Rapid Identification of <i>Escherichia coli</i> 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) <...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Adriana Calderaro, Mirko Buttrini, Benedetta Farina, Sara Montecchini, Monica Martinelli, Federica Crocamo, Maria Cristina Arcangeletti, Carlo Chezzi, Flora De Conto
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	colistin resistance MALDI-TOF MS Gram-negative bacteria Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/acacbd473dd64aa19e94ce9e316bc40e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:acacbd473dd64aa19e94ce9e316bc40e
record_format	dspace
spelling	oai:doaj.org-article:acacbd473dd64aa19e94ce9e316bc40e2021-11-25T18:24:16ZRapid Identification of <i>Escherichia coli</i> Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry10.3390/microorganisms91122102076-2607https://doaj.org/article/acacbd473dd64aa19e94ce9e316bc40e2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-2607/9/11/2210https://doaj.org/toc/2076-2607Colistin 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) <i>Escherichia coli</i> 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 <i>E. coli</i> 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.Adriana CalderaroMirko ButtriniBenedetta FarinaSara MontecchiniMonica MartinelliFederica CrocamoMaria Cristina ArcangelettiCarlo ChezziFlora De ContoMDPI AGarticlecolistin resistanceMALDI-TOF MSGram-negative bacteriaBiology (General)QH301-705.5ENMicroorganisms, Vol 9, Iss 2210, p 2210 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	colistin resistance MALDI-TOF MS Gram-negative bacteria Biology (General) QH301-705.5
spellingShingle	colistin resistance MALDI-TOF MS Gram-negative bacteria Biology (General) QH301-705.5 Adriana Calderaro Mirko Buttrini Benedetta Farina Sara Montecchini Monica Martinelli Federica Crocamo Maria Cristina Arcangeletti Carlo Chezzi Flora De Conto Rapid Identification of <i>Escherichia coli</i> Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry
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) <i>Escherichia coli</i> 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 <i>E. coli</i> 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	article
author	Adriana Calderaro Mirko Buttrini Benedetta Farina Sara Montecchini Monica Martinelli Federica Crocamo Maria Cristina Arcangeletti Carlo Chezzi Flora De Conto
author_facet	Adriana Calderaro Mirko Buttrini Benedetta Farina Sara Montecchini Monica Martinelli Federica Crocamo Maria Cristina Arcangeletti Carlo Chezzi Flora De Conto
author_sort	Adriana Calderaro
title	Rapid Identification of <i>Escherichia coli</i> Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry
title_short	Rapid Identification of <i>Escherichia coli</i> Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry
title_full	Rapid Identification of <i>Escherichia coli</i> Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry
title_fullStr	Rapid Identification of <i>Escherichia coli</i> Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry
title_full_unstemmed	Rapid Identification of <i>Escherichia coli</i> Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry
title_sort	rapid identification of <i>escherichia coli</i> colistin-resistant strains by maldi-tof mass spectrometry
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/acacbd473dd64aa19e94ce9e316bc40e
work_keys_str_mv	AT adrianacalderaro rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry AT mirkobuttrini rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry AT benedettafarina rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry AT saramontecchini rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry AT monicamartinelli rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry AT federicacrocamo rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry AT mariacristinaarcangeletti rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry AT carlochezzi rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry AT floradeconto rapididentificationofiescherichiacoliicolistinresistantstrainsbymalditofmassspectrometry
_version_	1718411217613094912

Rapid Identification of <i>Escherichia coli</i> Colistin-Resistant Strains by MALDI-TOF Mass Spectrometry

Ejemplares similares