Evidence map›Paper›PMID 40137140›Full record

ArticleMetabolites2025

Metabolomics-Based Study on the Anticonvulsant Mechanism of

Liang Chen, Jiaxin Li, Chengwei Fang, Jiepeng Wang

Erratum issuedAbstract read
In one paragraph

Article in Metabolites, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Liang ChenSchool of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China.
Jiaxin LiSchool of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China.
Chengwei FangSchool of Pharmacy, Guizhou University of Traditional Chinese Medicine, Guiyang 550025, China.
Jiepeng WangSchool of Basic Medical Sciences, Hebei University of Chinese Medicine, Shijiazhuang 050200, China.

Funding

National Natural Science Foundation of China 82160803
6 · The paper itself

Abstract

BACKGROUND/

objectivesEpilepsy is a common chronic and recurrent neurological disorder that poses a threat to human health, and

methodsAn epileptic model of rats was established using pentylenetetrazol. The potential targets and pathways of ATS were predicted by network pharmacology. Ultra Performance Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometrynce Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometryance Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometry and statistical analyses were used to profile plasma metabolites and identify ATS's effects on epilepsy.

resultsKyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that ATS was involved in regulating multiple signaling pathways, mainly including the neuroactive ligand-receptor interaction and GABAerGamma-aminobutyrate transaminaseAminobutyrate Transaminaseapse signaling pathway. ATS treatment restored 19 metabolites in epiGamma-aminobutyrate transaminaseminobutyrate Transaminase rats, affecting lysine, histidine, and purine metabolism. GABA-T was found as a new key target for treating epilepsy with ATS. The IC

conclusionsThese results improved our understanding of epilepsy and ATS treatment, potentially leading to better therapies. The identification of key metabolites and their associated pathways in this study offers potential novel therapeutic targets for epilepsy. By modulating these metabolites, future therapies could be designed to better manage the disorder. Moreover, the insights from network pharmacology can guide the development of more effective antiepileptic drugs, paving the way for improved clinical outcomes for patients.

Indexed as

Acorus tatarinowii SchottepilepsyGamma-aminobutyrate transaminasemetabolomicsplasma

Identifiers

PMID40137140
PMCPMC11944195

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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.