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

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

Automated summary

In this study, a label-free SERS method was developed for the accurate identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus using aptamer-guided AgNP enhancement and CNN classification. The method showed 100% identification accuracy for MSSA and MRSA, indicating its potential as a rapid detection tool for drug-sensitive and drug-resistant bacterial strains.