Objective To investigate the early effects of intervention with tanakan on retinal function in diabetic retinopathy(DR) after laser photocoagulation. Methods Prospective random controlled study was performed on 60 Patients (60 eyes) from 23 to 69 years old with DR(phase Ⅲ～Ⅳ). The multifocal electroretinograms (MERG) were tested with VERIS Ⅳ before, the 3rd day and the 7th day after photocoagulation. Results No significant differences were found in the latencies and response densities of N1,P1 and N2 between the two groups before photocoagulation. Compared with that before photocoagulation, three days after photocoagulation the latencies in tanakan group had no significant change. The response densities of N1,P1 and N2 reduced and the changes were much smaller than that in control. Three days after photocoagulation, the response densities of P1 and N2 in the central macula 5°area were much higher and the latencies of P1 and N2 were significantly shorter than that in control group. There were no significant differences in the response densities in the 7th day and the differences in the latencies between two groups still existed. Conclusion Tanakan may be effective in preventing the retina from damage of retinal photocoagulation in some degree in DR. (Chin J Ocul Fundus Dis, 2002, 18: 208-211)
Epilepsy is a chronic brain dysfunction disease with complex and diverse causes, but 70%-80% of patients do not have obvious characteristic phenotypic symptoms. In order to provide precise treatment for epilepsy patients, research on the genetic pathogenic factors and pathogenesis of epilepsy has attracted much attention. Different types of epilepsy are constantly found to be closely related to mutations in specific genes, such as SCN1A, KCNA2, KCNT1, GABRA1, TSCs, CDKL5, and so on. Therefore, the development of broad-spectrum antiepileptic drugs is very difficult. However, plant-based drugs or functional ingredients derived from traditional medicinal herbs, such as cannabinol, aconitine, and dodecenal, will expand the development of safer and more effective anti epileptic drugs.
Improving the rate of pathogen examination before antibiotic treatment is of great significance for clarifying pathogen diagnosis and curbing bacterial resistance, and is also one of the important goals for improving national medical quality and safety. In response to the current problem of low pathogen examination rates, Chengdu Women’s and Children’s Central Hospital adopts a FOCUS-PDCA model, has explored measures such as current situation investigation, root cause analysis, intervention plan formulation, countermeasure implementation, and effect evaluation to improve the rate of pathogen examination before antibiotic treatment in inpatients. This article mainly elaborates on the above model, which has practical significance for ensuring the rational use of antibiotics in inpatients.
目的:探讨大鼠外耳道腺的正常组织结构及年龄组间,雌雄动物间有无差异,以期为药物评价过程中药物对耳腺影响的评价提供客观的依据。方法:将20只同龄SD大鼠随即分为A和B两组,每组均雌雄各半,分别与12周和17周龄处死,取外耳道腺进行病理学分析。观察外耳道腺在不同年龄组间、雌雄组间有无区别。结果:A组动物雌雄组间外耳道腺的组织结构无明显差异。B组动物雌雄动物间外耳道腺的组织结构无明显差异。A组雌性动物与B组雌性动物间外耳道腺的组织结构无明显差异。A组雄性动物与B组雄性动物间外耳道腺的组织结构无明显差异。结论:SD大鼠外耳道腺的组织结构在雌雄动物间及年龄在12周及17周间无明显差异,因此在进行药物对耳腺的影响的评价过程中12周龄和17周龄的受试大鼠所得到的结果应具备可比性。
目的 了解我院住院患者抗菌药物的临床应用现状及存在问题，为临床合理使用抗菌药物提供参考。 方法 采用回顾性调查方法，对本院2008年11月-2009年4月的出院病历资料进行统计、分析。 结果 共调查病历1 000份，抗菌药物总使用率58.70％；其中预防用药使用率62.35％，治疗用药使用率37.65％；联合用药的比例为37.31％；不合理用药占19.76%。 结论 抗菌药物使用率较高，且使用存在一些不合理现象。医院应加强监管，对存在的问题应制订相应措施。
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.