English  |  正體中文  |  简体中文  |  Items with full text/Total items : 12145/12927 (94%)
Visitors : 848298      Online Users : 940
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/14321


    Title: Discrimination of methicillin-resistant staphylococcus aureus by MALDI-TOF mass spectrometry with machine learning techniques in patients with staphylococcus aureus bacteremia
    Authors: Kong, PH;Chiang, CH;Lin, TC;Kuo, SC;Li, CF;Hsiung, CA;Shiue, YL;Chiou, HY;Wu, LC;Tsou, HH
    Contributors: Institute of Population Health Sciences;National Institute of Infectious Diseases and Vaccinology
    Abstract: Early administration of proper antibiotics is considered to improve the clinical outcomes of Staphylococcus aureus bacteremia (SAB), but routine clinical antimicrobial susceptibility testing takes an additional 24 h after species identification. Recent studies elucidated matrix-assisted laser desorption/ionization time-of-flight mass spectra to discriminate methicillin-resistant strains (MRSA) or even incorporated with machine learning (ML) techniques. However, no universally applicable mass peaks were revealed, which means that the discrimination model might need to be established or calibrated by local strains' data. Here, a clinically feasible workflow was provided. We collected mass spectra from SAB patients over an 8-month duration and preprocessed by binning with reference peaks. Machine learning models were trained and tested by samples independently of the first six months and the following two months, respectively. The ML models were optimized by genetic algorithm (GA). The accuracy, sensitivity, specificity, and AUC of the independent testing of the best model, i.e., SVM, under the optimal parameters were 87%, 75%, 95%, and 87%, respectively. In summary, almost all resistant results were truly resistant, implying that physicians might escalate antibiotics for MRSA 24 h earlier. This report presents an attainable method for clinical laboratories to build an MRSA model and boost the performance using their local data.
    Date: 2022-05-16
    Relation: Pathogens. 2022 May 16;11(5):Article number 586.
    Link to: http://dx.doi.org/10.3390/pathogens11050586
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2076-0817&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000803182300001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85130682180
    Appears in Collections:[鄒小蕙] 期刊論文
    [邱弘毅] 期刊論文
    [熊昭] 期刊論文
    [郭書辰] 期刊論文

    Files in This Item:

    File Description SizeFormat
    PUB35631107.pdf2188KbAdobe PDF202View/Open


    All items in NHRI are protected by copyright, with all rights reserved.

    Related Items in TAIR

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback