Rapid SERS identification of methicillin-susceptible and methicillin-resistant <i>Staphylococcus aureus via</i> aptamer recognition and deep learning

Abstract

Here, we report a label-free surface-enhanced Raman scattering (SERS) method for the rapid and accurate identification of methicillin-susceptible <i>Staphylococcus aureus</i> (MSSA) and methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) based on aptamer-guided AgNP enhancement and convolutional neural network (CNN) classification. Sixty clinical isolates of <i>Staphylococcus aureus</i> (<i>S. aureus</i>), 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.