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Atorvastatin, a hydroxymethylglutaryl-CoA reductase inhibitor, and endothelin-1 (ET-1) receptor antagonist have been separately indicated to ameliorate disease progression in atherosclerosis. However, no study has evaluated the effect of their combination on atherosclerosis. The objective of the current study was to evaluate the direct in vivo effects of a combination regimen of atorvastatin and ET-1 receptor antagonist on male New Zealand white rabbit models of atherosclerosis (injury-induced). Thirty-two atherosclerotic rabbits were divided into four experimental groups: (a) injury group - fed high-fat diet; (b) ET-1 receptor antagonist preventive group - fed high-fat diet, but with intragastric administration of the ET-1 receptor antagonist, darusentan; (c) combined preventive group - fed high-fat diet, but with intragastric administration of both darusentan and atorvastatin; and (d) treatment group - fed high-fat diet for the first 8 weeks, followed by normal diet and intragastric administration of both darusentan and atorvastatin up to 16 weeks. A further eight non-atherosclerotic rabbits were fed normal diet and classified as the control group. At the end of 8 and 16 weeks, compared with the injury group, the combined preventive group had significant reduction in both the concentration of serum lipids and inflammatory factors and atherosclerosis formation, indicative of a multifaceted anti-atherosclerotic impact. The relative area of atherosclerotic lesions in the injury group (30.84%) was significantly higher than the control group (4.62%; p < 0.05). The combined preventive group showed a significantly robust effect on lowering serum lipid, inflammatory cytokines, and maintained homeostatic balance of free radicals, and important downstream effectors like ET-1 and matrixmetalloproteinase-9. Our data show that atorvastatin and ET-1 receptor antagonist co-administration may decrease lipid levels, stabilize plaques and relieve vascular inflammation. By reducing the plaque burden, this regimen may minimize the risk of atherosclerotic plaque rupture or arterial occlusion.

The application and research of artificial neural network in foundation deformation prediction, over the past decades and at home and abroad, are reviewed. Based on the two main aspects of foundation pit deformation, which are horizontal displacement of supporting structure and surface settlement around foundation, the evolution and corresponding research results of neural network prediction are reviewed and summarized when the input vector of neural network changes from historical deformation to multi-source data then to combination of them. On the basis of a thorough analysis and discussion, this paper points out the further research direction and development prospects.