Silver-Zeolite Combined to Polyphenol-Rich Extracts of Ascophyllum nodosum: Potential Active Role in Prevention of Periodontal Diseases
Bibliographic record
Abstract
The purpose of this study was to evaluate various biological effects of silver-zeolite and a polyphenol-rich extract of A. nodosum (ASCOP) to prevent and/or treat biofilm-related oral diseases. Porphyromonas gingivalis and Streptococcus gordonii contribute to the biofilm formation associated with chronic periodontitis. In this study, we evaluated in vitro antibacterial and anti-biofilm effects of silver-zeolite (Ag-zeolite) combined to ASCOP on P. gingivalis and S. gordonii growth and biofilm formation capacity. We also studied the anti-inflammatory and antioxidant capacities of ASCOP in cell culture models. While Ag-zeolite combined with ASCOP was ineffective against the growth of S. gordonii, it showed a strong bactericidal effect on P. gingivalis growth. Ag-zeolite combined with ASCOP was able to completely inhibit S. gordonii monospecies biofilm formation as well as to reduce the formation of a bi-species S. gordonii/P. gingivalis biofilm. ASCOP alone was ineffective towards the growth and/or biofilm formation of S. gordonii and P. gingivalis while it significantly reduced the secretion of inflammatory cytokines (TNFα and IL-6) by LPS-stimulated human like-macrophages. It also exhibited antioxidant properties and decreased LPS induced lipid peroxidation in gingival epithelial cells. These findings support promising use of these products in future preventive or therapeutic strategies against periodontal diseases.
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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".