Biology subjects

Winston, J.

Publications and source records attributed to Winston, J..

3 recordsLinked to original sources

bioRxiv2026

New Human IPSC Models of Late-onset Alzheimer's Disease Polygenic Risk Identify Multiple Impairments of Microglial Function

Genetic discoveries implicate microglia in late-onset Alzheimers disease (AD). We modelled AD in a powerful study of 51 human induced pluripotent stem cell (iPSC) microglia derived from high-polygenic risk AD or low-risk cognitively well individuals, sampled from a large cohort. We explored mitochondrial function, cytokine secretion, endocytosis, phagocytosis, lipid accumulation, calcium store release, and chemotaxis under basal conditions and immune challenge. High polygenic risk was independently associated with significant functional deficits in iPSC microglia under immune challenge, in mitochondrial ATP production (p=0.005, -13%), and inflammatory cytokine release (IL-6: p=0.018, -42%; TNF p=0.026, -38.5%). Furthermore, a selective deficit in amyloid-{beta} uptake was identified (p=0.00477, -5.9%). Deficits in inflammatory cytokine release were driven by APOE {varepsilon}4. These findings reflect primary changes in AD pathogenesis predating plaque formation and validate a human in vitro platform for late-onset AD to further understand disease mechanisms and screen drug or genetic therapies.

neuroscience↗

bioRxiv2022

Surveillance for SARS-CoV-2 in Ohio's wildlife, companion, and agricultural animals

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in humans in late 2019 and spread rapidly to become a global pandemic. A zoonotic spillover event from animal to human was identified as the presumed origin. Subsequently, reports began emerging regarding spillback events resulting in SARS-CoV-2 infections in multiple animal species. These events highlighted critical links between animal and human health while also raising concerns about the development of new reservoir hosts and potential viral mutations that could alter virulence and transmission or evade immune responses. Characterizing susceptibility, prevalence, and transmission between animal species became a priority to help protect animal and human health. In this study, we coalesced a large team of investigators and community partners to surveil for SARS-CoV-2 in domestic and free-ranging animals around Ohio between May 2020 and August 2021. We focused on species with known or predicted susceptibility to SARS-CoV-2 infection, highly congregated or medically compromised animals (e.g. shelters, barns, veterinary hospitals), and animals that had frequent contact with humans (e.g. pets, agricultural animals, zoo animals, or animals in wildlife hospitals). This included free-ranging deer (n=76), mink (n=57), multiple species of bats (n=65), and other wildlife in addition to domestic cats (n=275) and pigs (n= 184). In total, we tested 800 animals (34 species) via rRT-PCR for SARS-CoV-2 RNA. SARS-CoV-2 viral RNA was not detected in any of the tested animals despite a major peak in human SARS-CoV-2 cases that occurred in Ohio subsequent to the peak of animal samplings. Importantly, due to lack of validated tests for animals, we did not test for SARS-CoV-2 antibodies in this study, which limited our ability to assess exposure. While the results of this study were negative, the surveillance effort was critical and remains key to understanding, predicting, and preventing re-emergence of SARS-CoV-2 in humans or animals.

microbiology↗

bioRxiv2018

Improving preterm newborn identification in low-resource settings with machine learning

BackgroundGlobally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings.\n\nMethods and FindingsThis study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3 - 40.3). Among assessed newborns with complete data included in this analysis (n = 458), the median GA by ultrasound was 39.6 weeks (IQR: 38.4 - 40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796.\n\nConclusionsWe identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA.

bioinformatics↗