A Network Pharmacology-Based Study of the Molecular Mechanisms of Shaoyao-Gancao Decoction in Treating Parkinson's Disease.

Clicks: 321
ID: 103875
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #2 of 2 articles by views in interdisciplinary sciences, computational life sciences

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Parkinson's disease (PD) is another major neurodegenerative disorder following Alzheimer's disease, which not only seriously reduces the survival in patients, affecting patient's quality of life, but also imposes a tremendous burden on families and even the whole society. It is urgent to find out effective drugs without side effects. The present study applied a creative approach called network pharmacology to explore the active compounds and therapeutic targets of Shaoyao-Gancao Decoction (SYGCD) for treating PD. We identified a total of 48 active compounds mediating 30 PD-related targets to exert synergism, and the same target can be enriched in multiple signal pathways and biological processes, expounding that the decoction can exert synergistic effect on PD by multi-targets and multi-pathways. Furthermore, the molecular docking analysis showed that active compounds and targets can be well combined. These results highlighted the molecular mechanisms underlying the efficiency of SYGCD for PD treatment at a systematic level, investigating thoroughly the innovative therapeutic tactics for PD in traditional Chinese medicine.
Reference Key
li2020ainterdisciplinary Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Li, Liting;Qiu, Haiyan;Liu, Mimi;Cai, Yongming;
Journal interdisciplinary sciences, computational life sciences
Year 2020
DOI
10.1007/s12539-020-00359-7
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.