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Wastewater-borne viruses and bacteria, surveillance and biosensors at the interface of academia and field deployment

Authors
Singh, RajendraRyu, JaewonLee, Woo HyoungKang, Joo-HyonPark, SanghwaKim, Keugtae
Issue Date
Feb-2025
Publisher
Taylor & Francis
Keywords
Wastewater; environmental surveillance; water; contamination; epidemics; human health; healthcare; virus; bacteria; biosensor
Citation
Critical Reviews in Biotechnology, v.45, no.2, pp 413 - 433
Pages
21
Indexed
SCIE
SCOPUS
Journal Title
Critical Reviews in Biotechnology
Volume
45
Number
2
Start Page
413
End Page
433
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/22429
DOI
10.1080/07388551.2024.2354709
ISSN
0738-8551
1549-7801
Abstract
Wastewater is a complex, but an ideal, matrix for disease monitoring and surveillance as it represents the entire load of enteric pathogens from a local catchment area. It captures both clinical and community disease burdens. Global interest in wastewater surveillance has been growing rapidly for infectious diseases monitoring and for providing an early warning of potential outbreaks. Although molecular detection methods show high sensitivity and specificity in pathogen monitoring from wastewater, they are strongly limited by challenges, including expensive laboratory settings and prolonged sample processing and analysis. Alternatively, biosensors exhibit a wide range of practical utility in real-time monitoring of biological and chemical markers. However, field deployment of biosensors is primarily challenged by prolonged sample processing and pathogen concentration steps due to complex wastewater matrices. This review summarizes the role of wastewater surveillance and provides an overview of infectious viral and bacterial pathogens with cutting-edge technologies for their detection. It emphasizes the practical utility of biosensors in pathogen monitoring and the major bottlenecks for wastewater surveillance of pathogens, and overcoming approaches to field deployment of biosensors for real-time pathogen detection. Furthermore, the promising potential of novel machine learning algorithms to resolve uncertainties in wastewater data is discussed. [GRAPHICS]
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