The Role of Implant Coronal Surface Properties on Early Adhesion of <i>Streptococcus Oralis</i>—An In Vitro Comparative Study
Bibliographic record
Abstract
Dental implant coronal surfaces designed with the primary goal of maintaining crestal bone levels may also promote bacterial adhesion, leading to soft tissue inflammation and peri-implant bone loss. Achieving an optimal surface roughness that minimizes bacterial adhesion while preserving crestal bone is crucial. It is hypothesized that a specific threshold surface roughness value may exist below which, and above which, initial bacterial adhesion does not statistically change. This study evaluated 12 commercially available and 2 custom-designed implant surfaces for their physicochemical properties and initial bacterial adhesion, as represented by Streptococcus oralis (S. oralis) the dominant initial colonizer of the successive waves of bacterial consortia that result in plaque and biofilm formation. Implants were immersed in a S. oralis suspension for 4 h, after which microbial viability was assessed. Marked differences were observed in surface roughness, chemical composition, and wettability, and S. oralis adhesion. Surfaces with Sa > 1 μm had significantly more adherent bacteria after 4 h compared to those with Sa < 1 μm, despite complexity. Adding nanotopography to dual-acid etched surfaces further reduced bacterial adhesion compared to surfaces without these features. The role of chemical composition and wettability was less influential than roughness. In conclusion, there is a cut-off threshold roughness around Sa = 1 μm, above which the adhesion of bacteria increases significantly to a plateau level; while below which, bacterial adhesion is equivalent to a machined surface despite the surface texture of the implant collar.
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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".