In Vivo and In Vitro Studies of Epithelial Cell Behavior around Titanium Implants with Machined and Rough Surfaces
Bibliographic record
Abstract
BACKGROUND: The surface roughness of a dental implant affects the epithelial wound healing process and may significantly enhance implant prognosis. PURPOSE: We explored the influence of surface roughness on peri-implant epithelium (PIE) sealing and down-growth by comparing machine-surfaced (Ms) and rough-surfaced (Rs) implants. MATERIALS AND METHODS: (1) Maxillary first molars were extracted from rats and replaced with Ms or Rs implants. (2) We also compared changes in the morphology of cultured rat oral epithelial cells (OECs) grown on Ms or Rs titanium (Ti) plates. RESULTS: (1) After 4 weeks, the PIE around Ms and Rs implants showed a similar structure to junctional epithelium (JE). At 16 weeks, Rs implants appeared to form a weak epithelial seal at the tissue-implant interface and exhibited markedly less PIE down-growth than Ms implants but was deeper than that observed in natural teeth. (2) We observed less expression of adhesion proteins in OECs cultured on Rs plates than in cells grown on Ms plates. Additionally, cell adherence, migration, and proliferation on Rs plates were lower, whereas apoptosis was reduced on Ms plates. CONCLUSION: Ms implants are a better choice for integration with an epithelial wound healing process.
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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 | 0.001 |
| 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".