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Computer Research and Modeling, 2020, Volume 12, Issue 6, Pages 1409–1426
DOI: https://doi.org/10.20537/2076-7633-2020-12-6-1409-1426
(Mi crm857)
 

ANALYSIS AND MODELING OF COMPLEX LIVING SYSTEMS

Cytokines as indicators of the state of the organism in infectious diseases. Experimental data analysis

A. A. Yakovleva, A. I. Abakumovab, A. V. Kostyushkoc, E. V. Markelovac

a Institute of Automation and Control Processes FEB RAS, 5 Radio st., Vladivostok, 690041, Russia
b Far Eastern Federal University, 10 Ajax Bay, Russky Island, Vladivostok, 690922, Russia
c Pacific State Medical University, 2 Ostryakova ave., Vladivostok, 690002, Russia
References:
Abstract: When person‘s diseases is result of bacterial infection, various characteristics of the organism are used for observation the course of the disease. Currently, one of these indicators is dynamics of cytokine concentrations are produced, mainly by cells of the immune system. There are many types of these low molecular weight proteins in human body and many species of animals. The study of cytokines is important for the interpretation of functional disorders of the body’s immune system, assessment of the severity, monitoring the effectiveness of therapy, predicting of the course and outcome of treatment. Cytokine response of the body indicating characteristics of course of disease. For research regularities of such indication, experiments were conducted on laboratory mice. Experimental data are analyzed on the development of pneumonia and treatment with several drugs for bacterial infection of mice. As drugs used immunomodulatory drugs “Roncoleukin”, “Leikinferon” and “Tinrostim”. The data are presented by two types cytokines` concentration in lung tissue and animal blood. Multy-sided statistical ana non statistical analysis of the data allowed us to find common patterns of changes in the “cytokine profile” of the body and to link them with the properties of therapeutic preparations. The studies cytokine “Interleukin-10” (IL-10) and “Interferon Gamma” (IFN$\gamma$) in infected mice deviate from the normal level of infact animals indicating the development of the disease. Changes in cytokine concentrations in groups of treated mice are compared with those in a group of healthy (not infected) mice and a group of infected untreated mice. The comparison is made for groups of individuals, since the concentrations of cytokines are individual and differ significantly in different individuals. Under these conditions, only groups of individuals can indicate the regularities of the processes of the course of the disease. These groups of mice were being observed for two weeks. The dynamics of cytokine concentrations indicates characteristics of the disease course and efficiency of used therapeutic drugs. The effect of a medicinal product on organisms is monitored by the location of these groups of individuals in the space of cytokine concentrations. The Hausdorff distance between the sets of vectors of cytokine concentrations of individuals is used in this space. This is based on the Euclidean distance between the elements of these sets. It was found that the drug “Roncoleukin” and “Leukinferon” have a generally similar and different from the drug “Tinrostim” effect on the course of the disease.
Keywords: data processing, experiment, cytokine, immune system, pneumonia, statistics, approximation, Hausdorff distance.
Received: 27.02.2020
Revised: 08.10.2020
Accepted: 09.10.2020
Document Type: Article
UDC: 51-76
Language: Russian
Citation: A. A. Yakovlev, A. I. Abakumov, A. V. Kostyushko, E. V. Markelova, “Cytokines as indicators of the state of the organism in infectious diseases. Experimental data analysis”, Computer Research and Modeling, 12:6 (2020), 1409–1426
Citation in format AMSBIB
\Bibitem{YakAbaKos20}
\by A.~A.~Yakovlev, A.~I.~Abakumov, A.~V.~Kostyushko, E.~V.~Markelova
\paper Cytokines as indicators of the state of the organism in infectious diseases. Experimental data analysis
\jour Computer Research and Modeling
\yr 2020
\vol 12
\issue 6
\pages 1409--1426
\mathnet{http://mi.mathnet.ru/crm857}
\crossref{https://doi.org/10.20537/2076-7633-2020-12-6-1409-1426}
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