Rapid Detection of Heterogeneous Vancomycin-Intermediate Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight: Using a Machine Learning Approach and Unbiased Validation.
Rapid Detection of Heterogeneous Vancomycin-Intermediate Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight: Using a Machine Learning Approach and Unbiased Validation.
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基于基质辅助激光解吸电离飞行时间的异质性万古霉素中间葡萄球菌金黄色葡萄球菌的快速检测：使用机器学习方法和无偏见的验证。
DOI：
10.3389/fmicb.2018.02393
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发表时间：
2018
影响因子：
5.2
通讯作者：
Lu JJ
中科院分区：
文献类型：
--
作者：
Wang HY;Chen CH;Lee TY;Horng JT;Liu TP;Tseng YJ;Lu JJ
异质性万古霉素中间型金黄色葡萄球菌（hVISA）是一种对万古霉素具有隐性耐药性的新兴超级细菌。检测hVISA可以指导抗生素的正确给药。然而，hVISA不能在大多数临床微生物实验室检测，因为所需的诊断工具要么昂贵，耗时，或劳动密集型。相比之下，基质辅助激光解吸电离飞行时间（MALDI-TOF）是一种具有成本效益且快速的工具，有可能提供抗生素耐药性信息。为了分析复杂的MALDI-TOF质谱，可以使用机器学习（ML）算法来生成鲁棒的hVISA检测模型。在这项研究中，MALDI-TOF质谱从35 hVISA/万古霉素-中间体S获得。金黄色葡萄球菌（VISA）和90株万古霉素敏感的S.金黄色葡萄球菌分离株使用Etest和改良的群体分析曲线下面积确定分离株的万古霉素敏感性。ML算法，即决策树，k-最近邻，随机森林和支持向量机（SVM），进行了训练和验证，使用嵌套交叉验证，以提供无偏的验证结果。模型的曲线下面积范围为0.67至0.79，SVM衍生模型优于其他算法。将m/z 1132、2895、3176和6591处的峰记录为检测hVISA/VISA的信息峰。我们证明了可以通过使用ML分析MALDI-TOF质谱来检测hVISA/VISA。此外，由于采用了严格的验证方法，结果特别稳健。本研究建立的ML模型可以快速、准确地报告hVISA/VISA相关情况，指导临床合理使用抗生素治疗S.金黄色葡萄球菌感染。
Heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) is an emerging superbug with implicit drug resistance to vancomycin. Detecting hVISA can guide the correct administration of antibiotics. However, hVISA cannot be detected in most clinical microbiology laboratories because the required diagnostic tools are either expensive, time consuming, or labor intensive. By contrast, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) is a cost-effective and rapid tool that has potential for providing antibiotics resistance information. To analyze complex MALDI-TOF mass spectra, machine learning (ML) algorithms can be used to generate robust hVISA detection models. In this study, MALDI-TOF mass spectra were obtained from 35 hVISA/vancomycin-intermediate S. aureus (VISA) and 90 vancomycin-susceptible S. aureus isolates. The vancomycin susceptibility of the isolates was determined using an Etest and modified population analysis profile–area under the curve. ML algorithms, namely a decision tree, k-nearest neighbors, random forest, and a support vector machine (SVM), were trained and validated using nested cross-validation to provide unbiased validation results. The area under the curve of the models ranged from 0.67 to 0.79, and the SVM-derived model outperformed those of the other algorithms. The peaks at m/z 1132, 2895, 3176, and 6591 were noted as informative peaks for detecting hVISA/VISA. We demonstrated that hVISA/VISA could be detected by analyzing MALDI-TOF mass spectra using ML. Moreover, the results are particularly robust due to a strict validation method. The ML models in this study can provide rapid and accurate reports regarding hVISA/VISA and thus guide the correct administration of antibiotics in treatment of S. aureus infection.
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影响因子：
3.7
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
通讯作者：
Yahara K
影响因子：
14.2
作者：
Idelevich, E. A.;Sparbier, K.;Becker, K.
通讯作者：
Becker, K.
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：
7.4
作者：
Lu, Jang-Jih;Tsai, Fuu-Jen;Chen, Chao-Jung
通讯作者：
Chen, Chao-Jung
新型的单核苷酸变异与万古霉素中间葡萄球菌葡萄球菌中的万古霉素抗性有关。
影响因子：
3.9
作者：
Lin LC;Chang SC;Ge MC;Liu TP;Lu JJ
通讯作者：
Lu JJ