Effects of different hierarchical hybrid micro/nanostructure surfaces on implant osseointegration
Bibliographic record
Abstract
BACKGROUND: Hierarchical hybrid micro/nanostructure implant surfaces are considered to better mimic the hierarchical structure of bone and the nanostructures substantively influence osseointegration through managing cell behaviors. PURPOSE: To enhance implant osseointegration for further clinical application, we evaluated the material properties and osseointegration effects of hierarchical surfaces with different nano-morphologies, using a rat model. MATERIALS AND METHODS: Two representative surface fabrication methods, hydrofluoric (HF) acid etching combined with anodization (HF + AN) or magnetron sputtering (HF + MS), were selected. Sample material properties were evaluated by scanning electron microscopy, atomic force microscopy, X-ray diffraction, X-ray photoemission spectroscopy, and epoxy resin docking tensile test. Implants with different surfaces were inserted into the distal femurs of rats. After 12 weeks, osseointegration was examined by microcomputed tomography (micro-CT), histological, and biomechanical tests. RESULTS: Tensile testing demonstrated high bonding strength at coating/implant in the HF + MS group. Micro-CT revealed increased bone volume/total volume and significantly reduced trabecular separation in HF + MS versus other groups. Histological analysis showed significantly higher HF + MS bone-to-implant contact (74.78 ± 4.40%) versus HF + AN (65.11 ± 5.10%) and machined samples (56.03 ± 3.23%). The maximal HF + MS pull-out force increased by 33.7% versus HF + AN. CONCLUSIONS: These results indicated that HF + MS surfaces exhibited superior material property in terms of bonding strength and favorable implant osseointegration compared to other groups.
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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".