Evidence map›Paper›PMID 38591077›Full record

ArticleWorld journal of diabetes2024

Potential application of

Meng-Ying Che, Ling Yuan, Jiao Min, Duo-Jie Xu, Dou-Dou Lu, Wen-Jing Liu, Kai-Li Wang, Yan-Yan Wang, Yi Nan

Open access · diamondAbstract read
In one paragraph

Article in World journal of diabetes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Trial
  2. Review
  3. Uncovering the mechanistic basis ofPharmaceutical biology · 2025
    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

9 authors at 1 institution in 1 country.

Meng-Ying CheKey Laboratory of Ningxia Minority Medicine Modernization Ministry of Education, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Ling YuanCollege of Pharmacy, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Jiao MinKey Laboratory of Ningxia Minority Medicine Modernization Ministry of Education, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Duo-Jie XuTraditional Chinese Medicine College, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Dou-Dou LuSchool of Clinical Medicine, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Wen-Jing LiuKey Laboratory of Ningxia Minority Medicine Modernization Ministry of Education, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Kai-Li WangTraditional Chinese Medicine College, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Yan-Yan WangDepartment of Endocrinology, Yinchuan Hospital of Traditional Chinese Medicine, Yinchuan 750004, Ningxia Hui Autonomous Region, China.
Yi NanKey Laboratory of Ningxia Minority Medicine Modernization Ministry of Education, Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China. 20080011@nxmu.edu.cn.
Ningxia Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetic kidney disease (DKD) is one of the serious complications of diabetes mellitus, and the existing treatments cannot meet the needs of today's patients. Traditional Chinese medicine has been validated for its efficacy in DKD after many years of clinical application. However, the specific mechanism by which it works is still unclear. Elucidating the molecular mechanism of the

aimTo investigate the mechanism of the NRDP in DKD by network pharmacology combined with molecular docking, and then verify the initial findings by

methodsThe Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to screen active ingredient targets of NRDP. Targets for DKD were obtained based on the Genecards, OMIM, and TTD databases. The VENNY 2.1 database was used to obtain DKD and NRDP intersection targets and their Venn diagram, and Cytoscape 3.9.0 was used to build a "drug-component-target-disease" network. The String database was used to construct protein interaction networks. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Ontology analysis were performed based on the DAVID database. After selecting the targets and the active ingredients, Autodock software was used to perform molecular docking. In experimental validation using renal tubular epithelial cells (TCMK-1), we used the Cell Counting Kit-8 assay to detect the effect of NRDP on cell viability, with glucose solution used to mimic a hyperglycemic environment. Flow cytometry was used to detect the cell cycle progression and apoptosis. Western blot was used to detect the protein expression of STAT3, p-STAT3, BAX, BCL-2, Caspase9, and Caspase3.

resultsA total of 10 active ingredients and 85 targets with 111 disease-related signaling pathways were obtained for NRDP. Enrichment analysis of KEGG pathways was performed to determine advanced glycation end products (AGEs)-receptor for AGEs (RAGE) signaling as the core pathway. Molecular docking showed good binding between each active ingredient and its core targets.

conclusionNRDP may upregulate BCL-2 and STAT3 protein expression, and downregulate BAX, Caspase3, and Caspase9 protein expression, thus activating the AGE-RAGE signaling pathway, inhibiting the vitality of TCMK-1 cells, reducing their apoptosis. and arresting them in the G0/G1 phase to protect them from damage by high glucose.

Indexed as

Diabetic kidney diseaseExperimental validationMolecular dockingNardostachyos Radix et Rhizoma-rhubarbNetwork pharmacology

Identifiers

PMID38591077
PMCPMC10999050
OpenAlexW4394759641

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.