Objective To observe the therapeutic effect of central venous catheter drainage and intrapleural injection of urokinase on tuberculous pleurisy patients.Methods 60 hospitalized patients with tuberculous pleurisy were selected,and they were divided into two groupsby simple random grouping method.Both two groups received 3HRZE/6HR anti-tuberculosis treatment.30 patients in the observation group were treated with central venous catheter drainage and intrapleural injection of urokinase.30 patients in the control group were treated with conventional pleurocentesis.The duration of pleural effussion drainage,incidence of pleural thickening,hospitalization time and expense,and the adverse reaction rate were observed during treatment.Results In the observation group,the curative effect at 1 week was 46.7％,the duration of pleural effussion drainage was (20.5 ± 6.7)days,the incidence rate of pleural thickening was 26.7％,the hospitalization time was (9.4 ± 2.7) days,the hospitalization expense was (6 675.4 ± 1 818.4) RMB,the incidence rate of adverse reaction was 3.3％.In the control group,the curative effect at 1 week was 20.0％,the duration of pleural effussion drainage was (25.1 ± 7.7) days,the incidence rate of pleural thickening was 46.7％,the hospitalization time was (10.3 ± 2.8)days,the hospitalization expense was (7 508.9 ± 1 692.1) RMB,the incidence rate of adverse reaction was 20..0％.There were statistically significant differences between the two groups in the curative effect at 1 week (x2 =4.800,P =0.028),duration of pleural effussion drainage (t =2.484,P =0.016),incidence of pleural thickening (t =4.444,P =0.035) and incidence rate of adverse reaction (x2 =4.043,P =0.044).No statistically significant differences were observed between the two groups in hospitalization time(t =1.270,P =0.209) and expense (t =1.838,P =0.071).Conclusion In comparison to conventional pleurocentesis,the treatment of central venous catheter drainage and intrapleural injection of urokinase for tuberculous pleurisy is markedly efective,it is safe and Worthy of popularizing in clinical application.

Objective To investigate the psychological experience of patients with depression before coronary artery bypass grafting(CABG).Methods With the phenomenological research method in qualitative research, deep semi-structured interview was conducted to 14 patients, who would be treated with CABG in two Class Ⅲ grade A hospitals in Luoyang, with the data analyzed and summarized by the 7-step analytical method of Colaizzi.ResultsThe psychological experiences of patients with depression before CABG included two themes: negative psychological experience and positive psychological experience.Conclusions Psychological experience of patients with depression before CABG is complicated and fickle. Medical workers should pay attention to both their negative emotions and positive performance.

The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.