{"gname":"Weizmann Institute of Science","grp_id":"15","rels":[{"rel_title":"Cooperative molecular mimicry drives prolonged autoinflammation in multisystem inflammatory syndrome in children","rel_doi":"10.64898\/2026.04.03.26350001","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26350001","rel_abs":"Multisystem inflammatory syndrome in children (MIS-C) is a pediatric hyperinflammatory disease manifesting 4-6 weeks after SARS-CoV-2 infection. While the immunological hallmarks of MIS-C have been defined, few details regarding the underlying disease pathology have been resolved. To address this, we used a multiomics approach to profile the plasma and peripheral immune cells of 13 acute MIS-C patients, 18 recovered MIS-C follow-ups resampled over multiple time points (1-18 months), and 15 healthy pediatric controls. Despite rapid clinical disease resolution, circulating pro-inflammatory (IL-8, IL-6, IL-1, IL-1{beta}, TNF-{beta}) and TH2-type cytokines (IL-4, IL-5, IL-13) remained elevated up to three months post-MIS-C onset, revealing a subclinical inflammatory state that endures in recovered children. Surprisingly, the majority of patient-expanded TCRs recognizing SARS-CoV-2 epitopes were cross-reactive (75%, 12\/16 SARS-CoV-2 TCRs) for autoantigens related to prostaglandin biology and insulin metabolism, suggesting a breakdown of self-tolerance via SARS-CoV-2 molecular mimicry. Indeed, autoantibody screening confirmed that 13 gene targets with self-antigen peptides also exhibited elevated autoantibodies in MIS-C patients. Further, autoreactive TCR expansions lasted over time and correlated with cytokines involved in allergic inflammation. Together, our findings point to a mechanism of sustained autoimmunity wherein promiscuous TCRs recognize both viral and self-antigens that are activated during primary SARS-CoV-2 infection in children who develop MIS-C. Upon onset, these circulating cross-reactive T cells drive clinically apparent sterile autoinflammation that persists subclinically into convalescence.","rel_num_authors":9,"rel_authors":[{"author_name":"Haley E Randolph","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Ashley Richardson","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Sofija Buta","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Julie Samuels","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Nina N Brodsky","author_inst":"Yale University School of Medicine"},{"author_name":"Seunghee Kim-Schulze","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Carrie L Lucas","author_inst":"Yale University School of Medicine"},{"author_name":"Rebecca Trachtman","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Dusan Bogunovic","author_inst":"Columbia University Irving Medical Center"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Cooperative molecular mimicry drives prolonged autoinflammation in multisystem inflammatory syndrome in children","rel_doi":"10.64898\/2026.04.03.26350001","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26350001","rel_abs":"Multisystem inflammatory syndrome in children (MIS-C) is a pediatric hyperinflammatory disease manifesting 4-6 weeks after SARS-CoV-2 infection. While the immunological hallmarks of MIS-C have been defined, few details regarding the underlying disease pathology have been resolved. To address this, we used a multiomics approach to profile the plasma and peripheral immune cells of 13 acute MIS-C patients, 18 recovered MIS-C follow-ups resampled over multiple time points (1-18 months), and 15 healthy pediatric controls. Despite rapid clinical disease resolution, circulating pro-inflammatory (IL-8, IL-6, IL-1, IL-1{beta}, TNF-{beta}) and TH2-type cytokines (IL-4, IL-5, IL-13) remained elevated up to three months post-MIS-C onset, revealing a subclinical inflammatory state that endures in recovered children. Surprisingly, the majority of patient-expanded TCRs recognizing SARS-CoV-2 epitopes were cross-reactive (75%, 12\/16 SARS-CoV-2 TCRs) for autoantigens related to prostaglandin biology and insulin metabolism, suggesting a breakdown of self-tolerance via SARS-CoV-2 molecular mimicry. Indeed, autoantibody screening confirmed that 13 gene targets with self-antigen peptides also exhibited elevated autoantibodies in MIS-C patients. Further, autoreactive TCR expansions lasted over time and correlated with cytokines involved in allergic inflammation. Together, our findings point to a mechanism of sustained autoimmunity wherein promiscuous TCRs recognize both viral and self-antigens that are activated during primary SARS-CoV-2 infection in children who develop MIS-C. Upon onset, these circulating cross-reactive T cells drive clinically apparent sterile autoinflammation that persists subclinically into convalescence.","rel_num_authors":9,"rel_authors":[{"author_name":"Haley E Randolph","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Ashley Richardson","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Sofija Buta","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Julie Samuels","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Nina N Brodsky","author_inst":"Yale University School of Medicine"},{"author_name":"Seunghee Kim-Schulze","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Carrie L Lucas","author_inst":"Yale University School of Medicine"},{"author_name":"Rebecca Trachtman","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Dusan Bogunovic","author_inst":"Columbia University Irving Medical Center"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Imaging solute transportation along the posterior lymphatic pathway in the ocular glymphatic system in healthy human participants","rel_doi":"10.64898\/2026.04.03.26349283","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26349283","rel_abs":"Background: Recently, a posterior pathway for fluid drainage from the retina to the meningeal lymphatics in the optic nerve (ON) sheath was identified in rodents using intravitreal imaging tracers directly injected into the ocular-globe. Fluid and solute clearance along this pathway may be associated with many diseases. However, intravitreal tracers are rarely used in clinical imaging. As intravenous Gadolinium-based-contrast-agent (GBCA) can enter the globe via the blood-ocular-barriers, it may provide an alternative approach to image this pathway. Purpose: To establish a clinically feasible intravenous GBCA-based MRI approach for tracking fluid and solute transport along the posterior lymphatic pathway in the ocular glymphatic system. Materials & Methods: This prospective study was conducted from March 2021 to September 2022 in healthy participants. Dynamic-susceptibility-contrast-in-the-CSF (cDSC) MRI was performed before, immediately and 4 hours after intravenous-GBCA administration to track GBCA distribution in aqueous humor (AH) and cerebrospinal fluid (CSF) in regions-of-interest (ROIs) in the globe (anterior-cavity, vitreous-body), in the intraorbital and extraorbital ON, and in the intracranial CSF space proximal to the ON (chiasmatic-cistern, interpeduncular-cistern). Kruskal-Wallis tests with post-hoc Dunn's tests were used for group comparisons. Results: Sixteen healthy participants (mean age +\/- SD: 51 +\/- 21 years, 5 men) were recruited. Intravenous-GBCA enhancement was observed in all ROIs immediately after injection. At 4-hour-post-GBCA, the vitreous body showed a trend of smaller enhancement area (55 +\/- 11% versus 49 +\/- 11%, P=.14) and lower GBCA-concentration (0.044 +\/- 0.014 versus 0.028 +\/- 0.010 mmol\/L, P=.07) compared to immediate-post-GBCA. The intraorbital ON showed more widespread enhancement (39 +\/- 5% versus 59 +\/- 6%, P=.01) and significantly higher GBCA-concentration (0.023 +\/- 0.009 versus 0.059 +\/- 0.015 mmol\/L, P<.001) at 4-hour-post-GBCA. Conclusion: Dynamic fluid and solute transportation along the posterior lymphatic pathway in the ocular glymphatic system in healthy participants was measured by tracking intravenous-GBCAs entering the globe via the blood-ocular-barriers using cDSC-MRI.","rel_num_authors":17,"rel_authors":[{"author_name":"Xuehua Wen","author_inst":"Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimo"},{"author_name":"Yuanqi Sun","author_inst":"Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimo"},{"author_name":"Xinyi Zhou","author_inst":"Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimo"},{"author_name":"Yinghao Li","author_inst":"Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimo"},{"author_name":"Adrian Paez","author_inst":"Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimo"},{"author_name":"Jacob Varghese","author_inst":"Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimo"},{"author_name":"Jay J. Pillai MD","author_inst":"Division of Neuroradiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, United States"},{"author_name":"Linda Knutsson","author_inst":"F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States"},{"author_name":"Peter C.M. Van Zijl","author_inst":"Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimo"},{"author_name":"Richard Leigh","author_inst":"Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States"},{"author_name":"David O. Kamson","author_inst":"Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States"},{"author_name":"Christina R. Graley","author_inst":"Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States"},{"author_name":"Shiv Saidha","author_inst":"Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States"},{"author_name":"Arnold Bakker","author_inst":"Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States"},{"author_name":"Bryan K. Ward","author_inst":"Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States"},{"author_name":"Amir H. Kashani","author_inst":"Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland, United States"},{"author_name":"Jun Hua","author_inst":"F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Age-dependent acceleration of structural brain aging in medication-free major depressive disorder linked to neuroanatomical phenotype findings from COORDINATE-MDD consortium","rel_doi":"10.64898\/2026.03.31.26349338","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.03.31.26349338","rel_abs":"Background: Major depressive disorder (MDD) is associated with altered brain structure and evidence of accelerated brain aging. However, previous studies have been limited by clinical samples with mixed medication status and multiple mood states, modest sample sizes, small percentage of MDD individuals older than 65 years of age, and\/or reliance on summary-level data. Methods: Harmonized T1-weighted MRI from MDD (n = 645), all medication-free and in a current depressive episode, and matched healthy controls (n = 645), segmented into 145 regional volumes, from 11 sites in COORDINATE-MDD consortium. Brain age gap (BAG) was estimated using gradient boosting regression with nested cross-validation. Group differences in BAG (and age-corrected BAG [cBAG]) were examined across age strata. Regional contributions were evaluated using Shapley Additive exPlanations. Results: MDD was associated with significantly elevated cBAG compared with healthy controls (mean difference + 2.01 years). Age-stratified analyses showed no differences before mid-30s, with progressively larger gaps thereafter, reaching +6.85 years in MDD aged 55 and older. cBAG differed across neuroanatomical phenotypes associated with differential antidepressant response, cognitive impairment, increased adverse life events, increased self-harm and suicide attempts, and a pro-atherogenic metabolic profile. Key contributing regions included lateral and medial prefrontal regions, middle temporal gyrus, putamen, supplementary motor cortex, central operculum, and cerebellum. Conclusions: Accelerated structural brain aging in MDD is age-dependent and is most pronounced in a neuroanatomical phenotype associated with worse key clinical outcomes. The findings support neuroprogression models of MDD while demonstrating that cBAG is not a uniform feature of MDD and seem to be more strongly expressed in a specifically clinically vulnerable disease phenotype.","rel_num_authors":52,"rel_authors":[{"author_name":"Bhanu Sharma","author_inst":"McMaster University"},{"author_name":"Pedro L Ballester","author_inst":"Hospital for Sick Children"},{"author_name":"Luciano Minuzzi","author_inst":"McMaster University"},{"author_name":"Wenyi Xiao","author_inst":"University of East London"},{"author_name":"Mathilde Antoniades","author_inst":"University of Pennsylvania"},{"author_name":"Dhivya Srinivasan","author_inst":"University of Pennsylvania"},{"author_name":"Guray Erus","author_inst":"University of Pennsylvania"},{"author_name":"Jose Garcia","author_inst":"University of Pennsylvania"},{"author_name":"Yong Fan","author_inst":"University of Pennsylvania"},{"author_name":"Danilo Arnone","author_inst":"King's College London"},{"author_name":"Stephen Arnott","author_inst":"Rotman Research Institute"},{"author_name":"Taolin Chen","author_inst":"West China Hospital of Sichuan University"},{"author_name":"Ki Seung Choi","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Katharine Dunlop","author_inst":"University of Toronto"},{"author_name":"Cherise Chin Fatt","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Rachel D Woodham","author_inst":"University of East London"},{"author_name":"Beata Godlewska","author_inst":"University of Oxford"},{"author_name":"Stefanie Hassel","author_inst":"University of Calgary"},{"author_name":"Keith Ho","author_inst":"University Health Network"},{"author_name":"Andrew M McIntosh","author_inst":"University of Edinburgh"},{"author_name":"Kun Qin","author_inst":"West China Hospital of Sichuan University"},{"author_name":"Susan Rotzinger","author_inst":"University Health Network"},{"author_name":"Matthew Sacchet","author_inst":"Harvard University"},{"author_name":"Jonathan Savitz","author_inst":"Laureate Institute for Brain Research"},{"author_name":"Haochang Shou","author_inst":"University of Pennsylvania"},{"author_name":"Ashish Singh","author_inst":"University of Pennsylvania"},{"author_name":"Vibe Frokjaer","author_inst":"University Hospital Rigshospitalet"},{"author_name":"Melanie Ganz","author_inst":"University Hospital Rigshospitalet"},{"author_name":"Aleks Stolicyn","author_inst":"University of Edinburgh"},{"author_name":"Irina Strigo","author_inst":"University of California San Francisco"},{"author_name":"Duygu Tosun","author_inst":"University of California - San Francisco"},{"author_name":"Dongtao Wei","author_inst":"Southwest University"},{"author_name":"Ian Anderson","author_inst":"University of Manchester"},{"author_name":"Edward Craighead","author_inst":"Emory Unviersity"},{"author_name":"Bill Deakin","author_inst":"University of Manchester"},{"author_name":"Boadie Dunlop","author_inst":"Emory University School of Medicine"},{"author_name":"Rebecca Elliot","author_inst":"Manchester University"},{"author_name":"Qiyong Gong","author_inst":"West China Hospital of Sichuan University"},{"author_name":"Ian Gotlib","author_inst":"Stanford University"},{"author_name":"Catherine Harmer","author_inst":"University of Oxford"},{"author_name":"Sidney H Kennedy","author_inst":"University of Toronto"},{"author_name":"Gitte  Moos Knudsen","author_inst":"Rigshospitalet"},{"author_name":"Helen Mayberg","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Martin  P. Paulus","author_inst":"Laureate Institute for Brain Research"},{"author_name":"Jiang Qiu","author_inst":"Southwest University"},{"author_name":"Madhukar Trivedi","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Heather C Whalley","author_inst":"University of Edinburgh"},{"author_name":"Chao-Gan Yan","author_inst":"Institute of Psychology"},{"author_name":"Allan Young","author_inst":"King's College London"},{"author_name":"Christos Davatzikos","author_inst":"University of Pennsylvania"},{"author_name":"Cynthia H.Y. Fu","author_inst":"University of East London"},{"author_name":"Benicio N H.Y. Frey","author_inst":"McMaster University"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Uptake and retention in HIV care among pregnant and postpartum women living with HIV under different eras of vertical transmission prevention policies in sub-Saharan Africa: a systematic review and meta-analysis","rel_doi":"10.64898\/2026.04.02.26350030","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.02.26350030","rel_abs":"Objectives: This systematic review and meta-analysis (2010 - 2025) examines changes in uptake and retention rates among pregnant and postpartum women with HIV in sub-Saharan Africa as countries adopted Option B+ for preventing vertical transmission. Design and data sources: We searched PubMed, Embase, Cochrane Library, Scopus, and African Index Medicus from 10\/2021 - 05\/2025 for eligible studies that measured HIV care uptake or retention for pregnant\/postpartum women under prevention policies before or during Option B+. Study designs were limited to cohort, case-control, cross-sectional, or interventional studies. Exclusions were white papers, commentaries, modeling, cost-effectiveness, and qualitative studies. Data extraction and synthesis: Outcomes were (i) HIV care uptake defined as initiation of ART during pregnancy or prior to initial antenatal care (ANC) visit and (ii) proportion of women retained in HIV care as defined by study authors after ART initiation (or entry to antenatal care). These were synthesized in meta-analyses stratified by policy era (pre-Option B+ vs. Option B+) at different times for different countries. Comparisons between policy eras were made using relative risk with a 95% confidence interval. Pooled retention estimates at 6- and 12-months post ART initiation used crude relative risks (RR) with 95% confidence intervals (CI). Results: Among 4,752 articles, 82 from 17 countries were included; 60 reported HIV care uptake, 31 reported retention outcomes. Pooled HIV uptake rose by 8% (RR=1.08; 95% CI:1.06-1.09) and pooled retention in HIV care rose by 46% (RR=1.46; 95% CI:1.41-1.51) after Option B+ implementation. Pooled estimates of retention in care were 36.9% (95% CI: 13.9%, 59.9%) at 6 months post ART initiation before the implementation of Option B+ and 72.7% (95% CI: 66.3%, 79.1%) after implementation. Conclusion: HIV care uptake and retention improved after Option B+ implementation in 15 countries reporting results, but retention remains suboptimal for meeting UNAIDS 95-95-95 targets.","rel_num_authors":11,"rel_authors":[{"author_name":"Nelly N Jinga","author_inst":"Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa."},{"author_name":"Candice Hwang","author_inst":"Department of Medicine, Stanford University School of Medicine, Stanford, California, USA."},{"author_name":"Laura Rossouw","author_inst":"Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa."},{"author_name":"Kate Clouse","author_inst":"University of North Carolina, Greensboro, NC, USA"},{"author_name":"Cornelius Nattey","author_inst":"Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa."},{"author_name":"Bernard Mbwele","author_inst":"Department of Epidemiology, University of Dar es Salaam Mbeya College of Health and Allied Sciences, Mbeya, Tanzania."},{"author_name":"Nkosinathi Blessing Ngcobo","author_inst":"Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa."},{"author_name":"Molly Beestrum","author_inst":"Galter Health Sciences Library, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA."},{"author_name":"Mark D Huffman","author_inst":"Division of Cardiology, Department of Medicine, Washington University in St. Louis, USA"},{"author_name":"Matthew  P. Fox","author_inst":"Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA."},{"author_name":"Mhairi Maskew","author_inst":"Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa."}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Epigenetic Signatures in Monozygotic and Dizygotic Twins Discordant for Orofacial Clefts","rel_doi":"10.64898\/2026.04.07.26350251","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.07.26350251","rel_abs":"Introduction: Nonsyndromic cleft lip with or without cleft palate (NSCL\/P) is a common congenital malformation with complex etiology involving both genetic and environmental factors. Epigenetic mechanisms may mediate environmental contributions, but separating genetic from environmental effects remains challenging. Methods: We present an epigenome-wide association study with 32 monozygotic and 22 dizygotic twin pairs discordant for NSCL\/P on blood and saliva samples. Differential methylation analysis was conducted using linear models to identify CpG sites showing significant methylation differences between affected and unaffected twins followed by functional annotation and pathway enrichment analysis. Results: The top-ranked finding is a differentially methylated region comprising two CpG sites at the CYP26A1 locus, cg12110262 (P = 3.21x10-7) and cg15055355 (P = 1.39x10-3). CYP26A1 is essential for retinoic acid catabolism and craniofacial patterning. The chromatin regulator ANKRD11, which causes KBG syndrome featuring cleft palate was the second best hit. Differentially methylated CpG sites showed significant enrichment in craniofacial enhancers and overlap with multiple GWAS-validated cleft genes including VAX1, PVRL1, SMAD3, and PRDM16. Conclusions: Our findings implicate retinoic acid signaling and chromatin regulation in NSCL\/P etiology and demonstrate the value of discordant twin designs for distinguishing environmental from genetic epigenetic contributions to complex malformations.","rel_num_authors":10,"rel_authors":[{"author_name":"Aline L Petrin","author_inst":"University of Iowa College of Dentistry and Dental Clinics"},{"author_name":"Henry L Keen","author_inst":"University of Iowa Carver College of Medicine"},{"author_name":"Lindsey Dunlay","author_inst":"University of Iowa College of Dentistry and Dental Clinics"},{"author_name":"Xian J Xie","author_inst":"Florida State University College of Medicine"},{"author_name":"Erliang Zeng","author_inst":"University of Iowa College of Dentistry and Dental Clinics"},{"author_name":"Azeez Butali","author_inst":"University of Iowa College of Dentistry and Dental Clinics"},{"author_name":"Allen Wilcox","author_inst":"Epidemiology Branch of the National Institute of Environmental Health Sciences"},{"author_name":"Mary  L. Marazita","author_inst":"University of Pittsburgh"},{"author_name":"Jeffrey C. Murray","author_inst":"University of Iowa Carver College of Medicine"},{"author_name":"Lina Moreno-Uribe","author_inst":"University of Iowa College of Dentistry and Dental Clinics"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach","rel_doi":"10.64898\/2026.04.07.26349269","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.07.26349269","rel_abs":"Background Six years after its emergence, SARS-CoV-2 continues to have a substantial burden. The impact of vaccination and the optimal timing of its rollout remain uncertain given existing population immunity and variability in outbreak timing between summer and winter. Methods The US Scenario Modeling Hub convened its 19th round of ensemble projections for COVID-19 hospitalizations and deaths in the United States, where eight teams projected trajectories in each US state and nationally from April 2025 to April 2026 under five scenarios regarding vaccine recommendations and timing. Recommendations had two eligibility scenarios (high-risk individuals only and all-eligible) and two timing scenarios (classic start: mid-August, earlier start: late June). These were crossed to create four scenarios and were compared against a counterfactual scenario with no vaccination. Findings Compared to no vaccination, our ensemble projections estimated 90,000 (95% PI 53,000-126,000) hospitalizations averted in the high-risk and classic timing scenario across the US. Expanding to all-eligible age-groups averted an additional 26,000 (95% PI 14,000-39,000) hospitalizations, which when coupled with the early vaccination timing, was projected to further reduce national hospitalizations by 15,000 (95% PI -3,000-33,000). The majority of teams projected both summer and winter waves. Implications We project COVID-19 will cause significant hospitalizations and deaths in the US in the 2025-26 season and estimate significant benefits from a broad all-eligible vaccination recommendation. The results also suggest an additional benefit is likely to be gained from an earlier vaccination campaign. Funding Centers for Disease Control and Prevention; National Institute of Health (US), National Science Foundation (US)","rel_num_authors":35,"rel_authors":[{"author_name":"Anjalika Nande","author_inst":"University of Oxford"},{"author_name":"Soren L Larsen","author_inst":"University of Illinois, Urbana Champaign \/ University of California, Berkeley"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc."},{"author_name":"Jessica T Davis","author_inst":"Northeastern University"},{"author_name":"Shraddha Ramdas Bandekar","author_inst":"University of Texas at Austin"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia"},{"author_name":"Shi Chen","author_inst":"University of North Carolina at Charlotte"},{"author_name":"Lucie Contamin","author_inst":"University of Pittsburgh"},{"author_name":"Sung-mok Jung","author_inst":"National Unversity of Singapore"},{"author_name":"Emily Howerton","author_inst":"Princeton University"},{"author_name":"Katriona Shea","author_inst":"The Pennsylvania State University"},{"author_name":"Clara Bay","author_inst":"Northeastern University"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc."},{"author_name":"Kaiming Bi","author_inst":"The University of Texas Health Science Center at Houston"},{"author_name":"Anass Bouchnita","author_inst":"University of Texas at El Paso"},{"author_name":"Jiangzhuo Chen","author_inst":"University of Virginia"},{"author_name":"Matteo Chinazzi","author_inst":"Northeastern University"},{"author_name":"Spencer J Fox","author_inst":"Northern Arizona University"},{"author_name":"Alison L Hill","author_inst":"Johns Hopkins University"},{"author_name":"Harry Hochheiser","author_inst":"University of Pittsburgh"},{"author_name":"Joseph C Lemaitre","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Sara L Loo","author_inst":"Johns Hopkins University"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia"},{"author_name":"Lauren Ancel Meyers","author_inst":"University of Texas at Austin"},{"author_name":"Carl A.B Pearson","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Przemyslaw Porebski","author_inst":"University of Virginia"},{"author_name":"Emily Przykucki","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Claire P Smith","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia"},{"author_name":"Alessandro Vespignani","author_inst":"Northeastern University"},{"author_name":"Timothy C Willard","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Katie Yan","author_inst":"The Pennsylvania State University"},{"author_name":"Cecile Viboud","author_inst":"National Institutes of Health Fogarty International Center"},{"author_name":"Justin Lessler","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Shaun Truelove","author_inst":"Johns Hopkins University"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach","rel_doi":"10.64898\/2026.04.07.26349269","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.07.26349269","rel_abs":"Background Six years after its emergence, SARS-CoV-2 continues to have a substantial burden. The impact of vaccination and the optimal timing of its rollout remain uncertain given existing population immunity and variability in outbreak timing between summer and winter. Methods The US Scenario Modeling Hub convened its 19th round of ensemble projections for COVID-19 hospitalizations and deaths in the United States, where eight teams projected trajectories in each US state and nationally from April 2025 to April 2026 under five scenarios regarding vaccine recommendations and timing. Recommendations had two eligibility scenarios (high-risk individuals only and all-eligible) and two timing scenarios (classic start: mid-August, earlier start: late June). These were crossed to create four scenarios and were compared against a counterfactual scenario with no vaccination. Findings Compared to no vaccination, our ensemble projections estimated 90,000 (95% PI 53,000-126,000) hospitalizations averted in the high-risk and classic timing scenario across the US. Expanding to all-eligible age-groups averted an additional 26,000 (95% PI 14,000-39,000) hospitalizations, which when coupled with the early vaccination timing, was projected to further reduce national hospitalizations by 15,000 (95% PI -3,000-33,000). The majority of teams projected both summer and winter waves. Implications We project COVID-19 will cause significant hospitalizations and deaths in the US in the 2025-26 season and estimate significant benefits from a broad all-eligible vaccination recommendation. The results also suggest an additional benefit is likely to be gained from an earlier vaccination campaign. Funding Centers for Disease Control and Prevention; National Institute of Health (US), National Science Foundation (US)","rel_num_authors":35,"rel_authors":[{"author_name":"Anjalika Nande","author_inst":"University of Oxford"},{"author_name":"Soren L Larsen","author_inst":"University of Illinois, Urbana Champaign \/ University of California, Berkeley"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc."},{"author_name":"Jessica T Davis","author_inst":"Northeastern University"},{"author_name":"Shraddha Ramdas Bandekar","author_inst":"University of Texas at Austin"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia"},{"author_name":"Shi Chen","author_inst":"University of North Carolina at Charlotte"},{"author_name":"Lucie Contamin","author_inst":"University of Pittsburgh"},{"author_name":"Sung-mok Jung","author_inst":"National Unversity of Singapore"},{"author_name":"Emily Howerton","author_inst":"Princeton University"},{"author_name":"Katriona Shea","author_inst":"The Pennsylvania State University"},{"author_name":"Clara Bay","author_inst":"Northeastern University"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc."},{"author_name":"Kaiming Bi","author_inst":"The University of Texas Health Science Center at Houston"},{"author_name":"Anass Bouchnita","author_inst":"University of Texas at El Paso"},{"author_name":"Jiangzhuo Chen","author_inst":"University of Virginia"},{"author_name":"Matteo Chinazzi","author_inst":"Northeastern University"},{"author_name":"Spencer J Fox","author_inst":"Northern Arizona University"},{"author_name":"Alison L Hill","author_inst":"Johns Hopkins University"},{"author_name":"Harry Hochheiser","author_inst":"University of Pittsburgh"},{"author_name":"Joseph C Lemaitre","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Sara L Loo","author_inst":"Johns Hopkins University"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia"},{"author_name":"Lauren Ancel Meyers","author_inst":"University of Texas at Austin"},{"author_name":"Carl A.B Pearson","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Przemyslaw Porebski","author_inst":"University of Virginia"},{"author_name":"Emily Przykucki","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Claire P Smith","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia"},{"author_name":"Alessandro Vespignani","author_inst":"Northeastern University"},{"author_name":"Timothy C Willard","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Katie Yan","author_inst":"The Pennsylvania State University"},{"author_name":"Cecile Viboud","author_inst":"National Institutes of Health Fogarty International Center"},{"author_name":"Justin Lessler","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Shaun Truelove","author_inst":"Johns Hopkins University"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach","rel_doi":"10.64898\/2026.04.07.26349269","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.07.26349269","rel_abs":"Background Six years after its emergence, SARS-CoV-2 continues to have a substantial burden. The impact of vaccination and the optimal timing of its rollout remain uncertain given existing population immunity and variability in outbreak timing between summer and winter. Methods The US Scenario Modeling Hub convened its 19th round of ensemble projections for COVID-19 hospitalizations and deaths in the United States, where eight teams projected trajectories in each US state and nationally from April 2025 to April 2026 under five scenarios regarding vaccine recommendations and timing. Recommendations had two eligibility scenarios (high-risk individuals only and all-eligible) and two timing scenarios (classic start: mid-August, earlier start: late June). These were crossed to create four scenarios and were compared against a counterfactual scenario with no vaccination. Findings Compared to no vaccination, our ensemble projections estimated 90,000 (95% PI 53,000-126,000) hospitalizations averted in the high-risk and classic timing scenario across the US. Expanding to all-eligible age-groups averted an additional 26,000 (95% PI 14,000-39,000) hospitalizations, which when coupled with the early vaccination timing, was projected to further reduce national hospitalizations by 15,000 (95% PI -3,000-33,000). The majority of teams projected both summer and winter waves. Implications We project COVID-19 will cause significant hospitalizations and deaths in the US in the 2025-26 season and estimate significant benefits from a broad all-eligible vaccination recommendation. The results also suggest an additional benefit is likely to be gained from an earlier vaccination campaign. Funding Centers for Disease Control and Prevention; National Institute of Health (US), National Science Foundation (US)","rel_num_authors":35,"rel_authors":[{"author_name":"Anjalika Nande","author_inst":"University of Oxford"},{"author_name":"Soren L Larsen","author_inst":"University of Illinois, Urbana Champaign \/ University of California, Berkeley"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc."},{"author_name":"Jessica T Davis","author_inst":"Northeastern University"},{"author_name":"Shraddha Ramdas Bandekar","author_inst":"University of Texas at Austin"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia"},{"author_name":"Shi Chen","author_inst":"University of North Carolina at Charlotte"},{"author_name":"Lucie Contamin","author_inst":"University of Pittsburgh"},{"author_name":"Sung-mok Jung","author_inst":"National Unversity of Singapore"},{"author_name":"Emily Howerton","author_inst":"Princeton University"},{"author_name":"Katriona Shea","author_inst":"The Pennsylvania State University"},{"author_name":"Clara Bay","author_inst":"Northeastern University"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc."},{"author_name":"Kaiming Bi","author_inst":"The University of Texas Health Science Center at Houston"},{"author_name":"Anass Bouchnita","author_inst":"University of Texas at El Paso"},{"author_name":"Jiangzhuo Chen","author_inst":"University of Virginia"},{"author_name":"Matteo Chinazzi","author_inst":"Northeastern University"},{"author_name":"Spencer J Fox","author_inst":"Northern Arizona University"},{"author_name":"Alison L Hill","author_inst":"Johns Hopkins University"},{"author_name":"Harry Hochheiser","author_inst":"University of Pittsburgh"},{"author_name":"Joseph C Lemaitre","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Sara L Loo","author_inst":"Johns Hopkins University"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia"},{"author_name":"Lauren Ancel Meyers","author_inst":"University of Texas at Austin"},{"author_name":"Carl A.B Pearson","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Przemyslaw Porebski","author_inst":"University of Virginia"},{"author_name":"Emily Przykucki","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Claire P Smith","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia"},{"author_name":"Alessandro Vespignani","author_inst":"Northeastern University"},{"author_name":"Timothy C Willard","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Katie Yan","author_inst":"The Pennsylvania State University"},{"author_name":"Cecile Viboud","author_inst":"National Institutes of Health Fogarty International Center"},{"author_name":"Justin Lessler","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Shaun Truelove","author_inst":"Johns Hopkins University"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Reusing Blood Samples from a Hospital-based Cohort to Apixaban Plasma Concentrations","rel_doi":"10.64898\/2026.04.07.26350322","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.07.26350322","rel_abs":"In the management of atrial fibrillation, the most frequently prescribed oral anticoagulant is apixaban, given at a fixed dose of 5mg BID. Apixaban is predominantly metabolized by cytochrome P4503A4 (CYP3A4) and is also a substrate for the drug efflux transporter P-glycoprotein (P-gp). In nearly 300,000 Medicare patients with AF receiving apixaban, we previously showed that concomitant therapy with drugs that inhibit both CYP3A4 and P-gp, specifically amiodarone or diltiazem, significantly increased serious bleeding that caused hospitalization and\/or death. We hypothesized that this adverse effect was mediated by an increase in apixaban plasma concentrations caused by concomitant therapy that reduced drug elimination. Utilizing left-over samples obtained from clinically indicated blood draws that would typically be discarded, the Vanderbilt University Medical Center biobank BioVU contains >353,000 samples linked to de-identified electronic medical records (EMRs), with both DNA and plasma harvested. Of 35 samples drawn from patients taking apixaban 5mg BID, 5 were identified to be drawn from patients concomitantly taking drugs inhibiting both CYP3A4 and P-gp. Using a chromogenic anti-Xa assay, we found that plasma concentrations of apixaban were significantly higher (347{+\/-}64 ng\/mL; mean{+\/-}SEM) for patients receiving concomitant CYP3A4\/P-gp-inhibiting drugs compared to those not treated with these drugs (166{+\/-}67 ng\/mL; P=0.025, Mann Whitney). There were no differences between the 2 patient groups with respect to age, weight, or serum creatinine. The results of this pilot study provide preliminary data to support our hypothesis, and they demonstrate the practicality of obtaining pharmacokinetic data from a large cohort of plasma samples linked to deidentified EMRs. This approach could be used to define the role of apixaban levels in high-risk clinical scenarios and to better understand the relationship between drug levels and bleeding risk.","rel_num_authors":7,"rel_authors":[{"author_name":"Katherine T. Murray","author_inst":"Vanderbilt University School of Medicine"},{"author_name":"Daniel V. Fabbri","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Jeffrey S. Annis","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Cynthia R. Clark","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Jill M. Pulley","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Evan Brittain","author_inst":"Vanderbilt University Medical Center"},{"author_name":"David Gailani","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-04-08","rel_site":"medrxiv"},{"rel_title":"Structural Basis of Polypurine Track Strand Displacement by HIV-1 Reverse Transcriptase","rel_doi":"10.64898\/2026.04.07.717013","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.07.717013","rel_abs":"To complete reverse transcription, HIV-1 reverse transcriptase (RT) must displace the RNase H-resistant polypurine tract (PPT) primers. This enables synthesis of the long terminal repeats and formation of the central cDNA flap. However, the molecular mechanism of this PPT strand displacement (SD) has remained unknown, and no structural data exist on how a retroviral polymerase execute these reactions. We report the first cryo-EM structures of HIV-1 RT bound to nucleic acid substrates containing either a PPTRNA or PPTDNA displacement strand, with incoming dATP positioned at the polymerase catalytic site. These structures reveal key features of the PPT displacement mechanism by RT. Specifically, we observed a binding mode where the template nucleotide (T1) base-paired to the first displacement nucleotide (D1) undergoes a 90{degrees} rotation relative to the preceding template base (T0). This sharp template flip positions D1 [~]30 [A] away from the primers 3-end and is coordinated by RTp66 residues at the SD interface: F61 and R78 contact T1\/T0 to drive template translocation, while W24 engages both T1 and D1 to stabilize the displacement strand. Biochemical and virological mutagenesis experiments confirm that interactions with F61 and R78 are essential for both canonical cDNA polymerization and SD, whereas the W24-nucleotide interactions are required exclusively for SD but are dispensable for standard cDNA synthesis. These results contribute to the structural and functional understanding of PPT strand displacement by HIV-1 RT and reveal a distinct mechanistic vulnerability for the design of next-generation antiretrovirals.","rel_num_authors":7,"rel_authors":[{"author_name":"Xin Wen","author_inst":"Emory University"},{"author_name":"Rachel Lee","author_inst":"Emory University"},{"author_name":"Sri Dhanya Muppalla","author_inst":"Georgia Institute of Technology"},{"author_name":"William M McFadden","author_inst":"Emory University"},{"author_name":"Karen A Kirby","author_inst":"Emory University"},{"author_name":"Robert A Dick","author_inst":"Emory University"},{"author_name":"Stefan G Sarafianos","author_inst":"Emory University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"A MinD-like ATPase couples flagellation and cell division in spirochetes","rel_doi":"10.64898\/2026.04.08.717139","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.08.717139","rel_abs":"Spirochetes are evolutionarily distinct bacteria defined by their spiral morphology, unique means of motility, and periplasmic flagella (PFs). Because these filaments reside within the periplasm and are mechanically integrated with the cell body, their assembly must be precisely coordinated with cell growth and cytokinesis. However, the mechanism that couples flagellar biogenesis to cell division in spirochetes remains unclear. Using the Lyme disease spirochete Borrelia burgdorferi as a model, we identify FlhG (BB0269), a MinD-like ATPase, as a spatial regulator that links cell division to flagellar patterning. In wild-type cells, 7-11 long helical PFs originate from cell poles and assemble into ribbon-like bundles that wrap around the cell cylinder to drive motility. Deletion of flhG disrupts this ordered architecture, causing marked heterogeneity in flagellar number, defective ribbon assembly, aberrant septation, and severe motility impairment. Mechanistically, FlhG dynamically localizes to the poles and midcell during division, where it directs the positioning of FlhF, a signal recognition particle (SRP) -type GTPase controlling flagellar number and placement, and FliF, the MS-ring protein that nucleates flagellar assembly. Through this spatial regulation, FlhG coordinates flagellar assembly with cytokinetic progression. Together, these findings reveal a spatial regulatory mechanism coupling cell division to flagellation, providing insight into understanding how spirochetes coordinate their distinctive morphogenesis, flagellation and motility.\n\nSignificanceSpirochetes such as Borrelia burgdorferi, the causative agent of Lyme disease, rely on periplasmic flagella for motility and cell shape, yet how these structures are coordinated with cell division has remained unclear. We identify a MinD-like ATPase, FlhG, as a spatial regulator that couples flagellar assembly to cytokinesis. In contrast to its homologs in other bacteria, FlhG does not regulate flagellar protein levels but instead directs subcellular positioning of key assembly factors. By dynamically redistributing between the cell poles and division site, FlhG synchronizes flagellar patterning with septum formation. These findings uncover a previously unrecognized mechanism linking cell morphogenesis to the cell cycle and reveal how conserved ATPases can be repurposed to organize complex bacterial architectures.","rel_num_authors":6,"rel_authors":[{"author_name":"Chunhao Li","author_inst":"Philips Research Institute for Oral Health, School of Dentistry, Virginia Commonwealth University"},{"author_name":"Kai Zhang","author_inst":"Virginia Commonwealth University"},{"author_name":"Wangbiao Guo","author_inst":"Yale University"},{"author_name":"Michael J Lynch","author_inst":"Cornell University College of Arts and Sciences"},{"author_name":"Brian Crane","author_inst":"Cornell University"},{"author_name":"Jun Liu","author_inst":"Yale University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Intranasal Anti-CD3 Antibody Treatment Attenuates Post-COVID Neuroinflammation and Enhances Hippocampal Neurogenesis and Cognitive Function in Mice","rel_doi":"10.64898\/2026.04.07.716934","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.07.716934","rel_abs":"Cognitive impairment is a disabling feature of Long COVID, with data supporting neuroinflammation and maladaptive glial responses as primary drivers. Nasal administration of an anti-CD3 monoclonal antibody (aCD3 mAb) has shown therapeutic benefits in autoimmune and CNS disease models. Using a respiratory-restricted mild SARS-CoV-2 mouse model of Long COVID, we show that nasal anti-CD3 mAb, administered shortly after infection or during chronic neuroinflammation, increased brain FoxP3+ IL-10+ Tregs, reduced microglial and astrocytic gliosis in the white matter and hippocampus, restored neurogenesis, and improved short-term memory. Nasal aCD3 mAb reprogrammed microglia from an antigen-presenting, NF-{kappa}B-driven inflammatory state toward chemokine signaling, phagosome, and TGF {beta}-related regulatory phenotype. Patients with Long COVID with neurological symptoms had lower circulating Treg populations. These findings identify nasal administration of aCD3 mAb as a noninvasive strategy to control neuroinflammation, restore the neurogenic niche, and offer a novel approach to treating cognitive impairment in Long COVID.","rel_num_authors":12,"rel_authors":[{"author_name":"Peiwen Lu","author_inst":"Department of Immunobiology, Yale University, New Haven CT USA"},{"author_name":"Saef Izzy","author_inst":"Immunology of Brain Injury Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Patrick Da Silva","author_inst":"Immunology of Brain Injury Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Harm Tjebbe Imkamp","author_inst":"Immunology of Brain Injury Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Jonathan R. Christenson","author_inst":"Immunology of Brain Injury Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Taha Yahya","author_inst":"Immunology of Brain Injury Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Maryam Hazim Al Mansi","author_inst":"Immunology of Brain Injury Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Amir Alawi","author_inst":"Immunology of Brain Injury Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Thais G. Moreira","author_inst":"Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Michelle Monje","author_inst":"Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA"},{"author_name":"Howard L. Weiner","author_inst":"Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Akiko Iwasaki","author_inst":"Department of Immunobiology, Yale University, New Haven CT USA"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Decoding concept representations in aphasia after stroke","rel_doi":"10.64898\/2026.04.07.717076","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.07.717076","rel_abs":"Many stroke survivors with aphasia struggle to map their thoughts into words or motor plans. Neuroprostheses that decode concept representations could help these individuals communicate by predicting the words, phrases, or sentences that they are struggling to produce. Here we decoded concept representations measured using functional magnetic resonance imaging (fMRI) from participants with different aphasia profiles. The decoders generated continuous word sequences that could describe the concepts that the participants were hearing about, seeing, or imagining. To forecast how this approach would generalize across the heterogeneity of aphasia profiles, we characterized how stroke affects the anatomical organization and information capacity of conceptual processing. Mapping how concepts are organized across the brain, we found that conceptual tuning during non-linguistic processing was largely consistent between the participants with aphasia and neurologically healthy participants. Comparing information processing between the participants with aphasia and neurologically healthy participants, we found that both groups processed similar amounts of non-linguistic information. Our findings indicate that concept representations can be largely spared in individuals with aphasia and demonstrate how these representations can be decoded to support communication.","rel_num_authors":9,"rel_authors":[{"author_name":"Jerry Tang","author_inst":"The University of Texas at Austin"},{"author_name":"Carly Millanski","author_inst":"The University of Texas at Austin"},{"author_name":"Allison Chen","author_inst":"The University of Texas at Austin"},{"author_name":"Lisa D Wauters","author_inst":"The University of Texas at Austin"},{"author_name":"Jordyn Anders","author_inst":"The University of Texas at Austin"},{"author_name":"Shilpa Shamapant","author_inst":"Austin Speech Labs"},{"author_name":"Stephen M Wilson","author_inst":"The University of Queensland"},{"author_name":"Alexander G Huth","author_inst":"University of California, Berkeley"},{"author_name":"Maya Henry","author_inst":"The University of Texas at Austin"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Intrinsically disordered ligands for the control of receptor uptake by endocytosis","rel_doi":"10.64898\/2026.04.06.716760","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716760","rel_abs":"Endocytosis plays a crucial role in signaling, recycling, and degradation of receptors. Controlling endocytosis of specific receptors is therefore a major goal for both basic science and medicine. While antibody-induced dimerization can drive signaling-induced uptake of some receptors, the steric bulk of antibodies generally inhibits endocytosis, such that control over receptor uptake remains an unmet need. Recent work has demonstrated that attractive interactions between intrinsically disordered proteins drive inward membrane curvature, a key step in endocytosis. To harness this phenomenon for the control of receptor uptake, we designed chimeras that consisted of disordered domains fused to ligands for specific receptors. These chimeras condensed on membrane surfaces, driving receptor clustering and uptake via clathrin-mediated endocytosis. In contrast, chimeras that repelled one another resisted condensation, helping receptors escape endocytosis and remain on the cell surface. Taken together, these results suggest that by modulating the amino acid sequence of intrinsically disordered ligands, we can promote or hinder the internalization of specific receptors by endocytic pathways. More broadly, these findings suggest a generalizable strategy for controlling the plasma membrane lifetime of diverse receptors, opening up new pathways for modulating cellular behavior and delivering therapeutics.\n\nSignificance StatementCells regulate signaling by continuously internalizing receptors from their surfaces using endocytosis. Controlling receptor internalization would provide new tools for addressing diverse diseases. While antibodies can cluster specific receptors on the cell surface, their steric bulk and rigidity inhibit endocytosis. In contrast, here we demonstrate that engineered ligands containing intrinsically disordered domains form flexible complexes that precisely control receptor internalization. Disordered ligands that attract one another condense receptors at sites of endocytosis, driving uptake, while repulsive disordered ligands prevent condensation such that receptors remain on the cell surface. By tuning the amino acid sequence of the disordered domain, a ligand can be switched from promoting to inhibiting receptor internalization, offering the opportunity to control cell signaling and therapeutic delivery.","rel_num_authors":7,"rel_authors":[{"author_name":"Sujeong Park","author_inst":"The University of Texas at Austin"},{"author_name":"Irene Sarro","author_inst":"The University of Texas at Austin"},{"author_name":"Advika Kamatar","author_inst":"The University of Texas at Austin"},{"author_name":"Liping Wang","author_inst":"The University of Texas at Austin"},{"author_name":"Padmini Rangamani","author_inst":"University of California San Diego"},{"author_name":"Eileen M Lafer","author_inst":"University of Texas Health Science Center San Antonio"},{"author_name":"Jeanne Stachowiak","author_inst":"University of Texas at Austin"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"UQ-PhysiCell: An extensible Python framework for uncertainty quantification and model analysis in PhysiCell","rel_doi":"10.64898\/2026.04.06.716692","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716692","rel_abs":"Agent-based models (ABMs) are widely used to study complex multiscale biological systems, particularly in cancer research. However, their high-dimensional parameter spaces, stochasticity, and computational costs pose significant challenges for uncertainty quantification, calibration, and systematic comparison of competing mechanistic hypotheses. PhysiCell has evolved into a growing ecosystem of open-source tools supporting physics-based multicellular modeling, including model construction, visualization, and data integration. However, despite these advances, systematic support for uncertainty-aware model analysis, scalable parameter exploration, and formal calibration workflows remains limited. Here, we introduce UQ-PhysiCell, an open-source Python package that enables uncertainty quantification, calibration, and model selection for PhysiCell models using a modular and scalable workflow. UQ-PhysiCell acts as a manager of PhysiCell simulation inputs and outputs, including parameters, initial conditions, rules, and MultiCellDS-compliant objects, and provides automated orchestration of large ensembles of simulations. The framework supports multiple levels of parallelism to accelerate the analysis, including the parallel execution of independent simulations, stochastic replicates, and downstream analysis tasks. UQ-PhysiCell integrates seamlessly with established Python libraries for sensitivity analysis, optimization, Bayesian inference, and surrogate modeling, allowing users to construct customized pipelines that match their modeling goals and computational resource requirements. By decoupling model execution from statistical analysis and emphasizing extensibility and reproducibility, UQ-PhysiCell lowers the barrier to applying rigorous uncertainty-aware methodologies to agent-based modeling and supports the systematic evaluation of PhysiCell models in biological and biomedical research.\n\nAuthor summaryWe developed UQ-PhysiCell to address a key challenge in agent-based modeling: the systematic quantification of uncertainty in complex stochastic simulations. PhysiCell is widely used to model multicellular biological systems, particularly in cancer research; however, practical tools for uncertainty analysis, calibration, and model comparison are often developed in an ad hoc manner. This makes the results difficult to reproduce and limits the ability to rigorously evaluate competing biological hypotheses. UQ-PhysiCell provides a flexible Python framework that manages the inputs and outputs of PhysiCell simulations and enables large-scale computational analysis. We designed the software to be modular, allowing users to build their own analysis pipelines and combine different methodologies for sensitivity analysis, calibration, and model selection. Rather than enforcing a single workflow, UQ-PhysiCell supports customization to match specific scientific questions and computational requirements. To make uncertainty-aware analyses feasible for computationally intensive agent-based models, UQ-PhysiCell implements multiple parallelism strategies, enabling the concurrent execution of simulations, stochastic replicates, and downstream analyses. By promoting reproducibility, scalability, and methodological flexibility, UQ-PhysiCell helps researchers move beyond single best-fit simulations toward more reliable and interpretable computational modeling.","rel_num_authors":7,"rel_authors":[{"author_name":"Heber L. Rocha","author_inst":"Indiana University"},{"author_name":"Elmar Bucher","author_inst":"Indiana University"},{"author_name":"Shuming Zhang","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Atul Deshpande","author_inst":"Johns Hopkins University"},{"author_name":"Daniel R Bergman","author_inst":"University of Maryland Baltimore"},{"author_name":"Randy Heiland","author_inst":"Indiana University"},{"author_name":"Paul R Macklin","author_inst":"Indiana University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"PA-SfM: Tracker-free differentiable acoustic radiation for freehand 3D photoacoustic imaging","rel_doi":"10.64898\/2026.04.06.716718","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716718","rel_abs":"Three-dimensional (3D) handheld photoacoustic tomography typically relies on bulky and expensive external positioning trackers to correct motion artifacts, which severely limits its clinical flexibility and accessibility. To address this challenge, we present PA-SfM, a tracker-free framework that leverages exclusively single-modality photoacoustic data for both sensor pose recovery and high-fidelity 3D reconstruction via differentiable acoustic radiation modeling. Unlike traditional Structure-from-Motion (SfM) methods that formulate pose estimation as a geometry-driven optimization over visual features, PA-SfM integrates the acoustic wave equation into a differentiable programming pipeline. By leveraging a high-performance, GPU-accelerated acoustic radiation kernel, the framework simultaneously optimizes the 3D photoacoustic source distribution and the sensor array pose via gradient descent. To ensure robust convergence in freehand scenarios, we introduce a coarse-to-fine optimization strategy that incorporates geometric consistency checks and rigid-body constraints to eliminate motion outliers. We validated the proposed method through both numerical simulations and in-vivo rat experiments. The results demonstrate that PA-SfM achieves sub-millimeter positioning accuracy and restores high-resolution 3D vascular structures comparable to ground-truth benchmarks, offering a low-cost, softwaredefined solution for clinical freehand photoacoustic imaging. The source code is publicly available at https:\/\/github.com\/JaegerCQ\/PA-SfM.","rel_num_authors":13,"rel_authors":[{"author_name":"Shuang Li","author_inst":"Peking University"},{"author_name":"Jian Gao","author_inst":"Nanjing University"},{"author_name":"Chulhong Kim","author_inst":"Pohang University of Science and Technology"},{"author_name":"Seongwook Choi","author_inst":"Pohang University of Science and Technology"},{"author_name":"Qian Chen","author_inst":"Peking University"},{"author_name":"Yibing Wang","author_inst":"Peking University"},{"author_name":"Shuang Wu","author_inst":"Nanjing University"},{"author_name":"Yu Zhang","author_inst":"Peking University"},{"author_name":"Tingting Huang","author_inst":"Peking University"},{"author_name":"Yucheng Zhou","author_inst":"Peking University"},{"author_name":"Boxin Yao","author_inst":"Peking University"},{"author_name":"Yao Yao","author_inst":"Nanjing University"},{"author_name":"Changhui Li","author_inst":"Peking University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"A basophil-specific GPCR mediates the immune response to helminth infection","rel_doi":"10.64898\/2026.04.06.716327","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716327","rel_abs":"Basophils are rare innate immune cells that contribute to type 2 immunity in allergy and parasitic helminth infection, yet the receptors governing their activation remain poorly defined. Here, we identify the G protein-coupled receptor (GPCR) Mrgpra6 as a basophil-specific gene that mediates the host response to helminth infection. Mrgpra6 expression is restricted to basophils in blood and lung and is uniformly expressed across the population. Knockout of Mrgpra6 impairs early innate immunity to invading helminths, resulting in increased larval burden, elevated mortality, and disrupted parasite progression within the host. Transcriptional profiling of FACS-sorted basophils during helminth infection reveals how Mrgpra6 shapes the basophil transcriptional landscape needed for effective host defense. Together, these findings identify Mrgpra6 as a functional regulator of basophil-mediated anti-helminth immunity.","rel_num_authors":7,"rel_authors":[{"author_name":"Xinzhong Dong","author_inst":"Johns Hopkins University"},{"author_name":"Aleksander K Geske","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Rachel Pan","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Celeste Flores","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Taylor Follansbee","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Nathachit Limjunyawong","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Xintong Dong","author_inst":"University of Texas at Dallas"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Quantifying PD1 saturation by PDL1 in tumor tissue using a novel RNA aptamer-based assay","rel_doi":"10.64898\/2026.04.06.716702","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716702","rel_abs":"BackgroundTherapeutic agents targeting the PD1-PDL1 interaction are of great clinical value, however accurately predicting which patients are most likely to benefit is challenging. Improved predictive biomarkers for anti-PD1 therapy are clearly needed. Quantifying PD1 saturation by PDL1 in tumor tissue has the potential to serve as such a biomarker. Here we report a novel bioassay called the PD1 Ligand Receptor Complex Aptamer (LIRECAP) assay and demonstrate it can be used to quantify the saturation of PD1 by PDL1 in formalin-fixed paraffin-embedded tumor biospecimens.\n\nResultsThe PD1 LIRECAP assay was developed by identifying a pair of RNA aptamers. One aptamer preferentially binds to unoccupied PD1 (P aptamer) and the other to the PD1-PDL1 complex (C aptamer). P and C aptamers were added together to a formalin-fixed sample, and bound aptamer extracted. A 2-color qRT-PCR assay using a single set of primers was used to determine the ratio of the sample-bound C to P aptamers (C:P ratio) which reflected PD1 saturation by PDL1 in the sample. Quantification of PD1 saturation by PDL1 as determined by the PD1 LIRECAP assay correlated closely with PD1-mediated signaling and PD1-PDL1 proximity. Analysis of sarcoma FFPE biospecimens confirmed the assay is technically reproducible on clinical biospecimens. There were significant differences in PD1 saturation by PDL1 between patients as well as considerable intratumoral heterogeneity.\n\nConclusionsThe PD1 LIRECAP assay is novel assay that can be used to quantify PD1 saturation by PDL1 in clinical biospecimens. The assay is technically feasible, reproducible, and has the potential to serve as a superior predictive biomarker for PD1\/PDL1-based therapy. Similar assays based on this platform could be used in other systems and settings to quantify interaction between two molecules.","rel_num_authors":6,"rel_authors":[{"author_name":"Suresh Veeramani","author_inst":"University of Iowa"},{"author_name":"Chaobo Yin","author_inst":"University of Iowa"},{"author_name":"Nanmeng Yu","author_inst":"University of Iowa"},{"author_name":"Kristen L Coleman","author_inst":"University of Iowa"},{"author_name":"Brian J Smith","author_inst":"University of Iowa"},{"author_name":"George J Weiner","author_inst":"University of Iowa"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Hierarchical decoding of targeting tripeptide motif by the cytosolic iron-sulfur cluster assembly targeting complex","rel_doi":"10.64898\/2026.04.06.716496","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716496","rel_abs":"Iron-sulfur (Fe-S) clusters are essential cofactors required for diverse cellular processes, yet how the Fe-S cluster biogenesis machinery selectively recognizes apo-client proteins remain poorly understood. In eukaryotes, many cytosolic and nuclear Fe-S proteins are recruited to the cytosolic iron-sulfur cluster assembly (CIA) system through a short C-terminal targeting complex recognition (TCR) motif having a [ILM]-[DES]-FW] consensus. Currently, the physicochemical properties underlying this molecular recognition event are undefined. By combining quantitative binding measurements, bioinformatic analysis, and structural modeling, we define the molecular basis for TCR peptide recognition by the CIA targeting complex (CTC). This systematic energetic dissection reveals a hierarchy of binding determinants, in which the side chain and C-terminal carboxylate of the aromatic residue provide the dominant energetic contributor, whereas the upstream residues modulate affinity in a sequence context-dependent manner. Computational docking and molecular dynamics simulations identify an interfacial binding site at the Cia1-Cia2 interface that can accommodate these TCR moieties complementary interaction surfaces. Mutational analysis the identified interaction site is consistent with an aromatic pocket and an adjacent hydrophobic groove on Cia2 accommodating the TCRs terminal aromatic and antepenultimate aliphatic residues. Together, these results reveal the physicochemical decoding grammar by which the CTC recognizes targeting peptides with divergent sequences, illustrating how short targeting motifs can achieve both the specificity and adaptability required for Fe-S protein maturation.\n\nSignificance StatementIron-sulfur (Fe-S) clusters are essential metallocofactors requiring dedicated pathways for their assembly and insertion into proteins. How Fe-S cluster biogenesis systems selectively recognize their Fe-S binding client proteins in the crowded cellular environment remains poorly understood, in part because these machineries must engage dozens of clients rather than relying on the one-to-one metallochaperone-client pairings commonly used to assemble other types of metalloproteins. In eukaryotes, many cytosolic and nuclear Fe-S proteins are recruited to the cytosolic iron-sulfur assembly (CIA) machinery through a C-terminal targeting tripeptide motif. Here we combine biochemical measurements and structural modeling to define the molecular rules governing recognition of CIA targeting peptides. We show that a hierarchy of physicochemical peptide features, rather than a strict sequence consensus, guides recruitment of clients to the targeting complex, explaining how a single binding site can decode multiple targeting peptide signals.","rel_num_authors":6,"rel_authors":[{"author_name":"Anastasiya Buzuk","author_inst":"Boston University"},{"author_name":"Omeir Khan","author_inst":"Boston University"},{"author_name":"Soyoon Kang","author_inst":"Boston University"},{"author_name":"Leah Yim","author_inst":"Boston University"},{"author_name":"Sandor Vajda","author_inst":"Boston University"},{"author_name":"Deborah Perlstein","author_inst":"Boston University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"A High-throughput Fluorescence Polarization Assay for Screening Sirtuin Inhibitors","rel_doi":"10.64898\/2026.04.06.716694","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716694","rel_abs":"Sirtuins (SIRTs), which remove protein lysine acyl modifications, play crucial roles in diverse cellular processes, including metabolism, gene transcription, DNA damage repair, cell survival, and stress response. Several sirtuins are considered non-oncogene addiction of cancer cells and promising targets for anticancer drug development. High-throughput screening (HTS) methods for sirtuins are critical for the development of potent and isoform-selective sirtuin inhibitors, which are needed to validate the therapeutic potential. Herein, we designed and synthesized a fluorescent polarization (FP) tracer, KP-SC-1. Using this high-affinity tracer, we developed a robust, high-throughput FP competition assay for screening SIRT1-3 inhibitors. The assay was validated by testing known SIRT1-3 inhibitors. The assay can detect NAD+-independent SIRT1-3 inhibitors, as well as NAD+-dependent inhibitors, such as Ex-527 and TM. Finally, our assay showed satisfactory stability and outstanding performance in a pilot library screening. Compared to previous assays, the FP assay uses much less SIRT1-3 enzymes, a feature important for high-throughput library screening. We believe that the FP assay developed here will accelerate the discovery and development of SIRT1-3 inhibitors.","rel_num_authors":3,"rel_authors":[{"author_name":"Kewen Peng","author_inst":"The University of Chicago"},{"author_name":"Suryadeep Chakraborty","author_inst":"The University of Chicago"},{"author_name":"Hening Lin","author_inst":"Howard Hughes Medical Institute, The University of Chicago"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Density-dependent facilitation of livestock by small mammal ecosystem engineers","rel_doi":"10.64898\/2026.04.06.716644","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716644","rel_abs":"Small mammals and large herbivores have co-evolved in grasslands for millions of years, yet how they interplay remains unclear. On the Qinghai-Tibetan Plateau, plateau pikas (Ochotona curzoniae) are often considered pests that compete with livestock at high densities. Using field experiments, we show that pikas facilitate yaks (Bos grunniens) below a moderate density threshold ([~]200 active burrows\/ha). By selectively clipping tall poisonous forbs, especially Stellera chamaejasme, pikas reduced their cover by two-thirds, increased the abundance and protein content of palatable grasses and sedges, improved yak foraging efficiency, and enhanced weight gain by up to 67%. These results provide the first empirical evidence of a density-dependent transition from antagonism to facilitation between small and large herbivores. They highlight how moderate populations of ecosystem-engineering small mammals can sustain both biodiversity and pastoral productivity in rangelands.","rel_num_authors":16,"rel_authors":[{"author_name":"Zhiwei Zhong","author_inst":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences"},{"author_name":"Bingbo Ni","author_inst":"Northeast Institute of Geography and Agroecology, Chinese Academy of Science"},{"author_name":"Douglas Lawton","author_inst":"Arizona State University"},{"author_name":"Xiaofei Li","author_inst":"Jilin Agricultural University"},{"author_name":"Xiaona Zheng","author_inst":"Northeast Normal UniversityC"},{"author_name":"Huakun Zhou","author_inst":"Northwest Institute of Plateau Biology"},{"author_name":"Junhu Su","author_inst":"Gansu Agricultural University"},{"author_name":"Wenjin Li","author_inst":"Lanzhou University"},{"author_name":"Fujiang Hou","author_inst":"Lanzhou University"},{"author_name":"Zhenggang Guo","author_inst":"Lanzhou University"},{"author_name":"Quanmin Dong","author_inst":"Qinghai University"},{"author_name":"Shikui Dong","author_inst":"Beijing Forestry University"},{"author_name":"Christopher Dickman","author_inst":"The University of Sydney"},{"author_name":"Jens-Christian Svenning","author_inst":"Aarhus University"},{"author_name":"Ying Gao","author_inst":"Northeast Normal University"},{"author_name":"Zhibin Zhang","author_inst":"Institute of Zoology"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Thermodynamic rigidity of harmonic brain states relates to general mental ability in juvenile myoclonic epilepsy","rel_doi":"10.64898\/2026.04.06.715875","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.715875","rel_abs":"Cognitive difficulties are increasingly recognized in juvenile myoclonic epilepsy (JME), but scalable biomarkers linking resting-state brain dynamics to general mental ability remain limited. Here, we combined topological data analysis, graph signal processing, machine learning, inverse Langevin modeling, and biophysical simulations to test whether EEG-derived network dynamics capture individual differences in general mental ability in JME.\n\nWe studied 54 patients with JME and 45 healthy controls using resting-state high-density EEG and the raw estimated full-scale score derived from the Wechsler Abbreviated Scale of Intelligence (WASI), used here as an index of general mental ability. Subject-specific low-alpha activity was reconstructed with generalized eigendecomposition, and graph-derived features were extracted from the projection of topological and alpha-power signals onto the functional connectome, providing a graph-harmonic description of large-scale brain-state dynamics. In controls, dynamic EEG-derived features significantly predicted general mental ability, whereas the same framework failed in JME. Because prediction in controls was driven mainly by dynamic measures of smoothness (Dirichlet energy), we next examined the temporal organization of alpha-power smoothness using an inverse Langevin framework. Within the patient group, greater thermodynamic rigidity--that is, stronger confinement of fluctuations around preferred network states--was associated with lower general mental ability. Relative to controls, patients also showed lower thermodynamic noise, indicating a reduced tendency to explore alternative network regimes.\n\nBiophysical simulations suggested that reduced dendritic arborization can generate rigidity directly, whereas pharmacological stabilization of hyperexcitable circuits can shift the system toward a more rigid, lower-noise regime. Together, these findings suggest that cognition in JME is linked not only to altered resting-state network dynamics but also to stronger confinement of network-state fluctuations, with both intrinsic circuit abnormalities and treatment-related stabilization representing plausible routes to this rigid phenotype.","rel_num_authors":10,"rel_authors":[{"author_name":"Felipe Branco de Paiva","author_inst":"University of Helsinki"},{"author_name":"Meixian Zhao","author_inst":"Johns Hopkins University"},{"author_name":"Meishu Zhao","author_inst":"Johns Hopkins University"},{"author_name":"Santiago Philibert-Rosas","author_inst":"Washington University in St. Louis"},{"author_name":"Cameron J. Brace","author_inst":"Washington University in St. Louis"},{"author_name":"Erika Moe","author_inst":"University of Wisconsin-Madison"},{"author_name":"Steven E. Haworth","author_inst":"University of Wisconsin-Madison"},{"author_name":"Bruce P. Hermann","author_inst":"University of Wisconsin-Madison"},{"author_name":"Moo K. Chung","author_inst":"University of Wisconsin-Madison"},{"author_name":"Aaron F. Struck","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Thermodynamic rigidity of harmonic brain states relates to general mental ability in juvenile myoclonic epilepsy","rel_doi":"10.64898\/2026.04.06.715875","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.715875","rel_abs":"Cognitive difficulties are increasingly recognized in juvenile myoclonic epilepsy (JME), but scalable biomarkers linking resting-state brain dynamics to general mental ability remain limited. Here, we combined topological data analysis, graph signal processing, machine learning, inverse Langevin modeling, and biophysical simulations to test whether EEG-derived network dynamics capture individual differences in general mental ability in JME.\n\nWe studied 54 patients with JME and 45 healthy controls using resting-state high-density EEG and the raw estimated full-scale score derived from the Wechsler Abbreviated Scale of Intelligence (WASI), used here as an index of general mental ability. Subject-specific low-alpha activity was reconstructed with generalized eigendecomposition, and graph-derived features were extracted from the projection of topological and alpha-power signals onto the functional connectome, providing a graph-harmonic description of large-scale brain-state dynamics. In controls, dynamic EEG-derived features significantly predicted general mental ability, whereas the same framework failed in JME. Because prediction in controls was driven mainly by dynamic measures of smoothness (Dirichlet energy), we next examined the temporal organization of alpha-power smoothness using an inverse Langevin framework. Within the patient group, greater thermodynamic rigidity--that is, stronger confinement of fluctuations around preferred network states--was associated with lower general mental ability. Relative to controls, patients also showed lower thermodynamic noise, indicating a reduced tendency to explore alternative network regimes.\n\nBiophysical simulations suggested that reduced dendritic arborization can generate rigidity directly, whereas pharmacological stabilization of hyperexcitable circuits can shift the system toward a more rigid, lower-noise regime. Together, these findings suggest that cognition in JME is linked not only to altered resting-state network dynamics but also to stronger confinement of network-state fluctuations, with both intrinsic circuit abnormalities and treatment-related stabilization representing plausible routes to this rigid phenotype.","rel_num_authors":10,"rel_authors":[{"author_name":"Felipe Branco de Paiva","author_inst":"University of Helsinki"},{"author_name":"Meixian Zhao","author_inst":"Johns Hopkins University"},{"author_name":"Meishu Zhao","author_inst":"Johns Hopkins University"},{"author_name":"Santiago Philibert-Rosas","author_inst":"Washington University in St. Louis"},{"author_name":"Cameron J. Brace","author_inst":"Washington University in St. Louis"},{"author_name":"Erika Moe","author_inst":"University of Wisconsin-Madison"},{"author_name":"Steven E. Haworth","author_inst":"University of Wisconsin-Madison"},{"author_name":"Bruce P. Hermann","author_inst":"University of Wisconsin-Madison"},{"author_name":"Moo K. Chung","author_inst":"University of Wisconsin-Madison"},{"author_name":"Aaron F. Struck","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Low-Intensity Focused Ultrasound Enhances Meningeal Lymphatic Drainage for Preventing Cognitive Decline in Alzheimer's Disease","rel_doi":"10.64898\/2026.04.06.716653","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716653","rel_abs":"Meningeal lymphatic vessels (mLVs) are vital for brain waste clearance, making them a promising therapeutic target. However, effective modulation strategies for mLVs with translational potential remain underdeveloped. Here, we develop a low-intensity focused ultrasound (LIFU) strategy that precisely targets the vault cranial meninges to non-invasively facilitate mLVs drainage. Using models of Alzheimers disease (AD) and aging, we demonstrate that this approach promotes CSF drainage, prevents cognitive decline, and reduces pathological biomarkers. Mechanistically, RNA sequencing combined with calcium imaging in vitro reveals that LIFU activates the Piezo1 ion channel in lymphatic endothelial cells, whereas pharmacological inhibition of Piezo1 abolishes LIFUs therapeutic effects. Compliant with FDA safety guidelines, this LIFU protocol demonstrates strong clinical translatability. If its efficacy is clinically confirmed, LIFU offers a promising therapy for neurodegenerative diseases triggered by waste accumulation.","rel_num_authors":9,"rel_authors":[{"author_name":"Zhou Feng","author_inst":"Third Military Medical University"},{"author_name":"Jingming Hou","author_inst":"Third Military Medical University"},{"author_name":"Xiaoli Li","author_inst":"Beijing Normal University"},{"author_name":"Xingjun Xu","author_inst":"Third Military Medical University"},{"author_name":"Tao Jiang","author_inst":"Third Military Medical University"},{"author_name":"Caixin Zhu","author_inst":"Third Military Medical University"},{"author_name":"Yu Tang","author_inst":"Third Military Medical University"},{"author_name":"Yue Shu","author_inst":"Third Military Medical University"},{"author_name":"Qining Wang","author_inst":"Peking University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Negative affective states are not detected in rats following an intravenous self-administration regimen leading to incubation of oxycodone craving","rel_doi":"10.64898\/2026.04.06.716594","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716594","rel_abs":"In rats, cue-induced opioid craving intensifies (incubates) during abstinence from opioid self-administration and then remains high for a prolonged period. The prolonged plateau models persistent vulnerability to cue-induced craving and relapse in humans recovering from opioid use disorder. However, a very significant contributor to relapse vulnerability in these individuals is the presence of negative affective states that can persist for months to years, far beyond physical dependence. The goal of this study was to determine if the incubation of craving model recapitulates this aspect of relapse vulnerability. We began by comparing rats trained to self-administer oxycodone using a regimen leading to persistent elevation of cue-induced craving (6 h\/d x 10 d) and rats trained to self-administer saline. We assessed somatic withdrawal signs in early abstinence and conducted behavioral tests modeling negative affect (open field, social preference, sucrose preference, and elevated plus maze) in late abstinence. Some somatic withdrawal signs were greater in oxycodone rats on abstinence day (AD)1, but cumulative scores did not differ between groups on AD1-3. On AD41-46, no group differences were found in behavioral tests modeling negative affect. To compare early and late abstinenceperiods, a second cohort of rats self-administered saline and oxycodoneand then received two cue-induced seeking tests (AD1 and AD40; oxycodone rats exhibited incubation of craving) and two series of negative affect tests (AD2-7 and AD41-48). While some time-dependent changes in affect were observed within each group, they were suggestive of reduced anxiety-like behavior in oxycodone rats. Finally, because rats are single-housed during our incubation studies, we compared drug-naive rats after 8-9 weeks of single vs pair housing and found no difference in behavioral tests modeling negative affect. We conclude that the persistence of elevated cue-induced craving observed after a standard opioid incubation regimen is not accompanied by negative affective states, probably due to lower drug intake during the intravenous regimen compared to non-contingent escalating dose regimens typically used to study withdrawal signs. This does not negate the utility of the incubation model for studying cue-induced opioid craving and its neurobiological basis.","rel_num_authors":8,"rel_authors":[{"author_name":"Amanda M. Wunsch","author_inst":"National Center for Wellness & Recovery"},{"author_name":"Kimberley A. Mount","author_inst":"Division of Behavioral Health and Recovery, Washington State Health Care Authority"},{"author_name":"Asia Guzman","author_inst":"Oregon Health & Science University"},{"author_name":"Alex B. Kawa","author_inst":"University of New Mexico"},{"author_name":"Jonathan G. Westlake","author_inst":"Oregon Health & Science University"},{"author_name":"Hayley M. Kuhn","author_inst":"Oregon Health & Science University"},{"author_name":"Madelyn M. Beutler","author_inst":"Oregon Health & Science University"},{"author_name":"Marina E. Wolf","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Comparison of Extraction Methods for the Quantification of Phytohormones from Tomato Fruits and Leaves by LC-MS\/MS","rel_doi":"10.64898\/2026.04.06.716604","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716604","rel_abs":"Accurate, simultaneous, and efficient quantification of chemically diverse phytohormone species is a critical task towards understanding the complex system of phytohormone signaling pathways. Quantification of phytohormones with the commonly used technique liquid chromatography coupled to tandem mass spectrometry is susceptible to the influence of non-phytohormone components present in the sample, a phenomenon referred to as matrix effect. To reduce matrix effect, some phytohormone quantification methods include additional steps of cleanup of crude extracts. However, to what extent additional purification steps provide increased accuracy compared to simpler, less laborious methods is seldomly evaluated. We evaluated three previously described phytohormone extraction methods, two of which include solid-phase extraction and one that does not, in their ability to minimize matrix effect and generate accurate estimates of phytohormone species spanning six classifications, from fruit and leaf tissue of Solanum lycopersicum cv. Micro-Tom (tomato). Our results show that, while the methods that included solid phase extraction occasionally outperformed each other regarding matrix effect and\/or recovery efficiency for broad range of phytohormones, they rarely outperformed the simpler single-phase extraction method.\n\nShort AbstractAccurate, simultaneous quantification of chemically diverse phytohormones by LC-MS\/MS is frequently confounded by matrix effects, leading to the incorporation of additional purification steps. We systematically compared three published extraction protocols with or without solid-phase extraction in tomato tissues across six hormone classes. Solid-phase methods occasionally improved matrix suppression or recovery, but did not consistently outperform the single-phase approach, questioning the added value of extra cleanup steps, particularly when high-throughput is desired, as in the case of systems biology interrogations.","rel_num_authors":4,"rel_authors":[{"author_name":"Carlos A Juarez Guzman","author_inst":"Colorado State University"},{"author_name":"Linxing Yao","author_inst":"Colorado State University"},{"author_name":"Corey D Broeckling","author_inst":"Colorado State University"},{"author_name":"Cristiana T Argueso","author_inst":"Colorado State University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Early Epigenetic and Metabolic Responses to the Adipocyte Secretome Reveal Stress-Adaptive States in Triple-Negative Breast Cancer","rel_doi":"10.64898\/2026.04.06.716548","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716548","rel_abs":"Obesity is a well-established risk factor for triple-negative breast cancer (TNBC), yet how adipocyte-derived signals reprogram cancer cell metabolism and chromatin states remains poorly defined. Here, we investigate how adipocyte-driven lipogenesis reshapes metabolic-epigenetic coupling to support stress-adaptive cell states and functional changes in epithelial TNBC cells. Using an integrated multi-omic approach, we combine RNA sequencing (RNA-seq), chromatin accessibility (ATAC-seq), metabolic flux modeling, and functional metabolic assays in lipogenic BT-549 cells. Computational modeling trained on RNA-seq predicts shifts in metabolic pathway usage, including enhanced NAD-linked metabolism. RNA-seq reveals a predominance of gene activation, consistent with ATAC-seq data showing a strong bias toward increased accessibility. Regions of increased accessibility are enriched for stress-adaptive and antioxidant pathways, including superoxide dismutase 2 (SOD2) and metallothioneins (MT1F, MT1E, MT2A). Functionally, lipogenic cells exhibit increased spare respiratory capacity, altered ATP-linked respiration, elevated extracellular acidification, and reduced reactive oxygen species (ROS) accumulation, consistent with a bioenergetically flexible, stress-adaptive metabolic state. Together, these findings reveal that adipocyte-driven metabolic rewiring promotes selective chromatin opening and activation of stress-adaptive gene programs, enabling TNBC cells to buffer oxidative pressure for enhanced proliferation and survival after exposure to the adipocyte secretome.","rel_num_authors":9,"rel_authors":[{"author_name":"Ashley Townsel","author_inst":"Emory University"},{"author_name":"Maya Jaffe","author_inst":"Emory University"},{"author_name":"Shiyu He","author_inst":"Georgia Institute of Technology"},{"author_name":"Yifei Wu","author_inst":"Emory University"},{"author_name":"Asia Ingram","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Madison Tipton","author_inst":"The University of Colorado Anschutz Medical Campus"},{"author_name":"Melissa L. Kemp","author_inst":"Georgia Institute of Technology"},{"author_name":"Curtis J. Henry","author_inst":"The University of Colorado Anschutz Medical Campus"},{"author_name":"Karmella A. Haynes","author_inst":"Emory University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Geometry-enhanced protein language modeling enables discovery of novel antibiotic resistance genes","rel_doi":"10.64898\/2026.04.05.716581","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716581","rel_abs":"The global antibiotic resistome remains largely unexplored, not because antibiotic resistance genes (ARGs) are rare in the environment, but because many are evolutionarily distant from known ARGs. Current computational approaches primarily rely on sequence homology, and thus miss distant homologues. We develop GeoARG, a geometry-enhanced framework that integrates structural features with protein language models through knowledge distillation, enabling efficient large-scale screening using sequence input alone. Across multiple benchmarks, GeoARG substantially improves the detection of remotely homologous ARGs, particularly under low sequence identity and fragmented conditions. Large-scale metagenomic analysis uncovers 1,485 high-confidence ARG candidates that are highly divergent from known ARGs, expanding the phylogenetic and functional landscape of the resistome. Structural analyses further show that these candidates preserve active-site geometry and maintain stable ligand-binding configurations consistent with known resistance mechanisms. These results demonstrate that geometric constraints enable systematic expansion of the resistome and facilitate the discovery of evolutionarily distant yet functionally conserved genes. A public web server is available at https:\/\/ycclab.cuhk.edu.cn\/GeoARG\/.","rel_num_authors":20,"rel_authors":[{"author_name":"Xingqiao Lin","author_inst":"Xiamen University, Carnegie Mellon University"},{"author_name":"Jiahui Guan","author_inst":"The University of Hong Kong"},{"author_name":"Yue Hong","author_inst":"Xiamen University"},{"author_name":"Yilian Guo","author_inst":"The Chinese University of Hong Kong-Shenzhen"},{"author_name":"Yutao Yang","author_inst":"Xiamen University"},{"author_name":"Peilin Xie","author_inst":"The Chinese University of Hong Kong-Shenzhen"},{"author_name":"Zhihao Zhao","author_inst":"The Chinese University of Hong Kong-Shenzhen"},{"author_name":"Xingchen Liu","author_inst":"The Chinese University of Hong Kong-Shenzhen"},{"author_name":"Yixian Huang","author_inst":"Shenzhen University of Advanced Technology"},{"author_name":"Yujing Ye","author_inst":"Xiamen University"},{"author_name":"Yun Tang","author_inst":"National Yang Ming Chiao Tung University"},{"author_name":"Tzong-Yi Lee","author_inst":"National Yang Ming Chiao Tung University"},{"author_name":"Ying-Chih Chiang","author_inst":"The Chinese University of Hong Kong-Shenzhen"},{"author_name":"Leyi Wei","author_inst":"Macao Polytechnic University"},{"author_name":"Xiangrong Liu","author_inst":"Xiamen University"},{"author_name":"Junwen Wang","author_inst":"The University of Hong Kong"},{"author_name":"Yi Pan","author_inst":"Shenzhen University of Advanced Technology"},{"author_name":"Jijun Tang","author_inst":"Shenzhen University of Advanced Technology"},{"author_name":"Yao Pei","author_inst":"The University of Hong Kong"},{"author_name":"Lantian Yao","author_inst":"Xiamen University, Shenzhen University of Advanced Technology"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Successful dendritic cell vaccines require lasting in-situ TNF \u03b1 secretion to license antitumor CD8 + T cell cytotoxicity","rel_doi":"10.64898\/2026.04.06.716539","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716539","rel_abs":"Dendritic cells (DCs) are central to activating cytotoxic CD8 T cells, yet DC-based vaccines have achieved limited success against established tumors. To address this gap, we analyzed the transcriptomic and functional changes CD8 T cells undergo following interactions with DC subsets in lymphoid organs and tumor sites. This approach allowed us to map their trajectory from naive to fully cytotoxic effector cells. We found that classical DCs in lymphoid organs provide essential antigen presentation but fail to elicit cytotoxicity. Instead, antigenexperienced CD8 T cells require additional inflammatory signals, primarily through TNF, delivered at tumor sites by infiltrating myeloid DCs. Effective cytotoxic responses therefore depend on the synchronization of these distinct, temporally separated signals. Notably, tumor antigen-pulsed DC vaccines rapidly lose TNF expression after infiltrating tumors, limiting their efficacy. These findings establish a sequential model of T cell activation and suggest strategies to enhance the potency of DC-based immunotherapies.","rel_num_authors":21,"rel_authors":[{"author_name":"Aseel Radi Khateeb","author_inst":"Tel Aviv University"},{"author_name":"Nadine Santana Magal","author_inst":"Tel Aviv University"},{"author_name":"Kfir Inbal","author_inst":"Tel Aviv UNIVERSITY"},{"author_name":"Annette Gleiberman","author_inst":"Tel Aviv University"},{"author_name":"Ayelet Kaminitz","author_inst":"Tel Aviv University"},{"author_name":"Tomer Weiss","author_inst":"Tel Aviv University"},{"author_name":"Gal Verbin","author_inst":"Tel Aviv University"},{"author_name":"Alon Richter","author_inst":"Tel Aviv University"},{"author_name":"Amichai Zarfin","author_inst":"Tel Aviv University"},{"author_name":"Leen Farhat Younis","author_inst":"tel aviv university"},{"author_name":"Amit Gutwillig","author_inst":"tel aviv university"},{"author_name":"Adi Frish","author_inst":"tel aviv university"},{"author_name":"Eric Shifrut","author_inst":"tel aviv university"},{"author_name":"Inbal Reuveni Reuveni","author_inst":"tel aviv university"},{"author_name":"Adi Barzel","author_inst":"tel aviv university"},{"author_name":"Carmit Levi","author_inst":"tel aviv university"},{"author_name":"Peleg Rider","author_inst":"tel aviv university"},{"author_name":"Matthew H. Spitzer","author_inst":"University of California, San Francisco"},{"author_name":"Edgar G. Engleman","author_inst":"Center for Cancer Systems Biology"},{"author_name":"Asaf Madi","author_inst":"tel aviv university"},{"author_name":"Yaron Carmi","author_inst":"Tel-Aviv University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Successful dendritic cell vaccines require lasting in-situ TNF \u03b1 secretion to license antitumor CD8 + T cell cytotoxicity","rel_doi":"10.64898\/2026.04.06.716539","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716539","rel_abs":"Dendritic cells (DCs) are central to activating cytotoxic CD8 T cells, yet DC-based vaccines have achieved limited success against established tumors. To address this gap, we analyzed the transcriptomic and functional changes CD8 T cells undergo following interactions with DC subsets in lymphoid organs and tumor sites. This approach allowed us to map their trajectory from naive to fully cytotoxic effector cells. We found that classical DCs in lymphoid organs provide essential antigen presentation but fail to elicit cytotoxicity. Instead, antigenexperienced CD8 T cells require additional inflammatory signals, primarily through TNF, delivered at tumor sites by infiltrating myeloid DCs. Effective cytotoxic responses therefore depend on the synchronization of these distinct, temporally separated signals. Notably, tumor antigen-pulsed DC vaccines rapidly lose TNF expression after infiltrating tumors, limiting their efficacy. These findings establish a sequential model of T cell activation and suggest strategies to enhance the potency of DC-based immunotherapies.","rel_num_authors":21,"rel_authors":[{"author_name":"Aseel Radi Khateeb","author_inst":"Tel Aviv University"},{"author_name":"Nadine Santana Magal","author_inst":"Tel Aviv University"},{"author_name":"Kfir Inbal","author_inst":"Tel Aviv UNIVERSITY"},{"author_name":"Annette Gleiberman","author_inst":"Tel Aviv University"},{"author_name":"Ayelet Kaminitz","author_inst":"Tel Aviv University"},{"author_name":"Tomer Weiss","author_inst":"Tel Aviv University"},{"author_name":"Gal Verbin","author_inst":"Tel Aviv University"},{"author_name":"Alon Richter","author_inst":"Tel Aviv University"},{"author_name":"Amichai Zarfin","author_inst":"Tel Aviv University"},{"author_name":"Leen Farhat Younis","author_inst":"tel aviv university"},{"author_name":"Amit Gutwillig","author_inst":"tel aviv university"},{"author_name":"Adi Frish","author_inst":"tel aviv university"},{"author_name":"Eric Shifrut","author_inst":"tel aviv university"},{"author_name":"Inbal Reuveni Reuveni","author_inst":"tel aviv university"},{"author_name":"Adi Barzel","author_inst":"tel aviv university"},{"author_name":"Carmit Levi","author_inst":"tel aviv university"},{"author_name":"Peleg Rider","author_inst":"tel aviv university"},{"author_name":"Matthew H. Spitzer","author_inst":"University of California, San Francisco"},{"author_name":"Edgar G. Engleman","author_inst":"Center for Cancer Systems Biology"},{"author_name":"Asaf Madi","author_inst":"tel aviv university"},{"author_name":"Yaron Carmi","author_inst":"Tel-Aviv University"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Bridging Higher-Order Information Theory and Neuroimaging: A Voxel-Wise O-Information Framework","rel_doi":"10.64898\/2026.04.06.716652","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716652","rel_abs":"The brains functional organization has been extensively studied through pairwise connectivity analyses. While these approaches have provided important insights into brain network organization, they fall short in capturing the complexity of high-order functional interactions (HOI). Particularly relevant is the investigation of redundancy and synergy patterns -not addressable with pairwise interactions-, revealing fundamental mechanisms of brain integration and information processing across various cognitive functions and clinical conditions. Conventional neuroimaging software packages are primarily designed for classical (general linear model-like) analyses but lack native support for HOI metrics. To address this gap, this study introduces a novel framework that bridges high-order information theory with conventional neuroimaging analysis pipelines and is subsequently applied to resting-state functional MRI to demonstrate its practical utility. By representing HOI into voxel-level metrics, our approach allows standard neuroimaging analyses to probe complex multivariate dependencies. Moreover, voxel-level group-comparison analyses show age differences linked with reduced redundancy in default mode network interactions. These findings advance our understanding of the complex relationship between multivariate functional interactions, voxel-level neuroimaging, and behavior, highlighting novel analytic strategies to study high-order information processing underlying cognitive function and its alterations in pathological conditions.","rel_num_authors":11,"rel_authors":[{"author_name":"Borja Camino-Pontes","author_inst":"BioCruces Health Research Institute"},{"author_name":"Antonio Jimenez-Marin","author_inst":"BioCruces Health Research Institute"},{"author_name":"I\u00f1igo Tellaetxe-Elorriaga","author_inst":"BioCruces Health research Institute"},{"author_name":"Asier Erramuzpe Aliaga","author_inst":"BioCruces Health Research Institute"},{"author_name":"Ibai Diez","author_inst":"BioCruces Health Research Institute"},{"author_name":"Paolo Bonifazi","author_inst":"BioCruces Health research Institute"},{"author_name":"Marilyn Gatica","author_inst":"NU London: Northeastern University London"},{"author_name":"Fernando E Rosas","author_inst":"University of Sussex"},{"author_name":"Daniele Marinazzo","author_inst":"University of Ghent"},{"author_name":"Sebastiano Stramaglia","author_inst":"Universit`a degli Studi di Bari Aldo Moro"},{"author_name":"Jesus Cortes","author_inst":"BioCruces Health Research Institute"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Single-section multiplexed imaging enables comprehensive lung cancer diagnosis","rel_doi":"10.64898\/2026.04.05.716628","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716628","rel_abs":"Accurate and timely diagnosis is essential for effective lung cancer treatment. However, contemporary diagnostic workflows rely on sequential immunohistochemistry of small biopsy specimens, which can exhaust limited tissue, compromise diagnostic accuracy, and delay treatment decisions with clinical consequences. Here, we demonstrate that multiplexed imaging overcomes these limitations by enabling comprehensive lung cancer diagnosis from a single tissue section. We developed and validated a clinically informed multiplexed antibody panel that integrates tumor diagnosis and classification, predictive biomarker assessment, and tumor immune profiling. In diagnostic biopsies, multiplexed imaging achieved 96% concordance with standard pathological diagnosis, while enabling accurate automated PD-L1 scoring and rapid detection of clinically approved and emerging actionable targets. This approach preserves scarce tissue, supports quantitative computational analysis to streamline diagnosis, and generates research-grade spatial data while accelerating diagnostic workflow. Together, these findings establish multiplexed imaging as a robust, time and tissue-efficient framework for lung cancer diagnostics that bridges clinical care and translational discovery.","rel_num_authors":12,"rel_authors":[{"author_name":"Raz Ben-uri","author_inst":"The Weizmann Institute of Science"},{"author_name":"Tal Keidar Haran","author_inst":"Hadassah Hebrew University Medical Center"},{"author_name":"Yuval Bussi","author_inst":"Weizmann Institute of Science"},{"author_name":"Gilad Vainer","author_inst":"Hadassah Hebrew University Medical Center"},{"author_name":"Johnathan Arnon","author_inst":"Hadassah Hebrew University Medical Center"},{"author_name":"Nir Pillar","author_inst":"Hadassah Hebrew University Medical Center"},{"author_name":"Hasan Sourikh","author_inst":"Hadassah Hebrew University Medical Center"},{"author_name":"Inbal Fuchs","author_inst":"Hadassah Hebrew University Medical Center"},{"author_name":"Ofer Elhanani","author_inst":"Weizmann Institute of Science"},{"author_name":"Tzahi Neuman","author_inst":"Hadassah Hebrew University Medical Center"},{"author_name":"Eli Pikarsky","author_inst":"Hadassah Hebrew University Medical Center"},{"author_name":"Leeat Keren","author_inst":"Weizmann Institute of Science"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"A Non-Classical Neuroactive Steroid Exhibiting Potent, Efficacious GABA\tA Receptor Agonism and NMDA Receptor Inhibition","rel_doi":"10.64898\/2026.04.06.716659","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716659","rel_abs":"Neuroactive steroids modulate GABAA and NMDA receptors allosterically, typically requiring specific structural features for their activity. In this study, we characterize YX84, a novel neuroactive steroid bearing a 3{beta} sulfate and p-trifluoroacetylbenzyl alcohol attached in an ether linkage to a hydroxyl group at steroid carbon 17. This compound and similar analogues exhibit an atypical pharmacological profile, with three distinct actions at GABAA receptors. First, YX84 is a full agonist, with EC50 near 1 {micro}M and comparable efficacy to GABA at GABAA receptors in native hippocampal neurons. It presents as a full agonist relative to GABA at 4\/{delta} subunit-containing receptors. Second, YX84 acts as a slow-onset, potent positive allosteric modulator (PAM) of GABAA receptors at concentrations below those that gate a response. Finally, YX84 exhibits rapid desensitizing and\/or blocking kinetics; voltage dependence is consistent with a contribution of channel block. Structure- activity relationship analyses reveal that both functional groups are essential for gating activity, while classical requirements such as carbon 3 hydroxyl stereoselectivity and carbon 5 reduction are dispensable. YX84 also modestly inhibits NMDA receptor currents, suggesting weak negative allosteric modulation. Behavioral assays show that intraperitoneal administration of YX84 (30 mg\/kg) does not impair sensorimotor function, unlike allopregnanolone. These findings identify YX84 as a structurally distinct neuroactive steroid with dual receptor activity and favorable behavioral tolerability, offering a promising scaffold for therapeutic development targeting excitatory\/inhibitory imbalance in neuropsychiatric disorders if pharmacokinetic considerations can be overcome.","rel_num_authors":8,"rel_authors":[{"author_name":"Hong-Jin Shu","author_inst":"Washington University in St. Louis"},{"author_name":"Yuanjian Xu","author_inst":"Washington University in St. Louis"},{"author_name":"Mingxing Qian","author_inst":"Washington University in St. Louis"},{"author_name":"Ann Benz","author_inst":"Washington University in St. Louis"},{"author_name":"Carla M Yuede","author_inst":"Washington University in St. Louis"},{"author_name":"Douglas F Covey","author_inst":"Washington University in St. Louis"},{"author_name":"Charles F Zorumski","author_inst":"Washington University in St. Louis"},{"author_name":"Steven Mennerick","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Energetic analysis of Na+\/K+-ATPase using bond graphs","rel_doi":"10.64898\/2026.04.05.716446","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716446","rel_abs":"The sodium-potassium ATPase (NKA) consumes 19-28% of cellular ATP and is critical for maintaining ion homeostasis. Understanding its energetic efficiency is essential for comprehending cellular physiology and pathophysiology. We develop bond graph models of the NKA that ensure thermodynamic consistency by enforcing conservation of mass, charge, and energy. A simplified 6-state model captures biophysics comparable to a 15-state model while remaining computationally tractable. Through detailed energetic analysis, we demonstrate that under physiological conditions, approximately 65% of the energy from ATP hydrolysis is stored as chemical energy in ion gradients, 10% as electrical energy in the membrane potential, and 25% is dissipated as heat, yielding an overall efficiency of [~]75%. We investigate how the free energy of ATP hydrolysis ({Delta}GATP), intracellular Na+, and extracellular K+ affect NKA efficiency and activity. A critical threshold exists at {Delta}GATP {approx} - 48 kJ\/mol below which chemoelectrical transduction drops dramatically, consistent with NKA inhibition under ischemic conditions. The bond graph framework enables quantitative comparison of different NKA models and provides a systematic approach for analyzing ion pumps.\n\nSIGNIFICANCEThe sodium-potassium ATPase is one of the bodys most energy-consuming enzymes, yet its energetic efficiency and mechanisms remain incompletely understood. This study presents the first comprehensive energetic analysis using bond graph modeling, guaranteeing thermodynamic consistency. By demonstrating that simplified 6-state models capture essential energetic behaviors of complex 15-state models, we establish bond graphs as a powerful, tractable tool for energetic analysis, model comparison, model selection and validation. The bond graph approach can be applied to other transporters, offering a powerful tool for systems physiology and drug discovery.","rel_num_authors":4,"rel_authors":[{"author_name":"Weiwei Ai","author_inst":"Auckland Bioengineering Institute, University of Auckland, New Zealand"},{"author_name":"Peter J Hunter","author_inst":"University of Auckland"},{"author_name":"Michael Pan","author_inst":"School of Mathematics and Statistics, University of New South Wales, Australia"},{"author_name":"David Phillip Nickerson","author_inst":"University of Auckland"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Context-Dependent Reactive Antipredator Behavior of Chacma Baboons (Papio ursinus) Amidst Predator Recovery","rel_doi":"10.64898\/2026.04.05.716544","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716544","rel_abs":"Predation is a driving force in the ecology and evolution of prey, and primates exhibit diverse anti-predator strategies for minimizing risk. Because these behaviors can be costly, individuals must balance costs and benefits when responding to perceived threats. The cognitive capacity and behavioral plasticity of baboons make them an ideal taxon for studying the context-dependent variation in anti-predator strategies. Here, we used an autonomous, motion-activated playback experiment to study the behavioral responses of chacma baboons (Papio ursinus griseipes) to simulated predator encounters in Gorongosa National Park, Mozambique. We compared responses in 2021, when predator densities were relatively low, to responses in 2024, after predation increased due to lion (Panthera leo) population recovery and African wild dog (Lycaon pictus) reintroduction. We compared flight and vigilance responses to vocalizations of these common predators with responses to leopard (Panthera pardus), historically a key predator; spotted hyena (Crocuta crocuta), a rare predator; and cheetah (Acinonyx jubatus), absent historically and currently. We also assessed how responses varied with habitat, age-sex class, presence of offspring, and group size. Across 916 predator playbacks, baboons fled in 19% and displayed vigilance in 71% of trials. When predator density was higher, baboons displayed weakened antipredator responses, consistent with the risk allocation hypothesis. Baboons were more likely to flee in response to lion and wild dog cues. Juveniles fled more frequently than other demographic classes, while adult females with offspring were more vigilant. Overall, responses were highly heterogeneous, reflecting the substantial intraspecific variation and behavioral flexibility characteristic of baboons.","rel_num_authors":5,"rel_authors":[{"author_name":"Sophia Marissa Van Cuylenborg","author_inst":"University of British Columbia"},{"author_name":"Nicholas S Wright","author_inst":"Universtiy of British Columbia"},{"author_name":"Meredith S Palmer","author_inst":"Yale University"},{"author_name":"Susana Carvalho","author_inst":"Oxford University, Gorongosa National Park"},{"author_name":"Kaitlyn M Gaynor","author_inst":"University of British Columbia"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Global genomic diversity of the selfing nematode Caenorhabditis tropicalis correlates with geography","rel_doi":"10.64898\/2026.04.05.716573","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716573","rel_abs":"Self-fertilization reduces genetic diversity compared to outcrossing and hypothetically decreases the ability to adapt to diverse environments. Among Caenorhabditis nematodes, self-fertilization evolved three times independently in Caenorhabditis elegans, Caenorhabditis briggsae, and the more recently discovered Caenorhabditis tropicalis. To survey C. tropicalis genetic relatedness, the influence of geography and niche on species-wide variation, and the signatures of selection, we collected 785 wild strains, sequenced their genomes, and identified 622 distinct genotypes (isotypes). In contrast to C. elegans and C. briggsae, C. tropicalis relatedness shows substantial association with geography and no transcontinental selective sweeps or broadly sampled isotypes. Populations from the Hawaiian Islands or Taiwan harbor more genetic variation than populations from the Caribbean or Americas, suggesting a Pacific species origin similar to other members of the Elegans subclade. Punctuated genomic regions of extreme genetic variation pervade the genome. These hyper-divergent regions (HDRs) comprise less than 6% of the reference genome in any given strain despite harboring 73% of all variant sites and are enriched for genes likely involved in environmental adaptation. HDRs represent a shared genomic feature of self-fertilizing Caenorhabditis nematodes despite their independent evolutionary origins and suggest a mechanism to explain worldwide distributions despite low species-wide levels of genetic variation.","rel_num_authors":28,"rel_authors":[{"author_name":"Bowen Wang","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Nicolas D. Moya","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Robyn E. Tanny","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Michael E.G. Sauria","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Lance M. O Connor","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Ayeh Khorshidian","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Ryan McKeown","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Lewis Stevens","author_inst":"Tree of Life, Wellcome Sanger Institute, Hinxton, CB10 1RQ, UK"},{"author_name":"Claire Buchanan","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Timothy A. Crombie","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Department of Biomedical and Chemical Engineering and Sciences, Florida "},{"author_name":"Clayton M. Dilks","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Kathryn S. Evans","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Daniel E. Cook","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Gaotian Zhang","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris,"},{"author_name":"Loraina A. Stinson","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Nicole M. Roberto","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Daehan Lee","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419"},{"author_name":"Stefan Zdraljevic","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Charlie Gosse","author_inst":"Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris, 75005, France"},{"author_name":"Clotilde Gimond","author_inst":"Universite Cote d Azur, CNRS, Inserm, IBV, Nice, 06108, France"},{"author_name":"Mu-En Chen","author_inst":"Biodiversity Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 115201, Taiwan; Department of Life Science, National Taiwan Normal Univers"},{"author_name":"Viet Dai Dang","author_inst":"Biodiversity Research Center, Academia Sinica, Taipei, 115201, Taiwan; Southern Institute of Ecology, Institute of Applied Material Science, Vietnam Academy of "},{"author_name":"John Wang","author_inst":"Biodiversity Research Center, Academia Sinica, Taipei, 115201, Taiwan"},{"author_name":"Asher D. Cutter","author_inst":"Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S3B2, Canada"},{"author_name":"Matthew V. Rockman","author_inst":"Department of Biology and Center for Genomics & Systems Biology, New York University, New York, NY, 10003, USA"},{"author_name":"Marie-Anne Felix","author_inst":"Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris, 75005, France"},{"author_name":"Christian Braendle","author_inst":"Universite Cote d Azur, CNRS, Inserm, IBV, Nice, 06108, France"},{"author_name":"Erik C. Andersen","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Global genomic diversity of the selfing nematode Caenorhabditis tropicalis correlates with geography","rel_doi":"10.64898\/2026.04.05.716573","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716573","rel_abs":"Self-fertilization reduces genetic diversity compared to outcrossing and hypothetically decreases the ability to adapt to diverse environments. Among Caenorhabditis nematodes, self-fertilization evolved three times independently in Caenorhabditis elegans, Caenorhabditis briggsae, and the more recently discovered Caenorhabditis tropicalis. To survey C. tropicalis genetic relatedness, the influence of geography and niche on species-wide variation, and the signatures of selection, we collected 785 wild strains, sequenced their genomes, and identified 622 distinct genotypes (isotypes). In contrast to C. elegans and C. briggsae, C. tropicalis relatedness shows substantial association with geography and no transcontinental selective sweeps or broadly sampled isotypes. Populations from the Hawaiian Islands or Taiwan harbor more genetic variation than populations from the Caribbean or Americas, suggesting a Pacific species origin similar to other members of the Elegans subclade. Punctuated genomic regions of extreme genetic variation pervade the genome. These hyper-divergent regions (HDRs) comprise less than 6% of the reference genome in any given strain despite harboring 73% of all variant sites and are enriched for genes likely involved in environmental adaptation. HDRs represent a shared genomic feature of self-fertilizing Caenorhabditis nematodes despite their independent evolutionary origins and suggest a mechanism to explain worldwide distributions despite low species-wide levels of genetic variation.","rel_num_authors":28,"rel_authors":[{"author_name":"Bowen Wang","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Nicolas D. Moya","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Robyn E. Tanny","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Michael E.G. Sauria","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Lance M. O Connor","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Ayeh Khorshidian","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Ryan McKeown","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Lewis Stevens","author_inst":"Tree of Life, Wellcome Sanger Institute, Hinxton, CB10 1RQ, UK"},{"author_name":"Claire Buchanan","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Timothy A. Crombie","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Department of Biomedical and Chemical Engineering and Sciences, Florida "},{"author_name":"Clayton M. Dilks","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Kathryn S. Evans","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Daniel E. Cook","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Gaotian Zhang","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris,"},{"author_name":"Loraina A. Stinson","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Nicole M. Roberto","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Daehan Lee","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419"},{"author_name":"Stefan Zdraljevic","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Charlie Gosse","author_inst":"Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris, 75005, France"},{"author_name":"Clotilde Gimond","author_inst":"Universite Cote d Azur, CNRS, Inserm, IBV, Nice, 06108, France"},{"author_name":"Mu-En Chen","author_inst":"Biodiversity Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 115201, Taiwan; Department of Life Science, National Taiwan Normal Univers"},{"author_name":"Viet Dai Dang","author_inst":"Biodiversity Research Center, Academia Sinica, Taipei, 115201, Taiwan; Southern Institute of Ecology, Institute of Applied Material Science, Vietnam Academy of "},{"author_name":"John Wang","author_inst":"Biodiversity Research Center, Academia Sinica, Taipei, 115201, Taiwan"},{"author_name":"Asher D. Cutter","author_inst":"Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S3B2, Canada"},{"author_name":"Matthew V. Rockman","author_inst":"Department of Biology and Center for Genomics & Systems Biology, New York University, New York, NY, 10003, USA"},{"author_name":"Marie-Anne Felix","author_inst":"Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris, 75005, France"},{"author_name":"Christian Braendle","author_inst":"Universite Cote d Azur, CNRS, Inserm, IBV, Nice, 06108, France"},{"author_name":"Erik C. Andersen","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Global genomic diversity of the selfing nematode Caenorhabditis tropicalis correlates with geography","rel_doi":"10.64898\/2026.04.05.716573","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716573","rel_abs":"Self-fertilization reduces genetic diversity compared to outcrossing and hypothetically decreases the ability to adapt to diverse environments. Among Caenorhabditis nematodes, self-fertilization evolved three times independently in Caenorhabditis elegans, Caenorhabditis briggsae, and the more recently discovered Caenorhabditis tropicalis. To survey C. tropicalis genetic relatedness, the influence of geography and niche on species-wide variation, and the signatures of selection, we collected 785 wild strains, sequenced their genomes, and identified 622 distinct genotypes (isotypes). In contrast to C. elegans and C. briggsae, C. tropicalis relatedness shows substantial association with geography and no transcontinental selective sweeps or broadly sampled isotypes. Populations from the Hawaiian Islands or Taiwan harbor more genetic variation than populations from the Caribbean or Americas, suggesting a Pacific species origin similar to other members of the Elegans subclade. Punctuated genomic regions of extreme genetic variation pervade the genome. These hyper-divergent regions (HDRs) comprise less than 6% of the reference genome in any given strain despite harboring 73% of all variant sites and are enriched for genes likely involved in environmental adaptation. HDRs represent a shared genomic feature of self-fertilizing Caenorhabditis nematodes despite their independent evolutionary origins and suggest a mechanism to explain worldwide distributions despite low species-wide levels of genetic variation.","rel_num_authors":28,"rel_authors":[{"author_name":"Bowen Wang","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Nicolas D. Moya","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Robyn E. Tanny","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Michael E.G. Sauria","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Lance M. O Connor","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Ayeh Khorshidian","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"},{"author_name":"Ryan McKeown","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Lewis Stevens","author_inst":"Tree of Life, Wellcome Sanger Institute, Hinxton, CB10 1RQ, UK"},{"author_name":"Claire Buchanan","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Timothy A. Crombie","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Department of Biomedical and Chemical Engineering and Sciences, Florida "},{"author_name":"Clayton M. Dilks","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Kathryn S. Evans","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Daniel E. Cook","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Gaotian Zhang","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris,"},{"author_name":"Loraina A. Stinson","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Nicole M. Roberto","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Daehan Lee","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA; Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419"},{"author_name":"Stefan Zdraljevic","author_inst":"Department of Molecular Biosciences, Northwestern University, Evanston, IL, 60208, USA"},{"author_name":"Charlie Gosse","author_inst":"Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris, 75005, France"},{"author_name":"Clotilde Gimond","author_inst":"Universite Cote d Azur, CNRS, Inserm, IBV, Nice, 06108, France"},{"author_name":"Mu-En Chen","author_inst":"Biodiversity Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 115201, Taiwan; Department of Life Science, National Taiwan Normal Univers"},{"author_name":"Viet Dai Dang","author_inst":"Biodiversity Research Center, Academia Sinica, Taipei, 115201, Taiwan; Southern Institute of Ecology, Institute of Applied Material Science, Vietnam Academy of "},{"author_name":"John Wang","author_inst":"Biodiversity Research Center, Academia Sinica, Taipei, 115201, Taiwan"},{"author_name":"Asher D. Cutter","author_inst":"Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S3B2, Canada"},{"author_name":"Matthew V. Rockman","author_inst":"Department of Biology and Center for Genomics & Systems Biology, New York University, New York, NY, 10003, USA"},{"author_name":"Marie-Anne Felix","author_inst":"Institut de Biologie de l Ecole Normale Superieure, CNRS, Inserm, Paris, 75005, France"},{"author_name":"Christian Braendle","author_inst":"Universite Cote d Azur, CNRS, Inserm, IBV, Nice, 06108, France"},{"author_name":"Erik C. Andersen","author_inst":"Department of Biology, Johns Hopkins University, Baltimore, MD, 21218, USA"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Gut microbiome-dependent IL-1 signaling is a mediator of ACVR1R206H-driven heterotopic ossification","rel_doi":"10.64898\/2026.04.05.716562","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716562","rel_abs":"Inflammatory diseases cause significant morbidity and mortality, but their pathobiology is often difficult to dissect due to complex genetic-environmental interactions. Genetic forms of heterotopic ossification, such as fibrodysplasia ossificans progressiva (FOP), reduce genetic variability, allowing careful dissection of non-genetic drivers of inflammation. While >95% of FOP patients harbor the ACVR1R206H mutation, patients exhibit significant variability in disease progression, suggesting a role of environmental drivers. Here, we identify the gut microbiome as a regulator of inflammation-driven HO in FOP. Metagenomic profiling of cohabitating FOP\/unaffected sibling pairs revealed a pathogenic gut microbiome profile in FOP patients (Bray-Curtis, p < 0.05). In Pdgfr-Cre\/Acvr1R206H (FOP) mice, gut microbiome ablation by antibiotics reduced spontaneous HO formation (47.4% reduction, p < 0.05) and reduced plasma IL-1 pathway activity. IL-1{beta} blockade in FOP mice suppressed trauma-induced HO formation. These findings identify a gut microbiome-IL-1-HO axis with modifiable targets for developing treatments for HO and related inflammatory conditions.\n\nOne Sentence SummaryAntibiotic disruption of the gut microbiome reduces HO in FOP mice via an IL-1 mediated pathway.","rel_num_authors":23,"rel_authors":[{"author_name":"Hannah M Herzog","author_inst":"University of California San Francisco"},{"author_name":"Camille Fang","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Liam Lam","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Katherine Jin","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Ariane Zamarioli","author_inst":"Department of Orthopedics and Anesthesiology, Ribeirao Preto Medical School, University of Sao Paulo, Brazil."},{"author_name":"Ethan Dinh","author_inst":"Oral and Craniofacial Sciences Graduate Program, School of Dentistry, University of California, San Francisco, CA 94143"},{"author_name":"Chhedi Lal Gupta","author_inst":"ICMR-National Institute of Immunohaematology, Chandrapur Unit (ICMR-CRMCH), Chandrapur, Maharashtra, India"},{"author_name":"Aditi Sharma","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Tania Moody","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Jessica L Pierce","author_inst":"Divison of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322"},{"author_name":"Michael S Hohl","author_inst":"Divison of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322"},{"author_name":"Sarah W Takimoto","author_inst":"University of California, Los Angeles, Los Angeles CA 90095"},{"author_name":"Svetlana Lyalina","author_inst":"Gladstone Institutes, San Francisco, CA"},{"author_name":"Kelly L Wentworth","author_inst":"Endocrine Research Unit, Department of Medicine, San Francisco Veterans Affairs Health Care System, San Francisco, CA 94121, USA"},{"author_name":"Kristie Yu","author_inst":"University of California, Los Angeles, Los Angeles CA 90095"},{"author_name":"Vivian F Lu","author_inst":"Central Michigan University College of Medicine, Mount Pleasant, MI 4885"},{"author_name":"Isadora Isadora Mamikunian","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Natasha K Hunt","author_inst":"University of California, San Francisco"},{"author_name":"Susan Lynch","author_inst":"Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, San Francisco CA 94143"},{"author_name":"Katherine S Pollard","author_inst":"Gladstone Institutes, San Francisco, CA"},{"author_name":"Christopher J Hernandez","author_inst":"UC San Francisco"},{"author_name":"Daniel S Perrien","author_inst":"Divison of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322"},{"author_name":"Edward C Hsiao","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"Gut microbiome-dependent IL-1 signaling is a mediator of ACVR1R206H-driven heterotopic ossification","rel_doi":"10.64898\/2026.04.05.716562","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716562","rel_abs":"Inflammatory diseases cause significant morbidity and mortality, but their pathobiology is often difficult to dissect due to complex genetic-environmental interactions. Genetic forms of heterotopic ossification, such as fibrodysplasia ossificans progressiva (FOP), reduce genetic variability, allowing careful dissection of non-genetic drivers of inflammation. While >95% of FOP patients harbor the ACVR1R206H mutation, patients exhibit significant variability in disease progression, suggesting a role of environmental drivers. Here, we identify the gut microbiome as a regulator of inflammation-driven HO in FOP. Metagenomic profiling of cohabitating FOP\/unaffected sibling pairs revealed a pathogenic gut microbiome profile in FOP patients (Bray-Curtis, p < 0.05). In Pdgfr-Cre\/Acvr1R206H (FOP) mice, gut microbiome ablation by antibiotics reduced spontaneous HO formation (47.4% reduction, p < 0.05) and reduced plasma IL-1 pathway activity. IL-1{beta} blockade in FOP mice suppressed trauma-induced HO formation. These findings identify a gut microbiome-IL-1-HO axis with modifiable targets for developing treatments for HO and related inflammatory conditions.\n\nOne Sentence SummaryAntibiotic disruption of the gut microbiome reduces HO in FOP mice via an IL-1 mediated pathway.","rel_num_authors":23,"rel_authors":[{"author_name":"Hannah M Herzog","author_inst":"University of California San Francisco"},{"author_name":"Camille Fang","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Liam Lam","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Katherine Jin","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Ariane Zamarioli","author_inst":"Department of Orthopedics and Anesthesiology, Ribeirao Preto Medical School, University of Sao Paulo, Brazil."},{"author_name":"Ethan Dinh","author_inst":"Oral and Craniofacial Sciences Graduate Program, School of Dentistry, University of California, San Francisco, CA 94143"},{"author_name":"Chhedi Lal Gupta","author_inst":"ICMR-National Institute of Immunohaematology, Chandrapur Unit (ICMR-CRMCH), Chandrapur, Maharashtra, India"},{"author_name":"Aditi Sharma","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Tania Moody","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Jessica L Pierce","author_inst":"Divison of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322"},{"author_name":"Michael S Hohl","author_inst":"Divison of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322"},{"author_name":"Sarah W Takimoto","author_inst":"University of California, Los Angeles, Los Angeles CA 90095"},{"author_name":"Svetlana Lyalina","author_inst":"Gladstone Institutes, San Francisco, CA"},{"author_name":"Kelly L Wentworth","author_inst":"Endocrine Research Unit, Department of Medicine, San Francisco Veterans Affairs Health Care System, San Francisco, CA 94121, USA"},{"author_name":"Kristie Yu","author_inst":"University of California, Los Angeles, Los Angeles CA 90095"},{"author_name":"Vivian F Lu","author_inst":"Central Michigan University College of Medicine, Mount Pleasant, MI 4885"},{"author_name":"Isadora Isadora Mamikunian","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"},{"author_name":"Natasha K Hunt","author_inst":"University of California, San Francisco"},{"author_name":"Susan Lynch","author_inst":"Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, San Francisco CA 94143"},{"author_name":"Katherine S Pollard","author_inst":"Gladstone Institutes, San Francisco, CA"},{"author_name":"Christopher J Hernandez","author_inst":"UC San Francisco"},{"author_name":"Daniel S Perrien","author_inst":"Divison of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322"},{"author_name":"Edward C Hsiao","author_inst":"Division of Endocrinology and Metabolism and the Institute for Human Genetics, Department of Medicine, University of California, San Francisco, CA 94143"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"High-Low training is safe and effective in improving outcomes in a rodent model of chronic cervical spinal cord injury.","rel_doi":"10.64898\/2026.04.06.716770","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716770","rel_abs":"Repeated exposure to hypoxia (oxygen levels below sea-level atmospheric conditions, [~]21%) alternated with regular voluntary exercise, known colloquially as  Living High, Training Low, or simply  High-Low, is used by elite athletes to boost exercise benefits and athletic performance. While paradigms of High-Low training have been utilized by Olympic athletes for decades, the therapeutic potential of a High-Low regimen in the context of neurotrauma has yet to be investigated. This long-term experiment evaluated the independent and combined effects of repeated hypoxic exposure and voluntary exercise on functional outcomes within the context of preclinical spinal cord injury (SCI). We hypothesized that combinatorial High-Low training enhances functional recovery, beyond either exercise or repeated exposures to hypoxia alone, to improve outcomes after SCI. Adult female rats (n=62) underwent a high-cervical hemisection (LC2H) to model spinal cord injury. At 6 weeks post-SCI, treatment (access to exercise wheel, repeated exposure to normobaric hypoxia at rest, or alternation of both) began in the surviving subjects (n=49). Despite initiation of treatment beyond the acute post-injury phase, High-Low therapy significantly improved respiratory function and prevented the development of SCI-associated anxiety-like behaviors. Notably, repeated in vivo exposure to normobaric hypoxia induced a shift in peripheral T cell profiles, characterized by increased CD4+ and reduced CD8+ expression. These findings indicate that combining repeated exposure to hypoxia with voluntary exercise as a therapy could promote recovery in the existing spinal cord-injured population. Collectively, this work provides a foundational first step for further investigation of High-Low training as a rehabilitation therapy for individuals living with SCI.","rel_num_authors":12,"rel_authors":[{"author_name":"Daimen R.S. Britsch","author_inst":"University of Kentucky"},{"author_name":"Katherine M. Cotter","author_inst":"University of Kentucky"},{"author_name":"Connor M.J. Stuart","author_inst":"Universitatsklinikum Hamburg-Eppendorf"},{"author_name":"Jadwiga Turchan-Cholewo","author_inst":"University of Kentucky"},{"author_name":"Mary K. Colson","author_inst":"University of Kentucky"},{"author_name":"Edric D. Winford","author_inst":"Columbia University"},{"author_name":"Thomas A. Ujas","author_inst":"University of Kentucky"},{"author_name":"Jenny Lutshumba","author_inst":"University of Kentucky"},{"author_name":"Chris Calulot","author_inst":"University of Kentucky"},{"author_name":"John C. Gensel","author_inst":"University of Kentucky"},{"author_name":"Warren Alilain","author_inst":"University of Kentucky"},{"author_name":"Ann Marie Stowe","author_inst":"University of Kentucky"}],"rel_date":"2026-04-08","rel_site":"biorxiv"},{"rel_title":"An Empirical Assessment of Inferential Reproducibility of Linear Regression in Health and Biomedical Research Papers","rel_doi":"10.64898\/2026.04.07.26350296","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.07.26350296","rel_abs":"Background: In health research, variability in modelling decisions can lead to different conclusions even when the same data are analysed, a challenge known as inferential reproducibility. In linear regression analyses, incorrect handling of key assumptions, such as normality of the residuals and linearity, can undermine reproducibility. This study examines how violations of these assumptions influence inferential conclusions when the same data are reanalysed. Methods: We randomly sampled 95 health-related PLOS ONE papers from 2019 that reported linear regression in their methods. Data were available for 43 papers, and 20 were assessed for computational reproducibility, with three models per paper evaluated. The 14 papers that included a model at least partially computationally reproduced were then examined for inferential reproducibility. To assess the impact of assumption violations, differences in coefficients, 95% confidence intervals, and model fit were compared. Results: Of the fourteen papers assessed, only three were inferentially reproducible. The most frequently violated assumptions were normality and independence, each occurring in eight papers. Violations of independence were particularly consequential and were commonly associated with inferential failure. Although reproduced analyses often retained the same binary statistical significance classification as the original studies, confidence intervals were frequently wider, indicating greater uncertainty and reduced precision. Such uncertainty may affect the interpretation of results and, in turn, influence treatment decisions and clinical practice. Conclusion: Our findings demonstrate that substantial violations of key modelling assumptions often went undetected by authors and peer reviewers and, in many cases, were associated with inferential reproducibility failure. This highlights the need for stronger statistical education and greater transparency in modelling decisions. Rather than applying rigid or misinformed rules, such as incorrectly testing the normality of the outcome variable, researchers should adopt modelling frameworks guided by the research question and the study design. When assumptions are violated, appropriate alternatives, such as robust methods, bootstrapping, generalized linear models, or mixed-effects models, should be considered. Given that assumption violations were common even in relatively simple regression models, early and sustained collaboration with statisticians is critical for supporting robust, defensible, and clinically meaningful conclusions.","rel_num_authors":4,"rel_authors":[{"author_name":"Lee Jones","author_inst":"QUT: Queensland University of Technology"},{"author_name":"Adrian Barnett","author_inst":"QUT: Queensland University of Technology"},{"author_name":"Gunter Hartel","author_inst":"QIMR Berghofer"},{"author_name":"Dimitrios Vagenas","author_inst":"QUT: Queensland University of Technology"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Care Models for the Genetic Evaluation of Dilated Cardiomyopathy at Sites of the DCM Consortium.","rel_doi":"10.64898\/2026.04.06.26350275","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350275","rel_abs":"BackgroundClinical genetic evaluation for patients with dilated cardiomyopathy (DCM) is minimally implemented and models of care are not defined. To understand current genetics care for DCM, a systematic needs assessment was conducted.\n\nMethodsPrincipal Investigators (PIs) of the DCM Consortium convened at the Summer Scientific Symposium in July 2025. An electronic needs assessment was collected from the 24 PIs in advance to define current care models by evaluating which Heart Failure Society of America-recommended genetic evaluation components are conducted, by whom, and time required. Descriptive statistics were generated to characterize model features. Focus group discussions explored barriers and facilitators to implementing genetic services.\n\nResultsFour care models emerged from the PI responses: 1 - Traditional-Synchronous (25%, n=6, requiring the most time per patient), 2 - Traditional-Asynchronous (33%, n=8), 3 - Externally Sourced (17%, n=4), and 4 - Physician\/Advanced Practice Provider Conducted (25%, n=6, requiring the least time per patient). All models used genetic testing, whereas other components were implemented variably or not at all. Models 1 (15.7{+\/-}4.1) and 2 (15.4{+\/-}3.0) were rated more acceptable than Model 4 (9.8{+\/-}2.9; 1 vs 4: p=0.027; 2 vs 4, p=0.023). Notably, 88% of PIs used genetic information for treatment decisions, including ICD placement (83%; n=20) or cardiac transplant (63%; n=15). Major facilitator themes from focus group discussions included having a genetic counselor on the HF team and developing authoritative standards directing provision of DCM genetic services. Barrier themes included operational challenges, limited personnel, clinician under-recognition, need for new service delivery models, and billing\/reimbursement.\n\nConclusionsDCM genetic care models and components were highly variable across the 24 sites of the DCM Consortium, even though all sites discussed similar factors that enable or hinder implementing genetic services for DCM. Understanding the basis of practice model variability may provide insight to yield more scalable care approaches.\n\nClinical PerspectiveWhat is new?\n\nO_LIFour care models to deliver genetic evaluation services for the assessment of dilated cardiomyopathy (DCM) were identified across the 24 sites of the DCM Consortium.\nC_LIO_LIDespite universal use of genetic testing and shared barriers and facilitators across sites, substantial variability exists across and within identified model types, and clinical genetic evaluation is not routinely implemented for all patients with DCM.\nC_LI\n\nWhat are the clinical implications?\n\nO_LIFor Heart Failure (HF) centers seeking to implement genetics services for DCM patients and families, the DCM Consortium describes the scaffolding of current care models implemented at HF centers across the US to inform local program development.\nC_LIO_LIThe incompletely and variably implemented care models highlight the need for a standardized, scalable framework to enhance consistent and equitable integration of clinical genetic evaluation into routine DCM care.\nC_LI","rel_num_authors":51,"rel_authors":[{"author_name":"Elizabeth Jordan","author_inst":"The Ohio State University Wexner Medical Center Department of Internal Medicine"},{"author_name":"Tia Moscarello","author_inst":"Stanford University"},{"author_name":"Hibatallah Khafagy","author_inst":"The Ohio State University Wexner Medical Center Department of Internal Medicine"},{"author_name":"Patricia K Parker","author_inst":"The Ohio State University Wexner Medical Center Department of Internal Medicine"},{"author_name":"Phoenix Grover","author_inst":"Duke University Medical Center"},{"author_name":"Simone Weinman","author_inst":"NYU Langone Health"},{"author_name":"Joseph Liu","author_inst":"Cleveland Clinic"},{"author_name":"Alberta Nomo","author_inst":"The Ohio State University Wexner Medical Center Department of Internal Medicine"},{"author_name":"Naomi Barker","author_inst":"Medical University of South Carolina"},{"author_name":"Emily Brown","author_inst":"Johns Hopkins University"},{"author_name":"Akos Berthold","author_inst":"Inova Schar Heart and Vascular"},{"author_name":"Jessica Chowns","author_inst":"Perelman School of Medicine at the University of Pennsylvania"},{"author_name":"Susan Christian","author_inst":"University of Alberta"},{"author_name":"Amy Ekwurtzel","author_inst":"Emory University"},{"author_name":"Judy Fan","author_inst":"University of California Los Angeles"},{"author_name":"Monisha Kisling","author_inst":"Genome Medical"},{"author_name":"Daria Ma","author_inst":"Cedars-Sinai Medical Center Smidt Heart Institute"},{"author_name":"Erin M Miller","author_inst":"Cincinnati Children's Hospital Medical Center"},{"author_name":"Jessica Sweeney","author_inst":"Medstar Heart and Vascular Institute\/Georgetown University"},{"author_name":"Brian Reyes","author_inst":"The University of Texas Southwestern Medical Center"},{"author_name":"Nancy Robles","author_inst":"Stanford"},{"author_name":"Lisa von Wald","author_inst":"University of Minnesota Medical School"},{"author_name":"Wendy Flowers","author_inst":"DCM Consortium"},{"author_name":"Gregory Hershberger","author_inst":"DCM Consortium"},{"author_name":"Krishna G Aragam","author_inst":"Massachusetts General Hospital"},{"author_name":"Michael A. Burke","author_inst":"Emory University Division of Cardiology"},{"author_name":"Jamie Diamond","author_inst":"Emory University Medical Center"},{"author_name":"Mark H. Drazner","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Gregory A Ewald","author_inst":"Washington University in St Louis"},{"author_name":"Stephen Gottlieb","author_inst":"University of Maryland Baltimore"},{"author_name":"Garrie Joseph Haas","author_inst":"Ohio State University College of Medicine"},{"author_name":"Mark R. Hofmeyer","author_inst":"MedStar Heart and Vascular Institute"},{"author_name":"Gordon S Huggins","author_inst":"Tufts Medical Center"},{"author_name":"Javier Jimenez","author_inst":"Miami Cardiac & Vascular Institute"},{"author_name":"Daniel Judge","author_inst":"Medical University of South Carolina"},{"author_name":"Stuart D. Katz","author_inst":"New York University Grossman School of Medicine"},{"author_name":"Masataka Kawana","author_inst":"Stanford University"},{"author_name":"Evan Kransdorf","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Cindy M. Martin","author_inst":"Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital"},{"author_name":"Elena Minami","author_inst":"University of Washington"},{"author_name":"Anjali T. Owens","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Palak Shah","author_inst":"Inova Schar Heart and Vascular"},{"author_name":"Chetan Shenoy","author_inst":"University of Minnesota Medical School"},{"author_name":"Supriya Shore","author_inst":"University of Michigan Medical School"},{"author_name":"Frank Smart","author_inst":"LSU Health Sciences Center"},{"author_name":"Douglas Stoller","author_inst":"University of Nebraska Medical Center"},{"author_name":"Jose A Tallaj","author_inst":"The University of Alabama at Birmingham"},{"author_name":"W. H. Wilson Tang","author_inst":"Cleveland Clinic"},{"author_name":"Jessica Wang","author_inst":"UCLA Health Internal Medicine"},{"author_name":"Jane Wilcox","author_inst":"Northwestern University"},{"author_name":"Ray E Hershberger","author_inst":"Ohio State University College of Medicine"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Care Models for the Genetic Evaluation of Dilated Cardiomyopathy at Sites of the DCM Consortium.","rel_doi":"10.64898\/2026.04.06.26350275","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350275","rel_abs":"BackgroundClinical genetic evaluation for patients with dilated cardiomyopathy (DCM) is minimally implemented and models of care are not defined. To understand current genetics care for DCM, a systematic needs assessment was conducted.\n\nMethodsPrincipal Investigators (PIs) of the DCM Consortium convened at the Summer Scientific Symposium in July 2025. An electronic needs assessment was collected from the 24 PIs in advance to define current care models by evaluating which Heart Failure Society of America-recommended genetic evaluation components are conducted, by whom, and time required. Descriptive statistics were generated to characterize model features. Focus group discussions explored barriers and facilitators to implementing genetic services.\n\nResultsFour care models emerged from the PI responses: 1 - Traditional-Synchronous (25%, n=6, requiring the most time per patient), 2 - Traditional-Asynchronous (33%, n=8), 3 - Externally Sourced (17%, n=4), and 4 - Physician\/Advanced Practice Provider Conducted (25%, n=6, requiring the least time per patient). All models used genetic testing, whereas other components were implemented variably or not at all. Models 1 (15.7{+\/-}4.1) and 2 (15.4{+\/-}3.0) were rated more acceptable than Model 4 (9.8{+\/-}2.9; 1 vs 4: p=0.027; 2 vs 4, p=0.023). Notably, 88% of PIs used genetic information for treatment decisions, including ICD placement (83%; n=20) or cardiac transplant (63%; n=15). Major facilitator themes from focus group discussions included having a genetic counselor on the HF team and developing authoritative standards directing provision of DCM genetic services. Barrier themes included operational challenges, limited personnel, clinician under-recognition, need for new service delivery models, and billing\/reimbursement.\n\nConclusionsDCM genetic care models and components were highly variable across the 24 sites of the DCM Consortium, even though all sites discussed similar factors that enable or hinder implementing genetic services for DCM. Understanding the basis of practice model variability may provide insight to yield more scalable care approaches.\n\nClinical PerspectiveWhat is new?\n\nO_LIFour care models to deliver genetic evaluation services for the assessment of dilated cardiomyopathy (DCM) were identified across the 24 sites of the DCM Consortium.\nC_LIO_LIDespite universal use of genetic testing and shared barriers and facilitators across sites, substantial variability exists across and within identified model types, and clinical genetic evaluation is not routinely implemented for all patients with DCM.\nC_LI\n\nWhat are the clinical implications?\n\nO_LIFor Heart Failure (HF) centers seeking to implement genetics services for DCM patients and families, the DCM Consortium describes the scaffolding of current care models implemented at HF centers across the US to inform local program development.\nC_LIO_LIThe incompletely and variably implemented care models highlight the need for a standardized, scalable framework to enhance consistent and equitable integration of clinical genetic evaluation into routine DCM care.\nC_LI","rel_num_authors":51,"rel_authors":[{"author_name":"Elizabeth Jordan","author_inst":"The Ohio State University Wexner Medical Center Department of Internal Medicine"},{"author_name":"Tia Moscarello","author_inst":"Stanford University"},{"author_name":"Hibatallah Khafagy","author_inst":"The Ohio State University Wexner Medical Center Department of Internal Medicine"},{"author_name":"Patricia K Parker","author_inst":"The Ohio State University Wexner Medical Center Department of Internal Medicine"},{"author_name":"Phoenix Grover","author_inst":"Duke University Medical Center"},{"author_name":"Simone Weinman","author_inst":"NYU Langone Health"},{"author_name":"Joseph Liu","author_inst":"Cleveland Clinic"},{"author_name":"Alberta Nomo","author_inst":"The Ohio State University Wexner Medical Center Department of Internal Medicine"},{"author_name":"Naomi Barker","author_inst":"Medical University of South Carolina"},{"author_name":"Emily Brown","author_inst":"Johns Hopkins University"},{"author_name":"Akos Berthold","author_inst":"Inova Schar Heart and Vascular"},{"author_name":"Jessica Chowns","author_inst":"Perelman School of Medicine at the University of Pennsylvania"},{"author_name":"Susan Christian","author_inst":"University of Alberta"},{"author_name":"Amy Ekwurtzel","author_inst":"Emory University"},{"author_name":"Judy Fan","author_inst":"University of California Los Angeles"},{"author_name":"Monisha Kisling","author_inst":"Genome Medical"},{"author_name":"Daria Ma","author_inst":"Cedars-Sinai Medical Center Smidt Heart Institute"},{"author_name":"Erin M Miller","author_inst":"Cincinnati Children's Hospital Medical Center"},{"author_name":"Jessica Sweeney","author_inst":"Medstar Heart and Vascular Institute\/Georgetown University"},{"author_name":"Brian Reyes","author_inst":"The University of Texas Southwestern Medical Center"},{"author_name":"Nancy Robles","author_inst":"Stanford"},{"author_name":"Lisa von Wald","author_inst":"University of Minnesota Medical School"},{"author_name":"Wendy Flowers","author_inst":"DCM Consortium"},{"author_name":"Gregory Hershberger","author_inst":"DCM Consortium"},{"author_name":"Krishna G Aragam","author_inst":"Massachusetts General Hospital"},{"author_name":"Michael A. Burke","author_inst":"Emory University Division of Cardiology"},{"author_name":"Jamie Diamond","author_inst":"Emory University Medical Center"},{"author_name":"Mark H. Drazner","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Gregory A Ewald","author_inst":"Washington University in St Louis"},{"author_name":"Stephen Gottlieb","author_inst":"University of Maryland Baltimore"},{"author_name":"Garrie Joseph Haas","author_inst":"Ohio State University College of Medicine"},{"author_name":"Mark R. Hofmeyer","author_inst":"MedStar Heart and Vascular Institute"},{"author_name":"Gordon S Huggins","author_inst":"Tufts Medical Center"},{"author_name":"Javier Jimenez","author_inst":"Miami Cardiac & Vascular Institute"},{"author_name":"Daniel Judge","author_inst":"Medical University of South Carolina"},{"author_name":"Stuart D. Katz","author_inst":"New York University Grossman School of Medicine"},{"author_name":"Masataka Kawana","author_inst":"Stanford University"},{"author_name":"Evan Kransdorf","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Cindy M. Martin","author_inst":"Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital"},{"author_name":"Elena Minami","author_inst":"University of Washington"},{"author_name":"Anjali T. Owens","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Palak Shah","author_inst":"Inova Schar Heart and Vascular"},{"author_name":"Chetan Shenoy","author_inst":"University of Minnesota Medical School"},{"author_name":"Supriya Shore","author_inst":"University of Michigan Medical School"},{"author_name":"Frank Smart","author_inst":"LSU Health Sciences Center"},{"author_name":"Douglas Stoller","author_inst":"University of Nebraska Medical Center"},{"author_name":"Jose A Tallaj","author_inst":"The University of Alabama at Birmingham"},{"author_name":"W. H. Wilson Tang","author_inst":"Cleveland Clinic"},{"author_name":"Jessica Wang","author_inst":"UCLA Health Internal Medicine"},{"author_name":"Jane Wilcox","author_inst":"Northwestern University"},{"author_name":"Ray E Hershberger","author_inst":"Ohio State University College of Medicine"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"The effects on global health outcomes of switching from regular salt to potassium-enriched salt: a modelling study","rel_doi":"10.64898\/2026.04.06.26350270","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350270","rel_abs":"ObjectiveTo estimate the benefit and risk of replacing regular salt with potassium-enriched salt.\n\nDesignComparative risk assessment modelling.\n\nSettingWorldwide\n\nParticipantsAdult populations aged 25 and above.\n\nIntervention(1) worldwide replacement of all salt (discretionary salt used for seasoning or cooking in the home, and non-discretionary salt used in processed and restaurant foods); (2) worldwide replacement of just discretionary salt; (3) worldwide replacement of just non-discretionary salt; (4) replacement of discretionary salt just for people with diagnosed hypertension; and (5) replacement of discretionary salt just for people with treated hypertension.\n\nMain outcome measuresFor scenarios 1-3, we estimated benefits including deaths, new cases and disability-adjusted-life-years (DALYs) from cardiovascular disease and chronic kidney disease (CKD), from blood pressure-lowering as well as harms (CVD deaths) caused by hyperkalaemia among people with CKD stages G3-G5.\n\nResultsReplacement of all salt worldwide could prevent 2.96 (95% uncertainty interval 2.81-3.12) million deaths, 10.17 (9.59-10.70) million new cases of disease and 69.43 (65.61-72.92) million disability-adjusted life years (DALYs) each year. These figures represent 14.6%, 13.1% and 16.5% of the annual global disease burden attributable to CVD and CKD. Replacement of all discretionary salt (1.85, 1.74-1.97 million deaths) would have a greater impact on mortality than replacement of all non-discretionary salt (1.56, 1.46-1.67 million deaths). In people with CKD Stage G3-G5, there would be a net benefit - replacement of all salt would prevent 0.75 (0.71-0.80) million deaths but might cause 0.10 (0.09-0.11) million deaths from hyperkalaemia. Discretionary salt replacement only among diagnosed or treated hypertensives would prevent 0.59 (0.55-0.63) million and 0.48 (0.45-0.52) million deaths, respectively.\n\nConclusionSwitching regular salt to potassium-enriched salt appears to offer large potential for health gains under diverse scenarios, including for people with CKD.\n\nFundingThis work did not receive specific funding.\n\nWhat is already known on this topic- Excess dietary sodium and low dietary potassium intake both cause high blood pressure, which causes a significant burden of cardiovascular disease (CVD) and chronic kidney disease (CKD).\n- Efforts to cut dietary sodium intake as a strategy to control blood pressure have mostly been unsuccessful, with no country expected to meet the World Health Organisation (WHO) 2025 goal of reducing sodium intake by 30%.\n- Switching regular salt to potassium-enriched salt has shown clear protection against CVD and causes minimal change in taste and has low cost to scale up, but concerns remain about the potential of causing deaths due to hyperkalaemia among people with advanced chronic kidney disease.\n\n\nWhat this study adds- The study modelled five different possible approaches to the implementation of potassium-enriched salt that will suit a range of different circumstances, including countries with different levels of discretionary versus non-discretionary salt intake.\n- The study indicates switching to potassium-enriched salt can prevent very large numbers of CVD and CKD events worldwide, while the potential for causing harm in people with CKD is small in comparison.\n- There was also net benefit in analyses restricted to just people with CKD, where benefits of blood pressure lowering outweigh potential harms from hyperkalaemia.","rel_num_authors":11,"rel_authors":[{"author_name":"Liping Huang","author_inst":"The George Institute for Global Health"},{"author_name":"Xiaoyue Xu","author_inst":"School of Population Health, University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Kunihiro Matsushita","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"},{"author_name":"Tammy M Brady","author_inst":"Department of Paediatrics, Division of Nephrology, Johns Hopkins University School of Medicine and Dentistry, Baltimore, MD, USA"},{"author_name":"Lawrence J Appel","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"},{"author_name":"Ewout J Hoorn","author_inst":"Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherl"},{"author_name":"Maoyi Tian","author_inst":"School of Public Health, Harbin Medical University, Harbin, Heilongjiang, China"},{"author_name":"Leopold N Aminde","author_inst":"Public Health & Economics Modelling Group, School of Medicine, Griffith University, Gold Coast, QLD, Australia"},{"author_name":"Kathy Trieu","author_inst":"The George Institute for Global Health, UNSW, Sydney, NSW, Australia"},{"author_name":"Bruce Neal","author_inst":"The George Institute for Global Health, UNSW, Sydney, NSW, Australia"},{"author_name":"Matti Marklund","author_inst":"Department of Paediatrics, Division of Nephrology, Johns Hopkins University School of Medicine and Dentistry, Baltimore, MD, USA"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"The effects on global health outcomes of switching from regular salt to potassium-enriched salt: a modelling study","rel_doi":"10.64898\/2026.04.06.26350270","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350270","rel_abs":"ObjectiveTo estimate the benefit and risk of replacing regular salt with potassium-enriched salt.\n\nDesignComparative risk assessment modelling.\n\nSettingWorldwide\n\nParticipantsAdult populations aged 25 and above.\n\nIntervention(1) worldwide replacement of all salt (discretionary salt used for seasoning or cooking in the home, and non-discretionary salt used in processed and restaurant foods); (2) worldwide replacement of just discretionary salt; (3) worldwide replacement of just non-discretionary salt; (4) replacement of discretionary salt just for people with diagnosed hypertension; and (5) replacement of discretionary salt just for people with treated hypertension.\n\nMain outcome measuresFor scenarios 1-3, we estimated benefits including deaths, new cases and disability-adjusted-life-years (DALYs) from cardiovascular disease and chronic kidney disease (CKD), from blood pressure-lowering as well as harms (CVD deaths) caused by hyperkalaemia among people with CKD stages G3-G5.\n\nResultsReplacement of all salt worldwide could prevent 2.96 (95% uncertainty interval 2.81-3.12) million deaths, 10.17 (9.59-10.70) million new cases of disease and 69.43 (65.61-72.92) million disability-adjusted life years (DALYs) each year. These figures represent 14.6%, 13.1% and 16.5% of the annual global disease burden attributable to CVD and CKD. Replacement of all discretionary salt (1.85, 1.74-1.97 million deaths) would have a greater impact on mortality than replacement of all non-discretionary salt (1.56, 1.46-1.67 million deaths). In people with CKD Stage G3-G5, there would be a net benefit - replacement of all salt would prevent 0.75 (0.71-0.80) million deaths but might cause 0.10 (0.09-0.11) million deaths from hyperkalaemia. Discretionary salt replacement only among diagnosed or treated hypertensives would prevent 0.59 (0.55-0.63) million and 0.48 (0.45-0.52) million deaths, respectively.\n\nConclusionSwitching regular salt to potassium-enriched salt appears to offer large potential for health gains under diverse scenarios, including for people with CKD.\n\nFundingThis work did not receive specific funding.\n\nWhat is already known on this topic- Excess dietary sodium and low dietary potassium intake both cause high blood pressure, which causes a significant burden of cardiovascular disease (CVD) and chronic kidney disease (CKD).\n- Efforts to cut dietary sodium intake as a strategy to control blood pressure have mostly been unsuccessful, with no country expected to meet the World Health Organisation (WHO) 2025 goal of reducing sodium intake by 30%.\n- Switching regular salt to potassium-enriched salt has shown clear protection against CVD and causes minimal change in taste and has low cost to scale up, but concerns remain about the potential of causing deaths due to hyperkalaemia among people with advanced chronic kidney disease.\n\n\nWhat this study adds- The study modelled five different possible approaches to the implementation of potassium-enriched salt that will suit a range of different circumstances, including countries with different levels of discretionary versus non-discretionary salt intake.\n- The study indicates switching to potassium-enriched salt can prevent very large numbers of CVD and CKD events worldwide, while the potential for causing harm in people with CKD is small in comparison.\n- There was also net benefit in analyses restricted to just people with CKD, where benefits of blood pressure lowering outweigh potential harms from hyperkalaemia.","rel_num_authors":11,"rel_authors":[{"author_name":"Liping Huang","author_inst":"The George Institute for Global Health"},{"author_name":"Xiaoyue Xu","author_inst":"School of Population Health, University of New South Wales, Sydney, NSW, Australia"},{"author_name":"Kunihiro Matsushita","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"},{"author_name":"Tammy M Brady","author_inst":"Department of Paediatrics, Division of Nephrology, Johns Hopkins University School of Medicine and Dentistry, Baltimore, MD, USA"},{"author_name":"Lawrence J Appel","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"},{"author_name":"Ewout J Hoorn","author_inst":"Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, The Netherl"},{"author_name":"Maoyi Tian","author_inst":"School of Public Health, Harbin Medical University, Harbin, Heilongjiang, China"},{"author_name":"Leopold N Aminde","author_inst":"Public Health & Economics Modelling Group, School of Medicine, Griffith University, Gold Coast, QLD, Australia"},{"author_name":"Kathy Trieu","author_inst":"The George Institute for Global Health, UNSW, Sydney, NSW, Australia"},{"author_name":"Bruce Neal","author_inst":"The George Institute for Global Health, UNSW, Sydney, NSW, Australia"},{"author_name":"Matti Marklund","author_inst":"Department of Paediatrics, Division of Nephrology, Johns Hopkins University School of Medicine and Dentistry, Baltimore, MD, USA"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Challenges in the Computational Reproducibility of Linear Regression Analyses: An Empirical Study","rel_doi":"10.64898\/2026.04.07.26350286","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.07.26350286","rel_abs":"Background: Reproducibility concerns in health research have grown, as many published results fail to be independently reproduced. Achieving computational reproducibility, where others can replicate the same results using the same methods, requires transparent reporting of statistical tests, models, and software use. While data-sharing initiatives have improved accessibility, the actual usability of shared data for reproducing research findings remains underexplored. Addressing this gap is crucial for advancing open science and ensuring that shared data meaningfully support reproducibility and enable collaboration, thereby strengthening evidence-based policy and practice. Methods: A random sample of 95 PLOS ONE health research papers from 2019 reporting linear regression was assessed for data-sharing practices and computational reproducibility. Data were accessible for 43 papers. From the randomly selected sample, the first 20 papers with available data were assessed for computational reproducibility. Three regression models per paper were reanalysed. Results: Of the 95 papers, 68 reported having data available, but 25 of these lacked the data required to reproduce the linear regression models. Only eight of 20 papers we analysed were computationally reproducible. A major barrier to reproducing the analyses was the great difficulty in matching the variables described in the paper to those in the data. Papers sometimes failed to be reproduced because the methods were not adequately described, including variable adjustments and data exclusions. Conclusion: More than half (60%) of analysed studies were not computationally reproducible, raising concerns about the credibility of the reported results and highlighting the need for greater transparency and rigour in research reporting. When data are made available, authors should provide a corresponding data dictionary with variable labels that match those used in the paper. Analysis code, model specifications, and any supporting materials detailing the steps required to reproduce the results should be deposited in a publicly accessible repository or included as supplementary files. To increase the reproducibility of statistical results, we propose a Model Location and Specification Table (MLast), which tracks where and what analyses were performed. In conjunction with a data dictionary, MLast enables the mapping of analyses, greatly aiding computational reproducibility.","rel_num_authors":4,"rel_authors":[{"author_name":"Lee Vanessa Jones","author_inst":"QUT: Queensland University of Technology"},{"author_name":"Adrian Barnett","author_inst":"QUT: Queensland University of Technology"},{"author_name":"Gunter Hartel","author_inst":"QIMR Berghofer"},{"author_name":"Dimitrios Vagenas","author_inst":"QUT: Queensland University of Technology"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Religious beliefs and practices, political orientation, and distrust in healthcare predict attitudes toward mRNA vaccines in the United States","rel_doi":"10.64898\/2026.04.06.26350267","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350267","rel_abs":"Religion has contributed to societal divides regarding COVID-19 mRNA vaccines. In this study, we conducted a secondary analysis of a survey of U.S. adults (N=4939) focused on how religious affiliations, beliefs, and practices impact attitudes toward genetic and genomic activities, one of which was mRNA vaccines. The dataset included large samples of participants from six religious groups in the U.S. (Black Protestant, Catholic, Evangelical Protestant, Jewish, Mainline Protestant, and Muslim), as well as individuals who were atheist, agnostic, or spiritual. ANCOVA results indicated that Evangelical Protestant participants showed significantly less support for mRNA vaccines than other groups, while atheist participants were the most supportive. Muslim participants had the highest concerns, whereas atheist participants had the lowest. Regression analyses indicated the strongest predictors of support for mRNA vaccines were more spiritual community support for community health, followed by higher acceptance of evolution, more liberal political orientation, less distrust toward the healthcare system, higher frequency of attending religious activities, higher income, lower fundamentalist religious beliefs, and more spiritual community support for liberal reproductive and end of life views. The strongest predictors of concerns about mRNA vaccines were more distrust toward the healthcare system and more conservative political orientation, followed by less spiritual community support for community health, stronger beliefs about God in the body, more fundamentalist religious beliefs, and lower knowledge of genetics. The large sample size, and examination of a broad array of religious variables alongside distrust and political orientation offer new insights. These findings add to the literature on the culture wars surrounding mRNA vaccines, and can perhaps aid in future efforts to build trust and relationships between public health and religious communities.","rel_num_authors":5,"rel_authors":[{"author_name":"Erin  D. Solomon","author_inst":"Washington University in St Louis School of Medicine"},{"author_name":"Eu Gene Chin","author_inst":"Biola University Rosemead School of Psychology"},{"author_name":"Kari Baldwin","author_inst":"Washington University in St Louis School of Medicine"},{"author_name":"Lauren  L Baker","author_inst":"Washington University in St Louis School of Medicine"},{"author_name":"James  M. DuBois","author_inst":"Washington University in Saint Louis School of Medicine"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Multiplex Portuguese Families as a Lens into rare mutations and the Shared Genetic Architecture of Schizophrenia, Mood Disorders, and Autism Spectrum Disorders","rel_doi":"10.64898\/2026.04.06.26350177","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350177","rel_abs":"In an analysis of 173 multiplex families from the Portuguese Island Collection (PIC) we characterize the shared genetic architecture of serious mental illnesses (SMI) including schizophrenia (SZ), bipolar disorder (BP), major depression (MDD), and autism (ASD). Within this cohort, co-segregation of psychotic and mood disorders occurred in 28% of families, while 7% demonstrated co-segregation of intellectual disability or ASD with SZ and mood disorder phenotypes. Whole-genome sequencing (WGS) was performed on a three-generation PIC family to identify rare, large-effect variants. We identified an extremely rare predicted loss of function (LoF) mutation in the Chromodomain Helicase DNA Binding Protein 2 (CHD2) gene. These results demonstrate that high-density multiplex families in founder populations are a powerful resource for mapping rare, large-effect variants that cross clinical diagnostic boundaries, as the identified CHD2 mutation suggests that the disruption of a single neurodevelopmental gene may lead to diverse SMI phenotypes. By combining population and family-based methodologies, this approach leverages shared genetic backgrounds and environments to provide a unique opportunity for cellular studies to explore the biological mechanisms underlying SMI, offering significant potential to inform future functional research and identify novel therapeutic targets.","rel_num_authors":19,"rel_authors":[{"author_name":"Carlos N Pato","author_inst":"Rutgers University"},{"author_name":"Michele T Pato","author_inst":"Rutgers University"},{"author_name":"Jennifer Mulle","author_inst":"Rutgers University"},{"author_name":"Ronald P Hart","author_inst":"Rutgers University"},{"author_name":"Zhiping Pang","author_inst":"Rutgers University"},{"author_name":"James A Knowles","author_inst":"Rutgers University"},{"author_name":"Tarjinder Singh","author_inst":"Columbia University, NY Genome Center"},{"author_name":"Priya Maddhesiya","author_inst":"New York Genome Center"},{"author_name":"Celia Carvalho","author_inst":"Faculty of Social and Human Sciences, University of Azores"},{"author_name":"Alison Merikangas","author_inst":"Rutgers University"},{"author_name":"Helena Medeiros","author_inst":"Rutgers University"},{"author_name":"Tim B Bigdeli","author_inst":"SUNY Downstate"},{"author_name":"Hamed Kazemi","author_inst":"Department of Psychiatry, University of Arizona College of Medicine-Phoenix"},{"author_name":"John Drake","author_inst":"Department of Psychiatry, University of Arizona College of Medicine-Phoenix"},{"author_name":"Vladmir Vladimrov","author_inst":"Department of Psychiatry, University of Arizona College of Medicine-Phoenix"},{"author_name":"Brion Maher","author_inst":"Department of Mental Health, Johns Hopkins Bloomberg School of Public Health"},{"author_name":"Silviu-Alin Bacanu","author_inst":"Department of Psychiatry, Virginia Commonwealth University"},{"author_name":"Benjamin Neale","author_inst":"Broad Institute"},{"author_name":"Ayman Fanous","author_inst":"Department of Psychiatry, University of Arizona College of Medicine-Phoenix"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Multiplex Portuguese Families as a Lens into rare mutations and the Shared Genetic Architecture of Schizophrenia, Mood Disorders, and Autism Spectrum Disorders","rel_doi":"10.64898\/2026.04.06.26350177","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350177","rel_abs":"In an analysis of 173 multiplex families from the Portuguese Island Collection (PIC) we characterize the shared genetic architecture of serious mental illnesses (SMI) including schizophrenia (SZ), bipolar disorder (BP), major depression (MDD), and autism (ASD). Within this cohort, co-segregation of psychotic and mood disorders occurred in 28% of families, while 7% demonstrated co-segregation of intellectual disability or ASD with SZ and mood disorder phenotypes. Whole-genome sequencing (WGS) was performed on a three-generation PIC family to identify rare, large-effect variants. We identified an extremely rare predicted loss of function (LoF) mutation in the Chromodomain Helicase DNA Binding Protein 2 (CHD2) gene. These results demonstrate that high-density multiplex families in founder populations are a powerful resource for mapping rare, large-effect variants that cross clinical diagnostic boundaries, as the identified CHD2 mutation suggests that the disruption of a single neurodevelopmental gene may lead to diverse SMI phenotypes. By combining population and family-based methodologies, this approach leverages shared genetic backgrounds and environments to provide a unique opportunity for cellular studies to explore the biological mechanisms underlying SMI, offering significant potential to inform future functional research and identify novel therapeutic targets.","rel_num_authors":19,"rel_authors":[{"author_name":"Carlos N Pato","author_inst":"Rutgers University"},{"author_name":"Michele T Pato","author_inst":"Rutgers University"},{"author_name":"Jennifer Mulle","author_inst":"Rutgers University"},{"author_name":"Ronald P Hart","author_inst":"Rutgers University"},{"author_name":"Zhiping Pang","author_inst":"Rutgers University"},{"author_name":"James A Knowles","author_inst":"Rutgers University"},{"author_name":"Tarjinder Singh","author_inst":"Columbia University, NY Genome Center"},{"author_name":"Priya Maddhesiya","author_inst":"New York Genome Center"},{"author_name":"Celia Carvalho","author_inst":"Faculty of Social and Human Sciences, University of Azores"},{"author_name":"Alison Merikangas","author_inst":"Rutgers University"},{"author_name":"Helena Medeiros","author_inst":"Rutgers University"},{"author_name":"Tim B Bigdeli","author_inst":"SUNY Downstate"},{"author_name":"Hamed Kazemi","author_inst":"Department of Psychiatry, University of Arizona College of Medicine-Phoenix"},{"author_name":"John Drake","author_inst":"Department of Psychiatry, University of Arizona College of Medicine-Phoenix"},{"author_name":"Vladmir Vladimrov","author_inst":"Department of Psychiatry, University of Arizona College of Medicine-Phoenix"},{"author_name":"Brion Maher","author_inst":"Department of Mental Health, Johns Hopkins Bloomberg School of Public Health"},{"author_name":"Silviu-Alin Bacanu","author_inst":"Department of Psychiatry, Virginia Commonwealth University"},{"author_name":"Benjamin Neale","author_inst":"Broad Institute"},{"author_name":"Ayman Fanous","author_inst":"Department of Psychiatry, University of Arizona College of Medicine-Phoenix"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Connectomics-guided meta-learning for decoding and anticipatory prediction of sleep spindles from basal ganglia local field potentials in Parkinson's disease","rel_doi":"10.64898\/2026.04.01.26349783","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.01.26349783","rel_abs":"Sleep disturbances are pervasive, debilitating non-motor symptoms of Parkinsons disease (PD), where sleep spindle deficits directly drive cognitive decline and disease progression. Current adaptive deep brain stimulation (aDBS) for PD is largely limited to motor symptom management, with no established technical foundation for sleep spindle-targeted closed-loop modulation. The functional role of the basal ganglia in human sleep spindle regulation remains incompletely characterized, and no robust cross-subject pipeline exists to decode these transient events from clinically implanted DBS electrodes. Here, we developed a connectomics-guided meta-learning framework for cross-subject sleep spindle decoding and anticipatory prediction, using whole-night synchronized basal ganglia local field potential and polysomnography data from 17 PD patients with bilateral DBS implants. Our framework achieved 92.63% accuracy for concurrent spindle decoding and 83.44% accuracy for 2-second-ahead prediction, with optimal signals localized to the limbic subthalamic nucleus and <50 ms total latency meeting real-time closed-loop requirements. This work defines the neuroanatomical substrate of basal ganglia spindle signaling in PD, establishes the cross-subject spindle decoding pipeline for clinical DBS systems, and provides a critical translational foundation for sleep-targeted closed-loop aDBS to mitigate PD non-motor burden.","rel_num_authors":8,"rel_authors":[{"author_name":"Chenfei Ye","author_inst":"Harbin Institute of Technology (Shenzhen)"},{"author_name":"Jiahui Liao","author_inst":"Harbin Institute of Technology (Shenzhen)"},{"author_name":"Zixiao Yin","author_inst":"Beijing Tiantan Hospital, Capital Medical University"},{"author_name":"Yue Li","author_inst":"Harbin Institute of Technology (Shenzhen)"},{"author_name":"Yichen Xu","author_inst":"Beijing Tiantan Hospital, Capital Medical University"},{"author_name":"Houyou Fan","author_inst":"Beijing Tiantan Hospital, Capital Medical University"},{"author_name":"Ting Ma","author_inst":"Harbin Institute of Technology (Shenzhen)"},{"author_name":"Jianguo Zhang","author_inst":"Beijing Tiantan Hospital, Capital Medical University"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Disrupted Coupling of Heart Rate Dependent Brain Network Switching and Attentional Task Performance in Schizophrenia Spectrum Disorders","rel_doi":"10.64898\/2026.04.06.26350241","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350241","rel_abs":"Cognitive deficits are a core feature of schizophrenia, yet their neural mechanisms remain poorly understood. Network switching, a measure of how frequently brain networks change their interactions over time, has been linked to cognitive performance in healthy individuals and has been reported to be altered in schizophrenia. Recent evidence further suggests that the relationship between network switching and cognition depends on arousal, which is itself disrupted in schizophrenia. However, whether arousal-related alterations in network switching contribute to cognitive impairment in schizophrenia remains unclear. Here, we used concurrent resting-state functional MRI (fMRI) and pulse oximetry data from 39 healthy controls (HC), 27 psychiatric controls (PC), and 39 individuals with schizophrenia spectrum disorders (SSD) to examine whether network switching relates to indices of autonomic arousal. Additionally, in HC and SSD participants, we tested whether arousal moderated the association between network switching and performance on an attention task. We observed no group differences in autonomic arousal. However, PC exhibited higher dorsal default mode and anterior salience network switching rates compared to SSD participants. Additionally, autonomic arousal significantly moderated the relationship between network switching and cognitive performance in HC, an effect that was absent in SSD. Notably, these findings implicate network switching as a potential neural biomarker that differentiates PC from SSD. They also suggest that disrupted coupling between arousal state and network switching, rather than switching alone, may underlie cognitive dysfunction in SSD.","rel_num_authors":10,"rel_authors":[{"author_name":"Kimberly Kundert-Obando","author_inst":"Vanderbilt"},{"author_name":"Andrew Kittleson","author_inst":"Vanderbilt University"},{"author_name":"Shiyu Wang","author_inst":"Vanderbilt University"},{"author_name":"Haatef Pourmotabbed","author_inst":"Vanderbilt University"},{"author_name":"Ella Provancher","author_inst":"Vanderbilt University"},{"author_name":"Anna Machado","author_inst":"Vanderbilt University"},{"author_name":"Sohee Park","author_inst":"Vanderbilt University"},{"author_name":"Julia M Sheffield","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Heather Burrell Ward","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Catie Chang","author_inst":"Vanderbilt University"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Association Between Hospital Tiers and Cardiogenic Shock Mortality: Mitigating the Transfer Penalty Through a Regionalized Hub-and-Spoke Model","rel_doi":"10.64898\/2026.04.05.26350211","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.05.26350211","rel_abs":"BackgroundCardiogenic shock (CS) remains associated with high short-term mortality despite contemporary advances in care. The association between institutional cardiac capability and outcomes--particularly among transferred patients and after accounting for clinical instability--remains incompletely defined.\n\nObjectivesTo evaluate the association between hierarchical hospital cardiac capability and in-hospital mortality using a latent measure of acute physiologic severity.\n\nMethodsUsing the National Inpatient Sample (2016-2022), hospitals were classified into five hierarchical tiers ranging from non-PCI (Tier 1) to heart transplant\/durable LVAD centers (Tier 5). Generalized structural equation modeling (GSEM) assessed the relationship between hospital tier and mortality. A latent \"Acute Severity\" construct--comprising cardiac arrest, acute kidney and liver injury, and mechanical ventilation--was incorporated to model the effects of clinical instability\n\nResultsAmong an estimated 1,177,180 CS hospitalizations, most occurred at cardiac surgical and transplant\/LVAD centers. Crude mortality declined stepwise from non-PCI hospitals (64.5%) to transplant\/LVAD centers (36.5%). After adjustment, higher hospital tier was independently associated with lower mortality (Tier 2 OR 0.43 [95% CI 0.38-0.48]; Tier 3 OR 0.37 [0.32- 0.43]; Tier 4 OR 0.33 [0.30-0.38]; Tier 5 OR 0.35 [0.31-0.40]). Although transfer-in status was associated with increased mortality (OR 1.39 [1.33-1.46]), this association was attenuated at cardiac surgical and transplant\/LVAD centers, consistent with a mitigation of transfer associated risk.\n\nConclusionsHigher hospital cardiac capability is independently associated with lower mortality in CS. Advanced centers are associated with mitigation transfer-associated risk, supporting regionalized hub-and-spoke systems with early referral to high-capability centers.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC=\"FIGDIR\/small\/26350211v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (51K):\norg.highwire.dtl.DTLVardef@113e8c7org.highwire.dtl.DTLVardef@176786org.highwire.dtl.DTLVardef@8d99beorg.highwire.dtl.DTLVardef@68b764_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":8,"rel_authors":[{"author_name":"Ankur Sethi","author_inst":"Robert Wood Johnson University Hospital"},{"author_name":"Emily Hiltner","author_inst":"Robert Wood Johnson University Hospital"},{"author_name":"ashish awasthi","author_inst":"Robert Wood Johnson University Hospital"},{"author_name":"Casey Panebianco","author_inst":"Robert Wood Johnson University Hospital"},{"author_name":"Tana LaPlaca","author_inst":"Robert Wood Johnson Medical School"},{"author_name":"Nancy Rizzuto","author_inst":"Robert Wood Johnson University Hospital"},{"author_name":"Leonard Lee","author_inst":"Rutgers Robert Wood University Hospital"},{"author_name":"Mark Russo","author_inst":"Rutgers Health"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"Causal Machine Learning for Comparative Effectiveness of GLP-1 RA versus SGLT2i in Heart Failure Using Real-World EHR Data","rel_doi":"10.64898\/2026.04.06.26350259","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350259","rel_abs":"Clinicians lack precision medicine tools to estimate individualized treatment effects for patients with heart failure (HF). Causal machine learning leveraging electronic health records can estimate both average and individualized treatment effects, enabling estimation of treatment heterogeneity. Using Stony Brook University Hospital data, we compared the effectiveness of glucagon-like peptide-1 receptor agonists (GLP-1 RA) versus sodium-glucose cotransporter 2 inhibitors (SGLT2i) in patients with HF. Under a doubly robust framework, we found a stable population-average effect: GLP-1 RA was associated with a lower risk than SGLT2i for a 1-year composite outcome of all-cause mortality or HF-related hospitalization. Heterogeneity analyses provided limited evidence for individualized treatment selection, although subgroup tests identified loop diuretic use, body mass index, and estimated glomerular filtration rate as potential effect modifiers. While these models hold promise for translating observational data into actionable precision care, careful assessment of causal assumptions and rigorous validation are essential before clinical implementation.","rel_num_authors":5,"rel_authors":[{"author_name":"Grace Y Han","author_inst":"Renaissance School of Medicine at Stony Brook University"},{"author_name":"Andreas P Kalogeropoulos","author_inst":"Renaissance School of Medicine at Stony Brook University"},{"author_name":"Zach Butzin-Dozier","author_inst":"University of California, Berkeley"},{"author_name":"Rachel Wong","author_inst":"Renaissance School of Medicine at Stony Brook University"},{"author_name":"Fusheng Wang","author_inst":"Stony Brook University"}],"rel_date":"2026-04-07","rel_site":"medrxiv"},{"rel_title":"HIV-1 Reverse Transcriptase interactions with Long-acting NNRTI, Depulfavirine (VM1500A)","rel_doi":"10.64898\/2026.04.06.715899","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.715899","rel_abs":"Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are key components of combination antiretroviral therapy (ART) for the treatment of human immunodeficiency virus type 1 (HIV-1) infection, binding an allosteric pocket of reverse transcriptase (RT) and inhibiting viral replication. Although second-generation NNRTIs have improved potency and resistance profiles compared to first-generation NNRTIs, the continued emergence of resistant viral strains and the need for long-acting therapeutic options underscore the importance of developing next-generation compounds. Depulfavirine (VM1500A) is a potent NNRTI being developed as a long-acting formulation. Its prodrug, elsulfavirine (ESV), is approved for HIV-1 treatment in Eurasian countries as a once-daily oral regimen and has demonstrated favorable antiviral efficacy, pharmacokinetics, and tolerability in clinical studies. Here, we report the 2.4 [A] crystal structure of HIV-1 RT in complex with depulfavirine, revealing an extended binding conformation within the NNRTI pocket that reaches from the back of the binding pocket to the entrance. These interactions may shed light on mechanisms of resistance to the F227C mutation, with and without V106 substitution, and Y188L. Notably, depulfavirine maintains potent inhibition of common NNRTI-resistant RT variants, including K103N and Y181C. Combination studies of ESV with antivirals from diverse inhibitor categories demonstrated additive or near-synergistic activity with islatravir (ISL), cabotegravir (CAB), lenacapavir (LEN), and tenofovir (TDF). These findings highlight the broad resistance profile and potential of the depulfavirine combination.","rel_num_authors":5,"rel_authors":[{"author_name":"Alexa A Snyder","author_inst":"Emory University"},{"author_name":"Isabella L Kaufman","author_inst":"Emory University"},{"author_name":"Caitlin J Risener","author_inst":"Emory University"},{"author_name":"Karen A Kirby","author_inst":"Emory University"},{"author_name":"Stefan G Sarafianos","author_inst":"Emory University"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Myeloperoxidase promotes fibrosis by inhibiting cathepsin K to bias the lung toward ECM accumulation","rel_doi":"10.64898\/2026.04.05.713467","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.713467","rel_abs":"Pulmonary fibrosis (PF) involves excessive collagen accumulation, yet mechanisms shifting the balance of synthesis and degradation toward net deposition remain unclear. Myeloperoxidase (MPO) inversely correlates with survival in PF. Using the bleomycin model, we found MPO knockout (MPOko) mice were protected from fibrosis, and pharmacological MPO inhibition after peak inflammation (day 7) recapitulated this protection. MPO persisted in lung tissue 21 days post-injury despite neutrophil efflux, linking acute inflammation to sustained remodeling. Mechanistically, we identified that MPO inhibits Cathepsin K (CatK), a potent collagenolytic enzyme involved in fibrosis resolution. Notably, CatK gene expression (CTSK) is elevated in PF, suggesting post-translational inhibition of CatK. MPOko and inhibitor-treated mice exhibited elevated CatK activity after bleomycin; exogenous addition of pathophysiologic concentrations of MPO reduced CatK activity in mouse precision-cut lung slices and human fibroblasts. Biochemically, MPO reduced CatK activity to 33% of control. In two distinct cohorts of PF patients, we observed significantly increased MPO protein levels in platelet poor plasma and in lung tissue. In PF patients, 62% had MPO levels in platelet poor plasma exceeding healthy controls, while lung tissue from other PF patients showed significantly elevated MPO staining. Plasma levels were inversely correlated with decreased survival, FVC, and DLCO. These findings establish MPO as a post-translational inhibitor of CatK-mediated collagenolysis, revealing a mechanism linking acute inflammation to sustained fibrosis and suggest a patient subpopulation that may benefit from MPO-targeted therapy.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=54 SRC=\"FIGDIR\/small\/713467v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (17K):\norg.highwire.dtl.DTLVardef@4a5977org.highwire.dtl.DTLVardef@137d360org.highwire.dtl.DTLVardef@167fbforg.highwire.dtl.DTLVardef@11ce71b_HPS_FORMAT_FIGEXP  M_FIG C_FIG Myeloperoxidase persists in lung tissue after injury and inhibits cathepsin K activity, impairing collagen degradation and promoting extracellular matrix accumulation during pulmonary fibrosis.","rel_num_authors":8,"rel_authors":[{"author_name":"Patrick A Link","author_inst":"University of Iowa"},{"author_name":"Jack H Wellmerling","author_inst":"Mayo Clinic"},{"author_name":"Jeffrey A Meridew","author_inst":"Mayo Clinic"},{"author_name":"Hyogo Naoi","author_inst":"Mayo Clinic"},{"author_name":"YS Prakash","author_inst":"Mayo Clinic"},{"author_name":"Mauricio Rojas","author_inst":"Ohio State University"},{"author_name":"Eva M Carmona","author_inst":"Mayo Clinic"},{"author_name":"Daniel J Tschumperlin","author_inst":"Mayo Clinic"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Breast cancer interactions with osteoclasts generate osteoclast tumor hybrid like cells through dynamic non-canonical cell fusion and cell-in-cell processes","rel_doi":"10.64898\/2026.04.05.716538","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716538","rel_abs":"Macrophages\/osteoclasts are highly fusogenic cells that interact closely with bone-metastatic breast cancer cells. These cancer cells adapt to bone microenvironments by undergoing osteomimicry, acquiring bone-like phenotypes. Exploration using human breast cancer-bone metastases dataset revealed that a small population of epithelial breast cancer cells express osteoclast-like and osteomimicry genes at the single-cell level. Cell fusion and cell-in-cell (CIC) processes are two uncommon yet prognostically significant mechanisms in cancer. We showed that co-culture between murine breast cancer cells and osteoclasts yielded a unique osteoclast phenotype through dynamic cell-in-cell (CIC) interactions and fusion-like behaviours between pre-osteoclasts\/mature osteoclasts and breast tumor cells, resulting in osteoclast-tumor hybrid-like cells. These tumor cell interactions characterized by membrane retention and nuclear adjacency to host nuclei were consistently observed throughout osteoclast differentiation. Single-cell sequencing analysis and interpretative assays on hybrid-like cells revealed altered extracellular matrix (ECM) modification processes, immunoregulatory, and cancer-associated pathways compared to unfused osteoclasts. Tumor cells co-cultured with osteoclasts expressed hematopoietic and osteoclast-lineage factors more strongly than tumor cells cultured alone with their effects amplified under direct cell-cell contact. The presence of these hybrid-like cells was validated in human breast cancer-bone metastases. We propose that disseminated bone-tropic breast cancer cells were stimulated by osteoclasts to undergo a non-canonical, dynamic osteoclast differentiation and CIC formation to form hybrid-like cells that may facilitate bone metastatic lesions.","rel_num_authors":9,"rel_authors":[{"author_name":"King Hoo Lim","author_inst":"City University of Hong Kong"},{"author_name":"Damrongrat Siriwanna","author_inst":"City University of Hong Kong"},{"author_name":"Xining Li","author_inst":"Chinese University of Hong Kong"},{"author_name":"Eunice Dotse","author_inst":"City University of Hong Kong"},{"author_name":"Meijun Wang","author_inst":"City University of Hong Kong"},{"author_name":"Choa Mun","author_inst":"City University of Hong Kong"},{"author_name":"Yusong Li","author_inst":"City University of Hong Kong"},{"author_name":"Xin Wang","author_inst":"Chinese University of Hong Kong"},{"author_name":"Kwan Ting Chow","author_inst":"City University of Hong Kong"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"AI predictions and the expansion of scientific frontiers: Evidence from structural biology","rel_doi":"10.64898\/2026.04.06.716821","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716821","rel_abs":"Artificial intelligence holds the potential to expand the frontier of scientific research1, yet recent work has raised concern that it may instead narrow scientific attention to well-established areas2-4. Here, leveraging the 2021 release of AlphaFold25 as a quasi-experimental opportunity, we provide field-level evidence that AI can redirect collective attention toward more novel research targets. Tracking 245,396 experimental structures in the Protein Data Bank6, we show that a long-running decline in the study of novel proteins halted after AlphaFold2s release, with the shift concentrated among studies citing AlphaFold2 and targets with high-confidence predictions. This pattern extends to 248,191 downstream papers that consume structural knowledge, where engagement with genes lacking experimental structures and with understudied human genes increased since 2021. Amid rising concern that AI may reinforce scientific canons7-10, our findings offer an early field-level case where AI predictions expand scientific frontiers, consistent with the idea that the real-world consequences of AI on science depend on where their informational gains are greatest. These results suggest AI can complement human knowledge and redirect collective attention in science, with broad implications for emerging AI for science models.","rel_num_authors":3,"rel_authors":[{"author_name":"Mengyi Sun","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Sukwoong Choi","author_inst":"University at Albany, State University of New York"},{"author_name":"Yian Yin","author_inst":"Cornell University"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"RNA polymerase inhibitors reveal active-site motions essential for the nucleotide-addition cycle","rel_doi":"10.64898\/2026.04.06.716786","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.06.716786","rel_abs":"The nucleotide-addition cycle (NAC) of multi-subunit DNA-dependent RNA polymerases (RNAPs) involves coordinated conformational changes in conserved active-site structural elements, including the trigger loop (TL). The TL is open (unfolded) in most RNAP structures but can close (fold) in substrate-bound (post- or pre-translocated) states of the RNAP, promoting catalysis. TL closure has been associated with closure of another conserved structural element, the Rim-Helices\/F-loop (RH-FL), but the role of the RH-FL in the NAC is unclear. Antibiotic leads CBR9379 and AAP-SO2 inhibit the Escherichia coli and Mycobacterium tuberculosis RNAPs, respectively, by binding in a pocket formed by the bridge helix and RH-FL. The precise mechanism of action for these inhibitors is yet to be defined. We present cryo-electron microscopy structures showing that both compounds inhibit the RNAP NAC by preventing RH-FL closure, thereby allosterically destabilizing the closed TL. This work reveals a conserved mechanistic principle of RNAP catalysis across all domains of life and provides new insight for antibiotic design.","rel_num_authors":4,"rel_authors":[{"author_name":"Yukti Dhingra","author_inst":"The Rockefeller University"},{"author_name":"Robert Landick","author_inst":"University of Wisconsin-Madison, Wisconsin, USA"},{"author_name":"Elizabeth A Campbell","author_inst":"The Rockefeller Univeristy"},{"author_name":"Seth A Darst","author_inst":"The Rockefeller University"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Screening metatranscriptomes for ultrastable RNA secondary structures reveals hidden bacteriophages and novel capsid nanomaterials","rel_doi":"10.64898\/2026.04.05.716407","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716407","rel_abs":"Metatranscriptomics has transformed our view of RNA bacteriophage diversity, revealing vast numbers of single-stranded RNA (ssRNA) phages whose protein capsids can be engineered for biotechnology applications. However, many ssRNA phages remain hidden from current detection methods, which require protein-level similarity to known phages. Here we show that RNA structure provides an additional signal for the detection of ssRNA phages in metatranscriptomes, including hidden phages missed by prior protein-based methods. By computationally folding each contig and screening for exceptionally stable RNA secondary structures, we find evidence of thousands of previously unrecognized phages encoding novel coat proteins. We express a library of 12,000 such coat proteins in E. coli and find that most assemble into nuclease-resistant capsids. We determine the 3D structure of one such capsid by cryo-electron microscopy and demonstrate that it can be disassembled and reassembled in vitro to package heterologous RNA--a key step toward repurposing these particles as RNA delivery vehicles. We compile the newly discovered ssRNA phages with previously known ones into a database that contains sequence and structural information for over 460,000 unique RNA molecules and over 100,000 distinct coat proteins, providing a comprehensive resource for microbiology and nanomaterials research.","rel_num_authors":17,"rel_authors":[{"author_name":"Daniel A. Villarreal","author_inst":"San Diego State University"},{"author_name":"Nino Makasarashvili","author_inst":"San Diego State University"},{"author_name":"Aaryan Kapoor","author_inst":"San Diego State University"},{"author_name":"Max Root","author_inst":"San Diego State University"},{"author_name":"Matthew Campbell","author_inst":"San Diego State University"},{"author_name":"Skylar Gibson","author_inst":"San Diego State University"},{"author_name":"Conor Schiveley","author_inst":"San Diego State University"},{"author_name":"Amineh Rastandeh","author_inst":"San Diego State University"},{"author_name":"Sherry Baker","author_inst":"San Diego State University"},{"author_name":"Sundharraman Subramanian","author_inst":"Michigan State University"},{"author_name":"Uri Neri","author_inst":"Lawrence Berkeley National Laboratory"},{"author_name":"Carolyn E. Mills","author_inst":"University of California, Santa Barbara"},{"author_name":"Katelyn McNair","author_inst":"San Diego State University"},{"author_name":"Anca M. Segall","author_inst":"San Diego State University"},{"author_name":"Uri Gophna","author_inst":"Tel Aviv University"},{"author_name":"Kristin N. Parent","author_inst":"Michigan State University"},{"author_name":"Rees F. Garmann","author_inst":"San Diego State University"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Screening metatranscriptomes for ultrastable RNA secondary structures reveals hidden bacteriophages and novel capsid nanomaterials","rel_doi":"10.64898\/2026.04.05.716407","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716407","rel_abs":"Metatranscriptomics has transformed our view of RNA bacteriophage diversity, revealing vast numbers of single-stranded RNA (ssRNA) phages whose protein capsids can be engineered for biotechnology applications. However, many ssRNA phages remain hidden from current detection methods, which require protein-level similarity to known phages. Here we show that RNA structure provides an additional signal for the detection of ssRNA phages in metatranscriptomes, including hidden phages missed by prior protein-based methods. By computationally folding each contig and screening for exceptionally stable RNA secondary structures, we find evidence of thousands of previously unrecognized phages encoding novel coat proteins. We express a library of 12,000 such coat proteins in E. coli and find that most assemble into nuclease-resistant capsids. We determine the 3D structure of one such capsid by cryo-electron microscopy and demonstrate that it can be disassembled and reassembled in vitro to package heterologous RNA--a key step toward repurposing these particles as RNA delivery vehicles. We compile the newly discovered ssRNA phages with previously known ones into a database that contains sequence and structural information for over 460,000 unique RNA molecules and over 100,000 distinct coat proteins, providing a comprehensive resource for microbiology and nanomaterials research.","rel_num_authors":17,"rel_authors":[{"author_name":"Daniel A. Villarreal","author_inst":"San Diego State University"},{"author_name":"Nino Makasarashvili","author_inst":"San Diego State University"},{"author_name":"Aaryan Kapoor","author_inst":"San Diego State University"},{"author_name":"Max Root","author_inst":"San Diego State University"},{"author_name":"Matthew Campbell","author_inst":"San Diego State University"},{"author_name":"Skylar Gibson","author_inst":"San Diego State University"},{"author_name":"Conor Schiveley","author_inst":"San Diego State University"},{"author_name":"Amineh Rastandeh","author_inst":"San Diego State University"},{"author_name":"Sherry Baker","author_inst":"San Diego State University"},{"author_name":"Sundharraman Subramanian","author_inst":"Michigan State University"},{"author_name":"Uri Neri","author_inst":"Lawrence Berkeley National Laboratory"},{"author_name":"Carolyn E. Mills","author_inst":"University of California, Santa Barbara"},{"author_name":"Katelyn McNair","author_inst":"San Diego State University"},{"author_name":"Anca M. Segall","author_inst":"San Diego State University"},{"author_name":"Uri Gophna","author_inst":"Tel Aviv University"},{"author_name":"Kristin N. Parent","author_inst":"Michigan State University"},{"author_name":"Rees F. Garmann","author_inst":"San Diego State University"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Redox imbalance dictates dependence on GOT1 versus GOT2 for rod photoreceptor health during aging and stress","rel_doi":"10.64898\/2026.04.05.716322","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716322","rel_abs":"Photoreceptor (PR) loss causes vision loss in many blinding diseases, and effective therapies to prevent this cell loss are lacking. Aspartate aminotransferases (GOTs), located in the cytosol (GOT1) and mitochondria (GOT2), are key components of the malate-aspartate shuttle, which transfers reducing equivalents from cytosol to mitochondria. Previous work has implicated the GOTs as potential modulators of blinding retinal disease. To determine the roles of GOT1 and GOT2 in rod PRs, we generated rod PR-specific Got1 or Got2 conditional knockout mice (Got1 or Got2 cKO). We previously showed that Got1 cKO causes PR degeneration and is accompanied by NADH accumulation and a decreased retinal NAD+\/NADH ratio. Here, we show that NADH oxidation via metabolic or genetic means prolongs PR survival in Got1 cKO animals, implicating NADH accumulation, or reductive stress, as a key driver of PR degeneration. In contrast, Got2 cKO causes minimal PR degeneration and alterations in retinal NADH and the NAD+\/NADH ratio that oppose reductive stress. Interestingly, GOT2, but not GOT1, is decreased in multiple models of PR degeneration, including retinal detachment (RD) where the NAD+\/NADH ratio favors a reductive state. Notably, loss of Got2 in PRs demonstrates a neuroprotective effect after experimental RD suggesting decreased GOT2 expression may be part of a stress response to promote PR survival. Overall, this study illustrates the differential dependence on the GOTs for PR health, provides evidence that an overly reductive environment is detrimental to PR survival, and identifies GOT2 as a novel therapeutic target with potentially broad application in blinding diseases.","rel_num_authors":15,"rel_authors":[{"author_name":"Meini Chen","author_inst":"University of Michigan"},{"author_name":"Eric Weh","author_inst":"University of Michigan"},{"author_name":"Moloy Goswami","author_inst":"University of Michigan"},{"author_name":"Katherine M Weh","author_inst":"University of Michigan"},{"author_name":"Heather Hager","author_inst":"University of Michigan"},{"author_name":"Peter Sajjakulnukit","author_inst":"University of Michigan"},{"author_name":"Avi Weingarten","author_inst":"University of Michigan"},{"author_name":"Shubha Subramanya","author_inst":"University of Michigan"},{"author_name":"Nicholas Miller","author_inst":"University of Michigan"},{"author_name":"Sraboni Chaudhury","author_inst":"University of Michigan"},{"author_name":"Emma Piraino","author_inst":"University of Michigan"},{"author_name":"Navdeep S M Chandel","author_inst":"Northwestern University"},{"author_name":"Renee Ryals","author_inst":"Oregon Health & Science University"},{"author_name":"Costas A Lyssiotis","author_inst":"University of Michigan"},{"author_name":"Thomas J Wubben","author_inst":"University of Michigan"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Redox imbalance dictates dependence on GOT1 versus GOT2 for rod photoreceptor health during aging and stress","rel_doi":"10.64898\/2026.04.05.716322","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716322","rel_abs":"Photoreceptor (PR) loss causes vision loss in many blinding diseases, and effective therapies to prevent this cell loss are lacking. Aspartate aminotransferases (GOTs), located in the cytosol (GOT1) and mitochondria (GOT2), are key components of the malate-aspartate shuttle, which transfers reducing equivalents from cytosol to mitochondria. Previous work has implicated the GOTs as potential modulators of blinding retinal disease. To determine the roles of GOT1 and GOT2 in rod PRs, we generated rod PR-specific Got1 or Got2 conditional knockout mice (Got1 or Got2 cKO). We previously showed that Got1 cKO causes PR degeneration and is accompanied by NADH accumulation and a decreased retinal NAD+\/NADH ratio. Here, we show that NADH oxidation via metabolic or genetic means prolongs PR survival in Got1 cKO animals, implicating NADH accumulation, or reductive stress, as a key driver of PR degeneration. In contrast, Got2 cKO causes minimal PR degeneration and alterations in retinal NADH and the NAD+\/NADH ratio that oppose reductive stress. Interestingly, GOT2, but not GOT1, is decreased in multiple models of PR degeneration, including retinal detachment (RD) where the NAD+\/NADH ratio favors a reductive state. Notably, loss of Got2 in PRs demonstrates a neuroprotective effect after experimental RD suggesting decreased GOT2 expression may be part of a stress response to promote PR survival. Overall, this study illustrates the differential dependence on the GOTs for PR health, provides evidence that an overly reductive environment is detrimental to PR survival, and identifies GOT2 as a novel therapeutic target with potentially broad application in blinding diseases.","rel_num_authors":15,"rel_authors":[{"author_name":"Meini Chen","author_inst":"University of Michigan"},{"author_name":"Eric Weh","author_inst":"University of Michigan"},{"author_name":"Moloy Goswami","author_inst":"University of Michigan"},{"author_name":"Katherine M Weh","author_inst":"University of Michigan"},{"author_name":"Heather Hager","author_inst":"University of Michigan"},{"author_name":"Peter Sajjakulnukit","author_inst":"University of Michigan"},{"author_name":"Avi Weingarten","author_inst":"University of Michigan"},{"author_name":"Shubha Subramanya","author_inst":"University of Michigan"},{"author_name":"Nicholas Miller","author_inst":"University of Michigan"},{"author_name":"Sraboni Chaudhury","author_inst":"University of Michigan"},{"author_name":"Emma Piraino","author_inst":"University of Michigan"},{"author_name":"Navdeep S M Chandel","author_inst":"Northwestern University"},{"author_name":"Renee Ryals","author_inst":"Oregon Health & Science University"},{"author_name":"Costas A Lyssiotis","author_inst":"University of Michigan"},{"author_name":"Thomas J Wubben","author_inst":"University of Michigan"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Sulfide:quinone oxidoreductase drives mitochondrial supersulfide metabolism to regulate bioenergetics and longevity in eukaryotes","rel_doi":"10.64898\/2026.04.05.716515","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.05.716515","rel_abs":"Sulfide:quinone oxidoreductase (SQR) is a critical enzyme that maintains sulfur metabolism by oxidizing sulfide to supersulfides, currently defined as sulfur metabolites with six valence electrons and no charge that are covalently catenated with other sulfur atoms and excludes disulfides. While SQR is known to contribute to mitochondrial electron transport, its physiological impact on systemic energy metabolism and longevity remains largely undefined. In this study, we investigated the role of SQR in mitochondrial bioenergetics and aging using SQR-deficient Schizosaccharomyces pombe ({Delta}hmt2) and a mitochondria-selective SQR-deficient (Sqrdl{Delta}N\/{Delta}N) mice model. Functional analysis demonstrated that{Delta} hmt2 grew normally in glucose but not in glycerol, indicating impaired mitochondrial respiration. It showed reduced membrane potential, ATP, and lifespan. Consistent with the yeast findings, Sqrdl{Delta}N\/{Delta}N mice exhibited accumulated levels of hydrogen sulfide and persulfides, and demonstrated impaired mitochondrial energy metabolism. Furthermore, supersulfide donor supplementation selectively conferred lifespan extension in wild-type yeast, but not in SQR-deficient strain, and similarly improved mitochondrial function exclusively in wild-type mouse embryonic fibroblasts, with no benefit observed in SQR-mutant counterparts. Together, our findings demonstrate that mitochondrial SQR plays an essential role in sulfur respiration, critically supporting mitochondrial function and organismal longevity across eukaryotes.\n\nGraphic Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC=\"FIGDIR\/small\/716515v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (36K):\norg.highwire.dtl.DTLVardef@cfada1org.highwire.dtl.DTLVardef@904e79org.highwire.dtl.DTLVardef@1024b1org.highwire.dtl.DTLVardef@1d911bc_HPS_FORMAT_FIGEXP  M_FIG C_FIG HighlightsO_LIDeveloped an SQR-deficient S. pombe ({Delta}hmt2) model that exhibits sulfur metabolism, mitochondrial dysfunction, and shortened chronological lifespan\nC_LIO_LISulfide and supersulfide donors prolong yeast lifespan in a SQR-dependent manner\nC_LIO_LIMitochondrial SQR is essential for membrane potential formation and ATP production in yeast and mammals\nC_LI","rel_num_authors":12,"rel_authors":[{"author_name":"Jia Yao","author_inst":"Department of Redox Molecular Medicine, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan"},{"author_name":"Tetsuro Matsunaga","author_inst":"Department of Redox Molecular Medicine, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan"},{"author_name":"Akira Nishimura","author_inst":"Division of Agriculture, Graduate School of Arts and Sciences, Iwate University, Iwate 020-8550, Japan"},{"author_name":"Meg Shieh","author_inst":"Department of Chemistry, Brown University, Providence, Rhode Island 02912, USA"},{"author_name":"Tomoaki Ida","author_inst":"Department of Redox Molecular Medicine, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan"},{"author_name":"Minkyung Jung","author_inst":"Department of Redox Molecular Medicine, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan"},{"author_name":"Seiryo Ogata","author_inst":"Department of Environmental Medicine and Molecular Toxicology, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan"},{"author_name":"Tsuyoshi Takata","author_inst":"Department of Redox Molecular Medicine, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan"},{"author_name":"Uladzimir Barayeu","author_inst":"Max-Planck-Institute for Polymer Research, Mainz 55128, Germany"},{"author_name":"Hozumi Motohashi","author_inst":"Department of Medical Biochemistry, Graduate School of Medicine Tohoku University, Sendai 980-8575, Japan"},{"author_name":"Masanobu Morita","author_inst":"Department of Environmental Medicine and Molecular Toxicology, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan"},{"author_name":"Takaaki Akaike","author_inst":"Department of Redox Molecular Medicine, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Defining the RNA Modification Landscape of Multiple Myeloma Reveals METTL3-Dependent m6A Regulation of NEAT1","rel_doi":"10.64898\/2026.04.04.716518","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.04.716518","rel_abs":"RNA modifications play critical roles in gene regulation. N6-methyladenosine (m6A) is the most abundant modification on mRNA and long noncoding RNA (lncRNA) and regulates RNA processing, stability, and translation. RNA modifications are not well characterized in multiple myeloma (MM), a plasma cell malignancy characterized by relapse and disease progression, and the contribution of m6A-modified lncRNAs to the disease remains unclear. Here, we define the RNA modification landscape of MM by combining mass spectrometry, Nanopore Direct RNA sequencing, and methylated RNA immunoprecipitation sequencing. We identify 20 RNA modification types and > 15,000 m6A sites, including sites on 2,398 lncRNAs. Among these, we validate m6A sites on the paraspeckle-associated lncRNA NEAT1. Functional studies reveal that NEAT1 expression is regulated by the methyltransferase METTL3 and site-specific demethylation of a NEAT1 m6A site reduces MM cell viability. Single-cell RNA sequencing shows consistent NEAT1 enrichment in malignant plasma cells but minimal expression in healthy cells. These findings identify m6A-modified lncRNAs as key regulators of MM biology and establish NEAT1 as an epitranscriptomically controlled driver of MM cell survival.","rel_num_authors":21,"rel_authors":[{"author_name":"Prasanth Thunuguntla","author_inst":"Washington University School of Medicine, St. Louis, MO, 63108"},{"author_name":"Dhanusha Duraiyan","author_inst":"Washington University School of Medicine, St. Louis, MO, 63109"},{"author_name":"Catheryn Sizemore","author_inst":"Washington University School of Medicine, St. Louis, MO, 63110"},{"author_name":"Elizabeth Sulvaran-Guel","author_inst":"Washington University School of Medicine, St. Louis, MO, 63111"},{"author_name":"Richa Mishra","author_inst":"Washington University School of Medicine, St. Louis, MO, 63112"},{"author_name":"Jessica Camacho","author_inst":"Washington University School of Medicine, St. Louis, MO, 63113"},{"author_name":"Savannah Gonzales","author_inst":"Washington University School of Medicine, St. Louis, MO, 63114"},{"author_name":"Stephen Daly","author_inst":"Washington University School of Medicine, St. Louis, MO, 63115"},{"author_name":"Katelyn Bagwill","author_inst":"Washington University School of Medicine, St. Louis, MO, 63116"},{"author_name":"Dakota Colbert","author_inst":"Washington University School of Medicine, St. Louis, MO, 63117"},{"author_name":"Jaiyana King","author_inst":"Washington University School of Medicine, St. Louis, MO, 63118"},{"author_name":"Christ Samuel","author_inst":"Washington University School of Medicine, St. Louis, MO, 63119"},{"author_name":"Luke David-Pennington","author_inst":"Washington University School of Medicine, St. Louis, MO, 63120"},{"author_name":"Ashley Ki","author_inst":"Washington University School of Medicine, St. Louis, MO, 63121"},{"author_name":"Sydney Anderson","author_inst":"Washington University School of Medicine, St. Louis, MO, 63122"},{"author_name":"Carolina Bras Costa","author_inst":"Washington University School of Medicine, St. Louis, MO, 63123"},{"author_name":"Jin Zhang","author_inst":"Washington University School of Medicine, St. Louis, MO, 63124"},{"author_name":"Ravi Vij","author_inst":"Washington University School of Medicine, St. Louis, MO, 63125"},{"author_name":"Benjamin A Garcia","author_inst":"Washington University School of Medicine, St. Louis, MO, 63126"},{"author_name":"John DiPersio","author_inst":"Washington University School of Medicine, St. Louis, MO, 63127"},{"author_name":"Jessica Silva-Fisher","author_inst":"Washington University School of Medicine, St. Louis, MO, 63128"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Flow molecular dynamics simulations reveal mechanosensitive regulation of von Willebrand factor through glycan-modulated autoinhibitory modules","rel_doi":"10.64898\/2026.04.04.716521","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.04.716521","rel_abs":"Force-induced protein conformational changes govern many essential biological processes, yet their molecular mechanisms remain difficult to resolve. Von Willebrand factor (VWF), a central regulator of haemostasis, is activated by hydrodynamic forces in blood flow, but how mechanical signals propagate across its multidomain architecture is poorly understood. Here, we use flow molecular dynamics (FMD), a simulation framework that applies fluid forces via controlled solvent flow to interrogate mechanosensitive proteins. Using VWF as a model system, we reconstructed the complete mechanomodule (D'D3-A1-A2-A3; 1,109 residues) with native glycosylation by integrating crystallographic data and AlphaFold predictions. FMD simulations capture a force-driven transition from a compact, autoinhibited \"birds-nest\" ensemble to an extended, activated state, revealing asymmetric autoinhibitory strengths within the N'AIM and C'AIM modules of the A1 domain. By directly linking static structures to dynamic, force-regulated behaviour, this work establishes a generalizable platform for dissecting protein mechanosensitivity and enabling the rational design of force-responsive therapeutics.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=48 SRC=\"FIGDIR\/small\/716521v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@edba50org.highwire.dtl.DTLVardef@1630df4org.highwire.dtl.DTLVardef@292887org.highwire.dtl.DTLVardef@23cdb7_HPS_FORMAT_FIGEXP  M_FIG C_FIG Flow molecular dynamics simulations reveal that GPIb engages the A1 domain only after the disruption of key interdomain and intermodular interactions.","rel_num_authors":3,"rel_authors":[{"author_name":"Naveen Eugene Louis Richard Louis","author_inst":"University of Sydney"},{"author_name":"Yunduo Charles Zhao","author_inst":"University of Sydney"},{"author_name":"Lining Arnold Ju","author_inst":"The University of Sydney"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Genetic background shapes AI-predicted variant effects","rel_doi":"10.64898\/2026.04.04.715328","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.04.715328","rel_abs":"Predicting the consequences of genetic variants remains a major goal in biomedicine. Conventional approaches typically assess single-nucleotide variants in the context of a single reference genome, without accounting for genetic diversity that can modulate variant effects. Here we introduce the personalized variant effect predictor (pVEP) framework, which quantifies how genetic background across thousands of human genomes from globally diverse populations shapes computational predictions of clinical variant effects. Across deep learning models spanning protein structure, splicing, and noncoding regulation, pVEP reveals that many clinical variants exhibit heterogeneous predicted effects across haplotypes, with the same variant predicted to be pathogenic in some genetic backgrounds and benign in others. We find support for underlying molecular mechanisms, including shifts in predicted protein contacts and changes in splice-site recognition. Overall, personalized genomic context emerges as a systematically underappreciated variable in variant annotation and clinical interpretation, with particular implications for genetically diverse populations.","rel_num_authors":12,"rel_authors":[{"author_name":"Brian M Schilder","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Zihan Liu","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Jack J Desmarais","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"David Laub","author_inst":"University of California, San Diego"},{"author_name":"Fahimeh Rahimi","author_inst":"University of Florida"},{"author_name":"Palash Sethi","author_inst":"University of Florida"},{"author_name":"Lucas A Pereira","author_inst":"University of Florida"},{"author_name":"Mengyi M Sun","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Justin Block Kinney","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"David Martin McCandlish","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Juannan Zhou","author_inst":"University of Florida"},{"author_name":"Peter K Koo","author_inst":"Cold Spring Harbor Laboratory"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Genetic background shapes AI-predicted variant effects","rel_doi":"10.64898\/2026.04.04.715328","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.04.715328","rel_abs":"Predicting the consequences of genetic variants remains a major goal in biomedicine. Conventional approaches typically assess single-nucleotide variants in the context of a single reference genome, without accounting for genetic diversity that can modulate variant effects. Here we introduce the personalized variant effect predictor (pVEP) framework, which quantifies how genetic background across thousands of human genomes from globally diverse populations shapes computational predictions of clinical variant effects. Across deep learning models spanning protein structure, splicing, and noncoding regulation, pVEP reveals that many clinical variants exhibit heterogeneous predicted effects across haplotypes, with the same variant predicted to be pathogenic in some genetic backgrounds and benign in others. We find support for underlying molecular mechanisms, including shifts in predicted protein contacts and changes in splice-site recognition. Overall, personalized genomic context emerges as a systematically underappreciated variable in variant annotation and clinical interpretation, with particular implications for genetically diverse populations.","rel_num_authors":12,"rel_authors":[{"author_name":"Brian M Schilder","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Zihan Liu","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Jack J Desmarais","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"David Laub","author_inst":"University of California, San Diego"},{"author_name":"Fahimeh Rahimi","author_inst":"University of Florida"},{"author_name":"Palash Sethi","author_inst":"University of Florida"},{"author_name":"Lucas A Pereira","author_inst":"University of Florida"},{"author_name":"Mengyi M Sun","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Justin Block Kinney","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"David Martin McCandlish","author_inst":"Cold Spring Harbor Laboratory"},{"author_name":"Juannan Zhou","author_inst":"University of Florida"},{"author_name":"Peter K Koo","author_inst":"Cold Spring Harbor Laboratory"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Tau-induced mitochondrial reverse electron transport drives neurodegeneration","rel_doi":"10.64898\/2026.04.04.716514","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.04.716514","rel_abs":"Hyperphosphorylation and aggregation of the microtubule-associated protein tau are recognized as pathological hallmarks of tauopathies; however, the biological activity of tau that drives its pathophysiological effects remains poorly understood1-6. Mitochondrial dysfunction is a common feature of tauopathies7,8. Despite this, the mechanistic link between tau abnormalities and mitochondrial dysfunction, as well as its relationship to taus physiological function, remains unclear. Here, we demonstrate that tau regulates mitochondrial reverse electron transport (RET), which produces excess ROS, reduces the NAD+\/NADH ratio, and is activated by aging or stress. In flies, mice, and human induced pluripotent stem cells (hiPSC)-derived neurons, tau depletion eliminates stress-induced RET and confers significant stress resistance. Mechanistically, tau enters mitochondria and directly interacts with the mitochondrial complex I (C-I) subunit NDUFS3, enhancing RET activation in a phosphorylation-dependent manner that correlates with tau pathogenicity. Elevated RET further drives tau hyperphosphorylation, establishing a self-perpetuating pathological loop. Blocking tau entry into mitochondria or disrupting tau\/NDUFS3 interaction reduces tau-induced RET. Genetic or pharmacological inhibition of RET protects against tau-induced neurodegeneration across species. RET regulation represents a previously unrecognized normal function of tau that becomes pathological in disease, providing a therapeutic target for conditions characterized by tau abnormalities and mitochondrial dysfunction.","rel_num_authors":11,"rel_authors":[{"author_name":"Wen Li","author_inst":"Stanford University"},{"author_name":"Suman Rimal","author_inst":"Stanford University"},{"author_name":"Sunil Bhurtel","author_inst":"Stanford University"},{"author_name":"Lucas Yeung","author_inst":"Stanford University"},{"author_name":"Benjamin Lu","author_inst":"Stanford University"},{"author_name":"Lea T Grinberg","author_inst":"UC San Francisco"},{"author_name":"Salvatore Spina","author_inst":"University of California San Francisco"},{"author_name":"Inma Cobos","author_inst":"STANFORD UNIVERSITY"},{"author_name":"William W Seeley","author_inst":"University of California, San Francisco"},{"author_name":"Su Guo","author_inst":"UC San Francisco"},{"author_name":"Bingwei Lu","author_inst":"Stanford University"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"FunctionaL Assigning Sequence Homing (FLASH) maps phenotype to sequence with deep and machine learning","rel_doi":"10.64898\/2026.04.04.715981","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.04.715981","rel_abs":"Genome-wide association studies (GWAS) map genetic variation to a reference genome and correlate variants to phenotypes. Yet, GWAS and similar procedures have limitations, including an inability to predict phenotype on variants never seen during the discovery phase and difficulty integrating structural variants. Deep and machine learning alternatives have not been successful at consistent prediction of resistance phenotypes (Hu et al. 2024). Here, we introduce FLASH: a new interpretable, statistically-based deep learning framework that operates directly on raw sequencing reads. In over 35,000 isolates of bacteria, fungi and viruses, FLASH achieves uniformly high accuracy on independent test data, including on variation never seen in training, meeting or exceeding bespoke state of the art methods. FLASH identifies canonical drug targets ab initio and new pan-species predictors of virulence, including those lacking annotation and those only partially aligned to NCBI reference databases. Further, FLASH can predict phenotypes beyond the possibility of GWAS, such as bacterial host range of phage, a task that to our knowledge is impossible today. FLASH is simple to run, highly efficient and constitutes a new approach for predicting gene function and phenotype across the tree of life. It is especially valuable when bioethical concerns and the vast genetic complexity of pathogenic microbes limit the feasibility of experimental validation.","rel_num_authors":8,"rel_authors":[{"author_name":"Daniel J. Cotter","author_inst":"Department of Biomedical Data Science, Stanford University, Stanford, CA, USA"},{"author_name":"Marie-Claire Harrison","author_inst":"Department of Biomedical Data Science, Stanford University, Stanford, CA, USA"},{"author_name":"Arjun Rustagi","author_inst":"Department of Medicine, University of California, San Francisco, San Francisco, CA, USA"},{"author_name":"Peter L. Wang","author_inst":"Department of Biomedical Data Science, Stanford University, Stanford, CA, USA"},{"author_name":"Marek Kokot","author_inst":"Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland"},{"author_name":"Allison F. Carey","author_inst":"Department of Pathology, University of Utah, Salt Lake City, UT, 84112, USA"},{"author_name":"Sebastian Deorowicz","author_inst":"Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland"},{"author_name":"Julia Salzman","author_inst":"Department of Biomedical Data Science, of Biochemistry, and by Courtesy, of Statistics and of Biology, Stanford University, CA"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"A pseudo-phased genome assembly for Hemileia vastatrix reveals an isolate-specific chromosomal haploid trisomy","rel_doi":"10.64898\/2026.04.04.716458","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.04.716458","rel_abs":"Recurrent epidemics of coffee leaf rust, caused by the fungal pathogen Hemileia vastatrix, have constrained production of Arabica coffee for over 150 years. Here, we present a pseudo-phased, chromosome-level genome resource for H. vastatrix, isolate Hv178a, to guide research into disease management. The Hv178a genome assembly is 665 and 638 Mbp for haplotype A and B respectively, localised to 18 chromosomes. We determined that the genomes are highly repetitive at [~]90%, with a GC content of [~]33%. We present the full annotation of 13,760 and 17,998 protein coding genes, and we predicted 452 and 496 effectors in haplotype A and B respectively. Depth-based comparisons with 11 additional H. vastatrix isolates revealed increased chromosome 17 (chr17) copy number in Hv178a. Validation with qPCR supports a chr17 trisomy in Hv178a absent from the ancestral lineage and potentially explaining the observed change in virulence.","rel_num_authors":10,"rel_authors":[{"author_name":"Peri Tobias","author_inst":"University of Sydney"},{"author_name":"Richard J Edwards","author_inst":"The University of Western Australia"},{"author_name":"Jamie Botting","author_inst":"The University of Western Australia"},{"author_name":"Giullia di Lorenzo","author_inst":"Universidade de Lisboa, Portugal"},{"author_name":"Vera In\u00e1cio","author_inst":"Universidade de Lisboa, Portugal"},{"author_name":"In\u00eas Diniz","author_inst":"National Institute for Agricultural and Veterinary Research, Portugal"},{"author_name":"Maria do C\u00e9u Silva","author_inst":"Universidade de Lisboa, Portugal"},{"author_name":"Vitor V\u00e1rzea","author_inst":"Universidade de Lisboa, Portugal"},{"author_name":"Robert Park","author_inst":"University of Sydney"},{"author_name":"Dora Batista","author_inst":"Universidade de Lisboa, Portugal"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery","rel_doi":"10.64898\/2026.04.04.716470","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.04.04.716470","rel_abs":"Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages.","rel_num_authors":6,"rel_authors":[{"author_name":"Tianyu Liu","author_inst":"Yale University"},{"author_name":"Sihan Jiang","author_inst":"Yale University"},{"author_name":"Fan Zhang","author_inst":"CUHK"},{"author_name":"Kunyang Sun","author_inst":"University of California, Berkeley"},{"author_name":"Teresa Head-Gordon","author_inst":"UC Berkeley"},{"author_name":"Hongyu Zhao","author_inst":"Yale University"}],"rel_date":"2026-04-07","rel_site":"biorxiv"},{"rel_title":"Biologically informed geometry and force distribution improve task performance in agonist\/antagonist tendon-driven prosthetic hands","rel_doi":"10.64898\/2026.04.06.26350199","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.06.26350199","rel_abs":"Prosthetic devices balance functionality and usability to support activities of daily living (ADLs). However, many designs rely on rigid end effectors that, while anthropomorphic in form, lack biomimetic design principles. This mismatch increases cognitive and physical burden, reducing adoption rates. We developed the Human-inspired Actuator Modeling and Reconstruction (HAMR) process, a user-centered framework informed by individual morphology and functional needs, to generate customized agonist\/antagonist tendon-actuated end effectors. Using HAMR, we created the Tendon Actuated Prosthetic Hand (TAPH), which integrates human-derived geometry with adaptive force distribution to promote natural object interaction. In a study with 12 participants without limb difference, TAPH was compared to a structurally similar tendon-actuated hand with generalized anthropomorphic geometry across three ADL tasks of varying complexity. TAPH significantly improved task performance and reduced physical effort, mental workload, and frustration, particularly during gross motor tasks. For fine motor tasks, performance improved under stable conditions but not during tasks requiring dynamic precision and continuous coordination. These findings highlight the functional benefits of biologically informed prosthesis design and support biomimetic principles in enhancing performance and user experience.","rel_num_authors":2,"rel_authors":[{"author_name":"Lorena Isabel Velasquez","author_inst":"Johns Hopkins University"},{"author_name":"Jeremy Delaine Brown","author_inst":"Johns Hopkins University"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"MyGeneRisk Colon: A Web-Based Tool for Personalized Colorectal Cancer Risk Prediction Based on Genetics and Lifestyle","rel_doi":"10.64898\/2026.04.03.26349669","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26349669","rel_abs":"Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colon, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portals architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.","rel_num_authors":44,"rel_authors":[{"author_name":"Jiayin Zheng","author_inst":"Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania Perelman School of Medicine"},{"author_name":"Robert S Steinfelder","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Hang Yin","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Conghui Qu","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Minta Thomas","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Sushma S Thomas","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Cynthia Andrews","author_inst":"North Sound Accountable Communities of Health, Bellingham Washington, USA"},{"author_name":"Bianca Augusto","author_inst":"Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida, USA."},{"author_name":"Douglas C Corley","author_inst":"Division of Research, Kaiser Permanente Northern California, Oakland, California, USA."},{"author_name":"Jeffrey K Lee","author_inst":"Department of Gastroenterology, Kaiser Permanente San Francisco Medical Center, San Francisco, California, USA."},{"author_name":"Sonja I Berndt","author_inst":"Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA."},{"author_name":"Andrew T Chan","author_inst":"Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA."},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA."},{"author_name":"Chris Gignoux","author_inst":"Colorado Center for Personalized Medicine, University of Colorado - Anschutz Medical Campus, Aurora, Colorado, USA."},{"author_name":"Shauna R Goldberg","author_inst":"Kaiser Permanente Colorado Institute for Health Research, Aurora, Colorado, USA."},{"author_name":"Christopher A Haiman","author_inst":"Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, C"},{"author_name":"Jeroen R Huyghe","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Motoki Iwasaki","author_inst":"Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan."},{"author_name":"Loic Le Marchand","author_inst":"Population Sciences in the Pacific Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USA."},{"author_name":"Soo Chin Lee","author_inst":"National University Cancer Institute, Singapore, Singapore"},{"author_name":"Johana Melendez","author_inst":"Hillsborough College, Plant City, Fl. USA"},{"author_name":"Ivan Mesa","author_inst":"Hillsborough College, Tampa, FL, USA"},{"author_name":"Shuji Ogino","author_inst":"Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA."},{"author_name":"Viviam Sifontes","author_inst":"Moffitt Cancer Center, Tampa, FL, USA"},{"author_name":"Caroline Y Um","author_inst":"Department of Population Science, American Cancer Society, Atlanta, Georgia."},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA and Department of Oncology, Johns Hopkins Kimmel Cancer Ce"},{"author_name":"Larissa L White","author_inst":"Institute for Health Research, Kaiser Permanente Colorado, Aurora, Colorado, USA."},{"author_name":"Andrea Williams","author_inst":"Southside Coalition of Community Health Centers"},{"author_name":"Waverly Willis","author_inst":"Independent researcher"},{"author_name":"Alicja Wolk","author_inst":"Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden."},{"author_name":"Taiki Yamaji","author_inst":"Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan."},{"author_name":"Susan T Vadaparampil","author_inst":"Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida, USA."},{"author_name":"Gail P Jarvik","author_inst":"Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, Washington, USA."},{"author_name":"Andrea N Burnett-Hartman","author_inst":"Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado, USA."},{"author_name":"Roger L Milne","author_inst":"Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia."},{"author_name":"Elizabeth A Platz","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA."},{"author_name":"Jane C Figueiredo","author_inst":"Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA."},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nas"},{"author_name":"Robert J MacInnis","author_inst":"Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia."},{"author_name":"Julie R Palmer","author_inst":"Slone Epidemiology Center at Boston University, Boston Massachusetts"},{"author_name":"Stephanie L Schmit","author_inst":"Genomic Sciences and Systems Biology, Cleveland Clinic, Cleveland, Ohio, USA."},{"author_name":"Iris Landorp-Vogelaar","author_inst":"Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands."},{"author_name":"Ulrike Peters","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Li Hsu","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"MyGeneRisk Colon: A Web-Based Tool for Personalized Colorectal Cancer Risk Prediction Based on Genetics and Lifestyle","rel_doi":"10.64898\/2026.04.03.26349669","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26349669","rel_abs":"Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colon, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portals architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.","rel_num_authors":44,"rel_authors":[{"author_name":"Jiayin Zheng","author_inst":"Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania Perelman School of Medicine"},{"author_name":"Robert S Steinfelder","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Hang Yin","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Conghui Qu","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Minta Thomas","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Sushma S Thomas","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Cynthia Andrews","author_inst":"North Sound Accountable Communities of Health, Bellingham Washington, USA"},{"author_name":"Bianca Augusto","author_inst":"Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida, USA."},{"author_name":"Douglas C Corley","author_inst":"Division of Research, Kaiser Permanente Northern California, Oakland, California, USA."},{"author_name":"Jeffrey K Lee","author_inst":"Department of Gastroenterology, Kaiser Permanente San Francisco Medical Center, San Francisco, California, USA."},{"author_name":"Sonja I Berndt","author_inst":"Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA."},{"author_name":"Andrew T Chan","author_inst":"Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA."},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA."},{"author_name":"Chris Gignoux","author_inst":"Colorado Center for Personalized Medicine, University of Colorado - Anschutz Medical Campus, Aurora, Colorado, USA."},{"author_name":"Shauna R Goldberg","author_inst":"Kaiser Permanente Colorado Institute for Health Research, Aurora, Colorado, USA."},{"author_name":"Christopher A Haiman","author_inst":"Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, C"},{"author_name":"Jeroen R Huyghe","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Motoki Iwasaki","author_inst":"Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan."},{"author_name":"Loic Le Marchand","author_inst":"Population Sciences in the Pacific Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USA."},{"author_name":"Soo Chin Lee","author_inst":"National University Cancer Institute, Singapore, Singapore"},{"author_name":"Johana Melendez","author_inst":"Hillsborough College, Plant City, Fl. USA"},{"author_name":"Ivan Mesa","author_inst":"Hillsborough College, Tampa, FL, USA"},{"author_name":"Shuji Ogino","author_inst":"Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA."},{"author_name":"Viviam Sifontes","author_inst":"Moffitt Cancer Center, Tampa, FL, USA"},{"author_name":"Caroline Y Um","author_inst":"Department of Population Science, American Cancer Society, Atlanta, Georgia."},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA and Department of Oncology, Johns Hopkins Kimmel Cancer Ce"},{"author_name":"Larissa L White","author_inst":"Institute for Health Research, Kaiser Permanente Colorado, Aurora, Colorado, USA."},{"author_name":"Andrea Williams","author_inst":"Southside Coalition of Community Health Centers"},{"author_name":"Waverly Willis","author_inst":"Independent researcher"},{"author_name":"Alicja Wolk","author_inst":"Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden."},{"author_name":"Taiki Yamaji","author_inst":"Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan."},{"author_name":"Susan T Vadaparampil","author_inst":"Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida, USA."},{"author_name":"Gail P Jarvik","author_inst":"Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, Washington, USA."},{"author_name":"Andrea N Burnett-Hartman","author_inst":"Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado, USA."},{"author_name":"Roger L Milne","author_inst":"Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia."},{"author_name":"Elizabeth A Platz","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA."},{"author_name":"Jane C Figueiredo","author_inst":"Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA."},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nas"},{"author_name":"Robert J MacInnis","author_inst":"Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia."},{"author_name":"Julie R Palmer","author_inst":"Slone Epidemiology Center at Boston University, Boston Massachusetts"},{"author_name":"Stephanie L Schmit","author_inst":"Genomic Sciences and Systems Biology, Cleveland Clinic, Cleveland, Ohio, USA."},{"author_name":"Iris Landorp-Vogelaar","author_inst":"Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands."},{"author_name":"Ulrike Peters","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Li Hsu","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"MyGeneRisk Colon: A Web-Based Tool for Personalized Colorectal Cancer Risk Prediction Based on Genetics and Lifestyle","rel_doi":"10.64898\/2026.04.03.26349669","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26349669","rel_abs":"Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colon, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portals architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.","rel_num_authors":44,"rel_authors":[{"author_name":"Jiayin Zheng","author_inst":"Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania Perelman School of Medicine"},{"author_name":"Robert S Steinfelder","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Hang Yin","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Conghui Qu","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Minta Thomas","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Sushma S Thomas","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Cynthia Andrews","author_inst":"North Sound Accountable Communities of Health, Bellingham Washington, USA"},{"author_name":"Bianca Augusto","author_inst":"Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida, USA."},{"author_name":"Douglas C Corley","author_inst":"Division of Research, Kaiser Permanente Northern California, Oakland, California, USA."},{"author_name":"Jeffrey K Lee","author_inst":"Department of Gastroenterology, Kaiser Permanente San Francisco Medical Center, San Francisco, California, USA."},{"author_name":"Sonja I Berndt","author_inst":"Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA."},{"author_name":"Andrew T Chan","author_inst":"Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA."},{"author_name":"Stephen J Chanock","author_inst":"Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA."},{"author_name":"Chris Gignoux","author_inst":"Colorado Center for Personalized Medicine, University of Colorado - Anschutz Medical Campus, Aurora, Colorado, USA."},{"author_name":"Shauna R Goldberg","author_inst":"Kaiser Permanente Colorado Institute for Health Research, Aurora, Colorado, USA."},{"author_name":"Christopher A Haiman","author_inst":"Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, C"},{"author_name":"Jeroen R Huyghe","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Motoki Iwasaki","author_inst":"Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan."},{"author_name":"Loic Le Marchand","author_inst":"Population Sciences in the Pacific Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USA."},{"author_name":"Soo Chin Lee","author_inst":"National University Cancer Institute, Singapore, Singapore"},{"author_name":"Johana Melendez","author_inst":"Hillsborough College, Plant City, Fl. USA"},{"author_name":"Ivan Mesa","author_inst":"Hillsborough College, Tampa, FL, USA"},{"author_name":"Shuji Ogino","author_inst":"Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA."},{"author_name":"Viviam Sifontes","author_inst":"Moffitt Cancer Center, Tampa, FL, USA"},{"author_name":"Caroline Y Um","author_inst":"Department of Population Science, American Cancer Society, Atlanta, Georgia."},{"author_name":"Kala Visvanathan","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA and Department of Oncology, Johns Hopkins Kimmel Cancer Ce"},{"author_name":"Larissa L White","author_inst":"Institute for Health Research, Kaiser Permanente Colorado, Aurora, Colorado, USA."},{"author_name":"Andrea Williams","author_inst":"Southside Coalition of Community Health Centers"},{"author_name":"Waverly Willis","author_inst":"Independent researcher"},{"author_name":"Alicja Wolk","author_inst":"Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden."},{"author_name":"Taiki Yamaji","author_inst":"Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan."},{"author_name":"Susan T Vadaparampil","author_inst":"Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida, USA."},{"author_name":"Gail P Jarvik","author_inst":"Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, Washington, USA."},{"author_name":"Andrea N Burnett-Hartman","author_inst":"Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado, USA."},{"author_name":"Roger L Milne","author_inst":"Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia."},{"author_name":"Elizabeth A Platz","author_inst":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA."},{"author_name":"Jane C Figueiredo","author_inst":"Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA."},{"author_name":"Wei Zheng","author_inst":"Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nas"},{"author_name":"Robert J MacInnis","author_inst":"Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia."},{"author_name":"Julie R Palmer","author_inst":"Slone Epidemiology Center at Boston University, Boston Massachusetts"},{"author_name":"Stephanie L Schmit","author_inst":"Genomic Sciences and Systems Biology, Cleveland Clinic, Cleveland, Ohio, USA."},{"author_name":"Iris Landorp-Vogelaar","author_inst":"Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands."},{"author_name":"Ulrike Peters","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."},{"author_name":"Li Hsu","author_inst":"Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA."}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Sound of Aging: Large-Scale Evidence for a Voice-Based Biological Clock","rel_doi":"10.64898\/2026.04.05.26350190","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.05.26350190","rel_abs":"Using 30-second voice recordings from 7,081 adults aged 40-70, we trained gender-specific models to estimate voice-predicted age (Voice Age). Voice Age correlated with chronological age comparably to established omic and physiological aging clocks, while capturing an independent dimension of biological aging. Accelerated vocal aging showed association with higher adiposity, impaired sleep physiology, and cardiometabolic risk markers, supporting voice as a scalable, non-invasive functional aging biomarker.","rel_num_authors":6,"rel_authors":[{"author_name":"David Krongauz","author_inst":"Weizmann Institute of Science"},{"author_name":"Yanir Marmor","author_inst":"Weizmann Institute of Science"},{"author_name":"Arad Zulti","author_inst":"Weizmann Institute of Science"},{"author_name":"Anastasia Godneva","author_inst":"Weizmann Institute of Science"},{"author_name":"Adina Weinberger","author_inst":"Weizmann Institute of Science"},{"author_name":"Eran Segal","author_inst":"Weizmann Institute of Science"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Self-reported sleep problems are associated with impaired daily-life gait quality and increased fall risk in older people","rel_doi":"10.64898\/2026.03.30.26349800","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.03.30.26349800","rel_abs":"BackgroundSleep problems are common in older people and have been associated with increased fall risk, but the mechanisms underlying this relationship remain unclear. Gait quality reflects balance control and neurological function and may provide insight into pathways linking sleep health and falls.\n\nMethodsData from 758 community-dwelling older people ([&ge;]65 years; mean age 75.8 years, 69.3% women) were analysed. Sleep problems were assessed at baseline using a self-reported item (Patient Health Questionnaire-9, question 3). Daily-life gait quality and habitual walking speed were derived from one week of wearable sensor monitoring. Falls and injurious falls were prospectively recorded over 12 months. Associations between sleep problems, gait quality, and fall incidence were examined using regression models adjusted for demographic, pain and cognitive factors, and use of sleeping medication.\n\nResultsSleep problems were reported by 43.9% of participants. Sleep problems were not associated with habitual walking speed, but were associated with lower gait quality in daily life (adjusted {beta} = -0.15, 95% CI -0.27 to -0.03). Participants reporting sleep problems had higher incidence rates of total falls (adjusted IRR = 1.42, 95% CI 1.07 to 1.90) and injurious falls (adjusted IRR = 1.50, 95% CI 1.07 to 2.10).\n\nConclusionsSelf-reported sleep problems were associated with impaired real-world gait quality and substantially higher rates of falls and injurious falls in older people. These findings suggest that sleep problems may increase fall risk by altering balance control rather than by reducing walking speed. Sleep should be considered when managing fall risk, and fall risk should be considered in older people with sleep complaints.\n\nKEY POINTS BOXO_ST_ABSKey pointsC_ST_ABSO_LISleep problems in older people were not associated with habitual walking speed, but were associated with lower gait quality in daily life.\nC_LIO_LIPeople reporting sleep problems had 42% higher rates of falls, and 50% higher rates of injurious falls.\nC_LI\n\nWhy does this paper matter?This paper highlights that sleep problems are an important and under-recognised marker of fall risk among older people. It advocates for the need to consider sleep in fall risk management and fall risk in those presenting with sleep complaints.","rel_num_authors":6,"rel_authors":[{"author_name":"Kimberley S van Schooten","author_inst":"Neuroscience Research Australia, Randwick, New South Wales, Australia"},{"author_name":"Andrew Vakulin","author_inst":"Flinders Health and Medical Research Institute (FHMRI), Sleep Health, Flinders University, Adelaide, South Australia, Australia"},{"author_name":"Rajani Khanal","author_inst":"Flinders Health and Medical Research Institute (FHMRI), Sleep Health, Flinders University, Adelaide, South Australia, Australia"},{"author_name":"Kelly Sansom","author_inst":"Flinders Health and Medical Research Institute (FHMRI), Sleep Health, Flinders University, Adelaide, South Australia, Australia"},{"author_name":"James Bletsas","author_inst":"School of Biomedical Science, University of New South Wales, Kensington, New South Wales, Australia"},{"author_name":"Kim Delbaere","author_inst":"School of Health Sciences, University of New South Wales, Kensington, New South Wales, Australia"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Shared Genetic Architecture Between Kidney Function and Alzheimer Disease Across Ancestries","rel_doi":"10.64898\/2026.04.04.26350158","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.04.26350158","rel_abs":"Epidemiological studies have consistently shown that chronic kidney disease is associated with increased Alzheimer disease risk. However, the underlying genetic architecture connecting these two conditions remains largely unexplored beyond genome-wide correlation analyses. Here, we conducted the first comprehensive, multi-ancestry, large-scale genetic investigation to identify shared genetic components between kidney function and Alzheimer disease.\n\nWe leveraged large-scale genome-wide association study summary statistics for estimated glomerular filtration rate (N{approx}1.5 million European, N{approx}145,000 African ancestry) and late-onset Alzheimer disease (N=63,926 and N=398,058 in two European cohorts; N=9,168 in African ancestry) corrected for competing risk bias. We deployed a novel analytical framework integrating linkage disequilibrium score regression and polygenic risk score analysis, local analysis of [co]variant association, conjunctional false discovery rate analysis with Bayesian colocalization and fine-mapping, and bidirectional cis-Mendelian randomization to identify vertical pleiotropy.\n\nDespite the absence of genome-wide genetic correlation (rg {approx} 0, p > 0.1), local genetic analysis uncovered striking regional heterogeneity. Sixteen pleiotropic loci were identified in individuals of European ancestry (conjunctional false discovery rate < 0.05), including APOE, PICALM, SPI1, and EFTUD1, alongside 15 loci with significant local genetic correlations. Fine-mapping revealed that most pleiotropic loci harbored distinct causal variants for kidney function and Alzheimer disease, indicating horizontal pleiotropy. An APOE {varepsilon}4-defining allele (rs429358) was the sole variant with shared causality across both traits. We identified vertical pleiotropy using cis-Mendelian randomization at the PICALM and EFTUD1 loci, providing evidence that kidney function-related genetic variants can causally affect Alzheimer disease risk at specific genomic loci. In contrast, loci such as CD2AP, MAT1A, and SYMPK demonstrated horizontal pleiotropy, reflecting shared upstream biological pathways rather than direct causal mediation. Notably, APOE was the only pleiotropic locus shared between European and African ancestry groups, underscoring marked ancestry-specific genetic architectures with critical implications for risk prediction and therapeutic translation.\n\nAlzheimer disease and kidney function share genetic components at specific loci rather than genome-wide, with mixed directional effects and horizontal pleiotropy explaining the absent global correlation despite strong local signals. At a subset of loci, we identified directional effects linking kidney genetic determinants to Alzheimer disease risk using cis-Mendelian randomization, supporting a complex kidney-brain genetic axis. Most overlap reflects horizontal pleiotropy, with limited loci showing vertical pleiotropy. APOE was the only shared locus across ancestries, underscoring ancestry-specific architectures with implications for risk prediction. The multi-scale approach used here also provides a methodological framework for dissecting complex disease relationships missed by traditional genome-wide analyses.","rel_num_authors":14,"rel_authors":[{"author_name":"Diya Yang","author_inst":"Case Western Reserve University"},{"author_name":"Yihe Yang","author_inst":"Case Western Reserve University"},{"author_name":"Nicholas R. Ray","author_inst":"Columbia University"},{"author_name":"Mengxuan Li","author_inst":"Case Western Reserve University"},{"author_name":"Penelope Benchek","author_inst":"Case Western Reserve University"},{"author_name":"Dana C. Crawford","author_inst":"Case Western Reserve University"},{"author_name":"John F. O'Toole","author_inst":"Cleveland Clinic \/ Case Western Reserve University"},{"author_name":"John R. Sedor","author_inst":"Cleveland Clinic \/ Case Western Reserve University"},{"author_name":"Christiane Reitz","author_inst":"Columbia University"},{"author_name":"Audrey Lynn","author_inst":"Case Western Reserve University"},{"author_name":"Xiaofeng Zhu","author_inst":"Case Western Reserve University"},{"author_name":"Jonathan L. Haines","author_inst":"Case Western Reserve University"},{"author_name":"- Alzheimer's Disease Genetics Consortium (ADGC)","author_inst":""},{"author_name":"William S. Bush","author_inst":"Case Western Reserve University"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Shared Genetic Architecture Between Kidney Function and Alzheimer Disease Across Ancestries","rel_doi":"10.64898\/2026.04.04.26350158","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.04.26350158","rel_abs":"Epidemiological studies have consistently shown that chronic kidney disease is associated with increased Alzheimer disease risk. However, the underlying genetic architecture connecting these two conditions remains largely unexplored beyond genome-wide correlation analyses. Here, we conducted the first comprehensive, multi-ancestry, large-scale genetic investigation to identify shared genetic components between kidney function and Alzheimer disease.\n\nWe leveraged large-scale genome-wide association study summary statistics for estimated glomerular filtration rate (N{approx}1.5 million European, N{approx}145,000 African ancestry) and late-onset Alzheimer disease (N=63,926 and N=398,058 in two European cohorts; N=9,168 in African ancestry) corrected for competing risk bias. We deployed a novel analytical framework integrating linkage disequilibrium score regression and polygenic risk score analysis, local analysis of [co]variant association, conjunctional false discovery rate analysis with Bayesian colocalization and fine-mapping, and bidirectional cis-Mendelian randomization to identify vertical pleiotropy.\n\nDespite the absence of genome-wide genetic correlation (rg {approx} 0, p > 0.1), local genetic analysis uncovered striking regional heterogeneity. Sixteen pleiotropic loci were identified in individuals of European ancestry (conjunctional false discovery rate < 0.05), including APOE, PICALM, SPI1, and EFTUD1, alongside 15 loci with significant local genetic correlations. Fine-mapping revealed that most pleiotropic loci harbored distinct causal variants for kidney function and Alzheimer disease, indicating horizontal pleiotropy. An APOE {varepsilon}4-defining allele (rs429358) was the sole variant with shared causality across both traits. We identified vertical pleiotropy using cis-Mendelian randomization at the PICALM and EFTUD1 loci, providing evidence that kidney function-related genetic variants can causally affect Alzheimer disease risk at specific genomic loci. In contrast, loci such as CD2AP, MAT1A, and SYMPK demonstrated horizontal pleiotropy, reflecting shared upstream biological pathways rather than direct causal mediation. Notably, APOE was the only pleiotropic locus shared between European and African ancestry groups, underscoring marked ancestry-specific genetic architectures with critical implications for risk prediction and therapeutic translation.\n\nAlzheimer disease and kidney function share genetic components at specific loci rather than genome-wide, with mixed directional effects and horizontal pleiotropy explaining the absent global correlation despite strong local signals. At a subset of loci, we identified directional effects linking kidney genetic determinants to Alzheimer disease risk using cis-Mendelian randomization, supporting a complex kidney-brain genetic axis. Most overlap reflects horizontal pleiotropy, with limited loci showing vertical pleiotropy. APOE was the only shared locus across ancestries, underscoring ancestry-specific architectures with implications for risk prediction. The multi-scale approach used here also provides a methodological framework for dissecting complex disease relationships missed by traditional genome-wide analyses.","rel_num_authors":14,"rel_authors":[{"author_name":"Diya Yang","author_inst":"Case Western Reserve University"},{"author_name":"Yihe Yang","author_inst":"Case Western Reserve University"},{"author_name":"Nicholas R. Ray","author_inst":"Columbia University"},{"author_name":"Mengxuan Li","author_inst":"Case Western Reserve University"},{"author_name":"Penelope Benchek","author_inst":"Case Western Reserve University"},{"author_name":"Dana C. Crawford","author_inst":"Case Western Reserve University"},{"author_name":"John F. O'Toole","author_inst":"Cleveland Clinic \/ Case Western Reserve University"},{"author_name":"John R. Sedor","author_inst":"Cleveland Clinic \/ Case Western Reserve University"},{"author_name":"Christiane Reitz","author_inst":"Columbia University"},{"author_name":"Audrey Lynn","author_inst":"Case Western Reserve University"},{"author_name":"Xiaofeng Zhu","author_inst":"Case Western Reserve University"},{"author_name":"Jonathan L. Haines","author_inst":"Case Western Reserve University"},{"author_name":"- Alzheimer's Disease Genetics Consortium (ADGC)","author_inst":""},{"author_name":"William S. Bush","author_inst":"Case Western Reserve University"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Clinical and pathological characteristics of thin cutaneous melanomas with rapid recurrence.","rel_doi":"10.64898\/2026.04.04.26350182","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.04.26350182","rel_abs":"BackgroundAlthough thin, T1 melanomas have an excellent cure rate with surgery alone, >25% of melanoma deaths originate from thin melanomas (TMs). There is, therefore, an urgent need to improve the identification and management of patients with TMs at high risk of recurrence.\n\nMethodsPatients with T1 melanoma and recurrence [&le;] 2 years of diagnosis (T1 rapid group) were compared to patients with T1 melanoma and recurrence [&ge;]10 years after diagnosis (T1 late group).\n\nResults442 patients from 14 sites were included: 310 and 132 patients in the T1 rapid and late groups, respectively. Median age at primary melanoma diagnosis was 51 years [15-85], 272 (62%) male, 254 (58%) superficial spreading and 101 (23%) head\/neck primary. The majority (73%) of recurrences in the T1 rapid group were locoregional. Using univariable logistic regression analysis, age >65 years (p<0.0001), lentigo maligna (LM) melanoma subtype (p=0.025), head\/neck primary site (p=0.0065), mitoses [&ge;]1\/mm2 (p=0.0181) and ulceration (p=0.0087) were significantly associated with T1 rapid recurrence compared to T1 late recurrence. Using multivariable analysis, age >65 years (p=0.0010), mitoses [&ge;]1\/mm2 (p=0.049) and ulceration (p=0.037) remained significant.\n\nConclusionsRapid recurrence of TM is associated with age >65 years, LM subtype, head\/neck primary site, mitoses [&ge;]1\/mm2 and ulceration.","rel_num_authors":19,"rel_authors":[{"author_name":"Prachi Bhave","author_inst":"Sir Peter MacCallum Cancer Centre Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia Department of Bioinformatics, Walter and E"},{"author_name":"Terence Wong","author_inst":"Melanoma Institute Australia, The University of Sydney, New South Wales, Australia"},{"author_name":"Kim Margolin","author_inst":"St Johns Cancer Institute, California, USA"},{"author_name":"Lotte Hoeijmakers","author_inst":"Department of Medical Oncology, Netherlands Cancer Institute, Netherlands"},{"author_name":"Johanna Mangana","author_inst":"Department of Dermatology, University Hospital Zurich, Zurich, Switzerland Faculty of Medicine, University of Zurich, Zurich, Switzerland"},{"author_name":"Maria Grazia Vitale","author_inst":"Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori IRCCS Fondazione G.Pascale, Napoli, Italy"},{"author_name":"Paolo A Ascierto","author_inst":"Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori IRCCS Fondazione G.Pascale, Napoli, Italy Universita di Napoli Federico I"},{"author_name":"Andrea Maurichi","author_inst":"Department of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy"},{"author_name":"Mario Santinami","author_inst":"Department of Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy"},{"author_name":"Georgina Heddle","author_inst":"Department of Dermatology, Queen Elizabeth Hospital, Adelaide, South Australia, Australia"},{"author_name":"Clara Allayous","author_inst":"AP-HP Department of Dermatology, Hopital Saint-Louis, Paris, France"},{"author_name":"Celeste Lebbe","author_inst":"Universite Paris Cite, AP-HP Dermato-oncology and CIC, Cancer institute APHP, nord Paris cite, Saint Louis Hospital, Paris, France"},{"author_name":"Adnan Kattak","author_inst":"Department of Medical Oncology, Fiona Stanley Hospital & Edith Cowan University, Perth, Western Australia, Australia"},{"author_name":"Stephan Forchhammer","author_inst":"Department of Dermatology, Eberhard Karls University, Tuebingen, Germany"},{"author_name":"Jolien I Kessels","author_inst":"Department of Dermatology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, University Hospital Brussels, Brussels, Belgium"},{"author_name":"Peter Lau","author_inst":"Sir Charles Gairdener Hospital, University of Western Australia, Perth, Western Australia, Australia"},{"author_name":"Serigne N Lo","author_inst":"Melanoma Institute Australia, The University of Sydney, New South Wales, Australia Faculty of Medicine and Health, The University of Sydney, Sydney, New South W"},{"author_name":"Anthony A Papenfuss","author_inst":"Department of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia"},{"author_name":"Grant A McArthur","author_inst":"Sir Peter MacCallum Cancer Centre Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Cell-free chromatin epigenomic profiling enables non-invasive pancreatic cancer cell-state identification","rel_doi":"10.64898\/2026.04.02.26349987","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.02.26349987","rel_abs":"Classical and basal-like transcriptional subtypes of pancreatic ductal adenocarcinoma (PDAC) are prognostic and may predict response to different chemotherapy regimens and RAS inhibitors. Current subtyping methods rely on tissue biopsies and remain challenging to integrate into clinical workflows. Herein, we present a novel approach for non-invasive subtyping of PDAC based on epigenomic profiling of circulating tumor DNA (ctDNA). In a multi-omics cohort of patient-derived xenografts, we identify highly recurrent regulatory elements associated with classical and basal-like PDAC. We then demonstrate that these epigenomic signatures can identify PDAC subtype from plasma epigenomic profiling in a multi-institutional cohort of patients with metastatic PDAC and integrate information from circulating histone modifications and DNA methylation to develop the Pancreatic Integrated Epigenomic Score (PIES). PIES is concordant with tissue-based labels and captures transcriptional subtype heterogeneity observed within biopsies. Furthermore, it improves prognostication over tissue-based subtyping suggestive of the recovery of ground truth tumor biology from plasma ctDNA. Our work provides a proof-of-concept for a circulating biomarker that enables transcriptional subtyping and informs therapeutic decisions in pancreatic cancer.\n\nSignificanceTranscriptional subtyping of pancreatic cancer can improve prognostication and inform treatment selection. Current subtyping approaches rely on tissue biopsy, are challenging to implement in clinical practice, and are limited by tumor heterogeneity and sampling error. Herein, we introduce a cell-free DNA-based epigenomic assay capable of inferring pancreatic cancer subtypes noninvasively.","rel_num_authors":43,"rel_authors":[{"author_name":"Karl Semaan","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Marc Eid","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Damien Vasseur","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Gunsagar S. Gulati","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Cibelle Lima","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Elio Ibrahim","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Ji-Heui Seo","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"John J. Canniff","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Hunter Savignano","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Alexander Jordan","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Leigh Culane","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Noa Philips","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Rashad Nawfal","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Aislyn Schalck","author_inst":"MD Anderson Cancer Center"},{"author_name":"Andressa Dias Costa","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Elizabeth A. Andrews","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Emma C. Coleman","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Talal El Zarif","author_inst":"Yale School of Medicine"},{"author_name":"Garyoung Gary Lee","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Razane El Hajj Chehade","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Ze Zhang","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Gaelle Nafeh","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Wassim Daoud Khatoun","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"James Brady","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Zhenjie Jin","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Paulo Da Silva Cordeiro","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Brad Fortunato","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"David Peng","author_inst":"MD Anderson Cancer Center"},{"author_name":"Christopher Vellano","author_inst":"MD Anderson Cancer Center"},{"author_name":"Tim Heffernan","author_inst":"MD Anderson Cancer Center"},{"author_name":"Antoine Hollebecque","author_inst":"Department of Medical Oncology, Gustave Roussy, Villejuif, France"},{"author_name":"Antoine Italiano","author_inst":"Department of Medical Oncology, Gustave Roussy, Villejuif, France"},{"author_name":"Brandon M. Huffman","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"James M. Cleary","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Jacob E. Berchuck","author_inst":"Winship Cancer Institute of Emory University"},{"author_name":"Toni K. Choueiri","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Kimberly Perez","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Jonathan Nowak","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Andrew J. Aguirre","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Brian M. Wolpin","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Sylvan C Baca","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Matthew L. Freedman","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Harshabad Singh","author_inst":"Dana-Farber Cancer Institute"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Backfill Bayesian Ordered Lattice Design for Phase I Clinical Trials","rel_doi":"10.64898\/2026.04.02.26350086","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.02.26350086","rel_abs":"The Bayesian Ordered Lattice Design (BOLD) method for Phase I clinical trials is extended to address an important challenge. It is widely understood that conventional Phase I trial designs are not consistently effective in determining safe and active dose levels. The US FDA launched the Project Optimus, aimed at reforming the paradigms of dose optimization and selection. We propose a backfill BOLD design (BF-BOLD) that centers on BOLD for dose-finding but also adds an activity evaluation for each patient. Our method for determining the optimal biological dose (OBD) first involves identifying the maximum tolerated dose (MTD) and then assessing activity rates among dose levels below the identified MTD. This approach is straight-forward and does not require complex statistical modeling. The results of the simulation indicate that performing dose-finding trials with backfilling can both enhance safety and activity assessment, thereby improving treatment sustainability while also preserving the potential for efficacy of the Recommended Phase II Dose (RP2D). We also demonstrate the applicability of the backfill design for reducing overdose rates, and as a more attractive alternative to small-scale dose expansion trials that follow dose escalation. Backfill designs are an important design approach for early phase trials.","rel_num_authors":2,"rel_authors":[{"author_name":"GI-MING WANG","author_inst":"Case Comprehensive Cancer Center"},{"author_name":"Curtis Tatsuoka","author_inst":"University of Maryland Baltimore"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Racial and Ethnic Differences in Cesarean Delivery Across Insurance Types, United States, 2014-2024","rel_doi":"10.64898\/2026.04.04.26350151","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.04.26350151","rel_abs":"IntroductionCesarean delivery accounts for nearly one-third of U.S. births and is associated with substantial maternal morbidity and health care costs. Persistent racial disparities have been documented, yet the structural factors contributing to these differences remain incompletely understood. The extent to which insurance coverage shapes racial disparities in cesarean delivery remains unclear.\n\nObjectiveTo evaluate the independent and interactive associations of race\/ethnicity and insurance coverage with cesarean delivery in the United States.\n\nMethodsPopulation-based retrospective cohort study using singleton live births recorded in the United States Vital Statistics Natality files from 2014 to 2024. Multivariable logistic regression was used to estimate the independent effects of race\/ethnicity and insurance status on cesarean delivery, including interaction terms to test effect modification, using national birth certificate data. Models were adjusted for maternal demographics, clinical factors, and temporal covariates. Adjusted odds ratios, predicted probabilities, and absolute risk differences were derived from post-estimation marginal effects. The main outcome measure was cesarean delivery (yes vs no).\n\nResultsAmong 41,543,568 deliveries from 2014 to 2024, 13,312,221 (32.0%) were cesarean deliveries. After adjustment, both race and ethnicity and insurance status were independently associated with cesarean delivery. Compared with non-Hispanic White women, non-Hispanic Black women had higher odds of cesarean delivery (odds ratio [OR], 1.22; 95% CI, 1.22-1.23). Relative to uninsured women, those with private insurance had 59% higher odds of cesarean delivery (OR, 1.59; 95% CI, 1.58-1.60). Significant interaction effects were observed, indicating that insurance coverage modified racial and ethnic differences in cesarean delivery. Non-Hispanic Black women had the highest predicted probabilities across all insurance categories, with the largest absolute disparities observed among uninsured women.\n\nConclusionRacial and ethnic differences in cesarean delivery persist in the United States and are modified by insurance coverage, suggesting that coverage-related differences may contribute to inequities in obstetric care.","rel_num_authors":11,"rel_authors":[{"author_name":"Oluwasegun Akinyemi","author_inst":"Howard University College of Medicine"},{"author_name":"Mojisola Fasokun","author_inst":"University of Alabama at Birmingham"},{"author_name":"Delia Singleton","author_inst":"Howard University College of Medicine"},{"author_name":"Fadeke Ogunyankin","author_inst":"Cook Children's Health Care System: Cook Children's Medical Center"},{"author_name":"Samar Khalil","author_inst":"Howard University College of Medicine"},{"author_name":"Kaelyn Gordon","author_inst":"Howard University College of Medicine"},{"author_name":"Miriam Michael","author_inst":"Howard University College of Medicine"},{"author_name":"Kakra Hughes","author_inst":"Howard University College of Medicine"},{"author_name":"Guoyang Luo","author_inst":"Inova Health System: Inova"},{"author_name":"Shari Lawson","author_inst":"Howard University College of Medicine"},{"author_name":"Eke Ahizechukwu","author_inst":"Johns Hopkins School of Medicine: The Johns Hopkins University School of Medicine"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Obstructive Sleep Apnea is Associated with Peri-Lead Edema Following Deep Brain Stimulation for Parkinson's Disease","rel_doi":"10.64898\/2026.04.05.26350193","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.05.26350193","rel_abs":"BackgroundPeri-lead edema (PLE) occurs in up to 15% of Deep Brain Stimulation (DBS) cases, can cause morbidity, and its etiology remains unknown. We hypothesized that PLE represents a secondary brain injury modulated by hypoxemia, and that patients with obstructive sleep apnea (OSA) are at elevated risk.\n\nMethodsWe conducted a retrospective case-control study of 121 Parkinsons disease (PD) patients undergoing DBS at a single center (2019-2024). PLE severity was quantified by CT volumetric segmentation and Hounsfield unit (HU) measures. Perioperative SpO2 and PaO2 were recorded. Polysomnography (PSG) was available in 26 patients; and the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) was administered retrospectively.\n\nResultsSymptomatic PLE occurred in 12 patients (9.9%), with onset at 3.5 (2-9) days postoperatively. PLE patients had higher body mass index (p = 0.022) and higher OSA prevalence (75% vs. 30%; p = 0.002). Perioperative SpO2 was lower in the PLE group in both the operating room and post-anesthesia care unit (PACU; p < 0.05); PaO2 was lower in the PACU (p = 0.037). In the PSG subgroup, REM Sleep Behavior Disorder (RBD) incidence was lower in PLE patients (20% vs. 60%; unadjusted p = 0.048), and PLE severity correlated significantly with sleep-related hypoxemia and respiratory indices. RBDSQ scores were positively associated with edema density (normalized HU: rho = 0.86, p = 0.024).\n\nConclusionsOSA and perioperative hypoxemia are associated with symptomatic PLE following DBS, while RBD appears protective. Preoperative sleep evaluation and optimized perioperative airway management warrant prospective investigation as PLE prevention strategies.","rel_num_authors":12,"rel_authors":[{"author_name":"Evgeniya Kornilov","author_inst":"Weizmann Institute of Science"},{"author_name":"Uri Alkan","author_inst":"Department of Otorhinolaryngology, Head and Neck Surgery, Rabin Medical Center, Petach-Tikva, Israel"},{"author_name":"Ella Harari","author_inst":"Department of Neurosurgery, Rabin Medical center, Petach Tikva, Israel"},{"author_name":"Karam Azem","author_inst":"Department of Anesthesiology, Beilinson Hospital, Rabin Medical Centre"},{"author_name":"Shlomo Fireman","author_inst":"Department of Anesthesiology, Beilinson Hospital, Rabin Medical Centre"},{"author_name":"Eilat Kahana","author_inst":"Department of Anesthesiology, Beilinson Hospital, Rabin Medical Centre"},{"author_name":"Johnathan Reiner","author_inst":"Department of Neurology, Rabin Medical Center, Petach-Tikva, Israel."},{"author_name":"Eilat Sapirstein","author_inst":"Department of Neurosurgery, Rabin Medical center, Petach Tikva, Israel."},{"author_name":"Gal Sela","author_inst":"Department of Neurology, Rabin Medical Center, Petach-Tikva, Israel."},{"author_name":"Amir Glik","author_inst":"Department of Neurology, Rabin Medical Center, Petach-Tikva, Israel."},{"author_name":"Shai Fein","author_inst":"Department of Anesthesiology, Beilinson Hospital, Rabin Medical Centre"},{"author_name":"Idit Tamir","author_inst":"Department of Neurosurgery, Rabin Medical center, Petach Tikva, Israel."}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Hierarchical Barycentric Multimodal Representation Learning for Medical Image Analysis","rel_doi":"10.64898\/2026.04.05.26350202","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.05.26350202","rel_abs":"Multimodal medical image analysis exploits complementary information from multiple data sources (e.g., multi-contrast Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DTI), and Positron Emission Tomography (PET)) to enhance diagnostic accuracy and support clinical decision-making. Central to this process is the learning of robust representations that capture both modality-invariant and modality-specific features, which can then be leveraged for downstream tasks such as MRI segmentation and normative modeling of population-level variation and individual deviations. However, learning robust and generalizable representations becomes particularly challenging in the presence of missing modalities and heterogeneous data distributions. Most existing methods address this challenge primarily from a statistical perspective, yet they lack a theoretical understanding of the underlying geometric behavior--such as how probability mass is allocated across modalities. In this paper, we introduce a generalized geometric perspective for multimodal representation learning grounded in the concept of barycenters, which unifies a broad class of existing methods under a common theoretical perspective. Building on this barycentric formulation, we propose a novel approach that leverages generalized Wasserstein barycenters with hierarchical modality-specific priors to better preserve the geometry of unimodal distributions and enhance representation quality. We evaluated our framework on two key multimodal tasks--brain tumor MRI segmentation and normative modeling--demonstrating consistent improvements over a variety of multimodal approaches. Our results highlight the potential of scalable, theoretically grounded approaches to advance robust and generalizable representation learning in medical imaging applications.","rel_num_authors":6,"rel_authors":[{"author_name":"Peijie Qiu","author_inst":"Mallinckrodt Institute of Radiology,  Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Zhaoqi An","author_inst":"Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA"},{"author_name":"Sungmin Ha","author_inst":"Mallinckrodt Institute of Radiology,  Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Sayantan Kumar","author_inst":"National Library or Medicine, National Institutes on Health"},{"author_name":"Xiaobing Yu","author_inst":"Mallinckrodt Institute of Radiology,  Washington University School of Medicine, St. Louis, MO, USA"},{"author_name":"Aristeidis Sotiras","author_inst":"Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk Modeling","rel_doi":"10.64898\/2026.04.05.26350197","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.05.26350197","rel_abs":"Type 2 diabetes case reports describe complex clinical courses, but their timelines are often expressed in language that is difficult to reuse in longitudinal modeling. To address this gap, we developed a textual time-series corpus of 136 PubMed Open Access single-patient case reports involving glucagon-like peptide 1 receptor agonists, with clinical events associated with their most probable reference times. We evaluated automated LLM timeline extraction against gold-standard timelines annotated by clinical domain experts, assessing how well systems recovered clinical events and their timings. The best-performing LLM produced high event coverage (GPT5; 0.871) and reliable temporal sequencing across symptoms (GPT5; 0.843), diagnoses, treatments, laboratory tests, and outcomes. As a downstream demonstration, time-to-event analyses in diabetes suggested lower risk of respiratory sequelae among GLP-1 users versus non-users (HR=0.259, p!0.05), consistent with prior reports of improved respiratory outcomes. Temporal annotations and code will be released upon acceptance.","rel_num_authors":2,"rel_authors":[{"author_name":"Sayantan Kumar","author_inst":"National Library of Medicine, National Institutes on Health"},{"author_name":"Jeremy Weiss","author_inst":"National Library of Medicine, National Institutes on Health"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Network and receptor architectures shape brain morphometry in addiction","rel_doi":"10.64898\/2026.04.03.26348150","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26348150","rel_abs":"Substance use disorders (SUD) are chronic conditions with devastating effects on brain health, functioning, and survival. In this study, we compared brain morphometry of 2,782 individuals with SUD to 1,951 controls and assessed the topographic overlap of these differences with brain connectivity and receptor architecture. Across SUD, we identified a morphometric signature involving frontal, parietal, temporal and limbic systems that overlapped with cortical hub regions and harbored cortical and subcortical disease epicenters. Findings were highly consistent across six substances and numerous robustness and generalizability analyses. Transdiagnostic comparisons showed high spatial overlap of SUD epicenters with those of schizophrenia and bipolar disorder, suggesting shared network-constrained cortical differences. Finally, multivariate mapping revealed that SUD brain differences aligned with two neurotransmitter axes contrasting cannabinoid-opioid and dopaminergic systems. These findings indicate that addiction-related brain differences are shaped by connectome and neurotransmitter architecture, positioning brain network and neurochemical organization as key principles of SUD-related brain alterations.","rel_num_authors":75,"rel_authors":[{"author_name":"Foivos Georgiadis","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Beatrice A. Milano","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; Institute of Life Sciences, Sant'Anna School of Adv"},{"author_name":"Sara Lariviere","author_inst":"Department of Medical Imaging and Radiation Sciences, Universite de Sherbrooke, Sherbrooke, QC, Canada"},{"author_name":"Kent E. Hutchinson","author_inst":"Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Vince Calhoun","author_inst":"Department of Psychology, School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA; Department of Psychiatry and Behavioral Sciences, Emory Unive"},{"author_name":"Chiang-Shan Ray Li","author_inst":"Department of Psychiatry and Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA"},{"author_name":"Reza Momenan","author_inst":"Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health (NIH), Bethesda, MD, USA"},{"author_name":"Rajita Sinha","author_inst":"Department of Psychiatry, Yale University School of Medicine, Yale Interdisciplinary Stress Center, New Haven, CT, USA"},{"author_name":"Dick Veltman","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Ruth van Holst","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Anneke Goudriaan","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Maartje Luijten","author_inst":"Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands"},{"author_name":"Martine Groefsema","author_inst":"Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands"},{"author_name":"Henrik Walter","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Tristram Lett","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Reinout Wiers","author_inst":"Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Lianne Schmaal","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Julianne Flanagan","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Bernice Porjesz","author_inst":"Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY, USA"},{"author_name":"Jonathan Ipser","author_inst":"Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa"},{"author_name":"Justin Boehmer","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Nicola Canessa","author_inst":"IUSS Cognitive Neuroscience (ICoN) Center, Scuola Universitaria Superiore IUSS, 27100, Pavia, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuro"},{"author_name":"Ramiro Salas","author_inst":"Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Edythe London","author_inst":"Department of Psychiatry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA"},{"author_name":"Martin Paulus","author_inst":"Laureate Institute for Brain Research, Tulsa, OK, USA"},{"author_name":"Dan Stein","author_inst":"Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa"},{"author_name":"Samantha Brooks","author_inst":"School of Psychology, Faculty of Health, Liverpool John Moores University, UK"},{"author_name":"Liesbeth Reneman","author_inst":"Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Anouk Schrantee","author_inst":"Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Francesca Filbey","author_inst":"Department of Psychology, University of Texas at Dallas, Richardson, TX, USA"},{"author_name":"Rob Hester","author_inst":"Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Murat Yucel","author_inst":"Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia"},{"author_name":"Valentina Lorenzetti","author_inst":"Healthy Brain and Mind Research Centre, Neuroscience of Addiction and Mental Health Program, Australian Catholic University, Fitzroy, VIC, Australia; Clinical P"},{"author_name":"Nadia Solowij","author_inst":"Faculty of the Arts, Social Sciences and Humanities, School of Psychology, Wollongong, Australia, University of Wollongong, Wollongong, NSW, Australia"},{"author_name":"Rocio Martin-Santos","author_inst":"Department of Psychiatry and Psychology, Hospital Clinic, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Centro de Investigacion en Red de "},{"author_name":"Albert Batalla","author_inst":"Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands"},{"author_name":"Janna Cousijn","author_inst":"Neuroscience of Addiction Lab, Center for Substance Use and Addiction Research, Department of Psychology, Education and Child Studies, Erasmus School of Social "},{"author_name":"Edith Pomarol-Clotet","author_inst":"FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain"},{"author_name":"Eduardo A. Garza-Villarreal","author_inst":"Instituto de Neurobiologia, Universidad Nacional Autonoma de Mexico (UNAM) campus Juriquilla, Queretaro, Mexico"},{"author_name":"Marco Leyton","author_inst":"Department of Psychiatry, McGill University, Montreal Neurological Institute, Montreal, QC, Canada"},{"author_name":"Elliot Stein","author_inst":"Neuroimaging Research Branch, National Institute on Drug Abuse (NIDA), National Institutes of Health, Baltimore, MD, USA"},{"author_name":"Cleo L. Crunelle","author_inst":"Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Department of Psychiatry, Brussels, Belgium"},{"author_name":"Anne M. Kaag","author_inst":"Department of Clinical Neuropsychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Antonio Verdejo-Garcia","author_inst":"Department of Clinical Psychology, School of Psychology, Monash University, Melbourne, VIC, Australia"},{"author_name":"John J. Foxe","author_inst":"Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA"},{"author_name":"Kathleen T. Brady","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Aimee McRae-Clark","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Alain Dagher","author_inst":"Department of Psychiatry, McGill University, Montreal Neurological Institute, Montreal, QC, Canada"},{"author_name":"Amelie Haugg","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marc Walter","author_inst":"Clinic of Psychiatry and Psychotherapy, Psychiatric Services Aargau, Windisch, Switzerland"},{"author_name":"Andre Schmidt","author_inst":"Department of Psychiatry (UPK), University of Basel, Basel, Switzerland"},{"author_name":"Anne Lingford-Hughes","author_inst":"Division of Psychiatry, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK"},{"author_name":"Louise M. Paterson","author_inst":"Division of Psychiatry, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK"},{"author_name":"Angelica M. Morales","author_inst":"Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States"},{"author_name":"Dara G. Ghahremani","author_inst":"Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA"},{"author_name":"Chuan Fan","author_inst":"First Affiliated Hospital of Anhui Medical University, Hefei, China"},{"author_name":"Etna J. E. Engeli","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marcus Herdener","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Boris B. Quednow","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland; Neuroscience Center"},{"author_name":"Erich Seifritz","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland; Neuroscience Center"},{"author_name":"Philipp Homan","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marco De Pieri","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland"},{"author_name":"Silke Bachmann","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; University Hospital for Psychiatry, Psychotherapy a"},{"author_name":"Daniele Zullino","author_inst":"Division of Addiction Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Swit"},{"author_name":"Justine Y. Hansen","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Bratislav Misic","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Sophia I. Thomopoulos","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Devarshi Pancholi","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Anthony Juliano","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Hugh Garavan","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Sofie L. Valk","author_inst":"Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Julich, 52425 Julich, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, 0"},{"author_name":"Boris C. Bernhardt","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Matthias Kirschner","author_inst":"Dept of Adult Psychiatry and Psychotherapy, Univ. Hospital of Psychiatry Zurich (PUK), Univ. of Zurich, Zurich, Switzerland; Dept of Adult Psychiatry, Univ. Hos"},{"author_name":"- ENIGMA Addiction Working Group","author_inst":""}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Network and receptor architectures shape brain morphometry in addiction","rel_doi":"10.64898\/2026.04.03.26348150","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26348150","rel_abs":"Substance use disorders (SUD) are chronic conditions with devastating effects on brain health, functioning, and survival. In this study, we compared brain morphometry of 2,782 individuals with SUD to 1,951 controls and assessed the topographic overlap of these differences with brain connectivity and receptor architecture. Across SUD, we identified a morphometric signature involving frontal, parietal, temporal and limbic systems that overlapped with cortical hub regions and harbored cortical and subcortical disease epicenters. Findings were highly consistent across six substances and numerous robustness and generalizability analyses. Transdiagnostic comparisons showed high spatial overlap of SUD epicenters with those of schizophrenia and bipolar disorder, suggesting shared network-constrained cortical differences. Finally, multivariate mapping revealed that SUD brain differences aligned with two neurotransmitter axes contrasting cannabinoid-opioid and dopaminergic systems. These findings indicate that addiction-related brain differences are shaped by connectome and neurotransmitter architecture, positioning brain network and neurochemical organization as key principles of SUD-related brain alterations.","rel_num_authors":75,"rel_authors":[{"author_name":"Foivos Georgiadis","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Beatrice A. Milano","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; Institute of Life Sciences, Sant'Anna School of Adv"},{"author_name":"Sara Lariviere","author_inst":"Department of Medical Imaging and Radiation Sciences, Universite de Sherbrooke, Sherbrooke, QC, Canada"},{"author_name":"Kent E. Hutchinson","author_inst":"Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Vince Calhoun","author_inst":"Department of Psychology, School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA; Department of Psychiatry and Behavioral Sciences, Emory Unive"},{"author_name":"Chiang-Shan Ray Li","author_inst":"Department of Psychiatry and Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA"},{"author_name":"Reza Momenan","author_inst":"Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health (NIH), Bethesda, MD, USA"},{"author_name":"Rajita Sinha","author_inst":"Department of Psychiatry, Yale University School of Medicine, Yale Interdisciplinary Stress Center, New Haven, CT, USA"},{"author_name":"Dick Veltman","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Ruth van Holst","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Anneke Goudriaan","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Maartje Luijten","author_inst":"Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands"},{"author_name":"Martine Groefsema","author_inst":"Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands"},{"author_name":"Henrik Walter","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Tristram Lett","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Reinout Wiers","author_inst":"Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Lianne Schmaal","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Julianne Flanagan","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Bernice Porjesz","author_inst":"Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY, USA"},{"author_name":"Jonathan Ipser","author_inst":"Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa"},{"author_name":"Justin Boehmer","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Nicola Canessa","author_inst":"IUSS Cognitive Neuroscience (ICoN) Center, Scuola Universitaria Superiore IUSS, 27100, Pavia, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuro"},{"author_name":"Ramiro Salas","author_inst":"Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Edythe London","author_inst":"Department of Psychiatry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA"},{"author_name":"Martin Paulus","author_inst":"Laureate Institute for Brain Research, Tulsa, OK, USA"},{"author_name":"Dan Stein","author_inst":"Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa"},{"author_name":"Samantha Brooks","author_inst":"School of Psychology, Faculty of Health, Liverpool John Moores University, UK"},{"author_name":"Liesbeth Reneman","author_inst":"Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Anouk Schrantee","author_inst":"Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Francesca Filbey","author_inst":"Department of Psychology, University of Texas at Dallas, Richardson, TX, USA"},{"author_name":"Rob Hester","author_inst":"Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Murat Yucel","author_inst":"Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia"},{"author_name":"Valentina Lorenzetti","author_inst":"Healthy Brain and Mind Research Centre, Neuroscience of Addiction and Mental Health Program, Australian Catholic University, Fitzroy, VIC, Australia; Clinical P"},{"author_name":"Nadia Solowij","author_inst":"Faculty of the Arts, Social Sciences and Humanities, School of Psychology, Wollongong, Australia, University of Wollongong, Wollongong, NSW, Australia"},{"author_name":"Rocio Martin-Santos","author_inst":"Department of Psychiatry and Psychology, Hospital Clinic, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Centro de Investigacion en Red de "},{"author_name":"Albert Batalla","author_inst":"Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands"},{"author_name":"Janna Cousijn","author_inst":"Neuroscience of Addiction Lab, Center for Substance Use and Addiction Research, Department of Psychology, Education and Child Studies, Erasmus School of Social "},{"author_name":"Edith Pomarol-Clotet","author_inst":"FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain"},{"author_name":"Eduardo A. Garza-Villarreal","author_inst":"Instituto de Neurobiologia, Universidad Nacional Autonoma de Mexico (UNAM) campus Juriquilla, Queretaro, Mexico"},{"author_name":"Marco Leyton","author_inst":"Department of Psychiatry, McGill University, Montreal Neurological Institute, Montreal, QC, Canada"},{"author_name":"Elliot Stein","author_inst":"Neuroimaging Research Branch, National Institute on Drug Abuse (NIDA), National Institutes of Health, Baltimore, MD, USA"},{"author_name":"Cleo L. Crunelle","author_inst":"Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Department of Psychiatry, Brussels, Belgium"},{"author_name":"Anne M. Kaag","author_inst":"Department of Clinical Neuropsychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Antonio Verdejo-Garcia","author_inst":"Department of Clinical Psychology, School of Psychology, Monash University, Melbourne, VIC, Australia"},{"author_name":"John J. Foxe","author_inst":"Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA"},{"author_name":"Kathleen T. Brady","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Aimee McRae-Clark","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Alain Dagher","author_inst":"Department of Psychiatry, McGill University, Montreal Neurological Institute, Montreal, QC, Canada"},{"author_name":"Amelie Haugg","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marc Walter","author_inst":"Clinic of Psychiatry and Psychotherapy, Psychiatric Services Aargau, Windisch, Switzerland"},{"author_name":"Andre Schmidt","author_inst":"Department of Psychiatry (UPK), University of Basel, Basel, Switzerland"},{"author_name":"Anne Lingford-Hughes","author_inst":"Division of Psychiatry, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK"},{"author_name":"Louise M. Paterson","author_inst":"Division of Psychiatry, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK"},{"author_name":"Angelica M. Morales","author_inst":"Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States"},{"author_name":"Dara G. Ghahremani","author_inst":"Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA"},{"author_name":"Chuan Fan","author_inst":"First Affiliated Hospital of Anhui Medical University, Hefei, China"},{"author_name":"Etna J. E. Engeli","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marcus Herdener","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Boris B. Quednow","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland; Neuroscience Center"},{"author_name":"Erich Seifritz","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland; Neuroscience Center"},{"author_name":"Philipp Homan","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marco De Pieri","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland"},{"author_name":"Silke Bachmann","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; University Hospital for Psychiatry, Psychotherapy a"},{"author_name":"Daniele Zullino","author_inst":"Division of Addiction Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Swit"},{"author_name":"Justine Y. Hansen","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Bratislav Misic","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Sophia I. Thomopoulos","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Devarshi Pancholi","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Anthony Juliano","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Hugh Garavan","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Sofie L. Valk","author_inst":"Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Julich, 52425 Julich, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, 0"},{"author_name":"Boris C. Bernhardt","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Matthias Kirschner","author_inst":"Dept of Adult Psychiatry and Psychotherapy, Univ. Hospital of Psychiatry Zurich (PUK), Univ. of Zurich, Zurich, Switzerland; Dept of Adult Psychiatry, Univ. Hos"},{"author_name":"- ENIGMA Addiction Working Group","author_inst":""}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Network and receptor architectures shape brain morphometry in addiction","rel_doi":"10.64898\/2026.04.03.26348150","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26348150","rel_abs":"Substance use disorders (SUD) are chronic conditions with devastating effects on brain health, functioning, and survival. In this study, we compared brain morphometry of 2,782 individuals with SUD to 1,951 controls and assessed the topographic overlap of these differences with brain connectivity and receptor architecture. Across SUD, we identified a morphometric signature involving frontal, parietal, temporal and limbic systems that overlapped with cortical hub regions and harbored cortical and subcortical disease epicenters. Findings were highly consistent across six substances and numerous robustness and generalizability analyses. Transdiagnostic comparisons showed high spatial overlap of SUD epicenters with those of schizophrenia and bipolar disorder, suggesting shared network-constrained cortical differences. Finally, multivariate mapping revealed that SUD brain differences aligned with two neurotransmitter axes contrasting cannabinoid-opioid and dopaminergic systems. These findings indicate that addiction-related brain differences are shaped by connectome and neurotransmitter architecture, positioning brain network and neurochemical organization as key principles of SUD-related brain alterations.","rel_num_authors":75,"rel_authors":[{"author_name":"Foivos Georgiadis","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Beatrice A. Milano","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; Institute of Life Sciences, Sant'Anna School of Adv"},{"author_name":"Sara Lariviere","author_inst":"Department of Medical Imaging and Radiation Sciences, Universite de Sherbrooke, Sherbrooke, QC, Canada"},{"author_name":"Kent E. Hutchinson","author_inst":"Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Vince Calhoun","author_inst":"Department of Psychology, School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA; Department of Psychiatry and Behavioral Sciences, Emory Unive"},{"author_name":"Chiang-Shan Ray Li","author_inst":"Department of Psychiatry and Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA"},{"author_name":"Reza Momenan","author_inst":"Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health (NIH), Bethesda, MD, USA"},{"author_name":"Rajita Sinha","author_inst":"Department of Psychiatry, Yale University School of Medicine, Yale Interdisciplinary Stress Center, New Haven, CT, USA"},{"author_name":"Dick Veltman","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Ruth van Holst","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Anneke Goudriaan","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Maartje Luijten","author_inst":"Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands"},{"author_name":"Martine Groefsema","author_inst":"Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands"},{"author_name":"Henrik Walter","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Tristram Lett","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Reinout Wiers","author_inst":"Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Lianne Schmaal","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Julianne Flanagan","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Bernice Porjesz","author_inst":"Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY, USA"},{"author_name":"Jonathan Ipser","author_inst":"Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa"},{"author_name":"Justin Boehmer","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Nicola Canessa","author_inst":"IUSS Cognitive Neuroscience (ICoN) Center, Scuola Universitaria Superiore IUSS, 27100, Pavia, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuro"},{"author_name":"Ramiro Salas","author_inst":"Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Edythe London","author_inst":"Department of Psychiatry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA"},{"author_name":"Martin Paulus","author_inst":"Laureate Institute for Brain Research, Tulsa, OK, USA"},{"author_name":"Dan Stein","author_inst":"Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa"},{"author_name":"Samantha Brooks","author_inst":"School of Psychology, Faculty of Health, Liverpool John Moores University, UK"},{"author_name":"Liesbeth Reneman","author_inst":"Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Anouk Schrantee","author_inst":"Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Francesca Filbey","author_inst":"Department of Psychology, University of Texas at Dallas, Richardson, TX, USA"},{"author_name":"Rob Hester","author_inst":"Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Murat Yucel","author_inst":"Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia"},{"author_name":"Valentina Lorenzetti","author_inst":"Healthy Brain and Mind Research Centre, Neuroscience of Addiction and Mental Health Program, Australian Catholic University, Fitzroy, VIC, Australia; Clinical P"},{"author_name":"Nadia Solowij","author_inst":"Faculty of the Arts, Social Sciences and Humanities, School of Psychology, Wollongong, Australia, University of Wollongong, Wollongong, NSW, Australia"},{"author_name":"Rocio Martin-Santos","author_inst":"Department of Psychiatry and Psychology, Hospital Clinic, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Centro de Investigacion en Red de "},{"author_name":"Albert Batalla","author_inst":"Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands"},{"author_name":"Janna Cousijn","author_inst":"Neuroscience of Addiction Lab, Center for Substance Use and Addiction Research, Department of Psychology, Education and Child Studies, Erasmus School of Social "},{"author_name":"Edith Pomarol-Clotet","author_inst":"FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain"},{"author_name":"Eduardo A. Garza-Villarreal","author_inst":"Instituto de Neurobiologia, Universidad Nacional Autonoma de Mexico (UNAM) campus Juriquilla, Queretaro, Mexico"},{"author_name":"Marco Leyton","author_inst":"Department of Psychiatry, McGill University, Montreal Neurological Institute, Montreal, QC, Canada"},{"author_name":"Elliot Stein","author_inst":"Neuroimaging Research Branch, National Institute on Drug Abuse (NIDA), National Institutes of Health, Baltimore, MD, USA"},{"author_name":"Cleo L. Crunelle","author_inst":"Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Department of Psychiatry, Brussels, Belgium"},{"author_name":"Anne M. Kaag","author_inst":"Department of Clinical Neuropsychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Antonio Verdejo-Garcia","author_inst":"Department of Clinical Psychology, School of Psychology, Monash University, Melbourne, VIC, Australia"},{"author_name":"John J. Foxe","author_inst":"Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA"},{"author_name":"Kathleen T. Brady","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Aimee McRae-Clark","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Alain Dagher","author_inst":"Department of Psychiatry, McGill University, Montreal Neurological Institute, Montreal, QC, Canada"},{"author_name":"Amelie Haugg","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marc Walter","author_inst":"Clinic of Psychiatry and Psychotherapy, Psychiatric Services Aargau, Windisch, Switzerland"},{"author_name":"Andre Schmidt","author_inst":"Department of Psychiatry (UPK), University of Basel, Basel, Switzerland"},{"author_name":"Anne Lingford-Hughes","author_inst":"Division of Psychiatry, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK"},{"author_name":"Louise M. Paterson","author_inst":"Division of Psychiatry, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK"},{"author_name":"Angelica M. Morales","author_inst":"Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States"},{"author_name":"Dara G. Ghahremani","author_inst":"Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA"},{"author_name":"Chuan Fan","author_inst":"First Affiliated Hospital of Anhui Medical University, Hefei, China"},{"author_name":"Etna J. E. Engeli","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marcus Herdener","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Boris B. Quednow","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland; Neuroscience Center"},{"author_name":"Erich Seifritz","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland; Neuroscience Center"},{"author_name":"Philipp Homan","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marco De Pieri","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland"},{"author_name":"Silke Bachmann","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; University Hospital for Psychiatry, Psychotherapy a"},{"author_name":"Daniele Zullino","author_inst":"Division of Addiction Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Swit"},{"author_name":"Justine Y. Hansen","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Bratislav Misic","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Sophia I. Thomopoulos","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Devarshi Pancholi","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Anthony Juliano","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Hugh Garavan","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Sofie L. Valk","author_inst":"Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Julich, 52425 Julich, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, 0"},{"author_name":"Boris C. Bernhardt","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Matthias Kirschner","author_inst":"Dept of Adult Psychiatry and Psychotherapy, Univ. Hospital of Psychiatry Zurich (PUK), Univ. of Zurich, Zurich, Switzerland; Dept of Adult Psychiatry, Univ. Hos"},{"author_name":"- ENIGMA Addiction Working Group","author_inst":""}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Network and receptor architectures shape brain morphometry in addiction","rel_doi":"10.64898\/2026.04.03.26348150","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26348150","rel_abs":"Substance use disorders (SUD) are chronic conditions with devastating effects on brain health, functioning, and survival. In this study, we compared brain morphometry of 2,782 individuals with SUD to 1,951 controls and assessed the topographic overlap of these differences with brain connectivity and receptor architecture. Across SUD, we identified a morphometric signature involving frontal, parietal, temporal and limbic systems that overlapped with cortical hub regions and harbored cortical and subcortical disease epicenters. Findings were highly consistent across six substances and numerous robustness and generalizability analyses. Transdiagnostic comparisons showed high spatial overlap of SUD epicenters with those of schizophrenia and bipolar disorder, suggesting shared network-constrained cortical differences. Finally, multivariate mapping revealed that SUD brain differences aligned with two neurotransmitter axes contrasting cannabinoid-opioid and dopaminergic systems. These findings indicate that addiction-related brain differences are shaped by connectome and neurotransmitter architecture, positioning brain network and neurochemical organization as key principles of SUD-related brain alterations.","rel_num_authors":75,"rel_authors":[{"author_name":"Foivos Georgiadis","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Beatrice A. Milano","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; Institute of Life Sciences, Sant'Anna School of Adv"},{"author_name":"Sara Lariviere","author_inst":"Department of Medical Imaging and Radiation Sciences, Universite de Sherbrooke, Sherbrooke, QC, Canada"},{"author_name":"Kent E. Hutchinson","author_inst":"Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Vince Calhoun","author_inst":"Department of Psychology, School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA; Department of Psychiatry and Behavioral Sciences, Emory Unive"},{"author_name":"Chiang-Shan Ray Li","author_inst":"Department of Psychiatry and Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA"},{"author_name":"Reza Momenan","author_inst":"Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institutes of Health (NIH), Bethesda, MD, USA"},{"author_name":"Rajita Sinha","author_inst":"Department of Psychiatry, Yale University School of Medicine, Yale Interdisciplinary Stress Center, New Haven, CT, USA"},{"author_name":"Dick Veltman","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Ruth van Holst","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Anneke Goudriaan","author_inst":"Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Maartje Luijten","author_inst":"Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands"},{"author_name":"Martine Groefsema","author_inst":"Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands"},{"author_name":"Henrik Walter","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Tristram Lett","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Reinout Wiers","author_inst":"Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Lianne Schmaal","author_inst":"Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Julianne Flanagan","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Bernice Porjesz","author_inst":"Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY, USA"},{"author_name":"Jonathan Ipser","author_inst":"Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa"},{"author_name":"Justin Boehmer","author_inst":"Department of Psychiatry and Psychotherapy, Charite - Universitatsmedizin Berlin, Berlin, Germany"},{"author_name":"Nicola Canessa","author_inst":"IUSS Cognitive Neuroscience (ICoN) Center, Scuola Universitaria Superiore IUSS, 27100, Pavia, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuro"},{"author_name":"Ramiro Salas","author_inst":"Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Edythe London","author_inst":"Department of Psychiatry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA"},{"author_name":"Martin Paulus","author_inst":"Laureate Institute for Brain Research, Tulsa, OK, USA"},{"author_name":"Dan Stein","author_inst":"Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa"},{"author_name":"Samantha Brooks","author_inst":"School of Psychology, Faculty of Health, Liverpool John Moores University, UK"},{"author_name":"Liesbeth Reneman","author_inst":"Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Anouk Schrantee","author_inst":"Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Francesca Filbey","author_inst":"Department of Psychology, University of Texas at Dallas, Richardson, TX, USA"},{"author_name":"Rob Hester","author_inst":"Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Murat Yucel","author_inst":"Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia"},{"author_name":"Valentina Lorenzetti","author_inst":"Healthy Brain and Mind Research Centre, Neuroscience of Addiction and Mental Health Program, Australian Catholic University, Fitzroy, VIC, Australia; Clinical P"},{"author_name":"Nadia Solowij","author_inst":"Faculty of the Arts, Social Sciences and Humanities, School of Psychology, Wollongong, Australia, University of Wollongong, Wollongong, NSW, Australia"},{"author_name":"Rocio Martin-Santos","author_inst":"Department of Psychiatry and Psychology, Hospital Clinic, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Centro de Investigacion en Red de "},{"author_name":"Albert Batalla","author_inst":"Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands"},{"author_name":"Janna Cousijn","author_inst":"Neuroscience of Addiction Lab, Center for Substance Use and Addiction Research, Department of Psychology, Education and Child Studies, Erasmus School of Social "},{"author_name":"Edith Pomarol-Clotet","author_inst":"FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain"},{"author_name":"Eduardo A. Garza-Villarreal","author_inst":"Instituto de Neurobiologia, Universidad Nacional Autonoma de Mexico (UNAM) campus Juriquilla, Queretaro, Mexico"},{"author_name":"Marco Leyton","author_inst":"Department of Psychiatry, McGill University, Montreal Neurological Institute, Montreal, QC, Canada"},{"author_name":"Elliot Stein","author_inst":"Neuroimaging Research Branch, National Institute on Drug Abuse (NIDA), National Institutes of Health, Baltimore, MD, USA"},{"author_name":"Cleo L. Crunelle","author_inst":"Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Department of Psychiatry, Brussels, Belgium"},{"author_name":"Anne M. Kaag","author_inst":"Department of Clinical Neuropsychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands"},{"author_name":"Antonio Verdejo-Garcia","author_inst":"Department of Clinical Psychology, School of Psychology, Monash University, Melbourne, VIC, Australia"},{"author_name":"John J. Foxe","author_inst":"Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA"},{"author_name":"Kathleen T. Brady","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Aimee McRae-Clark","author_inst":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA"},{"author_name":"Alain Dagher","author_inst":"Department of Psychiatry, McGill University, Montreal Neurological Institute, Montreal, QC, Canada"},{"author_name":"Amelie Haugg","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marc Walter","author_inst":"Clinic of Psychiatry and Psychotherapy, Psychiatric Services Aargau, Windisch, Switzerland"},{"author_name":"Andre Schmidt","author_inst":"Department of Psychiatry (UPK), University of Basel, Basel, Switzerland"},{"author_name":"Anne Lingford-Hughes","author_inst":"Division of Psychiatry, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK"},{"author_name":"Louise M. Paterson","author_inst":"Division of Psychiatry, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK"},{"author_name":"Angelica M. Morales","author_inst":"Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States"},{"author_name":"Dara G. Ghahremani","author_inst":"Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA"},{"author_name":"Chuan Fan","author_inst":"First Affiliated Hospital of Anhui Medical University, Hefei, China"},{"author_name":"Etna J. E. Engeli","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marcus Herdener","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Boris B. Quednow","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland; Neuroscience Center"},{"author_name":"Erich Seifritz","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland; Neuroscience Center"},{"author_name":"Philipp Homan","author_inst":"Department of Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich (PUK), University of Zurich, Zurich, Switzerland"},{"author_name":"Marco De Pieri","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland"},{"author_name":"Silke Bachmann","author_inst":"Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland; University Hospital for Psychiatry, Psychotherapy a"},{"author_name":"Daniele Zullino","author_inst":"Division of Addiction Psychiatry, Department of Psychiatry, University Hospitals of Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Swit"},{"author_name":"Justine Y. Hansen","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Bratislav Misic","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Sophia I. Thomopoulos","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Re"},{"author_name":"Devarshi Pancholi","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Anthony Juliano","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Hugh Garavan","author_inst":"Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT, USA"},{"author_name":"Sofie L. Valk","author_inst":"Institute for Neuroscience and Medicine (INM-7), Forschungszentrum Julich, 52425 Julich, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, 0"},{"author_name":"Boris C. Bernhardt","author_inst":"Montreal Neurological Institute, McGill University, Montreal, QC, Canada"},{"author_name":"Matthias Kirschner","author_inst":"Dept of Adult Psychiatry and Psychotherapy, Univ. Hospital of Psychiatry Zurich (PUK), Univ. of Zurich, Zurich, Switzerland; Dept of Adult Psychiatry, Univ. Hos"},{"author_name":"- ENIGMA Addiction Working Group","author_inst":""}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Empiric tuberculosis treatment and 12-month mortality among sputum GeneXpert-negative adults living with HIV in Uganda in the era of widespread Antiretroviral therapy: A prospective cohort study","rel_doi":"10.64898\/2026.04.04.26350152","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.04.26350152","rel_abs":"BackgroundIn sub-Saharan Africa where both tuberculosis (TB) and HIV are prevalent, empiric TB treatment in people living with HIV (PLHIV) persists due to limited sensitivity of sputum-based TB tests. We evaluated mortality among molecular test-negative presumptive TB adult PLHIV in a population where the majority are or have been on antiretroviral therapy (ART), comparing mortality between those who received empiric TB treatment and those who did not.\n\nMaterials and MethodsFrom November 2017 to December 2020, Xpert-negative presumptive TB adult PLHIV were recruited at Mulago Referral Hospital and Kisenyi Health Centre-IV in Kampala, Uganda. Clinical data including TB symptoms, chest X-ray, and empiric TB treatment decision were collected. Laboratory investigations included CD4 cell count, serum cryptococcal antigen (CrAg), urine TB-lipoarabinomannan (TB-LAM), microbiological blood cultures, and sputum mycobacterial growth indicator tube (MGIT) cultures. Participants were followed monthly for 12 months to ascertain vital status.\n\nResultsOverall, 300 participants were enrolled; 61.3% inpatients, 55.7% female, median age 37 (IQR 29-45) years, 82.3% on ART, median CD4 206 cells\/mm3 (IQR 36-507). Of the 300 participants, 68 (22.7%) received empiric TB treatment, of which 53 (77.9%) were inpatients. 12-month mortality was 31.0% (93\/300); 91.4% among inpatients, 72% within three months post-enrolment. Mortality was higher among those who received empiric TB treatment (51.5 vs. 30.2 per 1,000 person-months; p=0.013) compared to those who did not. TB cultures were positive in 5.0% (15\/300), of whom seven (46.7%) were also TB-LAM positive. CrAg was positive in 12.3% and 3.7% had positive blood culture.\n\nConclusionWe found high mortality among Xpert-negative PLHIV, particularly those who received empiric TB treatment, despite high ART coverage. Cryptococcal antigenemia and bacteremia were not uncommon. In presence of negative Xpert results in PLHIV, clinicians should perform extensive laboratory evaluations to identify possible comorbidities or alternative non-TB diagnosis.","rel_num_authors":10,"rel_authors":[{"author_name":"Lydia Nakiyingi","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Bernard Kikaire","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Sarah Nakayenga","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Louis Kamulegeya","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Elizabeth Nakabugo","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Juliet  Nkugwa Asio","author_inst":"Uganda Virus Research Institute"},{"author_name":"Bernard Bagaya","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Willy Ssengooba","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Harriet Mayanja-Kizza","author_inst":"Makerere University College of Health Sciences"},{"author_name":"Yukari  C Manabe","author_inst":"Johns Hopkins University"}],"rel_date":"2026-04-06","rel_site":"medrxiv"},{"rel_title":"Papillary muscles, ventricular loading, and atrial remodelling as beat-to-beat determinants of functional mitral regurgitation: an exploratory Granger causality study","rel_doi":"10.64898\/2026.04.03.26350122","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26350122","rel_abs":"BackgroundFunctional mitral regurgitation results from interacting mechanisms whose relative contributions vary between atrial and ventricular subtypes and shift dynamically within each heartbeat, producing temporal patterns that static analyses cannot capture.\n\nObjectivesTo identify which structural determinants predict mitral regurgitation variability beat to beat using Granger causality within vector autoregression, focusing on papillary muscle dynamics across subtypes.\n\nMethodsFrame-level echocardiographic time series from 41 patients (21 atrial, 20 ventricular; 1,959 frames) were z-score standardised within patient. Individual (lag 3) and pooled (lag 2) vector autoregression models tested whether left ventricular volume, left atrial volume, papillary muscle length, and annulus diameter Granger-predict mitral regurgitation area.\n\nResultsIndividual models revealed marked heterogeneity. In pooled analysis, left ventricular volume was the strongest Granger predictor at short lags (atrial p=0.011; ventricular p=0.006), while left atrial volume emerged at longer lags (lag 7: atrial p=0.043; ventricular p=0.011). Systolic papillary muscle length was not predictive. Full-cycle analysis revealed a subtype-specific dissociation: papillary muscle length Granger-predicted regurgitation only in the ventricular subtype (p=0.001), while regurgitation predicted papillary muscle displacement only in the atrial subtype (p<0.001). Left ventricular volume dominated within-beat prediction but lost cross-beat relevance in the ventricular subtype, while left atrial volume gained cross-beat predictive relevance in the atrial subtype. No structural determinant correlated with severity cross-sectionally.\n\nConclusionsBeat-to-beat vector autoregression and Granger modelling reveals heterogeneous, subtype-specific temporal patterns with distinct temporal windows of predictability for ventricular loading and papillary geometry. This framework may support patient-specific temporal phenotyping of functional mitral regurgitation.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC=\"FIGDIR\/small\/26350122v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (28K):\norg.highwire.dtl.DTLVardef@d0b391org.highwire.dtl.DTLVardef@1bd2b60org.highwire.dtl.DTLVardef@6ac20forg.highwire.dtl.DTLVardef@ea6f34_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":18,"rel_authors":[{"author_name":"Csilla Andrea Eotvos","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Cluj, Romania"},{"author_name":"Teodora Avram","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca, Romania"},{"author_name":"Eric Daniel Blendea","author_inst":"Emil Racovita National College, Cluj-Napoca, Romania"},{"author_name":"Matei Ioan Munteanu","author_inst":"Institut Polytechnique, Paris, France"},{"author_name":"Adrian Florin Bubuianu","author_inst":"Department of Computer Science, Babes Bolyai University, Cluj-Napoca, Romania"},{"author_name":"Madalina Patricia Moldovan","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca, Cluj, Romania"},{"author_name":"Petra Hedesiu","author_inst":"School of Engineering, Columbia University, New York, USA"},{"author_name":"Roxana Daiana Lazar","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca, Cluj, Romania"},{"author_name":"Iulia Georgiana Zehan","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca, Cluj, Romania"},{"author_name":"Adriana Daniela Sarb","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca, Cluj, Romania"},{"author_name":"Giorgia Coseriu","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca, Cluj, Romania"},{"author_name":"Patricia Schiop-Tentea","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca, Cluj, Romania"},{"author_name":"Diana Larisa Mocan-Hognogi","author_inst":"Emergency Clinical County Hospital, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania"},{"author_name":"Roxana Chiorescu","author_inst":"Emergency Clinical County Hospital, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania"},{"author_name":"Sorin Pop","author_inst":"Emergency Clinical County Hospital, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania"},{"author_name":"Laura Diosan","author_inst":"Department of Computer Science, Babes Bolyai University, Cluj-Napoca, Romania"},{"author_name":"E. Kevin Heist","author_inst":"Massachusetts General Hospital, Harvard Medical School, Boston, MA 02215, USA"},{"author_name":"Dan Blendea","author_inst":"Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca, Cluj, Romania"}],"rel_date":"2026-04-05","rel_site":"medrxiv"},{"rel_title":"Host Factors Modulate Nirmatrelvir-Ritonavir Efficacy in COVID-19 Patients: A Viral Dynamics Modeling Study","rel_doi":"10.64898\/2026.04.03.26350141","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26350141","rel_abs":"Antiviral therapies such as nirmatrelvir-ritonavir are widely used for COVID-19, yet their real-world effectiveness and sources of heterogeneity in treatment response remain incompletely understood. Here, we integrate longitudinal viral load data from a large cohort of SARS-CoV-2 BA.2-infected patients in Shanghai (n=48,243) with a mechanistic within-host viral dynamics model coupled to pharmacokinetic\/pharmacodynamic principles to quantify in vivo antiviral efficacy. We estimate that nirmatrelvir-ritonavir reduces viral production by approximately 55% on average. Treatment response exhibits substantial heterogeneity, with higher efficacy observed in vaccinated individuals and reduced efficacy in older adults. Sensitivity analyses demonstrate that the vaccination effect is robust across model specifications, whereas age-related differences depend on assumptions about early viral kinetics, highlighting structural identifiability challenges when analyzing sparse real-world data. These findings provide a mechanistic interpretation of heterogeneous treatment effects and establish a generalizable framework for integrating real-world clinical data with within-host models to inform antiviral optimization and personalized treatment strategies.","rel_num_authors":26,"rel_authors":[{"author_name":"Yiyu Liao","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Yan Wang","author_inst":"School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Yuqian Wang","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Jingwen Ai","author_inst":"Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China"},{"author_name":"Boon Kiat Law","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Derrick Lim","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Jiaxin Zhou","author_inst":"School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Hongyu Wang","author_inst":"Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China"},{"author_name":"Yanpeng Wu","author_inst":"School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Po Ying Chia","author_inst":"National Centre for Infectious Diseases, Singapore"},{"author_name":"Hoong Kai Chua","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Conrad En Zuo Chan","author_inst":"Communicable Diseases Agency, Singapore"},{"author_name":"Joshua T. Schiffer","author_inst":"Fred Hutchinson Cancer Center, Seattle, WA, USA"},{"author_name":"Katherine Owens","author_inst":"Fred Hutchinson Cancer Center, Seattle, WA, USA"},{"author_name":"Shadisadat Esmaeili","author_inst":"Fred Hutchinson Cancer Center, Seattle, WA, USA"},{"author_name":"Benjamin J. Cowling","author_inst":"School of Public Health, The University of Hong Kong, Hong Kong SAR, China"},{"author_name":"Matthew E. Cove","author_inst":"National University Hospital, Singapore"},{"author_name":"Hiroki Saito","author_inst":"St Marianna University Yokohama Seibu Hospital, Yokohama, Japan"},{"author_name":"Liang En Wee","author_inst":"Department of Infectious Diseases, Singapore General Hospital, Singapore"},{"author_name":"Barnaby E. Young","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Tat Ming Ng","author_inst":"Division of Pharmacy, Tan Tock Seng Hospital, Singapore"},{"author_name":"Eric Chun Yong Chan","author_inst":"Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore, Singapore"},{"author_name":"Marco Ajelli","author_inst":"Indiana University School of Public Health-Bloomington, Bloomington, IN, USA"},{"author_name":"Wenhong Zhang","author_inst":"Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China"},{"author_name":"Hongjie Yu","author_inst":"School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Keisuke Ejima","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"}],"rel_date":"2026-04-05","rel_site":"medrxiv"},{"rel_title":"Host Factors Modulate Nirmatrelvir-Ritonavir Efficacy in COVID-19 Patients: A Viral Dynamics Modeling Study","rel_doi":"10.64898\/2026.04.03.26350141","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26350141","rel_abs":"Antiviral therapies such as nirmatrelvir-ritonavir are widely used for COVID-19, yet their real-world effectiveness and sources of heterogeneity in treatment response remain incompletely understood. Here, we integrate longitudinal viral load data from a large cohort of SARS-CoV-2 BA.2-infected patients in Shanghai (n=48,243) with a mechanistic within-host viral dynamics model coupled to pharmacokinetic\/pharmacodynamic principles to quantify in vivo antiviral efficacy. We estimate that nirmatrelvir-ritonavir reduces viral production by approximately 55% on average. Treatment response exhibits substantial heterogeneity, with higher efficacy observed in vaccinated individuals and reduced efficacy in older adults. Sensitivity analyses demonstrate that the vaccination effect is robust across model specifications, whereas age-related differences depend on assumptions about early viral kinetics, highlighting structural identifiability challenges when analyzing sparse real-world data. These findings provide a mechanistic interpretation of heterogeneous treatment effects and establish a generalizable framework for integrating real-world clinical data with within-host models to inform antiviral optimization and personalized treatment strategies.","rel_num_authors":26,"rel_authors":[{"author_name":"Yiyu Liao","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Yan Wang","author_inst":"School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Yuqian Wang","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Jingwen Ai","author_inst":"Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China"},{"author_name":"Boon Kiat Law","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Derrick Lim","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Jiaxin Zhou","author_inst":"School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Hongyu Wang","author_inst":"Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China"},{"author_name":"Yanpeng Wu","author_inst":"School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Po Ying Chia","author_inst":"National Centre for Infectious Diseases, Singapore"},{"author_name":"Hoong Kai Chua","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Conrad En Zuo Chan","author_inst":"Communicable Diseases Agency, Singapore"},{"author_name":"Joshua T. Schiffer","author_inst":"Fred Hutchinson Cancer Center, Seattle, WA, USA"},{"author_name":"Katherine Owens","author_inst":"Fred Hutchinson Cancer Center, Seattle, WA, USA"},{"author_name":"Shadisadat Esmaeili","author_inst":"Fred Hutchinson Cancer Center, Seattle, WA, USA"},{"author_name":"Benjamin J. Cowling","author_inst":"School of Public Health, The University of Hong Kong, Hong Kong SAR, China"},{"author_name":"Matthew E. Cove","author_inst":"National University Hospital, Singapore"},{"author_name":"Hiroki Saito","author_inst":"St Marianna University Yokohama Seibu Hospital, Yokohama, Japan"},{"author_name":"Liang En Wee","author_inst":"Department of Infectious Diseases, Singapore General Hospital, Singapore"},{"author_name":"Barnaby E. Young","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"},{"author_name":"Tat Ming Ng","author_inst":"Division of Pharmacy, Tan Tock Seng Hospital, Singapore"},{"author_name":"Eric Chun Yong Chan","author_inst":"Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore, Singapore"},{"author_name":"Marco Ajelli","author_inst":"Indiana University School of Public Health-Bloomington, Bloomington, IN, USA"},{"author_name":"Wenhong Zhang","author_inst":"Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China"},{"author_name":"Hongjie Yu","author_inst":"School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Keisuke Ejima","author_inst":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore"}],"rel_date":"2026-04-05","rel_site":"medrxiv"},{"rel_title":"Mutation-specific impairment of TET2 and DNMT3A enzymatic activity predicts clonal hematopoiesis disease risk","rel_doi":"10.64898\/2026.04.03.26350108","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26350108","rel_abs":"Clonal hematopoiesis of indeterminate potential (CHIP) driven by somatic mutations in TET2 and DNMT3A is present in >10% of adults over 60 and confers substantial risk for hematologic malignancy and cardiovascular disease, yet the majority of patients with CHIP do not progress to disease. Analyzing 1,020,538 individuals across three biobanks (UK Biobank, All of Us, BioVU), we show that a discrete subset of enzymatically disruptive mutations -- TET2 loss-of-function variants and the DNMT3A R882 hotspot -- account for the majority of clinical risk in these genes and exhibit the strongest clonal fitness advantage. Because DNMT3A and TET2 encode enzymes that modulate DNA methylation, we reasoned that peripheral blood methylation patterns should reflect the functional impact of individual mutations, enabling a direct readout of enzymatic dysfunction in CHIP patients. We developed and validated methylation-based activity scores for TET2 and DNMT3A as patient specific biomarkers that quantify enzymatic activity. These scores capture functional heterogeneity across mutation subtypes, predict disease risk comparably to clinical risk scores such as the Clonal Hematopoiesis Risk Score and the AHA PREVENT cardiovascular risk model. Integrating the activity score with the clinical models substantially improves prediction of incident cytopenia, myeloid neoplasm, and major adverse cardiovascular events. These findings establish that TET2 and DNMT3A CHIP pathogenicity is proportional to the degree of enzymatic disruption conferred by specific variants, and nominate methylation-based activity scores as a functional biomarker for individualized CHIP risk stratification and monitoring therapeutic response.","rel_num_authors":12,"rel_authors":[{"author_name":"Yash Pershad","author_inst":"Vanderbilt University"},{"author_name":"Kun Zhao","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Joseph C Van Amburg","author_inst":"Vanderbilt University"},{"author_name":"Robert W Corty","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Alyssa C Parker","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Alexander James Silver","author_inst":"University of Michigan"},{"author_name":"Yara F Almadani","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Ashwin Kishtagari","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Emily Hodges","author_inst":"Vanderbilt University School of Medicine"},{"author_name":"Michael R Savona","author_inst":"Vanderbilt University Medical Center"},{"author_name":"J. Brett Heimlich","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Alexander G Bick","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-04-05","rel_site":"medrxiv"},{"rel_title":"A Hybrid Machine Learning Framework for Early Prediction of Chronic Kidney Disease Progression Using Longitudinal Claims Data: An XGBoost-LSTM Ensemble with Temporal Attention","rel_doi":"10.64898\/2026.04.03.26349862","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.03.26349862","rel_abs":"BackgroundChronic kidney disease (CKD) affects approximately 850 million individuals worldwide and remains a leading cause of morbidity, premature mortality, and escalating healthcare costs. Despite the availability of clinical biomarkers, CKD progression to end-stage renal disease (ESRD) is frequently identified late, limiting opportunities for preventive intervention. Conventional predictive models have relied predominantly on static cross-sectional laboratory values, failing to capture the temporal dynamics of disease trajectory that longitudinal claims data can provide.\n\nObjectiveThis study proposes a novel hybrid machine learning framework -- XGBoost-LSTM-Attention (XLA) -- that integrates gradient-boosted feature selection with long short-term memory (LSTM) networks and a temporal attention mechanism to improve early prediction of CKD progression from Stage 3 to Stages 4\/5 or ESRD using longitudinal claims-based features.\n\nMethodsWe conducted two complementary analyses. Primary analysis: a cross-sectional validation using real NHANES 2015-2018 data (n=701 CKD Stage 3 adults) predicting significant proteinuria (UACR [&ge;]30 mg\/g) from clinical features excluding UACR. Supplementary analysis: an NHANES-calibrated longitudinal cohort (n=8,412) with simulated quarterly measurements demonstrated XLA performance under real-world longitudinal data conditions. All models were evaluated using 5-fold stratified cross-validation.\n\nResultsIn the primary NHANES cross-sectional analysis, the XLA framework achieved AUC-ROC of 0.684 (95% CI: 0.641-0.727), with all models performing comparably (AUC 0.684-0.710), confirming that cross-sectional clinical features alone provide limited signal for proteinuria prediction and underscoring the necessity of UACR measurement. In the longitudinal supplementary analysis, XLA achieved AUC-ROC of 0.994 versus 0.939 for the best cross-sectional baseline (+5.5%), demonstrating that temporal trajectory features -- particularly eGFR slope and RAAS adherence trends -- confer substantial incremental predictive value when longitudinal data are available.\n\nConclusionThe XLA framework demonstrates meaningful advantages over traditional approaches when applied to longitudinal claims data. Cross-sectional findings highlight the irreplaceable role of direct UACR measurement in CKD risk stratification. Together, these results provide actionable evidence for both the limitations of static prediction and the promise of trajectory-based approaches in value-based care programs managing large CKD populations.","rel_num_authors":3,"rel_authors":[{"author_name":"JASWANT NARENDRA SAXENA","author_inst":"CGI"},{"author_name":"Devi Vara Prasad Potturu","author_inst":"Yale University"},{"author_name":"Ananya Nagraj","author_inst":"Stevens Institute of Technology"}],"rel_date":"2026-04-04","rel_site":"medrxiv"},{"rel_title":"Proteomic-Based Aging Clocks and MRI Markers of Cerebral Small Vessel Disease: ARIC and MESA","rel_doi":"10.64898\/2026.04.02.26350087","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.02.26350087","rel_abs":"BackgroundThis study investigates whether proteomic aging clocks (PACs) are associated with cerebral small vessel disease (CSVD).\n\nMethodsWe included participants from two US community-based cohorts: the Atherosclerosis Risk in Communities (ARIC) Study and the Multi-Ethnic Study of Atherosclerosis (MESA) Study. These analyses leveraged PACs that were developed in ARIC using proteomics measured by SomaScan in midlife (Visit 2; mean age 56 y; n=1,486) and late-life (Visit 5; mean age 76 y; n=1,496), trained on chronological age. Proteomic age acceleration (PAA) was calculated as residuals from regressing PACs on chronological age. 3T brain MRI data were collected in late-life. We examined associations of PAA with log-transformed white matter hyperintensity (WMH) volume using linear regression and with the presence of microbleeds, and subcortical, lacunar, and cortical infarcts using logistic regression. Associations of PACs with WMH volume and microbleeds were tested in MESA using proteins measured at Exam 1 (mean age 57 y; n=932) and Exam 5 (mean age 66 y; n=934). All associations were quantified per 5-year increase in PAA. All models were adjusted for demographics and cardiovascular risk factors.\n\nResultsIn ARIC, higher midlife PAA was associated with greater WMH volume (percent difference: 25% [95% CI: 13%, 39%]) and higher odds of subcortical infarcts (OR: 1.24 [1.02, 1.51]). Late-life PAA was associated with all CSVD markers: WMH volume (percent difference: 20% [8%, 34%]), cerebral microbleeds (OR: 1.40 [1.15, 1.69]), subcortical (OR: 1.80 [1.47, 2.22]), lacunar (OR: 1.80 [1.46, 2.23]), and cortical infarcts (OR: 1.39 [1.07, 1.82]). In MESA, higher late-life PAA was associated with greater WMH volume (28% [3%, 58%]) but not with microbleeds.\n\nConclusionAccelerated proteomic aging is associated with a higher prevalence of MRI markers of CSVD, most predominantly in late-life. Understanding this relationship may help stratify those at higher risk of CSVD at an early stage.","rel_num_authors":22,"rel_authors":[{"author_name":"Saeun Park","author_inst":"University of Minnesota Twin Cities"},{"author_name":"Shuo Wang","author_inst":"University of Minnesota School of Medicine, Minneapolis, MN"},{"author_name":"Jialing Liu","author_inst":"Division of Biostatistics, University of Minnesota"},{"author_name":"Timothy M. Hughes","author_inst":"Wake Forest University School of Medicine, Winston Salem, NC"},{"author_name":"Erika P. Raven","author_inst":"New York University Grossman School of Medicine"},{"author_name":"Jelle Veraart","author_inst":"New York University Langone"},{"author_name":"Mohamad Habes","author_inst":"The University of Texas Health Science Center at San Antonio May Cancer Center"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Rajat Deo","author_inst":"University of Pennsylvania"},{"author_name":"Wendy S. Post","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Jerome I. I. Rotter","author_inst":"The Lundquist Institute"},{"author_name":"Alexis C. Wood","author_inst":"Baylor College of Medicine"},{"author_name":"Peter Ganz","author_inst":"University of California, San Francisco; Zuckerberg San Francisco General Hospital"},{"author_name":"Behnam Sabayan","author_inst":"Hennepin Healthcare Research Institute"},{"author_name":"Weihong Tang","author_inst":"University of Minnesota, School of Public Health, Division of Epidemology and Community Health"},{"author_name":"Josef Coresh","author_inst":"New York University Grossman School of Medicine"},{"author_name":"James S. Pankow","author_inst":"University of Minnesota"},{"author_name":"Keenan A. Walker","author_inst":"National Institutes of Health"},{"author_name":"Pamela L. Lutsey","author_inst":"University of Minnesota"},{"author_name":"Weihua Guan","author_inst":"University of Minnesota Twin Cities"},{"author_name":"Anna E. Prizment","author_inst":"Universtiy of Minnesota"},{"author_name":"Sanaz Sedaghat","author_inst":"Division of Epidemiology and Community Health, School of Public Health, University of Minnesota"}],"rel_date":"2026-04-04","rel_site":"medrxiv"},{"rel_title":"Proteomic-Based Aging Clocks and MRI Markers of Cerebral Small Vessel Disease: ARIC and MESA","rel_doi":"10.64898\/2026.04.02.26350087","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.02.26350087","rel_abs":"BackgroundThis study investigates whether proteomic aging clocks (PACs) are associated with cerebral small vessel disease (CSVD).\n\nMethodsWe included participants from two US community-based cohorts: the Atherosclerosis Risk in Communities (ARIC) Study and the Multi-Ethnic Study of Atherosclerosis (MESA) Study. These analyses leveraged PACs that were developed in ARIC using proteomics measured by SomaScan in midlife (Visit 2; mean age 56 y; n=1,486) and late-life (Visit 5; mean age 76 y; n=1,496), trained on chronological age. Proteomic age acceleration (PAA) was calculated as residuals from regressing PACs on chronological age. 3T brain MRI data were collected in late-life. We examined associations of PAA with log-transformed white matter hyperintensity (WMH) volume using linear regression and with the presence of microbleeds, and subcortical, lacunar, and cortical infarcts using logistic regression. Associations of PACs with WMH volume and microbleeds were tested in MESA using proteins measured at Exam 1 (mean age 57 y; n=932) and Exam 5 (mean age 66 y; n=934). All associations were quantified per 5-year increase in PAA. All models were adjusted for demographics and cardiovascular risk factors.\n\nResultsIn ARIC, higher midlife PAA was associated with greater WMH volume (percent difference: 25% [95% CI: 13%, 39%]) and higher odds of subcortical infarcts (OR: 1.24 [1.02, 1.51]). Late-life PAA was associated with all CSVD markers: WMH volume (percent difference: 20% [8%, 34%]), cerebral microbleeds (OR: 1.40 [1.15, 1.69]), subcortical (OR: 1.80 [1.47, 2.22]), lacunar (OR: 1.80 [1.46, 2.23]), and cortical infarcts (OR: 1.39 [1.07, 1.82]). In MESA, higher late-life PAA was associated with greater WMH volume (28% [3%, 58%]) but not with microbleeds.\n\nConclusionAccelerated proteomic aging is associated with a higher prevalence of MRI markers of CSVD, most predominantly in late-life. Understanding this relationship may help stratify those at higher risk of CSVD at an early stage.","rel_num_authors":22,"rel_authors":[{"author_name":"Saeun Park","author_inst":"University of Minnesota Twin Cities"},{"author_name":"Shuo Wang","author_inst":"University of Minnesota School of Medicine, Minneapolis, MN"},{"author_name":"Jialing Liu","author_inst":"Division of Biostatistics, University of Minnesota"},{"author_name":"Timothy M. Hughes","author_inst":"Wake Forest University School of Medicine, Winston Salem, NC"},{"author_name":"Erika P. Raven","author_inst":"New York University Grossman School of Medicine"},{"author_name":"Jelle Veraart","author_inst":"New York University Langone"},{"author_name":"Mohamad Habes","author_inst":"The University of Texas Health Science Center at San Antonio May Cancer Center"},{"author_name":"Ruth Dubin","author_inst":"University of Texas Southwestern Medical Center"},{"author_name":"Rajat Deo","author_inst":"University of Pennsylvania"},{"author_name":"Wendy S. Post","author_inst":"Johns Hopkins University School of Medicine"},{"author_name":"Jerome I. I. Rotter","author_inst":"The Lundquist Institute"},{"author_name":"Alexis C. Wood","author_inst":"Baylor College of Medicine"},{"author_name":"Peter Ganz","author_inst":"University of California, San Francisco; Zuckerberg San Francisco General Hospital"},{"author_name":"Behnam Sabayan","author_inst":"Hennepin Healthcare Research Institute"},{"author_name":"Weihong Tang","author_inst":"University of Minnesota, School of Public Health, Division of Epidemology and Community Health"},{"author_name":"Josef Coresh","author_inst":"New York University Grossman School of Medicine"},{"author_name":"James S. Pankow","author_inst":"University of Minnesota"},{"author_name":"Keenan A. Walker","author_inst":"National Institutes of Health"},{"author_name":"Pamela L. Lutsey","author_inst":"University of Minnesota"},{"author_name":"Weihua Guan","author_inst":"University of Minnesota Twin Cities"},{"author_name":"Anna E. Prizment","author_inst":"Universtiy of Minnesota"},{"author_name":"Sanaz Sedaghat","author_inst":"Division of Epidemiology and Community Health, School of Public Health, University of Minnesota"}],"rel_date":"2026-04-04","rel_site":"medrxiv"}]}