Document Type
Journal Article
Publication Title
Microbiology Spectrum
Volume
11
Issue
2
PubMed ID
36877048
Publisher
American Society for Microbiology
School
School of Medical and Health Sciences
RAS ID
56684
Funders
National Natural Science Foundation of China (81871734, 82072380, 82272423, 31900022) / Key R&D Program of Jiangsu Province (BE2020646) / Research Foundation for Advanced Talents of Guangdong Provincial People’s Hospital (KJ012021097) / Xuzhou Key R&D Plan Social Development Project (no. KC22300) / Jiang-Su Qing-Lan Project (2020) / Science and Technology Innovation Team of Young Scientists (no. TD202001)
Abstract
Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-To-Treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenemsensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE: This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.
DOI
10.1128/spectrum.04126-22
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Lyu, J. W., Zhang, X. D., Tang, J. W., Zhao, Y. H., Liu, S. L., Zhao, Y., ... & Gu, B. (2023). Rapid prediction of multidrug-resistant klebsiella pneumoniae through deep learning analysis of sers spectra. Microbiology Spectrum, 11(2), Article e04126-22. https://doi.org/10.1128/spectrum.04126-22