Increased expression of activating factors in large osteoclasts could explain their excessive activity in osteolytic diseases
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Bibliographic record
Abstract
Large osteoclasts (>or=10 nuclei) predominate at sites of pathological bone resorption. We hypothesized this was related to increased resorptive activity of large osteoclasts and have demonstrated previously that larger osteoclasts are 8-fold more likely to be resorbing than small osteoclasts (2-5 nuclei). Here we ask whether these differences in resorptive activity can be explained by differences in expression of factors involved in osteoclast signaling, fusion, attachment, and matrix degradation. Authentic rabbit osteoclasts and osteoclasts derived from RAW264.7 cells showed similar increases in c-fms expression (1.7- to 1.8-fold) in large osteoclasts suggesting that RAW cells are a viable system for further analysis. We found 2- to 4.5-fold increases in the expression of the integrins alpha(v) and beta(3), the proteases proMMP9, matMMP9 and pro-cathepsinK, and in activating receptors RANK, IL-1R1, and TNFR1 in large osteoclasts. In contrast, small osteoclasts had higher expression of the fusion protein SIRPalpha1 and the decoy receptor IL-1R2. The higher expression of activation receptors and lower expression of IL-1R2 in large osteoclasts suggest they are hyperresponsive to extracellular factors. This is supported by the observation that the resorptive activity in large osteoclasts was more responsive to IL-1beta, and that this increased activity was inhibited by the IL-1 receptor antagonist, IL-1ra. This increased responsiveness of large osteoclasts to IL-1 may, in part, explain the pathological bone loss noted in inflammatory diseases. The heterogeneity in receptor expression and the differential response to cytokines and their antagonists could prove useful for selective inhibition of large osteoclasts actively engaged in pathological bone loss.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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