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Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry
Colistin resistance is one of the major threats for global public health, requiring reliable and rapid susceptibility testing methods. The aim of this study was the evaluation of a MALDI-TOF mass spectrometry (MS) peak-based assay to distinguish colistin resistant (colR) from susceptible (colS) Esch...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623207/ https://www.ncbi.nlm.nih.gov/pubmed/34835336 http://dx.doi.org/10.3390/microorganisms9112210 |
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author | Calderaro, Adriana Buttrini, Mirko Farina, Benedetta Montecchini, Sara Martinelli, Monica Crocamo, Federica Arcangeletti, Maria Cristina Chezzi, Carlo De Conto, Flora |
author_facet | Calderaro, Adriana Buttrini, Mirko Farina, Benedetta Montecchini, Sara Martinelli, Monica Crocamo, Federica Arcangeletti, Maria Cristina Chezzi, Carlo De Conto, Flora |
author_sort | Calderaro, Adriana |
collection | PubMed |
description | Colistin resistance is one of the major threats for global public health, requiring reliable and rapid susceptibility testing methods. The aim of this study was the evaluation of a MALDI-TOF mass spectrometry (MS) peak-based assay to distinguish colistin resistant (colR) from susceptible (colS) Escherichia coli strains. To this end, a classifying algorithm model (CAM) was developed, testing three different algorithms: Genetic Algorithm (GA), Supervised Neural Network (SNN) and Quick Classifier (QC). Among them, the SNN- and GA-based CAMs showed the best performances: recognition capability (RC) of 100% each one, and cross validation (CV) of 97.62% and 100%, respectively. Even if both algorithms shared similar RC and CV values, the SNN-based CAM was the best performing one, correctly identifying 67/71 (94.4%) of the E. coli strains collected: in point of fact, it correctly identified the greatest number of colS strains (42/43; 97.7%), despite its lower ability in identifying the colR strains (15/18; 83.3%). In conclusion, although broth microdilution remains the gold standard method for testing colistin susceptibility, the CAM represents a useful tool to rapidly screen colR and colS strains in clinical practice. |
format | Online Article Text |
id | pubmed-8623207 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86232072021-11-27 Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry Calderaro, Adriana Buttrini, Mirko Farina, Benedetta Montecchini, Sara Martinelli, Monica Crocamo, Federica Arcangeletti, Maria Cristina Chezzi, Carlo De Conto, Flora Microorganisms Article Colistin resistance is one of the major threats for global public health, requiring reliable and rapid susceptibility testing methods. The aim of this study was the evaluation of a MALDI-TOF mass spectrometry (MS) peak-based assay to distinguish colistin resistant (colR) from susceptible (colS) Escherichia coli strains. To this end, a classifying algorithm model (CAM) was developed, testing three different algorithms: Genetic Algorithm (GA), Supervised Neural Network (SNN) and Quick Classifier (QC). Among them, the SNN- and GA-based CAMs showed the best performances: recognition capability (RC) of 100% each one, and cross validation (CV) of 97.62% and 100%, respectively. Even if both algorithms shared similar RC and CV values, the SNN-based CAM was the best performing one, correctly identifying 67/71 (94.4%) of the E. coli strains collected: in point of fact, it correctly identified the greatest number of colS strains (42/43; 97.7%), despite its lower ability in identifying the colR strains (15/18; 83.3%). In conclusion, although broth microdilution remains the gold standard method for testing colistin susceptibility, the CAM represents a useful tool to rapidly screen colR and colS strains in clinical practice. MDPI 2021-10-24 /pmc/articles/PMC8623207/ /pubmed/34835336 http://dx.doi.org/10.3390/microorganisms9112210 Text en © 2021 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 Calderaro, Adriana Buttrini, Mirko Farina, Benedetta Montecchini, Sara Martinelli, Monica Crocamo, Federica Arcangeletti, Maria Cristina Chezzi, Carlo De Conto, Flora Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry |
title | Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry |
title_full | Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry |
title_fullStr | Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry |
title_full_unstemmed | Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry |
title_short | Rapid Identification of Escherichia coli Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry |
title_sort | rapid identification of escherichia coli colistin-resistant strains by maldi-tof mass spectrometry |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623207/ https://www.ncbi.nlm.nih.gov/pubmed/34835336 http://dx.doi.org/10.3390/microorganisms9112210 |
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