Uysal Ciloglu Fatma, Saridag Ayse Mine, Kilic Ibrahim Halil, Tokmakci Mahmut, Kahraman Mehmet, Aydin Omer
Department of Biomedical Engineering, Erciyes University, Kayseri 38039, Turkey.
Analyst. 2020 Nov 23;145(23):7559-7570. doi: 10.1039/d0an00476f.
To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.
为对抗抗生素耐药性，通过对病原体进行快速诊断来选择合适的抗生素极为重要。传统技术需要复杂的样品制备和耗时的过程，不适用于快速诊断。为解决这一问题，我们将表面增强拉曼光谱与机器学习技术相结合，用于快速鉴定耐甲氧西林和对甲氧西林敏感的革兰氏阳性金黄色葡萄球菌菌株以及革兰氏阴性嗜肺军团菌（对照组）。总共使用了10株耐甲氧西林金黄色葡萄球菌（MRSA）、3株对甲氧西林敏感的金黄色葡萄球菌（MSSA）和6株嗜肺军团菌分离株。获得的光谱显示出高重现性和可重复性，且信噪比高。主成分分析（PCA）、层次聚类分析（HCA）以及各种监督分类算法被用于区分金黄色葡萄球菌菌株和嗜肺军团菌。尽管肉眼观察时MRSA和MSSA光谱之间没有显著差异，但一些峰强度比，如732/958、732/1333和732/1450，证明可能存在显示它们之间差异的重要指标。在包括支持向量机（SVM）、决策树（DT）和朴素贝叶斯（NB）在内的传统分类器中，k近邻（kNN）分类算法表现出卓越的分类性能，准确率达97.8%。我们的结果表明，表面增强拉曼光谱结合机器学习可用于检测耐药菌和敏感菌，且该技术是一种非常有前景的临床应用工具。
