Evidence map›Paper›PMID 38934276›Full record

ArticleCombinatorial chemistry & high throughput screening2025

Effects and Mechanism of

Long Chen, Xiaohua Jiang, Hui Liu, Jin Zhang

Abstract read
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Article in Combinatorial chemistry & high throughput screening, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Long ChenDepartment of Infectious Disease, The First Affiliated Hospital of University of South China, Hengyang, 421000 China.
Xiaohua JiangDepartment of Infectious Disease, The First Affiliated Hospital of University of South China, Hengyang, 421000 China.
Hui LiuDepartment of Infectious Disease, The First Affiliated Hospital of University of South China, Hengyang, 421000 China.
Jin ZhangDepartment of Infectious Disease, The First Affiliated Hospital of University of South China, Hengyang, 421000 China.

Funding

Scientific Research Program Fund of Hunan Provincial Health Commission 202203083535
6 · The paper itself

Abstract

aimsThis study aims to investigate the effects and mechanism of Morinda Officinalis How (MOH), a lianoid shrub with potential therapeutic properties, on Metabolism- Associated Fatty Liver Disease (MAFLD).

objectiveThe objective of this study was to construct a MOH-MAFLD network prediction model and explore the effect of MOH on MAFLD and its underlying mechanism in vivo.

methodsScreening of MAFLD targets was performed using the DisGeNET database. Venny database was used to establish the MOH-MAFLD interaction network map, while the STRING database was applied to assess the Protein-Protein Interaction (PPI) network. The central target gene was screened using Gene Ontology (GO) function analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.

resultsGO function enrichment analysis revealed that MOH affected MAFLD through apoptosis and estrogen-related pathways. KEGG pathway enrichment and PPI network analysis indicated that MOH might mitigate MAFLD by reducing apoptosis and improving lipid metabolism. Additionally, 6 weeks of MOH treatment in rats decreased caspase-3 levels and increased Bcl-2, Estrogen receptor α(Esr1), and JUN proteins, thus ameliorating MAFLD progression.

conclusionMOH could delay the progression of MAFLD by affecting estrogen-related pathways, reducing cell stress, and inhibiting apoptosis.

Indexed as

Drugs, Chinese HerbalFatty LiverMorindaPlant ExtractsAnimalsApoptosisLipid MetabolismMaleProtein Interaction MapsRatsRats, Sprague-DawleyDrugs, Chinese HerbalPlant Extractscaspase 3c-JUN N-terminal kinasediseaseESR1.fatty liverMetabolismMorinda officinalis How

Identifiers

PMID38934276

What OpenQuestion holds

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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.