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Development and validation of a hierarchical machine learning method using MALDI-TOF mass spectrometry for rapid SCCmec typing and PVL detection in MRSA: a multi-centre study.

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Title: Development and validation of a hierarchical machine learning method using MALDI-TOF mass spectrometry for rapid SCCmec typing and PVL detection in MRSA: a multi-centre study.
Authors: Lin TH; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC., Jian MJ; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC., Mitsumoto-Kaseida F; Department of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan., Kaku N; Department of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan., Chung HY; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.; Graduate Institute of Medical Science, National Defense Medical Center, Taipei, Taiwan, ROC., Chang CK; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC., Perng CL; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC., Wang YC; Division of Infectious Diseases and Tropical Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC., Wang CC; Department of Pediatrics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC., Chen YH; Department of Neurological Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC., Yanagihara K; Department of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan., Shang HS; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
Source: Emerging microbes & infections [Emerg Microbes Infect] 2025 Dec; Vol. 14 (1), pp. 2525264. Date of Electronic Publication: 2025 Jul 17.
Publication Type: Journal Article; Multicenter Study; Validation Study
Journal Info: Publisher: Taylor & Francis Country of Publication: United States NLM ID: 101594885 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2222-1751 (Electronic) Linking ISSN: 22221751 NLM ISO Abbreviation: Emerg Microbes Infect Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Development and validation of a hierarchical machine learning method using MALDI-TOF mass spectrometry for rapid SCCmec typing and PVL detection in MRSA: a multi-centre study.
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  Data: <searchLink fieldCode="AU" term="%22Lin+TH%22">Lin TH</searchLink>; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.<br /><searchLink fieldCode="AU" term="%22Jian+MJ%22">Jian MJ</searchLink>; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.<br /><searchLink fieldCode="AU" term="%22Mitsumoto-Kaseida+F%22">Mitsumoto-Kaseida F</searchLink>; Department of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.<br /><searchLink fieldCode="AU" term="%22Kaku+N%22">Kaku N</searchLink>; Department of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.<br /><searchLink fieldCode="AU" term="%22Chung+HY%22">Chung HY</searchLink>; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.; Graduate Institute of Medical Science, National Defense Medical Center, Taipei, Taiwan, ROC.<br /><searchLink fieldCode="AU" term="%22Chang+CK%22">Chang CK</searchLink>; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.<br /><searchLink fieldCode="AU" term="%22Perng+CL%22">Perng CL</searchLink>; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.<br /><searchLink fieldCode="AU" term="%22Wang+YC%22">Wang YC</searchLink>; Division of Infectious Diseases and Tropical Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.<br /><searchLink fieldCode="AU" term="%22Wang+CC%22">Wang CC</searchLink>; Department of Pediatrics, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.<br /><searchLink fieldCode="AU" term="%22Chen+YH%22">Chen YH</searchLink>; Department of Neurological Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.<br /><searchLink fieldCode="AU" term="%22Yanagihara+K%22">Yanagihara K</searchLink>; Department of Laboratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.<br /><searchLink fieldCode="AU" term="%22Shang+HS%22">Shang HS</searchLink>; Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC.
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  Data: <searchLink fieldCode="JN" term="%22101594885%22">Emerging microbes & infections</searchLink> [Emerg Microbes Infect] 2025 Dec; Vol. 14 (1), pp. 2525264. <i>Date of Electronic Publication: </i>2025 Jul 17.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Taylor+%26+Francis%22">Taylor & Francis </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101594885 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2222-1751 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2222221751%22">22221751 </searchLink><i>NLM ISO Abbreviation: </i>Emerg Microbes Infect <i>Subsets: </i>MEDLINE
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              Text: 2025 Dec
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