The Role of p53 in Skeletal Muscle Adaptation During Exercise: A Literature Review
Bibliographic record
Abstract
Introduction: Due to its natural relationship with physiological health, skeletal muscle has been studied in a variety of contexts. Most commonly, it is analyzed during exercise to determine the adaptations caused by specific homeostatic imbalances. These imbalances pushed for more research in p53, a tumour suppressor known for regulating cellular stability. Methods: This literature review will be a narrative review using primary studies to determine the role of p53 in hypertrophy, mitochondrial biogenesis, and angiogenesis of skeletal muscles during exercise. Results: Differences in gene expression related to hypertrophy, mitochondrial biogenesis, and angiogenesis were observed during skeletal muscle adaptations dependent on p53 content and activity during and after exercise. Discussion: p53 content level was shown to contribute to skeletal muscle atrophy immediately following exercise, while having minimal effect on mitochondrial biogenesis. Rather, p53 activity was seen to be a more likely effector of mitochondrial levels. Moreover, through indirect pathways, p53 appears to negatively correlate with increases of angiogenesis in skeletal muscle. Conclusion: Research on p53 continues to express the importance of the protein beyond its role as a tumour suppressor. This review highlights alternative roles of p53 by analyzing its interactions in relation to exercise-induced adaptations of skeletal muscle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".