Effect of BioAggregate on osteoclast differentiation and inflammatory bone resorption <i>in vivo</i>
Bibliographic record
Abstract
AIM: To investigate the effect of BioAggregate (Innovative Bioceramix, Vancouver, BC, Canada) on lipopolysaccharide (LPS)-induced bone destruction in vivo and to compare its performance with that of mineral trioxide aggregate (MTA; Dentsply Tulsa Dental, Tulsa, OK, USA). METHODOLOGY: Mouse bone marrow macrophages (BMMs) were primary cultured and treated with several concentrations of BioAggregate and MTA extracts. Cell viability was measured with a Cell Counting Kit-8 assay, whilst in vitro osteoclast differentiation was evaluated with tartrate-resistant acid phosphatase (TRAP) staining. LPS-induced mouse calvarial bone destruction model was established to assess the effect of BioAggregate and MTA extracts in vivo. Mice were killed on day 7, and calvarial bones were prepared for microcomputed tomography scanning, histologic analysis and double-immunofluorescence staining. Statistical tests used were one-way anova followed by Student-Newman-Keuls test. RESULTS: BioAggregate extracts displayed no obvious cytotoxicity to BMMs and significantly inhibited (P < 0.01) the differentiation of RANKL-stimulated BMMs. Comparable effects were induced by MTA. BioAggregate and MTA extracts markedly reduced (P < 0.01) osteoclast numbers and attenuated (P < 0.05) bone resorption in LPS-challenged mouse calvaria. The expression levels of osteoclastogenic cathepsin K and its upstream regulator nuclear factor of activated T-cell cytoplasmic 1 and c-Fos were also decreased by BioAggregate and MTA extracts. CONCLUSIONS: BioAggregate and MTA showed comparable inhibitory effect on osteoclast differentiation and inflammatory bone resorption in vivo.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".