Rui Zhang

Objective: To systematically analyze the potential therapeutic mechanism of licochalcone A against Alzheimer's disease by network pharmacology. Methods: The Comparative Toxicogenomics Database (CTD) and GeneCards database were screened, and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the overlapping genes of Alzheimer's disease and licochalcone A, followed by Protein-Protein Interaction (PPI) analysis and visualization. Results: A total of 64 potential overlapping targets between licochalcone A and Alzheimer's disease were identified, among which CASP3, AKT1 and BCL2 were core targets with high degree values. The annotated genes were enriched in signal transduction, metabolism and immune system. Conclusion: Licochalcone A may exert anti-Alzheimer's disease effects by targeting and regulating inflammatory response, participating in biological processes such as signal transduction and energy metabolism, which provides a theoretical basis for the development of multi-target drugs for Alzheimer's disease.

Fewer studies have focused on the interaction of metal mixtures with hypertension, especially in Chinese community-dwelling elderly. In addition, the relationship between metal exposure and hypertension may be attenuated or strengthened by the presence of multiple chronic diseases in older adults. In this study, inductively coupled plasma mass spectrometry was used to detect the levels of 12 metals in the urine of 693 elderly people in the Yinchuan community. We employed Directed Acyclic Graphs (DAG) to select variables for adjustment in the model. Conditional logistic regression model and restricted cubic spline analysis (RCS) were used to explore the association between and dose–response relationship between metal concentrations in urine and hypertension. Quantile g-computation and Bayesian kernel machine regression (BKMR) to analyze the association of individual urinary metal concentrations and metal mixtures with hypertension risk. Urinary concentrations of 12 metals (vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, thallium, and lead) were higher in the hypertension group than in the non-hypertension group. In the RCS models, the urinary concentrations of vanadium, iron, and lead showed a linear dose–response relationship with hypertension risk. Quantile g-computation analyses showed cadmium contributed the largest positive weights. The BKMR models showed that the positive slope of lead became steep at higher concentrations of urinary iron when the other three metals were at the median. We found that exposure to metal mixtures was associated with the risk of hypertension and a significant positive interaction between urinary iron and lead. Further research is needed to confirm our findings and elucidate the underlying mechanisms of the interaction between metals and hypertension.