Focal adhesion formation of primary human gingival fibroblast on hydrothermally and <scp>in‐sol‐made TiO<sub>2</sub></scp>‐coated titanium
Bibliographic record
Abstract
Abstract Optimal cell adhesion of the gingival fibroblasts to dental implants is important for maintaining good implant integration. The aim of this study was to discover, if the nanoporous TiO2‐coating on titanium alloy substrates is able to increase the cell adhesion of the human gingival fibroblasts (HGF). The study consisted of three differently produced titanium groups: hydrothermally produced TiO2‐coating (HT), novel TiO2‐coating made in sol (SOL), and noncoated control group. Primary HGF cells were initiated from gingival biopsies from patients having a third molar extraction. HGF were cultivated on titanium discs for 2 and 24 h to determine the initial attachment with confocal microscope. The cell spreading and adhesion protein signals were measured. In addition, expression of adhesion proteins vinculin, paxillin, and focal adhesion kinase (FAK) were measured after 3 days of cultivation by using Western Blotting. Higher protein levels of paxillin, vinculin, and FAK were induced on both coated discs compared to noncoated discs. The difference was statistically significant (p < 0.05) concerning expression of paxillin. The cell spreading was significantly larger on SOL discs after 2 and 24 h when comparing to noncoated controls. The confocal microscope analyses revealed significantly higher adhesion protein signals on both HT‐ and SOL‐coated titanium compared to control group. This study showed, that both methods to produce TiO2‐coatings are able to increase HGF adhesion protein expression and cell spreading on titanium surface. Accordingly, the coatings can potentially improve the gingival attachment to titanium implant surfaces.
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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".