English
Русский
Abstract in RussianDevelopment of a visualization model for immunological parameters to predict the severity of cardiovascular diseases DOI: 10.29188/2712-9217-2025-11-4-17-25
- Krylov A.S. – Junior Researcher, DIANARK LLC; Moscow, Russia; https://orcid.org0000-0001-7085-3437
- Myagkova M.A. – Dr. Sci. (Biol.), Professor, Leading Researcher, Institute of Physiologically Active Compounds, Federal Research Center for Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences; Chernogolovka, Moscow Region, Russia; https://orcid.org0000-0001-7831-7663
- Bobrova Z.V. – Researcher, DIANARK LLC; Moscow, Russia; https://orcid.org0000-0002-8073-8763
- Petrochenko S.N. – general director DIANARK LLC; Moscow, Russia; https://orcid.org/0000-0003-3656-9007
13 Introduction. This study presents the practical application of a developed software suite designed for the visualization and analysis of immunological parameters in patients with cardiovascular diseases (CVD). The core of the software is an original three-dimensional mathematical balance model that integrates laboratory immunochemical data into an interactive graphical system. Visualization is achieved through dynamic changes in the tilt angle of a virtual platform, reflecting the cumulative state of key immunological parameters associated with CVD pathogenesis.
Materials and methods. We investigated the levels of natural antibodies (n-Abs) to key bioregulators—serotonin, dopamine, histamine, and angiotensin II—in patients with various forms of CVD. The analysis included three groups: 1) patients with arterial hypertension (n=45), 2) patients with combined pathology (hypertension and coronary artery disease, n=53), and 3) a control group (n=41). Antibody levels were determined using ELISA.
Results. A statistically significant increase in n-Ab levels was observed in all patient groups compared to controls, with the most pronounced changes in patients with mixed pathology. A mathematical balance model was proposed to integrate laboratory data and predict CVD severity. The results were validated by clinical assessments of the examined patients.
Conclusions. The developed software features a user-friendly interface, adjustable settings, and a scale for measuring the platform’s tilt angle. This system can be applied in telemedicine for disease progression prediction and clinical decision support.
