Molecular Mechanism of the Role of Apigenin in the Treatment of Hyperlipidemia: A Network Pharmacology Approach.

The therapeutic effect of apigenin (APG) on hyperlipidemia was investigated using network pharmacology combined with molecular docking strategy, and the potential targets of APG in the treatment of hyperlipidemia were explored. Genetic Ontology Biological Process (GOBP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of common targets were performed. Then, molecular docking was used to predict the binding mode of APG to the target. Finally, Sprague Dawley rats were used to establish a hyperlipidemia model. The expression levels of insulin (INS) and vascular endothelial growth factor A (VEGFA) mRNA in each group were detected by quantitative reverse transcription-polymerase chain reaction. Network pharmacological studies revealed that the role of APG in the treatment of hyperlipidemia was through the regulation of INS, VEGFA, tumor necrosis factor, epidermal growth factor receptor, matrix metalloprotein 9, and other targets, as well as through the regulation of the hypoxia-inducible factor 1 (HIF-1) signaling pathway, fluid shear stress, and atherosclerosis signaling pathways, vascular permeability; APG also participated in the regulation of glucose metabolism and lipid metabolism, and acted on vascular endothelial cells, and regulated vascular tone. Molecular docking showed that APG binds to the target with good efficiency. Experiments showed that after APG treatment, the expression levels of INS and VEGFA mRNA in the model group were significantly decreased (p<0.01). In conclusion, APG has multiple targets and affects pathways involved in the treatment of hyperlipidemia by regulating the HIF-1 signaling pathway, fluid shear stress, and the atherosclerosis pathway.

A Pilot Study on the Satisfaction of Long-Term Care Services in Taiwan.

For many developed countries and regions, long-term care is becoming an important issue due to demographic changes and an increasing willingness and need of family members to let the elderly be taken care of by non-family members. Thus, effectively managing long-term care needs has become a major societal concern. In this paper, the public attitude towards long-term care and the satisfaction of long-term care services in Taiwan are examined. First, internal consistency reliability, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are applied to delete unimportant indicators. Second, structural equation modeling (SEM) is used to determine which indicators have a statistically significant influence on the public attitude toward long-term care and on the satisfaction of long-term care services. Third, artificial neural network (ANN) is applied to understand the relative importance of the indicators in influencing the public attitude and satisfaction of long-term care services. The contribution of this study is significant because some of the factors investigated in the study should be stressed by the government or institutions to provide more satisfactory services to the elderly and their families.