COMPARATIVE ANALYSIS OF MORPHOLOGICAL DISORDERS AND CHANGES IN THE PROOXIDANT-ANTIOXIDANT SYSTEM IN ACUTE EXPERIMENTAL ISCHEMIA-REPERFUSION USING NEURAL NETWORK CLUSTERING
DOI:
https://doi.org/10.11603/mie.1996-1960.2022.1-2.13109Abstract
Background. The effective use of information technologies makes it possible to provide a comparative analysis of many factors in scientific medical research, which is especially important when using significant digital information in experimental morphology.
Materials and Methods. The experimental model of ischemic-reperfusion lesion is represented by five groups of rats with reperfusion terms of 1 and 2 hours, 1 day, 7 and 14 days (6 animals in each group). The control group consisted of 6 animals. Acute ischemia was caused by the imposition of SWAT rubber bundles on the hind limbs of rats for 2 h under thiopental sodium anesthesia. For a deeper analysis and clustering of the study groups, in order to optimize the prognosis of ischemia-reperfusion lesions, a neural network approach was used by using the Neuro XL Classifier add-in for Microsoft Excel.
Results. It was established systemic disorders appeared, which were manifested by changes in the biochemical parameters of blood serum, parameters of the processes of peroxide oxidation of lipids and antioxidant protection. According to neural network clustering the greatest prognostic value for the detection of severity of morphological disorders in the early reperfusion period have the combined changes in creatinine, cholesterol, alanine aminotransferase, aspartate aminotransferase, conjugated triene and TBK-active products levels.
Conclusions. In order to optimize the prediction of the development of morphological disorders in experimental acute ischemia-reperfusion based on combined changes in biochemical indicators, a method of analyzing the results of the study using neural network clustering is proposed.
References
Mintser, O. P., Zaliskyi, V. M. (2019). Transformuvannia znan z aterohenezu: vykorystannia nanoasotsiiovanykh biotekhnolohii i merezhevoho analizu. [Transformation of knowledge of atherogenesis: the use of nano associated biotechnologies and network analysis]. Medychna informatyka ta inzheneriia, 1, 424. [In Ukrainian].
Vakulenko, D. V., Martseniuk, V. P., Vakulenko, L. O. et al. (2019). Cardiovascular system adaptability to exercise according to morphological, temporal, spectral and correlation analysis of oscillograms. Family Medicine & Primary Care Review, 21 (3), 253-263.
Epstein, R. M., Franks, P., Fiscella, K. et al. (2005). Measuring patient-centered communication in patient-physician consultations: theoretical and practical issues. Soc. Sci. Med., 61, 1516-1528.
Selskyy, P. R., Vakulenko, D. V., Televiak, A. T., Veresiuk, T. O. (2018). On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering. Family Medicine & Primary Care Review, 20 (2), 171-5.
Wu, M.-Y., Yiang, G.-T., Liao, W.-T., Tsai, A. P.-Y. et al. (2018). Current Mechanistic Concepts in Ischemia and Reperfusion Injury. Cell Physiol. Biochem, 46, 1650-1667.
Khomenko, I. P., Korol, S. O., Matviichuk, B. V. (2018). Klinichno-orhanizatsiini osoblyvosti nadannia travmatolohichnoi dopomohy poranenym iz defektamy miakykh tkanyn pry vohnepalnykh ta minnovybukhovykh ushkodzhenniakh kintsivok. [Clinical and organizational peculiarities of traumatological care for the injured with soft tissue defects in inflammatory and mine-explosive injuries of the extremities]. Travma, 19 (5), 125-9. [In Ukrainian].
Garcia-de-la-Asuncion, J., Perez-Solaz, A., Carrau, M. et al. (2013). Different oxidative stress marker levels in blood from the operated knee or the antecubital vein in patients undergoing knee surgery: a tourniquet-induced ischemia-reperfusion model. Redox Report, 17 (5), 194-199.
Henyk, S. M., Symchych, A.V. (2016). Reperfuziinyi syndrom pislia revaskuliaryzatsii ishemii nyzhnikh kintsivok [Reperfusion syndrome after revascularization of ischemia of the lower extremities]. Heart and vessels, 3, 104-108.
Horalskyi, L. P., Khomych, V. T., Kononskyi, O. I. (2011). Osnovy histolohichnoi tekhniky i morfofunktsionalni metody doslidzhen u normi ta pry patolohii. [Fundamentals of histological technique and morphofunctional methods of research in normal and pathology]. Polissia, Zhytomyr: 288. [In Ukrainian].
Makarova, V. H., Makarova, М. N. (2013). Spravochnik. Fiziolohicheskiie, biochimicheskiie i biometricheskiie normy eksperimentalnych zhivotnych. [Directory. Physiological, biochemical and biometric parameters of the norm of experimental animals]. Lema Publishing House, 116. [In Russian].
Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford: Oxford University Press, 504.
Du, K. L. (2010). Clustering: A neural network approach. Neural network, 23 (1), 89-107.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Journal Medical Informatics and Engineering allows the author(s) to hold the copyright without registration
The majority of Medical Informatics and Engineering Open Access journals publish open access articles under the terms of the Creative Commons Attribution (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The remaining journals offer a choice of licenses.
This journal is available through Creative Commons (CC) License CC-BY 4.0
