Neutrophil-induced skeletal muscle damage: a calculated and controlled response following hindlimb unloading and reloading
Bibliographic record
Abstract
Neutrophils phagocyte necrotic debris and release cytokines, enzymes, and oxidative factors. In the present study, we investigated the contribution of neutrophils to muscle injury, dysfunction, and recovery using an unloading and reloading model. Mice were submitted to 10 days of hindlimb unloading and were transiently depleted in neutrophils with anti-Ly6G/Ly6C antibody prior to reloading. Leukocyte accumulation and muscle function were assessed immunohistologically and functionally in vitro. In addition, soleus muscles submitted to unloading and reloading were incubated in vitro with LPS (100 microg/ml) to determine whether exogenous stimulus would activate neutrophil response and produce extensive muscle damage. Contractile properties were recorded every hour for 6 h, and muscles were subsequently incubated in procion orange to assess muscle damage. Neutrophil depletion affected neither the loss in muscle force nor the time of recovery in atrophied and reloaded soleus muscles. However, atrophied and reloaded soleus muscles that contained high concentration of neutrophils experienced a 20% greater loss in force than atrophied and reloaded soleus muscles depleted in neutrophils following in vitro incubation with LPS. Procion orange dye also confirmed that neutrophils induced a 2.5-fold increase in muscle membrane damage in the presence of LPS. These results show that neutrophil infiltration during modified mechanical loading is highly regulated and efficiently eliminated, with no significant muscle fiber injury unless the activation state of neutrophils is modified by the presence of LPS.
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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) | 0.001 | 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".