The aging of the population in developed countries is a trend of major medical and social significance. In this regard, the study of the etiology and pathogenesis of neurodegenerative diseases, as well as the search for effective treatment methods, is of particular relevance. For a long time, it was believed that metabolic waste products were drained from the brain parenchyma’s interstitial fluid into the ventricular system. However, the discovery of the brain’s glymphatic system has significantly advanced our understanding of the mechanisms underlying pathologies associated with impaired clearance of metabolites from the brain. This scientific review outlines the main directions in the study of the functional morphology of the glymphatic system under normal conditions. It provides a detailed description of two theories of cerebrospinal fluid outflow and presents a critical analysis of both Russian and international research data. Under normal conditions, the function of the glymphatic system is influenced by heart rate, intracranial pressure, pulse and arterial pressure, as well as the phase of the respiratory cycle. In addition, sleep quality, head position during sleep, and exposure to toxic substances directly affect glymphatic system activity. The review also highlights recent data on the glymphatic system of the visual organs. Further research into the morphofunctional characteristics of the glymphatic system under normal conditions may greatly expand our fundamental understanding of disease pathogenesis and contribute to the development of new approaches to treatment and prevention.

Chronic venous disease (CVD) is one of the most common vascular disorders, affecting millions of people worldwide. Owing to the variability of clinical symptoms and the subjective nature of their interpretation, diagnosing CVD at an early stage is complicated, making it crucial for patients to consult a specialist. It was hypothesized that an artificial intelligence (AI) model could accurately classify CVD (C0-C2 Clinical, Etiology, Anatomy, and Pathophysiology (CEAP) clinical class) from lower limb photographs. Therefore, this study aimed to develop and validate such a model. A multicenter cross-sectional study (NCT17122021) was conducted from May 2020 to January 2024 in accordance with the Declaration of Helsinki. A dataset of 10,745 lower limb photographs was collected using smartphones and professional cameras across several Russian clinics, then standardized and anonymized before model training. CEAP clinical class was determined by consensus among three surgeons experienced in phlebology. The AI model IVENUS was developed to automatically assess lower limb photographs and classify early-stage CVD according to the CEAP clinical classification (C0-C2). The model was trained using a deep learning approach based on the Swin Transformer V2 architecture. To improve model robustness and reduce overfitting, Gaussian blurring and color jitter were applied as data augmentation methods during training. The standard performance metrics (sensitivity, recall, specificity, accuracy, and precision) were calculated. The dataset consisted of 673 lower limbs of stage C0, 4445 lower limbs of stage C1, and 5627 lower limbs of stage C2. The overall diagnostic accuracy in the external validation subset of 1622 photographs was 84.8%, with a precision of 84.3%, sensitivity of 84.3%, and specificity of 92.3%. The AI model IVENUS demonstrated high diagnostic value for early-stage CVD, sufficient for its application as a clinical decision support system. Therefore, this model may support patient self-screening and telemedicine triage and may be used by specialists for automated patient routing and tracking of treatment progress.

In the modern understanding of the system of vascular trophic support, the haemomicrocirculatory bed is a sequential connection of arterioles, precapillaries, capillaries, postcapillaries and venules, ordered by their location in tissues, as well as the presence of arteriolo-venular anastomoses. Differentiation of various links of the microcirculatory bed is based not only on the size of the vessel lumen, but also on the peculiarities of the structure of their walls and the functions performed. The main metabolic link of the human circulatory system is the capillary bed, in which the presence of three types of capillaries is currently described, the distinction of which is established by the peculiarities of the structure of their walls. At the same time, morphological differences of capillary wall structure determine essential differences in their functions, which causes a number of critical comments on the validity of uniting capillaries into one type of vessels only in the absence of diameter differences. In addition, when forming an objective morphofunctional assessment of the organization of the haemomicrocirculatory bed of an organ and/or tissue, it is necessary to evaluate topographic-anatomical regularities of the architectonics and mutual location of different types of capillaries in the vascular network of organs and tissues, which can be determined using modern methods of morphometry. The results of further research in this direction can become new knowledge about the regularities of blood supply organization, allowing to detail the role of various microvessels in the organization of microhemodynamics and metabolic processes in organs and tissues in norm and pathology, as well as find an important applied use in the development of new methods of selective drug delivery to the focus of pathological process in various human diseases.

Objective. To quantitatively evaluate the effectiveness of artificial intelligence (AI)-based technologies in diseases of the venous system. Material and methods. A systematic literature search was performed across the PubMed, Cochrane Library, and Google Scholar databases. Original studies utilizing AI models in venous pathology, including venous thromboembolism (VTE) and chronic venous disease (CVD), were included in the analysis. Study endpoints were the diagnostic and prognostic performance metrics, pooled using a random-effects model. Results. Of the 606 evaluated publications, 100 studies were included in the analysis addressing prediction (n=76) and diagnosis (n=24) of VTE (n=86) and CVD (n=14). For diagnostic models of VTE and CVD, the pooled accuracy, sensitivity, and specificity at internal validation were: 0.93 (95% CI: 0.87—0.96) and 0.98 (95% CI: 0.91—0.99); 0.89 (95% CI: 0.81—0.96) and 0.89 (95% CI: 0.80—0.95); 0.93 (95% CI: 0.87—0.98) and 0.94 (95% CI: 0.67—0.99), respectively. For predictive models of VTE, the pooled accuracy, sensitivity, specificity, precision, and AUC-ROC at internal validation were: 0.85 (95% CI: 0.83—0.88), 0.78 (95% CI: 0.72—0.85), 0.86 (95% CI: 0.80—0.92), 0.60 (95% CI: 0.45—0.74), and 0.86 (95% CI: 0.84—0.88), respectively. For predictive models of CVD, the pooled AUC was 0.88 (95% CI: 0.82—0.95). External validation was performed in only 23% of cases, and the obtained metrics were inferior to internal validation results. Conclusion. AI-based technologies demonstrate high diagnostic and moderate predictive performance in pathology of the venous system. However, further studies with external validation are required to assess implementation into clinical practice.