OPTIMIZATION OF PREDICTION OF THE DEVELOPMENT OF MORPHOLOGICAL DISORDERS AFTER EXPERIMENTAL ACUTE ISCHEMIA-REPERFUSION BASED ON COMBINED CHANGES IN BIOCHEMICAL PARAMETERS BY MEANS OF CORRELATION ANALYSIS AND NEURAL NETWORK CLUSTERING
DOI:
https://doi.org/10.11603/mie.1996-1960.2019.4.11017Keywords:
acute ischemia-reperfusion, morphological changes, correlation analysis, neural network clustering.Abstract
Background. The study proposes a method for optimizing the prediction of the development of ischemic disorders by experimental acute ischemia-reperfusion based on combined changes in biochemical parameters. The approach is based on the calculation of correlation coefficients and the use of neural network clustering.
The aim of the study is to propose a method for optimizing the prediction of the severity of morphological disorders in the early reperfusion period after experimental acute ischemia-reperfusion based on combined changes in biochemical parameters by means of correlation analysis and neural network clustering.
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 (18 animals in each group). The control group consisted of 15 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. Histological and morphometric examination of skeletal muscle was conducted at the Department of Pathological Anatomy with the section course and forensic medicine of the I. Gorbachevsky Ternopil National Medical University by conventional methods. Statistical data processing with calculating of Mann-Whitney's U-criterion and Spearman's correlation coefficient was performed using the Microsoft Excel (2010) software package according to conventional methods. 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 and Conclusions. By means of correlation analysis, the average strength of the direct correlation between the indicators of the average area of muscle fibers and the indices of alanine aminotransferase (+0,5) and aspartate aminotransferase (+0,5) is revealed, which indicates the predominance of catabolism processes in ischemic muscle tissue. 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 and alanine aminotransferase levels.
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