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.
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.
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DOI：
10.1371/journal.pone.0194212
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Yahara K
中科院分区：
文献类型：
--
作者：
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;Yahara K
万古霉素中度耐药金黄色葡萄球菌（VISA）和异质性VISA（hVISA）与治疗失败相关。hVISA仅含有最小抑制浓度增加的细胞亚群，并且其检测是有问题的，因为其通过标准敏感性测试被归类为万古霉素敏感的，并且用于其检测的金标准方法在临床微生物学实验室中是不切实际的。最近，一个研究小组开发了一种机器学习分类器，用于区分VISA和hVISA与万古霉素敏感的S。根据基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）数据，对金黄色葡萄球菌（VSSA）进行鉴定。尽管如此，hVISA分类的灵敏度被发现为76%，并且该程序没有完全自动化的图形用户界面。在这里，我们开发了一种更准确的机器学习分类器，用于通过MALDI-TOF MS数据区分日本医院MRSA分离株中的hVISA与VSSA和VISA。该分类器显示出99%的hVISA分类灵敏度。此外，我们阐明了制备样品和获得MALDI-TOF MS数据的程序，并开发了一体化软件hVISA Classifier，该软件具有图形用户界面，可自动进行分类，便于医务工作者使用;可在https://github.com/bioprojects/hVISAclassifier上公开获取。该系统对于在临床微生物实验室中筛选MRSA分离株的hVISA表型是有用和实用的，因此应该改善MRSA感染的治疗。
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.
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DOI：
10.1007/s10096-009-0741-5
发表时间：
2009-08-01
影响因子：
4.5
作者：
Fong, R. K. C.;Low, J.;Kurup, A.
通讯作者：
Kurup, A.
影响因子：
5.2
作者：
Sader, Helio S.;Jones, Ronald N.;Rybak, Michael J.
通讯作者：
Rybak, Michael J.
影响因子：
9.4
作者：
Saffert, Ryan T.;Cunningham, Scott A.;Patel, Robin
通讯作者：
Patel, Robin
影响因子：
9.4
作者：
Francois, P;Koessler, T;Schrenzel, J
通讯作者：
Schrenzel, J
影响因子：
11.8
作者：
Liu, Catherine;Bayer, Arnold;Chambers, Henry F.
通讯作者：
Chambers, Henry F.