The influence of environmental factors on the detection and quantification of SARS-CoV-2 variants in dormitory wastewater at a primarily undergraduate institution

Microbiology Spectrum, Год журнала: 2025, Номер unknown

Опубликована: Янв. 10, 2025

ABSTRACT Testing for the causative agent of coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome 2 (SARS-CoV-2), has been crucial in tracking spread and informing public health decisions. Wastewater-based epidemiology helped to alleviate some strain testing through broader, population-level surveillance, applied widely on college campuses. However, questions remain about impact various sampling methods, target types, environmental factors, infrastructure variables SARS-CoV-2 detection. Here, we present a data set over 800 wastewater samples that sheds light influence variety these factors quantification using droplet digital PCR (ddPCR) from building-specific sewage infrastructure. We consistently quantified significantly higher number copies virus per liter nucleocapsid (N2) compared 1 (N1), regardless method (grab vs composite). further show dormitory-specific differences abundance, including correlations dormitory population size. Environmental like precipitation temperature little no load, with exception temperatures grab sample data. observed gene copy numbers Omicron variant than Delta within ductile iron pipes but difference abundance (N1 or N2) across three different pipe types our set. Our results indicate contextual should be considered when interpreting wastewater-based epidemiological IMPORTANCE viral RNA is shed by symptomatic asymptomatic infected individuals, allowing its genetic material detected wastewater. used measure several dormitories Appalachian State University campus examined quantification. Changes were based type, as well trends variants method. These highlight value applying data-inquiry practices this study better contextualize results.

Язык: Английский

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Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making

Viruses, Год журнала: 2025, Номер 17(1), С. 109 - 109

Опубликована: Янв. 15, 2025

Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater-based Surveillance (WBS), has proven a valuable strategy for studying the prevalence infectious diseases within populations time- resource-efficient manner, as samples are representative all cases catchment area, whether they clinically reported or not. However, analysis interpretation WBS datasets decision-making during public health emergencies, such COVID-19 pandemic, remains an area opportunity. In this article, database obtained from sampling at treatment plants (WWTPs) university campuses Monterrey Mexico City between 2021 2022 was used to train simple clustering- regression-based risk assessment models allow informed prevention control measures high-affluence facilities, even if working with low-dimensionality limited number observations. When dividing weekly data points based on seven-day average daily new were above certain threshold, resulting clustering model could differentiate weeks surges clinical reports periods them 87.9% accuracy rate. Moreover, provided satisfactory forecasts one week (80.4% accuracy) two (81.8%) into future. prediction (R2 = 0.80, MAPE 72.6%), likely because insufficient dimensionality database. Overall, while simple, WBS-supported can provide relevant insights decision-makers epidemiological outbreaks, regression algorithms using still be improved.

Язык: Английский

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Advanced nanosensors for smart healthcare

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 329 - 345

Опубликована: Янв. 1, 2025

Язык: Английский

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Recent Advances in Biosensor Technologies for Meat Production Chain

Foods, Год журнала: 2025, Номер 14(5), С. 744 - 744

Опубликована: Фев. 22, 2025

Biosensors are innovative and cost-effective analytical devices that integrate biological recognition elements (bioreceptors) with transducers to detect specific substances (biomolecules), providing a high sensitivity specificity for the rapid accurate point-of-care (POC) quantitative detection of selected biomolecules. In meat production chain, their application has gained attention due increasing demand enhanced food safety, quality assurance, fraud detection, regulatory compliance. can foodborne pathogens (Salmonella, Campylobacter, Shiga-toxin-producing E. coli/STEC, L. monocytogenes, etc.), spoilage bacteria indicators, contaminants (pesticides, dioxins, mycotoxins), antibiotics, antimicrobial resistance genes, hormones (growth promoters stress hormones), metabolites (acute-phase proteins as inflammation markers) at different modules along from livestock farming packaging in farm-to-fork (F2F) continuum. By real-time data biosensors enable early interventions, reducing health risks (foodborne outbreaks) associated contaminated meat/meat products or sub-standard products. Recent advancements micro- nanotechnology, microfluidics, wireless communication have further sensitivity, specificity, portability, automation biosensors, making them suitable on-site field applications. The integration blockchain Internet Things (IoT) systems allows acquired management, while artificial intelligence (AI) machine learning (ML) enables processing, analytics, input risk assessment by competent authorities. This promotes transparency traceability within fostering consumer trust industry accountability. Despite biosensors' promising potential, challenges such scalability, reliability complexity matrices, approval still main challenges. review provides broad overview most relevant aspects current state-of-the-art development, challenges, opportunities prospective applications regular use safety monitoring, clarifying perspectives.

Язык: Английский

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Academic institution extensive, building-by-building wastewater-based surveillance platform for SARS-CoV-2 monitoring, clinical data correlation, and potential national proxy

PLOS Global Public Health, Год журнала: 2025, Номер 5(5), С. e0003756 - e0003756

Опубликована: Май 9, 2025

In this work, we report on the performance of an extensive, building-by-building wastewater surveillance platform deployed across 38 locations largest private university system in Mexico, spanning 19 32 states, to detect SARS-CoV-2 genetic materials during COVID-19 pandemic. Sampling took place weekly from January 2021 and June 2022. Data 343 sampling sites was clustered by campus state evaluated through its correlation with seven-day average daily new cases each cluster. Statistically significant linear correlations (p-values below 0.05) were found 25 campuses 13 states. Moreover, evaluate effectiveness epidemiologic containment measures taken institution potential as representative points for future public health emergencies Monterrey Metropolitan Area, between viral loads samples be stronger Dulces Nombres, treatment plant city (Pearson coefficient: 0.6456, p-value: 6.36710 −8 ), than study 0.4860, 8.288x10 −5 ). However, when comparing data after urban mobility returned pre-pandemic levels, levels both became comparable (0.894 0.865 Nombres). This work provides a basic framework implementation analysis similar decentralized platforms address sanitary emergencies, allowing efficient return priority in-person activities while preventing becoming transmission hotspots.

Язык: Английский

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