Evidence map›Paper›PMID 34925533›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2021

Revealing the Mechanism of

Yi Xie, Kainan Zhou, Yan Wang, Shuhan Yang, Suying Liu, Xueqian Wang, Ying Zhang

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 28% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
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

7 authors at 2 institutions in 1 country.

Yi XieGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0002-5476-1679
Kainan ZhouGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0003-1077-3245
Yan WangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0002-3134-1567
Shuhan YangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0003-0241-0129
Suying LiuGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0001-6077-4778
Xueqian WangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0002-5694-222X
Ying ZhangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0002-9299-1889
Chinese Academy of Medical Sciences & Peking Union Medical College · CNBeijing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer-related fatigue (CRF) is an increasingly appreciated complication in cancer patients, which severely impairs their quality of life for a long time.

objectiveTo systematically analyze the mechanism of AR against CRF by network pharmacology.

methodsTCMSP was searched to obtain the active compounds and targets of AR. The active compound-target (AC-T) network was established and exhibited by related visualization software. The GeneCards database was searched to acquire CRF targets, and the intersection targets with AR targets were used to make the Venny diagram. The protein-protein interaction (PPI) network of intersection targets was established, and further, the therapeutic core targets were selected by topological parameters. The selected core targets were uploaded to Metascape for GO and KEGG analysis. Finally, AutoDock Vina and PyMOL were employed for molecular docking validation.

results16 active compounds of AR were obtained, such as quercetin, kaempferol, 7-O-methylisomucronulatol, formononetin, and isorhamnetin. 57 core targets were screened, such as AKT1, TP53, VEGFA, IL-6, and CASP3. KEGG analysis manifested that the core targets acted on various pathways, including 137 pathways such as TNF, IL-17, and the AGE-RAGE signaling pathway. Molecular docking demonstrated that active compounds docked well with the core targets.

conclusionThe mechanism of AR in treating CRF involves multiple targets and multiple pathways. The present study laid a theoretical foundation for the subsequent research and clinical application of AR and its extracts against CRF.

Identifiers

PMID34925533
PMCPMC8674051
OpenAlexW4200400901

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.