Bone sialoprotein plays a functional role in bone formation and osteoclastogenesis
Bibliographic record
Abstract
Bone sialoprotein (BSP) and osteopontin (OPN) are both highly expressed in bone, but their functional specificities are unknown. OPN knockout (-/-) mice do not lose bone in a model of hindlimb disuse (tail suspension), showing the importance of OPN in bone remodeling. We report that BSP(-/-) mice are viable and breed normally, but their weight and size are lower than wild-type (WT) mice. Bone is undermineralized in fetuses and young adults, but not in older (> or =12 mo) BSP(-/-) mice. At 4 mo, BSP(-/-) mice display thinner cortical bones than WT, but greater trabecular bone volume with very low bone formation rate, which indicates reduced resorption, as confirmed by lower osteoclast surfaces. Although the frequency of total colonies and committed osteoblast colonies is the same, fewer mineralized colonies expressing decreased levels of osteoblast markers form in BSP(-/-) versus WT bone marrow stromal cultures. BSP(-/-) hematopoietic progenitors form fewer osteoclasts, but their resorptive activity on dentin is normal. Tail-suspended BSP(-/-) mice lose bone in hindlimbs, as expected. In conclusion, BSP deficiency impairs bone growth and mineralization, concomitant with dramatically reduced bone formation. It does not, however, prevent the bone loss resulting from loss of mechanical stimulation, a phenotype that is clearly different from OPN(-/-) mice.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".