Objective To identify potential drug target genes associated with idiopathic pulmonary fibrosis(IPF)and predict therapeutic candidates using a two-sample Mendelian randomization(MR)approach across the druggable genome.Methods Druggable genome data from the DGIdb database and Finan were integrated to identify overlapping genes.A two-sample MR analysis was performed to infer causal relationships between genes and IPF.Functional enrichment analyses,including Gene Ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG),were conducted to explore biological pathways.Drug-target interactions were predicted via DSigDB database screening,followed by molecular docking simulations to evaluate binding affinities.Results Among the 2588 overlapping druggable genes,thirty exhibited significant causal associations with IPF(P＜0.05).Four hub genes(NOD2,LATS2,LTA,and TCF7L2)were enriched in IPF-related pathways,notably Hippo and TNF signaling.Six potential therapeutics were identified:oxyphenbutazone,moexipril,α-galactosylceramide,GSK429286A,CGP74514A,and JW-7-24-1.Molecular docking confirmed strong binding affinities between these drugs and their targets.Conclusion This study has identified thirty druggable gene targets and six candidate drugs for IPF.The enrichment of hub genes in key pathways and validated drug-target interactions provide insights into IPF therapies.

Aiming at the dilemma of expensive and difficult maintenance, lack of technical data and insufficient maintenance force for modern medical equipment, an intelligent fault diagnosis expert system of multi-parameter monitor based on fault tree was proposed in this study. Firstly, the fault tree of multi-parameter monitor was established and analyzed qualitatively and quantitatively, then based on the analysis results of fault tree, the expert system knowledge base and inference engine were constructed and the overall framework of the system was determined, finally the intelligent fault diagnosis expert system for multi-parameter monitor was developed by using the page hypertext preprocessor (PHP) language, with an accuracy rate of 80% in fault diagnosis. The results showed that technology fusion on the basis of fault tree and expert system can effectively realize intelligent fault diagnosis of multi-parameter monitors and provide troubleshooting suggestions, which can not only provide experience accumulation for fault diagnosis of multi-parameter monitors, but also provide a new idea and technical support for fault diagnosis of medical equipment.
Expert Systems ; Monitoring, Physiologic