Theoretical and Experimental Approaches to Study of Biological Objects by Mathematical Methods Using the Example of Hormone Production in the Thyroid Gland
DOI:
https://doi.org/10.53933/sspmpm.v4i3.153Keywords:
biological system, expert systems, thyroid gland, follicular cell, pre-disease, markers of changes, hypothyroidism, correlation portrait, nodal dotsAbstract
The study of any biological object is a complex process that involves a number of successive stages, one of which tools can be a specially created expert system. It is advisable to present the conclusion about the studied biological object in clear forms of expression – quantitative or binary, which are the results of the practical application of the principles of absorption by some researched factors of others, a compromise between them or the prevailing alternative of the studied properties. The involvement of mathematical technologies in the identification and explanation of the regularities of the activity of biological objects requires the display of the results of their research using a mathematical language. This makes it possible to establish regularities in the course of biological processes and predict their consequences. Since any living system is formed from a large number of elements, the organism has a complex hierarchy of structural and functional levels of organization. A mandatory prerequisite for the activity of a biological system is a variety of states, each of them being characterized by its own characteristics – markers of change, which, according to the degree of completeness of the one state transformation into another, should be divided into markers of primary changes, markers of prevailing majority changes, and markers of final changes. Comprehensive application of the Semi-quantitative analysis of electronograms according to Ryabukha O. (2000) and her method for determining the profiles of hormonopoietic cells’ special capacities (2003) when studying the cytophysiology of the thyroid gland in normal and pathological conditions, it is possible to determine the specific link of the follicular cell’s specialized activity, in which there was a violation of hormonopoiesis, and to assess its intensity. The developed Conceptual apparatus of functional connections between organelles of hormone-producing cells when studying them by the Method of correlation analysis by creating intra- and intersystem correlation portraits reflects the features of mutual influences and interdependencies, which deepens the understanding of the intimate mechanisms of hormonopoiesis.
References
Murray J.D. Mathematical Biology: I. An Introduction. 3rd ed. S.S. Antman, J.E. Marsden, L. Sirovich (Eds.). New York (NY): Springer; 2002. 551 р. URL: http://dl.icdst.org/pdfs/files/27f6eba850c27d335ff3f93778d8057f.pdf.
Murray J.D. Mathematical Biology: ІІ. Spatial Models and Biomedical Applications (Interdisciplinary applied mathematics). 3rd ed; Vol. 18. New York (N.Y.): Springer; 2003. 811 р. URL: https://link.springer.com/book/10.1007/b98869.
Sydorova N.N., Kazmirchuk A.P., Sydorova L.L. Methods of mathematical prediction in biomedical research and their theoretical capabilities in predicting the course of COVID-19. Current Aspects of Military Medicine. 2020. Vol. 27. No. 1. P. 213−227. DOI: https://doi.org/10.32751/2310-4910-2020-27-21. DOI: https://doi.org/10.32751/2310-4910-2020-27-21
Vera J., Lischer C., Nenov M. et al. Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic. International journal of molecular sciences. 2021. Vol. 22. Iss. 2. Article ID 547. DOI: https://doi.org/10.3390/ijms22020547. DOI: https://doi.org/10.3390/ijms22020547
Ghaffari L.N., Loeffler C.M.L., Grajek J. et al. Classical mathematical models for prediction of response to chemotherapy and immunotherapy. PLoS Computational Biology. 2022. Vol. 18. Iss. 2. Article ID e1009822. DOI: https://doi.org/10.1371/journal.pcbi.1009822. DOI: https://doi.org/10.1371/journal.pcbi.1009822
Liu Y., Wu R., Yang A. Research on Medical Problems Based on Mathematical Models. Mathematics. 2023. Vol. 11. Iss. 13. Article ID 2842. DOI: https://doi.org/10.3390/math11132842. DOI: https://doi.org/10.3390/math11132842
Hossain M.B., Shama A., Adhikary A. et al. An explainable artificial intelligence framework for the predictive analysis of hypo and hyper thyroidism using machine learning algorithms. Human-Centric Intelligent Systems. 2023. Iss. 3. P. 211–231. DOI: https://doi.org/10.1007/s44230-023-00027-1. DOI: https://doi.org/10.1007/s44230-023-00027-1
Torres N.V., Santos G. The (Mathematical) Modeling Process in Biosciences. Frontiers in genetics. 2015. Vol. 6. P. 354. DOI: https://doi.org/10.3389/fgene.2015.00354. DOI: https://doi.org/10.3389/fgene.2015.00354
Saibene A., Assale M., Giltri M. Expert systems: Definitions, advantages and issues in medical field applications. Expert Systems with Applications. 2021. Vol.177. 114900. DOI: https://doi.org/10.1016/j.eswa.2021.114900. DOI: https://doi.org/10.1016/j.eswa.2021.114900
Schrödinger E. What is life? The physical aspect of the living cell. А textbook. Moscow: AST, 2018. 288 p. (Russian translation Cambridge, 1944).
Mintser O.P., Babintseva L.Y. New trends in the development of data presentation and management systems. Analytical view. Medical Informatics and Engineering. 2022. No. 1-2. P. 5–13. DOI: https://doi.org/10.11603/mie.1996-1960.2022.1-2.13104. DOI: https://doi.org/10.11603/mie.1996-1960.2022.1-2.13104
Ryabukha O.I. Perspectives of applying new approaches to the implementation of mathematical technologies in the study of cell activity. Medical Informatics and Engineering. 2018. No. 1. P. 67–75. DOI: https://doi.org/10.11603/mie.1996-1960.2018.1.8894. DOI: https://doi.org/10.11603/mie.1996-1960.2018.1.8894
Caplan M.J. Functional organization of the cell. In: W. F. Boron, E. L. Boulpaep (Eds.). Medical Physiology, 3rd ed. Philadelphia: Elsevier; 2016. pp. 8–46. URL: https://shop.elsevier.com/books/medical-physiology/boron/978-1-4557-4377-3.
Ryabukha O. Multidisciplinary studies of the thyroid gland’s synthetic activity under conditions of iodine deficiency using correlation analysis. SSP Modern Pharmacy and Medicine. 2023. Vol. 3. No. 3. P. 1–15. DOI: https://doi.org/10.53933/sspmpm.v3i3.104. DOI: https://doi.org/10.53933/sspmpm.v3i3.104
Miot H.A. Correlation analysis in clinical and experimental studies. Jornal vascular brasileiro. 2018. Vol. 17. No. 4. P. 275–279. DOI: https://doi.org/10.1590/1677-5449.174118. DOI: https://doi.org/10.1590/1677-5449.174118
Ryabukha O., Dronyuk I. The portraits creating method by correlation analysis of hormone-producing cells data. CEUR Workshop Proceedings-Series. 2018. Vol. 2255. P. 135–145. URL: http://ceur-ws.org/Vol-2255/paper13.pdf.
Ryabukha O., Dronyuk I. Applying of information technologies for study of the thyroid gland follicular thyrocytes’ synthetic activity. CEUR Workshop Proceedings-Series. 2020. Vol. 2753. P. 323–337. URL: http://ceur-ws.org/Vol-2753/paper23.pdf.
von Bertalanffy L. The history and status of General Systems Theory. The Academy of Management Journal. 1972. Vol. 15. No. 4. P. 407-426. URL: https://www.marilia.unesp.br/Home/Instituicao/Docentes/RosangelaCaldas/bertalanffy.pdf DOI: https://doi.org/10.5465/255139
Mossio M. Organization as an explanandum and an explanans of biology. In Organization in Biology. History, Philosophy and Theory of the Life Sciences. Springer International Publishing, 33, 2023. p. 2. DOI: https://doi.org/10.1007/978-3-031-38968-9. DOI: https://doi.org/10.1007/978-3-031-38968-9
Kumar A., Kishun J., Singh U. et al. Use of appropriate statistical tools in biomedical research: Current trend & status. The Indian journal of medical research. 2023. Vol. 157. No. 4. P. 353–357. DOI: https://doi.org/10.4103/ijmr.IJMR_809_20. DOI: https://doi.org/10.4103/ijmr.IJMR_809_20
Huang P., Zheng G.-L., Ma S. Ten-years research progress of natural language understanding based on perceptual formalization. In Proceedings of the 2nd International Conference on Intelligence Science (ICIS 2018), Nov 2018, Beijing, China. IFIP Advances in Information and Communication Technology, Springer, Cham. 2018. Vol 539, pp.191-200. DOI: https://doi.org/10.1007/978-3-030-01313-4_20. DOI: https://doi.org/10.1007/978-3-030-01313-4_20
Gupta G., Varanasi S., Basu K. et al. Formalizing informal logic and natural language deductivism. In Proceedings of the International Conference on Logic Programming (ICLP ‘21), Sep 20-21, 2021 Porto, Portugal: CEUR Workshop Proceedings-Series. 2021. Vol 2970. URL: https://ceur-ws.org/Vol-2970/gdepaper3.pdf.
Saji A., Kato Y., Matsubara S. A Model-Theoretic Formalization of Natural Language Inference Using Neural Network and Tableau Method. In Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation. 2022. Manila, Philippines: Association for Computational Linguistics. p.p. 430–437. URL: https://aclanthology.org/2022.paclic-1.48.
Gordon C.S., Matskevich S. Trustworthy formal natural language specifications. In Proceedings of the 2023 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!‘23), Oct. 25-27, 2023. Cascais, Portugal: arXiv:2310.03885. p.p. 50–70. DOI: https://doi.org/10.1145/3622758.3622890. DOI: https://doi.org/10.1145/3622758.3622890
Klyuchko O.M. Electronic Expert Systems for Biology and Medicine. Biotechnologia Acta. 2018. Vol. 11, No. 6, Р. 5-28. DOI: https://doi.org/10.15407/biotech11.06.005. DOI: https://doi.org/10.15407/biotech11.06.005
Ryabukha O.I. Conceptual approaches to the study of the thyroid gland at different levels of its integration into the body. Endocrinology and Disorders. 2020. Vol. 4. Iss. 1. DOI: https://doi.org/10.31579/2640-1045/047. DOI: https://doi.org/10.31579/2640-1045/047
Avtandilov G.G. Medical morphometry: A guide. Moscow: Meditsina; 1990. 384 p. URL: https://readrate.com/rus/books/meditsinskaya-morfometriya.
Avtandilov G.G. The Basics of quantitative pathological anatomy. Moscow: Meditsina; 2002. 240 p. URL: https://readrate.com/rus/books/osnovy-kolichestvennoy-patologicheskoy-anatomii.
Ryabukha O., Dronyuk I. Applying regression analysis to study the interdependence of thyroid, adrenal glands, liver, and body weight in hypothyroidism and hyperthyroidism. CEUR Workshop Proceedings-Series. 2019. Vol. 2488. P. 155–164. URL: http://ceur-ws.org/Vol-2488/paper13.PDF.
Ryabukha O., Greguš ml M. Correlation analysis as a thyroid gland, adrenal glands, and liver relationship tool for correcting hypothyroidism with organic and inorganic iodine. Procedia Computer Science. 2019. Vol. 160. P. 598-603. DOI: https://doi.org/10.1016/j.procs.2019.11.041. DOI: https://doi.org/10.1016/j.procs.2019.11.041
Сhaddock R.E. Interpretation of the coefficient of correlation. In: Principles and Methods of Statistics. Boston: Houghton Mifflin; 1925. pp. 303–304. URL: https://babel.hathitrust.org/cgi/pt?id=uc1.b3257183&view=1up&seq=323.
Altman D.G., Bland J.M. Statistics notes: variables and parameters. BMJ (Clinical research ed.). 1999. Vol. 318. No. 7199. P. 1667. DOI: https://doi.org/10.1136/bmj.318.7199.1667. DOI: https://doi.org/10.1136/bmj.318.7199.1667
Ryabukha O.I. Potentiated alimentary iodine deficiency: Features of relationships between the follicular thyrocytes’ energy capability profile ultrastructures when corrected by different doses of organic iodine. Hygiene of Populated Places. 2022. Iss. 72. P. 68–83. DOI: https://doi.org/10.32402/hygiene2022.72.068. DOI: https://doi.org/10.32402/hygiene2022.72.068
Ryabukha O. Innovative model for studying the features of hormono-poietic cells functioning based on characteristics of different aspects in their activity (as examplified by follicular thyrocytes). In: Scientific basis of modern medicine: collective monograph. Boston: Primedia eLaunch, 2020. pp. 171–181. DOI: https://doi.org/10.46299/isg.2020.MONO.MED.I. DOI: https://doi.org/10.46299/isg.2020.MONO.MED.I
Ryabukha O.I. Study of the follicular thyrocytes’ synthetic activity while taking inorganic iodine under conditions of alimentary iodine deficiency. Bulletin of problems in biology and medicine. 2017. Iss. 4. No. 3(141). P. 218–223. DOI: https://doi.org/10.29254/2077-4214-2017-4-3-141-218-223. DOI: https://doi.org/10.29254/2077-4214-2017-4-3-141-218-223
Ryabukha O. I. Application of mathematical approaches in medicine on the example of follicular thyrocytes secretory activity study. World of Medicine and Biology. 2019. No. 1. P. 181–187. DOI: https://doi.org/10.26724/2079-8334-2019-1-67-181. DOI: https://doi.org/10.26724/2079-8334-2019-1-67-181
Ryabukha O. Сorrelation portrait as a means to study the relationships of follicular thyrocytes ultrastructures: The profile of transport capability under the action of organic iodine in the conditions of alimentary iodine deficiency. Medical Informatics and Engineering. 2022. No. 1-2. P. 14–28. DOI: https://doi.org/10.11603/mie.1996-1960.2022.1-2.13107. DOI: https://doi.org/10.11603/mie.1996-1960.2022.1-2.13107
Ryabukha O.I. Study of ultrastructure profile of follicular thyrocytes’ transport capabilities by means of correlation analysis. Medical Informatics and Engineering. 2022. No. 3-4. P. 28–38. DOI: https://doi.org/10.11603/mie.1996-1960.2021.3-4.12638. DOI: https://doi.org/10.11603/mie.1996-1960.2021.3-4.12638
Ryabukha O. Features of relationships between ultrastructures of the energy capability profile of follicular thyrocytes in the correction of alimentary iodine deficiency with a low dose of organic and inorganic iodine. The Medical and Ecological Problems. 2022. Vol. 26, No. 3-4. P. 16–29. DOI: https://doi.org/10.31718/mep.2022.26.3-4.03. DOI: https://doi.org/10.31718/mep.2022.26.3-4.03
Uurtio V., Monteiro J.M., Kandola J. et al. A Tutorial on canonical correlation methods. ACM Computing Surveys. 2018. Vol. 50. No. 6. Article ID 95. DOI: https://doi.org/10.1145/3136624. DOI: https://doi.org/10.1145/3136624
Ryabukha O., Dronyuk I. Modern аpproaches to the applying of mathematical methods in the analysis of the transport direction of follicular thyrocytes. CEUR Workshop Proceedings-Series. 2021. Vol. 3038. P. 302–316. URL: http://ceur-ws.org/Vol-3038/paper19.pdf.
Ryabukha O.I. Substantiation of conceptual apparatus for mathematical studies on the hormone-producing cell activity. Bulletin of problems in biology and medicine. 2018. Iss. 3. No. 1(145). P. 234–237. DOI: https://doi.org/10.29254/2077-4214-2018-3-145-234-237. DOI: https://doi.org/10.29254/2077-4214-2018-3-145-234-237
Shapovalova V. The ICD-11 for the twenty-first century: the first view from the organizational, legal, clinical and pharmacological aspects. SSP Modern Pharmacy and Medicine. 2022. Vol. 2. No. 1. Р. 1-13. DOI: https://doi.org/10.53933/sspmpm.v2i1.37. DOI: https://doi.org/10.53933/sspmpm.v2i1.37
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Olha Ryabukha
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.