Evidence map›Paper›PMID 39502531›Full record

ArticleFrontiers in pharmacology2024

Effects of cannabidiol on AMPKα2 /HIF-1α/BNIP3/NIX signaling pathway in skeletal muscle injury.

Zhiquan Hou, Zhifang Wang, Jun Zhang, Yunen Liu, Zhonghua Luo

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. 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

5 authors.

Zhiquan Hou *Shenyang Medical College, Shenyang, China.
Zhifang Wang *College of physical education, Yanshan University, Qinhuangdao, China.
Jun ZhangCollege of physical education, Yanshan University, Qinhuangdao, China.
Yunen LiuShenyang Medical College, Shenyang, China.
Zhonghua LuoShenyang Medical College, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cannabidiol: (CBD) is a non-psychoactive natural active ingredient from cannabis plant, which has many pharmacological effects, including neuroprotection, antiemetic, anti-inflammatory and anti-skeletal muscle injury. However, the mechanism of its effect on skeletal muscle injury still needs further research. In order to seek a scientifically effective way to combat skeletal muscle injury during exercise, we used healthy SD rats to establish an exercise-induced skeletal muscle injury model by treadmill training, and systematically investigated the effects and mechanisms of CBD, a natural compound in the traditional Chinese medicine Cannabis sativa L., on combating skeletal muscle injury during exercise. CBD effectively improved the fracture of skeletal muscle tissue and reduced the degree of inflammatory cell infiltration. Biochemical indexes such as CK, T, Cor, LDH, SOD, MDA, and GSH-Px in serum of rats returned to normal. Combining transcriptome and network analysis results, CBD may play a protective role in exercise-induced skeletal muscle injury through HIF-1 signaling pathway. The experimental results implied that CBD could down-regulate the expression of IL-6, NF-κB, TNF-α, Keap1, AMPKα2, HIF-1α, BNIP3 and NIX, and raised the protein expression of IL-10, Nrf2 and HO-1. These results indicate that the protective effect of CBD on exercise-induced skeletal muscle injury may be related to the inhibition of oxidative stress and inflammation, thus inhibiting skeletal muscle injury through AMPKα2/HIF-1α/BNIP3/NIX signal pathways.

Indexed as

cannabidiolexercise-induced skeletal muscle injuryinflammationoxidative stresstranscriptome sequencing

Identifiers

PMID39502531
PMCPMC11536269

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.