Modern approaches to the comprehensive assessment of the human immune status under radiation exposure. Review (part 2)

«Radiation and Risk», 2025, vol. 34, No. 3, pp.43-60

DOI: 10.21870/0131-3878-2025-34-3-43-60

Authors

Khomenko P.O. – Jun. Researcher
Kodintseva E.A. – Researcher, Head of the Shared Research Facilities, C. Sc., Biol. URCRM FMBA of Russia. Contacts: 68-A Vorovsky Str., Chelyabinsk, 454141, Russia. Tel.: +7(351)232-79-23; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. .
Akleyev A.A. – Prof. of the Dep., MD. SUSMU MOH Russia.
¹ Urals Research Center for Radiation Medicine of the FMBA of Russia, Chelyabinsk
² South-Ural State Medical University of the Ministry of Healthcare of RF, Chelyabinsk

Abstract

The importance of an integrated approach to assessing the human immune status based on studying immunity indices in dynamics after radiation exposure is dictated not only by the need to form risk groups among irradiated individuals, but also by the special significance of studying the role of immune disorders in the pathogenesis of late effects of irradiation. However, in environmental immunology, in particular, when assessing the impact of ionizing radiation on the human body, such an analysis is extremely difficult, often impossible. In recent years, the features of the human immune status have been studied in combination with established or potential molecular genetic markers of hereditary predisposition to various pathological conditions with multifactorial etiology. The presented part of the analytical review contains modern scientific information on methodological approaches to the integrated assessment of the human immune status based on a comprehensive analysis of immunity indices, metabolic and hormonal status, as well as other mathematical and statistical methods and mathematical modeling of processes occurring in the immune system. Each approach is based on its own methodological framework, which is rapidly improving, but in practical application has both advantages and limitations.

Key words
human immune status, immune status assessment, mathematical models in immunology, statistical analysis, radiation immunology, environmental immunology, radiobiology, public health.

References

1. Khomenko P.О., Kodintseva E.A., Akleyev A.А. Modern approaches to the comprehensive assessment of the human immune status under radiation exposure. Review (part 1). Radiatsiya i risk – Radiation and Risk, 2025, vol. 34, no. 2, pp. 142-158. (In Russian).

2. Shmakova T.V., Vifleyemskiy A.B., Litovskaya A.V. Integral evaluation of immune homeostasis in rockets liquidators and role of this evaluation for prophylaxis. Meditsina truda i promyshlennaya ekologiya – Russian Journal of Occupational Health and Industrial Ecology, 2010, no. 12, pp. 5-10. (In Russian).

3. Petlenko S.V., Ivanov M.B., Losev S.P., Golubkov A.V., Komnatniy S.B., Bogdanova E.G., Pikalova L.V. The new approach to an integrates estimation of immune system of the person in conditions of influence of a complex of factors of objects of chemical hazard. Medline.ru, 2010, vol. 11, pp. 195-216. Available at: https://medline.ru/public/art/tom11/sample_art.phtml?n_art=17&n_tom=11&lng=eng&ysclid=m67mur4m9854 2947897 (Accessed 22.01.2025). (In Russian).

4. Rakitinskiy V.N., Yudina T.V., Saarkoppel L.M. Development of the problem of integral estimation of the functional state of working people. Laboratornaya sluzhba – Laboratory Service, 2013, vol. 2, no. 3, pp. 6-9. (In Russian).

5. Kryuchkova E.N., Saarkoppel L.M., Yudina T.V. Evaluation of the functional potential of an organism exposed to occupational hazards. Meditsina truda i promyshlennaya ekologiya – Russian Journal of Occupa-tional Health and Industrial Ecology, 2015, no. 9, pp. 79. (In Russian).

6. Zatsarenko S.V., Kuzmina E.G. Integral indicators of immunity in the detection of secondary immunodefi-ciencies and allergy. Rossiyskiy immunologicheskiy zhurnal – Russian Journal of Immunology, 2018, vol. 21, no. 4, pp. 662-664. (In Russian).

7. Travnikova O.E., Dobrodeeva L.K., Martynova N.A., Kalinin A.G. The mode of the complex estimation of the human's immune reactions. Ekologiya cheloveka – Human Ecology, 2009, no. 8, pp. 44-49. (In Russian).

8. Khoffman Dzh., Kuper-Villis E. The symmetry, the complexity and stability of a network theory of the immune system. Matematicheskie modeli v immunologii i meditsine – Mathematical Models in Immunology and Medi-cine. Moscow, Mir, 1986. pp. 110-123. (In Russian).

9. Lee H.Y., Topham D.J., Park S.Y., Hollenbaugh J., Treanor J., Mosmann T.R., Jin X., Ward B.M., Miao H., Holden-Wiltse J., Perelson A.S., Zand M., Wu H. Simulation and prediction of the adaptive immune response to influenza A virus infection. J. Virol., 2009, vol. 83, no. 14, pp. 7151-7165.

10. Bodnar M., Forys U. A model of immune system with time-dependent immune reactivity. Nonlinear Anal. Theory Methods Appl., 2009, vol. 70, no. 2, pp. 1049-1058.

11. Nesterenko V.G. Role of asymmetry in the immune network. Folia Biol., 1986, vol. 32, no. 4, pp. 256-272.

12. Stakheyeva M., Eidenzon D., Slonimskaya E., Patysheva M., Bogdashin I., Kolegova E., Grigoriev E., Choinzonov E., Cherdyntseva N. Integral characteristic of the immune system state predicts breast cancer outcome. Exp. Oncol., 2019, vol. 41, no. 1, pp. 32-38.

13. Stakheyeva M.N., Eidenzon D., Cherdyntseva N.V., Choinzonov E.L., Bogdashin I.V. Integral assessment of the immune system as a new criterion for prescribing effective immunotherapy in patients with malignant neoplasms. Rossiysky bioterapevtichesky zhurnal – Russian Journal of Biotherapy, 2018, vol. 17, no. 1s, pp. 68-69. (In Russian).

14. Zemscov A.M., Zemscov V.M., Zemscova V.A., Vorontsova Z.A., Zoloedov V.I. Innovative and analytical technologies according to the results of traditional immunological monitoring of patients. Vestnik novykh meditsinskikh tekhnologiy – Journal of New Medical Technologies, 2019, vol. 26, no. 2, pp. 40-43. (In Russian).

15. Sokolov E.I., Grishina T.I., Shtin S.R. An integrated approach to assessing the relationship between changes in immune status indicators and markers of nonspecific inflammation in coronary heart disease. Arkhiv vnutrenney meditsiny – The Russian Archives of Internal Medicine, 2013, no. 5(13), pp. 57-60. (In Russian).

16. Kriuchkova E.N., Istomin A.V., Saarkoppel L.M., Yatsyna I.V. The determinants of adaptive resources of organism of adolescents of various regions. Zdravookhraneniye Rossiyskoy Federatsii – Health Care of the Russian Federation, 2017, vol. 61, no. 3, pp. 143-147. (In Russian).

17. Yudina T.V., Saarkoppel L.M., Kryuchkova E.N. The integral approach to evaluation of heath condition of workers of hazard industries. Zdravookhraneniye Rossiyskoy Federatsii – Health Care of the Russian Federation, 2016, vol. 60, no. 2, pp. 101-104. (In Russian).

18. Smirnova O.A. Mathematical modeling of the effect of ionizing radiation on the mammalian immune system. Fizika elementarnykh chastits i atomnogo yadra – Physics of Elementary Particles and Atomic Nuclei, 1996, vol. 27, no. 1, pp. 243-292. (In Russian).

19. Smirnova O.A. Radiation and mammalian organism: model approach. Moscow, Izhevsk, Regular and chaotic dynamics, Institute of Computer Research, 2006. 224 p. (In Russian).

20. Chigvintsev V.M. Mathematical model for description of functioning and interrelation of the immune and neu-roendocrine systems taking into account the impact of chemical factors of the environment: diss. cand. sci. phys. and math. Perm, 2019. 160 p. (In Russian).

21. Perelson A.S., Weisbuch G. Immunology for physicists. Rev. Mod. Phys., 1997, vol. 69, no. 4, pp. 1219-1268.

22. Du S.Q., Yuan W. Mathematical modeling of interaction between innate and adaptive immune responses in COVID-19 and implications for viral pathogenesis. J. Med. Virol., 2020, vol. 92, no. 9, pp. 1615-1628.

23. Nowak M., May R.M. Virus dynamics: mathematical principles of immunology and virology: mathematical principles of immunology and virology. Oxford, Oxford University Press, 2000. 250 р.

24. Driver R.D. Ordinary and delay differential equations. New York, Springer, 2012. 503 р.

25. Fachada N., Lopes V., Rosa A. Agent-based modelling and simulation of the immune system: a review. EPIA 2007 – 13th Portuguese Conference on Artificial Intelligence. Portugal, 2007.

26. Chiacchio F., Pennisi M., Russo G., Motta S., Pappalardo F. Agent‐based modeling of the immune system: NetLogo, a promising framework. BioMed Res. Int., 2014, no. 1, pp. 907171. DOI: 10.1155/2014/907171.

27. Shinde S.B., Kurhekar M.P. Review of the systems biology of the immune system using agent‐based mo-dels. IET Syst. Biol., 2018, vol. 12, no. 3, pp. 83-92.

28. Quintela B.M., dos Santos R.W., Lobosco M. On the coupling of two models of the human immune response to an antigen. BioMed Res. Int., 2014, no. 1, pp. 410457. DOI: 10.1155/2014/410457.

29. Skobtsov Yu.A. Modern immunologic models and their applications. Vestnik MTGU im. N.E. Baumana. Seriya «Priborostroyeniye» – Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, 2022, no. 3(140), pp. 61-77. (In Russian).

30. Afanasova E.P. Prediction of development and outcomes, development of network and mathematical models for improvement of diagnostics and analysis of therapy of acute endometritis: diss. dr. sci. med. Kursk, 2014. 360 p. (In Russian).

31. Cappuccio A., Tieri P., Castiglione F. Multiscale modelling in immunology: a review. Brief. Bioinform., 2016, vol. 17, no. 3, pp. 408-418.

32. de Castro L.N., Von Zuben F.J. aiNet: an artificial immune network for data analysis. Data mining: a heuristic approach. Eds.: H.A. Abbass, R.A. Sarker, C.S. Newton. London, Idea Group Publ., 2001. P. 231-260.

33. Palsson S., Hickling T.P., Bradshaw-Pierce E.L., Zager M., Jooss K., O'Brien P.J., Spilker M.E., Palsson B.O., Vicini P. The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models. BMC Syst. Biol., 2013, no. 7, pp. 95. DOI: 10.1186/1752-0509-7-95.

34. Bratus′ A.S., Bocharov G.A., Ogorodnik E.S., Samokatov V.S. Mathematical model of adaptive immune response based on the process of recognition in the stochastic system “key-lock” by methods of artificial intelligence. Mathematical models and numerical methods in biology and medicine: Proceedings of the XV Conference, 1-3 November 2023. Moscow, INM RAS, 2023, pp. 1-12. (In Russian).

35. Pertseva M., Gao B., Neumeier D., Yermanos A., Reddy S.T. Applications of machine and deep learning in adaptive immunity. Annu. Rev. Chem. Biomol. Eng., 2021, vol. 12, pp. 39-62.

36. Curion F., Theis F.J. Machine learning integrative approaches to advance computational immunology. Genome Med., 2024, vol. 16, no. 1, pp. 80. DOI: 10.1186/s13073-024-01350-3.

37. Chin A., Rider N.L. Artificial intelligence in clinical immunology. Artificial Intelligence in Medicine. Cham, Springer International Publ., 2022, pp. 1397-1410.

38. Rusyaeva N.V., Golodnikov I.I., Kononenko I.V., Nikonova T.V., Shestakova M.V. Machine learning methods in the differential diagnosis of difficult-to-classify types of diabetes mellitus. Sakharnyy diabet – Diabetes Mellitus, 2023, vol. 26, no. 5, pp. 473-483. (In Russian).

39. Dmitriyeva N.Yu. Opportunities of machine learning for diagnostics of orphan diseases. Real′naya klinicheskaya praktika: dannyye i dokazatel′stva – Real-World Data & Evidence, 2023, vol. 3, no. 3, pp. 36-39. (In Russian).

40. Khanzode K.C.A., Sarode R.D. Advantages and disadvantages of artificial intelligence and machine lear-ning: a literature review. Int. J. Lib. Inf. Sci. (IJLIS), 2020, vol. 9, no. 1, pp. 30-36.

41. Duffy D., Rouilly V., Libri V., Hasan M., Beitz B., David M., Urrutia A., Bisiaux A., LaBrie S.T., Dubois A., Boneca I.G., Delval C., Thomas S., Rogge L., Schmolz M., Quintana-Murci L., Albert M.L. Functional analysis via standardized whole-blood stimulation systems defines the boundaries of a healthy immune response to complex stimuli. Immunity, 2014, vol. 40, no. 3, pp. 436-450.

42. Yarets Yu.I. Interpretation of immunogram results. Gomel′, GU «RNPTS RMiECH» Publ., 2020. 38 p. (In Russian).

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