Degradative Signaling in ATG7-Deficient Skeletal Muscle Following Cardiotoxin Injury
Bibliographic record
Abstract
Skeletal muscle is a complex tissue comprising multinucleated and post-mitotic cells (i.e., myofibers). Given this, skeletal muscle must maintain a fine balance between growth and degradative signals. A major system regulating the remodeling of skeletal muscle is autophagy, where cellular quality control is mediated by the degradation of damaged cellular components. The accumulation of damaged cellular material can result in elevated apoptotic signaling, which is particularly relevant in skeletal muscle given its post-mitotic nature. Luckily, skeletal muscle possesses the unique ability to regenerate in response to injury. It is unknown whether a relationship between autophagy and apoptotic signaling exists in injured skeletal muscle and how autophagy deficiency influences myofiber apoptosis and regeneration. In the present study, we demonstrate that an initial inducible muscle-specific autophagy deficiency does not alter apoptotic signaling following cardiotoxin injury. This finding is presumably due to the re-establishment of ATG7 levels following injury, which may be attributed to the contribution of a functional Atg7 gene from satellite cells. Furthermore, the re-expression of ATG7 resulted in virtually identical regenerative potential. Overall, our data demonstrate that catastrophic injury may “reset” muscle gene expression via the incorporation of nuclei from satellite cells.
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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".