抗菌剂
微生物学
尿
拉曼散射
拉曼光谱
医学
生物
光学
内科学
物理
作者
Weifeng Zhang,Xun Chen,Jing Zhang,Xiangmei Chen,Liqun Zhou,Pu Wang,Wei-Li Hong
标识
DOI:10.1016/j.medntd.2022.100132
摘要
Urinary tract infection with mixed microorganisms may lead to false-positive resistance detection. Current antimicrobial susceptibility testing (AST) performed in clinical laboratories is based on bacterial culture and takes a long time for mixed bacterial infections. Here, we propose a machine learning-based single-cell metabolism inactivation concentration (ML-MIC) model to achieve rapid AST for mixed bacterial infections. Using E. coli and S. aureus as a demonstration of mixed bacteria, we performed feature extraction and multi-feature analysis on stimulated Raman scattering (SRS) images of bacteria with the ML-MIC model to determine the subtypes and AST of the mixed bacteria. Furthermore, we assessed the AST of mixed bacteria in urine and obtained single-cell metabolism inactivation concentration in only 3 h. Collectively, we demonstrated that SRS imaging of bacterial metabolism can be extended to mixed bacterial infection cases for rapid AST.
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