Sphingomyelins of Local Fat Depots and Blood Serum as Promising Biomarkers of Cardiovascular Diseases.

Unlabelled: Assessment of the blood lipid spectrum does not always properly reflect local dysfunctional changes in the adipose tissue and prevents identification of all patients at high risk of cardiovascular diseases (CVD). Monitoring of changes in sphingomyelin levels allows to assess and anticipate the development and/or severity of these diseases, as well as to make sphingomyelins new therapeutic targets. was to evaluate the sphingomyelin spectrum of local fat depots and blood serum in connection with clinical and instrumental indicators in patients with coronary artery disease (CAD) and patients with degenerative acquired valvular heart disease (AVHD).

ST37 Distribution in the Moscow Region and Properties of Clinical and Foodborne Isolates.

of the phylogenetic lineage II (PLII) are common in the European environment and are hypovirulent. Despite this, they caused more than a third of the sporadic cases of listeriosis and multi-country foodborne outbreaks. ST37 is one of them.

Expression of Ceramide-Metabolizing Enzymes in the Heart Adipose Tissue of Cardiovascular Disease Patients.

Here, we examined the expression of ceramide metabolism enzymes in the subcutaneous adipose tissue (SAT), epicardial adipose tissue (EAT) and perivascular adipose tissue (PVAT) of 30 patients with coronary artery disease (CAD) and 30 patients with valvular heart disease (VHD) by means of quantitative polymerase chain reaction and fluorescent Western blotting. The EAT of patients with CAD showed higher expression of the genes responsible for ceramide biosynthesis (, , , , , , and ) and utilization (, ). PVAT was characterized by higher mRNA levels of , , , , and ceramide utilization enzyme ().

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.

Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.

Cardiometabolic Effects of Empagliflozin in Patients Undergoing Elective Percu-taneous Coronary Intervention for Type 2 Diabetes Mellitus.

Aim      To evaluate cardiometabolic effects of empagliflozin in patients with ischemic heart disease and type 2 diabetes mellitus (DM) following elective percutaneous coronary intervention (PCI).Materials and methods Patients meeting the inclusion/non-inclusion criteria were randomized into two groups of equal number using simple randomization with successively assigned numbers. Group 1 included 37 patients (18 men and 19 women) who gave their consent for the treatment with empagliflozin 10 mg/day in addition to their previous hypoglycemic therapy.