FACTORS INFLUENCING SKELETAL MUSCLE REMODELLING WITH LOADING
Bibliographic record
Abstract
Skeletal muscle is a plastic tissue that can adapt to several stimuli by changing its metabolic and contractile properties. Increased skeletal muscle mass can be brought on by loading through resistance exercise training, whereas decreased skeletal muscle mass can be brought on by reducing skeletal muscle contractile activity. Rates of muscle protein synthesis can be influenced by several factors broadly categorized as external or internal system variables. External system variables are environmental perturbations indispensable for activating internal system variables. Internal system variables are local, skeletal muscle-specific, biological processes that mechanistically underpin skeletal muscle adaptations. The overarching objective of the experiments conducted as part of this thesis was to discover the influence of external variables (resistance training program variables) and internal variables (long noncoding RNA) on skeletal muscle adaptations and characterize changes in skeletal muscle protein synthesis under various scenarios. In studies 1 and 2, we used systematic review and network meta-analytical methodology and discovered that resistance exercise training load, volume, and weekly frequency were important determinants of skeletal muscle adaptations. As an internal variable, the long-noncoding transcriptome is poorly characterized in skeletal muscle biology, and for study 3, we used five independent exercise studies to identify a novel long-noncoding RNA signature associated with resistance exercise-induced changes in lean mass. Lastly, in study 4, we characterized integrated rates of bulk muscle protein synthesis under distinct loading states in young, healthy men. We found that 14 days of single-leg immobilization was sufficient to induce rapid declines in muscle protein synthesis, whereas 4 sessions of resistance exercise increased muscle protein synthesis. Taken together, the findings of this thesis contribute substantially to our understanding the role of external and internal variables on skeletal muscle remodelling.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".