Novel Clinical mNGS-Based Machine Learning Model for Rapid Antimicrobial Susceptibility Testing of Acinetobacter baumannii-Reference-Cited by-同舟云学术

Novel Clinical mNGS-Based Machine Learning Model for Rapid Antimicrobial Susceptibility Testing of Acinetobacter baumannii

Published:2023-05-23 Issue:5 Volume:61 Page:
ISSN:0095-1137
Container-title:Journal of Clinical Microbiology
language:en
Short-container-title:J Clin Microbiol

Author:

Hu Xuejiao¹^ORCID,Zhao Yunhu¹^ORCID,Han Peng²,Liu Suling¹,Liu Weijiang¹,Mai Cong³,Deng Qianyun¹,Ren Jing⁴,Luo Jiajie²,Chen Fangyuan²,Jia Xuefeng²,Zhang Jing²,Rao Guanhua²,Gu Bing¹^ORCID

Affiliation:

1. Department of Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

2. Genskey Medical Technology Co., Ltd., Beijing, China

3. Department of Critical Care Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

4. Tianjin Medical University General Hospital, Tianjin, China

Abstract

Multidrug-resistant (MDR) bacteria are important public health problems. Antibiotic susceptibility testing (AST) currently uses time-consuming culture-based procedures, which cause treatment delays and increased mortality. We developed a machine learning model using Acinetobacter baumannii as an example to explore a fast AST approach using metagenomic next-generation sequencing (mNGS) data.

Funder

National science foundation of china

Young S&T Talent Training Program of Guangdong Provincial Association for S&T

Rasearch Foundation for advanced talents of Guangdong Provincial People’s Hospital

Basic and Applied Basic Research Foundation of Guangdong Province

Science and Technology Program of Guangzhou

Publisher

American Society for Microbiology

Subject

Microbiology (medical)

Link

https://journals.asm.org/doi/pdf/10.1128/jcm.01805-22