Dietary Cod Protein Improves the IGF1‐Akt/PKB Signaling Pathway in Rat Skeletal Muscle during Recovery from Injury
Bibliographic record
Abstract
This study was designed to assess the mechanisms by which dietary cod protein may facilitate skeletal muscle recovery from injury through analyses of the IGF1‐Akt/PKB signaling pathway. Male Wistar rats were fed isoenergetic diets containing either casein (C), cod protein (CP), or casein supplemented with a mixture of arginine, glycine, taurine and lysine (C+), matching their respective levels as in CP. Muscle injury was induced by bupivacaine injection and downstream IGF1‐Akt/PKB effectors were measured post‐injury. At day 2 post‐injury, C+ upregulated (p=0.0008) whereas CP tended to upregulate phospho‐Akt Thr308 compared with C (p=0.075), suggesting that the myogenic effect of CP is due in part to its high levels of arginine, glycine, taurine and lysine. At day 2 post‐injury, CP reduced the expression of MuRF‐1 by 38% compared with C (p=0.03), supporting a decrease in the ubiquitylation of muscle proteins and their degradation. At day 5 post‐injury, CP tended to increase the muscle IGF‐1 level compared with C (p=0.098); phospho‐Akt Ser473, a phosphorylation required for maximum activation of Akt in addition to phosphorylation at Thr308, was also increased (p<0.0001). These findings suggest that at early time‐points of recovery, CP can reduce muscle protein degradation through modulation of the ubiquitination process and upregulate protein synthesis through enhanced Akt activation. Supported by NSERC
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".