The global trend of population aging and increasing life expectancy leads to the need to develop and implement national strategies for promoting a healthy lifestyle and preventing chronic noncommunicable diseases at the population level. The article analyzes the best practices of world experience in popularizing healthy lifestyle among different age groups, concepts of maintaining health and increasing life expectancy, active longevity, as well as effective measures to prevent CVD. The analysis of publications posted in the electronic databases PubMed, CochraneLibrary, WoS, Scopus, eLibrary, CyberLeninka, 15 years deep, as well as documents of the United Nations on Sustainable Development Goals and the World Health Organization “Global Action Plan for the Prevention of Noncommunicable Diseases for 2013– 2020”, revised and extended in many ways until 2030.The rapid development of information technology in healthcare, the growth of data volumes and the complexity of diagnostic and treatment methods have led to the fact that artificial intelligence has begun to be used more and more often in this industry. The global experience of a number of Countries confirms that the prevention of chronic noncommunicable diseases and the formation of a healthy lifestyle are multi-level tasks that require a combination of political will, a developed healthcare system and public participation. 

Background. Acute intestinal infections (hereinafter referred to as AII) remain a significant public health problem due to their high virulence, especially among children, who are more susceptible to these pathogens due to underdeveloped immune systems and frequent poor hygiene. The incidence of AII is increasing worldwide, and resort towns are particularly vulnerable, where the main mode of transmission of this group of pathogens — through contaminated water — is much easier to realise. Objective. To analyse statistical data on the incidence of acute respiratory infections among children (0–17 years old) in Sevastopol in the period from 2019 to 2023 in order to identify epidemiological features and patterns of spread of these infections in the child population. Design and methods. The study was based on data provided by the territorial department of Rospotrebnadzor, which consisted of registered cases of established and unidentified etiology. All laboratory-confirmed diagnoses in patients over a five-year period were analysed. Descriptive statistics methods were used to process the data, including calculation of absolute and relative morbidity rates, as well as determination of the share of various infections in the overall morbidity structure. The data were visualised by constructing line graphs and histograms, which made it possible to identify the main trends and seasonal peaks in morbidity. Results. The conducted analysis showed a significant predominance of paediatric morbidity in the total structure of AII, reaching 71 %. Rotavirus infection dominated among the etiologically confirmed cases, accounting for 43 % of the total number of cases. Epidemiological monitoring revealed a well-defined seasonal dynamic with a maximum rise in morbidity in the summer-autumn period (3rd quarter), which corresponds to the characteristic epidemic cycle for this group of infections. The data obtained confirm the need to strengthen preventive measures in this time interval. Conclusion. The study allowed to identify patterns of AII morbidity and also showed a significant epidemiological role of rotavirus infection in the structure of child morbidity. The data obtained substantiate the need to optimise the regional system of surveillance and prevention with a focus on the identified seasonality. 

Introduction. Recent advances in pathogenesis of neurodegenerative diseases have shown that inflammation is a key factor of progression. The levels of T cells, NK cells, monocytes and neutrophils are found to be increased in amyotrophic lateral sclerosis (ALS) patients and are associated with disease progression. The aim of the study was to evaluate the dynamics of immunological parameters of bone marrow cells and clonal hematopoiesis in patients with ALS. Material and methods. The ALS group included 10 patients (M/F 4/6). The mean age was 53.9±9.9 years (95 % CI, 28–47). Whole-exome sequencing and immunophenotyping of CD34+ subsets in bone marrow cells were performed before the start of therapy (point 1) and during the first 6 months of follow-up (point 2). The control group included 10 bone marrow donors, mean age was 39.5±8.5 years (CI 95 %, 46–66, p=0.007). CHIP was detected in 3 cases (30 %) before therapy. Results. The peripheral blood mononuclear cells (PBMCs) were collected after four-day G-CSF administration. The mean number of collected CD34+ cells was 184.5±121.5x106 (95 % CI, 126.0–444.8). Patients received fludarabine 25 mg/ m2 /day, on days 1 and 2. To induce hematopoietic stem cell proliferation the harvested cells were incubated with human placenta double-stranded DNA fragments (Panagen®) ex vivo. The obtained CD34+ cells were reinfused intravenously 48 hours post fludarabine. A significant increase in the number of CD34+CD13+ and CD34+CD123+ HSCs was detected after immunomodulatory therapy and reinfusion of transdifferentiated CD34+ HSCs. The level of CD34+CD44+ in bone marrow significantly decreased. Levels of CD34+CD7+, CD34+CD2+ and CD34+CD56+ showed a trend toward increased mean value and widened confidence intervals compared with the values before treatment, although they did not reach statistical significance. After therapy, two cases demonstrated absence of CHIP while, one case showed a decrease in the allelic variant frequency (VAF). During follow-up the mean ALSFRS-R score did not change (40±1 points (CI 95 %, 37.5–40) vs. 40±2 (CI 95 %, 38–42.5), p>0.05). Conclusion. Our study is the first attempt to characterize the subsets of bone marrow HSCs in ALS. Our results have clinical significance, although they are limited and preliminary. First, they demonstrate that bone marrow is one of the organs responding to immune-mediated neuroinflammation. Second, the issue of whether the abnormal immune response leading to neurodegeneration can be restarted and corrected is raised. Moreover, preliminary results indicate a possible link between CHIP and ALS and point the way to eliminating aberrant clones. 

Acute stroke is one of the leading causes of death and disability worldwide. Millions of people experience this disease every year, with significant consequences for both patients and health systems. Diagnosis and treatment of stroke require fast and accurate decision-making, as time is a critical factor for a successful outcome. However, existing diagnostic methods such as magnetic resonance imaging, although they provide high imaging accuracy, require significant time and human resources. This creates the need to develop new approaches that can improve the effectiveness of diagnosis and prediction of stroke outcomes.

Artificial intelligence is actively developing and finding applications in various fields of medicine, including medical image analysis. The use of artificial intelligence to process MRI data opens up new possibilities for automated diagnosis and prediction of disease outcomes such as stroke. This improves diagnostic accuracy and reduces data analysis time, which is especially important in emergency situations. 

Objective. The purpose of this study is to review the application of deep neural network learning methods in the diagnosis and treatment of aortic aneurysm (AA), based on imaging methods, and special attention will also be paid to screening, diagnosis, lesion segmentation, surgical care, and outcome prediction. Methods. A review was conducted of scientific publications that used deep learning models, such as convolutional neural networks (SNN), in various aspects of AA diagnosis and treatment. Results. Deep learning models have demonstrated significant progress in the treatment and diagnosis of aortic aneurysms. For screening and diagnosis, models such as ResNet provide high accuracy in detecting aneurysms on contrast-free CT scans. Methods such as U-Net allow accurate measurement of aneurysm size and volume, which is important for planning the volume of surgery. Deep learning also helps in surgical procedures by accurately predicting stent position and postoperative complications. In addition, the models are able to accurately predict the progression of the aneurysm and the prognosis for the patient. Conclusions. Deep learning technologies demonstrate significant potential in improving the diagnosis, treatment, and control of aortic aneurysms. These advances can lead to a more accurate and personalized approach to patients, improving treatment outcomes for patients with this pathology. 

Rapidly developing neuroimaging technologies allow to accurately determine venous ischemia and venous infarction of the brain. Most of the works on this topic are devoted to the study of spontaneous cerebral venous thromboses. Common to such thromboses and venous injuries that occurred during the removal of skull base tumors is acute exclusion of cerebral veins. However, in other respects, clinical and diagnostic algorithms are often not similar. The article presents a generalization and analysis of the available literature on the possibilities of neuroimaging venous brain injuries in spontaneous and surgical conditions. Modern methods of angiographic and perfusion diagnostics are described using thrombosis of the dural sinuses and cortical veins as an example. A discussion of the possibility of using these techniques in neurosurgery is given. 

Relevance. Diabetes mellitus (DM) is characterized by a long, asymptomatic course, when heart already occur and disturbances of metabolism, but do not manifest clinically. To understand the need for therapeutic prevention of possible complications of DM, it is important to study the sex-specific features of the functional state of the heart and metabolism at the DM development. Objective. To study the sex-specific features of electrophysiological parameters of the heart and general metabolism indicators in male and female rats at the initial stages of DM 2. Materials and methods. DM was induced in 20 rats of both sexes by a single intraperitoneal administration of alloxan at a dose of 150 mg/kg. Electrocardiogram (ECG) was recorded noninvasively: RR, PQ, QRS, QT intervals, P, R, T wave amplitude, and RН heart rate were assessed. Metabolic parameters were determined: volumes of consumed O2 and exhaled CO2, heat production intensity (HPI, kcal/kg/h). All parameters were assessed before administration, on days 3 and 6 after alloxan administration. Results. On day 3 after alloxan administration, blood glucose levels increased relative to baseline: 5.9 times in males and 7.0 times in females. On day 6, the glucose level was higher than the initial level: 3.2 times in males and 1.5 times in females; moreover, in males it was 2.5 times higher than in females. Pathological ECG changes — rhythm irregularity, decreased amplitude of P and T waves below the isoline, widening of the QRS complex were detected in both males and females on days 3 and 6 of diabetes. Metabolic parameters decreased only in males at all observation periods. Conclusion. In the first week of diabetes, rats showed systolic and diastolic dysfunction of the heart, decreased metabolic intensity. The most pronounced changes were noted in males, which was due to a higher glycemia than in females.