Effect of Hydroxyapatite and Titania Nanostructures on Early In Vivo Bone Response
Bibliographic record
Abstract
PURPOSE: Hydroxyapatite (HA) or titania nanostructures were applied on smooth titanium implant cylinders. The aim was to investigate whether nano-HA may result in enhanced osseointegration compared to nano-titania structures. MATERIALS AND METHODS: Surface topography evaluation included detailed characterization of nano-size structures present at the implant surface combined with surface roughness parameters at the micro- and nanometer level of resolution. Microstructures were removed from the surface to ensure that bone response observed was dependent only on the nanotopography and/or chemistry of the surface. Early in vivo histological analyses of the bone response (4 weeks) were investigated in a rabbit model. RESULTS: In the present study, nano-titania-coated implants showed an increased coverage area and feature density, forming a homogenous layer compared to nano-HA implants. Bone contact values of the nano-titania implants showed a tendency to have a higher percentage as compared to the nano-HA implants (p = .1). CONCLUSION: Thus, no evidence of enhanced bone formation to nano-HA-modified implants was observed compared to nano-titania-modified implants. The presence of specific nanostructures dependent on the surface modification exhibiting different size and distribution did modulate in vivo bone response.
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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 | 0.001 |
| 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".