Adhesion Properties of Human Oral Epithelial‐Derived Cells to Zirconia
Bibliographic record
Abstract
Abstract Background Few studies have examined epithelial attachment to zirconia and the proliferative ability of epithelial cells on zirconia surfaces. Purpose To evaluate the adhesion properties of zirconia materials for epithelial cell attachment and compare this with titanium and alumina. Materials and Methods Human oral epithelial cells were cultured on smooth‐surfaced specimens of commercially pure titanium (cpTi), ceria‐stabilized zirconia/alumina nano‐composite (P‐NANOZR), yttria‐stabilized zirconia (Cercon), and alumina oxide (inCoris AL). The cell morphology, the cell viability and mRNA of integrin β4, laminin γ2, catenin δ2, and E‐cadherin were evaluated by SEM, Cell‐Counting Kit‐8, and real‐time PCR, respectively. Results Morphology of cells attached to specimens was similar among all groups. The viable cell numbers on Cercon and inCoris AL after 24 hours culture were significantly higher than for cpTi. Integrin β4, laminin γ2, and catenin δ2 mRNA expression was not different among all groups. However, at 3 and 24 hours after incubation, E‐cadherin mRNA expression in the P‐NANOZR group was significantly higher than for cpTi. Conclusion Zirconia may support binding of epithelial cells through hemidesmosomes comparable with titanium. Furthermore, P‐NANOZR may impart resistance to exogenous stimuli through strong intercellular contacts with peri‐implant mucosal cells when used as an abutment and implant superstructure.
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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".