The High Rate of Bone Resorption in Multiple Myeloma is due to RANK (Receptor Activator of Nuclear Factor-κB) and RANK Ligand Expression
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it