RAMAN SPECTROSCOPY FOR THE DETECTION OF NON-INFECTIOUS DISEASES
Ivan Bratchenko1,2, Lyudmila Bratchenko1,2, Alexander Zakharov3, Anna Neupokoeva3, Maria Skuratova4, Peter Lebedev3; 1Samara National Research University, Samara, Russia; 2Immanuel Kant Baltic Federal University, Kaliningrad, Russia; 3Samara State Medical University, Samara, Russia; 4Samara Clinics named after NI Pirogov, Samara, Russia.
Abstract
In modern world practice, promising diagnostic methods are emerging, such as "optical biopsy" and "liquid biopsy", which are used for specific diseases biomarkers detection in biological tissues and fluids. Optical methods have the potential to overcome the limitations of traditional methods of clinical analysis. One of the most promising methods of optical analysis (and optical biopsy) is a Raman spectroscopy, which can contribute to understanding of molecular basis of diseases and creation of new bioanalytical tools for the diagnosis of diseases. Since each type of biological tissue and biofluid has an individual molecular composition and, thus, a unique spectral profile resulting from the transition of a molecule from one vibrational-rotational state to another, a set of such individual states of functional groups of nucleic acids, proteins, lipids and carbohydrates makes it possible to characterize component composition of tissues, which ultimately makes it possible to isolate disease markers. Along with the use of optical biopsy methods, it is possible to apply a supersensitive technique for analyzing biofluids based on surface-enhanced Raman spectroscopy, which will be most effective for detecting low concentrations of disease markers in biological fluids. In the last decade, the development of nanotechnology has led to the creation of promising tools for solving new problems in the study of various human diseases, which is especially important for effective and targeted treatment and a deeper fundamental understanding of the biochemistry of diseases. Analysis of the diseases was based on deep learning using a separate one-dimensional convolutional neural network (CNN). The results of the spectral data for different diseases demonstrates that CNN significantly outperforms standard methods of analysis and allows for detection of non-communicable diseases with 95-100% accuracy.
Speaker
Ivan Bratchenko
Department of Laser and biotechnical systems, Samara National Research University
Russia
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