Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning.
Shu WangHao DongWanzhu ShenYong YangZhigang LiYong LiuChongwen WangBing GuLong ZhangPublished in: RSC advances (2021)
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.
Keyphrases
- methicillin resistant staphylococcus aureus
- staphylococcus aureus
- deep learning
- convolutional neural network
- label free
- drug resistant
- gold nanoparticles
- escherichia coli
- sensitive detection
- artificial intelligence
- biofilm formation
- machine learning
- multidrug resistant
- bioinformatics analysis
- acinetobacter baumannii
- loop mediated isothermal amplification
- emergency department
- high resolution
- cystic fibrosis
- mass spectrometry