Objective:To investigate the expression of myocardium connexin 43 (Cx43) in late exercise preconditioning (LEP) cardioprotection.Methods: Eight-week-old adult male Sprague Dawley rats were randomly assigned into four groups (n=8). Myocardial injury was judged in accordance with serum levels of cTnⅠ and NT-proBNP as well as hematoxylin basicfuchsin picric acid staining of myocardium.Cx43mRNA was detected byin situhybridization and qualified by real-time fluorescence quantitative PCR. Cx43 protein was localized by immunohistochemistry and its expression level was determined by western blotting.Results:The LEP obviously attenuated the myocardial ischemia/hypoxia injury caused by exhaustive exercise. There was no significant difference of Cx43mRNA level between the four groups. Cx43 protein level was decreased significantly in group EE (P<0.05). However, LEP produced a significant increase in Cx43 protein level (P<0.05), and the decreased Cx43 protein level in exhaustive exercise was significantly up-regulated by LEP (P<0.05).Conclusions:LEP protects rat heart against exhaustive exercise-induced myocardial injury by up-regulating the expression of myocardial Cx43.

In order to find the most suitable algorithm of T-wave end point detection for clinical detection, we tested three methods, which are not just dependent on the threshold value of T-wave end point detection, i. e. wavelet method, cumulative point area method and trapezium area method, in PhysioNet QT database (20 records with 3 569 beats each). We analyzed and compared their detection performance. First, we used the wavelet method to locate the QRS complex and T-wave. Then we divided the T-wave into four morphologies, and we used the three algorithms mentioned above to detect T-wave end point. Finally, we proposed an adaptive selection T-wave end point detection algorithm based on T-wave morphology and tested it with experiments. The results showed that this adaptive selection method had better detection performance than that of the single T-wave end point detection algorithm. The sensitivity, positive predictive value and the average time errors were 98.93%, 99.11% and (--2.33 ± 19.70) ms, respectively. Consequently, it can be concluded that the adaptive selection algorithm based on T-wave morphology improves the efficiency of T-wave end point detection.
Algorithms ; Electrocardiography ; Humans ; Wavelet Analysis