{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:27:41Z","timestamp":1793132861827,"version":"4.5.0"},"reference-count":21,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:00:00Z","timestamp":1742947200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Science, Research and Innovation Fund","award":["4295682"],"award-info":[{"award-number":["4295682"]}]},{"name":"Research and Diagnostic Center for Emerging Infectious Diseases","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p><jats:italic>Mycobacterium abscessus<\/jats:italic> (<jats:italic>M. abscessus<\/jats:italic>) infection is a significant public-health concern due to its resistance to multiple antibiotics and associated treatment challenges. There is a pressing need for a rapid and effective method capable of reliably identifying <jats:italic>M. abscessus<\/jats:italic> drug resistance. Our study aimed to investigate the capacity of matrix-assisted laser desorption\/ionization\u2013time-of-flight mass spectrometry (MALDI-TOF MS) to identify <jats:italic>M. abscessus<\/jats:italic> drug-resistant isolates, offering potential proteomic spectrum markers for detecting resistant strains in clinical diagnosis and treatment. With the aid of machine learning, particularly the decision-tree algorithm, predictive models demonstrated excellent performance with 100% sensitivity and specificity. Peaks at 4,062\u2009Da, 7,518\u2009Da, 8,359\u2009Da and 2,493\u2009Da were potential biomarkers that can distinguish between phenotypes resistant or susceptible to amikacin, linezolid, clarithromycin and cefoxitin, respectively. Besides diagnosing these phenotypes, the combination of machine learning and MALDI-TOF can identify patterns of resistance and susceptibility to various drugs in serially sampled isolates. In an analysis of nine serially collected samples from a single patient, MALDI-TOF could differentiate between <jats:italic>M. abscessus<\/jats:italic> strains resistant to three drugs\u2014amikacin, linezolid and clarithromycin\u2014and those completely susceptible to these drugs, based on distinct peak intensities. Furthermore, alterations in the patterns of amikacin and clarithromycin resistance\/susceptibility influenced the MALDI-TOF spectra in serial isolates, whereas changes in susceptibility to linezolid did not affect the patterns. Hence, MALDI-TOF could be considered an efficient and dependable method for identifying <jats:italic>M. abscessus<\/jats:italic> drug resistance. This diagnostic tool has the potential to streamline the traditionally lengthy process of antimicrobial susceptibility testing while maintaining reliable results.<\/jats:p>","DOI":"10.1371\/journal.pone.0319809","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:35:48Z","timestamp":1743010548000},"page":"e0319809","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":9,"title":["MALDI-TOF mass spectrometry discriminates drug-susceptible and -resistant strains in Mycobacterium abscessus"],"prefix":"10.1371","volume":"20","author":[{"given":"Tran 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