Application of the second-generation sequencing technology of metagenomics in the detection of pathogens in respiratory patients

The mNGS successfully identified etiological agents in 60 of the 65 cases, compared to tNGS, which yielded positive results in 42 cases, and conventional laboratory cultures, which detected pathogens in 24 cases. At the bacterial genus level, mNGS discerned 9 genera, 11 species, and 92 isolates of pathogenic bacteria, whereas tNGS identified 8 genera, 8 species, and 71 isolates. Conventional methods were less sensitive, detecting only 6 genera, 7 species, and 33 isolates. In terms of fungal detection, mNGS identified 4 fungal species, tNGS detected 4 isolates of the Candida genus, and conventional methods identified 2 isolates of the same genus. Viral detection at the species level revealed 10 species and 46 isolates by mNGS, whereas tNGS detected only 3 species and 7 isolates. The area under the receiver operating characteristic curve (AUC) with 95% confidence intervals for diagnosing pulmonary infections was 0.818 (0.671 to 0.966) for mNGS, 0.668 (0.475 to 0.860) for tNGS, and 0.721 (0.545 to 0.897) for conventional culture.The mNGS demonstrates superior diagnostic efficacy and pathogen detection breadth in critically ill patients with respiratory infections, offering a significant advantage by reducing the time to diagnosis. The enhanced sensitivity and comprehensive pathogen profiling of mNGS underscore its potential as a leading diagnostic tool in clinical microbiology.

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Publication Details

Journal
Journal of Microbiological Methods
Published
2024-08-13
DOI
https://doi.org/10.1016/j.mimet.2024.107021
Citations
5
Primary Topic
Respiratory viral infections research
Type
article
Field-Weighted Citation Impact
1.93

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article

Application of the second-generation sequencing technology of metagenomics in the detection of pathogens in respiratory patients

Liang Zhou, Kai Sheng, Shuyu Fang, Ali Yang et al.
5 citations
Journal of Microbiological Methods
Respiratory viral infections research
1.93
article

Application of the second-generation sequencing technology of metagenomics in the detection of pathogens in respiratory patients

Liang Zhou, Kai Sheng, Shuyu Fang, Ali Yang, Danfeng Zhang
article en
5 citations

Abstract

The mNGS successfully identified etiological agents in 60 of the 65 cases, compared to tNGS, which yielded positive results in 42 cases, and conventional laboratory cultures, which detected pathogens in 24 cases. At the bacterial genus level, mNGS discerned 9 genera, 11 species, and 92 isolates of pathogenic bacteria, whereas tNGS identified 8 genera, 8 species, and 71 isolates. Conventional methods were less sensitive, detecting only 6 genera, 7 species, and 33 isolates. In terms of fungal detection, mNGS identified 4 fungal species, tNGS detected 4 isolates of the Candida genus, and conventional methods identified 2 isolates of the same genus. Viral detection at the species level revealed 10 species and 46 isolates by mNGS, whereas tNGS detected only 3 species and 7 isolates. The area under the receiver operating characteristic curve (AUC) with 95% confidence intervals for diagnosing pulmonary infections was 0.818 (0.671 to 0.966) for mNGS, 0.668 (0.475 to 0.860) for tNGS, and 0.721 (0.545 to 0.897) for conventional culture.The mNGS demonstrates superior diagnostic efficacy and pathogen detection breadth in critically ill patients with respiratory infections, offering a significant advantage by reducing the time to diagnosis. The enhanced sensitivity and comprehensive pathogen profiling of mNGS underscore its potential as a leading diagnostic tool in clinical microbiology.

Journal of Microbiological MethodsVol. 225
Shanghai Jiao Tong University (CN), Tongren Hospital (CN), Shanghai Sunshine Rehabilitation Center (CN)
Ministry of Science and Technology of the People's Republic of China
Openalex Percentile: Top 13%
Respiratory viral infections research
1.93
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Application of the second-generation sequencing technology of metagenomics in the detection of pathogens in respiratory patients — Liang Zhou, Kai Sheng, et al. · Journal of Microbiological Methods (2024) | TGRS Research Map | TGRS