Dietary Cod Protein Improves Skeletal Muscle Regeneration Partly Through Its High Levels of Arginine, Lysine, Glycine and Taurine
Bibliographic record
Abstract
Feeding cod protein may beneficially influence the time course of inflammation during recovery following skeletal muscle injury. The objective of this study was to identify amino acids in cod protein having a beneficial impact on muscle repair. Male Wistar rats were fed isoenergetic diets containing either casein (C), cod protein (CP), or casein supplemented with arginine (1.9%), glycine (1.9%), taurine (0.7%) and lysine (1.7%) (C + ). After 21 d of ad libitum feeding, one tibialis anterior muscle (TA) was injured by injecting 200μl of bupivacaine while the contra‐lateral TA was injected with saline and served as sham. Rats fed CP exhibited higher muscle weight at days 5, 14, and 28 post‐injury and higher myofiber cross‐sectional area at days 5 and 28 post‐injury compared with C. CP and C + similarly modulated the inflammation as they decreased ED1 + macrophages and COX‐2 level at day 2 post injury compared with casein (p=0.01), and increased ED2 + macrophages compared with C. The C + diet upregulated (p=0.02) whereas the CP diet tended to upregulate myogenin expression at day 5 post‐injury compared with C (p=0.06). These data support the hypothesis that the beneficial impact of cod protein on skeletal muscle recovery is partly attributed to the anti‐inflammatory and myogenic action of its high arginine, lysine, glycine and taurine levels. Supported by The Natural Sciences and Engineering Research Council of Canada.
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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".