Evidence map›Paper›PMID 39636495›Full record

ArticleChinese journal of integrative medicine2025

Liang-Ge-San Decoction Ameliorates Acute Respiratory Distress Syndrome via Suppressing p38MAPK-NF-κ B Signaling Pathway.

Quan Li, Juan Chen, Meng-Meng Wang, Li-Ping Cao, Wei Zhang, Zhi-Zhou Yang, Yi Ren, Jing Feng, Xiao-Qin Han, Shi-Nan Nie and 1 more

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Article in Chinese journal of integrative medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Quan LiDepartment of Intensive Care Unit, Suqian First Hospital, Suqian, Jiangsu Province, 223800, China.
Juan ChenDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Meng-Meng WangDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Li-Ping CaoDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Wei ZhangDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Zhi-Zhou YangDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Yi RenDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Jing FengDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Xiao-Qin HanDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Shi-Nan NieDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
Zhao-Rui SunDepartment of Emergency Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China. sunzhr84@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore the potential effects and mechanisms of Liang-Ge-San (LGS) for the treatment of acute respiratory distress syndrome (ARDS) through network pharmacology analysis and to verify LGS activity through biological experiments.

methodsThe key ingredients of LGS and related targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. ARDS-related targets were selected from GeneCards and DisGeNET databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the Metascape Database. Molecular docking analysis was used to confirm the binding affinity of the core compounds with key therapeutic targets. Finally, the effects of LGS on key signaling pathways and biological processes were determined by in vitro and in vivo experiments.

resultsA total of LGS-related targets and 496 ARDS-related targets were obtained from the databases. Network pharmacological analysis suggested that LGS could treat ARDS based on the following information: LGS ingredients luteolin, wogonin, and baicalein may be potential candidate agents. Mitogen-activated protein kinase 14 (MAPK14), recombinant V-Rel reticuloendotheliosis viral oncogene homolog A (RELA), and tumor necrosis factor alpha (TNF-α) may be potential therapeutic targets. Reactive oxygen species metabolic process and the apoptotic signaling pathway were the main biological processes. The p38MAPK/NF-κ B signaling pathway might be the key signaling pathway activated by LGS against ARDS. Moreover, molecular docking demonstrated that luteolin, wogonin, and baicalein had a good binding affinity with MAPK14, RELA, and TNF α. In vitro experiments, LGS inhibited the expression and entry of p38 and p65 into the nucleation in human bronchial epithelial cells (HBE) cells induced by LPS, inhibited the inflammatory response and oxidative stress response, and inhibited HBE cell apoptosis (P<0.05 or P<0.01). In vivo experiments, LGS improved lung injury caused by ligation and puncture, reduced inflammatory responses, and inhibited the activation of p38MAPK and p65 (P<0.05 or P<0.01).

conclusionLGS could reduce reactive oxygen species and inflammatory cytokine production by inhibiting p38MAPK/NF-κ B signaling pathway, thus reducing apoptosis and attenuating ARDS.

Indexed as

Drugs, Chinese HerbalNF-kappa Bp38 Mitogen-Activated Protein KinasesRespiratory Distress SyndromeSignal TransductionAnimalsApoptosisHumansMaleMiceMolecular Docking SimulationNetwork PharmacologyDrugs, Chinese HerbalNF-kappa Bp38 Mitogen-Activated Protein Kinasesacute respiratory distress syndromeanimal experimentsLiang-Ge-Sanmolecular dockingnetwork pharmacology

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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.