ABSTRACT
Objective To investigate the expressions of Smad3 and Smad7 in patients with ulcerative colitis(UC)and their relation with clinicopathology.Methods The expressions of Smad3 and Smad7 were measured by immunohistochemistry with SABC method in 60 UC specimens and 16 normal colonic tissues.The association of expressions of Smad3 and Smad7 proteins with clinical staging,lesion extent and pathologic grading were retrospectively analyzed.Results The expression of Smad3 was significantly lower in UC patients than in normal controls(P＜0.05),however,there was no relation between Smad3 expression and lesion extent(P＞0.05).There was a negative correlation between the expression of Smad3 and histological grade(r=-0.283,P＜0.05).The expression of Smad7 was significantly higher in UC patients than in normal controls,and its expression in active disease was higher than that in clinical remission(Z=2.097,P=0.036).There was a positive correlation between the expression of Smad7 and histological grade(r_s=0.453,P=0.000),and no relation between Smad7 expression and lesion extent(r_s=0.066,P=0.614).The statistical analysis showed a negative correlation between Smad3 expression and Smad7 expression(r=-0.420,P＜0.05).Conclusion The abnormal expressions of Smad3 and Smad7 are correlated with pathogenesis of UC.Furthermore.Smad7 may serve as marker for disease activity of UC.
ABSTRACT
Objective To extract characteristic parameters of ECG signals a new method of non-invasive diagnosis for coronary heart disease with artificial neural network. Methods ECG signals were digitized with A/D converter and filtered to eliminating the noise. Span of QRS interval, R-R interval,and voltage of S-T segment of filtered ECG were detected. These 3 characteristics were as the input parameters of the input layer. Samples were trained with an improved 3-layers back propagation(BP) artificial neural network, as trained samples. The non-trained samples were recognized with these BP neural networks. Results After 12 samples had been trained about 1500 times, the BP neural network could accurately distinguish samples of coronary heart disease from the trained samples and also recognize 20 non-trained samples, 19 to be correct except one. Conclusion It is showed that based on BP network and characteristic parameters of ECG, a new and promising method of non-invasive diagnosis for coronary heart disease has been found.
ABSTRACT
Diabetes is a vulgar malady of metabolism and incretion. It is important to monitor and control the blood glucose for the diagnosis and treatment of diabetes. In particular, it is one of the most effective means for physicians or patients to do so through self-monitoring of blood glucose (SMBG) instruments. In this paper, SMBG instruments are discussed in detail and classified as the minimally invasive one, the non-invasive one and the continuous glucose monitoring system (CGMS). The needle or laser applied to blood sampling, the technology of the minimally invasive one is relatively mature, and the result of measurement is exact, but this way is achy for the patients. Reverse iontophoresis and spectral analysis adopted, the non-invasive has an increasing accuracy. The CGMS can perform the periodical measurement and record of the value of blood glucose automatically for several days.