Molecular events and signalling pathways involved in skeletal muscle disuse‐induced atrophy and the impact of countermeasures
Bibliographic record
Abstract
• Introduction • Overview of signalling pathways involved in disuse-induced muscle atrophy - Changes in protein synthesis and associated signalling pathways - Several pathways contribute to increased protein degradation but to different extents - Akt/FOXO transcriptional control and induction of the ubiquitin ligase pathway - NF-κB pathways activated during disuse atrophy - Additional events controlling muscle atrophy - Apoptotic pathways are involved depending on the atrophy models - Preferential role of myostatin in inhibiting hypertrophy rather than inducing atrophy • Different approaches to counteract muscle disuse-induced atrophy - Exercise and physical training - Nutritional aids - Manipulations of growth factors - Additional nutritional, ergogenic supplements and drugs - Combined countermeasures for optimized effects • Conclusion and perspectives Disuse-induced skeletal muscle atrophy occurs following chronic periods of inactivity such as those involving prolonged bed rest, trauma and microgravity environments. Deconditioning of skeletal muscle is mainly characterized by a loss of muscle mass, decreased fibre cross-sectional area, reduced force, increased fatigability, increased insulin resistance and transitions in fibre types. A description of the role of specific transcriptional mechanisms contributing to muscle atrophy by altering gene expression during muscle disuse has recently emerged and focused primarily on short period of inactivity. A better understanding of the transduction pathways involved in activation of proteolytic and apoptotic pathways continues to represent a major objective, together with the study of potential cross-talks in these cellular events. In parallel, evaluation of the impact of countermeasures at the cellular and molecular levels in short- and long-term disuse experimentations or microgravity environments should undoubtedly and synergistically increase our basic knowledge in attempts to identify new physical, pharmacological and nutritional targets to counteract muscle atrophy. These investigations are important as skeletal muscle atrophy remains an important neuromuscular challenge with impact in clinical and social settings affecting a variety of conditions such as those seen in aging, cancer cachexia, muscle pathologies and long-term space exploration.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".