The Roles of RANK/RANKL/OPG in Cardiac, Skeletal, and Smooth Muscles in Health and Disease
Bibliographic record
Abstract
Although their physiology and functions are very different, bones, skeletal and smooth muscles, as well as the heart have the same embryonic origin. Skeletal muscles and bones interact with each other to enable breathing, kinesis, and the maintenance of posture. Often, muscle and bone tissues degenerate synchronously under various conditions such as cancers, space travel, aging, prolonged bed rest, and neuromuscular diseases. In addition, bone tissue, skeletal and smooth muscles, and the heart share common signaling pathways. The RANK/RANKL/OPG pathway, which is essential for bone homeostasis, is also implicated in various physiological processes such as sarcopenia, atherosclerosis, and cardiovascular diseases. Several studies have reported bone-skeletal muscle crosstalk through the RANK/RANKL/OPG pathway. This review will summarize the current evidence indicating that the RANK/RANKL/OPG pathway is involved in muscle function. First, we will briefly discuss the role this pathway plays in bone homeostasis. Then, we will present results from various sources indicating that it plays a physiopathological role in skeletal, smooth muscle, and cardiac functions. Understanding how the RANK/RANKL/OPG pathway interferes in several physiological disorders may lead to new therapeutic approaches aimed at protecting bones and other tissues with a single treatment.
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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 | 0.002 |
| 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) | 0.003 | 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".