Regenerating Skeletal Muscle Compensates for the Impaired Macrophage Functions Leading to Normal Muscle Repair in Retinol Saturase Null Mice
Bibliographic record
Abstract
Skeletal muscle repair is initiated by local inflammation and involves the engulfment of dead cells (efferocytosis) by infiltrating macrophages at the injury site. Macrophages orchestrate the whole repair program, and efferocytosis is a key event not only for cell clearance but also for triggering the timed polarization of the inflammatory phenotype of macrophages into the healing one. While pro-inflammatory cytokines produced by the inflammatory macrophages induce satellite cell proliferation and differentiation into myoblasts, healing macrophages initiate the resolution of inflammation, angiogenesis, and extracellular matrix formation and drive myoblast fusion and myotube growth. Therefore, improper efferocytosis results in impaired muscle repair. Retinol saturase (RetSat) initiates the formation of various dihydroretinoids, a group of vitamin A derivatives that regulate transcription by activating retinoid receptors. Previous studies from our laboratory have shown that RetSat-null macrophages produce less milk fat globule-epidermal growth factor-factor-8 (MFG-E8), lack neuropeptide Y expression, and are characterized by impaired efferocytosis. Here, we investigated skeletal muscle repair in the tibialis anterior muscle of RetSat-null mice following cardiotoxin injury. Our data presented here demonstrate that, unexpectedly, several cell types participating in skeletal muscle regeneration compensate for the impaired macrophage functions, resulting in normal muscle repair in the RetSat-null mice.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".