| Title |
A network pharmacology approach to determine the synergetic mechanisms of herb couple for treating rheumatic arthritis
|
|---|---|
| Published in |
Drug Design, Development and Therapy, April 2018
|
| DOI | 10.2147/dddt.s161904 |
| Pubmed ID | |
| Authors |
Xi-xi Xu, Jian-ping Bi, Li Ping, Ping Li, Fei Li |
| Abstract |
The purpose of this study was to investigate the therapeutic mechanism(s) of Clematis chinensis Osbeck/Notopterygium incisum K.C. Ting ex H.T (CN). A network pharmacology approach integrating prediction of ingredients, target exploration, network construction, module partition and pathway analysis was used. This approach successfully helped to identify 12 active ingredients of CN, interacting with 13 key targets (Akt1, STAT3, TNFsf13, TP53, EPHB2, IL-10, IL-6, TNF, MAPK8, IL-8, RELA, ROS1 and STAT4). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that CN-regulated pathways were mainly classified into signal transduction and immune system. The present work may help to illustrate the mechanism(s) of action of CN, and it may provide a better understanding of antirheumatic effects. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Postgraduate | 3 | 15% |
| Other | 2 | 10% |
| Librarian | 2 | 10% |
| Student > Master | 2 | 10% |
| Student > Ph. D. Student | 1 | 5% |
| Other | 3 | 15% |
| Unknown | 7 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 20% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 10% |
| Biochemistry, Genetics and Molecular Biology | 2 | 10% |
| Arts and Humanities | 1 | 5% |
| Agricultural and Biological Sciences | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 9 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 April 2018.
All research outputs
#17,534,407
of 25,707,225 outputs
Outputs from Drug Design, Development and Therapy
#1,125
of 2,278 outputs
Outputs of similar age
#223,149
of 344,747 outputs
Outputs of similar age from Drug Design, Development and Therapy
#26
of 64 outputs
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We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.