A peptide coating preventing the attachment of <i>Porphyromonas gingivalis</i> on the surfaces of dental implants
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to investigate whether a peptide-based coating can prevent the adhesion of Porphyromonas gingivalis, a key human pathogen associated with periodontitis and peri-implantitis. BACKGROUND: Nonsurgical and surgical interventions have been used for the treatment of peri-implantitis; however, the effectiveness of these approaches is usually unsatisfactory. The main reason is that dental plaque on the surface of the implant is difficult to remove due to its rough surface and thread design. Recently, a peptide-based coating for implant surfaces that can reject the adhesion of Escherichia coli and improve the attachment of host cells was developed. METHODS: A salivary pellicle was created on the surfaces of peptide-coated bare discs and verified with anti-human immunoglobulin G, A and M, and anti-fibrinogen. Early colonizers, Veillonella parvula and Streptococcus sobrinus, and the later colonizer, Porphyromonas gingivalis, were labelled with green and red fluorescent dyes, respectively, and seeded on the discs. Bacterial attachment was semi-quantified by fluorescence intensity. RESULTS: The salivary pellicle was evenly distributed on the discs, with or without the peptide coating, with an average thickness of 3.84 µm. A multi-species dental biofilm was created on the salivary pellicle. The peptide coating resulted in an approximate 25% reduction in the attachment of Veillonella parvula and Streptococcus sobrinus, and a 50% reduction in Porphyromonas gingivalis, when compared to control, uncoated implant discs. CONCLUSION: The novel peptide-based coating can inhibit the attachment of Porphyromonas gingivalis. It may have the potential to impede the development of peri-implantitis.
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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".