Osseoconductivity of a Specific Streptavidin–Biotin–Fibronectin Surface Coating of Biotinylated Titanium Implants – A Rabbit Animal Study
Bibliographic record
Abstract
BACKGROUND: Biofunctionalized implant surfaces may accelerate bony integration and increase long-term stability. PURPOSE: The aim of the study was to evaluate the osseous reaction toward biomimetic titanium implants surfaces coated with quasicovalent immobilized fibronectin in an in vivo animal model. MATERIALS AND METHODS: A total of 84 implants (uncoated [control 1, n = 36], streptavidin-biotin coated [test 1, n = 24], streptavidin-biotin-fibronectin coated [test 2, n = 24]) were inserted 1 mm supracortically in the proximal tibia of 12 rabbits. The samples were examined after 3 and 6 weeks. Total bone-implant contact (tBIC; %), bone-implant contact in the cortical (cBIC; %) and in the spongious bone (sBIC; %) as well as the percentage of linear bone fill (PLF; %) were evaluated. RESULTS: After 3 weeks, streptavidin-biotin-fibronectin implants had a significant higher sBIC (p = .043) and PLF (p = .007) compared with the uncoated samples. After 6 weeks, this difference was significant for tBIC (p = .016) and cBIC (p < .001). Additionally, uncoated screws showed a significant higher sBIC when compared with the fibronectin coating (p < .001). Streptavidin-biotin-coated implants showed less bone growth at both time points of all examined parameters when compared with their counterparts (all p < .001). CONCLUSIONS: Quasicovalent immobilization of biotinylated fibronectin with the streptavidin-biotin-fibronectin system on smooth surface titanium shows a beneficial faster osseous healing in vivo. Besides, an antifouling effect of the streptavidin-biotin coating was proven.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".