Accelerated and Enhanced Bone Formation on Novel Simvastatin‐Loaded Porous Titanium Oxide Surfaces
Bibliographic record
Abstract
BACKGROUND: With increasing application of dental implants in poor-quality bones, the need for implant surfaces ensuring accelerated osseointegration and enhanced peri-implant bone regeneration is increased. PURPOSE: A study was performed to evaluate the osseointegration and bone formation on novel simvastatin-loaded porous titanium oxide surface. MATERIALS AND METHODS: Titanium screws were treated by micro-arc oxidation to form porous oxide surface and 25 or 50 μg of simvastatin was loaded. The nontreated control, micro-arc oxidized, and simvastatin-loaded titanium screws were surgically implanted into the proximal tibia of 16-week-old male Wistar rats (n = 36). Peri-implant bone volume, bone-implant contact, and mineral apposition rates were measured at 2 and 4 weeks. Data were analyzed by one-way analysis of variance followed by Tukey's post hoc test. RESULTS: New bone was formed directly on the implant surface in the bone marrow cavity in simvastatin-loaded groups since 2 weeks. Bone-implant contact values were significantly higher in simvastatin-loaded groups than control and micro-arc oxidized groups at both time points (p < .05). Peri-implant bone volume and mineral apposition rate of simvastatin-loaded groups were significantly higher than control and micro-arc oxidized groups at 2 weeks (p < .05). CONCLUSIONS: These data suggested that simvastatin-loaded porous titanium oxide surface provides faster osseointegration and peri-implant bone formation and it would be potentially applicable in poor-quality bones.
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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 | 0.000 | 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".