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Mendeley readers
Attention Score in Context
| Title |
A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer’s Disease
|
|---|---|
| Published in |
Frontiers in Pharmacology, June 2018
|
| DOI | 10.3389/fphar.2018.00668 |
| Pubmed ID | |
| Authors |
Tianduanyi Wang, Zengrui Wu, Lixia Sun, Weihua Li, Guixia Liu, Yun Tang |
| Abstract |
Traditional Chinese medicine (TCM) is typically prescribed as formula to treat certain symptoms. A TCM formula contains hundreds of chemical components, which makes it complicated to elucidate the molecular mechanisms of TCM. Here, we proposed a computational systems pharmacology approach consisting of network link prediction, statistical analysis, and bioinformatics tools to investigate the molecular mechanisms of TCM formulae. Taking formula Tian-Ma-Gou-Teng-Yin as an example, which shows pharmacological effects on Alzheimer's disease (AD) and its mechanism is unclear, we first identified 494 formula components together with corresponding 178 known targets, and then predicted 364 potential targets for these components with our balanced substructure-drug-target network-based inference method. With Fisher's exact test and statistical analysis we identified 12 compounds to be most significantly related to AD. The target genes of these compounds were further enriched onto pathways involved in AD, such as neuroactive ligand-receptor interaction, serotonergic synapse, inflammatory mediator regulation of transient receptor potential channel and calcium signaling pathway. By regulating key target genes, such as ACHE, HTR2A, NOS2, and TRPA1, the formula could have neuroprotective and anti-neuroinflammatory effects against the progression of AD. Our approach provided a holistic perspective to study the relevance between TCM formulae and diseases, and implied possible pharmacological effects of TCM components. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 27% |
| Student > Doctoral Student | 4 | 11% |
| Student > Bachelor | 3 | 8% |
| Researcher | 2 | 5% |
| Student > Postgraduate | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 13 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 16% |
| Medicine and Dentistry | 5 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 8% |
| Engineering | 2 | 5% |
| Neuroscience | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 16 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 10 February 2021.
All research outputs
#7,514,350
of 23,092,602 outputs
Outputs from Frontiers in Pharmacology
#3,308
of 16,446 outputs
Outputs of similar age
#129,029
of 329,072 outputs
Outputs of similar age from Frontiers in Pharmacology
#89
of 402 outputs
Altmetric has tracked 23,092,602 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 16,446 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,072 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 402 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.