Myeloma impairs mature osteoblast function but causes early expansion of osteo‐progenitors: temporal changes in bone physiology and gene expression in the KMS12BM model
Bibliographic record
Abstract
Myeloma bone disease results from an uncoupling of osteoclastic resorption and osteoblastic bone formation, but early changes in osteogenic function remain poorly defined. We used the KMS12BM xenograft model to investigate cellular and molecular events at early and late stages of disease. Lytic lesions and changes in osteoblast and osteoclast numbers occur late (8 weeks), however, micro-computed tomography of femora revealed significant reduction in bone volume at earlier disease stages (3 weeks) when tumour burden is low. Calcein labelling demonstrated reduced mineralization and bone formation at 3 weeks, suggesting functional impairment despite preserved osteoblast numbers. Osteo-progenitors from compact bone increased early (1 week), but fell at 3 weeks and were profoundly suppressed by 8 weeks. Exposure of osteoblast progenitors to multiple myeloma (MM) cells in vitro induced cell cycling, suggesting a mechanistic basis for early expansion of osteo-progenitors. We observed temporal changes in chemokine, osteogenic and osteoclastogenic genes in the stromal compartment. Notably, an early rise in CCL3 may underlie functional changes in mature osteoblasts at 3 weeks. Our data indicate that MM has distinct effects on mature osteoblasts and immature osteo-progenitors. Our findings argue for early clinical intervention to prevent bone changes that ultimately lead to the development of osteolytic disease.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".