Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms.

Autor:	Liu W; School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China., Tang JW; School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.; Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China., Mou JY; The First School of Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China., Lyu JW; Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China., Di YW; Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China., Liao YL; Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China., Luo YF; Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China., Li ZK; Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China., Wu X; School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China., Wang L; Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China.
Jazyk:	angličtina
Zdroj:	Frontiers in microbiology [Front Microbiol] 2023 Mar 08; Vol. 14, pp. 1101357. Date of Electronic Publication: 2023 Mar 08 (Print Publication: 2023).
DOI:	10.3389/fmicb.2023.1101357
Abstrakt:	Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E . coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E . coli . Therefore, discrimination of Shigella spp., from E . coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E . coli . As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E . coli strains and Shigella species (spp.), that is, S . dysenteriae , S . boydii , S . flexneri , and S . sonnei , based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E . coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E . coli , which facilitated its application potential for diarrheal prevention and control in clinical settings. Graphical abstract. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Liu, Tang, Mou, Lyu, Di, Liao, Luo, Li, Wu and Wang.)
Databáze:	MEDLINE
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