{"gname":"The Rockefeller University","grp_id":"2","rels":[{"rel_title":"An all-inclusive electrical impedance tomography system for remote disease monitoring of multiple vital organs","rel_doi":"10.64898\/2026.04.27.26351830","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351830","rel_abs":"Chronic cardiopulmonary, metabolic, and renal diseases represent an immense global health burden, yet access to organ-specific diagnostics remains limited outside of hospitals. Most clinical assessments rely on imaging or laboratory testing that is costly, infrastructure-dependent, and impractical for large-scale or longitudinal monitoring in community settings. Here, we introduce VitoCheck, a compact, user-friendly electrical impedance tomography (EIT) platform that provides non-invasive evaluation of lung, heart, liver, and kidney function within minutes. We first demonstrate system stability, spatial specificity, and spectral sensitivity through controlled phantom studies. We then validate VitoCheck in clinical cohorts by demonstrating accurate EIT-based predictions of standard diagnostic metrics, including spirometry-derived forced expiratory volumes, echocardiography-derived ejection fraction, ultrasound-derived liver fat scores, and blood serum-derived kidney filtration. User feedback further highlights the rapid scan workflow that supports deployment by non-specialists in decentralized environments. By combining portable and easy-to-use hardware with quantitative organ health analytics, VitoCheck enables scalable screening and proactive disease management for use in remote and out-of-clinic care.","rel_num_authors":11,"rel_authors":[{"author_name":"James H.W. Li","author_inst":"Hong Kong University of Science and Technology"},{"author_name":"Bradley J. Edelman","author_inst":"The University of Hong Kong"},{"author_name":"Wang C. Kwok","author_inst":"The University of Hong Kong"},{"author_name":"Michael Lawson","author_inst":"Hong Kong Centre for Cerebro-Cardiovascular Health Engineering"},{"author_name":"Ravi Bahukhandi","author_inst":"The University of Hong Kong"},{"author_name":"Henry Lui","author_inst":"Hong Kong Centre for Cerebro-Cardiovascular Health Engineering"},{"author_name":"Iris Yuwen Zhou","author_inst":"Harvard Medical School"},{"author_name":"Kevin C. Chan","author_inst":"Stanford University School of Medicine"},{"author_name":"Kai Hang Yiu","author_inst":"The University of Hong Kong"},{"author_name":"Erik Fung","author_inst":"Hong Kong University of Science and Technology"},{"author_name":"Russell W. Chan","author_inst":"The University of Hong Kong"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Treponema pallidum subsp. pallidum genetic population structure and relevance to syphilis prevention and treatment","rel_doi":"10.64898\/2026.04.27.26351061","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351061","rel_abs":"Syphilis, caused by the spirochete Treponema pallidum subsp. Pallidum (TPA), is rapidly resurging globally, particularly in low- and middle-income countries where the burden is increasingly concentrated. However, TPA genomic diversity and population structure in these settings remain poorly characterized. We investigated the global genetic diversity of syphilis spirochetes, sequencing 298 new TPA genomes from 11 countries across five continents, including underrepresented areas such as Argentina, Colombia, Malawi, Sri Lanka, and Vietnam. Combined with 1,409 public genomes, our dataset comprised 1,707 genomes. Hierarchical clustering identified six Nichols and five SS14-lineage subpopulations, with distinct subpopulations concentrated in Africa, East Asia, and the Americas, as well as previously unrecognized diversification within the globally dominant SS14 lineage. Concordance analysis showed that widely used multilocus sequencing typing methods recapitulate major Nichols-lineage subpopulations but have reduced discriminatory power for the SS14 lineage. Genome-wide Fixation index scans and targeted analyses of genes encoding outer membrane proteins prioritized for vaccine development demonstrated lineage- and subpopulation-specific patterns of genetic structure and selection. We observed strong diversifying selection acting on cell envelope assembly factors (BamA, LptD), selected FadL-like transporters, members of the T. pallidum repeat (Tpr) family, and efflux-associated outer membrane factors, alongside strictly conserved {beta}-barrel scaffolds. Macrolide resistance and reduced beta-lactam susceptibility marker prevalence varied by lineage and geographical region. These findings refine our understanding of TPA genetic diversity, delineate heterogeneous evolutionary trajectories across key vaccine-relevant loci, and underscore the importance of geographically representative genomic analyses to inform syphilis vaccine design and antimicrobial resistance monitoring.","rel_num_authors":49,"rel_authors":[{"author_name":"Farhang Aghakhanian","author_inst":"Institute for Global Health and Infectious Diseases , University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA"},{"author_name":"Nicole A.P. Lieberman","author_inst":"Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, Washington, USA"},{"author_name":"Christopher M. Hennelly","author_inst":"Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA"},{"author_name":"Jane S. Chen","author_inst":"Institute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA"},{"author_name":"Everton B. Bettin","author_inst":"Department of Medicine, UConn Health, Farmington, Connecticut, USA"},{"author_name":"B. Ethan Nunley","author_inst":"Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, Washington, USA"},{"author_name":"Wentao Chen","author_inst":"Dermatology Hospital, Southern Medical University, Guangdong Provincial Center for Skin Diseases and STD Control, Guangzhou, China"},{"author_name":"Ligang Yang","author_inst":"Dermatology Hospital, Southern Medical University, Guangdong Provincial Center for Skin Diseases and STD Control, Guangzhou, China"},{"author_name":"Mitch Matoga","author_inst":"Reproductive and Sexual Health Clinic, University of North Carolina Project Malawi, Lilongwe, Malawi"},{"author_name":"Irving F. Hoffman","author_inst":"1- Institute for Global Health and Infectious Diseases , University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 2- Reproductive and Sexua"},{"author_name":"Jaime M. Altcheh","author_inst":"1- Servicio Parasitologia- Chagas, Hospital de Ninos Ricardo Gutierrez, Capital Federal, Buenos Aires, Argentina 2- Instituto Multidisciplinario de Investigacio"},{"author_name":"Luciana N. Garcia","author_inst":"1- Servicio Parasitologia- Chagas, Hospital de Ninos Ricardo Gutierrez, Capital Federal, Buenos Aires, Argentina 2- Instituto Multidisciplinario de Investigacio"},{"author_name":"Andres Rabinovich","author_inst":"1- Servicio Parasitologia-Chagas, Hospital de Ninos Ricardo Gutierrez, Buenos Aires, Argentina 2- Instituto Multidisciplinario de Investigaciones en Patologias "},{"author_name":"Patricia Fernandez Pardal","author_inst":"Division de Dermatologia, Hospital Muniz, Buenos Aires, Argentina"},{"author_name":"Viviana Leiro","author_inst":"Division de Dermatologia, Hospital Muniz, Buenos Aires, Argentina"},{"author_name":"Ariyaratne K.A. Manathunge","author_inst":"National STD\/AIDS Control Programme, Colombo, Sri Lanka"},{"author_name":"Jayanthi P. Elwitigala","author_inst":"National STD\/AIDS Control Programme, Colombo, Sri Lanka"},{"author_name":"Sheila A. Lukehart","author_inst":"Department of Medicine, Division of Allergy and Infectious Diseases, Department of Global Health, University of Washington, Seattle, Washington, USA"},{"author_name":"Edward W. Hook III","author_inst":"Department of Medicine, University of Alabama, Birmingham, Birmingham, Alabama, USA"},{"author_name":"Mauro Romero Leal Passos","author_inst":"Department of Microbiology and Parasitology, Biomedical Institute, Fluminense Federal University, Niteroi, RJ, Brazil"},{"author_name":"Wilma Nancy Campos Arze","author_inst":"Department of Microbiology and Parasitology, Biomedical Institute, Fluminense Federal University, Niteroi, RJ, Brazil"},{"author_name":"Hugo Boechat Andrade","author_inst":"1- Department of Microbiology and Parasitology, Biomedical Institute, Fluminense Federal University, Niteroi, RJ, Brazil 2- Hospital Center, Evandro Chagas Nati"},{"author_name":"Fiona Mulcahy","author_inst":"Department of Genito Urinary Medicine and Infectious Diseases, St James Hospital, Dublin, Ireland"},{"author_name":"Qian-Qiu Wang","author_inst":"1- Institute of Dermatology, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China  2- National Center for STD Control, China Center"},{"author_name":"Rui-Li Zhang","author_inst":"Department of Dermatology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China"},{"author_name":"Cai-Xia Kou","author_inst":"Institute of Dermatology, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China,  National Center for STD Control, China Centers for"},{"author_name":"Silver K. Vargas","author_inst":"Center for Interdisciplinary Studies in Sexuality, AIDS and Society, Universidad Peruana Cayetano Heredia, Av. Honorio Delgado 430, 4314 Lima, Peru"},{"author_name":"Kelika A. Konda","author_inst":"1- Center for Interdisciplinary Studies in Sexuality, AIDS and Society, Universidad Peruana Cayetano Heredia, Av. Honorio Delgado 430, 4314 Lima, Peru 2- Depart"},{"author_name":"Michael Reyes Diaz","author_inst":"Center for Interdisciplinary Studies in Sexuality, AIDS and Society, Universidad Peruana Cayetano Heredia, Av. Honorio Delgado 430, 4314 Lima, Peru"},{"author_name":"Tran Veit Ha","author_inst":"Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA"},{"author_name":"Le Huu Doanh","author_inst":"Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Shu-Ichi Nakayama","author_inst":"Department of Bacteriology, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan"},{"author_name":"Yuki Ohama","author_inst":"Department of Bacteriology, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan"},{"author_name":"Makoto Ohnishi","author_inst":"Department of Bacteriology, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan"},{"author_name":"Bin Yang","author_inst":"Dermatology Hospital, Southern Medical University, Guangdong Provincial Center for Skin Diseases and STD Control, Guangzhou, China"},{"author_name":"David \u0160majs","author_inst":"Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czechia"},{"author_name":"Petra Posp\u00ed\u0161ilov\u00e1","author_inst":"Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czechia"},{"author_name":"Melissa J. Caimano","author_inst":"1- UConn Health, Farmington, Connecticut, USA 2- Connecticut Children's, Hartford, Connecticut, USA"},{"author_name":"Jonathan J. Juliano","author_inst":"1- Institute for Global Health and Infectious Diseases , University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA 2- Department of Epidemio"},{"author_name":"M. Anthony Moody","author_inst":"1- Department of Pediatrics, Division of Infectious Diseases, Duke University Medical Center, Durham, North Carolina, USA 2- Department of Integrative Immunolog"},{"author_name":"Juan C. Salazar","author_inst":"1- Departments of Pediatrics and Immunology , UConn Health, Farmington, Connecticut, USA 2- Connecticut Children's, Hartford, Connecticut, USA"},{"author_name":"Justin D. Radolf","author_inst":"1- Departments of Medicine, Pediatrics, Molecular Biology and Biophysics, Genetics and Genome Sciences, and Immunology, UConn Health, Farmington, Connecticut, U"},{"author_name":"Patricia Nadal-Bar\u00f3n","author_inst":"1- Department of Microbiology and Parasitology, Clinical Laboratories, Vall d Hebron University Hospital, Barcelona, Spain 2- Department of Genetics and Microbi"},{"author_name":"Linda H. Xu","author_inst":"Department of Medicine, DIvision of Allergy and infectious Diseases, University of Washington , Seattle, Washington, USA"},{"author_name":"Lorenzo Giacani","author_inst":"1- Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, Washington, USA 2- Department of Global Health, Unive"},{"author_name":"Kelly L. Hawley","author_inst":"1- Departments of Pediatrics and Medicine, UConn Health, Farmington, Connecticut, USA 2- Connecticut Children's, Hartford, Connecticut, USA"},{"author_name":"Alexander L. Greninger","author_inst":"1- Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, Washington, USA 2- Vaccine and Infectious Disease Division"},{"author_name":"Arlene C. Se\u00f1a","author_inst":"Institute for Global Health and Infectious Diseases , University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA"},{"author_name":"Jonathan B. Parr","author_inst":"Institute for Global Health and Infectious Diseases , University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"In-Hospital Mortality in Chagas vs Non-Chagas Heart Failure: A Nationwide Real-World Analysis From the Brazilian Public Health System","rel_doi":"10.64898\/2026.04.26.26351771","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.26.26351771","rel_abs":"Background: Chagas cardiomyopathy remains a major cause of heart failure (HF) in endemic regions and is increasingly recognized globally, yet data on in-hospital outcomes are limited. Objective: To assess whether Chagas disease is associated with higher in-hospital mortality among patients hospitalized with HF. Methods: We analyzed a nationwide administrative database from the Brazilian Unified Health System (DATASUS\/SIHSUS), including adults hospitalized with HF between April 2017 and August 2021. HF was identified using ICD10 code I50.x and Chagas disease using B57.x. The primary outcome was in hospital mortality, evaluated using multivariable Cox models. Results: Among 910,128 HF hospitalizations, 1,082 (0.12%) were associated with Chagas disease. Patients with Chagas were younger but had a more complex clinical profile and higher resource use. In-hospital mortality was higher in the Chagas group (25% vs 12%; p<0.001). After adjustment, Chagas disease remained independently associated with mortality (HR 1.54; 95% CI 1.35, 1.75; p<0.001). Conclusions: In this large real world cohort, Chagas disease was associated with higher in-hospital mortality and greater healthcare utilization. These findings reinforce the high risk nature of Chagas cardiomyopathy and point to the need for more targeted treatment strategies.","rel_num_authors":10,"rel_authors":[{"author_name":"Camila Nicolela Geraldo Martins","author_inst":"University of Campinas - Unicamp"},{"author_name":"Adriana Aparecida Bau","author_inst":"University of Campinas - UNICAMP"},{"author_name":"Guilherme Cordeiro","author_inst":"Nodian"},{"author_name":"Jose R Matos-Souza","author_inst":"University of Campinas - UNICAMP"},{"author_name":"Wilson Nadruz Jr.","author_inst":"University of Campinas - UNICAMP"},{"author_name":"Andrei C Sposito","author_inst":"Univerisity of Campinas - UNICAMP"},{"author_name":"Ahmad Masri","author_inst":"Oregon Health and Science University"},{"author_name":"Carlos Eduardo Rochitte","author_inst":"Heart Institute (InCor), University of Sao Paulo Medical School"},{"author_name":"Michael Jerosch-Herold","author_inst":"Brigham and Women's Hospital, Harvard Medical School"},{"author_name":"Otavio Rizzi Coelho-Filho","author_inst":"University of Campinas - Unicamp"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Real-World Dose Modifications for FOLFIRINOX in Pancreatic Cancer: Evaluating the Feasibility of a Machine-Learning Framework","rel_doi":"10.64898\/2026.04.27.26350002","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26350002","rel_abs":"Background: FOLFIRINOX is a cornerstone regimen for eligible patients with pancreatic ductal adenocarcinoma (PDAC), but its clinical benefit is limited by substantial toxicity and frequent dose modification. In real-world practice, dose modifications are often individualized, and the clinical factors associated with these decisions remain incompletely characterized. Objective: To develop and evaluate an electronic medical record (EMR)-based machine-learning framework for modeling cycle-specific FOLFIRINOX dose modification decisions in patients with PDAC. Methods: We included patients with PDAC who received FOLFIRINOX at UCSF oncology clinics between November 2011 and December 2023. Predictors included demographic, clinical, laboratory, and treatment variables derived from the EMR. Logistic regression, random forest, and XGBoost models were trained using group-based 5-fold cross-validation to predict cycle-specific dose modifications for 5-fluorouracil, irinotecan, and oxaliplatin. Model performance was evaluated using area under the receiver operating characteristic curve. Results: The cohort included 514 patients receiving FOLFIRINOX across 5,041 treatment cycles. The mean age was 59 years, 60% of patients were White, 41% had a history of smoking, and patients received a median of 6 chemotherapy cycles. More than 60% of patients required at least one dose modification during treatment. XGBoost demonstrated the highest performance across component drugs, with AUCs ranging from 0.53 to 0.70. Clinically plausible predictors of irinotecan and oxaliplatin dose modification included hepatic and renal function markers, cumulative drug exposure, treatment-related symptoms, and demographic or behavioral characteristics. Conclusion: We developed an EMR-based machine-learning framework to model real-world FOLFIRINOX dose modification and identified clinically plausible, routinely available predictors, particularly for irinotecan and oxaliplatin. Variable model performance suggests that dosing decisions are only partially captured by structured EMR data, highlighting both the limitations of current data-driven approaches and clinical domains where ML-based models may support individualized dosing and toxicity surveillance. Future informatics efforts should incorporate dose-modification rationale, patient-reported and functional outcomes, and validation across diverse practice settings.","rel_num_authors":4,"rel_authors":[{"author_name":"Akanksha Dua","author_inst":"University of California - San Francisco"},{"author_name":"Ziad Obermeyer","author_inst":"University of California - Berkeley"},{"author_name":"Atul J Butte","author_inst":"University of California, San Francisco"},{"author_name":"Travis Zack","author_inst":"University of California, San Francisco"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Real-World Dose Modifications for FOLFIRINOX in Pancreatic Cancer: Evaluating the Feasibility of a Machine-Learning Framework","rel_doi":"10.64898\/2026.04.27.26350002","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26350002","rel_abs":"Background: FOLFIRINOX is a cornerstone regimen for eligible patients with pancreatic ductal adenocarcinoma (PDAC), but its clinical benefit is limited by substantial toxicity and frequent dose modification. In real-world practice, dose modifications are often individualized, and the clinical factors associated with these decisions remain incompletely characterized. Objective: To develop and evaluate an electronic medical record (EMR)-based machine-learning framework for modeling cycle-specific FOLFIRINOX dose modification decisions in patients with PDAC. Methods: We included patients with PDAC who received FOLFIRINOX at UCSF oncology clinics between November 2011 and December 2023. Predictors included demographic, clinical, laboratory, and treatment variables derived from the EMR. Logistic regression, random forest, and XGBoost models were trained using group-based 5-fold cross-validation to predict cycle-specific dose modifications for 5-fluorouracil, irinotecan, and oxaliplatin. Model performance was evaluated using area under the receiver operating characteristic curve. Results: The cohort included 514 patients receiving FOLFIRINOX across 5,041 treatment cycles. The mean age was 59 years, 60% of patients were White, 41% had a history of smoking, and patients received a median of 6 chemotherapy cycles. More than 60% of patients required at least one dose modification during treatment. XGBoost demonstrated the highest performance across component drugs, with AUCs ranging from 0.53 to 0.70. Clinically plausible predictors of irinotecan and oxaliplatin dose modification included hepatic and renal function markers, cumulative drug exposure, treatment-related symptoms, and demographic or behavioral characteristics. Conclusion: We developed an EMR-based machine-learning framework to model real-world FOLFIRINOX dose modification and identified clinically plausible, routinely available predictors, particularly for irinotecan and oxaliplatin. Variable model performance suggests that dosing decisions are only partially captured by structured EMR data, highlighting both the limitations of current data-driven approaches and clinical domains where ML-based models may support individualized dosing and toxicity surveillance. Future informatics efforts should incorporate dose-modification rationale, patient-reported and functional outcomes, and validation across diverse practice settings.","rel_num_authors":4,"rel_authors":[{"author_name":"Akanksha Dua","author_inst":"University of California - San Francisco"},{"author_name":"Ziad Obermeyer","author_inst":"University of California - Berkeley"},{"author_name":"Atul J Butte","author_inst":"University of California, San Francisco"},{"author_name":"Travis Zack","author_inst":"University of California, San Francisco"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"The Carrier Delivery-Assist Catheter in Stroke Thrombectomy","rel_doi":"10.64898\/2026.04.27.26351898","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351898","rel_abs":"Introduction Delivering large-bore aspiration catheters through tortuous anatomy remains challenging during mechanical thrombectomy (MT). The Carrier delivery-assist catheter (DAC) was designed to facilitate aspiration catheter navigation, but multicenter data remain limited. We evaluated the efficiency and safety of the Carrier DAC. Methods We performed a multicenter retrospective study of prospectively collected data from patients undergoing MT at 15 U.S. Comprehensive Stroke Centers (September 2024?September 2025). Co-primary endpoints were puncture-to-clot engagement time and first-pass effect (FPE; eTICI 2c?3). A pre-specified single-center analysis compared upfront contact aspiration using the Carrier DAC versus standard 0.021? microcatheter techniques with identical aspiration catheter sizes. Results The multicenter cohort included 211 Carrier-assisted MTs. Median aspiration catheter inner diameter was 0.071?, with super-bore catheters used in 5.7%. Median puncture-to-clot time was 12 minutes, and FPE was achieved in 50.7%. Median puncture-to-reperfusion time was 20 minutes, and mFPE occurred in 74.4%. Parenchymal hematoma and subarachnoid hemorrhage occurred in 11.8% and 6.6%, respectively. Cavernous tortuosity did not affect primary endpoints. The single-center analysis included 242 patients. Carrier use was associated with shorter puncture-to-clot times and numerically higher FPE rates without increased hemorrhagic complications. Conclusions The Carrier DAC enables efficient navigation of large-bore aspiration catheters and may reduce procedural time while maintaining procedural safety. Prospective studies are warranted.","rel_num_authors":29,"rel_authors":[{"author_name":"Jaydevsinh Dolia","author_inst":"Emory University School of Medicine \/ Grady Memorial Hospital Marcus Stroke and Neuroscience Center"},{"author_name":"Theja Yelam","author_inst":"Emory University School of Medicine \/ Grady Memorial Hospital Marcus Stroke and Neuroscience Center"},{"author_name":"Jonathan A Grossberg","author_inst":"Emory University"},{"author_name":"Savio Batista dos Reis","author_inst":"Emory University School of Medicine \/ Grady Memorial Hospital Marcus Stroke and Neuroscience Center"},{"author_name":"Aqueel H Pabaney","author_inst":"Emory University"},{"author_name":"Mithilesh Siddu","author_inst":"Grady Memorial Hospital\/Emory University"},{"author_name":"Daniel Vela-Duarte","author_inst":"Palm Beach Neuroscience Institute - St. Mary?s Medical Center"},{"author_name":"Brian T Jankowitz","author_inst":"Hackensack Meridian JFK University Medical Center Neuroscience Institute"},{"author_name":"Omar Tanweer","author_inst":"Baylor College of Medicine"},{"author_name":"Jordan Xu","author_inst":"Boston Children's Hospital"},{"author_name":"Hugo H. Cuellar-Saenz","author_inst":"Louisiana State University Health Sciences Center"},{"author_name":"Rahul Shah","author_inst":"LSU Health Shreveport Department of Neurology"},{"author_name":"Isaac Josh Abecassis","author_inst":"The University of Louisville"},{"author_name":"Dale Ding","author_inst":"University of Louisville School of Medicine"},{"author_name":"Tapan Mehta","author_inst":"Ayer Neuroscience Institute, Hartford Healthcare"},{"author_name":"Sunil A. Sheth","author_inst":"The University of Texas Health Science Center at Houston"},{"author_name":"Joseph Naji Samaha","author_inst":"McGovern Medical School at The University of Texas Health Science Center at Houston"},{"author_name":"Sami Al Kasab","author_inst":"Medical University of south Carolina"},{"author_name":"Kevin A Shah","author_inst":"Zucker School of Medicine at Hofstra\/Northwell"},{"author_name":"Michael T. Froehler","author_inst":"Vanderbilt University"},{"author_name":"Amaan Ali","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Ameer E. Hassan","author_inst":"Valley Baptist Medical Center"},{"author_name":"Samantha Miller","author_inst":"Valley Baptist Medical Center"},{"author_name":"Jeffrey Miller","author_inst":"Cleveland Clinic Florida"},{"author_name":"Tareq Kass-Hout","author_inst":"The University of Chicago Urban Education Institute"},{"author_name":"Rami Z. Morsi","author_inst":"The University of Chicago"},{"author_name":"Kaustubh Limaye","author_inst":"Indiana University School of Medicine"},{"author_name":"Pedro N Martins","author_inst":"Emory University"},{"author_name":"Diogo C Haussen","author_inst":"Emory University School of Medicine \/ Grady Memorial Hospital Marcus Stroke and Neuroscience Center"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"The Carrier Delivery-Assist Catheter in Stroke Thrombectomy","rel_doi":"10.64898\/2026.04.27.26351898","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351898","rel_abs":"Introduction Delivering large-bore aspiration catheters through tortuous anatomy remains challenging during mechanical thrombectomy (MT). The Carrier delivery-assist catheter (DAC) was designed to facilitate aspiration catheter navigation, but multicenter data remain limited. We evaluated the efficiency and safety of the Carrier DAC. Methods We performed a multicenter retrospective study of prospectively collected data from patients undergoing MT at 15 U.S. Comprehensive Stroke Centers (September 2024?September 2025). Co-primary endpoints were puncture-to-clot engagement time and first-pass effect (FPE; eTICI 2c?3). A pre-specified single-center analysis compared upfront contact aspiration using the Carrier DAC versus standard 0.021? microcatheter techniques with identical aspiration catheter sizes. Results The multicenter cohort included 211 Carrier-assisted MTs. Median aspiration catheter inner diameter was 0.071?, with super-bore catheters used in 5.7%. Median puncture-to-clot time was 12 minutes, and FPE was achieved in 50.7%. Median puncture-to-reperfusion time was 20 minutes, and mFPE occurred in 74.4%. Parenchymal hematoma and subarachnoid hemorrhage occurred in 11.8% and 6.6%, respectively. Cavernous tortuosity did not affect primary endpoints. The single-center analysis included 242 patients. Carrier use was associated with shorter puncture-to-clot times and numerically higher FPE rates without increased hemorrhagic complications. Conclusions The Carrier DAC enables efficient navigation of large-bore aspiration catheters and may reduce procedural time while maintaining procedural safety. Prospective studies are warranted.","rel_num_authors":29,"rel_authors":[{"author_name":"Jaydevsinh Dolia","author_inst":"Emory University School of Medicine \/ Grady Memorial Hospital Marcus Stroke and Neuroscience Center"},{"author_name":"Theja Yelam","author_inst":"Emory University School of Medicine \/ Grady Memorial Hospital Marcus Stroke and Neuroscience Center"},{"author_name":"Jonathan A Grossberg","author_inst":"Emory University"},{"author_name":"Savio Batista dos Reis","author_inst":"Emory University School of Medicine \/ Grady Memorial Hospital Marcus Stroke and Neuroscience Center"},{"author_name":"Aqueel H Pabaney","author_inst":"Emory University"},{"author_name":"Mithilesh Siddu","author_inst":"Grady Memorial Hospital\/Emory University"},{"author_name":"Daniel Vela-Duarte","author_inst":"Palm Beach Neuroscience Institute - St. Mary?s Medical Center"},{"author_name":"Brian T Jankowitz","author_inst":"Hackensack Meridian JFK University Medical Center Neuroscience Institute"},{"author_name":"Omar Tanweer","author_inst":"Baylor College of Medicine"},{"author_name":"Jordan Xu","author_inst":"Boston Children's Hospital"},{"author_name":"Hugo H. Cuellar-Saenz","author_inst":"Louisiana State University Health Sciences Center"},{"author_name":"Rahul Shah","author_inst":"LSU Health Shreveport Department of Neurology"},{"author_name":"Isaac Josh Abecassis","author_inst":"The University of Louisville"},{"author_name":"Dale Ding","author_inst":"University of Louisville School of Medicine"},{"author_name":"Tapan Mehta","author_inst":"Ayer Neuroscience Institute, Hartford Healthcare"},{"author_name":"Sunil A. Sheth","author_inst":"The University of Texas Health Science Center at Houston"},{"author_name":"Joseph Naji Samaha","author_inst":"McGovern Medical School at The University of Texas Health Science Center at Houston"},{"author_name":"Sami Al Kasab","author_inst":"Medical University of south Carolina"},{"author_name":"Kevin A Shah","author_inst":"Zucker School of Medicine at Hofstra\/Northwell"},{"author_name":"Michael T. Froehler","author_inst":"Vanderbilt University"},{"author_name":"Amaan Ali","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Ameer E. Hassan","author_inst":"Valley Baptist Medical Center"},{"author_name":"Samantha Miller","author_inst":"Valley Baptist Medical Center"},{"author_name":"Jeffrey Miller","author_inst":"Cleveland Clinic Florida"},{"author_name":"Tareq Kass-Hout","author_inst":"The University of Chicago Urban Education Institute"},{"author_name":"Rami Z. Morsi","author_inst":"The University of Chicago"},{"author_name":"Kaustubh Limaye","author_inst":"Indiana University School of Medicine"},{"author_name":"Pedro N Martins","author_inst":"Emory University"},{"author_name":"Diogo C Haussen","author_inst":"Emory University School of Medicine \/ Grady Memorial Hospital Marcus Stroke and Neuroscience Center"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Sex-specific omics scores of sex hormones and associations with metabolic and sleep disorders","rel_doi":"10.64898\/2026.04.27.26351843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351843","rel_abs":"Introduction Sex hormones shape biological sex differences and alter the onset and severity of sleep and metabolic diseases in a sex-specific manner. To better understand relationships and underlying mechanisms, we develop summary proteomics and metabolomics scores for sex hormones and investigate their associations with sleep and metabolic disorders. Methods We used proteome- (n= 3680) and metabolome- wide (n= 1649) data from the baseline exam of the Multi-Ethnic Study of Atherosclerosis (MESA) cohort to develop female- and male-specific omics scores for sex hormones including total (Total T), bioavailable (Bio T), and free (Free T) testosterone, estradiol (E2) and sex hormone binding protein (SHBG). Each omics dataset was randomly split assigning 80% of participants to a training dataset and the remaining 20% to a test dataset. We applied linear regression with bootstrap standard errors, adjusting for age, BMI, self-reported race\/ ethnicity and study site, to identify sex hormone-associated proteins and metabolites (i.e FDR< .05). Lasso penalized regression was then used to select independent features, from which weighted protein (ProtS) and metabolite scores (MetS) were constructed as weighted sums, and examined in the validation dataset. Subsequently, we conducted sex-stratified association analysis of the validated omics scores using data from MESA baseline, exams 4 (proteomics) and 5 (proteomics, metabolomics) with sleep and metabolic phenotypes, timepoints where sex hormones were not measured. Results All constructed omics scores were significantly associated with their corresponding hormones in the test dataset. Higher omics scores of SHBG and lower omics scores of Free T were associated with lower diabetes risk in both sexes; and higher E2 scores with higher incident hypertension risk only in men. In males, Total T had protective diabetes associations, whereas in females they were linked to greater risk. Similarly, higher ProtS-Free T and lower ProtS-SHBG were associated with increased risk for OSA in both sexes. Finally, higher E2 scores were associated with higher risk of insomnia only in males. Conclusions Summary omics-based scores reveal sex-specific cross-sectional associations with sleep and incident metabolic disorders. These findings highlight the potential of these omics proxies to improve risk stratification and generate insights into mechanisms linking sex hormones with disease.","rel_num_authors":15,"rel_authors":[{"author_name":"Ziqing Wang","author_inst":"Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA"},{"author_name":"Tamar Sofer","author_inst":"Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA"},{"author_name":"Yu Zhang","author_inst":"Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA"},{"author_name":"Alexis C Wood","author_inst":"USDA\/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston TX"},{"author_name":"Mark D Benson","author_inst":"Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA"},{"author_name":"Kent D Taylor","author_inst":"The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Med"},{"author_name":"Xiuqing Guo","author_inst":"The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Med"},{"author_name":"Stephen S Rich","author_inst":"Department of Genome Sciences, University of Virginia, Charlottesville VA"},{"author_name":"Robert E Gerszten","author_inst":"Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA"},{"author_name":"Susan Redline","author_inst":"Division of Sleep Medicine, Brigham and Women's Hospital, Boston MA"},{"author_name":"Jerome I Rotter","author_inst":"The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Med"},{"author_name":"Peter Ganz","author_inst":"Department of Medicine, University of California, San Francisco, San Francisco, California"},{"author_name":"Peter Y Liu","author_inst":"The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Med"},{"author_name":"Rajat Deo","author_inst":"Division of Cardiovascular Medicine, Electrophysiology Section, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Ruth Dubin","author_inst":"Division of Nephrology, University of Texas Southwestern Medical Center, Dallas, TX, USA"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Adverse childhood family environment is associated with altered cardiovascular regulation during exercise among young adults in the Coronary Artery Risk Development in Young Adults (CARDIA) Study","rel_doi":"10.64898\/2026.04.24.26351696","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351696","rel_abs":"ABSTRACT: The purposes of this study were to determine whether adverse childhood family environment (ACFE) exposure is associated with altered hemodynamic responses to graded exercise in young adulthood, and whether this association was modified by sex and race in a large, population-based cohort. We hypothesized that ACFE exposure would be associated with an exaggerated exercise pressor response in young adulthood, independent of resting BP. We further hypothesized that the association between ACFE and the hemodynamic response to exercise would be stronger in females than males, and in Black versus White participants. Methods: Exercising blood pressure (BP) and heart rate (HR) responses were recorded during graded exercise testing and ACFE exposure was assessed among 3,417 young adults (mean age = 25 {+\/-} 4 y; 44% female; 46% Black). Linear mixed-effects models that included participant-specific random intercepts and random slopes were used to assess the relation between ACFE exposure and exercising systolic (SBP), diastolic, and mean BP, pulse pressure (PP), pulse pressure index (PPI), heart rate (HR), and rate pressure product (RPP). All models were adjusted for resting values of the hemodynamic outcome, as well as age, sex, race, study center, body mass index, current hypertension medication use, smoking status, and alcohol consumption. Results: Graded exercise hemodynamic responses were analyzed in 3,346-3,417 participants in the final models, providing 15,372-17,481 observations. Higher ACFE exposure was associated with lower SBP ({beta} = -0.304 mmHg\/ACFE, p = 0.033), HR ({beta} = -0.485 bpm\/ACFE, p<0.001), and RPP ({beta} = -83.404 bpm{middle dot}mmHg\/ACFE, p=0.002) at the lowest workload, but steeper workload-related increases in SBP (interaction {beta} = 0.044 mmHg{middle dot}MET-1{middle dot}ACFE-1, p=0.029), HR ({beta} = 0.061 bpm{middle dot}MET-1{middle dot}ACFE-1, p<0.001), RPP ({beta} = 10.16 bpm{middle dot}mmHg{middle dot}MET-1{middle dot}ACFE-1, p=0.025), and PP ({beta} = 0.052 mmHg{middle dot}MET-1{middle dot}ACFE-1, p=0.038) and PPI ({beta} = 0.000232 units{middle dot}MET-1{middle dot}ACFE-1, p=0.018). These findings were robust to additional adjustment for central adiposity, exercise capacity, and maximal heart rate and heart rate recovery. Conclusion: Our findings add nuanced evidence revealing that early adversity is associated with a demand-dependent shift in cardiovascular regulation, with attenuated responses at low demand, but more dramatic increases in pulsatile and myocardial load responses during progressive physiological stress.","rel_num_authors":7,"rel_authors":[{"author_name":"Nathaniel D. M. Jenkins","author_inst":"The University of Iowa"},{"author_name":"Austin T Robinson","author_inst":"Indiana University"},{"author_name":"Bjoern Hornikel","author_inst":"University of Alabama at Birmingham"},{"author_name":"Kara Marie Whitaker","author_inst":"The University of Iowa"},{"author_name":"David R Jacobs","author_inst":"University of Minnesota-Twin Cities"},{"author_name":"Kiarri N Kershaw","author_inst":"Feinberg School of Medicine, Northwestern University"},{"author_name":"Kelley Pettee Gabriel","author_inst":"The University of Alabama at Birmingham"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Estimated Impacts of Rotavirus Vaccine Recommendation Changes in the U.S.","rel_doi":"10.64898\/2026.04.27.26351857","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351857","rel_abs":"In January 2026, the United States Department of Health and Human Services downgraded the recommendation for infant immunization with rotavirus vaccine to one of shared clinical decision-making. We use a validated model for the transmission dynamics of rotavirus to predict the magnitude and timing of increases in the number of rotavirus hospitalizations in the US and in representative states given possible decreases in vaccine coverage. Rotavirus hospitalizations are likely to increase within two to three years following a drop in vaccine coverage, resulting in over 200,000 hospitalizations between July 2026-June 2031 if coverage were to drop to 20%. The burden is likely to fall disproportionately on southern states that currently experience higher rates of rotavirus hospitalization.","rel_num_authors":6,"rel_authors":[{"author_name":"Ernest Asare","author_inst":"Yale University School of Public Health"},{"author_name":"Jiye Kwon","author_inst":"Yale School of Public Health"},{"author_name":"Melanie H Chitwood","author_inst":"Yale School of Public Health"},{"author_name":"Stephanie Perniciaro","author_inst":"Yale School of Public Health"},{"author_name":"Gregg S. Gonsalves","author_inst":"Yale School of Public Health"},{"author_name":"Virginia E. Pitzer","author_inst":"Yale School of Public Health"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Challenges Facing Early-Career Physician-Scientists in the United States Amid Recent Policy Shifts: Findings from a National Survey","rel_doi":"10.64898\/2026.04.26.26351791","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.26.26351791","rel_abs":"Background: Amid persistent structural barriers and recent national policy changes, early-career physician-scientists face mounting challenges that threaten the sustainability of the biomedical research pipeline in the United States. Methods: We surveyed early career physician-scientists collecting demographic data, career development support, distribution of clinical and research responsibilities, funding, and perceived career challenges. The survey was distributed by email to the department chairs at 110 institutions in the United States. Results: A total of 175 surveys were completed. About half 50.8% (n=89) of respondents received a career development award, with 28.9% of respondents reporting limited institutional\/departmental support. The most reported challenges were balancing clinical, research, and educational responsibilities (72.5%, n=127); balancing work and family responsibilities (48%, n= 84); limited funding opportunities (48%, n=84); and under-compensation (34.3%, n=60). About 57.7% (n=101) of respondents had considered leaving academic medicine within the next two years, and 83.2% (n=139) indicated a >50% likelihood of doing so within five years. The most frequently cited reasons for attrition were funding challenges (72%, n=126), under-compensation (42.3%, n=74), feeling unhappy or stressed (40.6%, n=71), and burnout (37.7%, n=66). Furthermore, 43.9% (n=76) of respondents reported considering relocation outside the United States for better academic working conditions, and 10.4% (n=18) had already been contacted by institutions abroad. Conclusion: Early-career physician-scientists face substantial structural and financial challenges, with limited institutional support, high rates of burnout, and widespread intent to leave academia. These findings underscore an urgent need for sustained investment, targeted retention strategies, and policy reforms to stabilize and strengthen the physician-scientist workforce in the United States.","rel_num_authors":7,"rel_authors":[{"author_name":"Abdelrahman Abushouk","author_inst":"Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"author_name":"Aleksander Obradovic","author_inst":"Department of Medicine, Columbia University, New York, NY, USA"},{"author_name":"Alisha Faraz","author_inst":"Khan Lab school, Mountain View CA"},{"author_name":"Aisha Siebert","author_inst":"Department of Urology, Northwestern University, Chicago, IL, USA"},{"author_name":"Han Nuang Tun","author_inst":"Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire, USA; Department of Medical Science, Northeastern University, Bouve College of Clinical a"},{"author_name":"Evan Noch","author_inst":"Department of Neurology, UT Southwestern, Dallas, TX, USA"},{"author_name":"Jennifer MM Kwan","author_inst":"Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Challenges Facing Early-Career Physician-Scientists in the United States Amid Recent Policy Shifts: Findings from a National Survey","rel_doi":"10.64898\/2026.04.26.26351791","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.26.26351791","rel_abs":"Background: Amid persistent structural barriers and recent national policy changes, early-career physician-scientists face mounting challenges that threaten the sustainability of the biomedical research pipeline in the United States. Methods: We surveyed early career physician-scientists collecting demographic data, career development support, distribution of clinical and research responsibilities, funding, and perceived career challenges. The survey was distributed by email to the department chairs at 110 institutions in the United States. Results: A total of 175 surveys were completed. About half 50.8% (n=89) of respondents received a career development award, with 28.9% of respondents reporting limited institutional\/departmental support. The most reported challenges were balancing clinical, research, and educational responsibilities (72.5%, n=127); balancing work and family responsibilities (48%, n= 84); limited funding opportunities (48%, n=84); and under-compensation (34.3%, n=60). About 57.7% (n=101) of respondents had considered leaving academic medicine within the next two years, and 83.2% (n=139) indicated a >50% likelihood of doing so within five years. The most frequently cited reasons for attrition were funding challenges (72%, n=126), under-compensation (42.3%, n=74), feeling unhappy or stressed (40.6%, n=71), and burnout (37.7%, n=66). Furthermore, 43.9% (n=76) of respondents reported considering relocation outside the United States for better academic working conditions, and 10.4% (n=18) had already been contacted by institutions abroad. Conclusion: Early-career physician-scientists face substantial structural and financial challenges, with limited institutional support, high rates of burnout, and widespread intent to leave academia. These findings underscore an urgent need for sustained investment, targeted retention strategies, and policy reforms to stabilize and strengthen the physician-scientist workforce in the United States.","rel_num_authors":7,"rel_authors":[{"author_name":"Abdelrahman Abushouk","author_inst":"Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"author_name":"Aleksander Obradovic","author_inst":"Department of Medicine, Columbia University, New York, NY, USA"},{"author_name":"Alisha Faraz","author_inst":"Khan Lab school, Mountain View CA"},{"author_name":"Aisha Siebert","author_inst":"Department of Urology, Northwestern University, Chicago, IL, USA"},{"author_name":"Han Nuang Tun","author_inst":"Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire, USA; Department of Medical Science, Northeastern University, Bouve College of Clinical a"},{"author_name":"Evan Noch","author_inst":"Department of Neurology, UT Southwestern, Dallas, TX, USA"},{"author_name":"Jennifer MM Kwan","author_inst":"Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Challenges Facing Early-Career Physician-Scientists in the United States Amid Recent Policy Shifts: Findings from a National Survey","rel_doi":"10.64898\/2026.04.26.26351791","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.26.26351791","rel_abs":"Background: Amid persistent structural barriers and recent national policy changes, early-career physician-scientists face mounting challenges that threaten the sustainability of the biomedical research pipeline in the United States. Methods: We surveyed early career physician-scientists collecting demographic data, career development support, distribution of clinical and research responsibilities, funding, and perceived career challenges. The survey was distributed by email to the department chairs at 110 institutions in the United States. Results: A total of 175 surveys were completed. About half 50.8% (n=89) of respondents received a career development award, with 28.9% of respondents reporting limited institutional\/departmental support. The most reported challenges were balancing clinical, research, and educational responsibilities (72.5%, n=127); balancing work and family responsibilities (48%, n= 84); limited funding opportunities (48%, n=84); and under-compensation (34.3%, n=60). About 57.7% (n=101) of respondents had considered leaving academic medicine within the next two years, and 83.2% (n=139) indicated a >50% likelihood of doing so within five years. The most frequently cited reasons for attrition were funding challenges (72%, n=126), under-compensation (42.3%, n=74), feeling unhappy or stressed (40.6%, n=71), and burnout (37.7%, n=66). Furthermore, 43.9% (n=76) of respondents reported considering relocation outside the United States for better academic working conditions, and 10.4% (n=18) had already been contacted by institutions abroad. Conclusion: Early-career physician-scientists face substantial structural and financial challenges, with limited institutional support, high rates of burnout, and widespread intent to leave academia. These findings underscore an urgent need for sustained investment, targeted retention strategies, and policy reforms to stabilize and strengthen the physician-scientist workforce in the United States.","rel_num_authors":7,"rel_authors":[{"author_name":"Abdelrahman Abushouk","author_inst":"Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"author_name":"Aleksander Obradovic","author_inst":"Department of Medicine, Columbia University, New York, NY, USA"},{"author_name":"Alisha Faraz","author_inst":"Khan Lab school, Mountain View CA"},{"author_name":"Aisha Siebert","author_inst":"Department of Urology, Northwestern University, Chicago, IL, USA"},{"author_name":"Han Nuang Tun","author_inst":"Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire, USA; Department of Medical Science, Northeastern University, Bouve College of Clinical a"},{"author_name":"Evan Noch","author_inst":"Department of Neurology, UT Southwestern, Dallas, TX, USA"},{"author_name":"Jennifer MM Kwan","author_inst":"Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"One Size Fits All? Comparing Foundation and Task-specific Models for Retinal Fluid Segmentation","rel_doi":"10.64898\/2026.04.26.26351792","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.26.26351792","rel_abs":"Retinal fluids, detectable through optical coherence tomography (OCT), are key biomarkers for retinal diseases such as diabetic macular edema and age-related macular degeneration, guiding treatment decisions and monitoring response to therapy. Automated segmentation of retinal fluids could support large-scale clinical research and the development of clinical decision support tools. Recent ophthalmic foundation models trained on massive retinal imaging datasets show promise across many downstream tasks, including disease risk prediction and biomarker segmentation, but their performance relative to task-specific models for specialized clinical tasks remains unclear. We compared a task-specific segmentation model (RetiFluidNet) and an ophthalmic foundation model (VisionFM) using a standard benchmarking dataset containing 4,248 OCT images from 48 patients with three retinal diseases. Models were evaluated using three-fold cross-validation and assessed for pixel-level segmentation accuracy and patient-level fluid burden estimation. The task-specific model achieved higher segmentation performance and more consistent fluid quantification across devices. These findings suggest that, for retinal fluid segmentation, specialized task-specific models currently remain more reliable than general-purpose foundation models, highlighting the need for targeted adaptation before clinical deployment.","rel_num_authors":7,"rel_authors":[{"author_name":"Xiaoyu Sun","author_inst":"Washington University in St. Louis"},{"author_name":"Saiyu You","author_inst":"Washington University in St. Louis"},{"author_name":"Siqi Sun","author_inst":"Washington University in St. Louis"},{"author_name":"Cindy X. Cai","author_inst":"Wilmer Eye Institute, Johns Hopkins School of Medicine"},{"author_name":"Joanna Abraham","author_inst":"Washington University in St. Louis"},{"author_name":"Po-Yin Yen","author_inst":"Washington University in St. Louis"},{"author_name":"Linying Zhang","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"One Size Fits All? Comparing Foundation and Task-specific Models for Retinal Fluid Segmentation","rel_doi":"10.64898\/2026.04.26.26351792","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.26.26351792","rel_abs":"Retinal fluids, detectable through optical coherence tomography (OCT), are key biomarkers for retinal diseases such as diabetic macular edema and age-related macular degeneration, guiding treatment decisions and monitoring response to therapy. Automated segmentation of retinal fluids could support large-scale clinical research and the development of clinical decision support tools. Recent ophthalmic foundation models trained on massive retinal imaging datasets show promise across many downstream tasks, including disease risk prediction and biomarker segmentation, but their performance relative to task-specific models for specialized clinical tasks remains unclear. We compared a task-specific segmentation model (RetiFluidNet) and an ophthalmic foundation model (VisionFM) using a standard benchmarking dataset containing 4,248 OCT images from 48 patients with three retinal diseases. Models were evaluated using three-fold cross-validation and assessed for pixel-level segmentation accuracy and patient-level fluid burden estimation. The task-specific model achieved higher segmentation performance and more consistent fluid quantification across devices. These findings suggest that, for retinal fluid segmentation, specialized task-specific models currently remain more reliable than general-purpose foundation models, highlighting the need for targeted adaptation before clinical deployment.","rel_num_authors":7,"rel_authors":[{"author_name":"Xiaoyu Sun","author_inst":"Washington University in St. Louis"},{"author_name":"Saiyu You","author_inst":"Washington University in St. Louis"},{"author_name":"Siqi Sun","author_inst":"Washington University in St. Louis"},{"author_name":"Cindy X. Cai","author_inst":"Wilmer Eye Institute, Johns Hopkins School of Medicine"},{"author_name":"Joanna Abraham","author_inst":"Washington University in St. Louis"},{"author_name":"Po-Yin Yen","author_inst":"Washington University in St. Louis"},{"author_name":"Linying Zhang","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"SurvTEDVAE: A Disentangled Variational Autoencoder for Heterogeneous Treatment Effect Estimation with Time-to-Event Outcomes","rel_doi":"10.64898\/2026.04.26.26351790","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.26.26351790","rel_abs":"Estimating heterogeneous treatment effects (HTE) from observational health data is essential for precision medicine, yet existing methods often struggle with high-dimensional covariates and time-to-event outcomes common in electronic health records (EHRs). We propose SurvTEDVAE, a disentangled variational autoencoder designed for causal survival analysis. The model learns latent representations corresponding to instrumental factors, confounders, and outcome-dependent risk factors, and integrates a survival likelihood to model time-to-event outcomes with censoring. The learned representations are used to estimate conditional average treatment effects using downstream causal estimators. We evaluated SurvTEDVAE using a semi-synthetic ACTG dataset and a high-dimensional EHR-based hypertension cohort with over 20,000 covariates. Across both datasets, SurvTEDVAE achieved lower estimation error for heterogeneous treatment effects compared with meta-learning and causal survival forest approaches. These results demonstrate that disentangled representation learning can improve causal effect estimation for survival outcomes in high-dimensional real-world health data.","rel_num_authors":2,"rel_authors":[{"author_name":"William J.B. Powell","author_inst":"Washington University in St. Louis"},{"author_name":"Linying Zhang","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Impact of the Food4Moms Produce Prescription Program on Readiness for Healthy Eating, Fruit and Vegetable Intake, and Food Security","rel_doi":"10.64898\/2026.04.24.26351720","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.24.26351720","rel_abs":"Produce prescription programs (PRx) targeting different populations and conditions have been found to be effective. However, few have focused on pregnant women. The objectives of this study were to assess the impact of the Food4Moms (F4M) PRx on 1) healthy eating stages of change 2) intake of fresh produce, and 3) household food security among pregnant Latina women. F4M recruited low-income Latinas living in Greater Hartford, Connecticut that received a \"produce prescription\" from a Registered Dietitian based at the community-based organization (CBO) where the program was implemented. Participants were offered $100 per month for 10 months through Fresh Connect debit cards to purchase fresh produce from two food retailers or the equivalent value in fresh produce delivered at home. To be fully enrolled in F4M, participants had to complete a baseline survey and the first nutrition education interactive session. Enrolled participants were offered additional nutrition education sessions at the CBO and received text messages with nutrition tips as well as reminders to spend their remaining benefit balances towards the end of each month. A single-group pre-post study design was used to assess the impact of F4M 10 months after the card activation. No attrition bias was detected when comparing the characteristics of those completing (N=113) vs. those not completing the endline survey (N=41). Pre-post Wilcoxon signed-test or paired t-test analyses showed that F4M had a positive impact on healthy eating readiness (p < 0.001), the consumption of fruits (p < 0.001) and vegetables (p < 0.001), and household food security (p = 0.034). F4M is a promising community-engaged PRx program that may improve readiness for healthy eating, produce intake, and household food security. Implementation research is needed to find out how to effectively scale out and sustain programs like F4M. The study was registered in ClinicalTrials.gov (identifier: NCT05907616).","rel_num_authors":10,"rel_authors":[{"author_name":"Sofia Segura-P\u00e9rez","author_inst":"Hispanic Health Council"},{"author_name":"Katina Gionteris","author_inst":"Wholesome Wave"},{"author_name":"Amber Hromi-Fiedler","author_inst":"Yale University School of Public Health"},{"author_name":"Kathleen O'Connor Duffany","author_inst":"Yale University School of Public Health and Yale-Griffin CDC Prevention Research Center"},{"author_name":"Elizabeth Rhodes","author_inst":"Emory University Rollins School of Public Health"},{"author_name":"Sara Rodonis","author_inst":"Hispanic Health Council"},{"author_name":"Andrea Aleaga","author_inst":"Hispanic Health Council"},{"author_name":"Gilma Galdamez","author_inst":"Hispanic Health Council"},{"author_name":"Andrea Trist\u00e1n Urrutia","author_inst":"Drexel University"},{"author_name":"Rafael Perez-Escamilla","author_inst":"Yale University School of Public Health"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Lactoferrin and lysozyme for Kenyan children presenting with wasting and diarrhea: A 2 x 2 factorial randomized controlled trial","rel_doi":"10.64898\/2026.04.27.26351844","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351844","rel_abs":"Introduction: Lactoferrin and lysozyme are milk derived proteins with antimicrobial and anti-inflammatory properties. We tested if these supplements improved time-to-nutritional-recovery and reduced the incidence of new moderate or severe diarrhea (MSD) among children presenting to hospital with wasting and diarrhea. Methods: Medically-stable children aged 6-24 months with diarrhea and wasting were randomized to a 16-week course of lactoferrin, lysozyme, a combination of both, or placebo. Time-to-nutritional-recovery (mid-upper arm circumference [&ge;] 12.5cm) and incidence of new onset MSD were the primary outcomes observed over 6-months follow-up. Subgroup analyses included efficacy by wasting status (severe vs. moderate), stunting, age, inpatient\/outpatient, and adherence. Results: Among the 600 children randomized, 531 (88.5%) nutritionally recovered within 16-weeks; 63% among severely wasted children and 95% in children with moderate wasting. The wasting recovery rate in the combination arm was non significantly higher (HR: 1.23, 95%CI: 0.97, 1.57; p=0.083) than the placebo group. Children randomized to lactoferrin alone and lysozyme alone had nutritional recovery rates similar to placebo (HR: 0.94, 95% CI 0.74, 1.20; p=0.607 and HR: 0.91, 95% CI: 0.71, 1.17; p=0.462, respectively). Among severely wasted children, the combination arm had a higher recovery rate than placebo (HR: 2.76, 95% CI 1.49, 5.09; p=0.001), but not the individual lactoferrin (HR: 1.29, 95% CI 0.69, 2.41; p=0.427) and lysozyme (HR: 0.80, 95% CI: 0.40, 1.60; p=0.530) arms. Children randomized to intervention arms had comparable incidence of MSD (82.3-97.0 per 100 child-years) to the placebo arm (75.3 per 100 child-years). Conclusions: The combination of lactoferrin and lysozyme for 16 weeks modestly improved nutritional recovery time particularly among severely wasted children. If confirmed, there may be a role for enteric-targeted therapeutics as adjuvants to severe wasting management. Additional strategies are needed for the post-acute diarrhea recovery period.","rel_num_authors":23,"rel_authors":[{"author_name":"Kirky D Tickell","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Ruchi Tiwari","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Indi Trehan","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Joyce Otieno","author_inst":"Kenya Medical Research Institute, Nairobi, Kenya"},{"author_name":"Maureen Okello","author_inst":"Kenya Medical Research Institute, Nairobi, Kenya"},{"author_name":"Adeel Shah","author_inst":"Aga Khan University, Nairobi, Kenya"},{"author_name":"Lucia Keter","author_inst":"Kenya Medical Research Institute, Nairobi, Kenya"},{"author_name":"Emily Yoshioka","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Eric Ochola","author_inst":"Kenya Medical Research Institute, Nairobi, Kenya"},{"author_name":"Churchil Nyabinda","author_inst":"Kenya Medical Research Institute, Nairobi, Kenya"},{"author_name":"Doreen Rwigi","author_inst":"Kenya Medical Research Institute, Nairobi, Kenya"},{"author_name":"James M Njunge","author_inst":"8KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya"},{"author_name":"Eric Houpt","author_inst":"University of Virginia, Charlottesville, USA"},{"author_name":"James A Platts-Mills","author_inst":"University of Virginia, Charlottesville, USA"},{"author_name":"Jie Liu","author_inst":"Qingdao University, Qingdao, China"},{"author_name":"Barbra A Richardson","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Christine J McGrath","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Arianna R Means","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Mareme M Diakhate","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Grace J Stewart","author_inst":"University of Washington, Seattle, USA"},{"author_name":"Judd L Walson","author_inst":"Johns Hopkins University, Baltimore USA"},{"author_name":"Benson O Singa","author_inst":"Kenya Medical Research Institute, Nairobi, Kenya"},{"author_name":"Patricia B Pavlinac","author_inst":"University of Washington"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Reconciling neurocognitive and behavioral impulsivities through ecological assessment and multivariate modelling of cognitive control dynamics","rel_doi":"10.64898\/2026.04.27.26351677","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351677","rel_abs":"Impulsivity is a core dimension of ADHD and a transdiagnostic vulnerability factor for a wide range of adverse psychiatric and somatic outcomes, that could be mitigated through more effective screening of at-risk individuals. However, laboratory-based measures of impulsivity show weak convergence across paradigms and limited prediction of real-world behavior, constraining their utility. We tested whether combining repeated ecological assessment with computational modeling of response-time (RT) dynamics improves measurement of impulsivity and its cross-paradigm validity. Sixty participants, including adolescents with ADHD, individuals with 22q11.2 deletion syndrome, and healthy controls, completed a total of 1347 smartphone-based Balloon-Analogue-Risk-Task (D-BART) assessments repeatedly in daily life, alongside a single-session Conners CPT-3. RT was modeled using linear mixed-effects models as a function of objective risk and subjective uncertainty, with random effects capturing between- and within-person variability. Dynamic RT parameters were integrated with conventional performance metrics and related to CPT-3 variables using partial least squares analysis. External validity was evaluated against parent-rated behavioral symptoms. RT increased with both risk and uncertainty, consistent with adaptive modulation of speed-accuracy trade-offs. These effects varied substantially across individuals and repeated assessments. Dynamic RT parameters differentiated clinical from control participants, whereas traditional aggregate metrics did not. A PLS latent component linked D-BART and CPT-3 patterns and was associated with real-world hyperactivity\/impulsivity, whereas CPT-3-derived scores alone were not. Experimental manipulation of ecological sampling density directly impacted D-BART predictive accuracy. These findings show that ecological repetition combined with parsimonious RT-dynamics modeling enhances construct validity, cross-paradigm convergence, and behavioral relevance of impulsivity measures, providing a scalable framework for capturing dynamic cognitive-control processes.","rel_num_authors":7,"rel_authors":[{"author_name":"andrea imparato","author_inst":"Developmental Imaging and Psychopathology Laboratory, University of Geneva School of medicine, Geneva"},{"author_name":"Natacha Reich","author_inst":"Developmental Imaging and Psychopathology Laboratory, University of Geneva School of medicine, Geneva"},{"author_name":"Gregoire Riviere","author_inst":"Developmental Imaging and Psychopathology Laboratory, University of Geneva School of medicine, Geneva"},{"author_name":"Stephan Eliez","author_inst":"University of Geneva"},{"author_name":"Christopher Graser","author_inst":"Dana-Farber Cancer Institute, Harvard University, Boston, Massachusetts, United States of America"},{"author_name":"Maude Schneider","author_inst":"University of Geneva"},{"author_name":"Corrado Sandini","author_inst":"Developmental Imaging and Psychopathology Laboratory, University of Geneva School of medicine, Geneva"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"Co-expressed MicroRNAs Associated with An Elevated Psychometabolic Risk Phenotype in Women during Midlife","rel_doi":"10.64898\/2026.04.27.26351846","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351846","rel_abs":"Introduction The bidirectional relationship between depression and type 2 diabetes (T2D) is well-established. Women are disproportionately affected by their co-occurrence, particularly during midlife, yet sex- and age-specific studies on phenotypic and mechanistic factors underlying risk for their co-occurrence are limited. The purpose of this study was to identify combined risk profiles (i.e., depression, T2D) in women during midlife and to determine if microRNAs (miRs) that are associated with high-risk profiles provide mechanistic insights into multimorbidity. Materials and Methods This study included baseline data from women during midlife (ages 40-64 years) who participated in the Diabetes Prevention Program (DPP) (n = 603). Unsupervised k-means clustering was used to identify multimorbid risk profiles. Clinical characteristics included for risk profiling included Beck Depression Inventory (BDI-I), age, BMI, waist circumference, triglycerides, high HDL, FBG, and HbA1c. Associations between risk profiles and individual miRs and principal components of co-expressed miRs were determined via logistic regression models adjusted for participant race and ethnicity. False discovery rate (q< 0.05) was used to control for multiple comparisons. Results Two distinct profiles were identified, with the high-risk profile characterized by younger age yet higher adiposity, glycemic biomarkers, and depression symptom burden compared to the low-risk profile. MiR-320a and miR-320c were associated with increased odds of high-risk profile assignment, and a co-expression cluster enriched for miRs belonging to the miR-320 family (PC3) was significantly associated with increased odds of high-risk profile assignment. Across all models, Black race demonstrated at least threefold higher odds of high-risk profile assignment. Discussion These findings highlight distinct multimorbid risk profiles in women during midlife, emphasizing the potential utility of integrated, multidimensional approaches for risk stratification. Findings also revealed mechanisms that may underly risk for co-occurrence of T2D and depression in women during midlife and potential therapeutic targets for prevention and treatment.","rel_num_authors":7,"rel_authors":[{"author_name":"Kayla D. Longoria","author_inst":"The University of Texas Health Science Center at Houston"},{"author_name":"Benjamin Stroebel","author_inst":"UCSF"},{"author_name":"Meghana Gadgil","author_inst":"UCSF"},{"author_name":"Nicole Perez","author_inst":"New York University"},{"author_name":"Kimberly A. Lewis","author_inst":"UCLA Health"},{"author_name":"Sandra J. Weiss","author_inst":"UCSF"},{"author_name":"Elena Flowers","author_inst":"UC San Francisco"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"A Hybrid Framework for Accurate Melanoma Diagnosis: Leveraging Generative AI with Enhanced CNN+ Architectures","rel_doi":"10.64898\/2026.04.27.26351813","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.27.26351813","rel_abs":"Melanocytes become cancerous, forming tumors that may invade and destroy the surrounding tissues. When melanocytes acquire invasive characteristics, the anchored melanoma begins to damage the normal cells. Therefore, early intervention and diagnosis are essential to avoid high morbidity and mortality in malignant melanoma. However, It is challenging to distinguish the difference between malignant melanoma and benign clump of melanocytes. Based on a data set of 10,000 melanocyte tumors, this paper develops a new model system to improve the accuracy of distinguishing between benign and malignant melanocytes. In the first stage, the original CNN architectures are used, such as ResNet18, ResNet50, VGG11, and VGG16. Synthetic medical images, generated via a Diffusion Model to extract informative features from the original dataset, are used to train the CNN architectures. This approach improves classification accuracy from 91.1% to 92.9%. In the second stage, the fully connected layer of each neural network is replaced with a high-level classifier, XGBoost, to perform secondary clas- sification. This hybrid strategy further enhances performance, achieving up to 93.3% accuracy by using the synthetic images.","rel_num_authors":10,"rel_authors":[{"author_name":"Yiming Wu","author_inst":"Wenzhou-Kean University"},{"author_name":"Bojun Zhang","author_inst":"Wenzhou-Kean University"},{"author_name":"Yan Yan","author_inst":"Wenzhou-Kean University"},{"author_name":"Jiashu Li","author_inst":"Wenzhou-Kean University"},{"author_name":"Yue Wu","author_inst":"Wenzhou-Kean University"},{"author_name":"Sung Soo Kim","author_inst":"Wenzhou-Kean University"},{"author_name":"Kuan Huang","author_inst":"Kean University"},{"author_name":"Qiong Ye","author_inst":"Wenzhou-Kean University"},{"author_name":"Yingzhe Yu","author_inst":"First Affiliated Hospital of Ningbo University"},{"author_name":"Guanchao Tong","author_inst":"Wenzhou-Kean University"}],"rel_date":"2026-04-28","rel_site":"medrxiv"},{"rel_title":"SCIMETAR-seq tracks immunophenotype, demethylation, mutations, and transcriptomes in single cells undergoing HMA therapy","rel_doi":"10.64898\/2026.04.26.720516","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720516","rel_abs":"Myelodysplastic neoplasms (MDS) and related myeloid neoplasms such as chronic myelomonocytic leukaemia (CMML) are clonal haematopoietic stem cell disorders characterised by ineffective and dysplastic haematopoiesis. They are associated with peripheral cytopaenias, variable increases in immature blasts, and a risk of progression to acute myeloid leukaemia. Hypomethylating agents (HMA) can improve blood counts and reduce blasts, but responses are usually limited. Epigenetic rewiring of haematopoietic stem and progenitor cells (HSPC) by HMA enhances hematopoietic output but is influenced by clonal mosaicism, which requires tracking of response at the single cell level to achieve full understanding. We developed SCIMETAR-seq for single-cell interrogation of DNA methylation, target amplicons, and mRNA in FACS-indexed HSPC, then deployed SCIMETAR-seq on CD34+ HSPC from longitudinal HMA-treated patient BM in vitro and in vivo. HMA-induced LINE-1 (L1) demethylation was positively correlated with cell cycling; being lowest in quiescent HSC and highest in erythrocyte progenitors. Erythrocyte progenitor frequencies were particularly increased by HMA exposure. SRSF2 p.P95 genotype did not influence HMA-induced L1 demethylation but was enriched into cells with a CMP immunophenotype, which were transcriptionally biased away from MEP towards granulocytic progenitors. Despite a lack of L1 demethylation in quiescent HSC\/MPP after 7 days of HMA treatment in vivo, their transcriptomes were enriched for TNF-, TGF{beta}- and WNT-signaling, suggesting that extrinsic factors secreted by other BM cells in response to HMA mediates reprogramming of quiescent HSC during HMA therapy in vivo.","rel_num_authors":22,"rel_authors":[{"author_name":"Golam Sarower Bhuyan","author_inst":"School of Clinical Medicine, University of New South Wales, Sydney, Australia"},{"author_name":"Feng Yan","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Mary N.T. Nguyen","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Xiaoheng Zou","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Veena Gullapalli","author_inst":"School of Clinical Medicine, University of New South Wales, Sydney, Australia"},{"author_name":"Lachlin Vaughan","author_inst":"Westmead Hospital, Sydney, Australia"},{"author_name":"Olivia Stonehouse","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Henry R. Hampton","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Sylvie Shen","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Peter Truong","author_inst":"School of Clinical Medicine, University of New South Wales, Sydney, Australia"},{"author_name":"Ruchira Dissanayake","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Elaheh S. Ghodousi","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Swapna Joshi","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Forrest C. Koch","author_inst":"School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Hoi Man Chung","author_inst":"School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China"},{"author_name":"Fabio Zanini","author_inst":"School of Clinical Medicine, University of New South Wales, Sydney, Australia"},{"author_name":"Fatemeh Vafaee","author_inst":"School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Yuanhua Huang","author_inst":"School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China"},{"author_name":"Julie A.I. Thoms","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Omid Faridani","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Christopher J. Jolly","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"John E. Pimanda","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"SCIMETAR-seq tracks immunophenotype, demethylation, mutations, and transcriptomes in single cells undergoing HMA therapy","rel_doi":"10.64898\/2026.04.26.720516","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720516","rel_abs":"Myelodysplastic neoplasms (MDS) and related myeloid neoplasms such as chronic myelomonocytic leukaemia (CMML) are clonal haematopoietic stem cell disorders characterised by ineffective and dysplastic haematopoiesis. They are associated with peripheral cytopaenias, variable increases in immature blasts, and a risk of progression to acute myeloid leukaemia. Hypomethylating agents (HMA) can improve blood counts and reduce blasts, but responses are usually limited. Epigenetic rewiring of haematopoietic stem and progenitor cells (HSPC) by HMA enhances hematopoietic output but is influenced by clonal mosaicism, which requires tracking of response at the single cell level to achieve full understanding. We developed SCIMETAR-seq for single-cell interrogation of DNA methylation, target amplicons, and mRNA in FACS-indexed HSPC, then deployed SCIMETAR-seq on CD34+ HSPC from longitudinal HMA-treated patient BM in vitro and in vivo. HMA-induced LINE-1 (L1) demethylation was positively correlated with cell cycling; being lowest in quiescent HSC and highest in erythrocyte progenitors. Erythrocyte progenitor frequencies were particularly increased by HMA exposure. SRSF2 p.P95 genotype did not influence HMA-induced L1 demethylation but was enriched into cells with a CMP immunophenotype, which were transcriptionally biased away from MEP towards granulocytic progenitors. Despite a lack of L1 demethylation in quiescent HSC\/MPP after 7 days of HMA treatment in vivo, their transcriptomes were enriched for TNF-, TGF{beta}- and WNT-signaling, suggesting that extrinsic factors secreted by other BM cells in response to HMA mediates reprogramming of quiescent HSC during HMA therapy in vivo.","rel_num_authors":22,"rel_authors":[{"author_name":"Golam Sarower Bhuyan","author_inst":"School of Clinical Medicine, University of New South Wales, Sydney, Australia"},{"author_name":"Feng Yan","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Mary N.T. Nguyen","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Xiaoheng Zou","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Veena Gullapalli","author_inst":"School of Clinical Medicine, University of New South Wales, Sydney, Australia"},{"author_name":"Lachlin Vaughan","author_inst":"Westmead Hospital, Sydney, Australia"},{"author_name":"Olivia Stonehouse","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Henry R. Hampton","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Sylvie Shen","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Peter Truong","author_inst":"School of Clinical Medicine, University of New South Wales, Sydney, Australia"},{"author_name":"Ruchira Dissanayake","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Elaheh S. Ghodousi","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Swapna Joshi","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Forrest C. Koch","author_inst":"School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Hoi Man Chung","author_inst":"School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China"},{"author_name":"Fabio Zanini","author_inst":"School of Clinical Medicine, University of New South Wales, Sydney, Australia"},{"author_name":"Fatemeh Vafaee","author_inst":"School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Yuanhua Huang","author_inst":"School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China"},{"author_name":"Julie A.I. Thoms","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Omid Faridani","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"Christopher J. Jolly","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"},{"author_name":"John E. Pimanda","author_inst":"School of Biomedical Sciences, University of New South Wales, Sydney, Australia"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Insulin receptor substrate 2 (IRS2) confers resistance to PI3K pathway inhibition in PIK3CA mutant breast cancer","rel_doi":"10.64898\/2026.04.24.720709","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720709","rel_abs":"Activating mutations in PI3K are one of the most frequent mutations in breast cancer and are associated with worse patient outcomes in many breast cancer subtypes. Despite intense interest, cancer treatments that target the PI3K pathway have been only modestly effective due to intrinsic and acquired resistance mechanisms which reactivate PI3K signaling. Here, we characterize a feedback mechanism by which PI3K pathway inhibitors increase insulin receptor substrate 2 (IRS2) abundance and demonstrate the role of IRS2 in promoting resistance to these drugs. In PIK3CA mutant breast tumors and cell lines, there is a significant reduction in IRS2 mRNA and protein abundance which is reversed by PI3K pathway inhibition and mediated by the transcription factor FOXO3. PIK3CA mutations do not alter IRS1 expression. IRS2 confers resistance to PI3K pathway inhibition by sustaining PI3K signaling in PIK3CA mutant, but not wild-type breast cancer cells. Increased IRS2 abundance also correlates with PI3K pathway inhibitor resistance across PI3K mutant cancer cell lines from a variety of tissues. The clinical relevance of these findings is highlighted by the frequency of PI3K mutations in cancer and the identification of a new target to address the challenges associated with prior efforts to block the reactivation of PI3K signaling during PI3K inhibition.","rel_num_authors":9,"rel_authors":[{"author_name":"Michael W. Lero","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Jennifer S. Morgan","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Michael-Anthony Card","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Lihua Julie Zhu","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Junhui Li","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Rui Li","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Quyen Thu Bui","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Emma Mohlmann","author_inst":"University of Massachusetts Chan Medical School"},{"author_name":"Leslie M. Shaw","author_inst":"University of Massachusetts Chan Medical School"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Coenzyme A is a redox sensing cofactor for malic enzyme 2 regulating oxidative stress and mitochondrial metabolism","rel_doi":"10.64898\/2026.04.27.721221","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.27.721221","rel_abs":"Coenzyme A (CoA) is an essential cofactor required for numerous metabolic reactions, yet its ability to bind and regulate proteins remains poorly defined. Using a proteomic approach, we identified malic enzyme 2 (ME2) as a CoA-binding protein. ME2 uses NAD(P)+ to convert malate to pyruvate, generating NAD(P)H to support energy production and redox homeostasis. ME2 binds CoA at an allosteric site previously thought to bind NAD(P)+. Reduced CoA has minimal effect on ME2 activity, but the oxidized form, CoA disulfide, strongly activates ME2 by promoting ME2 tetramerization and a catalytically efficient closed conformation. Under oxidative stress, ME2 facilitates CoA disulfide formation, enhancing NADPH production and cellular defense against reactive oxygen species (ROS). Mice with ME2 mutation that cannot bind CoA show impaired muscle performance, elevated ROS, and mitochondrial dysfunction. These findings establish CoA as a redox-sensing cofactor, allowing cells to respond to ROS and promote mitochondrial metabolism, and expanding the function of this essential cofactor.","rel_num_authors":16,"rel_authors":[{"author_name":"Wenzhe Chen","author_inst":"The University of Chicago"},{"author_name":"Ornella D. Nelson","author_inst":"Cornell University"},{"author_name":"Xiaorong Li","author_inst":"China Agricultural University"},{"author_name":"Manfeng Zhang","author_inst":"China Agricultural University"},{"author_name":"Xuan Lu","author_inst":"The University of Chicago"},{"author_name":"Tao Yu","author_inst":"Cornell University"},{"author_name":"Cong-Hui Yao","author_inst":"The University of Chicago"},{"author_name":"Shuai Zhang","author_inst":"Cornell University"},{"author_name":"Yugang Zhang","author_inst":"Cornell University"},{"author_name":"Adam Francisco","author_inst":"Cornell University"},{"author_name":"Byunghyun Ahn","author_inst":"Cornell University"},{"author_name":"Em Lundberg","author_inst":"The University of Chicago"},{"author_name":"Minsu Song","author_inst":"The University of Chicago"},{"author_name":"Janane F. Rahbani","author_inst":"The University of Chicago"},{"author_name":"Zhongzhou Chen","author_inst":"China Agricultural University"},{"author_name":"Hening Lin","author_inst":"Howard Hughes Medical Institute, The University of Chicago"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Mechanisms of USP7\/MAGEL2 Complex Assembly and Its Mutational Disruption in Neurodevelopmental Diseases","rel_doi":"10.64898\/2026.04.24.720667","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720667","rel_abs":"The WASH complex regulates endosomal trafficking and is linked to several neurodevelopmental diseases, including Prader-Willi syndrome, Schaaf-Yang syndrome, and Hao-Fountain syndrome. Its function is tightly controlled by ubiquitination, maintained by the multi-subunit MUST complex containing both a ubiquitin ligase (MAGEL2\/TRIM27) and a deubiquitinase (USP7). However, the mechanism underlying the MUST complex assembly remains poorly understood. In this study, we investigate the assembly of USP7 and MAGEL2 components of the MUST complex using NMR spectroscopy, isothermal titration calorimetry, X-ray crystallography, and cellular assays. We show that the USP7\/MAGEL2 interaction is bipartite and multivalent. Two distinct domains of USP7, TRAF and UBL1-2, recognize two unstructured but evolutionarily conserved regions of MAGEL2, one of which contains multiple TRAF-binding sites. Furthermore, we determine the high-resolution crystal structure of the TRAF\/MAGEL2 complex and identify Hao-Fountain syndrome-linked mutations in USP7 that disrupt USP7\/MAGEL2 complex formation in vitro and in cells. These findings provide mechanistic insight into the pathogenic basis of Hao-Fountain syndrome and related Schaaf-Yang and Prader-Willi syndromes.","rel_num_authors":8,"rel_authors":[{"author_name":"Emilie J Korchak","author_inst":"UConn Health"},{"author_name":"Gabriela Soriano","author_inst":"UConn Health"},{"author_name":"Irina Semenova","author_inst":"UConn Health"},{"author_name":"Bing Hao","author_inst":"UConn Health"},{"author_name":"Denis Stepihar","author_inst":"Texas Tech University"},{"author_name":"Tara Bayat","author_inst":"Texas Tech University"},{"author_name":"Klementina Fon Tacer","author_inst":"Texas Tech University"},{"author_name":"Irina Bezsonova","author_inst":"UConn Health"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Superspreaders increase deleterious mutant burden and can accelerate the evolution of complex traits in pathogens","rel_doi":"10.64898\/2026.04.24.720758","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720758","rel_abs":"Degree heterogeneity in contact networks is known to accelerate the spread of infectious diseases through the presence of superspreaders, but its evolutionary consequences remain less understood. Here we study how network heterogeneity shapes the fate of competing pathogen strains in a stochastic susceptible infected susceptible framework. We show that heterogeneous networks act as strong suppressors of selection: both advantageous and disadvantageous mutants exhibit fixation probabilities close to neutral expectations, in stark contrast to well-mixed populations. We derive an analytical theory that captures this effect through a single suppression factor determined by network structure and infection dynamics, and validate it against simulations on synthetic and empirical contact networks. Mechanistically, suppression arises because most transmission events are effectively neutral, while selection acts only in rare configurations. As a consequence, heterogeneous networks substantially increase the persistence of deleterious mutants and elevate mutation selection balance, but they can either accelerate or decelerate multi-step evolutionary processes such as fitness valley crossing. Our results reveal a fundamental trade-off induced by superspreaders: while they enhance epidemic spread, they weaken selective pressures and thereby promote evolutionary diversification.","rel_num_authors":4,"rel_authors":[{"author_name":"Samrat S Mondal","author_inst":"University of California San Diego"},{"author_name":"Anastasia E. Madsen","author_inst":"University of California San Diego"},{"author_name":"Natalia L. Komarova","author_inst":"University of California San Diego"},{"author_name":"Dominik Wodarz","author_inst":"University of California San Diego"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Tipping points are typical in ecosystems with higher-order interactions","rel_doi":"10.64898\/2026.04.24.720639","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720639","rel_abs":"Whether species-rich communities erode gradually or collapse abruptly under environmental change is a central question in ecology. Classical pairwise theory predicts that coexistence is always lost gradually, through smooth declines to extinction, yet real ecological interactions are often strongly state-dependent -- shaped by nonlinearities that fixed pairwise coefficients cannot capture. Here we show that higher-order (nonlinear) interactions make abrupt, irreversible loss of coexistence a typical route to community collapse: across diverse random communities, the equilibrium supporting coexistence disappears suddenly at a fold bifurcation. Using polynomial homotopy continuation to track equilibria as environmental conditions change, we find that folds progressively dominate the boundary of the coexistence domain as nonlinearity strengthens, replacing the gradual extinctions of pairwise theory. Furthermore, the sign structure of higher-order interactions controls both the onset of tipping-points and whether biodiversity buffers or amplifies collapse. Because higher-order and nonlinear interactions are intimately linked, tipping points also arise generically in pairwise models with strong nonlinearity. Applying our continuation framework to a canonical model of plant-pollinator collapse, we formally resolve its bifurcation structure as fold-mediated, and we show that fold bifurcations are typical across published multispecies models spanning mutualistic, competitive, and consumer-resource interactions. These results challenge the expectation that monitoring abundances suffices to anticipate collapse, and unify structural-stability theory, which delineates the safe operating space for coexistence, with critical transition theory, which characterizes the nature of its boundaries.","rel_num_authors":6,"rel_authors":[{"author_name":"Pablo Lechon-Alonso","author_inst":"University of Chicago"},{"author_name":"Zachary R Miller","author_inst":"Yale Univeristy"},{"author_name":"Armun Liaghat","author_inst":"New York University"},{"author_name":"Paul Breiding","author_inst":"University of Osnabruck"},{"author_name":"Mercedes Pascual","author_inst":"New York University"},{"author_name":"Stefano Allesina","author_inst":"University of Chicago"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Tipping points are typical in ecosystems with higher-order interactions","rel_doi":"10.64898\/2026.04.24.720639","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720639","rel_abs":"Whether species-rich communities erode gradually or collapse abruptly under environmental change is a central question in ecology. Classical pairwise theory predicts that coexistence is always lost gradually, through smooth declines to extinction, yet real ecological interactions are often strongly state-dependent -- shaped by nonlinearities that fixed pairwise coefficients cannot capture. Here we show that higher-order (nonlinear) interactions make abrupt, irreversible loss of coexistence a typical route to community collapse: across diverse random communities, the equilibrium supporting coexistence disappears suddenly at a fold bifurcation. Using polynomial homotopy continuation to track equilibria as environmental conditions change, we find that folds progressively dominate the boundary of the coexistence domain as nonlinearity strengthens, replacing the gradual extinctions of pairwise theory. Furthermore, the sign structure of higher-order interactions controls both the onset of tipping-points and whether biodiversity buffers or amplifies collapse. Because higher-order and nonlinear interactions are intimately linked, tipping points also arise generically in pairwise models with strong nonlinearity. Applying our continuation framework to a canonical model of plant-pollinator collapse, we formally resolve its bifurcation structure as fold-mediated, and we show that fold bifurcations are typical across published multispecies models spanning mutualistic, competitive, and consumer-resource interactions. These results challenge the expectation that monitoring abundances suffices to anticipate collapse, and unify structural-stability theory, which delineates the safe operating space for coexistence, with critical transition theory, which characterizes the nature of its boundaries.","rel_num_authors":6,"rel_authors":[{"author_name":"Pablo Lechon-Alonso","author_inst":"University of Chicago"},{"author_name":"Zachary R Miller","author_inst":"Yale Univeristy"},{"author_name":"Armun Liaghat","author_inst":"New York University"},{"author_name":"Paul Breiding","author_inst":"University of Osnabruck"},{"author_name":"Mercedes Pascual","author_inst":"New York University"},{"author_name":"Stefano Allesina","author_inst":"University of Chicago"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Baltic Sea microbial cohorts exhibit catabolic specialization and anabolic interdependencies across environmental gradients","rel_doi":"10.64898\/2026.04.27.721049","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.27.721049","rel_abs":"Microbial communities are structured by environmental gradients and metabolic interactions, yet the genomic characteristics and metabolic functions of co-occurring populations remain underexplored. Here, we investigated co-occurring microbial cohorts across the Baltic Sea, a system characterized by strong salinity, temperature, and oxygen gradients. For this, we used a genomic catalog consisting of 701 species-representative genomes to recruit reads from 112 metagenomes and infer cohort structure, environmental distributions, and metabolic potential. We identified nine microbial cohorts that showed strong associations with environmental gradients, indicating deterministic assembly. Cohorts differed markedly in genomic traits, with the most abundant and prevalent taxa associated with smaller, streamlined genomes, while a low-oxygen cohort with larger genomes contributed disproportionately to nitrogen and sulfur transformations. Across cohorts, biosynthetic potential was unevenly distributed. Amino acid biosynthesis pathways were frequently complete, whereas B-vitamin pathways were typically incomplete and rarely encoded in full by individual genomes. Metabolites with low pathway completeness showed consistent taxonomic partitioning, with biosynthetic capabilities distributed across taxa rather than collectively encoded within cohorts. Together, these results show that Baltic Sea microbial cohorts are ecologically structured assemblages whose genomic repertoires reflect catabolic specialization and anabolic interdependencies. Our findings highlight microbial cohorts as a useful framework for linking environmental gradients, genome traits, and the organization of metabolic functions in natural microbial communities.","rel_num_authors":4,"rel_authors":[{"author_name":"Armando Pacheco-Valenciana","author_inst":"Stockholm University"},{"author_name":"Felix Milke","author_inst":"Carl von Ossietzky University of Oldenburg"},{"author_name":"Gerrit Wienhausen","author_inst":"Carl von Ossietzky University of Oldenburg"},{"author_name":"Sarahi L Garcia","author_inst":"Carl von Ossietzky University of Oldenburg"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Pro-Cognitive Effects of IgM Isotype Anti-NMDAR1 Autoantibodies in Mice","rel_doi":"10.64898\/2026.04.24.720689","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720689","rel_abs":"Natural anti-NMDAR1 autoantibodies are present at varying levels in the general human population, but their effects on cognitive function remain unclear. Recent human studies reported significant associations between higher blood levels of natural anti-NMDAR1 autoantibodies and potential neuroprotective outcomes in Alzheimer's disease, traumatic brain injury-associated depression and PTSD symptoms, and schizophrenia. However, whether these natural autoantibodies play a causal role in emotional and cognitive function has not been investigated. Since natural autoantibodies in human blood are predominantly of the IgM isotype, we immunized Aicda mutant mice to produce only IgM isotype anti-NMDAR1 autoantibodies without IgG and IgA isotypes. Mice were tested for sensorimotor gating and conditioned fear and extinction, cross species measures of information processing and emotional memory, respectively. Mice with higher levels of IgM anti-NMDAR1 autoantibodies exhibited significantly increased sensorimotor gating and improved fear extinction recall compared with mice with baseline levels of these autoantibodies. These findings indicate that IgM anti-NMDAR1 autoantibodies are pro-cognitive, unlike previous reports of poor cognition associated with IgG anti-NMDAR1 autoantibodies. Together, these studies suggest that IgM may hold therapeutic potential for a range of neurodegenerative, neurological, and psychiatric disorders.","rel_num_authors":7,"rel_authors":[{"author_name":"Jenna M DeWit","author_inst":"University of California San Diego"},{"author_name":"Tina Tebyanian","author_inst":"University of California San Diego"},{"author_name":"Alexandra Unapanta","author_inst":"University of California San Diego"},{"author_name":"Melonie N Vaughn","author_inst":"University of California San Diego"},{"author_name":"Susan B Powell","author_inst":"University of California San Diego"},{"author_name":"Victoria B Risbrough","author_inst":"University of California San Diego"},{"author_name":"Xianjin Zhou","author_inst":"University of California San Diego"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Metabolic and Anti-Proliferative Responses of Pancreatic Cancer Cells to Ultrasound and Nanobubble Treatment","rel_doi":"10.64898\/2026.04.24.720507","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720507","rel_abs":"Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies due to its dense stroma, which limits drug delivery and therapeutic efficacy. Ultrasound (US) mediated strategies using nanobubbles (NBs) offer a promising approach to enhance treatment, yet the biological effects of NB exposure and the timing of US application remain unclear. Here, we investigated how NB exposure with immediate (0h) or delayed (1h) US affects viability, proliferation, metabolism, and stress signaling in PANC-1 and BxPC-3 cells. Immediate US exposure in the presence of extracellular nanobubbles resulted in a greater reduction in cell viability at 24 h compared to delayed US application. Proliferation analysis showed that Ki67 positivity decreased following USNB treatments in both cell lines. Metabolically, NB treatment alone increased cellular activity, whereas combined USNB treatment reduced metabolic activity over time. Seahorse analysis revealed higher basal respiration in PANC-1 cells compared to BxPC-3 cells, consistent with a more glycolytic phenotype, while USNB treatment enhanced glycolytic responses, particularly in PANC-1. Moreover, stress responses were also more pronounced in PANC-1 cells, with HSP70 expression increasing up to 2-fold in NB incubated group and decreasing in USNB groups compared to untreated, whereas BxPC-3 cells exhibited only modest and opposite changes to PANC-1 in HSP70 expression decreasing with NB incubation. Treatment timing critically influenced outcomes, with immediate US producing stronger antiproliferative and cytotoxic effects, highlighting the importance of sequencing in USNB therapeutic strategies. Moreover, NBs alone stimulated metabolic and stress responses that may promote proliferation, whereas NBs combined with US induced stronger stress responses associated with metabolic reprogramming and reduced proliferation.","rel_num_authors":6,"rel_authors":[{"author_name":"Sila Appak-Baskoy","author_inst":"Toronto Metropolitan University"},{"author_name":"Muhammad Saad Khan","author_inst":"Toronto Metropolitan University"},{"author_name":"Farkhondeh Ghaderi","author_inst":"Toronto Metropolitan University"},{"author_name":"Agata A Exner","author_inst":"Case Western Reserve University"},{"author_name":"Michael C. Kolios","author_inst":"Toronto Metropolitan University"},{"author_name":"Imogen R. Coe","author_inst":"Toronto Metropolitan University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Fibroblast signaling influences macrophage-dependent, biomaterial-induced tissue remodeling","rel_doi":"10.64898\/2026.04.24.720640","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720640","rel_abs":"The ability to induce tissue regeneration on demand using biomaterials remains a major goal in biomedical research, yet significant challenges persist. Among the most advanced biomaterial models, the nanofiber-hydrogel composite has demonstrated a striking ability to induce soft adipose tissue remodeling at the injection site without incorporating exogenous biological cues. However, the underlying mechanisms that drive such a tissue response remain unclear. Here, we show that biomaterial-induced tissue remodeling is driven by sustained and controlled inflammation mediated by macrophages in strong communication with fibroblasts. Notably, both pro-inflammatory and anti-inflammatory signals remained elevated during this process in the long-term, challenging the prevailing notion that inflammation opposes remodeling. Using macrophage depletion in mice, we demonstrate that macrophages are essential for this process. Single-cell RNA sequencing further revealed robust fibroblast-to-macrophage signaling, contrasting with the conventional macrophage-to-fibroblast paradigm, and identified unique Spp1 macrophages and Ctla2a fibroblasts within the remodeling niche. These findings provide a comprehensive view of the immune landscape in biomaterial-induced tissue remodeling, highlighting key cellular interactions, prolonged kinetics, and unexpected signaling pathways. By defining key targets and fundamental principles, this work has broad implications for advancing biomaterial-induced tissue regeneration.","rel_num_authors":13,"rel_authors":[{"author_name":"Jessica L. Stelzel","author_inst":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"author_name":"Stuart J. Bauer","author_inst":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"author_name":"Bobby Y.X. Ni","author_inst":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"author_name":"Zhi-Cheng Yao","author_inst":"Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"author_name":"Victor M. Quiroz","author_inst":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"author_name":"Jamie L. Hernandez","author_inst":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"author_name":"Bryan L. McCarty","author_inst":"Turing Labs Inc. 4140 Abel Ave, Palo Alto, CA 94306, USA."},{"author_name":"Russell A. Martin","author_inst":"LifeSprout Inc. 101 W Dickman St, Suite 900, Baltimore, MD 21230, USA"},{"author_name":"Kailei D. Goodier","author_inst":"Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"author_name":"Valerie W. Wong","author_inst":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"author_name":"Sashank K. Reddy","author_inst":"Department of Plastic and Reconstructive Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"author_name":"Hai-Quan Mao","author_inst":"Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"author_name":"Joshua C. Doloff","author_inst":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Glymphatic function restored by \u03b11-noradrenergic antagonism alleviates headache allodynia in mice","rel_doi":"10.64898\/2026.04.24.720660","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720660","rel_abs":"Mild traumatic brain injury (mTBI) often leads to migraine-like post-traumatic headache (PTH), yet effective treatments are limited. Clinical and preclinical studies have shown that mTBI disrupts glymphatic transport of cerebrospinal fluid in the brain. We hypothesized that altered glymphatic transport might underlie facial allodynia commonly associated with migraine and PTH. A closed-head impact model was used to induce mTBI in mice. Facial allodynia, a symptom of PTH and migraine, was evaluated using periorbital von Frey testing. Glymphatic influx was assessed using slice-based imaging of a fluorescent tracer injected into the cisterna magna. Here we show that prazosin (PZN), an 1-noradrenergic receptor antagonist, restores glymphatic function and treats facial allodynia induced by calcitonin gene-related peptide (CGRP) and a nitric oxide donor in mice. In contrast, propranolol, a {beta}-noradrenergic receptor antagonist, was ineffective. Even in the absence of mTBI, CGRP reduced glymphatic function and PZN was able to restore glymphatic function in the dorsal cortex. Importantly, the role of glymphatic function was confirmed by the lack of PZN efficacy in aquaporin-4 knockout mice. These findings indicate that targeting 1-noradrenergic receptors to enhance glymphatic transport may offer a therapeutic strategy for treating migraine and PTH.","rel_num_authors":11,"rel_authors":[{"author_name":"Adriana Della Pietra","author_inst":"University of Iowa"},{"author_name":"Adisa Kuburas","author_inst":"University of Iowa"},{"author_name":"Mathew Sevao","author_inst":"University of Washington"},{"author_name":"Tristen M. Castillo","author_inst":"University of Iowa"},{"author_name":"Quinn K. Hanigan","author_inst":"University of Iowa"},{"author_name":"Thomas L. Duong","author_inst":"University of Iowa"},{"author_name":"Harold C. Flinn","author_inst":"University of Iowa"},{"author_name":"Emilie H. Partridge","author_inst":"University of Iowa"},{"author_name":"Murray A. Raskind","author_inst":"VA Puget Sound Health Care System and University of Washington"},{"author_name":"Jeffrey Iliff","author_inst":"VA Puget Sound Health Care System and University of Washington"},{"author_name":"Andrew Russo","author_inst":"University of Iowa"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Complex trait responses to complex environments: how do larval amphibians navigate co-occurring ecological demands that influence the same traits?","rel_doi":"10.64898\/2026.04.24.720614","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720614","rel_abs":"1. Many organisms alter phenotypically plastic traits in response to environmental cues to match their phenotypes with variable environments. In larval amphibians, development and growth rates respond to spatiotemporally variable mortality risk from predation, wetland drying, or resource limitation. However, these rates are also temperature-dependent for ectotherms. Although wild animals experience these factors simultaneously (e.g., thermal regimes, predation risk, resource limitation), most studies investigate their impacts in isolation, limiting our understanding of how they interact across ecological contexts. 2. Here we simultaneously exposed larval Plains Leopard Frogs (Lithobates blairi) to varying resource levels and predation risk treatments across a thermal regime to investigate the joint effects of these ecological drivers on growth and development rates and their consequences for size and vagility after metamorphosis. We crossed two predation treatments (waterborne cues from Procambarus gracilis fed L. blairi larvae, control water) with three food resource levels (5%, 25%, 50% of body mass) and six thermal regimes (diel {+\/-} 3{degrees}C cycles of 15, 20, 22, 24, 26, 28{degrees}C), replicating each combination five times for a total of 180 individuals. We recorded growth and development rates and completion of metamorphosis, then measured juvenile body size and jumping performance. 3. The number of larvae completing metamorphosis was primarily determined by temperature and temperature-dependent effects of resource limitation. Percent metamorphosis peaked at intermediate temperatures when resources were high and were higher in predation-risk treatments at the warmest temperatures. Under high resources, development and growth rates showed unimodal thermal responses that were absent when resources were constrained. Higher resources increased development rates, but proportional increases in growth maintained constant body size across temperatures. Post-metamorphic body size differed only by predation treatment, with predator-exposed individuals being smaller. Juvenile jumping performance increased with body size and individuals raised with high resources without predator cues exhibited the highest performance. 4. The absence of temperature effects on size at metamorphosis reflected unexpected coupling of growth and development rates across treatments, producing uniform body sizes. This pattern contrasts with the temperature-size rule and suggests that plastic responses may exhibit selection for a minimum viable size at metamorphosis.","rel_num_authors":7,"rel_authors":[{"author_name":"Stephanie A Bristow","author_inst":"Michigan State University"},{"author_name":"Samantha M Skerlec","author_inst":"University of Tennessee, Knoxville"},{"author_name":"Wyatt Mills","author_inst":"Wichita State University"},{"author_name":"Adam Rogers","author_inst":"Wichita State University"},{"author_name":"Amani Saber","author_inst":"Wichita State University"},{"author_name":"Krista J Ward","author_inst":"Wichita State University"},{"author_name":"Thomas M Luhring","author_inst":"Wichita State University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Branch extension and pruning share the same regulatory module in the developing Drosophila airways","rel_doi":"10.64898\/2026.04.24.720541","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720541","rel_abs":"Branched tubular organs represent a common solution to the problem of fluid transport in large animals. The growth of new branches has been extensively studied but the regulation of branch pruning remains underexplored. Here, we investigate branch removal in the stereotyped branching patterns of the developing Drosophila airways. After progenitor invagination, the tips of the distal airways generate a stereotyped branching pattern in each central metameric unit. An intriguing exception in the repeating patterns is the lack of branches targeting the visceral mesoderm (visceral primary branch, VB) in the third and nineth metameres. We show that these branches initially form but the localized expression of the pro-apoptotic gene reaper and resultant apoptosis prune them. We reveal that VB3\/9 pruning entails four sequential programs. First, a common distal outgrowth program promotes budding and extension of the primary branches, including VB3 and 9. Second, a VB identity program is established representing the ground state of all primary branches. Third, the Bithorax-Complex transcription factors define metameric identities and interfere with the VB program to induce reaper and apoptosis specifically in VB3\/9 in a concentration dependent manner. Finally, the default VB cell identity is transformed by extrinsic BMP\/Decapentaplegic and WNT\/Wingless into more derived primary branch identities and spared from pruning in metamers 3 and 9. Our results demonstrate that molecular and genetic circuits promoting branch emergence and extension can be regionally modified and deployed also for branch pruning.","rel_num_authors":4,"rel_authors":[{"author_name":"Ryo Matsuda","author_inst":"Stockholms Universitet"},{"author_name":"Chie Hosono","author_inst":"Stockholms Universitet"},{"author_name":"Kaoru Saigo","author_inst":"The University of Tokyo"},{"author_name":"Christos Samakovlis","author_inst":"Stockholm University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Proteome-aware organ proxy aging clocks","rel_doi":"10.64898\/2026.04.24.720503","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720503","rel_abs":"The development of minimally invasive multi-organ aging clocks, established through the deconvolution of plasma proteomics, has provided a convenient tool to assess the organ heterogeneity of aging. However, prior studies relied on bulk transcriptomic data for organ marker identification, which may lead to the potential misidentification of protein markers, and their research scope was largely confined to a few common diseases. To address these limitations, this study integrated multi-dimensional data to refine organ-enriched marker panels by incorporating organ-specific proteome information, and developed Proteome-Aware Organ Proxy Proteome Aging Clock (PAOPAC). PAOPAC exhibited decelerated biological age corresponding to improved physiological phenotypes across two independent external datasets, demonstrating its generalizability. We then leveraged PAOPAC to generate a comprehensive disease-aging landscape and to investigate the process of chronological and biological aging. Our analyses revealed that the majority of diseases are associated with an accelerated aging phenotype.","rel_num_authors":5,"rel_authors":[{"author_name":"Hao Xu","author_inst":"Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 1008"},{"author_name":"Jiawei Chen","author_inst":"Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 1008"},{"author_name":"Dongxu Chen","author_inst":"Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 1008"},{"author_name":"Kehang Mao","author_inst":"Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 1008"},{"author_name":"JING-DONG Jackie HAN","author_inst":"Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 1008"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"A Novel Mechanism of Cardiomyopathy: Toxic Peptides Dysregulate Calcium Transport","rel_doi":"10.64898\/2026.04.24.719962","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.719962","rel_abs":"A hallmark of dilated cardiomyopathy (DCM) is calcium mishandling, including reduced transport activity of the SERCA calcium pump in cardiac muscle cells. This has focused attention on SERCA as mechanism of disease and potential therapeutic target. Previously, diminished SERCA activity has been attributed to decreased protein expression, but recent studies suggest SERCA levels are unchanged in DCM. Thus, another mechanism must be responsible for the deficit. Since proteolysis is increased and proteosome function is impaired in DCM, we reasoned that accumulation of toxic protein fragments may contribute to SERCA dysfunction. In particular, previous studies showed diverse species of hydrophobic -helices can inhibit SERCA, so we hypothesized that SERCA may become congested with transmembrane peptides that mimic endogenous regulatory partners. We purified cell membranes from non-failing and DCM human ventricles and subjected them to mass spectrometry to identify protein species upregulated in DCM. Select candidates were screened for binding and inhibition of SERCA. Several small membrane proteins and membrane protein fragments bound avidly to SERCA and significantly reduced cellular calcium stores. The data suggest a novel pathophysiological mechanism in which transmembrane protein debris obstructs SERCA function and regulation, contributing to cardiac muscle dysfunction in heart failure.","rel_num_authors":9,"rel_authors":[{"author_name":"Taylor A Phillips","author_inst":"Loyola University Chicago"},{"author_name":"Jacob D Cunningham","author_inst":"Loyola University Chicago"},{"author_name":"Mary D Hernando","author_inst":"University of Alberta"},{"author_name":"Jaroslava Seflova","author_inst":"Loyola University Chicago"},{"author_name":"Laura A Sherer","author_inst":"Loyola University Chicago"},{"author_name":"Seby Edassery","author_inst":"Loyola University Chicago"},{"author_name":"Jonathan A. Kirk","author_inst":"Loyola University Chicago"},{"author_name":"Howard S Young","author_inst":"University of Alberta"},{"author_name":"Seth L Robia","author_inst":"Loyola University Chicago"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"An Isoform-Centric, Structure-Aware Framework for Protein Function Prediction and Evaluation, Instantiated in 3DisoDeepPF","rel_doi":"10.64898\/2026.04.24.720502","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720502","rel_abs":"Understanding functional diversity across protein isoforms remains a long-standing challenge with broad biological and translational implications, yet most computational methods are developed and benchmarked on a single reference protein per gene, limiting their ability to resolve isoform-specific functional differences. This challenge is compounded by the scarcity of isoform-resolved annotations and benchmarks. Here, we present an isoform-centric, structure-aware framework for the protein family (Pfam) domain and Gene Ontology (GO) term prediction. We implemented this framework in 3DisoDeepPF, which combines a dense graph combining sequence and structure similarity with multimodal representations, and evaluated 3DisoDeepPF in both conventional and isoform-resolved settings. Across conventional canonical benchmarks, 3DisoDeepPF showed strong performance relative to representative methods in both GO and Pfam prediction tasks. In an isoform-specific breast cancer atlas, 3DisoDeepPF remained stable under homology-controlled evaluation and detected Pfam changes among isoforms from the same gene. Additionally, 3DisoDeepPF provides evidence-tracing utilities that trace predicted labels to associated protein nodes, enabling supporting traceability and biological plausibility assessment.","rel_num_authors":20,"rel_authors":[{"author_name":"Felicia Jiang","author_inst":"Department of Surgery"},{"author_name":"Runhao Zhao","author_inst":"Independent Researcher"},{"author_name":"Feng Liang","author_inst":"Independent Researcher"},{"author_name":"Yinghan Zhang","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Taoyong Cui","author_inst":"Department of Computer Science and Engineering, The Chinese University of Hong Kong"},{"author_name":"Xiang Zhao","author_inst":"Independent Researcher"},{"author_name":"Xiangeng Wang","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"minghao Xu","author_inst":"Center for Machine Learning Research, Peking University, Beijing, China"},{"author_name":"Yi Shuai","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Tianli Luo","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Hualiang Yao","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Chenchen Xu","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Ziwei Wang","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Weixin Zeng","author_inst":"Independent Researcher"},{"author_name":"Xu Jiang","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Zhenchao Tang","author_inst":"AI for Life Sciences lab, Tencent, Shenzhen"},{"author_name":"Wentao Zhang","author_inst":"Center for Machine Learning Research, Peking University, Beijing, China"},{"author_name":"Pheng Ann Heng","author_inst":"Department of Computer Science and Engineering, The Chinese University of Hong Kong"},{"author_name":"Yu Li","author_inst":"Department of Computer Science and Engineering, The Chinese University of Hong Kong"},{"author_name":"Xin Wang","author_inst":"The Chinese University of Hong Kong"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"An Isoform-Centric, Structure-Aware Framework for Protein Function Prediction and Evaluation, Instantiated in 3DisoDeepPF","rel_doi":"10.64898\/2026.04.24.720502","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720502","rel_abs":"Understanding functional diversity across protein isoforms remains a long-standing challenge with broad biological and translational implications, yet most computational methods are developed and benchmarked on a single reference protein per gene, limiting their ability to resolve isoform-specific functional differences. This challenge is compounded by the scarcity of isoform-resolved annotations and benchmarks. Here, we present an isoform-centric, structure-aware framework for the protein family (Pfam) domain and Gene Ontology (GO) term prediction. We implemented this framework in 3DisoDeepPF, which combines a dense graph combining sequence and structure similarity with multimodal representations, and evaluated 3DisoDeepPF in both conventional and isoform-resolved settings. Across conventional canonical benchmarks, 3DisoDeepPF showed strong performance relative to representative methods in both GO and Pfam prediction tasks. In an isoform-specific breast cancer atlas, 3DisoDeepPF remained stable under homology-controlled evaluation and detected Pfam changes among isoforms from the same gene. Additionally, 3DisoDeepPF provides evidence-tracing utilities that trace predicted labels to associated protein nodes, enabling supporting traceability and biological plausibility assessment.","rel_num_authors":20,"rel_authors":[{"author_name":"Felicia Jiang","author_inst":"Department of Surgery"},{"author_name":"Runhao Zhao","author_inst":"Independent Researcher"},{"author_name":"Feng Liang","author_inst":"Independent Researcher"},{"author_name":"Yinghan Zhang","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Taoyong Cui","author_inst":"Department of Computer Science and Engineering, The Chinese University of Hong Kong"},{"author_name":"Xiang Zhao","author_inst":"Independent Researcher"},{"author_name":"Xiangeng Wang","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"minghao Xu","author_inst":"Center for Machine Learning Research, Peking University, Beijing, China"},{"author_name":"Yi Shuai","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Tianli Luo","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Hualiang Yao","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Chenchen Xu","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Ziwei Wang","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Weixin Zeng","author_inst":"Independent Researcher"},{"author_name":"Xu Jiang","author_inst":"Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital"},{"author_name":"Zhenchao Tang","author_inst":"AI for Life Sciences lab, Tencent, Shenzhen"},{"author_name":"Wentao Zhang","author_inst":"Center for Machine Learning Research, Peking University, Beijing, China"},{"author_name":"Pheng Ann Heng","author_inst":"Department of Computer Science and Engineering, The Chinese University of Hong Kong"},{"author_name":"Yu Li","author_inst":"Department of Computer Science and Engineering, The Chinese University of Hong Kong"},{"author_name":"Xin Wang","author_inst":"The Chinese University of Hong Kong"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Efficient murine cardiac phenotyping by combining synchrotron-based phase-contrast micro-CT, histology, immunofluorescence and spatial transcriptomics","rel_doi":"10.64898\/2026.04.24.720617","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720617","rel_abs":"Congenital heart disease is commonly studied using genetically modified mouse models, but characterization of complex three-dimensional (3D) cardiac anomalies remains technically challenging. Histology of fixed paraffin-embedded samples, typically used for structural and molecular analysis, is limited to two dimensions (2D). Synchrotron radiation-based phase-contrast micro-computed tomography (SRPC-CT) enables rapid, high-resolution, 3D imaging but had yet to be fully integrated with molecular tissue analysis. Importantly, SRPC-CT is nondestructive and compatible with paraffin-embedded tissue, potentially allowing integration with downstream molecular analyses such as multiplexed spatial transcriptomic profiling on tissue sections. Here, we present a pipeline combining SRPC-CT with complementary molecular approaches for rapid and accurate phenotyping of mouse hearts and for investigating disease mechanisms. We demonstrate the successful integration of high-resolution 3D imaging with spatial transcriptomics, fluorescent stainings, and histochemistry. Notably, all 2D modalities were applied sequentially to a single tissue section and registered within the 3D volume. This multimodal framework provides a powerful approach for linking structural and molecular information that is broadly applicable across biomedical research.","rel_num_authors":17,"rel_authors":[{"author_name":"Niccol\u00f2 Peruzzi","author_inst":"Lund University"},{"author_name":"Ayse Ceren Mutgan","author_inst":"Lund University"},{"author_name":"Kinga I Gawlik","author_inst":"Lund University"},{"author_name":"Timothy J Mead","author_inst":"Case Western Reserve University School of Medicine"},{"author_name":"Tuva Jerlhagen Forsgren","author_inst":"Lund University"},{"author_name":"Elin Lagervall","author_inst":"Lund University"},{"author_name":"Till Dreier","author_inst":"Excillum AB"},{"author_name":"Robin Kr\u00fcger","author_inst":"Lund University"},{"author_name":"Elna Lampei","author_inst":"Lund University"},{"author_name":"Jenny Romell","author_inst":"Excillium AB"},{"author_name":"Rapolas Spalinskas","author_inst":"Stockholm University and SciLifeLab"},{"author_name":"Madeleine Durbeej-Hjalt","author_inst":"Lund University"},{"author_name":"Suneel APTE","author_inst":"Cleveland Clinic Lerner Research Institute"},{"author_name":"Martin Bech","author_inst":"Lund University"},{"author_name":"Anne Bonnin","author_inst":"Paul Scherrer Institute: Paul Scherrer Institut"},{"author_name":"Katarina Tiklova","author_inst":"Stockholm University and SciLifeLab"},{"author_name":"Karin Tran-Lundmark","author_inst":"Lund University and Sk\u00e5ne University Hospital"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Normal Aging Limits Cortical Network Reorganization and Behavioral Recovery after Experimental Stroke","rel_doi":"10.64898\/2026.04.24.720447","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720447","rel_abs":"Stroke is the leading cause of chronic disability in the United States, and advancing age is associated with worse recovery. Despite this, relatively little is known about how aging influences the repair and reorganization of neural circuits and large-scale cortical networks after stroke. To address this question, we compared cortical network dynamics and behavioral recovery after focal photothrombotic stroke in forepaw somatosensory cortex in young (3-month-old) and aged (18-month-old) Thy1-GCaMP6f mice. Both young and aged mice developed significant somatomotor deficits after stroke; however, only young mice exhibited substantial behavioral recovery despite similar infarct volumes across groups. Two age-dependent effects on cortical network function emerged. First, somatosensory-evoked activity and somatosensory functional connectivity were disrupted in both cohorts early after stroke, but their trajectories diverged over time. Forepaw-evoked GCaMP responses in the affected cortex were similarly reduced in both groups early after stroke; yet by 7 weeks, responses recovered in young mice but remained persistently depressed in aged animals. Likewise, bihemispheric somatosensory functional connectivity was initially disrupted in both groups but improved between 1 and 7 weeks only in young mice. Second, global temporal measures of network function evolved differently after stroke. At baseline, stimulus-locked fidelity and interhemispheric coherence were higher in young than aged mice, but after stroke, these measures declined in young animals to levels comparable to aged mice and did not recover by 7 weeks. Stroke also altered large-scale cortical entrainment dynamics, and reductions in cortical entrainment area between baseline and 1-week post-stroke predicted long-term behavioral recovery across animals. Together, these findings indicate that impaired behavioral recovery in aged mice reflects a failure of damaged somatosensory networks to reorganize, whereas recovery in young mice occurs despite persistent degradation of global network fidelity and coherence. These results highlight age-dependent mechanisms of circuit repair after stroke and suggest a potential network-level basis for chronic deficits in stroke survivors.","rel_num_authors":8,"rel_authors":[{"author_name":"Asher J Albertson","author_inst":"Washington University School of Medicine"},{"author_name":"Ryan M Bowen","author_inst":"Washington University School of Medicine"},{"author_name":"Kyrillos Ayoub","author_inst":"Washington University School of Medicine"},{"author_name":"Rose A. Leon-Alvarado","author_inst":"Washington University School of Medicine"},{"author_name":"Brendon Wang","author_inst":"Washington University School of Medicine"},{"author_name":"Roman Patti","author_inst":"Washington University School of Medicine"},{"author_name":"Adam Q Bauer","author_inst":"Washington University School of Medicine"},{"author_name":"Jin-Moo Lee","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"The selective OX1R antagonist 1-SORA-51 reduces binge-like feeding behavior in male and female mice without detectable changes in dopamine.","rel_doi":"10.64898\/2026.04.24.720455","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720455","rel_abs":"Binge eating disorder (BED) is characterized by episodic overconsumption of palatable food and involves dysregulated motivational and arousal processes. The orexin (hypocretin) system, through its widespread projections to mesolimbic circuits, has been implicated in cue-driven reward seeking and escalated intake, raising the possibility that orexin receptor antagonists may modulate binge-like behavior. Here we evaluated the effects of a dual orexin receptor antagonist (DORA-22) and an OX1R-selective antagonist (1-SORA-51) in a cyclic intermittent high-fat access model that generates robust and reproducible binge-like intake in male and female mice. DORA-22 produced no detectable effect on consumption at either early (2 h) or extended (24 h) binge timepoints. In contrast, 1-SORA-51 significantly reduced high-fat intake during the initial 2 hours of access in both sexes, with no effect on 24-hour consumption, indicating a selective attenuation of the early phase of binge intake. Fiber photometry recordings of GRABDA2m fluorescence in the nucleus accumbens revealed that 1-SORA-51 did not alter baseline dopamine signals or the dopamine increase triggered by high-fat pellet delivery, demonstrating that its behavioral effects occur without detectable modulation of mesolimbic dopamine dynamics. Together, these findings identify OX1R antagonism as a strategy for suppressing the initiation of binge-like feeding and highlight the receptor-level specificity of orexin contributions to maladaptive overconsumption.","rel_num_authors":7,"rel_authors":[{"author_name":"Ralph DiLeone","author_inst":"Yale University"},{"author_name":"Joseph Richard Trinko","author_inst":"Yale University"},{"author_name":"Etienne Atangana","author_inst":"Yale University"},{"author_name":"Deven Diaz","author_inst":"Yale University"},{"author_name":"Avishag Ashkenazi","author_inst":"Yale University"},{"author_name":"Ethan P Foscue","author_inst":"Yale University"},{"author_name":"Edward M Kong","author_inst":"Yale University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Accurate ab initio gene prediction in eukaryotes with Tiberius in multiple clades","rel_doi":"10.64898\/2026.04.24.720536","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720536","rel_abs":"Eukaryotic genome annotation is currently bottlenecked by limitations in the generality, scalability and accuracy of computational methods. Deep learning approaches have recently achieved large improvements in ab initio gene prediction accuracy. We extend the deep learning-based ab initio gene predictor Tiberius beyond mammals by training lineage-specific models for Mesangiospermae, Fungi, Vertebrata, Insecta, Chlorophyta and Bacillariophyta. Across a benchmark of 33 species, Tiberius consistently achieves higher accuracy than the other evaluated ab initio methods, Helixer and ANNEVO, while also having the fastest runtimes overall. Compared with BRAKER3, which incorporates RNA-Seq and protein evidence, Tiberius approaches state-of-the-art accuracy in Mesangiospermae, Fungi, Bacillariophyta and Chlorophyta, while being on average 80 times faster when using a GPU. Availability and implementation: https:\/\/github.com\/Gaius-Augustus\/Tiberius","rel_num_authors":13,"rel_authors":[{"author_name":"Lars Gabriel","author_inst":"Institute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str 47, 17489 Greifswald, MV, Germany"},{"author_name":"Tomas Bruna","author_inst":"DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, 94720, California, USA"},{"author_name":"Asees Kaur","author_inst":"University of California Merced, Merced, CA 95343, USA"},{"author_name":"Anish Krishnan","author_inst":"Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, CA 94720, USA"},{"author_name":"Felix Ortmann","author_inst":"Institute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str 47, 17489 Greifswald, MV, Germany"},{"author_name":"Asaf Salamov","author_inst":"DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, 94720, California, USA"},{"author_name":"Samuel Talbot","author_inst":"Center for Quantitative Life Sciences, Oregon State University, OR 97331, Oregon, USA"},{"author_name":"Felix Becker","author_inst":"Institute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str 47, 17489 Greifswald, MV, Germany"},{"author_name":"Richard Krieg","author_inst":"Institute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str 47, 17489 Greifswald, MV, Germany"},{"author_name":"Christopher W Wheat","author_inst":"Department of Zoology, Stockholm University, Svante Arrhenius vaeg 18b, SE-106 91, Stockholm, Sweden"},{"author_name":"Igor V Grigoriev","author_inst":"DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, 94720, California, USA; Department of Plant and Microbial Biology"},{"author_name":"Mario Stanke","author_inst":"Institute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str 47, 17489 Greifswald, MV, Germany"},{"author_name":"Katharina J Hoff","author_inst":"Institute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str. 47, 17489 Greifswald ,  MV, Germany"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"The Value of Multi-Year Sampling for Detecting Fine-Scale Population Genetic Structure in Marine Fishes: A Case Study of Juvenile Southern Flounder","rel_doi":"10.64898\/2026.04.24.720543","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.24.720543","rel_abs":"Understanding population structure is critical for effective fisheries management in species with complex life histories and variable recruitment. Southern flounder (Paralichthys lethostigma) is a valuable flatfish species with declining populations in the Southeast United States. Improved management may depend on a better understanding of fine-scale and temporal population genetic structure in this region; however, such structure remains poorly characterized. To address our lack of understanding of the spatial and temporal population structure of this important species, we used double digest reduced-representation genome sequencing (ddRADSeq) on juveniles from estuaries in North Carolina and Texas between 2014 and 2023. We found significant genetic differentiation between the Gulf of Mexico and Atlantic populations, supporting the management of these regions as distinct stocks. By contrast, we detected significant variance in genetic structure within Texas and North Carolina populations that was not consistent across sampling years between estuaries in close proximity. The population genetic structure of southern flounder suggests significant, temporally variable genetic differences within estuarine locations that may result from variation in larval dispersal and recruitment patterns. Our findings highlight the value of integrating fine-scale, multi-year genetic data to capture temporal dynamics and avoid misleading conclusions based on single-year or broad-scale sampling.","rel_num_authors":5,"rel_authors":[{"author_name":"Sydney Harned","author_inst":"North Carolina State University"},{"author_name":"Jamie Mankiewicz","author_inst":"North Carolina State University"},{"author_name":"Russell Borski","author_inst":"North Carolina State University"},{"author_name":"John Godwin","author_inst":"North Carolina State University"},{"author_name":"Martha Burford Reiskind","author_inst":"North Carolina State University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Paired CRISPR screens identify mitochondrial metabolism and UBE2H as aneuploid-specific dependencies in human cancer cell lines","rel_doi":"10.64898\/2026.04.26.720636","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720636","rel_abs":"Aneuploidy is a hallmark of cancer and imposes widespread cellular stress, including proteotoxicity, transcriptional dysregulation, and increased metabolic demand. Although these stresses are predicted to create therapeutic vulnerabilities, the genetic dependencies of aneuploid cells remain incompletely characterized. Here, we performed paired CRISPR loss-of-function screens in isogenic aneuploid and near-euploid cancer cell line models to systematically identify aneuploidy-specific dependencies. Seven genome-wide paired screens identified ribosomes, rRNA processing, spliceosome-mediated RNA processing, proteasome subunits, and mitochondrial metabolism as top aneuploid-specific dependency gene groups. To identify therapeutically targetable aneuploid dependencies, we performed 18 additional paired CRISPR screens using a focused druggable genome library. This analysis identified the ubiquitin-conjugating enzyme UBE2H as a top aneuploid-selective dependency. Functional validation confirmed aneuploid cell dependency on UBE2H, and mechanistic analyses linked UBE2H to mitochondrial protein abundance, suggesting a role in maintaining mitochondrial proteostasis under aneuploid stress. Together, these findings define core cellular systems that support the viability of aneuploid cells and identify UBE2H as a potential therapeutic vulnerability connecting ubiquitin signaling to mitochondrial homeostasis.","rel_num_authors":15,"rel_authors":[{"author_name":"Klaske M Schukken","author_inst":"Yale University"},{"author_name":"Saron M Akalu","author_inst":"Yale University"},{"author_name":"Charles Zou","author_inst":"Yale University"},{"author_name":"Pranav K Kandikuppa","author_inst":"Yale University"},{"author_name":"Ryan A Hagenson","author_inst":"Yale University"},{"author_name":"Jessica L Keane","author_inst":"Stanford University"},{"author_name":"Mason P Lynch","author_inst":"Stanford University"},{"author_name":"Toyoki Yoshimoto","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Olaf Klingbeil","author_inst":"KU Leuven, VIB Center for Cancer Biology"},{"author_name":"Erin L. Sausville","author_inst":"Yale University"},{"author_name":"Sanat Mishra","author_inst":"Yale University"},{"author_name":"Christopher M Vakoc","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Zuzana Storchova","author_inst":"Rheinland Pfalzische Technische Universitat"},{"author_name":"Sarah J Aitken","author_inst":"Yale University"},{"author_name":"Jason Meyer Sheltzer","author_inst":"Stanford University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Paired CRISPR screens identify mitochondrial metabolism and UBE2H as aneuploid-specific dependencies in human cancer cell lines","rel_doi":"10.64898\/2026.04.26.720636","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720636","rel_abs":"Aneuploidy is a hallmark of cancer and imposes widespread cellular stress, including proteotoxicity, transcriptional dysregulation, and increased metabolic demand. Although these stresses are predicted to create therapeutic vulnerabilities, the genetic dependencies of aneuploid cells remain incompletely characterized. Here, we performed paired CRISPR loss-of-function screens in isogenic aneuploid and near-euploid cancer cell line models to systematically identify aneuploidy-specific dependencies. Seven genome-wide paired screens identified ribosomes, rRNA processing, spliceosome-mediated RNA processing, proteasome subunits, and mitochondrial metabolism as top aneuploid-specific dependency gene groups. To identify therapeutically targetable aneuploid dependencies, we performed 18 additional paired CRISPR screens using a focused druggable genome library. This analysis identified the ubiquitin-conjugating enzyme UBE2H as a top aneuploid-selective dependency. Functional validation confirmed aneuploid cell dependency on UBE2H, and mechanistic analyses linked UBE2H to mitochondrial protein abundance, suggesting a role in maintaining mitochondrial proteostasis under aneuploid stress. Together, these findings define core cellular systems that support the viability of aneuploid cells and identify UBE2H as a potential therapeutic vulnerability connecting ubiquitin signaling to mitochondrial homeostasis.","rel_num_authors":15,"rel_authors":[{"author_name":"Klaske M Schukken","author_inst":"Yale University"},{"author_name":"Saron M Akalu","author_inst":"Yale University"},{"author_name":"Charles Zou","author_inst":"Yale University"},{"author_name":"Pranav K Kandikuppa","author_inst":"Yale University"},{"author_name":"Ryan A Hagenson","author_inst":"Yale University"},{"author_name":"Jessica L Keane","author_inst":"Stanford University"},{"author_name":"Mason P Lynch","author_inst":"Stanford University"},{"author_name":"Toyoki Yoshimoto","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Olaf Klingbeil","author_inst":"KU Leuven, VIB Center for Cancer Biology"},{"author_name":"Erin L. Sausville","author_inst":"Yale University"},{"author_name":"Sanat Mishra","author_inst":"Yale University"},{"author_name":"Christopher M Vakoc","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Zuzana Storchova","author_inst":"Rheinland Pfalzische Technische Universitat"},{"author_name":"Sarah J Aitken","author_inst":"Yale University"},{"author_name":"Jason Meyer Sheltzer","author_inst":"Stanford University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Paired CRISPR screens identify mitochondrial metabolism and UBE2H as aneuploid-specific dependencies in human cancer cell lines","rel_doi":"10.64898\/2026.04.26.720636","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.26.720636","rel_abs":"Aneuploidy is a hallmark of cancer and imposes widespread cellular stress, including proteotoxicity, transcriptional dysregulation, and increased metabolic demand. Although these stresses are predicted to create therapeutic vulnerabilities, the genetic dependencies of aneuploid cells remain incompletely characterized. Here, we performed paired CRISPR loss-of-function screens in isogenic aneuploid and near-euploid cancer cell line models to systematically identify aneuploidy-specific dependencies. Seven genome-wide paired screens identified ribosomes, rRNA processing, spliceosome-mediated RNA processing, proteasome subunits, and mitochondrial metabolism as top aneuploid-specific dependency gene groups. To identify therapeutically targetable aneuploid dependencies, we performed 18 additional paired CRISPR screens using a focused druggable genome library. This analysis identified the ubiquitin-conjugating enzyme UBE2H as a top aneuploid-selective dependency. Functional validation confirmed aneuploid cell dependency on UBE2H, and mechanistic analyses linked UBE2H to mitochondrial protein abundance, suggesting a role in maintaining mitochondrial proteostasis under aneuploid stress. Together, these findings define core cellular systems that support the viability of aneuploid cells and identify UBE2H as a potential therapeutic vulnerability connecting ubiquitin signaling to mitochondrial homeostasis.","rel_num_authors":15,"rel_authors":[{"author_name":"Klaske M Schukken","author_inst":"Yale University"},{"author_name":"Saron M Akalu","author_inst":"Yale University"},{"author_name":"Charles Zou","author_inst":"Yale University"},{"author_name":"Pranav K Kandikuppa","author_inst":"Yale University"},{"author_name":"Ryan A Hagenson","author_inst":"Yale University"},{"author_name":"Jessica L Keane","author_inst":"Stanford University"},{"author_name":"Mason P Lynch","author_inst":"Stanford University"},{"author_name":"Toyoki Yoshimoto","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Olaf Klingbeil","author_inst":"KU Leuven, VIB Center for Cancer Biology"},{"author_name":"Erin L. Sausville","author_inst":"Yale University"},{"author_name":"Sanat Mishra","author_inst":"Yale University"},{"author_name":"Christopher M Vakoc","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Zuzana Storchova","author_inst":"Rheinland Pfalzische Technische Universitat"},{"author_name":"Sarah J Aitken","author_inst":"Yale University"},{"author_name":"Jason Meyer Sheltzer","author_inst":"Stanford University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"rel_title":"Adaptive loss of function accelerated the evolution of ancient and modern human cognition","rel_doi":"10.64898\/2026.04.27.721126","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.27.721126","rel_abs":"Methods to detect accelerated evolution have identified many genomic regions with unexpectedly rapid evolution in the human lineage-significantly more than in chimpanzees, our closest living relatives. However, these methods focus on accelerated sequence evolution of short non-coding regions, leaving open the questions of how to identify accelerated evolution of molecular function, as opposed to sequence, and whether accelerated evolution has shaped the human genome more broadly. Here, we introduce a new approach to detect accelerated evolution: Function Aware Statistical Test for Evolutionary Rates (FASTER). In contrast to previous methods, FASTER can detect not only accelerated evolution of sequence, but also of predicted function, and can be applied to any set of genomic regions. Applying this method to humans and chimpanzees, we identified protein-coding, untranslated (UTR), and non-coding regions with accelerated evolution of function. Across all these genomic levels, we consistently found more acceleration in conserved sites in the human lineage compared to chimpanzee, many of which are predicted to reduce protein stability or chromatin accessibility. Multiple lines of evidence suggest this human-acceleration was driven by positive selection on brain development and cognition which has continued to shape human evolution even in the past several thousand years. Collectively, these results demonstrate the power of genome-wide scans for the evolution of predicted function and specifically suggest that an accelerated rate of reduction in function-including widespread decreases in cis-regulatory activity-may have been a major driver of both ancient and recent human evolution.","rel_num_authors":5,"rel_authors":[{"author_name":"Alexander L Starr","author_inst":"Stanford University"},{"author_name":"Gabriella M Cale","author_inst":"California Institute of Technology"},{"author_name":"Leslie Magtanong","author_inst":"Stanford University"},{"author_name":"Michael E. Palmer","author_inst":"Stanford University"},{"author_name":"Hunter B Fraser","author_inst":"Stanford University"}],"rel_date":"2026-04-28","rel_site":"biorxiv"},{"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":"BackgroundVietnam 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.\n\nMethodsWe 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).\n\nResultsIsolates 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.\n\nConclusionsThe 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":"BackgroundCollege-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.\n\nMethodsData 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.\n\nResultsIntervention 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 in the intervention group (p <0.05). These demographic factors did not moderate the interventions 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).\n\nConclusionsMulticomponent 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 ObjectivesOlder adults with epilepsy have a 2- to 4-fold increased risk of dementia, including Alzheimers 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.\n\nMethodsEighty-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.\n\nResultsOnly 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-{varepsilon}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%).\n\nDiscussionAT(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":"BackgroundMicrovascular dysfunction is a key contributor to the development and progression of chronic kidney disease (CKD), yet direct and reproducible assessment of microvascular function in clinical CKD populations remains limited. Laser Doppler flowmetry (LDF) provides a noninvasive, dynamic assessment of skin microvascular blood flow and may serve as a surrogate measure of systemic microvascular health. However, the extent to which LDF-derived measures relate to kidney function, proteinuria, and kidney histopathology in CKD remains unclear.\n\nMethodsWe assessed cutaneous microvascular function in 150 participants with CKD (estimated glomerular filtration rate [eGFR] <90 mL\/min\/1.73 m{superscript 2}) 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. The percentage change in perfusion unit (PU) was calculated and used to define microvascular functional reserve. Associations between LDF-derived measures with eGFR and urine protein-to-creatinine ratio (uPCR) were assessed using multivariable linear regression adjusted for demographic and clinical covariates. Unsupervised k-means clustering was performed to identify microvascular phenotypes based on resting PU and microvascular function reserve. Associations of LDF measures with glomerulosclerosis (GS) and interstitial fibrosis and tubular atrophy (IFTA) were evaluated in a subset of participants (n = 20) who underwent clinically indicated kidney biopsies.\n\nResultsAmong 150 CKD participants, the mean (SD) age was 64 (14) years, 46% were female, 38% had diabetes, and 83% had hypertension. The mean eGFR was 42 (21) mL\/min\/1.73 m{superscript 2} 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) was associated with lower eGFR, independent of covariates. Baseline PU or percentage change in PU were not associated with uPCR. Unsupervised clustering identified four distinct microvascular phenotypes characterized by graded differences in resting perfusion and microvascular function reserve. Among participants with biopsy data, higher baseline PU and lower percentage change in PU were associated with greater severity of GS and IFTA.\n\nConclusionIn persons with CKD, elevated resting perfusion and impaired microvascular functional reserve were associated with lower eGFR. These findings suggest that LDF-derived measures capture clinically relevant alterations in systemic microvascular function and may serve as a noninvasive biomarker of kidney function and underlying histopathologic injury in CKD.","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":"PurposeTo 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.\n\nMaterials and MethodsIn 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.\n\nResultsPRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV (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).\n\nConclusionPRMD 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.\n\nKey PointsO_LIThis study introduces combined wavelet scattering and subspace learning for medical image segmentation, enabling accurate, interpretable voxel-level classification of emphysema and functional small airways disease on paired CT scans.\nC_LIO_LIThe proposed Deep Parametric Response Mapping method demonstrated 95% voxel-wise agreement with standard Parametric Response Mapping and stronger correlations with spirometric measures, enhancing the clinical relevance of CT-based phenotyping for Chronic Obstructive Pulmonary Disease.\nC_LIO_LIDeep Parametric Response Mapping significantly improved robustness to image noise--reducing overestimation of emphysema and functional small airways disease from [~]15% to <5% (P < 0.001)--and benefits from reduced data requirements due to the fixed, mathematically defined filters used in wavelet scattering.\nC_LI\n\nSummary StatementDeep Parametric Response Mapping improves the accuracy and noise robustness of CT-based classification of emphysema and functional small airways disease using feature-based representations, enhancing the reliability of COPD phenotyping.","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":"PurposeTo 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.\n\nMaterials and MethodsIn 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.\n\nResultsPRMD achieved 95% voxel-wise agreement with standard PRM (r = 0.98) while demonstrating significantly greater robustness under noise. PRMD showed stronger correlations with FEV (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).\n\nConclusionPRMD 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.\n\nKey PointsO_LIThis study introduces combined wavelet scattering and subspace learning for medical image segmentation, enabling accurate, interpretable voxel-level classification of emphysema and functional small airways disease on paired CT scans.\nC_LIO_LIThe proposed Deep Parametric Response Mapping method demonstrated 95% voxel-wise agreement with standard Parametric Response Mapping and stronger correlations with spirometric measures, enhancing the clinical relevance of CT-based phenotyping for Chronic Obstructive Pulmonary Disease.\nC_LIO_LIDeep Parametric Response Mapping significantly improved robustness to image noise--reducing overestimation of emphysema and functional small airways disease from [~]15% to <5% (P < 0.001)--and benefits from reduced data requirements due to the fixed, mathematically defined filters used in wavelet scattering.\nC_LI\n\nSummary StatementDeep Parametric Response Mapping improves the accuracy and noise robustness of CT-based classification of emphysema and functional small airways disease using feature-based representations, enhancing the reliability of COPD phenotyping.","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.\n\nSynopsisThis study elucidates the integrative impact of water chemistry and the nonlinear responses of individual water chemistry parameters on the occurrence and abundance of Mycobacterium spp. and Legionella spp. in drinking water using interpretable machine learning.\n\nTOC\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC=\"FIGDIR\/small\/26351579v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (52K):\norg.highwire.dtl.DTLVardef@91e321org.highwire.dtl.DTLVardef@1d6c336org.highwire.dtl.DTLVardef@aa3e5borg.highwire.dtl.DTLVardef@e3e881_HPS_FORMAT_FIGEXP  M_FIG C_FIG","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.\n\nSynopsisThis study elucidates the integrative impact of water chemistry and the nonlinear responses of individual water chemistry parameters on the occurrence and abundance of Mycobacterium spp. and Legionella spp. in drinking water using interpretable machine learning.\n\nTOC\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC=\"FIGDIR\/small\/26351579v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (52K):\norg.highwire.dtl.DTLVardef@91e321org.highwire.dtl.DTLVardef@1d6c336org.highwire.dtl.DTLVardef@aa3e5borg.highwire.dtl.DTLVardef@e3e881_HPS_FORMAT_FIGEXP  M_FIG C_FIG","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":"ImportanceAdverse 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.\n\nObjectiveTo 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.\n\nDesignCross-sectional study using data from a Yale Stress Center study between 2008 and 2012. Data analysis was conducted from July 2025 to January 2026.\n\nSettingCommunity-based sample from the greater New Haven, CT area.\n\nParticipantsA 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.\n\nMain Outcomes and MeasuresEpigenetic 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.\n\nResultsData 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.\n\nConclusionsGreater 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.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSIs neighborhood deprivation associated with epigenetic age acceleration, and if so, how do neighborhood- and individual-level factors impact this relationship?\n\nFindingsIn this cross-sectional study of 370 adults, greater neighborhood deprivation was associated with accelerated epigenetic aging across multiple second-generation clocks. Lifetime adversity partially mediated these associations, and the relationship between neighborhood deprivation and accelerated aging was stronger among Black participants than White participants.\n\nMeaningThese findings suggest that neighborhood conditions and lifetime stress contribute to accelerated biological aging and suggest that epigenetic aging may represent one biological pathway through which neighborhood-level racial inequalities contribute to health disparities.","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":"BackgroundStudies 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.\n\nMethodsWe 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 models ability to \"know what it doesnt 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.\n\nResultsThe model achieved modest but competitive classification performance (AUROC = .653 {+\/-} .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.\n\nConclusionsThis 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":"BackgroundStudies 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.\n\nMethodsWe 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 models ability to \"know what it doesnt 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.\n\nResultsThe model achieved modest but competitive classification performance (AUROC = .653 {+\/-} .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.\n\nConclusionsThis 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":"PurposeSurvivors 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.\n\nMethodsWe 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.\n\nResultsAmong 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).\n\nConclusionsFatigue 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.\n\nPLAIN ENGLISH SUMMARYSurvivors of severe COVID-19 illness 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. We found that the presence of fatigue inflated depression severity through symptoms of tiredness and difficulty sleeping, which are two of the nine items of a commonly used depression screening tool, known as the Patient Health Questionnaire-9 (PHQ-9). Depression screening tools that omit these two items, such as the PHQ-2, may better screen for depressive symptoms in PICS, minimizing the risk of overestimating depression symptoms and potentially misdiagnosis.","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":"PurposeSurvivors 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.\n\nMethodsWe 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.\n\nResultsAmong 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).\n\nConclusionsFatigue 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.\n\nPLAIN ENGLISH SUMMARYSurvivors of severe COVID-19 illness 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. We found that the presence of fatigue inflated depression severity through symptoms of tiredness and difficulty sleeping, which are two of the nine items of a commonly used depression screening tool, known as the Patient Health Questionnaire-9 (PHQ-9). Depression screening tools that omit these two items, such as the PHQ-2, may better screen for depressive symptoms in PICS, minimizing the risk of overestimating depression symptoms and potentially misdiagnosis.","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":"BackgroundPrognostication 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.\n\nMethodsWe developed and externally validated a machine learning model to predict favorable six-month functional outcome (GOS \"MD\"\/\"GR\" or GOSE [&ge;]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.\n\nResultsThe 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.\n\nConclusionA 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, which enabled visualization of rapid 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.\n\nImportanceAs bacterial whole genome sequencing (WGS) is increasingly used as a research tool for clinical samples, it is important to understand the pros and cons of different culture sampling methodologies. Here, we analyzed cases of persistent bacteremia due to methicillin-resistant Staphylococcus aureus by applying WGS to either each of five individual colonies isolated on blood culture plates (c5-seq) or the pooled bacterial population on each plate (p-seq). We found that c5-seq was a more practical and informative method to understand evolutionary dynamics.","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 Earths 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 IL-4, IL-5, or IL-13 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 KH-3 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, KH-3 accelerated GATA3 mRNA decay with minimal effects on RORC or TBX21 and selectively reduced Th2 cytokine secretion, while IL-10 and IL-2 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 KH-3 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.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC=\"FIGDIR\/small\/720195v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (27K):\norg.highwire.dtl.DTLVardef@16d8fd4org.highwire.dtl.DTLVardef@1fdd31dorg.highwire.dtl.DTLVardef@12f1b11org.highwire.dtl.DTLVardef@19a1c93_HPS_FORMAT_FIGEXP  M_FIG C_FIG","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 IL-4, IL-5, or IL-13 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 KH-3 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, KH-3 accelerated GATA3 mRNA decay with minimal effects on RORC or TBX21 and selectively reduced Th2 cytokine secretion, while IL-10 and IL-2 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 KH-3 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.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC=\"FIGDIR\/small\/720195v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (27K):\norg.highwire.dtl.DTLVardef@16d8fd4org.highwire.dtl.DTLVardef@1fdd31dorg.highwire.dtl.DTLVardef@12f1b11org.highwire.dtl.DTLVardef@19a1c93_HPS_FORMAT_FIGEXP  M_FIG C_FIG","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":"BackgroundThe 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.\n\nAimThis study explores whether trauma onset timing is associated with distinct resting-state functional connectivity (rsFC) pattern using data-driven approach.\n\nMethodsSeventy-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.\n\nResultsA two-cluster solution provided the most stable fit. The two generated clusters were significantly different in CT-only prevalence (p < 0.05; Cramers 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.\n\nConclusionData-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":"BackgroundThe 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.\n\nAimThis study explores whether trauma onset timing is associated with distinct resting-state functional connectivity (rsFC) pattern using data-driven approach.\n\nMethodsSeventy-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.\n\nResultsA two-cluster solution provided the most stable fit. The two generated clusters were significantly different in CT-only prevalence (p < 0.05; Cramers 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.\n\nConclusionData-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":"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":"IntroductionImproved 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.\n\nMethodsWe 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.\n\nResultsFrom 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.\n\nConclusionsThe 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. TSs 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":"IntroductionIntermittent 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.\n\nMethodsWe 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.\n\nResultsCompared 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.\n\nConclusionAdverse 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.\n\nKey MessagesO_LIIntermittent preventive treatment of malaria in pregnancy (IPTp) with a combination of dihydroartemisinin-piperaquine (DP) and sulfadoxine-pyrimethamine (SP) leads to lower birth weight compared to SP alone, but it is unclear whether effects persist through infancy and what mechanisms drive these differences.\nC_LIO_LIDP+SP provided some improvements to ponderal growth over SP by preventing active placental malaria, but these benefits were not large enough to offset negative effects associated with other prenatal factors.\nC_LIO_LIInfants born to mothers who received IPTp with DP+SP were at higher risk of growth faltering in the first year of life compared to DP or SP alone; while differences in weight-for-length subsided over time, some children developed chronic forms of malnutrition that may be difficult to recover from.\nC_LI","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":"IntroductionIntermittent 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.\n\nMethodsWe 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.\n\nResultsCompared 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.\n\nConclusionAdverse 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.\n\nKey MessagesO_LIIntermittent preventive treatment of malaria in pregnancy (IPTp) with a combination of dihydroartemisinin-piperaquine (DP) and sulfadoxine-pyrimethamine (SP) leads to lower birth weight compared to SP alone, but it is unclear whether effects persist through infancy and what mechanisms drive these differences.\nC_LIO_LIDP+SP provided some improvements to ponderal growth over SP by preventing active placental malaria, but these benefits were not large enough to offset negative effects associated with other prenatal factors.\nC_LIO_LIInfants born to mothers who received IPTp with DP+SP were at higher risk of growth faltering in the first year of life compared to DP or SP alone; while differences in weight-for-length subsided over time, some children developed chronic forms of malnutrition that may be difficult to recover from.\nC_LI","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":"PurposeTo evaluate patient satisfaction and preferences for portable versus table-mounted visual field (VF) devices in a rural telemedicine setting and identify influencing factors.\n\nMethodsWe 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.\n\nResultsAmong 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}{superscript 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}{superscript 2} (2) = 63.33, p<0.001, V = 0.34) and easiest to use (54.6%; {chi}{superscript 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.\n\nConclusionRural 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.\n\nWe 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).\n\nWe 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 30-45% of the total effect of pPS on T2D risk.\n\nIntegrating 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":"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"}]}