":"Introduction: Intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine (SP) has become less effective at preventing malaria due to rising parasite resistance. IPTp with dihydroartemisinin-piperaquine (DP) alone or in combination with SP (DP+SP) dramatically lowers the risk of malaria in pregnancy compared to SP but is associated with lower birthweight and early life wasting. We estimated the effect of IPTp-DP, DP+SP, and SP on infant growth outcomes and assessed possible treatment mechanisms through a causal mediation analysis. Methods: We used infant follow-up data (N=761) from a trial (NCT04336189) that randomized pregnant women to receive monthly IPTp-DP, SP, or DP+SP. We compared weight-for-length (WLZ) and length-for-age (LAZ) z-scores between treatment arms. We assessed possible mediation through pregnancy, birth, and infancy factors using interventional indirect effect models. Results: Compared to IPTp-SP, IPTp-DP+SP decreased mean WLZ by 0.18 [95% confidence interval (CI) -0.03, 0.39] between 1-3 months and 0.28 (95% CI 0.07, 0.49) between 4-6 months, with the largest differences among primigravidae. Lower risk of active placental malaria in IPTp-DP+SP helped reduce differences in mean WLZ vs IPTp-SP (+0.06, 95% CI 0.02, 0.10). The IPTp-DP+SP arm had up to 0.28 lower mean LAZ between 7-13 months compared to IPTp-DP, particularly among children who were wasted between 0-6 months; low birthweight had a persistent, mediating effect on linear growth. Conclusion: Adverse birth outcomes contributed to early growth faltering among children born to mothers receiving IPTp-DP+SP vs IPTp-SP, but the prevention of placental malaria partially counteracted the negative effects of IPTp-DP+SP on ponderal growth.","rel_num_authors":12,"rel_authors":[{"author_name":"Anna T Nguyen","author_inst":"Stanford University"},{"author_name":"Joaniter I Nankabirwa","author_inst":"Infectious Diseases Research Collaboration (IDRC); Makerere University College of Health Sciences"},{"author_name":"Abel Kakuru","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Michelle E Roh","author_inst":"Oregon Health & Science University; University of California San Francisco"},{"author_name":"Miriam Aguti","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Harriet Adrama","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Jimmy Kizza","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Peter Olwoch","author_inst":"Infectious Diseases Research Collaboration (IDRC)"},{"author_name":"Moses R Kamya","author_inst":"Infectious Diseases Research Collaboration (IDRC); Makerere University College of Health Sciences"},{"author_name":"Grant Dorsey","author_inst":"University of California San Francisco"},{"author_name":"Prasanna Jagannathan","author_inst":"Stanford University"},{"author_name":"Jade Benjamin-Chung","author_inst":"Stanford University; Chan Zuckerberg Biohub"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"Patient preferences for portable versus table-mounted visual field devices in rural Alabama: a mixed methods study within a telemedicine setting","rel_doi":"10.64898\/2026.04.23.26351565","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351565","rel_abs":"Purpose To evaluate patient satisfaction and preferences for portable versus table-mounted visual field (VF) devices in a rural telemedicine setting and identify influencing factors. Methods We conducted a sequential explanatory mixed methods study at three Federally Qualified Health Centers (FQHCs) within the Alabama Screening and Intervention for Glaucoma and eye Health through Telemedicine (AL-SIGHT) study. Participants completed VF testing with table-mounted Humphrey Field Analyzer (HFA), tablet-based Melbourne Rapid Fields (MRF), and virtual reality (VR)-based VisuALL perimeters. Participants rated satisfaction, comfort, ease of use, and future testing preference. Chi-square tests assessed differences in device preferences. Twelve participants completed semi-structured interviews to explore reasons underlying preferences. Qualitative data were analyzed in NVivo 14 using reflexive thematic analysis. Results Among 271 respondents (mean age 60.4 years; 62.4% women), 50.6% preferred VR-based, 35.1% tablet-based, and 14.4% table-mounted for future testing ({chi}2 (2) = 53.52, p<0.001, Cramers V = 0.31). Satisfaction was highest for VR-based (56.9% very satisfied), followed by tablet-based (49.4%), and HFA (38.0%). VR-based perimeter was most frequently selected as the most comfortable (55.7%; {chi}2 (2) = 63.33, p<0.001, V = 0.34) and easiest to use (54.6%; {chi}2 (2) = 71.96, p<0.001, V = 0.36). Preferences did not vary significantly across demographic variables (all p>0.05). Qualitative themes identified four key drivers: comfort and physical experience, visual experience, ease of use and interaction, and psychological and motivational factors. Portability and community suitability were valued. Conclusion Rural underserved patients strongly preferred portable visual field devices, particularly VR-based, over table-mounted HFA. Comfort, ergonomic flexibility, immersive visual experience, and simplicity of interaction were central determinants of preference. Portable perimetry may enhance patient-centered glaucoma monitoring within telemedicine programs and access in resource-limited settings.","rel_num_authors":7,"rel_authors":[{"author_name":"Ellen  Konadu Antwi-Adjei","author_inst":"University of Alabama at Birmingham"},{"author_name":"Sourav Datta","author_inst":"University of Alabama at Birmingham"},{"author_name":"Christopher  A. Girkin","author_inst":"University of California San Diego"},{"author_name":"Cynthia Owsley","author_inst":"University of Alabama at Birmingham"},{"author_name":"Lindsay  A. Rhodes","author_inst":"University of Alabama at Birmingham"},{"author_name":"Matthew Fifolt","author_inst":"University of Alabama at Birmingham"},{"author_name":"Lyne Racette","author_inst":"University of Alabama at Birmingham"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"Multi-omic signatures of genetic mechanisms inform on type 2 diabetes biology and patient heterogeneity","rel_doi":"10.64898\/2026.04.17.26351136","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351136","rel_abs":"Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and beta cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for up to 45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.","rel_num_authors":22,"rel_authors":[{"author_name":"Magdalena Sevilla-Gonzalez","author_inst":"Massachusetts General Hospital"},{"author_name":"Alan Magno Martinez-Munoz","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Paul A. Hanson","author_inst":"Massachusetts General Hospital"},{"author_name":"Sarah Hsu","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Xingyang Wang","author_inst":"Harvard School of Public Health"},{"author_name":"Kirk Smith","author_inst":"Massachusetts General Hospital"},{"author_name":"Zsu-Zsu Chen","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Lukasz Szczerbinski","author_inst":"Massachusetts General Hospital"},{"author_name":"Varinderpal Kaur","author_inst":"Massachusetts General Hospital"},{"author_name":"Kent D. Taylor","author_inst":"The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center"},{"author_name":"Alexis C. Wood","author_inst":"Baylor College of Medicine"},{"author_name":"Michael Y. Mi","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Hui Li","author_inst":"Chalmers University of Technology"},{"author_name":"Clemens Wittenbecher","author_inst":"Chalmers University of Technology"},{"author_name":"Robert E. Gerszten","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Steve Rich","author_inst":"University of Virginia School of Medicine"},{"author_name":"Jerome Rotter","author_inst":"The Lundquist Institute"},{"author_name":"Jun Li","author_inst":"Brigham and Women's Hospital"},{"author_name":"Josep M Mercader","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Alisa K Manning","author_inst":"Massachusetts General Hospital"},{"author_name":"Ravi V K Shah","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Miriam Udler","author_inst":"Massachusetts General Hospital"}],"rel_date":"2026-04-25","rel_site":"medrxiv"},{"rel_title":"A branching cell-fate decision in biofilm dispersal enables long-term surface persistence","rel_doi":"10.64898\/2026.04.24.720661","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720661","rel_abs":"Biofilms are the most ancient multicellular communities on Earth, representing a primitive developmental system that protects microbes from threats. Biofilm dispersal, whereby bacteria exit biofilms, is critical for the spread of pathogens to new infection sites. Here, using Vibrio cholerae, we show that dispersal events are accompanied by a branching cell-fate decision. While ~90% of cells disperse, a viable subpopulation remains within a residual matrix. This post-dispersal biofilm community (PDBC) is established by the matrix protein RbmA and adopts a specialized anabolic program that enhances tolerance to antibiotics and bacteriophages. Our findings reveal that PDBCs act as a resilient \"seed-bank\" capable of rapidly re-populating the niche without requiring de novo matrix biosynthesis, providing a mechanistic basis for the recurrence and spread of chronic infections.","rel_num_authors":3,"rel_authors":[{"author_name":"Sandhya Kasivisweswaran","author_inst":"Carnegie Mellon University"},{"author_name":"Jojo A Prentice","author_inst":"Yale University"},{"author_name":"Andrew  A. Bridges","author_inst":"Carnegie Mellon University"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"A branching cell-fate decision in biofilm dispersal enables long-term surface persistence","rel_doi":"10.64898\/2026.04.24.720661","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720661","rel_abs":"Biofilms are the most ancient multicellular communities on Earth, representing a primitive developmental system that protects microbes from threats. Biofilm dispersal, whereby bacteria exit biofilms, is critical for the spread of pathogens to new infection sites. Here, using Vibrio cholerae, we show that dispersal events are accompanied by a branching cell-fate decision. While ~90% of cells disperse, a viable subpopulation remains within a residual matrix. This post-dispersal biofilm community (PDBC) is established by the matrix protein RbmA and adopts a specialized anabolic program that enhances tolerance to antibiotics and bacteriophages. Our findings reveal that PDBCs act as a resilient \"seed-bank\" capable of rapidly re-populating the niche without requiring de novo matrix biosynthesis, providing a mechanistic basis for the recurrence and spread of chronic infections.","rel_num_authors":3,"rel_authors":[{"author_name":"Sandhya Kasivisweswaran","author_inst":"Carnegie Mellon University"},{"author_name":"Jojo A Prentice","author_inst":"Yale University"},{"author_name":"Andrew  A. Bridges","author_inst":"Carnegie Mellon University"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"Mitochondrial mechanics nucleates axonal jamming and swelling","rel_doi":"10.64898\/2026.04.23.720276","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720276","rel_abs":"Neuronal function requires precise spatial organization of mitochondria to meet localized energetic demand. However, the physical constraints governing mitochondrial transport in axons remain poorly defined. Bidirectional motor-driven trafficking inherently introduces the potential for collisions, but the implications of these interactions for transport failure and structural damage are not understood. Here, we develop an agent-based model that couples mitochondrial motility, morphology, and lifecycle dynamics to a deformable axonal boundary. We show that mitochondrial traffic jams emerge from a force balance between active propulsion and steric interactions, and that their severity is governed by organelle shape and mechanical properties. Elongated, mechanically rigid mitochondria remain aligned and are transported rapidly, whereas flexible, low-aspect-ratio mitochondria are prone to jamming and accumulation. Incorporating fission and fusion dynamics reveals that fission amplifies transport disruption by generating collision-prone populations, while fusion restores transport by producing anisotropic structures that navigate crowded environments more efficiently. Importantly, we find that sustained jamming generates mechanical stress on the axonal membrane, leading to deformation and swelling. Together, these results establish a physical framework linking mitochondrial dynamics to axonal integrity and provide testable predictions for how dysregulated fission-fusion balance can drive transport failure and structural pathology in neurons.","rel_num_authors":4,"rel_authors":[{"author_name":"Patrick S Noerr","author_inst":"University of California San Diego"},{"author_name":"Ahmed A Abushawish","author_inst":"Department of Neurobiology, School of Biological Sciences, University of California San Diego"},{"author_name":"Gulcin Pekkurnaz","author_inst":"Department of Neurobiology, School of Biological Sciences, University of California San Diego"},{"author_name":"Padmini Rangamani","author_inst":"Department of Pharmacology, School of Medicine, University of California San Diego; Department of Mechanical and Aerospace Engineering, University of California"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"Targeting WNK1 Releases Differentiation Block in Acute Myeloid Leukemia","rel_doi":"10.64898\/2026.04.22.720037","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720037","rel_abs":"Impaired differentiation is a hallmark of Acute Myeloid Leukemia (AML). Current differentiation therapies benefit only a small subset of AML patients, leaving a substantial gap in care for other subtypes. Identifying novel molecular drivers of maturation arrest is critical to expand differentiation induction to a broader range of AML patients. This study addresses this unmet clinical need, by identifying With-no-Lysine(K) kinase 1 (WNK1) as a novel regulator of AML differentiation arrest. We show that WNK1 expression and activity are elevated in AML patients. WNK1 inhibition induced differentiation accompanied by decreased growth and survival of AML cell lines and AML patient cells. It also inhibited self-renewal of AML patient cells in vitro and elicited significant anti-tumor activity in vivo in mouse models. Mechanistically, WNK1 inhibition derepressed the MEK-ERK-C\/EBP{beta} signaling axis and increased the expression of myeloid differentiation genes. Our findings reveal a novel role of WNK1 in promoting AML through differentiation arrest, posing WNK1 inhibition as a potential approach for AML differentiation therapy.","rel_num_authors":3,"rel_authors":[{"author_name":"Jordan Cress","author_inst":"Case Western Reserve University"},{"author_name":"Emily Katoni","author_inst":"Case Western Reserve University"},{"author_name":"Parameswaran Ramakrishnan","author_inst":"Case Western Reserve University"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"PIEZOs regulate oligodendrocyte sheath formation, expansion, and myelination potential","rel_doi":"10.64898\/2026.04.23.720488","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720488","rel_abs":"Myelination requires precise integration of physical cues by oligodendrocyte lineage cells (OLCs), but the molecular sensors that detect these cues remain incompletely understood. Here, we demonstrate that oligodendrocyte progenitor cells (OPCs) are sensitive to sub-micron changes in membrane displacement. Based on channel properties, RNA expression, and protein abundance, we find that the mechanosensitive ion channel PIEZO1 contributes to OPC mechanosensitivity. In vivo, zebrafish with oligodendrocyte (OL)-specific disruption of piezo1 have fewer sheaths per OL. Zebrafish with OL-specific piezo2 disruption also have fewer sheaths as well as decreased total myelin capacity over time. OL-specific disruption of both piezo1 and piezo2 caused more severe phenotypes, with reduced OPC volume, and in myelinating OLs, reduced sheath number, sheath length, and total myelin output. Furthermore, piezo1\/piezo2 disruption leads to sporadic sheath formation outside the normal developmental window. Our findings indicate that OLs use Piezo channels in vivo to influence sheath formation, expansion, and retractions.","rel_num_authors":8,"rel_authors":[{"author_name":"Adam M Coombs","author_inst":"Oregon Health & Science University"},{"author_name":"Dongeun Heo","author_inst":"Oregon Health & Science University"},{"author_name":"Daniel J Orlin","author_inst":"Oregon Health & Science University"},{"author_name":"Cody L Call","author_inst":"Oregon Health & Science University"},{"author_name":"Marie E Bechler","author_inst":"SUNY Upstate Medical University"},{"author_name":"Swetha E Murthy","author_inst":"Oregon Health & Science University"},{"author_name":"Ben Emery","author_inst":"Oregon Health & Science University"},{"author_name":"Kelly R Monk","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-04-25","rel_site":"biorxiv"},{"rel_title":"Behavioral and psychological symptoms of dementia: insights from a multivariate and network-based brain proteome-wide study","rel_doi":"10.64898\/2026.04.23.26351110","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351110","rel_abs":"Behavioral and psychological symptoms of dementia (BPSD) are common, profoundly troubling to patients and caregivers, and difficult to treat, yet their molecular underpinnings remain poorly understood. Here, we generated a large brain proteomic dataset with nine BPSD domains assessed in life from 376 donors from three cohorts. Protein associations with BPSD were examined using complementary approaches -- domain-specific BPSD, multi-domain BPSD, and latent factor modeling -- and integrated via cross-cohort meta-analysis. Four proteins (NMT1, DCAKD, DNPH1, and HIBADH) were associated with anxiety in dementia and five proteins (ABL1, SAP18, PLXND1, CTRB2, and LDHD) with multi-domain BPSD or BPSD latent factors after adjusting for sex, age, and other covariates (FDR < 0.05). Additionally, eight protein co-expression networks were associated with BPSD across cohorts. Together, these results link BPSD to dysregulation of synaptic signaling, protein folding, and humoral immune response, providing a molecular framework for therapeutic discovery.","rel_num_authors":9,"rel_authors":[{"author_name":"Selina M Vattathil","author_inst":"University of California, Davis"},{"author_name":"Duc M Duong","author_inst":"Emory University School of Medicine"},{"author_name":"Marla Gearing","author_inst":"Emory University School of Medicine"},{"author_name":"Nicholas T Seyfried","author_inst":"Emory University School of Medicine"},{"author_name":"Robert S. Wilson","author_inst":"Rush University Medical Center"},{"author_name":"David A. Bennett","author_inst":"Rush University Medical Center"},{"author_name":"Randall L. Woltjer","author_inst":"Oregon Health and Science University"},{"author_name":"Thomas S. Wingo","author_inst":"University of California, Davis"},{"author_name":"Aliza P. Wingo","author_inst":"University of California, Davis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Preconception metabolic-bariatric surgery and child health outcomes: Identification and cohort profile of the POSIT study protocol","rel_doi":"10.64898\/2026.04.22.26351521","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351521","rel_abs":"Preconception weight loss by metabolic-bariatric surgery (MBS) improves maternal-fetal outcomes, but little is known about its impact on offspring growth and health. The preconception bariatric surgery and child health outcomes (POSIT) study aims to estimate the effects of maternal MBS-induced preconception weight loss on infant and childhood body size, growth, and related outcomes. This report presents the methods used to construct the POSIT cohort and its baseline characteristics. This retrospective cohort study sampled members from a United States healthcare system aged 18 and older with a singleton, live birth to create three study groups: 1) a treatment group including women who underwent preconception MBS and subsequently became pregnant (n=1,374); 2) a control group matched to the MBS pre-surgery body mass index (BMI) (pre-surgery controls, n=13,740); and 3) a second control group matched to the MBS post-surgical, pre-pregnancy BMI (pre-pregnancy controls, n=13,740). MBS and pre-surgery BMI controls showed slight imbalances in that pre-surgery BMI controls were on average [~]6 months younger, had 0.6 lower BMI (44.5 kg\/m2) at the time of their pregnancy and were more likely to have become pregnant in earlier years than the MBS group prior to surgery. MBS and pre-pregnancy controls had comparable age (mean {+\/-} SD 33 {+\/-} 5 years), pre-pregnancy BMI (33 {+\/-} 6 kg\/m2), and year of delivery. Following matching, the MBS group had similar socioeconomic and health disparities as the pre-surgery control group, and both were worse than pre-pregnancy control group. Pregestational maternal comorbidity index improved after MBS and matched the pre-pregnancy controls. Upon extraction of offspring growth patterns and mediation analyses of maternal weight loss and metabolic responses to MBS, study findings will investigate effects of preconception weight loss by MBS on short- and long-term child health outcomes. Results will guide future studies focusing on improving maternal preconception weight and maternal-fetal outcomes.","rel_num_authors":10,"rel_authors":[{"author_name":"Jonathan  Q Purnell","author_inst":"Oregon Health & Science University"},{"author_name":"Darios Getahun","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Kimberly  K Vesco","author_inst":"Kaiser Permanente Northwest"},{"author_name":"Sijia Qiu","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Jiaxiao  M Shi","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Carmen  P Wong","author_inst":"Kaiser Permanente"},{"author_name":"Padma Koppolu","author_inst":"Kaiser Permanente Northwest"},{"author_name":"Theresa  M Im","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Caryn  E Oshiro","author_inst":"Kaiser Permanente"},{"author_name":"Janne Boone-Heinonen","author_inst":"University of California San Francisco"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Preconception metabolic-bariatric surgery and child health outcomes: Identification and cohort profile of the POSIT study protocol","rel_doi":"10.64898\/2026.04.22.26351521","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351521","rel_abs":"Preconception weight loss by metabolic-bariatric surgery (MBS) improves maternal-fetal outcomes, but little is known about its impact on offspring growth and health. The preconception bariatric surgery and child health outcomes (POSIT) study aims to estimate the effects of maternal MBS-induced preconception weight loss on infant and childhood body size, growth, and related outcomes. This report presents the methods used to construct the POSIT cohort and its baseline characteristics. This retrospective cohort study sampled members from a United States healthcare system aged 18 and older with a singleton, live birth to create three study groups: 1) a treatment group including women who underwent preconception MBS and subsequently became pregnant (n=1,374); 2) a control group matched to the MBS pre-surgery body mass index (BMI) (pre-surgery controls, n=13,740); and 3) a second control group matched to the MBS post-surgical, pre-pregnancy BMI (pre-pregnancy controls, n=13,740). MBS and pre-surgery BMI controls showed slight imbalances in that pre-surgery BMI controls were on average [~]6 months younger, had 0.6 lower BMI (44.5 kg\/m2) at the time of their pregnancy and were more likely to have become pregnant in earlier years than the MBS group prior to surgery. MBS and pre-pregnancy controls had comparable age (mean {+\/-} SD 33 {+\/-} 5 years), pre-pregnancy BMI (33 {+\/-} 6 kg\/m2), and year of delivery. Following matching, the MBS group had similar socioeconomic and health disparities as the pre-surgery control group, and both were worse than pre-pregnancy control group. Pregestational maternal comorbidity index improved after MBS and matched the pre-pregnancy controls. Upon extraction of offspring growth patterns and mediation analyses of maternal weight loss and metabolic responses to MBS, study findings will investigate effects of preconception weight loss by MBS on short- and long-term child health outcomes. Results will guide future studies focusing on improving maternal preconception weight and maternal-fetal outcomes.","rel_num_authors":10,"rel_authors":[{"author_name":"Jonathan  Q Purnell","author_inst":"Oregon Health & Science University"},{"author_name":"Darios Getahun","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Kimberly  K Vesco","author_inst":"Kaiser Permanente Northwest"},{"author_name":"Sijia Qiu","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Jiaxiao  M Shi","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Carmen  P Wong","author_inst":"Kaiser Permanente"},{"author_name":"Padma Koppolu","author_inst":"Kaiser Permanente Northwest"},{"author_name":"Theresa  M Im","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Caryn  E Oshiro","author_inst":"Kaiser Permanente"},{"author_name":"Janne Boone-Heinonen","author_inst":"University of California San Francisco"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Influenza vaccine effectiveness against influenza-associated hospitalizations and emergency department or urgent care encounters among children and adults - United States, 2024-25 season","rel_doi":"10.64898\/2026.04.22.26350853","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26350853","rel_abs":"BackgroundThe 2024-25 influenza season was the most severe in the United States (US) since 2017-18, with co-circulation of both influenza A virus subtypes (H1N1 and H3N2). Influenza vaccine effectiveness (VE) has varied by season, setting, and patient characteristics.\n\nMethodsUsing electronic healthcare encounter data from eight US states, we evaluated influenza vaccine effectiveness (VE) against influenza-associated hospitalizations and emergency department or urgent care (ED\/UC) encounters from October 2024-April 2025 among children aged 6 months-17 years and adults aged [&ge;]18 years. Using a test-negative, case-control design, we compared the odds of influenza vaccination between acute respiratory illness (ARI) encounters with a positive (cases) versus negative (controls) test for influenza by molecular assay, adjusting for confounders.\n\nResultsAnalyses included 108,618 encounters (5,764 hospitalizations and 102,854 ED\/UC encounters) among children and 309,483 encounters (76,072 hospitalizations and 233,411 ED\/UC encounters) among adults. Among children across care settings, 17.0% (6,097\/35,765) of cases versus 29.4% (21,449\/72,853) of controls were vaccinated. Among adults, 28.2% (21,832\/77,477) of cases versus 44.2% (102,560\/232,006) of controls were vaccinated. VE was 51% (95% confidence interval [95% CI]: 41-60%) against influenza-associated hospitalizations and 54% (95% CI: 52-55%) against influenza-associated ED\/UC encounters among children. VE was 43% (95% CI: 41-46%) against influenza-associated hospitalizations and 49% (95% CI: 47-50%) against influenza-associated ED\/UC encounters among adults.\n\nConclusionsInfluenza vaccination provided protection against influenza-associated hospitalizations and ED\/UC encounters among children and adults in the US during the severe 2024-25 influenza season.\n\nThese findings support influenza vaccination as an important tool to reduce influenza-associated disease.\n\nBrief SummaryDuring the 2024-25 influenza season, influenza vaccination provided protection against influenza-associated hospitalizations (43-51%) and emergency department or urgent care encounters (49-54%) among children and adults in the United States.","rel_num_authors":44,"rel_authors":[{"author_name":"Jennifer DeCuir","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Emily L. Reeves","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Zachary A. Weber","author_inst":"Westat Inc., Bethesda, Maryland, United States"},{"author_name":"Duck-Hye Yang","author_inst":"Westat Inc., Bethesda, Maryland, United States"},{"author_name":"Stephanie A. Irving","author_inst":"Kaiser Permanente Center for Health Research, Portland, Oregon, United States"},{"author_name":"Sara Y. Tartof","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States; Kaiser Permanente Bernard J. Tyson School of Me"},{"author_name":"Nicola P. Klein","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"Shaun J. Grannis","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; School of Medicine, Indiana University, Indianapolis, Indiana, U"},{"author_name":"Toan C. Ong","author_inst":"School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States"},{"author_name":"Sarah W. Ball","author_inst":"Westat, Inc. Bethesda, Maryland, United States"},{"author_name":"Malini B. DeSilva","author_inst":"HealthPartners Institute, Minneapolis, Minnesota, United States"},{"author_name":"Kristin Dascomb","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States"},{"author_name":"Allison L. Naleway","author_inst":"Kaiser Permanente Center for Health Research, Portland, Oregon, United States"},{"author_name":"Padma Koppolu","author_inst":"Kaiser Permanente Center for Health Research, Portland, Oregon, United States"},{"author_name":"S. Bianca Salas","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States"},{"author_name":"Lina S. Sy","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States"},{"author_name":"Bruno Lewin","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States; Kaiser Permanente Bernard J. Tyson School of Me"},{"author_name":"Richard Contreras","author_inst":"Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States"},{"author_name":"Ousseny Zerbo","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"John R. Hansen","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"Lawrence Block","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"Karen B. Jacobson","author_inst":"Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, California, United States"},{"author_name":"Brian E. Dixon","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; Fairbanks School of Public Health, Indiana University Indianapol"},{"author_name":"Colin Rogerson","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; School of Medicine, Indiana University, Indianapolis, Indiana, U"},{"author_name":"Thomas Duszynski","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; Fairbanks School of Public Health, Indiana University, Indianapo"},{"author_name":"William F. Fadel","author_inst":"Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, United States; Fairbanks School of Public Health, Indiana University, Indianapo"},{"author_name":"Michelle A. Barron","author_inst":"School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States"},{"author_name":"David Mayer","author_inst":"School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States"},{"author_name":"Catia Chavez","author_inst":"School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States"},{"author_name":"Adam Yates","author_inst":"Westat Inc., Bethesda, Maryland, United States"},{"author_name":"Lindsey Kirshner","author_inst":"Westat Inc., Bethesda, Maryland, United States"},{"author_name":"Charlene E. McEvoy","author_inst":"HealthPartners Institute, Minneapolis, Minnesota, United States"},{"author_name":"Omobosola O. Akinsete","author_inst":"HealthPartners Institute, Minneapolis, Minnesota, United States"},{"author_name":"Inih J. Essien","author_inst":"HealthPartners Institute, Minneapolis, Minnesota, United States"},{"author_name":"Tamara Sheffield","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States; Immunization Programs, Intermountain Healt"},{"author_name":"Daniel Bride","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States; Enterprise Analytics, Intermountain Health"},{"author_name":"Julie Arndorfer","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States"},{"author_name":"Josh Van Otterloo","author_inst":"Division of Infectious Diseases and Clinical Epidemiology, Intermountain Health, Salt Lake City, Utah, United States"},{"author_name":"Karthik Natarajan","author_inst":"Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States; Medical Informatics Services, NewYork-Presby"},{"author_name":"Caitlin S. Ray","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Amanda B. Payne","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Katherine Adams","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Brendan Flannery","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"},{"author_name":"Shikha Garg","author_inst":"National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, United States"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Racioethnic Disparities in Risk of Cardiometabolic Risk Factors and Cardiovascular Disease among Women Treated for Breast Cancer: The Pathways Heart Study","rel_doi":"10.64898\/2026.04.23.26351612","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351612","rel_abs":"Racial and ethnic disparities exist in cardiovascular disease (CVD) burden in the general population; yet surprisingly few studies have examined such disparities in breast cancer patients, who are at higher risk due to cardiotoxic therapy. To investigate incidence of CVD and cardiometabolic risk factors across Asian, non-Hispanic Black (NHB), Hispanic, and non-Hispanic White (NHW) women with a history of breast cancer. In 4,071 women with breast cancer from a prospective cohort, the incidence of cardiometabolic risk factors and CVD occurring after breast cancer diagnosis were analyzed with self-identified race and ethnicity (SIRE) and global genetic ancestry. Racial and ethnic differences existed in the prevalence of cardiometabolic risk factors and CVD before breast cancer diagnosis, which continued to manifest in incident cases after cancer treatment. Asian, NHB, and Hispanic women were all at higher risk of diabetes than NHW women. Nonetheless, only NHB women had higher risk of CVD events, and Hispanic women were at lower risk. The apparent lower risk of CVD in Asian women largely disappeared after adjustment for covariates. Similar differences across SIRE groups were found in the cardiotoxic chemotherapy subgroup and the subgroup without chemotherapy, except for any CVD and VTE showing modifying effects of cardiotoxic chemotherapy. Analyses of genetic ancestry revealed similar results to SIRE. Our study reveals racial and ethnic disparities in cardiometabolic risk factors and CVD events before and after breast cancer diagnosis. Clinical and research attention is warranted to bridge the population-level gaps in CVD morbidity and mortality.\n\nStatement of SignificanceOur study provides strong evidence for racial and ethnic disparities in cardiovascular disease before and after breast cancer diagnosis. Clinical and research attention is warranted to bridge these population-level gaps.","rel_num_authors":21,"rel_authors":[{"author_name":"Song Yao","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Alexa Zimbalist","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Haiyang Sheng","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Peter Fiorica","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Richard Cheng","author_inst":"University of Washington School of Medicine"},{"author_name":"Lucas Medicino","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Angela Omilian","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Qianqian Zhu","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Janise Roh","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Cecile Laurent","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Valerie Lee","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Isaac Ergas","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Carlos Iribarren","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Jamal Rana","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Mai Nguyen-Huynh","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Eileen Rillamas-Sun","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Dawn Hershman","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Christine Ambrosone","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Lawrence Kushi","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Heather Greenlee","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Marilyn Kwan","author_inst":"Kaiser Permanente Northern California"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Racioethnic Disparities in Risk of Cardiometabolic Risk Factors and Cardiovascular Disease among Women Treated for Breast Cancer: The Pathways Heart Study","rel_doi":"10.64898\/2026.04.23.26351612","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351612","rel_abs":"Racial and ethnic disparities exist in cardiovascular disease (CVD) burden in the general population; yet surprisingly few studies have examined such disparities in breast cancer patients, who are at higher risk due to cardiotoxic therapy. To investigate incidence of CVD and cardiometabolic risk factors across Asian, non-Hispanic Black (NHB), Hispanic, and non-Hispanic White (NHW) women with a history of breast cancer. In 4,071 women with breast cancer from a prospective cohort, the incidence of cardiometabolic risk factors and CVD occurring after breast cancer diagnosis were analyzed with self-identified race and ethnicity (SIRE) and global genetic ancestry. Racial and ethnic differences existed in the prevalence of cardiometabolic risk factors and CVD before breast cancer diagnosis, which continued to manifest in incident cases after cancer treatment. Asian, NHB, and Hispanic women were all at higher risk of diabetes than NHW women. Nonetheless, only NHB women had higher risk of CVD events, and Hispanic women were at lower risk. The apparent lower risk of CVD in Asian women largely disappeared after adjustment for covariates. Similar differences across SIRE groups were found in the cardiotoxic chemotherapy subgroup and the subgroup without chemotherapy, except for any CVD and VTE showing modifying effects of cardiotoxic chemotherapy. Analyses of genetic ancestry revealed similar results to SIRE. Our study reveals racial and ethnic disparities in cardiometabolic risk factors and CVD events before and after breast cancer diagnosis. Clinical and research attention is warranted to bridge the population-level gaps in CVD morbidity and mortality.\n\nStatement of SignificanceOur study provides strong evidence for racial and ethnic disparities in cardiovascular disease before and after breast cancer diagnosis. Clinical and research attention is warranted to bridge these population-level gaps.","rel_num_authors":21,"rel_authors":[{"author_name":"Song Yao","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Alexa Zimbalist","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Haiyang Sheng","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Peter Fiorica","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Richard Cheng","author_inst":"University of Washington School of Medicine"},{"author_name":"Lucas Medicino","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Angela Omilian","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Qianqian Zhu","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Janise Roh","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Cecile Laurent","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Valerie Lee","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Isaac Ergas","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Carlos Iribarren","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Jamal Rana","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Mai Nguyen-Huynh","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Eileen Rillamas-Sun","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Dawn Hershman","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Christine Ambrosone","author_inst":"Roswell Park Comprehensive Cancer Center"},{"author_name":"Lawrence Kushi","author_inst":"Kaiser Permanente Northern California"},{"author_name":"Heather Greenlee","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Marilyn Kwan","author_inst":"Kaiser Permanente Northern California"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Comparison of the Expert Guidelines With Artificial Intelligence-Driven Echocardiographic Assessment of Diastolic Function","rel_doi":"10.64898\/2026.04.23.26350072","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26350072","rel_abs":"BackgoundAccurate assessment of diastolic function and left ventricular (LV) filling pressure is central to heart failure diagnosis and risk stratification. Contemporary guideline algorithms rely on complex parameters that are not consistently available in routine clinical practice.\n\nObjectiveTo compare the diagnostic and prognostic performance of the 2016 American Society of Echocardiography\/European Association of Cardiovascular Imaging (ASE\/EACVI) and 2025 ASE guidelines with a deep learning model based on routinely acquired echocardiographic variables.\n\nMethodsThis study evaluated the guideline-based algorithms and a deep learning model in participants from the Atherosclerosis Risk in Communities (ARIC) cohort (n=5450) for prognostication and two invasive hemodynamic validation cohorts from the United States (n=83) and Japan (n=130) for detection of elevated left ventricular filling pressure.\n\nResultsIn the ARIC cohort, the deep learning model demonstrated superior prognostic performance compared with the 2016 and 2025 guidelines (C-index: 0.676 vs. 0.638 and 0.602, respectively; both p<0.001). Similar findings were observed among participants with preserved ejection fraction (C-index: 0.660 vs. 0.628 and 0.590; both p<0.001), with improved performance compared with the H2FPEF score (C-index: 0.660 vs. 0.607; p<0.001). In the US hemodynamic validation cohort, the deep learning model showed higher diagnostic performance than the 2025 guidelines (AUC: 0.879 vs. 0.822; p=0.041) and similar performance compared with the 2016 guidelines (AUC: 0.879 vs. 0.812; p=0.138). In the Japanese hemodynamic validation cohort, the deep learning model outperformed both guidelines (AUC: 0.816 vs. 0.634 and 0.694; both p<0.05).\n\nConclusionsA deep learning model leveraging routinely available echocardiographic parameters demonstrated improved diagnostic and prognostic performance compared with contemporary guideline-based approaches, potentially offering a scalable alternative for assessing diastolic function and left ventricular filling pressures.","rel_num_authors":12,"rel_authors":[{"author_name":"Marton Tokodi","author_inst":"Heart and Vascular Center, Semmelweis University and Department of Experimental Cardiology and Surgical Techniques, Semmelweis University, Budapest, Hungary"},{"author_name":"Nobuyuki Kagiyama","author_inst":"Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan"},{"author_name":"Ambarish Pandey","author_inst":"Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA"},{"author_name":"Yutaka Nakamura","author_inst":"Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan"},{"author_name":"Yuka Akama","author_inst":"Juntendo University Graduate School of Medicine"},{"author_name":"Sachiko Takamatsu","author_inst":"Department of Nursing, The Sakakibara Heart Institute of Okayama, Okayama, Japan"},{"author_name":"Misako Toki","author_inst":"Department of Clinical Laboratory, The Sakakibara Heart Institute of Okayama, Okayama, Japan"},{"author_name":"Takeshi Kitai","author_inst":"National Cerebral and Cardiovascular Center"},{"author_name":"Taiji Okada","author_inst":"Shimane University Faculty of Medicine"},{"author_name":"Carolyn SP Lam","author_inst":"Duke-National University of Singapore"},{"author_name":"Naveena Yanamala","author_inst":"Rutgers University, NJ, USA"},{"author_name":"Partho Sengupta","author_inst":"Rutgers, The State University of New Jersey"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Deep Learning Reveals the Modular Genetic Architecture of Cardiovascular Aging","rel_doi":"10.64898\/2026.04.22.26351478","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351478","rel_abs":"Chronological age is a potent determinant of clinical events, but it is conventionally treated as a linear function of time rather than a dynamic process shaped by genetics and tissue-specific senescence. Deep learning models derived from cardiovascular imaging offer an opportunity to quantify biological age across multiple domains and to examine the extent to which these measures capture shared or distinct vulnerabilities. Here, we applied deep learning to estimate biological age from electrocardiograms, cardiac MRI, carotid ultrasound, and retinal imaging, capturing electrical, structural, macrovascular, and microvascular domains in more than 100,000 UK Biobank participants. Genome-wide association and cross-trait heritability analyses showed that cardiovascular aging is not a singular process but a modular phenotype with distinct genetic determinants across modalities. Polygenic risk scores supported these distinct trajectories, showing that different biological age measures capture partly divergent biological processes with corresponding differences in clinical associations. Modality-specific genes also showcased distinct cell-type enrichment patterns. By deconvoluting aging into electrical, structural, macrovascular, and microvascular components, our results demonstrate that AI-derived age metrics capture distinct, disease-specific aging pathways. Ultimately, this modular framework positions deep learning-derived aging models not as holistic measures of health, but as domain-specific biomarkers of cardiovascular vulnerability.","rel_num_authors":5,"rel_authors":[{"author_name":"Ryan B Choi","author_inst":"Yale School of Medicine"},{"author_name":"Philip M Croon","author_inst":"Yale School of Medicine"},{"author_name":"Sudheesha Perera","author_inst":"Yale School of Medicine"},{"author_name":"Evangelos Oikonomou","author_inst":"Yale School of Medicine"},{"author_name":"Rohan Khera","author_inst":"Yale School of Medicine"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Genetic and Environmental Predictors of Seasonality and Seasonal Affective Disorder in Individuals with Depression","rel_doi":"10.64898\/2026.04.22.26351539","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351539","rel_abs":"BackgroundThe etiology and nosological status of seasonal affective disorder (SAD) as a specifier of depressive episodes versus a transdiagnostic disorder are the subject of debate. In this study, we investigated the underlying etiology of SAD and dimensional seasonality by examining their association with latitude and genetic risk for a range of traits, and investigated gene-environment interactions.\n\nMethodsThis study included 12,460 adults aged 18-90 with a history of depression from the Australian Genetics of Depression Study. Regression models included predictors for latitude (distance from equator) and polygenic scores for eight traits; major depressive disorder, bipolar disorder, anxiety disorders, chronotype, sleep duration, body mass index, vitamin D levels, and educational attainment. Outcomes were SAD status and general seasonality score.\n\nResultsSAD was positively associated with latitude (OR[95%CI] = 1.05[1.03-1.06], padjusted<0.001), and there was nominal evidence of additive and multiplicative interactions between chronotype genetic risk and latitude (OR = 0.99[0.99-0.99], padjusted=0.381; OR=0.98[0.97-0.99], padjusted=0.489). General seasonality score was associated with latitude (IRR=1.01[1.01-1.01], padjusted 0.001) and genetic risk for major depressive disorder (IRR =1.02[1.01-1.03], padjusted<0.001), bipolar disorder (IRR=1.02[1.01-1.03], padjusted=0.001), anxiety disorders (IRR=1.03[1.01-1.04], padjusted<0.001), vitamin D levels (OR=0.89[0.80-0.95], padjusted=0.048), and educational attainment (IRR=0.97[0.96-0.99], padjusted<0.001).\n\nConclusionsThese findings enhance understanding of SAD etiology, highlighting contributions of psychiatric genetic risk and geographic measures on seasonal behavior, and support examining seasonality as a continuous dimension.","rel_num_authors":7,"rel_authors":[{"author_name":"Floris Huider","author_inst":"Vrije Universiteit Amsterdam"},{"author_name":"Jacob Crouse","author_inst":"Brain and Mind Centre, The University of Sydney"},{"author_name":"Sarah Medland","author_inst":"QIMR Berghofer"},{"author_name":"Ian Hickie","author_inst":"Youth Mental Health and Technology Team, Brain and Mind Centre, The University of Sydney, Australia."},{"author_name":"Nick Martin","author_inst":"QIMR Berghofer"},{"author_name":"Jodi Thea Thomas","author_inst":"QIMR Berghofer Medical Research Institute"},{"author_name":"Brittany L Mitchell","author_inst":"QIMR Berghofer Medical Research Institute"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Phase 1a Evaluation of LP-184 in Recurrent Glioblastoma: Safety, Pharmacokinetics, and Translational Optimization of CNS Exposure","rel_doi":"10.64898\/2026.04.21.26351406","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351406","rel_abs":"PurposeLimited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and\/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER.\n\nPatientsLP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment.\n\nResultsPatients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data ([~]20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by [~] 80% and increases tumor LP-184 cytotoxicity 2-fold.\n\nConclusionsLP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial.\n\nStatement of translational relevanceTreatment failure in glioblastoma reflects inadequate drug brain exposure and DNA repair- mediated resistance. LP-184, a novel acylfulvene alkylator, generates MGMT-independent DNA lesions predominantly repaired by transcription-coupled NER. In a Phase 1a dose finding trial, LP-184 was well-tolerated at the recommended dose for expansion (RDE) in participants with advanced cancers, including recurrent glioblastoma. Plasma drug levels achieved predicted effective systemic exposures but not brain concentrations based on projected 20% brain penetrance. Pharmacokinetic modeling indicates that NER inhibition could increase tumor chemosensitivity with the addition of spironolactone. The optimal dosing regimen for spironolactone combined with LP-184 was identified in orthotopic PDX models, facilitating advancement to Phase 1b\/2a testing of LP-184 plus spironolactone.","rel_num_authors":9,"rel_authors":[{"author_name":"Karisa Schreck","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bachchu Lal","author_inst":"Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland"},{"author_name":"Jianli Zhou","author_inst":"Lantern Pharma Inc"},{"author_name":"Hernando Lopez Bertoni","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Matthias Holdhoff","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Reginald Ewesudo","author_inst":"Lantern Pharma Inc"},{"author_name":"Kishor Bhatia","author_inst":"Lantern Pharma Inc"},{"author_name":"Marc Chamberlain","author_inst":"Lantern Pharma Inc"},{"author_name":"John Laterra","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Shared Risk Genes and Casual Relationships across Sex Hormone Related Traits and Alzheimer's Disease","rel_doi":"10.64898\/2026.04.23.26351626","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351626","rel_abs":"BackgroundAlzheimers disease (AD) exhibits marked sex differences. While sex hormone levels across the lifespan likely contribute to this, little remains known about their causal impact and their relation to sex-biased genetic risk for AD. We therefore sought to identify potential shared genetic architectures, as well as causal genes and relationships, between sex hormone-related traits and AD risk.\n\nMethodsLarge-scale AD sex-stratified genome-wide association study (GWAS) results were available from case-control, proxy-based, and population-based cohorts, including the Alzheimers Disease Genetics Consortium, Alzheimers Disease Sequencing Project, UK Biobank, and FinnGen. Sex hormone-related trait GWAS were available for age at menarche, menopause, and voice breaking, as well as testosterone, sex hormone-binding globulin (SHBG), progesterone, follicle stimulating hormone, luteinizing hormone, and estradiol levels. Cross-trait conjunctional analyses were conducted to identify pleiotropic overlap between sex-hormone traits and AD, followed by prioritization of candidate causal sex-biased AD genes through quantitative trait locus genetic colocalization analyses. The potential regulatory impact of sex hormones on these genes was assessed through transcription factor motif analyses. Finally, sex-stratified mendelian randomization analyses were used to infer causal effects of sex hormones on AD risk.\n\nResultsGenome-wide pleiotropy analyses demonstrated enrichment of AD with testosterone, SHBG, and age-at-menarche traits in women. We identified 12 high-confidence pleiotropic loci, 9 of which showed stronger AD effect sizes in women (3 in men) and 8 that were novel. Genes at these loci were often causally implicated in brain tissues and enriched for promoter-associated androgen receptor transcription factor binding motifs. Mendelian randomization indicated higher bioavailable testosterone in women (OR:0.88; 95%-CI:0.82-0.96) and higher SHBG levels in men (OR:0.86; 95%-CI:0.77-0.96) were associated with lower AD risk.\n\nConclusionsOur findings reveal sex-specific shared genetic architectures between AD and sex hormone-related traits and nominate related genes that may drive sex-biases in AD risk. Several of the implicated female-biased genes are relevant to phosphatidylinositol and lipid metabolism, including Fatty Acid Desaturase 2 (FADS2). While we observed no causal effect of estradiol-related traits on AD risk, the protective effects of bioavailable testosterone in women and SHBG in men provide targets for sex-informed AD risk stratification and prevention strategies.","rel_num_authors":8,"rel_authors":[{"author_name":"chenyu yang","author_inst":"Washington University in St Louis"},{"author_name":"Noah Cook","author_inst":"Washington University in St. Louis"},{"author_name":"Youjie Zeng","author_inst":"Washington University in Saint Louis"},{"author_name":"Sathesh Kumar Sivasankaran","author_inst":"Washington University in St Louis"},{"author_name":"- FinnGen","author_inst":""},{"author_name":"Alex Decasien","author_inst":"National Institutes of Health"},{"author_name":"Shea J Andrews","author_inst":"University of California San Francisco"},{"author_name":"Michael E Belloy","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Shared Risk Genes and Casual Relationships across Sex Hormone Related Traits and Alzheimer's Disease","rel_doi":"10.64898\/2026.04.23.26351626","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351626","rel_abs":"BackgroundAlzheimers disease (AD) exhibits marked sex differences. While sex hormone levels across the lifespan likely contribute to this, little remains known about their causal impact and their relation to sex-biased genetic risk for AD. We therefore sought to identify potential shared genetic architectures, as well as causal genes and relationships, between sex hormone-related traits and AD risk.\n\nMethodsLarge-scale AD sex-stratified genome-wide association study (GWAS) results were available from case-control, proxy-based, and population-based cohorts, including the Alzheimers Disease Genetics Consortium, Alzheimers Disease Sequencing Project, UK Biobank, and FinnGen. Sex hormone-related trait GWAS were available for age at menarche, menopause, and voice breaking, as well as testosterone, sex hormone-binding globulin (SHBG), progesterone, follicle stimulating hormone, luteinizing hormone, and estradiol levels. Cross-trait conjunctional analyses were conducted to identify pleiotropic overlap between sex-hormone traits and AD, followed by prioritization of candidate causal sex-biased AD genes through quantitative trait locus genetic colocalization analyses. The potential regulatory impact of sex hormones on these genes was assessed through transcription factor motif analyses. Finally, sex-stratified mendelian randomization analyses were used to infer causal effects of sex hormones on AD risk.\n\nResultsGenome-wide pleiotropy analyses demonstrated enrichment of AD with testosterone, SHBG, and age-at-menarche traits in women. We identified 12 high-confidence pleiotropic loci, 9 of which showed stronger AD effect sizes in women (3 in men) and 8 that were novel. Genes at these loci were often causally implicated in brain tissues and enriched for promoter-associated androgen receptor transcription factor binding motifs. Mendelian randomization indicated higher bioavailable testosterone in women (OR:0.88; 95%-CI:0.82-0.96) and higher SHBG levels in men (OR:0.86; 95%-CI:0.77-0.96) were associated with lower AD risk.\n\nConclusionsOur findings reveal sex-specific shared genetic architectures between AD and sex hormone-related traits and nominate related genes that may drive sex-biases in AD risk. Several of the implicated female-biased genes are relevant to phosphatidylinositol and lipid metabolism, including Fatty Acid Desaturase 2 (FADS2). While we observed no causal effect of estradiol-related traits on AD risk, the protective effects of bioavailable testosterone in women and SHBG in men provide targets for sex-informed AD risk stratification and prevention strategies.","rel_num_authors":8,"rel_authors":[{"author_name":"chenyu yang","author_inst":"Washington University in St Louis"},{"author_name":"Noah Cook","author_inst":"Washington University in St. Louis"},{"author_name":"Youjie Zeng","author_inst":"Washington University in Saint Louis"},{"author_name":"Sathesh Kumar Sivasankaran","author_inst":"Washington University in St Louis"},{"author_name":"- FinnGen","author_inst":""},{"author_name":"Alex Decasien","author_inst":"National Institutes of Health"},{"author_name":"Shea J Andrews","author_inst":"University of California San Francisco"},{"author_name":"Michael E Belloy","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"BackgroundPatients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients.\n\nMethodsWe analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide.\n\nResultsIn CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004).\n\nConclusionsSVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.\n\nKey PointsO_LIPatients with kidney failure undergoing hemodialysis have 20-fold higher cardiovascular mortality compared to the general population, and conventional risk factors have low prognostic utility for these patients.\nC_LIO_LIBy applying large-scale circulating proteomics in two independent hemodialysis cohorts, we have discovered >20 novel proteins that predict major adverse cardiovascular events(MACE).\nC_LIO_LISushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1) surpassed >6000 individual proteins and clinical factors for predicting MACE.\nC_LI","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"BackgroundPatients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients.\n\nMethodsWe analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide.\n\nResultsIn CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004).\n\nConclusionsSVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.\n\nKey PointsO_LIPatients with kidney failure undergoing hemodialysis have 20-fold higher cardiovascular mortality compared to the general population, and conventional risk factors have low prognostic utility for these patients.\nC_LIO_LIBy applying large-scale circulating proteomics in two independent hemodialysis cohorts, we have discovered >20 novel proteins that predict major adverse cardiovascular events(MACE).\nC_LIO_LISushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1) surpassed >6000 individual proteins and clinical factors for predicting MACE.\nC_LI","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"BackgroundPatients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients.\n\nMethodsWe analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide.\n\nResultsIn CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004).\n\nConclusionsSVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.\n\nKey PointsO_LIPatients with kidney failure undergoing hemodialysis have 20-fold higher cardiovascular mortality compared to the general population, and conventional risk factors have low prognostic utility for these patients.\nC_LIO_LIBy applying large-scale circulating proteomics in two independent hemodialysis cohorts, we have discovered >20 novel proteins that predict major adverse cardiovascular events(MACE).\nC_LIO_LISushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1) surpassed >6000 individual proteins and clinical factors for predicting MACE.\nC_LI","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"BackgroundPatients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients.\n\nMethodsWe analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide.\n\nResultsIn CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004).\n\nConclusionsSVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.\n\nKey PointsO_LIPatients with kidney failure undergoing hemodialysis have 20-fold higher cardiovascular mortality compared to the general population, and conventional risk factors have low prognostic utility for these patients.\nC_LIO_LIBy applying large-scale circulating proteomics in two independent hemodialysis cohorts, we have discovered >20 novel proteins that predict major adverse cardiovascular events(MACE).\nC_LIO_LISushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1) surpassed >6000 individual proteins and clinical factors for predicting MACE.\nC_LI","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis","rel_doi":"10.64898\/2026.04.23.26348442","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26348442","rel_abs":"BackgroundPatients with kidney failure undergoing maintenance hemodialysis suffer high rates of major adverse cardiovascular events(MACE) that are not accurately predicted by traditional cardiovascular risk models. There is an urgent need to identify novel, modifiable cardiovascular risk factors for these patients.\n\nMethodsWe analyzed associations of 6287 circulating proteins with MACE among 1048 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort(CRIC) (14-year follow-up) with validation in the Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease study(PACE) (7-year follow-up). In both cohorts, proteins were measured shortly after dialysis initiation and one year later. We compared protein-based risk models derived by elastic net regression to the Pooled Cohort Equations(PCE) optimized for these cohorts(Refit PCE), and to an Expanded Refit PCE that included Troponin T and N-terminal pro-B-type natriuretic peptide.\n\nResultsIn CRIC, 149 proteins were associated with MACE at false discovery rate<0.05. Among 22 proteins significant at Bonferroni p<8x10-6, proteins that validated in PACE included Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), Complement component C7, R-spondin 4, Tenascin, Fibulin-3 and Fibulin-5. Complement pathways were prominent in network analyses. SVEP1 surpassed other markers by statistical significance, with CRIC HR per log2 1.8 (p=2.1x10-12) and HR per annual doubling 1.6 (p=6.8x10-6). For 2-year MACE, AUC(95%CI) for SVEP1 alone was 0.72(0.59, 0.84) in CRIC, and 0.73(0.63, 0.81) in PACE. SVEP1 surpassed the Expanded Refit PCE in CRIC (0.61 (0.48, 0.73)) (p=0.038). In the pooled CRIC + PACE cohort, SVEP1 AUC(95%CI) (0.79(0.70, 0.88)) surpassed Refit PCE (0.61(0.51, 0.72)) (p=0.004).\n\nConclusionsSVEP1, a 390 kDa protein unlikely to be renally cleared, surpassed over 6000 other proteins and by itself outperformed traditional clinical risk models in predicting MACE in two populations of patients undergoing maintenance hemodialysis. Future studies should provide mechanistic insights behind these findings.\n\nKey PointsO_LIPatients with kidney failure undergoing hemodialysis have 20-fold higher cardiovascular mortality compared to the general population, and conventional risk factors have low prognostic utility for these patients.\nC_LIO_LIBy applying large-scale circulating proteomics in two independent hemodialysis cohorts, we have discovered >20 novel proteins that predict major adverse cardiovascular events(MACE).\nC_LIO_LISushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1) surpassed >6000 individual proteins and clinical factors for predicting MACE.\nC_LI","rel_num_authors":26,"rel_authors":[{"author_name":"Yue Ren","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Tariq Shafi","author_inst":"Baylor Scott White, Temple, TX, USA"},{"author_name":"Mark R. Segal","author_inst":"University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Hongzhe Li","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Alexander R. Pico","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Min-Gyoung Shin","author_inst":"Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA"},{"author_name":"Jeffrey R. Schelling","author_inst":"Case Western Reserve University of School of Medicine, Cleveland, OH, USA"},{"author_name":"John D. Hulleman","author_inst":"University of Minnesota, Minneapolis, Minnesota, USA"},{"author_name":"Jiang He","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Changwei Li","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Hernan Rincon Choles","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Julia Brown","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Mirela A Dobre","author_inst":"Case Western Reserve University, Cleveland, OH, USA"},{"author_name":"Rupal Mehta","author_inst":"Northwestern Feinberg School of Medicine, IL USA"},{"author_name":"Rajat Deo","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA"},{"author_name":"Anand Srivastava","author_inst":"University of Illinois College of Medicine, Chicago, IL, USA"},{"author_name":"Jonathan Taliercio","author_inst":"Cleveland Clinic, Cleveland, OH, USA"},{"author_name":"Stephen M Sozio","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Bernard Jaar","author_inst":"Johns Hopkins University School of Medicine and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA"},{"author_name":"Michelle M Estrella","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Wei Chen","author_inst":"Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Glenn M Chertow","author_inst":"Stanford University School of Medicine, Stanford, CA, USA"},{"author_name":"Rulan Parekh","author_inst":"Womens College Hospital, Toronto, Ontario, Canada"},{"author_name":"Peter Ganz","author_inst":"University of California San Francisco School of Medicine, San Francisco, CA, USA"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"- CRIC Study Investigators","author_inst":""}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images","rel_doi":"10.64898\/2026.04.23.26351616","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351616","rel_abs":"Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging beyond structured electronic health record (EHR) data remains unclear in real-world settings. We developed a multimodal survival modeling framework integrating optical coherence tomography (OCT) and EHR data to predict time to visual improvement in patients with diabetic macular edema (DME), and evaluated how different ophthalmic foundation model representations contribute to prognostic performance.\n\nIn a retrospective cohort of 973 patients (1,450 eyes) receiving anti-vascular endothelial growth factor therapy, we compared multimodal models combining 22,227 EHR variables with 196,402 OCT images, with OCT embeddings derived from three ophthalmic foundation models (RETFound, EyeCLIP, and VisionFM). The EHR-only model showed minimal prognostic discrimination (C-index 0.50 [95% CI, 0.45-0.55]). Incorporating OCT improved performance, with the magnitude of improvement depending on the representation. EHR+RETFound achieved the strongest performance (C-index 0.59 [0.54-0.65]), followed by EHR+EyeCLIP (0.57 [0.52-0.62]) and EHR+VisionFM (0.56 [0.51-0.61]). Multimodal models, particularly EHR+RETFound, demonstrated improved risk stratification with clearer separation of Kaplan-Meier curves.\n\nPartial information decomposition revealed that prognostic information was dominated by modality-specific contributions, with OCT and EHR providing largely distinct signals and minimal shared information. The magnitude of OCT-specific contribution varied across foundation models and aligned with observed performance differences.\n\nThese findings indicate that OCT provides complementary prognostic value beyond structured clinical data, but gains are modest and depend strongly on representation choice. Our results highlight both the promise of multimodal modeling for personalized prognosis and the need for rigorous, context-specific evaluation of foundation models in real-world clinical settings.","rel_num_authors":11,"rel_authors":[{"author_name":"Siqi Sun","author_inst":"Washington University in St. Louis"},{"author_name":"Cindy X. Cai","author_inst":"Wilmer Eye Institute, . Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine"},{"author_name":"Ruochong Fan","author_inst":"Washington University in St. Louis"},{"author_name":"Saiyu You","author_inst":"Washington University in St. Louis"},{"author_name":"Diep Tran","author_inst":"Wilmer Eye Institute"},{"author_name":"P. Kumar Rao","author_inst":"Washington University in St. Louis"},{"author_name":"Marc A Suchard","author_inst":"University of California, Los Angeles"},{"author_name":"Yixin Wang","author_inst":"University of Michigan, Ann Arbor"},{"author_name":"Cecilia S Lee","author_inst":"Washington University In St Louis: Washington University in St Louis"},{"author_name":"Aaron Y Lee","author_inst":"Washington University in St Louis"},{"author_name":"Linying Zhang","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images","rel_doi":"10.64898\/2026.04.23.26351616","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351616","rel_abs":"Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging beyond structured electronic health record (EHR) data remains unclear in real-world settings. We developed a multimodal survival modeling framework integrating optical coherence tomography (OCT) and EHR data to predict time to visual improvement in patients with diabetic macular edema (DME), and evaluated how different ophthalmic foundation model representations contribute to prognostic performance.\n\nIn a retrospective cohort of 973 patients (1,450 eyes) receiving anti-vascular endothelial growth factor therapy, we compared multimodal models combining 22,227 EHR variables with 196,402 OCT images, with OCT embeddings derived from three ophthalmic foundation models (RETFound, EyeCLIP, and VisionFM). The EHR-only model showed minimal prognostic discrimination (C-index 0.50 [95% CI, 0.45-0.55]). Incorporating OCT improved performance, with the magnitude of improvement depending on the representation. EHR+RETFound achieved the strongest performance (C-index 0.59 [0.54-0.65]), followed by EHR+EyeCLIP (0.57 [0.52-0.62]) and EHR+VisionFM (0.56 [0.51-0.61]). Multimodal models, particularly EHR+RETFound, demonstrated improved risk stratification with clearer separation of Kaplan-Meier curves.\n\nPartial information decomposition revealed that prognostic information was dominated by modality-specific contributions, with OCT and EHR providing largely distinct signals and minimal shared information. The magnitude of OCT-specific contribution varied across foundation models and aligned with observed performance differences.\n\nThese findings indicate that OCT provides complementary prognostic value beyond structured clinical data, but gains are modest and depend strongly on representation choice. Our results highlight both the promise of multimodal modeling for personalized prognosis and the need for rigorous, context-specific evaluation of foundation models in real-world clinical settings.","rel_num_authors":11,"rel_authors":[{"author_name":"Siqi Sun","author_inst":"Washington University in St. Louis"},{"author_name":"Cindy X. Cai","author_inst":"Wilmer Eye Institute, . Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine"},{"author_name":"Ruochong Fan","author_inst":"Washington University in St. Louis"},{"author_name":"Saiyu You","author_inst":"Washington University in St. Louis"},{"author_name":"Diep Tran","author_inst":"Wilmer Eye Institute"},{"author_name":"P. Kumar Rao","author_inst":"Washington University in St. Louis"},{"author_name":"Marc A Suchard","author_inst":"University of California, Los Angeles"},{"author_name":"Yixin Wang","author_inst":"University of Michigan, Ann Arbor"},{"author_name":"Cecilia S Lee","author_inst":"Washington University In St Louis: Washington University in St Louis"},{"author_name":"Aaron Y Lee","author_inst":"Washington University in St Louis"},{"author_name":"Linying Zhang","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer","rel_doi":"10.64898\/2026.04.23.26351499","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351499","rel_abs":"Purpose: To test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. Methods: We conducted a multi-cohort study including CALGB 9344 (anthracycline +\/- paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrell's C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen\/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. Results: A total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. Conclusion: Histology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit.","rel_num_authors":25,"rel_authors":[{"author_name":"Frederick Matthew Howard","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Anran Li","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sara Kochanny","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Megan Sullivan","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Elbio Martin Flores","author_inst":"Department of Pathology, Ingalls Memorial Hospital, Harvey, IL, USA"},{"author_name":"James Dolezal","author_inst":"Geisinger Cancer Institute, Danville, PA, USA"},{"author_name":"Galina Khramtsova","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sasha Hassan","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Riley Medenwald","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Poornima Saha","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Cheng Fan","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Linda McCart","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Mark Watson","author_inst":"Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Lauren R Teras","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"Clara Bodelon","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"Alpa V Patel","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"W Fraser Symmans","author_inst":"Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Ann Partridge","author_inst":"Dana-Farber Cancer Institute, Boston, MA, USA"},{"author_name":"Lisa Carey","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Olofunmilayo I. Olopade","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Daniel Stover","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Charles Perou","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Katharine Yao","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Alexander T Pearson","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Dezheng Huo","author_inst":"Department of Public Health Sciences, University of Chicago, Chicago, IL, USA"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer","rel_doi":"10.64898\/2026.04.23.26351499","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351499","rel_abs":"Purpose: To test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. Methods: We conducted a multi-cohort study including CALGB 9344 (anthracycline +\/- paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrell's C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen\/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. Results: A total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. Conclusion: Histology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit.","rel_num_authors":25,"rel_authors":[{"author_name":"Frederick Matthew Howard","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Anran Li","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sara Kochanny","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Megan Sullivan","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Elbio Martin Flores","author_inst":"Department of Pathology, Ingalls Memorial Hospital, Harvey, IL, USA"},{"author_name":"James Dolezal","author_inst":"Geisinger Cancer Institute, Danville, PA, USA"},{"author_name":"Galina Khramtsova","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Sasha Hassan","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Riley Medenwald","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Poornima Saha","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Cheng Fan","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Linda McCart","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Mark Watson","author_inst":"Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Lauren R Teras","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"Clara Bodelon","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"Alpa V Patel","author_inst":"Department of Population Science, American Cancer Society, Atlanta, GA, USA"},{"author_name":"W Fraser Symmans","author_inst":"Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA"},{"author_name":"Ann Partridge","author_inst":"Dana-Farber Cancer Institute, Boston, MA, USA"},{"author_name":"Lisa Carey","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Olofunmilayo I. Olopade","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Daniel Stover","author_inst":"The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA"},{"author_name":"Charles Perou","author_inst":"Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Katharine Yao","author_inst":"Endeavor Health Cancer Institute, Evanston, IL, USA"},{"author_name":"Alexander T Pearson","author_inst":"Department of Medicine, University of Chicago, Chicago, IL"},{"author_name":"Dezheng Huo","author_inst":"Department of Public Health Sciences, University of Chicago, Chicago, IL, USA"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Plasma proteomics link menopause timing to brain aging and dementia risk","rel_doi":"10.64898\/2026.04.23.26351500","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.23.26351500","rel_abs":"Earlier menopause is a risk factor for several age-related diseases, including dementia. The biological pathways linking menopause timing to later-life brain aging are not understood. Leveraging large-scale plasma proteomics in postmenopausal women from the UK Biobank (N=15,012), earlier menopause was associated with upregulation of pro-inflammatory and extracellular matrix degradation pathways, plus accelerated aging across proteomic clocks of organ and cellular aging, including brain and oligodendrocyte aging. Elevated GDF15, a canonical aging marker, was the top protein correlate of earlier menopause. We observed robust replication of menopause timing proteomic shifts in the Womens Health Initiative Long Life Study (N=1,210). In UKB, proteins associated with earlier menopause, including GDF15, exhibited concordant associations with incident dementia risk and brain atrophy, cerebral small vessel disease burden, and white matter microstructural integrity. Collectively, our findings identify proteomic signatures linking ovarian aging to brain aging, providing a framework to inform interventions to reduce dementia risk.","rel_num_authors":16,"rel_authors":[{"author_name":"Madeline Wood Alexander","author_inst":"University of Toronto"},{"author_name":"Brendan Wood","author_inst":"University of Toronto"},{"author_name":"Hamilton See-Hwee Oh","author_inst":"Mount Sinai"},{"author_name":"Veronica Augustina Bot","author_inst":"Stanford University"},{"author_name":"Julia Borger","author_inst":"UCSF"},{"author_name":"Francesca Galbiati","author_inst":"UCSF"},{"author_name":"Keenan  A. Walker","author_inst":"National Institute on Aging"},{"author_name":"Susan M. Resnick","author_inst":"NIA"},{"author_name":"Heather M Ochs-Balcom","author_inst":"SUNY"},{"author_name":"Tony Wyss-Coray","author_inst":"Stanford University"},{"author_name":"Charles Kooperberg","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Alexander P. Reiner","author_inst":"Fred Hutchinson Cancer Research Center"},{"author_name":"Emily G Jacobs","author_inst":"UCSB"},{"author_name":"Jennifer S Rabin","author_inst":"Sunnybrook Health Sciences Centre"},{"author_name":"Kaitlin B Casaletto","author_inst":"UCSF"},{"author_name":"Rowan Saloner","author_inst":"University of California, San Francisco"}],"rel_date":"2026-04-24","rel_site":"medrxiv"},{"rel_title":"Culture of preimplantation embryos in media containing L-proline increases intracellular GSH concentration throughout development","rel_doi":"10.64898\/2026.04.23.720483","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720483","rel_abs":"Careful balance of the redox status of the embryo and reduction of oxidative stress is crucial in early development. Here we show that the culture of preimplantation mouse embryos in the conditionally non-essential amino acid L-proline (Pro) increases the intracellular concentration of the potent antioxidant glutathione as shown by staining of 2-cell, 4-cell and 8-cell embryos with tetrafluoroterephthalonitrile (4F-2CN). Further, liquid-chromatography\/mass spectrometry showed increased GSH levels in all Pro-treated preimplantation stages of development compared to controls. The GSH:GSSG ratio also showed a Pro-dependent increase. Overall, our results indicate that the beneficial effect of Pro in preimplantation embryo culture is due to the reduction in oxidative stress mediated through an increase in cellular GSH concentration.","rel_num_authors":3,"rel_authors":[{"author_name":"Madeleine LM Hardy","author_inst":"Australian National University"},{"author_name":"Michael B. Morris","author_inst":"University of Sydney"},{"author_name":"Margot L Day","author_inst":"The university of sydney"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Single cell eQTL mapping reveals convergent glial-neuronal risk architecture in Parkinson's disease","rel_doi":"10.64898\/2026.04.24.720642","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720642","rel_abs":"Synucleinopathies affect ~15 million people and are classically divided into neuronal (Parkinson's disease(PD), dementia with Lewy bodies) and glial (multiple system atrophy) disorders. Here we challenge this dichotomy. We functionally fine-map 90 PD GWAS signals across nine cell types in cortex and substantia nigra using disease-context, population-scale single-nucleus eQTL meta-analysis (N = 1,197), bulk brain eQTL analysis (N = 1,182), and Mendelian randomization. A stringent causal framework integrates single-nucleus allelic imbalance (snASE) with orthogonal validation. We identify 125 functional risk genes for 50 loci--nearly doubling supported genes--and assign genes and cell types to over half of GWAS signals. Unexpectedly, 51% of risk genes are regulated in glia, particularly oligodendrocytes and their precursors. Across cell types, risk converges on a shared glial--neuronal vesiculopathy network. These findings uncover a convergent glial-neuronal risk architecture and establish a single cell atlas for context-aware gene discovery and precision therapeutics for PD.","rel_num_authors":22,"rel_authors":[{"author_name":"Zechuan Lin","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Jacob Parker","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Vanitha Nithianandam","author_inst":"Department of Pathology, Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts, USA"},{"author_name":"Sakthikumar Mathivanan","author_inst":"Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA 92037"},{"author_name":"Tao Wang","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Zhixiang Liao","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Sean K. Simmons","author_inst":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA  02142, USA"},{"author_name":"Idil Tuncali","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Xian Adiconis","author_inst":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA  02142, USA"},{"author_name":"Nathan Haywood","author_inst":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA  02142, USA"},{"author_name":"Beatrice Weykopf","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Xufei Teng","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Monika Sharma","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Jie Yuan","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Clare Baecher-Allan","author_inst":"Department of Neurology, Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts, USA"},{"author_name":"Xianjun Dong","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"},{"author_name":"Thomas G. Beach","author_inst":"Banner Sun Health Research, Sun City, AZ  85351, USA"},{"author_name":"Geidy E. Serrano","author_inst":"Banner Sun Health Research, Sun City, AZ  85351, USA"},{"author_name":"Joshua Z. Levin","author_inst":"Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA  02142, USA"},{"author_name":"Suchun Zhang","author_inst":"Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA 92037"},{"author_name":"Mel B. Feany","author_inst":"Department of Pathology, Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts, USA"},{"author_name":"Clemens R. Scherzer","author_inst":"Stephen and Denise Adams Center for Parkinsons Disease Research of Yale School of Medicine, New Haven, CT 06510"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"EMPIRE: The Ellipse Model for Phylogenetic Inference of Range Evolution","rel_doi":"10.64898\/2026.04.23.720387","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720387","rel_abs":"Many phylogenetic models of historical biogeography exist for describing how lineages move and evolve over time. Here, we present the Ellipse Model for Phylogenetic Inference of Range Evolution (empire), which models the movement and splitting of species range ellipses in continuous space, summarizing important attributes of each range, such as its position, size, and orientation. The framework allows us to reconstruct ancestral range ellipses, investigate rates governing important processes like movement, expansion, and elongation, and examine the spatial context of speciation, including asymmetric range inheritance at cladogenesis. We apply empire to the Australian Sphenomorphinae, a group of skinks whose diversification has coincided with substantial climatic change over the past [~]36 million years. We find that speciation events are positively associated with aridification, while daughter lineages post-speciation do not tend to show evidence of ecological partitioning.","rel_num_authors":3,"rel_authors":[{"author_name":"Sarah Kathryn Swiston","author_inst":"Washington University in Saint Louis"},{"author_name":"Sean W McHugh","author_inst":"Washington University in Saint Louis"},{"author_name":"Michael J Landis","author_inst":"Washington University in Saint Louis"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Additive baselines furnish no evidence for epistasis learning by MULTI-evolve","rel_doi":"10.64898\/2026.04.23.719915","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.719915","rel_abs":"Recent work from Tran et al. (Science, 2026) introduced MULTI-evolve, a framework for protein engineering that combines single-mutant nomination via a protein language model (PLM) or a deep mutational scan (DMS), experimental single- and double-mutant characterization, and neural networks to engineer hyperactive multimutant proteins. The authors attribute the framework's performance to \"epistasis-aware modeling\" and claim that their neural networks \"learn the epistatic landscape\" and \"identify synergistic interactions\" from limited double-mutant training data. Here we show that MULTI-evolve's multimutant predictions are almost perfectly correlated with an additive model across all three engineering applications (APEX, dCasRx, and HuABC2), such that the engineering of multimutants reduces to combining beneficial mutations with the largest additive effects--a standard protein engineering strategy for over four decades. We also find that MULTI-evolve's neural networks do not outperform an additive model in held-out test set predictions. Finally, we revisit a DMS benchmark finding presented as evidence of epistasis learning and show that it is expected even under a null additive model due to an elementary statistical phenomenon. Indeed, we fit an additive model to the benchmark data and reproduce the pattern purported to demonstrate epistasis learning.","rel_num_authors":3,"rel_authors":[{"author_name":"Gian Marco Visani","author_inst":"University of Washington, Seattle"},{"author_name":"Aayush Verma","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"William S. DeWitt","author_inst":"University of Washington, Seattle"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Life history stage-dependent nuclear selection in chimeric fungi","rel_doi":"10.64898\/2026.04.23.718558","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.718558","rel_abs":"A single hyphal compartment of the filamentous fungus \\textit{Neurospora crassa} may contain tens or hundreds of nuclei, sharing macromolecules with each other, and, via a continuous cytoplasm, with the nuclei in other compartments. Nuclear lineages acquire mutations with each mitosis, which, combined with the autonomous mitosis of nuclei, has fueled speculation that multilevel selection may occur, both upon the mycelium, and upon individual nuclear populations. Here, we combine experiments on fungal chimera formed from two auxotrophically and epitopically labeled nuclear populations, with specially created microscopy toolkit for extracting the proportions of the two nucleotypes to analyze the strength of nuclear-level selective forces at different stages in the fungal life history. We find strong nucleotype-selective forces during spore-germination and establishment of the mycelium, and no evidence of selection on nuclei inside a growing mycelium. The kinetics of mycelial initiation from individual spores therefore allow for the selection of nuclear compositions best adapted to the fungus' environment, in accordance with the hypothesized function of unicellular life history stages for purging deleterious mutations.","rel_num_authors":7,"rel_authors":[{"author_name":"Jiayu Li","author_inst":"Princeton University"},{"author_name":"Ariel Fitzmorris","author_inst":"University of California, Los Angeles"},{"author_name":"Justina Martelli","author_inst":"University of California, Los Angeles"},{"author_name":"Alex Mela","author_inst":"University of California, Berkeley"},{"author_name":"Louise Glass","author_inst":"University of California, Berkeley"},{"author_name":"Amy S. Gladfelter","author_inst":"Duke University"},{"author_name":"Marcus Roper","author_inst":"University of California Los Angeles"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Adaptive molecular convergence is pervasive across deep time and largely decoupled from phenotypic convergence","rel_doi":"10.64898\/2026.04.23.718300","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.718300","rel_abs":"Researchers often infer evolutionary repeatability when selection scans implicate homologous genes in repeatedly evolved traits or ecologies. However, the causes and frequency of genome-scale molecular convergence remain unresolved, particularly over deep time. We show that adaptive molecular convergence--excess convergence of nonsynonymous substitutions, consistent with positive selection--is pervasive across Medusozoa. Molecular convergence declines over time but persists among lineages separated by >600 million years, exceeding null expectations based on random overlap. However, lineages sharing repeatedly evolved phenotypes (eyes, medusa loss, upright colonies) do not exhibit elevated molecular convergence relative to other comparisons. Instead, convergence is non-randomly distributed across genes and enriched for environment-facing functions, including metabolism, immunity, and xenobiotic processing, suggesting that widespread reuse of genes reflects multifaceted organism-environment interactions.","rel_num_authors":7,"rel_authors":[{"author_name":"Cory A Berger","author_inst":"University of California, Santa Barbara"},{"author_name":"Marina I. Stoilova","author_inst":"University of Kansas"},{"author_name":"Rebecca M Varney","author_inst":"University of Nebraska-Lincoln"},{"author_name":"Sam C. Abrams","author_inst":"UC Santa Barbara"},{"author_name":"Maria Pia Miglietta","author_inst":"Texas A and M"},{"author_name":"Paulyn Cartwright","author_inst":"The University of Kansas"},{"author_name":"Todd H. Oakley","author_inst":"UC Santa Barbara"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Virus-like antigen display delivers a stand-alone danger signal through the BCR that circumvents tolerance","rel_doi":"10.64898\/2026.04.23.720482","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.23.720482","rel_abs":"How B cells discriminate self from foreign antigens remains a central question, given inherent autoreactivity of the mature B cell receptor (BCR) repertoire. Soluble antigen (sAg) induces tolerance, whereas patterned antigen display on virus-like particles (pAg) triggers robust B cell responses that can proceed without T cell help. Here, we show how this divergence arises early in BCR signaling. Unlike sAg, pAg can bypass a Lyn-dependent negative feedback loop to trigger digital signaling, such that ultra-low concentrations of pAg produce strong and sustained Ca2+ responses. Surprisingly, pAg drives maximal nuclear NF-{kappa}B but limited NFAT, whereas sAg does the opposite, reflecting differential production of diacylglycerol. Consequently, sAg induced an NFAT-dependent anergy program, whereas pAg evaded this state and instead engaged a cMyc-driven program that partially resembles a TLR-dependent danger response. Our findings reveal how proximal signaling directs distinct transcriptional fate to enable immunogenic B cell responses to virus-like antigen display.","rel_num_authors":12,"rel_authors":[{"author_name":"Julianne Riggs","author_inst":"UCSF"},{"author_name":"Alexander J. Ritter","author_inst":"UCSF"},{"author_name":"Francois X. P. Bourassa","author_inst":"Princeton University"},{"author_name":"Alexander R. Meyer","author_inst":"University of Michigan"},{"author_name":"Erika Kay-Tsumagari","author_inst":"University of Michigan"},{"author_name":"Wei-Yun Wholey","author_inst":"University of Michigan"},{"author_name":"Jeremy Libang","author_inst":"UCSF"},{"author_name":"James L. Mueller","author_inst":"UCSF"},{"author_name":"Wen Lu","author_inst":"UCSF"},{"author_name":"Ned S. Wingreen","author_inst":"Princeton University"},{"author_name":"Wei Cheng","author_inst":"University of Michigan"},{"author_name":"Julie Zikherman","author_inst":"UCSF"}],"rel_date":"2026-04-24","rel_site":"biorxiv"},{"rel_title":"Turnip mosaic virus-based gRNA delivery system for plant genome editing","rel_doi":"10.64898\/2026.04.22.720221","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.720221","rel_abs":"Plant virus-based gRNA delivery systems offer a rapid alternative to stable transformation for CRISPR-mediated genome editing, but potyvirus-based platforms in Cas9-expressing plants are still underexplored. Here, we developed a turnip mosaic virus (TuMV)-based system for gRNA delivery in Cas9-expressing Nicotiana benthamiana and tested whether Csy4-mediated gRNA processing could improve editing efficiency. A TuMV construct carrying a gRNA targeting PHYTOENE DESATURASE (NbPDS) induced detectable editing in both infiltrated and systemic tissues, although editing frequencies were low. Incorporation of the bacterial endoribonuclease Csy4 increased editing efficiencies in the two NbPDS genes, raising editing in infiltrated leaves to 7.1-13.8% for NbPDSa and 7.6-23.0% for NbPDSb, while lower but reproducible editing was detectable in systemic leaves. The TuMV-Csy4 platform also supported editing of a second endogenous target, MAGNESIUM CHELATASE SUBUNIT H (NbChlH), and enabled multiplex editing of NbPDS and NbChlH regardless of guide order. Editing efficiencies were consistently higher in infiltrated leaves than in systemic leaves, and no visible photobleaching or chlorosis was observed in systemic tissues despite confirmed molecular editing. To assess the potential for heritable editing, a tRNA<S>Ile<\/S> mobility element was fused to the NbPDS gRNA. Although this construct increased somatic editing, no albino progeny were recovered after screening approximately 20,000 seedlings, indicating that heritable editing was not achieved under these conditions. Together, these results establish TuMV as a platform for Cas9-based gRNA delivery and show that Csy4-mediated processing improves editing efficiency, supports multiplex targeting, and demonstrates the feasibility of potyvirus-based genome editing systems in plants.","rel_num_authors":4,"rel_authors":[{"author_name":"Ekkachai Khwanbua","author_inst":"Iowa State University"},{"author_name":"Ryan R. Lappe","author_inst":"Iowa State University"},{"author_name":"Austin A. Bierl","author_inst":"Iowa State University"},{"author_name":"Steven Whitham","author_inst":"Iowa State University"}],"rel_date":"2026-04-24","rel_site":"biorxiv"}]}