Can Cod Protein Improve Skeletal Muscle Repair Following Injury?
Bibliographic record
Abstract
It has been recently shown that cod protein can modulate the production of pro‐inflammatory cytokines. We therefore postulated that it may regulate muscle regeneration and promote muscle mass recovery. The aim of this study was to determine the effects of dietary cod protein on skeletal muscle repair after injury compared with casein. Sixty four male Wistar rats were assigned to isoenergetic diets composed of either dietary cod protein or casein. After 21 d of ad libitum feeding, one tibialis anterior muscle (TA) was injured by bupivacaine injection (100μl) at the half proximal region while the contra‐lateral TA was injected with saline and served as sham. TA muscles collected at time 0 served as control. At day 14 post‐injury, values for muscle weight in both contra‐lateral (0.017) and injured groups (0.014) were higher in rats fed the cod protein diet, indicating better muscle mass recovery than in those fed the casein diet. Conversely, the cod protein diet led to lower pro‐inflammatory ED1 + macrophage density in both injured (P=0.008) and contra‐lateral sham (P=0.029) at day 24 post‐injury, suggesting that cod protein modulates the time course of inflammatory cell trafficking. Globally these data suggest that cod protein favors muscle growth and influences the inflammatory response, which potentially impact on recovery following injury. Supported by The Natural Sciences and Engineering Research Council of Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".