Research on the Correlation Between rs2110385 Polymorphisms of the Visfatin Gene and Nonproliferative Diabetic Retinopathy

Objective:To investigate the association between rs2110385 polymorphisms of the visfatin gene and the risk of type 2 diabetic retinopathy(DR).Methods:172 Han subjects were selected from Xi’an Shaanxi Province;140 patients with type 2 diabetes mellitus(T2DM)and 32 normal controls(NC)were selected from our hospital.Patients with diabetes were divided into a non-DR group(T2DM)(n=69)and a nonproliferative diabetic retinopathy Group(DR)(n=71)after dilated fundus photography and fundus fluorescein angiography.rs2110385/AluⅠgenotypes were detected by standardized polymerase chain reaction-restriction fragment length polymorphism(PCR-RFLP),and the differences in the detection rates of different genotypes in the above populations were compared.Results:1)The visfatin level in the DR Group was significantly higher than that in the NC and T2DM groups(P<0.05).2)The frequency of GG genotype and G allele of rs2110385 in the DR Group were higher than those in the T2DM and NC groups(80.3,69.6,50.0,86.6,79,65.6,P<0.05).3)There were significant differences in allele frequency and genotype frequency distribution of rs2110385 between the DR Group and the NC group(P<0.01).Conclusion:Visfatin increased in the nonproliferative diabetic retinopathy group and could be a potential indicator for the clinical prediction of DR.The G allele of the rs2110385 polymorphic site may be related to the risk of DR.

Model Predictive Control for Vapor Compression Cycle of Refrigeration Process

A model predictive controller based on a novel structure selection criterion for the vapor compression cycle （VCC） of refrigeration process is proposed in this paper. Firstly, those system variables are analyzed which exert significant influences on the system performance. Then the structure selection criterion, a trade-off between computation complexity and model performance, is applied to different model structures, and the results are utilized to determine the optimized model structure for controller design. The controller based on multivariable model predictive control （MPC） strategy is designed, and the optimization problem for the reduced order models is formulated as a constrained minimization problem. The effectiveness of the proposed MPC controller is verified on the experimental rig.