The Effect of <scp>NiTi</scp> Brush, Polishing Brush, and Chemical Agent on the Dental Implant Surface Morphology and Cytocompatibility
Bibliographic record
Abstract
OBJECTIVES: To in vitro investigate the effect of different implant surface decontamination methods and treatment storing conditions on implant surface morphology and cell viability. MATERIALS AND METHODS: Titanium disks with a sand-blasted and acid-etched surface (Promote, PRO) were treated with diamond polishing brushes (BRUSH), nickel-titanium brushes (NITI), or phenol and sulfuric acid-gel (GEL). The disks were stored in saline (-S) or left exposed to air overnight (-A). Untreated (PRO) and machined (MACHINED) disks were used as controls. GEL samples were treated for the 60 s, while the operative time was recorded for BRUSH and NITI. The samples were subjected to scanning electron microscopy (SEM), surface roughness measurements, and cell viability (SaOS-2 cells, 7 days) assessment. RESULTS: The operative time was shorter for NITI than for BRUSH (p = 0.017). The original surface morphology (PRO) was not altered in the GEL group, in contrast with what was observed for BRUSH and NITI. The type of storage did not influence the surface morphology. No significant differences in Sa and Sz were observed among the groups, except for MACHINED, which presented lower Sa values (p < 0.05). Cells were able to proliferate on all surfaces. NITI-S showed significantly higher cell viability compared to all groups (p ≤ 0.001), except for NITI-A and MACHINED. Among the treated groups, only one additional significant difference was found, as NITI-A performed better than GEL-S. CONCLUSIONS: None of the investigated protocols compromised the cytocompatibility of the titanium dental implant surface. The best results were registered in the NITI group when the samples were stored in saline. Future studies should confirm the effectiveness of the proposed methods in removing bacterial biofilm from contaminated 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".