{"gname":"National Taiwan University","grp_id":"39","rels":[{"rel_title":"Effects of collaborative clinical visit agenda-setting interventions: A systematic review and meta-analysis","rel_doi":"10.64898\/2026.08.30.26361729","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.30.26361729","rel_abs":"Background Agenda-setting is a fundamental patient-centered communication practice in which a clinician works with a patient to elicit, propose, and organize topics for discussion during a clinical encounter. Various agenda-setting interventions have been developed, including patient-facing tools and clinician training, but their effects have not been systematically evaluated. We aimed to determine the effects of these interventions on encounter, patient, care partner, and clinician outcomes. Methods We searched grey literature and seven databases, including PubMed, from inception through July 2025 for randomized and non-randomized comparative studies of interventions designed to promote or improve clinical visit agenda-setting. Two reviewers independently screened articles and extracted data, with a third reviewer resolving conflicts. We assessed risk of bias using RoB 2 for randomized studies and ROBINS-I for non-randomized studies. We conducted random effects meta-analyses when outcomes were sufficiently comparable, assessed heterogeneity using I2, and rated certainty of evidence using GRADE. Post hoc exploratory subgroup analyses examined study design, adjustment status, and intervention structure. Results Twenty-nine articles describing 22 unique studies met the inclusion criteria, including 13 randomized and nine non-randomized studies. Agenda-setting interventions increased the occurrence of agenda-setting (risk ratio 5.43, 95% confidence interval (CI) 2.06 to 14.28, I2=34.6%) and favored the intervention for concerns addressed when measured as a continuous outcome (standardized mean difference (SMD) 0.37, 95% CI 0.16 to 0.57, I2=65.3%) and overall clinician satisfaction (SMD 0.50, 95% CI 0.23 to 0.78, I2=0.0%). There were no clear differences in the number of concerns raised (mean difference (MD) 0.21, 95% CI -0.19 to 0.61, I2=59.6%), visit duration (MD 0.64 minutes, 95% CI -0.83 to 2.12, I2=51.4%), or overall patient satisfaction (SMD 0.05, 95% CI -0.05 to 0.15, I2=47.0%). Potentially important heterogeneity was present for four of these six outcomes. Post hoc exploratory subgroup analyses did not provide clear evidence that effects varied by study design, adjustment status, or intervention structure. Risk of bias was often high, serious, or critical, and certainty of evidence was low or very low for all pooled outcomes. Conclusions To our knowledge, this is the first comprehensive synthesis of clinical visit agenda-setting interventions. Such interventions may increase the occurrence of agenda-setting and the extent to which patient concerns are addressed without increasing visit length. However, the certainty of evidence was low or very low, and the available evidence does not establish a superior intervention structure.","rel_num_authors":14,"rel_authors":[{"author_name":"Ailyn Sierpe","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Renata W. Yen","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Annika Milliman","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Elizabeth Cady","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Boyoung Ahn","author_inst":"The Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD 21205, United States"},{"author_name":"Anne E. Dade","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Anna Marie Devito","author_inst":"Hartford HealthCare Cancer Institute, Hartford HealthCare, 195 Retreat Ave, Hartford, CT 06103, United States"},{"author_name":"Bradley A. Eckert","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Vismaya V. Gopalan","author_inst":"The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, 1 Medical Center Dr, Lebanon, NH 03756, United "},{"author_name":"Stephanie C. Krasinski","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Meredith A. MacMartin","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Sophia G. Musacchio","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"},{"author_name":"Jingyi Zhang","author_inst":"University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States"},{"author_name":"Catherine H. Saunders","author_inst":"Dartmouth Health, 1 Medical Center Dr, Lebanon, NH 03756, United States"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Relation of Self-Reported Race and Genetic Ancestry to Hypertension Prevalence Among Hispanics\/Latinos: The Hispanic Community Health Study\/Study of Latinos","rel_doi":"10.64898\/2026.09.01.26361995","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361995","rel_abs":"Background. The imprecision of current metrics to capture the complex genetic admixture and racial identity among Hispanic\/Latino individuals in the United States [US] is a concern. We examined the relationship of self-reported race and genetic ancestry with hypertension [HTN] among Hispanics\/Latinos. Methods. Cross-sectional study of the Hispanic Community Health Study\/Study of Latinos (HCHS\/SOL), including 10,586 Hispanic\/Latino unrelated adults. Genetic ancestry: West African [AA], Amerindian [AI], and European [EA]. Self-reported race: White, Black, Native American, or Multiple\/Missing (More than one race or Unknown\/Not reported\/Refused). HTN: systolic (SBP) [&ge;]130 mmHg, diastolic blood pressure (DBP) [&ge;]80 mmHg, and\/or use of HTN medications. Age- and sex adjusted models were used. Results. Self-reported race was White (38{middle dot}6%), Black (3{middle dot}6%), Native American (4{middle dot}1%), and Multiple\/Missing (53{middle dot}7%), with Unknown\/Not reported\/Refused representing 32{middle dot}7%. Black and White Hispanics\/Latinos had the greatest AA (55{middle dot}7%) and EA (69{middle dot}3%) ancestries, respectively. Each 10% AA increase was associated with OR 1{middle dot}15, SBP beta +0{middle dot}9 mmHg, and DBP beta +0{middle dot}7 mmHg. Conversely, each 10% AI increase was associated with OR 0{middle dot}83, SBP beta -0{middle dot}4 mmHg, and DBP beta -0{middle dot}6 mmHg. HTN prevalence was highest among those with Black race or in the highest AA quantile (45{middle dot}6% and 48{middle dot}0%, respectively), and lowest among those with Native American race or in the highest AI quantile (37{middle dot}6% and 26{middle dot}7%, respectively). Conclusion. One-third of Hispanics\/Latinos did not self-report race. Black or White self-reporting race did somewhat relate to AA or EA ancestry, respectively. HTN profiles were related to self-reported race and genetic ancestry in this admixed population.","rel_num_authors":16,"rel_authors":[{"author_name":"Raul Antonio Montanez-Valverde","author_inst":"Montefiore Medical Group"},{"author_name":"Vivian Kim","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Priscilla Duran-Luciano","author_inst":"Albert Einstein College of Medicine Department of Medicine"},{"author_name":"Yawen Yuan","author_inst":"Albert Einstein College of Medicine\/Montefiore Medical Center"},{"author_name":"Tamar Sofer","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Robert C. Kaplan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Linda C Gallo","author_inst":"San Diego State University"},{"author_name":"Gregory A. Talavera","author_inst":"San Diego State University KPBS"},{"author_name":"Krista M Perreira","author_inst":"The University of North Carolina at Chapel Hill Innovate Carolina"},{"author_name":"Martha L. Daviglus","author_inst":"Institute for Minority Health Research, University of Illinois-Chicago"},{"author_name":"Sylvia E. Rosas","author_inst":"Joslin Diabetes Center"},{"author_name":"Maria M. Llabre","author_inst":"University of Miami"},{"author_name":"Tali Elfassy","author_inst":"University of Miami Miller School of Medicine"},{"author_name":"Xihao Li","author_inst":"UNC Gillings School of Global Public Health"},{"author_name":"Carmen R. Isasi","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Carlos Jose Rodriguez","author_inst":"Albert Einstein College of Medicine and Montefiore Medical Center"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Relation of Self-Reported Race and Genetic Ancestry to Hypertension Prevalence Among Hispanics\/Latinos: The Hispanic Community Health Study\/Study of Latinos","rel_doi":"10.64898\/2026.09.01.26361995","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361995","rel_abs":"Background. The imprecision of current metrics to capture the complex genetic admixture and racial identity among Hispanic\/Latino individuals in the United States [US] is a concern. We examined the relationship of self-reported race and genetic ancestry with hypertension [HTN] among Hispanics\/Latinos. Methods. Cross-sectional study of the Hispanic Community Health Study\/Study of Latinos (HCHS\/SOL), including 10,586 Hispanic\/Latino unrelated adults. Genetic ancestry: West African [AA], Amerindian [AI], and European [EA]. Self-reported race: White, Black, Native American, or Multiple\/Missing (More than one race or Unknown\/Not reported\/Refused). HTN: systolic (SBP) [&ge;]130 mmHg, diastolic blood pressure (DBP) [&ge;]80 mmHg, and\/or use of HTN medications. Age- and sex adjusted models were used. Results. Self-reported race was White (38{middle dot}6%), Black (3{middle dot}6%), Native American (4{middle dot}1%), and Multiple\/Missing (53{middle dot}7%), with Unknown\/Not reported\/Refused representing 32{middle dot}7%. Black and White Hispanics\/Latinos had the greatest AA (55{middle dot}7%) and EA (69{middle dot}3%) ancestries, respectively. Each 10% AA increase was associated with OR 1{middle dot}15, SBP beta +0{middle dot}9 mmHg, and DBP beta +0{middle dot}7 mmHg. Conversely, each 10% AI increase was associated with OR 0{middle dot}83, SBP beta -0{middle dot}4 mmHg, and DBP beta -0{middle dot}6 mmHg. HTN prevalence was highest among those with Black race or in the highest AA quantile (45{middle dot}6% and 48{middle dot}0%, respectively), and lowest among those with Native American race or in the highest AI quantile (37{middle dot}6% and 26{middle dot}7%, respectively). Conclusion. One-third of Hispanics\/Latinos did not self-report race. Black or White self-reporting race did somewhat relate to AA or EA ancestry, respectively. HTN profiles were related to self-reported race and genetic ancestry in this admixed population.","rel_num_authors":16,"rel_authors":[{"author_name":"Raul Antonio Montanez-Valverde","author_inst":"Montefiore Medical Group"},{"author_name":"Vivian Kim","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Priscilla Duran-Luciano","author_inst":"Albert Einstein College of Medicine Department of Medicine"},{"author_name":"Yawen Yuan","author_inst":"Albert Einstein College of Medicine\/Montefiore Medical Center"},{"author_name":"Tamar Sofer","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Robert C. Kaplan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Linda C Gallo","author_inst":"San Diego State University"},{"author_name":"Gregory A. Talavera","author_inst":"San Diego State University KPBS"},{"author_name":"Krista M Perreira","author_inst":"The University of North Carolina at Chapel Hill Innovate Carolina"},{"author_name":"Martha L. Daviglus","author_inst":"Institute for Minority Health Research, University of Illinois-Chicago"},{"author_name":"Sylvia E. Rosas","author_inst":"Joslin Diabetes Center"},{"author_name":"Maria M. Llabre","author_inst":"University of Miami"},{"author_name":"Tali Elfassy","author_inst":"University of Miami Miller School of Medicine"},{"author_name":"Xihao Li","author_inst":"UNC Gillings School of Global Public Health"},{"author_name":"Carmen R. Isasi","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Carlos Jose Rodriguez","author_inst":"Albert Einstein College of Medicine and Montefiore Medical Center"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Proteoform-resolved neoGFAP as a diagnostic and prognostic biomarker across the TBI--MCI--AD continuum in Veterans","rel_doi":"10.64898\/2026.09.01.26361845","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361845","rel_abs":"Service members with traumatic brain injury are at approximately two- to four-fold higher risk of Alzheimer's disease or related dementias than those without such an injury, with risk increasing with injury severity. The amyloid\/tau\/neurodegeneration biomarker framework treats amyloid, tau, and neurodegeneration as independent axes but omits astroglial injury, despite evidence that reactive astrogliosis (indexed by glial fibrillary acidic protein, GFAP) must be elevated for cognitive decline to occur in amyloid-positive individuals. Total GFAP immunoassays aggregate intact protein with multiple calpain- and caspase-cleaved proteoforms, blurring the biological signal. We compared a calpain-cleaved GFAP neoepitope, the glial fibrillary acidic protein neoepitope (neoGFAP), against total GFAP across the full traumatic brain injury--mild cognitive impairment--Alzheimer's disease continuum in Veterans using a two-stage plasma-to-cerebrospinal-fluid biomarker approach. A plasma triage gate combining phosphorylated tau 217 and amyloid beta 42 was applied to 367 unique subjects; a cerebrospinal-fluid benchmarking cohort of 57 subjects (controls, chronic blast traumatic brain injury, mild cognitive impairment, and Alzheimer's disease) received head-to-head neoGFAP and total GFAP measurement. In the whole benchmarking cohort, neoGFAP discriminated mild cognitive impairment plus Alzheimer's disease from non-Alzheimer subjects with an area under the receiver-operating-characteristic curve of 0.81 versus 0.73 for total GFAP, a trend-level advantage that did not reach nominal significance. Within the gate-positive, amyloid-committed subset of 23 subjects, neoGFAP dominance became significant by McNemar's exact test (six discordant subjects favored neoGFAP, none the reverse). Across diagnostic contrasts, neoGFAP outperformed total GFAP for Alzheimer's disease versus control and, importantly for Veterans, for mild cognitive impairment versus chronic blast-exposed Veterans without cognitive impairment. In chronic blast injury, neoGFAP was paradoxically depleted relative to controls, consistent with tissue sequestration of aggregated proteoform fragments. Unbiased proteomic profiling confirmed coordinated elevation across astrocytic, neuronal, mitochondrial, and microglial compartments. An exploratory subject-level reclassification improved accuracy from 71.1 percent using plasma alone to 79.5 percent with added cerebrospinal-fluid markers and age. In a same-cohort ProQuantum replication (n=57), CSF neoGFAP preserved its discrimination advantage over total GFAP for MCI+AD versus non-AD (AUROC 0.76 vs 0.72; cross-platform Spearman {rho}=0.84), while plasma neoGFAP achieved AUROC 0.90, comparable to pTau217 (0.92) and exceeding A{beta}42\/40 (0.84). In this small sample, neoGFAP is a superior proteoform-resolved diagnostic and prognostic biomarker across the continuum and supports adding an astroglial-proteoform axis to amyloid\/tau\/neurodegeneration biomarker frameworks in high-risk populations.","rel_num_authors":24,"rel_authors":[{"author_name":"William E. Haskins","author_inst":"Gryphon Bio, South San Francisco, CA, USA; Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Kevin K. Wang","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA; Foundation for Applied Molecular Evolution, Alachua, FL, USA"},{"author_name":"Guangzheng Cai","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA; Foundation for Applied Molecular Evolution, Alachua, FL, USA"},{"author_name":"Khadija Boukholda","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA"},{"author_name":"Eman Elbayoumi","author_inst":"Morehouse School of Medicine, Atlanta, GA, USA"},{"author_name":"Ruchi Bajpai","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Devin Jackson","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Katie Tehas","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Kristy Radeker","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Anthony DeLizza","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Caroline Popper","author_inst":"Gryphon Bio, South San Francisco, CA, USA"},{"author_name":"Martin Kiendl","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Sigrun Badrnya","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Markus Miholits","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Stefan Jellbauer","author_inst":"Thermo Fisher Scientific, Carlsbad, CA, USA"},{"author_name":"Todd Kilbaugh","author_inst":"Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Franklin Okumu","author_inst":"Owl Therapeutics, Cambridge, MA, USA"},{"author_name":"Ava Puccio","author_inst":"University of Pittsburgh, Pittsburgh, PA, USA"},{"author_name":"Raquel C. Gardner","author_inst":"Sheba Medical Center, Tel HaShomer, Israel; University of California San Francisco, San Francisco, CA, USA"},{"author_name":"Geoff Manley","author_inst":"University of California San Francisco, San Francisco, CA, USA"},{"author_name":"John B. Williamson","author_inst":"Brain Rehabilitation Research Center, Gainesville, FL, USA"},{"author_name":"Abigail B. Waters","author_inst":"Brain Rehabilitation Research Center, Gainesville, FL, USA"},{"author_name":"Gail Ge Li","author_inst":"VA Northwest Mental Illness Research, Education, and Clinical Center (VA NW MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA; Department of Psychiat"},{"author_name":"Elaine R. Peskind","author_inst":"VA Northwest Mental Illness Research, Education, and Clinical Center (VA NW MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA; Department of Psychiat"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Clinical deep sequencing to diagnose pathogenic mosaic variants in malformations of cortical development and epilepsy","rel_doi":"10.64898\/2026.09.01.26361943","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361943","rel_abs":"Background and Objectives: Deep sequencing of brain tissue in the research setting has established that mosaic variants are a major cause of malformations of cortical development (MCDs) and epilepsy. However, genetic testing in the clinical setting primarily detects germline variants using clinically accessible samples. We aimed to determine the diagnostic yield and clinical utility of deep sequencing in the clinical setting to identify pathogenic mosaic variants for this population. Methods: We performed a retrospective cohort analysis of individuals at Boston Children's Hospital with MCDs with or without epilepsy who received clinical deep sequencing between September 2017 and February 2026. Demographic, clinical, and genetic testing data were abstracted from the medical record. For individuals without systemic features, we classified brain tissue as an affected tissue sample. For individuals with systemic features, we classified brain or relevant non-brain tissue as affected. The primary outcome was the diagnostic yield of clinical deep sequencing performed using affected vs unaffected tissue samples. The secondary outcome was the clinical utility of genetic diagnoses. Results: Our cohort included 37 individuals (19\/37 (51%) female, 18\/37 (49%) male) with MCDs, of whom 35\/37 (95%) had epilepsy (25 with brain tissue samples available from epilepsy surgery) and 8\/37 (22%) had systemic features. Most (35\/37 (95%)) had dysplasia phenotypes on MRI and 12\/27 (44%) with pathology available had Focal Cortical Dysplasia Type I or II. The diagnostic yield was 53% (17\/32; 16 mosaic and 1 germline variant) when clinical deep sequencing was performed using an affected tissue sample vs 0% (0\/6) using an unaffected tissue sample (p=0.016). Of the diagnosed cases, 13\/17 (76%) had testing performed on brain tissue (1 with systemic features) and 4\/17 (24%) on non-brain tissue (3 buccal and 1 duodenal tissue, all with systemic features). All but one diagnosis involved the mTOR pathway. All diagnoses had clinical utility. Discussion: Clinical deep sequencing, when performed using an affected tissue sample, has high diagnostic yield and clinical utility for individuals with MCDs, especially dysplasia phenotypes, and epilepsy. Our findings support implementation of clinical deep sequencing for this population, especially as the genetic diagnoses have implications for emerging precision therapies.","rel_num_authors":14,"rel_authors":[{"author_name":"Katelyn Stone","author_inst":"Boston Children's Hospital"},{"author_name":"Gillian Prinzing","author_inst":"Boston Children's Hospital"},{"author_name":"Abbe Lai","author_inst":"Boston Children's Hospital"},{"author_name":"Lacey Smith","author_inst":"Boston Children's Hospital"},{"author_name":"Beth R Sheidley","author_inst":"Boston Children's Hospital"},{"author_name":"Meagan M Corliss","author_inst":"Washington University School of Medicine"},{"author_name":"Kevin Bowling","author_inst":"Washington University in St. Louis"},{"author_name":"Yang Cao","author_inst":"Washington University School of Medicine"},{"author_name":"Kimberly Wiltrout","author_inst":"Boston Children's Hospital"},{"author_name":"Scellig S.D. Stone","author_inst":"Boston Children's Hospital"},{"author_name":"Hart Lidov","author_inst":"Boston Children's Hospital"},{"author_name":"Edward Yang","author_inst":"Boston Children's Hospital"},{"author_name":"Annapurna Poduri","author_inst":"Boston Children's Hospital"},{"author_name":"Alissa M D'Gama","author_inst":"Boston Children's Hospital"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Hybrid risk scores integrating polygenic and clinical variables for endometriosis prediction","rel_doi":"10.64898\/2026.08.31.26361798","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361798","rel_abs":"Background: Endometriosis affects approximately 10% of reproductive-age women and is associated with substantial diagnostic delay and heterogeneous symptom presentation. Prior machine-learning prediction models have relied on comorbidity data alone or on small candidate-variant genetic scores, with inconsistent or incompletely reported performance. No study has combined a well-powered, multi-ancestry polygenic risk score (PRS) with environmental, reproductive, and symptom data in a single hybrid model. We developed and evaluated hybrid risk-prediction models integrating a genome-wide, multi-ancestry PRS with clinical and symptom data for endometriosis in the US-based All of Us Research Program. Methods: Among 69,376 participants (15,382 endometriosis cases, 53,994 controls) across six genetically inferred ancestry groups, we computed individual-level PRS values using PRS-CS weights derived from an independent, multi-ancestry GWAS. Five nested logistic regression, random forest, and XGBoost models progressively added age, ancestry, and within-ancestry genetic principal components (Model 1), environmental and reproductive factors (Model 2), symptom and comorbidity indicators (Model 3), all covariates combined (Model 4), and PRS x environment interactions (Model 5). Performance was assessed by AUROC in a held-out test set and 5-fold cross-validation, with class-weighted, Youden-optimized thresholds used for sensitivity, specificity, and predictive values; permutation importance identified top contributors. Pairwise AUROC differences were tested with a Holm-corrected DeLong-type test. Results: Discrimination improved from AUROC 0.63 (PRS, age, ancestry, principal components) to 0.72 for the full model, driven mainly by symptom and comorbidity data. XGBoost consistently outperformed logistic regression and random forest. The PRS ranked among the top individual predictors by permutation importance in nearly every model, alongside age, while genetic and demographic information alone gave only modest discrimination, and PRS x environment interactions did not improve on environmental factors alone. Threshold optimization yielded balanced sensitivity and specificity (~0.67\/0.65) versus near-zero sensitivity at a default threshold. Conclusions: Combining the PRS with symptom and comorbidity data gave the best discrimination compared to solely a well-powered, multi-ancestry PRS as a predictor of endometriosis. This study clarifies both the promise and current limits of hybrid genetic-clinical prediction for endometriosis and points to symptom-based phenotyping, molecular subtyping, and external validation as priorities.","rel_num_authors":2,"rel_authors":[{"author_name":"Oksana Goroshchuk","author_inst":"Yale University School of Medicine"},{"author_name":"Dora Koller","author_inst":"Institut de Recerca Sant Pau"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"A target trial emulation study to estimate the causal effect of intravenous iron use during pregnancy and its effect on haematological and birth outcomes in Pakistan","rel_doi":"10.64898\/2026.08.29.26361700","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.29.26361700","rel_abs":"Background: Despite several trials on the hematological outcomes of intravenous (IV) iron in pregnancy, only few have examined its effect on birth outcomes. We estimated the causal effect of IV-iron on moderate or severe anaemia and birth outcomes. Methods: Women presenting to routine antenatal care in Pakistan with haemoglobin <10 g\/dL were eligible for treatment. We used target trial emulation (TTE) methodology to estimate the effect of IV-iron treatment within 14 days of anaemia identification, compared to no treatment, on anaemia status at follow-up. A modified TTE analysis examined birth outcomes at delivery for singleton pregnancies, including birthweight, size-for-gestational-age, and mortality. We conducted a separate TTE for each of five gestational-age periods and pooled the results of each TTE. Results: We screened 3115 pregnancies of which 1715 were eligible for IV-iron; 1043 participants were treated during pregnancy. Those who received IV-iron had half the risk of moderate or severe anaemia in pregnancy compared with no treatment (pooled relative risk (RR) 0.40; 95% confidence interval (CI): 0.27, 0.59). The pooled effect of IV-iron on stillbirth suggested an 83% risk reduction (95% CI 55-94%), and trends were similar for perinatal and neonatal mortality. Conclusion: IV-iron treatment improved haematological status in pregnant women and was associated with a large reduction in stillbirth. Given limited data from randomised trials regarding fetal death and treatment earlier in pregnancy, this study contributes important information to the potential benefit of IV-iron in contexts where anaemia and its sequelae are a major public health problem.","rel_num_authors":17,"rel_authors":[{"author_name":"Nida Salman Yazdani Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Erin Oakley Ms.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Amna Khan Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Muhammad Farrukh Qazi Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Shayan Khakwani Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Asad Sheikh Mr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Azqa Mazhar Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Uzma Muhammad Iqbal Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Jaime Marquis Ms.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Bushra Liaqat Dr.","author_inst":"Department of Obstetrics & Gynaecology, Koohi Goth Women's Hospital Karachi, Karachi, Sindh, Pakistan"},{"author_name":"Kaveeta Kumari Dr.","author_inst":"Department of Obstetrics & Gynaecology, Creek General Hospital, Karachi, Sindh, Pakistan"},{"author_name":"Ellen C. Caniglia Dr.","author_inst":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA"},{"author_name":"Aneeta Hotwani Ms.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Imran Nisar Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Fyezah Jehan Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"},{"author_name":"Emily R. Smith Dr.","author_inst":"Department of Global Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA"},{"author_name":"Zahra Hoodbhoy Dr.","author_inst":"Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Sindh, Pakistan"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Evaluating Clinical Foundation Models for Early Alzheimer's Disease and Related Dementia Prediction from Longitudinal EHRs","rel_doi":"10.64898\/2026.09.01.26361933","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361933","rel_abs":"Early identification of Alzheimer's disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.","rel_num_authors":5,"rel_authors":[{"author_name":"Shahla Farzana","author_inst":"Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA"},{"author_name":"Ash Arian","author_inst":"Pritzker School of Medicine, University of Chicago, Chicago, IL, USA"},{"author_name":"Tatjana Rundek","author_inst":"University of Miami"},{"author_name":"Moise Desvarieux","author_inst":"Mailman School of Public Health, Columbia University"},{"author_name":"Habibul Ahsan","author_inst":"Department of Family Medicine, Biological Sciences Division, University of Chicago Medicine, Chicago, IL, USA"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"Evaluating Clinical Foundation Models for Early Alzheimer's Disease and Related Dementia Prediction from Longitudinal EHRs","rel_doi":"10.64898\/2026.09.01.26361933","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.01.26361933","rel_abs":"Early identification of Alzheimer's disease and related dementias (ADRD) remains challenging despite its importance for timely intervention, management of modifiable risk factors, and care planning. We developed and evaluated ADRD onset prediction models using longitudinal electronic health records (EHRs) from the All of Us Research Program at clinically meaningful lead times of 6, 12, 24, and 36 months before diagnosis, benchmarking interpretable count-based representations against four publicly available pretrained clinical foundation models (CLMBR-T, GPT-style, LLaMA-style, and Mamba) across multiple ADRD phenotype definitions. Count-based models consistently achieved the highest discrimination and calibration across all cohorts and prediction horizons. Predictive performance declined with increasing lead time for all approaches; however, the performance gap between count-based and pretrained representations progressively narrowed, with foundation models achieving comparable AUROC of 0.719 (compared to the AUROC of 0.738 of count-based model) at the 36-month horizon while providing higher sensitivity and F1 scores under a fixed operating threshold. External validation with zero-shot evaluation on UChicago EHRs exhibited limited generalizability for count-based and pretrained clinical foundation model based representations. These findings demonstrate that transparent count-based EHR representations remain the strongest overall approach for ADRD onset prediction, while pretrained clinical foundation models provide complementary advantages for long-term risk identification and establish a benchmark for evaluating transferable clinical representations in temporal ADRD risk prediction.","rel_num_authors":5,"rel_authors":[{"author_name":"Shahla Farzana","author_inst":"Institute for Population and Precision Health, University of Chicago, Chicago, IL, USA"},{"author_name":"Ash Arian","author_inst":"Pritzker School of Medicine, University of Chicago, Chicago, IL, USA"},{"author_name":"Tatjana Rundek","author_inst":"University of Miami"},{"author_name":"Moise Desvarieux","author_inst":"Mailman School of Public Health, Columbia University"},{"author_name":"Habibul Ahsan","author_inst":"Department of Family Medicine, Biological Sciences Division, University of Chicago Medicine, Chicago, IL, USA"}],"rel_date":"2026-09-03","rel_site":"medrxiv"},{"rel_title":"The human metabolite - protein interactome reveals a global layer of cellular coordination","rel_doi":"10.64898\/2026.08.28.747909","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.28.747909","rel_abs":"Metabolites are substrates, products, cofactors, and regulators, but protein-protein interaction networks do not represent their potential to organize proteins across conventional pathway boundaries. Using the LIGMAP virtual-screening algorithm, we mapped 308 human metabolite codes to pockets in monomers, dimer interfaces, and non-interface sites in dimers and represented attractive or repulsive COLIG states involving pairs of metabolites in the same pocket. On a fixed cohort of 3,938 proteins, the mean coverage of 104 strict non-enzyme pathways was 44.6% for LIGMAP, 70.1% for STRING, and 82.5% for STRING+LIGMAP; the union placed 86.3% of eligible proteins in the largest connected component and 95.1% in the two largest components. STRING+LIGMAP protein coverage was 88.0% for 68 enzyme-only pathways and 88.9% for 1,283 mixed pathways. In pathway-held-out, degree-matched prediction, adding LIGMAP to degree plus STRING increased the mean area under the precision-recall curve from 0.651 to 0.660 (paired P = 0.024); adding BioLiP2 increased it to 0.663 (paired P = 0.005). Ancient-only and non-ancient-only subnetworks were each globally connected; ancient features were denser, whereas non-ancient features covered more proteins and pathways. At the full 5,426-protein scale, retaining only features assigned to 2-100 proteins recovered 698 of 1,691 strict non-enzyme reference edges (41.3%) and exceeded both protein-label and exact bipartite degree-preserving nulls. Uncapped recovery approached saturation and lost identity-selective enrichment. Experimentally established metabolite-dependent complexes validate the local mechanism independently of LIGMAP; LIGMAP fully recovered two of seven stringent direct mechanisms and all three broader serial axes examined. Our findings reveal a global metabolite-mediated architecture with the capacity to coordinate proteins across otherwise distinct cellular systems.","rel_num_authors":2,"rel_authors":[{"author_name":"Jeffrey Skolnick","author_inst":"Georgia Institute of Technology"},{"author_name":"Bharath Srinivasan","author_inst":"Cancer Research Horizons"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Sea stickleback genome reveals repeated chromosomal rearrangements in sticklebacks","rel_doi":"10.64898\/2026.08.30.747834","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747834","rel_abs":"Sticklebacks (Gasterosteidae) encompass model organisms which are of particular interest for evolutionary and ecological genomics. Within Gasterosteidae, chromosome number is variable (2n=40-46) and independent fusions of homologous chromosomes have been proposed. The sea stickleback (or fifteen-spined stickleback, Spinachia spinachia ) has the lowest known number of chromosomes (2n=40) and hence is crucial in understanding chromosome evolution among sticklebacks, but is so far missing in genomic datasets. Here, we present a high-quality diploid genome assembly of S. spinachia. PacBio HiFi and Hi-C reads were assembled into a genome of 407.5 Mb in size, consisting of 20 chromosomes, with an N50 of 6.6 Mb and 98.96% complete single-copy BUSCO genes. A phylogenetic tree inferred across five stickleback species and four outgroup genomes from 19,156 genes, alongside synteny analyses and ancestral chromosome reconstructions, confirms S. spinachia as the sister species to the four-spined stickleback (Apeltes quadracus) and not as the sister group to all other sticklebacks as once thought. It has one species-specific chromosome fusion and shares two fusions with the three-spined stickleback (Gasterosteus aculeatus), none of which are present in its sister species. One of these fusions is also present in Pungitius, leading to reinterpretion of this fusion as ancestral to Gasterosteidae, with subsequent fission in Apeltes. This implies a lower ancestral chromosome number in Gasterosteidae (2n=44) than previously thought. The other fusion shared with G. aculeatus presents a case of convergence. Our results suggest that karyotype evolution in Gasterosteidae has been shaped by ancestral chromosome fusion, convergent fusion, and secondary fission.","rel_num_authors":25,"rel_authors":[{"author_name":"Jule Drewalowski","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Sergei Kliver","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Leon Hilgers","author_inst":"Senckenberg, Leibniz Institution for Biodiversity and Earth System Research, Senckenberganlage 25, 60325 Frankfurt, Germany"},{"author_name":"Peter Rask M\u00f8ller","author_inst":"Natural History Museum of Denmark, University of Copenhagen, Denmark"},{"author_name":"Sarah ST Mak","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Iva Kova\u010di\u0107","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Bent Petersen","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Joseph Nesme","author_inst":"Department of Biology, University of Copenhagen, Denmark"},{"author_name":"Ann M Mc Cartney","author_inst":"Institute of Clinical and Translational Sciences, University of California, Irvine, CA, USA"},{"author_name":"Alice Mouton","author_inst":"CARE, Laboratoire des Transitions, University of Liege, Belgium"},{"author_name":"Giulio Formenti","author_inst":"Vertebrate Genome Lab, Rockefeller University, New York City, USA"},{"author_name":"Hannes Svardal","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Genevieve Diedericks","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Henrique G Leit\u00e3o","author_inst":"Department of Biology, University of Antwerp, Antwerp, Belgium"},{"author_name":"Rosa Fern\u00e1ndez","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Nuria Escudero","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Judit Salces-Ortiz","author_inst":"Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain"},{"author_name":"Claudio Ciofi","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Chiara Natali","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Maria Angela Diroma","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Alessio Iannucci","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Marco Sollitto","author_inst":"Department of Biology, University of Florence, Sesto Fiorentino (FI), Italy"},{"author_name":"Michael Hiller","author_inst":"Senckenberg, Leibniz Institution for Biodiversity and Earth System Research, Senckenberganlage 25, 60325 Frankfurt, Germany"},{"author_name":"M Thomas P Gilbert","author_inst":"Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Denmark"},{"author_name":"Josefin Stiller","author_inst":"Department of Biology, University of Copenhagen, Denmark"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multi-kingdom microbial diversity and interaction landscapes in mosquitoes revealed by 5,163 individual meta-transcriptomes","rel_doi":"10.64898\/2026.09.02.748197","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748197","rel_abs":"Mosquitoes are pathogen vectors embedded within diverse microbial ecosystems. However, the nature and interactions among their multi-kingdom microbiome remain poorly understood. We conducted a nationwide single-mosquito meta-transcriptomic survey of 5,163 mosquitoes representing 100 species across China, integrating viral discovery with marker-gene profiling of bacteria, archaea, fungi, and other eukaryotic microbes. From this, we identified 1,606 microbial species-level taxa, including extensive novel diversity, and revealed pronounced host species-specific organization of mosquito-associated communities. We detected 34 pathogens or potential pathogens of human or animal relevance, whose prevalence, abundance, host range, and geographic distribution defined distinct epidemiological patterns. Network analysis uncovered pervasive cross-kingdom microbial associations, including candidate antiviral relationships involving Wolbachia and other microbial taxa. Our study establishes a detailed view of the full-spectrum microbiome and provides a resource and conceptual framework for studying vector competence, pathogen emergence, and microbiome-informed mosquito-borne disease control.","rel_num_authors":40,"rel_authors":[{"author_name":"Qin-Yu Gou","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Wei-Chen Wu","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Pei-Bo Shi","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Geng-Yan Luo","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yuan-Fei Pan","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jing Wang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yan Gao","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Kai-Jie Liu","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Hai-Long Zhao","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Yun Feng","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"Kun Li","author_inst":"National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, "},{"author_name":"Wei-Hong Yang","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"De Wu","author_inst":"Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China."},{"author_name":"Shi-Jia Le","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Gen-Yang Xin","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Min-Wu Peng","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Yu-Qi Liao","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Chun-Hui Yang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Shi-Qiang Mei","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jia-Ming Huang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jin-Xia Cheng","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Xin Hou","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Jian-Bin Kong","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Xin-Xin Chen","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Bing Zhang","author_inst":"Xinjiang Key Laboratory of Molecular Biology for Endemic Diseases, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi, China."},{"author_name":"Zi-Rui Ren","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Jun-Hua Li","author_inst":"State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China."},{"author_name":"Xin Jin","author_inst":"State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, China."},{"author_name":"Juan Wang","author_inst":"Department of Viral and Rickettsial Disease Control, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease C"},{"author_name":"Tong-Qing An","author_inst":"State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China."},{"author_name":"Xin-Yi Huang","author_inst":"State Key Laboratory of Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China."},{"author_name":"Jie Cui","author_inst":"Department of Infectious Diseases, National Medical Center for Infectious Diseases, Huashan Hospital, Institute of Infection and Health Research, Fudan Universi"},{"author_name":"John-Sebastian Eden","author_inst":"Centre for Virus Research, Westmead Institute for Medical Research, Westmead, New South Wales, Australia.;School of Medical Sciences, The University of Sydney, "},{"author_name":"Gong Cheng","author_inst":"New Cornerstone Science Laboratory, Tsinghua University-Peking University Joint Center for Life Sciences, School of Basic Medical Sciences, Tsinghua University,"},{"author_name":"De-Yin Guo","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Guo-Dong Liang","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Ce"},{"author_name":"Edward C. Holmes","author_inst":"School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia."},{"author_name":"Zi-Qing Deng","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"},{"author_name":"Mang Shi","author_inst":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University"},{"author_name":"Da-Xi Wang","author_inst":"BGI Research, Beijing 100083, China.;Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI Research, Shenzhen, China.;State Key Laboratory of Genome a"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Rebuilding microbiome diversity theory on the closed simplex","rel_doi":"10.64898\/2026.09.02.748976","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748976","rel_abs":"Ecological diversity theory links diversity within local communities to diversity of higher-level ensembles, but this scale structure is largely absent from microbiome analysis. Alpha diversity is usually treated as a within-sample summary, whereas \"beta diversity\" often denotes pairwise dissimilarity and gamma diversity is rarely explicit. We restore the local-regional architecture for environmental, host-associated and longitudinal microbiomes and distinguish regional beta diversity from pairwise dissimilarity and predictor-associated compositional variation. To quantify these objects for sparse compositions, we introduce Hellinger-Riemann intrinsic coordinates (HRIC), a one-to-one, bounded normal-coordinate representation of the closed simplex that retains exact zeros. The same coordinates yield Simplex Hellinger alpha and gamma diversity, additive regional beta diversity, taxon contributions, pairwise dissimilarity and model-explained dispersion. Simulations established the correspondence between HRIC dispersion and the between-condition component of PERMANOVA. Across Arctic and North Atlantic communities, local diversity relative to each regional benchmark covaried similarly with vertical environmental gradients despite partly different taxon-level associations. In a randomized autologous faecal microbiota transplantation trial, recipients returned earlier towards their personal pre-transplant compositions, whereas the alpha-diversity difference was smaller and less precise. Explicit local and regional referents therefore connect diversity partitioning with compositional analysis across microbial systems.","rel_num_authors":2,"rel_authors":[{"author_name":"Yiqian Zhang","author_inst":"The Ohio State University"},{"author_name":"Zihan Zhu","author_inst":"Yale University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"A Lymphomimetic Synthetic Immune Niche, Consisting of CCL21 and ICAM1, Accelerates the Expansion of Potent CAR T-cells","rel_doi":"10.64898\/2026.08.30.748076","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748076","rel_abs":"Background: Chimeric Antigen Receptor (CAR) T-cell therapy has transformed the treatment of hematologic malignancies, yet, its broader clinical application often faces major challenges, including slow expansion rates, variable transduction efficiency, exhaustion, and functional heterogeneity. Recent studies have demonstrated that a Synthetic Immune Niche (SIN) composed of immobilized CCL21 and ICAM1 promotes the proliferation of murine and human T-cells while preserving their cytotoxic potency. In this study, we explored the capacity of immobilized CCL21 and ICAM1 to enhance the production of highly potent CAR T-cells by facilitating both their expansion and cytotoxic capacity. Methods: CD19-directed CAR T-cells were generated from PBMCs of healthy donors using a clinical-grade protocol. Following retroviral transduction, the cells were expanded on CCL21 and ICAM1 coated plates, or on uncoated control plates. The effects of the SIN treatment on CAR T-cell expansion, morphology, physical properties, phenotypic markers, and potency were systematically evaluated. Results: SIN exposure significantly enhanced CAR T-cell expansion, achieving a 9.3-fold higher total cell yield by day 13, compared with control cultures. This proliferative advantage persisted even after withdrawal of the cells from the synthetic niche on day 10. SIN stimulation preferentially expanded the CAR-transduced population, increasing CAR T-cell frequencies from 45.4% to 77.6%, resulting in a 14.6-fold increase in the absolute number of CAR T-cells compared with untreated cultures. Morphological and phenotypic analyses revealed distinct activated cell morphology, manifested by increased cell size, polarity and granularity, and elevated expression of the activation markers CD137 and CD69. Importantly, CAR T-cells transiently exposed to the SIN retained cytokine secretion and cytotoxic activity comparable to that of continuously SIN-treated cells, indicating that the SIN effect is persistent. Overall, SIN-conditioned CAR T-cells displayed robust antigen-dependent IFN-{gamma} secretion and cytotoxicity against CD19-expressing target cells. Conclusion: Stimulation of CAR T-cells with a synthetic immune niche consisting of immobilized CCL21 and ICAM1 enhances overall expansion while selectively enriching the CAR-transduced population, thereby substantially increasing both the number and prominence of therapeutically relevant CAR T-cells. This effect is accompanied by a persistent activation phenotype and high functional potency. We propose that incorporating SIN stimulation into CAR T-cell manufacturing represents a simple and scalable strategy for improving CAR T-cell yield while maintaining high cytotoxic efficacy.","rel_num_authors":5,"rel_authors":[{"author_name":"Karin Brezinger-Dayan","author_inst":"Rabin Medical Center"},{"author_name":"Sofi Yado","author_inst":"The Weizmann Institute of Science"},{"author_name":"Rawan Zoabi","author_inst":"The Weizmann Institute of Science"},{"author_name":"Benjamin Geiger","author_inst":"The Weizmann Institute of Science"},{"author_name":"Michal J Besser","author_inst":"Rabin Medical Center"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"A Lymphomimetic Synthetic Immune Niche, Consisting of CCL21 and ICAM1, Accelerates the Expansion of Potent CAR T-cells","rel_doi":"10.64898\/2026.08.30.748076","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748076","rel_abs":"Background: Chimeric Antigen Receptor (CAR) T-cell therapy has transformed the treatment of hematologic malignancies, yet, its broader clinical application often faces major challenges, including slow expansion rates, variable transduction efficiency, exhaustion, and functional heterogeneity. Recent studies have demonstrated that a Synthetic Immune Niche (SIN) composed of immobilized CCL21 and ICAM1 promotes the proliferation of murine and human T-cells while preserving their cytotoxic potency. In this study, we explored the capacity of immobilized CCL21 and ICAM1 to enhance the production of highly potent CAR T-cells by facilitating both their expansion and cytotoxic capacity. Methods: CD19-directed CAR T-cells were generated from PBMCs of healthy donors using a clinical-grade protocol. Following retroviral transduction, the cells were expanded on CCL21 and ICAM1 coated plates, or on uncoated control plates. The effects of the SIN treatment on CAR T-cell expansion, morphology, physical properties, phenotypic markers, and potency were systematically evaluated. Results: SIN exposure significantly enhanced CAR T-cell expansion, achieving a 9.3-fold higher total cell yield by day 13, compared with control cultures. This proliferative advantage persisted even after withdrawal of the cells from the synthetic niche on day 10. SIN stimulation preferentially expanded the CAR-transduced population, increasing CAR T-cell frequencies from 45.4% to 77.6%, resulting in a 14.6-fold increase in the absolute number of CAR T-cells compared with untreated cultures. Morphological and phenotypic analyses revealed distinct activated cell morphology, manifested by increased cell size, polarity and granularity, and elevated expression of the activation markers CD137 and CD69. Importantly, CAR T-cells transiently exposed to the SIN retained cytokine secretion and cytotoxic activity comparable to that of continuously SIN-treated cells, indicating that the SIN effect is persistent. Overall, SIN-conditioned CAR T-cells displayed robust antigen-dependent IFN-{gamma} secretion and cytotoxicity against CD19-expressing target cells. Conclusion: Stimulation of CAR T-cells with a synthetic immune niche consisting of immobilized CCL21 and ICAM1 enhances overall expansion while selectively enriching the CAR-transduced population, thereby substantially increasing both the number and prominence of therapeutically relevant CAR T-cells. This effect is accompanied by a persistent activation phenotype and high functional potency. We propose that incorporating SIN stimulation into CAR T-cell manufacturing represents a simple and scalable strategy for improving CAR T-cell yield while maintaining high cytotoxic efficacy.","rel_num_authors":5,"rel_authors":[{"author_name":"Karin Brezinger-Dayan","author_inst":"Rabin Medical Center"},{"author_name":"Sofi Yado","author_inst":"The Weizmann Institute of Science"},{"author_name":"Rawan Zoabi","author_inst":"The Weizmann Institute of Science"},{"author_name":"Benjamin Geiger","author_inst":"The Weizmann Institute of Science"},{"author_name":"Michal J Besser","author_inst":"Rabin Medical Center"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"De novo design of ligand binding proteins using large language models alone","rel_doi":"10.64898\/2026.09.02.748987","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748987","rel_abs":"Protein design has rapidly advanced with the advent of sequence- and structure-based machine learning models. However, reasoned design, which applies physicochemical principles and rules derived from sequence-structure-function relationships, has not seen the same benefits from generative machine learning models. Here, we test the ability of common large language models (LLMs; e.g. Claude, ChatGPT, and Gemini) to consider design principles to generate de novo proteins that bind metals and lipophilic small molecules without copying existing sequences. Common LLMs alone are able to 1) generate protein sequences to adopt a desired fold and bind the target ligand and 2) explain the principles that motivate the design choices. Following structure prediction and filtering, we selected a small set of designs (6 to 12 designs per query) for experimental validation, affording metal binders in one round of LLM-based design (25% hit rate) and perfluorooctanoic acid binders in two rounds (25% hit rate in the second round of design). Importantly, the LLMs produce detailed justification to accompany the de novo designed sequences, providing a conceptual framework on which designs can be evaluated. While the successful designs have some deviations from the prompted parameters and LLM-articulated design rationale, these campaigns provide a case study that highlights the utility of LLMs in making protein design more comprehensible and accessible to users without sophisticated design expertise.","rel_num_authors":5,"rel_authors":[{"author_name":"Nam Hyeong Kim","author_inst":"University of California, San Francisco"},{"author_name":"A. Katherine Hatstat","author_inst":"University of California, San Francisco"},{"author_name":"Hyunil Jo","author_inst":"University of California, San Francisco"},{"author_name":"Yibing Wu","author_inst":"University of California, San Francisco"},{"author_name":"William F. DeGrado","author_inst":"University of California, San Francisco"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multivalent Anti-ACE2 Nanobodies Confer Broad Pan-Sarbecovirus Protection","rel_doi":"10.64898\/2026.09.01.748598","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748598","rel_abs":"The continual emergence of SARS-CoV-2 variants that rapidly evade conventional spike-directed neutralizing antibodies, together with the ongoing risk of cross-species spillover and new sarbecovirus outbreaks, underscores the need to develop broadly acting, escape-resistant therapeutic agents. Here, we optimized a nanobody discovery pipeline incorporating competition-based yeast surface display assays to isolate single-chain variable heavy chain-only antibody domains (VHHs or nanobodies) that bind human ACE2 and inhibit SARS-CoV-2 entry. Dimeric VHHs, as well as bivalent and tetravalent Fc-fusion proteins exhibited markedly increased antiviral activity, blocking a broad panel of SARS-CoV-2 variants and diverse sarbecoviruses at low-nanomolar to picomolar concentrations. These agents did not affect ACE2 enzymatic function or cell surface expression. The VHH-Fc fusion proteins had favorable pharmacokinetics and conferred prophylactic protection in mouse models of both SARS-CoV-2 and SARS-CoV infection, showcasing their potential as broadly acting receptor-targeted biologics against pandemic-threat viruses.","rel_num_authors":13,"rel_authors":[{"author_name":"Athanasios D. Bakasis","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Rachel Patejak","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Peter C. Fridy","author_inst":"Laboratory of Cellular and Structural Biology, Rockefeller University, 1230 York Ave, Box 213, New York, NY 10021, USA"},{"author_name":"Jesse Jenkins","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Miranda Aldis","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Viren A. Baharani","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Lakshmi Sriram","author_inst":"Laboratory of Cell Cycle Genetics, The Rockefeller University, New York, NY 10021, USA"},{"author_name":"Kelly R. Molloy","author_inst":"Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, USA"},{"author_name":"Brian T. Chait","author_inst":"Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, USA"},{"author_name":"Michael P. Rout","author_inst":"Laboratory of Cellular and Structural Biology, Rockefeller University, 1230 York Ave, Box 213, New York, NY 10021, USA"},{"author_name":"Frederick R. Cross","author_inst":"Laboratory of Cell Cycle Genetics, The Rockefeller University, New York, NY 10021, USA"},{"author_name":"Paul D. Bieniasz","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"},{"author_name":"Theodora Hatziioannou","author_inst":"Laboratory of Retrovirology, The Rockefeller University, New York, NY 10065"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"A low-dimensional, generalizable encoding manifold for auditory cortex","rel_doi":"10.64898\/2026.08.30.747962","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747962","rel_abs":"Neural populations in auditory cortex (AC) perform sensory computations that support stimulus category decoding and flexible behavior. The underlying geometry and generalizability of these computations for natural stimuli remain poorly understood, particularly at the single-neuron level, where neurons display wide-ranging tuning specificity and temporal acuity. To address this gap, we developed ACNet, a foundation model of cortical sound encoding, to predict the time-varying activity of >3000 neurons in AC of ferrets. Training data included 42 hours of natural sounds spanning over 100 categories and were collected from multiple recording sites across multiple animals. The model achieved state-of-the-art response prediction accuracy. Model activity was succinctly captured by a low-dimensional neural manifold, which generalized (>80% of variance) across animals. Analysis of ACNet activations revealed the emergence of rate-based, sparse sound coding across layers, a prominent feature of the auditory cortex. These transformations were concomitant with the emergence of more accurate and neurally aligned auditory category decoding, even though ACNet was not explicitly trained to categorize sounds. The model also revealed tuning differences between anatomically distinct cell types. Taken together, our results demonstrate that foundation models of sensory systems can reveal generalizable computations by large neural populations.","rel_num_authors":4,"rel_authors":[{"author_name":"Satyabrata Parida","author_inst":"Oregon Health & Science University"},{"author_name":"Jereme C Wingert","author_inst":"Oregon Health & Science University"},{"author_name":"Jonah D Stickney","author_inst":"Oregon Health & Science University"},{"author_name":"Stephen V David","author_inst":"Oregon Health and Science University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Understanding cephalopod regeneration: impact of sexual maturation, injury severity and general anesthesia on octopus arm restoration","rel_doi":"10.64898\/2026.08.29.747974","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.747974","rel_abs":"Octopuses exhibit remarkable regenerative capacity, as shown by their ability to fully restore a whole arm. During this process, a complex appendage containing muscle, connective tissue, vasculature, suckers, sensory structures, peripheral nerves and the axial nerve cord (ANC) is regrown. Although the morphology of octopus regeneration is increasingly well described, the physiological variables that determine regenerative speed, including the potential contribution of ion-channel-dependent processes, remain poorly understood. In this work we examined the influence of reproductive state, injury severity, biological sex and magnesium chloride (MgCl2) anesthesia, on the rate of longitudinal arm regrowth. Reproductive stage was the strongest determinant of regenerative performance: pre-reproductive stage (pre-RS) animals showed robust regrowth, whereas reproductive stage (RS) animals regenerated significantly slower. In pre-RS animals, regeneration speed scaled strongly with injury severity, whereas this relationship was absent in RS animals. Biological sex did not independently predict regeneration speed, and MgCl2 exposure showed no long-term inhibitory effect on regeneration. Our final model explained 81.4% of variance in regeneration speed, with reproductive state and injury severity having major effects. These findings demonstrate that sexual maturation and optic-gland-mediated senescence act as an irreversible physiological block against somatic regeneration, and provide empirical support for mechanosensory and bioelectric-dependent scaling models of cephalopod limb restoration.","rel_num_authors":6,"rel_authors":[{"author_name":"Samraggi Chakraborty","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Lukasz Bijoch","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Fan-Che Kung","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Ping-Jui Hsieh","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Zhen-Hong Yang","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"},{"author_name":"Kuo-Sheng Lee","author_inst":"Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Geometric reshaping of task-relevant representations in the primary visual cortex supports perceptual decisions","rel_doi":"10.64898\/2026.08.29.747961","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.747961","rel_abs":"Discriminating between two stimuli requires that their neural representations become separable by downstream readouts. This can be achieved geometrically, by disentangling the manifolds that population responses form in neural state space. Such reorganization has been observed in associative and motor areas, but never at the earliest stage of cortical processing. While task learning is known to impact neuronal representations in the primary visual cortex (V1), it is unknown if the population geometry is also reshaped to support perceptual decisions. We imaged V1 populations in mice trained on a Go\/NoGo orientation discrimination task of increasing difficulty, and in naive mice passively viewing the same stimuli. Training reshaped the representational geometry so that the population responses were better linearly separable. A static compression made the Go and NoGo manifolds more compact and lower-dimensional from the earliest response, while a dynamic separation drove them further apart through the trial. Together, those transformations increased manifold capacity and readout accuracy. This reorganization made the Go-NoGo relationship in the neural state space more stable in Trained animals than in Naive ones. Within this learned geometry, the position of individual trials along the Go-NoGo axis predicted the animals' decision probabilities. Learning therefore promotes a disentangled and stable representational geometry that feeds the decision process.","rel_num_authors":4,"rel_authors":[{"author_name":"Julien Corbo","author_inst":"Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark"},{"author_name":"Leyla Roksan Caglar","author_inst":"Windreich Department of AI and Human Health, Icahn School of Medicine at Mount Sinai"},{"author_name":"O. Batuhan Erkat","author_inst":"Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark"},{"author_name":"Pierre-Olivier Polack","author_inst":"Center for Molecular and Behavioral Neuroscience, Rutgers University - Newark"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Biased signaling via NtsR1 restrains food intake and weight gain in obese mice","rel_doi":"10.64898\/2026.08.30.748142","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748142","rel_abs":"Neurotensin receptor 1 (NTSR1) activation suppresses feeding and promotes weight loss but is limited by adverse effects associated with Gq signaling. SBI-553 is a {beta}-arrestin-biased allosteric modulator of NTSR1 that avoids these effects. We evaluated the effects of SBI-553 (5 or 12 mg\/kg, i.p.) on food intake and body weight in lean and diet-induced obese mice. Neither dose altered metabolic parameters, locomotor activity, or wheel running. SBI-553 did not affect ad libitum feeding in lean mice; however, both doses acutely reduced high-fat diet intake in obese mice. While 5 mg\/kg had no effect on hunger-induced feeding, 12 mg\/kg suppressed refeeding in both chow- and high-fat-fed mice of both sexes and reduced weight regain in obese mice. These findings identify SBI-553 as a potential strategy for reducing food intake and supporting weight loss.","rel_num_authors":9,"rel_authors":[{"author_name":"Jariel Ramirez-Virella","author_inst":"Michigan State University"},{"author_name":"Katherine Black","author_inst":"Michigan State University"},{"author_name":"Netanya F Dennis","author_inst":"Michigan State University"},{"author_name":"Raluca Bugescu","author_inst":"Michigan State University"},{"author_name":"Katie Thompson","author_inst":"Michigan State University"},{"author_name":"Steven H Olsen","author_inst":"Sanford Burnham Prebys"},{"author_name":"Lauren M Slosky","author_inst":"University of Minnesota Medical School"},{"author_name":"Zoe A McElligott","author_inst":"University of North Carolina Chapel Hill"},{"author_name":"Gina Marie Leinninger","author_inst":"Michigan State University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Biased signaling via NtsR1 restrains food intake and weight gain in obese mice","rel_doi":"10.64898\/2026.08.30.748142","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748142","rel_abs":"Neurotensin receptor 1 (NTSR1) activation suppresses feeding and promotes weight loss but is limited by adverse effects associated with Gq signaling. SBI-553 is a {beta}-arrestin-biased allosteric modulator of NTSR1 that avoids these effects. We evaluated the effects of SBI-553 (5 or 12 mg\/kg, i.p.) on food intake and body weight in lean and diet-induced obese mice. Neither dose altered metabolic parameters, locomotor activity, or wheel running. SBI-553 did not affect ad libitum feeding in lean mice; however, both doses acutely reduced high-fat diet intake in obese mice. While 5 mg\/kg had no effect on hunger-induced feeding, 12 mg\/kg suppressed refeeding in both chow- and high-fat-fed mice of both sexes and reduced weight regain in obese mice. These findings identify SBI-553 as a potential strategy for reducing food intake and supporting weight loss.","rel_num_authors":9,"rel_authors":[{"author_name":"Jariel Ramirez-Virella","author_inst":"Michigan State University"},{"author_name":"Katherine Black","author_inst":"Michigan State University"},{"author_name":"Netanya F Dennis","author_inst":"Michigan State University"},{"author_name":"Raluca Bugescu","author_inst":"Michigan State University"},{"author_name":"Katie Thompson","author_inst":"Michigan State University"},{"author_name":"Steven H Olsen","author_inst":"Sanford Burnham Prebys"},{"author_name":"Lauren M Slosky","author_inst":"University of Minnesota Medical School"},{"author_name":"Zoe A McElligott","author_inst":"University of North Carolina Chapel Hill"},{"author_name":"Gina Marie Leinninger","author_inst":"Michigan State University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Automated detection of Loa loa: a field trial in Cameroon","rel_doi":"10.64898\/2026.08.29.745114","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.745114","rel_abs":"Onchocerciasis (river blindness) is targeted for elimination through mass administration (MDA) of ivermectin (IVM) to endemic populations. In areas where loiasis, caused by the blood-borne filarial parasite Loa loa, is co-endemic, IVM MDA faced significant challenges because individuals harboring more than 30,000 L. loa microfilaria (mf)\/mL of blood are at high risk of developing serious adverse events (SAEs) that can sometimes be fatal. An alternative strategy was developed for safe IVM distribution: the so-called 'Test and Not Treat' (TaNT), which identifies and excludes individuals with high mf density from IVM MDA and treats only those with minimal risk. TaNT requires a point-of-care diagnostic device to rapidly quantify L. loa parasites in field conditions. The NTDscope is a handheld device which captures bright-field videos of whole blood in capillaries. An onboard algorithm detects and counts the live microfilaria of L. loa via movement of red blood cells. This paper describes (i) a new detection algorithm; and (ii) a May 2025 field trial in Cameroon using the NTDscope with new algorithm (550 patients). Results for the TaNT use case (i.e. flagging cases with L. loa mf densities > 30,000 mf\/mL): 94% to 97% sensitivity, and 94% to 97% specificity. The results indicate that the NTDscope and new algorithm provide a greater margin of safety than the predecessor device, and can potentially offer rapid, effective field detection of high mf infections to enable scalability of the TaNT strategy.","rel_num_authors":15,"rel_authors":[{"author_name":"Linda Djune-Yemeli","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Charles B Delahunt","author_inst":"University of Washington, Seattle, WA"},{"author_name":"Steve M Tchana","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Jean G Bopda","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Yves A Balog","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Yannick Y Nzeuhang","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"},{"author_name":"Dipayan Banik","author_inst":"Global Health Labs (ceased operations in Dec 2025)"},{"author_name":"Matthew D Keller","author_inst":"Global Health Labs (ceased operations in Dec 2025)"},{"author_name":"Ethan Spencer","author_inst":"Global Health Labs (ceased operations in Dec 2025)"},{"author_name":"Maria D de Leon Derby","author_inst":"Department of Bioengineering, University of California, Berkeley, California"},{"author_name":"Zaina L Moussa","author_inst":"Department of Bioengineering, University of California, Berkeley, California"},{"author_name":"Daniel A Fletcher","author_inst":"Department of Bioengineering, University of California, Berkeley, California"},{"author_name":"Isaac I Bogoch","author_inst":"Division of General Internal Medicine, Toronto General Hospital, University Health Network, Toronto, Canada"},{"author_name":"Anne-Laure M Le-Ny","author_inst":"Global Health Labs (ceased operations in Dec 2025)"},{"author_name":"Joseph Kamgno","author_inst":"Higher Institute for Scientific and Medical Research (ISM)"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multi-hit STAG2 mutations define a high-risk subset of MDS and reveal convergent evolutionary targeting of cohesin","rel_doi":"10.64898\/2026.09.02.749005","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749005","rel_abs":"STAG2 is the most frequently mutated cohesin gene in myeloid neoplasms, yet the significance of multiple mutations within this X-linked tumor suppressor remains unknown. We analyzed a cohort of 1,967 adult patients with myeloid neoplasms and identified 233 cases (12%) harboring STAG2 mutations, including 38 cases (16%) with multiple STAG2 hits. Patients with multi-hit STAG2 mutations exhibited increased multilineage dysplasia compared with single-hit cases and experienced inferior overall survival, an effect driven primarily by patients with myelodysplastic syndromes (MDS). To investigate the molecular basis of recurrent STAG2 acquisition, we performed long-read sequencing in representative cases with phaseable STAG2 mutations. In the informative case examined, distinct truncating STAG2 mutations did not co-occur on the same DNA molecule, supporting independent acquisition rather than stepwise allelic inactivation. Cohort-level variant allele frequency patterns were consistent with recurrent evolutionary targeting of STAG2 across related clonal populations. Together, these findings support a model in which multi-hit STAG2 mutations arise through convergent evolution and define a biologically distinct, adverse-risk subset of MDS.","rel_num_authors":18,"rel_authors":[{"author_name":"Eno-obong Blessing Udoh","author_inst":"Cleveland Clinic Lerner College of Medicine"},{"author_name":"Yi Chen","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Matteo D'Addona","author_inst":"Scuola Medica Salernitana, University of Salerno, Department of Medicine, Surgery and Dentistry, Salerno, Italy"},{"author_name":"Edna Stewart","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Rong Deng","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Felipe de Almeida Sartori","author_inst":"Hospital Israelita Albert Einstein, Sao Paulo, Brazil"},{"author_name":"Serhan Unlu","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Zachary Brady","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Ruowei Zhu","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Xiaoyi Cheng","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Viviana Scoca","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Jane J Xu","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Sergei Doulatov","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Karl Theil","author_inst":"Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH"},{"author_name":"David Bosler","author_inst":"Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH"},{"author_name":"Valeria Visconte","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Jaroslaw P Maciejewski","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Aaron D Viny","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Multi-hit STAG2 mutations define a high-risk subset of MDS and reveal convergent evolutionary targeting of cohesin","rel_doi":"10.64898\/2026.09.02.749005","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.749005","rel_abs":"STAG2 is the most frequently mutated cohesin gene in myeloid neoplasms, yet the significance of multiple mutations within this X-linked tumor suppressor remains unknown. We analyzed a cohort of 1,967 adult patients with myeloid neoplasms and identified 233 cases (12%) harboring STAG2 mutations, including 38 cases (16%) with multiple STAG2 hits. Patients with multi-hit STAG2 mutations exhibited increased multilineage dysplasia compared with single-hit cases and experienced inferior overall survival, an effect driven primarily by patients with myelodysplastic syndromes (MDS). To investigate the molecular basis of recurrent STAG2 acquisition, we performed long-read sequencing in representative cases with phaseable STAG2 mutations. In the informative case examined, distinct truncating STAG2 mutations did not co-occur on the same DNA molecule, supporting independent acquisition rather than stepwise allelic inactivation. Cohort-level variant allele frequency patterns were consistent with recurrent evolutionary targeting of STAG2 across related clonal populations. Together, these findings support a model in which multi-hit STAG2 mutations arise through convergent evolution and define a biologically distinct, adverse-risk subset of MDS.","rel_num_authors":18,"rel_authors":[{"author_name":"Eno-obong Blessing Udoh","author_inst":"Cleveland Clinic Lerner College of Medicine"},{"author_name":"Yi Chen","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Matteo D'Addona","author_inst":"Scuola Medica Salernitana, University of Salerno, Department of Medicine, Surgery and Dentistry, Salerno, Italy"},{"author_name":"Edna Stewart","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Rong Deng","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Felipe de Almeida Sartori","author_inst":"Hospital Israelita Albert Einstein, Sao Paulo, Brazil"},{"author_name":"Serhan Unlu","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Zachary Brady","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Ruowei Zhu","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Xiaoyi Cheng","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Viviana Scoca","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Jane J Xu","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Sergei Doulatov","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"},{"author_name":"Karl Theil","author_inst":"Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH"},{"author_name":"David Bosler","author_inst":"Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH"},{"author_name":"Valeria Visconte","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Jaroslaw P Maciejewski","author_inst":"Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland, OH"},{"author_name":"Aaron D Viny","author_inst":"Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Single-cell multi-omics maps clonal IEL expansion and epithelial remodelling in refractory coeliac disease","rel_doi":"10.64898\/2026.08.30.747985","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747985","rel_abs":"Background Refractory coeliac disease type 1 (RCD1) lacks defining molecular markers, and the immune and epithelial mechanisms sustaining intestinal injury remain poorly understood. Objective To define the clonal immune and epithelial states that distinguish RCD1 from active coeliac disease (ACD) and determine their spatial organisation in the duodenal mucosa. Design We integrated single-cell RNA sequencing, CITE-seq surface proteomics and paired T-cell receptor sequencing of duodenal immune and epithelial compartments from Healthy controls (n = 6), ACD (n = 7), RCD1 (n = 9) and RCD2 (n = 2), with spatial transcriptomics in a subset of biopsies. Results RCD1 showed widespread TCR{beta} and TCR{gamma}{delta} clonal expansion across multiple IEL states, extending beyond previously defined mutation-bearing aberrant clones. Distinct IEL populations converged on a shared programme of adaptive persistence, innate-like signalling, metabolic fitness and cytoskeletal remodelling; GZMK expression marked both clonally expanded and non-clonal disease-associated states. In parallel, RCD1 epithelium showed loss of mature absorptive cell states and expansion of stress-associated, immune-interacting and regenerative programmes. Transit-amplifying cells acquired differentiation and tissue-remodelling signatures, while enteroendocrine cells expanded and developed a sensory-neurosecretory programme involving TRPA1, TRPV1, vesicle trafficking and NEUROD1 regulon activity. Spatial transcriptomics localised regenerative and enteroendocrine-associated epithelial programmes adjacent to immune-visible epithelial regions and KLRK1\/GZMK-expressing IEL-rich niches in refractory tissue. Conclusion RCD1 represents a distinct mucosal state characterised by coordinated clonal IEL adaptation and epithelial remodelling, rather than simple amplification of ACD, providing a cellular framework for persistent tissue injury and disease stratification.","rel_num_authors":27,"rel_authors":[{"author_name":"Michael Li","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"},{"author_name":"Arman Safavi","author_inst":"The Westmead Institute for Medical Research"},{"author_name":"Jerome Samir","author_inst":"Mutinex, Sydney, Australia"},{"author_name":"Raymond Louie","author_inst":"University of New South Wales, Kensington NSW, Sydney, Australia"},{"author_name":"Esmaeil Roohparvar Basmenj","author_inst":"University of New South Wales, Kensington NSW, Sydney, Australia"},{"author_name":"Martina Bonomi","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"},{"author_name":"Thiruni Adikari","author_inst":"University of Oxford, Department of Paediatrics"},{"author_name":"Brian Gloss","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Jun Xing","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Katherine Jackson","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Andrew Calcino","author_inst":"James Cook University, Townsville, QLD, Australia"},{"author_name":"Dan Suan","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Valentina Vaira","author_inst":"University of Milan, Milan, Italy"},{"author_name":"Claire Milthorpe","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Melinda Hardy","author_inst":"Walter and Eliza Hall Institute of Medical Research (WEHI), Victoria, Australia"},{"author_name":"Etienne Masle-Farquhar","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Scott Read","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Matt Field","author_inst":"James Cook University, Townsville, QLD, Australia"},{"author_name":"Luca Elli","author_inst":"University of Milan, Milan, Italy"},{"author_name":"Marco Vincenzo Lenti","author_inst":"Department of Internal Medicine and Therapeutics, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy"},{"author_name":"Antonio Di Sabatino","author_inst":"Department of Internal Medicine and Therapeutics, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy"},{"author_name":"Rachele Ciccocioppo","author_inst":"University of Chieti, Gastroenterology & Endoscopy Unit, Department of Medicine and Ageing, Italy"},{"author_name":"Jason A. Tye-Din","author_inst":"Walter and Eliza Hall Institute of Medical Research (WEHI), Victoria, Australia"},{"author_name":"Golo Ahlenstiel","author_inst":"Western Sydney University, Western Sydney Local Health District, Sydney NSW, Australia"},{"author_name":"Christopher C. Goodnow","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Mandeep Singh","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Fabio Luciani","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Single-cell multi-omics maps clonal IEL expansion and epithelial remodelling in refractory coeliac disease","rel_doi":"10.64898\/2026.08.30.747985","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747985","rel_abs":"Background Refractory coeliac disease type 1 (RCD1) lacks defining molecular markers, and the immune and epithelial mechanisms sustaining intestinal injury remain poorly understood. Objective To define the clonal immune and epithelial states that distinguish RCD1 from active coeliac disease (ACD) and determine their spatial organisation in the duodenal mucosa. Design We integrated single-cell RNA sequencing, CITE-seq surface proteomics and paired T-cell receptor sequencing of duodenal immune and epithelial compartments from Healthy controls (n = 6), ACD (n = 7), RCD1 (n = 9) and RCD2 (n = 2), with spatial transcriptomics in a subset of biopsies. Results RCD1 showed widespread TCR{beta} and TCR{gamma}{delta} clonal expansion across multiple IEL states, extending beyond previously defined mutation-bearing aberrant clones. Distinct IEL populations converged on a shared programme of adaptive persistence, innate-like signalling, metabolic fitness and cytoskeletal remodelling; GZMK expression marked both clonally expanded and non-clonal disease-associated states. In parallel, RCD1 epithelium showed loss of mature absorptive cell states and expansion of stress-associated, immune-interacting and regenerative programmes. Transit-amplifying cells acquired differentiation and tissue-remodelling signatures, while enteroendocrine cells expanded and developed a sensory-neurosecretory programme involving TRPA1, TRPV1, vesicle trafficking and NEUROD1 regulon activity. Spatial transcriptomics localised regenerative and enteroendocrine-associated epithelial programmes adjacent to immune-visible epithelial regions and KLRK1\/GZMK-expressing IEL-rich niches in refractory tissue. Conclusion RCD1 represents a distinct mucosal state characterised by coordinated clonal IEL adaptation and epithelial remodelling, rather than simple amplification of ACD, providing a cellular framework for persistent tissue injury and disease stratification.","rel_num_authors":27,"rel_authors":[{"author_name":"Michael Li","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"},{"author_name":"Arman Safavi","author_inst":"The Westmead Institute for Medical Research"},{"author_name":"Jerome Samir","author_inst":"Mutinex, Sydney, Australia"},{"author_name":"Raymond Louie","author_inst":"University of New South Wales, Kensington NSW, Sydney, Australia"},{"author_name":"Esmaeil Roohparvar Basmenj","author_inst":"University of New South Wales, Kensington NSW, Sydney, Australia"},{"author_name":"Martina Bonomi","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"},{"author_name":"Thiruni Adikari","author_inst":"University of Oxford, Department of Paediatrics"},{"author_name":"Brian Gloss","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Jun Xing","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Katherine Jackson","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Andrew Calcino","author_inst":"James Cook University, Townsville, QLD, Australia"},{"author_name":"Dan Suan","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Valentina Vaira","author_inst":"University of Milan, Milan, Italy"},{"author_name":"Claire Milthorpe","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Melinda Hardy","author_inst":"Walter and Eliza Hall Institute of Medical Research (WEHI), Victoria, Australia"},{"author_name":"Etienne Masle-Farquhar","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Scott Read","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Matt Field","author_inst":"James Cook University, Townsville, QLD, Australia"},{"author_name":"Luca Elli","author_inst":"University of Milan, Milan, Italy"},{"author_name":"Marco Vincenzo Lenti","author_inst":"Department of Internal Medicine and Therapeutics, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy"},{"author_name":"Antonio Di Sabatino","author_inst":"Department of Internal Medicine and Therapeutics, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy"},{"author_name":"Rachele Ciccocioppo","author_inst":"University of Chieti, Gastroenterology & Endoscopy Unit, Department of Medicine and Ageing, Italy"},{"author_name":"Jason A. Tye-Din","author_inst":"Walter and Eliza Hall Institute of Medical Research (WEHI), Victoria, Australia"},{"author_name":"Golo Ahlenstiel","author_inst":"Western Sydney University, Western Sydney Local Health District, Sydney NSW, Australia"},{"author_name":"Christopher C. Goodnow","author_inst":"Garvan Institute of Medical Research, Darlinghurst NSW, Sydney, Australia"},{"author_name":"Mandeep Singh","author_inst":"The Westmead Institute for Medical Research, Westmead NSW, Sydney, Australia"},{"author_name":"Fabio Luciani","author_inst":"University of New South Wales; The Westmead Institute for Medical Research"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Stroke and Alzheimer's disease have distinct consequences on neurovascular function but synergize to increase amyloid deposition","rel_doi":"10.64898\/2026.08.29.746901","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.746901","rel_abs":"INTRODUCTION: We examined whether stroke induces chronic cerebrovascular dysfunction and thereby exacerbates A{beta} pathology. METHODS: We exposed Tg2576 mice to a transient mild subcortical ischemia and examined cerebrovascular function at chronic timepoints as well as reactive astrocytes and A{beta} deposition post-mortem. RESULTS: Baseline cerebral blood flow and cerebrovascular reactivity (CVR) are both influenced by anesthesia regimen. A{beta} strongly impairs CVR at earlier ages, while mild ischemia impairs CVR at later ages. Mild ischemia reduces neurovascular coupling more strongly than A{beta} and synergistically increases A{beta} deposition in Tg2576 mice. DISCUSSION: Stroke-induced cerebrovascular dysfunction and astrocyte reactivity persist for long periods after the injury. Although there is variability in the spatiotemporal progression of ischemia- and A{beta}-induced vascular impairments, they generally follow the pattern of Thal staging. Surprisingly, the ischemia+A{beta} group showed the least cerebrovascular dysfunction yet an exacerbation of A{beta} deposition, suggesting that these pathologies are connected but not tightly coupled.","rel_num_authors":8,"rel_authors":[{"author_name":"Ozama Ismail","author_inst":"Oregon Health & Science University"},{"author_name":"Simone S Woodruff","author_inst":"Oregon Health & Science University"},{"author_name":"Benjamin Zimmerman","author_inst":"Oregon Health & Science University"},{"author_name":"Wenri Zhang","author_inst":"Oregon Health & Science University"},{"author_name":"Teresa L Stackhouse","author_inst":"Oregon Health & Science University"},{"author_name":"Martin M Pike","author_inst":"Oregon Health & Science University"},{"author_name":"Randy L Woltjer","author_inst":"Oregon Health & Science University"},{"author_name":"Anusha Mishra","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Rapid spatial cognition in mice, with and without neocortex and hippocampus","rel_doi":"10.64898\/2026.08.30.747945","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.747945","rel_abs":"Rapid learning, memory, and generalization are often attributed to circuits of the neocortex and hippocampus, but their specific role remains unclear. To examine these cognitive abilities together in individual mice, we observed mice navigating the Manhattan Maze, a reconfigurable 3D labyrinth. Naive wildtype mice improved within two rewards, approached efficient paths within about 20 rewards, retained a 9-turn route overnight, and learned faster in new configurations. Much of the few-shot improvement follows from a rule-based forward bias that emerged even before any reward. In a maze with loops, where that rule is less useful, mice learned within a few rewards to prefer one bottleneck corridor far from the reward location, while choices elsewhere stayed flexible. To begin linking these different components of learning to brain function, we presented the same task to mutant mice that lack the hippocampus and most of the neocortex. They were impaired during initial exploration, where repetitive scanning made them about 3-fold slower to obtain the first few rewards. Past that stage, learning, retention over weeks to months, and generalization to new mazes were largely preserved. Learning this fast is hard to reconcile with trial and error, in which value propagates backwards from the reward. A neuromorphic circuit model instead accounts for the bottleneck choice: it builds a map of the environment without reward, and needs neither cortical nor hippocampal circuit motifs. Therefore, learning is possible without the involvement of neocortex and hippocampus. Structure learned before the first reward, rather than the reward itself, may be what makes few-shot learning possible.","rel_num_authors":8,"rel_authors":[{"author_name":"Jieyu Zheng","author_inst":"California Institute of Technology"},{"author_name":"Rog\u00e9rio Guimar\u00e3es","author_inst":"California Institute of Technology"},{"author_name":"Zeynep Turan","author_inst":"California Institute of Technology"},{"author_name":"Anwesha Das","author_inst":"California Institute of Technology"},{"author_name":"Jennifer Y Hu","author_inst":"California Institute of Technology"},{"author_name":"Katelyn Sadorf","author_inst":"California Institute of Technology"},{"author_name":"Pietro Perona","author_inst":"California Institute of Technology"},{"author_name":"Markus Meister","author_inst":"California Institute of Technology"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Programmable Self-Assembly of RNA Nanostructures with >100 Unique Components","rel_doi":"10.64898\/2026.09.02.748990","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748990","rel_abs":"Sophisticated biomolecular functions often arise from large, precisely organized architectures, motivating efforts to construct increasingly complex structures through programmed nucleic acid self-assembly. Although RNA offers a richer repertoire of structural motifs and biological functions than DNA, RNA nanostructures constructed to date have remained substantially less complex and less scalable than their DNA counterparts, most notably DNA origami and DNA bricks, which rely on large libraries of synthetic single-stranded (ss) DNA strands. Directly adapting these strategies to RNA, however, faces two major barriers: (i) the high cost of producing large libraries of distinct synthetic ssRNA strands, and (ii) the limited stability of ssRNA under conditions commonly used for assembling multicomponent nucleic acid nanostructures. Here we present the double-stranded RNA (dsRNA) bricks approach, which addresses both barriers by adopting principles inspired by natural RNA systems: (i) many distinct RNA components (\"bricks\") are encoded within a single precursor transcript and released by enzymatic processing, (ii) each brick's predominantly double-stranded nature enhances stability. The resulting dsRNA bricks self-assemble through programmable branched kissing-loop interactions. Using this approach, we constructed complex two- and three-dimensional RNA nanostructures with more than 100 distinct components, representing, to our knowledge, the largest and most compositionally complex RNA nanoarchitectures reported to date. Overall, the dsRNA bricks approach establishes a scalable and robust framework for constructing complex RNA nanostructures with compositional and architectural sophistication approaching that of DNA-based systems, thereby opening new opportunities for programmable RNA materials and RNA-based devices.","rel_num_authors":19,"rel_authors":[{"author_name":"Liangxiao Chen","author_inst":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA"},{"author_name":"Zhishang Li","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Jun Yan","author_inst":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA"},{"author_name":"Swarup Dey","author_inst":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA"},{"author_name":"Cong Li","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Alexandra Petrova","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Abhay Prasad","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Deeksha Satyabola","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Kira DeVore","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Xinyi Tu","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Yanzhe Qu","author_inst":"School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Shiyumou Wang","author_inst":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA"},{"author_name":"Gengshi Wu","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Po-Lin Chiu","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Di Wen","author_inst":"Earle A. Chiles Research Institute, Robert W. Franz Cancer Center, Providence Portland Medical Center, Portland, OR, USA"},{"author_name":"Joseph Che-Yen Wang","author_inst":"Department of Cell and Biological Systems, Pennsylvania State University College of Medicine, Hershey, PA, USA"},{"author_name":"Hao Yan","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"},{"author_name":"Peng Yin","author_inst":"Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA"},{"author_name":"Di Liu","author_inst":"School of Molecular Sciences, Arizona State University, Tempe, AZ, USA"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"E-cadherin-mediated neighborhood surveillance dictates pre-malignant outcomes","rel_doi":"10.64898\/2026.09.02.748765","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748765","rel_abs":"Stratified epithelia accumulate oncogenic mutations throughout life, yet overgrowths are rare. How epithelia detect and eliminate aberrant clones remains poorly understood. Using a mouse model of oncogenic clonal mosaicism in the skin, we find that pre-malignant epidermal cells redistribute E-cadherin to interfaces shared with wild-type neighbors, generating local tension heterogeneity that triggers elimination by cell competition. We show that gain or loss of E-cadherin can each drive competitive elimination, and although mechanical routes differ, both establish tension heterogeneity between neighbors, rather than any absolute adhesion state, as the critical determinant of epidermal fitness. This mechanism carries the seeds of its own failure: these tension differentials precipitate clonal sorting, depleting the wild-type contacts that surveillance requires. Pre-malignant cells then become supercompetitors, eliminating wild-type neighbors and expanding hyperplastically. Mechanical heterogeneity therefore endows tissues with an active, yet inherently fragile error-correction system whose collapse initiates a switch in competitive status, increasing tumorigenesis susceptibility.","rel_num_authors":16,"rel_authors":[{"author_name":"Elizabeth A.N. Thompson","author_inst":"Rockefeller University"},{"author_name":"Clara Schimmer","author_inst":"Max Perutz Labs, University of Vienna"},{"author_name":"Tatiana Omelchenko","author_inst":"Rockefeller University"},{"author_name":"Michele Paganini","author_inst":"Max Perutz Labs, University of Vienna"},{"author_name":"Jesse S.S. Novak","author_inst":"Rockefeller University"},{"author_name":"Sienna T. Muller","author_inst":"Max Perutz Labs, University of Vienna"},{"author_name":"Alain R. Bonny","author_inst":"Rockefeller University"},{"author_name":"Marina Schernthanner","author_inst":"Rockefeller University"},{"author_name":"Sairaj M. Sajjath","author_inst":"Rockefeller University"},{"author_name":"Charlotte J. Bell","author_inst":"Rockefeller University"},{"author_name":"Alp Gorgulu","author_inst":"Rockefeller University"},{"author_name":"S. Martina Parigi","author_inst":"Rockefeller University"},{"author_name":"Katherine S. Stewart","author_inst":"Rockefeller University, Lunenfeld Tanenbaum Research Institute"},{"author_name":"Priyam Banerjee","author_inst":"Rockefeller University"},{"author_name":"Elaine Fuchs","author_inst":"Rockefeller University, Howard Hughes Medical Institute"},{"author_name":"Stephanie J. Ellis","author_inst":"Max Perutz Labs, University of Vienna"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Joint ancestry inference reveals the landscape of archaic introgression in admixed populations","rel_doi":"10.64898\/2026.08.29.748036","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.748036","rel_abs":"Studying the evolutionary history of archaic segments in recently admixed individuals requires inferring both continental and archaic ancestry in admixed genomes. Here, we present TRACTINATOR, the first deep-learning method for simultaneous inference of continental and archaic ancestry in admixed human genomes. The model combines SNP sequences, population allele-frequency information, and S* statistics to improve both inference tasks. By learning relationships between haplotypes and population allele frequencies, TRACTINATOR can generalize across genomic regions and even across different genomic datasets. We train our model using both real and synthetic data, and show that augmenting with synthetic data improves accuracy for both continental and archaic ancestry inference. Finally, we apply TRACTINATOR to admixed Latin American populations from the 1,000 Genomes Project, revealing how archaic ancestry is distributed within chromosomal segments of African, European and Indigenous American ancestry in Latin American individuals. For candidates of adaptive introgression, we also infer whether the archaic haplotype was introduced via European or Indigenous American ancestors.","rel_num_authors":7,"rel_authors":[{"author_name":"Jazeps Medina Tretmanis","author_inst":"Brown University"},{"author_name":"Valeria A\u00f1orve-Garibay","author_inst":"Brown University"},{"author_name":"David Peede","author_inst":"Brown University"},{"author_name":"Mayra M Ba\u00f1uelos","author_inst":"Brown University"},{"author_name":"Mar\u00eda C \u00c1vila Arcos","author_inst":"Universidad Nacional Aut\u00f3noma de M\u00e9xico"},{"author_name":"Flora Jay","author_inst":"Universit\u00e9 Paris-Saclay, CNRS, INRIA, Interdisciplinary Laboratory of Numerical Sciences"},{"author_name":"Emilia Huerta-Sanchez","author_inst":"Brown University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Chronic opioid-associated immune dysregulation among people living with HIV","rel_doi":"10.64898\/2026.08.31.748399","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748399","rel_abs":"Objectives: Persistent immune dysregulation contributes to chronic disease among people living with HIV (PWH), even after viral suppression with antiretroviral therapy (ART). Although chronic opioid exposure is associated with adverse clinical outcomes, its impact on immune homeostasis during ART remains incompletely understood. We investigated whether opioid use disorder (OUD) is associated with persistent systemic and cellular immune dysregulation despite ART-mediated reductions in HIV viral load (VL). Methods: Peripheral blood was collected longitudinally from PWH with OUD (PWH\/OUD+) and detectable HIV VL during 6 months of optimized ART (months 0, 3, and 6). A reference cohort of PWH without OUD (PWH\/OUD-) and suppressed HIV VL provided a single blood sample. Immune profiling included plasma inflammatory biomarkers, multiplex cytokine analyses, spectral flow cytometry, and assessment of monocyte cytokine responses following lipopolysaccharide (LPS) stimulation. Mixed-effects models adjusted for HIV VL and VL-stratified analyses were performed. Results: PWH\/OUD+ exhibited persistent immune dysregulation despite reductions in HIV VL. Plasma sCD163, sCD14, fractalkine, and I-TAC remained elevated, whereas TGF-{beta}1 was reduced. OUD was associated with expansion of CD16 monocytes and altered expression of CCR2, CD38, and CD11b. CD4 and CD8 T cells, NK cells, and B cells also exhibited persistent alterations in markers of activation, metabolism, and trafficking. Monocytes from PWH\/OUD+ displayed attenuated cytokine responses following LPS stimulation. Conclusions: OUD is associated with persistent systemic and cellular immune dysfunction in PWH despite ART-mediated viral suppression, supporting opioid exposure as an independent contributor to chronic immune dysregulation that may promote inflammation, immune dysfunction, and long-term HIV-associated comorbidities. Keywords: HIV, Opioid-use disorder, innate immunity, cytokine","rel_num_authors":8,"rel_authors":[{"author_name":"Brent Bever","author_inst":"Oregon Health and Science University"},{"author_name":"Michelle Underwood","author_inst":"Oregon Health and Science University"},{"author_name":"Byung Park","author_inst":"Oregon Health and Science University"},{"author_name":"Susan Pereira Ribeiro","author_inst":"Emory University School of Medicine"},{"author_name":"Ryan R Cook","author_inst":"Oregon Health and Science University"},{"author_name":"Lynn Kunkel","author_inst":"Oregon Health and Science University"},{"author_name":"P. Todd Korthuis","author_inst":"Oregon Health and Science University"},{"author_name":"Christina Lancioni","author_inst":"Oregon Health and Science University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Unbiased and scalable reduction of diverse bacterial genomes","rel_doi":"10.64898\/2026.09.02.748983","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748983","rel_abs":"The genome is a complex, integrated system where the functions and regulatory interactions of its many components remain poorly understood. Genome minimization aims to reduce genomic complexity by removing non-essential elements to reveal the fundamental building blocks of cellular life. However, current minimization strategies are often slow and species-specific due to a reliance on prior information, and limited to producing single, isolated strains, which obscures the diverse ways a genome can adapt to large-scale DNA removal. Here we show the development and application of Stochastic Lineage-based Iterative Minimization (SLIM) a modular, high-throughput platform for unbiased genome reduction across phylogenetically diverse bacteria. We apply SLIM to generate a library of genome-reduced Escherichia coli lineages. We then interrogate the lineages, identifying both universal and lineage-specific transcriptional and translational reprogramming in response to deletions. We demonstrate that these expression dynamics drive environment-dependent fitness, allowing us to pinpoint a single gene deletion in one genome-reduced lineage as the driver of a measurable environmental growth defect. Beyond E. coli, we successfully deploy SLIM in phylogenetically distinct bacterial taxa to rapidly reduce the genomes of Shigella flexneri and Pseudomonas putida, distinct genus and order respectively from E. coli, without species-specific optimization. Our results establish a scalable, generalizable framework for navigating the vast landscape of minimized genomes, providing a powerful new tool for functional discovery and the rational design of synthetic genomic chassis.","rel_num_authors":7,"rel_authors":[{"author_name":"Mikel Lipschitz","author_inst":"California Institute of Technology"},{"author_name":"Baiyi Quan","author_inst":"California Institute of Technology"},{"author_name":"Indeever Madireddy","author_inst":"California Institute of Technology"},{"author_name":"Gohta Aihara","author_inst":"California Institute of Technology"},{"author_name":"Marisa Bennett","author_inst":"California Institute of Technology"},{"author_name":"Tsui-Fen Chou","author_inst":"California Institute of Technology"},{"author_name":"Kaihang Wang","author_inst":"California Institute of Technology"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Synergistic targeting of EP300\/CBP and EYA co-activators collapses the rhabdomyosarcoma core regulatory circuit","rel_doi":"10.64898\/2026.09.02.748955","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748955","rel_abs":"Rhabdomyosarcoma (RMS) is a multi-subtype, high-risk pediatric sarcoma with a low mutational burden. The mutations found in RMS often alter genes involved in transcriptional control. Approaches to target dysregulated RMS transcription have remained elusive. Here, we develop a novel approach to target RMS transcription comprising simultaneous targeting of two distinctly acting transcriptional co-activators. We discover a common identity-controlling pan-RMS core regulatory circuit (CRC) composed of oncogenic and lineage-specific myogenic master transcription factors (mTFs). Using a super-enhancer-based reporter screen, we identify the EP300\/CBP inhibitor A485 as a potent inhibitor of the pan-RMS CRC, though with efficacy-limiting toxicities. To enhance efficacy, we identify the mTF-binding co-activator EYA2 as a co-factor of this pan-RMS CRC and exploit a new second-generation EYA1\/2 inhibitor, LG1-34, to disrupt its function. Combined co-activator inhibition inactivates the CRC and synergistically reduces RMS growth. This strategy dually targets CRC-associated co-activators to cooperatively suppress the RMS transcriptome and enforce cell death.","rel_num_authors":36,"rel_authors":[{"author_name":"Annika L Gustafson","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Stephanie Nance","author_inst":"St. Jude Research Hospital"},{"author_name":"Berkley Gryder","author_inst":"Case Western Reserve School of Medicine"},{"author_name":"Noha A.M. Shendy","author_inst":"St. Jude Research Hospital"},{"author_name":"Lars Wick","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Grace McKay-Corkum","author_inst":"St. Jude Research Hospital"},{"author_name":"K Elaine Ritter","author_inst":"St. Jude Research Hospital"},{"author_name":"Stephen Connor Purdy","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Arthur R Wolin","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Sheera R Rosenbaum","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Sabateeshan Mathavarajah","author_inst":"Massachusetts General Hospital Research Institute"},{"author_name":"Nickerson A Demelfi","author_inst":"Massachusetts General Hospital Research Institute"},{"author_name":"Yueyang Wang","author_inst":"Massachusetts General Hospital Research Institute"},{"author_name":"Yang Zhang","author_inst":"St. Jude Research Hospital"},{"author_name":"Mark Zimmerman","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Anoop Kavirayani","author_inst":"St. Jude Research Hospital"},{"author_name":"Erin A Citarella","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Vernon J Ebegboni","author_inst":"St. Jude Research Hospital"},{"author_name":"John W Hardin","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Alexander LaVeck","author_inst":"University of Colorado, Boulder"},{"author_name":"Xiang Wang","author_inst":"University of Colorado Boulder"},{"author_name":"Neekesh Dharia","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Andrew L Hong","author_inst":"Emory University"},{"author_name":"Guillaume Kugener","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Jesse S Boehm","author_inst":"The Broad Institute of MIT and Harvard"},{"author_name":"Jennifer A Roth","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Javed Khan","author_inst":"National Cancer Institute"},{"author_name":"Francisca Vazquez","author_inst":"The Broad Institute of MIT and Harvard"},{"author_name":"Kristin Artinger","author_inst":"The University of Minnesota"},{"author_name":"Rui Zhao","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"David M Langenau","author_inst":"Massachusetts General Hospital Research Institute"},{"author_name":"Jun Qi","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Kimberly Stegmaier","author_inst":"Dana-Farber Cancer Institute"},{"author_name":"Brian Abraham","author_inst":"St. Jude Children's Research Hospital"},{"author_name":"Heide Ford","author_inst":"University of Colorado Anschutz Medical Campus"},{"author_name":"Adam D Durbin","author_inst":"St. Jude Children's Research Hospital"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Modelling and measuring effects of shear stress in extrusion bioprinting of endothelial- epithelial cell co-cultures","rel_doi":"10.64898\/2026.09.02.748801","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748801","rel_abs":"Extrusion-based bioprinting enables the development of tissue-like constructs; however, the impact of printing-associated shear stress on cell viability and function remains a critical consideration. To address this, we developed a comprehensive workflow combining rheological characterization, computational fluid dynamics (CFD) modelling, and experimental validation to predict and assess shear stress effects during bioprinting. The rheological properties of gelatin methacryloyl (GelMA) at 5 % (w\/v, 20 {degrees}C) and 10 % (30 {degrees}C) concentrations were modelled, comparing various non-Newtonian regression models. CFD simulations were validated using micro-particle image velocimetry, showing agreement between predicted and measured velocities. The impact of bioprinting-associated shear stress on cell viability was assessed using a co-culture of human umbilical vein endothelial cells and breast epithelial cells. Immediate post-printing analysis revealed increased apoptosis in GelMA 5 % (w\/v, 20 {degrees}C), although 10 % (w\/v) GelMA demonstrated higher shear stress levels compared to 5 % GelMA. After 1 day of culture in crosslinked hydrogels, apoptosis increased in extrusion pressure, demonstrating the impact of low levels of acute shear stress. This workflow provides a robust methodology for predicting acute shear stress impacts during bioprinting, laying the foundation for future optimization studies.","rel_num_authors":6,"rel_authors":[{"author_name":"Jordan W Davern","author_inst":"Queensland University of Technology"},{"author_name":"Angus Weekes","author_inst":"Queensland University of Technology"},{"author_name":"Jorge Amaya Catano","author_inst":"Queensland University of Technology"},{"author_name":"Christoph Meinert","author_inst":"Gelomics Pty Ltd"},{"author_name":"Laura Bray","author_inst":"Queensland University of Technology"},{"author_name":"Travis J Klein","author_inst":"Queensland University of Technology"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"From concentration to export: resource contrasts and bee traits shape pollinator spillover to crops","rel_doi":"10.64898\/2026.08.29.747339","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.747339","rel_abs":"Floral plantings can either concentrate bees or export them to adjacent crops, yet the ecological conditions influencing these outcomes remain unclear. Here, we develop a mathematical model as proof of concept for our previous integrative hypothesis: concentrator and exporter outcomes can arise as alternative, context-dependent outcomes of the same underlying resource-selection process. Using bees as a model and focusing specifically on spillover from floral plantings to crops, we identified resource-specific thresholds separating concentration- and export-favoring conditions. Our model translates differences in relative patch attractiveness into context-dependent concentration and export outcomes and generates resource-specific, testable predictions about the conditions favoring pollinator movement into crops. In our simulations, the concentrator-exporter transition occurred at a lower flowering-intensity contrast than at pollen or nectar contrasts, which suggests that flowering intensity may provide an initial cue for bee movement, whereas nectar and pollen rewards refine or sustain bee responses once crops are perceived as attractive. Spillover thresholds differed among resource contrasts, whereas response steepness varied across bee-trait and community scenarios. Under the model's trait-sensitivity formulation, predicted spillover probability responded more strongly to flowering contrast for specialists than for generalists; colony size amplified this response, whereas bee richness dampened it. Together, these patterns show how flowering and resource contrasts interact with bee traits and community context to shape predicted spillover. Our results confirm that the concentrator and exporter hypotheses can be understood as context-dependent outcomes of the same ecological process rather than as mutually exclusive alternatives. Experimental tests of the predicted thresholds conducted in the field could reveal when and where floral plantings are most likely to promote bee spillover to crops, potentially supporting crop pollination.","rel_num_authors":5,"rel_authors":[{"author_name":"Cristina Kita","author_inst":"Universidade de Sao Paulo"},{"author_name":"Isabel Alves-dos-Santos","author_inst":"Universidade de Sao Paulo"},{"author_name":"Michael Hrncir","author_inst":"Universidade de Sao Paulo"},{"author_name":"Renata Muylaert","author_inst":"The University of Sydney"},{"author_name":"Marco A. R. Mello","author_inst":"University of Sao Paulo"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Burning down the mouse: Effects of wildfire and post-fire reseeding on Sin Nombre virus prevalence in its reservoir host","rel_doi":"10.64898\/2026.09.02.748549","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748549","rel_abs":"Worldwide, physical habitats and biological communities are being reshaped by wildfire. Such changes have obvious potential to alter pathogen ecology, yet few studies, outside of those focused on ectoparasites, have tested the effects of wildfire on pathogen prevalence in wildlife. Even fewer have focused on the impacts of strategies for post-fire remediation on pathogen circulation. Here, we investigated the effect of wildfire and wildfire mitigation on the prevalence and viral load of Sin Nombre virus (SNV) infection in its reservoir host, the western deer mouse, using a study design of matched burned, unburned, and post-burn reseeded sites in northern New Mexico. In total, we screened 411 individual deer mice for SNV via RT-qPCR and analyzed the effect of wildfire and wildfire mitigation on relative mouse abundance, individual infection probability, site-level prevalence and viral load. Relative abundance was highest at reseeded sites, and model selection indicated that habitat structure, particularly the gradient from closed canopy to open-herbaceous habitat, was consistently associated with increased relative abundance. Similarly, SNV prevalence was significantly higher in reseeded sites than either burned or unburned sites but did not differ in burned versus unburned sites. Viral load did not differ between burn history types, and zero-inflated gamma hurdle models did not identify any strong predictors of viral load. Serendipitously, we were also able to investigate the impacts of an El Nino Southern Oscillation (ENSO) cycle on patterns of infection in a subsample of sites that were studied during and one year after an ENSO year and found that SNV prevalence increased significantly post-ENSO. Both reseeding and ENSO deliver resource pulses that can support increases in mouse density and thereby enhance SNV transmission. The impacts of post-fire management practices on SNV prevalence in its reservoir host that were revealed in this study should be considered when implementing such strategies and when utilizing treated areas.","rel_num_authors":7,"rel_authors":[{"author_name":"Carleen N. Silva","author_inst":"New Mexico State University"},{"author_name":"Frances M. Twohig","author_inst":"University of New Mexico"},{"author_name":"Matthew E. Gompper","author_inst":"New Mexico State University"},{"author_name":"Robert A. Nofchissey","author_inst":"University of New Mexico"},{"author_name":"Aaron C. Young","author_inst":"New Mexico State University"},{"author_name":"Steven B. Bradfute","author_inst":"University of New Mexico"},{"author_name":"Kathryn A. Hanley","author_inst":"New Mexico State University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Ribosomal proteins are major substrates of starvation-induced endosomal microautophagy in Drosophila.","rel_doi":"10.64898\/2026.09.01.748611","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748611","rel_abs":"Maintenance of cellular homeostasis requires tight coordination between protein synthesis and degradation, particularly at old age and under conditions of stress including starvation. Autophagy contributes to sustain this balance by degrading cytoplasmic proteins and organelles. It thus is essential to prevent the accumulation of damaged proteins and organelles and to recycle nutrients. Of the three forms of autophagy, macroautophagy, chaperone mediated autophagy, and (endosomal) microautophagy (e-MI), the latter remains the least well understood. During e-MI, cytosolic substrate proteins are captured into late endosomes via ESCRT-dependent multivesicular body formation and then degraded in late endosomes or lysosomes. e-MI is thought to contribute to protein quality control under basal conditions and under stress. Importantly, very little is known about the endogenous substrates of e-MI in flies and thus about its physiological role. Performing integrative multi-omic analyses in Drosophila larval fat body that has functions similar to mammalian liver and adipose tissue, we identified 153 high-confidence endogenous e-MI substrates with the degradation of ribosomal proteins by e-MI being the most strongly affected functional category. Generally, we found that starvation caused the depletion of proteins involved in translation, aminoacyl-tRNA synthesis, and ribosomal biogenesis, without affecting their level of transcripts. Importantly, we observe a striking specificity between e-MI and macroautophagy, as the two pathways largely target distinct protein sets including different subsets of ribosomal proteins. Our metabolomic analysis further shows that genetic inhibition of e-MI reverses the reduced levels of amino acid caused by starvation. Together, our findings reveal ribosome turnover as a central physiological function of Drosophila e-MI and establish e-MI as a pathway driving metabolic adaptation during starvation.","rel_num_authors":5,"rel_authors":[{"author_name":"Prasoon Jaya","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Satya Surabhi","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Jennifer Aguilan","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Simone Sidoli","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Andreas Jenny","author_inst":"Albert Einstein College of Medicine"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"A cognitive representation in primary visual cortex modulated by vision","rel_doi":"10.64898\/2026.08.29.748029","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.748029","rel_abs":"Primary visual cortex (V1) is a critical substrate for mammalian vision. Traditionally, visual inputs are thought to be the main drivers of V1 activity, with internal signals playing a modulatory role. Here we show that this relationship is inverted for a large fraction of V1 neurons. In rats completing a navigation task in darkness, these neurons encoded progress along physically distinct paths with a shared turn structure. Under illumination, visual stimuli gain-modulated this path-invariant activity rather than replacing it with stimulus-driven responses. Path-invariant V1 neurons were also preferentially coordinated with hippocampal ensembles during sharp-wave ripples, linking them to a brain-wide network involved in learning. These findings establish that an internal model of the world can serve as a primary driver of activity in sensory cortex.","rel_num_authors":10,"rel_authors":[{"author_name":"Kyu Hyun Lee","author_inst":"UCSF"},{"author_name":"Philip Adenekan","author_inst":"University of Rochester"},{"author_name":"Fan Gao","author_inst":"UCSF"},{"author_name":"Jenny Zhou","author_inst":"Lawrence Livermore National Lab"},{"author_name":"Jose Hernandez","author_inst":"Lawrence Livermore National Lab"},{"author_name":"Allison Yorita","author_inst":"Lawrence Livermore National Lab"},{"author_name":"Razi Haque","author_inst":"Lawrence Livermore National Lab"},{"author_name":"Kenneth Kay","author_inst":"University of Rochester"},{"author_name":"Massimo Scanziani","author_inst":"University of California San Francisco and Howard Hughes Medical Institute"},{"author_name":"Loren Frank","author_inst":"UCSF and HHMI"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Cortical Hierarchy Dynamically Organizes Large-Scale Neural Propagation","rel_doi":"10.64898\/2026.08.29.747943","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.29.747943","rel_abs":"Flexible behaviour depends on the continuous coordination of sensory-driven and internally guided processing, yet whether the cortical hierarchy spanning lower-order sensory to higher-order association systems dynamically organizes large-scale cortical propagation over time remains unclear. Here we combined source-resolved magnetoencephalography with Riemannian cortical-flow modelling to derive hierarchy consistency, a moment-to-moment measure of the alignment between cortical propagation and the principal sensory-to-association functional gradient. We found that large-scale cortical propagation was dynamically organized by the cortical hierarchy. Hierarchy consistency exhibited a reproducible low-frequency periodic component that defined a characteristic timescale for the continuous updating of propagation direction. This dynamic organization was coordinated by a distributed cortical switchboard spanning the default-mode, salience, control and limbic systems, and was constrained by structural connectivity and network-control architecture. It flexibly adapted to behavioural demands, with hierarchy consistency increasing across both sensorimotor and working-memory states, while its characteristic periodicity shifted in a task-dependent manner. Moreover, hierarchy-related propagation dynamics were systematically reorganized across ageing and associated with higher-order cognitive function. Together, these findings establish the cortical hierarchy as a dynamic organizing principle that continuously shapes the direction and temporal evolution of large-scale cortical propagation to support adaptive behaviour.","rel_num_authors":8,"rel_authors":[{"author_name":"Xiaobo Liu","author_inst":"McGill University"},{"author_name":"Bin Wan","author_inst":"Max Plank INstitution"},{"author_name":"Wenyue Liu","author_inst":"Max Planck Institute for Human Cognitive and Brain Sciences"},{"author_name":"Runhao Lu","author_inst":"McGill University"},{"author_name":"Sanwang Wang","author_inst":"Peaking University"},{"author_name":"Xihan Zhang","author_inst":"Yale University"},{"author_name":"Li Dong","author_inst":"UESTC"},{"author_name":"Tengteng Fan","author_inst":"Peaking University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Hexose-6-phosphate dehydrogenase deficiency disrupts hepatic fatty acid homeostasis and induces triglyceride accumulation","rel_doi":"10.64898\/2026.09.01.748570","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748570","rel_abs":"Hexose-6-phosphate dehydrogenase (H6PD) catalyzes the first two steps of an endoplasmic reticulum-specific pentose phosphate pathway, regenerating luminal NADPH levels in the process. Its function remains insufficiently well understood. Since expression of H6PD is notably high in the liver, we aimed to assess its role in hepatic metabolism. Considering the central role of the liver in lipid synthesis, breakdown and storage, we focused our efforts specifically on studying the effect of H6PD on hepatic lipid metabolism. An H6PD knockout mice strain was generated and characterized by liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based lipidomic and proteomic analyses of liver tissue. Lipidomics analysis revealed an overall increase in hepatic triglycerides and a specific increase in unsaturated long-chain triglycerides in H6PD knockout mice. Intracellular lipid accumulation was confirmed through Nile Red staining of liver sections. Functional enrichment analysis of proteomics data from the H6PD deficient mice identified a corresponding upregulation of multiple fatty acid metabolism-associated pathways. Additionally, an H6PD knockout AML12 cell line was generated through CRISPR\/Cas9 and characterized by lipid staining and functional assays to assess metabolic outcomes. Loss of H6PD led to intracellular lipid accumulation, reduced mitochondrial {beta}-oxidation and increased sensitivity to lipotoxicity, even though fatty acids remained the cells' primary mitochondrial fuel. Ultimately, our results indicate that H6PD plays an as-of-yet undescribed role in hepatic lipid metabolism, implying a link between the availability of NADPH within the endoplasmic reticulum and fatty acid homeostasis.","rel_num_authors":9,"rel_authors":[{"author_name":"Gabriele Sakalauskaite","author_inst":"Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland"},{"author_name":"Sophie Ebert","author_inst":"Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland"},{"author_name":"Julien Arthur Allard","author_inst":"Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland"},{"author_name":"Michael Zogg","author_inst":"Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland"},{"author_name":"Isabel Meister","author_inst":"Laboratory of Biomedical Analysis and Metabolomics, School of Pharmaceutical Sciences, University of Geneva, 1211 Geneva, Switzerland"},{"author_name":"Julia Birk","author_inst":"Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland"},{"author_name":"Alex Schmidt","author_inst":"Proteomics Core Facility, Biozentrum, University of Basel, 4056 Basel, Switzerland"},{"author_name":"Jamal Bouitbir","author_inst":"Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland"},{"author_name":"Alex Odermatt","author_inst":"Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Living multicellular systems induce decodable spatial patterns in bacterial collectives","rel_doi":"10.64898\/2026.09.02.748853","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748853","rel_abs":"Living systems continuously modify their environments through chemical, mechanical, metabolic and bioelectrical activity. Whether a presence of a multicellular system can be encoded into the emergent spatial organization of another living collective in a distributed and decodable way is unknown. Here we show that motile Bacillus subtilis populations reorganize their spatial and ionic collective states in response to nearby Xenopus embryos and Xenobots. The bacteria in a liquid culture formed autonomous motility-dependent patterns that were redirected by living targets into attraction halos, which tracked target position at a distance. Extracellular levels of potassium amplified attraction, altered local potassium dynamics, and coupled target presence to global pattern complexity. Self-supervised machine learning further identified distributed bacterial spatial signatures predictive of Xenopus embryo vs. Xenobot presence at a distance from the target. Together, these findings suggest that bacterial collectives can encode information about the state of other biota in their environment, revealing a previously unrecognized form of inter-kingdom interaction between living morphogenetic systems.","rel_num_authors":9,"rel_authors":[{"author_name":"Elena G. Sergeeva","author_inst":"Allen Discovery Center at Tufts University"},{"author_name":"Federico Pigozzi","author_inst":"Allen Discovery Center at Tufts University"},{"author_name":"Thomas F. Varley","author_inst":"University of Vermont"},{"author_name":"Colin J. Comerci","author_inst":"Division of Biological Sciences, University of California San Diego"},{"author_name":"Zhe Zhao","author_inst":"Division of Biological Sciences, University of California San Diego"},{"author_name":"Robert Brucker","author_inst":"Allen Discovery Center at Tufts University"},{"author_name":"G\u00fcrol M. S\u00fcel","author_inst":"Division of Biological Sciences, University of California San Diego"},{"author_name":"Joshua C. Bongard","author_inst":"University of Vermont"},{"author_name":"Michael Levin","author_inst":"Allen Discovery Center at Tufts University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"TALE-independent transcriptional activation of the rice executor gene Xa23 is regulated via histone acetylation during zygote development","rel_doi":"10.64898\/2026.09.02.748781","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748781","rel_abs":"Transcription activator-like effectors (TALEs) from Xanthomonas activate transcription of executor (E) genes in host plants, leading to cell death and thereby restricting proliferation of biotrophic pathogens. Because E gene transcripts had only been detected upon activation by cognate Xanthomonas TALEs, E genes were thought to function exclusively in plant immunity. Here, we detect TALE-independent transcription of the rice E gene Xa23 in zygotes 4-6 hours after gamete fusion. Histone deacetylase inhibition induces Xa23 transcription in unfertilized egg cells, implicating histone acetylation in Xa23 regulation. We identified potential cis-regulatory elements and transcription start sites associated with native Xa23 transcription during zygote development. Together, our findings suggest that Xa23 is a developmentally regulated gene with a native role during early zygote development. This supports a previously proposed model in which E genes have native functions in development, while fortuitous upstream polymorphisms can create TALE-binding sites that convert them into immune executors.","rel_num_authors":12,"rel_authors":[{"author_name":"Kyrylo Schenstnyi","author_inst":"University of California, Berkeley"},{"author_name":"Kasidit Rattanawong","author_inst":"Tokyo Metropolitan University"},{"author_name":"Aya Satoh","author_inst":"Tokyo Metropolitan University"},{"author_name":"Annett Strauss","author_inst":"Eberhard Karl University of Tuebingen"},{"author_name":"Natalie Faiss","author_inst":"Eberhard Karl University of Tuebingen"},{"author_name":"Danalyn Rose Holmes","author_inst":"Eberhard Karl University of Tuebingen"},{"author_name":"Laura Redzich","author_inst":"Heinrich Heine University of Duesseldorf"},{"author_name":"Erika Toda","author_inst":"University of Tokyo"},{"author_name":"Hanifah Aini","author_inst":"Tokyo Metropolitan University"},{"author_name":"Atsuko Kinoshita","author_inst":"Tokyo Metropolitan University"},{"author_name":"Thomas Lahaye","author_inst":"Eberhard Karl University of Tuebingen"},{"author_name":"Takashi Okamoto","author_inst":"Tokyo Metropolitan University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Convergent stochastic assembly governs reef biofilm microbiomes across ecologically distinct benthic substrates","rel_doi":"10.64898\/2026.09.02.748686","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748686","rel_abs":"Understanding the processes that shape microbial biodiversity and community structure is a key objective of the field of microbial ecology. The processes driving assembly of benthic biofilm bacteria on functionally important reef substrates, such as crustose coralline algae (CCA) and calcium carbonate, are not well understood, despite their critical contributions to the maintenance of biodiversity and ecosystem function on reefs. To characterize the patterns of community assembly and biogeography on these substrates, climax biofilm bacterial communities from 11 reef sites were collected, and full 16S small subunit rRNA genes were sequenced. Though CCA- and carbonate-associated communities demonstrated different diversity, composition, and correlations with environmental conditions, communities on both substrates were assembled according to similar processes. Stochastic processes dominated assembly on both substrates, primarily drift with moderate influence from dispersal limitation and selection. Sub-communities of habitat generalists and specialists, as well as rare and abundant taxa, experienced disparate patterns of assembly that remained consistent between substrates, highlighting the importance of individual taxa traits in shaping community assembly. These results provide insight into the factors shaping benthic biofilm bacterial assembly and biogeography in a tropical reef ecosystem and contribute to understanding of reef resilience in the face of environmental change.","rel_num_authors":2,"rel_authors":[{"author_name":"Jordan Alexandra Sims","author_inst":"University of California, Berkeley"},{"author_name":"Jennifer L Salerno","author_inst":"George Mason University"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"SARAF represses the mild hypothermia response through the regulation of JUN","rel_doi":"10.64898\/2026.09.02.748854","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748854","rel_abs":"The mild hypothermia response (MHR) is a conserved mammalian cytoprotective program activated upon exposure to mild hypothermia (32 degrees C) that contributes to the neuroprotective effects of therapeutic hypothermia following hypoxic injury. Although rapid changes in intracellular calcium occur upon cooling, the mechanisms linking calcium dynamics to the activation of core MHR factors such as SP1 and RBM3 remain incompletely defined. In this study, we used siRNA-mediated knockdown (KD) of candidate regulators in conjunction with novel mild hypothermia indicator (MHI) reporters to identify upstream modulators of MHR-associated transcription. We identify SARAF, a negative regulator of store-operated calcium entry (SOCE), as a repressor of both SP1 and RBM3 under normothermic conditions. SARAF depletion is associated with increased intracellular calcium release and enhanced SP1- and RBM3-linked transcriptional outputs. We identify JUN as an important downstream factor mediating SARAF depletion-dependent de-repression of the MHR and demonstrate that it undergoes activation rapidly upon cooling. Finally, SARAF depletion conferred significant cytoprotection against hypoxia-induced early apoptosis. Collectively, these findings establish SARAF as an upstream regulator of MHR-associated transcription and provide a functional link between cold-induced intracellular calcium dynamics and the induction of core MHR effectors.","rel_num_authors":6,"rel_authors":[{"author_name":"Kijin Jang","author_inst":"University of Iceland."},{"author_name":"Kevin Ostacolo","author_inst":"Landspitali University Hospital, Reykjavik, Iceland."},{"author_name":"Valdimar Sveinsson","author_inst":"University of Iceland."},{"author_name":"Michael A Beer","author_inst":"Johns Hopkins University, Baltimore, MD, USA."},{"author_name":"Kimberley Jade Anderson","author_inst":"Landspitali University Hospital, Iceland."},{"author_name":"Hans Tomas Bjornsson","author_inst":"University of Iceland."}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Herpes simplex virus 1 subverts the mitochondrial network to support the infection: A lesson on mitochondrial versatility","rel_doi":"10.64898\/2026.09.02.748825","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748825","rel_abs":"Herpes simplex virus 1 (HSV-1) infects approximately 67% of the population worldwide. It establishes lifelong reservoirs in sensory neurons and has been linked to several diseases including neuronal dysfunction. Disruption of mitochondrial homeostasis is a hallmark of HSV-1 infection, however a molecular understanding of these changes and their significance is not yet well defined. HSV-1 infection causes a UL12.5-dependent inhibition of mitochondrial biogenesis through the loss of mitochondrial DNA and mitochondrial transcription factors, PGC-1 (peroxisome proliferator-activated receptor-gamma co-activator) and TFAM (mitochondrial transcription factor). Conversely, UL12.5-independent mechanisms inhibit mitochondrial fusion by activating the OMA1 metallopeptidase that cleaves the inner mitochondrial membrane fusion protein OPA1 (optic atrophy protein 1) and by down-modulating the outer mitochondrial membrane fusion protein MFN2 (mitofusin 2). This inhibition of fusion results in a smaller mitochondrial network that clusters to perinuclear regions, likely supplying energy for viral replication and envelopment. The inner mitochondrial membrane protein TIM23 is also down-modulated during infection in a UL12.5-independent mechanism. Failure of the virus to promote these changes negatively impacts the infection. Despite these changes, mitochondria are protected from mitophagy due to the viral-induced degradation of several mitophagy adaptor proteins, whereby damaged mitochondrial components, including mitochondrial DNA, are extruded via extracellular vesicles. These mitochondrial changes still support functions necessary for HSV-1 infection. Basal cell respiration is preserved, while spare respiratory capacity and extracellular acidification rates increase, indicating glycolytic activity. Mitochondrial membrane potential is also preserved. Overall, our studies provide mechanistic insight into how HSV-1 impacts mitochondria, which could contribute to viral pathogenesis.","rel_num_authors":5,"rel_authors":[{"author_name":"Rabina Saud","author_inst":"University of Kansas Medical Center"},{"author_name":"Kimberly Foster-Lemieur","author_inst":"University of Kansas Medical Center"},{"author_name":"Brett Duguay","author_inst":"Dalhousie University"},{"author_name":"Russell Swerdlow","author_inst":"University of Kansas Medical Center"},{"author_name":"Maria Kalamvoki","author_inst":"University of Kansas Medical Center"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Shared neurogenesis onset is sufficient to explain bilateral matching in the vertebrate retina","rel_doi":"10.64898\/2026.09.02.748850","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.02.748850","rel_abs":"Bilateral symmetry is a hallmark of many paired organs and often essential for optimal functionality. The vertebrate eyes are a prominent example of this, as the matched development of the two retinas is required for accurate visual processing. While macroscopic aspects of symmetry emergence across systems have been investigated, how bilateral matching is maintained once cells start to differentiate remains less understood. Here we address this question using the zebrafish retina as a model to follow neurogenic programs in vivo at single-cell resolution. We perform quantitative 3D live imaging of both retinas simultaneously and directly compare neurogenesis onset and propagation within and across embryos. We find that neurogenic waves initiate at the retinal poles and progress towards the mid-retina in a conserved spatiotemporal pattern. Within embryos, the two eyes exhibit highly similar neurogenesis dynamics when it comes to timing of neurogenesis onset, cell number increase, and spatial wave progression. Across embryos, however, variability is larger. While these observations hint at active inter-retinal coordination, a stochastic model predicts that a shared onset of neurogenesis can be sufficient to explain bilateral matching. Targeted genetic perturbation experiments support this prediction. We find that altering wave propagation affects patterning but not bilateral similarity. Disrupting neurogenesis onset timing, however, reduces bilateral symmetry between eyes. Thus, the combination of experiment and theory identifies synchronized neurogenesis onset as a key determinant of bilateral symmetry, revealing a minimal principle for how reproducible development of paired organs can emerge from stochastic processes.","rel_num_authors":4,"rel_authors":[{"author_name":"Diana Garcia-Morales","author_inst":"Gulbenkian Institute for Molecular Medicine (GIMM), 2780-156 Oeiras, Portugal"},{"author_name":"Adolfo Alsina","author_inst":"GISC, Rey Juan Carlos University"},{"author_name":"Guillaume Salbreux","author_inst":"Department of Genetics and Evolution, University of Geneva"},{"author_name":"Caren Norden","author_inst":"University of Cambridge"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Non-Covalent Poly(ADP-ribose) Signaling Organizes a Circadian E3 Ligase Network in the Brain","rel_doi":"10.64898\/2026.08.30.748055","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.30.748055","rel_abs":"Although PAR biology has traditionally been studied through covalent PARylation, non-covalent PAR-binding proteins provide an additional mechanism for interpreting transient PAR signals and converting them into downstream regulatory programs. Among these effectors, E3 ubiquitin ligases are uniquely positioned to couple PAR sensing to selective ubiquitination, thereby integrating stress signaling with proteostatic control. Because circadian systems depend heavily on temporally coordinated protein turnover, we hypothesized that PAR-binding E3 ligases may form a circadian-structured regulatory layer within the brain. To test this, we integrated GTEx v10 brain transcriptomics, GWAS Catalog gene-mapped associations, CIRCA circadian phase annotations, and Human Protein Atlas single-cell transcriptomic resources to characterize the organization of PAR-binding E3 ubiquitin ligases across neural tissues. Across the brain, the E3 ligase repertoire was broadly deployed yet regionally structured, with cerebellar and cortical enrichment patterns preserved within the PAR-binding subset. Representative ligases spanning circadian regulation, DNA repair, and neurodegeneration-relevant pathways displayed distinct abundance and regional-variability archetypes across GTEx brain regions. Human genetic analyses demonstrated that E3 ligases associated with cognition-, neurodegeneration-, and sleep\/circadian-related phenotypes were disproportionately PAR-binding, supporting convergence between PAR-responsive ubiquitin regulation and disease-relevant biology. Circadian phase analyses further revealed that PAR-binding ligases occupy structured, non-random circadian windows within the broader E3 background, including distinct co-phasing relationships with BMAL1 and CRY1. Finally, cell-type enrichment analyses identified microglia as the dominant compartment for circadian-linked and PAR-binding circadian E3 weighting within the brain E3 program. Together, these findings support a systems-level framework in which non-covalent PAR-binding E3 ubiquitin ligases constitute a brain-deployed, circadian-organized regulatory layer that couples PAR signaling to time-dependent ubiquitin control in neural systems.","rel_num_authors":2,"rel_authors":[{"author_name":"Sajiv Harikrishnan","author_inst":"Johns Hopkins University"},{"author_name":"Sung-Ung Kang","author_inst":"Johns Hopkins School of Medicine"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Mycoplasmal endosymbionts of Trichomonas vaginalis are associated with reduced risk for Chlamydia trachomatis endometrial infection in asymptomatic, coinfected, women.","rel_doi":"10.64898\/2026.08.31.748291","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748291","rel_abs":"Trichomonas vaginalis is a protozoan parasite that causes trichomoniasis, the most common curable non-viral sexually transmitted infection, and Chlamydia trachomatis is a bacterial pathogen that can ascend to the upper genital tract and cause pelvic inflammatory disease, infertility, and ectopic pregnancy. T. vaginalis harbors bacterial endosymbionts, including Candidatus Malacoplasma girerdii, an obligate symbiont, and Metamycoplasma hominis, which can live freely or symbiotically. In a 16S rRNA sequencing study of the cervicovaginal microbiome of women at high risk for chlamydial infection, Ca. M. girerdii abundance was one of 13 features predicting lack of chlamydial spread to the endometrium, despite no direct association between T. vaginalis infection and reduced chlamydial ascension. Investigating the relationship between these microorganisms further, we found that T. vaginalis vaginal abundance correlated positively with chlamydial burden in women whose infection was confined to the cervix, while a nonsignificant inverse relationship was seen in women with endometrial spread. Among participants with high chlamydial burden, Ca. M. girerdii was detected exclusively in women without endometrial infection. Both endosymbionts trended toward more frequent detection, and higher abundance, in coinfected women without endometrial spread, while M. hominis abundance correlated strongly with T. vaginalis burden in this group. These findings suggest that mycoplasmal endosymbionts of T. vaginalis, rather than T. vaginalis itself, are microbial factors limiting chlamydial ascension, and point to a three-way interaction between parasite, endosymbiont, and bacterial pathogen that shapes upper genital tract C. trachomatis infection risk.","rel_num_authors":10,"rel_authors":[{"author_name":"Xuejun Sun","author_inst":"The University of North Carolina at Chapel Hill"},{"author_name":"Kacy Savannah Yount","author_inst":"The University of North Carolina at Chapel Hill"},{"author_name":"Nefer N Batsuli","author_inst":"The University of North Carolina at Chapel Hill School of Medicine"},{"author_name":"Camille Campbell","author_inst":"The University of North Carolina at Chapel Hill School of Medicine"},{"author_name":"Sangmi Jeong","author_inst":"NC State University"},{"author_name":"Tammy S Tollison","author_inst":"NC State University"},{"author_name":"Xinxia Peng","author_inst":"NC State University"},{"author_name":"Toni Darville","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Xiaojing Zheng","author_inst":"The University of North Carolina at Chapel Hill"},{"author_name":"Catherine M O'Connell","author_inst":"The University of North Carolina at Chapel Hill"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Sequence and epigenetic characterization of chromosome 21 centromeres in a family with recurrent Trisomy 21","rel_doi":"10.64898\/2026.08.31.748295","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.748295","rel_abs":"Trisomy 21 (T21) is the most common genetic cause of intellectual disability, yet the molecular mechanisms underlying maternal meiosis I errors--responsible for ~70% of free T21 cases--remain poorly understood. In this preliminary study, we used long-read sequencing and genome assembly to investigate the DNA sequence and epigenetic features of chromosome 21 (chr21) centromeres in a family with recurrent free T21 due to maternal meiosis I errors. The mother, who had two affected and three unaffected children, showed no mosaicism or structural rearrangements. One of her two chr21 centromeres lacked a pronounced centromere dip region (CDR), displaying instead a diffuse hypomethylation pattern (dCDR) with much higher methylated CpG levels (55%) compared to its homologue (36%). This dCDR was transmitted to an unaffected child and the affected proband analyzed, suggesting it was present in one of the maternal chr21 since she was at least 32 years of age. Chr21 dCDRs were not observed in seven young mothers with children with T21 or previously described in the literature in 108 population haplotypes. We hypothesize that dCDRs may weaken kinetochore function, increasing nondisjunction risk, and propose two models linking such epigenetic variation to maternal age-related T21 risk. These findings highlight the value of complete centromere characterization in families with children with T21 and suggest centromere methylation status of chr21 as a potential T21 risk factor for future investigation.","rel_num_authors":6,"rel_authors":[{"author_name":"F. Kumara Mastrorosa","author_inst":"University of Washington"},{"author_name":"Kendra Hoekzema","author_inst":"University of Washington"},{"author_name":"Marcelo Ayllon","author_inst":"University of Washington"},{"author_name":"Periklis Makrythanasis","author_inst":"University of Athens"},{"author_name":"Stylianos E. Antonarakis","author_inst":"University of Geneva"},{"author_name":"Evan E. Eichler","author_inst":"University of Washington"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"N6-methyladenosine regulates Influenza A virus mRNA stability yet is rarely found on genomic RNA","rel_doi":"10.64898\/2026.09.01.748482","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748482","rel_abs":"Previous studies have found widespread N6-methyladenosine (m6A methylation) on all forms of Influenza A virus (IAV) RNA, with m6A found critical for viral replication, pathogenicity as well as viral RNA packaging. Here we applied the latest quantitative technologies to revisit the methylation landscape on the anti-sense genomic RNA of IAV. Unexpectedly, upon Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS\/MS) analysis of IAV virion -extracted genomic RNA, we detected very little m6A regardless of production from human cells or chicken eggs. Concordantly, Nanopore direct RNA sequencing also detected an overall low occurrence and stoichiometry (generally <5%) of m6A across all viral genomic RNA segments, compared with abundant m6A sites on viral mRNAs at ~20-30% m6A. Cross validation with glyoxal- and nitrite-mediated deamination of unmethylated adenosines (GLORI) confirmed multiple m6A sites on viral mRNA yet very few m6A on the genomic RNA. This paucity of m6A on genomic RNA makes it unlikely that m6A contributes to viral RNA packaging. Knockdown or pharmacological inhibition of the m6A methyltransferase METTL3 as well as the reader protein YTHDF2 both reduced viral mRNA levels and infectious viral particle production, with YTHDF2 promoting viral mRNA stability. Thus, the presence of m6A on IAV transcripts is indeed proviral, yet it is the mRNAs instead of genomic RNAs that are methylated at functionally relevant levels. Lastly, we provide proof of concept that a METTL3 small molecule inhibitor can be antiviral, and propose that m6A-targeted antivirals would mainly impact the intracellular gene expression phase of IAV replication.","rel_num_authors":11,"rel_authors":[{"author_name":"Hsiu-Yi Wu","author_inst":"Institute of Biomedical Sciences Academia Sinica"},{"author_name":"Wan-Ju Tung","author_inst":"Institute of Biomedical Sciences Academia Sinica"},{"author_name":"Ramreishang Wungmaiwo","author_inst":"Institute of Biomedical Sciences Academia Sinica"},{"author_name":"Pei-Yi Alma Su","author_inst":"Kaohsiung Medical University College of Medicine"},{"author_name":"Hung-Wei Hsu","author_inst":"Institute of Biomedical Sciences Academia Sinica"},{"author_name":"Ankit Gupta","author_inst":"National Institute of Environmental Health Sciences"},{"author_name":"Brian N Papas","author_inst":"National Institute of Environmental Health Sciences"},{"author_name":"Marcos Morgan","author_inst":"National Institute of Environmental Health Sciences"},{"author_name":"Wen-Chun Liu","author_inst":"Biomedical Translation Research Center"},{"author_name":"I-Hsuan Wang","author_inst":"Institute of Biomedical Sciences Academia Sinica"},{"author_name":"Kevin Tsai","author_inst":"Institute of Biomedical Sciences Academia Sinica"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Microbial valerate is associated with CAR T dysbiosis and its supplementation enhances CAR T function in B-cell lymphoma","rel_doi":"10.64898\/2026.09.01.748711","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748711","rel_abs":"Anaerobe-depleting antibiotic exposure is associated with inferior progression-free survival after CD19 CAR T-cell therapy in large B-cell lymphoma, yet the cellular mechanisms linking gut dysbiosis to the CAR T-cell product and whether this imprint is reversible have remained undefined. In two independent CAR-T candidate cohorts, low stool valerate at the time of CAR-T eligibility identified a multi-metabolite-deficient dysbiotic gut microbiome state marked by depletion of fiber-fermenting commensals and loss of carbohydrate-fermentation, SCFA-biosynthesis, and amino-acid metabolism pathways. Reanalysis of single-cell RNA sequencing from 42 lymphoma patients stratified by piperacillin-tazobactam\/imipenem\/meropenem (PIM) exposure revealed that PIM-exposed CAR T-cell products were CD4-skewed, with significantly elevated AP-1\/immediate-early gene (IEG) and cellular activation signatures that together predicted inferior progression-free survival. Ex vivo conditioning of CAR T-cells with valerate produced a chromatin and transcription factor program distinct from butyrate or propionate, characterized by KLF\/SP\/EGR family engagement, KLF4 promoter opening, and broad induction of AP-1\/IEG and MHC class II transcripts, whereas butyrate drove broader chromatin remodeling with TBX21\/EOMES\/NF-{kappa}B gains and KLF2 promoter closure, and propionate induced an NFY-centered program with preferential commitment to low-mitochondrial-content states. Untargeted metabolomics confirmed valerate uptake and mitochondrial {beta}-oxidation in CAR T-cells, while dietary sodium valerate supplementation in meropenem-treated mice bearing A20 lymphoma significantly reduced tumor burden and extended survival compared with CAR T-cells alone. These findings identify stool valerate as a bedside-deployable biomarker of dysbiosis-imprinted CAR T-cell dysfunction and support ex vivo or dietary valerate supplementation as a clinically tractable strategy to improve CAR-T anti-tumor function in patients with disrupted gut microbiomes.","rel_num_authors":28,"rel_authors":[{"author_name":"Lubna Rehman","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Wenting Song","author_inst":"Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Department of Bioinformatics and Computational Biology, T"},{"author_name":"Abdur Rehman","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Kamini Singh","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Shiv Govind Rawat","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Faezeh Darbaniyan","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Cao Cuong Le","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Federico Mario Aletti","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Jinsheng Weng","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Jingwei Liu","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Xiayoun Cheng","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Yongfu Tang","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Sridevi Patchva","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Matthew D. Richard","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Fuliang Chu","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Jingjing Cao","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Christopher R. Flowers","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Elizabeth J. Shpall","author_inst":"Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Mark R. Tanner","author_inst":"Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Johannes Fahrmann","author_inst":"Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Eran Elinav","author_inst":"Department of Systems Immunology, Weizmann Institute of Science, Israel; Microbiome and Cancer Division, German Cancer Research Center, Heidelberg, Germany"},{"author_name":"Christoph K. Stein-Thoeringer","author_inst":"Department of Internal Medicine I and M3 Research Institute, University Clinic Tuebingen, Tuebingen, Germany"},{"author_name":"Michael D. Jain","author_inst":"Department of Stem Cell Transplantation and Cellular Therapy, Moffitt Cancer Center, FL, USA"},{"author_name":"Robert R. Jenq","author_inst":"Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA"},{"author_name":"Abhinav Jain","author_inst":"Epigenomics Core, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Sattva S. Neelapu","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA"},{"author_name":"Ye Zheng","author_inst":"Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Department of Bioinformatics and Computational Biology, T"},{"author_name":"Neeraj Y Saini","author_inst":"Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Department of Stem Cell Transplantation and Cellular"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Neogenin-1 marks myeloid-primed fetal hematopoietic stem cells that undergo progressive lineage-restriction with age","rel_doi":"10.64898\/2026.09.01.747091","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.747091","rel_abs":"During aging, hematopoietic stem cells (HSCs) increasingly shift from balanced to myeloid-biased differentiation, resulting in reduced lymphoid output and impaired adaptive immunity. The question of whether this lineage bias is established in a subset of HSCs during early development or primarily emerges with aging warrants further investigation. Here, we investigate whether myeloid-biased HSCs (my-HSCs) are established at the fetal liver stage by specifically examining Neogenin-1 (NEO1), a previously defined marker of my-HSCs. We identify two distinct populations of Hoxb5+ HSCs in the fetal liver: NEO1+ and NEO1-, with NEO1+ HSCs exhibiting transcriptional and functional characteristics consistent with my-HSCs. With age, my-HSC-associated transcriptional programs become increasingly reinforced across the Hoxb5+ pHSC compartment, with NEO1+ cells showing early enrichment of this program and both NEO1+ and NEO1- cells acquiring broader myeloid-biased features in aging. These findings suggest that lineage programming can begin early in development and is further shaped by age-related changes, potentially contributing to the functional decline observed in the aging hematopoietic system.","rel_num_authors":16,"rel_authors":[{"author_name":"Allison Banuelos","author_inst":"Stanford University School of Medicine"},{"author_name":"Michelle Baez","author_inst":"Stanford University School of Medicine"},{"author_name":"Leyla Yilmaz","author_inst":"Stanford University School of Medicine"},{"author_name":"Elle Koren-Sedova","author_inst":"Stanford University School of Medicine"},{"author_name":"Monika Zukowska","author_inst":"Stanford University School of Medicine"},{"author_name":"Andrew T Burden","author_inst":"Stanford University School of Medicine"},{"author_name":"Leah Swartzrock-Willner","author_inst":"Stanford University School of Medicine"},{"author_name":"Uyen Le","author_inst":"Stanford University School of Medicine"},{"author_name":"Allison Zhang","author_inst":"Stanford University School of Medicine"},{"author_name":"Nardin Georgeos","author_inst":"Stanford University School of Medicine"},{"author_name":"Benjamin Ohene-Gambill","author_inst":"Stanford University School of Medicine"},{"author_name":"Bowen Zheng","author_inst":"Stanford University School of Medicine"},{"author_name":"Nicole Womack-Gambrel","author_inst":"Stanford University School of Medicine"},{"author_name":"Ruby Honjol","author_inst":"Stanford University School of Medicine"},{"author_name":"Rahul Sinha","author_inst":"Stanford University School of Medicine"},{"author_name":"Irving L Weissman","author_inst":"Stanford University School of Medicine"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Combined Image-Based Profiling and Biochemical Analysis of GCaMP Overexpression Effects on Mammalian Cells","rel_doi":"10.64898\/2026.09.01.748672","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748672","rel_abs":"Protein-based fluorescent sensors are a powerful addition to the biology toolbox for their ability to be stably expressed within living organisms, tissues, cells, and subcellular compartments, with the capacity to report on the presence of specific target molecules or other analytes. At the same time, sensor components will unavoidably present opportunities for unintended interaction with endogenous cellular machinery, potentially confounding both sensor function and cell health. Interactions with host components may not be readily predictable during the sensor design process, especially when simultaneously optimizing many other sensor parameters such as fluorescence response, dynamic range, and kinetics. Characterizing effects of sensor expression on cells is currently a laborious ad hoc process; new methods to characterize the cell expression effects of sensors and their variants could dramatically improve sensor design pipelines, laying the groundwork to recognize potentially problematic expression side effects earlier in the iterative design and testing workflow. Here, we take a dual high-content imaging-based and biochemical approach to examine sensor interactions with native cell biology, focusing on the widely used GCaMP calcium sensor. We identify a morphology-based signature of the cellular effects of high sensor expression in a neuroblastoma cell line. Subsequently, we identify biochemical interactions between GCaMP and a component of the mammalian cytoskeleton and track morphological features in sensor-expressing cells that lack these structural components. Our findings present an entry point for engineering new minimally cross-reactive sensor versions given a contextual biological understanding of sensor overexpression. We anticipate that as this and related workflows are incorporated into sensor engineering pipelines, bioorthogonality can be more systematically assessed and prioritized in diverse sensor scaffolds.","rel_num_authors":3,"rel_authors":[{"author_name":"Loren L Looger","author_inst":"University of California, San Diego"},{"author_name":"Laura Beebe","author_inst":"University of California, San Diego"},{"author_name":"Lin Tian","author_inst":"Max Planck Florida Institute for Neuroscience"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Cardiomyocyte prohibitin ablation reprograms cardiac metabolism revealing a pathogenic role for mTORC1 in dilated cardiomyopathy","rel_doi":"10.64898\/2026.09.01.748696","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748696","rel_abs":"Maintaining cardiac structure and function throughout the lifespan requires a delicate balance in carbon allocation between energetic and biosynthetic processes. At the nexus of this balance are prohibitins-1 and -2 (PHB1, 2) which form a ring-like complex in mitochondrial and plasma membranes responsible for coordinating cellular growth, metabolism and autophagy. Here we describe how ablation of the PHB complex in cardiomyocytes of adult mice (cPHB1KO) causes unrestrained mechanistic target of rapamycin complex 1 (mTORC1) activity and a Warburg-like reprogramming of glucose metabolism in heart toward enhanced de novo amino acid biosynthesis. These changes are accompanied by disruptions in mitochondrial Ca2+ handling and impaired autophagy, leading to severe dilated cardiomyopathy and mortality within 12 weeks. Using pharmacological and nutritional approaches, we further show that mTORC1 inhibition attenuates pathologic cardiac remodeling only in female cPHB1KO mice. Our findings illustrate novel mechanisms linking the PHB complex with altered carbon flux and pathogenesis of cardiomyopathy.","rel_num_authors":11,"rel_authors":[{"author_name":"Ran Huo","author_inst":"University of Iowa-College of Pharmacy"},{"author_name":"Sarah Torrence","author_inst":"University of Iowa- College of Pharmacy"},{"author_name":"Kaitlyn A Berns","author_inst":"University of Iowa- College of Pharmacy"},{"author_name":"Rachel M Crawford","author_inst":"University of Iowa- College of Pharmacy"},{"author_name":"Amany Alowaisi","author_inst":"University of Iowa- College of Pharmacy"},{"author_name":"Jolonda Mahoney","author_inst":"University of Iowa- College of Pharmacy"},{"author_name":"Biyi Chen","author_inst":"University of Iowa- College of Pharmacy"},{"author_name":"Qian J Shi","author_inst":"University of Iowa"},{"author_name":"Benjamin Darbro","author_inst":"Univ. Iowa"},{"author_name":"Long-Sheng Song","author_inst":"University of Iowa"},{"author_name":"ETHAN J ANDERSON","author_inst":"University of Iowa | College of Pharmacy"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Implementation and calibration of the Vaganov-Shashkin model in the virtualRings R package","rel_doi":"10.64898\/2026.09.01.748354","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748354","rel_abs":"Process-based tree growth models provide a mechanistic framework for investigating how climate conditions regulate tree growth across daily to annual time scales. Yet, their broader application across species and environments is constrained by the limited accessibility in open-source environments and the difficulty of estimating physiological parameters that are rarely measured directly. Here, we present virtualRings, a new R package integrating the Vaganov-Shashkin model (VSM) and the RINGS3 models, and focus on the implementation and calibration of VSM. Using tree-ring width observations from seven Northern Hemisphere sites across various environmental conditions, we compared the traditional bootstrap-based calibration approach with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). CMA-ES improved agreement between simulated and observed radial tree growth and provided an efficient approach for model parameter estimation. We further evaluated practical CMA-ES settings to balance computational cost and performance and discussed its potential limitations. The virtualRings package provides an open and reproducible platform for tree growth simulation, facilitating the application of important process-based models across species and environments and the investigation of how temperature and moisture constraints regulate daily tree-ring formation across spatial and temporal scales.","rel_num_authors":9,"rel_authors":[{"author_name":"Feng Wang","author_inst":"University of Iowa"},{"author_name":"Jeff W Atkins","author_inst":"Virginia Commonwealth University"},{"author_name":"Kevin J Anchukaitis","author_inst":"University of Arizona"},{"author_name":"Erika K Wise","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Xiuchen Jiang","author_inst":"University of Iowa"},{"author_name":"Bao Yang","author_inst":"Nanjing University"},{"author_name":"Dominique Arseneault","author_inst":"Universite du Quebec a Rimouski"},{"author_name":"Etienne Boucher","author_inst":"Universite du Quebec a Montreal"},{"author_name":"Matthew P Dannenberg","author_inst":"University of Iowa"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Sobetirome, a thyroid hormone receptor beta agonist, is a potential therapeutic agent for pulmonary fibrosis","rel_doi":"10.64898\/2026.08.31.747360","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.08.31.747360","rel_abs":"Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease with limited treatment options. Our group previously identified the antifibrotic potential of thyroid hormone, triiodothyronine (T3); however, clinical translation of thyroid hormone therapy is limited by its systemic adverse effects. In this study, we investigate whether sobetirome, a selective and well tolerated thyroid hormone receptor beta (THRB) agonist, offers antifibrotic benefits of thyroid hormone while minimizing systemic toxicity. Our study reveals that sobetirome, administered via intraperitoneal or inhalational routes, effectively mitigates bleomycin-induced pulmonary fibrosis in mice, with no evidence of toxicity. We identified that sobetirome restores mitochondrial homeostasis via activating the THRB-PPARGC1a axis. This protects alveolar type II epithelial cells from injury-induced apoptosis while selectively inducing apoptosis and metabolic reprogramming in apoptosis resistant IPF fibroblasts. Cell-specific deletion of Ppargc1a in either alveolar epithelial cells or fibroblasts abolishes sobetirome-mediated protection, establishing PPARGC1a as an essential mediator of therapeutic response. Importantly, sobetirome reverses fibrosis-associated transcriptional programs in human IPF lung tissue, reducing expression of key fibrosis-associated genes, including collagen I alpha 1 (COL1A1), collagen III alpha 1 (COL3A1), periostin (POSTN), cathepsin K (CTSK), and Chitinase 3 Like 1 (CHI3L1), while promoting extracellular matrix remodeling, epithelial restoration, and tissue homeostasis. Collectively, our findings identify THRB activation as a novel metabolic strategy for reversing pulmonary fibrosis. Across complementary in vitro, in vivo, and human ex vivo models, sobetirome restores mitochondrial function, modulates apoptotic pathways in pathogenic cells, and promotes fibrosis resolution, highlighting its potential as a lung-targeted therapeutic approach for IPF and other fibrotic lung diseases.","rel_num_authors":19,"rel_authors":[{"author_name":"Thomas Barnthaler","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"shuiz Ding","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Taylor Sterling Adams","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Kady-Ann Rose","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Reina Rangel","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Johad Khoury","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Melika Salimi","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Sabina Anderson","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Liqin Lin","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Karen Velasco Alzate","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Fernando Poli De Frias","author_inst":"Baylor College of Medicine, Houston, Texas, USA"},{"author_name":"Giuseppe Deluliis","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Fadi Nicola","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Carlos Cosme Jr.","author_inst":"Yale University School of Medicine, New Haven, CT, USA."},{"author_name":"Aurelien Justet","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Xinran Liu","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Ivan Rosas","author_inst":"Baylor College of Medicine, Houston, Texas, USA"},{"author_name":"Naftali Kaminski","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Farida Ahangari","author_inst":"Yale University School of Medicine, New Haven, CT, USA"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"Cryo-EM structures reveal the mechanism of phosphatidylserine remodeling by membrane-bound glycerophospholipid O-acyltransferase 1","rel_doi":"10.64898\/2026.09.01.748671","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.01.748671","rel_abs":"Lands cycle remodeling of glycerophospholipid acyl chains is crucial for cells to maintain appropriate membrane composition. Glycerophospholipids are cleaved at the glycerol sn2-position by phospholipase A. The lysophospholipids are reacylated by enzymes of the membrane-bound O-acyltransferase (MBOAT) family to incorporate specific fatty-acyl chains to adjust membrane properties. How MBOAT enzymes recognize specific acyl-CoA donors, select lysophospholipid acceptors, and release products is unclear. Phosphatidylserine (PS), a critical anionic phospholipid, controls membrane surface charge, signaling-protein recruitment, and cell-death-associated membrane recognition, and PS acyl-chain remodeling is linked to ferroptosis resistance. Here, we showed that MBOAT1 preferentially generates monounsaturated fatty acid-containing PS from lyso-PS. High-resolution cryo-electron microscopy structures of human MBOAT1 captured distinct binding poses of the fatty acyl donor, lyso-PS acceptor, and PS product. With lipidomics, enzymology and molecular dynamics simulations, these structures reveal the mechanism and pathway of MBOAT1-dependent PS remodeling.","rel_num_authors":9,"rel_authors":[{"author_name":"Leehyeon Kim","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Siyoung Kim","author_inst":"University of Chicago"},{"author_name":"Ritchie Ly","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Luke Cohen-Abeles","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Pei Liu","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Xuejun Jiang","author_inst":"Memorial Sloan-Kettering Cancer Center"},{"author_name":"Dohoon Kwon","author_inst":"Pohang University of Science and Technology (POSTECH)"},{"author_name":"Robert V Farese Jr.","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Tobias C Walther","author_inst":"Memorial Sloan Kettering Cancer Center"}],"rel_date":"2026-09-03","rel_site":"biorxiv"},{"rel_title":"From Housing to Hotspots: Integrating a Housing-Based Measure of Individual Socioeconomic Status with Geospatial Analysis to Target Colorectal Cancer Screening in Rural Communities","rel_doi":"10.64898\/2026.08.28.26361444","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361444","rel_abs":"Purpose: This study assesses the association between colorectal cancer (CRC) screening and a validated, housing-based measure of individual-level socioeconomic status (SES, called HOUSES hereafter) within rural communities and determines whether HOUSES-integrated geospatial analysis can be used to tailor interventions. Methods: We used CRC screening data from a subset of Mayo Clinic Midwest patients living in cities without ready access to routine care in the Mayo Clinic Health System in 2019 to represent rural communities. At the individual level, we assessed the association between CRC screening rates and the HOUSES index, adjusting for age, sex, race\/ethnicity, comorbidity, distance from home address to clinic, and area deprivation index, using a multilevel mixed-effects logistic regression model. Additionally, we conducted geospatial analysis to examine the correlation between hotspots of 1) lower CRC screening rates and 2) lower SES of the subject population (HOUSES quartile 1). Findings: Among 34,489 individuals (median age 64.0 years, 52.4% female), those with the lowest SES (HOUSES Q1) had 37% lower odds of being CRC screening adherent than those with the highest SES (HOUSES Q4) (adj. OR [95% CI]: 0.63 [0.58-0.69]). In the 14 identified HOUSES Q1 hotspots, there was a significant correlation in counts of HOUSES Q1 and low CRC screening (correlation coefficient=0.81). Conclusion: Lower SES was significantly associated with lower CRC screening among rural populations. HOUSES-enabled geospatial analysis identified geographic hotspots with lower CRC screening rates for targeted interventions to address disparities in CRC screening in rural communities. HOUSES may be a useful digital tool for cancer preventive care and research.","rel_num_authors":30,"rel_authors":[{"author_name":"Rebecca Yao","author_inst":"University of Chicago"},{"author_name":"Chung-Il Wi","author_inst":"Mayo Clinic"},{"author_name":"Madison J Beenken","author_inst":"Mayo Clinic"},{"author_name":"Dave Watson","author_inst":"Mayo Clinic"},{"author_name":"Philip H Wheeler","author_inst":"Mayo Clinic"},{"author_name":"Mike Finch","author_inst":"Children's Minnesota Research Institute"},{"author_name":"Dan P Kelleher","author_inst":"Mayo Clinic"},{"author_name":"Gokhan Anil","author_inst":"Mayo Clinic"},{"author_name":"Trent Anderson","author_inst":"Mayo Clinic Health System"},{"author_name":"Kathy Madden","author_inst":"Mayo Clinic Health System"},{"author_name":"Scott H Okuno","author_inst":"Mayo Clinic"},{"author_name":"Folakemi T Odedina","author_inst":"Mayo Clinic"},{"author_name":"Erin C Westfall","author_inst":"Mayo Clinic Health System"},{"author_name":"Eunice Y Park","author_inst":"Mayo Clinic"},{"author_name":"Pravesh Sharma","author_inst":"Mayo Clinic Health System"},{"author_name":"Sagar Dugani","author_inst":"Mayo Clinic"},{"author_name":"Randy M Foss","author_inst":"Mayo Clinic Health System"},{"author_name":"Brandon H Hidaka","author_inst":"Mayo Clinic Health System"},{"author_name":"Jessica L Sosso","author_inst":"Mayo Clinic Health System"},{"author_name":"Shivani Sabarish","author_inst":"Mayo Clinic"},{"author_name":"Gurpreet Singh","author_inst":"Mayo Clinic"},{"author_name":"Nahyr Lugo-Fagundo","author_inst":"Mayo Clinic"},{"author_name":"James Howick","author_inst":"Mayo Clinic"},{"author_name":"W. Ray Kim","author_inst":"Mayo Clinic"},{"author_name":"Andrew D Calvin","author_inst":"Mayo Clinic"},{"author_name":"Cheryl L. Walker-Mcgill","author_inst":"Carolina Complete Health"},{"author_name":"Lior Rennert","author_inst":"Clemson University"},{"author_name":"Young J Juhn","author_inst":"Mayo Clinic"},{"author_name":"James R Cerhan","author_inst":"Mayo Clinic"},{"author_name":"Brian A Lynch","author_inst":"Mayo Clinic"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Immune Checkpoint Blockade Modifies Drug-Associated Toxicity Across Phenotypes and Time","rel_doi":"10.64898\/2026.08.31.26361880","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361880","rel_abs":"ImportanceImmune checkpoint inhibitors (ICIs) produce diverse immune toxicities, but whether checkpoint blockade also modifies associations between other drugs and adverse events is poorly understood.\n\nObjectiveTo define ICI-associated toxicity organization and determine whether drug-associated adverse events and onset vary with ICI exposure and checkpoint pathway.\n\nDesign and SettingCross-sectional analysis of deduplicated FAERS reports from 2016 through 2025; analyses performed in 2026.\n\nParticipantsAmong 13,701,106 deduplicated reports, 2,365,269 were cancer associated and 256,940 contained an ICI. Median age among cancer reports with observed age was 66 years (IQR, 56-75 years); 1,031,999 (43.6%) were female and 1,003,154 (42.4%) were male.\n\nExposuresICI exposure in any reported drug role, individual primary-suspect drugs, and checkpoint-pathway exposure.\n\nMain Outcomes and MeasuresReporting odds ratios (ORs), cross-organ adverse-event communities, adjusted primary-suspect drug x ICI interaction ORs for Stevens-Johnson syndrome\/toxic epidermal necrolysis (SJS\/TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), interstitial nephritis, drug-induced liver injury (DILI), and vomiting (VOM), and accelerated failure-time model time ratios for documented onset.\n\nResultsOf 3001 eligible Preferred Terms in cancer-associated reports, 2091 differed at a false discovery rate (FDR) less than .05. Four cross-organ toxicity communities were identified. Of 138 eligible drug-phenotype pairs, 65 had FDR-significant interactions, including moxifloxacin-SJS\/TEN amplification (interaction OR, 101.72; 95% CI, 39.11-264.55), enfortumab vedotin-SJS\/TEN attenuation (interaction OR, 0.17; 95% CI, 0.13-0.23), and omeprazole-interstitial nephritis amplification (interaction OR, 10.35; 95% CI, 7.62-14.05). Among 60,324 reports contributing to temporal analyses, ICI exposure was associated with longer adjusted documented time to onset for 5 of 6 phenotypes (time ratios, 1.37-1.59) but not AGEP (time ratio, 0.99; 95% CI, 0.67-1.46). Temporal associations also differed across checkpoint pathways.\n\nConclusions and RelevanceICIs were associated with a structured cross-organ toxicity landscape, phenotype-specific modification of drug-associated adverse events, and distinct temporal patterns across checkpoint pathways. These findings support checkpoint blockade as a modifier of drug-associated toxicity and motivate longitudinal and mechanistic validation.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSWhat is the structure of toxicity associated with immune checkpoint blockade?\n\nFindingsIn this cross-sectional study of 13,701,106 deduplicated adverse-event reports, ICI exposure defined a broad toxicity landscape containing 4 cross-organ communities. Sixty-five drug x ICI interactions were identified across 5 immune-mediated phenotypes and a control (vomiting), and documented time to onset differed by phenotype and checkpoint pathway.\n\nMeaningCheckpoint blockade may create an altered immune state in which the phenotype and timing of drug-associated toxicity depend on both the accompanying drug and the checkpoint pathway inhibited.","rel_num_authors":7,"rel_authors":[{"author_name":"Eric M Mukherjee","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Amir Asiaee","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Dodi Park","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Matthew S Krantz","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Cosby A Stone Jr.","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Michelle Martin-Pozo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Elizabeth Jane Phillips","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"The accuracy of urine-based mycobacterial antigens to detect childhood tuberculosis using an ultrasensitive immunoassay","rel_doi":"10.64898\/2026.08.28.26361530","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361530","rel_abs":"Background. Urine-based testing offers a promising non-sputum approach for diagnosing paediatric tuberculosis. However, the currently available lipoarabinomannan (LAM) assay shows limited sensitivity in children and is primarily indicated for those living with HIV. Co-detection of LAM with Mycobacterium tuberculosis (Mtb) proteins in urine could provide complementary pathogen-derived biomarkers that improve diagnostic performance. Methods. We developed an ultrasensitive multiplex electrochemiluminescence (ECL) immunoassay to measure Ag85B, CFP-10, ESAT-6, MPT32, and MPT64 in urine. We determined the analytical limits of detection and evaluated the diagnostic performance of individual proteins and LAM using urine samples from children with Confirmed, Unconfirmed, and Unlikely pulmonary tuberculosis enrolled across five high-burden countries (The Gambia, India, Peru, South Africa, and Uganda). Performance was assessed overall, by HIV and nutritional status, and across biomarker combinations. Findings. Urine samples from 630 children were analysed (median age was 4 years [IQR 2-8]; 44% female, 15% living with HIV, 19% underweight, 24% with Confirmed tuberculosis). The ECL assay achieved femtomolar limits of detection (1.5 to 4.0 fM). The sensitivity and specificity of individual Mtb proteins were 12-33% and 98-100%, respectively. Ag85B had the highest sensitivity (33%, 95% CI 26-41) for Confirmed tuberculosis and was similar to LAM. A four-antigen signature (Ag85B, MPT64, MPT32, LAM) was 50% sensitive (95% CI 42-58) and 94% specific (95% CI 90-96), and was significantly more sensitive than LAM alone, in particular among those without HIV. An additional sixteen (10%) of children with Unconfirmed TB had at least one Mtb protein or LAM detected. Interpretation. Multiple Mtb proteins are detectable in paediatric urine with high specificity, and multi-antigen signatures can augment sensitivity versus LAM alone. These findings demonstrate the potential of multi-antigen urine detection for childhood TB and define analytical targets for the development of future point-of-care diagnostics. Funding. National Institutes of Health.","rel_num_authors":24,"rel_authors":[{"author_name":"Esin Nkereuwem","author_inst":"London School of Hygiene and Tropical Medicine"},{"author_name":"Salvia Misaghian","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Devan Jaganath","author_inst":"University of California San Francisco"},{"author_name":"Roger I Calderon","author_inst":"Advanced Research and Health"},{"author_name":"Juaneta Luiz","author_inst":"University of Cape Town"},{"author_name":"Mandar Paradkar","author_inst":"Byramjee Jeejeebhoy Government Medical College"},{"author_name":"Peter Wambi","author_inst":"WALIMU"},{"author_name":"Robert Castro","author_inst":"University of California San Francisco"},{"author_name":"Rutuja Nerurkar","author_inst":"University of California San Francisco"},{"author_name":"Mingyue Wang","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Jacob Wohlstadter","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"Molly F Franke","author_inst":"Harvard Medical School"},{"author_name":"Beate Kampmann","author_inst":"Charit\u00e9 Universit\u00e4tsmedizin Berlin"},{"author_name":"Aarti Kinikar","author_inst":"Byramjee Jeejeebhoy Government Medical College"},{"author_name":"Heather J Zar","author_inst":"University of Cape Town"},{"author_name":"Mark Segal","author_inst":"University of California San Francisco"},{"author_name":"Midori Kato-Maeda","author_inst":"University of California San Francisco"},{"author_name":"Jeffrey M Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Danielle Swaney","author_inst":"University of California San Francisco"},{"author_name":"Adithya Cattamanchi","author_inst":"University of California Irvine"},{"author_name":"Joel D Ernst","author_inst":"University of California San Francisco"},{"author_name":"Eric Wobudeya","author_inst":"WALIMU"},{"author_name":"George Sigal","author_inst":"Meso Scale Diagnostics, LLC."},{"author_name":"- The Combo Study","author_inst":""}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Social Determinants of Health in HIV\/HBV Coinfection Compared with HIV and HBV Monoinfection: A Framework for Dynamic Social Vulnerability","rel_doi":"10.64898\/2026.08.31.26361856","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361856","rel_abs":"Human immunodeficiency virus (HIV) and hepatitis B virus (HBV) coinfection is associated with accelerated liver disease, but whether coinfection is associated with newly documented social determinants of health (SDoH) is unclear. We conducted a retrospective cohort study using TriNetX across 110 U.S. healthcare organizations (2010-2026). We propensity score matched adults with HIV\/HBV to adults with HIV or HBV monoinfection. We organized newly documented SDoH indicators using a dynamic individual-level framework with four clinically recognized domains of social disadvantage: material vulnerability, healthcare access and engagement, interpersonal adversity, and psychosocial vulnerability. Matched cohorts included 10,071 HIV\/HBV-HIV pairs and 9,659 HIV\/HBV-HBV pairs (mean age, 47 years; 79% male; 66% non-White; median follow-up, 3.3 years). Over 178,900 person-years, HIV\/HBV was associated with higher risk of the primary SDoH composite compared with HIV (11.5% vs 9.7%; incidence rate, 2.50 vs 1.97 per 100 person-years; hazard ratio [HR], 1.25; 95% confidence interval [CI], 1.15-1.37) and HBV (11.0% vs 6.4%; incidence rate, 2.39 vs 1.67; HR, 1.50; 95% CI, 1.35-1.67). HIV\/HBV was also associated with higher material vulnerability and healthcare access and engagement composites in both comparisons, including housing instability, food insecurity, financial insecurity, insurance instability, and care disengagement\/nonadherence (HR range, 1.22-3.33 vs HIV; 1.31-1.94 vs HBV). In the HBV comparison, HIV\/HBV was additionally associated with interpersonal adversity, primary support stressors, and violence or victimization (HR range, 1.36-2.16). Findings were robust across sensitivity analyses. HIV\/HBV was associated with more newly documented SDoH than monoinfection, supporting dynamic SDoH assessment.","rel_num_authors":9,"rel_authors":[{"author_name":"George Yendewa","author_inst":"Case Western Reserve University"},{"author_name":"Tayoot Chengsupanimit","author_inst":"Case Western Reserve University"},{"author_name":"Ali Dehghani","author_inst":"Case Western Reserve University"},{"author_name":"Ali Ahmed","author_inst":"University of Pennsylvania"},{"author_name":"Amir Mohareb","author_inst":"Harvard Medical School"},{"author_name":"Michael Freeman","author_inst":"Case Western Reserve University"},{"author_name":"Chari Cohen","author_inst":"Hepatitis B Foundation"},{"author_name":"Ighovwerha Ofotokun","author_inst":"Case Western Reserve University"},{"author_name":"Karine Dube","author_inst":"University of Pennsylvania"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Cross-System Meta-Analysis of Machine Learning Predictors Identifies Value-Specific Risk Drivers and Interactions Underlying Acute Kidney Injury","rel_doi":"10.64898\/2026.08.31.26361849","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361849","rel_abs":"Background: Current understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific biomarker values or physiologic thresholds influence susceptibility. We aimed to synthesize knowledge from machine learning models trained across multiple health systems to identify generalizable, value-specific risk drivers and biomarker interactions contributing to AKI risk. Methods: We analyzed electronic health records (EHRs) from 785,497 adult inpatients between 2010 and 2019 across nine U.S. academic medical centers within PCORnet. Interpretable gradient boosting machine models were independently developed at each health system to quantify predictor-outcome associations. Meta-regression was applied to integrate these site-level results, characterize nonlinear value-risk relationships, and identify bivariate interactions between predictors. Results: Meta-analysis revealed consistent, value-specific risk drivers across health systems. An increase in glucose from 100 mg\/dL to 140 mg\/dL was associated with a 1.46-fold higher risk of AKI. Chloride and anion gap also demonstrated elevated AKI risk with risk increases overlapping portions of their reference ranges, with anion gap showing a 1.14-fold increase across 4-12 mmol\/L and chloride a 1.28-fold increase across 96-100 mEq\/L. Electrolytes including potassium, calcium, and sodium showed quadratic associations with AKI risk. Bivariate meta-regression identified interactions between key predictors, highlighting pathways that jointly modulate AKI risk. Conclusion: This cross-system meta-analysis synthesizes machine learning-derived evidence into clinically interpretable knowledge, revealing how specific biomarker ranges and interactions modulate AKI risk. By moving beyond surface-level associations to quantitative, generalizable physiologic thresholds, these findings provide actionable insights to enhance risk stratification and personalized prevention in hospital care.","rel_num_authors":18,"rel_authors":[{"author_name":"Ho Yin Chan","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Deyi Li","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Alan S.L. Yu","author_inst":"Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"John A. Kellum","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Dana Y. Fuhrman","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Qi Xu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Elizabeth A Chrischilles","author_inst":"Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, Iowa, USA"},{"author_name":"Lindsay G. Cowell","author_inst":"Department of Health Data Science and Biostatistics, Peter O Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Sravani Chandaka","author_inst":"Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"Alfred Jerrod Anzalone","author_inst":"Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA"},{"author_name":"Jacob Kean","author_inst":"Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA"},{"author_name":"Kathleen M. McTigue","author_inst":"Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Abu Saleh Mohammad Mosa","author_inst":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA"},{"author_name":"Bradley Taylor","author_inst":"CTSI Center for Biomedical Informatics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA"},{"author_name":"Mahanaz Syed","author_inst":"Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA"},{"author_name":"Lemuel R. Waitman","author_inst":"Department of Biomedical and Health Informatics, University of Missouri-Kansas City, Kansas City, Missouri, USA"},{"author_name":"Yong Hu","author_inst":"Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China"},{"author_name":"Mei Liu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Cross-System Meta-Analysis of Machine Learning Predictors Identifies Value-Specific Risk Drivers and Interactions Underlying Acute Kidney Injury","rel_doi":"10.64898\/2026.08.31.26361849","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361849","rel_abs":"Background: Current understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific biomarker values or physiologic thresholds influence susceptibility. We aimed to synthesize knowledge from machine learning models trained across multiple health systems to identify generalizable, value-specific risk drivers and biomarker interactions contributing to AKI risk. Methods: We analyzed electronic health records (EHRs) from 785,497 adult inpatients between 2010 and 2019 across nine U.S. academic medical centers within PCORnet. Interpretable gradient boosting machine models were independently developed at each health system to quantify predictor-outcome associations. Meta-regression was applied to integrate these site-level results, characterize nonlinear value-risk relationships, and identify bivariate interactions between predictors. Results: Meta-analysis revealed consistent, value-specific risk drivers across health systems. An increase in glucose from 100 mg\/dL to 140 mg\/dL was associated with a 1.46-fold higher risk of AKI. Chloride and anion gap also demonstrated elevated AKI risk with risk increases overlapping portions of their reference ranges, with anion gap showing a 1.14-fold increase across 4-12 mmol\/L and chloride a 1.28-fold increase across 96-100 mEq\/L. Electrolytes including potassium, calcium, and sodium showed quadratic associations with AKI risk. Bivariate meta-regression identified interactions between key predictors, highlighting pathways that jointly modulate AKI risk. Conclusion: This cross-system meta-analysis synthesizes machine learning-derived evidence into clinically interpretable knowledge, revealing how specific biomarker ranges and interactions modulate AKI risk. By moving beyond surface-level associations to quantitative, generalizable physiologic thresholds, these findings provide actionable insights to enhance risk stratification and personalized prevention in hospital care.","rel_num_authors":18,"rel_authors":[{"author_name":"Ho Yin Chan","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Deyi Li","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Alan S.L. Yu","author_inst":"Division of Nephrology and Hypertension and the Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"John A. Kellum","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Dana Y. Fuhrman","author_inst":"Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Qi Xu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"},{"author_name":"Elizabeth A Chrischilles","author_inst":"Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, Iowa, USA"},{"author_name":"Lindsay G. Cowell","author_inst":"Department of Health Data Science and Biostatistics, Peter O Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas, USA"},{"author_name":"Sravani Chandaka","author_inst":"Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA"},{"author_name":"Alfred Jerrod Anzalone","author_inst":"Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska, USA"},{"author_name":"Jacob Kean","author_inst":"Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA"},{"author_name":"Kathleen M. McTigue","author_inst":"Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"},{"author_name":"Abu Saleh Mohammad Mosa","author_inst":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA"},{"author_name":"Bradley Taylor","author_inst":"CTSI Center for Biomedical Informatics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA"},{"author_name":"Mahanaz Syed","author_inst":"Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA"},{"author_name":"Lemuel R. Waitman","author_inst":"Department of Biomedical and Health Informatics, University of Missouri-Kansas City, Kansas City, Missouri, USA"},{"author_name":"Yong Hu","author_inst":"Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China"},{"author_name":"Mei Liu","author_inst":"Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Secondhand Cannabis Smoke Exposure: Prevalence, Personal Use, and Neurocognitive Trajectories Over Time in Adolescents in the United States","rel_doi":"10.64898\/2026.08.31.26361835","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361835","rel_abs":"Background: As cannabis policy changes in the United States, secondhand cannabis smoke (SCS) is increasingly common, including within families. However, prevalence of exposure and clinical correlates over time in adolescents are not fully understood. Objectives: (1) To estimate the prevalence of SCS and personal cannabis use in US-based teens exposed to SCS, and (2) examine the cognitive trajectories of adolescents exposed to SCS compared to non-exposed peers. Methods: Data from the Adolescent Brain Cognitive Development (ABCD) Study was used. Participants (n=11,316 of full cohort with follow-up data; n=776 with self-reported family SCS exposure) attended yearly visits from ages 11-17, completing substance use interviews, toxicological testing, and the NIH Toolbox Cognitive battery. Youth with SCS but no personal cannabis use (n=419; 47% female) were matched on prenatal substance exposure, family substance use history, and sociodemographics to non-SCS exposed and non-cannabis-using youth with a 1:2 ratio (Controls n=838). Linear mixed-effects models assessed cognitive performance by SCS*age interactions, accounting for random effects of subject and family. Covariates included sex and alcohol, nicotine, and other substance use. Secondary models analyzed performance by cumulative waves of reported SCS exposure interacting with age. Results: Of the full cohort, 6.9% (n=776) reported exposure to SCS. Of these individuals, 46% endorsed lifetime personal cannabis use by age 17, relative to 20% of non-SCS exposed youth (OR=3.83[95%CI:3.29,4.44]). Within matched participants, SCS*age demonstrated a significant interaction on attention and inhibitory control ({beta}=-0.32, p=.028), with SCS demonstrating reduced improvement over time. More waves of exposure were also associated with worse performance over time ({beta}=-0.39, p=.057). Discussion: Almost half of those who had been exposed to SCS endorsed personal cannabis use. Cognitive findings were domain specific, similar to findings in secondhand tobacco: SCS exposed youth showed restricted improvement in attention and inhibitory control by age 17. Public health and policymakers should make efforts to curb youth SCS exposure, given the potential for risk which has not been fully explored to date.","rel_num_authors":6,"rel_authors":[{"author_name":"Jenicca Bastien","author_inst":"University of California, San Diego"},{"author_name":"Kimberly Garcia","author_inst":"University of California, San Diego"},{"author_name":"Alexander L. Wallace","author_inst":"University of California, San Diego"},{"author_name":"Ryan M Sullivan","author_inst":"University of California, San Diego"},{"author_name":"Eunha Hoh","author_inst":"San Diego State University"},{"author_name":"Natasha E Wade","author_inst":"University of California, San Diego"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Towards Electronic Health Records-Based Paediatric Growth References: Results from the SwissPedGrowth Project","rel_doi":"10.64898\/2026.08.28.26361619","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26361619","rel_abs":"BACKGROUND: We used anthropometric data from electronic health records (EHRs) of Swiss childrens hospitals to evaluate growth references and estimate centile curves. METHODS: We received EHRs extracted from seven Swiss childrens hospitals and analysed two samples: all children with a height, weight, body mass index (BMI), or head circumference recording, and a subsample restricted to children without diseases potentially affecting growth, weighted to represent the general population. We calculated mean z-scores based on the World Health Organization growth references adopted for Switzerland in 2011 (CH-WHO 2011) and current Swiss growth references (Swiss 2026). We estimated sex-specific centile curves in the subsample using generalised additive models for location, scale, and shape. RESULTS: We included 213,868 children with height, 448,002 with weight, 209,244 with BMI, and 67,397 with head circumference recordings. Mean z-scores in the all children sample were (CH-WHO 2011; Swiss 2026): height (0.10; -0.19), weight (0.16; -0.09), BMI (0.04; -0.07), head circumference (-0.28, -0.28); and in the subsample: height (0.34; 0.00), weight (0.27; 0.01), BMI (0.18; 0.05), and head circumference (0.04; 0.01). The 50th height, weight, BMI, and head circumference centiles of girls and boys in the subsample closely followed those of Swiss 2026, with slightly wider 3rd and 97th centiles in infancy and adolescence. CONCLUSION: Height, weight, BMI, and head circumference centiles aligned well with the Swiss 2026 growth references in Switzerland, demonstrating that hospital EHRs could contribute to future growth references.","rel_num_authors":20,"rel_authors":[{"author_name":"Lorenz Manuel Leuenberger","author_inst":"University of Bern"},{"author_name":"Yara Shoman","author_inst":"University of Bern"},{"author_name":"Franco Romero","author_inst":"University of Bern"},{"author_name":"Mari Sasaki","author_inst":"University of Bern"},{"author_name":"Xeni Deligianni","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Nicole Goebel","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Rebeca Mozun","author_inst":"University Children's Hospital Zurich"},{"author_name":"Julia Anna Bielicki","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Marie-Anne Burckhardt","author_inst":"University children's hospital Basel (UKBB)"},{"author_name":"Christoph Saner","author_inst":"Department of Pediatrics, Inselspital, Bern University Hospital"},{"author_name":"Valerie Schwitzgebel","author_inst":"University of Geneva"},{"author_name":"Michael Hauschild","author_inst":"Lausanne University Hospital (CHUV)"},{"author_name":"Franziska Righini Grunder","author_inst":"Children's Hospital of Central Switzerland"},{"author_name":"Pascal Mueller","author_inst":"Children's Hospital of Eastern Switzerland"},{"author_name":"Luregn J Schlapbach","author_inst":"University Children`s Hospital Zurich"},{"author_name":"Oskar Jenni","author_inst":"University Children's Hospital Zurich"},{"author_name":"Ben Daniel Spycher","author_inst":"University of Bern"},{"author_name":"Claudia Elisabeth Kuehni","author_inst":"University of Bern"},{"author_name":"Fabien N. Belle","author_inst":"University of Bern"},{"author_name":"- SwissPedHealth consotrium","author_inst":""}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States","rel_doi":"10.64898\/2026.08.31.26361843","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361843","rel_abs":"Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.","rel_num_authors":97,"rel_authors":[{"author_name":"Jessica T. Davis","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Gursharn Kaur","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Annabella Hines","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA; STI Federal, Sault Ste. Marie, MI, USA"},{"author_name":"Michal Ben-Nun","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"Srinivasan Venkatramanan","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Logan Brooks","author_inst":"University of California, Berkeley, Berkeley, CA, USA"},{"author_name":"Sarabeth Mathis","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Marco Ajelli","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Maria Litvinova","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Allisandra G. Kummer","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Paulo Cesar Ventura","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"Shreeya Mhade","author_inst":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomingt"},{"author_name":"David Weber","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Dmitry Shemetov","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Nat DeFries","author_inst":"Carnegie Mellon University, Pittsburgh, PA, USA"},{"author_name":"Daniel J. McDonald","author_inst":"The University of British Columbia, Vancouver, BC, Canada"},{"author_name":"Teresa Yamana","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rodrigo Zepeda-Tello","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Jeffrey Shaman","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Rami Yaari","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Sen Pei","author_inst":"Columbia University, New York, NY, USA"},{"author_name":"Alexander Webber","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Li Shandross","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Evan Ray","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Spencer Wadsworth","author_inst":"University of Connecticut, Storrs, CT, USA"},{"author_name":"Jarad Niemi","author_inst":"Iowa State University, Ames, IA, USA"},{"author_name":"William T. Redman","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Luke Mullany","author_inst":"Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA"},{"author_name":"Richard Posner","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Abhishek Mallela","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Yen Ting Lin","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"William S. Hlavacek","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Adam Smart","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Amir Aman Gill","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Avery Drennan","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Bria Jayde Fiebiger","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ely Finn Miller","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Jaechoul Lee","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Joseph R. Mihaljevic","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Kylie Ann Geist","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Maya Baltz","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ozbej Bernik","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Y-Minh B. Truong","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ye Chen","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Colin James Grosvenor","author_inst":"Oregon State University, Corvallis, OR, USA"},{"author_name":"Mauricio Santillana","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Candice Djorno","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Jiecheng Lu","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Shihao Yang","author_inst":"Georgia Institute of Technology, Atlanta, GA, USA"},{"author_name":"Fred Lu","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Leonardo Clemente","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Austin G. Meyer","author_inst":"Northeastern University, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Clara Bay","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Alessandra Urbinati","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nicolo Gozzi","author_inst":"ISI Foundation, Turin, Italy"},{"author_name":"Matteo Chinazzi","author_inst":"The Roux Institute, Northeastern University, Portland, ME, USA; Northeastern University, Boston, MA, USA"},{"author_name":"Minami Ueda","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Nima Moghaddas","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Remy LeWinter","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Sara Venturini","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Stefania Fiandrino","author_inst":"ISI Foundation, Turin, Italy; Sapienza, University of Rome, Rome, Italy"},{"author_name":"Alessandro Vespignani","author_inst":"MOBS Lab, Northeastern University, Boston, MA, USA"},{"author_name":"Spencer J. Fox","author_inst":"Northern Arizona University, Flagstaff, AZ, USA"},{"author_name":"Ehsan Suez","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Mariah Salcedo","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Rajath Prabhakar","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"B. K. M. Case","author_inst":"University of Georgia, Athens, GA, USA"},{"author_name":"Amanda Perofsky","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Cecile Viboud","author_inst":"Fogarty International Center, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"James Turtle","author_inst":"Predictive Science Inc., San Diego, CA, USA"},{"author_name":"VP Nagraj","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Amy Benefield","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Desiree Williams","author_inst":"Signature Science, LLC, Austin, TX, USA"},{"author_name":"Graham C. Gibson","author_inst":"Los Alamos National Laboratory, Los Alamos, NM, USA"},{"author_name":"Lauren Meyers","author_inst":"The University of Texas at Austin, Austin, TX, USA"},{"author_name":"Edward Thommes","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Christopher van Bommel","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Rhiannon Loster","author_inst":"Public Health Ontario, Toronto, ON, Canada"},{"author_name":"Benjamin Benteke Longaou","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Monica Cojocaru","author_inst":"University of Guelph, Guelph, ON, Canada"},{"author_name":"Pengfei Yue","author_inst":"University of Waterloo, Waterloo, ON, Canada"},{"author_name":"Alexander Rodriguez","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Ruipu Li","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Sonika Potnis","author_inst":"University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Nicholas G. Reich","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Thomas Robacker","author_inst":"University of Massachusetts Amherst, Amherst, MA, USA"},{"author_name":"Joseph Lemaitre","author_inst":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"},{"author_name":"Aniruddha Adiga","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Bryan Lewis","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Madhav Marathe","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Nibir Chandra Mandal","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Stephen D. Turner","author_inst":"University of Virginia, Charlottesville, VA, USA"},{"author_name":"Naren Ramakrishnan","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Yiqi Su","author_inst":"Virginia Tech, Blacksburg, VA, USA"},{"author_name":"Michael Johansson","author_inst":"Northeastern University, Boston, MA, USA"},{"author_name":"Matthew Biggerstaff","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"},{"author_name":"Rebecca K. Borchering","author_inst":"Centers for Disease Control and Prevention, Atlanta, GA, USA"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Cost-Outcome Variation in Percutaneous Mechanical Circulatory Support: A National Value-of-Care Analysis","rel_doi":"10.64898\/2026.08.31.26361851","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361851","rel_abs":"Background: Percutaneous mechanical circulatory support (pMCS) is increasingly used in critically ill patients, yet its value in relation to cost and outcomes remains unclear. We evaluated national variation in utilization, outcomes, and cost, and introduced a value of care framework integrating risk-adjusted outcomes and expenditures. Methods: We performed a retrospective cohort study using the National Inpatient Sample to identify non-elective hospitalizations of critically ill patients undergoing intra-aortic balloon pump (IABP) or percutaneous left ventricular assist device (pLVAD) placement using ICD-10 codes. Multivariable logistic regression and generalized linear models were used to estimate expected outcomes and costs. Observed-to-expected (O\/E) ratios were calculated, and a value index was derived to compare procedural strategies. Results: A total of 57,910 weighted hospitalizations were included (IABP 78%, pLVAD 22%). In-hospital mortality exceeded 30% across regions. Significant regional variation was observed, with the West demonstrating the highest costs and the Midwest the lowest (p<0.001). Mean hospital charges were higher for pLVAD compared with IABP ($403,731 vs $320,769). Both strategies achieved outcomes better than expected after risk adjustment (O\/E 0.92); however, costs were higher than expected for both, with greater relative cost inflation observed for IABP (O\/E 1.41) and higher absolute costs for pLVAD. In value-of-care analysis, IABP was associated with lower cost and comparable outcomes, while pLVAD demonstrated higher cost without proportional outcome improvement. Conclusion: Substantial variation exists in the cost, outcomes, and value of pMCS strategies. While both IABP and pLVAD achieve favorable risk-adjusted outcomes, pLVAD is associated with higher costs without commensurate clinical benefit.","rel_num_authors":4,"rel_authors":[{"author_name":"Joshua D Greendyk","author_inst":"Corewell Health Grand Rapids"},{"author_name":"William E Allen","author_inst":"Boston Medical Center"},{"author_name":"Afif Hossain","author_inst":"Rutgers New Jersey Medical School"},{"author_name":"Zachariya Trichas","author_inst":"Rutgers New Jersey Medical School"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Multidimensional Social Vulnerability and Hepatic and Extrahepatic Outcomes in Adults With HIV\/HBV Coinfection in the United States","rel_doi":"10.64898\/2026.08.31.26361853","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361853","rel_abs":"Background: HIV\/HBV coinfection is associated with substantial liver-related morbidity and mortality, yet the impact of social vulnerability (SV) on clinical outcomes has not been systematically assessed. We evaluated associations of multidimensional SV with mortality, hepatic, virologic, and extrahepatic organ outcomes among adults with HIV\/HBV. Methods: We conducted a retrospective cohort study using TriNetX data from 110 U.S. healthcare organizations (2010-2026). We propensity score matched adults with HIV\/HBV with and without documented SV 1:1 (2,024 per group). SV was defined using a four-domain framework encompassing material, healthcare access and engagement, interpersonal, and psychosocial vulnerability. Results: Over 15,900 person-years, SV was associated with higher mortality (hazard ratio [HR], 2.06; 95% confidence interval [CI], 1.72-2.47), liver composite events (HR, 1.37; 95% CI, 1.07-1.76), hepatic decompensation (HR, 1.94; 95% CI, 1.39-2.70), hepatic failure (HR, 2.39; 95% CI, 1.53-3.73), HBV viremia (HR, 1.69; 95% CI, 1.32-2.16), and HIV viremia (HR, 2.05; 95% CI, 1.71-2.46). SV was also associated with major adverse cardiovascular events (HR, 1.47), chronic kidney disease (HR, 1.49), and diabetes (HR, 1.25). Multidomain SV generally showed stronger associations than single-domain SV for most hepatic and virologic outcomes, with HR ranges of 1.76-2.62 versus 1.35-1.76 for single-domain SV. Healthcare access and engagement vulnerability was most consistently associated with mortality and hepatic outcomes. Conclusions: SV was associated with mortality, hepatic disease, impaired HIV\/HBV control, extrahepatic organ morbidity, and acute care utilization in adults with HIV\/HBV. SV assessment may improve risk stratification and identify actionable intervention targets during HIV\/HBV care.","rel_num_authors":10,"rel_authors":[{"author_name":"George Yendewa","author_inst":"Case Western Reserve University"},{"author_name":"Tayoot Chengsupanimit","author_inst":"Case Western Reserve University"},{"author_name":"Ali Dehghani","author_inst":"Case Western Reserve University"},{"author_name":"Ali Ahmed","author_inst":"University of Pennsylvania"},{"author_name":"Amir Mohareb","author_inst":"Harvard Medical School"},{"author_name":"Chari Cohen","author_inst":"Hepatitis B Foundation"},{"author_name":"Michael Freeman","author_inst":"Case Western Reserve University"},{"author_name":"H. Nina Kim","author_inst":"University of Washington"},{"author_name":"Ighovwerka Ofotokun","author_inst":"Case Western Reserve University"},{"author_name":"Karine Dube","author_inst":"University of Pennsylvania"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Making Accelerating Medicines Partnership Data Findable and Interoperable through a Common Data Model: Extending OMOP for Multi-Source Multimodal Data","rel_doi":"10.64898\/2026.08.31.26361831","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361831","rel_abs":"SysBio FAIRplex is a Common Fund Venture Program that catalogs and indexes data from the Accelerating Medicines Partnership(R) (AMP(R)) Program through a federated model in which data hosts retain custody of their datasets. The central piece of this work is the SysBio Common Data Model (SysBio CDM). AMP is a precompetitive public-private partnership started in 2014 that unites the resources of NIH and private partners to improve our understanding of disease pathways and transform current models for developing new treatments by: - identifying new targets, biomarkers, and development paradigms; - developing leading-edge tools and technologies; - collecting large-scale datasets and supporting analytics for open analysis by the public; and - generating consensus platforms and procedures. A multidisciplinary Task Force was chartered to design the SysBio CDM by extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model into the -omics domain. The Task Force produced a Minimum Viable Product comprising nine OMOP tables; four extension tables for assay and file metadata; and a Common Data Element (CDE) Registry to specify field semantics. This manuscript describes the deliverable: the underlying design choices, the criteria applied in selecting and constructing the extension tables, how the extended model supports multimodal data integration across AMP projects, and what further work to support additional -omics modalities would entail. As an auxiliary methodology, the paper also describes the AI-assisted CDE harmonization workflow used to populate the model.","rel_num_authors":13,"rel_authors":[{"author_name":"Cole Tindall","author_inst":"DataTecnica, LLC"},{"author_name":"Rodney Alan Long Jr.","author_inst":"Data Tecnica, LLC"},{"author_name":"Bart Naughton","author_inst":"Eisai Inc."},{"author_name":"Brandy M Mapes","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Dave Vismer","author_inst":"Technome"},{"author_name":"Halcyon G Skinner","author_inst":"Verily Health"},{"author_name":"Jessica Malenfant","author_inst":"Sage Bionetworks"},{"author_name":"Mano R Maurya","author_inst":"University of California, San Diego"},{"author_name":"Mike A Nalls","author_inst":"Data Tecnica, LLC"},{"author_name":"Srinivasan Ramachandran","author_inst":"University of California, San Diego"},{"author_name":"Trang Nguyen","author_inst":"The Broad Institute of MIT and Harvard"},{"author_name":"Mette A Peters","author_inst":"DataTecnica, LLC"},{"author_name":"Richard H Scheuermann","author_inst":"Division of Intramural Research, National Library of Medicine, National Institutes of Health"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Making Accelerating Medicines Partnership Data Findable and Interoperable through a Common Data Model: Extending OMOP for Multi-Source Multimodal Data","rel_doi":"10.64898\/2026.08.31.26361831","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.31.26361831","rel_abs":"SysBio FAIRplex is a Common Fund Venture Program that catalogs and indexes data from the Accelerating Medicines Partnership(R) (AMP(R)) Program through a federated model in which data hosts retain custody of their datasets. The central piece of this work is the SysBio Common Data Model (SysBio CDM). AMP is a precompetitive public-private partnership started in 2014 that unites the resources of NIH and private partners to improve our understanding of disease pathways and transform current models for developing new treatments by: - identifying new targets, biomarkers, and development paradigms; - developing leading-edge tools and technologies; - collecting large-scale datasets and supporting analytics for open analysis by the public; and - generating consensus platforms and procedures. A multidisciplinary Task Force was chartered to design the SysBio CDM by extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model into the -omics domain. The Task Force produced a Minimum Viable Product comprising nine OMOP tables; four extension tables for assay and file metadata; and a Common Data Element (CDE) Registry to specify field semantics. This manuscript describes the deliverable: the underlying design choices, the criteria applied in selecting and constructing the extension tables, how the extended model supports multimodal data integration across AMP projects, and what further work to support additional -omics modalities would entail. As an auxiliary methodology, the paper also describes the AI-assisted CDE harmonization workflow used to populate the model.","rel_num_authors":13,"rel_authors":[{"author_name":"Cole Tindall","author_inst":"DataTecnica, LLC"},{"author_name":"Rodney Alan Long Jr.","author_inst":"Data Tecnica, LLC"},{"author_name":"Bart Naughton","author_inst":"Eisai Inc."},{"author_name":"Brandy M Mapes","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Dave Vismer","author_inst":"Technome"},{"author_name":"Halcyon G Skinner","author_inst":"Verily Health"},{"author_name":"Jessica Malenfant","author_inst":"Sage Bionetworks"},{"author_name":"Mano R Maurya","author_inst":"University of California, San Diego"},{"author_name":"Mike A Nalls","author_inst":"Data Tecnica, LLC"},{"author_name":"Srinivasan Ramachandran","author_inst":"University of California, San Diego"},{"author_name":"Trang Nguyen","author_inst":"The Broad Institute of MIT and Harvard"},{"author_name":"Mette A Peters","author_inst":"DataTecnica, LLC"},{"author_name":"Richard H Scheuermann","author_inst":"Division of Intramural Research, National Library of Medicine, National Institutes of Health"}],"rel_date":"2026-09-02","rel_site":"medrxiv"},{"rel_title":"Evaluating Cognitive Impact of Traumatic Brain Injury and Risk for Post-Traumatic Epilepsy","rel_doi":"10.64898\/2026.08.30.26361760","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.30.26361760","rel_abs":"ObjectivePost-traumatic epilepsy (PTE) is a common sequela of traumatic brain injury (TBI). Research indicates that individuals with PTE tend to experience greater cognitive difficulties compared to those with TBI alone. However, it is plausible that a distinct cognitive profile exists that distinguishes between TBI cases with and without PTE. We aimed to identify longitudinal changes in cognitive measures among TBI patients to better assess the changes associated with developing PTE.\n\nSettingOutpatient.\n\nParticipantsProspective subjects who had suffered TBI within 6 months post-injury (TBI-6M, n=32), retrospective subjects with pre-existing PTE diagnoses (PTE, n=20), and healthy control subjects (HC, n=41).\n\nDesignWe examined cognitive performance for TBI patients within 6 months post-injury, then again within 12 months (TBI-12M, n=26), and within 18-months (TBI-18M, n=25), and compared this with cognitive performance among HC and PTE.\n\nMain MeasuresCognitive tests administered yielded 15 test components for analysis. We utilized linear mixed effects modeling to examine cohort-level differences cognitive function.\n\nResults11\/15 tests showed a significant performance deficit in the PTE subjects compared to HC. TBI-6M was not significantly different from the PTE subjects; with time, 9\/15 tests showed some degree of recovery in TBI subjects. Tests for information processing speed\/working memory and executive function showed strong recovery (TBI-6M vs. TBI-18M, SDMT written: p<0.0001, SDMT oral and COWAT: p<0.001). Tests for visual attention\/working memory also showed a smaller but significant recovery (TBI-18M vs. PTE, p<0.05). By contrast, tests for verbal memory [HVLT-R Delayed Recall] showed chronic impairment in TBI (TBI-18M vs HC, p<0.0001). TBI subjects generally trend towards recovery in cognitive performance post-TBI.\n\nConclusionsInformation processing speed\/working memory are strong indicators for TBI recovery, while auditory learning\/memory shows chronic impairment. The stagnation of recovery in cognitive domains typically characterized by robust recovery may correlate with an elevated risk of developing PTE.","rel_num_authors":8,"rel_authors":[{"author_name":"Taylor Zink","author_inst":"Robert Wood Johnson Medical School"},{"author_name":"Henry Noren","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Daniel Valdivia","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Christine Yohn","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Jasdeep Hundal","author_inst":"Jersey Shore University Medical Center, Hackensack-Meridian Health"},{"author_name":"Spencer Chen","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"David Scarisbrick","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Hai Sun","author_inst":"Rutgers Robert Wood Johnson Medical School"}],"rel_date":"2026-09-01","rel_site":"medrxiv"},{"rel_title":"A Measurement-Based Care Strategy for Buprenorphine-Naloxone Treatment (Bup-MBC): Development of an EHR-Integrated Intervention","rel_doi":"10.64898\/2026.08.27.26361539","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.27.26361539","rel_abs":"IntroductionRisk of recurrent opioid use during buprenorphine-naloxone (bup-nx) treatment is dynamic and remains elevated after initiation, with vulnerability shaped in part by treatment intensity and gaps between visits, yet routine outpatient care relies on episodic encounters and retrospective data. This mismatch can delay recognition of emerging instability and limit timely treatment adjustments. This paper reports the development and specification of an intervention strategy to address this mismatch.\n\nMethodsWe used a structured, multi-phase design process to specify and configure a measurement-based care (MBC) strategy for bup-nx treatment (Bup-MBC) in outpatient addiction clinics through three phases: (1) a systematic review of patient-reported outcome measures (PROMs) for substance use treatment; (2) a qualitative needs assessment using the Theoretical Domains Framework and COM-B (Capability, Opportunity, Motivation-Behavior) model to identify gaps in risk monitoring, agency, and trust; and (3) iterative co-design with multidisciplinary clinicians to refine workflow fit and trust-preserving use of data. Patients informed item and feedback content during the needs assessment but did not participate in the co-design cycles.\n\nResultsBup-MBC integrates (1) brief between-visit PROMs (e.g., withdrawal, craving, adherence); (2) immediate non-punitive patient feedback; (3) clinician-facing summaries and non-directive prompts in the electronic health record (EHR); and (4) an opt-in between-visit outreach pathway with predefined safety triggers, all configured within existing EHR and patient portal infrastructure. It targets patient and clinician capability to recognize changes in risk, opportunity for action through structured monitoring and visit preparation, and trust and agency through non-punitive communication, without adding substantial burden. The full measure set, severity bands, and question-to-action map are provided as supplementary material. Key trade-offs included prioritizing single-item measures for feasibility, balancing opt-in outreach with safety overrides, and assuming routine clinician use of summaries.\n\nConclusionThis development study specifies an EHR-integrated MBC strategy for outpatient bup-nx treatment. As single-center design work with co-design limited to clinicians and delivery contingent on portal or text-message access, its outputs are hypotheses about mechanism and fit rather than demonstrated effects. Feasibility studies are needed to evaluate uptake, acceptability, workflow fit, and effects on treatment.","rel_num_authors":11,"rel_authors":[{"author_name":"Thomas Reese","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Carolyn Audet","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Jessica Ancker","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Adam Wright","author_inst":"Vanderbilt University Medical Center"},{"author_name":"David Marcovitz","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Kristopher  A Kast","author_inst":"Vanderbilt University Medical Center"},{"author_name":"John Bridges","author_inst":"Ohio State University"},{"author_name":"Hilary Tindle","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Mauli Shah","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Amanda von Horn","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Michael E Matheny","author_inst":"Vanderbilt University"}],"rel_date":"2026-09-01","rel_site":"medrxiv"},{"rel_title":"Dynamic Clinical States and Transitions During the First 72 Hours of Intensive Care After Acute Stroke","rel_doi":"10.64898\/2026.08.30.26361738","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.30.26361738","rel_abs":"BackgroundThe condition of a patient with acute stroke often changes within hours of ICU admission. Prognostic work here targets fixed endpoints predicted from admission data, and trajectory phenotyping assigns one label per patient. We used longitudinal ICU data to identify interpretable dynamic clinical states, characterize transitions between them, and relate the current state to later events.\n\nMethodsRetrospective cohort study of 6368 adults with acute stroke in MIMIC-IV v3.1. The first 72 h were divided into twelve 6-hour windows, and a hidden Markov model was fitted to 21 neurological, physiological and organ-support variables. State number was chosen against criteria fixed before fitting: statistical fit, restart stability, state occupancy and clinical interpretability. Generalized estimating equations related the current state to new mechanical ventilation and vasopressor use within 12 h, and to ICU death within 72 h. Eleven sensitivity analyses assessed the robustness of the state solution.\n\nResultsFour states were selected: neurologically preserved-low support, neurological impairment- low support, impairment-renal dysfunction and impairment-respiratory support (63.3%, 7.8%, 11.8% and 17.1% of windows). Within 72 h, 40.3% of patients changed state at least once, and transitions ran in both directions rather than along a single severity gradient. States were identified without outcome data, yet ICU mortality by last state ranged from 2.9% to 43.9%. Adjusted for age, sex, subtype and Charlson index, the current state remained associated with organ-support escalation and death. State prevalence differed by at most 1.1 percentage points between training and test sets, and 10 of 11 sensitivity analyses gave a stable four-state solution (ARI 0.754-0.955).\n\nConclusionsThe early ICU course of acute stroke can be represented as movement among a small number of clinically interpretable states. The representation was reproducible in a held-out set and across admission eras, but requires validation in an independent database before any clinical use.","rel_num_authors":3,"rel_authors":[{"author_name":"Ping LEI","author_inst":"Institute of Global Health, University of Geneva, Geneva, Switzerland; School of Public Health, Fudan University, Shanghai, China"},{"author_name":"Yanyi XU","author_inst":"Department of Environmental Health, School of Public Health, Fudan University, Shanghai 200032, China"},{"author_name":"Yuxia ZHANG","author_inst":"Department of Nursing, Zhongshan Hospital, Fudan University, Shanghai, China"}],"rel_date":"2026-09-01","rel_site":"medrxiv"},{"rel_title":"Genotype-guided isoniazid dosing harmonizes drug exposure in 3HP tuberculosis preventive therapy","rel_doi":"10.64898\/2026.08.27.26360825","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.27.26360825","rel_abs":"RationalePolymorphisms in the N-acetyltransferase 2 (NAT2) gene explain much of the interindividual variation in isoniazid (INH) metabolism and determine risk of toxicities. However, there is limited evidence to guide INH dose adjustment according to the NAT2 acetylator profile in weekly rifapentine-INH tuberculosis preventive therapy (TPT).\n\nObjectivesWe evaluated whether NAT2 genotype-guided INH dose adjustment harmonizes drug exposure across acetylator phenotypes during 3HP and derived phenotype-specific dose recommendations.\n\nMethodsIn a prospective, multicenter, within-subject PK trial (NCT05413551), adults initiating 3HP in Brazil were assigned genotype-guided INH doses (slow: 5 mg\/kg [&le;]300 mg; intermediate: 15 mg\/kg [&le;]900 mg; rapid: 25 mg\/kg [&le;]1,500 mg) alongside a standard 900 mg flat dose on an alternate occasion. AUC0-24 and C24 were estimated from serial blood samples; a two-compartment Michaelis-Menten population PK model characterized NAT2 effects on clearance.\n\nMeasurements and Main ResultsAmong 228 participants, 47.4% (108\/228) were intermediate, 43.4% (99\/228) slow, and 9.2% (21\/228) rapid acetylators. Genotype-guided dosing reduced AUC0-24 variability approximately two-fold versus standard dosing (CV 58.8% vs 76.8%) and increased exposure uniformity (median AUC0- 24 27.2 [IQR 18.8-41.3] vs 43.2 [27.3-71.0] mg{middle dot}h\/L). Among slow acetylators, C24 >0.15 {micro}g\/mL decreased from 27\/42 (64%) with standard dosing to 1\/42 (2%) with genotype-guided dosing (P<0.0001). In 104 participants with intensive PK sampling, rapid acetylators receiving guided doses had AUC0-24similar to standard-dose intermediate acetylators (42.8 vs 39.5 mg{middle dot}h\/L; P=.63). Monte Carlo simulations supported doses of 600, 900, and 1,200 mg for slow, intermediate, and rapid acetylators, respectively.\n\nConclusionsNAT2-guided isoniazid dosing reduced variation in drug levels, averting very low and high AUC and C24. These findings inform genotype-stratified dosing of INH for TPT, which might reduce toxicities and improve outcomes.","rel_num_authors":15,"rel_authors":[{"author_name":"Kesia da Silva","author_inst":"Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA"},{"author_name":"Samuel Sarkodie","author_inst":"University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Karina Marques","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Patricia Vieira","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Roberto Dias de Oliveira Sr.","author_inst":"State University of Mato Grosso do Sul"},{"author_name":"Paulo Cesar Pereira dos Santos","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Marco Antonio Moreira Puga","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Allyson Guimar\u00e3es Costa","author_inst":"Universidade Federal do Amazonas"},{"author_name":"Jo\u00e3o Paulo Gregorio Machado","author_inst":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil"},{"author_name":"Renata Spener-Gomes","author_inst":"Funda\u00e7\u00e3o Medicina Tropical Dr. Heitor Vieira Dourado, Manaus, AM, Brazil"},{"author_name":"Eunsol Yang","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Rada Savic","author_inst":"Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Doctor Heitor Vieira Dourado Tropical Medicine Foundation: Fundacao de Medicina Tropical Doutor Heitor Vieira Dourado"},{"author_name":"Julio Croda","author_inst":"Oswaldo Cruz Foundation, Campo Grande, MS, Brazil"},{"author_name":"Jason R Andrews","author_inst":"Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA"}],"rel_date":"2026-09-01","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@9b88d2org.highwire.dtl.DTLVardef@133934dorg.highwire.dtl.DTLVardef@10bd71corg.highwire.dtl.DTLVardef@5797cf_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@9b88d2org.highwire.dtl.DTLVardef@133934dorg.highwire.dtl.DTLVardef@10bd71corg.highwire.dtl.DTLVardef@5797cf_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@9b88d2org.highwire.dtl.DTLVardef@133934dorg.highwire.dtl.DTLVardef@10bd71corg.highwire.dtl.DTLVardef@5797cf_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@9b88d2org.highwire.dtl.DTLVardef@133934dorg.highwire.dtl.DTLVardef@10bd71corg.highwire.dtl.DTLVardef@5797cf_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@9b88d2org.highwire.dtl.DTLVardef@133934dorg.highwire.dtl.DTLVardef@10bd71corg.highwire.dtl.DTLVardef@5797cf_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinical Reference Percentiles for AI-derived Epicardial Adipose Tissue: A Multicenter Study","rel_doi":"10.64898\/2026.08.28.26360111","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.28.26360111","rel_abs":"Background and AimsEpicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles.\n\nMethodsIn this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator.\n\nResultsPercentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA-indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001).\n\nConclusionAge- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26360111v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@9b88d2org.highwire.dtl.DTLVardef@133934dorg.highwire.dtl.DTLVardef@10bd71corg.highwire.dtl.DTLVardef@5797cf_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":37,"rel_authors":[{"author_name":"Assiata Kamagate","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mikolaj Buchwald","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Robert Jack Henry Miller","author_inst":"University of Calgary, Libin Cardiovascular Institute of Alberta"},{"author_name":"Shaun Khanna","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Tara Zuhair Kassem","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jacek Kwiecinski","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Renee Bullock-Palmer","author_inst":"Deborah Heart and Lung Center"},{"author_name":"Wenhao Zhang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Anna M Marcinkiewicz","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Mark Lemley","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aditya Killekar","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Paul B Kavanagh","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene RS Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles California"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Robert A deKemp","author_inst":"Ottawa Heart Institute"},{"author_name":"Ronny R Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Wanda Acampa","author_inst":"University Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Thomas L Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Daniel S Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Heterogeneity in pre-vaccination population immunity can contribute to variability in vaccine effectiveness estimates","rel_doi":"10.64898\/2026.08.29.26361716","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.29.26361716","rel_abs":"Vaccine effectiveness (VE) estimates can vary widely between years and populations, even for the same vaccine. Estimated VE is known to be sensitive to susceptible depletion and differences in pre-vaccination infection risk between vaccinated and unvaccinated populations. However, how variation in pre-vaccination risk within and between the two groups affects VE estimates over time remains unclear. This uncertainty is especially important given negative VE estimates. We investigated the difference between estimated VE and true vaccine protection considering continuous distributions of pre-vaccination infection risk under three scenarios. When the vaccinated and unvaccinated populations differ in their mean risk, estimated VE can be higher or lower than true vaccine protection. Similar patterns arise when both populations share identical means but different risk distributions. Finally, if infection-derived immunity lasts longer than vaccine protection, annual VE estimates can vary by tens of percentage points between years despite constant true vaccine protection. These theoretical results underscore that VE studies estimate contrasting risk between vaccinated and unvaccinated individuals in a particular time and place, and VE estimates can vary counterintuitively between years and populations even with constant vaccine-induced protection. Explaining variability in estimated VE thus requires a more complete understanding of populations distributions of infection risk.","rel_num_authors":5,"rel_authors":[{"author_name":"Alexander N Pillai","author_inst":"University of Chicago"},{"author_name":"Sang Woo Park","author_inst":"Seoul National University"},{"author_name":"Marc Lipsitch","author_inst":"Stanford University"},{"author_name":"Benjamin J Cowling","author_inst":"The University of Hong Kong"},{"author_name":"Sarah Cobey","author_inst":"University of Chicago"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Heterogeneity in pre-vaccination population immunity can contribute to variability in vaccine effectiveness estimates","rel_doi":"10.64898\/2026.08.29.26361716","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.29.26361716","rel_abs":"Vaccine effectiveness (VE) estimates can vary widely between years and populations, even for the same vaccine. Estimated VE is known to be sensitive to susceptible depletion and differences in pre-vaccination infection risk between vaccinated and unvaccinated populations. However, how variation in pre-vaccination risk within and between the two groups affects VE estimates over time remains unclear. This uncertainty is especially important given negative VE estimates. We investigated the difference between estimated VE and true vaccine protection considering continuous distributions of pre-vaccination infection risk under three scenarios. When the vaccinated and unvaccinated populations differ in their mean risk, estimated VE can be higher or lower than true vaccine protection. Similar patterns arise when both populations share identical means but different risk distributions. Finally, if infection-derived immunity lasts longer than vaccine protection, annual VE estimates can vary by tens of percentage points between years despite constant true vaccine protection. These theoretical results underscore that VE studies estimate contrasting risk between vaccinated and unvaccinated individuals in a particular time and place, and VE estimates can vary counterintuitively between years and populations even with constant vaccine-induced protection. Explaining variability in estimated VE thus requires a more complete understanding of populations distributions of infection risk.","rel_num_authors":5,"rel_authors":[{"author_name":"Alexander N Pillai","author_inst":"University of Chicago"},{"author_name":"Sang Woo Park","author_inst":"Seoul National University"},{"author_name":"Marc Lipsitch","author_inst":"Stanford University"},{"author_name":"Benjamin J Cowling","author_inst":"The University of Hong Kong"},{"author_name":"Sarah Cobey","author_inst":"University of Chicago"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"GLP-1 Receptor Agonist Initiation and Anti-VEGF Treatment Frequency in Diabetic Macular Edema: an IRIS(R) Registry Cohort Study","rel_doi":"10.64898\/2026.08.29.26361426","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.29.26361426","rel_abs":"PurposeTo evaluate whether initiation of GLP-1 receptor agonists (GLP-1RAs) is associated with anti-VEGF treatment burden in type 2 diabetes patients with diabetic macular edema (DME) in the IRIS(R) Registry (Intelligent Research in Sight).\n\nMethodsIncident GLP-1RA initiators were matched 1:1 with controls via Mahalanobis distance matching (9,896 pairs; N=19,792) on sociodemographics, DME risk factors, and factors influencing GLP-1RA prescription including hypertension, obesity, chronic kidney disease. A longitudinal mixed-effects event-study model evaluated monthly anti-VEGF injection frequency over a 36-month window (12 months before through 24 months after initiation), adjusting for DME duration. Visual acuity (VA) and central subfield thickness (CST) were secondary outcomes.\n\nResultsFollowing GLP-1RA initiation, anti-VEGF injection trajectories did not significantly differ between the matched GLP-1RA and control cohorts (interaction coefficients -0.18 to 1.59, P>0.05). Likewise, no differences in VA were observed between cohorts (-0.05 to 0.04 logMAR, P>0.05) or CST (-14.12 to 33.58 {micro}m, P>0.05).\n\nConclusionIn these matched cohorts, GLP-1RA initiation was not associated with the trajectory of anti-VEGF use or changes in VA or CST.\n\nPrecisWe used the American Academy of Ophthalmology IRIS(R) Registry (Intelligent Research in Sight) to identify patients with DME. In 19,792 matched patients, there was no significant reduction in injection frequency post GLP1-RA initiation and no significant change in VA or CST.","rel_num_authors":11,"rel_authors":[{"author_name":"Preeti Nagalamadaka","author_inst":"Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School"},{"author_name":"Connor J Ross","author_inst":"Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School"},{"author_name":"Joshua B. Gilbert","author_inst":"Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School"},{"author_name":"Hannah Stillman","author_inst":"The Warren Alpert Medical School of Brown University"},{"author_name":"Sophia Y. Ghauri","author_inst":"The Warren Alpert Medical School of Brown University"},{"author_name":"Sophia M. Dutton","author_inst":"The Warren Alpert Medical School of Brown University"},{"author_name":"William Kearney","author_inst":"Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School"},{"author_name":"Josephine H. Li","author_inst":"Diabetes Unit, Massachusetts General Hospital and Department of Medicine, Mass General Brigham"},{"author_name":"Aaron Leong","author_inst":"Diabetes Unit, Massachusetts General Hospital and Department of Medicine, Mass General Brigham"},{"author_name":"Rishi P. Singh","author_inst":"Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School"},{"author_name":"Magdalena G Krzystolik","author_inst":"Massachusetts Eye and Ear Department of Ophthalmology, Harvard Medical School"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Arm Angle Moderates the Association Between Fastball Usage and Elbow\/Forearm Injury in MLB Pitchers","rel_doi":"10.64898\/2026.08.29.26361727","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.29.26361727","rel_abs":"BackgroundNewly available arm angle data offers a new dimension to understand rising rates of arm injury in MLB pitchers.\n\nPurposeTo evaluate the relationship between arm angle, pitch characteristics, and elbow and forearm injury in MLB pitchers.\n\nStudy DesignRetrospective cohort study; Level of evidence, 3\n\nMethodsStatcast data from 2020 to 2025 and MLB injured list (IL) data were used to evaluate arm angle and pitch characteristics in relation to elbow and forearm injuries. Results are presented with and without requirements on prior season workload and for same-season and next-season injury incidence. A generalized additive model (GAM) was used to capture non-linear dependence and interactions between selected features and injury incidence to the elbow or forearm. Average marginal effect (AME) odds ratios are reported for main effect terms.\n\nResultsN = 3,812 pitcher-seasons were included. 29% pitchers who underwent UCLR did so in the same season as a forearm injury (tmean = 44, tmedian = 27 days to surgery). Arm angle, fastball usage, and their interaction were the three most predictive features. Arm angle was positively related to incidence of injury ([Formula] = 1.014), fastball usage was inversely related to incidence of injury ([Formula] = 0.243), and arm angle moderated the effect fastball usage at high arm angle, where increased usage was no longer protective. Slider velocity ([Formula] = 1.072), spin rate ([Formula] = 1.001), and usage ([Formula] = 2.039) also significantly predicted injury risk. Fastball velocity was not significant in any fit, with [Formula] = 0.999 across all fits. Fit-level Nagelkerke R2 values ranged from .019 to .052.\n\nConclusionFastball usage and arm angle, not velocity, predicted elbow and forearm injury risk among MLB pitchers, and arm angle was the single most predictive feature. The heterogeneity of risk factors as a function of arm angle, and the novelty of MLB arm angle data, may explain why fastball usage has been previously underexplored as a risk factor.","rel_num_authors":4,"rel_authors":[{"author_name":"Connor Richards","author_inst":"University of California, San Diego"},{"author_name":"D. Taylor La Salle","author_inst":"Point Loma Nazarene University"},{"author_name":"Oscar Vila Dieguez","author_inst":"University of California, San Diego"},{"author_name":"Samuel R Ward","author_inst":"University of California, San Diego"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Slider Horizontal Movement and Arm Angle are Associated with Elbow and Forearm Injury Incidence in MLB Pitchers","rel_doi":"10.64898\/2026.08.30.26361739","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.30.26361739","rel_abs":"BackgroundSweeping sliders with large horizontal break are hypothesized to be associated with arm injury, and prior work suggests a link between slider usage, arm angle, and injury.\n\nPurposeTo evaluate whether arm angle and slider horizontal movement are associated with elbow or forearm injury risk in MLB pitchers.\n\nStudy DesignRetrospective cohort study; Level of evidence, 3\n\nMethodsStatcast data from 2020-2025 and injury data were used to study the relationship between sliders and arm injuries. Pitchers with at least 30 innings pitched (IP) were evaluated for same- and next-season injury incidence to the Elbow, Forearm, or Elbow\/Forearm. A generalized additive model (GAM) related pitch-level variables to injury incidence, and average marginal effect (AME) odds ratios are reported for main effects.\n\nResultsAll fits were significant at p < .01. Fits for same-season (N = 1,957, p = .00008, R2 = .066) and next-season (N = 2,511, p = .00005, R2 = .053) Elbow\/Forearm injury were significant at p < .001. Same-season (R2 = .061) and next-season (R2 = .055) Forearm injury fits had p = .001. Main effects for slider glove-side movement and fastball usage, their interactions with arm angle, and arm angle main effects were the most predictive features. Across the three fits where the slider glove-side movement main effect was significant, odds ratios ranged from 1.03 to 1.07, meaning that each additional inch of slider glove-side movement was associated with a 3% to 7% increase in the observed injury incidence. Furthermore, this effect was magnified at high arm angle and muted at low arm angle.\n\nConclusionWe observed that slider horizontal movement, arm angle, and their interaction were significantly associated with incidence of Forearm and combined Elbow\/Forearm injury. This effect was weak or non-existent at low arm angles and strong at high arm angles, suggesting that arm angle moderates the risk of slider horizontal movement, and providing evidence that sliders with large horizontal break (e.g. sweepers) may pose an injury risk.","rel_num_authors":4,"rel_authors":[{"author_name":"Connor Richards","author_inst":"University of California, San Diego"},{"author_name":"D Taylor La Salle","author_inst":"Point Loma Nazarene University"},{"author_name":"Oscar Vila Dieguez","author_inst":"University of California, San Diego"},{"author_name":"Samuel R Ward","author_inst":"University of California, San Diego"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"CPT\/HCPCS Code Recommendation from Clinical Notes: A Comparative Evaluation of AI Methods","rel_doi":"10.64898\/2026.08.29.26361731","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.29.26361731","rel_abs":"Automatic coding from clinical notes has been studied extensively for International Classification of Diseases (ICD) codes, yet broad Current Procedural Terminology (CPT) and Healthcare Common Procedure Coding System (HCPCS) recommendation remains comparatively underexplored. Existing studies often focus on one specialty, a limited code vocabulary, or a single model family, leaving it unclear how different artificial intelligence (AI) paradigms perform under a common, clinically meaningful evaluation. We formulate CPT and HCPCS coding as an AI-assisted recommendation task in which a physician or professional coder reviews a short, ranked list of candidate codes supported by the clinical note. Using operative notes from Vanderbilt University Medical Center (VUMC) and discharge summaries from Medical Information Mart for Intensive Care IV (MIMIC-IV), we compare lexical retrieval, Clinical-Longformer, GPT-5.6-Sol, MedGemma-27B, and an inspectable agentic-style retrieve-and-verify system under a controlled review budget. Micro-averaged recall within a fixed number of recommendations measures whether reference codes reach the reviewable list; micro-F1 is reported only where reference labels are sufficiently complete. Zero-shot GPT-5.6-Sol achieves the highest recall within five and ten candidates: 0.717 and 0.800 on VUMC and lower-bound values of 0.689 and 0.738 on MIMIC-IV. The retrieve-and-verify system reaches 0.695 and 0.784 on VUMC and lower-bound values of 0.575 and 0.657 on MIMIC-IV, with a candidate-linked evidence window attached to each retained recommendation. Diagnostic analyses reveal distinct failure sources, including output-length underfilling, confusion among closely related codes, out-of-knowledge-base generation, and incomplete evidence support. These findings establish a systematic evaluation framework for procedure-code recommendation and identify practical requirements for future systems that are accurate, review-efficient, and grounded in clinical evidence.","rel_num_authors":6,"rel_authors":[{"author_name":"Qingyuan Song","author_inst":"Vanderbilt University"},{"author_name":"Congning Ni","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Weixin Liu","author_inst":"Vanderbilt University"},{"author_name":"Yike Li","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Bradley A. Malin","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Zhijun Yin","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"The Right to Sexual and Reproductive Health among Migrant Workers in Taiwan: Stakeholder Perspectives through an AAAQ Analysis","rel_doi":"10.64898\/2026.08.26.26361458","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.26.26361458","rel_abs":"BackgroundGlobal labor migration from LMIC to higher-income destinations has expanded rapidly, placing increasing pressure on destination-country health. Existing research on cross-border migrant workers has focused largely on occupational health, general healthcare utilization, and disease-specific risks, while there is considerably less evidence on their sexual and reproductive health. This study contributes to this understudied field by examining the policy and health-system factors that shape the sexual and reproductive health services for migrant workers in Taiwan.\n\nMethodsA qualitative study was conducted in Taiwan between November 2025 and August 2026. 22 stakeholders were purposively recruited from academia, healthcare, nongovernmental organizations, government, labor brokerage, and employers. Data were collected through semi-structured interviews and small focus groups. Interviews were conducted in Mandarin Chinese, transcribed verbatim, and translated into English. Data were analyzed using framework analysis combining deductive coding based on the AAAQ framework with inductive coding of implementation and contextual themes.\n\nResultsGaps were identified across all four AAAQ dimensions. Participants described limited migrant- responsive SRH programming; physical, financial, administrative, social, and information barriers; shortcomings in linguistic and cultural responsiveness; and weaknesses in interpretation, coordination, and continuity of care, despite generally favorable views of Taiwans clinical quality.\n\nConclusionsOur findings show that broad insurance coverage and strong clinical capacity do not by themselves ensure the realization of migrant workers SRHR. In Taiwan, rights were mediated through labor brokerage, gendered live-in work arrangements, and fragmented governance across health, labor, immigration, and social-welfare systems. Improving migrant SRHR therefore requires stronger implementation of existing protections, reduced dependence on informal intermediaries, and more integrated institutional responsibility for cross-sector migrant health needs.","rel_num_authors":5,"rel_authors":[{"author_name":"Patrick Tang","author_inst":"Harvard University"},{"author_name":"Michelle Wei-Hsuan Lu","author_inst":"National Taiwan University"},{"author_name":"Kai-Tsun Yeung","author_inst":"University of Michigan at Ann Arbor"},{"author_name":"Bruce Jiajie Guo","author_inst":"Emory University"},{"author_name":"Ko-Fei Natalie Wei","author_inst":"TES"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Clinically Generalisable End-to-End Graph Learning for CT Image-Based Multitask Stroke Diagnosis","rel_doi":"10.64898\/2026.08.26.26360026","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.26.26360026","rel_abs":"Stroke remains a leading cause of mortality and long-term disability worldwide, yet rapid diagnosis is often limited by the shortage of trained radiologists, particularly in resource-constrained settings. Automated analysis of CT imaging offers a potential solution, but existing methods often struggle to achieve clinically generalisable performance while jointly addressing multiple diagnostic tasks. Here we present the Intelligent Integrated Stroke Diagnosis System (IISDS), an end-to-end deep learning framework built upon StrokeGNN, a graph-based architecture that integrates 3D contextual feature extraction with U-Net-based 2D lesion segmentation to enable comprehensive stroke analysis from non-contrast CT scans. IISDS performs stroke subtype classification, lesion segmentation and lesion volume estimation within a unified pipeline. To develop and validate the system, we collected and curated BGD-ISD through a collaboration between AI researchers, neurologists, radiologists and clinicians, resulting in a large multi-centre dataset comprising 1,507 CT scans from 597 stroke cases acquired across six hospitals and medical centres in Bangladesh. Across BGD-ISD and multiple publicly available datasets, IISDS achieves state-of-the-art performance on all tasks, improving segmentation accuracy by [&ge;] 0.011 Dice score, reducing lesion volume estimation error by [&ge;] 0.3 average symmetric surface distance (ASSD), and increasing classification performance by [&ge;] 0.018 area under the receiver operating characteristic curve (AUC) compared with existing approaches. These results demonstrate the potential of graph-based deep learning to enable clinically generalisable, automated and scalable stroke diagnosis from CT imaging, supporting rapid clinical decision-making, particularly in healthcare environments with limited access to expert radiological interpretation. The BGD-ISD dataset will be made openly available to the research community upon publication at: https:\/\/github.com\/Zhicheng-Lu\/stroke_ct.","rel_num_authors":21,"rel_authors":[{"author_name":"Zhicheng Lu","author_inst":"Charles Sturt University"},{"author_name":"Shahadat Uddin","author_inst":"The University of Sydney"},{"author_name":"Sergio Uribe","author_inst":"Monash University"},{"author_name":"Sam White","author_inst":"Adelaide University"},{"author_name":"Rodrigo Tomazini Martins","author_inst":"Mater Misericordiae Hospital"},{"author_name":"Shayne Chau","author_inst":"Charles Sturt University"},{"author_name":"Abu Syed Md. Mosaddek","author_inst":"Uttara Adhunik Medical College"},{"author_name":"Md. Siddiqul Islam","author_inst":"American International University-Bangladesh"},{"author_name":"Nusratun Nahar","author_inst":"Southeast University"},{"author_name":"A.K.M. Azad","author_inst":"Imam Mohammad Ibn Saud Islamic University"},{"author_name":"K. M. Nazmul Hossain","author_inst":"Jahangirnagar University"},{"author_name":"Habib Sadat Choudhury","author_inst":"International Medical College"},{"author_name":"K. M. Rakibul Hasan","author_inst":"Paediatric Neuro Care & Research (PNR)"},{"author_name":"Nabil Mosaddek","author_inst":"Military Institute of Science and Technology (MIST)"},{"author_name":"Samia Rahman","author_inst":"Popular Diagnostic Centre"},{"author_name":"Md. Mostaque Hossain","author_inst":"International Medical College"},{"author_name":"K. M. Mehedi Hasan Sizar","author_inst":"IbnaSina Diagnostic & Imaging Center"},{"author_name":"Claudio Angione","author_inst":"Teesside University"},{"author_name":"Pietro Lio","author_inst":"University of Cambdridge"},{"author_name":"Md Tauhidul Islam","author_inst":"Stanford University"},{"author_name":"Mohammad Ali Moni","author_inst":"University of Queensland"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Multi-organ aging quantified from routine chest CT predicts chronic disease risk and mortality","rel_doi":"10.64898\/2026.08.26.26361434","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.26.26361434","rel_abs":"Biological aging occurs heterogeneously across individuals and organs. However, current measures of biological age incompletely capture organ-specific differences in health and disease risk. Because chest CT visualizes multiple thoracic organs, it offers an opportunity to quantify structural aging across organ systems. Here, we developed MOSAIC-Age, a framework characterizing eight organ-specific aging clocks on chest CT. The clocks were developed and validated using 9,971 CT scans from CT-RATE and MIDRC, and subsequently locked and applied to two independent prospective cohorts with 35,293 participants from the National Lung Screening Trial and Genetic Epidemiology of COPD study. CT-derived biological age gaps (BAGs) were examined in relation to lifestyle and socioeconomic factors, prevalent comorbidities, incident chronic diseases, and all-cause and cause-specific mortality. Higher BAGs, indicating organs that appeared older on CT than expected for their chronological age, were broadly associated with adverse health characteristics, chronic disease burden, and increased mortality risk. Multiple disease outcomes were associated with aging across several organs, whereas in multivariable analyses including all eight organ-specific BAGs, the remaining associations were more organ specific. A greater number of markedly older-appearing organs and a faster pace of aging were each associated with higher mortality. Together, these findings demonstrate that routine chest CT captures both shared and organ-specific patterns of biological aging and establish CT-derived organ aging as a quantitative imaging biomarker for assessing multi-organ health and long-term disease risk.","rel_num_authors":34,"rel_authors":[{"author_name":"Junya Sato","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Morteza Salehjahromi","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Anas Zafar","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Amgad Muneer","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Xinyan Xu","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Erjia Zhu","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Natalie I. Vokes","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Tina Cascone","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Xiuning Le","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Mehmet Altan","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Eric E. Gardner","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Ajay Sheshadri","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Edwin J. Ostrin","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Ameen A. Salahudeen","author_inst":"University of Illinois Cancer Center"},{"author_name":"Tianhong Li","author_inst":"UC Davis Comprehensive Cancer Center"},{"author_name":"Miriam Merad","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Aadel A. Chaudhuri","author_inst":"Mayo Clinic"},{"author_name":"David E. Gerber","author_inst":"UT Southwestern Medical Center"},{"author_name":"Fernando U. Kay","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Myrna C.B. Godoy","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Brett W. Carter","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Girish S. Shroff","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Lauren A. Byers","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Caroline Chung","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"David Jaffray","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"David Rice","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Zhongxing Liao","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Joe Y. Chang","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Ara A. Vaporciyan","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Don L. Gibbons","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Carol C. Wu","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"John V. Heymach","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Jianjun Zhang","author_inst":"The University of Texas MD Anderson Cancer Center"},{"author_name":"Jia Wu","author_inst":"The University of Texas MD Anderson Cancer Center"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Comparative effectiveness of preventive strategies against medically-attended respiratory syncytial virus in U.S. infants during the first six months of life, 2023-2025","rel_doi":"10.64898\/2026.08.25.26361361","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.25.26361361","rel_abs":"Key pointsO_ST_ABSQuestionC_ST_ABSAmong infants eligible to receive protection from maternal vaccination or infant long-acting monoclonal antibodies, what is the effectiveness RSV preventive strategies when accounting for real-world delays in administration?\n\nFindingsIn this target trial emulation study of 120,586 mother-infant pairs from U.S. claims data, long-acting monoclonal antibody effectiveness was sensitive to implementation delays, rendering preventive strategies that protected infants at or close to birth more effective at preventing respiratory disease than a monoclonal antibody strategy that reflected real-world implementation.\n\nMeaningStrategies, such as maternal vaccination or monoclonal antibodies provided within the first week of life, that protect infants at birth or close to birth are especially valuable in settings where delayed long-acting monoclonal antibody receipt is likely.\n\nImportanceMaternal vaccination and long-acting monoclonal antibodies are now available in the U.S. to prevent RSV. Long-acting monoclonal antibody administration in the U.S. commonly occurs after hospital discharge in outpatient settings, leaving some infants unprotected early in life when severe RSV risk is highest. Comparative effectiveness between the two interventions and whether delays affect effectiveness estimates have not been quantified.\n\nObjectiveTo evaluate the effectiveness of infant long-acting monoclonal antibody strategies and a maternal vaccination strategy, each compared to no intervention, and the comparative effectiveness of intervention strategies when accounting for real-world delays in monoclonal antibody receipt.\n\nDesignCohort study using target trial emulation to compare four strategies for prevention of RSV-related outcomes.\n\nSettingThe U.S. between 2023 and 2025 using a nationwide database of employer-sponsored commercial insurance claims.\n\nParticipants120,586 commercially insured mother-infants, whose infants were born in the U.S. during the 2023-2024 or 2024-2025 RSV season. Infants who could not be paired with their mothers record, did not enroll in commercial insurance within 75 days from birth, received palivizumab, and had an implausible birth date were excluded.\n\nInterventionsComparison of four RSV prevention strategies: (i) maternal RSVpreF; (ii) long-acting monoclonal antibody given within the first week of life (mAb as intended); (iii) long-acting monoclonal antibody given within a six-month grace period from birth (mAb within grace period); and (iv) a control.\n\nMain outcomes and measuresEffectiveness against first RSV-associated hospitalization and medically-attended RSV illness was summarized using adjusted hazard ratios (aHR) and estimated using an inverse propensity weighting approach, with weights accounting for maternal age, maternal comorbidities affecting pregnancy, obstetric and newborn complications, season, region, and birth timing relative to October 1. A weighted Kaplan Meier estimator was used to estimate strategy-specific cumulative incidence of RSV outcomes over time.\n\nResultsIn the first five weeks of life, the mAb within grace period strategy doubled the hazard of RSV hospitalization (aHR: 2.0 [95% CI: 1.0-4.9]) and increased the hazard of medically-attended RSV (aHR: 1.6 [95% CI: 1.0-2.7]) compared to the maternal RSVpreF strategy. The hazard for RSV hospitalization was similar for the mAb as intended strategy compared to the maternal RSVpreF strategy (aHR = 0.9 [95% CI: 0.3-1.9]).\n\nConclusions and relevanceRSVpreF and monoclonal antibodies were similarly effective when monoclonal antibodies were administered close to birth, but when accounting for real-world delays in monoclonal antibody receipt, the maternal RSVpreF strategy was more effective than the mAb within grace period strategy.","rel_num_authors":9,"rel_authors":[{"author_name":"Sara S Kim","author_inst":"Rollins School of Public Health, Emory University"},{"author_name":"Seth Zissette Zissette","author_inst":"Bloomberg School of Public Health, Johns Hopkins University"},{"author_name":"Connor Van Meter","author_inst":"Rollins School of Public Health, Emory University"},{"author_name":"Machi Shiiba","author_inst":"Rollins School of Public Health, Emory University"},{"author_name":"Marina Bruck","author_inst":"Rollins School of Public Health, Emory University"},{"author_name":"Ashley Tippett","author_inst":"Emory School of Medicine"},{"author_name":"Satosih Kamidani","author_inst":"Emory School of Medicine; The Center for Childhood Infections and Vaccines of Children's Healthcare of Atlanta"},{"author_name":"David Benkeser","author_inst":"Rollins School of Public Health, Emory University"},{"author_name":"Elizabeth  Rogawski McQuade","author_inst":"Rollins School of Public Health, Emory University"}],"rel_date":"2026-08-31","rel_site":"medrxiv"},{"rel_title":"Spectral and melanopic dose calibration of consumer see-through extended-reality glasses for controlled retinal photostimulation","rel_doi":"10.64898\/2026.08.26.26361398","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.08.26.26361398","rel_abs":"Light reaching the retina is a primary regulator of human circadian physiology, acting largely through melanopsin-expressing retinal ganglion cells with peak short-wavelength sensitivity. Delivering known, repeatable retinal doses outside the laboratory is difficult because conventional light sources leave viewing geometry, gaze, and ambient conditions uncontrolled. Consumer extended-reality (XR) glasses fix a bright binocular display in constant geometry relative to the eye, but their suitability as calibrated photic stimulators has not been established. Here we validate a commercial micro-OLED XR display (VITURE Luma Ultra) for controlled retinal photostimulation. A purpose-built host application renders exact 8-bit RGB stimuli while independently controlling hardware brightness and logging all intensity-determining state; spectral radiance was measured at the retinal position of a 3D-printed phantom head with an open-source miniature spectroradiometer, anchored to absolute units by a luminance transfer calibration. The blue primary peaks at 461 nm (FWHM 43 nm), is spectrally invariant across a >10-fold intensity range, and at maximum output delivers an estimated 299 lx melanopic equivalent daylight illuminance, above consensus daytime recommendations, while remaining roughly two orders of magnitude below photobiological safety limits. The red primary is visually effective with minimal melanopic drive (melanopic DER 0.10), enabling spectrally shifted evening stimulation. Unlike the immersive virtual-reality headsets previously used for calibrated light delivery, the see-through form factor preserves the wearers view of the surroundings--relevant for clinical monitoring in supervised settings such as the intensive care unit. These results show that consumer XR glasses can serve as a dose-calibrated platform for wearable photostimulation using an open-source measurement chain, and provide groundwork for application-layer dose-response studies.","rel_num_authors":2,"rel_authors":[{"author_name":"Matt Gaidica","author_inst":"Washington University"},{"author_name":"Matthew Rosengart","author_inst":"Washington University"}],"rel_date":"2026-08-31","rel_site":"medrxiv"}]}