| Autor: |
Adriana Calderaro, Mirko Buttrini, Benedetta Farina, Sara Montecchini, Monica Martinelli, Federica Crocamo, Maria Cristina Arcangeletti, Carlo Chezzi, Flora De Conto |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Microorganisms, Vol 9, Iss 11, p 2210 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2076-2607 |
| DOI: |
10.3390/microorganisms9112210 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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