According to the World Health Organization, ischemic stroke is the second leading cause of death in the world. Frequently, it is caused by brachiocephalic artery (BCA) atherosclerosis. Timely detection of atherosclerosis and its unstable course can allow for a timely response to potentially dangerous changes and reduce the risk of vascular complications. Omics technologies allow us to identify new biomarkers that we can use in diagnosing diseases. This research included 90 blood plasma samples. The study group comprised 52 patients with severe atherosclerotic lesions BCA, and the control group comprised 38 patients with no BCA atherosclerosis. Targeted and panoramic lipidomic profiling of their blood plasma was carried out. There was a statistically significant difference (p < 0.05) in the values of the indices saturated fatty acids (FAs), unsaturated FAs, monounsaturated FAs, omega-3, and omega-6. Based on the results on the blood plasma lipidome, we formed models that have a fairly good ability to determine atherosclerotic lesions of the brachiocephalic arteries, as well as a model for identifying unstable atherosclerotic plaques. According only to the panoramic lipidome data, divided into groups according to stable and unstable atherosclerotic plaques, a significant difference was taken into account: p value < 0.05 and abs (fold change) > 2. Unfortunately, we did not observe significant differences according to the established plasma panoramic lipidome criteria between patients with stable and unstable plaques. Omics technologies allow us to obtain data about any changes in the body. According to our data, statistically significant differences in lipidomic profiling were obtained when comparing groups with or without BCA atherosclerosis.

Background:: Diabetic retinopathy is the most common complication of diabetes mellitus and is one of the leading causes of vision impairment globally, which is also relevant for the Russian Federation. Objective:: To evaluate the diagnostic efficiency of a convolutional neural network trained for the detection of diabetic retinopathy and estimation of its severity in fundus images of the Russian population. Methods:: In this cross-sectional multicenter study, the training data set was obtained from an open source and relabeled by a group of independent retina specialists; the sample size was 60,000 eyes. The test sample was recruited prospectively, 1186 fundus photographs of 593 patients were collected. The reference standard was the result of independent grading of the diabetic retinopathy stage by ophthalmologists. Results:: Sensitivity and specificity were 95.0% (95% CI; 90.8-96.4) and 96.8% (95% CI; 95.5- 99.0), respectively; positive predictive value – 98.8% (95% CI; 97.6-99.2); negative predictive value – 87.1% (95% CI, 83.4-96.5); accuracy – 95.9% (95% CI; 93.3-97.1); Kappa score – 0.887 (95% CI; 0.839-0.946); F1score – 0.909 (95% CI; 0.870-0.957); area under the ROC-curve – 95.9% (95% CI; 93.3-97.1). There was no statistically significant difference in diagnostic accuracy between the group with isolated diabetic retinopathy and those with hypertensive retinopathy as a concomitant diagnosis. Conclusion:: The method for diagnosing DR presented in this article has shown its high accuracy, which is consistent with the existing world analogues, however, this method should prove its clinical efficiency in large multicenter multinational controlled randomized studies, in which the reference diagnostic method would be unified and less subjective than an ophthalmologist.

According to the World Health Organisation ischemic stroke is the second leading cause of death in the world. More frequent it is caused by brachiocephalic arteries (BCA) atherosclerosis. Timely detection of the atherosclerosis and it`s unstable course can allow a timely response to potentially dangerous changes and reduce the risk of vascular complications. Omics technologies allow us to identify new biomarkers that we can use in diagnosing diseases. The research included 90 blood plasma samples. The first group - 52 patients with severe atherosclerotic lesions, second group - 38 patients with no BCA atherosclerosis. Targeted and panoramic lipidomic profiling of blood plasma was carried out. There is a statistically significant difference (р

This paper presents a methodology to generate synthetic pulse wave database. Each virtual subject is generated with the help of one-dimensional hemodynamics model of systemic circulation with lumped model of the left heart. This paper describes and compares two parameter optimization methods: unscented Kalman filter and Bayesian optimization. As a case study, an experiment is conducted to predict cardio-ankle vascular index (CAVI) values for real individuals with a machine learning algorithm trained on a synthetic population. The average error of 6.5% is achieved

Ischemic cardiovascular events (ischemic stroke, myocardial infarction) are the most common complications of cardiovascular diseases. One of the leading mechanisms of these complications is atherosclerosis. Lipids play an important role in plaque development. Recently, the lipidome has been of greatest interest, since it may have a prognostic value in atherosclerosis development. With an increase in the circulation of proatherogenic lipidomic biomarkers, the risk of atheroscle­rosis destabilization and ischemic complications increases.The conducted studies made it possible to create additional risk stratification scales, for example, Cardiovascular Event Risk Test (CERT) 1 and 2. They make it possible to estimate the residual risk in patients taking statins. The lipidome examination in extracranial artery atherosclerosis can identify those plaques that have signs of instability, and therefore are dangerous for ischemic stroke development.The review describes the features of plasma lipidome in various cardiovascular diseases associated with atherosclerosis.