Integrating quantitative PCR and Bayesian statistics in quantifying human adenoviruses in small volumes of source water.

Autor: Wu J; Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, United States. Electronic address: jianyong.wu@alumni.unc.edu., Gronewold AD; NOAA, Great Lakes Environmental Research Laboratory, Ann Arbor, MI 48108, United States., Rodriguez RA; Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, United States., Stewart JR; Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, United States., Sobsey MD; Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, United States.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2014 Feb 01; Vol. 470-471, pp. 255-62. Date of Electronic Publication: 2013 Oct 18.
DOI: 10.1016/j.scitotenv.2013.09.026
Abstrakt: Rapid quantification of viral pathogens in drinking and recreational water can help reduce waterborne disease risks. For this purpose, samples in small volume (e.g. 1L) are favored because of the convenience of collection, transportation and processing. However, the results of viral analysis are often subject to uncertainty. To overcome this limitation, we propose an approach that integrates Bayesian statistics, efficient concentration methods, and quantitative PCR (qPCR) to quantify viral pathogens in water. Using this approach, we quantified human adenoviruses (HAdVs) in eighteen samples of source water collected from six drinking water treatment plants. HAdVs were found in seven samples. In the other eleven samples, HAdVs were not detected by qPCR, but might have existed based on Bayesian inference. Our integrated approach that quantifies uncertainty provides a better understanding than conventional assessments of potential risks to public health, particularly in cases when pathogens may present a threat but cannot be detected by traditional methods.
(© 2013 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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