Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy.

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

SERS Signature of SARS-CoV-2 in Saliva and Nasopharyngeal Swabs: Towards Perspective COVID-19 Point-of-Care Diagnostics.

In this study, the intrinsic surface-enhanced Raman spectroscopy (SERS)-based approach coupled with chemometric analysis was adopted to establish the biochemical fingerprint of SARS-CoV-2 infected human fluids: saliva and nasopharyngeal swabs. The numerical methods, partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC), facilitated the spectroscopic identification of the viral-specific molecules, molecular changes, and distinct physiological signatures of pathetically altered fluids. Next, we developed the reliable classification model for fast identification and differentiation of negative CoV(-) and positive CoV(+) groups. The PLS-DA calibration model was described by a great statistical value-RMSEC and RMSECV below 0.3 and R2cal at the level of ~0.7 for both type of body fluids. The calculated diagnostic parameters for SVMC and PLS-DA at the stage of preparation of calibration model and classification of external samples simulating real diagnostic conditions evinced high accuracy, sensitivity, and specificity for saliva specimens. Here, we outlined the significant role of neopterin as the biomarker in the prediction of COVID-19 infection from nasopharyngeal swab. We also observed the increased content of nucleic acids of DNA/RNA and proteins such as ferritin as well as specific immunoglobulins. The developed SERS for SARS-CoV-2 approach allows: (i) fast, simple and non-invasive collection of analyzed specimens; (ii) fast response with the time of analysis below 15 min, and (iii) sensitive and reliable SERS-based screening of COVID-19 disease.

SERS-based sensor for the detection of sexually transmitted pathogens in the male swab specimens: A new approach for clinical diagnosis.

The surface-enhanced Raman scattering (SERS) has been widely tested for its usefulness in microbiological studies, providing many information-rich spectra which are a kind of 'whole-organism fingerprint' and enabling identification of bacterial species. Here we show, previously not considered, the comprehensive SERS-chemometric analysis of five bacterial pathogens, namely Neisseria gonorrhoeae, Mycoplasma hominis, Mycoplasma genitalium, Ureaplasma urealyticum, and Haemophilus ducreyi, all being responsible for sexually transmitted diseases (STDs). In the designed biosensor, the direct, intrinsic format of the spectroscopic analysis was adopted for the SERS-based screening of gonorrhea and chlamydiosis due to vibrational analysis of men's urethra swabs. Our experiments demonstrated that the applied method enables identification the individual species of the Neisseria genus with high accuracy. In order to differentiate the sexually transmitted pathogens and to classify the clinical samples of male urethra swabs, three multivariate methods were used. In the external validation the created models correctly classified the men's urethra swabs with prediction accuracy reaching 89% for SIMCA and 100% for PLS-DA. As a result, the developed protocol enables: (i) simple and non-invasive analysis of clinical samples (the collection of urethra swabs specimens could be carried out at different points of care, such as doctor's office); (ii) fast analysis (<15 min); (iii) culture-free identification; (iv) sensitive and reliable SERS-based diagnosis of STD. The simplicity of the developed detection procedure, supported by high sensitivity, reproducibility, and specificity, open a new path in the improvement of the point-of-care applications.