Alterations of protein turnover underlying disuse atrophy in human skeletal muscle
Bibliographic record
Abstract
Unloading-induced atrophy is a relatively uncomplicated form of muscle loss, dependent almost solely on the loss of mechanical input, whereas in disease states associated with inflammation (cancer cachexia, AIDS, burns, sepsis, and uremia), there is a procatabolic hormonal and cytokine environment. It is therefore predictable that muscle loss mainly due to disuse alone would be governed by mechanisms somewhat differently from those in inflammatory states. We suggest that in vivo measurements made in human subjects using arterial-venous balance, tracer dilution, and tracer incorporation are dynamic and thus robust by comparison with static measurements of mRNA abundance and protein expression and/or phosphorylation in human muscle. In addition, measurements made with cultured cells or in animal models, all of which have often been used to infer alterations of protein turnover, appear to be different from results obtained in immobilized human muscle in vivo. In vivo measurements of human muscle protein turnover in disuse show that the primary variable that changes facilitating the loss of muscle mass is protein synthesis, which is reduced in both the postabsorptive and postprandial states; muscle proteolysis itself appears not to be elevated. The depressed postprandial protein synthetic response (a phenomenon we term "anabolic resistance") may even be accompanied by a diminished suppression of proteolysis. We therefore propose that most of the loss of muscle mass during disuse atrophy can be accounted for by a depression in the rate of protein synthesis. Thus the normal diurnal fasted-to-fed cycle of protein balance is disrupted and, by default, proteolysis becomes dominant but is not enhanced.
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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".