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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".