Evidence map›Paper›PMID 27999264›Full record

ArticleInternational journal of molecular sciences2016

Systematic Understanding of Mechanisms of a Chinese Herbal Formula in Treatment of Metabolic Syndrome by an Integrated Pharmacology Approach.

Meimei Chen, Fafu Yang, Xuemei Yang, Xinmei Lai, Yuxing Gao

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
4.7field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Meimei ChenCollege of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, China. chenmeimei1984@163.com.
Fafu YangCollege of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, China. yangfafu@fjnu.edu.cn.
Xuemei YangCollege of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China. mei_tcm@163.com.
Xinmei LaiCollege of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China. keerer1990@163.com.
Yuxing GaoCollege of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China. gaoxingchem@xmu.edu.cn.
Fujian University of Traditional Chinese Medicine · CNFujian Normal University · CNXiamen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic syndrome (MS) is becoming a worldwide health problem. Wendan decoction (WDD)-a famous traditional Chinese medicine formula-has been extensively employed to relieve syndromes related to MS in clinical practice in China. However, its pharmacological mechanisms still remain vague. In this study, a comprehensive approach that integrated chemomics, principal component analysis, molecular docking simulation, and network analysis was established to elucidate the multi-component and multi-target mechanism of action of WDD in treatment of MS. The compounds in WDD were found to possess chemical diversity, complexity and drug-likeness compared to MS drugs. Six nuclear receptors were obtained to have strong binding affinity with 217 compounds of five herbs in WDD. The importance roles of targets and herbs were also identified due to network parameters. Five compounds from

Indexed as

Drugs, Chinese HerbalGlucoseHumansLipid MetabolismMedicine, Chinese TraditionalMetabolic SyndromeMolecular Docking SimulationPrincipal Component AnalysisDrugs, Chinese HerbalGlucosewendanmetabolic syndromemolecular dockingnetwork analysisWendan decoction

Identifiers

PMID27999264
PMCPMC5187914
OpenAlexW2563694709

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.