Aim: To investigate the effects of the major component of high-density lipoprotein apolipoprotein A-I (apoA-I) on the development of atherosclerosis in LPS-challenged ApoE -/-mice and the underlying mechanisms. Methods: Male ApoE-KO mice were daily injected with LPS (25 μg, sc) or PBS for 4 weeks. The LPS-challenged mice were intravenously injected with rAAV-apoA-I-GFP or rAAV-GFP. After the animals were killed, blood, livers and aortas were collected for biochemical and histological analyses. For ex vivo experiments, the abdominal cavity macrophages were harvested from each treatment group of mice, and cultured with autologous serum, then treated with LPS. Results: Chronic administration of LPS in ApoE -/-mice significantly increased the expression of inflammatory cytokines (TNF-α, IL-1β, IL-6, and MCP-1), increased infiltration of inflammatory cells, and enhanced the development of atherosclerosis. In LPS-challenged mice injected with rAAV-apoA-I-GFP, viral particles and human apoA-I were detected in the livers, total plasma human apoA-I levels were grammatically increased; HDL-cholesterol level was significantly increased, TG and TC were slightly increased. Furthermore, overexpression of apoA-I significantly suppressed the expression of proinflammatory cytokines, reduced the infiltration of inflammatory cells, and decreased the extent of atherosclerotic lesions. Moreover, overexpression of apoA-I significantly increased the expression of the cytokine mRNA-destabilizing protein tristetraprolin (TTP), and phosphorylation of JAK2 and STAT3 in aortas. In ex vivo mouse macrophages, the serum from mice overexpressing apoA-I significantly increased the expression of TTP, accompanied by accelerated decay of mRNAs of the inflammatory cytokines. Conclusion: ApoA-I potently suppresses LPS-induced atherosclerosis by inhibiting the inflammatory response possibly via activation of STAT3 and upregulation of TTP.

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Abstract. Centroid-Based Classifier (CBC) is one of the most widely used text classification method due to its theoretical simplicity and computational efficiency. However, the accuracy of CBC is not satisfactory when it deals with the skewed distributed data. In this paper, we propose a new classification model named as Gravitation Model (GM) to solve the model misfit of CBC. In the proposed model, we give each category a mass factor to indicate its distribution in vector space and this factor can be learned from training data. We provide the performance comparisons with CBC and its improved methods based on the results of experiments conducted on twelve real datasets, which show that the proposed gravitation model consistently outperforms CBC. Furthermore, it reaches the same performance as the best centroid-based classifier and is more stable than the best one.