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Prediction of the anti-inflammatory effects of bioactive components of a Hippocampus species-based TCM formulation on chronic kidney disease using network pharmacology  ( SCI-EXPANDED收录)   被引量:2

文献类型:期刊文献

英文题名:Prediction of the anti-inflammatory effects of bioactive components of a Hippocampus species-based TCM formulation on chronic kidney disease using network pharmacology

作者:Zhang, Lingyu[1];Lu, Sitong[1];Hu, Zhang[1];Liao, Mingneng[1];Li, Chengpeng[1];Kong, Songzhi[1]

机构:[1]Guangdong Ocean Univ, Fac Chem & Environm Sci, Zhanjiang 524088, Peoples R China

年份:2021

卷号:20

期号:11

起止页码:2355

外文期刊名:TROPICAL JOURNAL OF PHARMACEUTICAL RESEARCH

收录:SCI-EXPANDED(收录号:WOS:000726345100018)、、Scopus(收录号:2-s2.0-85120872350)、WOS

基金:The authors gratefully acknowledge the financial support from Guangdong Provincial Natural Science Foundation of China (no. 2016A030308009) , Project of Science and Technology Plan of Zhanjiang (nos. 2019A01017 and 2020A01026) , Project of Enhancing School with Innovation of Guangdong Ocean University (no. 2017KTSCX090) and Postgraduate Education Innovation Program of Guangdong Ocean University (no. 201926) .

语种:英文

外文关键词:Hippocampus; Chronic kidney disease; Network pharmacology; Paeonol inflammation; AGE-RAGE signaling pathway

外文摘要:Purpose: To systematically study and predict the therapeutic targets and signaling pathways of Hippocampus (HPC) against chronic kidney disease (CKD) using network pharmacology. Methods: By combining database mining, literature searching, screening of disease targets, and network construction, the effects of various components of HPC on several proteins related to CKD were predicted and the active compounds were screened. Genes related to the selected compounds were linked using the SEA database. The correlation between CKD and genes was determined using OMIM, DisGenNet, and GeneCards databases. Pathway-enrichment analyses of overlapping genes were undertaken using online databases. Results: A total of 144 compounds in HPC were identified. Analyses of clusters suggest that the active components of HPC and the target genes against the inflammation caused by CKD were due to 10 compounds and 25 genes. Metascape results showed that these HPC targets are related to CKD inflammation. Conclusion: The active components of HPC and the target genes against CKD inflammation are involved in multiple signaling pathways, such as AGE-RAGE, TLR, TNF, and NF-Kappa B. This work provides scientific evidence to support the clinical use of HPC against CKD.

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