Evaluation of a Most Probable Number Method for Detection and Quantification of Legionella pneumophila

Author:

Niu Chunyan,Zhang YajieORCID,Zhang Yong

Abstract

The detection and enumeration of Legionella pneumophila (L. pneumophila) in water is crucial for water quality management, human health and has been a research hotspot worldwide. Due to the time-consuming and complicated operation of the plate culture method, it is necessary to adopt a fast and effective method for application. The present study aimed to comprehensively evaluate the performance and applicability of the MPN method by comparing its qualitative and quantitative results with the GB/T 18204.5-2013 and ISO methods, respectively. The qualitative results showed that 372 samples (53%) were negative for both methods; 315 samples (45%) were positively determined by the MPN method, compared with 211 samples (30%) using GB/T 18204.5-2013. The difference in the detection rate between the two methods was statistically significant. In addition, the quantitative results showed that the concentration of L. pneumophila by the MPN method was greater than ISO 11731 and the difference was statistically significant. However, the two methods were different but highly correlated (r = 0.965, p < 0.001). The specificity and sensitivity of the MPN method were 89.85% and 95.73%, respectively. Overall, the results demonstrated that the MPN method has higher sensitivity, a simple operation process and good application prospects in the routine monitoring of L. pneumophila from water samples.

Publisher

MDPI AG

Subject

Infectious Diseases,Microbiology (medical),General Immunology and Microbiology,Molecular Biology,Immunology and Allergy

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3