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-PLSR Analysis of Vaginal Microflora: Towards the Spectral Library of Microorganisms.

The accurate identification of microorganisms belonging to vaginal microflora is crucial for establishing which microorganisms are responsible for microbial shifting from beneficial symbiotic to pathogenic bacteria and understanding pathogenesis leading to vaginosis and vaginal infections. In this study, we involved the surface-enhanced Raman spectroscopy (SERS) technique to compile the spectral signatures of the most significant microorganisms being part of the natural vaginal microbiota and some vaginal pathogens. Obtained data will supply our still developing spectral SERS database of microorganisms. The SERS results were assisted by Partial Least Squares Regression (PLSR), which visually discloses some dependencies between spectral images and hence their biochemical compositions of the outer structure. In our work, we focused on the most common and typical of the reproductive system microorganisms (Lactobacillus spp. and Bifidobacterium spp.) and vaginal pathogens: bacteria (e.g., Gardnerella vaginalis, Prevotella bivia, Atopobium vaginae), fungi (e.g., Candida albicans, Candida glabrata), and protozoa (Trichomonas vaginalis). The obtained results proved that each microorganism has its unique spectral fingerprint that differentiates it from the rest. Moreover, the discrimination was obtained at a high level of explained information by subsequent factors, e.g., in the inter-species distinction of Candida spp. the first three factors explain 98% of the variance in block Y with 95% of data within the X matrix, while in differentiation between Lactobacillus spp. and Bifidobacterium spp. (natural flora) and pathogen (e.g., Candida glabrata) the information is explained at the level of 45% of the Y matrix with 94% of original data. PLSR gave us insight into discriminating variables based on which the marker bands representing specific compounds in the outer structure of microorganisms were found: for Lactobacillus spp. 1400 cm-1, for fungi 905 and 1209 cm-1, and for protozoa 805, 890, 1062, 1185, 1300, 1555, and 1610 cm-1. Then, they can be used as significant marker bands in the analysis of clinical subjects, e.g., vaginal swabs.

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.

Brain tumour homogenates analysed by surface-enhanced Raman spectroscopy: Discrimination among healthy and cancer cells.

One of the biggest challenge for modern medicine is to make a discrimination among healthy and cancerous tissues. Therefore, nowadays big effort of scientist are devoted to find a new way for as fast as possible diagnosis with as much as possible accuracy in distinguishing healthy from cancerous tissues. That issues are probably the most important in the case of brain tumours, when the diagnosis time plays a great role. Herein we present the surface-enhanced Raman spectroscopy (SERS) together with the principal component analysis (PCA) used to identify the spectra of different brain specimens, healthy and tumour tissues homogenates. The presented analyses include three sets of brain tissues as control samples taken from healthy objects (one set consists of samples from four brain lobes and both hemispheres; eight samples) and the brain tumours from five patients (two Anaplastic Astrocytoma and three Glioblastoma samples). Results prove that tumour brain samples can be discriminated well from the healthy tissues by using only three main principal components, with 96% of accuracy. The largest influence onto the calculated separation is attributed to the spectral regions corresponding in SERS spectra to vibrations of the L-Tryptophan (1450, 1278 cm-1), protein (1300 cm-1), phenylalanine and Amide-I (1005, 1654 cm-1). Therefore, the presented method may open the way for the probable application as a very fast diagnosis tool alternative for conventionally used histopathology or even more as an intraoperative diagnostic tool during brain tumour surgery.