Ginsenoside Rg5 promotes muscle regeneration via p38MAPK and Akt/mTOR signaling
Bibliographic record
Abstract
Skeletal muscles play a key role in physical activity and energy metabolism. The loss of skeletal muscle mass can cause problems related to metabolism and physical activity. Studies are being conducted to prevent such diseases by increasing the mass and regeneration capacity of muscles. Ginsenoside Rg5 has been reported to exhibit a broad range of pharmacological activities. However, studies on the effects of Rg5 on muscle differentiation and growth are scarce. To investigate the effects of Rg5 on myogenesis, C2C12 myoblasts were induced to differentiate with Rg5, followed by immunoblotting, immunostaining, and qRT-PCR for myogenic markers and promyogenic signaling (p38MAPK). Immunoprecipitation confirmed that Rg5 increased the interaction between MyoD and E2A via p38MAPK. To investigate the effects of Rg5 on prevention of muscle mass loss, C2C12 myotubes were treated with dexamethasone to induce muscle atrophy. Immunoblotting, immunostaining, and qRT-PCR were performed for myogenic markers, Akt/mTOR signaling for protein synthesis, and atrophy-related genes (Atrogin-1 and MuRF1). Rg5 promoted C2C12 myoblast differentiation through phosphorylation of p38MAPK and MyoD/E2A heterodimerization. Furthermore, Rg5 stimulated C2C12 myotube hypertrophy via phosphorylation of Akt/mTOR. Phosphorylation of Akt induces FoxO3a phosphorylation, which reduces the expression of Atrogin-1 and MuRF1. This study provides an understanding of how Rg5 promotes myogenesis and hypertrophy and prevents dexamethasone-induced muscle atrophy. The study is the first, to the best of our knowledge, to show that Rg5 promotes muscle regeneration and to suggest that Rg5 can be used for therapeutic intervention of muscle weakness and atrophy, including cancer cachexia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".