Beneficial effects of cod protein on skeletal muscle repair following injury
Bibliographic record
Abstract
This study examined the effect of peanut and cod proteins on post-damage skeletal muscle repair, compared with casein. We hypothesized that because of their high arginine content, these proteins would improve the resolution of inflammation and muscle mass recovery following injury. One hundred and twenty-eight male Wistar rats were assigned to isoenergetic diets composed of casein and peanut (experiment 1) or cod protein (experiment 2). After 21 days of feeding, one tibialis anterior muscle (TA) was injured with bupivacaine, while the contralateral TA was injected with saline (sham muscle). Measurements were taken at days 0, 3, 14, and 24 post-injury. Compared with casein, peanut protein reduced muscle mass at days 0 (-12%, p = 0.005) and 14 post-injury in the injured muscle (-13%, p = 0.04), and lowered myofiber cross-sectional area in both the sham (-21%, p = 0.008) and injured muscles (-26%, p = 0.05) at day 24 post-injury, showing that peanut protein has a weak potential to support muscle growth. At day 14 post-injury, muscle mass in the sham (13%, p = 0.02) and injured muscles (12%, p = 0.01) was higher in cod-protein-fed rats, indicating better muscle mass recovery, than in casein-fed rats. Cod protein tended (p = 0.06) to decrease the density of neutrophils (-24%) at day 14 post-injury in the injured muscle, and to decrease the density of ED1(+) macrophages at day 24 post-injury in both sham (-29%, p = 0.03) and injured (-40%, p = 0.01) muscles. No effects were observed for peanut protein. These data indicate that cod protein is better for promoting growth and regeneration of skeletal muscle after trauma, partly because of the improved resolution of inflammation.
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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) | 0.000 | 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".