Evaluation of a novel oral mucosa in vitro implantation model for analysis of molecular interactions with dental abutment surfaces
Bibliographic record
Abstract
BACKGROUND: Abutment surfaces are being designed to promote gingival soft tissue attachment and integration. This forms a seal around prosthetics and consequently ensures long-term implant survival. New scalable and reproducible models are necessary to evaluate and quantify the performance of these surfaces. PURPOSE: To evaluate a novel implantation model by histomorphometric and immunohistochemical characterization of the interactions between human oral gingival tissue and titanium abutments with either novel anodized or conventional machined surface. MATERIALS AND METHODS: Abutments were inserted into an organotypic reconstructed human gingiva (RHG) model consisting of differentiated gingival epithelium cells on a fibroblast populated lamina propria hydrogel following a tissue punch. Epithelial attachment, down-growth along the abutment surface, and phenotype were assessed via histomorphology, scanning electron microscopy, and immunohistochemistry 10 days after implantation. RESULTS: The down-growing epithelium transitioned from a gingival margin to a sulcular and junctional epithelium. The sulcus depth and junctional epithelial length were similar to previously reported pre-clinical and clinical lengths. A collagen IV/laminin 5 basement membrane formed between the epithelium and the underlying connective tissue. The RHG expanded in thickness approximately 2-fold at the abutment surface. The model allowed the evaluation of protein expression of adhering soft tissue cells for both tested abutments. CONCLUSIONS: The RHG model is the first in vitro 3D model to enable the assessment of not only human epithelial tissue attachment to dental abutments but also the expression of protein markers involved in soft tissue attachment and integration. The two abutments showed no noticeable difference in epithelial attachment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".