THE FACTOR ANALYSIS DATA ABOUT THE STRUCTURE OF CANIS FAMILIARIS THYROID GLAND UNDER DIFFERENT SEASONAL ENVIRONMENTAL PARAMETERS

The use of factor analysis in constructing mathematical models allows us to identify hidden relationships between the variables under study. The structural organization of the thyroid gland is characterized by histological and topographic features of the relationship between the functional (follicles) and regulatory (C-thyrocytes, mast cells) links of the different zones of the organ and their dynamic plasticity in animals under the influence of exogenous seasonal, heliomagnetic and geomagnetic influences. The aim of the study was to evaluate the histological and topographic organization of the structural elements of the thyroid gland of dogs (Canis familiaris) under the influence of various conditions of the annual environmental seasons. The object of the study was digitized transverse histological sections of the central part of the right lobe of the thyroid gland of domestic male dogs (n = 16). To study the influence of meteorological factors on the structure of the gland, the material was obtained in the winter and summer seasons of the year, 8 animals each. Histological analysis was performed on sections stained by various methods (hematoxylin and eosin, azure II-eosin), including immunohistochemical (C-thyrocytes, fibronectin, PCNA - proliferating cell nuclear antigen, thyroglobulin, von Willebrand factor). In the ImageJ program, after preliminary geometric and photometric calibration, the area and optical density of cellular and tissue structures of the thyroid gland were measured, including relative to the center in all its zones. A total of 67 variables were studied, combined into groups of exogenous, endogenous organismic and endogenous organ characteristics. 10 factors were identified that influenced 77% of the total variance of the analyzed parameters. The composition of the factors is presented as follows: factor 1 includes quantitative indicators of C-thyrocytes (16.7% of the total variance). Factor 2 is represented by quantitative and functional indicators of mast cells (13.7% of the variance). Factor 3 characterizes the state of the functional link of the organ - follicles (12.9% of the variance); the fourth factor is the degree of saturation of C-thyrocytes with granules (7.69% of the variance). Finally, seasonal environmental influences (daylight hours, average daily temperature together with the index of proliferative activity of cells form the fifth factor (5.85% of the variance of the signs).

1. Abduvaliev A, Saydalieva M, Hidirova M et al. Mathematical Modeling of the Thyroid Regulatory Mechanisms. Am J Med Sci Med. 2015;3(3):28-32. https://doi.org/10.12691/ajmsm-3-3-1

2. Kolpak EP, Terekhina EI. Matematicheskaya model' funktsionirovaniya shchitovidnoy zhelezy. Molodoy ucheny. 2018;4(190):1-7. In Russian

3. Kalmin OV, Kalmin OO. Mathematical Modeling of Morphometric Parameters of Thyroid Gland Structure. Moscow Workshop on Electronic and Networking Technologies, MWENT 2020 - Proceedings, Moscow, 11-13 marta 2020 goda. – Moscow. 2020;9067485. https://doi.org/10.1109/MWENT47943.2020.9067485

4. Borovikov VP. Populyarnoe vvedenie v sovremenny analiz dannykh i mashinnoe obuchenie na STATISTICA. Moskva: Goryachaya liniya-Telekom. 2024. – 354s. In Russian

5. Howard MC. A systematic literature review of exploratory factor analyses in management. J Bus Res. 2023;164(1):113969. https://doi.org/10.1016/j.jbusres.2023.113969

6. Bezdenezhnykh AV, Goncharov VV. Faktornaya model' strukturnoy organizatsii shchitovidnoy zhelezy pri dlitel'nom bege. Sovremennye problemy nauki i obrazovaniya. 2025;1. In Russian. https://doi.org/10.17513/spno.33895

7. Bezdenezhnykh AV, Petrova NI. Metodika topicheskoy diagnostiki morfologicheskikh parametrov shchitovidnoy zhelezy. Morfologija. 2000;114(4):91-94. In Russian

8. Kulaichev AP. Metody i sredstva kompleksnogo statisticheskogo analiza dannykh: uchebnoe posobie. 5-e izd., pererab. i dop. M.: INFRA-M. 2022. – 484s. In Russian

9. Trendafilov N, Hirose K. Exploratory factor analysis. In book: In International Encyclopedia of Education (Fourth Edition). Elsevier Science. 2023;600-606. https://doi.org/10.1016/B978-0-12-818630-5.10015-6

10. Fávero LP, Belfiore P. Principal Component Factor Analysis. In book: Data Science for Business and Decision Making. Academic Press. 2019;383-438. https://doi.org/10.1016/B978-0-12-811216-8.00012-4