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Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning–based spectroscopic analysis

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Abstract

The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.

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Acknowledgements

We acknowledge the support from the Discipline Construction Project of Guangdong Medical University (4SG21022G).

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Author notes

  1. Qiuyue Fu and Yanjiao Zhang contributed equally to this work.

Authors and Affiliations

  1. Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China

    Qiuyue Fu, Peng Wang, Xun Qiu & Shaoxin Li

  2. School of Basic Medicine, Guangdong Medical University, Dongguan, 523808, China

    Yanjiao Zhang

  3. Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, 523808, Guangdong, China

    Jiang Pi & Junfa Xu

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

    Zhusheng Guo & Ya Huang

  5. Guangdong Provincial Key Laboratory of Molecular Diagnosis, Guangdong Medical University, Dongguan, 523808, Guangdong, China

    Yi Zhao

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  1. Qiuyue Fu
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Contributions

Junfa Xu and Shaoxin Li provided experimental design and technical assistance. Qiuyue Fu and Xun Qiu performed the experiments and analyzed data. Peng Wang constructed the CNN model construction. Qiuyue Fu and Yanjiao Zhang wrote this paper. Jiang Pi was responsible for manuscript revision. Ya Huang and Zhusheng Guo provided clinical bacterial samples. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Shaoxin Li or Junfa Xu.

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Fu, Q., Zhang, Y., Wang, P. et al. Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning–based spectroscopic analysis. Anal Bioanal Chem 413, 7401–7410 (2021). https://doi.org/10.1007/s00216-021-03691-z

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  • DOI: https://doi.org/10.1007/s00216-021-03691-z

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