Bibliographic record
Abstract
The loss of skeletal muscle mass under a wide range of acute and chronic maladies is associated with poor prognosis, reduced quality of life, and increased mortality. Decades of research indicate the importance of skeletal muscle for whole body metabolism, glucose homeostasis, as well as overall health and wellbeing. This tissue's remarkable ability to rapidly and effectively adapt to changing environmental cues is a double-edged sword. Physiological adaptations that are beneficial throughout life become maladaptive during atrophic conditions. The atrophic program can be activated by mechanical, oxidative, and energetic distress, and is influenced by the availability of nutrients, growth factors, and cytokines. Largely governed by a transcription-dependent mechanism, this program impinges on multiple protein networks including various organelles as well as biosynthetic and quality control systems. Although modulating muscle function to prevent and treat disease is an enticing concept that has intrigued research teams for decades, a lack of thorough understanding of the molecular mechanisms and signaling pathways that control muscle mass, in addition to poor transferability of findings from rodents to humans, has obstructed efforts to develop effective treatments. Here, we review the progress made in unraveling the molecular mechanisms responsible for the regulation of muscle mass, as this continues to be an intensive area of research.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".