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Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae

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Abstract

Carbapenem-resistant Enterobacteriaceae (CRE) is a major pathogen that poses a serious threat to human health. Unfortunately, currently, there are no effective measures to curb its rapid development. To address this, an in-depth study on the surface-enhanced Raman spectroscopy (SERS) of 22 strains of 7 categories of CRE using a gold silver composite SERS substrate was conducted. The residual networks with an attention mechanism to classify the SERS spectrum from three perspectives (pathogenic bacteria type, enzyme-producing subtype, and sensitive antibiotic type) were performed. The results show that the SERS spectrum measured by the composite SERS substrate was repeatable and consistent. The SERS spectrum of CRE showed varying degrees of species differences, and the strain difference in the SERS spectrum of CRE was closely related to the type of enzyme-producing subtype. The introduced attention mechanism improved the classification accuracy of the residual network (ResNet) model. The accuracy of CRE classification for different strains and enzyme-producing subtypes reached 94.0% and 96.13%, respectively. The accuracy of CRE classification by pathogen sensitive antibiotic combination reached 93.9%. This study is significant for guiding antibiotic use in CRE infection, as the sensitive antibiotic used in treatment can be predicted directly by measuring CRE spectra. Our study demonstrates the potential of combining SERS with deep learning algorithms to identify CRE without culture labels and classify its sensitive antibiotics. This approach provides a new idea for rapid and accurate clinical detection of CRE and has important significance for alleviating the rapid development of resistance to CRE.

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The author confirms that all data generated or analyzed during the course of this study have been included in this article. The network model code involved in this article can be obtained through consultation with the corresponding author.

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Funding

This work was supported by the National Natural Science Foundation of China (Nos. 21876116, 82372353, and 81974299), the Guangdong Natural Science Foundation of China (No. 2021A1515011733 and 2023A1515010636), the GuangDong Basic and Applied Basic Research Foundation (2023A1515010938), the Guangdong Medical Science and Technology Research Found Project (B2023415), the Dongguan Science and Technology Commissioner Project (20231800500552), and the Social Development Science and Technology Project in Dongguan City (20211800900702).

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Author notes

  1. Wen Wang, Xin Wang, and Ya Huang contributed equally to this work.

Authors and Affiliations

  1. Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Guangdong Medical University Dongguan First Affiliated Hospital, School of Medical Technology, Guangdong Medical University, Dongguan, 523808, Guangdong, China

    Wen Wang, Xin Wang, Xianglin Fang, Yanguang Cong, Luzhu Chen, Jingyi Zhong, Ruoyi Li & Shaoxin Li

  2. Donghua Hospital Laboratory Department, Dongguan, 523808, Guangdong, China

    Ya Huang & Zhusheng Guo

  3. Dongguan Key Laboratory of Environmental Medicine, School of Basic Medicine, Guangdong Medical University, Dongguan, 523808, Guangdong, China

    Yi Zhao, Zhi Tang & Yanjiao Zhang

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  1. Wen Wang
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  2. Xin Wang
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Contributions

Shaoxin Li, Zhusheng Guo, and Yanjiao Zhang provided experimental design and technical assistance; Wen Wang and Ya Huang performed the experiments and analyzed data; Xin Wang and Xianglin Fang constructed the deep learning model; Wen Wang, Xin Wang, and Ya Huang wrote this paper; Luzhu Chen, Jingyi Zhong, and Ruoyi Li conducted preliminary research on the project; Ya Huang and Zhusheng Guo provided clinical bacterial samples; Yi Zhao provided technical assistance; Zhi Tang and Yanguang Cong were responsible for reviewing the manuscript. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Zhusheng Guo, Yanjiao Zhang or Shaoxin Li.

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Wang, W., Wang, X., Huang, Y. et al. Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae. Anal Bioanal Chem 416, 2465–2478 (2024). https://doi.org/10.1007/s00216-024-05209-9

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