An Antibacterial Surface on Dental Implants, Based on the Photocatalytic Bactericidal Effect
Bibliographic record
Abstract
BACKGROUND: It is well known that the moderately roughened surfaces of dental implants enhance direct bone-implant contact. However, rough implant surfaces, as compared to smooth surfaces, are thought to pose a higher risk of bacterial infection when exposed to the oral cavity. PURPOSE: This study was focused on evaluating the photocatalytic bactericidal effects of anatase titanium dioxide (TiO(2)) on gram-negative anaerobic bacteria known to be associated with periimplantitis. MATERIALS AND METHODS: A film of photocatalytic anatase TiO(2) was added onto the surface of commercially pure titanium disks by plasma source ion implantation (PSII) followed by annealing. The photocatalytic properties of the film were confirmed by the degradation of methylene blue. Actinobacillus actinomycetemcomitans and Fusobacterium nucleatum cells were incubated anaerobically and seeded on the disk. The disks were then exposed to ultraviolet A (UVA) illumination from black light in an anaerobic environment. After illumination, the number of viable cells was counted in terms of colony-forming units. RESULTS: The anatase TiO(2) film added by the PSII method and annealing exhibited a strong photocatalytic reaction under UVA illumination. The viability of both types of bacteria on the photocatalytic TiO(2) film was suppressed to less than 1% under UVA illumination within 120 minutes. CONCLUSION: The bactericidal effect of the TiO(2) photocatalyst is of great use for sterilizing the contaminated surface of dental implants.
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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) | 0.001 | 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 | 0.001 | 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".