mTORC1 and the regulation of skeletal muscle anabolism and mass
Bibliographic record
Abstract
The mass and integrity of skeletal muscle is vital to whole-body substrate metabolism and health. Indeed, defects in muscle metabolism and functions underlie or exacerbate diseases like diabetes, rheumatoid arthritis, and cancer. Physical activity and nutrition are the 2 most important environmental factors that can affect muscle health. At the molecular level, the mammalian target of rapamycin complex 1 (mTORC1) is a critical signalling complex that regulates muscle mass. In response to nutrition and resistance exercise, increased muscle mass and activation of mTORC1 occur in parallel. In this review, we summarize recent findings on mTORC1 and its regulation in skeletal muscle in response to resistance exercise, alone or in combination with intake of protein or amino acids. Because increased activity of the complex is implicated in the development of muscle insulin resistance, obesity, and some cancers (e.g., ovarian, breast), drugs that target mTORC1 are being developed or are in clinical trials. However, various cancers are associated with extensive muscle wasting, due in part to tumour burden and malnutrition. This muscle wasting may also be a side effect of anticancer drugs. Because loss of muscle mass is associated not only with metabolic abnormalities but also dose limiting toxicity, we review the possible implications for skeletal muscle of long-term inhibition of mTORC1, especially in muscle wasting conditions.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".