To investigate the change of the portal venous pressure (PVP), conjugated glycocholic acid (CGA) and pancreatic glucagon (PG) concentration in rats peripheral and portal venous blood in the course of experimental liver cirrhosis induced with carbon tetrachloride and to investigate the mentioned changes after portalazygous devascularization. The authors found that in the early stage of cirrhosis the PVP and the concentration of CGA and PG in peripheral venous blood were increased markedly, CGA in portal vein was decreased and PG in portal vein was increased in early stage of liver cirrhosis.With the extent of liver cirrhosis the indexes above changed more markedly. After portalazygous devascularization the concentration of CGA in peripheral vein in the cirrhotic rats was increased but PVP, the concentration of CGA in portal vein and PG in peripheral and portal vein did not change.There was no change in nornal rats. The results suggest that the variation in CGA in peripheral vein can accurately reflect the degree of damage to liver cells, thus making the diagnosis of liver cirrhosis earlier and judging the degree and prognosis of liver cirrhosis.The concentration of PG in portal venous and peripheral vein relate to PVP in liver cirrhosis.Portalazygous devascularization can maintain PVP and PG in portal vein and do not affect liver function of the control rats but it can damage liver cell in cirrhotic rats.
Electroencephalogram(EEG) analysis has important reference value in the diagnosis of epilepsy. The automatic classification of epileptic EEG can be used to judge the patient’s situation in time,which is of great significance in clinical application. In order to solve the problem that the recognition accuracy is not high by using the single feature of EEG signals and avoid the influence of wavelet basis function selection on recognition results,a method of automatic discrimination of epileptic EEG signals based on S transform and permutation entropy is proposed. Firstly, the original signals are decomposed by discrete S transform, and then we calculate the fluctuation index of coefficients of each rhythm and combine the permutation entropy of EEG signals into a feature vector and use Real AdaBoost classifier to discriminate the epileptic EEG signals in muti-period. In this study, we used the epilepsy database from University of Bonn. Three groups of EEG signals, including the data of normal people with their eyes open, the data collected inside of the epileptic foci from patients during their interictal period and the data during their ictal period, were used to test effectiveness. The results of this study showed that the fluctuation index of each rhythm could be used to characterize the normal, interictal and ictal epileptic EEG signals effectively, and the recognition accuracy of multiple features was much higher than that of single feature. The average recognition accuracy could reach 98.13%. Compared with time-frequency feature extraction method or nonlinear feature extraction method only,the recognition accuracy was increased by more than 1.2% and 8.1% respectively, which was superior to the methods recorded in many other literatures. Therefore, this method has a good application prospect in diagnosis of epilepsy.