Evidence map›Paper›PMID 31827565›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2019

A Network Pharmacology Approach to Reveal the Underlying Mechanisms of

Qiang Zeng, Longfei Li, Yu Jin, Zongzheng Chen, Lihong Duan, Meiqun Cao, Min Ma, Zhengzhi Wu

Abstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
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

21 citing papers in PubMed.

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  17. Combined Metabolomics and Network Toxicology to Explore the Molecular Mechanism ofEvidence-based complementary and alternative medicine : eCAM · 2021
    Article
  18. A Network Pharmacology Approach to Reveal the Underlying Mechanisms ofEvidence-based complementary and alternative medicine : eCAM · 2021
    Article
  19. A Network Pharmacology Approach to Explore the Mechanisms ofEvidence-based complementary and alternative medicine : eCAM · 2021
    Article
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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

8 authors.

Qiang ZengIntegrated Chinese and Western Medicine Postdoctoral Research Station, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China.
Longfei LiLaboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China.
Yu JinThe First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen 518035, China.
Zongzheng ChenIntegrated Chinese and Western Medicine Postdoctoral Research Station, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China.
Lihong DuanThe First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen 518035, China.
Meiqun CaoThe First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen 518035, China.ORCID https://orcid.org/0000-0003-0375-5348
Min MaIntegrated Chinese and Western Medicine Postdoctoral Research Station, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China.ORCID https://orcid.org/0000-0003-3602-6846
Zhengzhi WuThe First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen 518035, China.ORCID https://orcid.org/0000-0002-8819-609X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the potential active compounds and underlying mechanisms of

methodsThe active components of PLP were collected from Traditional Chinese Medicine System Pharmacology (TCMSP) database, and their possible target proteins were predicted using TCMSP, SwissTargetPrediction, and STITCH databases. The putative AD-related target proteins were identified from Therapeutic Target Database (TTD), GeneCards, and MalaCards database. The compound-target-disease network interactions were established to obtain the key targets about PLP acting on AD by network topology analysis. Then, the function annotation and signaling pathways of key targets were performed by GO and KEGG enrichment analysis using DAVID tools. Finally, the binding capacity between active ingredients and key targets was validated by molecular docking using SystemsDock tools.

resultsThere were 7 active compounds involving in 151 predicted targets identified in PLP. Besides, a total of 160 AD-related targets were identified. Among these targets, 30 shared targets of PLP and AD were acquired. After topological analysis of the PLP potential target-AD target network, 33 key targets that were highly responsible for the therapeutic effects of PLP on AD were obtained. Further GO and KEGG enrichment analysis showed that these key targets were significantly involved in multiple biological processes and pathways which participated in cell apoptosis and inflammatory response and maintained the function of neurons to accomplish the anti-AD activity. The molecular docking analysis verified that the 7 active compounds had definite affinity with the key targets.

conclusionsThe ameliorative effects of PLP on AD were predicted to be associated with regulating neural cell apoptosis, inflammatory response, and neurotrophy via various pathways such as PI3K-Akt signaling pathway, MAPK signaling pathway, and neurotrophin signaling pathway.

Identifiers

PMID31827565
PMCPMC6885190

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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.