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Articles by Yonghua Wang
Total Records ( 3 ) for Yonghua Wang
  Min Zhu , Marc Bifano , Xu Xu , Yonghua Wang , Frank LaCreta , Dennis Grasela and Marc Pfister
  Entecavir is a guanosine nucleoside analogue approved for the treatment of chronic hepatitis B virus (HBV) infection. The impact of human immunodeficiency virus (HIV) coinfection on the pharmacokinetics (PK) of entecavir was examined by nonlinear mixed-effects modeling. Plasma concentration data from HIV- and HBV-coinfected patients were analyzed in conjunction with data from HBV-monoinfected patients, and HIV coinfection was tested as a covariate on oral clearance (CL/F). The estimated population averages of intercompartmental clearance and the volumes of distribution in the central and peripheral compartments obtained with a 1-mg dose were 34.2 liters/h (interindividual variability, 30.2%), 115 liters (interindividual variability, 39.2%), and 1,830 liters (interindividual variability, 74%), respectively. CL/F was found to be a function of creatinine clearance, but HIV confection did not show any effect on CL/F. The geometric mean (GM) of individual Bayesian estimates of the steady-state area under the concentration-time curve following 1-mg daily doses were 39.3 and 38.8 ng·h/ml in HIV- and HBV-coinfected and HBV-monoinfected patients, respectively. The adjusted GM ratio (1.01; 90% confidence interval, 0.91 to 1.12) was within the bioequivalence criteria boundary (0.80 to 1.25). In conclusion, the proposed model adequately described the entecavir PK in HBV- and HIV-coinfected patients and HBV-monoinfected patients, and the entecavir exposures were comparable in the two patient populations.
  Huanzhuo Ye , Nan Li , Hao Feng and Yonghua Wang
  The financial institutions concern on the customer credit evaluation. Highly effective and accurate evaluation models can help the financial institutions to reduce the risk and loss. There are several commonly used evaluation models, for example, the logistic regression, decision tree, support vector machine, artificial neural network, etc. We use the same data set to test these models and give the advantages and disadvantages of these models. Among these models, there is not a unified view that which model is the best because eacxh one has their advantages. The logistic regression is the most stable model while the decision tree is the lowest. in stability, MLP-ANN has the better accuracy rate than other models.
  Akhtar Hussain Shar , Mohamed Shahen , Liujingjing , Shenfengxia and Yonghua Wang
  Cancer is an alarming condition whose occurrence has increased dramatically in the last few decades. This, in turn, has increased the medicinal cost and health care burdens of society. Unfortunately, a lack of effective and safe pharmacological therapy and strategies has been found to overcome this issue. Now-a-days nose cancer is an alarming health disorder that requires scientific attention to find an effective and safe pharmacological approach to overcome this serious issue. Currently, various treatments are used for this life threatening disease. Chinese traditional medicine like ginseng is one of the choices used as an effective and safe therapeutic tool for the treatment of cancer. Some previously published studies proved that ginseng not only kills the cancer cells but also inhibits the further multiplication of cancer cells with minimum side effects compared to the rest of treatment therapies. However, the exact mechanism of action behind ginseng’s ability to kill cancer cells has not been completely explored, there are still many research areas waiting to be studied, such as molecular pathways involved in the ginseng-derived flavonoids that attack cancer cells. The target of this review is to highlight the importance of the medicinal effects of ginseng and their flavonoids used in cancer therapy. Another objective of this paper is to attract the attention of the scientific community, to find out the mechanism of action behind ginseng herbal medicine in order to develop safe and non-toxic drugs for the cure of nose cancer.
 
 
 
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