{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T23:17:13Z","timestamp":1789168633264,"version":"build-2803163510"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Next Generation EU-MUR PNRR Extended Partnership initiative on Emerging Infectious Diseases","award":["PE00000007"],"award-info":[{"award-number":["PE00000007"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Pathogens"],"abstract":"<jats:p>Trichophyton indotineae is an emerging dermatophyte species responsible for recalcitrant and terbinafine-resistant dermatophytosis, raising concerns over diagnostic accuracy and treatment efficacy. This study aimed to improve the identification and resistance profiling of T. indotineae by integrating molecular methods with machine learning-assisted analysis of MALDI-TOF mass spectra. A total of 56 clinical isolates within the Trichophyton mentagrophytes complex were analyzed using ITS and ERG1 gene sequencing, antifungal susceptibility testing, and MALDI-TOF MS profiling. Terbinafine resistance was detected in 23 isolates and correlated with specific ERG1 mutations, including F397L, L393S, F415C, and A448T. While conventional MALDI-TOF MS failed to reliably distinguish T. indotineae from closely related species, unsupervised statistical methods (PCA and hierarchical clustering) revealed distinct spectral groupings. Supervised machine learning algorithms, particularly PLS-DA and SVM, achieved 100% balanced accuracy in species classification using 10-fold cross-validation. Biomarker analysis identified discriminatory spectral peaks for both T. indotineae and T. mentagrophytes (3417.29 m\/z and 3423.53 m\/z). These results demonstrate that combining MALDI-TOF MS with multivariate analysis and machine learning improves diagnostic resolution and may offer a practical alternative to sequencing in resource-limited settings. This approach could enhance the routine detection of terbinafine-resistant T. indotineae and support more targeted antifungal therapy.<\/jats:p>","DOI":"10.3390\/pathogens14100986","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T16:43:54Z","timestamp":1759250634000},"page":"986","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Integrating Machine Learning and Molecular Methods for Trichophyton indotineae Identification and Resistance Profiling Using MALDI-TOF Spectra"],"prefix":"10.3390","volume":"14","author":[{"given":"Vittorio","family":"Ivagnes","sequence":"first","affiliation":[{"name":"Dipartimento di Scienze di Laboratorio ed Ematologiche, Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4757-7256","authenticated-orcid":false,"given":"Elena","family":"De Carolis","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze di Laboratorio ed Ematologiche, Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlotta","family":"Magr\u00ec","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze di Laboratorio ed Ematologiche, Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0435-9347","authenticated-orcid":false,"given":"Manuel J.","family":"Arroyo","sequence":"additional","affiliation":[{"name":"Clover Bioanalytical Software S.L., 18016 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomina","family":"Pavan","sequence":"additional","affiliation":[{"name":"Department of Microbiology, San Bortolo Hospital, 36100 Vicenza, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8221-7542","authenticated-orcid":false,"given":"Anna Cristina Maria","family":"Prigitano","sequence":"additional","affiliation":[{"name":"Department Biomedical Sciences for Health, Universit\u00e0 degli Studi di Milano, 20122 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2028-7462","authenticated-orcid":false,"given":"Anuradha","family":"Chowdhary","sequence":"additional","affiliation":[{"name":"Medical Mycology Unit, Department of Microbiology, Vallabhbhai Patel Chest Institute, University of Delhi, Delhi 110007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9780-7059","authenticated-orcid":false,"given":"Maurizio","family":"Sanguinetti","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze di Laboratorio ed Ematologiche, Fondazione Policlinico Universitario \u201cA. Gemelli\u201d IRCCS, 00168 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Martinez-Rossi, N.M., Peres, N.T.A., Bitencourt, T.A., Martins, M.P., and Rossi, A. (2021). State-of-the-Art Dermatophyte Infections: Epidemiology Aspects, Pathophysiology, and Resistance Mechanisms. J. Fungi, 7.","DOI":"10.3390\/jof7080629"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kruithoff, C., Gamal, A., McCormick, T.S., and Ghannoum, M.A. (2023). Dermatophyte Infections Worldwide: Increase in Incidence and Associated Antifungal Resistance. Life, 14.","DOI":"10.20944\/preprints202311.1060.v1"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, H., Sun, F., Wang, C., Ye, J., Xia, P., Wang, W., and Wu, Y. (2024). A Systematic Analysis of the Global, Regional, and National Burden of Fungal Skin Diseases from 1990 to 2021. 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