Interleukin-15 as a myokine: mechanistic insight into its effect on skeletal muscle metabolism
Bibliographic record
Abstract
Interleukin (IL)-15 is a cytokine with important immunological functions. It is highly expressed in skeletal muscle and is believed to be a myokine, a hypothesis supported by the rapid increase in circulating levels of IL-15 in response to exercise. Treatment with high doses of IL-15 results in metabolic adaptations such as improved insulin sensitivity and whole-body fatty acid oxidation and protection from high-fat-diet-induced obesity and insulin resistance. IL-15 secreted by contracting muscle may therefore act as an endocrine factor to improve adiposity and energy metabolism in different tissues. Most studies have used supraphysiological doses of IL-15 that do not represent circulating IL-15 in response to exercise. However, evidence shows that IL-15 levels are higher in muscle interstitium and that IL-15 might improve muscle glucose homeostasis and oxidative metabolism in an autocrine/paracrine manner. Nevertheless, how IL-15 signals in skeletal muscle to improve muscle energy metabolism is not understood completely, especially because the absence of the α subunit of the IL-15 receptor (IL-15Rα) results in a phenotype similar to that of overexpressing/oversecreting IL-15 in mice. In this article, we review the literature to propose a model for the regulation of IL-15 by the soluble form of IL-15Rα to explain why some findings in the literature seem, at first glance, to be contradictory.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".