Sclerostin expression in trabecular bone is downregulated by osteoclasts
Bibliographic record
Abstract
Abstract Bone tissues have trabecular bone with a high bone turnover and cortical bone with a low turnover. The mechanisms by which the turnover rate of these bone tissues is determined remain unclear. Osteocytes secrete sclerostin, a Wnt/β-catenin signaling antagonist, and inhibit bone formation. We found that sclerostin expression in cortical bone is more marked than in trabecular bone inSostreporter mice. Leukemia inhibitory factor (LIF) secreted from osteoclasts reportedly suppressed sclerostin expression and promoted bone formation. Here, we report that osteoclasts downregulate sclerostin expression in trabecular bone and promote bone turnover. Treatment of C57BL/6 mice with an anti-RANKL antibody eliminated the number of osteoclasts and LIF-positive cells in trabecular bone. The number of sclerostin-positive cells was increased in trabecular bone, while the number of β-catenin-positive cells and bone formation were decreased in trabecular bone. Besides,Tnfsf11heterozygous (Rankl+/−) mice exhibited a decreased number of LIF-positive cells and increased number of sclerostin-positive cells in trabecular bone.Rankl+/−mice exhibited a decreased number of β-catenin-positive cells and reduced bone formation in trabecular bone. Furthermore, in cultured osteoclasts, RANKL stimulation increasedLifmRNA expression, suggesting that RANKL signal increased LIF expression. In conclusion, osteoclasts downregulate sclerostin expression and promote trabecular bone turnover.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".