The effects of anti-sense interleukin-5 gene transferred by recombinant adeno-associated virus in allergic rats

The accumulation and infiltration of eosinophils in airways is one of the most important characteristics of asthma, and is mediated partly by secretion of IL-5 from Th2 lymphocytes. It is well known that interleukin-5 (IL-5) played an important role in the regulation of eosinophils. In this study, an anti-sense IL-5 gene transferred by recombinant adeno-associated virus (rAAV-ASIL-5) was prepared to transfect allergic rats. It was found that the expression of IL-5 protein in plasma and BALF were inhibited significantly. The rAAV-ASIL-5-mediated suppression of total cell counts in peripheral blood and BALF were also observed. Moreover, rAAV-ASIL-5 remarkably reduced the eosinophil counts in peripheral blood and BALF, as well as the expression of ECP protein in plasma and BALF. The inflammation in lungs of rAAV-ASIL-5 pretreated rats also became slighter when compared with allergic rats. Otherwise, no apparent pathological damage to vital organs of rats was found. In conclusion, recombinant adeno-associated virus-mediated delivery of anti-sense IL-5 gene inhibited the accumulation of eosinophils and the airways inflammation in rat model of allergic asthma via suppressing IL-5 expression. It suggested the feasibility of rAAV-ASIL-5 in the gene therapy for allergic asthma and other eosinophilic diseases

Privacy Preserving K-means Clustering with Chaotic Distortion

Randomized data distortion is a popular method used to mask the data for preserving the privacy. But the appropriateness of this method was questioned because of its possibility of disclosing original data. In this paper, the chaos system, with its unique characteristics of sensitivity on initial condition and unpredictability, is advocated to distort the original data with sensitive information for privacy preserving k-means clustering. The chaotic distortion procedure is proposed and three performance metrics specifically for k-means clustering are developed. We use a large scale experiment (with 4 real world data sets and corresponding reproduced 40 data sets) to evaluate its performance. Our study shows that the proposed approach is effective; it not only can protect individual privacy but also maintain original information of cluster cente