Control of skeletal muscle atrophy in response to disuse: clinical/preclinical contentions and fallacies of evidence
Bibliographic record
Abstract
Muscle wasting resulting wholly or in part from disuse represents a serious medical complication that, when prolonged, can increase morbidity and mortality. Although much knowledge has been gained over the past half century, the underlying etiology by which disuse alters muscle proteostasis remains enigmatic. Multidisciplinary and novel methodologies are needed to fill gaps and overcome barriers to improved patient care. The present review highlights seminal concepts from a symposium at Experimental Biology 2016. These proceedings focus on 1) the role of insulin resistance in mediating disuse-induced changes in muscle protein synthesis (MPS) and breakdown (MPB), as well as cross-talk between carbohydrate and protein metabolism; 2) the relative importance of MPS/MPB in mediating involuntary muscle loss in humans and animals; 3) interpretative limitations associated with MPS/MPB "markers," e.g., MuRF1/MAFbx mRNA; and finally, 4) how OMIC technologies can be leveraged to identify molecular pathways (e.g., ATF4, p53, p21) mediating disuse atrophy. This perspective deals primarily with "simple atrophy" due to unloading. Nonetheless, it is likely that disuse is a pervasive contributor to muscle wasting associated with catabolic disease-related atrophy (i.e., due to associated sedentary behaviour of disease burden). Key knowledge gaps and challenges are identified to stimulate discussion and identify opportunities for translational research. Data from animal and human studies highlight both similarities and differences. Integrated preclinical and clinical research is encouraged to better understand the metabolic and molecular underpinnings and translational relevance,for disuse atrophy. These approaches are crucial to clinically prevent or reverse muscle atrophy, thereby reestablishing homeostasis and recovery.
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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 | 0.031 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".