{"messages":[{"status":"ok","cursor":"20","count":30,"total":31442}], "collection":[{"rel_doi":"10.64898\/2026.03.30.715311","rel_title":"Intranasal immunization with live-attenuated RSV-vectored SARS-CoV-2 vaccines elicits antigen-specific systemic and mucosal immunity and protects against viral challenge and natural infection","rel_date":"2026-03-31","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.30.715311","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.03.29.711974","rel_title":"Panmap: Scalable phylogeny-guided alignment, genotyping, and placement on pangenomes","rel_date":"2026-03-30","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.29.711974","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"bioinformatics"},{"rel_doi":"10.64898\/2026.03.27.26349524","rel_title":"Twelve Distinct Laboratory Methods Used to Measure SARS-CoV-2 in Wastewaters throughout a Three-Year Ontario-Wide, Canada Study: Impact on Public Health Interpretation of Disease Incidence","rel_date":"2026-03-30","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.27.26349524","rel_abs":"Wastewater and environmental monitoring (WEM) was a critical public health surveillance tool for SARS-CoV-2 surveillance during the COVID-19 Pandemic. However, substantial methodological heterogeneity across laboratories continues to challenge the interpretation and thus compromise the actionability of resulting WEM measurements. This study quantifies interlaboratory concordance in SARS-CoV-2 WEM measurements using influent wastewater samples collected between September 2021 and January 2024 at a single wastewater treatment facility within the Ontario Wastewater Surveillance Initiative, analyzed independently by 12 laboratories using their routine methods. In the absence of a known true viral concentration, interlaboratory WEM measurements were evaluated against a facility-specific longitudinal benchmark derived from routine surveillance at the source facility and correlated to clinical surveillance metrics. Concordance was assessed across four WEM measurement units commonly used in practice: SARS-CoV-2 copies\/mL, SARS-CoV-2 copies\/copies of PMMoV, and their standardized counterpart wastewater viral activity level (WVAL) units of WVAL-standardized SARS-CoV-2 copies\/mL and WVAL-standardized SARS-CoV-2 copies\/copies of PMMoV. Measurements in each unit were analyzed using complementary analytical frameworks, including categorical concordance metrics, principal component analysis, and linear mixed-effects modelling. Across the study period, interlaboratory measurements consistently captured benchmark temporal dynamics, including major peaks and periods of low activity, but showed substantial variation in magnitude and public-health interpretation across laboratory methods. Concordance was strongest during epidemiological extremes and deteriorated during transitional periods, increasing the risk of misclassification with potentially implications for public health decision-making. To explore potential laboratory methodological drivers of agreement, associations between the benchmark concordance and the laboratory-specific concentration, extraction, and RT-qPCR analytical steps were assessed using Fishers exact tests, alongside extracted-mass threshold analyses. No single methodological factor showed a statistically significant association with benchmark concordance in this study; however, several parameters, including RNA template volume, total RT-qPCR reaction volume, and extracted mass of analyzed settled solids, may warrant further investigation in future studies.","rel_num_authors":21,"rel_authors":[{"author_name":"Nada Hegazy","author_inst":"University of Ottawa"},{"author_name":"K. Ken Peng","author_inst":"Simon Fraser University; University of Ottawa"},{"author_name":"Johanna de Haan-Ward","author_inst":"University of Ottawa"},{"author_name":"Elizabeth Renouf","author_inst":"University of Ottawa"},{"author_name":"Elizabeth Mercier","author_inst":"University of Ottawa"},{"author_name":"Shen Wan","author_inst":"University of Ottawa"},{"author_name":"X. Joan Hu","author_inst":"Simon Fraser University"},{"author_name":"Charmaine Dean","author_inst":"University of Waterloo"},{"author_name":"Mark Servos","author_inst":"University of Waterloo"},{"author_name":"Elizabeth Edwards","author_inst":"University of Toronto"},{"author_name":"Gustavo Ybazeta","author_inst":"Health Sciences North Research Institute"},{"author_name":"Marc Habash","author_inst":"University of Guelph"},{"author_name":"Lawrence Goodridge","author_inst":"University of Guelph"},{"author_name":"R. Stephen Brown","author_inst":"Queen's University"},{"author_name":"Sarah Jane Payne","author_inst":"Queen's University"},{"author_name":"Andrea Kirkwood","author_inst":"Ontario Tech University"},{"author_name":"Christopher Kyle","author_inst":"Trent University"},{"author_name":"R. Michael McKay","author_inst":"University of Windsor"},{"author_name":"Kimberly Gilbride","author_inst":"Tornoto Metropolitan University"},{"author_name":"Christopher DeGroot","author_inst":"Western University"},{"author_name":"Robert Delatolla","author_inst":"University of Ottawa"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"epidemiology"},{"rel_doi":"10.64898\/2026.03.27.26349516","rel_title":"Increased Risk of Pulmonary Embolism Following SARS-CoV-2 Activity in Ontario, Canada","rel_date":"2026-03-30","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.27.26349516","rel_abs":"BackgroundSARS-CoV-2 infection is an established prothrombotic trigger, yet the population-level temporal relationship between circulating viral activity and pulmonary embolism (PE) remains poorly characterized. We aimed to evaluate the short-term association between respiratory viral activity and PE hospitalizations, accounting for specific temporal lags.\n\nMethodsWe conducted a population-level time-series analysis of incident PE hospitalizations in Ontario, Canada, from 2011 to 2024. Using distributed lag non-linear models, we assessed the association between standardized weekly activity levels of SARS-CoV-2, influenza A\/B, and respiratory syncytial virus (RSV) and PE risk over a 5-week lag period. Relative risks (RR) per standard deviation (SD) increase in viral activity were estimated via negative binomial regression using cross-basis terms to account for both exposure-response and lag-response non-linearities. Models were adjusted for Fourier seasonal terms and secular trends.\n\nFindingsAmong 70,670 incident PE cases identified between 2011 and 2024, SARS-CoV-2 activity demonstrated a significant temporal association with PE. A cumulative RR increase of 20% per SD in SARS-CoV-2 activity was observed over the five weeks following exposure (RR 1.20; 95% CI 1.05-1.37). The risk followed a distinct delay trajectory: weekly cumulative RRs peaked at week 3 (RR 1.21; 95% CI 1.01-1.45). For the 2020-2024 period, influenza A also showed an association peaking at week 3 without statistical significance (RR 1.17; 95% CI 0.95-1.45).\n\nInterpretationIncreased population-level SARS-CoV-2 activity is associated with a heightened risk of PE, peaking at approximately the third week. This delayed peak suggests a protracted thrombo-inflammatory window, likely driven by sustained endothelial injury. These findings highlight the vascular burden of COVID-19 and suggest that infection prevention measures, including vaccination, may provide significant downstream protection against thromboembolic disease.","rel_num_authors":3,"rel_authors":[{"author_name":"Clara Eunyoung Lee","author_inst":"University of Toronto"},{"author_name":"Natalie J. Wilson","author_inst":"University of Toronto"},{"author_name":"David Fisman","author_inst":"University of Toronto"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"epidemiology"},{"rel_doi":"10.64898\/2026.03.28.714969","rel_title":"Structure of SARS-CoV-2 spike in complex with its co-receptor the neuronal cell adhesion protein contactin 1","rel_date":"2026-03-29","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.28.714969","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"biochemistry"},{"rel_doi":"10.64898\/2026.03.25.714316","rel_title":"Characterization of Self-Incompatibility Genes in Brassica rapa var. Toria and Yellow sarson","rel_date":"2026-03-28","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.25.714316","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"plant biology"},{"rel_doi":"10.64898\/2026.03.27.714475","rel_title":"Impact of viral membrane oxidation on SARS-CoV-2 spike protein transmembrane anchoring stability","rel_date":"2026-03-27","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.27.714475","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"biochemistry"},{"rel_doi":"10.64898\/2026.03.26.26347671","rel_title":"A protocol for assessment of interventions using a computational phenotype for Long COVID","rel_date":"2026-03-27","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.26.26347671","rel_abs":"BackgroundLong COVID presents with one or multiple symptoms or diagnosable conditions after SARS-CoV-2 infection. To study whether use of the antiviral remdesivir in persons hospitalized with acute COVID-19 is associated with reduced Long COVID, we created a computational phenotype for Long COVID.\n\nMethodsIn electronic health records (EHR) from a multistate healthcare system (US), hospital admissions from 5\/1\/20 - 9\/30\/22 were reviewed. The study group was hospitalized with acute COVID-19 and the control group was hospitalized for other reasons without prior SARS-CoV-2 infection. The populations were balanced with overlap weights based on a high-dimensional propensity score of pre-specified variables and the top 100 comorbidities differing between the groups. Hazard ratios (HR) were calculated for the combined primary outcome: U09.9 (Post-Covid Conditions) or any incident secondary outcome from 90 to 365 days after admission. Secondary outcomes included 27 individual incident diagnoses, corrected for multiplicity with Holm-Bonferroni.\n\nResultsAdmissions included 45,540 with, and 409,186 without COVID-19 during the study period, evaluable for the primary outcome. After weighting, standardized difference was < 0.01 for all measured confounders including demographic and clinical features. In the COVID+ and non-COVID groups 38.0% and 29.3% met the combined primary outcome, respectively. Weighted HR (95%CI) for the primary outcome was 1.37 (1.35, 1.40), p < 0.0001. All secondary outcomes were associated with the COVID+ group, when adjusted for multiplicity. Incident diagnoses with strong associations (HR > 2) included thromboembolism, hair loss, diabetes mellitus, obesity, and hypoxia. Anosmia\/dysgeusia was associated with COVID, but wide confidence intervals reflected few charted diagnoses.\n\nConclusionsManifestations of Long COVID at population scale are detectable as part of routine symptoms and clinical diagnoses in the EHR after admissions for COVID-19, compared with all other hospital admissions. This a prior computational phenotype for Long COVID will be used to assess whether remdesivir use is associated with decreased Long COVID.","rel_num_authors":10,"rel_authors":[{"author_name":"Amitabh Amitabh Gunjan","author_inst":"Providence Global Healthcare Innovation Center, Hyderabad, India"},{"author_name":"Lawrence Huang","author_inst":"Institute for Systems Biology, Seattle, WA, USA"},{"author_name":"Anudeep Appe","author_inst":"Providence Global Healthcare Innovation Center, Hyderabad, India"},{"author_name":"Paul A. McKelvey","author_inst":"Providence Health and Services, Portland, OR, USA"},{"author_name":"Heather A. Algren","author_inst":"Swedish Center for Research and Innovation, Seattle, WA, USA"},{"author_name":"Mark Berry","author_inst":"Gilead Sciences, Inc., Foster City, CA, USA"},{"author_name":"Essy Mozaffari","author_inst":"Gilead Sciences, Inc., Foster City, CA, USA"},{"author_name":"Bill J. Wright","author_inst":"Providence Health and Services, Portland, OR, USA"},{"author_name":"Jennifer J. Hadlock","author_inst":"Institute for Systems Biology, Seattle, WA, USA"},{"author_name":"Jason D. Goldman","author_inst":"Swedish Center for Research and Innovation, Seattle, WA, USA"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"infectious diseases"},{"rel_doi":"10.64898\/2026.03.24.26349229","rel_title":"A Demographic Look at Cancer Treatment Behaviors during the COVID-19 Pandemic","rel_date":"2026-03-26","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.24.26349229","rel_abs":"Abstract\/SummaryO_ST_ABSBackgroundC_ST_ABSWhile numerous studies have explored the relationship between COVID-19 and cancer, few have specifically examined the significant impact of the pandemic on cancer patients, particularly concerning their treatments and appointments.\n\nObjectivesThis study aims to investigate cancer treatment behaviors during the COVID-19 pandemic.\n\nMethodsThis retrospective quantitative study utilized data from the Centers for Disease Control and Preventions National Health Interview Survey of 2020. The inclusion criteria were as follows: studies conducted within the United States; patients diagnosed with COVID-19 since the pandemic began; patients diagnosed with cancer within the United States; patients undergoing cancer treatment or in remission since the start of the pandemic; patients who experienced a change, delay, or cancellation of treatment due to the COVID-19 pandemic; patients who experienced a change or delay in cancer care due to the COVID-19 pandemic; patients with a weakened immune system due to prescriptions; and patients who took prescription medication within the past 12 months. The variables were analyzed against population characteristics, including age, race, gender, cancer type, and COVID-19 status. Python Jupyter Notebook (packaged by Anaconda Navigator in R Studio, version 6.4.8), Microsoft Excel for data cleaning and assessment, and SPSS were used for statistical analyses.\n\nResultsChi-Square Analysis (p<.05) revealed significant associations between cancer treatment and gender (p=0.009), other cancer treatments and age (p<.001) and education (p<.001), changes in other cancer treatments and gender (p=0.045), race (p<.001), age (p<.001), and education (p=.013), and prescribed medication and gender (p=.009), family income (p<.001), and age (p<.001).\n\nConclusionThe COVID-19 pandemic has significantly impacted cancer care in the U.S., affecting the delivery of treatments. Additional government funding is necessary to help medical facilities develop programs for off-site treatment delivery, to better prepare for future pandemics, and avoid repeating past challenges.","rel_num_authors":1,"rel_authors":[{"author_name":"Jonathan M Acosta Morales","author_inst":"SUNY Downstate"}],"version":"1","license":"cc_by","type":"PUBLISHAHEADOFPRINT","category":"oncology"},{"rel_doi":"10.64898\/2026.03.21.26348591","rel_title":"Disentangling the Shared and Differential Genetic Architecture Between COVID-19 and Other Respiratory Disorders: A Multi-Omics Genome-Wide Analysis","rel_date":"2026-03-26","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.21.26348591","rel_abs":"BackgroundA bidirectional relationship has been observed between COVID-19 and respiratory disorders, where respiratory comorbidities increase severity and COVID-19 induces respiratory sequelae. The underlying biological and genetic mechanisms remain unclear. While previous studies have identified overlapping genetic loci, few have systematically disentangled the genetic factors shared between these conditions versus those specific to COVID-19, particularly at a multi-omics level.\n\nMethodsWe developed and applied a unified analytical framework to compare three COVID-19 phenotypes with eight respiratory disorders (including asthma, COPD, IPF, and pneumonia). Utilizing the cofdr method for shared genetic signal analysis and DDx\/mtCOJO for differentiation, we integrated genome-wide association statistics with multi-omics data (transcriptome, splicing, and proteome). This approach allowed for the simultaneous identification of shared genetic signals (concordant or discordant) and disease-specific variants across expression (TWAS), alternative splicing (spTWAS), and protein abundance (PWAS).\n\nResultsWe delineated a comprehensive atlas of 214 differential and numerous shared loci across 24 pairwise comparisons. The shared genetic architecture was characterized by pleiotropic effects in genes such as ATP11A (exhibiting opposing effects in COVID-19 vs. IPF) and GSDMB (shared with COPD). Crucially, differentiation analysis revealed that severe COVID-19 is genetically distinct from other respiratory infections (e.g., pneumonia and influenza) through dysregulated Type I\/III interferon signaling and specific defects in alveolar epithelial and macrophage function, as well as GM-CSF\/surfactant metabolism pathways. These findings provide direct genetic evidence supporting the use of GM-CSF modulators and interferon-lambda for COVID-19 treatment, therapies that have already entered clinical trials. Furthermore, multi-trait conditional analysis prioritized FYCO1 and HCN3 as potential COVID-19-specific risk genes. Splicing analysis underscored the critical role of alternative splicing in both shared and differential architectures, highlighting IFNAR2 isoform regulation as a key discriminator between COVID-19 and other respiratory traits.\n\nConclusionThis study provides the first genome-wide, multi-omics map revealing the shared and differential genetic landscapes of COVID-19 and other respiratory phenotypes. By uncovering specific molecular mechanisms that distinguish COVID-19 pathology, specifically involving surfactant homeostasis and interferon pathways, our findings offer novel insights for targeted drug repurposing and precision risk stratification.","rel_num_authors":4,"rel_authors":[{"author_name":"Xiao Xue","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Yu-Ping LIN","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Yaning FENG","author_inst":"Zhejiang Chinese Medical University;The Chinese University of Hong Kong"},{"author_name":"Hon-Cheong SO","author_inst":"The Chinese University of Hong Kong"}],"version":"1","license":"cc_no","type":"PUBLISHAHEADOFPRINT","category":"genetic and genomic medicine"},{"rel_doi":"10.64898\/2026.03.24.26349101","rel_title":"An Assessment of Correctional Officer's Health Beliefs in Relationship to COVID-19 Vaccine Uptake and Hesitancy.","rel_date":"2026-03-26","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.24.26349101","rel_abs":"IntroductionDuring the COVID-19 pandemic, incarcerated populations faced heightened risk of exposure due to healthcare barriers, restrictive environments, and pre-existing health conditions. Consequently, Correctional Officers (COs) faced increased risk of COVID-19 exposure. Given the health benefits of COVID-19 vaccination and the rise in vaccine hesitancy, this study examined the relationship between COs health beliefs and COVID-19 vaccine uptake.\n\nMethodsA health beliefs survey was administered to Massachusetts-based COs (n=118). Chi-squared Automatic Interaction Detection modeling and logistic regression was utilized to analyze the survey data.\n\nResultsCOs with higher trust in vaccines and a prior positive COVID-19 test were most likely to get vaccinated voluntarily. Those with low trust in vaccines and no previous positive COVID-19 test were least likely to receive the vaccine.\n\nConclusionDespite the severe impact of COVID-19 in correctional settings, and the evidence of vaccine efficacy against hospitalization and death, vaccine uptake among COs remains low.","rel_num_authors":7,"rel_authors":[{"author_name":"Bethany Hedden-Clayton","author_inst":"Wayne State University"},{"author_name":"Ariel L Roddy","author_inst":"Northern Arizona University"},{"author_name":"Juliette K Roddy","author_inst":"Northern Arizona University"},{"author_name":"Yvane Ngassa","author_inst":"Tufts University School of Medicine"},{"author_name":"Bridget Pickard","author_inst":"Tufts Medical Center"},{"author_name":"Rachel Annabelle Tam","author_inst":"Boston Medical Center"},{"author_name":"Alysse G Wurcel","author_inst":"Boston Medical Center"}],"version":"1","license":"cc_no","type":"PUBLISHAHEADOFPRINT","category":"public and global health"},{"rel_doi":"10.64898\/2026.03.19.712934","rel_title":"Experimental SARS-CoV-2 infection using horseshoe bats","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.19.712934","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"microbiology"},{"rel_doi":"10.64898\/2026.03.21.713333","rel_title":"IL-1\u03b2 and TNF drive endothelial dysfunction and coagulopathy in acute COVID-19.","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.21.713333","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"cell biology"},{"rel_doi":"10.64898\/2026.03.23.713300","rel_title":"Cross-coronavirus host susceptibility loci influence disease severity through immune mediators","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.23.713300","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"genetics"},{"rel_doi":"10.64898\/2026.03.23.713574","rel_title":"Modulation of liposome membranes by the C-terminal domain of the coronavirus envelope protein","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.23.713574","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"biophysics"},{"rel_doi":"10.64898\/2026.03.20.713312","rel_title":"Somatic evolution of a cross-reactive germline antibody that expands its breadth to neutralize new SARS-CoV-2 variants","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.20.713312","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.03.24.713916","rel_title":"Coronavirus envelope protein drives iron sensing disorder by hijacking the TAp73-FDXR axis","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.24.713916","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"microbiology"},{"rel_doi":"10.64898\/2026.03.24.713966","rel_title":"SARS-CoV-2 PLpro Drives Epithelial Barrier Disruption Across Drosophila and Mammalian Epithelia","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.24.713966","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"cell biology"},{"rel_doi":"10.64898\/2026.03.23.26349139","rel_title":"Phylogenetic Insights into SARS-CoV-2 Introductions and Spread in Georgia","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.23.26349139","rel_abs":"The spread of successive novel COVID-19 variants presented a challenge for outbreak surveillance, epidemiology, and emergency responses. Monitoring the emergence and spread of SARS-CoV-2 variants is essential to allocate limited public health resources and optimize control efforts. Global collaboration among the scientific community enabled large-scale viral surveillance and sequencing efforts. However, translating these vast datasets into actionable public health inferences requires rapid statistical methodologies, scalable workflows, and robust frameworks.\n\nIn this study, we focused on the Delta epidemic wave in Georgia by applying a hybrid maximum likelihood (ML) and Bayesian phylodynamic approach. We characterized the Delta variant introduction to Georgia and its subsequent local spread. Our analysis of 9,783 Delta sequences collected between August 1, 2020 and January 25, 2022 detected at least 344 introductions into Georgia, resulting in 34 highly-supported local clusters. On average, clusters circulated for one month before the earliest detected sequence, highlighting critical delays in detection. While most clusters remained small, a few introduction events led to large, sustained outbreaks. We jointly inferred the statewide transmission network, estimated from all locally circulating clusters with a modified Bayesian discrete trait phylogeographic reconstruction of statewide health districts. We showed that South Central, Georgia was a major source of transmission, despite having smaller numbers of infected people, compared to major metropolitan areas.\n\nOur study addresses the urgent need for methodologies and data-driven recommendations for public health practice, particularly given large, dynamic, and integrated datasets. By identifying key geographic sources and sinks of transmission, our findings can guide resource allocation and prepare for future epidemics among high-risk populations. Additionally, by characterizing introduction events, local circulation, and detection lags, we highlight critical gaps in surveillance. These gaps can inform outbreak investigation and response, such as targeted contact tracing and testing.","rel_num_authors":6,"rel_authors":[{"author_name":"Gabriella E Veytsel","author_inst":"University of Georgia Institute of Bioinformatics"},{"author_name":"Leke Lyu","author_inst":"Emory University Department of Biostatistics and Bioinformatics"},{"author_name":"Guppy Stott","author_inst":"University of Georgia Institute of Bioinformatics"},{"author_name":"Ludy Carmola","author_inst":"University of Georgia Department of Infectious Diseases"},{"author_name":"Hope Dishman","author_inst":"Georgia Department of Public Health"},{"author_name":"Justin Bahl","author_inst":"University of Georgia Department of Epidemiology and Biostatistics"}],"version":"1","license":"cc_by","type":"PUBLISHAHEADOFPRINT","category":"public and global health"},{"rel_doi":"10.64898\/2026.03.23.26349117","rel_title":"Household Size and Age as Primary Drivers of COVID-19 Infection Among Priority Populations in Australia","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.23.26349117","rel_abs":"BackgroundThe COVID-19 pandemic exacerbated health disparities globally, with certain populations experiencing disproportionate disease burdens. In Australia, COVID-19 deaths occurred disproportionately among first-generation migrants. This study examined risk factors for COVID-19 infection in a Victorian cohort recruited from priority populations, including healthcare workers, people with chronic health conditions, and culturally and linguistically diverse (CALD) communities.\n\nMethodsWe conducted a cross-sectional analysis of participants from the Optimise longitudinal cohort study (September 2020-December 2023). The primary outcome was the self-reported count of confirmed COVID-19 infections (PCR or rapid antigen test positive) from December 2019 to December 2023. We used Poisson regression to examine associations between baseline sociodemographic characteristics and infection count, calculating unadjusted and adjusted incidence rate ratios (IRRs) with 95% confidence intervals (CIs).\n\nResultsOf 433 participants (median age 51 years, 75% female), 25% reported no infections, 48% reported one infection, and 27% reported two or more infections. In univariate analysis, CALD status (IRR=1.24,95%CI:1.02-1.50) and larger household size (2-5 people, IRR=1.71,95%CI:1.14-2.50) were associated with higher infection rates, while chronic health conditions (IRR=0.73, 95%CI:0.61-0.88) and older age (IRR=0.54, 95%CI:0.43-0.67) were associated with lower infection rates. In adjusted analysis, younger age (18-34 years vs [&ge;]55 years: aIRR=0.63,95%CI:0.48-0.82) and medium household size (living alone vs 2-5 person household: aIRR=1.42, 95%CI:1.11-1.83) remained significant predictors. CALD status and socioeconomic status showed no independent association with infection risk after adjustment for household size and age.\n\nConclusionCOVID-19 infection risk in this Victorian cohort was driven by younger age and larger household size rather than CALD status or socioeconomic status, suggesting that housing density and age, rather than cultural or socioeconomic characteristics, determined infection patterns. Future pandemic preparedness should prioritise policies enabling safe quarantine and isolation for individuals in larger households and workplace protections and economic security for younger essential workers.","rel_num_authors":11,"rel_authors":[{"author_name":"Shanti Narayanasamy","author_inst":"Duke University"},{"author_name":"Aimee Altermatt","author_inst":"Burnet Institute"},{"author_name":"Wai Chung Tse","author_inst":"Burnet Institute"},{"author_name":"Lisa Gibbs","author_inst":"University of Melbourne School of Population Health: The University of Melbourne School of Population and Global Health"},{"author_name":"Anna Wilkinson","author_inst":"Burnet Institute"},{"author_name":"Katherine Heath","author_inst":"Burnet Institute"},{"author_name":"Mark Stoove","author_inst":"Burnet Institute"},{"author_name":"Nick Scott","author_inst":"Burnet Institute"},{"author_name":"Katherine Gibney","author_inst":": The Peter Doherty Institute for Infection and Immunity"},{"author_name":"Margaret Hellard","author_inst":"Burnet Institute"},{"author_name":"Alisa Pedrana","author_inst":"Burnet Institute"}],"version":"1","license":"cc_by","type":"PUBLISHAHEADOFPRINT","category":"infectious diseases"},{"rel_doi":"10.64898\/2026.03.23.26349096","rel_title":"Wastewater-Based Genomic Surveillance of SARS-CoV-2 Variant Circulation in Two Informal Urban Settlements in Nairobi, Kenya","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.23.26349096","rel_abs":"BackgroundSARS-CoV-2 genomic surveillance data remain limited in most low and middle-income countries (LMICs), resulting in significant gaps in the understanding of variant circulation and evolution. Wastewater-based epidemiology (WBE) presents a non-invasive, cost-effective, and population-representative surveillance approach that can complement clinical testing, particularly in densely populated urban informal settlements with limited healthcare access. This study aimed to pilot wastewater-based genomic surveillance as a multifaceted public health tool in Kenya.\n\nMethodsA prospective study was conducted using wastewater samples collected from two WHO-validated environmental surveillance sites -- Eastleigh A (Kamukunji sub-county) and Mathare (Starehe sub-county) -- in Nairobi, Kenya, between December 2022 and October 2023. A total of 272 samples were collected using Moore swabs at a frequency of two to three times per week. Samples were concentrated using Nanotrap(R) Magnetic Virus Particles, and nucleic acid was extracted using the Qiagen QIAamp Viral RNA Mini Kit. SARS-CoV-2 was detected using RT-PCR (TaqPath COVID-19 CE-IVD RT-PCR Kit). Library preparation for whole-genome sequencing was performed using the Illumina COVIDSeq kit, and sequencing was conducted on the Illumina MiSeq platform. Bioinformatic analysis was performed using Terra.bio and RStudio, and phylogenetic analysis included sequences abstracted from GISAID.\n\nResultsOf 272 samples, 238 (87.5%) tested positive with a cycle threshold (Ct) value of less than 36. Genomic analysis of 181 sequences identified Omicron as the predominant circulating variant, detected in 59% of samples. Other variants included XBB (16%), XBB.2.3(10%), XBB.1.9.X (5%), and additional minor variants. These findings were concordant with clinical sequencing data from Kenya over the same period.\n\nConclusionsWastewater-based genomic surveillance reliably reflected SARS-CoV-2 variant trends observed in clinical data. This approach provides early signals of variant emergence and evolution, offering a cost-effective complement to clinical surveillance in resource-limited settings.","rel_num_authors":17,"rel_authors":[{"author_name":"Leonard Kingwara","author_inst":"Kenya National Public Health Institute"},{"author_name":"Rukia Sarah Madada","author_inst":"Kenya National Public Health Institute"},{"author_name":"Vera Morangi","author_inst":"Kenya National Public Health Institute"},{"author_name":"Shalyn Akasa","author_inst":"Kenya National Public Health Institute"},{"author_name":"Victor Kiprutto","author_inst":"Kenya National Public Health Institute"},{"author_name":"Okonji Julie","author_inst":"Association of Public Health Laboratories"},{"author_name":"Richard Muthoka","author_inst":"Kenya National Public Health Institute"},{"author_name":"Charles Rombo","author_inst":"Kenya National Public Health Institute"},{"author_name":"Kanana Kimonye","author_inst":"Kenya National Public Health Institute"},{"author_name":"Emmanuel Okunga","author_inst":"Kenya National Public Health Institute"},{"author_name":"Moses Masika","author_inst":"University of Nairobi College of Health Sciences: University of Nairobi Faculty of Health Sciences"},{"author_name":"Edwin Ochieng","author_inst":"Association of Public Health Laboratories"},{"author_name":"Rufus Nyaga","author_inst":"Association of Public Health Laboratories"},{"author_name":"Osborn Otieno","author_inst":"Global Fund: The Global Fund to Fight AIDS Tuberculosis and Malaria"},{"author_name":"Fatim Cham","author_inst":"Global Fund: The Global Fund to Fight AIDS Tuberculosis and Malaria"},{"author_name":"Noah Hull","author_inst":"Association of Public Health Laboratories"},{"author_name":"Kamene Kimenye","author_inst":"Kenya National Public Health Institute"}],"version":"1","license":"cc_by","type":"PUBLISHAHEADOFPRINT","category":"epidemiology"},{"rel_doi":"10.64898\/2026.03.23.26349084","rel_title":"Beyond COVID-19 Deaths: Cause-Specific Analysis of Excess Mortality in Russia","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.23.26349084","rel_abs":"During the COVID-19 pandemic, European mortality exhibited a marked East-West divide in both timing and magnitude, echoing longstanding longevity disparities in this region. Russia sits on the Eastern side: early restrictions were short-lived, and vaccine uptake remained low amid historically limited trust in government and science. Using weekly national and monthly regional mortality data disaggregated by age, sex, and cause of death, we estimated excess mortality from March 2020 to December 2021 using generalised additive models. We identify two major mortality peaks (late 2020-early 2021 and late 2021) and estimate 1,044,914 excess deaths, well above the 595,815 officially registered COVID-19 deaths. Non-COVID-19 excess was larger during the first peak, especially at ages 15-44. Cardiovascular diseases accounted for roughly 60% of the non-COVID-19 excess and we find no evidence of excess mortality from cancer or external causes. Among women, excess deaths were concentrated at older ages, whereas among men they clustered at working and older working ages, only partly reflecting differences in age structure. The highest excess mortality was found in the most populous regions, particularly the Central European and Volga parts. Temporal and spatial inconsistencies in cause-of-death coding may obscure indirect mortality burden and hinder the associated policy response.\n\nHighlights- Russia had 1,044,914 excess deaths in 2020-21, about twice official COVID-19 deaths.\n- These discrepancies varied over time and across regions.\n- Cardiovascular deaths drove most non-COVID excess mortality.\n- We find no evidence of excess mortality from external causes of death.\n- Autopsy-based COVID-19 assignment may have increased misclassification","rel_num_authors":4,"rel_authors":[{"author_name":"Ekaterina Degtiareva","author_inst":"University of Oxford"},{"author_name":"Sergey Timonin","author_inst":"The Australian National University"},{"author_name":"Andrea Tilstra","author_inst":"University of Oxford"},{"author_name":"Jos\u00e9 Manuel Aburto","author_inst":"London School of Hygiene and Tropical Medicine"}],"version":"1","license":"cc_by","type":"PUBLISHAHEADOFPRINT","category":"epidemiology"},{"rel_doi":"10.64898\/2026.03.23.26349092","rel_title":"Higher SARS-CoV-2 Transmission Burden Among Racialized Individuals: Evidence from Canadian Serology Data","rel_date":"2026-03-25","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.23.26349092","rel_abs":"IntroductionCOVID-19 transmission has not been evenly distributed across racial groups, with exposure being shaped by social and structural factors. The emergence of highly transmissible variants (i.e., Omicron) dramatically increased infection rates. However, it remains unclear whether racial disparities in transmission disappeared or persisted over the course of the pandemic.\n\nObjectiveTo understand how SARS-CoV-2 transmission differed by race in Canada and whether those disparities changed with the Omicron variant.\n\nMethodsWe analyzed cross-sectional SARS-CoV-2 seroprevalence data from the Canadian Blood Services serosurveillance program (June 2020 to April 2023) using a previously described dynamic susceptible-infection model, while accounting for seroreversion. Race-specific force of infection was estimated for the pre-Omicron and Omicron periods (with the emergence of Omicron defined as beginning December 26, 2021).\n\nResultsPrior to Omicron, racialized individuals had a 74% higher force of infection (IRR = 2.205; 95% CI: 2.115-2.299). During the Omicron period, infection rates rose significantly within each racial group relative to the pre-Omicron period, with a 55.52-fold increase among White individuals and a 31.27-fold increase among racialized individuals. Despite this, racialized individuals remained disproportionately affected following the emergence of Omicron, with 24% higher infection rates than those of their White counterparts (IRR = 1.242; 95% CI: 1.231-1.253).\n\nConclusionWidespread transmission during Omicron did not result in epidemiologic equity, as racialized populations continued to experience higher infection risk despite crude seroprevalence depicting convergence.","rel_num_authors":4,"rel_authors":[{"author_name":"Simran K Mann","author_inst":"University of Toronto"},{"author_name":"Natalie J. Wilson","author_inst":"University of Toronto"},{"author_name":"Clara Eunyoung Lee","author_inst":"University of Toronto"},{"author_name":"David Fisman","author_inst":"University of Toronto"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"infectious diseases"},{"rel_doi":"10.64898\/2026.03.19.712870","rel_title":"SARS-CoV-2 Defective Viral Genomes from Distinct Genomic Regions Drive Divergent Interferon Responses","rel_date":"2026-03-24","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.19.712870","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"microbiology"},{"rel_doi":"10.64898\/2026.03.20.26348947","rel_title":"Shifts in the pathogen spectrum and epidemiology of respiratory tract infections in the post-COVID-19 era: A study from Quzhou, Eastern China","rel_date":"2026-03-24","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.20.26348947","rel_abs":"BackgroundAlthough the relaxation of COVID-19 containment measures in China has altered the transmission dynamics of respiratory pathogens, regional data on post-pandemic epidemiological characteristics remain limited.\n\nObjectiveThis study aimed to investigate the pathogen spectrum and epidemiological characteristics of acute respiratory infections (ARIs) in Quzhou City from 2023 to 2024, providing a scientific basis for local prevention and control strategies.\n\nMethodsA total of 2,800 respiratory specimens were collected from November 2023 to July 2024, comprising 1,960 influenza-like illness (ILI) cases from outpatient\/emergency departments and 840 severe acute respiratory infection (SARI) cases from inpatient departments. All samples were tested for 13 common respiratory pathogens using multiplex fluorescence quantitative PCR. Etiological and epidemiological analyses were performed based on detection results and case information.\n\nResultsThe overall ARI positivity rate was 59.28% (1,660\/2,800), with a male-to-female ratio of 1.07:1 (1,447\/1,353). The three most prevalent pathogens were influenza virus (Flu, 23.21%, 650\/2,800), Streptococcus pneumoniae (SP, 13.14%, 368\/2,800), and adenovirus (ADV, 8.39%, 235\/2,800). Single pathogen infections accounted for 73.55% (1,221\/1,660) of positive cases, while co-infections with two or more pathogens accounted for 26.45% (439\/1,660), yielding an overall co-infection rate of 15.68% (439\/2,800). No significant gender difference was observed in detection rates. However, significant differences were found across case types, temporal periods, age groups, and geographic regions (P < 0.01). Children aged [&le;]5 years exhibited the highest positivity rate (78.00%, 378\/525), while adults aged [&ge;]65 years showed the lowest (34.53%, 144\/417). Among surveillance regions, Kaihua County had the highest positivity rate (72.47%), and Changshan County the lowest (40.55%).\n\nConclusionsMultiple respiratory pathogens and co-infections are prevalent in Quzhou City, with distinct age-specific and seasonal patterns. These findings underscore the need for continuous multi-pathogen surveillance and integrated prevention strategies for influenza and other respiratory infectious diseases in the post-pandemic era.","rel_num_authors":6,"rel_authors":[{"author_name":"Ruijun Yang","author_inst":"Quzhou Center for Disease Control and Prevention"},{"author_name":"Min Wang","author_inst":"Quzhou Center for Disease Control and Prevention"},{"author_name":"Lei Lyu","author_inst":"Quzhou Center for Disease Control and Prevention"},{"author_name":"Jialing You","author_inst":"Quzhou Center For Disease Control and Prevention"},{"author_name":"Shiteng Huang","author_inst":"Quzhou Center for Disease Control and Prevention"},{"author_name":"Bingdong Zhan","author_inst":"Quzhou Center for Disease Control and Prevention"}],"version":"1","license":"cc_by","type":"PUBLISHAHEADOFPRINT","category":"pathology"},{"rel_doi":"10.64898\/2026.03.16.26348020","rel_title":"Feasibility, Acceptability, and Cost of Community-Based Self-monitoring among Sex Workers Testing Positive for COVID-19 in Zimbabwe: A Mixed-methods Study.","rel_date":"2026-03-23","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.16.26348020","rel_abs":"BackgroundSex workers struggled to adhere to isolation guidelines following COVID-19 diagnosis because of financial pressure to keep working. We co-developed and evaluated for feasibility, acceptability, and cost an intervention for promoting isolation and community-based self-monitoring for COVID-19.\n\nMethodsSex workers testing positive for COVID-19 received the following co-developed intervention: i) risk-differentiated support, including immediate hospitalization and\/or treatment for serious illness, and community-based self-monitoring for those at risk of progressing to severe illness, ii) food packs lasting two weeks. Using Proctors Framework, we interviewed purposively selected health-workers and sex workers before intervention implementation (26 sex workers and 24 health workers) and during implementation (8 sex workers of whom 5 tested positive, and 5 health workers) to evaluate the intervention. We determined intervention development and implementation costs using program data.\n\nResultsThe intervention was implemented between March-June 2023. Sex workers and health workers reported that the intervention was highly acceptable and was implemented with fidelity. Food packs were highly appreciated; participants said they promoted isolation although vulnerability to non-food financial pressures persisted. Unanticipated impacts were increased testing uptake following introduction of food packs. Self-monitoring at home was acceptable although fear of stigma prevented some participants from seeking the needed support. The cost per sex worker testing positive was $49 and $54 respectively excluding\/including intervention co-development costs.\n\nConclusionA co-developed intervention for promoting isolation and community-based self-monitoring for COVID-19 was feasible and acceptable, with costs comparing favorably with similar interventions. Addressing stigma could optimise implementation and potential for future pandemics.","rel_num_authors":10,"rel_authors":[{"author_name":"Itai Kabonga","author_inst":"Centre for Sexual Health and HIV\/AIDS Research"},{"author_name":"Collin Mangenah","author_inst":"Centre for Sexual Health and HIV\/AIDS Research"},{"author_name":"Constancia Watadzaushe","author_inst":"Centre for Sexual Health and HIV\/AIDS Research"},{"author_name":"Claudius Madanhire","author_inst":"Centre for Sexual Health and HIV\/AIDS Research"},{"author_name":"Nancy Ruhode","author_inst":"Centre for Sexual Health and HIV\/AIDS Research"},{"author_name":"Yasmin Dunkley","author_inst":"London School of Hygiene & Tropical Medicine"},{"author_name":"Hatzold Karin","author_inst":"PSI: Population Services International"},{"author_name":"Elizabeth L. Corbett","author_inst":"London School of Hygiene & Tropical Medicine"},{"author_name":"Frances  M. Cowan","author_inst":"Liverpool School of Tropical Medicine"},{"author_name":"Euphemia L Sibanda","author_inst":"Centre for Sexual Health and HIV\/AIDS Research"}],"version":"1","license":"cc_by","type":"PUBLISHAHEADOFPRINT","category":"public and global health"},{"rel_doi":"10.64898\/2026.03.18.26348530","rel_title":"Microtesla Magnetic Therapy for cognitive impairment in post-acute sequelae of SARS CoV-2: A randomized controlled feasibility study","rel_date":"2026-03-23","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.18.26348530","rel_abs":"BackgroundCognitive impairment has significant implications for function and quality of life and is common in individuals with post-acute sequelae of SARS CoV-2, also known as long COVID (LC). Emerging evidence suggests that sustained neuroinflammation, cerebrovascular dysfunction, and mitochondrial impairment are contributors to cognitive symptoms. Microtesla Magnetic Therapy (MMT) is a low-amplitude radiofrequency magnetic field intervention that has demonstrated anti-inflammatory and neuroprotective effects in preclinical models, suggesting it may be valuable in the management of cognitive impairment from LC and other neurological disorders. This study is the first randomized controlled trial to evaluate MMT for LC-related cognitive impairment.\n\nObjectiveTo evaluate the feasibility, safety, and preliminary efficacy of an at-home MMT intervention in individuals with moderate-to-severe cognitive impairment from LC.\n\nMethodsIn this prospective feasibility study, 30 participants with LC-related cognitive impairment were randomized (2:1) to receive active or sham MMT. Participants self-administered 15-minute treatments at home with remote monitoring twice weekly for 4 weeks using a head-worn device that delivered a nonthermal radiofrequency magnetic field to the whole brain. Feasibility was defined as completion of at least 80% of prescribed treatments and all study visits. Secondary outcomes included safety, cognitive function, and self-reported mood and quality of life assessed at baseline, post-treatment (Week 4), and follow-up (Week 8).\n\nResultsFeasibility was high, with 100% treatment adherence among participants who completed the study and strong usability ratings for at-home administration. There were no device-related adverse events. Compared with sham, participants receiving active MMT showed significantly greater improvements from baseline to Week 8 in WAIS-IV Digit Span Sequencing (p= 0.026), HVLT-R Recall (p= 0.044), and D-KEFS Color Naming (p= 0.049). Additional measures of attention, processing speed, and executive function demonstrated favorable trends in the active group. Emotional well-being, assessed by the SF-36, improved significantly in the active group at Week 8 compared with sham (p= 0.017), and mood symptoms showed clinically meaningful improvement.\n\nConclusionsAdministration of the MMT intervention at home was feasible, safe, and well tolerated in individuals with cognitive impairment from LC. Preliminary findings suggest sustained clinically meaningful improvements in multiple cognitive domains and mood following treatment.\n\nTrial RegistrationClinicalTrials.gov NCT06739668, https:\/\/clinicaltrials.gov\/study\/NCT06739668, 2024-12-17","rel_num_authors":9,"rel_authors":[{"author_name":"Alexandra Canori","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Eric Watson","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Devanshi Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Arianna Fiorentino","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Christopher Santiago","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"David Maltz","author_inst":"Fareon, Inc"},{"author_name":"Blake Gurfein","author_inst":"Fareon, Inc"},{"author_name":"David Putrino","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Jacqueline Becker","author_inst":"Icahn School of Medicine at Mount Sinai"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"psychiatry and clinical psychology"},{"rel_doi":"10.64898\/2026.03.22.712163","rel_title":"Resistome, Virulome, Mobilome, And Biosynthetic Gene Clusters Adaptations of Acinetobacter Baumannii Mexican Strains Across the Pre- and of the COVID-19 Period: Insights from Whole-Genome Sequencing","rel_date":"2026-03-23","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.22.712163","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"microbiology"},{"rel_doi":"10.64898\/2026.03.19.26348833","rel_title":"Autoantibody landscapes in neurological Long COVID and post-COVID cognitive impairment show heterogeneity without a shared disease signature","rel_date":"2026-03-22","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.19.26348833","rel_abs":"BackgroundNeurological Long COVID (n-LC) includes persistent cognitive and autonomic symptoms after SARS-CoV-2 infection. Prior studies of post-COVID conditions have described diverse humoral autoreactivity, but findings are heterogeneous, and it remains unclear whether n-LC is associated with a consistent CNS-directed humoral signature.\n\nMethodsWe performed a cross-cohort case-control analysis to detect autoantibodies in cerebrospinal fluid (CSF) and serum from n-LC participants. In the Yale COVID Mind Study cohort, CSF from n-LC participants and from pre-pandemic and post-COVID asymptomatic controls was assessed by mouse brain immunofluorescence and proteome-wide phage immunoprecipitation sequencing (PhIP-Seq), with candidate reactivities evaluated by orthogonal assays and supervised modeling. In the Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (IDCRP EPICC) cohort, post-COVID sera collected prior to iPhone- or iPad-based cognitive screening were profiled by PhIP-Seq and compared between participants with and without cognitive impairment.\n\nResultsCSF immunoreactivity on mouse brain tissue was observed in both n-LC and controls, with similar overall frequencies, although n-LC participants more often showed nuclear-predominant staining patterns. PhIP-Seq identified sparse, largely patient-specific peptide reactivities to nuclear and neuronal proteins in CSF and serum. Supervised models provided limited discrimination between cases and controls. Candidate autoantigens had limited disease specificity on orthogonal testing. EPICC serum autoantibody profiling similarly failed to distinguish individuals with and without cognitive impairment.\n\nConclusionsAcross cohorts and compartments, n-LC did not exhibit a shared autoantibody signature. These findings support the absence of a dominant, common CNS autoantibody-mediated mechanism in n-LC.\n\nFundingGrants HU00012020067, HU00012120103, HU00011920111, R01NS125693, R01MH125737, R01AI157488 from Defense health program and NIH.","rel_num_authors":23,"rel_authors":[{"author_name":"Debanjana Chakravarty","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA; University of California San Fr"},{"author_name":"Ravi Dandekar","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA; University of California, San F"},{"author_name":"Vishal D. Lashkari","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco,  San Francisco,CA, USA; University of California, San F"},{"author_name":"Iris Tilton","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco,  San Francisco,CA, USA; University of California, San F"},{"author_name":"Lindsay McAlpine","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Jennifer Chiarella","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Allison Nelson","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Thomas Ngo","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA; University of California, San F"},{"author_name":"PeiXi Chen","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA; University of California, San F"},{"author_name":"Grace Wang","author_inst":"Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA"},{"author_name":"Aditi Saxena","author_inst":"Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA"},{"author_name":"Bryan Castillo-Rojas","author_inst":"Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA"},{"author_name":"Kelsey Zorn","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA; Department of Biochemistry and "},{"author_name":"David R. Tribble","author_inst":"Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesd"},{"author_name":"Timothy H. Burgess","author_inst":"Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesd"},{"author_name":"Leah H. Rubin","author_inst":"Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Department of Molecular and Comparative P"},{"author_name":"Stephanie A. Richard","author_inst":"Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesd"},{"author_name":"Brian K. Agan","author_inst":"Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesd"},{"author_name":"Simon D. Pollett","author_inst":"Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesd"},{"author_name":"Shelli Farhadian","author_inst":"Yale University School of Medicine, New Haven, CT, USA"},{"author_name":"Serena Spudich","author_inst":"Yale University School of Medicine, New Haven, CT, USA; Yale Center for Mind and Brain Health, New Haven, CT, USA"},{"author_name":"Samuel J. Pleasure","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA; University of California, San F"},{"author_name":"Michael R. Wilson","author_inst":"Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA; University of California, San F"}],"version":"1","license":"cc_no","type":"PUBLISHAHEADOFPRINT","category":"infectious diseases"},{"rel_doi":"10.64898\/2026.03.19.26348823","rel_title":"SARS-CoV-2 and the Pandemic Surge in Invasive Group A Streptococcal Disease","rel_date":"2026-03-22","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.19.26348823","rel_abs":"BackgroundMultiple countries reported unprecedented increases in invasive group A streptococcal (iGAS) disease following widespread SARS-CoV-2 circulation. Whether this surge reflects reduced pathogen exposure during non-pharmaceutical interventions (\"immunity debt\") or effects of SARS-CoV-2 infection on host immunity remains unresolved.\n\nMethodsWe conducted a population-based time-series analysis of weekly iGAS incidence in central Ontario, Canada (population {approx}11 million) from March 2011 through March 2024 (676 weeks). Using negative binomial panel regression, we modeled acute (2-week lagged) and cumulative SARS-CoV-2 exposure while adjusting for seasonality, secular trends, age, and sex. Population attributable fractions (PAFs) were estimated by counterfactual prediction. Specificity was assessed through negative control analyses (influenza, RSV). The immunity debt hypothesis was evaluated using cumulative streptococcal exposure as a predictor of iGAS.\n\nResultsAmong 2,906 iGAS episodes, 34.3% during the pandemic period were associated with acute SARS-CoV-2 effects (range by age group: 16.5-39.1%). Models incorporating cumulative SARS-CoV-2 burden showed markedly better fit ({Delta}AIC=-157.5); cumulative exposure was strongly associated with iGAS (IRR 1.193, 95% CI 1.151-1.235), increasing the estimated PAF to 66.7%. Cumulative effects were strongest in children (IRR 1.309). SARS-CoV-2 was comparably associated with non-invasive streptococcal disease, with no increase in invasion propensity. Cumulative streptococcal exposure was not protective (overall IRR 1.000, p=0.730); where significant, the association was positive, opposite to immunity debt predictions.\n\nConclusionsCumulative SARS-CoV-2 burden was strongly associated with pandemic-era iGAS incidence. Cumulative streptococcal exposure did not support the immunity debt hypothesis. These ecological findings are consistent with SARS-CoV-2-associated immune dysregulation and warrant individual-level confirmation.","rel_num_authors":6,"rel_authors":[{"author_name":"David Fisman","author_inst":"University of Toronto"},{"author_name":"Clara Eunyoung Lee","author_inst":"University of Toronto"},{"author_name":"Natalie Wilson","author_inst":"University of Toronto"},{"author_name":"Michelle Barton","author_inst":"Western University"},{"author_name":"Simran K Mann","author_inst":"University of Toronto"},{"author_name":"Ashleigh Tuite","author_inst":"Public Health Agency of Canada"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"infectious diseases"}]}