Disuse atrophy delays and reduces amino acid induced activation of key translational signaling proteins in humans
Bibliographic record
Abstract
We aimed to determine the effect of muscle disuse on activation by mixed amino acid (AA) feeding of three anabolic signaling proteins: mTOR, eIF2Bε and p70s6k. 10 men and 2 women wore an immobilizing knee brace for 14d, reducing muscle by CSA (5±1%, P<0.001). Subjects then received infusions of mixed AA at either 44 mg kg −1 h −1 or 261 mg kg −1 h −1 . Quadriceps biopsies were taken from both legs when fasted and fed (1, 2, 4 h). After 1 h AA infusion phosphorylation of mTOR (Ser2448), eIF2Bε (Ser 539) and p70s6k (Thr389) in the non‐immobilized leg increased by 35–56% (P<0.05), with a return to baseline by 2 h (p70s6k) or 4 h (mTOR, eIF2Bε). In the immobilized leg mTOR phosphorylation did not significantly increase at all after AA infusion whereas phosphorylation of eIF2Bε increased by only 22% at 2 h (P<0.05); phosphorylation of p70s6k increased by only 32 % at 1 h (P<0.05), but remained elevated until 4 h. p70s6k phosphorylation was 22 % higher (P=0.05) in the immobilized leg in the fasted state. These findings, together with an observed depressed AA‐induced muscle protein synthetic response, suggest a dampened response of the underlying translational control mechanisms with disuse atrophy in humans. We propose that a reduction in the feeding‐induced stimulation of muscle protein synthesis is of major importance in causing muscle loss with disuse in humans. Support: NSERC, CIHR, UK BBSRC, and EC EXEGENESIS
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".