<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Rodríguez-Temporal</style></author><author><style face="normal" font="default" size="100%">Laura Herrera</style></author><author><style face="normal" font="default" size="100%">Fernando Alcaide</style></author><author><style face="normal" font="default" size="100%">Diego Domingo</style></author><author><style face="normal" font="default" size="100%">Genevieve Héry-Arnaud</style></author><author><style face="normal" font="default" size="100%">Jakko van Ingen</style></author><author><style face="normal" font="default" size="100%">An Van den Bossche</style></author><author><style face="normal" font="default" size="100%">André Ingebretsen</style></author><author><style face="normal" font="default" size="100%">Clémence Beauruelle</style></author><author><style face="normal" font="default" size="100%">Eva Terschlüsen</style></author><author><style face="normal" font="default" size="100%">Samira Boarbi</style></author><author><style face="normal" font="default" size="100%">Vila, Neus</style></author><author><style face="normal" font="default" size="100%">Manuel J Arroyo</style></author><author><style face="normal" font="default" size="100%">Gema Méndez</style></author><author><style face="normal" font="default" size="100%">Patricia Muñoz</style></author><author><style face="normal" font="default" size="100%">Luis Mancera</style></author><author><style face="normal" font="default" size="100%">María Jesús Ruiz-Serrano</style></author><author><style face="normal" font="default" size="100%">Belén Rodriguez-Sanchez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of Mycobacterium abscessus Subspecies by MALDI-TOF Mass Spectrometry and Machine Learning.</style></title><secondary-title><style face="normal" font="default" size="100%">J Clin Microbiol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Mycobacterium</style></keyword><keyword><style  face="normal" font="default" size="100%">Mycobacterium abscessus</style></keyword><keyword><style  face="normal" font="default" size="100%">Mycobacterium Infections, Nontuberculous</style></keyword><keyword><style  face="normal" font="default" size="100%">Nontuberculous Mycobacteria</style></keyword><keyword><style  face="normal" font="default" size="100%">Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023 Jan 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">61</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Mycobacterium abscessus is one of the most common and pathogenic nontuberculous mycobacteria (NTM) isolated in clinical laboratories. It consists of three subspecies: M. abscessus subsp. , M. abscessus subsp. , and M. abscessus subsp. . Due to their different antibiotic susceptibility pattern, a rapid and accurate identification method is necessary for their differentiation. Although matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has proven useful for NTM identification, the differentiation of M. abscessus subspecies is challenging. In this study, a collection of 325 clinical isolates of M. abscessus was used for MALDI-TOF MS analysis and for the development of machine learning predictive models based on MALDI-TOF MS protein spectra. Overall, using a random forest model with several confidence criteria (samples by triplicate and similarity values &amp;gt;60%), a total of 96.5% of isolates were correctly identified at the subspecies level. Moreover, an improved model with Spanish isolates was able to identify 88.9% of strains collected in other countries. In addition, differences in culture media, colony morphology, and geographic origin of the strains were evaluated, showing that the latter had an impact on the protein spectra. Finally, after studying all protein peaks previously reported for this species, two novel peaks with potential for subspecies differentiation were found. Therefore, machine learning methodology has proven to be a promising approach for rapid and accurate identification of subspecies of M. abscessus using MALDI-TOF MS.&lt;/p&gt;
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