Mathematical modelling in the study of the pathogenesis of viral hepatitis in children
https://doi.org/10.46563/1560-9561-2022-25-1-28-31
EDN: fyihcf
Abstract
The aim of the work is to form a mathematical model of viral hepatitis based on structural modelling, discriminant and factorial analysis of laboratory parameters of patients.
Materials and methods. The data array included the results of a comprehensive laboratory testing of 109 children with viral hepatitis B and C (33 parameters).
Results. Seven main factors in the pathogenesis of viral hepatitis in children reflect the severity of endogenous intoxication and disorders of protein metabolism, modification of the mode of functioning of enzymes, and supramolecular multi-enzyme complexes in conditions of progression of the first two components.
Conclusion. The identified factors of the pathogenesis of viral hepatitis may reflect the endogenous intoxication during disease progression, the state of detoxification enzyme systems, and the further fibrotic changes in the liver.
Contribution:
Martusevich A.K. — the concept and design of the study, the collection and processing of material, writing the text;
Galova E.A. — providing material for research;
Korkotashvili L.V. — carrying out mathematical modeling;
Popovicheva A.N. — editing the text of the article.
Аll co-authors — аpproval of the final version of the article, responsibility for the integrity of all parts of the article.
Informed consent: informed consent was received from the patients parents for the publication of a description of the clinical case.
Acknowledgment. The study had no sponsorship.
Conflict of interest. The authors declare no conflict of interest.
Received: January 14, 2022
Accepted: February 17, 2022
Published: March 15, 2022
About the Authors
Andrey K. MartusevichRussian Federation
Elena A. Galova
Russian Federation
Aleksandra N. Popovicheva
Russian Federation
Junior researcher, Laboratory of medical biophysics of the University Clinic, Privolzhsky Research Medical University, Nizhny Novgorod, 603950, Russian Federation.
e-mail: alexandra.popovichus@yandex.ru
References
1. Aston P.J. A new model for the dynamics of hepatitis C infection: derivation, analysis and implications. Viruses. 2018; 10(4): 195. https://doi.org/10.3390/v10040195
2. Scheel T.K.H., Rice C.M. Understanding the hepatitis C virus life cycle paves the way for highly effective therapies. Nat Med. 2013; 19: 837–49. https://doi.org/10.1038/nm.3248
3. Shin E.C., Han J.W., Kang W., Kato T., Kim S.J., Zhong J., et al. The beginning of ending hepatitis C virus: A summary of the 26th international symposium on hepatitis C virus and related viruses. Viruses. 2020; 12(3): 302. https://doi.org/10.3390/v12030302
4. Stanaway J.D., Flaxman A.D., Naghavi M., Fitzmaurice C., Vos T., Abubakar I., et al. The global burden of viral hepatitis from 1990 to 2013: findings from the Global Burden of Disease Study 2013. Lancet. 2016; 388: 1081–8. https://doi.org/10.1016/S0140-6736(16)30579-7
5. Klasse P.J. Molecular determinants of the ratio of inert to infectious virus particles. Prog Mol Biol Transl Sci. 2015; 129: 285–326. https://doi.org/10.1016/bs.pmbts.2014.10.012
6. Pradeep K.S., Medhi S., Asim M., Das B.C., Gondal R., Kar P. Evaluation of adefovir & lamivudine in chronic hepatitis B: correlation with HBV viral kinetic, hepatic-necro inflammation & fibrosis. Indian J Med Res. 2011; 133(1): 50–6.
7. Hao W., Komar H.M., Hart P.A., Conwell D.L., Lesinski G.B., Friedman A. Mathematical model of chronic pancreatitis. PNAS. 2017; 114(19): 5011–6. https://doi.org/10.1073/pnas.1620264114
8. Kim Y., Lawler S., Nowicki M.O., Chiocca E.A., Friedman A. A mathematical model for pattern formation of glioma cells outside the tumor spheroid core. J Theor Biol. 2009; 260(3): 359–71. https://doi.org/10.1016/j.jtbi.2009.06.025
9. Siewe N., Yakubu A.A., Satoskar A.R., Friedman A. Immune response to infection by leishmania: A mathematical model. Mathematical Biosciences. 2016; 276: 28–43. https://doi.org/10.1016/j.mbs.2016.02
10. Siewe N., Yakubu A.A., Satoskar A.R., Friedman A. Granuloma formation in leishmaniasis: A Mathematical model. J Theor Biol. 2017; 412: 48–60. https://doi.org/10.1016/j.jtbi.2016.10.004
11. Friedman A., Hao W. Mathematical Modeling of Liver Fibrosis. Mat Biosc Eng. 2017; 14(1): 143–64. https://doi.org/10.3934/mbe.2017010
12. Friedman A., Siewe N. Chronic hepatitis B virus and liver fibrosis: A mathematical model. PLoS One. 2018; 13(4): e0195037. https://doi.org/10.1371/journal.pone.0195037
13. Graw F., Perelson A.S. Modeling Viral Spread. Annu Rev Virol. 2016; 3: 555–72. https://doi.org/10.1146/annurev-virology-110615-042249
14. Hao W., Rovin B.H., Friedman A. Mathematical model of renal interstitial fibrosis. Proc Natl Acad Sci. 2014; 111(39): 14193–8. https://doi.org/10.1073/pnas.1413970111
15. Zhao S., Su Z., Lu Y. A mathematical model of hepatitis B virus transmission and its application for vaccination strategy in China. Int J Epidemiol. 2000; 29(4): 744–52. https://doi.org/10.1093/ije/29.4.744
16. Kalemera M., Mincheva D., Grove J., Illingworth C.J.R. Building a mechanistic mathematical model of hepatitis C virus entry. PLoS Comput Biol. 2019; 15(3): e1006905. https://doi.org/10.1371/journal.pcbi.1006905
17. Kamyad A.V., Akbari R., Heydari A.A., Heydari A. Mathematical modeling of transmission dynamics and optimal control of vaccination and treatment for hepatitis B virus. Comp Math Methods Med. 2014. 475451; 2014: 1–15. https://doi.org/10.1155/2014/475451
18. Ciupe S.M., Ribiero R.M., Nelson P.W., Perelson A.S. Modeling the mechanisms of acute hepatitis B virus infection. J Theor Biol. 2007; 247: 23–35. https://doi.org/10.1016/j.jtbi.2007.02.017
19. Padmanabhan P., Dixit N.M. Mathematical model of viral kinetics in vitro estimates the number of E2-CD81 complexes necessary for hepatitis C virus entry. PLoS Comput Biol. 2011; 7: e1002307. https://doi.org/10.1371/journal.pcbi.1002307
20. Aunins T.R., Marsh K.A., Subramanya G., Uprichard S.L., Perelson A.S., Chatterjee A. Intracellular hepatitis C virus modeling predicts infection dynamics and viral protein mechanisms. J Virol. 2018; 92(11): e02098–17. https://doi.org/10.1128/JVI.02098-17
21. Dixit N.M., Layden-Almer J.E., Layden T.J., Perelson A.S. Modelling how ribavirin improves interferon response rates in hepatitis C virus infection. Nature. 2004; 432: 922–4. https://doi.org/10.1038/nature03153
Review
For citations:
Martusevich A.K., Galova E.A., Popovicheva A.N. Mathematical modelling in the study of the pathogenesis of viral hepatitis in children. Russian Pediatric Journal. 2022;25(1):28-31. (In Russ.) https://doi.org/10.46563/1560-9561-2022-25-1-28-31. EDN: fyihcf