Molecular Mechanism of the Effect of Zhizhu Pill on Gastroesophageal Reflux Disease Based on Network Pharmacology and Molecular Docking

Prediction and analysis of genetic effect in idiopathic pulmonary fibrosis and gastroesophageal reflux disease

With increasing research on idiopathic pulmonary fibrosis (IPF) and gastroesophageal reflux disease (GERD), more and more studies have indicated that GERD is associated with IPF, but the underlying pathological mechanisms remain unclear. The aim of the present study is to identify and analyse the differentially expressed genes (DEGs) between IPF and GERD and explore the relevant molecular mechanisms via bioinformatics analysis. Four GEO datasets (GSE24206, GSE53845, GSE26886, and GSE39491) were downloaded from the GEO database, and DEGs between IPF and GERD were identified with the online tool GEO2R. Subsequently, a series of bioinformatics analyses are conducted, including Kyoto Encyclopaedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses, the PPI network, biological characteristics, TF-gene interactions, TF-miRNA coregulatory networks, and the prediction of drug molecules. Totally, 71 genes were identified as DEGs in IPF and GERD. Five KEGG pathways, including Amoebiasis, Protein digestion and absorption, Relaxin signalling pathway, AGE-RAGE signalling pathway in diabetic complications, and Drug metabolism - cytochrome P450, were significantly enriched. In addition, eight hub genes, including POSTN, MMP1, COL3A1, COL1A2, CXCL12, TIMP3, VCAM1, and COL1A1 were selected from the PPI network by Cytoscape software. Then, five hub genes (MMP1, POSTN, COL3A1, COL1A2, and COL1A1) with high diagnostic values for IPF and GERD were validated by GEO datasets. Finally, TF-gene and miRNA interaction was identified with hub genes and predicted drug molecules for the IPF and GERD. And the results suggest that cetirizine, luteolin, and pempidine may have great potential therapeutic value in IPF and GERD. This study will provide novel strategies for the identification of potential biomarkers and valuable therapeutic targets for IPF and GERD.

Research hotspots and trends of artificial intelligence in rheumatoid arthritis: A bibliometric and visualized study

Artificial intelligence (AI) applications on rheumatoid arthritis (RA) are becoming increasingly popular. In this bibliometric study, we aimed to analyze the characteristics of publications relevant to the research of AI in RA, thereby developing a thorough overview of this research topic. Web of Science was used to retrieve publications on the application of AI in RA from 2003 to 2022. Bibliometric analysis and visualization were performed using Microsoft Excel (2019), R software (4.2.2) and VOSviewer (1.6.18). The overall distribution of yearly outputs, leading countries, top institutions and authors, active journals, co-cited references and keywords were analyzed. A total of 859 relevant articles were identified in the Web of Science with an increasing trend. USA and China were the leading countries in this field, accounting for 71.59% of publications in total. Harvard University was the most influential institution. Arthritis Research & Therapy was the most active journal. Primary topics in this field focused on estimating the risk of developing RA, diagnosing RA using sensor, clinical, imaging and omics data, identifying the phenotype of RA patients using electronic health records, predicting treatment response, tracking the progression of the disease and predicting prognosis and developing new drugs. Machine learning and deep learning algorithms were the recent research hotspots and trends in this field. AI has potential applications in various fields of RA, including the risk assessment, screening, early diagnosis, monitoring, prognosis determination, achieving optimal therapeutic outcomes and new drug development for RA patients. Incorporating machine learning and deep learning algorithms into real-world clinical practice will be a future research hotspot and trend for AI in RA. Extensive collaboration to improve model maturity and robustness will be a critical step in the advancement of AI in healthcare.

Exploring the components and mechanisms of Shen-qi-wang-mo granule in the treatment of retinal vein occlusion by UPLC-Triple TOF MS/MS and network pharmacology

This study aimed to explore the substance basis and mechanisms of Shen-qi-wang-mo Granule (SQWMG), a traditional Chinese medicine prescription that had been clinically utilized to treat retinal vein occlusion (RVO) for 38 years. Components in SQWMG were analyzed by UPLC-Triple-TOF/MS and a total of 63 components were identified with ganoderic acids (GA) being the largest proportion. Potential targets of active components were retrieved from SwissTargetPrediction. RVO-related targets were acquired from related disease databases. Core targets of SQWMG against RVO were acquired by overlapping the above targets. The 66 components (including 5 isomers) and 169 targets were obtained and concluded into a component-target network. Together with biological enrichment analysis of targets, it revealed the crucial role of the "PI3K-Akt signaling pathway", "MAPK signaling pathway" and their downstream factor iNOS and TNF-α. The 20 key targets of SQWMG in treating RVO were acquired from the network and pathway analysis. The effects of SQWMG on targets and pathways were validated by molecular docking based on AutoDock Vina and qPCR experiment. The molecular docking showed great affinity for these components and targets, especially on ganoderic acids (GA) and alisols (AS), which were both triterpenoids and qPCR exhibited remarkably reduced inflammatory factor gene expression through regulation of these two pathways. Finally, the key components were also identified from rat serum after treatment of SQWMG.