Mast cells can modulate leukocyte accumulation and skeletal muscle function following hindlimb unloading
Bibliographic record
Abstract
Rodent hindlimb suspension is widely used to induce inflammation and muscle impairment. We set out to define the role of mast cells in neutrophil and macrophage recruitment and muscle recovery after unloading-reloading. We hypothesized that mechanical perturbation would stimulate release of proinflammatory substances by mast cells, which would influence leukocyte recruitment and muscle function. Rats were suspended for 10 days and injected with a mast cell inhibitor (cromolyn) or stimulator (compound 48/80) or a placebo before reloading. Leukocyte accumulation and muscle function were assessed using immunohistological staining and measurements of contractile properties in vitro. Our results showed that mechanical loading activated mast cells, thereby influencing leukocyte recruitment in the early reloading periods. Indeed, the inhibition of mast cell degranulation significantly reduced the number of neutrophil cell profiles in reloaded soleus muscle, whereas mast cell activation provoked a significant increase in the number of neutrophil cell profiles in uninjured muscle. However, the inhibition of mast cell degranulation also led to a significant increase in the number of ED1+ macrophage cell profiles. These perturbations in the inflammatory response caused by mast cell inhibition induced a short protective effect on the loss of muscle force after 1 day of reloading but delayed the return to the normal contractile properties of muscles after 14 days of reloading. These results indicate that mechanical loading can induce mast cell degranulation, which can influence leukocyte influx and muscle function, and also highlighted the possibility that leukocytes may play a dual role in skeletal muscles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".