Differential Behavior of Fibroblasts and Epithelial Cells on Structured Implant Abutment Materials: A Comparison of Materials and Surface Topographies
Bibliographic record
Abstract
PURPOSE: The aim of this study was to compare the proliferation and attachment behavior of fibroblasts and epithelial cells on differently structured abutment materials. MATERIALS AND METHODS: Three different surface topographies were prepared on zirconia and titanium alloy specimens and defined as follows: machined (as delivered without further surface modification), smooth (polished), and rough (sandblasted). Energy-dispersive X-ray spectroscopy, topographical analysis, and water contact angle measurements were used to analyze the surface properties. Fibroblasts (HGF1) and epithelial cells (HNEpC) grown on the specimens were investigated 24 hours and 72 hours after seeding and counted using fluorescence imaging. To investigate adhesion, the abundance and arrangement of the focal adhesion protein vinculin were evaluated by immunocytochemistry. RESULTS: Similar surface topographies were created on both materials. Fibroblasts exhibited significant higher proliferation rates on comparable surface topographies of zirconia compared with the titanium alloy. The proliferation of fibroblasts and epithelial cells was optimal on different substrate/topography combinations. Cell spreading was generally higher on polished and machined surfaces than on sandblasted surfaces. Rough surfaces provided favorable properties in terms of cellular adhesion of fibroblasts but not of epithelial cells. CONCLUSIONS: Our data support complex soft tissue cell-substrate interactions: the fibroblast and epithelial cell response is influenced by both the material and surface topography.
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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".