Recycle, repair, recover: the role of autophagy in modulating skeletal muscle repair and post-exercise recovery
Bibliographic record
Abstract
Skeletal muscle is a highly plastic tissue that can adapt relatively rapidly to a range of stimuli. In response to novel mechanical loading, e.g. unaccustomed resistance exercise, myofibers are disrupted and undergo a period of ultrastructural remodeling to regain full physiological function, normally within 7 days. The mechanisms that underpin this remodeling are believed to be a combination of cellular processes including ubiquitin-proteasome/calpain-mediated degradation, immune cell infiltration, and satellite cell proliferation/differentiation. A relatively understudied system that has the potential to be a significant contributing mechanism to repair and recovery is the autophagolysosomal system, an intracellular process that degrades damaged and redundant cellular components to provide constituent metabolites for the resynthesis of new organelles and cellular structures. This review summarizes our current understanding of the autophagolysosomal system in the context of skeletal muscle repair and recovery. In addition, we also provide hypothetical models of how this system may interact with other processes involved in skeletal muscle remodeling and provide avenues for future research to improve our understanding of autophagy in human skeletal muscle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".