Ischemia‐reperfusion modulates the inflammatory and regenerative response to muscle contusion injury
Bibliographic record
Abstract
Numerous coordinated events must occur for complete regeneration of skeletal muscle following injury. When regeneration is incomplete, muscle is replaced by fibrotic scar tissue. It is our hypothesis that the inflammatory response normally facilitates muscle regeneration, but can be modulated by oxidative stress to exacerbate fibrosis. In this study, we used ischemia and reperfusion (I‐R) to generate oxidative stress and modulate inflammation and fibrosis after skeletal muscle injury. Male F344/BN rats, aged 2 months, were assigned to one of four groups: (1) Uninjured control, (2) Clip compression (200g) of the tibialis anterior (TA) muscle, (3) Femoral artery and vein occluded for two hours, causing I‐R insult, (4) Clip compression of the TA, two hours after I‐R. The relative levels of GP91 phox (oxidative species marker), MPO (neutrophil marker), CINC‐1 (neutrophil chemoattractant), and CD68 (macrophage marker) were quantitated at 8 and 72h post‐injury. Clip‐injured I‐R muscle had elevated GP91 phox (304 ± 70%) when compared to I‐R (102 ± 66%) or injured muscle alone (122 ± 22%). At 8 hours, MPO activity and CINC‐1 levels were elevated in both ischemic (1.83 ± 0.14 U/mg and 85.4 ± 20.5 pg/mg, respectively) and non‐ischemic (1.58 ± 0.40 U/mg and 86.5 ± 13.2 pg/mg) clip‐injured muscle compared to controls (0.12 ± 0.05 U/mg and 1.8 ± 0.1 pg/mg). By 3 days, MPO levels decreased, but remained elevated in the clip‐injured I‐R muscle (1.22 ± 0.10 U/mg) compared to clip‐injury alone (0.65 ± 0.13 U/mg). At 21 days post‐injury, increased collagen deposition and fibrotic tissue were observed in clip‐injured muscle preceded by I‐R. We conclude that oxidative stress modulates the inflammatory response, inhibiting regeneration and exacerbating fibrosis. (Supported by NSERC and NSHRF)
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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 | 0.001 |
| 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".