Neutrophil-induced skeletal muscle damage: a calculated and controlled response following hindlimb unloading and reloading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it