Predictive models for COVID-19 detection using routine blood tests and machine learning

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning

Investigation of the electric field distribution in the human brain based on MRI and EEG data

This work is devoted to the development of the approach to restoration of the spatial-temporal distribution of electric field in the human brain. This field was estimated from the model derived from the Maxwell’s equations with boundary conditions corresponding to electric potentials at the EEG electrodes, which are located on the surface of the head according to the standard “10-20” scheme. The MRI data were used for calculation of the spatial distribution of the electrical conductivity of biotissues in the human brain. The study of the electric field distribution using our approach was carried out for the healthy child and the child with autism. The research was carried out using the equipment of the Tomsk Regional Common Use Center of Tomsk State University

Investigation of glycation products by THz time-domain spectroscopy

Glycation is the non-enzymatic reaction betwee

Paraffin embedded cancer tissue 2D terahertz imaging and machine learning analysis

Absorption spectra of paraffin-embedded prostate and adenocarcinoma cancer tissues an

Imitation of ultra-sharp light focusing within turbid tissue-like scattering medium by using time-independent Helmholtz equation and method Monte Carlo

Based on time-independent Helmholtz equation and its solution in frame of inhomogeneous approximation a hybrid computational method for imitation of propagation of bounded laser beam focused into biological tissue is introduced. The biological tissue is simulated as a semi-infinite randomly inhomogeneous medium. The developed approach is intended to model laser beams in the super-sharp focusing mode. The results of modeling of laser light focusing into the turbid tissue-like scattering medium with lenses of various shapes are presented

The study of spectral changes in THz range in normal and pathological skin in vivo depending on the dehydration methods used

The terahertz (THz) attenuated total reflectance (ATR) imaging of normal and pathological skin under the action of various dehydration agents was carried out in vivo. Studies were conducted on animal models (the mouse), patients with diabetes, and healthy volunteers. For measurements, each animal was leaned against the ATR prism of the skin surface, and several locations in the skin of each animal were analyzed. Places on the skin for analysis were chosen so that the intensity spectra of the THz signal were practically the same for selected points. THz spectra measurements were carried out every 10 minutes within 45 minutes interval under the action of a dehydration agent. 40% glucose was shown to provide the most effective improving tissue optical clearing effect in the THz range