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Comparative Study of Two Methods of Enteric Virus Detection and Enteric Virus Relationship with Bacterial Indicator in Poyang Lake, Jiangxi, China.

Xiaotong WenHuilie ZhengFang YuanHui ZhuDuyi KuangZhiqiang ShenYuanan LuZhaokang Yuan
Published in: International journal of environmental research and public health (2019)
Currently, water contaminated with fecal matter poses a threat to public health and safety. Thus, enteric viruses are tested for as a part of water quality indicator assays; however, enteric viruses have not yet been listed in the criteria. Effective and sensitive methods for detecting enteric viruses are required in order to increase water safety. This study utilized enteric viruses as possible alternative indicators of water quality to examine fresh water in six sites in Poyang Lake, Nanchang, Jiangxi Province. The presence of norovirus geno-groups II (NoV GII), enteroviruses (EoV) and adenoviruses (AdV) were determined using Tianjin's protocol and Hawaii's protocol during a six month period from 2016-2017. The former used an electropositive material method for viral concentration and Taqman-q reverse transcription polymerase chain reaction (RT-PCR) to detect enteric viruses; while the latter used a filtration-based method for viral concentration and RT-PCR for enteric virus detection. There is a statistically significant difference between Tianjin's method and Hawaii's method for the detection of enteric viruses, such as NoV GII, EoV, and AdV (n = 36, p < 0.001). The enteric viruses showed no significant positive correlation with bacteria indicators (n = 36, p > 0.05). These data stress the need for additional indicators when establishing water quality systems, and the possibility of using enteric viruses as water quality indicators. It has become essential to improve shortcomings in order to search for an adequate method to detect enteric viruses in water and to implement such method in water quality monitoring.
Keyphrases
  • water quality
  • public health
  • randomized controlled trial
  • real time pcr
  • genetic diversity
  • sars cov
  • high throughput
  • risk assessment
  • transcription factor
  • heavy metals
  • machine learning
  • deep learning
  • big data