Evidence map›Paper›PMID 34306139›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2021

Systematic Elucidation of the Mechanism of

Xiaomin Kang, De Jin, Yuqing Zhang, Rongrong Zhou, Yuehong Zhang, Fengmei Lian

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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  3. Mechanisms of action ofFrontiers in pharmacology · 2024
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  5. Anti-Inflammatory Activities of the Ethanol Extract ofMolecules (Basel, Switzerland) · 2021
    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

6 authors at 2 institutions in 1 country.

Xiaomin KangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0002-3002-3786
De JinGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0001-6565-9496
Yuqing ZhangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0001-9601-1887
Rongrong ZhouGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0002-6886-1531
Yuehong ZhangGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0001-8024-9751
Fengmei LianGuang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.ORCID https://orcid.org/0000-0003-2222-7487
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

backgroundDiabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes, which seriously affects the physical and mental health of patients.

objectiveTo investigate the mechanism of SL in the treatment of DPN based on network pharmacology.

methodsThe active ingredients of SL were screened by related databases. The compound targets were collected by the target prediction platforms. The DPN-related targets were gathered through disease databases. The intersection targets were obtained by uploading the compound targets and disease targets to Venny 2.1.0, and a compound-target network was constructed by Cytoscape3.7.2. The protein-protein interaction (PPI) relationships were obtained by the STRING11.0 database. Genome Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID6.8 database. Molecular docking of key compounds and core targets was performed by DockThor.

resultsA total of 29 compounds and 51 intersection targets with potential therapeutic effects on DPN were obtained. The compound-target network construction resulted in four key compounds: protostemonine, 3-deoxysappanchalcone, 7,3',4'-trihydroxy-3-benzyl-2H-chromene, and o-12'-methylergocornine. PPI network analysis yielded 10 core targets: AKT1, MAPK3, CXCL8, TNF, OPRM1, MTOR, STAT3, MAPK8, SIRT1, and HSP90AA1. KEGG analysis resulted in 82 signaling pathways (

conclusionSL had the mechanism of multiple ingredients, multiple targets, and multiple pathways in the treatment of DPN. This study provided a scientific basis for further research on the treatment of DPN with SL and its extracts.

Identifiers

PMID34306139
PMCPMC8263209
OpenAlexW3176697057

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Registered trials

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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.