[Based on a Markov model, cost-effectiveness analysis of influenza vaccination among people aged 60 years and older in Shenzhen].

Objective: To assess the cost-effectiveness of influenza vaccination among people aged 60 years and older in Shenzhen. Methods: A Markov state transition model was constructed to evaluate the cost-effectiveness of annual influenza vaccination for preventing influenza infection compared with no vaccination among the elderly from the social perspective. Allowing seasonal variation of influenza activity, the model followed a five-year cohort using weekly cycles. We employed once the Chinese gross domestic product (GDP) per capita in 2019 (70 892 yuan) as the willingness-to-pay (WTP) threshold and calculated the net monetary benefit (NMB) with costs and quality-adjusted life-years (QALYs) discounted at 5% annually. The impact of parameter uncertainty on the results was examined using one-way and probabilistic sensitivity analyses (PSA). Results: The base case amounted to approximately 35 yuan of cost-saving and a net gain of 0.007 QALYs. Correspondingly, the NMB was 529 yuan per vaccinated person. One-way sensitivity analyses showed that the NMB was relatively sensitive to changes in the attack rate of influenza and vaccine effectiveness. Based on the results of PSA with 1 000 Monte Carlo simulations, influenza vaccination had a probability of being cost-effective in 100% of the repetitions. Conclusions: The present study provides evidence that influenza vaccination is a cost-saving disease prevention strategy for people aged 60 years and older in Shenzhen.

[Bioinformatics analysis of key genes and prognosis-related genes during the onset of hepatocellular carcinoma].

Objective: To screen and analyze the differentially-expressed genes (DEGs) in primary hepatocellular carcinoma tissues and adjacent tissues using bioinformatics methods to explore the molecular mechanism of the occurrence and prognosis of primary hepatocellular carcinoma. Methods: GSE76427 data set was collected through GEO database, and DEGs were identified using GEO2R online analysis. Go and KEGG databases were used for enrichment and functional annotation of DEGs. Protein interaction network was built based on the STRING database and Cytoscape software to analyze the key genes of hepatocellular carcinoma, and the survival curve of these key genes were analyzed using the GEPIA database. Results: A total of 74 hepatocellular carcinoma DEGs were screened, of which 3 and 71 were up-and-down-regulated genes. The results of GO enrichment analysis showed that the down-regulated DEGs were mainly involved in cell response to cadmium and zinc ions, negative growth regulation, heterologous metabolic processes and hormone-mediated signaling pathways. KEGG pathway enrichment analysis results showed that the down-regulated DEGs pathway were mainly involved in retinol metabolism, chemical carcinogenesis, drug metabolism-cytochrome P450, cytochrome P450 metabolizing xenobiotics, tryptophan metabolism and caffeine metabolism. Protein interaction network had screened out 10 down-regulated core genes: MT1G, MT1F, MT1X, MT1E, MT1H, insulin-like growth factor 1, FOS, CXCL12, EGR1, and BGN. Among them, the insulin-like growth factor 1 was related to the prognosis of primary hepatocellular carcinoma. Conclusion: Bioinformatics analysis results of HCC chip data showed that 10 key genes may play a key role in the occurrence and development of HCC and the insulin like growth factor 1 is associated with the prognosis of primary hepatocellular carcinoma.