↓ Skip to main content

Network Pharmacology Studies on the Bioactive Compounds and Action Mechanisms of Natural Products for the Treatment of Diabetes Mellitus: A Review

Overview of attention for article published in Frontiers in Pharmacology, February 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user
facebook
1 Facebook page

Citations

dimensions_citation
121 Dimensions

Readers on

mendeley
206 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Network Pharmacology Studies on the Bioactive Compounds and Action Mechanisms of Natural Products for the Treatment of Diabetes Mellitus: A Review
Published in
Frontiers in Pharmacology, February 2017
DOI 10.3389/fphar.2017.00074
Pubmed ID
Authors

Weiwei Li, Guoqi Yuan, Yuxiang Pan, Cong Wang, Haixia Chen

Abstract

Diabetes mellitus (DM) is a kind of chronic and metabolic disease, which can cause a number of diseases and severe complications. Network pharmacology approach is introduced to study DM, which can combine the drugs, target proteins and disease and form drug-target-disease networks. Network pharmacology has been widely used in the studies of the bioactive compounds and action mechanisms of natural products for the treatment of DM due to the multi-components, multi-targets, and lower side effects. This review provides a balanced and comprehensive summary on network pharmacology from current studies, highlighting different bioactive constituents, related databases and applications in the investigations on the treatment of DM especially type 2. The mechanisms related to type 2 DM, including α-amylase and α-glucosidase inhibitory, targeting β cell dysfunction, AMPK signal pathway and PI3K/Akt signal pathway are summarized and critiqued. It suggests that the network pharmacology approach cannot only provide a new research paradigm for natural products, but also improve the current antidiabetic drug discovery strategies. Furthermore, we put forward the perspectives on the reasonable applications of network pharmacology for the therapy of DM and related drug discovery.

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 206 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 206 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 14%
Student > Master 22 11%
Researcher 18 9%
Student > Bachelor 12 6%
Student > Postgraduate 9 4%
Other 47 23%
Unknown 69 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 15%
Pharmacology, Toxicology and Pharmaceutical Science 28 14%
Biochemistry, Genetics and Molecular Biology 17 8%
Chemistry 12 6%
Medicine and Dentistry 12 6%
Other 26 13%
Unknown 80 39%
Attention Score in Context

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 10 May 2017.
All research outputs
#18,534,624
of 22,955,959 outputs
Outputs from Frontiers in Pharmacology
#8,302
of 16,230 outputs
Outputs of similar age
#238,185
of 311,210 outputs
Outputs of similar age from Frontiers in Pharmacology
#113
of 199 outputs
Altmetric has tracked 22,955,959 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 16,230 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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 311,210 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 199 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.