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Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Bloodstream infection (BSI) is characterized by the presence of viable microorganisms in the bloodstream and may induce systemic immune responses. Early and appropriate antibiotic usage is crucial to effectively treating BSI. However, conventional culture-based microbiological diagnostics are time-consuming and cannot provide timely bacterial identification for subsequent antimicrobial susceptibility test (AST) and clinical decision-making. To address this issue, modern microbiological diagnostics have been developed, such as surface-enhanced Raman scattering (SERS), which is a sensitive, label-free, and quick bacterial detection method measuring specific bacterial metabolites. In this study, we aim to integrate a new deep learning (DL) method, Vision Transformer (ViT), with bacterial SERS spectral analysis to build the SERS-DL model for rapid identification of Gram type, species, and resistant strains. To demonstrate the feasibility of our approach, we used 11,774 SERS spectra obtained directly from eight common bacterial species in clinical blood samples without artificial introduction as the training dataset for the SERS-DL model. Our results showed that ViT achieved excellent identification accuracy of 99.30% for Gram type and 97.56% for species. Moreover, we employed transfer learning by using the Gram-positive species identifier as a pre-trained model to perform the antibiotic-resistant strain task. The identification accuracy of methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) can reach 98.5% with only 200-dataset requirement. In summary, our SERS-DL model has great potential to provide a quick clinical reference to determine the bacterial Gram type, species, and even resistant strains, which can guide early antibiotic usage in BSI.

Deep Learning-Assisted Surface-Enhanced Raman Scattering for Rapid Bacterial Identification.

Molecular information contained in bodily fluids (ex. Blood, urine, saliva, or tears) can be minutely obtained through label-free surface-enhanced Raman spectroscopy (SERS). However, the resulting SERS spectra require complex analysis to transform such spectral information into accurate diagnostics. Herein, we review how scientists and technologists are employing SERS and artificial intelligence (AI) to carry out prediction, classification, spectral variation detection, and pattern recognition tasks to extract molecular information and generate diagnostic models or staging platforms based on disease-related molecular variations (reflected in the analyzed spectra). The employed SERS substrates are critically discussed and AI methods applied to assist SERS-based diagnostics are also elaborated. Particular applications such as the AI-assisted diagnosis of cancer, infectious diseases, and other illnesses (including stroke and Alzheimer's) are also covered. Besides, our perspective to push forward the frontiers of this exciting field toward smart diagnostics and their clinical translation is offered. 

Toward smart diagnostics via artificial intelligence-assisted surface-enhanced Raman spectroscopy

Combining Dense Au Nanoparticle Layers and 2D Surface-Enhanced Raman Scattering Arrays for the Identification of Mutant Cyanobacteria Using Machine Learning

The efficacy of mineral medicines varies greatly between different origins. Therefore, investigating a method to quickly identify similar mineral medicines is meaningful. In this paper, a visual classification and identification model of Raman spectroscopy combined with principal component analysis (PCA) and support vector machine (SVM) algorithms was developed to rapidly classify and identify carbonate and sulfate mineral medicines. The results reveal that although the Raman spectra are too similar to distinguish by naked eye, the PCA-SVM algorithm can perform accurate classification and identification, and its accuracy, precision, recall and F1-score parameters all reach 100%. The proposed method is rapid, accurate, nondestructive, convenient, portable, and low cost, and has important application value for the classification, identification and quality supervision of various carbonate and sulfate mineral medicines. 

https://doi.org/10.1007/s44211-022-00224-1

Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm

Crohn's disease (CD) is an idiopathic chronic inflammatory bowel disease without a cure. Most of the CD patients are firstly diagnosed by invasive endoscopy, and clinical and pathological examinations are further required to confirm the diagnosis. Hence, the development of a non-invasive, rapid and accurate diagnosis method for CD patients is essential. In this study, urine samples from 95 CD patients (including 58 active CD (aCD) patients and 37 inactive CD (iCD) patients) and 48 healthy controls (HC) were investigated by surface-enhanced Raman spectroscopy (SERS). The statistical analysis of the three groups (i.e., CD/HC, aCD/HC and iCD/HC) was performed on the measured data. Principal component analysis (PCA)-support vector machine (SVM) and PCA-linear discriminant analysis (LDA) were then employed to establish classification models to distinguish between patients and HC. For the average SERS spectra of patients and HC, the Raman peaks belonging to lipids, proteins and nucleic acids were stronger in patients than those in HC. It showed that the classification accuracy of CD/HC based on PCA-SVM was higher than that of PCA-LDA (82.5% vs. 69.9%). And the classification accuracy of aCD/HC based on PCA-SVM was higher than that of iCD/HC (86.8% vs. 76.5%). The classification model we established distinguished between aCD and HC with 86.2% sensitivity and 87.5% specificity. It indicates that the metabolic change of patients could be identified by measuring urine with SERS, and aCD and HC could be distinguished more effectively. Our findings are helpful for clinicians to diagnose CD patients and monitor the progress and recurrence of the disease.

Non-invasive diagnosis of Crohn's disease based on SERS combined with PCA-SVM.

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Non-invasive diagnosis of Crohn's disease based on SERS combined with PCA-SVM.