Efficacy of compound Xiatianwu tablets in elderly patients with osteoporotic distal radius fractures / 中国中药杂志

Xiatianwu tablet is based on the theory of traditional Chinese medicine (TCM), combined with modern TCM pharmacology and selected 33 famous traditional Chinese crude drugs to compose. Its recipe helps cure rheumatism, relax tendons, promote blood circulation to relieve pain, et al. Although Xiatianwu tablets are widely applied to clinical remedy such as rheumatic arthritis, lumbar disc hernia, osteoarthritis and so on, there is no report about its application in fracture. This article is to observe the efficacy of compound Xiatianwu tablets in elderly patients with osteoporotic distal radius fractures and its impact on the wrist function and complications. 180 elderly patients with osteoporotic distal radius fractures, from January 2011 to June 2014, were divided into observation group and control group by the method of random number table, each group had 90 cases. The control group were gave Caltrate D after manipulative reduction and plaster immobilization, observation group were treated with compound Xiatianwu tablets in the basis of the control group. Efficacy, wrist function and complication rates were observed in two groups after treatment. The excellent and good rate was 95.56% in observation group better than 77.78% in control group, the difference was statistically significant (χ2 = 4.712, P < 0.05). The complication rate in observation group was significantly lower compared with the control group (P < 0.05). This study shows that compound Xiatianwu tablets can improve the efficacy in elderly patients with osteoporotic distal radius fractures, reduce the incidence of complications and relieve the pain of patients which plays a significant role in improving the quality of life.

Effect of the quality of the interaction data on predicting protein function from protein-protein interactions.

Protein function prediction is an important issue in the post-genomic era. When protein function is deduced from protein interaction data, the traditional methods treat each interaction sample equally, where the qualities of the interaction samples are seldom taken into account. In this paper, we investigate the effect of the quality of protein-protein interaction data on predicting protein function. Moreover, two improved methods, weight neighbour counting method (WNC) and weight chi-square method (WCHI), are proposed by considering the quality of interaction samples with the neighbour counting method (NC) and chi-square method (CHI). Experimental results have shown that the qualities of interaction samples affect the performances of protein function prediction methods seriously. It is also demonstrated that WNC and WCHI methods outperform NC and CHI methods in protein function prediction when example weights are chosen properly.

Evaluation of similarity measures for gene expression data and their correspondent combined measures.

It is commonly considered that genes with similar expression profiles are functional related. And there are many ways to measure the similarity of gene expression data. Especially with the advent of lots of biological information, new combined measures have been constructed by combining the biological information with different similarity measures. However, it is not clear that what is the most suitable and effective measure for gene expression data and what is the most suitable measure to construct the most effective combined measure. In this paper, several similarity measures are analyzed and two new similarity measures are proposed. Their correspondent combined measures are also constructed by incorporating Gene Ontology annotations. All these measures are evaluated by their effectiveness in detecting functionally links in several different gene expression data by comparing with the protein-protein interaction database. The results show that the newly proposed measures and their correspondent combined measures are very effective and suitable for different datasets. And our methodology is applicable to evaluate new similarity measures and detect the best measure for a certain dataset.