Tungsten Increases Sex-Specific Osteoclast Differentiation in Murine Bone
Bibliographic record
Abstract
Tungsten is a naturally occurring metal that is increasingly used in industry and medical devices, and is labeled as an emerging environmental contaminant. Like many metals, tungsten accumulates in bone. Our previous data indicate that tungsten decreases differentiation of osteoblasts, bone-forming cells. Herein, we explored the impact of tungsten on osteoclast differentiation, which function in bone resorption. We observed significantly elevated osteoclast numbers in the trabecular bone of femurs following oral exposure to tungsten in male, but not female mice. In order to explore the mechanism(s) by which tungsten increases osteoclast number, we utilized in vitro murine primary and cell line pre-osteoclast models. Although tungsten did not alter the adhesion of osteoclasts to the extracellular matrix protein, vitronectin, we did observe that tungsten enhanced RANKL-induced differentiation into tartrate-resistant acid phosphatase (TRAP)-positive mononucleated osteoclasts. Importantly, tungsten alone had no effect on differentiation or on the number of multinucleated TRAP-positive osteoclasts. Enhanced RANKL-induced differentiation correlated with increased gene expression of differentiated osteoclast markers Nfatc1, Acp5, and Ctsk. Although tungsten did not alter the RANK surface receptor expression, it did modulate its downstream signaling. Co-exposure of tungsten and RANKL resulted in sustained positive p38 signaling. These findings demonstrate that tungsten enhances sex-specific osteoclast differentiation, and together with previous findings of decreased osteoblastogenesis, implicate tungsten as a modulator of bone homeostasis.
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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 | 0.001 |
| 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) | 0.002 | 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".