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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...

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Autores principales: Calderaro, Adriana, Buttrini, Mirko, Farina, Benedetta, Montecchini, Sara, Martinelli, Monica, Crocamo, Federica, Arcangeletti, Maria Cristina, Chezzi, Carlo, De Conto, Flora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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