Shrimp Protein Hydrolysate Modulates the Timing of Proinflammatory Macrophages in Bupivacaine-Injured Skeletal Muscles in Rats
Bibliographic record
Abstract
This study was designed to determine whether marine-derived proteins other than cod could have beneficial effects on inflammation following muscle injury. Macrophage and neutrophil densities were measured from bupivacaine-injured tibialis anterior muscle of rats fed isoenergetic diets containing either shrimp hydrolysate (Shr), casein hydrolysate (CaH), or whole casein (Ca). In this study, Shr reduced ED1+-macrophages at day 2 ( p=0.013 ), day 5 ( p=0.006 ), and day 14 after injury ( p=0.038 ) compared with Ca, indicating faster resolution of inflammation in Shr. Except for day 2 after injury where Shr led to lower ED1+-macrophages compared with CaH ( p=0.006 ), both Shr and CaH responded similarly at days 5, 14, and 28 after injury. This findings suggest that beneficial effects of Shr on ED1+-cells might be related to generation of anti-inflammatory peptides through the hydrolysis process, in addition to its high content of anti-inflammatory amino acids. However, while increasing myofiber cross-sectional area in noninjured muscles compared with both Ca and CaH, Shr failed to have a positive effect in corresponding injured muscles. These data indicate that shrimp hydrolysate can facilitate resolution of inflammation after muscle injury mainly through modulating proinflammatory macrophage accumulation but have less effect on optimal recovery in terms of muscle mass and fiber size.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".