<i>Inonotus obliquus</i> upregulates muscle regeneration and augments function through muscle oxidative metabolism
Bibliographic record
Abstract
Skeletal muscle wasting related to aging or pathological conditions is critically associated with the increased incidence and prevalence of secondary diseases including cardiovascular diseases, metabolic syndromes, and chronic inflammations.Much effort is made to develop agents to enhance muscle metabolism and function.Inonotus obliquus (I.obliquus; IO) is a mushroom popularly called chaga and has been widely employed as a folk medicine for inflammation, cardiovascular diseases, diabetes, and cancer in Eastern Europe and Asia.However, its effect on muscle health has not been explored.Here, we aimed to investigate the beneficial effect of IO extract in muscle regeneration and metabolism.The treatment of IO in C2C12 myoblasts led to increased myogenic differentiation and alleviation of dexamethasoneinduced myotube atrophy.Network pharmacological analysis using the identified specific chemical constituents of IO extracts predicted protein kinase B (AKT)-dependent mechanisms to promote myogenesis and muscle regeneration.Consistently, IO treatment resulted in the activation of AKT, which suppressed muscle-specific ubiquitin E3 ligases induced by dexamethasone.IO treatment in mice improved the regeneration of cardiotoxin-injured muscles accompanied by elevated proliferation and differentiation of muscle stem cells.Furthermore, it elevated the mitochondrial content and muscle oxidative metabolism accompanied by the induction of peroxisome proliferator-activated receptor γ coactivator α (PGC-1α).Our current data suggest that IO is a promising natural agent in enhancing muscle regenerative capacity and oxidative metabolism thereby preventing muscle wasting.
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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 | 0.000 |
| 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".