{"gname":"University of Illinois Chicago","grp_id":"43","rels":[{"rel_title":"A Multidimensional Immune Signature Predicts Susceptibility to Omicron Infection in Vaccinated Individuals","rel_doi":"10.64898\/2026.08.31.26361844","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361844","rel_abs":"Substantial inter-individual variation in susceptibility to viral infection persists despite widespread vaccination, and its immunological basis remains poorly understood. We investigated how innate and adaptive immune responses contribute to susceptibility to SARS-CoV-2 infection during the early COVID-19 pandemic. We compared two groups of vaccinated individuals who either remained uninfected or became infected during the first Omicron wave. Blood samples were collected at baseline and 24 weeks later. Peripheral blood mononuclear cells (PBMCs) and polymorphonuclear neutrophils (PMNs) were isolated and stimulated with the TLR7\/8 agonist R848 to assess innate responses. PBMCs were stimulated with SARS-CoV-2 peptide pools and highly purified inactivated viruses (ancestral and Omicron BA.1) to evaluate adaptive immunity. Prior to infection, individuals in the infected group exhibited reduced CD4 and CD8 T cells proliferative responses, alongside with increased TNF production across all stimulation conditions, despite largely comparable immune phenotypes, indicating a pre-existing functional immune deficit. Following infection, T-cell proliferation and IFN-gamma production were partially restored in response to viral antigens, although responses to Omicron BA.1 remained suboptimal. This functional deficit was accompanied by heightened inflammatory activity, including increased TNF and IFN-gamma production, elevated anti-nucleocapsid IgG3 levels, higher frequencies of B cells and myeloid cells, reduced circulating interferon-inducible T-cell Alpha Chemoattractant (I-TAC) concentrations, and a modest impairment in PMN IL-8 responses. Notably, these alterations were detectable prior to infection and persisted thereafter, indicating that they represent determinants rather than consequences of viral infection. Importantly, beyond differences in the magnitude of immune responses, protection was associated with the degree of functional coordination within the humoral compartment, as reflected by the relationship between Spike-binding antibodies and neutralizing activity. Together, these results demonstrate that susceptibility to Omicron infection is linked to a pre-existing and persistent functional immune imbalance affecting both innate and adaptive arms of immunity.","rel_num_authors":19,"rel_authors":[{"author_name":"Hend Jarras","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Wilfried Wenceslas Bazie","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Programme de Recherche sur les Maladies In"},{"author_name":"Isalie Blais","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Benjamin Goyer","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Julien Boucher","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Arielle Pakenham","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Kimberly Dancause-Caron","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Henintsoa Rabezanahary","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Mathieu Theriault","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Kim Santerre","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Marc-Andre Langlois","author_inst":"Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada"},{"author_name":"Philippe A Tessier","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"},{"author_name":"Jean-Francois Masson","author_inst":"Department of Chemistry, Institut Courtois, Quebec Center for Advanced Materials, Regroupement quebecois sur les materiaux de pointe, and Centre interdisciplina"},{"author_name":"Joelle N Pelletier","author_inst":"Department of Chemistry, Department of Biochemistry, Universite de Montreal, Montreal, QC, Canada, PROTEO- The Quebec Network for Research on Protein Function, "},{"author_name":"Nicholas Brousseau","author_inst":"Direction des risques biologiques, Institut national de sante publique du Quebec, Quebec, QC, Canada"},{"author_name":"Denis Boudreau","author_inst":"Departement de chimie et Centre d optique, photonique et laser COPL, Universite Laval, Quebec, QC, Canada"},{"author_name":"Sylvie Trottier","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Centre de recherche en infectiologie de l "},{"author_name":"Mariana Baz","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"},{"author_name":"Caroline Gilbert","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"A Multidimensional Immune Signature Predicts Susceptibility to Omicron Infection in Vaccinated Individuals","rel_doi":"10.64898\/2026.08.31.26361844","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361844","rel_abs":"Substantial inter-individual variation in susceptibility to viral infection persists despite widespread vaccination, and its immunological basis remains poorly understood. We investigated how innate and adaptive immune responses contribute to susceptibility to SARS-CoV-2 infection during the early COVID-19 pandemic. We compared two groups of vaccinated individuals who either remained uninfected or became infected during the first Omicron wave. Blood samples were collected at baseline and 24 weeks later. Peripheral blood mononuclear cells (PBMCs) and polymorphonuclear neutrophils (PMNs) were isolated and stimulated with the TLR7\/8 agonist R848 to assess innate responses. PBMCs were stimulated with SARS-CoV-2 peptide pools and highly purified inactivated viruses (ancestral and Omicron BA.1) to evaluate adaptive immunity. Prior to infection, individuals in the infected group exhibited reduced CD4 and CD8 T cells proliferative responses, alongside with increased TNF production across all stimulation conditions, despite largely comparable immune phenotypes, indicating a pre-existing functional immune deficit. Following infection, T-cell proliferation and IFN-gamma production were partially restored in response to viral antigens, although responses to Omicron BA.1 remained suboptimal. This functional deficit was accompanied by heightened inflammatory activity, including increased TNF and IFN-gamma production, elevated anti-nucleocapsid IgG3 levels, higher frequencies of B cells and myeloid cells, reduced circulating interferon-inducible T-cell Alpha Chemoattractant (I-TAC) concentrations, and a modest impairment in PMN IL-8 responses. Notably, these alterations were detectable prior to infection and persisted thereafter, indicating that they represent determinants rather than consequences of viral infection. Importantly, beyond differences in the magnitude of immune responses, protection was associated with the degree of functional coordination within the humoral compartment, as reflected by the relationship between Spike-binding antibodies and neutralizing activity. Together, these results demonstrate that susceptibility to Omicron infection is linked to a pre-existing and persistent functional immune imbalance affecting both innate and adaptive arms of immunity.","rel_num_authors":19,"rel_authors":[{"author_name":"Hend Jarras","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Wilfried Wenceslas Bazie","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Programme de Recherche sur les Maladies In"},{"author_name":"Isalie Blais","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Benjamin Goyer","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Julien Boucher","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Arielle Pakenham","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Kimberly Dancause-Caron","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Henintsoa Rabezanahary","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Mathieu Theriault","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Kim Santerre","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada"},{"author_name":"Marc-Andre Langlois","author_inst":"Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada"},{"author_name":"Philippe A Tessier","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"},{"author_name":"Jean-Francois Masson","author_inst":"Department of Chemistry, Institut Courtois, Quebec Center for Advanced Materials, Regroupement quebecois sur les materiaux de pointe, and Centre interdisciplina"},{"author_name":"Joelle N Pelletier","author_inst":"Department of Chemistry, Department of Biochemistry, Universite de Montreal, Montreal, QC, Canada, PROTEO- The Quebec Network for Research on Protein Function, "},{"author_name":"Nicholas Brousseau","author_inst":"Direction des risques biologiques, Institut national de sante publique du Quebec, Quebec, QC, Canada"},{"author_name":"Denis Boudreau","author_inst":"Departement de chimie et Centre d optique, photonique et laser COPL, Universite Laval, Quebec, QC, Canada"},{"author_name":"Sylvie Trottier","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Centre de recherche en infectiologie de l "},{"author_name":"Mariana Baz","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"},{"author_name":"Caroline Gilbert","author_inst":"Axe Maladies infectieuses et immunitaires, Centre de recherche du CHU de Quebec-Universite Laval, Quebec, QC, Canada, Departement de Microbiologie-Infectiologie"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Secondary causes among adult patients presenting with first-episode psychosis to acute medical settings in Hong Kong: A 10-Year retrospective study","rel_doi":"10.64898\/2026.09.02.26362044","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362044","rel_abs":"Background The prevalence and pattern of secondary causes of first-onset psychosis (FEP) in Asian populations are understudied. Our objectives are to investigate the prevalence and pattern of secondary causes of FEP presenting in an acute medical setting in a metropolitan, Chinese-predominant population, and to investigate the prevalence and pattern of undiagnosed conditions presenting as psychosis. Method This is a retrospective observational study. We reviewed medical records of patients referred to the consultation psychiatry team at a tertiary acute teaching hospital in Hong Kong from January 2015 to Apr 2025. The inclusion criteria of the study are 1) age 18-64 at the time of the assessment, and 2) FEP confirmed by the consultation liaison team. Patients diagnosed with delirium were excluded. Result Among the 384 patients included in the study (mean age = 40.0 {+\/-}13.7 years, 69% female), secondary causes of psychosis were found in 9.6% (n=37) of the cohort. Substance use is the most common secondary cause found (n=11, 2.9% of all FEPs) overall. Among those with secondary psychoses, patients' underlying conditions were revealed only by the workup in relation to the FEP in 16 of them, of which definitive or probable autoimmune encephalitis (n=5) and early-onset dementia (n=4) were the most common conditions uncovered. Conclusion The prevalence of secondary psychoses in our FEP cohort is lower than published international figures. Clinicians need to be aware of the suspicious clinical features suggestive of autoimmune encephalitis and early-onset dementia in FEP patients with otherwise unremarkable past history and toxicology test.","rel_num_authors":6,"rel_authors":[{"author_name":"Steven Wai Ho Chau","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Lam KW Lam","author_inst":"Department of Psychiatry, Prince of Wales Hospital, Hong Kong"},{"author_name":"Matthew PM Yu","author_inst":"Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China"},{"author_name":"Yuen Cheuk Wong","author_inst":"Department of General Adult Psychiatry, Castle Peak Hospital, Hong Kong, China"},{"author_name":"Joseph CC Choi","author_inst":"Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong, China"},{"author_name":"Howan HW Leung","author_inst":"5.\tDivision of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Hong Kong, China"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Socio-demographic and environmental factors amplify typhoon-related excess mortality in Japan","rel_doi":"10.64898\/2026.09.01.26362002","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26362002","rel_abs":"Mechanisms shaping population vulnerability to typhoon-related mortality remain poorly understood. Constructing a Bayesian spatio-temporal model, we linked 12.9 million deaths across Japan from 2010 to 2019 to population-weighted typhoon wind exposure and assessed effect modification by income, natural hazard vulnerability and healthcare access. Typhoon exposure was associated with 2,426 cumulative excess deaths [95% credible interval: 139, 4,632] among adults [&ge;]70 years, with mortality increasing within 0-1 weeks of exposure and more strongly in areas with limited healthcare access and greater hazard vulnerability. Among individuals <70 years, cumulative excess mortality was uncertain [781 deaths; -309 to 1,900], but delayed mortality increases were concentrated in lower-income and landslide-prone areas. These distinct patterns suggest that typhoon mortality reflects an interaction between acute exposure, demographic ageing and geographically uneven adaptive capacity, highlighting the need to incorporate local vulnerability into climate-resilient health systems.","rel_num_authors":5,"rel_authors":[{"author_name":"Lisa Yamasaki","author_inst":"Harvard T.H. Chan School of Public Health"},{"author_name":"Hiroaki Murayama","author_inst":"International University of Health and Welfare"},{"author_name":"Paul LC Chua","author_inst":"The University of Tokyo"},{"author_name":"Masahiro Hashizume","author_inst":"The University of Tokyo"},{"author_name":"Robbie M Parks","author_inst":"Columbia University"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Very low-calorie diet reduces hepatic steatosis and remodels circulating metabolite-microRNAs networks in metabolic dysfunction-associated steatotic liver disease: A pilot study","rel_doi":"10.64898\/2026.09.01.26361664","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361664","rel_abs":"Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major cause of chronic liver disease, with weight loss as the pivotal therapeutic strategy. However, the metabolic and molecular adaptations underlying rapid weight loss remain incompletely defined. In this pilot study, women with obesity and MASLD but without diabetes consumed a very low-calorie diet (VLCD) for 8 weeks. Clinical parameters, hepatic steatosis measured by controlled attenuation parameter (CAP), circulating metabolites, and microRNAs (miRs) were assessed before and after the dietary intervention. Integrated correlation and hierarchical clustering analyses were performed to identify molecular networks associated with clinical improvement. VLCD was well tolerated, resulting in significant weight loss (~11%) with ~80% adherence. Significant improvements in metabolic parameters were observed, including fat mass, waist circumference, blood pressure, insulinemia, HOMA-IR, HbA1c, and triglycerides, with unchanged liver enzymes. Hepatic steatosis decreased markedly, as indicated by a reduction in CAP, while stiffness remained unchanged. Metabolomic profiling revealed elevated ketone bodies and broad reductions in amino acid levels, consistent with enhanced fatty acid oxidation and a catabolic metabolic state. Correlation analysis identified distinct metabolite signatures associated with hepatic steatosis, with changes in CAP positively associated with changes in amino acids and inversely associated with changes in ketone bodies and tricarboxylic acid cycle intermediates. Circulating miRs underwent selective rather than global remodeling, with only a limited subset showing strong associations with clinical parameters, including CAP and HOMA-IR. Specifically, VLCD altered the circulating levels of miR-148a-3p, miR-140-3p, miR-10b-5p, and miR-345-5p. Integration of metabolomic and miR datasets identified coordinated metabolite-miR modules involving glucose metabolism, branched-chain amino acid catabolism, mitochondrial metabolism, purine metabolism, microbial metabolites, and cellular redox pathways. These findings demonstrate that improvement in hepatic steatosis during VLCD-induced weight loss is accompanied by coordinated remodeling of circulating metabolite-miR networks. Integrated multi-omics analysis identifies candidate molecular signatures associated with metabolic adaptation and highlights circulating miR-metabolite modules as potential biomarkers of therapeutic response in MASLD.","rel_num_authors":13,"rel_authors":[{"author_name":"Paroma Deb","author_inst":"University of Iowa"},{"author_name":"Darin Bagar","author_inst":"University of Iowa"},{"author_name":"Prashant Kumar","author_inst":"University of Iowa"},{"author_name":"Leon Sun","author_inst":"University of Iowa"},{"author_name":"Ethan Chen","author_inst":"University of Iowa"},{"author_name":"Ravinder Reddy Gaddam","author_inst":"University of Iowa"},{"author_name":"Lorrana F Ferretto","author_inst":"University of Iowa"},{"author_name":"Constance R Shelsky","author_inst":"University of Iowa"},{"author_name":"Antonio J Sanchez","author_inst":"University of Iowa"},{"author_name":"Himani Thakkar","author_inst":"University of Iowa"},{"author_name":"Bhagirath Chaurasia","author_inst":"University of Iowa"},{"author_name":"Ajit Vikram","author_inst":"University of Iowa"},{"author_name":"Marcelo Lima DG Correia","author_inst":"University of Iowa"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Nanopore sequencing panel for saliva-based host pharmacogenomic screening in anti-tubercular therapy","rel_doi":"10.64898\/2026.09.02.26362034","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26362034","rel_abs":"Abstract Rationale: Host genotypes can predict subtherapeutic anti-tubercular drug exposures and treatment-associated toxicities. Screening for these variants could enable personalized dosing, but scalable assays for second-line drugs are lacking. Objectives: We developed a nanopore sequencing panel to detect host variants affecting anti-tuberculosis drug troughs and toxicities, and evaluated its performance as a saliva-based screening tool. Methods: We designed a 16-plex panel targeting 23 variants (21 clinically validated, 2 predicted actionable) relevant to linezolid, bedaquiline, clofazimine, moxifloxacin, and ethambutol exposure. We first sequenced 50 Coriell DNA (1000 Genomes Project) to benchmark accuracy against Illumina, then sequenced saliva from 202 individuals treated for drug-resistant tuberculosis in India using MinION Mk1C (R10.4). Plasma trough concentrations and toxicity frequencies were stratified by genotype. Data were analyzed using in-house pipelines. Measurements and Main Results: The panel showed high coverage in saliva (median 3,609X). Several suggestive genotype-phenotype trends reached nominal significance in distinct subsets. Among patients on high-dose moxifloxacin (800mg daily), UGT1A1 rs3755319 A>C was associated with higher troughs in heterozygotes (6\/14, p<0.01) and homozygous alternates (4\/14, p<0.05). Among patients with linezolid-associated toxicity dose-reduced to 300mg, ABCB1 rs2032582 A>C homozygous alternates (7\/98) had significantly lower Cmin versus wild-type (p<0.05) and heterozygotes (p<0.01); neither association held at standard dosing. Linezolid toxicity was more frequent among ABCB1 rs1128503 A>G heterozygotes versus homozygous reference (58.3% vs. 29.1%), and UGT1A1 rs4148323 G>A heterozygotes showed higher moxifloxacin toxicity rates than wild-type (42.9% vs. 14.3%). Conclusions: Portable, saliva-based sequencing reliably detects pharmacogenetic variants and could inform pre-treatment screening for drug exposure or toxicity.","rel_num_authors":16,"rel_authors":[{"author_name":"Priyanka Yadav","author_inst":"Institute of Bioinformatics, International Tech Park, Bangalore, Karnataka, India"},{"author_name":"Swarup A. V. Shah","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Aishwarya S. Babu","author_inst":"1. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India 2. Institute of Bioinformatics, International Tech Park, Bangalore, Karnataka, India"},{"author_name":"Minal Paradkar","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Shruthi Vasanthaiah","author_inst":"1. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India 2. Institute of Bioinformatics, International Tech Park, Bangalore, Karnataka, India"},{"author_name":"Karthick Vasudevan","author_inst":"1. Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India 2. Institute of Bioinformatics, International Tech Park, Bangalore, Karnataka, India"},{"author_name":"Prerna R. Arora","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Rohan V. Lokhande","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Heeral U.B. Pandya","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Paolo Denti","author_inst":"Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa"},{"author_name":"Camilla Rodrigues","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Jason  R. Andrews","author_inst":"Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, California, USA"},{"author_name":"Akhilesh Pandey","author_inst":"Department of Laboratory Medicine and Pathology, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA"},{"author_name":"Jeffrey A Tornheim","author_inst":"Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA"},{"author_name":"Tester F. Ashavaid","author_inst":"PD Hinduja Hospital and Medical Research Centre, Mumbai, Maharashtra, India"},{"author_name":"Renu Verma","author_inst":"Institute of bioinformatics"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Cell-type-resolved somatic variant discovery from bulk long-read sequencing","rel_doi":"10.64898\/2026.09.01.26361966","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361966","rel_abs":"Somatic mutations arise throughout life, with functional consequences tied to the cell populations in which they occur. Genome-wide studies measure somatic variations in bulk tissue, whereas single-cell approaches resolve cell identity but provide limited sensitivity for complex alleles. Here we developed SniffCell, which uses DNA methylation carried on native long reads to assign somatic variant-supporting molecules to methylation-resolvable cell types. SniffCell builds cell-type-discriminatory methylation signatures across eight tissues, assigns long reads to cell types, and provides cell-type-specific variant calling. Across peripheral blood mononuclear cells and brain benchmarks, SniffCell recovered sorted cell identities and validated cell-type-specific variant assignments using purified immune-cell, neuronal, and oligodendrocyte fractions. In blood, SniffCell recovered lineage-restricted antigen receptor rearrangements and localized a somatic tandem-repeat expansion to T cells. In the frontal cortex, SniffCell identified recurrent neuron-specific tandem-repeat expansions in genes including FGF14, LRRC7 and SH3RF3. Across three brain cohorts comprising 172 donors, recurrent neuron-associated expansions were enriched for GAA-rich motifs. In donors with matched blood, and diverged more strongly from the inherited repeat length, whereas oligodendrocyte-associated alleles more often tracked it. SniffCell transforms native bulk long-read genomes into a cell-type-aware resource for somatic variant discovery and reveals recurrent somatic instability in human tissues at cell-type resolution.","rel_num_authors":24,"rel_authors":[{"author_name":"Yilei Fu","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Caoimhe Morley","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Lauren M. Masters","author_inst":"Department of Molecular and Human Genetics, Baylor College of Medicine, TX, USA"},{"author_name":"Adam C. English","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Yiming Zhu","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Abraham G. Moller","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Luis F. Paulin","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Ben Thompson","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Ester Kalef-Ezra","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"George Weissenberger","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Helen Shen","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Melissa Meridith","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Arianna Manini","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Dominic Horner","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Xylena Reed","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Donna Muzny","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Zane Jaunmuktane","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Ziad M. Khan","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Heer Mehta","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Winston Timp","author_inst":"Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA"},{"author_name":"Kimberley Billingsley","author_inst":"Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of He"},{"author_name":"Graham S. Erwin","author_inst":"Department of Molecular and Human Genetics, Baylor College of Medicine, TX, USA"},{"author_name":"Christos Proukakis","author_inst":"UCL Queen Square Institute of Neurology, University College London, UK; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, "},{"author_name":"Fritz J. Sedlazeck","author_inst":"Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, TX, USA; D"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Rest-activity and circadian rhythm parameters relate to cognition and disability outcomes in multiple sclerosis","rel_doi":"10.64898\/2026.08.31.26361636","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361636","rel_abs":"OBJECTIVE: To prioritize novel measures of disease progression, we examined whether actigraphy-derived rest-activity rhythm (RAR) parameters relate to cognitive performance as well as disability in a multiple sclerosis (MS) cohort enrolled in a prospective brain donation program. METHODS: RAR parameters were assessed using a wrist actigraphy device (AX3 Axivity Actiwatch, Axivity Ltd.) over two weeks. The primary outcome measure was the Symbol Digit Modalities Test (SDMT, N=222). Secondary outcomes included Brixton Spatial Anticipation Test and self-reported disability. In 76 participants, volumetric measures were derived from repurposed clinical magnetic resonance imaging (MRI) data. We applied linear and logistic regression models, adjusting for age, sex, education, time since MS diagnosis, and body mass index. RESULTS: After correction for multiple comparisons, higher intradaily variability (IV) of RAR and lower relative amplitude were associated with worse SDMT performance; higher IV was also associated with greater odds of disability. A broader set of RAR parameters was associated with disability measure. No MRI parameters were related to RAR in the subset of individuals with available MRI data, although we note suggestive associations with hippocampal and choroid plexus volumes warranting further investigation. INTERPRETATION: More robust circadian rhythms were related to better cognition. These results highlight the utility of actigraphy and its more nuanced measures beyond the simple summaries of activity levels that quantitate the extent of motor disability. Selected RAR features may be an effective non-invasive approach to capture clinically relevant quantitative measures of brain function for persons with MS.","rel_num_authors":12,"rel_authors":[{"author_name":"Katrin Wolfova","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Charles White","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Nasim Montazeri Ghahjaverestan","author_inst":"Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada"},{"author_name":"Tenzing Choeying","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Rodolfo Arevalo","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Kaho Onomichi","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Levi Davis","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Victoria M Leavitt","author_inst":"Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA."},{"author_name":"Korhan Buyukturkoglu","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Claire Riley","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"},{"author_name":"Andrew Lim","author_inst":"Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre University of Toronto, ON, Canada"},{"author_name":"Philip De Jager","author_inst":"Columbia Multiple Sclerosis Center, Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Interpretable photoacoustic phenotyping of distal microcirculation for peripheral artery disease diagnosis with exploratory perioperative assessment","rel_doi":"10.64898\/2026.09.02.26361270","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.02.26361270","rel_abs":"Peripheral artery disease (PAD) spans a continuum from large-vessel obstruction to distal microvascular dysfunction, yet routine non-invasive tests, including the ankle-brachial index (ABI), do not provide structurally resolved assessment of the foot microvascular bed and may be unreliable in the setting of medial arterial calcification or perioperative follow-up. Here we developed a clinic-oriented multispectral compound-scanning photoacoustic tomography system (MCPATS) for compression-free distal toe imaging, and an interpretable photoacoustic tomography distal microcirculation score, termed PACT-DMS, for phenotyping PAD-related distal vascular abnormalities. PACT-DMS was derived from anatomically standardized distal toe sections and integrated seven prespecified vascular features spanning trunk-vessel morphology, microvascular distribution and pulsation-related dynamics through a traceable linear support vector machine. In a prospective single-centre cohort of 45 participants, the bilateral fusion PACT-DMS model distinguished patients with PAD from healthy controls with an area under the receiver operating characteristic curve of 0.964 (95% CI, 0.907-1.000) and an accuracy of 91.1% (95% CI, 82.2%-97.8%) under subject-level leave-one-out cross-validation, supported by complementary robustness analyses. Exploratory analyses further showed that PACT-DMS identified abnormal distal vascular phenotypes in 6 of 9 clinically diagnosed PAD limbs with non-abnormal ABI and visualized distal vascular-bed changes before and after revascularization. These findings support MCPATS-enabled interpretable photoacoustic vascular phenotyping as a candidate adjunctive approach for distal microcirculatory assessment in PAD; larger multicentre studies with external validation and prespecified analysis protocols will be required to define its clinical role.","rel_num_authors":12,"rel_authors":[{"author_name":"Handi Deng","author_inst":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University; Institute for Intelligent He"},{"author_name":"Tianhao Yuwen","author_inst":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University; Institute for Intelligent He"},{"author_name":"Zipeng Li","author_inst":"Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital"},{"author_name":"Jiaxuan Xiang","author_inst":"Beijing Tsingpai Technology Co., Ltd."},{"author_name":"Yizhou Bai","author_inst":"Department of Thyroid and Breast Surgery, Beijing Tsinghua Changgung Hospital"},{"author_name":"Naiyue Zhang","author_inst":"Beijing Tsingpai Technology Co., Ltd."},{"author_name":"Wubing Fu","author_inst":"Beijing Tsingpai Technology Co., Ltd."},{"author_name":"Xiaojun Wang","author_inst":"Beijing Tsingpai Technology Co., Ltd."},{"author_name":"Jianming Guo","author_inst":"Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University"},{"author_name":"Weiwei Wu","author_inst":"Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital"},{"author_name":"Cheng Ma","author_inst":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University"},{"author_name":"Ming-Yuan Liu","author_inst":"Department of Vascular Surgery, Beijing Friendship Hospital, Capital Medical University; Beijing Institute of Vascular Surgery"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Defining use cases for biomarkers and tests across tuberculosis infection, disease and treatment: An international consensus and prioritisation exercise","rel_doi":"10.64898\/2026.09.01.26361598","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361598","rel_abs":"Background Translation of tuberculosis (TB) biomarker and diagnostic research into tools that improve patient and public health outcomes has been slow, partly because no internationally agreed framework exists defining the use cases that new biomarkers and tests should address. We aimed to identify, validate, and prioritise use cases for TB biomarkers and tests across Mycobacterium tuberculosis (Mtb) infection, TB disease, TB treatment, and post-TB care, through an international consensus process. Methods and Findings We conducted a scoping review of the literature, guidelines, and target product profiles (24 documents; 69 candidate use cases consolidated to 13), followed by a hybrid RAND\/UCLA modified Delphi consensus process involving 185 identified interest-holders, including clinicians, researchers, diagnostic developers, industry, funders, civil society, national TB programmes, and policymakers (including WHO representatives). Interest-holders completed an online survey rating agreement with each use case, and attended a consensus meeting to discuss use cases with less than 80% agreement, followed by a further validation meeting. Eleven use cases were retained across four TB care pathway stages: three for Mtb infection, four for disease detection, three for treatment optimisation, and one for post-TB care. Highest-priority use cases were detection of drug-resistant TB, prediction of progression from infection to disease, identification of current Mtb infection, and improved diagnosis of active TB disease. Conclusions This consensus exercise provides the first comprehensive, prioritised framework of use cases for TB biomarkers and tests, spanning the full care pathway. These eleven priority use cases can guide investment, focus biomarker discovery, and inform future target product profiles, funding calls, and policy development. Applying the framework to the current biomarker pipeline is a key next step to address gaps between innovation and priority needs.","rel_num_authors":24,"rel_authors":[{"author_name":"Sacha Wright","author_inst":"University College London"},{"author_name":"Federico Fama","author_inst":"University College London"},{"author_name":"Angus de Wilton","author_inst":"University of Bristol"},{"author_name":"Ellen Steward","author_inst":"University College London"},{"author_name":"Francesca Saluzzo","author_inst":"Scientific Institute San Raffaele: IRCCS Ospedale San Raffaele"},{"author_name":"Chiara Sepulcri","author_inst":"Scientific Institute San Raffaele: IRCCS Ospedale San Raffaele"},{"author_name":"James Greenan-Barrett","author_inst":"University College London"},{"author_name":"Khay Mar Aung","author_inst":"The University of Sydney"},{"author_name":"Thi  Mai Nguyen","author_inst":"The University of Sydney"},{"author_name":"Nora Engel","author_inst":"Vrije Universiteit Amsterdam"},{"author_name":"Seda Yerlikaya","author_inst":"Heidelberg University"},{"author_name":"Hanif Esmail","author_inst":"University College London"},{"author_name":"Salome Charalambous","author_inst":"University of the Witwatersrand Johannesburg"},{"author_name":"Cecily Miller","author_inst":"World Health Organization"},{"author_name":"Ruvandhi Nathavitharana","author_inst":"Harvard Medical School"},{"author_name":"Luan  Nguyen Quang Vo","author_inst":"Karolinska Institutet"},{"author_name":"Mikashmi Kohli","author_inst":"Foundation for Innovative New Diagnostics: FIND"},{"author_name":"Delia Goletti","author_inst":"Istituto Nazionale per le Malattie Infettive: Istituto Nazionale Malattie Infettive Lazzaro Spallanzani"},{"author_name":"Daniela Cirillo","author_inst":"Scientific Institute San Raffaele: IRCCS Ospedale San Raffaele"},{"author_name":"Mahdad Noursadeghi","author_inst":"University College London"},{"author_name":"Claudia  Maria Denkinger","author_inst":"Heidelberg University"},{"author_name":"Emily  Lai-Ho MacLean","author_inst":"The University of Sydney"},{"author_name":"Rishi  K Gupta","author_inst":"University College London"},{"author_name":"Ankur Gupta-Wright","author_inst":"Imperial College London \/ North Bristol NHS Trust \/ University of Heidelberg"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Transcranial photobiomodulation rebalances cortical excitation\/inhibition in young adults with attention deficit\/hyperactivity disorder","rel_doi":"10.64898\/2026.09.01.26361884","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361884","rel_abs":"Background: Attention deficit\/hyperactivity disorder (ADHD) is a neurodevelopmental condition lacking mechanistically grounded interventions. Here, we tested whether transcranial photobiomodulation (tPBM) can restore neural homeostasis in ADHD patients. Methods: In a randomized, double-blind, sham-controlled crossover design, 28 young adults with ADHD completed a two-week intervention, receiving active (150 mW) and sham (0 mW) stimulation over the right prefrontal cortex for 16 minutes with concurrent electroencephalography (EEG) recording, alongside 29 healthy controls providing a normative reference. Results: Behaviorally, tPBM improved working memory K scores in the ADHD group, with performance closer to typical levels. Across sensor and source levels, tPBM progressively increased relative alpha power, steepened the aperiodic exponent, and enhanced neural complexity, as indexed by multiscale entropy, with widespread effects spanning frontoparietal and attention systems, extending to sensory and default-mode regions, collectively indicating a shift toward normative neural dynamics. Notably, these changes--consistent with rebalanced excitation\/inhibition dynamics--predict behavioral improvements in working memory. Conclusions: Together, our findings identify tPBM as a candidate approach for restoring excitation\/inhibition balance and normalizing large-scale neural dynamics in ADHD patients, providing a mechanistic foundation for its therapeutic potential.","rel_num_authors":8,"rel_authors":[{"author_name":"Xuye Yuan","author_inst":"Beijing Normal University"},{"author_name":"Yiyang Wang","author_inst":"Beijing Normal University"},{"author_name":"Chen Dang","author_inst":"Shanghai Jiao Tong University"},{"author_name":"Hongyu Liu","author_inst":"Peking University"},{"author_name":"Lili Yang","author_inst":"Beijing Normal University"},{"author_name":"Dongwei Li","author_inst":"Beijing Normal University"},{"author_name":"Li Sun","author_inst":"Peking University Sixth Hospital"},{"author_name":"Yan Song","author_inst":"Beijing Normal University"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Drought and Syphilis Exposure in Zambia, Uganda, and Tanzania: Are There Urban\/Rural Disparities?","rel_doi":"10.64898\/2026.08.31.26361827","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361827","rel_abs":"Eastern and southern Africa are highly affected by drought, and projections indicate that droughts will become more common in the coming decades. Whilst there has been research on the impact of drought on HIV, attributable to its effect on food insecurity and increased risky sexual behaviour, the link between drought and other sexually transmitted infections (STIs), particularly syphilis, remains largely unexplored. This study therefore assesses the association between drought and active syphilis, history of syphilis, and recovery from syphilis in Zambia, Uganda, and Tanzania, while examining disparities between urban and rural settings. It uses data on 75,225 people from Population-based HIV Impact Assessment surveys (2016-17), which include biomarker information, combined with rainfall data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset to define drought in the two years prior to the survey. Multivariate logistic regression models with country-level fixed effects show that in urban areas, drought was associated with a significant increase in the probability of having active syphilis (average marginal effects (AME) = 0.5%, 95% confidence interval (CI): 0.2%-1.0%) and of having ever had syphilis (AME = 2%, 95% CI: 0.8%-4%). No significant association was found in rural areas. Exposure to drought was not associated with recovery from syphilis in either setting, though confidence intervals were wide. Our results underscore the importance of strengthening healthcare systems to make them more resilient to drought shocks in urban areas, and the need to reinforce syphilis prevention and treatment programmes in those areas when such shocks occur.","rel_num_authors":7,"rel_authors":[{"author_name":"Arlette Simo Fotso","author_inst":"Institut National d'Etudes Demograpiques: INED"},{"author_name":"Charline Maltat Charline Maltat","author_inst":"Haute Ecole de Demographie"},{"author_name":"Baurice Gbaguidi-Sekpon","author_inst":"Haute Ecole de Demographie"},{"author_name":"Hakim Marzouk","author_inst":"Haute Ecole de Demographie"},{"author_name":"Adam Trickey","author_inst":"Population Health Sciences, University of Bristol"},{"author_name":"Andrea Low","author_inst":"Columbia University, Mailman School of Public Health"},{"author_name":"Valentine Becquet","author_inst":"Institut National d'Etudes Demographiques"}],"rel_date":"2026-09-04","rel_site":"medrxiv"},{"rel_title":"Direct measurement of PET hydrolase interfacial kinetics reveals catalysis-independent surface remodelling","rel_doi":"10.64898\/2026.09.02.749009","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749009","rel_abs":"How PET hydrolases engage solid plastic has been inferred almost entirely from soluble analogs and surfactant-stabilised nanoparticle suspensions, and reported affinities span orders of magnitude. Here we measure it directly, depositing thin amorphous PET films onto gold surface plasmon resonance chips and following enzyme binding to authentic polymer in real time. Four PET hydrolases, LCC, LCC-ICCG, IsPETase-EHA and TfCut2, all bind with nanomolar apparent affinity and surface residence half-lives of tens of minutes. LCC-ICCG dissociates at 4.0 x 10^-1, within twofold of its kcat measured on authentic PET, indicating that turnover is limited by disengagement rather than by ester hydrolysis. During association, we observe non-monophasic responses for these enzymes, including, for several, a signal fall while enzyme is still flowing over the surface. This decline persists in a catalytically inactivated variant, is absent in control proteins and a structurally homologous non-plastic degrading cutinase, and cannot arise from movement of the bound enzyme alone. The polymer surface must therefore be altered non-catalytically by PET hydrolase binding. The process runs at different rates for different enzymes on an identical film, is suppressed by dilute Triton X-100, and is modulated biphasically by PET degradation products. Altogether, we conclude that enzymatic PET depolymerisation involves a catalysis-independent chain-mobilisation step that soluble assays cannot report.","rel_num_authors":5,"rel_authors":[{"author_name":"Kailey J Petz","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"},{"author_name":"Arnaud Boudigou","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"},{"author_name":"Laura E Dickson","author_inst":"University of Ottawa, Department of Chemical and Biological Engineering"},{"author_name":"Benoit H Lessard","author_inst":"University of Ottawa, Department of Chemical and Biological Engineering"},{"author_name":"Adam M Damry","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Direct measurement of PET hydrolase interfacial kinetics reveals catalysis-independent surface remodelling","rel_doi":"10.64898\/2026.09.02.749009","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749009","rel_abs":"How PET hydrolases engage solid plastic has been inferred almost entirely from soluble analogs and surfactant-stabilised nanoparticle suspensions, and reported affinities span orders of magnitude. Here we measure it directly, depositing thin amorphous PET films onto gold surface plasmon resonance chips and following enzyme binding to authentic polymer in real time. Four PET hydrolases, LCC, LCC-ICCG, IsPETase-EHA and TfCut2, all bind with nanomolar apparent affinity and surface residence half-lives of tens of minutes. LCC-ICCG dissociates at 4.0 x 10^-1, within twofold of its kcat measured on authentic PET, indicating that turnover is limited by disengagement rather than by ester hydrolysis. During association, we observe non-monophasic responses for these enzymes, including, for several, a signal fall while enzyme is still flowing over the surface. This decline persists in a catalytically inactivated variant, is absent in control proteins and a structurally homologous non-plastic degrading cutinase, and cannot arise from movement of the bound enzyme alone. The polymer surface must therefore be altered non-catalytically by PET hydrolase binding. The process runs at different rates for different enzymes on an identical film, is suppressed by dilute Triton X-100, and is modulated biphasically by PET degradation products. Altogether, we conclude that enzymatic PET depolymerisation involves a catalysis-independent chain-mobilisation step that soluble assays cannot report.","rel_num_authors":5,"rel_authors":[{"author_name":"Kailey J Petz","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"},{"author_name":"Arnaud Boudigou","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"},{"author_name":"Laura E Dickson","author_inst":"University of Ottawa, Department of Chemical and Biological Engineering"},{"author_name":"Benoit H Lessard","author_inst":"University of Ottawa, Department of Chemical and Biological Engineering"},{"author_name":"Adam M Damry","author_inst":"University of Ottawa, Department of Chemistry and Biomolecular Sciences"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Air Pollution, Early Adversity, and Amygdala: Environmental Correlates of Psychopathology in Preadolescents","rel_doi":"10.64898\/2026.08.31.748438","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748438","rel_abs":"Early life adversity (ELA) and ambient fine particulate matter (PM2.5) are hypothesized to be environmental risk factors for altered brain structure and psychopathology, but their unique and interactive effects in childhood remain unclear. This study examines the interactive associations of ELA and annual average residential PM2.5 exposure (total mass and 15 components) on total amygdala and basolateral amygdala subregion volumes and psychopathology symptoms in a subset of children (N=3,601, 45% assigned female at birth, 9-10 years) from the Adolescent Brain Cognitive Development Study. Linear mixed-effects models, adjusting for sociodemographic factors, co-pollutants, and neuroimaging covariates, showed that ELA was associated with greater bifactor model-defined general, specific internalizing, and specific externalizing symptoms of psychopathology. PM2.5 moderated ELA associations with specific externalizing symptoms, with greater symptoms in those exposed to higher ELA and higher PM2.5 exposures. Among youth with higher ELA, smaller basolateral paralaminar volumes were linked to greater general and specific externalizing symptoms. These findings underscore the importance of considering psychosocial and physical environmental co-exposures when identifying children at risk for psychopathology.","rel_num_authors":11,"rel_authors":[{"author_name":"Amanda C Del Giacco","author_inst":"University of Southern California"},{"author_name":"Michael A Rosario","author_inst":"University of Southern California"},{"author_name":"Carlos Cardenas-Iniguez","author_inst":"University of Southern California"},{"author_name":"Nitya Chawla","author_inst":"Scripps College"},{"author_name":"Alan Wen","author_inst":"University of Southern California"},{"author_name":"Kirthana Sukumaran","author_inst":"University of Southern California"},{"author_name":"L. Nate Overholtzer","author_inst":"University of Southern California"},{"author_name":"Jiu-Chiuan Chen","author_inst":"University of Southern California"},{"author_name":"Benjamin B Lahey","author_inst":"University of Chicago"},{"author_name":"Tyler M Moore","author_inst":"University of Pennsylvania"},{"author_name":"Megan Herting","author_inst":"University of Southern California"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Pangenome alignment reveals global diversity and evolution of human centromeric regions","rel_doi":"10.64898\/2026.09.03.749043","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749043","rel_abs":"Centromeres play essential roles in chromosome segregation and genome stability, yet they remain among the least characterized regions of the human genome. Despite advances in long-read sequencing and complete genome assembly, the extreme repetitiveness and structural complexity of these regions still challenge population-scale analysis, obscuring their mutational dynamics. The Human Pangenome Reference Consortium has now accurately assembled over 6,000 centromeres, providing an opportunity to catalog global centromere variation. However, centromeric regions have been systematically excluded from pangenome alignments due to the technical challenge of aligning their highly repetitive tandem arrays and extreme structural variability. Here we introduce Centrolign, a graph-based multiple sequence alignment tool that combines a uniqueness-driven objective function with partial-order partial-order alignment to accurately align alpha satellite higher-order repeats. By prioritizing rare matches within tandem arrays and leveraging extended centromere-spanning haplotypes formed by suppressed recombination, Centrolign produces progressive multiple sequence alignments that preserve ancestral repeat organization. Applied across human centromeres, these alignments reveal the phylogenetic structure of similar satellite array haplotypes and enable precise estimation of variation rates, structural variant frequencies, and spatial patterns of mutation within satellite arrays. Integrating Centrolign graphs with repeat annotation tools and pangenome mapping algorithms allows accurate variant calling and genotyping from long reads without prior assembly. Moreover, we show that centromere haplotypes can be accurately subtyped with k-mers alone. Together, these advances establish a robust framework for incorporating centromeres into broader pangenomes, and population genomics in general, advancing our understanding of human genome evolution and diversity.","rel_num_authors":19,"rel_authors":[{"author_name":"Jordan Eizenga","author_inst":"University of California Santa Cruz"},{"author_name":"Mira Mastoras","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Julian Keith Lucas","author_inst":"UC Santa Cruz"},{"author_name":"Julian Menendez","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Faith Okamoto","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Glenn Hickey","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Prajna Hebbar","author_inst":"University of California Santa Cruz"},{"author_name":"Sasha A. Langley","author_inst":"Department of Evolution and Ecology, University of California, Davis, CA, USA"},{"author_name":"Hailey Loucks","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Fedor Ryabov","author_inst":"Centre for Biomedical Research and Technology, HSE University, Moscow, Russia; The Center for Bio- and Medical Technologies, Moscow, Russia"},{"author_name":"Yulia Zybina","author_inst":"UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA"},{"author_name":"Mobin Asri","author_inst":"UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA"},{"author_name":"J Matthew Franklin","author_inst":"Department of Genetics, Stanford University, Palo Alto, CA 94304, USA."},{"author_name":"Nicolas Altemose","author_inst":"Stanford University"},{"author_name":"- Human Pangenome Reference Consortium","author_inst":""},{"author_name":"Ivan A Alexandrov","author_inst":"Russian Academy of Medical Sciences"},{"author_name":"Charles H. Langley","author_inst":"University of California"},{"author_name":"Benedict Paten","author_inst":"UCSC"},{"author_name":"Karen H Miga","author_inst":"University of California, Santa Cruz"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Genome-wide evolutionary shifts shape the emergence of dominant circulating clones in Mycobacterium abscessus","rel_doi":"10.64898\/2026.09.03.749045","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.03.749045","rel_abs":"Mycobacterium abscessus (Mab) infections are increasingly associated with dominant circulating clones (DCCs), yet the evolutionary processes underlying their emergence remain poorly understood. Here, we analyzed 11,314 globally collected Mab genomes to investigate genome-wide evolutionary changes during the emergence of seven DCCs. We established a conservative core-genome analytical framework that integrates population-wide gene conservation with read-level validation to minimize the influence of assembly-derived variation on downstream evolutionary inference. Applying this framework to the global Mab population, we defined a stable core genome of 3,001 genes, representing a conservative lower bound across currently sequenced Mab populations. Core-genome analysis revealed a substantial decline in recombination relative to mutation following DCC expansion, indicating a broad shift towards mutation-dominated clonal evolution. Selective pressures also changed across this transition: 29 genes shifted from purifying to positive selection, consistent with continued adaptation during DCC expansion, whereas 19 showed the opposite pattern, suggesting increased functional constraint after clonal establishment. Although the accessory genes acquired differed among DCCs, gains consistently exceeded losses during DCC formation and showed functional convergence in environmental sensing, metabolism, metal homeostasis and stress responses. Together, these findings reveal consistent evolutionary shifts across independently emerged DCCs, with convergence occurring primarily in evolutionary processes and adaptive functions rather than through a single shared genetic determinant.","rel_num_authors":6,"rel_authors":[{"author_name":"Chendi Zhu","author_inst":"Beijing chest hospital"},{"author_name":"Yu Zhou","author_inst":"Beijing chest hospital"},{"author_name":"Mingxing Ni","author_inst":"Beijing chest hospital"},{"author_name":"Zhuofan Huang","author_inst":"Beijing chest hospital"},{"author_name":"Zhenyu Wang","author_inst":"Beijing chest hospital"},{"author_name":"Weimin Li","author_inst":"Beijing chest hospital"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Evaluating performance bias in face-to-BMI vision transformer models across diverse human populations","rel_doi":"10.64898\/2026.09.02.748815","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748815","rel_abs":"Computer vision models that estimate body mass index (BMI) from facial features offer a non-invasive, low-cost alternative to physical measurement, with uses in telemedicine, emergency care where a scale or measuring tools arent available, automated self-monitoring, and large-scale epidemiological research. Most of these models, however, are trained on government records, social media images, and celebrity photographs, sources that introduce dataset biases and fail to represent the general public. This study tests how well a face-to-BMI machine learning model generalizes across populations, specifically how morphological diversity and population-specific training data affect cross-cultural accuracy. We trained and evaluated Vision Transformer (ViT-H\/14) models on paired BMI measurements and facial photographs from four Indigenous populations: the Orang Asli of Malaysia, the Ju\/hoansi of Southern Africa, the Sama residing in the Philippines, and the Tsimane of Bolivia. To evaluate how training data composition affects predictions, we compared four training strategies, from single-population models (focal models) to models trained on the full combined global dataset (global models). In-distribution training always produced the best performance. Models exposed to a target populations morphology, whether focal or global, consistently predicted BMI most accurately for that population. But when a target population differed from the training sample, adding more cross-cultural variation to training improved out-of-distribution predictions. Therefore, training on a populations own data works best when that data exists, and training on data spanning a wide range of human morphology is the strongest fallback when it doesnt. These findings suggest that while target population training data produces the most accurate results, training on datasets that capture global morphological variation substantially improves performance in unrepresented populations. Broader diversity in training data is essential for developing machine learning health tools that generalize reliably across human populations.","rel_num_authors":17,"rel_authors":[{"author_name":"Jordie Hoffman","author_inst":"U of U: University of Utah"},{"author_name":"Michael Gurven","author_inst":"UC Santa Barbara: University of California Santa Barbara"},{"author_name":"Hillard Kaplan","author_inst":"Chapman University"},{"author_name":"Jonathan Stieglitz","author_inst":"Toulouse School of Economics Recherche"},{"author_name":"Benjamin  C. Trumble","author_inst":"Arizona State University"},{"author_name":"Bret Beheim","author_inst":"Max-Planck-Institute for Evolutionary Anthropology: Max-Planck-Institut fur evolutionare Anthropologie"},{"author_name":"Paul  L. Hooper","author_inst":"University of New Mexico - Albuquerque: The University of New Mexico"},{"author_name":"Richard  B. Lee","author_inst":"University of Toronto"},{"author_name":"Julia  R. Phelps","author_inst":"Arizona State University"},{"author_name":"Kim Hill","author_inst":"Arizona State University"},{"author_name":"Brian  F. Codding","author_inst":"University of California Santa Barbara"},{"author_name":"Simon Brewer","author_inst":"U of U: University of Utah"},{"author_name":"Yvonne  A. L. Lim","author_inst":"Universiti Malaya"},{"author_name":"Amanda  J. Lea","author_inst":"Vanderbilt University"},{"author_name":"Ian  J. Wallace","author_inst":"University of New Mexico - Albuquerque: The University of New Mexico"},{"author_name":"Vivek  V. Venkataraman","author_inst":"University of Calgary"},{"author_name":"Thomas  S. Kraft","author_inst":"University of Utah"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Long-range linkage maintained male-specific loci on a young Y chromosome prior to recombination shutdown","rel_doi":"10.64898\/2026.08.31.748364","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748364","rel_abs":"Suppression of recombination on the Y chromosome maintains linkage between male-specific loci and is commonly established by inversions. Here, we describe a young, inversion-free Y chromosome (neo-Y) in Drosophila albomicans with a unique history and paradoxical signature of exchange. Prior to recombination shutdown in males, it repeatedly recombined with the X-linked counterpart (neo-X) but at the same time preserved complete long-range linkage of the chromosome ends. By assembling multiple neo-Ys chromosomes and QTL-mapping, we show that double crossovers maintained linkage between the male-sex determining region at one end to sexually antagonistic alleles and a locus essential for spermatogenesis at the other. We argue that such long-range linkage of distal sex-specific loci disfavors inversions but instead encourages the emergence of achiasmy.","rel_num_authors":5,"rel_authors":[{"author_name":"May Wang","author_inst":"University of British Columbia"},{"author_name":"Mohammadebrahim Akhavizadegan","author_inst":"University of British Columbia"},{"author_name":"Jen-Yu Wang","author_inst":"University of California - Irvine"},{"author_name":"Ching-Ho Chang","author_inst":"Academia Sinica"},{"author_name":"Kevin H-C Wei","author_inst":"University of British Columbia"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Unmasking the large and highly repetitive genome of Phlox reveals a complex evolutionary history of speciation with gene flow","rel_doi":"10.64898\/2026.09.01.748350","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748350","rel_abs":"The large size and high repetitive content of many plant genomes have hindered elucidation of how the fundamental processes of evolution give rise to speciation. Here we present chromosome-level assemblies of 6 gigabase genomes for four closely related Phlox wildflower species. We generate extensive population genetic data for the three well-studied annual species P. drummondii, P. cuspidata, and P. roemeriana, including structural variation from whole-genome long-read resequencing data. The whole-genome assemblies reveal extensive differences in amount and distribution of genetic variation within and between species, reflective of differences in life-history strategies, mating systems, and edaphic specialization. The population genetic data exposes a history of widespread and consistent gene flow between all three annual Phlox species throughout their divergence and speciation. Our unmasking of structural variants and repetitive elements exposes rich and dynamic forms of genetic variation that show strong phylogenetic signal and pervasive patterns of gene flow. Our results lay a foundation for untangling the highly complex genomes of plants to make advancements in our understanding of the processes of speciation and divergence.","rel_num_authors":7,"rel_authors":[{"author_name":"Felix L Wu","author_inst":"Harvard University"},{"author_name":"Danielle E Khost","author_inst":"Harvard University"},{"author_name":"Patrick F McKenzie","author_inst":"Harvard University"},{"author_name":"Samridhi Chaturvedi","author_inst":"Tulane University"},{"author_name":"Grace A Burgin","author_inst":"Colgate University"},{"author_name":"Timothy B Sackton","author_inst":"Harvard University"},{"author_name":"Robin Hopkins","author_inst":"Harvard University"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Overlapping flower and pollen production in two imperiled pitcher plants raises conservation challenges","rel_doi":"10.64898\/2026.09.02.748929","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748929","rel_abs":"Balancing sexual reproduction with vegetative growth poses challenges for many plants. Additionally, reproductive effort, such as production of pollen or flowers, is not always correlated with numbers of viable and germinable seeds. Energetic tradeoffs like these can be particularly fraught for imperiled species and might be especially complicated in landscapes with a possibility of interspecific hybridization. In this multi-year study, we track the reproductive effort, flowering phenology, and reproductive output of two interfertile Sarracenia (pitcher plants). We show that reproductive effort varies among years and sites, and that production of pollen and flowers does not always result in higher seed output. We demonstrate that phenological overlap in the timing of flower production and pollen viability could enable hybridization, and that the more imperiled taxon produces fewer seeds per flower. Results have implications for conservation of these pitcher plants, management of hybridization in these and other systems, and general principles of reproductive allocation.","rel_num_authors":13,"rel_authors":[{"author_name":"Rebecca  E. Hale","author_inst":"University of North Carolina at Asheville"},{"author_name":"Caroline Kennedy","author_inst":"University of North Carolina Asheville"},{"author_name":"Wayne Morgan","author_inst":"NCSU: North Carolina State University"},{"author_name":"Todd Brasseur","author_inst":"University of North Carolina Asheville"},{"author_name":"Elizabeth Companion","author_inst":"University of North Carolina Asheville"},{"author_name":"William Gay","author_inst":"University of North Carolina Asheville"},{"author_name":"Kristen Hillegass","author_inst":"University of Kentucky"},{"author_name":"Alyssa Lynch","author_inst":"NCSU CALS: NC State University College of Agriculture and Life Sciences"},{"author_name":"Michelle Paredes","author_inst":"University of North Carolina Asheville"},{"author_name":"Gabi Parker","author_inst":"University of North Carolina Asheville"},{"author_name":"Lila Uzell","author_inst":"Friends of Virgin Islands National Park"},{"author_name":"Mars Zappia","author_inst":"University of North Carolina Asheville"},{"author_name":"Jennifer Rhode Ward","author_inst":"University of North Carolina Asheville"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"\u03b12\u03b4-2 mediates coupling of presynaptic calcium entry to vesicle release in hippocampal parvalbumin-expressing interneurons","rel_doi":"10.64898\/2026.08.31.748347","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748347","rel_abs":"The 2{delta} family of auxiliary voltage-gated calcium channel (VGCC) subunits have critical but incompletely understood roles in brain function. Parvalbumin-positive (PV+) interneurons in the hippocampus highly express the 2{delta}-2 isoform, and mice lacking 2{delta}-2 exhibit spontaneous seizures. Thus, we examined PV+ neuron-mediated synaptic inhibition in acutely prepared brain slices from 2{delta}-2 knockout (KO) mice. In the inner molecular layer of the dentate gyrus, 2{delta}-2 KO mice demonstrated an increase in the excitation\/inhibition ratio of synaptic inputs onto granule cells. We then used optogenetics to activate PV+ interneurons, which produced dramatically smaller inhibitory synaptic currents in granule cells from 2{delta}-2 KO mice. There was a reduction in PV+ inputs onto granule cells as determined by immunostaining. Functionally, these inputs had a lower probability of GABA release and a decreased readily releasable pool of vesicles compared to littermate controls. VGCC coupling to presynaptic vesicle release was also reduced in dentate gyrus PV+ cells in 2{delta}-2 KO mice, based on manipulations of intracellular and extracellular calcium. Together, our data indicate that 2{delta}-2 plays a critical role in PV+ interneuron-mediated synaptic inhibition, which may contribute to seizures in 2{delta}-2 mutant mice.","rel_num_authors":4,"rel_authors":[{"author_name":"Allison J Ellingson","author_inst":"Oregon Health & Science University"},{"author_name":"Emma C Jerome","author_inst":"Oregon Health& Science University"},{"author_name":"Ashlynn A Gallagher","author_inst":"Oregon Health & Science University"},{"author_name":"Eric Schnell","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation","rel_doi":"10.64898\/2026.08.31.748303","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748303","rel_abs":"Small interfering RNAs (siRNAs) are short guide RNAs that recruit the RNA-induced silencing complex (RISC) to complementary target sites on messenger RNAs (mRNAs), triggering Ago2-mediated cleavage and gene silencing. siRNA design requires compact candidate sets that cover a target while preserving efficacy, specificity, and practical sequence constraints. Existing pipelines usually enumerate candidate windows, assign a canonical guide to each window, and then rank preconstructed siRNA--mRNA pairs. This has produced strong pairwise efficacy predictors, but leaves a candidate-construction gap: candidate positions and guide sequences are fixed before the model begins to rank them. We address this gap by decomposing siRNA candidate construction into two generative decisions: where to place candidates within an mRNA segment, and what constrained guide variants to consider at a candidate position. We instantiate this framework as siProGenA, using a Discrete Denoising Diffusion Probabilistic Model (D3PM) for mRNA-conditioned position proposal and a Bayesian Flow Network (BFN) for temperature-controlled guide generation. On 62 positive test segments, the diversity-aware final library reaches Hit@1 = 0.790 and Hit@5 = 0.903. In a measured-site controlled Stage~2 evaluation, seed- and cleavage-preserving variants outscore the canonical complement for 89.8% of measured sites, with supporting gains across additional computational scorers, random-mismatch controls, and biophysical diagnostics. Together, the results support a modular proposal--generation view of siRNA candidate construction for prioritizing compact candidate sets.","rel_num_authors":6,"rel_authors":[{"author_name":"Zhiqi Ma","author_inst":"The Chinese University of Hong Kong\uff0cShenzhen"},{"author_name":"Jiale Zhou","author_inst":"School of Engineering, Westlake University"},{"author_name":"Rubo Wang","author_inst":"Shanghai Artificial Intelligence Laboratory"},{"author_name":"Zhipeng Deng","author_inst":"School of Engineering, Westlake University"},{"author_name":"Zhijian Wu","author_inst":"School of Engineering, Westlake University"},{"author_name":"Yefeng Zheng","author_inst":"School of Engineering, Westlake University"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"BROOQS: Spectral Methods Resolve Level-1 Hybridization Cycles without Tests of Symmetry","rel_doi":"10.64898\/2026.08.31.748319","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748319","rel_abs":"Modern phylogenomic analyses often seek to reconstruct both vertical and reticulate evolutionary histories. While the prevalence of non-vertical evolution is increasingly appreciated, inferring networks remains conceptually challenging and computationally demanding. Following the success of quartet-based methods for handling gene tree discordance, several quartet-based network inference methods have been developed. A key insight of these methods is that level-1 networks can be constructed by first building a multifurcating tree called tree-of-blobs and then resolving each polytomy into a cycle. This two-step approach makes the problem easier both conceptually and computationally. However, these quartet-based methods often rely on noisy statistical tests of asymmetry in quartet frequencies. Moreover, they either enumerate all quartets, losing some scalability, or subsample them, losing information. We introduce BROOQS, a quartet-based method for resolving trees of blobs into a level-1 phylogenetic network. BROOQS efficiently aggregates information from all quartets around a blob without enumerating them, builds a pairwise similarity matrix, and uses robust spectral ordering algorithms to recover the cyclic ordering without relying on individual quartet symmetry tests. We prove theoretically that our spectral method is consistent under the network multi-species coalescent (NMSC) model. Across simulated and empirical datasets, BROOQS consistently improves accuracy and scalability compared to existing methods and extends to thousands of taxa.","rel_num_authors":2,"rel_authors":[{"author_name":"Shayesteh Arasti","author_inst":"University of California San Diego"},{"author_name":"Siavash Mirarab","author_inst":"University of California San Diego"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Alpha-linolenic acid and dietary protein minimally but differentially regulate white adipose tissue lipolysis in female mice fed moderate-fat diets","rel_doi":"10.64898\/2026.08.31.748352","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748352","rel_abs":"Omega-3 polyunsaturated fatty acids (n-3 PUFA) influence white adipose tissue (WAT) lipid buffering capacity; however, sex-specific regulation remains understudied. We recently reported that male mice fed a diet containing high alpha-linolenic acid (ALA) had increased WAT mass, reduced serum triglycerides and elevated lipolysis compared to mice fed a diet containing recommended levels of ALA, independent of background dietary protein. The current study examined whether subcutaneous and visceral WAT (scWAT, vWAT) lipolytic activity was altered in female C57BL\/6N mice (n=16\/group) fed low-ALA (1% energy) or high-ALA (3% energy) diets containing skim milk protein (SMP) or soy protein isolate (SPI) for 8 weeks. Body weight, WAT depot weights and serum triglycerides were unchanged in response to ALA content. Lipolytic markers were mostly unchanged by ALA content except for an increase in adipose triglyceride lipase (ATGL) in vWAT, while diets containing SPI modestly reduced serum cholesterol levels and increased total hormone-sensitive lipase (HSL) content in vWAT. Collectively, WAT lipolytic markers in female mice showed minimal response to diets containing high ALA content, unlike that previously reported in male mice. These results highlight the importance of considering both sexes to ensure generalizability of findings when investigating diet regulation of WAT lipid metabolism.","rel_num_authors":7,"rel_authors":[{"author_name":"Mathieu J Clavet","author_inst":"University of Guelph"},{"author_name":"Siobhan E Woods","author_inst":"University of Guelph"},{"author_name":"Melissa Gonzalez-Soto","author_inst":"University of Guelph"},{"author_name":"Alexa N King","author_inst":"University of Guelph"},{"author_name":"Frederic Capel","author_inst":"INRAE-UCA"},{"author_name":"David C Wright","author_inst":"University of British Columbia"},{"author_name":"David M Mutch","author_inst":"University of Guelph"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Recurrent beta-gamma interactions between olfactory bulb and piriform cortex support cross-sniff perceptual continuity in humans","rel_doi":"10.64898\/2026.08.31.747807","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.747807","rel_abs":"Olfactory perception relies on active sampling, where each inhalation delivers a discrete packet of sensory input. In humans, successive inhalations can be separated by several seconds, yet odors are perceived as continuous and stable. How the brain maintains this perceptual continuity across temporally discrete samples remains unknown. Here, we used electrobulbogram (EBG) recordings in 48 participants to demonstrate that successive sniffs are not processed independently but are linked through recurrent oscillatory dynamics between the olfactory bulb (OB) and piriform cortex (PC). Specifically, first-sniff alpha\/beta activity in the OB and OB-PC functional connectivity predicted second-sniff gamma power, while transfer entropy indicated a directional temporal dependence from first-sniff alpha\/beta to second-sniff gamma dynamics. In parallel, second-sniff gamma activity selectively tracked subjective odor valence prior to inhalation onset. At the network level, the PC exhibited stronger alpha\/beta-band connectivity with orbitofrontal, insular, and prefrontal regions during the first sniff than during the second, suggesting that early evaluative processing provides a contextual signal that is carried forward to shape subsequent sensory representations. These results demonstrate that the human OB-PC circuit carries evaluative information across inhalations through directed alpha\/beta-to-gamma interactions, providing a mechanism for maintaining perceptual continuity in a sensory system defined by temporally discrete sampling.","rel_num_authors":6,"rel_authors":[{"author_name":"Frans Nord\u00e9n","author_inst":"Karolinska Institutet"},{"author_name":"Anja L Winter","author_inst":"Karolinska Institutet"},{"author_name":"Leslie M Kay","author_inst":"The University of Chicago"},{"author_name":"Artin Arshamian","author_inst":"Karolinska Institutet"},{"author_name":"Mikael Lundqvist","author_inst":"Karolinska Institutet"},{"author_name":"Johan N Lundstr\u00f6m","author_inst":"Karolinska Institutet"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Linking coronary microvascular structure and function in preclinical models of coronary microvascular disease","rel_doi":"10.64898\/2026.08.31.748256","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748256","rel_abs":"Despite growing awareness of the importance of the coronary microvasculature in cardiac health and disease, Coronary Microvascular Disease (CMVD) remains poorly understood, underdiagnosed, and without targeted therapies. Preclinical models and quantitative tools to measure CMVD are needed. Here, we use two novel quantitative methods to assess coronary microvascular structure (multi-fractal spectrum analysis) and function (Single Photon Emission Computed Tomography (SPECT)-based intramyocardial blood volume (IMBV) imaging) in mouse models of CMVD. Change in IMBV ({bigtriangleup}IMBV), which serves as a quantitative measure of vasodilatory capacity or microvascular function, was reduced with aging, driven by a significant decrease in female mice. Male mice on ApoE-\/- background, fed a high-fat diet (HFD) for 6 months, or both had significantly reduced {bigtriangleup}IMBV. We used immunofluorescence to assess both traditional capillary density and global branching structure and vessel heterogeneity using multifractal spectrum analysis. Both were significantly reduced in all groups, and linear regression modeling showed that they were independently associated with {bigtriangleup}IMBV. Finally, we used {bigtriangleup}IMBV to assess the effects of widely used control Adeno-associated viral (AAV) vectors on coronary microvascular function. AAV-overexpression of GFP did not affect function, but Cre-recombinase compromised coronary microvascular structure and function by 16 weeks. In summary, quantitative assessments of coronary microvascular structure and function highlight changes consistent with CMVD seen with aging, female sex, and metabolic insults. Functional changes are partially driven, but not fully defined, by changes in the underlying structure, highlighting the important and incomplete link between structure and function.","rel_num_authors":10,"rel_authors":[{"author_name":"Mansi B. Kumar","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Varun Kanangat","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Matthew Woods","author_inst":"San Diego State University"},{"author_name":"Octavio Lopez","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Li Li","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Eric Blankemeyer","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Donna M. Conlon","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Uduak George","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Scott D. Metzler","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Marie A. Guerraty","author_inst":"University of Pennsylvania Perelman School of Medicine"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"CAMSAP3 loss of function models suggest causative role in generalized genetic epilepsy","rel_doi":"10.64898\/2026.09.01.744686","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.744686","rel_abs":"Advancements in next generation sequencing have led to the discovery of hundreds of human epilepsy gene associations. Newly associated genes require functional validation to establish causation and to inform patient treatment in the clinic. A recent exome trio analysis identified predicted pathogenic variants in two patients with generalized epilepsy in the gene CAMSAP3. CAMSAP3 regulates non-centrosomal microtubule dynamics, and the acetylation necessary for normal axonal differentiation and migration. We show that overexpression of patient variants leads to protein degradation and dysregulation of microtubule acetylation in cultured HEK cells. Camsap3 knockout zebrafish also exhibit increased axonal microtubule acetylation as well as epileptic features such as seizure-like swimming behaviors, aberrant inhibitory interneuron development and epileptiform via local field potential. Together these data suggest that CAMSAP3 plays an important role in generalized genetic epilepsy.","rel_num_authors":10,"rel_authors":[{"author_name":"Christopher Mark LaCoursiere","author_inst":"Boston Childrens Hospital"},{"author_name":"Zachary Stayn","author_inst":"Harvard University"},{"author_name":"Hannah Hepner","author_inst":"Harvard University"},{"author_name":"Sneham Tiwari","author_inst":"Boston Children's Hospital"},{"author_name":"Joseph Pascucci","author_inst":"Boston Children's Hospital"},{"author_name":"Chariton Moschopoulos","author_inst":"Boston Children's Hospital"},{"author_name":"Lacey Smith","author_inst":"Boston Children's Hospital"},{"author_name":"Hyun Yong Koh","author_inst":"Baylor College of Medicine"},{"author_name":"Parul Chaudhary","author_inst":"Boston Children's Hospital"},{"author_name":"Annapurna Poduri","author_inst":"Boston Children's Hospital"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs","rel_doi":"10.64898\/2026.08.31.748296","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748296","rel_abs":"Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2 enriched, and two basal associated clusters. The basal clusters separated into an immune rich region marked by T cell-inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer.","rel_num_authors":11,"rel_authors":[{"author_name":"Sonali Arora","author_inst":"FHCRC"},{"author_name":"Ramya Suresh","author_inst":"fred hutch cancer center"},{"author_name":"Nik Holland","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Gregory Glatzer","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Matt Jensen","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Eric Q Konnick","author_inst":"University of Washington"},{"author_name":"Colin Pritchard","author_inst":"Univ. Washington"},{"author_name":"Yi Li","author_inst":"Baylor College of Medicine"},{"author_name":"Heather A Parsons","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Sara A Hurvitz","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Eric C Holland","author_inst":"Fred Hutch Cancer Center"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs","rel_doi":"10.64898\/2026.08.31.748296","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748296","rel_abs":"Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2 enriched, and two basal associated clusters. The basal clusters separated into an immune rich region marked by T cell-inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer.","rel_num_authors":11,"rel_authors":[{"author_name":"Sonali Arora","author_inst":"FHCRC"},{"author_name":"Ramya Suresh","author_inst":"fred hutch cancer center"},{"author_name":"Nik Holland","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Gregory Glatzer","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Matt Jensen","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Eric Q Konnick","author_inst":"University of Washington"},{"author_name":"Colin Pritchard","author_inst":"Univ. Washington"},{"author_name":"Yi Li","author_inst":"Baylor College of Medicine"},{"author_name":"Heather A Parsons","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Sara A Hurvitz","author_inst":"Fred Hutch Cancer Center"},{"author_name":"Eric C Holland","author_inst":"Fred Hutch Cancer Center"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"A Compendium of 49 Experimental SBS Signatures for Decoding Human Cancer Mutational Processes","rel_doi":"10.64898\/2026.08.31.748400","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748400","rel_abs":"Human cancer genomes harbor distinct mutational patterns that reflect past processes of DNA damage and repair. However, the precise attribution of these signatures to specific chemical carcinogens lacks a standardized experimental reference framework. To address this gap, we curated 4,282 genome-wide sequencing datasets from 42 model systems across five species exposed to 146 cancer-risk agents. This platform yielded 49 robust experimental single-base substitution signatures (eSS), with 28 matching 19 established COSMIC signatures and 21 defining novel mutational processes. We reconstructed 24 COSMIC signatures, assigning candidate etiologies to five signatures of unknown origin and revising two contested assignments. Pan-cancer decomposition detected four eSS-like mutational processes enriched in smokers across 4,951 tumors. Lastly, independent single-molecule sequencing of primary human organoids reproduced these profiles with high fidelity, confirming true platform-independent biological reproducibility across complex human models. This eSS repertoire provides a reference that links human mutational processes to mechanistic classes of DNA damage.","rel_num_authors":8,"rel_authors":[{"author_name":"Maria Zhivagui","author_inst":"University of Nevada Las Vegas"},{"author_name":"Jessica N Au","author_inst":"UC San Diego"},{"author_name":"Sanskruti Sharma","author_inst":"University of Nevada, Las Vegas"},{"author_name":"Peter T. Nguyen","author_inst":"University of Nevada, Las Vegas"},{"author_name":"Shams Al-Azzam","author_inst":"UC San Diego"},{"author_name":"Jiang Zhang","author_inst":"UC San Diego"},{"author_name":"Mark Barnes","author_inst":"UC San Diego"},{"author_name":"Ludmil B Alexandrov","author_inst":"UC San Diego"}],"rel_date":"2026-09-04","rel_site":"biorxiv"},{"rel_title":"Effects of collaborative clinical visit agenda-setting interventions: A systematic review and meta-analysis","rel_doi":"10.64898\/2026.08.30.26361729","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.30.26361729","rel_abs":"BackgroundAgenda-setting is a fundamental patient-centered communication practice in which a clinician works with a patient to elicit, propose, and organize topics for discussion during a clinical encounter. Various agenda-setting interventions have been developed, including patient-facing tools and clinician training, but their effects have not been systematically evaluated. We aimed to determine the effects of these interventions on encounter, patient, care partner, and clinician outcomes.\n\nMethodsWe searched grey literature and seven databases, including PubMed, from inception through July 2025 for randomized and non-randomized comparative studies of interventions designed to promote or improve clinical visit agenda-setting. Two reviewers independently screened articles and extracted data, with a third reviewer resolving conflicts. We assessed risk of bias using RoB 2 for randomized studies and ROBINS-I for non-randomized studies. We conducted random effects meta-analyses when outcomes were sufficiently comparable, assessed heterogeneity using I2, and rated certainty of evidence using GRADE. Post hoc exploratory subgroup analyses examined study design, adjustment status, and intervention structure.\n\nResultsTwenty-nine articles describing 22 unique studies met the inclusion criteria, including 13 randomized and nine non-randomized studies. Agenda-setting interventions increased the occurrence of agenda-setting (risk ratio 5.43, 95% confidence interval (CI) 2.06 to 14.28, I2=34.6%) and favored the intervention for concerns addressed when measured as a continuous outcome (standardized mean difference (SMD) 0.37, 95% CI 0.16 to 0.57, I2=65.3%) and overall clinician satisfaction (SMD 0.50, 95% CI 0.23 to 0.78, I2=0.0%). There were no clear differences in the number of concerns raised (mean difference (MD) 0.21, 95% CI -0.19 to 0.61, I2=59.6%), visit duration (MD 0.64 minutes, 95% CI -0.83 to 2.12, I2=51.4%), or overall patient satisfaction (SMD 0.05, 95% CI -0.05 to 0.15, I2=47.0%). Potentially important heterogeneity was present for four of these six outcomes. Post hoc exploratory subgroup analyses did not provide clear evidence that effects varied by study design, adjustment status, or intervention structure. Risk of bias was often high, serious, or critical, and certainty of evidence was low or very low for all pooled outcomes.\n\nConclusionsTo our knowledge, this is the first comprehensive synthesis of clinical visit agenda-setting interventions. Such interventions may increase the occurrence of agenda-setting and the extent to which patient concerns are addressed without increasing visit length. However, the certainty of evidence was low or very low, and the available evidence does not establish a superior intervention structure.","rel_num_authors":14,"rel_authors":[{"author_name":"Ailyn Sierpe","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Renata W. Yen","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Annika Milliman","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Elizabeth Cady","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Boyoung Ahn","author_inst":"The Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD 21205, United States"},{"author_name":"Anne E. Dade","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Anna Marie Devito","author_inst":"Hartford HealthCare Cancer Institute, Hartford HealthCare, 195 Retreat Ave, Hartford, CT 06103, United States"},{"author_name":"Bradley A. Eckert","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Vismaya V. Gopalan","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Stephanie C. Krasinski","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Meredith A. MacMartin","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Sophia G. Musacchio","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Jingyi Zhang","author_inst":"University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States"},{"author_name":"Catherine H. Saunders","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Relation of Self-Reported Race and Genetic Ancestry to Hypertension Prevalence Among Hispanics\/Latinos: The Hispanic Community Health Study\/Study of Latinos","rel_doi":"10.64898\/2026.09.01.26361995","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361995","rel_abs":"Background. The imprecision of current metrics to capture the complex genetic admixture and racial identity among Hispanic\/Latino individuals in the United States [US] is a concern. We examined the relationship of self-reported race and genetic ancestry with hypertension [HTN] among Hispanics\/Latinos. Methods. Cross-sectional study of the Hispanic Community Health Study\/Study of Latinos (HCHS\/SOL), including 10,586 Hispanic\/Latino unrelated adults. Genetic ancestry: West African [AA], Amerindian [AI], and European [EA]. Self-reported race: White, Black, Native American, or Multiple\/Missing (More than one race or Unknown\/Not reported\/Refused). HTN: systolic (SBP) [&ge;]130 mmHg, diastolic blood pressure (DBP) [&ge;]80 mmHg, and\/or use of HTN medications. Age- and sex adjusted models were used. Results. Self-reported race was White (38{middle dot}6%), Black (3{middle dot}6%), Native American (4{middle dot}1%), and Multiple\/Missing (53{middle dot}7%), with Unknown\/Not reported\/Refused representing 32{middle dot}7%. Black and White Hispanics\/Latinos had the greatest AA (55{middle dot}7%) and EA (69{middle dot}3%) ancestries, respectively. Each 10% AA increase was associated with OR 1{middle dot}15, SBP beta +0{middle dot}9 mmHg, and DBP beta +0{middle dot}7 mmHg. Conversely, each 10% AI increase was associated with OR 0{middle dot}83, SBP beta -0{middle dot}4 mmHg, and DBP beta -0{middle dot}6 mmHg. HTN prevalence was highest among those with Black race or in the highest AA quantile (45{middle dot}6% and 48{middle dot}0%, respectively), and lowest among those with Native American race or in the highest AI quantile (37{middle dot}6% and 26{middle dot}7%, respectively). Conclusion. One-third of Hispanics\/Latinos did not self-report race. Black or White self-reporting race did somewhat relate to AA or EA ancestry, respectively. HTN profiles were related to self-reported race and genetic ancestry in this admixed population.","rel_num_authors":16,"rel_authors":[{"author_name":"Raul Antonio Montanez-Valverde","author_inst":"Montefiore Medical Group"},{"author_name":"Vivian Kim","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Priscilla Duran-Luciano","author_inst":"Albert Einstein College of Medicine Department of Medicine"},{"author_name":"Yawen Yuan","author_inst":"Albert Einstein College of Medicine\/Montefiore Medical Center"},{"author_name":"Tamar Sofer","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Robert C. Kaplan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Linda C Gallo","author_inst":"San Diego State University"},{"author_name":"Gregory A. Talavera","author_inst":"San Diego State University KPBS"},{"author_name":"Krista M Perreira","author_inst":"The University of North Carolina at Chapel Hill Innovate Carolina"},{"author_name":"Martha L. Daviglus","author_inst":"Institute for Minority Health Research, University of Illinois-Chicago"},{"author_name":"Sylvia E. Rosas","author_inst":"Joslin Diabetes Center"},{"author_name":"Maria M. Llabre","author_inst":"University of Miami"},{"author_name":"Tali Elfassy","author_inst":"University of Miami Miller School of Medicine"},{"author_name":"Xihao Li","author_inst":"UNC Gillings School of Global Public Health"},{"author_name":"Carmen R. Isasi","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Carlos Jose Rodriguez","author_inst":"Albert Einstein College of Medicine and Montefiore Medical Center"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Relation of Self-Reported Race and Genetic Ancestry to Hypertension Prevalence Among Hispanics\/Latinos: The Hispanic Community Health Study\/Study of Latinos","rel_doi":"10.64898\/2026.09.01.26361995","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361995","rel_abs":"Background. The imprecision of current metrics to capture the complex genetic admixture and racial identity among Hispanic\/Latino individuals in the United States [US] is a concern. We examined the relationship of self-reported race and genetic ancestry with hypertension [HTN] among Hispanics\/Latinos. Methods. Cross-sectional study of the Hispanic Community Health Study\/Study of Latinos (HCHS\/SOL), including 10,586 Hispanic\/Latino unrelated adults. Genetic ancestry: West African [AA], Amerindian [AI], and European [EA]. Self-reported race: White, Black, Native American, or Multiple\/Missing (More than one race or Unknown\/Not reported\/Refused). HTN: systolic (SBP) [&ge;]130 mmHg, diastolic blood pressure (DBP) [&ge;]80 mmHg, and\/or use of HTN medications. Age- and sex adjusted models were used. Results. Self-reported race was White (38{middle dot}6%), Black (3{middle dot}6%), Native American (4{middle dot}1%), and Multiple\/Missing (53{middle dot}7%), with Unknown\/Not reported\/Refused representing 32{middle dot}7%. Black and White Hispanics\/Latinos had the greatest AA (55{middle dot}7%) and EA (69{middle dot}3%) ancestries, respectively. Each 10% AA increase was associated with OR 1{middle dot}15, SBP beta +0{middle dot}9 mmHg, and DBP beta +0{middle dot}7 mmHg. Conversely, each 10% AI increase was associated with OR 0{middle dot}83, SBP beta -0{middle dot}4 mmHg, and DBP beta -0{middle dot}6 mmHg. HTN prevalence was highest among those with Black race or in the highest AA quantile (45{middle dot}6% and 48{middle dot}0%, respectively), and lowest among those with Native American race or in the highest AI quantile (37{middle dot}6% and 26{middle dot}7%, respectively). Conclusion. One-third of Hispanics\/Latinos did not self-report race. Black or White self-reporting race did somewhat relate to AA or EA ancestry, respectively. HTN profiles were related to self-reported race and genetic ancestry in this admixed population.","rel_num_authors":16,"rel_authors":[{"author_name":"Raul Antonio Montanez-Valverde","author_inst":"Montefiore Medical Group"},{"author_name":"Vivian Kim","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Priscilla Duran-Luciano","author_inst":"Albert Einstein College of Medicine Department of Medicine"},{"author_name":"Yawen Yuan","author_inst":"Albert Einstein College of Medicine\/Montefiore Medical Center"},{"author_name":"Tamar Sofer","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Robert C. Kaplan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Linda C Gallo","author_inst":"San Diego State University"},{"author_name":"Gregory A. Talavera","author_inst":"San Diego State University KPBS"},{"author_name":"Krista M Perreira","author_inst":"The University of North Carolina at Chapel Hill Innovate Carolina"},{"author_name":"Martha L. Daviglus","author_inst":"Institute for Minority Health Research, University of Illinois-Chicago"},{"author_name":"Sylvia E. Rosas","author_inst":"Joslin Diabetes Center"},{"author_name":"Maria M. Llabre","author_inst":"University of Miami"},{"author_name":"Tali Elfassy","author_inst":"University of Miami Miller School of Medicine"},{"author_name":"Xihao Li","author_inst":"UNC Gillings School of Global Public Health"},{"author_name":"Carmen R. Isasi","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Carlos Jose Rodriguez","author_inst":"Albert Einstein College of Medicine and Montefiore Medical Center"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Proteoform-resolved neoGFAP as a diagnostic and prognostic biomarker across the TBI--MCI--AD continuum in Veterans","rel_doi":"10.64898\/2026.09.01.26361845","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361845","rel_abs":"Service members with traumatic brain injury are at approximately two- to four-fold higher risk of Alzheimers disease or related dementias than those without such an injury, with risk increasing with injury severity. The amyloid\/tau\/neurodegeneration biomarker framework treats amyloid, tau, and neurodegeneration as independent axes but omits astroglial injury, despite evidence that reactive astrogliosis (indexed by glial fibrillary acidic protein, GFAP) must be elevated for cognitive decline to occur in amyloid-positive individuals. Total GFAP immunoassays aggregate intact protein with multiple calpain- and caspase-cleaved proteoforms, blurring the biological signal. We compared a calpain-cleaved GFAP neoepitope, the glial fibrillary acidic protein neoepitope (neoGFAP), against total GFAP across the full traumatic-brain-injury-mild-cognitive-impairment-Alzheimers-disease continuum in Veterans using a two-stage plasma-to-cerebrospinal-fluid biomarker approach. A plasma triage gate combining phosphorylated tau 217 and amyloid beta 42 was applied to 367 unique subjects; a cerebrospinal-fluid benchmarking cohort of 57 subjects (controls, chronic blast traumatic brain injury, mild cognitive impairment, and Alzheimers disease) received head-to-head neoGFAP and total GFAP measurement. In the whole benchmarking cohort, neoGFAP discriminated mild cognitive impairment plus Alzheimers disease from non-Alzheimer subjects with an area under the receiver-operating-characteristic curve of 0.81 versus 0.73 for total GFAP, a trend-level advantage that did not reach nominal significance. Within the gate-positive, amyloid-committed subset of 23 subjects, neoGFAP dominance became significant by McNemars exact test (six discordant subjects favoured neoGFAP, none the reverse). Across diagnostic contrasts, neoGFAP outperformed total GFAP for Alzheimers disease versus control and, importantly for Veterans, for mild cognitive impairment versus chronic blast-exposed Veterans without cognitive impairment. In chronic blast injury, neoGFAP was paradoxically depleted relative to controls, consistent with tissue sequestration of aggregated proteoform fragments. Unbiased proteomic profiling confirmed coordinated elevation across astrocytic, neuronal, mitochondrial, and microglial compartments. An exploratory subject-level reclassification improved accuracy from 71.1 percent using plasma alone to 79.5 percent with added cerebrospinal-fluid markers and age. In a same-cohort ProQuantum replication (n=57), CSF neoGFAP preserved its discrimination advantage over total GFAP for MCI+AD versus non-AD (AUROC 0.76 vs 0.72; cross-platform Spearman {rho}=0.84), while plasma neoGFAP achieved AUROC 0.90, comparable to pTau217 (0.92) and exceeding A{beta}42\/40 (0.84). In this small sample, neoGFAP is a superior proteoform-resolved diagnostic and prognostic biomarker across the continuum and supports adding an astroglial-proteoform axis to amyloid\/tau\/neurodegeneration biomarker frameworks in high-risk populations.\n\nAbbreviated summaryHaskins and colleagues report that a calpain-cleaved GFAP neoepitope (neoGFAP) outperforms total GFAP for diagnostic and prognostic classification across the traumatic-brain-injury-mild-cognitive-impairment-Alzheimers-disease continuum in Veterans, supporting the addition of an astroglial-proteoform axis to amyloid\/tau\/neurodegeneration biomarker frameworks in high-risk populations.","rel_num_authors":24,"rel_authors":[{"author_name":"William E. Haskins","author_inst":"Gryphon Bio, South San Francisco, CA, USA; Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Kevin K. Wang","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA; Foundation for Applied Molecular Evolution, Alachua, FL, USA"},{"author_name":"Guangzheng Cai","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA; Foundation for Applied Molecular Evolution, Alachua, FL, USA"},{"author_name":"Khadija Boukholda","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA"},{"author_name":"Eman Elbayoumi","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA"},{"author_name":"Ruchi Bajpai","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Devin Jackson","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Katie Tehas","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Kristy Radeker","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Anthony DeLizza","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Caroline Popper","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Martin Kiendl","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Sigrun Badrnya","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Markus Miholits","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Stefan Jellbauer","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Todd Kilbaugh","author_inst":"Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Franklin Okumu","author_inst":"Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Ava Puccio","author_inst":"University of Pittsburgh, Pittsburgh, PA, USA"},{"author_name":"Raquel C. Gardner","author_inst":"Sheba Medical Center, Tel HaShomer, Israel; University of California San Francisco, San Francisco, CA, USA"},{"author_name":"Geoff Manley","author_inst":"University of California San Francisco, San Francisco, CA, USA"},{"author_name":"John B. Williamson","author_inst":"Brain Rehabilitation Research Center, Gainesville, FL, USA"},{"author_name":"Abigail B. Waters","author_inst":"Brain Rehabilitation Research Center, Gainesville, FL, USA"},{"author_name":"Gail Ge Li","author_inst":"VA Northwest Mental Illness Research, Education, and Clinical Center (VA NW MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA; Department of Psychiat"},{"author_name":"Elaine R. Peskind","author_inst":"VA Northwest Mental Illness Research, Education, and Clinical Center (VA NW MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA; Department of Psychiat"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Clinical deep sequencing to diagnose pathogenic mosaic variants in malformations of cortical development and epilepsy","rel_doi":"10.64898\/2026.09.01.26361943","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361943","rel_abs":"Background and Objectives: Deep sequencing of brain tissue in the research setting has established that mosaic variants are a major cause of malformations of cortical development (MCDs) and epilepsy. However, genetic testing in the clinical setting primarily detects germline variants using clinically accessible samples. We aimed to determine the diagnostic yield and clinical utility of deep sequencing in the clinical setting to identify pathogenic mosaic variants for this population. Methods: We performed a retrospective cohort analysis of individuals at Boston Children's Hospital with MCDs with or without epilepsy who received clinical deep sequencing between September 2017 and February 2026. Demographic, clinical, and genetic testing data were abstracted from the medical record. For individuals without systemic features, we classified brain tissue as an affected tissue sample. For individuals with systemic features, we classified brain or relevant non-brain tissue as affected. The primary outcome was the diagnostic yield of clinical deep sequencing performed using affected vs unaffected tissue samples. The secondary outcome was the clinical utility of genetic diagnoses. Results: Our cohort included 37 individuals (19\/37 (51%) female, 18\/37 (49%) male) with MCDs, of whom 35\/37 (95%) had epilepsy (25 with brain tissue samples available from epilepsy surgery) and 8\/37 (22%) had systemic features. Most (35\/37 (95%)) had dysplasia phenotypes on MRI and 12\/27 (44%) with pathology available had Focal Cortical Dysplasia Type I or II. The diagnostic yield was 53% (17\/32; 16 mosaic and 1 germline variant) when clinical deep sequencing was performed using an affected tissue sample vs 0% (0\/6) using an unaffected tissue sample (p=0.016). Of the diagnosed cases, 13\/17 (76%) had testing performed on brain tissue (1 with systemic features) and 4\/17 (24%) on non-brain tissue (3 buccal and 1 duodenal tissue, all with systemic features). All but one diagnosis involved the mTOR pathway. All diagnoses had clinical utility. Discussion: Clinical deep sequencing, when performed using an affected tissue sample, has high diagnostic yield and clinical utility for individuals with MCDs, especially dysplasia phenotypes, and epilepsy. Our findings support implementation of clinical deep sequencing for this population, especially as the genetic diagnoses have implications for emerging precision therapies.","rel_num_authors":14,"rel_authors":[{"author_name":"Katelyn Stone","author_inst":"Boston Children's Hospital"},{"author_name":"Gillian Prinzing","author_inst":"Boston Children's Hospital"},{"author_name":"Abbe Lai","author_inst":"Boston Children's Hospital"},{"author_name":"Lacey Smith","author_inst":"Boston Children's Hospital"},{"author_name":"Beth R Sheidley","author_inst":"Boston Children's Hospital"},{"author_name":"Meagan M Corliss","author_inst":"Washington University School of Medicine"},{"author_name":"Kevin Bowling","author_inst":"Washington University in St. Louis"},{"author_name":"Yang Cao","author_inst":"Washington University School of Medicine"},{"author_name":"Kimberly Wiltrout","author_inst":"Boston Children's Hospital"},{"author_name":"Scellig S.D. Stone","author_inst":"Boston Children's Hospital"},{"author_name":"Hart Lidov","author_inst":"Boston Children's Hospital"},{"author_name":"Edward Yang","author_inst":"Boston Children's Hospital"},{"author_name":"Annapurna Poduri","author_inst":"Boston Children's Hospital"},{"author_name":"Alissa M D'Gama","author_inst":"Boston Children's Hospital"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Hybrid risk scores integrating polygenic and clinical variables for endometriosis prediction","rel_doi":"10.64898\/2026.08.31.26361798","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361798","rel_abs":"Background: Endometriosis affects approximately 10% of reproductive-age women and is associated with substantial diagnostic delay and heterogeneous symptom presentation. Prior machine-learning prediction models have relied on comorbidity data alone or on small candidate-variant genetic scores, with inconsistent or incompletely reported performance. No study has combined a well-powered, multi-ancestry polygenic risk score (PRS) with environmental, reproductive, and symptom data in a single hybrid model. We developed and evaluated hybrid risk-prediction models integrating a genome-wide, multi-ancestry PRS with clinical and symptom data for endometriosis in the US-based All of Us Research Program. Methods: Among 69,376 participants (15,382 endometriosis cases, 53,994 controls) across six genetically inferred ancestry groups, we computed individual-level PRS values using PRS-CS weights derived from an independent, multi-ancestry GWAS. Five nested logistic regression, random forest, and XGBoost models progressively added age, ancestry, and within-ancestry genetic principal components (Model 1), environmental and reproductive factors (Model 2), symptom and comorbidity indicators (Model 3), all covariates combined (Model 4), and PRS x environment interactions (Model 5). Performance was assessed by AUROC in a held-out test set and 5-fold cross-validation, with class-weighted, Youden-optimized thresholds used for sensitivity, specificity, and predictive values; permutation importance identified top contributors. Pairwise AUROC differences were tested with a Holm-corrected DeLong-type test. Results: Discrimination improved from AUROC 0.63 (PRS, age, ancestry, principal components) to 0.72 for the full model, driven mainly by symptom and comorbidity data. XGBoost consistently outperformed logistic regression and random forest. The PRS ranked among the top individual predictors by permutation importance in nearly every model, alongside age, while genetic and demographic information alone gave only modest discrimination, and PRS x environment interactions did not improve on environmental factors alone. Threshold optimization yielded balanced sensitivity and specificity (~0.67\/0.65) versus near-zero sensitivity at a default threshold. Conclusions: Combining the PRS with symptom and comorbidity data gave the best discrimination compared to solely a well-powered, multi-ancestry PRS as a predictor of endometriosis. This study clarifies both the promise and current limits of hybrid genetic-clinical prediction for endometriosis and points to symptom-based phenotyping, molecular subtyping, and external validation as priorities.","rel_num_authors":2,"rel_authors":[{"author_name":"Oksana Goroshchuk","author_inst":"Yale University School of Medicine"},{"author_name":"Dora Koller","author_inst":"Institut de Recerca Sant Pau"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"A target trial emulation study to estimate the causal effect of intravenous iron use during pregnancy and its effect on haematological and birth outcomes in Pakistan","rel_doi":"10.64898\/2026.08.29.26361700","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.29.26361700","rel_abs":"Background: Despite several trials on the hematological outcomes of intravenous (IV) iron in pregnancy, only few have examined its effect on birth outcomes. We estimated the causal effect of IV-iron on moderate or severe anaemia and birth outcomes. Methods: Women presenting to routine antenatal care in Pakistan with haemoglobin <10 g\/dL were eligible for treatment. We used target trial emulation (TTE) methodology to estimate the effect of IV-iron treatment within 14 days of anaemia identification, compared to no treatment, on anaemia status at follow-up. A modified TTE analysis examined birth outcomes at delivery for singleton pregnancies, including birthweight, size-for-gestational-age, and mortality. We conducted a separate TTE for each of five gestational-age periods and pooled the results of each TTE. Results: We screened 3115 pregnancies of which 1715 were eligible for IV-iron; 1043 participants were treated during pregnancy. Those who received IV-iron had half the risk of moderate or severe anaemia in pregnancy compared with no treatment (pooled relative risk (RR) 0.40; 95% confidence interval (CI): 0.27, 0.59). The pooled effect of IV-iron on stillbirth suggested an 83% risk reduction (95% CI 55-94%), and trends were similar for perinatal and neonatal mortality. Conclusion: IV-iron treatment improved haematological status in pregnant women and was associated with a large reduction in stillbirth. Given limited data from randomised trials regarding fetal death and treatment earlier in pregnancy, this study contributes important information to the potential benefit of IV-iron in contexts where anaemia and its sequelae are a major public health problem.","rel_num_authors":17,"rel_authors":[{"author_name":"Nida Salman Yazdani Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Erin Oakley Ms.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Amna Khan Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Muhammad Farrukh Qazi Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Shayan Khakwani Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Asad Sheikh Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Azqa Mazhar Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Uzma Muhammad Iqbal Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Jaime Marquis Ms.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Bushra Liaqat Dr.","author_inst":"Department of Obstetrics & Gynaecology, Koohi Goth Women's Hospital Karachi, Karachi, Sindh, Pakistan"},{"author_name":"Kaveeta Kumari Dr.","author_inst":"Department of Obstetrics & Gynaecology, Creek General Hospital, Karachi, Sindh, Pakistan"},{"author_name":"Ellen C. Caniglia Dr.","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA"},{"author_name":"Aneeta Hotwani Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Imran Nisar Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Fyezah Jehan Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Emily R. Smith Dr.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Zahra Hoodbhoy Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Evaluating Clinical Foundation Models for Early Alzheimer's Disease and Related Dementia Prediction from Longitudinal EHRs","rel_doi":"10.64898\/2026.09.01.26361933","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361933","rel_abs":"Early identification of Alzheimer's disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.","rel_num_authors":5,"rel_authors":[{"author_name":"Shahla Farzana","author_inst":"Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA"},{"author_name":"Ash Arian","author_inst":"Pritzker School of Medicine, University of Chicago, Chicago, IL, USA"},{"author_name":"Tatjana Rundek","author_inst":"University of Miami"},{"author_name":"Moise Desvarieux","author_inst":"Mailman School of Public Health, Columbia University"},{"author_name":"Habibul Ahsan","author_inst":"Department of Family Medicine, Biological Sciences Division, University of Chicago Medicine, Chicago, IL, USA"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Evaluating Clinical Foundation Models for Early Alzheimer's Disease and Related Dementia Prediction from Longitudinal EHRs","rel_doi":"10.64898\/2026.09.01.26361933","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361933","rel_abs":"Early identification of Alzheimer's disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.","rel_num_authors":5,"rel_authors":[{"author_name":"Shahla Farzana","author_inst":"Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA"},{"author_name":"Ash Arian","author_inst":"Pritzker School of Medicine, University of Chicago, Chicago, IL, USA"},{"author_name":"Tatjana Rundek","author_inst":"University of Miami"},{"author_name":"Moise Desvarieux","author_inst":"Mailman School of Public Health, Columbia University"},{"author_name":"Habibul Ahsan","author_inst":"Department of Family Medicine, Biological Sciences Division, University of Chicago Medicine, Chicago, IL, USA"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"The human metabolite - protein interactome reveals a global layer of cellular coordination","rel_doi":"10.64898\/2026.08.28.747909","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.28.747909","rel_abs":"Metabolites are substrates, products, cofactors, and regulators, but protein-protein interaction networks do not represent their potential to organize proteins across conventional pathway boundaries. Using the LIGMAP virtual-screening algorithm, we mapped 308 human metabolite codes to pockets in monomers, dimer interfaces, and non-interface sites in dimers and represented attractive or repulsive COLIG states involving pairs of metabolites in the same pocket. On a fixed cohort of 3,938 proteins, the mean coverage of 104 strict non-enzyme pathways was 44.6% for LIGMAP, 70.1% for STRING, and 82.5% for STRING+LIGMAP; the union placed 86.3% of eligible proteins in the largest connected component and 95.1% in the two largest components. STRING+LIGMAP protein coverage was 88.0% for 68 enzyme-only pathways and 88.9% for 1,283 mixed pathways. In pathway-held-out, degree-matched prediction, adding LIGMAP to degree plus STRING increased the mean area under the precision-recall curve from 0.651 to 0.660 (paired P = 0.024); adding BioLiP2 increased it to 0.663 (paired P = 0.005). Ancient-only and non-ancient-only subnetworks were each globally connected; ancient features were denser, whereas non-ancient features covered more proteins and pathways. At the full 5,426-protein scale, retaining only features assigned to 2-100 proteins recovered 698 of 1,691 strict non-enzyme reference edges (41.3%) and exceeded both protein-label and exact bipartite degree-preserving nulls. Uncapped recovery approached saturation and lost identity-selective enrichment. Experimentally established metabolite-dependent complexes validate the local mechanism independently of LIGMAP; LIGMAP fully recovered two of seven stringent direct mechanisms and all three broader serial axes examined. Our findings reveal a global metabolite-mediated architecture with the capacity to coordinate proteins across otherwise distinct cellular systems.","rel_num_authors":2,"rel_authors":[{"author_name":"Jeffrey Skolnick","author_inst":"Georgia Institute of Technology"},{"author_name":"Bharath Srinivasan","author_inst":"Cancer Research Horizons"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Sea stickleback genome reveals repeated chromosomal rearrangements in sticklebacks","rel_doi":"10.64898\/2026.08.30.747834","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747834","rel_abs":"Sticklebacks (Gasterosteidae) encompass model organisms which are of particular interest for evolutionary and ecological genomics. Within Gasterosteidae, chromosome number is variable (2n=40-46) and independent fusions of homologous chromosomes have been proposed. The sea stickleback (or fifteen-spined stickleback, Spinachia spinachia ) has the lowest known number of chromosomes (2n=40) and hence is crucial in understanding chromosome evolution among sticklebacks, but is so far missing in genomic datasets. Here, we present a high-quality diploid genome assembly of S. spinachia. PacBio HiFi and Hi-C reads were assembled into a genome of 407.5 Mb in size, consisting of 20 chromosomes, with an N50 of 6.6 Mb and 98.96% complete single-copy BUSCO genes. A phylogenetic tree inferred across five stickleback species and four outgroup genomes from 19,156 genes, alongside synteny analyses and ancestral chromosome reconstructions, confirms S. spinachia as the sister species to the four-spined stickleback (Apeltes quadracus) and not as the sister group to all other sticklebacks as once thought. It has one species-specific chromosome fusion and shares two fusions with the three-spined stickleback (Gasterosteus aculeatus), none of which are present in its sister species. One of these fusions is also present in Pungitius, leading to reinterpretion of this fusion as ancestral to Gasterosteidae, with subsequent fission in Apeltes. This implies a lower ancestral chromosome number in Gasterosteidae (2n=44) than previously thought. The other fusion shared with G. aculeatus presents a case of convergence. Our results suggest that karyotype evolution in Gasterosteidae has been shaped by ancestral chromosome fusion, convergent fusion, and secondary fission.","rel_num_authors":25,"rel_authors":[{"author_name":"Jule Drewalowski","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Sergei Kliver","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Leon Hilgers","author_inst":"Senckenberg, Leibniz Institution for Biodiversity and Earth System Research, Senckenberganlage 25, 60325 Frankfurt, Germany"},{"author_name":"Peter Rask M\u00f8ller","author_inst":"Natural History Museum of Denmark, University of Copenhagen, Denmark"},{"author_name":"Sarah ST Mak","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Iva Kova\u010di\u0107","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Bent Petersen","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Joseph Nesme","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Ann M Mc Cartney","author_inst":"Institute of Clinical and Translational Sciences, University of California, Irvine, CA, USA"},{"author_name":"Alice Mouton","author_inst":"CARE, Laboratoire des Transitions, University of Liege, Belgium"},{"author_name":"Giulio Formenti","author_inst":"Vertebrate Genome Lab, Rockefeller University, New York City, USA"},{"author_name":"Hannes Svardal","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Genevieve Diedericks","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Henrique G Leit\u00e3o","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Rosa Fern\u00e1ndez","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Nuria Escudero","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Judit Salces-Ortiz","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Claudio Ciofi","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Chiara Natali","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Maria Angela Diroma","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Alessio Iannucci","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Marco Sollitto","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Michael Hiller","author_inst":"Senckenberg, Leibniz Institution for Biodiversity and Earth System Research, Senckenberganlage 25, 60325 Frankfurt, Germany"},{"author_name":"M Thomas P Gilbert","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Josefin Stiller","author_inst":"Department of Biology, University of Copenhagen, Denmark"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multi-kingdom microbial diversity and interaction landscapes in mosquitoes revealed by 5,163 individual meta-transcriptomes","rel_doi":"10.64898\/2026.09.02.748197","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748197","rel_abs":"Mosquitoes are pathogen vectors embedded within diverse microbial ecosystems. However, the nature and interactions among their multi-kingdom microbiome remain poorly understood. We conducted a nationwide single-mosquito meta-transcriptomic survey of 5,163 mosquitoes representing 100 species across China, integrating viral discovery with marker-gene profiling of bacteria, archaea, fungi, and other eukaryotic microbes. From this, we identified 1,606 microbial species-level taxa, including extensive novel diversity, and revealed pronounced host species-specific organization of mosquito-associated communities. We detected 34 pathogens or potential pathogens of human or animal relevance, whose prevalence, abundance, host range, and geographic distribution defined distinct epidemiological patterns. Network analysis uncovered pervasive cross-kingdom microbial associations, including candidate antiviral relationships involving Wolbachia and other microbial taxa. Our study establishes a detailed view of the full-spectrum microbiome and provides a resource and conceptual framework for studying vector competence, pathogen emergence, and microbiome-informed mosquito-borne disease control.","rel_num_authors":40,"rel_authors":[{"author_name":"Qin-Yu Gou","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Wei-Chen Wu","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Pei-Bo Shi","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Geng-Yan Luo","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yuan-Fei Pan","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jing Wang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yan Gao","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Kai-Jie Liu","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Hai-Long Zhao","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Yun Feng","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"Kun Li","author_inst":"National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, "},{"author_name":"Wei-Hong Yang","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"De Wu","author_inst":"Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China."},{"author_name":"Shi-Jia Le","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Gen-Yang Xin","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Min-Wu Peng","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yu-Qi Liao","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Chun-Hui Yang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Shi-Qiang Mei","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jia-Ming Huang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jin-Xia Cheng","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Xin Hou","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jian-Bin Kong","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Xin-Xin Chen","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Bing Zhang","author_inst":"Xinjiang Key Laboratory of Molecular Biology for Endemic Diseases, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China."},{"author_name":"Zi-Rui Ren","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Jun-Hua Li","author_inst":"State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China."},{"author_name":"Xin Jin","author_inst":"State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China."},{"author_name":"Juan Wang","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"Tong-Qing An","author_inst":"State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China."},{"author_name":"Xin-Yi Huang","author_inst":"State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China."},{"author_name":"Jie Cui","author_inst":"Department of Infectious Diseases, National Medical Center for Infectious Diseases, Huashan Hospital, Institute of Infection and Health Research, Fudan Universi"},{"author_name":"John-Sebastian Eden","author_inst":"Centre for Virus Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.;School of Medical Sciences, The University of Sydney, "},{"author_name":"Gong Cheng","author_inst":"New Cornerstone Science Laboratory, Tsinghua University-Peking University Joint Center for Life Sciences, School of Basic Medical Sciences, Tsinghua University,"},{"author_name":"De-Yin Guo","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Guo-Dong Liang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Ce"},{"author_name":"Edward C. Holmes","author_inst":"School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia."},{"author_name":"Zi-Qing Deng","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Mang Shi","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Da-Xi Wang","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Rebuilding microbiome diversity theory on the closed simplex","rel_doi":"10.64898\/2026.09.02.748976","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748976","rel_abs":"Ecological diversity theory links diversity within local communities to diversity of higher-level ensembles, but this scale structure is largely absent from microbiome analysis. Alpha diversity is usually treated as a within-sample summary, whereas \"beta diversity\" often denotes pairwise dissimilarity and gamma diversity is rarely explicit. We restore the local-regional architecture for environmental, host-associated and longitudinal microbiomes and distinguish regional beta diversity from pairwise dissimilarity and predictor-associated compositional variation. To quantify these objects for sparse compositions, we introduce Hellinger-Riemann intrinsic coordinates (HRIC), a one-to-one, bounded normal-coordinate representation of the closed simplex that retains exact zeros. The same coordinates yield Simplex Hellinger alpha and gamma diversity, additive regional beta diversity, taxon contributions, pairwise dissimilarity and model-explained dispersion. Simulations established the correspondence between HRIC dispersion and the between-condition component of PERMANOVA. Across Arctic and North Atlantic communities, local diversity relative to each regional benchmark covaried similarly with vertical environmental gradients despite partly different taxon-level associations. In a randomized autologous faecal microbiota transplantation trial, recipients returned earlier towards their personal pre-transplant compositions, whereas the alpha-diversity difference was smaller and less precise. Explicit local and regional referents therefore connect diversity partitioning with compositional analysis across microbial systems.","rel_num_authors":2,"rel_authors":[{"author_name":"Yiqian Zhang","author_inst":"The Ohio State University"},{"author_name":"Zihan Zhu","author_inst":"Yale University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"A Lymphomimetic Synthetic Immune Niche, Consisting of CCL21 and ICAM1, Accelerates the Expansion of Potent CAR T-cells","rel_doi":"10.64898\/2026.08.30.748076","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748076","rel_abs":"Background: Chimeric Antigen Receptor (CAR) T-cell therapy has transformed the treatment of hematologic malignancies, yet, its broader clinical application often faces major challenges, including slow expansion rates, variable transduction efficiency, exhaustion, and functional heterogeneity. Recent studies have demonstrated that a Synthetic Immune Niche (SIN) composed of immobilized CCL21 and ICAM1 promotes the proliferation of murine and human T-cells while preserving their cytotoxic potency. In this study, we explored the capacity of immobilized CCL21 and ICAM1 to enhance the production of highly potent CAR T-cells by facilitating both their expansion and cytotoxic capacity. Methods: CD19-directed CAR T-cells were generated from PBMCs of healthy donors using a clinical-grade protocol. Following retroviral transduction, the cells were expanded on CCL21 and ICAM1 coated plates, or on uncoated control plates. The effects of the SIN treatment on CAR T-cell expansion, morphology, physical properties, phenotypic markers, and potency were systematically evaluated. Results: SIN exposure significantly enhanced CAR T-cell expansion, achieving a 9.3-fold higher total cell yield by day 13, compared with control cultures. This proliferative advantage persisted even after withdrawal of the cells from the synthetic niche on day 10. SIN stimulation preferentially expanded the CAR-transduced population, increasing CAR T-cell frequencies from 45.4% to 77.6%, resulting in a 14.6-fold increase in the absolute number of CAR T-cells compared with untreated cultures. Morphological and phenotypic analyses revealed distinct activated cell morphology, manifested by increased cell size, polarity and granularity, and elevated expression of the activation markers CD137 and CD69. Importantly, CAR T-cells transiently exposed to the SIN retained cytokine secretion and cytotoxic activity comparable to that of continuously SIN-treated cells, indicating that the SIN effect is persistent. Overall, SIN-conditioned CAR T-cells displayed robust antigen-dependent IFN-{gamma} secretion and cytotoxicity against CD19-expressing target cells. Conclusion: Stimulation of CAR T-cells with a synthetic immune niche consisting of immobilized CCL21 and ICAM1 enhances overall expansion while selectively enriching the CAR-transduced population, thereby substantially increasing both the number and prominence of therapeutically relevant CAR T-cells. This effect is accompanied by a persistent activation phenotype and high functional potency. We propose that incorporating SIN stimulation into CAR T-cell manufacturing represents a simple and scalable strategy for improving CAR T-cell yield while maintaining high cytotoxic efficacy.","rel_num_authors":5,"rel_authors":[{"author_name":"Karin Brezinger-Dayan","author_inst":"Rabin Medical Center"},{"author_name":"Sofi Yado","author_inst":"The Weizmann Institute of Science"},{"author_name":"Rawan Zoabi","author_inst":"The Weizmann Institute of Science"},{"author_name":"Benjamin Geiger","author_inst":"The Weizmann Institute of Science"},{"author_name":"Michal J Besser","author_inst":"Rabin Medical Center"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"A Lymphomimetic Synthetic Immune Niche, Consisting of CCL21 and ICAM1, Accelerates the Expansion of Potent CAR T-cells","rel_doi":"10.64898\/2026.08.30.748076","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748076","rel_abs":"Background: Chimeric Antigen Receptor (CAR) T-cell therapy has transformed the treatment of hematologic malignancies, yet, its broader clinical application often faces major challenges, including slow expansion rates, variable transduction efficiency, exhaustion, and functional heterogeneity. Recent studies have demonstrated that a Synthetic Immune Niche (SIN) composed of immobilized CCL21 and ICAM1 promotes the proliferation of murine and human T-cells while preserving their cytotoxic potency. In this study, we explored the capacity of immobilized CCL21 and ICAM1 to enhance the production of highly potent CAR T-cells by facilitating both their expansion and cytotoxic capacity. Methods: CD19-directed CAR T-cells were generated from PBMCs of healthy donors using a clinical-grade protocol. Following retroviral transduction, the cells were expanded on CCL21 and ICAM1 coated plates, or on uncoated control plates. The effects of the SIN treatment on CAR T-cell expansion, morphology, physical properties, phenotypic markers, and potency were systematically evaluated. Results: SIN exposure significantly enhanced CAR T-cell expansion, achieving a 9.3-fold higher total cell yield by day 13, compared with control cultures. This proliferative advantage persisted even after withdrawal of the cells from the synthetic niche on day 10. SIN stimulation preferentially expanded the CAR-transduced population, increasing CAR T-cell frequencies from 45.4% to 77.6%, resulting in a 14.6-fold increase in the absolute number of CAR T-cells compared with untreated cultures. Morphological and phenotypic analyses revealed distinct activated cell morphology, manifested by increased cell size, polarity and granularity, and elevated expression of the activation markers CD137 and CD69. Importantly, CAR T-cells transiently exposed to the SIN retained cytokine secretion and cytotoxic activity comparable to that of continuously SIN-treated cells, indicating that the SIN effect is persistent. Overall, SIN-conditioned CAR T-cells displayed robust antigen-dependent IFN-{gamma} secretion and cytotoxicity against CD19-expressing target cells. Conclusion: Stimulation of CAR T-cells with a synthetic immune niche consisting of immobilized CCL21 and ICAM1 enhances overall expansion while selectively enriching the CAR-transduced population, thereby substantially increasing both the number and prominence of therapeutically relevant CAR T-cells. This effect is accompanied by a persistent activation phenotype and high functional potency. We propose that incorporating SIN stimulation into CAR T-cell manufacturing represents a simple and scalable strategy for improving CAR T-cell yield while maintaining high cytotoxic efficacy.","rel_num_authors":5,"rel_authors":[{"author_name":"Karin Brezinger-Dayan","author_inst":"Rabin Medical Center"},{"author_name":"Sofi Yado","author_inst":"The Weizmann Institute of Science"},{"author_name":"Rawan Zoabi","author_inst":"The Weizmann Institute of Science"},{"author_name":"Benjamin Geiger","author_inst":"The Weizmann Institute of Science"},{"author_name":"Michal J Besser","author_inst":"Rabin Medical Center"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"De novo design of ligand binding proteins using large language models alone","rel_doi":"10.64898\/2026.09.02.748987","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748987","rel_abs":"Protein design has rapidly advanced with the advent of sequence- and structure-based machine learning models. However, reasoned design, which applies physicochemical principles and rules derived from sequence-structure-function relationships, has not seen the same benefits from generative machine learning models. Here, we test the ability of common large language models (LLMs; e.g. Claude, ChatGPT, and Gemini) to consider design principles to generate de novo proteins that bind metals and lipophilic small molecules without copying existing sequences. Common LLMs alone are able to 1) generate protein sequences to adopt a desired fold and bind the target ligand and 2) explain the principles that motivate the design choices. Following structure prediction and filtering, we selected a small set of designs (6 to 12 designs per query) for experimental validation, affording metal binders in one round of LLM-based design (25% hit rate) and perfluorooctanoic acid binders in two rounds (25% hit rate in the second round of design). Importantly, the LLMs produce detailed justification to accompany the de novo designed sequences, providing a conceptual framework on which designs can be evaluated. While the successful designs have some deviations from the prompted parameters and LLM-articulated design rationale, these campaigns provide a case study that highlights the utility of LLMs in making protein design more comprehensible and accessible to users without sophisticated design expertise.","rel_num_authors":5,"rel_authors":[{"author_name":"Nam Hyeong Kim","author_inst":"University of California, San Francisco"},{"author_name":"A. Katherine Hatstat","author_inst":"University of California, San Francisco"},{"author_name":"Hyunil Jo","author_inst":"University of California, San Francisco"},{"author_name":"Yibing Wu","author_inst":"University of California, San Francisco"},{"author_name":"William F. DeGrado","author_inst":"University of California, San Francisco"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multivalent Anti-ACE2 Nanobodies Confer Broad Pan-Sarbecovirus Protection","rel_doi":"10.64898\/2026.09.01.748598","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748598","rel_abs":"The continual emergence of SARS-CoV-2 variants that rapidly evade conventional spike-directed neutralizing antibodies, together with the ongoing risk of cross-species spillover and new sarbecovirus outbreaks, underscores the need to develop broadly acting, escape-resistant therapeutic agents. Here, we optimized a nanobody discovery pipeline incorporating competition-based yeast surface display assays to isolate single-chain variable heavy chain-only antibody domains (VHHs or nanobodies) that bind human ACE2 and inhibit SARS-CoV-2 entry. Dimeric VHHs, as well as bivalent and tetravalent Fc-fusion proteins exhibited markedly increased antiviral activity, blocking a broad panel of SARS-CoV-2 variants and diverse sarbecoviruses at low-nanomolar to picomolar concentrations. These agents did not affect ACE2 enzymatic function or cell surface expression. The VHH-Fc fusion proteins had favorable pharmacokinetics and conferred prophylactic protection in mouse models of both SARS-CoV-2 and SARS-CoV infection, showcasing their potential as broadly acting receptor-targeted biologics against pandemic-threat viruses.","rel_num_authors":13,"rel_authors":[{"author_name":"Athanasios D. Bakasis","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Rachel Patejak","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Peter C. Fridy","author_inst":"Laboratory of Cellular and Structural Biology, Rockefeller University, 1230 York Ave, Box 213, New York, NY 10021, USA"},{"author_name":"Jesse Jenkins","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Miranda Aldis","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Viren A. Baharani","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Lakshmi Sriram","author_inst":"Laboratory of Cell Cycle Genetics, The Rockefeller University, New York, NY 10021, USA"},{"author_name":"Kelly R. Molloy","author_inst":"Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, USA"},{"author_name":"Brian T. Chait","author_inst":"Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, USA"},{"author_name":"Michael P. Rout","author_inst":"Laboratory of Cellular and Structural Biology, Rockefeller University, 1230 York Ave, Box 213, New York, NY 10021, USA"},{"author_name":"Frederick R. Cross","author_inst":"Laboratory of Cell Cycle Genetics, The Rockefeller University, New York, NY 10021, USA"},{"author_name":"Paul D. Bieniasz","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Theodora Hatziioannou","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"A low-dimensional, generalizable encoding manifold for auditory cortex","rel_doi":"10.64898\/2026.08.30.747962","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747962","rel_abs":"Neural populations in auditory cortex (AC) perform sensory computations that support stimulus category decoding and flexible behavior. The underlying geometry and generalizability of these computations for natural stimuli remain poorly understood, particularly at the single-neuron level, where neurons display wide-ranging tuning specificity and temporal acuity. To address this gap, we developed ACNet, a foundation model of cortical sound encoding, to predict the time-varying activity of >3000 neurons in AC of ferrets. Training data included 42 hours of natural sounds spanning over 100 categories and were collected from multiple recording sites across multiple animals. The model achieved state-of-the-art response prediction accuracy. Model activity was succinctly captured by a low-dimensional neural manifold, which generalized (>80% of variance) across animals. Analysis of ACNet activations revealed the emergence of rate-based, sparse sound coding across layers, a prominent feature of the auditory cortex. These transformations were concomitant with the emergence of more accurate and neurally aligned auditory category decoding, even though ACNet was not explicitly trained to categorize sounds. The model also revealed tuning differences between anatomically distinct cell types. Taken together, our results demonstrate that foundation models of sensory systems can reveal generalizable computations by large neural populations.","rel_num_authors":4,"rel_authors":[{"author_name":"Satyabrata Parida","author_inst":"Oregon Health & Science University"},{"author_name":"Jereme C Wingert","author_inst":"Oregon Health & Science University"},{"author_name":"Jonah D Stickney","author_inst":"Oregon Health & Science University"},{"author_name":"Stephen V David","author_inst":"Oregon Health and Science University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Understanding cephalopod regeneration: impact of sexual maturation, injury severity and general anesthesia on octopus arm restoration","rel_doi":"10.64898\/2026.08.29.747974","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.747974","rel_abs":"Octopuses exhibit remarkable regenerative capacity, as shown by their ability to fully restore a whole arm. During this process, a complex appendage containing muscle, connective tissue, vasculature, suckers, sensory structures, peripheral nerves and the axial nerve cord (ANC) is regrown. Although the morphology of octopus regeneration is increasingly well described, the physiological variables that determine regenerative speed, including the potential contribution of ion-channel-dependent processes, remain poorly understood. In this work we examined the influence of reproductive state, injury severity, biological sex and magnesium chloride (MgCl2) anesthesia, on the rate of longitudinal arm regrowth. Reproductive stage was the strongest determinant of regenerative performance: pre-reproductive stage (pre-RS) animals showed robust regrowth, whereas reproductive stage (RS) animals regenerated significantly slower. In pre-RS animals, regeneration speed scaled strongly with injury severity, whereas this relationship was absent in RS animals. Biological sex did not independently predict regeneration speed, and MgCl2 exposure showed no long-term inhibitory effect on regeneration. Our final model explained 81.4% of variance in regeneration speed, with reproductive state and injury severity having major effects. These findings demonstrate that sexual maturation and optic-gland-mediated senescence act as an irreversible physiological block against somatic regeneration, and provide empirical support for mechanosensory and bioelectric-dependent scaling models of cephalopod limb restoration.","rel_num_authors":6,"rel_authors":[{"author_name":"Samraggi Chakraborty","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Lukasz Bijoch","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Fan-Che Kung","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Ping-Jui Hsieh","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Zhen-Hong Yang","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Kuo-Sheng Lee","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Geometric reshaping of task-relevant representations in the primary visual cortex supports perceptual decisions","rel_doi":"10.64898\/2026.08.29.747961","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.747961","rel_abs":"Discriminating between two stimuli requires that their neural representations become separable by downstream readouts. This can be achieved geometrically, by disentangling the manifolds that population responses form in neural state space. Such reorganization has been observed in associative and motor areas, but never at the earliest stage of cortical processing. While task learning is known to impact neuronal representations in the primary visual cortex (V1), it is unknown if the population geometry is also reshaped to support perceptual decisions. We imaged V1 populations in mice trained on a Go\/NoGo orientation discrimination task of increasing difficulty, and in naive mice passively viewing the same stimuli. Training reshaped the representational geometry so that the population responses were better linearly separable. A static compression made the Go and NoGo manifolds more compact and lower-dimensional from the earliest response, while a dynamic separation drove them further apart through the trial. Together, those transformations increased manifold capacity and readout accuracy. This reorganization made the Go-NoGo relationship in the neural state space more stable in Trained animals than in Naive ones. Within this learned geometry, the position of individual trials along the Go-NoGo axis predicted the animals' decision probabilities. Learning therefore promotes a disentangled and stable representational geometry that feeds the decision process.","rel_num_authors":4,"rel_authors":[{"author_name":"Julien Corbo","author_inst":"Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark"},{"author_name":"Leyla Roksan Caglar","author_inst":"Windreich Department of AI and Human Health, Icahn School of Medicine at Mount Sinai"},{"author_name":"O. Batuhan Erkat","author_inst":"Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark"},{"author_name":"Pierre-Olivier Polack","author_inst":"Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Biased signaling via NtsR1 restrains food intake and weight gain in obese mice","rel_doi":"10.64898\/2026.08.30.748142","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748142","rel_abs":"Neurotensin receptor 1 (NTSR1) activation suppresses feeding and promotes weight loss but is limited by adverse effects associated with Gq signaling. SBI-553 is a {beta}-arrestin-biased allosteric modulator of NTSR1 that avoids these effects. We evaluated the effects of SBI-553 (5 or 12 mg\/kg, i.p.) on food intake and body weight in lean and diet-induced obese mice. Neither dose altered metabolic parameters, locomotor activity, or wheel running. SBI-553 did not affect ad libitum feeding in lean mice; however, both doses acutely reduced high-fat diet intake in obese mice. While 5 mg\/kg had no effect on hunger-induced feeding, 12 mg\/kg suppressed refeeding in both chow- and high-fat-fed mice of both sexes and reduced weight regain in obese mice. These findings identify SBI-553 as a potential strategy for reducing food intake and supporting weight loss.","rel_num_authors":9,"rel_authors":[{"author_name":"Jariel Ramirez-Virella","author_inst":"Michigan State University"},{"author_name":"Katherine Black","author_inst":"Michigan State University"},{"author_name":"Netanya F Dennis","author_inst":"Michigan State University"},{"author_name":"Raluca Bugescu","author_inst":"Michigan State University"},{"author_name":"Katie Thompson","author_inst":"Michigan State University"},{"author_name":"Steven H Olsen","author_inst":"Sanford Burnham Prebys"},{"author_name":"Lauren M Slosky","author_inst":"University of Minnesota Medical School"},{"author_name":"Zoe A McElligott","author_inst":"University of North Carolina Chapel Hill"},{"author_name":"Gina Marie Leinninger","author_inst":"Michigan State University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Biased signaling via NtsR1 restrains food intake and weight gain in obese mice","rel_doi":"10.64898\/2026.08.30.748142","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748142","rel_abs":"Neurotensin receptor 1 (NTSR1) activation suppresses feeding and promotes weight loss but is limited by adverse effects associated with Gq signaling. SBI-553 is a {beta}-arrestin-biased allosteric modulator of NTSR1 that avoids these effects. We evaluated the effects of SBI-553 (5 or 12 mg\/kg, i.p.) on food intake and body weight in lean and diet-induced obese mice. Neither dose altered metabolic parameters, locomotor activity, or wheel running. SBI-553 did not affect ad libitum feeding in lean mice; however, both doses acutely reduced high-fat diet intake in obese mice. While 5 mg\/kg had no effect on hunger-induced feeding, 12 mg\/kg suppressed refeeding in both chow- and high-fat-fed mice of both sexes and reduced weight regain in obese mice. These findings identify SBI-553 as a potential strategy for reducing food intake and supporting weight loss.","rel_num_authors":9,"rel_authors":[{"author_name":"Jariel Ramirez-Virella","author_inst":"Michigan State University"},{"author_name":"Katherine Black","author_inst":"Michigan State University"},{"author_name":"Netanya F Dennis","author_inst":"Michigan State University"},{"author_name":"Raluca Bugescu","author_inst":"Michigan State University"},{"author_name":"Katie Thompson","author_inst":"Michigan State University"},{"author_name":"Steven H Olsen","author_inst":"Sanford Burnham Prebys"},{"author_name":"Lauren M Slosky","author_inst":"University of Minnesota Medical School"},{"author_name":"Zoe A McElligott","author_inst":"University of North Carolina Chapel Hill"},{"author_name":"Gina Marie Leinninger","author_inst":"Michigan State University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Automated detection of Loa loa: a field trial in Cameroon","rel_doi":"10.64898\/2026.08.29.745114","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.745114","rel_abs":"Onchocerciasis (river blindness) is targeted for elimination through mass administration (MDA) of ivermectin (IVM) to endemic populations. In areas where loiasis, caused by the blood-borne filarial parasite Loa loa, is co-endemic, IVM MDA faced significant challenges because individuals harboring more than 30,000 L. loa microfilaria (mf)\/mL of blood are at high risk of developing serious adverse events (SAEs) that can sometimes be fatal. An alternative strategy was developed for safe IVM distribution: the so-called 'Test and Not Treat' (TaNT), which identifies and excludes individuals with high mf density from IVM MDA and treats only those with minimal risk. TaNT requires a point-of-care diagnostic device to rapidly quantify L. loa parasites in field conditions. The NTDscope is a handheld device which captures bright-field videos of whole blood in capillaries. An onboard algorithm detects and counts the live microfilaria of L. loa via movement of red blood cells. This paper describes (i) a new detection algorithm; and (ii) a May 2025 field trial in Cameroon using the NTDscope with new algorithm (550 patients). Results for the TaNT use case (i.e. flagging cases with L. loa mf densities > 30,000 mf\/mL): 94% to 97% sensitivity, and 94% to 97% specificity. The results indicate that the NTDscope and new algorithm provide a greater margin of safety than the predecessor device, and can potentially offer rapid, effective field detection of high mf infections to enable scalability of the TaNT strategy.","rel_num_authors":15,"rel_authors":[{"author_name":"Linda Djune-Yemeli","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Charles B Delahunt","author_inst":"University of Washington, Seattle, WA"},{"author_name":"Steve M Tchana","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Jean G Bopda","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Yves A Balog","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Yannick Y Nzeuhang","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Dipayan Banik","author_inst":"Global Health Labs (ceased operations in Dec 2025)"},{"author_name":"Matthew D Keller","author_inst":"Global Health Labs (ceased operations in Dec 2025)"},{"author_name":"Ethan Spencer","author_inst":"Global Health Labs (ceased operations in Dec 2025)"},{"author_name":"Maria D de Leon Derby","author_inst":"Department of Bioengineering, University of California, Berkeley, California"},{"author_name":"Zaina L Moussa","author_inst":"Department of Bioengineering, University of California, Berkeley, California"},{"author_name":"Daniel A Fletcher","author_inst":"Department of Bioengineering, University of California, Berkeley, California"},{"author_name":"Isaac I Bogoch","author_inst":"Division of General Internal Medicine, Toronto General Hospital, University Health Network, Toronto, Canada"},{"author_name":"Anne-Laure M Le-Ny","author_inst":"Global Health Labs (ceased operations in Dec 2025)"},{"author_name":"Joseph Kamgno","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multi-hit STAG2 mutations define a high-risk subset of MDS and reveal convergent evolutionary targeting of cohesin","rel_doi":"10.64898\/2026.09.02.749005","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749005","rel_abs":"STAG2 is the most frequently mutated cohesin gene in myeloid neoplasms, yet the significance of multiple mutations within this X-linked tumor suppressor remains unknown. We analyzed a cohort of 1,967 adult patients with myeloid neoplasms and identified 233 cases (12%) harboring STAG2 mutations, including 38 cases (16%) with multiple STAG2 hits. Patients with multi-hit STAG2 mutations exhibited increased multilineage dysplasia compared with single-hit cases and experienced inferior overall survival, an effect driven primarily by patients with myelodysplastic syndromes (MDS). To investigate the molecular basis of recurrent STAG2 acquisition, we performed long-read sequencing in representative cases with phaseable STAG2 mutations. In the informative case examined, distinct truncating STAG2 mutations did not co-occur on the same DNA molecule, supporting independent acquisition rather than stepwise allelic inactivation. Cohort-level variant allele frequency patterns were consistent with recurrent evolutionary targeting of STAG2 across related clonal populations. Together, these findings support a model in which multi-hit STAG2 mutations arise through convergent evolution and define a biologically distinct, adverse-risk subset of MDS.","rel_num_authors":18,"rel_authors":[{"author_name":"Eno-obong Blessing Udoh","author_inst":"Cleveland Clinic Lerner College of Medicine"},{"author_name":"Yi Chen","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Matteo D'Addona","author_inst":"Scuola Medica Salernitana, University of Salerno, Department of Medicine, Surgery and Dentistry, Salerno, Italy"},{"author_name":"Edna Stewart","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Rong Deng","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Felipe de Almeida Sartori","author_inst":"Hospital Israelita Albert Einstein, Sao Paulo, Brazil"},{"author_name":"Serhan Unlu","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Zachary Brady","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Ruowei Zhu","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Xiaoyi Cheng","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Viviana Scoca","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Jane J Xu","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Sergei Doulatov","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Karl Theil","author_inst":"Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH"},{"author_name":"David Bosler","author_inst":"Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH"},{"author_name":"Valeria Visconte","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Jaroslaw P Maciejewski","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Aaron D Viny","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multi-hit STAG2 mutations define a high-risk subset of MDS and reveal convergent evolutionary targeting of cohesin","rel_doi":"10.64898\/2026.09.02.749005","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749005","rel_abs":"STAG2 is the most frequently mutated cohesin gene in myeloid neoplasms, yet the significance of multiple mutations within this X-linked tumor suppressor remains unknown. We analyzed a cohort of 1,967 adult patients with myeloid neoplasms and identified 233 cases (12%) harboring STAG2 mutations, including 38 cases (16%) with multiple STAG2 hits. Patients with multi-hit STAG2 mutations exhibited increased multilineage dysplasia compared with single-hit cases and experienced inferior overall survival, an effect driven primarily by patients with myelodysplastic syndromes (MDS). To investigate the molecular basis of recurrent STAG2 acquisition, we performed long-read sequencing in representative cases with phaseable STAG2 mutations. In the informative case examined, distinct truncating STAG2 mutations did not co-occur on the same DNA molecule, supporting independent acquisition rather than stepwise allelic inactivation. Cohort-level variant allele frequency patterns were consistent with recurrent evolutionary targeting of STAG2 across related clonal populations. Together, these findings support a model in which multi-hit STAG2 mutations arise through convergent evolution and define a biologically distinct, adverse-risk subset of MDS.","rel_num_authors":18,"rel_authors":[{"author_name":"Eno-obong Blessing Udoh","author_inst":"Cleveland Clinic Lerner College of Medicine"},{"author_name":"Yi Chen","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Matteo D'Addona","author_inst":"Scuola Medica Salernitana, University of Salerno, Department of Medicine, Surgery and Dentistry, Salerno, Italy"},{"author_name":"Edna Stewart","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Rong Deng","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Felipe de Almeida Sartori","author_inst":"Hospital Israelita Albert Einstein, Sao Paulo, Brazil"},{"author_name":"Serhan Unlu","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Zachary Brady","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Ruowei Zhu","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Xiaoyi Cheng","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Viviana Scoca","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Jane J Xu","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Sergei Doulatov","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Karl Theil","author_inst":"Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH"},{"author_name":"David Bosler","author_inst":"Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH"},{"author_name":"Valeria Visconte","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Jaroslaw P Maciejewski","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Aaron D Viny","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Single-cell multi-omics maps clonal IEL expansion and epithelial remodelling in refractory coeliac disease","rel_doi":"10.64898\/2026.08.30.747985","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747985","rel_abs":"Background Refractory coeliac disease type 1 (RCD1) lacks defining molecular markers, and the immune and epithelial mechanisms sustaining intestinal injury remain poorly understood. Objective To define the clonal immune and epithelial states that distinguish RCD1 from active coeliac disease (ACD) and determine their spatial organisation in the duodenal mucosa. Design We integrated single-cell RNA sequencing, CITE-seq surface proteomics and paired T-cell receptor sequencing of duodenal immune and epithelial compartments from Healthy controls (n = 6), ACD (n = 7), RCD1 (n = 9) and RCD2 (n = 2), with spatial transcriptomics in a subset of biopsies. Results RCD1 showed widespread TCR{beta} and TCR{gamma}{delta} clonal expansion across multiple IEL states, extending beyond previously defined mutation-bearing aberrant clones. Distinct IEL populations converged on a shared programme of adaptive persistence, innate-like signalling, metabolic fitness and cytoskeletal remodelling; GZMK expression marked both clonally expanded and non-clonal disease-associated states. In parallel, RCD1 epithelium showed loss of mature absorptive cell states and expansion of stress-associated, immune-interacting and regenerative programmes. Transit-amplifying cells acquired differentiation and tissue-remodelling signatures, while enteroendocrine cells expanded and developed a sensory-neurosecretory programme involving TRPA1, TRPV1, vesicle trafficking and NEUROD1 regulon activity. Spatial transcriptomics localised regenerative and enteroendocrine-associated epithelial programmes adjacent to immune-visible epithelial regions and KLRK1\/GZMK-expressing IEL-rich niches in refractory tissue. Conclusion RCD1 represents a distinct mucosal state characterised by coordinated clonal IEL adaptation and epithelial remodelling, rather than simple amplification of ACD, providing a cellular framework for persistent tissue injury and disease stratification.","rel_num_authors":27,"rel_authors":[{"author_name":"Michael Li","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"},{"author_name":"Arman Safavi","author_inst":"The Westmead Institute for Medical Research"},{"author_name":"Jerome Samir","author_inst":"Mutinex, Sydney, Australia"},{"author_name":"Raymond Louie","author_inst":"University of New South Wales, Kensington NSW, Sydney, Australia"},{"author_name":"Esmaeil Roohparvar Basmenj","author_inst":"University of New South Wales, Kensington NSW, Sydney, Australia"},{"author_name":"Martina Bonomi","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"},{"author_name":"Thiruni Adikari","author_inst":"University of Oxford, Department of Paediatrics"},{"author_name":"Brian Gloss","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Jun Xing","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Katherine Jackson","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Andrew Calcino","author_inst":"James Cook University, Townsville, QLD, Australia"},{"author_name":"Dan Suan","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Valentina Vaira","author_inst":"University of Milan, Milan, Italy"},{"author_name":"Claire Milthorpe","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Melinda Hardy","author_inst":"Walter and Eliza Hall Institute of Medical Research (WEHI), Victoria, Australia"},{"author_name":"Etienne Masle-Farquhar","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Scott Read","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Matt Field","author_inst":"James Cook University, Townsville, QLD, Australia"},{"author_name":"Luca Elli","author_inst":"University of Milan, Milan, Italy"},{"author_name":"Marco Vincenzo Lenti","author_inst":"Department of Internal Medicine and Therapeutics, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy"},{"author_name":"Antonio Di Sabatino","author_inst":"Department of Internal Medicine and Therapeutics, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy"},{"author_name":"Rachele Ciccocioppo","author_inst":"University of Chieti, Gastroenterology & Endoscopy Unit, Department of Medicine and Ageing, Italy"},{"author_name":"Jason A. Tye-Din","author_inst":"Walter and Eliza Hall Institute of Medical Research (WEHI), Victoria, Australia"},{"author_name":"Golo Ahlenstiel","author_inst":"Western Sydney University, Western Sydney Local Health District, Sydney NSW, Australia"},{"author_name":"Christopher C. Goodnow","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Mandeep Singh","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Fabio Luciani","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Single-cell multi-omics maps clonal IEL expansion and epithelial remodelling in refractory coeliac disease","rel_doi":"10.64898\/2026.08.30.747985","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747985","rel_abs":"Background Refractory coeliac disease type 1 (RCD1) lacks defining molecular markers, and the immune and epithelial mechanisms sustaining intestinal injury remain poorly understood. Objective To define the clonal immune and epithelial states that distinguish RCD1 from active coeliac disease (ACD) and determine their spatial organisation in the duodenal mucosa. Design We integrated single-cell RNA sequencing, CITE-seq surface proteomics and paired T-cell receptor sequencing of duodenal immune and epithelial compartments from Healthy controls (n = 6), ACD (n = 7), RCD1 (n = 9) and RCD2 (n = 2), with spatial transcriptomics in a subset of biopsies. Results RCD1 showed widespread TCR{beta} and TCR{gamma}{delta} clonal expansion across multiple IEL states, extending beyond previously defined mutation-bearing aberrant clones. Distinct IEL populations converged on a shared programme of adaptive persistence, innate-like signalling, metabolic fitness and cytoskeletal remodelling; GZMK expression marked both clonally expanded and non-clonal disease-associated states. In parallel, RCD1 epithelium showed loss of mature absorptive cell states and expansion of stress-associated, immune-interacting and regenerative programmes. Transit-amplifying cells acquired differentiation and tissue-remodelling signatures, while enteroendocrine cells expanded and developed a sensory-neurosecretory programme involving TRPA1, TRPV1, vesicle trafficking and NEUROD1 regulon activity. Spatial transcriptomics localised regenerative and enteroendocrine-associated epithelial programmes adjacent to immune-visible epithelial regions and KLRK1\/GZMK-expressing IEL-rich niches in refractory tissue. Conclusion RCD1 represents a distinct mucosal state characterised by coordinated clonal IEL adaptation and epithelial remodelling, rather than simple amplification of ACD, providing a cellular framework for persistent tissue injury and disease stratification.","rel_num_authors":27,"rel_authors":[{"author_name":"Michael Li","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"},{"author_name":"Arman Safavi","author_inst":"The Westmead Institute for Medical Research"},{"author_name":"Jerome Samir","author_inst":"Mutinex, Sydney, Australia"},{"author_name":"Raymond Louie","author_inst":"University of New South Wales, Kensington NSW, Sydney, Australia"},{"author_name":"Esmaeil Roohparvar Basmenj","author_inst":"University of New South Wales, Kensington NSW, Sydney, Australia"},{"author_name":"Martina Bonomi","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"},{"author_name":"Thiruni Adikari","author_inst":"University of Oxford, Department of Paediatrics"},{"author_name":"Brian Gloss","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Jun Xing","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Katherine Jackson","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Andrew Calcino","author_inst":"James Cook University, Townsville, QLD, Australia"},{"author_name":"Dan Suan","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Valentina Vaira","author_inst":"University of Milan, Milan, Italy"},{"author_name":"Claire Milthorpe","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Melinda Hardy","author_inst":"Walter and Eliza Hall Institute of Medical Research (WEHI), Victoria, Australia"},{"author_name":"Etienne Masle-Farquhar","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Scott Read","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Matt Field","author_inst":"James Cook University, Townsville, QLD, Australia"},{"author_name":"Luca Elli","author_inst":"University of Milan, Milan, Italy"},{"author_name":"Marco Vincenzo Lenti","author_inst":"Department of Internal Medicine and Therapeutics, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy"},{"author_name":"Antonio Di Sabatino","author_inst":"Department of Internal Medicine and Therapeutics, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy"},{"author_name":"Rachele Ciccocioppo","author_inst":"University of Chieti, Gastroenterology & Endoscopy Unit, Department of Medicine and Ageing, Italy"},{"author_name":"Jason A. Tye-Din","author_inst":"Walter and Eliza Hall Institute of Medical Research (WEHI), Victoria, Australia"},{"author_name":"Golo Ahlenstiel","author_inst":"Western Sydney University, Western Sydney Local Health District, Sydney NSW, Australia"},{"author_name":"Christopher C. Goodnow","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Mandeep Singh","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Fabio Luciani","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Stroke and Alzheimer's disease have distinct consequences on neurovascular function but synergize to increase amyloid deposition","rel_doi":"10.64898\/2026.08.29.746901","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.746901","rel_abs":"INTRODUCTION: We examined whether stroke induces chronic cerebrovascular dysfunction and thereby exacerbates A{beta} pathology. METHODS: We exposed Tg2576 mice to a transient mild subcortical ischemia and examined cerebrovascular function at chronic timepoints as well as reactive astrocytes and A{beta} deposition post-mortem. RESULTS: Baseline cerebral blood flow and cerebrovascular reactivity (CVR) are both influenced by anesthesia regimen. A{beta} strongly impairs CVR at earlier ages, while mild ischemia impairs CVR at later ages. Mild ischemia reduces neurovascular coupling more strongly than A{beta} and synergistically increases A{beta} deposition in Tg2576 mice. DISCUSSION: Stroke-induced cerebrovascular dysfunction and astrocyte reactivity persist for long periods after the injury. Although there is variability in the spatiotemporal progression of ischemia- and A{beta}-induced vascular impairments, they generally follow the pattern of Thal staging. Surprisingly, the ischemia+A{beta} group showed the least cerebrovascular dysfunction yet an exacerbation of A{beta} deposition, suggesting that these pathologies are connected but not tightly coupled.","rel_num_authors":8,"rel_authors":[{"author_name":"Ozama Ismail","author_inst":"Oregon Health & Science University"},{"author_name":"Simone S Woodruff","author_inst":"Oregon Health & Science University"},{"author_name":"Benjamin Zimmerman","author_inst":"Oregon Health & Science University"},{"author_name":"Wenri Zhang","author_inst":"Oregon Health & Science University"},{"author_name":"Teresa L Stackhouse","author_inst":"Oregon Health & Science University"},{"author_name":"Martin M Pike","author_inst":"Oregon Health & Science University"},{"author_name":"Randy L Woltjer","author_inst":"Oregon Health & Science University"},{"author_name":"Anusha Mishra","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Rapid spatial cognition in mice, with and without neocortex and hippocampus","rel_doi":"10.64898\/2026.08.30.747945","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747945","rel_abs":"Rapid learning, memory, and generalization are often attributed to circuits of the neocortex and hippocampus, but their specific role remains unclear. To examine these cognitive abilities together in individual mice, we observed mice navigating the Manhattan Maze, a reconfigurable 3D labyrinth. Naive wildtype mice improved within two rewards, approached efficient paths within about 20 rewards, retained a 9-turn route overnight, and learned faster in new configurations. Much of the few-shot improvement follows from a rule-based forward bias that emerged even before any reward. In a maze with loops, where that rule is less useful, mice learned within a few rewards to prefer one bottleneck corridor far from the reward location, while choices elsewhere stayed flexible. To begin linking these different components of learning to brain function, we presented the same task to mutant mice that lack the hippocampus and most of the neocortex. They were impaired during initial exploration, where repetitive scanning made them about 3-fold slower to obtain the first few rewards. Past that stage, learning, retention over weeks to months, and generalization to new mazes were largely preserved. Learning this fast is hard to reconcile with trial and error, in which value propagates backwards from the reward. A neuromorphic circuit model instead accounts for the bottleneck choice: it builds a map of the environment without reward, and needs neither cortical nor hippocampal circuit motifs. Therefore, learning is possible without the involvement of neocortex and hippocampus. Structure learned before the first reward, rather than the reward itself, may be what makes few-shot learning possible.","rel_num_authors":8,"rel_authors":[{"author_name":"Jieyu Zheng","author_inst":"California Institute of Technology"},{"author_name":"Rog\u00e9rio Guimar\u00e3es","author_inst":"California Institute of Technology"},{"author_name":"Zeynep Turan","author_inst":"California Institute of Technology"},{"author_name":"Anwesha Das","author_inst":"California Institute of Technology"},{"author_name":"Jennifer Y Hu","author_inst":"California Institute of Technology"},{"author_name":"Katelyn Sadorf","author_inst":"California Institute of Technology"},{"author_name":"Pietro Perona","author_inst":"California Institute of Technology"},{"author_name":"Markus Meister","author_inst":"California Institute of Technology"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Programmable Self-Assembly of RNA Nanostructures with >100 Unique Components","rel_doi":"10.64898\/2026.09.02.748990","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748990","rel_abs":"Sophisticated biomolecular functions often arise from large, precisely organized architectures, motivating efforts to construct increasingly complex structures through programmed nucleic acid self-assembly. Although RNA offers a richer repertoire of structural motifs and biological functions than DNA, RNA nanostructures constructed to date have remained substantially less complex and less scalable than their DNA counterparts, most notably DNA origami and DNA bricks, which rely on large libraries of synthetic single-stranded (ss) DNA strands. Directly adapting these strategies to RNA, however, faces two major barriers: (i) the high cost of producing large libraries of distinct synthetic ssRNA strands, and (ii) the limited stability of ssRNA under conditions commonly used for assembling multicomponent nucleic acid nanostructures. Here we present the double-stranded RNA (dsRNA) bricks approach, which addresses both barriers by adopting principles inspired by natural RNA systems: (i) many distinct RNA components (\"bricks\") are encoded within a single precursor transcript and released by enzymatic processing, (ii) each brick's predominantly double-stranded nature enhances stability. The resulting dsRNA bricks self-assemble through programmable branched kissing-loop interactions. Using this approach, we constructed complex two- and three-dimensional RNA nanostructures with more than 100 distinct components, representing, to our knowledge, the largest and most compositionally complex RNA nanoarchitectures reported to date. Overall, the dsRNA bricks approach establishes a scalable and robust framework for constructing complex RNA nanostructures with compositional and architectural sophistication approaching that of DNA-based systems, thereby opening new opportunities for programmable RNA materials and RNA-based devices.","rel_num_authors":19,"rel_authors":[{"author_name":"Liangxiao Chen","author_inst":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA"},{"author_name":"Zhishang Li","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Jun Yan","author_inst":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA"},{"author_name":"Swarup Dey","author_inst":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA"},{"author_name":"Cong Li","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Alexandra Petrova","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Abhay Prasad","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Deeksha Satyabola","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Kira DeVore","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Xinyi Tu","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Yanzhe Qu","author_inst":"School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Shiyumou Wang","author_inst":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA"},{"author_name":"Gengshi Wu","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Po-Lin Chiu","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Di Wen","author_inst":"Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Portland Medical Center, Portland, OR, USA"},{"author_name":"Joseph Che-Yen Wang","author_inst":"Department of Cell and Biological Systems, Pennsylvania State University College of Medicine, Hershey, PA, USA"},{"author_name":"Hao Yan","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Peng Yin","author_inst":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA"},{"author_name":"Di Liu","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"E-cadherin-mediated neighborhood surveillance dictates pre-malignant outcomes","rel_doi":"10.64898\/2026.09.02.748765","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748765","rel_abs":"Stratified epithelia accumulate oncogenic mutations throughout life, yet overgrowths are rare. How epithelia detect and eliminate aberrant clones remains poorly understood. Using a mouse model of oncogenic clonal mosaicism in the skin, we find that pre-malignant epidermal cells redistribute E-cadherin to interfaces shared with wild-type neighbors, generating local tension heterogeneity that triggers elimination by cell competition. We show that gain or loss of E-cadherin can each drive competitive elimination, and although mechanical routes differ, both establish tension heterogeneity between neighbors, rather than any absolute adhesion state, as the critical determinant of epidermal fitness. This mechanism carries the seeds of its own failure: these tension differentials precipitate clonal sorting, depleting the wild-type contacts that surveillance requires. Pre-malignant cells then become supercompetitors, eliminating wild-type neighbors and expanding hyperplastically. Mechanical heterogeneity therefore endows tissues with an active, yet inherently fragile error-correction system whose collapse initiates a switch in competitive status, increasing tumorigenesis susceptibility.","rel_num_authors":16,"rel_authors":[{"author_name":"Elizabeth A.N. Thompson","author_inst":"Rockefeller University"},{"author_name":"Clara Schimmer","author_inst":"Max Perutz Labs, University of Vienna"},{"author_name":"Tatiana Omelchenko","author_inst":"Rockefeller University"},{"author_name":"Michele Paganini","author_inst":"Max Perutz Labs, University of Vienna"},{"author_name":"Jesse S.S. Novak","author_inst":"Rockefeller University"},{"author_name":"Sienna T. Muller","author_inst":"Max Perutz Labs, University of Vienna"},{"author_name":"Alain R. Bonny","author_inst":"Rockefeller University"},{"author_name":"Marina Schernthanner","author_inst":"Rockefeller University"},{"author_name":"Sairaj M. Sajjath","author_inst":"Rockefeller University"},{"author_name":"Charlotte J. Bell","author_inst":"Rockefeller University"},{"author_name":"Alp Gorgulu","author_inst":"Rockefeller University"},{"author_name":"S. Martina Parigi","author_inst":"Rockefeller University"},{"author_name":"Katherine S. Stewart","author_inst":"Rockefeller University, Lunenfeld Tanenbaum Research Institute"},{"author_name":"Priyam Banerjee","author_inst":"Rockefeller University"},{"author_name":"Elaine Fuchs","author_inst":"Rockefeller University, Howard Hughes Medical Institute"},{"author_name":"Stephanie J. Ellis","author_inst":"Max Perutz Labs, University of Vienna"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Joint ancestry inference reveals the landscape of archaic introgression in admixed populations","rel_doi":"10.64898\/2026.08.29.748036","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.748036","rel_abs":"Studying the evolutionary history of archaic segments in recently admixed individuals requires inferring both continental and archaic ancestry in admixed genomes. Here, we present TRACTINATOR, the first deep-learning method for simultaneous inference of continental and archaic ancestry in admixed human genomes. The model combines SNP sequences, population allele-frequency information, and S* statistics to improve both inference tasks. By learning relationships between haplotypes and population allele frequencies, TRACTINATOR can generalize across genomic regions and even across different genomic datasets. We train our model using both real and synthetic data, and show that augmenting with synthetic data improves accuracy for both continental and archaic ancestry inference. Finally, we apply TRACTINATOR to admixed Latin American populations from the 1,000 Genomes Project, revealing how archaic ancestry is distributed within chromosomal segments of African, European and Indigenous American ancestry in Latin American individuals. For candidates of adaptive introgression, we also infer whether the archaic haplotype was introduced via European or Indigenous American ancestors.","rel_num_authors":7,"rel_authors":[{"author_name":"Jazeps Medina Tretmanis","author_inst":"Brown University"},{"author_name":"Valeria A\u00f1orve-Garibay","author_inst":"Brown University"},{"author_name":"David Peede","author_inst":"Brown University"},{"author_name":"Mayra M Ba\u00f1uelos","author_inst":"Brown University"},{"author_name":"Mar\u00eda C \u00c1vila Arcos","author_inst":"Universidad Nacional Aut\u00f3noma de M\u00e9xico"},{"author_name":"Flora Jay","author_inst":"Universit\u00e9 Paris-Saclay, CNRS, INRIA, Interdisciplinary Laboratory of Numerical Sciences"},{"author_name":"Emilia Huerta-Sanchez","author_inst":"Brown University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Chronic opioid-associated immune dysregulation among people living with HIV","rel_doi":"10.64898\/2026.08.31.748399","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748399","rel_abs":"Objectives: Persistent immune dysregulation contributes to chronic disease among people living with HIV (PWH), even after viral suppression with antiretroviral therapy (ART). Although chronic opioid exposure is associated with adverse clinical outcomes, its impact on immune homeostasis during ART remains incompletely understood. We investigated whether opioid use disorder (OUD) is associated with persistent systemic and cellular immune dysregulation despite ART-mediated reductions in HIV viral load (VL). Methods: Peripheral blood was collected longitudinally from PWH with OUD (PWH\/OUD+) and detectable HIV VL during 6 months of optimized ART (months 0, 3, and 6). A reference cohort of PWH without OUD (PWH\/OUD-) and suppressed HIV VL provided a single blood sample. Immune profiling included plasma inflammatory biomarkers, multiplex cytokine analyses, spectral flow cytometry, and assessment of monocyte cytokine responses following lipopolysaccharide (LPS) stimulation. Mixed-effects models adjusted for HIV VL and VL-stratified analyses were performed. Results: PWH\/OUD+ exhibited persistent immune dysregulation despite reductions in HIV VL. Plasma sCD163, sCD14, fractalkine, and I-TAC remained elevated, whereas TGF-{beta}1 was reduced. OUD was associated with expansion of CD16 monocytes and altered expression of CCR2, CD38, and CD11b. CD4 and CD8 T cells, NK cells, and B cells also exhibited persistent alterations in markers of activation, metabolism, and trafficking. Monocytes from PWH\/OUD+ displayed attenuated cytokine responses following LPS stimulation. Conclusions: OUD is associated with persistent systemic and cellular immune dysfunction in PWH despite ART-mediated viral suppression, supporting opioid exposure as an independent contributor to chronic immune dysregulation that may promote inflammation, immune dysfunction, and long-term HIV-associated comorbidities. Keywords: HIV, Opioid-use disorder, innate immunity, cytokine","rel_num_authors":8,"rel_authors":[{"author_name":"Brent Bever","author_inst":"Oregon Health and Science University"},{"author_name":"Michelle Underwood","author_inst":"Oregon Health and Science University"},{"author_name":"Byung Park","author_inst":"Oregon Health and Science University"},{"author_name":"Susan Pereira Ribeiro","author_inst":"Emory University School of Medicine"},{"author_name":"Ryan R Cook","author_inst":"Oregon Health and Science University"},{"author_name":"Lynn Kunkel","author_inst":"Oregon Health and Science University"},{"author_name":"P. Todd Korthuis","author_inst":"Oregon Health and Science University"},{"author_name":"Christina Lancioni","author_inst":"Oregon Health and Science University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Unbiased and scalable reduction of diverse bacterial genomes","rel_doi":"10.64898\/2026.09.02.748983","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748983","rel_abs":"The genome is a complex, integrated system where the functions and regulatory interactions of its many components remain poorly understood. Genome minimization aims to reduce genomic complexity by removing non-essential elements to reveal the fundamental building blocks of cellular life. However, current minimization strategies are often slow and species-specific due to a reliance on prior information, and limited to producing single, isolated strains, which obscures the diverse ways a genome can adapt to large-scale DNA removal. Here we show the development and application of Stochastic Lineage-based Iterative Minimization (SLIM) a modular, high-throughput platform for unbiased genome reduction across phylogenetically diverse bacteria. We apply SLIM to generate a library of genome-reduced Escherichia coli lineages. We then interrogate the lineages, identifying both universal and lineage-specific transcriptional and translational reprogramming in response to deletions. We demonstrate that these expression dynamics drive environment-dependent fitness, allowing us to pinpoint a single gene deletion in one genome-reduced lineage as the driver of a measurable environmental growth defect. Beyond E. coli, we successfully deploy SLIM in phylogenetically distinct bacterial taxa to rapidly reduce the genomes of Shigella flexneri and Pseudomonas putida, distinct genus and order respectively from E. coli, without species-specific optimization. Our results establish a scalable, generalizable framework for navigating the vast landscape of minimized genomes, providing a powerful new tool for functional discovery and the rational design of synthetic genomic chassis.","rel_num_authors":7,"rel_authors":[{"author_name":"Mikel Lipschitz","author_inst":"California Institute of Technology"},{"author_name":"Baiyi Quan","author_inst":"California Institute of Technology"},{"author_name":"Indeever Madireddy","author_inst":"California Institute of Technology"},{"author_name":"Gohta Aihara","author_inst":"California Institute of Technology"},{"author_name":"Marisa Bennett","author_inst":"California Institute of Technology"},{"author_name":"Tsui-Fen Chou","author_inst":"California Institute of Technology"},{"author_name":"Kaihang Wang","author_inst":"California Institute of Technology"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Synergistic targeting of EP300\/CBP and EYA co-activators collapses the rhabdomyosarcoma core regulatory circuit","rel_doi":"10.64898\/2026.09.02.748955","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748955","rel_abs":"Rhabdomyosarcoma (RMS) is a multi-subtype, high-risk pediatric sarcoma with a low mutational burden. The mutations found in RMS often alter genes involved in transcriptional control. Approaches to target dysregulated RMS transcription have remained elusive. Here, we develop a novel approach to target RMS transcription comprising simultaneous targeting of two distinctly acting transcriptional co-activators. We discover a common identity-controlling pan-RMS core regulatory circuit (CRC) composed of oncogenic and lineage-specific myogenic master transcription factors (mTFs). Using a super-enhancer-based reporter screen, we identify the EP300\/CBP inhibitor A485 as a potent inhibitor of the pan-RMS CRC, though with efficacy-limiting toxicities. To enhance efficacy, we identify the mTF-binding co-activator EYA2 as a co-factor of this pan-RMS CRC and exploit a new second-generation EYA1\/2 inhibitor, LG1-34, to disrupt its function. Combined co-activator inhibition inactivates the CRC and synergistically reduces RMS growth. This strategy dually targets CRC-associated co-activators to cooperatively suppress the RMS transcriptome and enforce cell death.","rel_num_authors":36,"rel_authors":[{"author_name":"Annika L Gustafson","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Stephanie Nance","author_inst":"St. Jude Research Hospital"},{"author_name":"Berkley Gryder","author_inst":"Case Western Reserve School of Medicine"},{"author_name":"Noha A.M. Shendy","author_inst":"St. Jude Research Hospital"},{"author_name":"Lars Wick","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Grace McKay-Corkum","author_inst":"St. Jude Research Hospital"},{"author_name":"K Elaine Ritter","author_inst":"St. Jude Research Hospital"},{"author_name":"Stephen Connor Purdy","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Arthur R Wolin","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Sheera R Rosenbaum","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Sabateeshan Mathavarajah","author_inst":"Massachusetts General Hospital Research Institute"},{"author_name":"Nickerson A Demelfi","author_inst":"Massachusetts General Hospital Research Institute"},{"author_name":"Yueyang Wang","author_inst":"Massachusetts General Hospital Research Institute"},{"author_name":"Yang Zhang","author_inst":"St. Jude Research Hospital"},{"author_name":"Mark Zimmerman","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Anoop Kavirayani","author_inst":"St. Jude Research Hospital"},{"author_name":"Erin A Citarella","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Vernon J Ebegboni","author_inst":"St. Jude Research Hospital"},{"author_name":"John W Hardin","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Alexander LaVeck","author_inst":"University of Colorado, Boulder"},{"author_name":"Xiang Wang","author_inst":"University of Colorado Boulder"},{"author_name":"Neekesh Dharia","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Andrew L Hong","author_inst":"Emory University"},{"author_name":"Guillaume Kugener","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Jesse S Boehm","author_inst":"The Broad Institute of MIT and Harvard"},{"author_name":"Jennifer A Roth","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Javed Khan","author_inst":"National Cancer Institute"},{"author_name":"Francisca Vazquez","author_inst":"The Broad Institute of MIT and Harvard"},{"author_name":"Kristin Artinger","author_inst":"The University of Minnesota"},{"author_name":"Rui Zhao","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"David M Langenau","author_inst":"Massachusetts General Hospital Research Institute"},{"author_name":"Jun Qi","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Kimberly Stegmaier","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Brian Abraham","author_inst":"St. Jude Children's Research Hospital"},{"author_name":"Heide Ford","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Adam D Durbin","author_inst":"St. Jude Children's Research Hospital"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Modelling and measuring effects of shear stress in extrusion bioprinting of endothelial- epithelial cell co-cultures","rel_doi":"10.64898\/2026.09.02.748801","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748801","rel_abs":"Extrusion-based bioprinting enables the development of tissue-like constructs; however, the impact of printing-associated shear stress on cell viability and function remains a critical consideration. To address this, we developed a comprehensive workflow combining rheological characterization, computational fluid dynamics (CFD) modelling, and experimental validation to predict and assess shear stress effects during bioprinting. The rheological properties of gelatin methacryloyl (GelMA) at 5 % (w\/v, 20 {degrees}C) and 10 % (30 {degrees}C) concentrations were modelled, comparing various non-Newtonian regression models. CFD simulations were validated using micro-particle image velocimetry, showing agreement between predicted and measured velocities. The impact of bioprinting-associated shear stress on cell viability was assessed using a co-culture of human umbilical vein endothelial cells and breast epithelial cells. Immediate post-printing analysis revealed increased apoptosis in GelMA 5 % (w\/v, 20 {degrees}C), although 10 % (w\/v) GelMA demonstrated higher shear stress levels compared to 5 % GelMA. After 1 day of culture in crosslinked hydrogels, apoptosis increased in extrusion pressure, demonstrating the impact of low levels of acute shear stress. This workflow provides a robust methodology for predicting acute shear stress impacts during bioprinting, laying the foundation for future optimization studies.","rel_num_authors":6,"rel_authors":[{"author_name":"Jordan W Davern","author_inst":"Queensland University of Technology"},{"author_name":"Angus Weekes","author_inst":"Queensland University of Technology"},{"author_name":"Jorge Amaya Catano","author_inst":"Queensland University of Technology"},{"author_name":"Christoph Meinert","author_inst":"Gelomics Pty Ltd"},{"author_name":"Laura Bray","author_inst":"Queensland University of Technology"},{"author_name":"Travis J Klein","author_inst":"Queensland University of Technology"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"From concentration to export: resource contrasts and bee traits shape pollinator spillover to crops","rel_doi":"10.64898\/2026.08.29.747339","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.747339","rel_abs":"Floral plantings can either concentrate bees or export them to adjacent crops, yet the ecological conditions influencing these outcomes remain unclear. Here, we develop a mathematical model as proof of concept for our previous integrative hypothesis: concentrator and exporter outcomes can arise as alternative, context-dependent outcomes of the same underlying resource-selection process. Using bees as a model and focusing specifically on spillover from floral plantings to crops, we identified resource-specific thresholds separating concentration- and export-favoring conditions. Our model translates differences in relative patch attractiveness into context-dependent concentration and export outcomes and generates resource-specific, testable predictions about the conditions favoring pollinator movement into crops. In our simulations, the concentrator-exporter transition occurred at a lower flowering-intensity contrast than at pollen or nectar contrasts, which suggests that flowering intensity may provide an initial cue for bee movement, whereas nectar and pollen rewards refine or sustain bee responses once crops are perceived as attractive. Spillover thresholds differed among resource contrasts, whereas response steepness varied across bee-trait and community scenarios. Under the model's trait-sensitivity formulation, predicted spillover probability responded more strongly to flowering contrast for specialists than for generalists; colony size amplified this response, whereas bee richness dampened it. Together, these patterns show how flowering and resource contrasts interact with bee traits and community context to shape predicted spillover. Our results confirm that the concentrator and exporter hypotheses can be understood as context-dependent outcomes of the same ecological process rather than as mutually exclusive alternatives. Experimental tests of the predicted thresholds conducted in the field could reveal when and where floral plantings are most likely to promote bee spillover to crops, potentially supporting crop pollination.","rel_num_authors":5,"rel_authors":[{"author_name":"Cristina Kita","author_inst":"Universidade de Sao Paulo"},{"author_name":"Isabel Alves-dos-Santos","author_inst":"Universidade de Sao Paulo"},{"author_name":"Michael Hrncir","author_inst":"Universidade de Sao Paulo"},{"author_name":"Renata Muylaert","author_inst":"The University of Sydney"},{"author_name":"Marco A. R. Mello","author_inst":"University of Sao Paulo"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Burning down the mouse: Effects of wildfire and post-fire reseeding on Sin Nombre virus prevalence in its reservoir host","rel_doi":"10.64898\/2026.09.02.748549","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748549","rel_abs":"Worldwide, physical habitats and biological communities are being reshaped by wildfire. Such changes have obvious potential to alter pathogen ecology, yet few studies, outside of those focused on ectoparasites, have tested the effects of wildfire on pathogen prevalence in wildlife. Even fewer have focused on the impacts of strategies for post-fire remediation on pathogen circulation. Here, we investigated the effect of wildfire and wildfire mitigation on the prevalence and viral load of Sin Nombre virus (SNV) infection in its reservoir host, the western deer mouse, using a study design of matched burned, unburned, and post-burn reseeded sites in northern New Mexico. In total, we screened 411 individual deer mice for SNV via RT-qPCR and analyzed the effect of wildfire and wildfire mitigation on relative mouse abundance, individual infection probability, site-level prevalence and viral load. Relative abundance was highest at reseeded sites, and model selection indicated that habitat structure, particularly the gradient from closed canopy to open-herbaceous habitat, was consistently associated with increased relative abundance. Similarly, SNV prevalence was significantly higher in reseeded sites than either burned or unburned sites but did not differ in burned versus unburned sites. Viral load did not differ between burn history types, and zero-inflated gamma hurdle models did not identify any strong predictors of viral load. Serendipitously, we were also able to investigate the impacts of an El Nino Southern Oscillation (ENSO) cycle on patterns of infection in a subsample of sites that were studied during and one year after an ENSO year and found that SNV prevalence increased significantly post-ENSO. Both reseeding and ENSO deliver resource pulses that can support increases in mouse density and thereby enhance SNV transmission. The impacts of post-fire management practices on SNV prevalence in its reservoir host that were revealed in this study should be considered when implementing such strategies and when utilizing treated areas.","rel_num_authors":7,"rel_authors":[{"author_name":"Carleen N. Silva","author_inst":"New Mexico State University"},{"author_name":"Frances M. Twohig","author_inst":"University of New Mexico"},{"author_name":"Matthew E. Gompper","author_inst":"New Mexico State University"},{"author_name":"Robert A. Nofchissey","author_inst":"University of New Mexico"},{"author_name":"Aaron C. Young","author_inst":"New Mexico State University"},{"author_name":"Steven B. Bradfute","author_inst":"University of New Mexico"},{"author_name":"Kathryn A. Hanley","author_inst":"New Mexico State University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Ribosomal proteins are major substrates of starvation-induced endosomal microautophagy in Drosophila.","rel_doi":"10.64898\/2026.09.01.748611","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748611","rel_abs":"Maintenance of cellular homeostasis requires tight coordination between protein synthesis and degradation, particularly at old age and under conditions of stress including starvation. Autophagy contributes to sustain this balance by degrading cytoplasmic proteins and organelles. It thus is essential to prevent the accumulation of damaged proteins and organelles and to recycle nutrients. Of the three forms of autophagy, macroautophagy, chaperone mediated autophagy, and (endosomal) microautophagy (e-MI), the latter remains the least well understood. During e-MI, cytosolic substrate proteins are captured into late endosomes via ESCRT-dependent multivesicular body formation and then degraded in late endosomes or lysosomes. e-MI is thought to contribute to protein quality control under basal conditions and under stress. Importantly, very little is known about the endogenous substrates of e-MI in flies and thus about its physiological role. Performing integrative multi-omic analyses in Drosophila larval fat body that has functions similar to mammalian liver and adipose tissue, we identified 153 high-confidence endogenous e-MI substrates with the degradation of ribosomal proteins by e-MI being the most strongly affected functional category. Generally, we found that starvation caused the depletion of proteins involved in translation, aminoacyl-tRNA synthesis, and ribosomal biogenesis, without affecting their level of transcripts. Importantly, we observe a striking specificity between e-MI and macroautophagy, as the two pathways largely target distinct protein sets including different subsets of ribosomal proteins. Our metabolomic analysis further shows that genetic inhibition of e-MI reverses the reduced levels of amino acid caused by starvation. Together, our findings reveal ribosome turnover as a central physiological function of Drosophila e-MI and establish e-MI as a pathway driving metabolic adaptation during starvation.","rel_num_authors":5,"rel_authors":[{"author_name":"Prasoon Jaya","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Satya Surabhi","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Jennifer Aguilan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Simone Sidoli","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Andreas Jenny","author_inst":"Albert Einstein College of Medicine"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"A cognitive representation in primary visual cortex modulated by vision","rel_doi":"10.64898\/2026.08.29.748029","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.748029","rel_abs":"Primary visual cortex (V1) is a critical substrate for mammalian vision. Traditionally, visual inputs are thought to be the main drivers of V1 activity, with internal signals playing a modulatory role. Here we show that this relationship is inverted for a large fraction of V1 neurons. In rats completing a navigation task in darkness, these neurons encoded progress along physically distinct paths with a shared turn structure. Under illumination, visual stimuli gain-modulated this path-invariant activity rather than replacing it with stimulus-driven responses. Path-invariant V1 neurons were also preferentially coordinated with hippocampal ensembles during sharp-wave ripples, linking them to a brain-wide network involved in learning. These findings establish that an internal model of the world can serve as a primary driver of activity in sensory cortex.","rel_num_authors":10,"rel_authors":[{"author_name":"Kyu Hyun Lee","author_inst":"UCSF"},{"author_name":"Philip Adenekan","author_inst":"University of Rochester"},{"author_name":"Fan Gao","author_inst":"UCSF"},{"author_name":"Jenny Zhou","author_inst":"Lawrence Livermore National Lab"},{"author_name":"Jose Hernandez","author_inst":"Lawrence Livermore National Lab"},{"author_name":"Allison Yorita","author_inst":"Lawrence Livermore National Lab"},{"author_name":"Razi Haque","author_inst":"Lawrence Livermore National Lab"},{"author_name":"Kenneth Kay","author_inst":"University of Rochester"},{"author_name":"Massimo Scanziani","author_inst":"University of California San Francisco and Howard Hughes Medical Institute"},{"author_name":"Loren Frank","author_inst":"UCSF and HHMI"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"From Housing to Hotspots: Integrating a Housing-Based Measure of Individual Socioeconomic Status with Geospatial Analysis to Target Colorectal Cancer Screening in Rural Communities","rel_doi":"10.64898\/2026.08.28.26361444","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361444","rel_abs":"PurposeThis study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions.\n\nMethodsWe used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race\/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1).\n\nFindingsAmong 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81).\n\nConclusionLower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.","rel_num_authors":30,"rel_authors":[{"author_name":"Rebecca Yao","author_inst":"University of Chicago"},{"author_name":"Chung-Il Wi","author_inst":"Mayo Clinic"},{"author_name":"Madison J Beenken","author_inst":"Mayo Clinic"},{"author_name":"Dave Watson","author_inst":"Mayo Clinic"},{"author_name":"Philip H Wheeler","author_inst":"Mayo Clinic"},{"author_name":"Mike Finch","author_inst":"Children's Minnesota Research Institute"},{"author_name":"Dan P Kelleher","author_inst":"Mayo Clinic"},{"author_name":"Gokhan Anil","author_inst":"Mayo Clinic"},{"author_name":"Trent Anderson","author_inst":"Mayo Clinic Health System"},{"author_name":"Kathy Madden","author_inst":"Mayo Clinic Health System"},{"author_name":"Scott H Okuno","author_inst":"Mayo Clinic"},{"author_name":"Folakemi T Odedina","author_inst":"Mayo Clinic"},{"author_name":"Erin C Westfall","author_inst":"Mayo Clinic Health System"},{"author_name":"Eunice Y Park","author_inst":"Mayo Clinic"},{"author_name":"Pravesh Sharma","author_inst":"Mayo Clinic Health System"},{"author_name":"Sagar Dugani","author_inst":"Mayo Clinic"},{"author_name":"Randy M Foss","author_inst":"Mayo Clinic Health System"},{"author_name":"Brandon H Hidaka","author_inst":"Mayo Clinic Health System"},{"author_name":"Jessica L Sosso","author_inst":"Mayo Clinic Health System"},{"author_name":"Shivani Sabarish","author_inst":"Mayo Clinic"},{"author_name":"Gurpreet Singh","author_inst":"Mayo Clinic"},{"author_name":"Nahyr Lugo-Fagundo","author_inst":"Mayo Clinic"},{"author_name":"James Howick","author_inst":"Mayo Clinic"},{"author_name":"W. Ray Kim","author_inst":"Mayo Clinic"},{"author_name":"Andrew D Calvin","author_inst":"Mayo Clinic"},{"author_name":"Cheryl L. Walker-Mcgill","author_inst":"Carolina Complete Health"},{"author_name":"Lior Rennert","author_inst":"Clemson University"},{"author_name":"Young J Juhn","author_inst":"Mayo Clinic"},{"author_name":"James R Cerhan","author_inst":"Mayo Clinic"},{"author_name":"Brian A Lynch","author_inst":"Mayo Clinic"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Immune Checkpoint Blockade Modifies Drug-Associated Toxicity Across Phenotypes and Time","rel_doi":"10.64898\/2026.08.31.26361880","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361880","rel_abs":"ImportanceImmune checkpoint inhibitors (ICIs) produce diverse immune toxicities, but whether checkpoint blockade also modifies associations between other drugs and adverse events is poorly understood.\n\nObjectiveTo define ICI-associated toxicity organization and determine whether drug-associated adverse events and onset vary with ICI exposure and checkpoint pathway.\n\nDesign and SettingCross-sectional analysis of deduplicated FAERS reports from 2016 through 2025; analyses performed in 2026.\n\nParticipantsAmong 13,701,106 deduplicated reports, 2,365,269 were cancer associated and 256,940 contained an ICI. Median age among cancer reports with observed age was 66 years (IQR, 56-75 years); 1,031,999 (43.6%) were female and 1,003,154 (42.4%) were male.\n\nExposuresICI exposure in any reported drug role, individual primary-suspect drugs, and checkpoint-pathway exposure.\n\nMain Outcomes and MeasuresReporting odds ratios (ORs), cross-organ adverse-event communities, adjusted primary-suspect drug x ICI interaction ORs for Stevens-Johnson syndrome\/toxic epidermal necrolysis (SJS\/TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), interstitial nephritis, drug-induced liver injury (DILI), and vomiting (VOM), and accelerated failure-time model time ratios for documented onset.\n\nResultsOf 3001 eligible Preferred Terms in cancer-associated reports, 2091 differed at a false discovery rate (FDR) less than .05. Four cross-organ toxicity communities were identified. Of 138 eligible drug-phenotype pairs, 65 had FDR-significant interactions, including moxifloxacin-SJS\/TEN amplification (interaction OR, 101.72; 95% CI, 39.11-264.55), enfortumab vedotin-SJS\/TEN attenuation (interaction OR, 0.17; 95% CI, 0.13-0.23), and omeprazole-interstitial nephritis amplification (interaction OR, 10.35; 95% CI, 7.62-14.05). Among 60,324 reports contributing to temporal analyses, ICI exposure was associated with longer adjusted documented time to onset for 5 of 6 phenotypes (time ratios, 1.37-1.59) but not AGEP (time ratio, 0.99; 95% CI, 0.67-1.46). Temporal associations also differed across checkpoint pathways.\n\nConclusions and RelevanceICIs were associated with a structured cross-organ toxicity landscape, phenotype-specific modification of drug-associated adverse events, and distinct temporal patterns across checkpoint pathways. These findings support checkpoint blockade as a modifier of drug-associated toxicity and motivate longitudinal and mechanistic validation.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSWhat is the structure of toxicity associated with immune checkpoint blockade?\n\nFindingsIn this cross-sectional study of 13,701,106 deduplicated adverse-event reports, ICI exposure defined a broad toxicity landscape containing 4 cross-organ communities. Sixty-five drug x ICI interactions were identified across 5 immune-mediated phenotypes and a control (vomiting), and documented time to onset differed by phenotype and checkpoint pathway.\n\nMeaningCheckpoint blockade may create an altered immune state in which the phenotype and timing of drug-associated toxicity depend on both the accompanying drug and the checkpoint pathway inhibited.","rel_num_authors":7,"rel_authors":[{"author_name":"Eric M Mukherjee","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Amir Asiaee","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Dodi Park","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Matthew S Krantz","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Cosby A Stone Jr.","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Michelle Martin-Pozo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Elizabeth Jane Phillips","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"The accuracy of urine-based mycobacterial antigens to detect childhood tuberculosis using an ultrasensitive immunoassay","rel_doi":"10.64898\/2026.08.28.26361530","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361530","rel_abs":"BackgroundUrine-based testing offers a promising non-sputum approach for diagnosing paediatric tuberculosis. However, the currently available lipoarabinomannan (LAM) assay shows limited sensitivity in children and is primarily indicated for those living with HIV. Co-detection of LAM with Mycobacterium tuberculosis (Mtb) proteins in urine could provide complementary pathogen-derived biomarkers that improve diagnostic performance.\n\nMethodsWe developed an ultrasensitive multiplex electrochemiluminescence (ECL) immunoassay to measure Ag85B, CFP-10, ESAT-6, MPT32, and MPT64 in urine. We determined the analytical limits of detection and evaluated the diagnostic performance of individual proteins and LAM using urine samples from children with Confirmed, Unconfirmed, and Unlikely pulmonary tuberculosis enrolled across five high-burden countries (The Gambia, India, Peru, South Africa, and Uganda). Performance was assessed overall, by HIV and nutritional status, and across biomarker combinations.\n\nFindingsUrine samples from 630 children were analysed (median age was 4 years [IQR 2-8]; 44% female, 15% living with HIV, 19% underweight, 24% with Confirmed tuberculosis). The ECL assay achieved femtomolar limits of detection (1{middle dot}5 to 4{middle dot}0 fM). The sensitivity and specificity of individual Mtb proteins were 12-33% and 98-100%, respectively. Ag85B had the highest sensitivity (33%, 95% CI 26-41) for Confirmed tuberculosis and was similar to LAM. A four-antigen signature (Ag85B, MPT64, MPT32, LAM) was 50% sensitive (95% CI 42-58) and 94% specific (95% CI 90-96), and was significantly more sensitive than LAM alone, in particular among those without HIV. An additional sixteen (10%) of children with Unconfirmed TB had at least one Mtb protein or LAM detected.\n\nInterpretationMultiple Mtb proteins are detectable in paediatric urine with high specificity, and multi-antigen signatures can augment sensitivity versus LAM alone. These findings demonstrate the potential of multi-antigen urine detection for childhood TB and define analytical targets for the development of future point-of-care diagnostics.\n\nFundingNational Institutes of Health.\n\nRESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe examined the literature for peer-reviewed research articles on the accuracy of biomarker- based urine tests for pulmonary tuberculosis in children <15 years old. We used PubMed and Google Scholar, with the search terms \"child\", \"tuberculosis\", \"urine\", and \"diagnosis\" regardless of language from July 2016 to July 2026. We excluded articles on host-based markers and extrapulmonary tuberculosis. Molecular urine assays, including Xpert MTB\/RIF, have limited sensitivity to detect childhood pulmonary tuberculosis. Most of the research on urine-based diagnostics has focused on detection of lipoarabinomannan (LAM), which has had variable sensitivity and specificity in children against a microbiological reference standard. Accuracy is higher in those with HIV, and current guidelines only recommend LAM for adults and children with HIV.\n\nAdded value of this studyWe developed an ultrasensitive multiplex immunoassay to detect and measure Mtb-specific proteins in urine samples from children with presumptive tuberculosis in five high-burden countries. We found that Mtb proteins could be detected in paediatric urine samples with high specificity, and Ag85B had similar sensitivity as LAM. A four-marker panel (Ag85B, MPT32, MPT64, and LAM) improved sensitivity over LAM alone without a significant loss of specificity, in particular among children without HIV.\n\nImplications of all the available evidenceMulti-antigen urine tests can improve sensitivity over single marker assays, and have the potential to provide non-sputum, point-of-care tuberculosis detection in children regardless of HIV status.","rel_num_authors":24,"rel_authors":[{"author_name":"Esin Nkereuwem","author_inst":"London School of Hygiene and Tropical Medicine"},{"author_name":"Salvia Misaghian","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Devan Jaganath","author_inst":"University of California San Francisco"},{"author_name":"Roger I Calderon","author_inst":"Advanced Research and Health"},{"author_name":"Juaneta Luiz","author_inst":"University of Cape Town"},{"author_name":"Mandar Paradkar","author_inst":"Byramjee Jeejeebhoy Government Medical College"},{"author_name":"Peter Wambi","author_inst":"WALIMU"},{"author_name":"Robert Castro","author_inst":"University of California San Francisco"},{"author_name":"Rutuja Nerurkar","author_inst":"University of California San Francisco"},{"author_name":"Mingyue Wang","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Jacob Wohlstadter","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Molly F Franke","author_inst":"Harvard Medical School"},{"author_name":"Beate Kampmann","author_inst":"Charit\u00e9 Universit\u00e4tsmedizin Berlin"},{"author_name":"Aarti Kinikar","author_inst":"Byramjee Jeejeebhoy Government Medical College"},{"author_name":"Heather J Zar","author_inst":"University of Cape Town"},{"author_name":"Mark Segal","author_inst":"University of California San Francisco"},{"author_name":"Midori Kato-Maeda","author_inst":"University of California San Francisco"},{"author_name":"Jeffrey M Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Danielle Swaney","author_inst":"University of California San Francisco"},{"author_name":"Adithya Cattamanchi","author_inst":"University of California Irvine"},{"author_name":"Joel D Ernst","author_inst":"University of California San Francisco"},{"author_name":"Eric Wobudeya","author_inst":"WALIMU"},{"author_name":"George Sigal","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"- The Combo Study","author_inst":""}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Social Determinants of Health in HIV\/HBV Coinfection Compared with HIV and HBV Monoinfection: A Framework for Dynamic Social Vulnerability","rel_doi":"10.64898\/2026.08.31.26361856","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361856","rel_abs":"Human immunodeficiency virus (HIV) and hepatitis B virus (HBV) coinfection is associated with accelerated liver disease, but whether coinfection is associated with newly documented social determinants of health (SDoH) is unclear. We conducted a retrospective cohort study using TriNetX across 110 U.S. healthcare organizations (2010-2026). We propensity score matched adults with HIV\/HBV to adults with HIV or HBV monoinfection. We organized newly documented SDoH indicators using a dynamic individual-level framework with four clinically recognized domains of social disadvantage: material vulnerability, healthcare access and engagement, interpersonal adversity, and psychosocial vulnerability. Matched cohorts included 10,071 HIV\/HBV-HIV pairs and 9,659 HIV\/HBV-HBV pairs (mean age, 47 years; 79% male; 66% non-White; median follow-up, 3.3 years). Over 178,900 person-years, HIV\/HBV was associated with higher risk of the primary SDoH composite compared with HIV (11.5% vs 9.7%; incidence rate, 2.50 vs 1.97 per 100 person-years; hazard ratio [HR], 1.25; 95% confidence interval [CI], 1.15-1.37) and HBV (11.0% vs 6.4%; incidence rate, 2.39 vs 1.67; HR, 1.50; 95% CI, 1.35-1.67). HIV\/HBV was also associated with higher material vulnerability and healthcare access and engagement composites in both comparisons, including housing instability, food insecurity, financial insecurity, insurance instability, and care disengagement\/nonadherence (HR range, 1.22-3.33 vs HIV; 1.31-1.94 vs HBV). In the HBV comparison, HIV\/HBV was additionally associated with interpersonal adversity, primary support stressors, and violence or victimization (HR range, 1.36-2.16). Findings were robust across sensitivity analyses. HIV\/HBV was associated with more newly documented SDoH than monoinfection, supporting dynamic SDoH assessment.","rel_num_authors":9,"rel_authors":[{"author_name":"George Yendewa","author_inst":"Case Western Reserve University"},{"author_name":"Tayoot Chengsupanimit","author_inst":"Case Western Reserve University"},{"author_name":"Ali Dehghani","author_inst":"Case Western Reserve University"},{"author_name":"Ali Ahmed","author_inst":"University of Pennsylvania"},{"author_name":"Amir Mohareb","author_inst":"Harvard Medical School"},{"author_name":"Michael Freeman","author_inst":"Case Western Reserve University"},{"author_name":"Chari Cohen","author_inst":"Hepatitis B Foundation"},{"author_name":"Ighovwerha Ofotokun","author_inst":"Case Western Reserve University"},{"author_name":"Karine Dube","author_inst":"University of Pennsylvania"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Cross-System Meta-Analysis of Machine Learning Predictors Identifies Value-Specific Risk Drivers and Interactions Underlying Acute Kidney Injury","rel_doi":"10.64898\/2026.08.31.26361849","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361849","rel_abs":"BackgroundCurrent understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific biomarker values or physiologic thresholds influence susceptibility. We aimed to synthesize knowledge from machine learning models trained across multiple health systems to identify generalizable, value-specific risk drivers and biomarker interactions contributing to AKI risk.\n\nMethodsWe analyzed electronic health records (EHRs) from 785,497 adult inpatients between 2010 and 2019 across nine U.S. academic medical centers within PCORnet. Interpretable gradient boosting machine models were independently developed at each health system to quantify predictor-outcome associations. Meta-regression was applied to integrate these site-level results, characterize nonlinear value-risk relationships, and identify bivariate interactions between predictors.\n\nResultMeta-analysis revealed consistent, value-specific risk drivers across health systems. An increase in glucose from 100 mg\/dL to 140 mg\/dL was associated with a 1.46-fold higher risk of AKI. Chloride and anion gap also demonstrated elevated AKI risk with risk increases overlapping portions of their reference ranges, with anion gap showing a 1.14-fold increase across 4-12 mmol\/L and chloride a 1.28-fold increase across 96-100 mEq\/L. Electrolytes including potassium, calcium, and sodium showed quadratic associations with AKI risk. Bivariate meta-regression identified interactions between key predictors, highlighting pathways that jointly modulate AKI risk.\n\nConclusionThis cross-system meta-analysis synthesizes machine learning-derived evidence into clinically interpretable knowledge, revealing how specific biomarker ranges and interactions modulate AKI risk. By moving beyond surface-level associations to quantitative, generalizable physiologic thresholds, these findings provide actionable insights to enhance risk stratification and personalized prevention in hospital care.\n\nHighlightsO_LICross-system meta-analysis uncovered generalizable, value-specific AKI risk drivers\nC_LIO_LIGlucose, chloride, and anion gap within reference ranges linked to higher AKI risk\nC_LIO_LIKey predictor interactions suggest coordinated pathways jointly modulating AKI risk\nC_LI","rel_num_authors":18,"rel_authors":[{"author_name":"Ho Yin Chan","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Deyi Li","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Alan S.L. Yu","author_inst":"Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"John A. Kellum","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Dana Y. Fuhrman","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Qi Xu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Elizabeth A Chrischilles","author_inst":"Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, Iowa, USA"},{"author_name":"Lindsay G. Cowell","author_inst":"Department of Health Data Science and Biostatistics, Peter O Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Sravani Chandaka","author_inst":"Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"Alfred Jerrod Anzalone","author_inst":"Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA"},{"author_name":"Jacob Kean","author_inst":"Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA"},{"author_name":"Kathleen M. McTigue","author_inst":"Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Abu Saleh Mohammad Mosa","author_inst":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA"},{"author_name":"Bradley Taylor","author_inst":"CTSI Center for Biomedical Informatics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA"},{"author_name":"Mahanaz Syed","author_inst":"Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA"},{"author_name":"Lemuel R. Waitman","author_inst":"Department of Biomedical and Health Informatics, University of Missouri-Kansas City, Kansas City, Missouri, USA"},{"author_name":"Yong Hu","author_inst":"Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China"},{"author_name":"Mei Liu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Cross-System Meta-Analysis of Machine Learning Predictors Identifies Value-Specific Risk Drivers and Interactions Underlying Acute Kidney Injury","rel_doi":"10.64898\/2026.08.31.26361849","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361849","rel_abs":"BackgroundCurrent understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific biomarker values or physiologic thresholds influence susceptibility. We aimed to synthesize knowledge from machine learning models trained across multiple health systems to identify generalizable, value-specific risk drivers and biomarker interactions contributing to AKI risk.\n\nMethodsWe analyzed electronic health records (EHRs) from 785,497 adult inpatients between 2010 and 2019 across nine U.S. academic medical centers within PCORnet. Interpretable gradient boosting machine models were independently developed at each health system to quantify predictor-outcome associations. Meta-regression was applied to integrate these site-level results, characterize nonlinear value-risk relationships, and identify bivariate interactions between predictors.\n\nResultMeta-analysis revealed consistent, value-specific risk drivers across health systems. An increase in glucose from 100 mg\/dL to 140 mg\/dL was associated with a 1.46-fold higher risk of AKI. Chloride and anion gap also demonstrated elevated AKI risk with risk increases overlapping portions of their reference ranges, with anion gap showing a 1.14-fold increase across 4-12 mmol\/L and chloride a 1.28-fold increase across 96-100 mEq\/L. Electrolytes including potassium, calcium, and sodium showed quadratic associations with AKI risk. Bivariate meta-regression identified interactions between key predictors, highlighting pathways that jointly modulate AKI risk.\n\nConclusionThis cross-system meta-analysis synthesizes machine learning-derived evidence into clinically interpretable knowledge, revealing how specific biomarker ranges and interactions modulate AKI risk. By moving beyond surface-level associations to quantitative, generalizable physiologic thresholds, these findings provide actionable insights to enhance risk stratification and personalized prevention in hospital care.\n\nHighlightsO_LICross-system meta-analysis uncovered generalizable, value-specific AKI risk drivers\nC_LIO_LIGlucose, chloride, and anion gap within reference ranges linked to higher AKI risk\nC_LIO_LIKey predictor interactions suggest coordinated pathways jointly modulating AKI risk\nC_LI","rel_num_authors":18,"rel_authors":[{"author_name":"Ho Yin Chan","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Deyi Li","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Alan S.L. Yu","author_inst":"Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"John A. Kellum","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Dana Y. Fuhrman","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Qi Xu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Elizabeth A Chrischilles","author_inst":"Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, Iowa, USA"},{"author_name":"Lindsay G. Cowell","author_inst":"Department of Health Data Science and Biostatistics, Peter O Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Sravani Chandaka","author_inst":"Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"Alfred Jerrod Anzalone","author_inst":"Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA"},{"author_name":"Jacob Kean","author_inst":"Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA"},{"author_name":"Kathleen M. McTigue","author_inst":"Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Abu Saleh Mohammad Mosa","author_inst":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA"},{"author_name":"Bradley Taylor","author_inst":"CTSI Center for Biomedical Informatics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA"},{"author_name":"Mahanaz Syed","author_inst":"Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA"},{"author_name":"Lemuel R. Waitman","author_inst":"Department of Biomedical and Health Informatics, University of Missouri-Kansas City, Kansas City, Missouri, USA"},{"author_name":"Yong Hu","author_inst":"Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China"},{"author_name":"Mei Liu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Secondhand Cannabis Smoke Exposure: Prevalence, Personal Use, and Neurocognitive Trajectories Over Time in Adolescents in the United States","rel_doi":"10.64898\/2026.08.31.26361835","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361835","rel_abs":"BackgroundAs cannabis policy changes in the United States, secondhand cannabis smoke (SCS) is increasingly common, including within families. However, prevalence of exposure and clinical correlates over time in adolescents are not fully understood.\n\nObjectives(1) To estimate the prevalence of SCS and personal cannabis use in US-based teens exposed to SCS, and (2) examine the cognitive trajectories of adolescents exposed to SCS compared to non- exposed peers.\n\nMethodsData from the Adolescent Brain Cognitive Development (ABCD) Study was used. Participants (n=11,316 of full cohort with follow-up data; n=776 with self-reported family SCS exposure) attended yearly visits from ages 11-17, completing substance use interviews, toxicological testing, and the NIH Toolbox Cognitive battery. Youth with SCS but no personal cannabis use (n=419; 47% female) were matched on prenatal substance exposure, family substance use history, and sociodemographics to non- SCS exposed and non-cannabis-using youth with a 1:2 ratio (Controls n=838). Linear mixed-effects models assessed cognitive performance by SCS*age interactions, accounting for random effects of subject and family. Covariates included sex and alcohol, nicotine, and other substance use. Secondary models analyzed performance by cumulative waves of reported SCS exposure interacting with age.\n\nResultsOf the full cohort, 6.9% (n=776) reported exposure to SCS. Of these individuals, 46% endorsed lifetime personal cannabis use by age 17, relative to 20% of non-SCS exposed youth (OR=3.83[95%CI:3.29,4.44]). Within matched participants, SCS*age demonstrated a significant interaction on attention and inhibitory control ({beta}=-0.32, p=.028), with SCS demonstrating reduced improvement over time. More waves of exposure were also associated with worse performance over time ({beta}=-0.39, p=.057).\n\nDiscussionAlmost half of those who had been exposed to SCS endorsed personal cannabis use. Cognitive findings were domain specific, similar to findings in secondhand tobacco: SCS exposed youth showed restricted improvement in attention and inhibitory control by age 17. Public health and policymakers should make efforts to curb youth SCS exposure, given the potential for risk which has not been fully explored to date.","rel_num_authors":6,"rel_authors":[{"author_name":"Jenicca Bastien","author_inst":"University of California, San Diego"},{"author_name":"Kimberly Garcia","author_inst":"University of California, San Diego"},{"author_name":"Alexander L. Wallace","author_inst":"University of California, San Diego"},{"author_name":"Ryan M Sullivan","author_inst":"University of California, San Diego"},{"author_name":"Eunha Hoh","author_inst":"San Diego State University"},{"author_name":"Natasha E Wade","author_inst":"University of California, San Diego"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Towards Electronic Health Records-Based Paediatric Growth References: Results from the SwissPedGrowth Project","rel_doi":"10.64898\/2026.08.28.26361619","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361619","rel_abs":"BACKGROUNDWe used anthropometric data from electronic health records (EHRs) of Swiss childrens hospitals to evaluate growth references and estimate centile curves.\n\nMETHODSWe received EHRs extracted from seven Swiss childrens hospitals and analysed two samples: all children with a height, weight, body mass index (BMI), or head circumference recording, and a subsample restricted to children without diseases potentially affecting growth, weighted to represent the general population. We calculated mean z-scores based on the World Health Organization growth references adopted for Switzerland in 2011 (CH-WHO 2011) and current Swiss growth references (Swiss 2026). We estimated sex-specific centile curves in the subsample using generalised additive models for location, scale, and shape.\n\nRESULTSWe included 213,868 children with height, 448,002 with weight, 209,244 with BMI, and 67,397 with head circumference recordings. Mean z-scores in the  all children sample were (CH- WHO 2011; Swiss 2026): height (0.10; -0.19), weight (0.16; -0.09), BMI (0.04; -0.07), head circumference (-0.28, -0.28); and in the subsample: height (0.34; 0.00), weight (0.27; 0.01), BMI (0.18; 0.05), and head circumference (0.04; 0.01). The 50th height, weight, BMI, and head circumference centiles of girls and boys in the subsample closely followed those of Swiss 2026, with slightly wider 3rd and 97th centiles in infancy and adolescence.\n\nCONCLUSIONHeight, weight, BMI, and head circumference centiles aligned well with the Swiss 2026 growth references in Switzerland, demonstrating that hospital EHRs could contribute to future growth references.","rel_num_authors":20,"rel_authors":[{"author_name":"Lorenz Manuel Leuenberger","author_inst":"University of Bern"},{"author_name":"Yara Shoman","author_inst":"University of Bern"},{"author_name":"Franco Romero","author_inst":"University of Bern"},{"author_name":"Mari Sasaki","author_inst":"University of Bern"},{"author_name":"Xeni Deligianni","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Nicole Goebel","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Rebeca Mozun","author_inst":"University Children's Hospital Zurich"},{"author_name":"Julia Anna Bielicki","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Marie-Anne Burckhardt","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Christoph Saner","author_inst":"Department of Pediatrics, Inselspital, Bern University Hospital"},{"author_name":"Valerie Schwitzgebel","author_inst":"University of Geneva"},{"author_name":"Michael Hauschild","author_inst":"Lausanne University Hospital (CHUV)"},{"author_name":"Franziska Righini Grunder","author_inst":"Children's Hospital of Central Switzerland"},{"author_name":"Pascal Mueller","author_inst":"Children's Hospital of Eastern Switzerland"},{"author_name":"Luregn J Schlapbach","author_inst":"University Children`s Hospital Zurich"},{"author_name":"Oskar Jenni","author_inst":"University Children's Hospital Zurich"},{"author_name":"Ben Daniel Spycher","author_inst":"University of Bern"},{"author_name":"Claudia Elisabeth Kuehni","author_inst":"University of Bern"},{"author_name":"Fabien N. Belle","author_inst":"University of Bern"},{"author_name":"- SwissPedHealth consotrium","author_inst":""}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDCs FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Cost-Outcome Variation in Percutaneous Mechanical Circulatory Support: A National Value-of-Care Analysis","rel_doi":"10.64898\/2026.08.31.26361851","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361851","rel_abs":"BackgroundPercutaneous mechanical circulatory support (pMCS) is increasingly used in critically ill patients, yet its value in relation to cost and outcomes remains unclear. We evaluated national variation in utilization, outcomes, and cost, and introduced a value-of-care framework integrating risk-adjusted outcomes and expenditures.\n\nMethodsWe performed a retrospective cohort study using the National Inpatient Sample to identify non-elective hospitalizations of critically ill patients undergoing intra-aortic balloon pump (IABP) or percutaneous left ventricular assist device (pLVAD) placement using ICD-10 codes. Multivariable logistic regression and generalized linear models were used to estimate expected outcomes and costs. Observed-to-expected (O\/E) ratios were calculated, and a value index was derived to compare procedural strategies.\n\nResultsA total of 57,910 weighted hospitalizations were included (IABP 78%, pLVAD 22%). In-hospital mortality exceeded 30% across regions. Significant regional variation was observed, with the West demonstrating the highest costs and the Midwest the lowest (p<0.001). Mean hospital charges were higher for pLVAD compared with IABP ($403,731 vs $320,769). Both strategies achieved outcomes better than expected after risk adjustment (O\/E 0.92); however, costs were higher than expected for both, with greater relative cost inflation observed for IABP (O\/E 1.41) and higher absolute costs for pLVAD. In value-of-care analysis, IABP was associated with lower cost and comparable outcomes, while pLVAD demonstrated higher cost without proportional outcome improvement.\n\nConclusionSubstantial variation exists in the cost, outcomes, and value of pMCS strategies. While both IABP and pLVAD achieve favorable risk-adjusted outcomes, pLVAD is associated with higher costs without commensurate clinical benefit.","rel_num_authors":4,"rel_authors":[{"author_name":"Joshua D Greendyk","author_inst":"Corewell Health Grand Rapids"},{"author_name":"William E Allen","author_inst":"Boston Medical Center"},{"author_name":"Afif Hossain","author_inst":"Rutgers New Jersey Medical School"},{"author_name":"Zachariya Trichas","author_inst":"Rutgers New Jersey Medical School"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multidimensional Social Vulnerability and Hepatic and Extrahepatic Outcomes in Adults With HIV\/HBV Coinfection in the United States","rel_doi":"10.64898\/2026.08.31.26361853","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361853","rel_abs":"BackgroundHIV\/HBV coinfection is associated with substantial liver-related morbidity and mortality, yet the impact of social vulnerability (SV) on clinical outcomes has not been systematically assessed. We evaluated associations of multidimensional SV with mortality, hepatic, virologic, and extrahepatic organ outcomes among adults with HIV\/HBV.\n\nMethodsWe conducted a retrospective cohort study using TriNetX data from 110 U.S. healthcare organizations (2010-2026). We propensity score matched adults with HIV\/HBV with and without documented SV 1:1 (2,024 per group). SV was defined using a four-domain framework encompassing material, healthcare access and engagement, interpersonal, and psychosocial vulnerability.\n\nResultsOver 15,900 person-years, SV was associated with higher mortality (hazard ratio [HR], 2.06; 95% confidence interval [CI], 1.72-2.47), liver composite events (HR, 1.37; 95% CI, 1.07-1.76), hepatic decompensation (HR, 1.94; 95% CI, 1.39-2.70), hepatic failure (HR, 2.39; 95% CI, 1.53-3.73), HBV viremia (HR, 1.69; 95% CI, 1.32-2.16), and HIV viremia (HR, 2.05; 95% CI, 1.71-2.46). SV was also associated with major adverse cardiovascular events (HR, 1.47), chronic kidney disease (HR, 1.49), and diabetes (HR, 1.25). Multidomain SV generally showed stronger associations than single-domain SV for most hepatic and virologic outcomes, with HR ranges of 1.76-2.62 versus 1.35-1.76 for single-domain SV. Healthcare access and engagement vulnerability was most consistently associated with mortality and hepatic outcomes.\n\nConclusionsSV was associated with mortality, hepatic disease, impaired HIV\/HBV control, extrahepatic organ morbidity, and acute care utilization in adults with HIV\/HBV. SV assessment may improve risk stratification and identify actionable intervention targets during HIV\/HBV care.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26361853v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (35K):\norg.highwire.dtl.DTLVardef@847c91org.highwire.dtl.DTLVardef@e5167org.highwire.dtl.DTLVardef@26312org.highwire.dtl.DTLVardef@b6507e_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":10,"rel_authors":[{"author_name":"George Yendewa","author_inst":"Case Western Reserve University"},{"author_name":"Tayoot Chengsupanimit","author_inst":"Case Western Reserve University"},{"author_name":"Ali Dehghani","author_inst":"Case Western Reserve University"},{"author_name":"Ali Ahmed","author_inst":"University of Pennsylvania"},{"author_name":"Amir Mohareb","author_inst":"Harvard Medical School"},{"author_name":"Chari Cohen","author_inst":"Hepatitis B Foundation"},{"author_name":"Michael Freeman","author_inst":"Case Western Reserve University"},{"author_name":"H. Nina Kim","author_inst":"University of Washington"},{"author_name":"Ighovwerka Ofotokun","author_inst":"Case Western Reserve University"},{"author_name":"Karine Dube","author_inst":"University of Pennsylvania"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Making Accelerating Medicines Partnership Data Findable and Interoperable through a Common Data Model: Extending OMOP for Multi-Source Multimodal Data","rel_doi":"10.64898\/2026.08.31.26361831","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361831","rel_abs":"SysBio FAIRplex is a Common Fund Venture Program1 that catalogs and indexes data from the Accelerating Medicines Partnership(R) (AMP(R)) Program2 through a federated model in which data hosts retain custody of their datasets. The central piece of this work is the SysBio Common Data Model (SysBio CDM). AMP is a precompetitive public-private partnership started in 2014 that unites the resources of NIH and private partners to improve our understanding of disease pathways and transform current models for developing new treatments by: O_LIidentifying new targets, biomarkers, and development paradigms;\nC_LIO_LIdeveloping leading-edge tools and technologies;\nC_LIO_LIcollecting large-scale datasets and supporting analytics for open analysis by the public; and\nC_LIO_LIgenerating consensus platforms and procedures.\nC_LI A multidisciplinary Task Force was chartered to design the SysBio CDM by extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model3 into the -omics domain. The Task Force produced a Minimum Viable Product comprising nine OMOP tables; four extension tables for assay and file metadata; and a Common Data Element (CDE) Registry to specify field semantics. This manuscript describes the deliverable: the underlying design choices, the criteria applied in selecting and constructing the extension tables, how the extended model supports multimodal data integration across AMP projects, and what further work to support additional -omics modalities would entail. As an auxiliary methodology, the paper also describes the AI-assisted CDE harmonization workflow used to populate the model.","rel_num_authors":13,"rel_authors":[{"author_name":"Cole Tindall","author_inst":"DataTecnica, LLC"},{"author_name":"Rodney Alan Long Jr.","author_inst":"Data Tecnica, LLC"},{"author_name":"Bart Naughton","author_inst":"Eisai Inc."},{"author_name":"Brandy M Mapes","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Dave Vismer","author_inst":"Technome"},{"author_name":"Halcyon G Skinner","author_inst":"Verily Health"},{"author_name":"Jessica Malenfant","author_inst":"Sage Bionetworks"},{"author_name":"Mano R Maurya","author_inst":"University of California, San Diego"},{"author_name":"Mike A Nalls","author_inst":"Data Tecnica, LLC"},{"author_name":"Srinivasan Ramachandran","author_inst":"University of California, San Diego"},{"author_name":"Trang Nguyen","author_inst":"The Broad Institute of MIT and Harvard"},{"author_name":"Mette A Peters","author_inst":"DataTecnica, LLC"},{"author_name":"Richard H Scheuermann","author_inst":"Division of Intramural Research, National Library of Medicine, National Institutes of Health"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Making Accelerating Medicines Partnership Data Findable and Interoperable through a Common Data Model: Extending OMOP for Multi-Source Multimodal Data","rel_doi":"10.64898\/2026.08.31.26361831","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361831","rel_abs":"SysBio FAIRplex is a Common Fund Venture Program1 that catalogs and indexes data from the Accelerating Medicines Partnership(R) (AMP(R)) Program2 through a federated model in which data hosts retain custody of their datasets. The central piece of this work is the SysBio Common Data Model (SysBio CDM). AMP is a precompetitive public-private partnership started in 2014 that unites the resources of NIH and private partners to improve our understanding of disease pathways and transform current models for developing new treatments by: O_LIidentifying new targets, biomarkers, and development paradigms;\nC_LIO_LIdeveloping leading-edge tools and technologies;\nC_LIO_LIcollecting large-scale datasets and supporting analytics for open analysis by the public; and\nC_LIO_LIgenerating consensus platforms and procedures.\nC_LI A multidisciplinary Task Force was chartered to design the SysBio CDM by extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model3 into the -omics domain. The Task Force produced a Minimum Viable Product comprising nine OMOP tables; four extension tables for assay and file metadata; and a Common Data Element (CDE) Registry to specify field semantics. This manuscript describes the deliverable: the underlying design choices, the criteria applied in selecting and constructing the extension tables, how the extended model supports multimodal data integration across AMP projects, and what further work to support additional -omics modalities would entail. As an auxiliary methodology, the paper also describes the AI-assisted CDE harmonization workflow used to populate the model.","rel_num_authors":13,"rel_authors":[{"author_name":"Cole Tindall","author_inst":"DataTecnica, LLC"},{"author_name":"Rodney Alan Long Jr.","author_inst":"Data Tecnica, LLC"},{"author_name":"Bart Naughton","author_inst":"Eisai Inc."},{"author_name":"Brandy M Mapes","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Dave Vismer","author_inst":"Technome"},{"author_name":"Halcyon G Skinner","author_inst":"Verily Health"},{"author_name":"Jessica Malenfant","author_inst":"Sage Bionetworks"},{"author_name":"Mano R Maurya","author_inst":"University of California, San Diego"},{"author_name":"Mike A Nalls","author_inst":"Data Tecnica, LLC"},{"author_name":"Srinivasan Ramachandran","author_inst":"University of California, San Diego"},{"author_name":"Trang Nguyen","author_inst":"The Broad Institute of MIT and Harvard"},{"author_name":"Mette A Peters","author_inst":"DataTecnica, LLC"},{"author_name":"Richard H Scheuermann","author_inst":"Division of Intramural Research, National Library of Medicine, National Institutes of Health"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Evaluating Cognitive Impact of Traumatic Brain Injury and Risk for Post-Traumatic Epilepsy","rel_doi":"10.64898\/2026.08.30.26361760","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.30.26361760","rel_abs":"ObjectivePost-traumatic epilepsy (PTE) is a common sequela of traumatic brain injury (TBI). Research indicates that individuals with PTE tend to experience greater cognitive difficulties compared to those with TBI alone. However, it is plausible that a distinct cognitive profile exists that distinguishes between TBI cases with and without PTE. We aimed to identify longitudinal changes in cognitive measures among TBI patients to better assess the changes associated with developing PTE.\n\nSettingOutpatient.\n\nParticipantsProspective subjects who had suffered TBI within 6 months post-injury (TBI-6M, n=32), retrospective subjects with pre-existing PTE diagnoses (PTE, n=20), and healthy control subjects (HC, n=41).\n\nDesignWe examined cognitive performance for TBI patients within 6 months post-injury, then again within 12 months (TBI-12M, n=26), and within 18-months (TBI-18M, n=25), and compared this with cognitive performance among HC and PTE.\n\nMain MeasuresCognitive tests administered yielded 15 test components for analysis. We utilized linear mixed effects modeling to examine cohort-level differences cognitive function.\n\nResults11\/15 tests showed a significant performance deficit in the PTE subjects compared to HC. TBI-6M was not significantly different from the PTE subjects; with time, 9\/15 tests showed some degree of recovery in TBI subjects. Tests for information processing speed\/working memory and executive function showed strong recovery (TBI-6M vs. TBI-18M, SDMT written: p<0.0001, SDMT oral and COWAT: p<0.001). Tests for visual attention\/working memory also showed a smaller but significant recovery (TBI-18M vs. PTE, p<0.05). By contrast, tests for verbal memory [HVLT-R Delayed Recall] showed chronic impairment in TBI (TBI-18M vs HC, p<0.0001). TBI subjects generally trend towards recovery in cognitive performance post-TBI.\n\nConclusionsInformation processing speed\/working memory are strong indicators for TBI recovery, while auditory learning\/memory shows chronic impairment. The stagnation of recovery in cognitive domains typically characterized by robust recovery may correlate with an elevated risk of developing PTE.","rel_num_authors":8,"rel_authors":[{"author_name":"Taylor Zink","author_inst":"Robert Wood Johnson Medical School"},{"author_name":"Henry Noren","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Daniel Valdivia","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Christine Yohn","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Jasdeep Hundal","author_inst":"Jersey Shore University Medical Center, Hackensack-Meridian Health"},{"author_name":"Spencer Chen","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"David Scarisbrick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Hai Sun","author_inst":"Rutgers Robert Wood Johnson Medical School"}],"rel_date":"2026-09-01","rel_site":"medrxiv"},{"rel_title":"A Measurement-Based Care Strategy for Buprenorphine-Naloxone Treatment (Bup-MBC): Development of an EHR-Integrated Intervention","rel_doi":"10.64898\/2026.08.27.26361539","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.27.26361539","rel_abs":"IntroductionRisk of recurrent opioid use during buprenorphine-naloxone (bup-nx) treatment is dynamic and remains elevated after initiation, with vulnerability shaped in part by treatment intensity and gaps between visits, yet routine outpatient care relies on episodic encounters and retrospective data. This mismatch can delay recognition of emerging instability and limit timely treatment adjustments. This paper reports the development and specification of an intervention strategy to address this mismatch.\n\nMethodsWe used a structured, multi-phase design process to specify and configure a measurement-based care (MBC) strategy for bup-nx treatment (Bup-MBC) in outpatient addiction clinics through three phases: (1) a systematic review of patient-reported outcome measures (PROMs) for substance use treatment; (2) a qualitative needs assessment using the Theoretical Domains Framework and COM-B (Capability, Opportunity, Motivation-Behavior) model to identify gaps in risk monitoring, agency, and trust; and (3) iterative co-design with multidisciplinary clinicians to refine workflow fit and trust-preserving use of data. Patients informed item and feedback content during the needs assessment but did not participate in the co-design cycles.\n\nResultsBup-MBC integrates (1) brief between-visit PROMs (e.g., withdrawal, craving, adherence); (2) immediate non-punitive patient feedback; (3) clinician-facing summaries and non-directive prompts in the electronic health record (EHR); and (4) an opt-in between-visit outreach pathway with predefined safety triggers, all configured within existing EHR and patient portal infrastructure. It targets patient and clinician capability to recognize changes in risk, opportunity for action through structured monitoring and visit preparation, and trust and agency through non-punitive communication, without adding substantial burden. The full measure set, severity bands, and question-to-action map are provided as supplementary material. Key trade-offs included prioritizing single-item measures for feasibility, balancing opt-in outreach with safety overrides, and assuming routine clinician use of summaries.\n\nConclusionThis development study specifies an EHR-integrated MBC strategy for outpatient bup-nx treatment. As single-center design work with co-design limited to clinicians and delivery contingent on portal or text-message access, its outputs are hypotheses about mechanism and fit rather than demonstrated effects. Feasibility studies are needed to evaluate uptake, acceptability, workflow fit, and effects on treatment.","rel_num_authors":11,"rel_authors":[{"author_name":"Thomas Reese","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Carolyn Audet","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Jessica Ancker","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Adam Wright","author_inst":"Vanderbilt University Medical Center"},{"author_name":"David Marcovitz","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Kristopher  A Kast","author_inst":"Vanderbilt University Medical Center"},{"author_name":"John Bridges","author_inst":"Ohio State University"},{"author_name":"Hilary Tindle","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Mauli Shah","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Amanda von Horn","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Michael E Matheny","author_inst":"Vanderbilt University"}],"rel_date":"2026-09-01","rel_site":"medrxiv"},{"rel_title":"Dynamic Clinical States and Transitions During the First 72 Hours of Intensive Care After Acute Stroke","rel_doi":"10.64898\/2026.08.30.26361738","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.30.26361738","rel_abs":"BackgroundThe condition of a patient with acute stroke often changes within hours of ICU admission. Prognostic work here targets fixed endpoints predicted from admission data, and trajectory phenotyping assigns one label per patient. We used longitudinal ICU data to identify interpretable dynamic clinical states, characterize transitions between them, and relate the current state to later events.\n\nMethodsRetrospective cohort study of 6368 adults with acute stroke in MIMIC-IV v3.1. The first 72 h were divided into twelve 6-hour windows, and a hidden Markov model was fitted to 21 neurological, physiological and organ-support variables. State number was chosen against criteria fixed before fitting: statistical fit, restart stability, state occupancy and clinical interpretability. Generalized estimating equations related the current state to new mechanical ventilation and vasopressor use within 12 h, and to ICU death within 72 h. Eleven sensitivity analyses assessed the robustness of the state solution.\n\nResultsFour states were selected: neurologically preserved-low support, neurological impairment- low support, impairment-renal dysfunction and impairment-respiratory support (63.3%, 7.8%, 11.8% and 17.1% of windows). Within 72 h, 40.3% of patients changed state at least once, and transitions ran in both directions rather than along a single severity gradient. States were identified without outcome data, yet ICU mortality by last state ranged from 2.9% to 43.9%. Adjusted for age, sex, subtype and Charlson index, the current state remained associated with organ-support escalation and death. State prevalence differed by at most 1.1 percentage points between training and test sets, and 10 of 11 sensitivity analyses gave a stable four-state solution (ARI 0.754-0.955).\n\nConclusionsThe early ICU course of acute stroke can be represented as movement among a small number of clinically interpretable states. The representation was reproducible in a held-out set and across admission eras, but requires validation in an independent database before any clinical use.","rel_num_authors":3,"rel_authors":[{"author_name":"Ping LEI","author_inst":"Institute of Global Health, University of Geneva, Geneva, Switzerland; School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Yanyi XU","author_inst":"Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China"},{"author_name":"Yuxia ZHANG","author_inst":"Department of Nursing, Zhongshan Hospital, Fudan University, Shanghai, China"}],"rel_date":"2026-09-01","rel_site":"medrxiv"},{"rel_title":"Genotype-guided isoniazid dosing harmonizes drug exposure in 3HP tuberculosis preventive therapy","rel_doi":"10.64898\/2026.08.27.26360825","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.27.26360825","rel_abs":"RationalePolymorphisms in the N-acetyltransferase 2 (NAT2) gene explain much of the interindividual variation in isoniazid (INH) metabolism and determine risk of toxicities. However, there is limited evidence to guide INH dose adjustment according to the NAT2 acetylator profile in weekly rifapentine-INH tuberculosis preventive therapy (TPT).\n\nObjectivesWe evaluated whether NAT2 genotype-guided INH dose adjustment harmonizes drug exposure across acetylator phenotypes during 3HP and derived phenotype-specific dose recommendations.\n\nMethodsIn a prospective, multicenter, within-subject PK trial (NCT05413551), adults initiating 3HP in Brazil were assigned genotype-guided INH doses (slow: 5 mg\/kg [&le;]300 mg; intermediate: 15 mg\/kg [&le;]900 mg; rapid: 25 mg\/kg [&le;]1,500 mg) alongside a standard 900 mg flat dose on an alternate occasion. AUC0-24 and C24 were estimated from serial blood samples; a two-compartment Michaelis-Menten population PK model characterized NAT2 effects on clearance.\n\nMeasurements and Main ResultsAmong 228 participants, 47.4% (108\/228) were intermediate, 43.4% (99\/228) slow, and 9.2% (21\/228) rapid acetylators. Genotype-guided dosing reduced AUC0-24 variability approximately two-fold versus standard dosing (CV 58.8% vs 76.8%) and increased exposure uniformity (median AUC0- 24 27.2 [IQR 18.8-41.3] vs 43.2 [27.3-71.0] mg{middle dot}h\/L). Among slow acetylators, C24 >0.15 {micro}g\/mL decreased from 27\/42 (64%) with standard dosing to 1\/42 (2%) with genotype-guided dosing (P<0.0001). In 104 participants with intensive PK sampling, rapid acetylators receiving guided doses had AUC0-24similar to standard-dose intermediate acetylators (42.8 vs 39.5 mg{middle dot}h\/L; P=.63). Monte Carlo simulations supported doses of 600, 900, and 1,200 mg for slow, intermediate, and rapid acetylators, respectively.\n\nConclusionsNAT2-guided isoniazid dosing reduced variation in drug levels, averting very low and high AUC and C24. These findings inform genotype-stratified dosing of INH for TPT, which might reduce toxicities and improve outcomes.","rel_num_authors":15,"rel_authors":[{"author_name":"Kesia da Silva","author_inst":"Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA"},{"author_name":"Samuel Sarkodie","author_inst":"University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Karina Marques","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Patricia Vieira","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Roberto Dias de Oliveira Sr.","author_inst":"State University of Mato Grosso do Sul"},{"author_name":"Paulo Cesar Pereira dos Santos","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Marco Antonio Moreira Puga","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Allyson Guimar\u00e3es Costa","author_inst":"Universidade Federal do Amazonas"},{"author_name":"Jo\u00e3o Paulo Gregorio Machado","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Renata Spener-Gomes","author_inst":"Funda\u00e7\u00e3o Medicina Tropical Dr. Heitor Vieira Dourado, Manaus, AM, Brazil"},{"author_name":"Eunsol Yang","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Rada Savic","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Doctor Heitor Vieira Dourado Tropical Medicine Foundation: Fundacao de Medicina Tropical Doutor Heitor Vieira Dourado"},{"author_name":"Julio Croda","author_inst":"Oswaldo Cruz Foundation, Campo Grande, MS, Brazil"},{"author_name":"Jason R Andrews","author_inst":"Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA"}],"rel_date":"2026-09-01","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@1f489d9org.highwire.dtl.DTLVardef@18637ccorg.highwire.dtl.DTLVardef@b97275org.highwire.dtl.DTLVardef@1099c38_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@1f489d9org.highwire.dtl.DTLVardef@18637ccorg.highwire.dtl.DTLVardef@b97275org.highwire.dtl.DTLVardef@1099c38_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@1f489d9org.highwire.dtl.DTLVardef@18637ccorg.highwire.dtl.DTLVardef@b97275org.highwire.dtl.DTLVardef@1099c38_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@1f489d9org.highwire.dtl.DTLVardef@18637ccorg.highwire.dtl.DTLVardef@b97275org.highwire.dtl.DTLVardef@1099c38_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@1f489d9org.highwire.dtl.DTLVardef@18637ccorg.highwire.dtl.DTLVardef@b97275org.highwire.dtl.DTLVardef@1099c38_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"}]}