Ischaemia–reperfusion modulates inflammation and fibrosis of skeletal muscle after contusion injury
Bibliographic record
Abstract
Regeneration of skeletal muscle following injury is dependent on numerous factors including age, the inflammatory response, revascularization, gene expression of myogenic and growth factors and the activation and proliferation of endogenous progenitor cells. It is our hypothesis that oxidative stress preceding a contusion injury to muscle modulates the inflammatory response to inhibit muscle regeneration and enhance fibrotic scar formation. Male F344/BN rats were assigned to one of four groups. Group 1: uinjured control; Group 2: ischaemic occlusion of femoral vessels for 2 h followed by reperfusion (I-R); Group 3: contusion injury of the tibialis anterior (TA); Group 4: I-R, then contusion injury. The acute inflammatory response (8 h, 3 days) was determined by expression of the chemokine CINC-1, TGF-beta1, IFN-gamma and markers of neutrophil (myeloperoxidase) and macrophage (CD68) activity and recruitment. Acute oxidative stress caused by I-R and/or contusion, was determined by measuring GP91(phox) and lipid peroxidation. Muscle recovery (21 days) was assessed by examining the fibrosis after I-R and contusion injuries to the TA with Sirius Red staining and quantification of collagen I expression. Consistent with our hypothesis, I-R preceding contusion increased all markers of the acute inflammatory response and oxidative stress after injury and elevated the expression of collagen. We conclude that ischaemia-induced oxidative stress exacerbated the inflammatory response and enhanced fibrotic scar tissue formation after injury. This response may be attributable to increased levels of TGF-beta1 and diminished expression of IFN-gamma in the ischaemic contused muscle.
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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) | 0.001 | 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".