{"gname":"University of Sydney","grp_id":"50","rels":[{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals","rel_doi":"10.64898\/2026.04.21.26351329","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351329","rel_abs":"Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of Significance To understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.","rel_num_authors":87,"rel_authors":[{"author_name":"Candelaria Vergara","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Zhanmo Ni","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Jun Zhong","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"David McKean","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Katelyn E. Connelly","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Samuel O. Antwi","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Jacksonville FL USA"},{"author_name":"Alan A Arslan","author_inst":"Departments of Obstetrics and Gynecology and Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Paige M. Bracci","author_inst":"Department of Epidemiology and Biostatistics,  University of California San Francisco San Francisco CA USA"},{"author_name":"Mengmeng Du","author_inst":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center,  New York NY USA"},{"author_name":"Steven Gallinger","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Jeanine Genkinger","author_inst":"Department of Epidemiology Columbia University New York NY USA"},{"author_name":"Christopher A Haiman","author_inst":"Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles CA USA"},{"author_name":"Manal Hassan","author_inst":"Department of Gastrointestinal Medical Oncology Houston TX USA"},{"author_name":"Rayjean J. Hung","author_inst":"Lunenfeld-Tanenbaum Research Institute,  Sinai Health System and University of Toronto,  Toronto Ontario Canada"},{"author_name":"Chad Huff","author_inst":"Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston TX USA"},{"author_name":"Charles Kooperberg","author_inst":"Division of Public Health Sciences Fred Hutchinson Cancer Center Seattle WA  USA"},{"author_name":"Fay Kastrinos","author_inst":"Division of Digestive and Liver Diseases Columbia University Irving Medical Center New York NY USA; Herbert Irving Comprehensive Cancer Center Columbia Universi"},{"author_name":"Loic LeMarchand","author_inst":"Cancer Epidemiology Program  University of Hawaii Cancer Center  Honolulu  HI  USA"},{"author_name":"WooHyung Lee","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Shannon M. Lynch","author_inst":"Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia PA USA"},{"author_name":"Stephen C Moore","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Ann L. Oberg","author_inst":"Department of Quantitative Health Sciences  Rochester MN USA"},{"author_name":"Margaret A Park","author_inst":"Department of GI Oncology and Department of Biostatistics and Bioinformatics  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Jennifer B Permuth","author_inst":"Department of Cancer Epidemiology  H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA"},{"author_name":"Harvey A. Risch","author_inst":"Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA"},{"author_name":"Paul Scheet","author_inst":"Dept of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX USA"},{"author_name":"Ann Schwartz","author_inst":"Department of Oncology Wayne State University School of Medicine, Detroit, MI Detriot MI USA"},{"author_name":"Xiao-Ou Shu","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center  Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashv"},{"author_name":"Rachael Z Stolzenberg-Solomon","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Brian M Wolpin","author_inst":"Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School, Harvard University Boston  MA  USA"},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology Department of Medicine, Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine Nashvi"},{"author_name":"Demetrius Albanes","author_inst":"Metabolic Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Gabriella Andreotti","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"William R. Bamlet","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Laura Beane-Freeman","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Sonja I Berndt","author_inst":"Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesd"},{"author_name":"Paul Brennan","author_inst":"International Agency for Research on Cancer Lyon France"},{"author_name":"Julie E Buring","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA"},{"author_name":"Natalia Cabrera-Castro","author_inst":"Department of Epidemiology Murcia Regional Health Council Murcia Spain"},{"author_name":"Daniele Campa","author_inst":"Unit of Genetics. Department of Biology University of Pisa Pisa Italy"},{"author_name":"Federico Canzian","author_inst":"Genomic Epidemiology Group  German Cancer Research Center (DKFZ)  Heidelberg  Germany"},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Yu Chen","author_inst":"Department of Population Health NYU Grossman School of Medicine NYU Perlmutter Comprehensive Cancer Center New York  NY USA"},{"author_name":"Charles C Chung","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"A. Heather Eliassen","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Channing Division of Network Medicine Department of Medicine Brigham and "},{"author_name":"J. Michael Gaziano","author_inst":"Division of Preventive Medicine  Brigham and Womens Hospital  Boston  MA  USA; Division of Aging  Brigham and Womens Hospital  Boston  MA  USA; Boston VA Health"},{"author_name":"Graham G Giles","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  Melbourne  VIC  Australia; Centre for Epidemiology and Biostatistics  Melbourne School of Population and "},{"author_name":"Edward L Giovannucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Michael Goggins","author_inst":"Department of Pathology Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"Phyllis J Goodman","author_inst":"SWOG Statistical Center  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Belynda Hicks","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Amy Hutchinson","author_inst":"Cancer Genomics Research Laboratory Frederick National Lab for Cancer Research Frederick MD USA"},{"author_name":"Miranda R Jones","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA"},{"author_name":"Verena Katzke","author_inst":"Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany"},{"author_name":"Manolis Kogevinas","author_inst":"ISGlobal  Centre for Research in Environmental Epidemiology (CREAL)  Barcelona  Spain; Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?n"},{"author_name":"Robert C. Kurtz","author_inst":"Gastroenterology, Hepatology, and Nutrition Service Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Daniel Laheru","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA"},{"author_name":"I-Min Lee","author_inst":"Division of Preventive Medicine  Department of Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Epidemiology  Harvard T.H. Chan School of P"},{"author_name":"Nu?ria Malats","author_inst":"Genetic and Molecular Epidemiology Group  Spanish National Cancer Research Center (CNIO) Madrid  Spain; CIBERONC Madrid  Spain"},{"author_name":"Roger Milne","author_inst":"Cancer Epidemiology Division  Cancer Council Victoria  East Melbourne  VIC  Australia; Precision Medicine School of Clinical Sciences at Monash Health Monash Un"},{"author_name":"Lorelei Mucci","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA"},{"author_name":"Rachel E. Neale","author_inst":"Department of Population Health QIMR Berghofer Medical Research Institute Queensland Australia"},{"author_name":"Irene Orlow","author_inst":"Department of Epidemiology and Biostatistics,  Memorial Sloan Kettering Cancer Center New York NY USA"},{"author_name":"Alpa V Patel","author_inst":"Epidemiology Research Program  American Cancer Society  Atlanta  GA USA"},{"author_name":"Laia Peruchet","author_inst":"Imperial College London Unitied Kingdom"},{"author_name":"Ulrike Peters","author_inst":"Division of Public Health Sciences  Fred Hutchinson Cancer Research Center  Seattle  WA  USA"},{"author_name":"Miquel Porta","author_inst":"Hospital del Mar Institute of Medical Research (IMIM)  Universitat Auto?noma de Barcelona  Barcelona  Spain"},{"author_name":"Kari G. Rabe","author_inst":"Department of Quantitative Health Sciences Mayo Clinic College of Medicine Rochester MN USA"},{"author_name":"Francisco X Real","author_inst":"Epithelial Carcinogenesis Group Tumor Biology Programme Spanish National Cancer Research Center (CNIO)  Madrid Spain; Department of Medicine and Life Sciences U"},{"author_name":"Fulvio Ricceri","author_inst":"Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH) Department of Clinical and Biological Sciences University of Turin Turin Italy"},{"author_name":"Nathaniel Rothman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Howard D Sesso","author_inst":"Division of Preventive Medicine Brigham and Women?s Hospital  Boston  MA  USA; Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  US"},{"author_name":"Veronica W Setiawan","author_inst":"Department of Population and Public Health Sciences Keck School of Medicine University of Southern California Los Angeles, CA USA"},{"author_name":"Debra Silverman","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Melissa C Southey","author_inst":"Precision Medicine School of Clinical Sciences at Monash Health Monash University Clayton VIC Australia; Department of Clinical Pathology The University of Melb"},{"author_name":"Meir J Stampfer","author_inst":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston  MA  USA; Department of Nutrition Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Geoffrey S Tobias","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Caroline Um","author_inst":"Department of Population Science American Cancer Society  Atlanta  GA USA"},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology Johns Hopkins School of Public Health Baltimore MD USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopki"},{"author_name":"Jean Wactawski-Wende","author_inst":"Department of Epidemiology and Environmental Health University of Buffalo  Buffalo NY USA"},{"author_name":"Nicolas Wentzensen","author_inst":"Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Walter C Willett","author_inst":"Department of Nutrition Harvard T. H. Chan School of Public Health Boston  MA  USA; Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston"},{"author_name":"Herbert Yu","author_inst":"Epidemiology Program  University of Hawaii Cancer Center Honolulu  HI  USA"},{"author_name":"Peter Kraft","author_inst":"Trans-Divisional Research Program (TDRP) Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Priya Duggal","author_inst":"Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"author_name":"Laufey T Amundadottir","author_inst":"Laboratory of Translational Genomics Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health Bethesda  MD USA"},{"author_name":"Alison P. Klein","author_inst":"Department of Oncology, Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA; Department of Epidemiology Johns Hopkins Sc"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Generalizable Deep Learning Framework for Radiotherapy Dose Prediction Across Cancer Sites, Prescriptions and Treatment Modalities","rel_doi":"10.64898\/2026.04.17.26350770","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26350770","rel_abs":"Optimizing radiotherapy dose distributions remain a resource-intensive bottleneck. Existing AI-based dose prediction methods often have limited generalizability because they rely on small, heterogeneous datasets. We present nnDoseNetv2, an auto-configured, end-to-end framework for dose prediction across diverse disease sites (head and neck, prostate, breast, and lung), prescription levels (1.5-84 Gy), and treatment modalities (IMRT, VMAT, and 3D-CRT). By integrating machine-specific beam geometry with 3D structural information, the framework is designed to generalize across varied clinical scenarios. A single multi-site model was trained on 1,000 clinical plans. On sites seen during training, performance was comparable to specialized site-specific models. On unseen sites (liver and whole brain), the model outperformed site-specific models, with mean absolute errors of 2.46% and 6.97% of prescription, respectively. These results suggest that geometric awareness can bridge disparate anatomical domains while eliminating the need for site-specific model maintenance, providing a scalable and high-fidelity approach for personalized radiotherapy planning.","rel_num_authors":9,"rel_authors":[{"author_name":"Ho-hsin Chang","author_inst":"University of Alabama at Birmingham"},{"author_name":"Rex Cardan","author_inst":"University of Alabama at Birmingham"},{"author_name":"Ritish Nedunoori","author_inst":"University of Alabama at Birmingham"},{"author_name":"John Fiveash","author_inst":"University of Alabama at Birmingham"},{"author_name":"Richard Popple","author_inst":"University of Alabama at Birmingham"},{"author_name":"Sandeep Bodduluri","author_inst":"University of Alabama at Birmingham"},{"author_name":"Dennis N Stanley","author_inst":"University of Alabama at Birmingham"},{"author_name":"Joseph Harms","author_inst":"Washington University"},{"author_name":"Carlos Cardenas","author_inst":"University of Alabama at Birmingham"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Artificial Intelligence Agents in Mental Health: A Systematic Review and Meta Analysis","rel_doi":"10.64898\/2026.04.21.26351365","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351365","rel_abs":"The rapid rise of large language models (LLMs) and foundation models has accelerated efforts to build artificial intelligence (AI) agents for mental health assessment, triage, psychotherapy support and clinical decision assistance. Yet a gap persists between healthcare and AI-focused work: while both communities use the language of \"agents,\" clinical research largely describes monolithic chatbots, whereas AI studies emphasize agentic properties such as autonomous planning, multi-agent coordination, tool and database use and integration with multimodal mental health data streams. In this Review, we conduct a systematic analysis of mental health AI agent systems from 2023 to 2025 using a six-dimensional audit framework: (i) system type (base model lineage, interface modality and workflow composition, from rule-based tools to role-aware multi-agent foundation-model systems), (ii) data scope (modalities and provenance, from elicited self-report and chatbot dialogues to electronic health records, biosensing and synthetic corpora), (iii) mental health focus (mapped to ICD-11 diagnostic groupings), (iv) demographics (age strata, geography and sex representation), (v) downstream tasks (screening\/triage, clinical decision support, therapeutic interventions, documentation, ethical-legal support and education\/simulation) and (vi) evaluation types (automated metrics, language quality benchmarks, safety stress tests, expert review and clinician or patient involvement). Across this corpus, we find that most systems (1) concentrate on depression, anxiety and suicidality, with sparse coverage of severe mental illness, neurocognitive disorders, substance use and complex comorbidity; (2) rely heavily on text-based self-report rather than clinically verified longitudinal data or genuinely multimodal inputs; (3) are implemented as single-agent chatbots powered by general-purpose LLMs rather than role-structured, workflow-integrated pipelines; and (4) are evaluated primarily via offline metrics or vignette-based scenarios, with few prospective, clinician- or patient-in-the-loop studies. At the same time, an emerging class of agentic systems assigns foundation models explicit roles as planners, retrieval agents, safety auditors or supervisors coordinating other models and tools. These multi-agent, tool-augmented workflows promise personalization, safety monitoring and greater transparency, but they also introduce new risks around reliability, bias amplification, privacy, regulatory accountability and the blurring of clinical versus non-clinical roles. We conclude by outlining priorities for the next generation of mental health AI agents: clinically grounded, role-aware multi-agent architectures; transparent and privacy-preserving use of clinical and elicited data; demographic and cultural broadening beyond predominantly Western adult samples; and evaluation pipelines that progress from offline benchmarks to longitudinal, real-world studies with routine safety auditing and clear governance of responsibilities between agents and human clinicians.","rel_num_authors":20,"rel_authors":[{"author_name":"Lexuan Zhu","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Wenkong Wang","author_inst":"Shandong University"},{"author_name":"Zhiying Liang","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Wenjia Tan","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Bingyi Chen","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Xinxin Lin","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Zhengdong Wu","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Huizi Yu","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Xiang Li","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Jiyuan Jiao","author_inst":"University of Maryland"},{"author_name":"Sijia He","author_inst":"University of Michigan"},{"author_name":"Guangxin Dai","author_inst":"Shandong University"},{"author_name":"Jiahui Niu","author_inst":"Shandong University"},{"author_name":"Yi Zhong","author_inst":"Peking University Sixth Hospital"},{"author_name":"Wenyue Hua","author_inst":"Microsoft Research"},{"author_name":"Ngan Yin Chan","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Lin Lu","author_inst":"Peking University Third Hospital"},{"author_name":"Yun Kwok Wing","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Xin Ma","author_inst":"Shandong University"},{"author_name":"Lizhou Fan","author_inst":"The Chinese University of Hong Kong"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Missed Opportunities for Stroke Prevention in Hypertensive Patients: A Retrospective Case-Control Study","rel_doi":"10.64898\/2026.04.21.26351407","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26351407","rel_abs":"Background: Hypertension is the leading modifiable risk factor for ischemic stroke, yet the adequacy of preventative hypertension care in routine clinical practice remains suboptimal. Whether gaps in hypertension management represent missed opportunities for stroke prevention remains unclear. Objective: To evaluate the association between hypertension care delivery and the risk of incident ischemic stroke. Methods: We conducted a retrospective, matched, nested case-control study among adults with hypertension using electronic health record data from a large regional health system (2010-2024). Patients with a first-ever ischemic stroke were matched 1:2 to controls on age, sex, race and ethnicity, and calendar time. Three care metrics were assessed during follow-up: (1) outpatient visits with blood pressure (BP) measurement per year; (2) number of antihypertensive medication ingredients; and (3) medication intensification score. Conditional logistic regression estimated adjusted odds ratios (aORs). Results: The study included 13,476 cases and 26,952 matched controls (N = 40,428). Mean (SD) age was 64.8 (12.2) years, 54.1% were female, and mean follow-up was 2,497 (1,308) days. Cases had fewer BP visits per year (median, 2.50 vs. 3.01; p < 0.001), similar number of medication ingredients (2.00 vs 2.00), and lower treatment intensification scores (-0.211 vs -0.125). In adjusted models, >5 BP visits per year was associated with lower stroke odds (aOR, 0.55; 95% CI, 0.51-0.59) compared with [&le;]1 visit. Use of 2-3 medication ingredients (vs 0) was also associated with reduced stroke odds (aOR, 0.80; 95% CI, 0.75-0.86), whereas >3 ingredients was not significant. The highest quartile of treatment intensification showed the strongest association (aOR, 0.47; 95% CI, 0.44-0.51). Findings were consistent across subgroup and sensitivity analyses, including strata defined by baseline SBP and follow-up SBP. Conclusions: Greater engagement in hypertension care was associated with lower odds of ischemic stroke, suggesting that gaps in routine management may represent missed opportunities for prevention.","rel_num_authors":12,"rel_authors":[{"author_name":"Huanhuan Yang","author_inst":"Yale University"},{"author_name":"Yuntian Liu","author_inst":"Yale University"},{"author_name":"Chungsoo Kim","author_inst":"Yale School of Medicine"},{"author_name":"Chenxi Huang","author_inst":"Yale University"},{"author_name":"Mitsuaki Sawano","author_inst":"Yale School of Medicine"},{"author_name":"Patrick Young","author_inst":"Yale School of Medicine"},{"author_name":"Jacob McPadden","author_inst":"Yale New Haven Hospital"},{"author_name":"Mark Anderson","author_inst":"Sentara Health"},{"author_name":"John S Burrows","author_inst":"Sentara Health"},{"author_name":"Harlan M Krumholz","author_inst":"Yale University"},{"author_name":"John E Brush","author_inst":"Sentara Health"},{"author_name":"Yuan Lu","author_inst":"Yale University"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Validation of 3D-DXA-Derived Proximal Femur Measurements Against QCT Across International Clinical Cohorts","rel_doi":"10.64898\/2026.04.22.26351450","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.22.26351450","rel_abs":"Three-dimensional dual-energy X-ray absorptiometry (3D-DXA) reconstructs proximal femur models from standard scans to estimate cortical and trabecular bone parameters. The aim of this study was to evaluate 3D-DXA against quantitative computed tomography (QCT) across independent international cohorts. The study included 537 subjects from four cohorts: an adult population from Spain, a postmenopausal female population from the United States, an osteoarthrosis population and a young population, both from Japan. Subjects underwent both 3D-DXA and QCT imaging. Accuracy was assessed using linear regression and Bland-Altman analysis to evaluate systematic and random errors. 3D-DXA parameters strongly correlated with QCT across all datasets, with correlation coefficients between 0.82 and 0.97. Random errors were consistent across cohorts and ranged between 16.55 and 19.91 mg\/cm3 for integral volumetric bone mineral density (vBMD), between 13.52 and 18.47 mg\/cm3 for trabecular vBMD, and between 9.13 and 11.37 mg\/cm2 for cortical surface bone mineral density (sBMD). Systematic errors ranged between -14.84 and 4.50 mg\/cm3 for integral vBMD, between -8.31 and 14.41 mg\/cm3 for trabecular vBMD, and between -5.58 and 3.21 mg\/cm2 for cortical sBMD. The variations in systematic errors were likely attributable to differences in QCT acquisition protocols. Overall, these results demonstrate consistent agreement between 3D-DXA and QCT across sex, age, ethnicity, geographic regions, and clinical profiles. Taken together, these findings support the use of 3D-DXA as an accurate, non-invasive, and clinically accessible technology for advanced assessment of the cortical and trabecular compartments of the proximal femur.","rel_num_authors":7,"rel_authors":[{"author_name":"Marta I. Bracco","author_inst":"3D-Shaper Medical, Barcelona, Spain"},{"author_name":"Dennis M. Black","author_inst":"University of California San Francisco, CA, USA"},{"author_name":"Teruki Sone","author_inst":"Kawasaki Medical School, Kurashiki, Japan"},{"author_name":"Luis del Rio","author_inst":"CETIR ASCIRES, Barcelona, Spain"},{"author_name":"Silvana Di Gregorio","author_inst":"CETIR ASCIRES, Barcelona, Spain"},{"author_name":"Jorge Malouf","author_inst":"Mineral Metabolism Unit, Grup Creu Groga, Mataro, Spain."},{"author_name":"Ludovic Humbert","author_inst":"3D-Shaper Medical, Barcelona, Spain"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Rare protein-disrupting variants in NPY5R, DLGAP1 and MAPK8IP3 segregate with OCD in two multiplex pedigrees potentially implicating energy homeostasis and post-synaptic signalling in molecular etiology.","rel_doi":"10.64898\/2026.04.21.26350600","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26350600","rel_abs":"Obsessive compulsive disorder (OCD) is significantly heritable, but only a fraction of the contributory genetic variation has been identified, and the molecular etiology involved remains obscure. Identifying rare contributory variants of large effect would be an important milestone in helping to elucidate the mechanisms involved. Analysis of densely affected pedigrees is a potentially useful strategy to bypass the sample size challenges of standard case-control approaches. Here we performed whole genome sequencing (WGS) of 25 individuals across two multiplex OCD pedigrees. We prioritised rare variants using a Bayesian inference approach which incorporates variant pathogenicity and co-segregation with OCD. In the first pedigree, we identified a highly deleterious missense variant in NPY5R, carried by the majority of affected individuals. This gene is brain-expressed and has previously been implicated in panic disorder and internet addiction GWAS studies. In the second pedigree, we identified a large deletion of DLGAP1 and a missense variant in MAPK8IP3, that perfectly co-segregated in a specific branch of the family: both genes have previously been implicated in OCD and autism. Both genes contribute to a protein interaction network including ERBB4 and RAPGEF1 which we had previously identified in a large Tourette Syndrome pedigree. Our analysis suggests that both energy homeostasis and downstream signalling from the post-synaptic density may both be important avenues for future research.","rel_num_authors":10,"rel_authors":[{"author_name":"Cathal Ormond","author_inst":"Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Mathieu Cap","author_inst":"Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Yi-Chieh Chang","author_inst":"Department of Psychiatry, McKnight Brain Institute, Center for OCD, Anxiety, and Related Disorders, University of Florida, Gainesville, Florida, United States o"},{"author_name":"Niamh Ryan","author_inst":"Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Denise Chavira","author_inst":"Department of Psychology, University of California Los Angeles, Los Angeles, California, United States of America"},{"author_name":"Kyle Williams","author_inst":"Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America"},{"author_name":"Jon E Grant","author_inst":"Department of Psychiatry & Behavioral Neuroscience, University of Chicago, Chicago, Illinois, United States of America"},{"author_name":"Carol Mathews","author_inst":"Department of Psychiatry, McKnight Brain Institute, Center for OCD, Anxiety, and Related Disorders, University of Florida, Gainesville, Florida, United States o"},{"author_name":"Elizabeth A Heron","author_inst":"Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland"},{"author_name":"Aiden Corvin","author_inst":"Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Dublin, Ireland"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Common Substrates of Early Illness Severity: Clinical, Genetic, and Brain Evidence","rel_doi":"10.64898\/2026.04.21.26350991","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26350991","rel_abs":"Background: The severity of positive psychotic symptoms largely defines emerging psychosis syndromes. However, depressive and negative symptoms are strongly psychologically and biologically interlinked. A transdiagnostic exploration of symptom severity across early illness syndromes could enhance the understanding of shared common factors and future trajectories of mental illness. We aimed to identify subgroups based on the severity of positive, negative, and depressive symptoms and assess relationships with: 1) premorbid functioning, 2) longitudinal illness course, 3) genetic risk, and 4) brain volume differences. Methods: We analysed 749 participants from a multisite, naturalistic, longitudinal (18 months) cohort study of: clinical high risk for psychosis (n=147), recent onset psychosis (n=161), and healthy controls (n=286), and recent onset depression (n=155). Participants were stratified into subgroups based on severity of baseline positive, negative, and depression symptoms. Baseline and longitudinal differences between groups for clinical, functioning, and polygenic risk scores (schizophrenia, depression, cross-disorder) were assessed with ANOVAs and linear mixed models. Voxel-based morphometry was used to examine whole-brain grey matter volume differences. Discovery findings were replicated in a held-out sample (n=610). Results: Participants were stratified into no (n=241), mild (n=50), moderate (n=182), and severe symptom (n=254) subgroups. The mean (SD) age was 25.3 (6.0) and 344 (47.3%) were male. Symptom severity was associated with poorer premorbid functioning and illness trajectory, greater genetic risk, and lower brain volume. Findings were not confounded by the original study groups or symptoms and were largely replicated. Conclusions and relevance: Transdiagnostic symptom severity is linked to shared aetiologies, prognoses, and biological markers across diagnoses and illness stages. Such commonalities could guide therapeutic selection and future research aiming to detect unique contributions to specific psychopathologies.","rel_num_authors":46,"rel_authors":[{"author_name":"Rochelle Ruby Ye","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia"},{"author_name":"Clara Vetter","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Sidhant Chopra","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia"},{"author_name":"Stephen Wood","author_inst":"Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia"},{"author_name":"Aswin Ratheesh","author_inst":"Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia"},{"author_name":"Shane Cross","author_inst":"Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University, Melbourne, Australia"},{"author_name":"Jente Meijer","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia"},{"author_name":"Anoja Tahanabalasingam","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia"},{"author_name":"Paris Lalousis","author_inst":"Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom"},{"author_name":"Nora Penzel","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Linda A. Antonucci","author_inst":"Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy"},{"author_name":"Shalaila S. Haas","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Madalina-Octavia Buciuman","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Rachele Sanfelici","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Lisa-Maria Neuner","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Maria Fernanda Urquijo-Castro","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"David Popovic","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Theresa Lichtenstein","author_inst":"Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany"},{"author_name":"Marlene Rosen","author_inst":"Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany"},{"author_name":"Katharine Chisholm","author_inst":"School of Psychology, University of Sussex, Falmer, East Sussex, England"},{"author_name":"Alexandra Korda","author_inst":"Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lubeck, Lubeck, Germany"},{"author_name":"Georg Romer","author_inst":"Department of Child and Adolescent Psychiatry, Pyschosomatics and Psychotherapy, University of Muenster, Germany"},{"author_name":"Carlo Maj","author_inst":"Center for Human Genetics, University of Marburg, Marbug, Germany"},{"author_name":"Anastasia Theodoridou","author_inst":"Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland"},{"author_name":"Anita Ricecher-Rossler","author_inst":"Department of Psychiatry, University of Basel, Switzerland"},{"author_name":"Christos Pantelis","author_inst":"Department of Psychiatry, University of Melbourne & Melbourne Health, Melbourne, Australia"},{"author_name":"Jarmo Hietala","author_inst":"Depart of Psychiatry, University of Turku, Turku, Finland"},{"author_name":"Rebekka Lencer","author_inst":"Institution for Translational Psychiatry, University of Muenster, Muenster, Germany"},{"author_name":"Alessandro Bertolino","author_inst":"Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy"},{"author_name":"Stefan Borgwardt","author_inst":"Department of Psychiatry and Psychotherapy, University of Lubeck, Lubeck, Germany"},{"author_name":"Markus Noethen","author_inst":"Institute of Human Genetics, University of Bonn, Bonn, Germany"},{"author_name":"Paolo Brambilla","author_inst":"Department of Neurosciences and Mental Health, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico of Milan, Milan, Italy"},{"author_name":"Stephan Ruhrmann","author_inst":"Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany"},{"author_name":"Eva Meisenzahl","author_inst":"Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Dusseldorf, Germany"},{"author_name":"Raimo K. R. Salonkangas","author_inst":"Department of Psychiatry, University of Turku, Finland"},{"author_name":"Joseph Kambeitz","author_inst":"Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany"},{"author_name":"Lana Kambeitz-Ilankovic","author_inst":"Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany"},{"author_name":"Peter Falkai","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Rachel Upthegrove","author_inst":"Department of Psychiatry, University of Oxford, United Kingdom"},{"author_name":"Frauke Schultze-Lutter","author_inst":"Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Dusseldorf, Germany"},{"author_name":"Nikolaos Koutsouleris","author_inst":"Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany"},{"author_name":"Patrick McGorry","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia"},{"author_name":"Cassandra Wannan","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia"},{"author_name":"Barnaby Nelson","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia"},{"author_name":"Dominic Dwyer","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia"},{"author_name":"- PRONIA Consortium","author_inst":"-"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Patterns of maternal transport in a state with levels of maternal care and no formal perinatal regions","rel_doi":"10.64898\/2026.04.20.26351263","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351263","rel_abs":"Purpose: To characterize maternal transport patterns in Iowa, a state with levels of maternal care and without formal perinatal regions, and assess whether transport decisions reflect efficient, risk-appropriate coordination. Methods: We analyzed 2010-2023 Iowa birth records, which included 2,251 maternal transports between obstetric facilities across 106 unique routes. We characterized transport patterns and applied a community detection algorithm to identify \"communities\" of obstetric facilities that disproportionately transport among themselves. Findings: Suburban and rural counties have elevated transport rates compared to urban counties. 2,189 transports (97%) were from lower- to higher-level facilities. Among these, 2,037 (93%) were to Level III tertiary care centers. 567 transports (25.2%) bypassed a closer facility offering an equivalent or higher level of care than its destination facility. Health system affiliation was associated with bypassing transport, indicating potential organizational rather than purely geographic drivers of transport decisions. Three \"communities\" of obstetric facilities largely shaped by geographic proximity were identified. Conclusions: Although Iowa does not have formal perinatal regions, patterns of maternal transport are mostly in line with three de facto regions. Some potential inefficiencies were identified, such as obstetric facilities transporting to a farther facility when a closer facility offered the same level of care or higher. These findings may help identify opportunities to enhance care coordination among obstetric facilities, optimize maternal transport networks, and improve regionalization of maternal care.","rel_num_authors":5,"rel_authors":[{"author_name":"Jingyu Li","author_inst":"Georgia Institute of Technology"},{"author_name":"Lauren N Steimle","author_inst":"Georgia Institute of Technology"},{"author_name":"Margaret Carrel","author_inst":"University of Iowa"},{"author_name":"Riley A Byrd","author_inst":"Georgia Institute of Technology"},{"author_name":"Stephanie M Radke","author_inst":"University of Iowa"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Patterns of maternal transport in a state with levels of maternal care and no formal perinatal regions","rel_doi":"10.64898\/2026.04.20.26351263","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351263","rel_abs":"Purpose: To characterize maternal transport patterns in Iowa, a state with levels of maternal care and without formal perinatal regions, and assess whether transport decisions reflect efficient, risk-appropriate coordination. Methods: We analyzed 2010-2023 Iowa birth records, which included 2,251 maternal transports between obstetric facilities across 106 unique routes. We characterized transport patterns and applied a community detection algorithm to identify \"communities\" of obstetric facilities that disproportionately transport among themselves. Findings: Suburban and rural counties have elevated transport rates compared to urban counties. 2,189 transports (97%) were from lower- to higher-level facilities. Among these, 2,037 (93%) were to Level III tertiary care centers. 567 transports (25.2%) bypassed a closer facility offering an equivalent or higher level of care than its destination facility. Health system affiliation was associated with bypassing transport, indicating potential organizational rather than purely geographic drivers of transport decisions. Three \"communities\" of obstetric facilities largely shaped by geographic proximity were identified. Conclusions: Although Iowa does not have formal perinatal regions, patterns of maternal transport are mostly in line with three de facto regions. Some potential inefficiencies were identified, such as obstetric facilities transporting to a farther facility when a closer facility offered the same level of care or higher. These findings may help identify opportunities to enhance care coordination among obstetric facilities, optimize maternal transport networks, and improve regionalization of maternal care.","rel_num_authors":5,"rel_authors":[{"author_name":"Jingyu Li","author_inst":"Georgia Institute of Technology"},{"author_name":"Lauren N Steimle","author_inst":"Georgia Institute of Technology"},{"author_name":"Margaret Carrel","author_inst":"University of Iowa"},{"author_name":"Riley A Byrd","author_inst":"Georgia Institute of Technology"},{"author_name":"Stephanie M Radke","author_inst":"University of Iowa"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Design and preliminary safety validation of a hybrid deterministic-AI triage system for multilingual primary healthcare: a WhatsApp-based vignette study in South Africa","rel_doi":"10.64898\/2026.04.21.26349781","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.21.26349781","rel_abs":"Background: Fragmented patient flow and inadequate pre-arrival triage contribute to avoidable morbidity in South African primary healthcare (PHC). No multilingual digital triage system currently operates across all 11 official South African languages while aligning with the South African Triage Scale (SATS). This paper describes the design, safety architecture, and preliminary safety assessment of BIZUSIZO, a WhatsApp-based AI-assisted triage and patient navigation system with a deterministic clinical safety layer. Methods: BIZUSIZO delivers SATS-aligned clinical triage via WhatsApp Business API, combining AI-assisted free-text classification with a deterministic clinical rules engine that hard-codes overrides for eight categories of life-threatening emergency across four languages, independent of AI. One hundred and twenty clinical vignettes were designed using SATS discriminators, written in patient language across four South African languages (English, isiZulu, isiXhosa, and Afrikaans; 30 cases per language), and processed through the complete triage pipeline. Results were scored against gold standard triage levels assigned by the developer using SATS clinical discriminators, with clinical governance review from a registered nurse. Results: The system achieved 0% under-triage (0\/117 evaluable cases), with zero RED emergencies missed across all languages tested. Exact concordance was 81.7% (98\/117), exceeding the 70% SATS validation benchmark, with 99.1% agreement within one SATS level. The deterministic rules engine correctly identified all 20 life-threatening emergency presentations with 100% sensitivity, independent of AI. Quadratic weighted Cohen's kappa was 0.910, indicating almost perfect agreement. Conclusions: In a developer-led preliminary safety assessment, a clinically-governed AI-assisted digital triage system achieved zero missed RED emergencies across 117 evaluable vignettes in four South African languages, with a deterministic safety layer providing AI-independent emergency detection. These findings support progression to formal prospective validation with real patients. Formal concordance validation across all 11 supported languages and against prospective nurse triage decisions is needed before clinical deployment.","rel_num_authors":1,"rel_authors":[{"author_name":"Bongekile E Nkosi-Mjadu","author_inst":"University of California, Berkeley"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Determinants of DNA-sequence-based Diagnostic Yield in the CSER Consortium","rel_doi":"10.64898\/2026.04.20.26351140","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351140","rel_abs":"Purpose: Diagnostic yield from exome and genome sequencing varies widely across studies. It remains unclear how much of this variation reflects patient-level factors (e.g., sex, clinical features, race\/ethnicity, genetic ancestry) versus site-level practices such as sequencing modality or variant interpretation workflows. We aimed to quantify the contributions of these factors to diagnostic outcomes across five U.S. clinical sequencing sites. Methods: We performed a cross-sectional analysis of 3,008 prenatal, neonatal, and pediatric cases from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium (2017-2023). Clinical indications spanned neurodevelopmental, neurological, immunological, metabolic, craniofacial, skeletal, cardiac, prenatal, and oncologic presentations. Genetic ancestry was inferred from sequencing data, and variants were interpreted using ACMG\/AMP guidelines to classify DNA-based diagnoses. Generalized linear mixed models were used to estimate associations between diagnostic yield and fixed effects (sex, prenatal status, isolated cancer, number of clinical indications, sequencing modality, race\/ethnicity, and genetic ancestry), while modeling study site as a random effect to quantify between-site variation. Results: The overall diagnostic yield was 19.0%. Multiple clinical indications (OR=1.47, 95% CI 1.20-1.80, p<0.001) were associated with higher diagnostic yield, and male sex (OR=0.80, 95% CI 0.66-0.96, p=0.017) and prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012) were associated with lower yield. Sequencing modality, race\/ethnicity, genetic ancestry, and isolated cancer were not statistically significantly associated with diagnostic outcomes. A model without fixed effects attributed ~10% of variance in diagnostic yield to between-site differences. After adjusting for covariates, site-level variance decreased to 5.7%, indicating consistent variation across sites not explained by measured patient factors. Conclusion: Across five sites, patient-level clinical features influenced diagnostic yield, but substantial site-level variation remained even after adjustment. Differences in variant interpretation, or case-classification practices may contribute to this residual variability. Further efforts to increase consistency in exome and genome-sequencing diagnostic workflows may help reduce inter-site differences.","rel_num_authors":19,"rel_authors":[{"author_name":"Yusuph Mavura","author_inst":"Department of Biomedical Data Science, Stanford University, Palo Alto, CA"},{"author_name":"David Crosslin","author_inst":"BioMed. Informatics and Genomics, John W. Deming Dept. of Med., Tulane Univ. Sch. of Med., New Orleans, LA"},{"author_name":"Kathleen DM Ferar","author_inst":"Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY."},{"author_name":"James MJ Lawlor","author_inst":"HudsonAlpha Inst. for Biotechnology, Huntsville, AL"},{"author_name":"John M. Greally","author_inst":"Departments of Genetics and Pediatrics, Albert Einstein College of Medicine, Bronx, NY"},{"author_name":"Lucia Hindorff","author_inst":"NHGRI, NIH, Bethesda, MD"},{"author_name":"Gail P Jarvik","author_inst":"Medicine (Medical Genetics) and Genome Sciences, Univ. of Washington Med. Ctr., Seattle, WA"},{"author_name":"Sara Kalla","author_inst":"HGSC - Baylor Coll. of Med., Houston, TX"},{"author_name":"Barbara A Koenig","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Program in Bioethics, University of California San Francisco, San Franc"},{"author_name":"Mark Kvale","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA"},{"author_name":"Pui-Yan Kwok","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Cardiovascular Research Institute and Department of Dermatology, Univer"},{"author_name":"Mary Norton","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecolo"},{"author_name":"Sharon E. Plon","author_inst":"Molecular and Human Genetics Dept., Baylor Coll. Med., Houston, TX; Pediatrics-Hematology-Oncology Dept, Baylor Coll. of Med., Houston, TX; Cancer Genomics Prog"},{"author_name":"Bradford C. Powell","author_inst":"Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC"},{"author_name":"Anne Slavotinek","author_inst":"Division of Human Genetics, Cincinnati Children's Hospital Medical Center, OH"},{"author_name":"Michelle L Thompson","author_inst":"Department of Pathology and Immunology, Washington University, St. Louis, MO"},{"author_name":"Alice B Popejoy","author_inst":"Division of Epidemiology, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, CA; Population Sciences and Health D"},{"author_name":"Eimear E. Kenny","author_inst":"The Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY; Center for Translational Genomics, Icahn School of Medicine, New York, "},{"author_name":"Neil Risch","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California "}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Determinants of DNA-sequence-based Diagnostic Yield in the CSER Consortium","rel_doi":"10.64898\/2026.04.20.26351140","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351140","rel_abs":"Purpose: Diagnostic yield from exome and genome sequencing varies widely across studies. It remains unclear how much of this variation reflects patient-level factors (e.g., sex, clinical features, race\/ethnicity, genetic ancestry) versus site-level practices such as sequencing modality or variant interpretation workflows. We aimed to quantify the contributions of these factors to diagnostic outcomes across five U.S. clinical sequencing sites. Methods: We performed a cross-sectional analysis of 3,008 prenatal, neonatal, and pediatric cases from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium (2017-2023). Clinical indications spanned neurodevelopmental, neurological, immunological, metabolic, craniofacial, skeletal, cardiac, prenatal, and oncologic presentations. Genetic ancestry was inferred from sequencing data, and variants were interpreted using ACMG\/AMP guidelines to classify DNA-based diagnoses. Generalized linear mixed models were used to estimate associations between diagnostic yield and fixed effects (sex, prenatal status, isolated cancer, number of clinical indications, sequencing modality, race\/ethnicity, and genetic ancestry), while modeling study site as a random effect to quantify between-site variation. Results: The overall diagnostic yield was 19.0%. Multiple clinical indications (OR=1.47, 95% CI 1.20-1.80, p<0.001) were associated with higher diagnostic yield, and male sex (OR=0.80, 95% CI 0.66-0.96, p=0.017) and prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012) were associated with lower yield. Sequencing modality, race\/ethnicity, genetic ancestry, and isolated cancer were not statistically significantly associated with diagnostic outcomes. A model without fixed effects attributed ~10% of variance in diagnostic yield to between-site differences. After adjusting for covariates, site-level variance decreased to 5.7%, indicating consistent variation across sites not explained by measured patient factors. Conclusion: Across five sites, patient-level clinical features influenced diagnostic yield, but substantial site-level variation remained even after adjustment. Differences in variant interpretation, or case-classification practices may contribute to this residual variability. Further efforts to increase consistency in exome and genome-sequencing diagnostic workflows may help reduce inter-site differences.","rel_num_authors":19,"rel_authors":[{"author_name":"Yusuph Mavura","author_inst":"Department of Biomedical Data Science, Stanford University, Palo Alto, CA"},{"author_name":"David Crosslin","author_inst":"BioMed. Informatics and Genomics, John W. Deming Dept. of Med., Tulane Univ. Sch. of Med., New Orleans, LA"},{"author_name":"Kathleen DM Ferar","author_inst":"Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY."},{"author_name":"James MJ Lawlor","author_inst":"HudsonAlpha Inst. for Biotechnology, Huntsville, AL"},{"author_name":"John M. Greally","author_inst":"Departments of Genetics and Pediatrics, Albert Einstein College of Medicine, Bronx, NY"},{"author_name":"Lucia Hindorff","author_inst":"NHGRI, NIH, Bethesda, MD"},{"author_name":"Gail P Jarvik","author_inst":"Medicine (Medical Genetics) and Genome Sciences, Univ. of Washington Med. Ctr., Seattle, WA"},{"author_name":"Sara Kalla","author_inst":"HGSC - Baylor Coll. of Med., Houston, TX"},{"author_name":"Barbara A Koenig","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Program in Bioethics, University of California San Francisco, San Franc"},{"author_name":"Mark Kvale","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA"},{"author_name":"Pui-Yan Kwok","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Cardiovascular Research Institute and Department of Dermatology, Univer"},{"author_name":"Mary Norton","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecolo"},{"author_name":"Sharon E. Plon","author_inst":"Molecular and Human Genetics Dept., Baylor Coll. Med., Houston, TX; Pediatrics-Hematology-Oncology Dept, Baylor Coll. of Med., Houston, TX; Cancer Genomics Prog"},{"author_name":"Bradford C. Powell","author_inst":"Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC"},{"author_name":"Anne Slavotinek","author_inst":"Division of Human Genetics, Cincinnati Children's Hospital Medical Center, OH"},{"author_name":"Michelle L Thompson","author_inst":"Department of Pathology and Immunology, Washington University, St. Louis, MO"},{"author_name":"Alice B Popejoy","author_inst":"Division of Epidemiology, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, CA; Population Sciences and Health D"},{"author_name":"Eimear E. Kenny","author_inst":"The Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY; Center for Translational Genomics, Icahn School of Medicine, New York, "},{"author_name":"Neil Risch","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California "}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Determinants of DNA-sequence-based Diagnostic Yield in the CSER Consortium","rel_doi":"10.64898\/2026.04.20.26351140","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351140","rel_abs":"Purpose: Diagnostic yield from exome and genome sequencing varies widely across studies. It remains unclear how much of this variation reflects patient-level factors (e.g., sex, clinical features, race\/ethnicity, genetic ancestry) versus site-level practices such as sequencing modality or variant interpretation workflows. We aimed to quantify the contributions of these factors to diagnostic outcomes across five U.S. clinical sequencing sites. Methods: We performed a cross-sectional analysis of 3,008 prenatal, neonatal, and pediatric cases from the NHGRI Clinical Sequencing Evidence-Generating Research (CSER) consortium (2017-2023). Clinical indications spanned neurodevelopmental, neurological, immunological, metabolic, craniofacial, skeletal, cardiac, prenatal, and oncologic presentations. Genetic ancestry was inferred from sequencing data, and variants were interpreted using ACMG\/AMP guidelines to classify DNA-based diagnoses. Generalized linear mixed models were used to estimate associations between diagnostic yield and fixed effects (sex, prenatal status, isolated cancer, number of clinical indications, sequencing modality, race\/ethnicity, and genetic ancestry), while modeling study site as a random effect to quantify between-site variation. Results: The overall diagnostic yield was 19.0%. Multiple clinical indications (OR=1.47, 95% CI 1.20-1.80, p<0.001) were associated with higher diagnostic yield, and male sex (OR=0.80, 95% CI 0.66-0.96, p=0.017) and prenatal status (OR=0.63, 95% CI 0.44-0.90, p=0.012) were associated with lower yield. Sequencing modality, race\/ethnicity, genetic ancestry, and isolated cancer were not statistically significantly associated with diagnostic outcomes. A model without fixed effects attributed ~10% of variance in diagnostic yield to between-site differences. After adjusting for covariates, site-level variance decreased to 5.7%, indicating consistent variation across sites not explained by measured patient factors. Conclusion: Across five sites, patient-level clinical features influenced diagnostic yield, but substantial site-level variation remained even after adjustment. Differences in variant interpretation, or case-classification practices may contribute to this residual variability. Further efforts to increase consistency in exome and genome-sequencing diagnostic workflows may help reduce inter-site differences.","rel_num_authors":19,"rel_authors":[{"author_name":"Yusuph Mavura","author_inst":"Department of Biomedical Data Science, Stanford University, Palo Alto, CA"},{"author_name":"David Crosslin","author_inst":"BioMed. Informatics and Genomics, John W. Deming Dept. of Med., Tulane Univ. Sch. of Med., New Orleans, LA"},{"author_name":"Kathleen DM Ferar","author_inst":"Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY."},{"author_name":"James MJ Lawlor","author_inst":"HudsonAlpha Inst. for Biotechnology, Huntsville, AL"},{"author_name":"John M. Greally","author_inst":"Departments of Genetics and Pediatrics, Albert Einstein College of Medicine, Bronx, NY"},{"author_name":"Lucia Hindorff","author_inst":"NHGRI, NIH, Bethesda, MD"},{"author_name":"Gail P Jarvik","author_inst":"Medicine (Medical Genetics) and Genome Sciences, Univ. of Washington Med. Ctr., Seattle, WA"},{"author_name":"Sara Kalla","author_inst":"HGSC - Baylor Coll. of Med., Houston, TX"},{"author_name":"Barbara A Koenig","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Program in Bioethics, University of California San Francisco, San Franc"},{"author_name":"Mark Kvale","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA"},{"author_name":"Pui-Yan Kwok","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Cardiovascular Research Institute and Department of Dermatology, Univer"},{"author_name":"Mary Norton","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecolo"},{"author_name":"Sharon E. Plon","author_inst":"Molecular and Human Genetics Dept., Baylor Coll. Med., Houston, TX; Pediatrics-Hematology-Oncology Dept, Baylor Coll. of Med., Houston, TX; Cancer Genomics Prog"},{"author_name":"Bradford C. Powell","author_inst":"Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC"},{"author_name":"Anne Slavotinek","author_inst":"Division of Human Genetics, Cincinnati Children's Hospital Medical Center, OH"},{"author_name":"Michelle L Thompson","author_inst":"Department of Pathology and Immunology, Washington University, St. Louis, MO"},{"author_name":"Alice B Popejoy","author_inst":"Division of Epidemiology, Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, CA; Population Sciences and Health D"},{"author_name":"Eimear E. Kenny","author_inst":"The Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY; Center for Translational Genomics, Icahn School of Medicine, New York, "},{"author_name":"Neil Risch","author_inst":"Institute for Human Genetics, University of California San Francisco, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California "}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Proposed Classification System for the 445 nm Blue Light Laser for Treatment of Laryngeal Lesions","rel_doi":"10.64898\/2026.04.20.26351290","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351290","rel_abs":"Objective: Only preliminary investigations on the use of the 445 nanometer wavelength blue light laser (BLL) for various laryngeal pathologies have been described. Currently, no standard exists for reporting treatment technique and tissue effect with this modality. Here, we aim to establish and validate a classification system to describe laser-induced tissue effects. Study Design: Retrospective video-based study for classification development and reliability validation. Methods: Video recordings from procedures performed with the BLL by multiple academic laryngologists were retrospectively reviewed. A preliminary 6-point classification (BLL 1-6) was developed based on expert consensus. Thirteen additional procedural clips were independently rated utilizing the classification schema to assess perceived tissue effect, and measure inter- and intra-rate reliability. Results: The final 5-point classification system (BLL 1-5) included angiolysis, blanching, tissue vaporization, ablation with mechanical tissue removal, and cutting. The consensus of the combined reviewers in rating all cases was 89% (58 of 65). Complete consensus was not achieved in 11% (7\/65) of cases. Of those incorrect, 57% (4\/7) were of clips illustrating the BLL-2 classification. Intra-rater reliability amongst the reviewers was 100%. Conclusion: Tissue effect of the 445 nm blue light laser can reliably be standardized with this proposed classification system. This rating system can be used to facilitate future systematic study of outcomes and effective communication between laryngologists and trainees.","rel_num_authors":8,"rel_authors":[{"author_name":"Mahnoor Khan","author_inst":"University of South Florida Morsani College of Medicine"},{"author_name":"Atif M Islam","author_inst":"University of South Florida Morsani College of Medicine"},{"author_name":"Yassmeen Abdel-Aty","author_inst":"University of South Florida Morsani College of Medicine"},{"author_name":"David Rosow","author_inst":"University of Miami Miller School of Medicine"},{"author_name":"Pavan Mallur","author_inst":"Harvard Medical School, Beth Israel Deaconess Medical Center"},{"author_name":"Michael Johns","author_inst":"University of Southern California, Keck School of Medicine"},{"author_name":"Clark Alan Rosen","author_inst":"University of California San Francisco"},{"author_name":"Yael E Bensoussan","author_inst":"University of South Florida Morsani College of Medicine"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"The burden of neurogenic orthostatic hypotension in patients with multiple system atrophy: a real-world study","rel_doi":"10.64898\/2026.04.20.26351214","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351214","rel_abs":"Background: Although neurogenic orthostatic hypotension (nOH) is a common and debilitating feature of multiple system atrophy (MSA), little is known about the burden of symptoms in the real world. Objectives: To design and conduct a cross-sectional community-based research survey targeting patients with MSA, with and without nOH. Methods: We recruited patients with MSA to complete an anonymous online survey covering three core themes: 1) timely diagnosis, 2) nOH pharmacotherapy and refractory symptoms, and 3) confidence in physician knowledge. Responses were grouped by pre-specified diagnostic certainty levels. Relationships between symptoms, function, and pharmacotherapy were assessed using univariate and multivariate methods. Results: We analyzed 259 respondents with a self-reported diagnosis of MSA (age: M=64.38, SD=8.09 years; 44% female). In total, 42% also had a diagnosis nOH; 40% had symptoms highly suspicious of nOH, but no diagnosis; and 21% reported having never had their blood pressure measured in the standing position at a clinical visit. Treatment with a pressor agent was independently associated with the presence of other symptoms of autonomic failure. Each additional nOH symptom reported increased the odds of requiring pharmacotherapy by 18%. Yet, despite anti-hypotensive medication use, 97% of patients reported limitations in their ability to bathe, cook, or arise from a chair\/bed with 76% needing caregiver support for refractory nOH symptoms. Conclusions: This cross-sectional representative sample shows nOH is underrecognized and undertreated in MSA patients, leading to substantial functional limitations. It is our hope that these findings are leveraged for planning future trials and advocating for better treatments","rel_num_authors":14,"rel_authors":[{"author_name":"Matthew J Kmiecik","author_inst":"Theravance Biopharma US, LLC"},{"author_name":"Leah O'Brien","author_inst":"Theravance Biopharma US, LLC"},{"author_name":"Molly Szpyhulsky","author_inst":"Theravance Biopharma US, LLC"},{"author_name":"Valeria Iodice","author_inst":"University College London Queen Square Institute of Neurology"},{"author_name":"Roy Freeman","author_inst":"Harvard Medical School"},{"author_name":"Jens Jordan","author_inst":"University of Cologne"},{"author_name":"Italo Biaggioni","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Horacio Kaufmann","author_inst":"New York University School of Medicine"},{"author_name":"Ross Vickery","author_inst":"Theravance Biopharma US, LLC"},{"author_name":"Aine Miller","author_inst":"Theravance Biopharma US, LLC"},{"author_name":"Emma Saunders","author_inst":"Multiple System Atrophy Trust"},{"author_name":"Emma Rushton","author_inst":"Multiple System Atrophy Trust"},{"author_name":"Lesley Valle","author_inst":"Klick Health"},{"author_name":"Lucy Norcliffe-Kaufmann","author_inst":"Theravance Biopharma US, LLC"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Plasma inflammatory markers and brain white matter microstructure in late middle-aged and older adults","rel_doi":"10.64898\/2026.04.20.26351124","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351124","rel_abs":"Chronic inflammation is a common feature of aging and is observed across various age-related neurodegenerative diseases, including Alzheimer's disease (AD). It has, however, been challenging to develop measurements of brain structure directly linked to peripheral measures of neuroinflammation. This cross-sectional study examined whether plasma levels of markers related to inflammation are associated with diffusion magnetic resonance imaging (dMRI) measures of white matter microstructure: mean diffusivity (MD) and Neurite Orientation Dispersion and Density Imaging (NODDI) free water fraction (FWF) and orientation dispersion index (ODI). Participants included 457 dementia-free individuals (mean age=63.82, SD=7.63). Blood plasma markers related to inflammation included two measures of systemic inflammation, (1) high-sensitivity C-reactive protein (CRP), and (2) a composite of pro-inflammatory cytokines (IL-1a, IL-1b, IL-2, IL-6, IL-8, TNF-a, TNF-b), as well as (3) glial fibrillary acidic protein (GFAP), a measure of astrocytic activation. Higher cytokine composite levels were associated with higher values of all three measures (FWF, ODI, MD) in cerebral white matter, and with higher ODI in the cerebellar peduncles. Higher CRP levels were associated with higher ODI in cerebral and cerebellar white matter. Associations with GFAP were not significant after adjusting for multiple comparisons. Results were consistent after accounting for plasma biomarkers of AD pathology (p-tau181\/AB42). Thus, higher levels of peripheral pro-inflammatory markers are associated with white matter microstructure (higher FWF, ODI, and MD), supporting the view that these dMRI-based metrics are sensitive to inflammatory processes. Additionally, the sensitivity of dMRI-based measures to inflammation may differ by inflammatory marker types.","rel_num_authors":12,"rel_authors":[{"author_name":"Siona Mishra","author_inst":"Johns Hopkins University"},{"author_name":"Corinne Pettigrew","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Chidi Ugonna","author_inst":"University of Arizona"},{"author_name":"Nan-kuei Chen","author_inst":"University of Arizona"},{"author_name":"Jennifer B Frye","author_inst":"University of Arizona"},{"author_name":"Kristian P Doyle","author_inst":"University of Arizona"},{"author_name":"Lee Ryan","author_inst":"University of Arizona"},{"author_name":"Marilyn Albert","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Sara Grace Ho","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Abhay Moghekar","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Anja Soldan","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Elizabeth R Paitel","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"The Impact of Malnutrition on Host Responses to Severe Infection in Adults: A Multicenter Analysis from Uganda","rel_doi":"10.64898\/2026.04.20.26351315","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351315","rel_abs":"Objective: Studies of nutritional status and host responses during severe and critical illness have focused predominantly on obesity; in contrast, the relationship between undernutrition, host responses, and clinical outcomes in adults hospitalized with severe infection remains poorly defined. We sought to determine whether severe undernutrition is associated with distinct host responses and clinical outcomes in adults hospitalized with severe infection. Design: Prospective cohort study. Setting: Two public referral hospitals in Uganda. Patients: Non-pregnant adults ([&ge;]18 yr) hospitalized with severe, undifferentiated infection. Interventions: None. Measurements and Main Results: We analyzed clinical data and serum Olink proteomic data from 432 participants (median age, 45 yr [IQR, 31-57 yr]; 44% male). Overall, 213 participants (49%) met prespecified criteria for undernutrition, including 52 (12%) with severe undernutrition. Clinically, severe undernutrition was associated with HIV coinfection, microbiologically diagnosed tuberculosis, greater physiological instability, and higher mortality. After adjustment for age, sex, illness duration, study site, and HIV, malaria, and tuberculosis coinfection, severe undernutrition was associated with higher expression of proteins involved in pro-inflammatory immune signaling, endothelial and vascular remodeling, hypoxia and oxidative stress responses, and extracellular matrix remodeling, together with lower expression of proteins linked to growth signaling, anticoagulant regulation, and lipid homeostasis. Conclusions: Severe undernutrition is associated with a distinct high-risk clinical phenotype and biologic signature in adults hospitalized with severe infection. These findings suggest that undernutrition may potentiate key domains of sepsis pathobiology, with implications for strengthening nutritional support and informing host-directed treatment strategies in low- and middle-income countries where malnutrition is common.","rel_num_authors":19,"rel_authors":[{"author_name":"Gabriel Conte Cortez Martins","author_inst":"Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA"},{"author_name":"Julius J. Lutwama","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Nicholas Owor","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Joyce Namulondo","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Jesse E. Ross","author_inst":"Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, N"},{"author_name":"Xuan Lu","author_inst":"Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, N"},{"author_name":"Ignatius Asasira","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Tonny Kiyingi","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Christopher Nsereko","author_inst":"Entebbe Regional Referral Hospital, Ministry of Health, Entebbe, Uganda"},{"author_name":"John Bosco Nsubuga","author_inst":"Entebbe Regional Referral Hospital, Ministry of Health, Entebbe, Uganda"},{"author_name":"Joseph Shinyale","author_inst":"Entebbe Regional Referral Hospital, Ministry of Health, Entebbe, Uganda"},{"author_name":"Moses Kiwubeyi","author_inst":"Tororo General Hospital, Ministry of Health, Tororo, Uganda"},{"author_name":"Rittah Nankwanga","author_inst":"Tororo General Hospital, Ministry of Health, Tororo, Uganda"},{"author_name":"Kai Nie","author_inst":"Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Steven J. Reynolds","author_inst":"Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"John Kayiwa","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Seunghee Kim-Schulze","author_inst":"Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Barnabas Bakamutumaho","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Matthew Cummings","author_inst":"Columbia University"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"The Impact of Malnutrition on Host Responses to Severe Infection in Adults: A Multicenter Analysis from Uganda","rel_doi":"10.64898\/2026.04.20.26351315","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351315","rel_abs":"Objective: Studies of nutritional status and host responses during severe and critical illness have focused predominantly on obesity; in contrast, the relationship between undernutrition, host responses, and clinical outcomes in adults hospitalized with severe infection remains poorly defined. We sought to determine whether severe undernutrition is associated with distinct host responses and clinical outcomes in adults hospitalized with severe infection. Design: Prospective cohort study. Setting: Two public referral hospitals in Uganda. Patients: Non-pregnant adults ([&ge;]18 yr) hospitalized with severe, undifferentiated infection. Interventions: None. Measurements and Main Results: We analyzed clinical data and serum Olink proteomic data from 432 participants (median age, 45 yr [IQR, 31-57 yr]; 44% male). Overall, 213 participants (49%) met prespecified criteria for undernutrition, including 52 (12%) with severe undernutrition. Clinically, severe undernutrition was associated with HIV coinfection, microbiologically diagnosed tuberculosis, greater physiological instability, and higher mortality. After adjustment for age, sex, illness duration, study site, and HIV, malaria, and tuberculosis coinfection, severe undernutrition was associated with higher expression of proteins involved in pro-inflammatory immune signaling, endothelial and vascular remodeling, hypoxia and oxidative stress responses, and extracellular matrix remodeling, together with lower expression of proteins linked to growth signaling, anticoagulant regulation, and lipid homeostasis. Conclusions: Severe undernutrition is associated with a distinct high-risk clinical phenotype and biologic signature in adults hospitalized with severe infection. These findings suggest that undernutrition may potentiate key domains of sepsis pathobiology, with implications for strengthening nutritional support and informing host-directed treatment strategies in low- and middle-income countries where malnutrition is common.","rel_num_authors":19,"rel_authors":[{"author_name":"Gabriel Conte Cortez Martins","author_inst":"Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA"},{"author_name":"Julius J. Lutwama","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Nicholas Owor","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Joyce Namulondo","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Jesse E. Ross","author_inst":"Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, N"},{"author_name":"Xuan Lu","author_inst":"Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, N"},{"author_name":"Ignatius Asasira","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Tonny Kiyingi","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Christopher Nsereko","author_inst":"Entebbe Regional Referral Hospital, Ministry of Health, Entebbe, Uganda"},{"author_name":"John Bosco Nsubuga","author_inst":"Entebbe Regional Referral Hospital, Ministry of Health, Entebbe, Uganda"},{"author_name":"Joseph Shinyale","author_inst":"Entebbe Regional Referral Hospital, Ministry of Health, Entebbe, Uganda"},{"author_name":"Moses Kiwubeyi","author_inst":"Tororo General Hospital, Ministry of Health, Tororo, Uganda"},{"author_name":"Rittah Nankwanga","author_inst":"Tororo General Hospital, Ministry of Health, Tororo, Uganda"},{"author_name":"Kai Nie","author_inst":"Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Steven J. Reynolds","author_inst":"Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"John Kayiwa","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Seunghee Kim-Schulze","author_inst":"Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Barnabas Bakamutumaho","author_inst":"Department of Arbovirology, Emerging and Re-emerging Infectious Diseases, Uganda Virus Research Institute, Entebbe, Uganda"},{"author_name":"Matthew Cummings","author_inst":"Columbia University"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"THRB splice site variants lead to exon 4 skipping and TR\u03b21 gain-of-function syndrome","rel_doi":"10.64898\/2026.04.15.26349265","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.15.26349265","rel_abs":"Background: Heterozygous c.283+1G>A and c.283G>A variants in the THRB gene, encoding for thyroid hormone receptor (TR){beta}1 and {beta}2, lead to autosomal dominant macular dystrophy (ADMD). We report the detailed clinical characterization of two first-degree relatives with ADMD, heterozygous for THRB c.283+1G>A, and an unrelated ADMD patient with a novel variant, c.283G>C. The genomic and molecular consequences of both variants were studied. Methods: gDNA and mRNA were obtained from leukocytes. Clinical characterization included biochemistry, bone density and body composition, ECG, echocardiography, ultrasound, audiometry and color-vision. In vitro assays investigated TR function and DNA binding. Results: The patients manifested no resistance to thyroid hormone beta (RTH{beta}) and had normal FT4 and TSH. Detailed studies in two patients showed no goiter, tachycardia, hypercholesterinemia or hepatic steatosis. Hearing was not impaired. Both had impaired color vision and reduced bone density. RT-PCR from all three patients revealed skipping of exon 4 exclusive to TR{beta}1, producing a deletion of 87 amino acids in the N-terminal domain (TR{beta}1{Delta}NTD). In vitro, DNA-binding affinity of TR{beta}1{Delta}NTD to DR4-TRE with or without RXR was comparable to TR{beta}1WT. Surprisingly, TR{beta}1{Delta}NTD was transcriptionally twice more active than TR{beta}1WT with a similar EC50 for T3, demonstrating gain-of-function of TR{beta}1{Delta}NTD. THRA expression in leukocytes was increased by 3-fold compared to unrelated controls and different from RTH{beta} patients. Conclusion: These THRB splice site variants produce TR{beta}1 exon 4 skipping, resulting in a gain-of-function mutant, TR{beta}1{Delta}NTD. This explains the dominant ADMD phenotype devoid of RTH{beta} and suggests a TR{beta}1 gain-of-function syndrome.","rel_num_authors":13,"rel_authors":[{"author_name":"Georg Sebastian Hones","author_inst":"Department of Endocrinology, Diabetes and Metabolism and Division of Laboratory Research, Center for Rare Endocrine Diseases\/Endo-ERN, University Hospital Essen"},{"author_name":"Xiao-Hui Liao","author_inst":"Department of Medicine, The University of Chicago, Chicago, IL USA"},{"author_name":"Elisa Annabelle Mahler","author_inst":"Department of Ophthalmology, University Hospital Bonn, Bonn, Germany"},{"author_name":"Philipp Herrmann","author_inst":"Department of Ophthalmology, University Hospital Bonn, Bonn, Germany"},{"author_name":"Anja Eckstein","author_inst":"Department of Ophthalmology, Center for Endocrine Medicine, University Hospital Essen, Essen, Germany"},{"author_name":"Dagmar Fuhrer","author_inst":"Department of Endocrinology, Diabetes and Metabolism and Division of Laboratory Research, Center for Rare Endocrine Diseases\/Endo-ERN, University Hospital Essen"},{"author_name":"Jesus Moreno Castillo","author_inst":"Department of Medicine, The University of Chicago, Chicago, IL USA."},{"author_name":"John Chiang","author_inst":"Molecular Vision Laboratory Hillsboro, Oregon, USA"},{"author_name":"Andrea Louise Vincent","author_inst":"Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Science University of Auckland, Auckland, and Eye Department Greenla"},{"author_name":"Roy E Weiss","author_inst":"Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA"},{"author_name":"Alexandra Mihaela Dumitrescu","author_inst":"Department of Medicine, The University of Chicago, Chicago, IL USA"},{"author_name":"Samuel Refetoff","author_inst":"Department of Medicine, The University of Chicago, Chicago, IL USA."},{"author_name":"Lars C Moeller","author_inst":"Department of Endocrinology, Diabetes and Metabolism and Division of Laboratory Research, Center for Rare Endocrine Diseases\/Endo-ERN, University Hospital Essen"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Patterns and Predictors of Dropout in Maternal Continuum of Care: A Comprehensive Analysis in Bangladesh","rel_doi":"10.64898\/2026.04.20.26351272","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351272","rel_abs":"Dropout from essential maternal health services across pregnancy, childbirth, and the postnatal period remains a major barrier to improving maternal and neonatal outcomes in Bangladesh. This study examined stage-specific dropout patterns along the maternal continuum of care and identified factors associated with discontinuation. We analysed nationally representative data from the Bangladesh Demographic and Health Survey 2022 for 5,162 women with a recent live birth. Dropout from antenatal care, skilled birth attendance, and postnatal care was examined using multivariable logistic regression to estimate adjusted odds ratios and 95% confidence intervals, with comparisons to BDHS 2017 to 18 and assessment of regional variation. Only 44% of women received four or more antenatal care visits. Of these, 33% delivered with a skilled birth attendant, and among those receiving both antenatal care and skilled delivery, only 15% received postnatal care within 48 hours. Overall, 57% dropped out before completing adequate antenatal care, with additional dropouts between antenatal care and delivery (10%) and between delivery and postnatal care (18%). Compared with 2017 to 18, overall dropout from the maternal continuum of care more than doubled in 2022 (5.0% to 11.7%), driven by increased antenatal care dropout, while skilled birth attendance dropout declined and postnatal care dropout increased slightly. Higher maternal education, household wealth, media exposure, and women decision-making power were consistently associated with lower odds of dropout, whereas higher birth order increased dropout risk. Substantial regional variation was observed, with the highest overall dropout in Sylhet and the lowest in Khulna. High dropout from the maternal continuum of care in Bangladesh occurs predominantly at the antenatal care stage and is shaped by socioeconomic status, birth order, women access to information, and regional disparities. Strengthening early antenatal engagement and women decision-making autonomy is critical to improving continuity of maternal care and reducing preventable maternal and neonatal risks.","rel_num_authors":6,"rel_authors":[{"author_name":"Sabrina Sharmin Priyanka","author_inst":"Neglected Tropical Diseases Research Group, Global Health Program, Kirby Institute, University of New South Wales, Sydney, Australia"},{"author_name":"Md. Sazzad Hossan Sujon","author_inst":"Department of Statistics and Data Science, Jahangirnagar University, Savar, Dhaka, Bangladesh"},{"author_name":"Amita Farzana","author_inst":"Dendreon Pharmaceuticals, LLC"},{"author_name":"Dibbya Prava Dasgupta","author_inst":"Centre for Health Services and Policy Research, School of Population and Public Health, University of British Columbia, Vancouver, Canada"},{"author_name":"Golam Sarower Bhuyan","author_inst":"University of New South Wales"},{"author_name":"Nazia Binte Ali","author_inst":"Department of Global Health, Milken School of Public Health, George Washington University"}],"rel_date":"2026-04-22","rel_site":"medrxiv"},{"rel_title":"Two closely related \u03b2-1,2-xylosyltransferases differentially impact fungal glycan synthesis","rel_doi":"10.64898\/2026.04.21.719979","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719979","rel_abs":"Cryptococcus neoformans is an opportunistic fungal pathogen that causes pulmonary infection in immunocompromised patients, which in severe cases leads to fatal meningoencephalitis. Cryptococcus exhibits unique glycobiology that plays important roles in pathogenesis. Unlike model yeast and other common fungal pathogens, Cryptococcus incorporates xylose, a five-carbon monosaccharide, into its glycans. One trimer motif, which consists of xylose in {beta}-1,2 linkage to the reducing mannose of an -1,3-mannose dimer, occurs in key cryptococcal glycoconjugates that include protein N- and O-linked glycans, glycosylinositol phosphorylceramides (GIPCs), and the capsule polysaccharides glucuronoxylomannan (GXM) and glucuronoxylomannogalactan (GXMGal). We previously identified cryptococcal {beta}-1,2-xylosyltransferase 1 (Cxt1), which catalyzes formation of this motif in GIPCs, GXM, and GXMGal. Here, we report the discovery of a second enzyme, cryptococcal {beta}-1,2-xylosyltransferase 2 (Cxt2). Through characterization of cells that lack one or both corresponding genes (CXT1 and CXT2), we have dissected the biological roles of these enzymes, which are overlapping but not identical. Notably, Cxt1 and Cxt2 co-localize in the Golgi, influence capsule in a strain-dependent manner, and together are responsible for all xylose addition to O-glycans. Overall, our work highlights unique roles of these two enzymes and fills a gap in understanding of cryptococcal glycan synthesis.","rel_num_authors":9,"rel_authors":[{"author_name":"Daphne Boodwa-Ko","author_inst":"Washington University in St. Louis"},{"author_name":"J. Stacey Klutts","author_inst":"Southern Arizona Veteran Affairs Health Care System"},{"author_name":"Kazuhiro Aoki","author_inst":"Medical College of Wisconsin"},{"author_name":"Michael L. Skowyra","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Indrani Bose","author_inst":"Western Carolina University"},{"author_name":"Daniel P. Agustinho","author_inst":"Baylor College of Medicine"},{"author_name":"Mayumi Ishihara-Aoki","author_inst":"Medical College of Wisconsin"},{"author_name":"Michael Tiemeyer","author_inst":"Complex Carbohydrate Research Center"},{"author_name":"Tamara L. Doering","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Structure of the Chimalliviridae bacteriophage Goslar reveals host recognition and infection machinery","rel_doi":"10.64898\/2026.04.21.720040","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720040","rel_abs":"Large-genome bacteriophages (phages) of the Chimalliviridae family possess a distinctive life cycle, building compartments inside infected bacterial cells that broadly protect their genomes from host defenses. Here, we use cryoelectron microscopy to determine a high-resolution structure of the E. coli-infecting Chimalliviridae phage Goslar, generating a composite model with 2,888 protein chains from 28 different structural proteins, totaling over 1.4 million amino acid residues. Our structure reveals the architecture of the Goslar capsid, portal, tail, and baseplate, highlighting structural similarities and differences with other tailed phages. Combining our structural data with quantitative mass spectrometry of Goslar virions, we identify several high-copy virion-associated proteins that likely play key roles when injected into host cells upon infection. We also identify a Chimalliviridae-specific family of proteins that form long, flexible filaments anchored at the phage baseplate and which incorporate carbohydrate-binding domains, suggesting a key role in host recognition.","rel_num_authors":7,"rel_authors":[{"author_name":"Dwaipayan Basu","author_inst":"UC San Diego"},{"author_name":"Yajie Gu","author_inst":"UC San Diego"},{"author_name":"Ying-Xing Li","author_inst":"UC San Diego"},{"author_name":"Taylor Forman","author_inst":"UC San Diego"},{"author_name":"Majid Ghassemian","author_inst":"UC San Diego"},{"author_name":"Joe Pogliano","author_inst":"University of California, San Diego"},{"author_name":"Kevin D Corbett","author_inst":"University of California, San Diego"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Conserved catalytic activity of immune TIR domains in animals","rel_doi":"10.64898\/2026.04.20.719644","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719644","rel_abs":"The Toll\/interleukin-1 receptor (TIR) domain is important for immune signaling across bacteria, plants, and animals. In human innate immunity, TIR domains are known to function as adaptors mediating protein-protein interactions, yet studies in bacteria and plants revealed that TIR domains often act as enzymes that produce immune signaling molecules. Here, we show that TIR domains from evolutionarily diverse animals have conserved active sites, implying that they can function as enzymes. In vitro experiments with animal TIRs show that the TIR domain of several Toll-like receptors (TLRs), including that of human TLR4, can produce cyclic ADP-ribose (cADPR), revealing an enzymatic activity previously unknown for TLR TIRs. We show that production of cADPR is a conserved feature of TIR domains across the animal tree of life, implying a role for this molecule in animal TIR signaling. Finally, we report a TIR domain from green algae that synthesizes 3'cADPR, suggesting conservation of 3'cADPR signaling between bacteria and eukaryotes. Our results reveal that the catalytic activity of TIR domains is widespread in animals and conserved across the tree of life.","rel_num_authors":9,"rel_authors":[{"author_name":"Bohdana Hurieva","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel"},{"author_name":"Ilya Osterman","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel"},{"author_name":"Alla H. Falkovich","author_inst":"Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel"},{"author_name":"Erez Yirmiya","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel"},{"author_name":"Azita Leavitt","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel"},{"author_name":"Jeremy Garb","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel"},{"author_name":"Ohad Roth","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel"},{"author_name":"Gil Amitai","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel"},{"author_name":"Rotem Sorek","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Long-range allosteric communication, double mutant cycles, and energetic coupling in SARS-CoV-2 spike protein","rel_doi":"10.64898\/2026.04.20.719543","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719543","rel_abs":"SARS-CoV-2 spike protein is continuously evolving, leading to new variants. While mutations in the receptor-binding domain (RBD) enhance binding to the ACE2 receptor and evade neutralizing antibodies, the function of mutations in the N-terminal domain (NTD) remains poorly understood. Using two independent methods, surface plasmon resonance (SPR) and cryo-EM, we show that NTD mutations increase the population of spike protein with the RBD in the up conformational state. SPR association and dissociation kinetics of spike binding to ACE2 and antibodies, analyzed using a coupled equilibrium model, determined the relative populations and indicated that the RBD-up-to-down transition is rate-limiting relative to the RBD-down-to-up transition. Advanced model fitting of cryo-EM Coulomb potential maps confirmed the populations. The combined effect of NTD and RBD mutations exceeds the sum of their individual effects, indicating long-range allosteric communication and energetic coupling within the spike protein.","rel_num_authors":3,"rel_authors":[{"author_name":"Alexandra Lucas","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Vamseedhar Rayaprolu","author_inst":"Pacific Northwest Cryo-EM Center, Oregon Health & Science University, Portland, OR, USA."},{"author_name":"Krishna Mallela","author_inst":"University of Colorado Anschutz Medical Campus"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"A High-Throughput Platform for Rapidly Adapting DNA Aptamers to SARS-CoV-2 Evolution","rel_doi":"10.64898\/2026.04.21.719937","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719937","rel_abs":"Rapid pathogen evolution, exemplified by SARS-CoV-2 during the COVID-19 pandemic, threatens pub-lic health by eroding the effectiveness of vaccines, therapeutics, and diagnostic tools through continuous viral mutation. Although spike protein targeting monoclonal antibodies (mAbs) were developed within 10-12 months of the initial outbreak to serve as key theranostic agents, their redesign has struggled to keep pace with viral evolution, rendering many neutralizing antibodies ineffective. Here we demonstrate a novel platform that integrates a random-rational hybrid library diversification with high-throughput MiSeq screening to evolve aptamers as highly versatile recognition elements that can be easily reprogrammed to bind the spike proteins of emerging SARS-CoV-2 strains. Using a repurposed next-generation sequencing (NGS) platform, interactions between 3 different spike proteins and 11,806 unique aptamer variant de-signs can be effectively screened within a few days. Our starting point is a 40-nt aptamer that binds strongly to the wild-type (WT) spike protein but shows reduced and no affinity toward its Delta and Omi-cron strains, respectively. With this starting aptamer diversified, our rapid screening method yielded one double mutant that exhibits 4-fold improvement in binding to the Delta spike protein and another double mutant that converts its binding to the Omicron spike protein from no detectable affinity to the kd of nano-molar range. A selective WT binder was also identified with no binding the two variants of interest. Using this pipeline, we identified bases not previously recognized as part of the motif that contribute critically to spike protein binding. Moreover, our pipeline integrates screening data analysis with molecular dynamics simulations, providing insights into aptamer-protein binding interactions. A sensor was developed based on the identified WT-selective binder, enabling highly specific detection of the WT spike protein with min-imal cross-reactivity and robust performance in 40% serum. Together, these results demonstrate that ap-tamers can be rapidly optimized to bind new variants or selectively recognize a specific strain using the repurposed NGS platform. This work highlights the platform as a highly adaptive technology capable of obtaining aptamers within days to keep pace with rapidly evolving pathogens in future pandemics.","rel_num_authors":19,"rel_authors":[{"author_name":"Yujie He","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Zhenglin Yang","author_inst":"Department of Chemistry, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Yu-An Kuo","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Yuting Wu","author_inst":"Department of Chemistry, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Diego Fonseca-Albert","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Kyle K Le","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Jeffrey G Guo","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Yanxing Wang","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Anh-Thu Nguyen","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Yuan-I Chen","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Sohyun Kim","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Wei-Ru Chen","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Saeed Seifi","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Soonwoo Hong","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Trung Duc Nguyen","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Yinong Chen","author_inst":"Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"author_name":"Pengyu Ren","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"},{"author_name":"Yi Lu","author_inst":"Department of Chemistry, University of Texas at Austin, Austin, TX, USA."},{"author_name":"Hsin-Chih Yeh","author_inst":"Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Acoustically activatable drug-loaded nanodroplets for mechanochemical therapy in solid tumors","rel_doi":"10.64898\/2026.04.20.719550","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719550","rel_abs":"Stimulus-responsive nanomedicines promise spatiotemporally controlled therapy, yet most systems rely on passive delivery and lack precise, externally programmable activation while maintaining clinical compatibility. Here we engineer sub-200 nm, perfluorocarbon (PFC)-core nanodroplets (NDs) that integrate efficient core drug loading, physiological stability, and acoustically programmable activation within a single nanoscale agent. These NDs are fabricated using microfluidic nanoassembly to achieve controlled size and composition, and are designed to encapsulate fluorinated payloads directly within the liquid core. Upon exposure to a sequential dual-frequency ultrasound (US) paradigm, the NDs undergo acoustic droplet vaporization followed by low-frequency cavitation, enabling spatially confined mechanical disruption and on-demand payload release within clinically relevant acoustic limits. These properties are engineered to overcome physicochemical barriers in solid tumors, including dense extracellular matrix and restricted drug penetration. This approach achieves enhanced payload release and induces potent mechanochemical cytotoxicity in vitro. In vivo, NDs exhibit prolonged circulation and tumor accumulation, while US activation drives substantial tissue fractionation, control drug release, and increases subsequent nanoparticle uptake. When applied to a solid tumor model, this combined mechanochemical strategy improves tumor control and significantly extends survival compared to either modality alone. These acoustically activatable NDs provide a versatile system for stimulus-responsive, site-targeted drug delivery and mechanical tumor disruption, with strong potential for clinical translation.","rel_num_authors":5,"rel_authors":[{"author_name":"Tiran Bercovici","author_inst":"Tel Aviv University"},{"author_name":"Mike Bismuth","author_inst":"Tel Aviv University"},{"author_name":"Meir Goldsmith","author_inst":"Tel Aviv University"},{"author_name":"Dan Peer","author_inst":"Tel Aviv University"},{"author_name":"Tali Ilovitsh","author_inst":"Tel Aviv University"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"A narrow thermodynamic design window governs selective membrane permeabilization and antiviral activity of amphipathic peptides","rel_doi":"10.64898\/2026.04.20.719566","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719566","rel_abs":"Designing molecules that selectively target therapeutically relevant membranes, such as viral envelopes, while sparing host cells is challenging: these membranes closely resemble host bilayers, so selectivity must exploit subtle lipid composition and curvature differences and demands precise tuning of affinity and hydrophobicity, yet curated sequence-specificity data are scarce. Here we show that selective membrane permeabilization and membrane-selective activity of amphipathic peptides are governed by a narrow thermodynamic design window defined by membrane curvature affinity and molecular hydrophobicity. Using a physics-driven generative workflow combining evolutionary molecular dynamics and a transformer predictor (PMIpred), we systematically explored and thermodynamically mapped peptide sequence space de novo without reliance on natural templates or experimental training data. Across four design generations we synthesized and experimentally characterized 43 peptides. Mapping functional activity onto a low-dimensional free-energy landscape reveals a confined thermodynamic \"sweet spot\" separating weak membrane binding from excessive hydrophobic association and cytotoxicity. Peptides operating within this regime efficiently permeabilize model membranes while maintaining low cellular toxicity. Antiviral activity against Zika virus and HIV-1 emerges in the same region but depends sensitively on membrane lipid composition. Quantitative thermodynamic design rules emerge for membrane-active peptides, illustrating how low-dimensional free-energy landscapes can guide the engineering of selective interactions at soft-matter interfaces.","rel_num_authors":9,"rel_authors":[{"author_name":"Niek van Hilten","author_inst":"Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands;  Present Address: Department of Pharmaceutical Chemistry, Cardiovascular Research Ins"},{"author_name":"Pascal van Maltitz","author_inst":"Institute of Molecular Virology, Ulm University Medical Center, Ulm, Germany"},{"author_name":"Dennis Aschmann","author_inst":"Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands; Present Address: Van 't Hoff Institute for Molecular Sciences, University of Amsterda"},{"author_name":"Maria Hoernke","author_inst":"Physical Chemistry, Martin-Luther-University, Halle (S.), Germany; Pharmaceutical Technology and Biopharmacy, Institute of Pharmaceutical Sciences, University o"},{"author_name":"Maximilian Krebs","author_inst":"Department of Physics, Technical University Dortmund, Dortmund, Germany"},{"author_name":"Jeroen Methorst","author_inst":"Department of Physics, Technical University Dortmund, Dortmund, Germany;  Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands"},{"author_name":"Alexander Kros","author_inst":"Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands"},{"author_name":"Jan M\u00fcnch","author_inst":"Institute of Molecular Virology, Ulm University Medical Center, Ulm, Germany"},{"author_name":"Herre Jelger Risselada","author_inst":"Department of Physics, Technical University Dortmund, Dortmund, Germany"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Concordia: Spatial Domain Detection via Augmented Graphs for Population-Level Spatial Proteomics","rel_doi":"10.64898\/2026.04.19.719422","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719422","rel_abs":"A key step in analyzing population-level spatial proteomic data is to delineate consistently defined spatial domains across samples. Domain detection is particularly challenging for cancer tissues, which have complex spatial domains with elongated or branching geometries. To address these challenges, we present Concordia, a Graph Neural Network (GNN)-based framework that uses augmented graphs to capture complex spatial domains, and it is designed to analyze thousands of tissues simultaneously to obtain consistently defined domains. Applied to a lung cancer dataset, Concordia uncovers a spatially defined cancer associated fibroblast subset linked to clinical outcomes that cannot be identified using protein expression alone.","rel_num_authors":3,"rel_authors":[{"author_name":"Si Liu","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Li Hsu","author_inst":"Fred Hutchinson Cancer Center"},{"author_name":"Wei Sun","author_inst":"Fred Hutchinson Cancer Center"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Asymmetric crosstalk between the BMP and TGF\u03b2 pathways resolves signaling ambiguity","rel_doi":"10.64898\/2026.04.20.719570","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719570","rel_abs":"The BMP and TGF{beta} signaling pathways control cellular fate decisions in diverse biological contexts, often playing opposing roles. Despite extensive knowledge of these pathways, understanding how cells respond to environments containing these opposing cues remains a challenge. Here, we systematically analyze the activation of these pathways under combinatorial signaling environments. We find that TGF{beta} ligands inhibit BMP signaling, while BMP ligands enhance TGF{beta} transcriptional response across concentrations, ligand variants, and cell types. This asymmetric crosstalk results in the activation of a TGF{beta}-biased transcriptional response, even under mixed signaling conditions, effectively reducing signal ambiguity, with implications for processes such as EMT. We show that this crosstalk originates downstream of the SMAD proteins phosphorylation. Using mathematical models, we predict, and experimentally verify, that promiscuous interactions between SMAD proteins provide the mechanism for the observed crosstalk. Our findings challenge the canonical models, suggesting an active role for mediator proteins in determining biological responses.","rel_num_authors":14,"rel_authors":[{"author_name":"Johannes M Auth","author_inst":"Weizmann Institute of Science"},{"author_name":"Inbal Eizenberg-Magar","author_inst":"Weizmann Institute of Science"},{"author_name":"omer erez","author_inst":"Weizmann Institute of Science"},{"author_name":"Omer Zachar","author_inst":"Weizmann Institute of Science"},{"author_name":"Merav D Shmueli","author_inst":"Weizmann Institute of Science"},{"author_name":"Inbal Zigdon","author_inst":"Weizmann Institute of Science"},{"author_name":"Achinoam Shoham","author_inst":"Weizmann Institute of Science"},{"author_name":"Vladimir Mindel","author_inst":"Weizmann Institute of Science"},{"author_name":"Meytar Asulin Berrebi","author_inst":"Weizmann Institute of Science"},{"author_name":"Ariel Tennenhouse","author_inst":"Weizmann Institute of Science"},{"author_name":"Neta Bar-Hai","author_inst":"Oncology Institute, Sheba Medical Center"},{"author_name":"Rakefet Ben-Yishay","author_inst":"Oncology Institute, Sheba Medical Center"},{"author_name":"Dana Ishay-Ronen","author_inst":"Oncology Institute, Sheba Medical Center"},{"author_name":"Yaron E Antebi","author_inst":"Weizmann Institute of Science"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Dynamics of the end-of-life phase explained by the saturating removal model","rel_doi":"10.64898\/2026.04.20.719588","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719588","rel_abs":"End of life is characterized by a phase of rapid physiological decline and high morbidity, phenotypically observed as the \"Smurf\" phase in Drosophila, metabolic end-of-life dysregulation in mice, and end-stage frailty in humans. Existing two-phase aging models often conceptualize this end-of-life phase as a discrete biological state. Here, we demonstrate that a continuous stochastic model of damage accumulation, the saturating removal (SR) model, captures these multi-species morbidity dynamics. By defining the end-of-life phase as a stochastic crossing of a sub-lethal damage threshold, the SR model accurately reproduces empirical end-of-life dynamics across flies, mice, and humans. The model predicts a surprising temporary reduction in hazard shortly after entering the end-of-life phase, resulting in a U-shaped hazard curve, consistent with the empirical data in all three organisms. It also correctly predicts a shortening twilight phenomenon where the mean duration of the end-of-life phase decreases the later its onset. We conclude that end-of-life dynamics are consistent with universal features of a driver of aging crossing a threshold for end-of-life morbidity and then a threshold for death.","rel_num_authors":4,"rel_authors":[{"author_name":"Naveh Raz","author_inst":"Weizmann Institute of Science"},{"author_name":"Glen Pridham","author_inst":"Weizmann Institute of Science"},{"author_name":"Michael Rera","author_inst":"Functional and Adaptive Biology"},{"author_name":"Uri Alon","author_inst":"Weizmann institute"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"PSoup: an R package for simulating biological networks from a qualitative perspective","rel_doi":"10.64898\/2026.04.19.719106","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719106","rel_abs":"Mathematical modelling is essential for understanding how complex biological systems respond to genetic, physiological, and environmental changes. Existing approaches, however, often require trade-offs between mechanistic detail, model size, parameter uncertainty, and interpretability. Ordinary differential equation (ODE) models capture biochemical processes with quantitative precision but can demand extensive parameterisation. In contrast, large statistical and machine-learning models rely on substantial datasets and frequently lack mechanistic transparency. Qualitative approaches such as Boolean networks improve scalability but may oversimplify biological behaviour. To address some of these limitations, we present PSoup, an R package that automatically converts knowledge graphs into transparent, parameter-free, qualitative models. PSoup uses algebraic update rules designed around a fixed, biologically interpretable baseline, enabling predictions of relative change across diverse perturbations without requiring kinetic parameters. This design allows PSoup to integrate information across biological scales and from heterogeneous experimental sources. We evaluated PSoup using the well-studied shoot branching network of Bertheloot et al. (2019), which ncorporates hormonal (auxin, strigolactone, cytokinin) and metabolic (sucrose) regulation. Across 78 experimental conditions, PSoup correctly predicted 88.5% of perturbation outcomes, including 89.5% accuracy for unique, biologically consistent comparisons. We further demonstrate how PSoup can distinguish among alternative plausible network topologies, revealing how structural differences influence emergent system behaviour. PSoup offers an intuitive, accessible, and mathematically transparent framework for exploring biological networks. Its capacity to integrate diverse knowledge and test alternative hypotheses positions it as a powerful tool for biological discovery and a valuable complement to existing modelling approaches.","rel_num_authors":5,"rel_authors":[{"author_name":"Nicole Z Fortuna","author_inst":"The University of Queensland"},{"author_name":"Brodie A.J. Lawson","author_inst":"Queensland University of Technology - QUT: Queensland University of Technology"},{"author_name":"Christos Mitsanis","author_inst":"The University of Queensland"},{"author_name":"Kevin Burrage","author_inst":"Queensland University of Technology - Gardens Point Campus"},{"author_name":"Christine A. Beveridge","author_inst":"The University of Queensland"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"A broad-host-range Rhizobium rhizogenes strain enables transient expression across diverse crops and establishes functional assays in faba bean","rel_doi":"10.64898\/2026.04.20.719558","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719558","rel_abs":"Agrobacterium-mediated transient expression has revolutionized plant research, enabling numerous landmark discoveries across diverse areas of plant biology. Yet this powerful approach remains largely confined to solanaceous species, leaving most economically important crop families without a comparable rapid assay platform. Here, we show that an engineered Rhizobium rhizogenes strain, AS109, mediates efficient transient expression across diverse dicot species spanning multiple taxonomic families, consistently outperforming commonly used laboratory agrobacterial strains. Leveraging the broad host range of AS109, we establish a suite of functional assays in faba bean (Vicia faba), including protein localisation, RNA interference-mediated gene silencing, cell-surface elicitor recognition screens, nucleotide-binding leucine-rich repeat receptor (NLR) activation, and infection cell biology at the host-pathogen interface. We further demonstrate that both singleton NLRs and sensor-helper NLR pairs from Solanaceae retain effector recognition and cell death activity when transferred into faba bean, establishing a rapid platform for evaluating cross-family transferability of disease-resistance genes. AS109 thus provides an accessible and versatile chassis for functional genomics in non-model crops, bridging the widening gap between hypothesis generation and experimental validation across diverse plant species.","rel_num_authors":20,"rel_authors":[{"author_name":"Freddie King","author_inst":"Imperial College London"},{"author_name":"Juan Carlos Lopez-Agudelo","author_inst":"Academia Sinica"},{"author_name":"Christopher Stephens","author_inst":"University of Cambridge"},{"author_name":"May Htet Aung","author_inst":"Academia Sinica"},{"author_name":"Tarhan Ibrahim","author_inst":"Imperial College London"},{"author_name":"Enoch Lok Him Yuen","author_inst":"Imperial College London"},{"author_name":"Ao Chen","author_inst":"Imperial College London"},{"author_name":"Nick Moritz Eilmann","author_inst":"Imperial College London"},{"author_name":"Saskia Jenkins","author_inst":"Imperial College London"},{"author_name":"Cristina Vuolo","author_inst":"Imperial College London"},{"author_name":"Yueh-Ning Swee","author_inst":"Academia Sinica"},{"author_name":"Wei-Jia Liu","author_inst":"Academia Sinica"},{"author_name":"Samuel Bruty","author_inst":"John Innes Centre"},{"author_name":"AmirAli Toghani","author_inst":"The Sainsbury Laboratory"},{"author_name":"Chih-Horng Kuo","author_inst":"Academia Sinica"},{"author_name":"Erh-Min Lai","author_inst":"Academia Sinica"},{"author_name":"Jiorgos Kourelis","author_inst":"Imperial College London"},{"author_name":"Lida Derevnina","author_inst":"University of Cambridge"},{"author_name":"Chih-Hang Wu","author_inst":"Academia Sinica"},{"author_name":"Tolga Osman Bozkurt","author_inst":"Imperial College London"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Prolonged cold exposure enhances regeneration potential in Arabidopsis","rel_doi":"10.64898\/2026.04.20.719581","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719581","rel_abs":"Prolonged cold exposure over winter impacts plant growth and development but its role beyond flowering regulation remains underexplored. In this study, we show that extended cold enhances regenerative capacity, promoting both callus formation and shoot regeneration in Arabidopsis. This enhancement is mediated by the cold-induced AP2\/ERF transcription factors C-REPEAT\/DRE-BINDING FACTOR 1 (CBF1), CBF2 and CBF3 which interact with the histone acetyltransferase HISTONE ACETYLTRANSFERASE OF THE GNAT FAMILY 1 (HAG1). The CBFs recruit HAG1 to the loci of key regeneration regulators, such as WUSCHEL-RELATED HOMEOBOX 5 (WOX5), to promote their expression via histone acetylation. Our findings thus uncover an epigenetic mechanism by which prolonged cold primes plants for enhanced regeneration, highlighting how environmental cues influence developmental plasticity in plants.","rel_num_authors":13,"rel_authors":[{"author_name":"Fu-Yu Hung","author_inst":"National Chung Hsing University"},{"author_name":"Yetkin Caka Ince","author_inst":"RIKEN Center for Sustainable Resource Science"},{"author_name":"Ayako Kawamura","author_inst":"RIKEN Center for Sustainable Resource Science"},{"author_name":"Arika Takebayashi","author_inst":"RIKEN Center for Sustainable Resource Science"},{"author_name":"Yu Chen","author_inst":"VIB Gent Plant Systems Biology Center"},{"author_name":"Takuya Nagae","author_inst":"RIKEN Center for Sustainable Resource Science"},{"author_name":"Akira Iwase","author_inst":"RIKEN Center for Sustainable Resource Science"},{"author_name":"Noriko Takeda-Kamiya","author_inst":"RIKEN Center for Sustainable Resource Science"},{"author_name":"Kiminori Toyooka","author_inst":"RIKEN Center for Sustainable Resource Science"},{"author_name":"Dongbo Shi","author_inst":"RIKEN Center for Sustainable Resource Science"},{"author_name":"Miguel Angel Moreno-Risueno","author_inst":"Center for Plant Biotechnology and Genomics UPM-INIA"},{"author_name":"Keqiang Wu","author_inst":"National Taiwan University"},{"author_name":"Keiko Sugimoto","author_inst":"RIKEN Center for Sustainable Resource Science"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"CHORD: a framework for cross-species single-cell integration across gene, cell and cell-type levels","rel_doi":"10.64898\/2026.04.19.719426","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719426","rel_abs":"Quantifying cross-species relationships among cell types from single-cell transcriptomic data can reveal both conserved and divergent patterns of cell-type hierarchies. However, existing cross-species integration methods can be limited in modeling genes beyond orthologs by leveraging cell-type-resolved transcriptional context, or in learning explicit type-level representations. Here we present CHORD, a cross-species integration framework that jointly learns representations of genes, cells and cell types. We demonstrate that CHORD can integrate cross-species single-cell atlases and support cell-type annotation with unknown cell-type detection. In the frog--zebrafish embryogenesis and mammalian motor cortex atlases, CHORD infers cell-type trees that place conserved cell types from different species in relative proximity and summarize hierarchical relationships among cell types. CHORD also supports cross-species comparison of continuous phenotypic variation by placing embryonic cells along an aligned developmental timeline. CHORD further yields gene embeddings that capture orthologous and functional relationships, and gene importance scores linking genes to cell types.","rel_num_authors":13,"rel_authors":[{"author_name":"Yitao Lin","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Xunuo Zhu","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Xuheng Zhou","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Xue Zhang","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Guoxin Cai","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Wenyi Zhao","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Jingqi Zhou","author_inst":"School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China"},{"author_name":"Jie Liu","author_inst":"Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China"},{"author_name":"Qiang Zhu","author_inst":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"},{"author_name":"Menghan Zhang","author_inst":"State Key Laboratory of Genetics and Development of Complex Phenotypes, Center for Evolutionary Biology, Human Phenome Institute, Zhangjiang Fudan International"},{"author_name":"Binbin Zhou","author_inst":"School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China"},{"author_name":"Xun Gu","author_inst":"Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA"},{"author_name":"Zhan Zhou","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"CHORD: a framework for cross-species single-cell integration across gene, cell and cell-type levels","rel_doi":"10.64898\/2026.04.19.719426","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719426","rel_abs":"Quantifying cross-species relationships among cell types from single-cell transcriptomic data can reveal both conserved and divergent patterns of cell-type hierarchies. However, existing cross-species integration methods can be limited in modeling genes beyond orthologs by leveraging cell-type-resolved transcriptional context, or in learning explicit type-level representations. Here we present CHORD, a cross-species integration framework that jointly learns representations of genes, cells and cell types. We demonstrate that CHORD can integrate cross-species single-cell atlases and support cell-type annotation with unknown cell-type detection. In the frog--zebrafish embryogenesis and mammalian motor cortex atlases, CHORD infers cell-type trees that place conserved cell types from different species in relative proximity and summarize hierarchical relationships among cell types. CHORD also supports cross-species comparison of continuous phenotypic variation by placing embryonic cells along an aligned developmental timeline. CHORD further yields gene embeddings that capture orthologous and functional relationships, and gene importance scores linking genes to cell types.","rel_num_authors":13,"rel_authors":[{"author_name":"Yitao Lin","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Xunuo Zhu","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Xuheng Zhou","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Xue Zhang","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Guoxin Cai","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Wenyi Zhao","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"},{"author_name":"Jingqi Zhou","author_inst":"School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China"},{"author_name":"Jie Liu","author_inst":"Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China"},{"author_name":"Qiang Zhu","author_inst":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"},{"author_name":"Menghan Zhang","author_inst":"State Key Laboratory of Genetics and Development of Complex Phenotypes, Center for Evolutionary Biology, Human Phenome Institute, Zhangjiang Fudan International"},{"author_name":"Binbin Zhou","author_inst":"School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China"},{"author_name":"Xun Gu","author_inst":"Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA"},{"author_name":"Zhan Zhou","author_inst":"State Key Laboratory of Advanced Drug Delivery and Release Systems & Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sci"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Scalable, Generalizable, and Uncertainty-Aware Integration of Spatial Multi-Omics Across Diverse Modalities and Platforms with SCIGMA","rel_doi":"10.64898\/2026.04.19.718223","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.718223","rel_abs":"Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic, and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multi-modal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multi- omics integration. SCIGMA introduces a novel uncertainty-aware contrastive learning objective and multi-view graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike existing methods, SCIGMA provides spatially resolved uncertainty estimates, improving interpretability and identifying regions of biological or technical heterogeneity. SCIGMA is the first spatial multi-omics method to support integration of up to five modalities - as demonstrated on Spatial-Mux-Seq data - and its modular framework is extensible to future technologies with even more modalities. It also scales to over one million spatial locations, enabling analysis of ultra-high-resolution datasets such as VisiumHD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues, and 9 platforms. On benchmarkable datasets, SCIGMA outperformed existing methods in spatial domain detection, modality preservation, feature reconstruction, and reproducibility. Across applications, it uncovered biologically meaningful structures, refined spatial domains, and modality-specific regulatory programs, while its uncertainty estimates revealed tissue regions with potential biological or technical variation. Together, SCIGMA provides a robust, flexible, and future-ready solution for scalable spatial multi-modal integration.","rel_num_authors":3,"rel_authors":[{"author_name":"Seowon Chang","author_inst":"Brown University"},{"author_name":"Alexander Fleischmann","author_inst":"Brown University"},{"author_name":"Ying Ma","author_inst":"Brown University"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"ZDHHC13 is a likely pseudoenzyme protein S-acyltransferase that functions via a non-canonical mechanism","rel_doi":"10.64898\/2026.04.20.719575","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719575","rel_abs":"S-acylation is the addition of fatty acids to cysteine residues to regulate protein function and localization. S-acylation is catalyzed by the ZDHHC (Asp-His-His-Cys) family of protein S-acyltransferases (PATs), which S-acylate protein substrates by first auto-S-acylating the catalytic cysteine of the DHHC active site followed by transfer to the substrate. ZDHHC13 and ZDHHC17 are related ankyrin repeat domain (ANK) PATs that S-acylate multiple neuronal proteins, including huntingtin (HTT), the protein mutated in Huntington disease. However, unlike ZDHHC17 and other human PATs, ZDHHC13 possesses a non-canonical DQHC active site. As the first histidine is essential for auto-S-acylation, it is unclear if ZDHHC13 is catalytically active. Our phylogenetic analysis of eukaryotic ANK-containing PATs shows that ZDHHC13 orthologues are more divergent compared to ZDHHC17. While the ZDHHC17 DHHC is highly conserved, the motif varies among ZDHHC13 orthologues, with some vertebrate lineages containing a serine in place of the catalytic cysteine. Interestingly, we found that the ZDHHC13 S-acylation is lower than that of ZDHHC17, but the ZDHHC13 catalytic cysteine is indeed S-acylated. While expression of wild type (WT) ZDHHC13 in ZDHHC13 deficient HEK293T cells increased S-acylation of a HTT1-588 fragment, surprisingly, expression of catalytically dead DQHS ZDHHC13 was still able to facilitate HTT1-588 S-acylation equally. This suggests the ZDHHC13 catalytic cysteine is not required for S-acylation of target proteins, suggesting ZDHHC13 may coordinate another PAT. Indeed, we identified ZDHHC13 in high-molecular weight complexes. Our results indicate that ZDHHC13 is a likely pseudoenzyme that may function via a non-conventional mechanism reliant on other PATs. This work broadens our understanding of the function of this non-canonical PAT.","rel_num_authors":7,"rel_authors":[{"author_name":"Andrey A Petropavlovskiy","author_inst":"University of Guelph"},{"author_name":"Alysha M Church","author_inst":"University of Guelph"},{"author_name":"Amelia H Doerksen","author_inst":"University of Guelph"},{"author_name":"Denver A Bakhareva","author_inst":"University of Guelph"},{"author_name":"Etienne P Sellar","author_inst":"University of Guelph"},{"author_name":"Nisandi N Herath","author_inst":"University of Guelph"},{"author_name":"Shaun S Sanders","author_inst":"University of Guelph"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Understanding cryptic diversity within the honeypot ant species complex of Myrmecocystus mendax","rel_doi":"10.64898\/2026.04.20.719579","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719579","rel_abs":"Cryptic species diversity, overlooked due to extreme morphological similarity, is a common phenomenon among ants. The honeypot ant genus Myrmecocystus (Wesmael, 1838; Formicinae: Lasiini) likely features multiple cryptic species, as previously suggested by phylogenetic studies based on ultraconserved elements (UCEs). Here, this work is expanded upon by examining 140 specimens and 2,508 UCE loci, with a particular focus on the M. mendax species complex from the southwestern USA and northern Mexico. Phylogenomic and population genomic analyses revealed five distinct M. mendax-like lineages and identified two potential cases of cryptic species diversity, one within samples matching the morphology of M. mendax and another within samples conforming to M. placodops. Most specimens morphologically identified as M. mendax formed a well-supported monophyletic group sister to M. melliger assigned individuals, with evidence for ongoing hybridization between both species in the Madrean Sky Islands along the USA-Mexico border. Patterns in the main M. mendax clade also suggest adaptive divergence across ecological gradients, warranting further investigation. Overall, these findings highlight the power of UCE-based genomic data in phylogenetic reconstructions and population genetic analyses to better resolve cryptic species diversity, and clarify complex evolutionary histories shaped by introgression and incomplete lineage sorting.","rel_num_authors":9,"rel_authors":[{"author_name":"Magnus Wolf","author_inst":"Institute for Evolution and Biodiversity (IEB), University of Muenster, Huefferstr. 1, Muenster, Germany"},{"author_name":"Nils Rensing","author_inst":"Institute for Evolution and Biodiversity (IEB), University of Muenster, Huefferstr. 1, Muenster, Germany"},{"author_name":"Hannah Neuhaus","author_inst":"Institute for Evolution and Biodiversity (IEB), University of Muenster, Huefferstr. 1, Muenster, Germany"},{"author_name":"Tobias van Elst","author_inst":"Animal Genomics, ETH Zurich, Universitaetstrasse 2, Zurich, Switzerland"},{"author_name":"Ti H Eriksson","author_inst":"Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125"},{"author_name":"Marek Borowiec","author_inst":"Department of Agricultural Biology and C. P. Gillette Museum, Colorado State University, 1177H Campus Delivery, Fort Collins, Colorado 80523, USA"},{"author_name":"Philip S Ward","author_inst":"Department of Entomology and Nematology, University of California, Davis, California 95616, USA"},{"author_name":"Robert A Johnson","author_inst":"School of Life Sciences, Arizona State University, 427 E Tyler Mall, Tempe, AZ 85287-1501, USA"},{"author_name":"Juergen Gardau","author_inst":"Institute for Evolution and Biodiversity (IEB), University of Muenster, Huefferstr. 1, Muenster, Germany"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Visualization of peripheral nerves in developing and regenerating limbs using a novel peripherin reporter line of Xenopus laevis","rel_doi":"10.64898\/2026.04.19.719517","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719517","rel_abs":"Peripherin (PRPH) is a class III intermediate filament protein expressed in peripheral nerves and upregulated during axon outgrowth and regeneration. In this study, we developed a transgenic Xenopus laevis line for long-term in vivo visualization of the peripheral nervous system. Deletion and motif analyses identified cis-regulatory regions within the promoter and intron 1 that are important for neuronal expression of the X. laevis prph gene. Stable lines exhibited robust EGFP reporter activity in developing neural primordia in embryos and in the peripheral nerves of tadpoles. Transgenic tadpoles enabled in vivo imaging of peripheral nerves throughout limb development. During larval limb regeneration, we observed modest early nerve entry into the blastema, recapitulating that seen in early limb development. In contrast, post-metamorphic limb blastemas displayed extensive innervation from the early phase of regeneration. Moreover, increased reporter activity in the nerves of the regenerating adult forelimb suggests regeneration-associated regulation of peripheral innervation and its potential role in blastema formation. This transgenic line will serve as a versatile tool for analyzing such large-scale neural remodeling across development, metamorphosis, and regeneration.","rel_num_authors":8,"rel_authors":[{"author_name":"Miyuki Suzuki","author_inst":"California Institute of Technology"},{"author_name":"Yosuke Kato","author_inst":"University of Hyogo"},{"author_name":"Rima Mizuno","author_inst":"The Graduate University for Advanced Studies\/National Institute for Basic Biology"},{"author_name":"Hiroshi Yajima","author_inst":"Gotemba Nishi High School"},{"author_name":"Shinichirou Miura","author_inst":"Aichi Gakuin University"},{"author_name":"Tetsuya Endo","author_inst":"Aichi Gakuin University"},{"author_name":"Makoto Mochii","author_inst":"University of Hyogo"},{"author_name":"Ken-ichi T Suzuki","author_inst":"National Institute for Basic Biology"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Role of Alanine Transaminase in Retinal Metabolic Homeostasis: Potential therapeutic target in retinal diseases","rel_doi":"10.64898\/2026.04.19.719493","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719493","rel_abs":"Purpose: Alanine transaminases (ALT), encoded by the GPT gene, catalyzes the reversible conversion of pyruvate and glutamate to alanine and alpha-ketoglutarate, thereby correlating carbohydrate and amino acid metabolism. However, its role in the human neural retina remains unclear. This study aimed to explore the expression, localization, and metabolic function of ALT in the human neural retina and its potential involvement in retinal diseases. Methods: ALT1 and ALT2 expression and localization were examined in the retinas of healthy and diabetic retinopathy (DR) donors via immunoblotting and immunofluorescence. ALT function was assessed in ex vivo human retinal explants using pharmacological inhibition with beta-chloro-L-alanine (BCLA), followed by the analyses of enzyme activity, tissue injury, and transcriptomic responses. Stable-isotope tracing with 13C- and 15N-labelled substrates combined with GC-MS was used to define ALT-dependent carbon and nitrogen fluxes in macular and peripheral retinas. Redox level (NADPH\/NADP+) was also evaluated under tert-butyl hydroperoxide-induced oxidative stress. Results: ALT1 and ALT2 were both expressed in the human neural retina, with prominent localization in Muller glia and photoreceptor inner segments. ALT1 displayed a diffuse cytoplasmic distribution, whereas ALT2 demonstrated a punctate pattern consistent with mitochondrial localization. In DR retinas, ALT1 expression was spatially disorganized and heterogeneous, while ALT2 remained comparatively preserved. Inhibition of ALT with BCLA markedly reduced ALT activity without causing overt cytotoxicity or major transcriptional changes. Isotope tracing demonstrated that retinal ALT predominantly channels pyruvate-derived carbon into alanine, whereas alanine was minimally contributed to pyruvate production under basal conditions. ALT inhibition suppressed alanine synthesis and release, redirected nitrogen flux towards glutamate, glutamine, and aspartate, and uncovered distinct metabolic adaptations in macular but not peripheral retinas. Under oxidative stress, ALT inhibition induced the decrease of NADP+\/NADPH ratio and LDH release, indicating improved redox balance and reduced tissue injury. Conclusions: ALT is previously unrecognized as a regulator of carbon and nitrogen partitioner in the human neural retina, contributing to redox homeostasis under stress. The altered distribution of ALT1 in DR retina and the protective metabolic effects of ALT inhibition suggest ALT as a potential contributor to retinal metabolic vulnerability and a candidate therapeutic target in retinal diseases.","rel_num_authors":12,"rel_authors":[{"author_name":"Qin Chen","author_inst":"Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China"},{"author_name":"Ting Zhang","author_inst":"Macula Research Group, Save Sight Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia"},{"author_name":"Jingwen Zeng","author_inst":"Macula Research Group, Save Sight Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia"},{"author_name":"Michelle Yam","author_inst":"Macula Research Group, Save Sight Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia"},{"author_name":"Sora Lee","author_inst":"Macula Research Group, Save Sight Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia"},{"author_name":"Fanfan Zhou","author_inst":"Molecular Drug Development Group, Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia"},{"author_name":"Meidong Zhu","author_inst":"New South Wales Tissue Bank, New South Wales Organ and Tissue Donation Service, Sydney, NSW, 2000, Australia"},{"author_name":"Ming Zhang","author_inst":"Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China"},{"author_name":"Fang Lu","author_inst":"Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, China"},{"author_name":"Jianhai Du","author_inst":"Departments of Ophthalmology and Visual Sciences and Biochemistry and Molecular Medicine, West Virginia University, Morgantown, WV, 26506, United States"},{"author_name":"Mark Gillies","author_inst":"Macula Research Group, Save Sight Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia"},{"author_name":"Ling Zhu","author_inst":"Macula Research Group, Save Sight Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Ultra-long stable biomimetic nanoparticle Click-ed-to-cancer membrane for anti-cancer treatment","rel_doi":"10.64898\/2026.04.19.719453","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719453","rel_abs":"Cancer cell membrane coated biomimetic nanoparticles have been shown to be highly efficient in cellular uptake, homotypic tumour targeting, and the ability to suppress tumour growth compared to uncoated nanoparticles. Long duration anti-cancer treatment regimens require highly stable cancer cell membrane coated biomimetic nanoparticle. To manufacture such highly stable cancer cell membrane coated biomimetic nanoparticle, we used Click-chemistry to encapsulate cancer cell membrane on nanoparticles. In situ characterization was done to confirm the functionality of the novel Click-chemistry based formulation to encapsulate cancer cell membrane on nanoparticles. Gold nanoparticles were encapsulated with the cell membranes of cell lines of lung adenocarcinoma, malignant melanoma, high-grade serous epithelial ovarian cancer, colorectal cancer, oral cancer, esophageal adenocarcinoma, adenoid cystic carcinoma of salivary gland, and breast cancer. Functional group analysis, size, morphology, and surface charge confirmed long-stability of the biomimetic nanoparticles after incubating in complete growth medium for 12-months.","rel_num_authors":6,"rel_authors":[{"author_name":"Rajdeep Chakraborty","author_inst":"Macquarie University"},{"author_name":"Ria Shah","author_inst":"Macquarie University"},{"author_name":"Masuma Akter","author_inst":"Macquarie University"},{"author_name":"Mohammad-Ali Shahbazi","author_inst":"University of Groningen"},{"author_name":"Anastasiia Tukova","author_inst":"Macquarie University"},{"author_name":"Kerwin Shannon","author_inst":"University of Sydney"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Parallel processing chains span cytoarchitectures to organize association cortex","rel_doi":"10.64898\/2026.04.21.717753","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.717753","rel_abs":"Task fMRI and electrophysiology have revealed distributed, linked cortical patches with shared category preferences (e.g., faces, objects, places) smaller than cytoarchitectonic areas. Resting-state functional connectivity (RSFC) similarly showed that somato-cognitive action network (SCAN) nodes interleave with effectors (foot, hand, mouth), subdividing the precentral gyrus. Here, using multiple precision functional mapping (PFM) modalities (RSFC, task, lags), we discovered that most of association cortex is organized like face processing and SCAN, with small, discrete patches interconnected into chains. Such patch-chains densely tile prefrontal cortex but are largely absent from primary cortex. Cortico-striatal connectivity is organized such that patches of the same chain connect to the same striatal location. Within chains, infra-slow fMRI signals are ordered in time. RSFC-defined chains align with task fMRI localizers (e.g., visual, motor, pain). Chains are absent at birth and emerge in the first year of life, suggesting their formation is at least partially experience-driven. Cytoarchitectonic areas are subdivided by patches, and patches in the same chain are distributed across different cytoarchitectures. Chains represent parallel ordered processing streams that are separated by information domain and behavioral goals, not cytoarchitectonics. Functional subdivision of architectonics into smaller patches, interlinked to form cross-architecture chains, enable greater parallelization and flexible specialization of processing.","rel_num_authors":32,"rel_authors":[{"author_name":"Evan M Gordon","author_inst":"Washington University School of Medicine"},{"author_name":"Aishwarya Rajesh","author_inst":"Washington University School of Medicine"},{"author_name":"Roselyne J Chauvin","author_inst":"Washington University School of Medicine"},{"author_name":"Alyssa Labonte","author_inst":"Washington University School of Medicine"},{"author_name":"Babatunde Adeyemo","author_inst":"Washington University School of Medicine"},{"author_name":"Ally Dworetsky","author_inst":"Washington University School of Medicine"},{"author_name":"Charles J Lynch","author_inst":"Weill Cornell Medicine"},{"author_name":"Samuel R Krimmel","author_inst":"Washington University School of Medicine"},{"author_name":"Philip Cho","author_inst":"Washington University School of Medicine"},{"author_name":"Anxu Wang","author_inst":"Washington University School of Medicine"},{"author_name":"Noah J Baden","author_inst":"Washington University School of Medicine"},{"author_name":"Kristen M Scheidter","author_inst":"Washington University School of Medicine"},{"author_name":"Julia Monk","author_inst":"Washington University School of Medicine"},{"author_name":"Athanasia Metoki","author_inst":"Washington University School of Medicine"},{"author_name":"Jianxun Ren","author_inst":"Chanping Laboratory"},{"author_name":"Tomoyuki Nishino","author_inst":"Washington University School of Medicine"},{"author_name":"Youngeun Park","author_inst":"Sungkyunkwan University"},{"author_name":"Emily Rafka","author_inst":"Washington University School of Medicine"},{"author_name":"John R Pruett Jr.","author_inst":"Washington University School of Medicine"},{"author_name":"Adam Kepecs","author_inst":"Washington University School of Medicine"},{"author_name":"Hesheng Liu","author_inst":"Chanping Laboratory"},{"author_name":"Damien A Fair","author_inst":"University of Minnesota"},{"author_name":"Conor Liston","author_inst":"Weill Cornell Medicine"},{"author_name":"Choong-Wan Woo","author_inst":"Sungkyunkwan University"},{"author_name":"Benjamin P Kay","author_inst":"Washington University School of Medicine"},{"author_name":"Scott Marek","author_inst":"Washington University School of Medicine"},{"author_name":"Steven E Petersen","author_inst":"Washington University School of Medicine"},{"author_name":"Chad M Sylvester","author_inst":"Washington University School of Medicine"},{"author_name":"Rebecca F Schwarzlose","author_inst":"Washington University School of Medicine"},{"author_name":"Marcus E Raichle","author_inst":"Washington University School of Medicine"},{"author_name":"Timothy O Laumann","author_inst":"Washington University School of Medicine"},{"author_name":"Nico U F Dosenbach","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Spatiotemporal profiling reveals the role of inflammatory niche in driving prostate cancer","rel_doi":"10.64898\/2026.04.19.719485","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719485","rel_abs":"Prostate cancer (PCa) is a lethal malignancy that displays profound resistance to immune checkpoint blockade (ICB), via mechanisms that are poorly understood. Here, we investigate the causes of CD8 T cell exhaustion and mechanisms of tumor progression in a PCa animal model, by single cell and spatial profiling, along a time course, following orthotopic transplantation of RB1\/TP53\/PTEN-deficient mouse organoids, competent to express neoantigens. The resulting tumors were castration resistant, consisting of largely basal and L2 malignant cells with upregulated inflammatory gene programs, and a specific spatial distribution of macrophages, cancer associated fibroblast (CAF) subtypes, and CD8 T-cells that was not previously reported. Using Zman-seq, we demonstrate that the effector function of tumor-infiltrating CD8 T cells was rapidly impaired as early as 24hrs after their infiltration, likely driven by signals from proinflammatory macrophages, Ccl2-Jak2+ inflammatory CAFs, and malignant basal cells, thus driving resistance to ICB. Interestingly, dual blockade of JAK1\/2 and PD1 induced potent anti-tumor effects in tumor epithelial cells, decreased malignant epithelial cells and pro-inflammatory macrophages, and increased the proportion of normal (Pi16+) fibroblasts in the TME. Our results underscore the therapeutic potential of targeting JAK1\/2 to enhance the efficacy of ICB, providing a rationale for clinical investigation of this combination in PCa.","rel_num_authors":22,"rel_authors":[{"author_name":"Abbas Nazir","author_inst":"Genentech, Inc."},{"author_name":"Hanchen Wang","author_inst":"Genentech, Inc."},{"author_name":"Ziyu Lu","author_inst":"The Rockefeller University"},{"author_name":"Jeff Lau","author_inst":"Genentech, Inc."},{"author_name":"Frank Peale","author_inst":"Genentech, Inc."},{"author_name":"Raj Jesudason","author_inst":"Genentech, Inc."},{"author_name":"Kelli A Connolly","author_inst":"Department of Immunobiology, Yale University School of Medicine, New Haven, USA"},{"author_name":"Zaneta Andrusivova","author_inst":"Genentech, Inc."},{"author_name":"Julia Lau","author_inst":"Genentech, Inc."},{"author_name":"Sarah Gierke","author_inst":"Genentech, Inc."},{"author_name":"Linna Peng","author_inst":"Genentech, Inc."},{"author_name":"Sara Chan","author_inst":"Genentech, Inc."},{"author_name":"Jian Jiang","author_inst":"Genentech, Inc."},{"author_name":"Sandra Rost","author_inst":"Genentech, Inc."},{"author_name":"Eric Lubeck","author_inst":"Genentech, Inc."},{"author_name":"Marco De Simone","author_inst":"Genentech, Inc."},{"author_name":"Bench Daniel","author_inst":"Genentech, Inc."},{"author_name":"Lisa M McGinnis","author_inst":"Genentech, Inc."},{"author_name":"Danilo Maddalo","author_inst":"Genentech, Inc."},{"author_name":"Nikhil S Joshi","author_inst":"Department of Immunobiology, Yale University School of Medicine, New Haven, USA"},{"author_name":"Levi A Garraway","author_inst":"Genentech, Inc."},{"author_name":"Aviv Regev","author_inst":"Genentech, Inc."}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Spatiotemporal profiling reveals the role of inflammatory niche in driving prostate cancer","rel_doi":"10.64898\/2026.04.19.719485","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719485","rel_abs":"Prostate cancer (PCa) is a lethal malignancy that displays profound resistance to immune checkpoint blockade (ICB), via mechanisms that are poorly understood. Here, we investigate the causes of CD8 T cell exhaustion and mechanisms of tumor progression in a PCa animal model, by single cell and spatial profiling, along a time course, following orthotopic transplantation of RB1\/TP53\/PTEN-deficient mouse organoids, competent to express neoantigens. The resulting tumors were castration resistant, consisting of largely basal and L2 malignant cells with upregulated inflammatory gene programs, and a specific spatial distribution of macrophages, cancer associated fibroblast (CAF) subtypes, and CD8 T-cells that was not previously reported. Using Zman-seq, we demonstrate that the effector function of tumor-infiltrating CD8 T cells was rapidly impaired as early as 24hrs after their infiltration, likely driven by signals from proinflammatory macrophages, Ccl2-Jak2+ inflammatory CAFs, and malignant basal cells, thus driving resistance to ICB. Interestingly, dual blockade of JAK1\/2 and PD1 induced potent anti-tumor effects in tumor epithelial cells, decreased malignant epithelial cells and pro-inflammatory macrophages, and increased the proportion of normal (Pi16+) fibroblasts in the TME. Our results underscore the therapeutic potential of targeting JAK1\/2 to enhance the efficacy of ICB, providing a rationale for clinical investigation of this combination in PCa.","rel_num_authors":22,"rel_authors":[{"author_name":"Abbas Nazir","author_inst":"Genentech, Inc."},{"author_name":"Hanchen Wang","author_inst":"Genentech, Inc."},{"author_name":"Ziyu Lu","author_inst":"The Rockefeller University"},{"author_name":"Jeff Lau","author_inst":"Genentech, Inc."},{"author_name":"Frank Peale","author_inst":"Genentech, Inc."},{"author_name":"Raj Jesudason","author_inst":"Genentech, Inc."},{"author_name":"Kelli A Connolly","author_inst":"Department of Immunobiology, Yale University School of Medicine, New Haven, USA"},{"author_name":"Zaneta Andrusivova","author_inst":"Genentech, Inc."},{"author_name":"Julia Lau","author_inst":"Genentech, Inc."},{"author_name":"Sarah Gierke","author_inst":"Genentech, Inc."},{"author_name":"Linna Peng","author_inst":"Genentech, Inc."},{"author_name":"Sara Chan","author_inst":"Genentech, Inc."},{"author_name":"Jian Jiang","author_inst":"Genentech, Inc."},{"author_name":"Sandra Rost","author_inst":"Genentech, Inc."},{"author_name":"Eric Lubeck","author_inst":"Genentech, Inc."},{"author_name":"Marco De Simone","author_inst":"Genentech, Inc."},{"author_name":"Bench Daniel","author_inst":"Genentech, Inc."},{"author_name":"Lisa M McGinnis","author_inst":"Genentech, Inc."},{"author_name":"Danilo Maddalo","author_inst":"Genentech, Inc."},{"author_name":"Nikhil S Joshi","author_inst":"Department of Immunobiology, Yale University School of Medicine, New Haven, USA"},{"author_name":"Levi A Garraway","author_inst":"Genentech, Inc."},{"author_name":"Aviv Regev","author_inst":"Genentech, Inc."}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Delineating the Transcriptional and Phenotypic Impact from Biotherapeutic Glycoengineering","rel_doi":"10.64898\/2026.04.21.719832","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719832","rel_abs":"Glycosylation is critical to biopharmaceutical activity, stability, and pharmacokinetics. While production cells can be engineered to produce better protein glycoforms, glycans decorate thousands of host cell proteins, and it remains unclear how glycoengineering impacts the host cell. To decipher the cell response to glycoengineering, we studied a library of 166 glycoengineered CHO-K1 cell clones representing 54 different glycosyltransferase modifications. Through integrated analysis of glycomics, RNA-Seq, and phenotypic data, we discovered that glycoengineered mutants clustered into three distinct groups (wild-type-like, Moderate, and Substantial) based on their glycosylation patterns. Different glycosyltransferase families exhibited distinct phenotypic signatures: St3gal modifications increased growth rate and cell density, B4galt knockouts affected cell size, and Mgat knockouts enhanced cell viability. Notably, we found specific cellular reprogramming associated with each glycosyltransferase family, including alterations in energy metabolism, stress responses, and DNA repair mechanisms. These findings were validated in an independent set of 30 glycoengineered CHO-S cell lines, expressing a panel of 10 recombinant proteins. Our extensive analysis reveals phenotypic changes resulting from glycoengineering, identifies their molecular bases, and provides crucial insights for controlling glycosylation during therapeutic protein production.","rel_num_authors":14,"rel_authors":[{"author_name":"Haining Li","author_inst":"University of California San Diego"},{"author_name":"WAN-TIEN CHIANG","author_inst":"University of California, San Diego"},{"author_name":"Vahid H Gazestani","author_inst":"University of California, San Diego"},{"author_name":"Bokan Bao","author_inst":"University of California, San Diego"},{"author_name":"Shangzhong Li","author_inst":"University of California, San Diego"},{"author_name":"Patrice Menard","author_inst":"Technical University of Denmark"},{"author_name":"Johnny Arnsdorf","author_inst":"Technical University of Denmark"},{"author_name":"Zulfya Sukhova Dalgaard","author_inst":"Technical University of Denmark"},{"author_name":"Sara Petersen Bjorn","author_inst":"Technical University of Denmark"},{"author_name":"Karen Kathrine Brondum","author_inst":"Technical University of Denmark"},{"author_name":"Anders Holmgaard Hansen","author_inst":"Technical University of Denmark"},{"author_name":"Sanne Schoffelen","author_inst":"Technical University of Denmark"},{"author_name":"Bjorn G Voldborg","author_inst":"Technical University of Denmark"},{"author_name":"Nathan E Lewis","author_inst":"University of Georgia"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Isoform-specific cofactor recruitment through the intrinsically disordered N-terminus of p63 underlies differential transcriptional activities","rel_doi":"10.64898\/2026.04.19.719484","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719484","rel_abs":"The transcription factor p63 is critical for epithelial development and implicated in tumorigenesis. However, our understanding of the role of p63 in development and disease has been complicated by its diverse isoforms. As a member of the p53 family member of genes, TP63 encodes for numerous isoforms, including the N-terminal variants TAp63 and {triangleup}Np63, which are generated through alternative promoter usage. TAp63 and {triangleup}Np63 share various structural domains, including the DNA-binding domain, and primarily differ in their N-terminus which consists of intrinsically disordered regions (IDRs). The isoforms are known to have different functions, including tumor suppression in the case of TAp63 and pro-tumor formation for {triangleup}Np63, but how the N-terminus contributes to isoform-specific gene regulatory effects has yet to be elucidated. Using both genomic and TurboID proximity-labeling proteomic approaches, we show that the N-terminus mediates differential interactions with cofactors that have direct effects on isoform function, specifically the regulation of apoptosis. We find that the N-terminus of TAp63 interacts with more transcriptional machinery, leading to stronger transcriptional activity by TAp63 than {triangleup}Np63. However, {triangleup}Np63 maintains interactions with coactivators, suggesting it can retain some transactivation capabilities. Strikingly, the N-terminus of TAp63 displays enriched interactions with chromatin modifiers, including the histone acetyltransferase KAT2A, that result in TAp63-specific binding at inaccessible sites. We find that an IDR-mediated interaction with KAT2A is involved in regulation of apoptosis by TAp63. Collectively, our results suggest a model in which TAp63 and {triangleup}Np63 broadly share genomic occupancy, but differential interactions with cofactors contribute to isoform-specific regulation by TAp63 and {triangleup}Np63.","rel_num_authors":5,"rel_authors":[{"author_name":"Marina F Nogueira","author_inst":"Washington University in St. Louis"},{"author_name":"Michael J Moore","author_inst":"Washington University in St. Louis"},{"author_name":"Aparna R Biswas","author_inst":"Washington University in St. Louis"},{"author_name":"Michael P Meers","author_inst":"Washington University in St. Louis"},{"author_name":"Sidharth V Puram","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"EA-PheWAS: Integrating Phenotype Embeddings with PheWAS for Enhanced Gene-Phenotype Discovery","rel_doi":"10.64898\/2026.04.21.720031","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720031","rel_abs":"Phenome-wide association studies (PheWAS) enable systematic exploration of relationships between genetic variants and clinical phenotypes derived from electronic health records (EHRs). Conventional regression-based PheWAS treats phenotypes separately and relies on binary phenotype representations, which limits statistical power for rare variants and rare phenotypes and reduces the ability to detect associations with phenotypes that are distributed across clinical codes. To address this limitation, we first developed EmbedPheScan, a phenotype embedding-based prioritization framework that summarizes the phenotypic profiles of rare loss-of-function variant carriers in a continuous embedding space. We then proposed EA-PheWAS by combining these embedding-derived signals with conventional regression-based PheWAS results using the aggregated Cauchy association test. Using the UK Biobank whole-exome sequencing and EHR data, we show that the proposed methods maintain appropriate false-positive control. We then performed genome-wide phenome scans across all genes and across biologically defined gene classes to evaluate EA-PheWAS relative to conventional PheWAS and EmbedPheScan, consistently finding that EA-PheWAS outperformed the other two methods. We illustrate the utility of EA-PheWAS focusing on four genes representing distinct scenarios, including strong-effect disease genes (PKD1, PKD2), genes with large numbers of rare LoF carriers (NF1), and genes with extremely sparse carrier counts (FBN1).","rel_num_authors":7,"rel_authors":[{"author_name":"Wangjie Zheng","author_inst":"Department of Biostatistics, Yale University"},{"author_name":"Tianyu Liu","author_inst":"Interdepartmental Program in Computational Biology and Bioinformatics, Yale University"},{"author_name":"Leqi Xu","author_inst":"Department of Biostatistics, Yale University"},{"author_name":"Yuhan Xie","author_inst":"Department of Biostatistics, Yale University"},{"author_name":"Yueqian Jing","author_inst":"Department of Biostatistics, Yale University"},{"author_name":"Haoran Shao","author_inst":"Department of Biostatistics, Yale University"},{"author_name":"Hongyu Zhao","author_inst":"Department of Biostatistics, Yale University"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Nematic order in cellular tissues: a standardized framework and anomalous defect dynamics","rel_doi":"10.64898\/2026.04.22.719598","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.22.719598","rel_abs":"Cellular monolayers often exhibit orientational order, with nematic alignment of cell shape and cytoskeletal structures governing tissue-scale collective dynamics. Despite extensive studies, a unified analysis framework for characterizing active nematics in living systems remains partial, and key discrepancies with theory persist. Here, we present a systematic and comparative analysis of nematic order and tissue flow dynamics across twelve distinct cell types. We quantify the impact of analysis parameters and provide data-driven guidelines to improve reproducibility and cross-study comparability. Across all nematic systems, we uncover remarkably consistent static properties, supporting the universality of nematic behavior in living tissues. By combining orientation-field analysis with velocity-field measurements and numerical simulations, we show that all examined systems display contractile active nematic signatures, with characteristic flow structures around topological defects. However, direct tracking of individual defects reveals subdiffusive dynamics, in stark contrast with the superdiffusive, self-propelled motion predicted by the hydrodynamic theory of active nematics. Our results establish a standardized framework for nematic analysis in biological systems and highlight fundamental limitations of current active nematic models in describing defect dynamics in living tissues.","rel_num_authors":4,"rel_authors":[{"author_name":"Nicolas Rembert","author_inst":"University of Geneva"},{"author_name":"Mathieu Dedenon","author_inst":"University of Geneva"},{"author_name":"Aurelien Roux","author_inst":"University of Geneva"},{"author_name":"Claire A. Dessalles","author_inst":"CNRS"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"MCT1 governs a metabolic checkpoint at pachytene during spermatogenesis","rel_doi":"10.64898\/2026.04.21.720010","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.720010","rel_abs":"The transition from mitosis to meiosis represents a fundamental cell-fate decision that requires coordinated remodeling of transcriptional and metabolic programs. While key transcriptional regulators of meiotic entry have been defined, how metabolic flux directly governs this process remains unclear. Here, we identify a monocarboxylate transporter1 (MCT1)-dependent metabolic checkpoint that controls meiotic progression in mammalian spermatogenesis. Through integrative single-cell transcriptomics, metabolic profiling, and computational perturbation modeling, we show that Stra8-driven meiotic initiation is coupled to a metabolic switch favoring monocarboxylic acid metabolism, prominently involving MCT1 (encoded by Slc16a1). Germ cell-specific deletion of Slc16a1 results in a complete arrest at the pachytene stage, characterized by defective homologous recombination, persistent DNA damage, and failure to activate the meiotic transcriptional program. Multi-omic analyses reveal that loss of MCT1 induces a metabolic stress-like state, suppresses expression of key meiotic regulators, and disrupts progression through the pachytene checkpoint. Mechanistically, we demonstrate that MCT1-mediated lactate influx drives histone H4 lysine 12 lactylation (H4K12la) at promoters of meiotic genes, thereby epigenetically licensing their expression. In the absence of MCT1, H4K12la deposition is lost at meiotic loci and redistributed toward stress-response pathways. Together, our findings suggest MCT1-mediated metabolism as an instructive signal that integrates metabolic state with epigenetic regulation to govern meiotic cell-fate progression, defining a previously unrecognized metabolic checkpoint at pachytene.","rel_num_authors":3,"rel_authors":[{"author_name":"Xiaoyu Zhang","author_inst":"University of Kansas Medical Center"},{"author_name":"Yan Liu","author_inst":"University of Kansas Medical Center"},{"author_name":"Ning Wang","author_inst":"University of Kansas Medical Center"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"RNA exosome-mediated RNA surveillance governs developmental timing in the human cerebellum","rel_doi":"10.64898\/2026.04.19.719460","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719460","rel_abs":"Defects in RNA metabolism are a defining feature of neurodevelopmental disease, yet the contribution of RNA decay pathways to human brain development remains poorly understood. Notably, mutations in genes encoding ubiquitously expressed RNA surveillance machinery often cause highly tissue-selective disease, underscoring a central paradox in human biology. The RNA exosome is a conserved ribonuclease complex long considered a housekeeping machine for RNA turnover, yet recessive mutations in genes encoding structural subunits of the complex disproportionately cause neurological disease, suggesting an instructive role in nervous system development. Here, we show that the RNA exosome regulates the temporal progression of gene expression programs during human cerebellar differentiation. Using CRISPR-engineered human cerebellar organoids modeling EXOSC3 variants, we find that RNA exosome dysfunction does not broadly alter transcript abundance, but instead disrupts transitions between developmental states. Mutant organoids exhibit persistence of early transcriptional programs, impaired maturation of Purkinje and rhombic lip-derived lineages, and altered cellular composition. These defects are accompanied by disorganized laminar architecture and reduced coordination of neuronal activity, despite preserved intrinsic excitability. More broadly, our findings suggest that defects in RNA decay represent a general mechanism underlying neurodevelopmental disease. Together, this work establishes RNA surveillance as a key determinant of developmental timing, neural identity, and disease.","rel_num_authors":12,"rel_authors":[{"author_name":"Nina A Barr","author_inst":"University of Southern California"},{"author_name":"Bozhidar Boltov","author_inst":"University of Southern California"},{"author_name":"Rylee E Kang","author_inst":"University of Southern California"},{"author_name":"Jash J Gada","author_inst":"University of Southern California"},{"author_name":"Matthew J Wade","author_inst":"University of Southern California"},{"author_name":"Ethan N Tjoa","author_inst":"University of Southern California"},{"author_name":"Vivian Lee","author_inst":"University of Southern California"},{"author_name":"Anoothi Seth","author_inst":"University of Southern California"},{"author_name":"Kafui Dzirasa","author_inst":"Duke University"},{"author_name":"Ashleigh E Schaffer","author_inst":"Oregon Health and Science University"},{"author_name":"Hyunmin Kim","author_inst":"Case Western Reserve University"},{"author_name":"Derrick J Morton","author_inst":"University of Southern California"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"RNA exosome-mediated RNA surveillance governs developmental timing in the human cerebellum","rel_doi":"10.64898\/2026.04.19.719460","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719460","rel_abs":"Defects in RNA metabolism are a defining feature of neurodevelopmental disease, yet the contribution of RNA decay pathways to human brain development remains poorly understood. Notably, mutations in genes encoding ubiquitously expressed RNA surveillance machinery often cause highly tissue-selective disease, underscoring a central paradox in human biology. The RNA exosome is a conserved ribonuclease complex long considered a housekeeping machine for RNA turnover, yet recessive mutations in genes encoding structural subunits of the complex disproportionately cause neurological disease, suggesting an instructive role in nervous system development. Here, we show that the RNA exosome regulates the temporal progression of gene expression programs during human cerebellar differentiation. Using CRISPR-engineered human cerebellar organoids modeling EXOSC3 variants, we find that RNA exosome dysfunction does not broadly alter transcript abundance, but instead disrupts transitions between developmental states. Mutant organoids exhibit persistence of early transcriptional programs, impaired maturation of Purkinje and rhombic lip-derived lineages, and altered cellular composition. These defects are accompanied by disorganized laminar architecture and reduced coordination of neuronal activity, despite preserved intrinsic excitability. More broadly, our findings suggest that defects in RNA decay represent a general mechanism underlying neurodevelopmental disease. Together, this work establishes RNA surveillance as a key determinant of developmental timing, neural identity, and disease.","rel_num_authors":12,"rel_authors":[{"author_name":"Nina A Barr","author_inst":"University of Southern California"},{"author_name":"Bozhidar Boltov","author_inst":"University of Southern California"},{"author_name":"Rylee E Kang","author_inst":"University of Southern California"},{"author_name":"Jash J Gada","author_inst":"University of Southern California"},{"author_name":"Matthew J Wade","author_inst":"University of Southern California"},{"author_name":"Ethan N Tjoa","author_inst":"University of Southern California"},{"author_name":"Vivian Lee","author_inst":"University of Southern California"},{"author_name":"Anoothi Seth","author_inst":"University of Southern California"},{"author_name":"Kafui Dzirasa","author_inst":"Duke University"},{"author_name":"Ashleigh E Schaffer","author_inst":"Oregon Health and Science University"},{"author_name":"Hyunmin Kim","author_inst":"Case Western Reserve University"},{"author_name":"Derrick J Morton","author_inst":"University of Southern California"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences","rel_doi":"10.64898\/2026.04.19.719533","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.19.719533","rel_abs":"Across the last three decades, functional magnetic resonance imaging (fMRI) research - through both resting-state (rsfMRI) and task-based (tfMRI) studies - has greatly advanced our understanding regarding the neural basis of cognition. Yet the mechanistic relationship between rsfMRI and tfMRI is still poorly understood. In particular, it remains unclear how and why the brain activation patterns observed during the resting state are linked to cognitive functioning and individual differences present during task performance. Here, we test a unifying computational account which postulates that task contexts modulate the nonlinear attractor landscape and associated dynamical properties of the brain present under resting conditions, and further that the nature of this modulation is impacted by meaningful cognitive individual differences. To test this account, we develop a joint rsfMRI-tfMRI modeling and analysis framework called Mesoscale Individualized NeuroDynamics with eXogenous inputs (MINDy-X) and apply it to resting and N-back working memory task data from the Human Connectome Project. We first validated that the joint model can simulate and predict both rsfMRI and tfMRI data accurately, consistent with a common underlying dynamical system. Analyses of this joint model revealed that task-related modulation bifurcated the predominantly multistable attractor dynamics present during the resting state towards a predominantly monostable dynamics observed during N-back task states. This topological shift was also accompanied by a geometric reconfiguration, with the task state characterized by an enrichment of dynamical attractor \"motifs\" clustered around the frontoparietal (FPN) and default mode (DMN) networks. Task-related modulations of this attractor landscape were further subject to clear individual differences, such that individuals who did not exhibit a shift in attractor topology were more error-prone and less cautious in responding, while closer geometric proximity to the FPN and DMN motifs explained additional aspects of task performance. N-back behavior was best characterized by the combination of topological and geometric properties present in both task and rest states, suggesting that they each account for unique aspects of individual variability. The current work supports a novel computational framework for understanding the whole-brain neural activity patterns observed during rsfMRI and tfMRI as reflecting different states within a common non-linear dynamical system. This framework provides a new vocabulary for characterizing cognitive functioning in terms of the unique geometric and topological configuration of the associated attractor landscapes, with the potential for wide application in many domains of basic and clinical neuroscience research.","rel_num_authors":4,"rel_authors":[{"author_name":"Ruiqi Chen","author_inst":"Washington University in St. Louis"},{"author_name":"Hayoung Song","author_inst":"Washington University in St. Louis"},{"author_name":"ShiNung Ching","author_inst":"Washington University in St. Louis"},{"author_name":"Todd S Braver","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Cerebellum violates Marr-Albus predictions to train synapses on long-term anticipatory goals","rel_doi":"10.64898\/2026.04.21.719977","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719977","rel_abs":"The cerebellar role in motor adaptation rests - according to the foundational theories of Marr and Albus - on the detection of (near)coincident activity between parallel fiber (PF) and climbing fiber (CF) inputs onto Purkinje cells. Supported by numerous in vitro studies, these theories predict synaptic adaptation based on temporal precision detected in time windows of 0ms to ~100ms. These predictions have not been tested in intact animals. Using two-photon imaging from cerebellar Crus I in intact, awake mice, we show here that coincident stimulation of the PF and CF inputs does not initiate plasticity. Rather, long-term depression (LTD) is reliably evoked by ramping activity of the PF pathway that precedes a CF burst by 400ms. These observations demonstrate a cerebellar plasticity that is not about precision in coincidence with CF signaling. Rather, it shows that cerebellar learning centers on the evaluation of anticipatory PF signals by the CF input.","rel_num_authors":2,"rel_authors":[{"author_name":"Christian Hansel","author_inst":"University of Chicago"},{"author_name":"Ting-Feng Lin","author_inst":"University of Chicago"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"\u03b1-Synuclein Facilitates Spontaneous Dopamine Release in a Calcium- and Phosphorylation-Dependent Manner","rel_doi":"10.64898\/2026.04.20.719002","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719002","rel_abs":"-Synuclein (aSyn) is central to Parkinson's disease pathogenesis, yet its native physiological role at the presynapse remain poorly defined. Here, super-resolution imaging in dopaminergic neurons reveals that endogenous aSyn localises within nanometres of L-type voltage-gated calcium channels (LTCC), with closer proximity under both spontaneous neuronal activity and stimulated conditions compared to when extracellular calcium is chelated. Blocking Ca2+\/calmodulin-dependent kinase II (CaMKII) reduces aSyn clustering at LTCC under spontaneous activity, suggesting that calcium entry and downstream calcium-dependent kinase activity contribute to aSyn localisation. Moreover, quantitative single-molecule analyses indicate that calcium increases the abundance of both total and serine129 phosphorylated (pS129) aSyn in synaptosomes under spontaneous conditions, and NMR analysis reveals that both calcium and S129 phosphorylation increase the binding affinity of aSyn to synaptic vesicles. Functional assays further demonstrate that LTCC blockade elevates intracellular DA levels exclusively in the presence of aSyn under spontaneous but not stimulated conditions. Finally, biochemical fractionation and multi-colour single-molecule imaging reveal that aSyn preferentially associates with small vesicles that are not obligately coupled to full-fusion associated recycling pools. These results suggest that aSyn acts as a calcium- and phosphorylation-regulated modulator of spontaneous DA release through pathways that are largely independent of full-fusion recycling mechanisms, and that pS129 aSyn is not solely a pathological marker but may also reflects physiological regulation. Together, these insights provide a framework for understanding how therapeutic strategies targeting aSyn may impact its normal synaptic functions.","rel_num_authors":17,"rel_authors":[{"author_name":"Yuqing Feng","author_inst":"University of Cambridge"},{"author_name":"Amberley D. Stephens","author_inst":"University of Cambridge"},{"author_name":"Pedro Vallejo Ramirez","author_inst":"University of Cambridge"},{"author_name":"Eugene V. Mosharov","author_inst":"Columbia University"},{"author_name":"Alfonso De Simone","author_inst":"University of Naples Federico II"},{"author_name":"Giuliana Fusco","author_inst":"University of Naples Federico II"},{"author_name":"Stanislaw Makarchuk","author_inst":"University of Cambridge"},{"author_name":"Marius Brockhoff","author_inst":"University of Cambridge"},{"author_name":"Ana Fernandez-Villegas","author_inst":"University of Cambridge"},{"author_name":"Colin Hockings","author_inst":"University of Cambridge"},{"author_name":"Edward Ward","author_inst":"University of Cambridge"},{"author_name":"Pedro Magalh\u00e3es","author_inst":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne"},{"author_name":"Senthil Kumar","author_inst":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne"},{"author_name":"Nino F. L\u00e4ubli","author_inst":"University of Cambridge"},{"author_name":"Hilal A. Lashuel","author_inst":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne"},{"author_name":"Clemens F. Kaminski","author_inst":"University of Cambridge"},{"author_name":"Gabriele S. Kaminski Schierle","author_inst":"University of Cambridge"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Children and adults use distinct neurocognitive mechanisms to support successful memory-based inference","rel_doi":"10.64898\/2026.04.21.719709","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719709","rel_abs":"Reasoning depends on the ability to connect information across distinct experiences to derive knowledge that was never directly observed, and this capacity exhibits protracted developmental improvement that extends into emerging adulthood. Despite extensive behavioral work, it remains unclear whether developmental gains in inference reflect quantitative strengthening of a single mechanism or qualitative changes in the knowledge representations and computations that support inference decisions. Here, we tested the hypothesis that improvements in inference are linked to maturation of the hippocampus and posterior parietal cortex, resulting in age-related differences in how inference decisions are computed. We predicted that children (7-12 years) would rely on an iterative retrieval mechanism, requiring retrieval and combination of multiple distinct memories at the time of inference, whereas adults would be able to directly retrieve inferred relationships from structured representations that encode shared relations across experiences. Using functional MRI combined with computational modeling of response times, we show that hippocampal activity predicts successful inference via an iterative retrieval mechanism in both children and adults. Critically, only in adults does angular gyrus activity predict inference via a distinct, direct retrieval mechanism, consistent with access to inferred relationships represented as either integrated memories or in geometrically aligned neural spaces that organize events according to their shared relational structure. These findings identify a developmental shift in the neural mechanisms that support inference, demonstrating that maturation of posterior parietal cortex enables access to representations that capture derived linkages across experiences, fundamentally changing how inference decisions are computed across development.","rel_num_authors":6,"rel_authors":[{"author_name":"Christine Coughlin","author_inst":"University of Illinois Chicago"},{"author_name":"Margaret L. Schlichting","author_inst":"University of Toronto"},{"author_name":"Neal W Morton","author_inst":"University of Wisconsin Milwaukee"},{"author_name":"Katherine R. Sherrill","author_inst":"University of Texas at Austin"},{"author_name":"Michelle Moreau","author_inst":"University of Texas at Austin"},{"author_name":"Alison R. Preston","author_inst":"University of Texas at Austin"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Hierarchical and non-hierarchical network flows generate complementary representational dynamics in human visual cortex","rel_doi":"10.64898\/2026.04.21.719298","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.21.719298","rel_abs":"Hierarchy is considered a fundamental organizing principle of visual cortex, but its functional implications remain debated given the presence of direct (non-hierarchical) connections. Building on recent advances in measuring direct region-to-region functional connectivity in the human brain, and in using that connectivity (rather than, e.g., visual classification training) to construct deep neural network models, we tested the hypothesis that hierarchical and direct connectivity pathways make distinct contributions to the generation of visual functionality. Detailed measurement of visual functionality, connectivity, and their interaction was achieved using 7T MRI and empirical neural network (ENN) models parameterized by empirical connectivity estimates. The classic V1 to V4 hierarchy was recovered in terms of (i) network distance from V1 along the human brain's direct region-to-region resting-state functional connectome and (ii) on-task representational transformation distance (visual representation dissimilarity) from V1. In silico ENN lesion experiments revealed that hierarchical pathways (V1[-&gt;]V2[-&gt;]V3[-&gt;]V4) reduce the dimensionality of neural representations relative to more rapid and high-dimensional representational contributions from direct pathways (e.g., V1[-&gt;]V4). These findings reveal distinct but complementary roles of hierarchical and direct pathways in generating cortical functionality.","rel_num_authors":7,"rel_authors":[{"author_name":"Alexandros Tzalavras","author_inst":"Rutgers University, Newark"},{"author_name":"David E Osher","author_inst":"The Ohio State University"},{"author_name":"Carrisa Veronica Cocuzza","author_inst":"Rutgers University"},{"author_name":"Lakshman Nallan Chakravarthula","author_inst":"Rutgers University, Newark"},{"author_name":"Ravi D Mill","author_inst":"Rutgers University, Newark"},{"author_name":"Kirsten L Peterson","author_inst":"Rutgers University, Newark"},{"author_name":"Michael W Cole","author_inst":"Rutgers University, Newark"}],"rel_date":"2026-04-22","rel_site":"biorxiv"},{"rel_title":"Sexual Function and Clitoral Anatomy after Vaginal Surgery with and without Midurethral Sling","rel_doi":"10.64898\/2026.04.20.26351291","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.20.26351291","rel_abs":"Importance: Sexual dysfunction can occur after midurethral sling (MUS) and transvaginal prolapse surgery. It remains unclear whether these procedures impact the clitoris, despite its role in sexual function and proximity to the MUS and vagina. Objectives: To compare postoperative sexual function and clitoral features by MUS and vaginal surgery approach after transvaginal prolapse repair with\/without concomitant MUS. Design: Cross-sectional ancillary study of magnetic resonance imaging (MRI) and sexual function data from the Defining Mechanisms of Anterior Vaginal Wall Descent study. Setting: Eight clinical sites in the US Pelvic Floor Disorders Network. Participants: 88 women with uterovaginal prolapse who underwent vaginal mesh hysteropexy or vaginal hysterectomy with uterosacral ligament suspension with\/without MUS between 2013-2015. Data were analyzed between September 2021-June 2023. Exposures: Between June 2014-May 2018, participants underwent pelvic MRI 30-42 months after surgery, or earlier if reoperation was desired. Sexual activity and function at baseline and 24-48-month follow-up were evaluated using the Pelvic Organ Prolapse\/Incontinence Sexual Questionnaire, IUGA-Revised (PISQ-IR). Clitoral features were obtained from postoperative MRI-based 3-dimensional models. Main Outcomes and Measures: PISQ-IR scores and clitoral features (size, position). Results: Eighty-two women (median [range] age, 65 [47-79] years) were analyzed: 45 MUS (22 hysteropexy, 23 hysterectomy) and 37 No-MUS (19 hysteropexy, 18 hysterectomy). Postoperatively, 25 MUS, 12 No-MUS, 20 hysteropexy, and 17 hysterectomy patients were sexually active (SA). Overall, within the MUS and vaginal surgery groups, sexual function remained unchanged or improved (most PISQ-IR change from baseline scores were [&ge;]0) among SA and NSA women. Among SA women after surgery, the MUS group (vs No-MUS) had a poorer PISQ-IR arousal\/orgasm (SA-AO) score (median, 3.5 vs 4.3; P=.02). The hysteropexy group (vs hysterectomy) had less improvement in PISQ-IR SA-AO score (median, 0.0 vs 0.3; P=.01). Women with MUS (vs without) had a smaller clitoral glans thickness (median, 9.0 mm vs 10.0 mm; P=.008) and clitoral body volume (median, 2783.5 mm3 vs 3587.4 mm3; P=.01). Conclusions and Relevance: SA women with MUS (vs without) or hysteropexy (vs hysterectomy) experienced poorer postoperative sexual function. MUS was linked to a smaller clitoris. Future studies should explore surgery-induced changes in clitoral anatomy and sexual function.","rel_num_authors":12,"rel_authors":[{"author_name":"Shaniel T Bowen","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Pamela A Moalli","author_inst":"Magee-Womens Hospital of the University of Pittsburgh"},{"author_name":"Rebecca G Rogers","author_inst":"Albany Medical Center"},{"author_name":"Marlene M Corton","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Uduak U Andy","author_inst":"University of Pennsylvania"},{"author_name":"Charles R Rardin","author_inst":"Alpert Medical School of Brown University"},{"author_name":"Michael E Hahn","author_inst":"University of California San Diego"},{"author_name":"Alison C Weidner","author_inst":"Duke University"},{"author_name":"David R Ellington","author_inst":"University of Alabama at Birmingham"},{"author_name":"Donna Mazloomdoost","author_inst":"Eunice Kennedy Shriver National Institute of Child Health and Human Development"},{"author_name":"Amaanti Sridhar","author_inst":"RTI International"},{"author_name":"Marie G Gantz","author_inst":"RTI International"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Family Constellations for All Clinical Conditions: A Systematic Review and Meta-analysis Showing a Lack of Supporting Evidence","rel_doi":"10.64898\/2026.04.19.26351231","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.19.26351231","rel_abs":"Introduction: Family Constellation Therapy (FCT) has been widely disseminated in clinical, public health, and judicial settings despite persistent concerns regarding its theoretical basis, safety, and the limited availability of rigorous randomised evidence supporting its clinical use. Objective: The aim of this systematic review is to assess the effects of FCT across all clinical conditions, explicitly considering both benefits and harms; and summarise the characteristics of studies and intervention settings used in randomised controlled trials of FCT. Methods: Following a prospectively registered protocol (CRD420251136190), we conducted a systematic search of seven databases (PubMed, EMBASE, APA PsycInfo, CENTRAL, BVS, Web of Science, and CINAHL) and grey literature (ICTRP and ProQuest database) without language or date restrictions to identify published and unpublished randomised controlled trials of FCT. Study selection, data extraction, risk of bias (RoB 2), and certainty of evidence (GRADE) were performed in duplicate. Statistical analyses followed a prospectively registered analysis plan with prespecified criteria for data pooling and for handling analytical limitations. Results: No reliable evidence was found to support the use of FCT for any condition across both clinical and non-clinical samples. All trials included were judged to be at high risk of bias and all comparisons were rated as very low-certainty evidence. Concerns regarding potential adverse effects were identified, and the available data was insufficient to establish the effectiveness of the intervention, precluding any clinical recommendation. Conclusion: Clinicians, policymakers, and consumers should reconsider adopting FCT while reliable evidence is not available.","rel_num_authors":3,"rel_authors":[{"author_name":"Filipe Luis Souza","author_inst":"University of New South Wales (UNSW)"},{"author_name":"Nathalia Cabral Souza","author_inst":"Independent researcher"},{"author_name":"Josimar A. de A. Mendes","author_inst":"University of Oxford"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"The MIND Study: Design, Feasibility, and Baseline Characteristics of a Smartphone-Based Migraine Cohort","rel_doi":"10.64898\/2026.04.14.26350866","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.14.26350866","rel_abs":"Objective: To describe the design, feasibility, and baseline characteristics of the Migraine Impact on Neurocognitive Dynamics (MIND) study, a 30-day smartphone-based cohort for high-frequency assessment of cognition and symptoms in adults with migraine. Background: Cognitive symptoms are an important component of migraine burden, but they are difficult to measure using single-visit testing or retrospective questionnaires. Repeated smartphone-based assessment may better capture real-world variability in cognition and symptoms. Methods: Adults meeting International Classification of Headache Disorders, 3rd edition, criteria for migraine were enrolled remotely and completed 30 days of once-daily ecological momentary assessments and mobile cognitive tasks delivered through the Mobile Monitoring of Cognitive Change platform. Baseline measures assessed demographics, migraine characteristics, disability, mood, stress, and treatment patterns. Feasibility was evaluated using enrollment, completion, and retention metrics. Results: A total of 177 participants enrolled (mean age 38.8 (SD 11.9) years; 79.7% female), including 80\/177 (45.2%) with chronic migraine. Across the 30-day protocol, 3688 daily assessments were completed, representing 70.8% of all possible study days, and 70.6% of participants completed at least 20 days of monitoring. Completion remained above 60% across study days. At baseline, chronic migraine was associated with greater burden than low-frequency and high-frequency episodic migraine, including higher MIDAS scores (98.6 vs. 38.7 and 70.3), more days with concentration difficulty (16.0 vs. 7.9 and 11.5), and more days with functional interference (18.5 vs. 7.6 and 13.0). Conclusions: The MIND study demonstrates the feasibility of high-frequency smartphone-based assessment of cognition and symptoms in migraine and provides a methodological foundation for future analyses of within-person cognitive and symptom dynamics across the migraine cycle.","rel_num_authors":4,"rel_authors":[{"author_name":"Babak Khorsand","author_inst":"University of California, Irvine"},{"author_name":"Devin Teichrow","author_inst":"University of California, Irvine Medical School"},{"author_name":"Richard B Lipton","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Ali Ezzati","author_inst":"University of California, Irvine"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Built environment characteristics and drowning mortality: A global satellite-based analysis of urbanisation, infrastructure, and water proximity","rel_doi":"10.64898\/2026.04.19.26351236","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.19.26351236","rel_abs":"Drowning remains a major global public health challenge, yet how built environment characteristics shape population-level drowning risk remains poorly understood. This study linked satellite-derived built environment data to subnational drowning mortality estimates across 203 regions in 12 countries from 2006-2021. It found that built environment associations with drowning mortality are complex, non-linear, and shaped by development context. Urban extent was strongly protective, while built area near water showed protection overall but increased risk when combined with high population crowding. Almost all drowning mortality variance occurred between regions rather than within regions over time, indicating risk is predominantly determined by place-based characteristics. Income-stratified analyses revealed profound heterogeneity: crowding was protective in low- to middle-income settings but near-null in high-income regions, while waterfront development captured very different realities across contexts. These findings highlight the importance of tailoring drowning prevention strategies to local built environment configurations and development contexts.","rel_num_authors":3,"rel_authors":[{"author_name":"Ryan Essex","author_inst":"The George Institute for Global Health"},{"author_name":"Samsung Lim","author_inst":"UNSW"},{"author_name":"Jagnoor Jagnoor","author_inst":"The George Institute for Global Health"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Temporal features of the built environment and associations with drowning mortality: A global satellite-based analysis","rel_doi":"10.64898\/2026.04.19.26351237","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.19.26351237","rel_abs":"Background: Drowning remains a major global public health challenge. This study examined whether the timing and trajectories of urbanisation-beyond the current built environment-are associated with subnational drowning mortality. Methods: We linked satellite-derived measures of built-environment change (GHSL), population crowding (WorldPop), surface water exposure (JRC Global Surface Water), and infrastructure proxies (VIIRS\/DMSP nighttime lights) to GBD 2021 drowning mortality estimates across 203 ADM1 regions in 12 countries (2006-2021; 3,248 region-year observations). Temporal predictors captured recent expansion, development \"newness\" (<10-year built share), acceleration\/volatility, and a crowdingxgrowth interaction. We screened predictors using LASSO (10-fold cross-validation) and fitted mixed-effects models with region random intercepts. Distributed-lag models tested temporal precedence and development age, and income-stratified models assessed heterogeneity. Results: Adding temporal predictors improved fit beyond contemporaneous built-environment measures (AIC=177; BIC=147). In adjusted models, crowdingxgrowth was strongly positively associated with drowning mortality, and a higher share of recent development was associated with higher mortality. Lag models showed a development age gradient: older built environment was most protective. Associations differed by income group, with several key coefficients reversing sign across strata. Discussion: Drowning mortality appears shaped by development histories as well as present-day conditions, with risk concentrated in rapidly changing, dense settings and the newest built environments. Cross-context heterogeneity suggests mechanisms and prevention priorities are unlikely to be uniform. Conclusions: Development timing and trajectories help explain subnational drowning mortality beyond current built form alone. Prevention and planning should prioritise transition-period safety strategies in newly developing and rapidly densifying areas.","rel_num_authors":3,"rel_authors":[{"author_name":"Ryan Essex","author_inst":"The George Institute for Global Health"},{"author_name":"Samsung Lim","author_inst":"UNSW"},{"author_name":"Jagnoor Jagnoor","author_inst":"The George Institute for Global Health"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Do Amyloid Trajectories Reach a Physiologic Ceiling? Evidence from Iterative Approximation and Simulation","rel_doi":"10.64898\/2026.04.14.26350359","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.14.26350359","rel_abs":"Qualitative models of Alzheimer's pathology often posit that amyloid accumulation follows a sigmoid curve, indicating that the rate of deposition wanes over time. Longitudinal PET data now allow us to investigate amyloid accumulation trajectories with greater detail and over longer follow-up periods. We combine inferences from simulated amyloid trajectories, empirical PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), and the sampled iterative local approximation algorithm (SILA) to assess whether amyloid accumulation reaches a physiologic ceiling. We find that SILA reliably detects a ceiling, when present, across a range of simulated scenarios that impose a sigmoid shape. When fit to empirical data from ADNI, however, SILA does not appear to indicate the presence of a ceiling. Thus, we conclude that amyloid trajectories may not reach a physiologic ceiling during the stages of Alzheimer's disease typically observed while patients remain under follow-up in cohort studies. Fits using SILA indicate that illustrative models of biomarker cascades, while useful tools for conceptualizing and interrogating pathologic processes, may not represent the shapes of amyloid trajectories accurately.","rel_num_authors":4,"rel_authors":[{"author_name":"Jason R. Gantenberg","author_inst":"Brown University"},{"author_name":"Renaud La Joie","author_inst":"University of California, San Francisco"},{"author_name":"Margo B. Heston","author_inst":"University of California, San Francisco"},{"author_name":"Sarah F. Ackley","author_inst":"Brown University"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Do Amyloid Trajectories Reach a Physiologic Ceiling? Evidence from Iterative Approximation and Simulation","rel_doi":"10.64898\/2026.04.14.26350359","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.14.26350359","rel_abs":"Qualitative models of Alzheimer's pathology often posit that amyloid accumulation follows a sigmoid curve, indicating that the rate of deposition wanes over time. Longitudinal PET data now allow us to investigate amyloid accumulation trajectories with greater detail and over longer follow-up periods. We combine inferences from simulated amyloid trajectories, empirical PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), and the sampled iterative local approximation algorithm (SILA) to assess whether amyloid accumulation reaches a physiologic ceiling. We find that SILA reliably detects a ceiling, when present, across a range of simulated scenarios that impose a sigmoid shape. When fit to empirical data from ADNI, however, SILA does not appear to indicate the presence of a ceiling. Thus, we conclude that amyloid trajectories may not reach a physiologic ceiling during the stages of Alzheimer's disease typically observed while patients remain under follow-up in cohort studies. Fits using SILA indicate that illustrative models of biomarker cascades, while useful tools for conceptualizing and interrogating pathologic processes, may not represent the shapes of amyloid trajectories accurately.","rel_num_authors":4,"rel_authors":[{"author_name":"Jason R. Gantenberg","author_inst":"Brown University"},{"author_name":"Renaud La Joie","author_inst":"University of California, San Francisco"},{"author_name":"Margo B. Heston","author_inst":"University of California, San Francisco"},{"author_name":"Sarah F. Ackley","author_inst":"Brown University"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Transcriptome-Wide Alternative Splicing Analysis Implicates Complex Events in Bipolar Disorder","rel_doi":"10.64898\/2026.04.19.26351209","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.19.26351209","rel_abs":"Alternative-splicing events (ASE) increase transcriptomic variability and play key roles in biological functions. The contribution of ASE to bipolar disorder (BD) remains largely unexplored. We performed a Transcriptome-Wide Alternative-Splicing Analysis (TWASA) to identify ASEs and genes potentially involved in BD. The study comprised 635 individuals: a discovery sample (DS) of 31 individuals from eight multiplex BD families (16 BD cases; 15 unaffected relatives), and a replication sample (RS) of 604 subjects (372 BD cases; 232 controls). Sequencing was conducted on RNA from lymphoblastoid cell lines (DS) and whole blood (RS). TWASA was performed using VAST-TOOLS (VT), rMATS (RM), and MAJIQ\/MOCCASIN (MCC). Gene-set association analyses of genes containing ASEs were performed across six psychiatric disorders. Novel ASE (nASE) were investigated in the DS using FRASER. Limited gene overlap was observed across TWASA tools. MCC identified 2,031 complex ASEs involving 1,508 genes, showing the strongest genetic association with BD across psychiatric phenotypes. Prioritization of MCC-identified ASE genes yielded 441 candidates, including DOCK2 as top candidate from the DS. Replication was obtained for 98 genes, five with an identical ASE, and four (RBM26, QKI, ANKRD36, and TATDN2) showing a concordant percentage-spliced-in direction with the DS. Finally, 578 nASE were identified in the DS, with no evidence of familial segregation or differences in ASE types. This first TWASA in BD reveals tool-specific variability, complex ASE for genes specifically associated with BD, and novel candidate genes for BD. Alternative transcript isoform abundance may represent a mechanism contributing to BD pathophysiology.","rel_num_authors":16,"rel_authors":[{"author_name":"Miriam Martinez-Jimenez","author_inst":"Centre for Molecular Biology Severo Ochoa, Spanish National Research Council & Autonomous University of Madrid, Madrid, Spain"},{"author_name":"Ines Garcia-Ortiz","author_inst":"Centre for Molecular Biology Severo Ochoa, Spanish National Research Council & Autonomous University of Madrid, Madrid, Spain"},{"author_name":"Diego Romero-Miguel","author_inst":"Centre for Molecular Biology Severo Ochoa, Spanish National Research Council & Autonomous University of Madrid, Madrid, Spain"},{"author_name":"Tomas Kavanagh","author_inst":"Brain and Mind Centre, School of Medical Sciences, University of Sydney, NSW, Australia"},{"author_name":"Lee L Marshall","author_inst":"Garvan Institute of Medical Research, Sydney, NSW, Australia"},{"author_name":"Rosa Ana Bello Sousa","author_inst":"Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain"},{"author_name":"Sergio Sanchez Alonso","author_inst":"Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain"},{"author_name":"Raquel Alvarez Garcia","author_inst":"Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Madrid, Spain"},{"author_name":"Sergio Benavente Lopez","author_inst":"Department of Psychiatry, University Hospital Infanta Elena, Valdemoro, Madrid, Spain"},{"author_name":"Ezequiel Di Stasio","author_inst":"Department of Psychiatry, General Hospital of Villalba, Madrid, Spain"},{"author_name":"Peter R Schofield","author_inst":"Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Enrique Baca-Garcia","author_inst":"Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain"},{"author_name":"Philip B Mitchell","author_inst":"Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Antony A Cooper","author_inst":"Garvan Institute of Medical Research, Sydney, NSW, Australia"},{"author_name":"Janice M Fullerton","author_inst":"School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Claudio Toma","author_inst":"Centro de Biologia Molecular Severo Ochoa"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Transcriptome-Wide Alternative Splicing Analysis Implicates Complex Events in Bipolar Disorder","rel_doi":"10.64898\/2026.04.19.26351209","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.19.26351209","rel_abs":"Alternative-splicing events (ASE) increase transcriptomic variability and play key roles in biological functions. The contribution of ASE to bipolar disorder (BD) remains largely unexplored. We performed a Transcriptome-Wide Alternative-Splicing Analysis (TWASA) to identify ASEs and genes potentially involved in BD. The study comprised 635 individuals: a discovery sample (DS) of 31 individuals from eight multiplex BD families (16 BD cases; 15 unaffected relatives), and a replication sample (RS) of 604 subjects (372 BD cases; 232 controls). Sequencing was conducted on RNA from lymphoblastoid cell lines (DS) and whole blood (RS). TWASA was performed using VAST-TOOLS (VT), rMATS (RM), and MAJIQ\/MOCCASIN (MCC). Gene-set association analyses of genes containing ASEs were performed across six psychiatric disorders. Novel ASE (nASE) were investigated in the DS using FRASER. Limited gene overlap was observed across TWASA tools. MCC identified 2,031 complex ASEs involving 1,508 genes, showing the strongest genetic association with BD across psychiatric phenotypes. Prioritization of MCC-identified ASE genes yielded 441 candidates, including DOCK2 as top candidate from the DS. Replication was obtained for 98 genes, five with an identical ASE, and four (RBM26, QKI, ANKRD36, and TATDN2) showing a concordant percentage-spliced-in direction with the DS. Finally, 578 nASE were identified in the DS, with no evidence of familial segregation or differences in ASE types. This first TWASA in BD reveals tool-specific variability, complex ASE for genes specifically associated with BD, and novel candidate genes for BD. Alternative transcript isoform abundance may represent a mechanism contributing to BD pathophysiology.","rel_num_authors":16,"rel_authors":[{"author_name":"Miriam Martinez-Jimenez","author_inst":"Centre for Molecular Biology Severo Ochoa, Spanish National Research Council & Autonomous University of Madrid, Madrid, Spain"},{"author_name":"Ines Garcia-Ortiz","author_inst":"Centre for Molecular Biology Severo Ochoa, Spanish National Research Council & Autonomous University of Madrid, Madrid, Spain"},{"author_name":"Diego Romero-Miguel","author_inst":"Centre for Molecular Biology Severo Ochoa, Spanish National Research Council & Autonomous University of Madrid, Madrid, Spain"},{"author_name":"Tomas Kavanagh","author_inst":"Brain and Mind Centre, School of Medical Sciences, University of Sydney, NSW, Australia"},{"author_name":"Lee L Marshall","author_inst":"Garvan Institute of Medical Research, Sydney, NSW, Australia"},{"author_name":"Rosa Ana Bello Sousa","author_inst":"Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain"},{"author_name":"Sergio Sanchez Alonso","author_inst":"Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain"},{"author_name":"Raquel Alvarez Garcia","author_inst":"Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Madrid, Spain"},{"author_name":"Sergio Benavente Lopez","author_inst":"Department of Psychiatry, University Hospital Infanta Elena, Valdemoro, Madrid, Spain"},{"author_name":"Ezequiel Di Stasio","author_inst":"Department of Psychiatry, General Hospital of Villalba, Madrid, Spain"},{"author_name":"Peter R Schofield","author_inst":"Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Enrique Baca-Garcia","author_inst":"Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain"},{"author_name":"Philip B Mitchell","author_inst":"Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Antony A Cooper","author_inst":"Garvan Institute of Medical Research, Sydney, NSW, Australia"},{"author_name":"Janice M Fullerton","author_inst":"School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Claudio Toma","author_inst":"Centro de Biologia Molecular Severo Ochoa"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Exploring the association of subnational drowning mortality and environmental exposures: A global analysis using satellite-derived data","rel_doi":"10.64898\/2026.04.19.26351234","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.19.26351234","rel_abs":"Introduction: Drowning risk begins with water exposure, yet population-water relationships have rarely been quantified at scale using environmental measures. This study explored whether satellite-derived data was associated with subnational drowning mortality and whether associations differed by country income level. Methods: We linked Global Burden of Disease (GBD 2021) age-standardised drowning mortality rates to satellite-derived exposures for 212 subnational regions across 12 countries (2006-2021; 3,392 region-years). Exposures were extracted via Google Earth Engine and standardised. Gamma-log generalised linear mixed models included region random intercepts and year fixed effects. Income-stratified models were estimated separately. Supplementary models assessed maritime vessel activity. Results: Near-water population percentage was the strongest correlate of drowning (IRR 1.40; 95% CI 1.33-1.47). Permanent water coverage was protective (IRR 0.80; 0.73-0.88), as were nighttime lights (IRR 0.96; 0.95-0.97) and hot days >30C (IRR 0.95; 0.92-0.99). Mean temperature (IRR 1.17; 1.11-1.23) and precipitation (IRR 1.03; 1.01-1.04) were positively associated. Near-water effects were consistent across income strata (LIC 1.25; MIC 1.31; HIC 1.24), while other predictors showed weak or inconsistent within-strata associations. Vessel activity was modestly associated with drowning in Global Fishing Watch models (IRR 1.05; 1.01-1.09) but not in Synthetic Aperture Radar models. Discussion: Satellite-derived indicators can characterise drowning risk at scale, with population proximity to water emerging as a robust cross-context correlate. Protective associations for permanent water suggest landscape configuration may shape risk beyond proximity alone, highlighting geospatial data's value for targeting prevention where surveillance is limited.","rel_num_authors":3,"rel_authors":[{"author_name":"Ryan Essex","author_inst":"The George Institute for Global Health"},{"author_name":"Samsung Lim","author_inst":"UNSW"},{"author_name":"Jagnoor Jagnoor","author_inst":"The George Institute for Global Health"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"Rethinking covariate adjustment in psychiatric biomarker research: a framework applied to UK Biobank blood samples","rel_doi":"10.64898\/2026.04.19.26351233","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.19.26351233","rel_abs":"Importance Blood-based biomarkers hold promise for psychiatric diagnosis and prognosis, yet clinical translation is constrained by poor reproducibility. Psychiatric biomarker studies are typically small, and demographic, behavioral, and temporal covariates often go undetected or cannot be adequately modeled. This may lead to residual confounding and unstable associations. Observations Leveraging UK Biobank data (N=~500,000), we systematically quantified how technical, demographic, behavioral, and temporal covariates influence 29 blood biomarkers commonly measured in research studies in psychiatry. Variance analyses showed substantial differences across biomarkers. Technical factors explained 1-6% and demographic factors explained 5-15% of the variance, with pronounced age-by-sex interactions for lipids and sex hormones. Behavioral covariates, particularly body mass index (BMI) and smoking, strongly influenced inflammatory markers. Temporal factors introduced systematic confounding. Chronotype was associated with blood collection time, multiple biomarkers exhibited marked diurnal rhythms (including testosterone, triglycerides, and immune markers), and inflammatory markers showed seasonal peaks in winter. In association analysis of biomarkers with major depression, bipolar disorder and schizophrenia, covariate adjustments attenuated or eliminated a substantial proportion of the biomarker-disorder associations, with BMI emerging as the dominant confounder. These findings demonstrate that such confounding structures exist and can be characterized in large cohorts, though specific biomarker-disorder relationships require validation in clinical samples. Conclusions and Relevance Poor reproducibility of biomarkers may not only stem from insufficient biological signal but also from inconsistent handling of confounders. We propose a systematic framework distinguishing technical factors (to be removed), demographic factors (addressed through adjustment or stratification), temporal factors (ideally controlled at design stages), and behavioral factors (requiring explicit causal reasoning). Associations robust to multiple adjustment strategies should be prioritized for clinical biomarker development. Standardized collection protocols, comprehensive covariate measurement, and transparent reporting across models are essential to improve reproducibility and identify biomarkers that reflect genuine illness-related pathophysiology.","rel_num_authors":5,"rel_authors":[{"author_name":"Mirim Shin","author_inst":"Brain and Mind Centre, The University of Sydney"},{"author_name":"Jacob J Crouse","author_inst":"Brain and Mind Centre, The University of Sydney"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, The University of Sydney"},{"author_name":"Naomi R. Wray","author_inst":"Department of Psychiatry, University of Oxford"},{"author_name":"Clara Albinana","author_inst":"Department of Psychiatry, University of Oxford"}],"rel_date":"2026-04-21","rel_site":"medrxiv"},{"rel_title":"A long-read RNA sequencing and polysome profiling framework reveals transposable element-driven transcript diversity and translational rewiring in glioblastoma","rel_doi":"10.64898\/2026.04.18.719388","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.18.719388","rel_abs":"Background: Transposable elements (TEs) account for over half of the human genome and are often derepressed in cancer. TEs can add cryptic splice sites, undergo exonization, and generate gene-TE fusion transcripts, but the combined effects of TEs on RNA processing and translation in glioblastoma stem cells (GSCs) remains incompletely elucidated. Results: We combined long-read RNA sequencing with polysome profiling in four patient-derived GSCs and two neural stem cell (NSC) controls to resolve TE-associated transcript diversity and its relationship to ribosomal engagement. Across GSCs, we identified 13,421 alternative splicing (AS) events, 3,077 of which contained TEs within 150 bp of splice junctions. AS sites proximal to TEs were associated with increased isoform switching compared to non-TE-associated AS sites (odds ratio 2.9 - 4.3). Moreover, AS isoforms generated from TE-proximal sites were more likely to exhibit altered ribosomal association (odds ratio 2.54). Directional shifts were observed, with shorter isoforms associating with monosome fractions and longer isoforms with polysome fractions. To enable systematic detection of gene - TE chimeric transcripts, we developed FuTER (Fusion TE Reporter), a long-read-based framework for identifying TE-associated fusions. Application to GSC datasets identified 78 GSC enriched fusion transcripts, several supported by breakpoint-spanning reads in polysome fractions, consistent with ribosome association. Conclusions: Our data suggest that TEs correlate with abnormal splicing activity and altered ribosome engagement in glioblastoma stem cells. By integrating long-read sequencing with polysome profiling and fusion detection, we establish a framework for analysis of TE-induced transcript diversity and its effects on cancer evolution and plasticity.","rel_num_authors":6,"rel_authors":[{"author_name":"Mattia Pizzagalli","author_inst":"Brown University"},{"author_name":"Shiven Sasipalli","author_inst":"Brown University"},{"author_name":"Owen Leary","author_inst":"Brown University"},{"author_name":"Lily Tran","author_inst":"Brown University"},{"author_name":"Brian Haas","author_inst":"Broad Institute"},{"author_name":"Nikos Tapinos","author_inst":"Brown University"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Redox-dependent lipophilicity of phenazine metabolites is modulated by intramolecular hydrogen bonds and controls their biological distribution","rel_doi":"10.64898\/2026.04.18.719255","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.18.719255","rel_abs":"Phenazines are redox-active microbial metabolites produced and secreted in diverse ecological contexts from soils to chronic infections. In these disparate environments phenazines can function variously as antibiotics, extracellular electron shuttles, and nutrient scavengers. Key to understanding the impact of these functions is a robust expectation of phenazine retention or diffusion in a given context. But predicting phenazine fate and transport is difficult because of the chemical complexity of their local microenvironments. To address this challenge, we measured the octanol water distribution coefficient (LogD) as a proxy for lipophilicity of three naturally occurring phenazines produced by the opportunistic pathogen Pseudomonas aeruginosa: phenazine-1-carboxylic acid, phenazine-1-carboxamide, and pyocyanin. We investigated the behavior of both oxidized and reduced forms of these phenazines across broad ionic strength and pH conditions. While the ionic context exerts only small effects, the pH and redox state contribute strongly and independently to changes in phenazine lipophilicity. The observed redox dependence is generally missed by existing lipophilicity calculation methods, yet the pH trends are expected. Additional LogD measurements with 1-hydroxyphenazine and unsubstituted phenazine, together with density functional theory modeling of phenazines in their reduced and oxidized forms, reveal that intramolecular hydrogen bonding contributes significantly to the increased lipophilicity of reduced phenazines that possess H-bond accepting substituents in the 1-position. These results explain phenazine behavior in a biological context: redox state alone significantly alters retention of pyocyanin in planktonic P. aeruginosa cells, with the reduced species being predominantly retained by membranes. We propose that the modulation of phenazine lipophilicity in response to the local redox environment has evolved to give a competitive advantage to bacteria by retaining or dispersing these bioactive molecules. Beyond improving our understanding of natural phenazine fate in diverse microbial contexts, our results emphasize an oft-overlooked theme relevant to rational drug and electrochemical shuttle design: redox state matters.","rel_num_authors":7,"rel_authors":[{"author_name":"Korbinian O. Thalhammer","author_inst":"California Institute of Technology"},{"author_name":"Matthew Scurria","author_inst":"University of California, Los Angeles"},{"author_name":"Jinyang Li","author_inst":"California Institute of Technology"},{"author_name":"Ines B Trindade","author_inst":"California Institute of Technology"},{"author_name":"Osvaldo Gutierrez","author_inst":"University of California, Los Angeles"},{"author_name":"Stuart J. Conway","author_inst":"University of California, Los Angeles"},{"author_name":"Dianne K. Newman","author_inst":"California Institute of Technology"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Phosphoinositide Variant Fuels 53BP1 Oligomerization and Higher-Order Assembly in the DNA Damage Response","rel_doi":"10.64898\/2026.04.18.710733","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.18.710733","rel_abs":"53BP1 nuclear bodies are dynamic and are endowed with properties that resemble biomolecular condensates, but molecular determinants that underlie transition of 53BP1 oligomerisation to its higher-order assembly at DNA double-strand breaks (DSBs) remain to be established. We found that 53BP1 condensation is stimulated by phosphatidylinositol 3-phosphates (PI(3)Ps) in vitro, and is effected via its C-terminal phospho-binding BRCTs. Consistently, mutational inactivation of 53BP1 BRCTs not only compromised PI(3)P binding, but also suppressed its ability to undergo optodroplet formation in vivo. We further show that swift 53BP1 oligomerisation following DNA damage precedes its stable assembly on DSB-flanking chromatin, requires its BRCT domain, and is suppressed by sequestration of nuclear PI(3)Ps. Taken together, we propose that PI(3)P binding underlies maturation of 53BP1 higher-order assembly on the damaged chromatin.","rel_num_authors":11,"rel_authors":[{"author_name":"Nan XIONG","author_inst":"School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R;"},{"author_name":"Gaofeng Cui","author_inst":"Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA"},{"author_name":"Xuanqi Xu","author_inst":"School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R;"},{"author_name":"Yawen Xie","author_inst":"School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R;"},{"author_name":"Shih-Chieh Ti","author_inst":"The University of Hong Kong"},{"author_name":"shikang liang","author_inst":"School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R;"},{"author_name":"Viji M. Draviam","author_inst":"Queen Mary University of London"},{"author_name":"Yang Liu","author_inst":"The University of Hong Kong"},{"author_name":"Cheng-han Yu","author_inst":"School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R"},{"author_name":"Georges Mer","author_inst":"Mayo Clinic"},{"author_name":"michael Shing Yan Huen","author_inst":"School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong S.A.R"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Dietary Sodium Restriction Reprograms Gut Microbial Fermentation and Reduces Host Energy Harvest","rel_doi":"10.64898\/2026.04.20.719706","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.20.719706","rel_abs":"Diet is a major determinant of gut microbiome structure and function, yet the role of dietary electrolytes, particularly sodium remains poorly defined. Here, we identify dietary sodium availability as a key regulator of gut microbial fermentation and host energy harvest. Using a controlled sodium-sufficient versus sodium deprived dietary intervention in rats, we integrated shotgun metagenomic sequencing, functional pathway analysis, targeted short-chain fatty acid (SCFA) quantification, and host physiological phenotyping. Sodium deprivation induced a coordinated restructuring of the gut microbiome, characterized by depletion of classical saccharolytic Firmicutes, including multiple Lactobacillus species, and enrichment of stress-tolerant, metabolically flexible taxa. Functional profiling revealed a shift away from growth-associated metabolic programs toward stress-adaptive and nutrient-scavenging pathways. Consistent with these changes, fecal concentrations of key SCFAs including acetate, butyrate, hexanoate, and valerate were significantly reduced, indicating impaired microbial fermentative capacity. These microbiome level alterations translated into measurable host phenotypes, including reduced cecal mass and attenuated weight gain, consistent with decreased microbial energy harvest. Together, these findings establish a functional link between luminal sodium availability, microbial metabolic efficiency, and host energy balance, extending the framework of diet microbiome interactions beyond macronutrients to include dietary electrolytes. This work identifies sodium as a previously under appreciated ecological constraint shaping gut microbial metabolism and suggests that modulation of dietary sodium intake may influence host metabolic outcomes through microbiome-mediated mechanisms","rel_num_authors":10,"rel_authors":[{"author_name":"Joshua Cornman-Homonoff","author_inst":"Department of Interventional Radiology, Yale School of Medicine, New Haven, Connecticut,  USA"},{"author_name":"Kumaran M Rajendran","author_inst":"Department of Biochemistry and Molecular Medicine, West Virginia University School of Medicine, Morgantown, WV, 26505-9193, USA"},{"author_name":"Saravanan Kolandaivelu","author_inst":"West Virginia University"},{"author_name":"Steven D Coon","author_inst":"Department of Biological Sciences, Fort Peck Community College, Poplar MT 59255, USA"},{"author_name":"Justin T Kupec","author_inst":"Department of Medicine, Division of Gastroenterology and Hepatology, West Virginia University School of Medicine, Morgantown, West Virginia, USA"},{"author_name":"Lei Wang","author_inst":"Department of Microbiology, West Virginia University School of Medicine, Morgantown, WV, 26505-9193, USA"},{"author_name":"Gangqing Hu","author_inst":"Department of Microbiology, West Virginia University School of Medicine, Morgantown, WV, 26505-9193, USA"},{"author_name":"Venkatakrishna R Jala","author_inst":"Department of Microbiology and Immunology, University of Louisville, Louisville, Kentucky,  USA"},{"author_name":"Geoffrey I Sandle","author_inst":"Leeds Institute of Medical Research, Leeds LS9 7TF, UK"},{"author_name":"Vazhaikkurichi M Rajendran","author_inst":"West Virginia University"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"A shared pathogen reservoir can tip widespread infection into mass mortality","rel_doi":"10.64898\/2026.04.17.719273","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.719273","rel_abs":"Pathogens that persist subclinically across many wildlife populations can drive mass mortality in others. Mass mortality is often abrupt, and the timing can be difficult to predict from host or habitat features alone. In a recent field study tracking ranavirus epizootics in wood frog (Rana sylvatica) breeding ponds, we found that no environmental or biotic feature reliably predicted die-off occurrence or timing. Instead, the trajectory of viral accumulation in the water column was the strongest dynamic predictor of mass mortality. Infected hosts shed virus throughout epizootics, but the influence of waterborne viral concentration on disease progression was apparent only near die-off onset. This pattern suggests a potential threshold-dependent feedback operating through the shared viral environment. Here, we develop a compartmental model linking waterborne viral concentration to the rate at which subclinical infections progress to clinical, high-shedding states within already-infected hosts. We show that a dose-dependent progression model generates the two-phase epizootic trajectory observed in natural die-offs: prolonged subclinical circulation followed by abrupt clinical transition after environmental virus crosses an escalation threshold. The model exhibits a sharp phase transition between subclinical circulation and mass mortality, governed mainly by the clinical-to-subclinical shedding ratio, host density, and pond volume. Existing explanations for die-off variation emphasize individual-level susceptibility, but our model demonstrates that dose-dependent environmental feedback, a mechanism not previously formalized at the population level, can generate the transition from subclinical infection to mass mortality without invoking individual variation in host susceptibility. This mechanism may apply in any system where hosts share a bounded environment, pathogen dose influences disease severity, and pathogen shedding increases with disease progression.","rel_num_authors":3,"rel_authors":[{"author_name":"Logan S Billet","author_inst":"Rutgers University"},{"author_name":"David K Skelly","author_inst":"Yale University"},{"author_name":"Erin L Sauer","author_inst":"Rutgers University"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"A shared pathogen reservoir can tip widespread infection into mass mortality","rel_doi":"10.64898\/2026.04.17.719273","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.719273","rel_abs":"Pathogens that persist subclinically across many wildlife populations can drive mass mortality in others. Mass mortality is often abrupt, and the timing can be difficult to predict from host or habitat features alone. In a recent field study tracking ranavirus epizootics in wood frog (Rana sylvatica) breeding ponds, we found that no environmental or biotic feature reliably predicted die-off occurrence or timing. Instead, the trajectory of viral accumulation in the water column was the strongest dynamic predictor of mass mortality. Infected hosts shed virus throughout epizootics, but the influence of waterborne viral concentration on disease progression was apparent only near die-off onset. This pattern suggests a potential threshold-dependent feedback operating through the shared viral environment. Here, we develop a compartmental model linking waterborne viral concentration to the rate at which subclinical infections progress to clinical, high-shedding states within already-infected hosts. We show that a dose-dependent progression model generates the two-phase epizootic trajectory observed in natural die-offs: prolonged subclinical circulation followed by abrupt clinical transition after environmental virus crosses an escalation threshold. The model exhibits a sharp phase transition between subclinical circulation and mass mortality, governed mainly by the clinical-to-subclinical shedding ratio, host density, and pond volume. Existing explanations for die-off variation emphasize individual-level susceptibility, but our model demonstrates that dose-dependent environmental feedback, a mechanism not previously formalized at the population level, can generate the transition from subclinical infection to mass mortality without invoking individual variation in host susceptibility. This mechanism may apply in any system where hosts share a bounded environment, pathogen dose influences disease severity, and pathogen shedding increases with disease progression.","rel_num_authors":3,"rel_authors":[{"author_name":"Logan S Billet","author_inst":"Rutgers University"},{"author_name":"David K Skelly","author_inst":"Yale University"},{"author_name":"Erin L Sauer","author_inst":"Rutgers University"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Sensory neurons inhibit invadopodia and metastasis via direct CGRP-RAMP1-cAMP signaling to cancer cells","rel_doi":"10.64898\/2026.04.17.719233","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.719233","rel_abs":"Breast cancer is globally the most common cancer among women. Although the five-year survival rate exceeds 80% for patients with localized disease, it drops to approximately 30% once metastasis occurs, underscoring the urgent need to define mechanisms that drive metastatic progression. Breast is a highly innervated organ and most of its innervation is sensory. However, whether sensory neurons can directly impact breast cancer cells remains an understudied topic. Here, we show that mammary tumors have increased CGRP+ sensory innervation. Using our novel microfluidic Device for Cancer cell-Axon Interaction Testing (DACIT), we demonstrate that the presence of axons strongly inhibits ECM-degrading ability of cancer cells. The sensory neuron secretome suppresses assembly and function of invadopodia, which are cancer cell protrusions controlling ECM degradation, and essential for intravasation and metastasis. We identify calcitonin gene-related peptide (CGRP) as the key component of the sensory neuron secretome responsible for the inhibitory effect. CGRP signaling occurs through the CRLR\/RAMP1 receptor complex expressed by breast cancer cells, inducing a rapid increase in intracellular cAMP levels in breast cancer cells, followed by an increase in RhoC activity and suppression of invadopodia and ECM degradation. Loss of RAMP1 function enhances 3D spheroid invasion, cancer cell motility in vivo and significantly increases the number and the size of lung metastatic foci. Consistently, in silico analyses of both mouse and human RNASeq data point to a link between increasingly invasive subtypes with a gradual decrease in expression of RAMP1 and CRLR. To validate in silico findings, we compare RAMP1 expression in the patient breast tumors with adjacent normal tissues, confirming the invasive breast tumors have reduced levels of RAMP1. Together, our findings identify a protective role for the paracrine CGRP signaling in limiting breast cancer invasion and metastasis. We also demonstrate how cancer cells circumvent CGRP inhibition by suppressing RAMP1 expression, highlighting CGRP-RAMP1-cAMP axis as a potential therapeutic target in breast cancer.","rel_num_authors":11,"rel_authors":[{"author_name":"Ines Velazquez Quesada","author_inst":"Temple University"},{"author_name":"Elizaveta Belova","author_inst":"Temple University"},{"author_name":"Afrooz Jarrah","author_inst":"Temple University"},{"author_name":"Maria Carolina Cesar Mariano","author_inst":"Temple University"},{"author_name":"Yasmine Dahleh","author_inst":"Temple University"},{"author_name":"Maira de Assis Lima","author_inst":"Einstein College of Medicine"},{"author_name":"Debora Barbosa Vendramini Costa","author_inst":"Henry Ford Health"},{"author_name":"Ralph Francescone","author_inst":"Henry Ford Health Sciences"},{"author_name":"Edna Cukierman","author_inst":"Fox Chase Cancer Center"},{"author_name":"Louis Hodgson","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Bojana Gligorijevic","author_inst":"Temple University"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Tissue-Specific Prevalence and Clonal Architecture of BRCA1\/2 LOH-Inducing Chromosomal Aneuploidy","rel_doi":"10.64898\/2026.04.17.718766","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.718766","rel_abs":"Germline pathogenic variants in BRCA1 and BRCA2 confer disproportionately elevated cancer risks in breast and ovarian tissues, yet the basis for this tissue specificity remains incompletely understood. Here, we integrate bulk-tumor aneuploidy analysis across 340,824 cancer cases from three independent cohorts (TCGA, ICGC PCAWG, and FoundationCore) with single-cell whole-genome sequencing from two independent studies to investigate whether tissue-specific patterns of chromosomal deletion contribute to this phenomenon. We find that breast and ovarian cancers are consistently enriched for deletions of chromosome arms 17q and 13q, harboring the BRCA1 and BRCA2 genes, respectively, relative to other solid tumor types, and that mutational timing analysis independently places these deletions among the earliest somatic events in these cancers. Phylogenetic reconstruction of single-cell data reveals that in pre-malignant breast tissue from germline BRCA1\/2 carriers, chr17q and chr13q deletions appear as localized subclonal events within small clades against a largely diploid background. In established malignancies, these same deletions are found within the dominant clonal lineages, accompanied by widespread genomic instability, consistent with clonal sweeps originating from early deletion events. These findings suggest that breast and ovarian cellular environments confer a selective advantage for chr17q and chr13q deletions, providing a mechanism that may contribute to the tissue-specific cancer risk observed in gBRCA1\/2 carriers.","rel_num_authors":8,"rel_authors":[{"author_name":"Xinfeng Wang","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Saumya Sisoudiya","author_inst":"Foundation Medicine, Inc"},{"author_name":"Mahad Bihie","author_inst":"McMaster University"},{"author_name":"Yves Greatti","author_inst":"Johns Hopkins University"},{"author_name":"Juli\u00e1n Grandvallet Contreras","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Tomi Jun","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Smruthy Sivakumar","author_inst":"Foundation Medicine"},{"author_name":"Kuan-lin Huang","author_inst":"Icahn School of Medicine at Mount Sinai"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Histone H4 acetyl-methyllysine marks accessible chromatin that resists compaction","rel_doi":"10.64898\/2026.04.17.718779","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.718779","rel_abs":"Certain regulatory DNA regions remain accessible even under conditions of widespread chromatin compaction. These regions are often marked by specific protein factors and histone modifications that help maintain their accessibility. Here, we examine the genomic landscape of acetyl-methyllysine (Kacme), a recently discovered histone post-translational modification. Across multiple systems, Kacme is highly enriched at sites of accessible chromatin, including active promoters, enhancers, silencers, and CTCF-binding sites. We find that Kacme is selectively retained at loci that resist condensation during mitosis, marks XIST and escapee regions on the inactive X chromosome in female cells and demarcates the boundaries of broad heterochromatin domains. Kacme-marked insulator elements block heterochromatin spreading and protect adjacent genes from transcriptional repression, even when H3K27me3 levels are pharmacologically elevated through KDM6A\/6B inhibition. Taken together, our findings establish the chromatin features associated with Kacme and support a model in which Kacme helps safeguard chromatin accessibility at loci that resist compaction.","rel_num_authors":8,"rel_authors":[{"author_name":"Andreas P Pintado-Urbanc","author_inst":"Yale University"},{"author_name":"Charlie L Brown","author_inst":"Yale University"},{"author_name":"Leah J Connor","author_inst":"Yale University"},{"author_name":"Joshua T Young","author_inst":"Yale University"},{"author_name":"Yeil Kim","author_inst":"Yale University"},{"author_name":"Elizabeth Black","author_inst":"Whitehead Institute for Biomedical Research"},{"author_name":"Lilian Kabeche","author_inst":"Yale University"},{"author_name":"Matthew Simon","author_inst":"Yale University"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"The tongue-brain axis mediates a hidden amino acid appetite","rel_doi":"10.64898\/2026.04.17.719133","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.719133","rel_abs":"Selecting a diet containing all essential amino acids (EAAs) is critical for health. Following EAA deprivation, animals can select a nutritiously complete food source; however, the underlying mechanisms in vertebrates remain unclear. In mice, we show that leucine deficiency activates hypothalamic agouti-related protein (AgRP) neurons, which project to the paraventricular thalamus (PVT) via gamma-aminobutyric acid and are required for EAA deficiency-induced leucine appetite in mice. Furthermore, the peripheral tongue amino acid sensor general control nonderepressive 2 (GCN2) mediates acute EAA appetite via AgRP neurons. Together, these findings identify a tongue-AgRP-PVT circuit underlying EAA appetite, which is important for the rapid and accurate selection of essential nutrients.","rel_num_authors":13,"rel_authors":[{"author_name":"Shangming Wu","author_inst":"Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences"},{"author_name":"Yeting Gan","author_inst":"Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University"},{"author_name":"Min Tang","author_inst":"Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University"},{"author_name":"Shanghai Chen","author_inst":"Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University"},{"author_name":"Peixiang Luo","author_inst":"Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University"},{"author_name":"Kexin Tong","author_inst":"Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University"},{"author_name":"Kan Liu","author_inst":"Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences"},{"author_name":"Haizhou Jiang","author_inst":"Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University"},{"author_name":"Xiaoxue Jiang","author_inst":"Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Fudan University"},{"author_name":"Fei Xiao","author_inst":"Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University"},{"author_name":"Wei Lv","author_inst":"Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences"},{"author_name":"Feixiang Yuan","author_inst":"Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University"},{"author_name":"Feifan Guo","author_inst":"Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro","rel_doi":"10.64898\/2026.04.17.719309","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.719309","rel_abs":"Spatial transcriptomics enables the study of how cells coordinate their molecular states within tissue, providing insight into both normal function and disease processes. A key challenge is to identify gene expression programs that vary continuously across space and are coordinated between cell types. We present CoPro, a computational framework for detecting the spatially coordinated progression of cellular states. CoPro can operate in both supervised and unsupervised modes to identify gene programs that co-vary within or between cell types, and to disentangle multiple overlapping spatial patterns. CoPro can be applied to single-cell-level spatial transcriptomics datasets, including MERFISH, SeqFISH+, Xenium, and histology-imputed transcriptomic data. We demonstrate the utility of CoPro with data collected from colon, brain, liver, and kidney tissues. In the colon, CoPro separates epithelial differentiation along the crypt axis from spatially localized inflammatory signals. In the aging liver, it identifies multiple aging-associated cellular programs superimposed on anatomical zonation. In the brain, the flexible kernel design enables the decoupling of the gene expression gradient along the dorsal-ventral and medial-lateral axes. In the kidney, CoPro identifies tubule-vasculature coordination that is essential in nephron function. These results demonstrate CoPro's utility for analyzing spatial coordination of gene expression in complex tissues and disentangling overlapping biological processes, such as anatomical organization and disease-associated variation.","rel_num_authors":11,"rel_authors":[{"author_name":"Zhen Miao","author_inst":"University of Pennsylvania"},{"author_name":"Yilong Qu","author_inst":"University of Pennsylvania"},{"author_name":"Sijia Huang","author_inst":"University of Pennsylvania"},{"author_name":"Linshan Laux","author_inst":"University of Minnesota"},{"author_name":"Samuel Peters","author_inst":"University of Minnesota"},{"author_name":"Alisha Aristel","author_inst":"University of Pennsylvania"},{"author_name":"Zhaojun Zhang","author_inst":"The Wharton School, University of Pennsylvania"},{"author_name":"Laura J Niedernhofer","author_inst":"University of Minnesota"},{"author_name":"Andrew McMahon","author_inst":"Caltech"},{"author_name":"Junhyong Kim","author_inst":"University of Pennsylvania"},{"author_name":"Nancy Zhang","author_inst":"University of Pennsylvania"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Noncoaxial Transcatheter Aortic Valve Deployment Creates Cusp-Specific Thrombogenic Microenvironments Through Altered Sinus Hemodynamics","rel_doi":"10.64898\/2026.04.17.719323","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.719323","rel_abs":"Objective: Transcatheter aortic valve replacement has transformed the management of aortic stenosis; however, adverse outcomes, including leaflet thrombosis and hypoattenuating leaflet thickening, remain clinically significant concerns. Flow disturbances resulting from valve canting may alter local hemodynamics and promote thrombogenic conditions. We investigated how modest transcatheter heart valve canting alters cusp-specific sinus flow and washout and promotes localized thrombogenic microenvironments associated with leaflet surface thrombus formation using particle image velocimetry, a physiologic blood loop, and tissue analysis. Approach and Results: A patient-derived aortic root model was used to evaluate the hemodynamic and thrombogenic effects of THV canting at -10o (anti-curvature), 0o (neutral), and +10o (along-curvature). High-resolution particle image velocimetry quantified sinus flow fields and washout characteristics, and complementary whole-blood loop experiments enabled histologic assessment of leaflet-associated thrombus formation. Canting redistributed systolic jet orientation and sinus recirculation in a direction-dependent manner while preserving global hemodynamic measurements. The most spatially constrained cusp showed the largest increase in stasis and the slowest washout. In the right coronary cusp, anti-curvature canting increased the fraction of sinus area with velocity magnitude <0.05 m\/s to 92% versus 43% in neutral and 10% in along-curvature deployments, and prolonged neo-sinus (T90) washout to 4.7 cycles versus 2.9 and 1.8 cycles, respectively. Histology localized surface-adherent platelet\/fibrin thrombus to these poorly washed regions, most prominently on the right coronary cusp leaflet in anti-curvature deployments. Left and noncoronary cusp responses shifted with tilt direction, indicating redistribution rather than uniform worsening of thrombogenic conditions. Conclusion: Even modest noncoaxial deployment is sufficient to create sinus-resolved thrombogenic microenvironments that are not captured by global gradient or effective orifice area. Deployment configuration is therefore a modifiable determinant of post-TAVR leaflet thrombosis risk and may contribute to HALT.","rel_num_authors":10,"rel_authors":[{"author_name":"Thangam Natarajan","author_inst":"Georgia Institute of Technology"},{"author_name":"Jae Hyun Kim","author_inst":"Georgia Institute of Technology"},{"author_name":"Christian D Salgado","author_inst":"Georgia Institute of Technology"},{"author_name":"Akshita Jha","author_inst":"Georgia Institute of Technology"},{"author_name":"Cole Baker","author_inst":"Oregon Health & Science University - West Campus"},{"author_name":"Stephanie Leigh Sellers","author_inst":"University of British Columbia and St. Paul's Hospital"},{"author_name":"Joseph E Aslan","author_inst":"Oregon Health & Science University"},{"author_name":"Monica T Hinds","author_inst":"Oregon Health and Science University"},{"author_name":"Ajit P. Yoganathan","author_inst":"Georgia Institute of Technology and Emory University"},{"author_name":"Lakshmi Prasad Dasi","author_inst":"Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Noncoaxial Transcatheter Aortic Valve Deployment Creates Cusp-Specific Thrombogenic Microenvironments Through Altered Sinus Hemodynamics","rel_doi":"10.64898\/2026.04.17.719323","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.17.719323","rel_abs":"Objective: Transcatheter aortic valve replacement has transformed the management of aortic stenosis; however, adverse outcomes, including leaflet thrombosis and hypoattenuating leaflet thickening, remain clinically significant concerns. Flow disturbances resulting from valve canting may alter local hemodynamics and promote thrombogenic conditions. We investigated how modest transcatheter heart valve canting alters cusp-specific sinus flow and washout and promotes localized thrombogenic microenvironments associated with leaflet surface thrombus formation using particle image velocimetry, a physiologic blood loop, and tissue analysis. Approach and Results: A patient-derived aortic root model was used to evaluate the hemodynamic and thrombogenic effects of THV canting at -10o (anti-curvature), 0o (neutral), and +10o (along-curvature). High-resolution particle image velocimetry quantified sinus flow fields and washout characteristics, and complementary whole-blood loop experiments enabled histologic assessment of leaflet-associated thrombus formation. Canting redistributed systolic jet orientation and sinus recirculation in a direction-dependent manner while preserving global hemodynamic measurements. The most spatially constrained cusp showed the largest increase in stasis and the slowest washout. In the right coronary cusp, anti-curvature canting increased the fraction of sinus area with velocity magnitude <0.05 m\/s to 92% versus 43% in neutral and 10% in along-curvature deployments, and prolonged neo-sinus (T90) washout to 4.7 cycles versus 2.9 and 1.8 cycles, respectively. Histology localized surface-adherent platelet\/fibrin thrombus to these poorly washed regions, most prominently on the right coronary cusp leaflet in anti-curvature deployments. Left and noncoronary cusp responses shifted with tilt direction, indicating redistribution rather than uniform worsening of thrombogenic conditions. Conclusion: Even modest noncoaxial deployment is sufficient to create sinus-resolved thrombogenic microenvironments that are not captured by global gradient or effective orifice area. Deployment configuration is therefore a modifiable determinant of post-TAVR leaflet thrombosis risk and may contribute to HALT.","rel_num_authors":10,"rel_authors":[{"author_name":"Thangam Natarajan","author_inst":"Georgia Institute of Technology"},{"author_name":"Jae Hyun Kim","author_inst":"Georgia Institute of Technology"},{"author_name":"Christian D Salgado","author_inst":"Georgia Institute of Technology"},{"author_name":"Akshita Jha","author_inst":"Georgia Institute of Technology"},{"author_name":"Cole Baker","author_inst":"Oregon Health & Science University - West Campus"},{"author_name":"Stephanie Leigh Sellers","author_inst":"University of British Columbia and St. Paul's Hospital"},{"author_name":"Joseph E Aslan","author_inst":"Oregon Health & Science University"},{"author_name":"Monica T Hinds","author_inst":"Oregon Health and Science University"},{"author_name":"Ajit P. Yoganathan","author_inst":"Georgia Institute of Technology and Emory University"},{"author_name":"Lakshmi Prasad Dasi","author_inst":"Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"Identification and Optimization of Kratom Strictosidine Pathway Enabled by Yeast Multiplex Engineering","rel_doi":"10.64898\/2026.04.16.719034","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.16.719034","rel_abs":"Monoterpene indole alkaloids (MIAs) are a major class of plant natural products with important pharmaceutical activities, yet the biosynthetic pathway to their universal precursor, strictosidine, has been fully elucidated in only Catharanthus roseus. In kratom (Mitragyna speciosa), only the first and last steps of strictosidine biosynthesis were previously known. Here, we applied multiplex pathway engineering in yeast to accelerate the discovery, reconstruction, and optimization of the kratom strictosidine pathway. Iterative multiplex integration and screening identified 13 functional kratom genes and enabled rapid validation of functional pathway modules, thereby completing the kratom strictosidine pathway from geranyl pyrophosphate and tryptophan. We also identified a vacuolar secologanin transporter, MsNPF2.6, which increased strictosidine production by 62% in yeast. Pathway optimization through the incorporation of nepetalactol-producing enzymes from other plants further supported strictosidine production in yeast from fed geraniol and tryptophan. These results establish the strictosidine pathway in kratom and highlight multiplex engineering as a powerful platform for rapid plant pathway discovery and optimization.","rel_num_authors":4,"rel_authors":[{"author_name":"Yinan Wu","author_inst":"the University of Chicago"},{"author_name":"Dong Oh Han","author_inst":"Cornell University"},{"author_name":"Franklin Leyang Gong","author_inst":"Cornell University"},{"author_name":"Sijin Li","author_inst":"Cornell University"}],"rel_date":"2026-04-21","rel_site":"biorxiv"},{"rel_title":"ICU admission and mortality in adult patients with influenza A\/H1N1-related pneumonia in Vietnam since the 2009 H1N1 pandemic: a 10-year cohort study","rel_doi":"10.64898\/2026.04.18.26351156","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.18.26351156","rel_abs":"The A(H1N1)pdm09 virus remains a major global health threat. This study examined the burden of ICU admission, mortality, and associated predictors among patients with A(H1N1)pdm09 pneumonia in a leading center for infectious diseases in Vietnam. Information on demographic, clinical, and laboratory characteristics, and outcomes was retrieved from medical records of adults admitted with influenza A(H1N1)pdm09 during 2009-2019. Among 729 cases, 21.7% (158\/729) developed pneumonia. Among 158 pneumonia cases, 36.7% (58\/158) developed moderate-to-severe acute respiratory distress syndrome (ARDS), and 15.2% (24\/158) received invasive ventilation. ICU admission and mortality rates were 48.7% (77\/158, 95%CI 41.1-56.5%) and 8.2% (13\/158, 95%CI 4.9-13.6%), respectively. Predictors of ICU admission included being >60 years old (adjusted OR [AOR] 13.864, 95%CI 2.185-87.956, P=0.005), comorbidities (AOR 6.527, 95%CI 1.710-24.915, P=0.006), AST (AOR 1.013, 95%CI 1.001-1.025, P=0.029), and moderate-to-severe ARDS (AOR 14.027, 95%CI 4.220-46.627, P<0.001). Predictors of mortality were invasive ventilation (AOR 55.355, 95%CI 1.486-2062.375, P=0.030) and double-dose oseltamivir or combination therapy (AOR 32.625, 95%CI 1.594-667.661, P=0.024). In conclusion, mortality is not rare in A(H1N1)pdm09 infection. Monitoring of older patients and those with comorbidities, liver enzyme elevation, or moderate-to-severe ARDS is essential for the timely detection of complications requiring intensive care.","rel_num_authors":13,"rel_authors":[{"author_name":"Minh  Quang Ho","author_inst":"HTD: Hospital for Tropical Diseases"},{"author_name":"Thuy  Bich Duong","author_inst":"FV Hospital: Franco Vietnamese Hospital"},{"author_name":"Tung  Le Nhu Nguyen","author_inst":"HTD: Hospital for Tropical Diseases"},{"author_name":"Nugraha Susilawati Tri","author_inst":"Sebelas Maret University: Universitas Sebelas Maret"},{"author_name":"Tam Bui","author_inst":"University Medical Center Ho Chi Minh City"},{"author_name":"Truc  Thanh Thai","author_inst":"University of Medicine and Pharmacy at Ho Chi Minh City"},{"author_name":"David  J. Muscatello","author_inst":"UNSW: University of New South Wales"},{"author_name":"Anthony  J. Sunjaya","author_inst":"UNSW: University of New South Wales"},{"author_name":"Siyu Chen","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Nguyen  Thanh Nguyen","author_inst":"Tam Anh general hospital Ho Chi Minh city"},{"author_name":"Thu  Minh Nguyen","author_inst":"HTD: Hospital for Tropical Diseases"},{"author_name":"Anh  Thi Kim Nguyen","author_inst":"OUCRU: Oxford University Clinical Research Unit"},{"author_name":"Cuong  Minh Duong","author_inst":"UNSW: University of New South Wales"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"ICU admission and mortality in adult patients with influenza A\/H1N1-related pneumonia in Vietnam since the 2009 H1N1 pandemic: a 10-year cohort study","rel_doi":"10.64898\/2026.04.18.26351156","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.18.26351156","rel_abs":"The A(H1N1)pdm09 virus remains a major global health threat. This study examined the burden of ICU admission, mortality, and associated predictors among patients with A(H1N1)pdm09 pneumonia in a leading center for infectious diseases in Vietnam. Information on demographic, clinical, and laboratory characteristics, and outcomes was retrieved from medical records of adults admitted with influenza A(H1N1)pdm09 during 2009-2019. Among 729 cases, 21.7% (158\/729) developed pneumonia. Among 158 pneumonia cases, 36.7% (58\/158) developed moderate-to-severe acute respiratory distress syndrome (ARDS), and 15.2% (24\/158) received invasive ventilation. ICU admission and mortality rates were 48.7% (77\/158, 95%CI 41.1-56.5%) and 8.2% (13\/158, 95%CI 4.9-13.6%), respectively. Predictors of ICU admission included being >60 years old (adjusted OR [AOR] 13.864, 95%CI 2.185-87.956, P=0.005), comorbidities (AOR 6.527, 95%CI 1.710-24.915, P=0.006), AST (AOR 1.013, 95%CI 1.001-1.025, P=0.029), and moderate-to-severe ARDS (AOR 14.027, 95%CI 4.220-46.627, P<0.001). Predictors of mortality were invasive ventilation (AOR 55.355, 95%CI 1.486-2062.375, P=0.030) and double-dose oseltamivir or combination therapy (AOR 32.625, 95%CI 1.594-667.661, P=0.024). In conclusion, mortality is not rare in A(H1N1)pdm09 infection. Monitoring of older patients and those with comorbidities, liver enzyme elevation, or moderate-to-severe ARDS is essential for the timely detection of complications requiring intensive care.","rel_num_authors":13,"rel_authors":[{"author_name":"Minh  Quang Ho","author_inst":"HTD: Hospital for Tropical Diseases"},{"author_name":"Thuy  Bich Duong","author_inst":"FV Hospital: Franco Vietnamese Hospital"},{"author_name":"Tung  Le Nhu Nguyen","author_inst":"HTD: Hospital for Tropical Diseases"},{"author_name":"Nugraha Susilawati Tri","author_inst":"Sebelas Maret University: Universitas Sebelas Maret"},{"author_name":"Tam Bui","author_inst":"University Medical Center Ho Chi Minh City"},{"author_name":"Truc  Thanh Thai","author_inst":"University of Medicine and Pharmacy at Ho Chi Minh City"},{"author_name":"David  J. Muscatello","author_inst":"UNSW: University of New South Wales"},{"author_name":"Anthony  J. Sunjaya","author_inst":"UNSW: University of New South Wales"},{"author_name":"Siyu Chen","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Nguyen  Thanh Nguyen","author_inst":"Tam Anh general hospital Ho Chi Minh city"},{"author_name":"Thu  Minh Nguyen","author_inst":"HTD: Hospital for Tropical Diseases"},{"author_name":"Anh  Thi Kim Nguyen","author_inst":"OUCRU: Oxford University Clinical Research Unit"},{"author_name":"Cuong  Minh Duong","author_inst":"UNSW: University of New South Wales"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Oral and plasma microbiome in the context of acute febrile illness","rel_doi":"10.64898\/2026.04.16.26351042","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.16.26351042","rel_abs":"Emerging infectious diseases and antimicrobial resistance (AMR) have surfaced as two major public health threats over the past two decades. Consequently, integrative surveillance systems capable of detecting both emerging pathogens and resistance-carrying bacteria are crucial. With advances in next-generation sequencing, simultaneous detection of pathogens and AMR is increasingly feasible. In this study, we used short-read metatranscriptomics complemented by total 16S rRNA metagenomic long-read sequencing to analyze paired oral and plasma samples from a cohort of febrile individuals at two locations in Senegal. Oral microbiomes differed in community composition between locations, and reduced diversity and richness were significantly associated with high fever. We identified at least one known pathogen in 15.33 % (23\/150) of samples, with Borrelia crocidurae as the most frequently detected pathogen. We detected both pathogenic and non-pathogenic viruses in oral (10\/72) and plasma (09\/78) samples. Finally, we observed a high frequency of genes associated with resistance and virulence: 10% of samples expressed at least one AMR gene (ARG), and 24% expressed virulence factor genes. Resistance to widely used beta-lactam antibiotics was the most prevalent. Our findings provide critical data on oral and plasma microbiomes in the context of acute febrile illness in Senegal while expanding understanding of circulating ARGs.","rel_num_authors":21,"rel_authors":[{"author_name":"Mouhamad Sy","author_inst":"Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, USA.; International Research and Training Center for Applied Genomics and"},{"author_name":"Tolla Ndiaye","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal.; Department of Microbiology and Immuno"},{"author_name":"Ritika Thakur","author_inst":"Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, USA."},{"author_name":"Amy Gaye","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal."},{"author_name":"Zoe C. Levine","author_inst":"Harvard\/MIT MD-PhD Program, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, United States."},{"author_name":"Bassirou Ngom","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal."},{"author_name":"Karina L. Bellavia","author_inst":"Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, USA."},{"author_name":"David Firer","author_inst":"Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, USA."},{"author_name":"Mariama Toure","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal."},{"author_name":"Ibrahima M. Ndiaye","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal."},{"author_name":"Younouss Diedhiou","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal."},{"author_name":"Amadou M. Mbaye","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal."},{"author_name":"Jules F. Gomis","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal."},{"author_name":"Katherine C. DeRuff","author_inst":"Broad Institute of MIT and Harvard, Cambridge, MA, United States."},{"author_name":"Awa B. Deme","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal."},{"author_name":"Mouhamadou Ndiaye","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal.; Department of Parasitology, Faculty o"},{"author_name":"Aida S. Badiane","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal.; Department of Parasitology, Faculty o"},{"author_name":"Marietou Faye Paye","author_inst":"Broad Institute of MIT and Harvard, Cambridge, MA, United States."},{"author_name":"Pardis C. Sabeti","author_inst":"Broad Institute of MIT and Harvard, Cambridge, MA, United States.; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, U"},{"author_name":"Daouda Ndiaye","author_inst":"International Research and Training Center for Applied Genomics and Health Surveillance (CIGASS) at UCAD, Dakar, Senegal.; Department of Parasitology, Faculty o"},{"author_name":"Katherine J. Siddle","author_inst":"Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, USA."}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Proteomic Age Acceleration in Multiple Sclerosis Precedes Symptom Onset and Associates with Severity","rel_doi":"10.64898\/2026.04.13.26350634","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.13.26350634","rel_abs":"Biological aging is accelerated in people with multiple sclerosis, but whether such acceleration occurs during the pre-symptomatic phase or varies by organ system is understudied.\n\nWe analyzed two independent proteomics datasets profiled using distinct platforms: the Johns Hopkins cohort profiled using the SomaScan platform (348 multiple sclerosis\/49 age-matched controls) and the Department of Defense cohort profiled using the Olink platform (134 multiple sclerosis\/79 age-matched controls), including 117 pre-symptomatic samples from people with multiple sclerosis (median lead time: 4.0 years), to estimate systemic and organ-specific proteomic age gaps using established clocks in pre-symptomatic and symptomatic phases, and assess their associations with severity.\n\nIn the Johns Hopkins cohort, people with multiple sclerosis demonstrated acceleration of systemic ({beta}=2.2, 95% CI 1.2-3.2, P<0.001, FDR<0.001), brain ({beta}=1.7, 95% CI 0.6-2.7, P=0.003, FDR=0.01), muscle ({beta}=2.5, 95% CI 1.3-3.7, P<0.001, FDR<0.001), and immune age ({beta}=1.8, 95% CI 0.6-2.9, P=0.003, FDR=0.01), with findings reproduced in the Department of Defense cohort for systemic ({beta}=0.7, 95% CI 0.0-1.4, P=0.04, FDR=0.34) and brain age (3.2 years, 95% CI 2.1-4.3, P<0.001, FDR<0.001). Proteomic age acceleration was evident prior to symptom onset [systemic: ({beta}=1.0, 95% CI 0.4-1.7, P=0.002, FDR=0.02); brain: ({beta}=2.4, 95% CI 1.2-3.7, P<0.001, FDR=0.002)], whereas no immune age acceleration was detected before or after onset. Higher systemic age gap was associated with greater global Age-Related Multiple Sclerosis Severity Score ({beta}=0.14, 95% CI 0.05-0.24, P=0.005, FDR=0.03) and slower walking speed ({beta}=0.02, 95% CI 0.01-0.03, P=0.006, FDR=0.04), while higher muscle age gap was associated with greater global Age-Related Multiple Sclerosis Severity Score ({beta}=0.17, 95% CI 0.10-0.24, P<0.001, FDR<0.001), poorer manual dexterity ({beta}=0.28, 95% CI 0.04-0.52, P=0.03, FDR=0.30), slower walking speed ({beta}=0.02, 95% CI 0.01-0.03, P=0.002, FDR=0.02), lower peripapillary retinal nerve fiber layer ({beta}= -0.26, 95% CI -0.41 to -0.10, P=0.001, FDR=0.02) and ganglion cell-inner plexiform layer thicknesses ({beta}= -0.35; 95% CI -0.65 to -0.05; P=0.02, FDR=0.30). Higher brain age gap was associated with several imaging measures, including lower whole-brain ({beta}= -0.002, 95% CI -0.003 to -0.001, P=0.002, FDR=0.02), and lower peripapillary retinal nerve fiber layer thickness ({beta}= -0.21, 95% CI -0.39 to -0.03, P=0.02, FDR=0.10).\n\nProteomic age acceleration in multiple sclerosis is detectable years before symptom onset and distinct organ-specific aging signatures are associated with disease severity. Proteomic aging may provide a biologically informative marker of early disease processes and a clinically relevant readout of disease heterogeneity.","rel_num_authors":15,"rel_authors":[{"author_name":"Fatemeh Siavoshi","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Julian Candia","author_inst":"National Institute on Aging"},{"author_name":"Dimitrios C Ladakis","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Blake E Dewey","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Angeliki Filippatou","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Matthew D Smith","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Elias S Sotirchos","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Shiv Saidha","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Jerry L Prince","author_inst":"Johns Hopkins University"},{"author_name":"Ahmed Abdelhak","author_inst":"University of California San Francisco (UCSF)"},{"author_name":"Ellen M Mowry","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Peter A Calabresi","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Keenan  A. Walker","author_inst":"National Institute on Aging"},{"author_name":"Kathryn C Fitzgerald","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Pavan Bhargava","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Proteomic Age Acceleration in Multiple Sclerosis Precedes Symptom Onset and Associates with Severity","rel_doi":"10.64898\/2026.04.13.26350634","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.13.26350634","rel_abs":"Biological aging is accelerated in people with multiple sclerosis, but whether such acceleration occurs during the pre-symptomatic phase or varies by organ system is understudied.\n\nWe analyzed two independent proteomics datasets profiled using distinct platforms: the Johns Hopkins cohort profiled using the SomaScan platform (348 multiple sclerosis\/49 age-matched controls) and the Department of Defense cohort profiled using the Olink platform (134 multiple sclerosis\/79 age-matched controls), including 117 pre-symptomatic samples from people with multiple sclerosis (median lead time: 4.0 years), to estimate systemic and organ-specific proteomic age gaps using established clocks in pre-symptomatic and symptomatic phases, and assess their associations with severity.\n\nIn the Johns Hopkins cohort, people with multiple sclerosis demonstrated acceleration of systemic ({beta}=2.2, 95% CI 1.2-3.2, P<0.001, FDR<0.001), brain ({beta}=1.7, 95% CI 0.6-2.7, P=0.003, FDR=0.01), muscle ({beta}=2.5, 95% CI 1.3-3.7, P<0.001, FDR<0.001), and immune age ({beta}=1.8, 95% CI 0.6-2.9, P=0.003, FDR=0.01), with findings reproduced in the Department of Defense cohort for systemic ({beta}=0.7, 95% CI 0.0-1.4, P=0.04, FDR=0.34) and brain age (3.2 years, 95% CI 2.1-4.3, P<0.001, FDR<0.001). Proteomic age acceleration was evident prior to symptom onset [systemic: ({beta}=1.0, 95% CI 0.4-1.7, P=0.002, FDR=0.02); brain: ({beta}=2.4, 95% CI 1.2-3.7, P<0.001, FDR=0.002)], whereas no immune age acceleration was detected before or after onset. Higher systemic age gap was associated with greater global Age-Related Multiple Sclerosis Severity Score ({beta}=0.14, 95% CI 0.05-0.24, P=0.005, FDR=0.03) and slower walking speed ({beta}=0.02, 95% CI 0.01-0.03, P=0.006, FDR=0.04), while higher muscle age gap was associated with greater global Age-Related Multiple Sclerosis Severity Score ({beta}=0.17, 95% CI 0.10-0.24, P<0.001, FDR<0.001), poorer manual dexterity ({beta}=0.28, 95% CI 0.04-0.52, P=0.03, FDR=0.30), slower walking speed ({beta}=0.02, 95% CI 0.01-0.03, P=0.002, FDR=0.02), lower peripapillary retinal nerve fiber layer ({beta}= -0.26, 95% CI -0.41 to -0.10, P=0.001, FDR=0.02) and ganglion cell-inner plexiform layer thicknesses ({beta}= -0.35; 95% CI -0.65 to -0.05; P=0.02, FDR=0.30). Higher brain age gap was associated with several imaging measures, including lower whole-brain ({beta}= -0.002, 95% CI -0.003 to -0.001, P=0.002, FDR=0.02), and lower peripapillary retinal nerve fiber layer thickness ({beta}= -0.21, 95% CI -0.39 to -0.03, P=0.02, FDR=0.10).\n\nProteomic age acceleration in multiple sclerosis is detectable years before symptom onset and distinct organ-specific aging signatures are associated with disease severity. Proteomic aging may provide a biologically informative marker of early disease processes and a clinically relevant readout of disease heterogeneity.","rel_num_authors":15,"rel_authors":[{"author_name":"Fatemeh Siavoshi","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Julian Candia","author_inst":"National Institute on Aging"},{"author_name":"Dimitrios C Ladakis","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Blake E Dewey","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Angeliki Filippatou","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Matthew D Smith","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Elias S Sotirchos","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Shiv Saidha","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Jerry L Prince","author_inst":"Johns Hopkins University"},{"author_name":"Ahmed Abdelhak","author_inst":"University of California San Francisco (UCSF)"},{"author_name":"Ellen M Mowry","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Peter A Calabresi","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Keenan  A. Walker","author_inst":"National Institute on Aging"},{"author_name":"Kathryn C Fitzgerald","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Pavan Bhargava","author_inst":"Johns Hopkins University School of Medicine"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Trial protocol: RadTARGET, a multicenter phase II randomized controlled trial evaluating focal radiotherapy boost with de-intensification of dose to non-suspicious prostate in patients with intermediate- or high-risk prostate cancer","rel_doi":"10.64898\/2026.04.18.26351182","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.18.26351182","rel_abs":"Definitive radiotherapy (RT) for prostate cancer (PC) with dose intensification and\/or focal boosting has excellent oncologic outcomes, but many patients experience adverse events. Dose escalation to the whole prostate improves outcomes at the expense of increased late adverse events. Intraprostatic recurrence after definitive RT typically occurs at the site of the primary tumor, suggesting that dose to the site of the dominant lesion is an important predictor of future failure. The efficacy and safety of tumor-focused RT compared to that of standard RT for definitive treatment of localized PC has not been assessed. RadTARGET (RAdiation Dose TAiloRing Guided by Enhanced Targeting) is a phase II randomized trial that aims to demonstrate superior safety of image-guided, tumor-focused RT compared to standard RT for acute genitourinary (GU) or gastrointestinal (GI) in the setting of definitive RT for intermediate- and high-risk PC. The study intervention is image-guided, tumor-focused RT with dose intensification of cancer visible on imaging and dose de-intensification to remaining prostate. Patients will be randomized to two arms: those who receive standard RT dose and those that receive tumor-focused RT. The study population will be patients with intermediate- or high-risk PC planning to undergo definitive RT with or without systemic therapy. The primary endpoint to compare between randomized arms is acute GU or GI grade [&ge;]2 adverse events. Participant and study duration are 5 years and 8 years, respectively. RadTARGET will compare the efficacy and safety of tumor-focused RT to that of standard RT for definitive treatment of localized PC. We hypothesize that the tumor-focused approach will substantially reduce adverse events after prostate RT while retaining high efficacy. If this hypothesis is confirmed, we will conclude that a phase III randomized control trial is warranted to formally establish oncologic non-inferiority compared to the current standard of whole-gland dose escalation.","rel_num_authors":20,"rel_authors":[{"author_name":"Anna Dornisch","author_inst":"UC San Diego"},{"author_name":"Mariluz Rojo Domingo","author_inst":"University of California San Diego"},{"author_name":"Roberta Vezza Alexander","author_inst":"UC San Diego"},{"author_name":"Christopher Charles Conlin","author_inst":"UC San Diego"},{"author_name":"Son Do","author_inst":"UC San Diego"},{"author_name":"Rana R McKay","author_inst":"UC San Diego"},{"author_name":"Vitali Moiseenko","author_inst":"UC San Diego"},{"author_name":"Michael A Liss","author_inst":"UC San Diego"},{"author_name":"Jasmine Liu","author_inst":"UC San Diego"},{"author_name":"Todd Pawlicki","author_inst":"UC San Diego"},{"author_name":"Samuel Pena","author_inst":"UC San Diego"},{"author_name":"Edmund M Qiao","author_inst":"UC San Diego"},{"author_name":"Brent S Rose","author_inst":"UC San Diego"},{"author_name":"Rhea Rupareliya","author_inst":"UC San Diego"},{"author_name":"Ajay P Sandhu","author_inst":"UC San Diego"},{"author_name":"Jessica Scholey","author_inst":"UC San Francisco"},{"author_name":"Steven N Seyedin","author_inst":"UC San Francisco"},{"author_name":"James J Urbanic","author_inst":"UC San Diego"},{"author_name":"Lee-Jen Wei","author_inst":"Harvard TH Chan School of Public Health"},{"author_name":"Tyler M Seibert","author_inst":"UC San Diego"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Trial protocol: RadTARGET, a multicenter phase II randomized controlled trial evaluating focal radiotherapy boost with de-intensification of dose to non-suspicious prostate in patients with intermediate- or high-risk prostate cancer","rel_doi":"10.64898\/2026.04.18.26351182","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.18.26351182","rel_abs":"Definitive radiotherapy (RT) for prostate cancer (PC) with dose intensification and\/or focal boosting has excellent oncologic outcomes, but many patients experience adverse events. Dose escalation to the whole prostate improves outcomes at the expense of increased late adverse events. Intraprostatic recurrence after definitive RT typically occurs at the site of the primary tumor, suggesting that dose to the site of the dominant lesion is an important predictor of future failure. The efficacy and safety of tumor-focused RT compared to that of standard RT for definitive treatment of localized PC has not been assessed. RadTARGET (RAdiation Dose TAiloRing Guided by Enhanced Targeting) is a phase II randomized trial that aims to demonstrate superior safety of image-guided, tumor-focused RT compared to standard RT for acute genitourinary (GU) or gastrointestinal (GI) in the setting of definitive RT for intermediate- and high-risk PC. The study intervention is image-guided, tumor-focused RT with dose intensification of cancer visible on imaging and dose de-intensification to remaining prostate. Patients will be randomized to two arms: those who receive standard RT dose and those that receive tumor-focused RT. The study population will be patients with intermediate- or high-risk PC planning to undergo definitive RT with or without systemic therapy. The primary endpoint to compare between randomized arms is acute GU or GI grade [&ge;]2 adverse events. Participant and study duration are 5 years and 8 years, respectively. RadTARGET will compare the efficacy and safety of tumor-focused RT to that of standard RT for definitive treatment of localized PC. We hypothesize that the tumor-focused approach will substantially reduce adverse events after prostate RT while retaining high efficacy. If this hypothesis is confirmed, we will conclude that a phase III randomized control trial is warranted to formally establish oncologic non-inferiority compared to the current standard of whole-gland dose escalation.","rel_num_authors":20,"rel_authors":[{"author_name":"Anna Dornisch","author_inst":"UC San Diego"},{"author_name":"Mariluz Rojo Domingo","author_inst":"University of California San Diego"},{"author_name":"Roberta Vezza Alexander","author_inst":"UC San Diego"},{"author_name":"Christopher Charles Conlin","author_inst":"UC San Diego"},{"author_name":"Son Do","author_inst":"UC San Diego"},{"author_name":"Rana R McKay","author_inst":"UC San Diego"},{"author_name":"Vitali Moiseenko","author_inst":"UC San Diego"},{"author_name":"Michael A Liss","author_inst":"UC San Diego"},{"author_name":"Jasmine Liu","author_inst":"UC San Diego"},{"author_name":"Todd Pawlicki","author_inst":"UC San Diego"},{"author_name":"Samuel Pena","author_inst":"UC San Diego"},{"author_name":"Edmund M Qiao","author_inst":"UC San Diego"},{"author_name":"Brent S Rose","author_inst":"UC San Diego"},{"author_name":"Rhea Rupareliya","author_inst":"UC San Diego"},{"author_name":"Ajay P Sandhu","author_inst":"UC San Diego"},{"author_name":"Jessica Scholey","author_inst":"UC San Francisco"},{"author_name":"Steven N Seyedin","author_inst":"UC San Francisco"},{"author_name":"James J Urbanic","author_inst":"UC San Diego"},{"author_name":"Lee-Jen Wei","author_inst":"Harvard TH Chan School of Public Health"},{"author_name":"Tyler M Seibert","author_inst":"UC San Diego"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Machine Learning Prediction of Disease Trajectories for Children with Juvenile Idiopathic Arthritis","rel_doi":"10.64898\/2026.04.18.26351165","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.18.26351165","rel_abs":"BackgroundDespite advances in therapy, optimal management of juvenile idiopathic arthritis (JIA) remains challenging. The ability to predict disease progression in JIA can improve personalized treatment decisions, but few reliable clinical predictors have been identified. We developed machine learning approaches to predict disease trajectories in children with JIA.\n\nMethodsUsing data from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry (years 2015-2024), we developed machine learning models to predict attainment of inactive disease in children with non-systemic JIA. We applied Dynamic Bayesian Networks (DBN) to model temporal dependencies and causal relationships, and Convolutional Neural Networks (CNN) to capture complex non-linear patterns. Model input included demographic factors, longitudinal clinical factors, and medication use in the preceding 12 months.\n\nFindingsA total of 8,093 participants were included. When tested on an independent test cohort, both DBN (AUC:0.76; precision:0.73; recall:0.83; F1-score:0.78; accuracy:0.71) and CNN (AUC:0.76; precision:0.71; recall:0.63; F1-score:0.67; accuracy:0.70) models achieved comparable performance in predicting inactive disease. Disease activity levels in the preceding 12 months, presence of enthesitis and uveitis were the strongest predictors. Causal relationships captured in the DBN model revealed suboptimal care patterns, likely shaped by insurance constraints and a predominantly reactive approach to JIA management.\n\nInterpretationOur study demonstrates that machine learning approaches can predict disease trajectories in JIA with good discriminative performance. Unlike prior studies that predict outcomes at single timepoints, our models are the first to predict inactive disease longitudinally. However, suboptimal care patterns in retrospective data limit models capacity to learn treatment-outcome relationships, underscoring critical opportunities to improve JIA care and the need for prospective comparative studies to better inform prediction models.\n\nFundingPatient-Centered Outcomes Research Institute (PCORI) Award (ME-2022C2-25573-IC).\n\nRESEARCH IN CONTEXT\n\nEvidence before this studyNumerous studies have sought to identify clinical predictors of JIA progression and outcomes. However, few reliable predictors have emerged and existing prediction models demonstrate limited performance. As a result, our ability to personalize treatment decisions based on individual risk of severe disease course remains limited.\n\nAdded value of this studyWe developed novel machine learning models that predict individualized disease trajectories in children with polyarticular and oligoarticular JIA using data from their preceding 12-month clinical course. These models demonstrated strong discriminative performance and outperformed previously published machine learning approaches in JIA. Unlike prior studies limited to single time-point predictions, our models are the first to predict inactive disease longitudinally, enabling a patient-specific projection of disease progression over time. Importantly, our findings also bright to light patterns of suboptimal care, likely driven by insurance constraints and a reactive treatment paradigm, underscoring critical opportunities to improve JIA management.\n\nImplications of all the available evidenceOur models have the potential to support clinical decision-making by enabling early identification of children with JIA at risk for unfavorable disease trajectories. In addition, the suboptimal care patterns and systems-level barriers identified through our analyses highlight priority areas for quality improvement initiatives and policy interventions to reduce gaps in JIA care delivery.","rel_num_authors":9,"rel_authors":[{"author_name":"Seungwon Lee","author_inst":"Computational Health Informatics Program, Boston Children's Hospital"},{"author_name":"Marie Davidian","author_inst":"Department of Statistics, North Carolina State University, Raleigh, NC"},{"author_name":"Marc D Natter","author_inst":"Computational Health Informatics Program, Boston Children's Hospital"},{"author_name":"Bryce B Reeve","author_inst":"Center for Health Measurement, Department of Population Health Sciences, Duke University School of Medicine, Durham, NC"},{"author_name":"Laura E Schanberg","author_inst":"Duke Clinical Research Institute, Duke University Medical Center, Durham, NC"},{"author_name":"Eleanor Belkin","author_inst":"Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD"},{"author_name":"Min-Lee Chang","author_inst":"Computational Health Informatics Program, Boston Children's Hospital"},{"author_name":"Yukiko Kimura","author_inst":"Joseph M Sanzari Children's Hospital, Hackensack Meridian School of Medicine, Hackensack, NJ"},{"author_name":"Mei-Sing Ong","author_inst":"Computational Health Informatics Program, Boston Children's Hospital"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Machine Learning Prediction of Disease Trajectories for Children with Juvenile Idiopathic Arthritis","rel_doi":"10.64898\/2026.04.18.26351165","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.18.26351165","rel_abs":"BackgroundDespite advances in therapy, optimal management of juvenile idiopathic arthritis (JIA) remains challenging. The ability to predict disease progression in JIA can improve personalized treatment decisions, but few reliable clinical predictors have been identified. We developed machine learning approaches to predict disease trajectories in children with JIA.\n\nMethodsUsing data from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry (years 2015-2024), we developed machine learning models to predict attainment of inactive disease in children with non-systemic JIA. We applied Dynamic Bayesian Networks (DBN) to model temporal dependencies and causal relationships, and Convolutional Neural Networks (CNN) to capture complex non-linear patterns. Model input included demographic factors, longitudinal clinical factors, and medication use in the preceding 12 months.\n\nFindingsA total of 8,093 participants were included. When tested on an independent test cohort, both DBN (AUC:0.76; precision:0.73; recall:0.83; F1-score:0.78; accuracy:0.71) and CNN (AUC:0.76; precision:0.71; recall:0.63; F1-score:0.67; accuracy:0.70) models achieved comparable performance in predicting inactive disease. Disease activity levels in the preceding 12 months, presence of enthesitis and uveitis were the strongest predictors. Causal relationships captured in the DBN model revealed suboptimal care patterns, likely shaped by insurance constraints and a predominantly reactive approach to JIA management.\n\nInterpretationOur study demonstrates that machine learning approaches can predict disease trajectories in JIA with good discriminative performance. Unlike prior studies that predict outcomes at single timepoints, our models are the first to predict inactive disease longitudinally. However, suboptimal care patterns in retrospective data limit models capacity to learn treatment-outcome relationships, underscoring critical opportunities to improve JIA care and the need for prospective comparative studies to better inform prediction models.\n\nFundingPatient-Centered Outcomes Research Institute (PCORI) Award (ME-2022C2-25573-IC).\n\nRESEARCH IN CONTEXT\n\nEvidence before this studyNumerous studies have sought to identify clinical predictors of JIA progression and outcomes. However, few reliable predictors have emerged and existing prediction models demonstrate limited performance. As a result, our ability to personalize treatment decisions based on individual risk of severe disease course remains limited.\n\nAdded value of this studyWe developed novel machine learning models that predict individualized disease trajectories in children with polyarticular and oligoarticular JIA using data from their preceding 12-month clinical course. These models demonstrated strong discriminative performance and outperformed previously published machine learning approaches in JIA. Unlike prior studies limited to single time-point predictions, our models are the first to predict inactive disease longitudinally, enabling a patient-specific projection of disease progression over time. Importantly, our findings also bright to light patterns of suboptimal care, likely driven by insurance constraints and a reactive treatment paradigm, underscoring critical opportunities to improve JIA management.\n\nImplications of all the available evidenceOur models have the potential to support clinical decision-making by enabling early identification of children with JIA at risk for unfavorable disease trajectories. In addition, the suboptimal care patterns and systems-level barriers identified through our analyses highlight priority areas for quality improvement initiatives and policy interventions to reduce gaps in JIA care delivery.","rel_num_authors":9,"rel_authors":[{"author_name":"Seungwon Lee","author_inst":"Computational Health Informatics Program, Boston Children's Hospital"},{"author_name":"Marie Davidian","author_inst":"Department of Statistics, North Carolina State University, Raleigh, NC"},{"author_name":"Marc D Natter","author_inst":"Computational Health Informatics Program, Boston Children's Hospital"},{"author_name":"Bryce B Reeve","author_inst":"Center for Health Measurement, Department of Population Health Sciences, Duke University School of Medicine, Durham, NC"},{"author_name":"Laura E Schanberg","author_inst":"Duke Clinical Research Institute, Duke University Medical Center, Durham, NC"},{"author_name":"Eleanor Belkin","author_inst":"Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD"},{"author_name":"Min-Lee Chang","author_inst":"Computational Health Informatics Program, Boston Children's Hospital"},{"author_name":"Yukiko Kimura","author_inst":"Joseph M Sanzari Children's Hospital, Hackensack Meridian School of Medicine, Hackensack, NJ"},{"author_name":"Mei-Sing Ong","author_inst":"Computational Health Informatics Program, Boston Children's Hospital"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Human vs AI Clinical Assessment: Benchmarking a Multimodal Foundation Model Against Multi-Center Expert Judgment on the Mental Status Examination.","rel_doi":"10.64898\/2026.04.17.26351105","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351105","rel_abs":"The Mental Status Examination (MSE) is the cornerstone of the psychiatric evaluation, yet validating artificial intelligence (AI) against the inherent variance of clinical judgment remains a critical bottleneck. Here we introduce a multi-center framework to benchmark the open-weight multimodal foundation model Qwen3-Omni against independent expert panels at two sites, UTHealth and Yale. Evaluating 396 classifications across 10 MSE domains and three longitudinal timepoints of increasing symptom severity, we found that experts achieved substantial agreement (Gwets AC1 = 0.87), whereas the model achieved only moderate alignment (AC1 = 0.70-0.72). Even as the models overall pathology prediction rate approximated the experts, the aggregate equilibrium masked a profound \"clinical reasoning gap\". Specifically, the model systematically over-predicted observable signs (e.g., speech, affect) while notably failing in inferential domains requiring the interpretation of latent mental content (e.g., delusions, perceptions). A 4-bit quantization analysis of the model confirmed this mechanistically: reducing model capacity disproportionately degraded inferential reasoning while preserving perceptual feature extraction. Furthermore, model-to-expert agreement degraded linearly as clinical complexity intensified across longitudinal visits (Accuracy: T0 = 84.8-87%; T1 = 80-82%; T2 = 71-73%), whereas expert consensus remained robust. Notably, model errors increased 2.3-to-3.4 fold where human experts disagreed. These findings establish inter-expert variance as an essential measurable baseline for psychiatric AI, demonstrating that true clinical translation requires models to move beyond multimodal perceptual extraction to achieve higher-order diagnostic reasoning.","rel_num_authors":10,"rel_authors":[{"author_name":"Benson Mwangi","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"},{"author_name":"Hammza Jabbar Abdl Sattar Hamoudi","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"},{"author_name":"Marsal Sanches","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"},{"author_name":"Nurhak Dogan","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"},{"author_name":"Pooja Chaudhary","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"},{"author_name":"Mon-Ju Wu","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"},{"author_name":"Giovana B. Zunta-Soares","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"},{"author_name":"Jair C. Soares","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"},{"author_name":"Andres Martin","author_inst":"Child Study Center, Yale School of Medicine, New Haven, CT, USA."},{"author_name":"Cesar A. Soutullo","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Behavioral Health Sciences, The University of Texas Health Science Center at Houston, Houston,TX, US"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Common Electrophysiology Biomarkers Collected at Home Robustly Track Depression Recovery With Deep Brain Stimulation","rel_doi":"10.64898\/2026.04.13.26350107","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.13.26350107","rel_abs":"Subcallosal cingulate cortex (SCC) deep brain stimulation (DBS) can provide relief for individuals with Treatment Resistant Depression (TRD), but ongoing clinical management remains challenging due to nonspecific symptom fluctuations that can obscure core depression recovery on standard rating scales. Objective, stable biomarkers that selectively track the therapeutic effects of SCC DBS are therefore essential for developing principled decision support systems to guide stimulation adjustments. Recent bidirectional DBS systems enable chronic recording of local field potentials (LFPs) and prior work using the Activa PC+S device identified an electrophysiological signature of stable clinical recovery. However, translation to practical clinical deployment requires demonstrating that this biomarker is robustly generalizable, specific to the impact of the DBS therapy, and deployable in real-world recording contexts. To address this need, we developed an at-home SCC LFP data collection platform (built on the Medtronic Summit RC+S system) enabling at home data collection for a new cohort of ten SCC DBS participants with TRD (ClinicalTrials.gov identifier NCT04106466). Using longitudinal LFP recordings collected from this system, we report findings demonstrating that the previously reported biomarker of stable recovery generalizes across subject cohorts and devices, is robust to common potential confounds (including time of day and stimulation status), and shows symptom specificity, sensitivity and stability necessary to support clinical decision making. Across both cohorts, biomarker changes show relationships to pre-DBS white matter structure and network function measured using diffusion MRI and resting-state functional MRI (rsFMRI). These findings replicating and extending previous findings support the biomarkers utility as a foundation for scalable, electrophysiology-informed decision support in SCC DBS.","rel_num_authors":25,"rel_authors":[{"author_name":"Elif Ceren Fitoz","author_inst":"Georgia Institute of Technology"},{"author_name":"Sankaraleengam Alagapan","author_inst":"Georgia Institute of Technology"},{"author_name":"Jungho Cha","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Ki Sueng Choi","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Martijn Figee","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Brian Kopell","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Mosadoluwa Obatusin","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Stephen Heisig","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Tanya Nauvel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Aida Razavilar","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Parisa Sarikhani","author_inst":"Georgia Institute of Technology"},{"author_name":"Isha Trivedi","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Jamie Gowatsky","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Jessa Alexander","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Romain Guignon","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Maryam Khalid","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Gutemberg Bobby Forestal","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Ha Neul Song","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Tim Dennison","author_inst":"University of Oxford"},{"author_name":"Shannon O'Neill","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Shreesh Karjagi","author_inst":"Georgia Institute of Technology"},{"author_name":"Allison C. Waters","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Patricio Riva-Posse","author_inst":"Emory University School of Medicine"},{"author_name":"Helen S. Mayberg","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Christopher J. Rozell","author_inst":"Georgia Institute of Technology"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Vital signs, demographics, and clinical events for low-birth-weight infants from four intensive care units","rel_doi":"10.64898\/2026.04.15.26350178","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.15.26350178","rel_abs":"Premature very low birth weight (VLBW) infants have high rates of mortality and morbidity from sepsis, necrotizing enterocolitis, and respiratory failure requiring intubation and mechanical ventilation. Earlier detection of cardiorespiratory deterioration using vital signs from continuous physiological monitoring may lead to more timely interventions and improved outcomes. To further this research area, we present PreMo, a publicly available dataset of continuous heart rate and oxygen saturation, demographics, clinical events, and outcomes for 3,829 VLBW patients from four Neonatal Intensive Care Units (NICUs) in the United States. The PreMo dataset consists of a collection of parquet files, RO-Crate metadata, and sample usage code scripts hosted on the University of Virginia LibraData Dataverse website.","rel_num_authors":14,"rel_authors":[{"author_name":"Ian German Mesner","author_inst":"University of Virginia"},{"author_name":"Doug E. Lake","author_inst":"University of Virginia"},{"author_name":"Sherry L. Kausch","author_inst":"University of Virginia"},{"author_name":"Katy N Krahn","author_inst":"University of Virginia"},{"author_name":"Angela Gummadi","author_inst":"University of Virginia"},{"author_name":"Timothy W. Clark","author_inst":"University of Virginia"},{"author_name":"Justin C. Niestroy","author_inst":"University of Virginia"},{"author_name":"Rakesh Sahni","author_inst":"Columbia University"},{"author_name":"Zachary A. Vesoulis","author_inst":"Washington University"},{"author_name":"David B. Gootenberg","author_inst":"Washington University"},{"author_name":"N. Ambalavanan","author_inst":"University of Alabama at Birmingham"},{"author_name":"Colm P. Travers","author_inst":"University of Alabama at Birmingham"},{"author_name":"Karen D. Fairchild","author_inst":"University of Virginia"},{"author_name":"Brynne A. Sullivan","author_inst":"University of Virginia"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"Vital signs, demographics, and clinical events for low-birth-weight infants from four intensive care units","rel_doi":"10.64898\/2026.04.15.26350178","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.15.26350178","rel_abs":"Premature very low birth weight (VLBW) infants have high rates of mortality and morbidity from sepsis, necrotizing enterocolitis, and respiratory failure requiring intubation and mechanical ventilation. Earlier detection of cardiorespiratory deterioration using vital signs from continuous physiological monitoring may lead to more timely interventions and improved outcomes. To further this research area, we present PreMo, a publicly available dataset of continuous heart rate and oxygen saturation, demographics, clinical events, and outcomes for 3,829 VLBW patients from four Neonatal Intensive Care Units (NICUs) in the United States. The PreMo dataset consists of a collection of parquet files, RO-Crate metadata, and sample usage code scripts hosted on the University of Virginia LibraData Dataverse website.","rel_num_authors":14,"rel_authors":[{"author_name":"Ian German Mesner","author_inst":"University of Virginia"},{"author_name":"Doug E. Lake","author_inst":"University of Virginia"},{"author_name":"Sherry L. Kausch","author_inst":"University of Virginia"},{"author_name":"Katy N Krahn","author_inst":"University of Virginia"},{"author_name":"Angela Gummadi","author_inst":"University of Virginia"},{"author_name":"Timothy W. Clark","author_inst":"University of Virginia"},{"author_name":"Justin C. Niestroy","author_inst":"University of Virginia"},{"author_name":"Rakesh Sahni","author_inst":"Columbia University"},{"author_name":"Zachary A. Vesoulis","author_inst":"Washington University"},{"author_name":"David B. Gootenberg","author_inst":"Washington University"},{"author_name":"N. Ambalavanan","author_inst":"University of Alabama at Birmingham"},{"author_name":"Colm P. Travers","author_inst":"University of Alabama at Birmingham"},{"author_name":"Karen D. Fairchild","author_inst":"University of Virginia"},{"author_name":"Brynne A. Sullivan","author_inst":"University of Virginia"}],"rel_date":"2026-04-20","rel_site":"medrxiv"},{"rel_title":"A loss of function variant in SLC30A8\/ZnT8 drives proteomic changes associated with lowered apoptosis in human stem cell-derived islets","rel_doi":"10.64898\/2026.04.17.26351108","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.17.26351108","rel_abs":"(1) Aims and hypothesisLoss-of-function mutations in SLC30A8, encoding the zinc ion (Zn2+) transporter ZnT8 in pancreatic beta cells, lower type 2 diabetes risk dose-dependently, but the underlying mechanisms remain unclear. Here, we combine proteomic, transcriptomic and functional approaches in human stem cell-derived islet-like clusters bearing common alleles or the inactivating variant R138X. We hypothesized that this variant protects against the deleterious effect of Zn2+ depletion on cell survival and function.\n\n(2) MethodsHuman embryonic stem cells INS(GFP\/w) (MEL1), and CRISPR\/Cas9-derived heterozygous or homozygous R138X lines were differentiated into stem cell-derived islet-like clusters. Intracellular Zn2+ levels were reduced using the chelator N,N,N',N'-tetrakis(2-pyridylmethyl)-1,2-ethanediamine (TPEN). Apoptosis was assessed by TUNEL staining and protein expression by immunofluorescence. Glucose-stimulated calcium (Ca2+) dynamics were measured using the intracellular probe (Cal590) and insulin secretion by homogenous time-resolved fluorescence. Transcriptomic profiling was performed by bulk mRNA sequencing and proteomics by liquid chromatography-tandem mass spectrometry.\n\n(3) ResultsIntracellular Zn2+ depletion increased apoptosis in wild-type islet-like clusters, whereas R138X clusters were protected. R138X heterozygous clusters showed a mild increase in GCG+ cells and R138X homozygous clusters exhibited increased NKX6.1+ cells, without affecting polyhormonal populations. These changes were reversed under Zn2+ depletion. Transcriptomic and proteomic analyses, assessing genotype effects while accounting for Zn2+ depletion, showed that R138X clusters (versus wild-type) exhibited upregulation of genes and proteins involved in vesicle trafficking, secretion, Ca{superscript 2} signaling and mitochondrial metabolism, consistent with enhanced glucose-stimulated insulin secretion in homozygous clusters. Conversely, genes and proteins associated with extracellular matrix remodeling, metal-ion handling, apoptosis and cellular stress were downregulated. R138X clusters displayed altered Ca2+ signaling, with decreased area under the curve and oscillation amplitude, but increased frequency. These differences were reversed by TPEN, while Zn2+ depletion impaired Ca2+ response in wild-type clusters. Despite lowered overall activity, R138X homozygous clusters showed enhanced overall cell-cell connectivity, reversed by TPEN treatment. The opposite effects were observed in R138X heterozygous clusters, showing improved connectivity and activity under Zn2+ depletion.\n\n(4) Conclusion and interpretationIntracellular Zn2+ depletion compromises islet-like cluster identity and function, while the R138X variant confers protection against these effects. Under Zn2+-depleted conditions, ZnT8 deficiency promotes a more mature and metabolically active state of the R138X clusters, with enhanced Ca2+ signaling and insulin secretion, supported by a structural remodeling and the downregulation of apoptosis and cellular stress. These findings highlight the therapeutic potential of targeting ZnT8 in type 2 diabetes and support its relevance for further improving cell-based therapies.\n\nResearch in ContextO_ST_ABSWhat is already know about this subject?C_ST_ABSO_LIRare inactivating mutations in the insulin granule-associated zinc transporter gene, SLC30A8\/ZnT8, drive lowered type 2 diabetes risk.\nC_LIO_LIPrevious studies have indicated that apoptosis is lowered, and glucose-stimulated insulin secretion enhanced, after ZnT8 inactivation.\nC_LIO_LIThe molecular mechanisms underlying these changes are unclear.\nC_LI\n\nWhat is the key question?O_LIHow do inactivating mutations in SL30A8\/ZnT8 lead to lowered apoptosis and enhanced insulin secretion from stem cell-derived islet-like clusters, and is altered susceptibility to intracellular zinc depletion involved?\nC_LI\n\nWhat are the new findings?O_LIThe rare inactivating R138X mutation in SLC30A8 leads to gene dose-dependent changes in the transcriptome and proteome of islet-like clusters.\nC_LIO_LIChanges include upregulation of maturity and downregulation of immaturity genes.\nC_LIO_LIDepletion of intracellular Zn2+ exaggerates the protective effects of the inactivating mutation on apoptosis and insulin secretion\nC_LI\n\nHow might this impact on clinical practice in the foreseeable future?O_LIOur findings suggest that careful monitoring of both dietary zinc intake and of circulating levels of zinc ions, whose effects are mitigated in SLC30A8 mutation carriers, may be helpful in some populations to lower diabetes risk.\nC_LI","rel_num_authors":7,"rel_authors":[{"author_name":"Marie Gasser","author_inst":"Centre Hospitalier de l'Universite de Montreal (CHUM) Research Center and Department of Medicine, University of Montreal, Montreal, QC, Canada"},{"author_name":"Ines Cherkaoui","author_inst":"Centre Hospitalier de l'Universite de Montreal (CHUM) Research Center and Department of Medicine, University of Montreal, Montreal, QC, Canada"},{"author_name":"Giada Ostinelli","author_inst":"Centre Hospitalier de l'Universite de Montreal (CHUM) Research Center and Department of Medicine, University of Montreal, Montreal, QC, Canada"},{"author_name":"Mathieu Ferron","author_inst":"Institut de Recherche Clinique de Montreal and Department of Medicine, University of Montreal, Montreal, QC, Canada"},{"author_name":"Qian Du","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Dieter Egli","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Guy Rutter","author_inst":"Imperial College London"}],"rel_date":"2026-04-20","rel_site":"medrxiv"}]}