The inflammatory response, a mixed blessing for muscle homeostasis and plasticity
Bibliographic record
Abstract
Skeletal muscle makes up almost half the body weight of heathy individuals and is involved in several vital functions, including breathing, thermogenesis, metabolism, and locomotion. Skeletal muscle exhibits enormous plasticity with its capacity to adapt to stimuli such as changes in mechanical loading, nutritional interventions, or environmental factors (oxidative stress, inflammation, and endocrine changes). Satellite cells and timely recruited inflammatory cells are key actors in muscle homeostasis, injury, and repair processes. Conversely, uncontrolled recruitment of inflammatory cells or chronic inflammatory processes leads to muscle atrophy, fibrosis and, ultimately, impairment of muscle function. Muscle atrophy and loss of function are reported to occur either in physiological situations such as aging, cast immobilization, and prolonged bed rest, as well as in many pathological situations, including cancers, muscular dystrophies, and several other chronic illnesses. In this review, we highlight recent discoveries with respect to the molecular mechanisms leading to muscle atrophy caused by modified mechanical loading, aging, and diseases. We also summarize current perspectives suggesting that the inflammatory process in muscle homeostasis and repair is a double-edged sword. Lastly, we review recent therapeutic approaches for treating muscle wasting disorders, with a focus on the RANK/RANKL/OPG pathway and its involvement in muscle inflammation, protection and regeneration processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".