The High Rate of Bone Resorption in Multiple Myeloma is due to RANK (Receptor Activator of Nuclear Factor-κB) and RANK Ligand Expression
Bibliographic record
Abstract
The excessive bone resorption observed in multiple myeloma may be due to the production of several osteoclast-activating factors either by the myeloma cells themselves or by the bone marrow microenvironment. These factors could act primarily via a common final pathway involving the recently-described members of the TNF receptor-ligand family: RANKL (Receptor Activator of NK-kappaB Ligand) and its corresponding RANK receptor that play a crucial role in osteoclast differentiation and activation, and osteoprotegerin (OPG), the physiological inhibitor of RANKL. RANKL expression by stromal cells is increased in myeloma and is associated with a concomitant decrease in OPG expression. This increase in RANKL-OPG ratio correlates with the extent of the myeloma bone disease. The RANKL-OPG imbalance could play a decisive role in the lytic bone lesions in myeloma, and this possibility is reinforced by several in-vivo studies that have assessed the effects of administering RANKL inhibitors in murine myeloma models. Treatment with either OPG: Fc or RANK: Fc decreased myeloma osteolysis in these models. RANKL blockade is also currently being evaluated in malignant osteolysis in humans. A therapeutic approach targeting the RANKL-RANK signaling pathway could be of great value, as RANKL inhibitors are potent anti-resorptive agents, affecting both myeloma-induced bone resorption and the tumor burden.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".