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Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study

In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phen...

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Autores principales: Liu, Wei, Tang, Jia-Wei, Lyu, Jing-Wen, Wang, Jun-Jiao, Pan, Ya-Cheng, Shi, Xin-Yi, Liu, Qing-Hua, Zhang, Xiao, Gu, Bing, Wang, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809336/
https://www.ncbi.nlm.nih.gov/pubmed/35107359
http://dx.doi.org/10.1128/spectrum.02409-21
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author Liu, Wei
Tang, Jia-Wei
Lyu, Jing-Wen
Wang, Jun-Jiao
Pan, Ya-Cheng
Shi, Xin-Yi
Liu, Qing-Hua
Zhang, Xiao
Gu, Bing
Wang, Liang
author_facet Liu, Wei
Tang, Jia-Wei
Lyu, Jing-Wen
Wang, Jun-Jiao
Pan, Ya-Cheng
Shi, Xin-Yi
Liu, Qing-Hua
Zhang, Xiao
Gu, Bing
Wang, Liang
author_sort Liu, Wei
collection PubMed
description In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phenotypic resistance. Thus, fast and accurate methods for the analysis of bacterial antibiotic resistance are in high demand for clinical applications. In this pilot study, we isolated 7 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains and 8 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from clinical samples. Surface-enhanced Raman spectroscopy (SERS) as a label-free and noninvasive method was employed for discriminating CSKP strains from CRKP strains through computational analysis. Eight supervised machine learning algorithms were applied for sample analysis. According to the results, all supervised machine learning methods could successfully predict carbapenem sensitivity and resistance in K. pneumoniae, with a convolutional neural network (CNN) algorithm on top of all other methods. Taken together, this pilot study confirmed the application potentials of surface-enhanced Raman spectroscopy in fast and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. IMPORTANCE With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.
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spelling pubmed-88093362022-02-09 Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study Liu, Wei Tang, Jia-Wei Lyu, Jing-Wen Wang, Jun-Jiao Pan, Ya-Cheng Shi, Xin-Yi Liu, Qing-Hua Zhang, Xiao Gu, Bing Wang, Liang Microbiol Spectr Research Article In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phenotypic resistance. Thus, fast and accurate methods for the analysis of bacterial antibiotic resistance are in high demand for clinical applications. In this pilot study, we isolated 7 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains and 8 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from clinical samples. Surface-enhanced Raman spectroscopy (SERS) as a label-free and noninvasive method was employed for discriminating CSKP strains from CRKP strains through computational analysis. Eight supervised machine learning algorithms were applied for sample analysis. According to the results, all supervised machine learning methods could successfully predict carbapenem sensitivity and resistance in K. pneumoniae, with a convolutional neural network (CNN) algorithm on top of all other methods. Taken together, this pilot study confirmed the application potentials of surface-enhanced Raman spectroscopy in fast and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. IMPORTANCE With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. American Society for Microbiology 2022-02-02 /pmc/articles/PMC8809336/ /pubmed/35107359 http://dx.doi.org/10.1128/spectrum.02409-21 Text en Copyright © 2022 Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Liu, Wei
Tang, Jia-Wei
Lyu, Jing-Wen
Wang, Jun-Jiao
Pan, Ya-Cheng
Shi, Xin-Yi
Liu, Qing-Hua
Zhang, Xiao
Gu, Bing
Wang, Liang
Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study
title Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study
title_full Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study
title_fullStr Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study
title_full_unstemmed Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study
title_short Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study
title_sort discrimination between carbapenem-resistant and carbapenem-sensitive klebsiella pneumoniae strains through computational analysis of surface-enhanced raman spectra: a pilot study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809336/
https://www.ncbi.nlm.nih.gov/pubmed/35107359
http://dx.doi.org/10.1128/spectrum.02409-21
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