Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

Abstract

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review

“…A total of 19 MRSA from 3 MSSA were analyzed and 33.975 SERS spectra were acquired from those isolates. Those spectra could discriminate MRSA from MSSA with 97.7% accuracy [ 132 ].…”

Investigation of mammalian cells expressing SARS-CoV-2 proteins by surface-enhanced Raman scattering and multivariate analysis

“…Many studies were carried out by preparing a number of different SERS substrates for biological applications such as tissue analysis, cell classification, and detection of viruses, bacteria, and cancer cells. [22][23][24][25][26][27][28][29] To date, various studies have been carried out for the detection of COVID-19 with SERS. [30][31][32][33][34][35][36][37][38] Generally, label-based SERS studies which indirectly detect analytes using antibody, aptamer, and Raman reporter molecules are carried out for the detection of COVID-19.…”

New Antibacterial Secondary Metabolites from a Marine-Derived Talaromyces sp. Strain BTBU20213036

“…The spread of multi-drug resistant microorganisms is getting more and more serious to humans [ 3 , 4 , 5 ]. It is estimated that 700,000 people died as a result of infections caused by antimicrobial resistant bacteria annually [ 6 ]. There is an urgent demand to screen new antibiotics in order to combat the infective diseases caused by drug-resistant bacteria.…”