1. 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.
- Author
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Asakura K, Azechi T, Sasano H, Matsui H, Hanaki H, Miyazaki M, Takata T, Sekine M, Takaku T, Ochiai T, Komatsu N, Shibayama K, Katayama Y, and Yahara K
- Subjects
- Anti-Bacterial Agents pharmacology, Humans, Methicillin-Resistant Staphylococcus aureus classification, Methicillin-Resistant Staphylococcus aureus genetics, Software, Staphylococcal Infections microbiology, Drug Resistance, Bacterial, Methicillin-Resistant Staphylococcus aureus drug effects, Microbial Sensitivity Tests methods, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization methods, Vancomycin pharmacology
- Abstract
Vancomycin-intermediately resistant Staphylococcus aureus (VISA) and heterogeneous VISA (hVISA) are associated with treatment failure. hVISA contains only a subpopulation of cells with increased minimal inhibitory concentrations, and its detection is problematic because it is classified as vancomycin-susceptible by standard susceptibility testing and the gold-standard method for its detection is impractical in clinical microbiology laboratories. Recently, a research group developed a machine-learning classifier to distinguish VISA and hVISA from vancomycin-susceptible S. aureus (VSSA) according to matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) data. Nonetheless, the sensitivity of hVISA classification was found to be 76%, and the program was not completely automated with a graphical user interface. Here, we developed a more accurate machine-learning classifier for discrimination of hVISA from VSSA and VISA among MRSA isolates in Japanese hospitals by means of MALDI-TOF MS data. The classifier showed 99% sensitivity of hVISA classification. Furthermore, we clarified the procedures for preparing samples and obtaining MALDI-TOF MS data and developed all-in-one software, hVISA Classifier, with a graphical user interface that automates the classification and is easy for medical workers to use; it is publicly available at https://github.com/bioprojects/hVISAclassifier. This system is useful and practical for screening MRSA isolates for the hVISA phenotype in clinical microbiology laboratories and thus should improve treatment of MRSA infections.
- Published
- 2018
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