Objective:To explore therapeutic effect of rosuvastatin combined clopidogrel bisulfate tablet on patients with coronary heart disease (CHD).Methods: A total of 124 patients, who were diagnosed as CHD by coronary angiography, were selected, randomly and equally divided into routine treatment group (received routine treatment,including clopidogrel bisulfate tablet) and combined treatment group (received rosuvastatin based on routine treatment group), both groups were treated for six months.Cardiac function, serum levels of angiotensin II (Ang II), vascular endothelial growth factor (VEGF) and nitric oxide (NO), and therapeutic effect were measured and compared between two groups before and after treatment.Results: Total effective rate of combined treatment group was significantly higher than that of routine treatment group (96.77% vs.83.87%, P=0.015).Compared with routine treatment group after treatment, there were significant rise in left ventricular ejection fraction [(61.89±7.02)% vs.(68.96±8.23)%] and NO level [(75.25±9.45) μmol/L vs.(82.25±10.22) μmol/L], and significant reductions in wall motion score index [(1.35±0.39)% vs.(1.11±0.29)%], levels of Ang Ⅱ [(102.25±6.93) ng/L vs.(52.99±5.36) ng/L] and VEGF [(328.25±23.41) ng/L vs.(228.69±22.69) ng/L] in combined treatment group, P<0.01 all.Conclusion: Rosuvastatin combined clopidogrel bisulfate tablet can effectively improve heart function, serum levels of Ang II, VEGF and NO in patients with coronary heart disease, the therapeutic effect is significant, which is worth extending.

Purpose: To intelligently diagnose whether there is bladder outlet obstruction (BOO) in female with decent detrusor contraction ability by focusing on urodynamic study (UDS) data. Materials and Methods: We retrospectively reviewed the UDS data of female patients during urination. Eleven easily accessible urinary flow indicators were calculated according to the UDS data of each patient during voiding period. Eight diagnosis models based on back propagation neural network with different input feature combination were constructed by analyzing the correlations between indicators and lower urinary tract dysfunction labels. Subsequently, the stability of diagnostic models was evaluated by five-fold cross-validation based on training data, while the performance was compared on test dataset. Results: UDS data from 134 female patients with a median age of 51 years (range, 27–78 years) were selected for our study.Among them, 66 patients suffered BOO and the remaining were normal. Applying the 5-fold cross-validation method, the model with the best performance achieved an area under the receiver operating characteristic curve (AUC) value of 0.949±0.060 using 9 UDS input features. The accuracy, sensitivity, and specificity for BOO diagnosis model in the testing process are 94.4%, 100%, and 89.3%, respectively. Conclusions The 9 significant indicators in UDS were employed to construct a diagnostic model of female BOO based on machine learning algorithm, which performs preferable classification accuracy and stability.