Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra-Reference-Cited by-同舟云学术

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra

Published:2023-04-13 Issue:2 Volume:11 Page:
ISSN:2165-0497
Container-title:Microbiology Spectrum
language:en
Short-container-title:Microbiol Spectr

Author:

Lyu Jing-Wen¹²,Zhang Xue Di¹³,Tang Jia-Wei⁴,Zhao Yun-Hu²,Liu Su-Ling²,Zhao Yue²,Zhang Ni²,Wang Dan⁵,Ye Long²,Chen Xiao-Li²,Wang Liang²⁶,Gu Bing¹²

Affiliation:

1. Department of Laboratory Medicine, School of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu Province, China

2. Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China

3. Laboratory Medicine, The Affiliated Xuzhou Infectious Diseases Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China

4. Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Jiangsu Province, Xuzhou, China

5. Laboratory Medicine, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China

6. School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia

Abstract

This study focuses on the simultaneous discrimination and prediction ofKlebsiella pneumoniaestrains 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.

Funder

National Natural Science Foundation of China

Key Research and Development Project of Jiangsu Province

Research Foundation for Advanced Talents of Guangdong Provincial People's hospitali

Jiang-Su Qing-Lan Project

Science and Technology Innovation Team of Young Scientists

Xuzhou Key R&D Plan Social Development Project

Publisher

American Society for Microbiology

Subject

Infectious Diseases,Cell Biology,Microbiology (medical),Genetics,General Immunology and Microbiology,Ecology,Physiology

Link

https://journals.asm.org/doi/pdf/10.1128/spectrum.04126-22

Reference55 articles.

1. World Health Organization. 2017. WHO publishes list of bacteria for which new antibiotics are urgently needed. https://www.who.int/news/item/27-02-2017-who-publishes-list-of-bacteria-for-which-new-antibiotics-are-urgently-needed.

2. Genomic Features Associated with the Degree of Phenotypic Resistance to Carbapenems in Carbapenem-Resistant Klebsiella pneumoniae

3. The Epidemiology of Carbapenem-Resistant Enterobacteriaceae: The Impact and Evolution of a Global Menace

4. Framework for optimisation of the clinical use of colistin and polymyxin B: the Prato polymyxin consensus

5. Risk Factors for Treatment Failure of Polymyxin B Monotherapy for Carbapenem-Resistant Klebsiella pneumoniae Infections

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