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Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms

Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic...

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Autores principales: Wang, Hsin-Yao, Kuo, Chi-Heng, Chung, Chia-Ru, Lin, Wan-Ying, Wang, Yu-Chiang, Lin, Ting-Wei, Yu, Jia-Ruei, Lu, Jang-Jih, Wu, Ting-Shu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9856018/
https://www.ncbi.nlm.nih.gov/pubmed/36672552
http://dx.doi.org/10.3390/biomedicines11010045
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author Wang, Hsin-Yao
Kuo, Chi-Heng
Chung, Chia-Ru
Lin, Wan-Ying
Wang, Yu-Chiang
Lin, Ting-Wei
Yu, Jia-Ruei
Lu, Jang-Jih
Wu, Ting-Shu
author_facet Wang, Hsin-Yao
Kuo, Chi-Heng
Chung, Chia-Ru
Lin, Wan-Ying
Wang, Yu-Chiang
Lin, Ting-Wei
Yu, Jia-Ruei
Lu, Jang-Jih
Wu, Ting-Shu
author_sort Wang, Hsin-Yao
collection PubMed
description Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic susceptibility testing (AST). Delayed effective antibiotics administration often causes unfavorable outcomes. Thus, we proposed a novel approach to identify subspecies and potential antibiotic resistance, guiding early and accurate treatment. Subspecies of MABC isolates were determined by secA1, rpoB, and hsp65. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI–TOF MS) spectra were analyzed, and informative peaks were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on spectrum. The models were validated by repeated five-fold cross-validation to avoid over-fitting. In total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072–0.9196) and outperformed other algorithms in discriminating abscessus from massiliense. We developed a MALDI–TOF based ML model for rapid and accurate MABC subspecies identification. Due to the significant correlation between subspecies and corresponding antibiotics resistance, this diagnostic tool guides a more precise and timelier MABC subspecies-specific treatment.
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spelling pubmed-98560182023-01-21 Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms Wang, Hsin-Yao Kuo, Chi-Heng Chung, Chia-Ru Lin, Wan-Ying Wang, Yu-Chiang Lin, Ting-Wei Yu, Jia-Ruei Lu, Jang-Jih Wu, Ting-Shu Biomedicines Article Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic susceptibility testing (AST). Delayed effective antibiotics administration often causes unfavorable outcomes. Thus, we proposed a novel approach to identify subspecies and potential antibiotic resistance, guiding early and accurate treatment. Subspecies of MABC isolates were determined by secA1, rpoB, and hsp65. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI–TOF MS) spectra were analyzed, and informative peaks were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on spectrum. The models were validated by repeated five-fold cross-validation to avoid over-fitting. In total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072–0.9196) and outperformed other algorithms in discriminating abscessus from massiliense. We developed a MALDI–TOF based ML model for rapid and accurate MABC subspecies identification. Due to the significant correlation between subspecies and corresponding antibiotics resistance, this diagnostic tool guides a more precise and timelier MABC subspecies-specific treatment. MDPI 2022-12-25 /pmc/articles/PMC9856018/ /pubmed/36672552 http://dx.doi.org/10.3390/biomedicines11010045 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Hsin-Yao
Kuo, Chi-Heng
Chung, Chia-Ru
Lin, Wan-Ying
Wang, Yu-Chiang
Lin, Ting-Wei
Yu, Jia-Ruei
Lu, Jang-Jih
Wu, Ting-Shu
Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms
title Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms
title_full Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms
title_fullStr Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms
title_full_unstemmed Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms
title_short Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms
title_sort rapid and accurate discrimination of mycobacterium abscessus subspecies based on matrix-assisted laser desorption ionization-time of flight spectrum and machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9856018/
https://www.ncbi.nlm.nih.gov/pubmed/36672552
http://dx.doi.org/10.3390/biomedicines11010045
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