快速表面增强拉曼光谱法鉴别甲氧西林敏感菌和耐甲氧西林菌的适配体识别及深度学习
Rapid SERS identification of methicillin-susceptible and methicillin-resistant aptamer recognition and deep learning.
作者信息
Wang Shu, Dong Hao, Shen Wanzhu, Yang Yong, Li Zhigang, Liu Yong, Wang Chongwen, Gu Bing, Zhang Long
机构信息
Hefei Institute of Physical Science, Chinese Academy of Sciences Hefei 230036 P. R China
University of Science and Technology of China Hefei 230036 P. R China.
出版信息
RSC Adv. 2021 Oct 25;11(55):34425-34431. doi: 10.1039/d1ra05778b.
Here, we report a label-free surface-enhanced Raman scattering (SERS) method for the rapid and accurate identification of methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) based on aptamer-guided AgNP enhancement and convolutional neural network (CNN) classification. Sixty clinical isolates of (), comprising 30 strains of MSSA and 30 strains of MRSA were used to build the CNN classification model. The developed method exhibited 100% identification accuracy for MSSA and MRSA, and is thus a promising tool for the rapid detection of drug-sensitive and drug-resistant bacterial strains.
在此，我们报告了一种基于适体引导的银纳米颗粒增强和卷积神经网络（CNN）分类的无标记表面增强拉曼散射（SERS）方法，用于快速准确地鉴定甲氧西林敏感（MSSA）和甲氧西林耐药（MRSA）菌株。使用60株临床分离株（包括30株MSSA和30株MRSA）构建CNN分类模型。所开发的方法对MSSA和MRSA的鉴定准确率为100%，因此是一种用于快速检测药敏和耐药菌株的有前途的工具。
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