Rapid and reliable techniques for virus identification are required in light of recurring epidemics and pandemics throughout the world. Several techniques have been distributed for testing the flow of patients. Polymerase chain reaction with reverse transcription is a reliable and sensitive, though not rapid, tool. The antibody-based strip is a rapid, though not reliable, and sensitive tool. A set of alternative tools is being developed to meet all the needs of the customer. Surface-enhanced Raman spectroscopy (SERS) provides the possibility of single molecule detection taking several minutes. Here, a multiplex lithographic SERS aptasensor was developed aiming at the detection of several respiratory viruses in one pot within 17 min. The four labeled aptamers were anchored onto the metal surface of four SERS zones; the caught viruses affect the SERS signals of the labels, providing changes in the analytical signals. The sensor was able to decode mixes of SARS-CoV-2 (severe acute respiratory syndrome coronavirus two), influenza A virus, respiratory syncytial virus, and adenovirus within a single experiment through a one-stage recognition process.

Surface-enhanced Raman scattering (SERS) is a powerful technique for decoding of 2-5-component mixes of analytes. Low concentrations of analytes and complex biological media are usually non-decodable with SERS. Recognition molecules, such as antibodies and aptamers, provide an opportunity for a specific binding of ultra-low contents of analyte dissolved in complex biological media. Different approaches have been proposed to provide changes in SERS intensity of an external label upon binding of ultra-low contents of the analytes. In this paper, we propose a SERS-based sensor for the rapid and sensitive detection of botulinum toxin type A. The silver nanoisland SERS substrate was functionalized using an aptamer conjugated with a Raman label. The binding of the target affects the orientation of the label, providing changes in an analytical signal. This trick allowed detecting botulinum toxin type A in a one-stage manner without additional staining with a monotonous dose dependence and a limit of detection of 2.4 ng/mL. The proposed sensor architecture is consistent with the multiarray detection systems for multiplex analyses.

The possibility of using Raman spectroscopy in determining the total hemoglobin content in the blood has been investigated. It has been established that a few lines in the Raman spectrum linearly depend on the hemoglobin concentration. It has been shown that SERS using silver colloids also gives a linear concentration dependence on the intensity of spectral lines.

Introduction:: Serum analysis is a promising method for the early diagnosis of cardiovascular diseases, as pathological processes are associated with complex biochemical changes. Raman spectroscopy enables the analysis of biological fluids at the molecular level; however, high concentrations of serum proteins can mask diagnostically significant low-molecular-weight components. Molecular weight filtering can improve spectral quality and diagnostic performance. This study aims to evaluate the effectiveness of serum filtering to optimize Raman spectral analysis in combination with machine learning methods to distinguish patients with cardiovascular diseases from healthy individuals. Methods:: This study uses Surface-Enhanced Raman Spectroscopy (SERS). SERS offers a substantial analytical advantage over conventional Raman spectroscopy because of its greatly enhanced sensitivity. While standard Raman spectroscopy often suffers from weak signal scattering in complex biological fluids such as serum, SERS enhances the Raman signal due to the effects of localized surface plasmon resonance occurring on metallic nanostructures. This improvement makes it possible to detect low-concentration biomolecules that would otherwise remain undetectable using conventional Raman scattering techniques. The Raman spectra of serum samples from 20 individuals, including patients with cardiovascular diseases and healthy controls, were analyzed. Serum was analyzed without filtering and after filtering using molecular weight cutoffs of 5 kDa, 30 kDa, and 100 kDa. Spectrum preprocessing included baseline correction, smoothing, and normalization. Principal component analysis was used for dimensionality reduction, followed by classification using logistic regression, support vector machines, random forests, and gradient boosting. Model performance was assessed using group and traditional cross-validation. Results and Discussion:: The best diagnostic performance was achieved with serum filtered with a 30 kDa cutoff. This fraction demonstrated excellent discrimination between the patients and control groups, with an area under the receiver operating characteristic curve reaching 99% and balanced sensitivity and specificity. Other filtering conditions and unfiltered serum yielded lower and less consistent results. Conclusion:: Serum filtering using a 30 kDa molecular weight cutoff significantly improves the performance of Raman spectroscopy combined with machine learning and represents an optimal approach for intelligent spectral analysis in cardiovascular disease diagnostics. However, due to the limited sample size (n=20), these findings are preliminary and require validation in larger independent cohorts.