Inhibitory effects of orthosilicic acid on osteoclastogenesis in <scp>RANKL</scp>‐stimulated <scp>RAW264</scp>.7 cells
Bibliographic record
Abstract
Numerous studies have reported on the positive effects of silicon (Si) on bone metabolism, particularly on the stimulatory effects of Si on osteoblast cells and on bone formation. Inhibitory effects of Si on osteoclast formation and bone resorption have also been demonstrated in vitro and are suggested to be mediated indirectly via stromal and osteoblast cells. Direct effects of Si on osteoclasts have been less studied and mostly using soluble Si, but no characterisation of the Si treatment solutions are provided. The aims of the present study were to (a) further investigate the direct inhibitory effects of Si on osteoclastogenesis in RANKL-stimulated RAW264.7 cells, (b) determine at what stage during osteoclastogenesis Si acts upon, and (c) determine if these effects can be attributed to the biologically relevant soluble orthosilicic acid specie. Our results demonstrate that silicon, at 50 μg/ml (or 1.8 mM), does not affect cell viability but directly inhibits the formation of TRAP+ multinucleated cells and the expression of osteoclast phenotypic genes in RAW264.7 cells. The inhibitory effect of Si was clearly associated with the early stages (first 24 hr) of osteoclastogenesis. Moreover, these effects can be attributed to the soluble orthosilicic acid specie.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".