{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T19:35:33Z","timestamp":1790624133452,"version":"4.1.0"},"reference-count":80,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001866","name":"Fonds National de la Recherche Luxembourg","doi-asserted-by":"publisher","award":["11823097"],"award-info":[{"award-number":["11823097"]}],"id":[{"id":"10.13039\/501100001866","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>While MALDI-TOF mass spectrometry (MS) is widely considered as the reference method for the rapid and inexpensive identification of microorganisms in routine laboratories, less attention has been addressed to its ability for detection of antimicrobial resistance (AMR). Recently, some studies assessed its potential application together with machine learning for the detection of AMR in clinical pathogens. The scope of this study was to investigate MALDI-TOF MS protein mass spectra combined with a prediction approach as an AMR screening tool for relevant foodborne pathogens, such as <jats:italic>Campylobacter coli<\/jats:italic> and <jats:italic>Campylobacter jejuni<\/jats:italic>. A One-Health panel of 224 <jats:italic>C. jejuni<\/jats:italic> and 116 <jats:italic>C. coli<\/jats:italic> strains was phenotypically tested for seven antimicrobial resistances, i.e., ciprofloxacin, erythromycin, tetracycline, gentamycin, kanamycin, streptomycin, and ampicillin, independently, and were submitted, after an on- and off-plate protein extraction, to MALDI Biotyper analysis, which yielded one average spectra per isolate and type of extraction. Overall, high performance was observed for classifiers detecting susceptible as well as ciprofloxacin- and tetracycline-resistant isolates. A maximum sensitivity and a precision of 92.3 and 81.2%, respectively, were reached. No significant prediction performance differences were observed between on- and off-plate types of protein extractions. Finally, three putative AMR biomarkers for fluoroquinolones, tetracyclines, and aminoglycosides were identified during the current study. Combination of MALDI-TOF MS and machine learning could be an efficient and inexpensive tool to swiftly screen certain AMR in foodborne pathogens, which may enable a rapid initiation of a precise, targeted antibiotic treatment.<\/jats:p>","DOI":"10.3389\/fmicb.2021.804484","type":"journal-article","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T06:44:40Z","timestamp":1645166680000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":70,"title":["Combination of MALDI-TOF Mass Spectrometry and Machine Learning for Rapid Antimicrobial Resistance Screening: The Case of Campylobacter spp."],"prefix":"10.3389","volume":"12","author":[{"given":"Maureen","family":"Feucherolles","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morgane","family":"Nennig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00f6ren L.","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Delphine","family":"Martiny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serge","family":"Losch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Penny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry-Michel","family":"Cauchie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Ragimbeau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.2903\/j.efsa.2019.5709","article-title":"Technical specifications on harmonised monitoring of antimicrobial resistance in zoonotic and indicator bacteria from food-producing animals and food.","volume":"17","author":"Aerts","year":"2019","journal-title":"EFSA J."},{"key":"B2","doi-asserted-by":"publisher","first-page":"361","DOI":"10.2217\/fmb-2017-0019","article-title":"Molecular antibiogram in diagnostic clinical microbiology: advantages and challenges.","volume":"12","author":"Arena","year":"2017","journal-title":"Future Microbiol."},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194212","article-title":"Rapid and easy detection of low-level resistance to vancomycin in methicillin-resistant Staphylococcus aureus by matrix-assisted laser desorption ionization time-of-flight mass spectrometry.","volume":"13","author":"Asakura","year":"2018","journal-title":"PLoS One"},{"key":"B4","doi-asserted-by":"publisher","first-page":"e51","DOI":"10.1093\/cid\/ciw118","article-title":"Implementing an antibiotic stewardship program: guidelines by the infectious diseases society of America and the society for healthcare epidemiology of America.","volume":"62","author":"Barlam","year":"2016","journal-title":"Clin. 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