Antibacterial, Antiadherence, Antiprotease, and Anti-Inflammatory Activities of Various Tea Extracts: Potential Benefits for Periodontal Diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Porphyromonas gingivalis is a key etiologic agent of chronic periodontitis. This Gram-negative anaerobic bacterium produces several virulence factors and can induce a host inflammatory response that contributes to periodontal disease. In the present study, we investigated green tea, white tea, oolong tea, and black tea extracts with a high polyphenol content for their effects on (i) the growth and adherence of P. gingivalis, (ii) the activity of host and bacterial proteases, and (iii) cytokine secretion by oral epithelial cells. All the tea extracts inhibited the growth of P. gingivalis (minimal inhibitory concentrations ranging from 200 to 500 μg/mL; minimal bactericidal concentrations=500 μg/mL). In addition, they dose dependently reduced the adherence of P. gingivalis to oral epithelial cells. Tea extracts also inhibited the catalytic activity of matrix metalloproteinase (MMP)-9, neutrophil elastase, and P. gingivalis collagenase. Lastly, the tea extracts dose dependently inhibited the secretion of interleukin (IL)-6, IL-8, and chemokine (C-C motif) ligand 5 (CCL-5) by P. gingivalis-stimulated oral epithelial cells. No marked differences in the various effects were observed among the four tea extracts. Extracts from green tea, white tea, oolong tea, and black tea show promise for controlling periodontal disease by their capacity to interfere with P. gingivalis growth and virulence properties, host destructive enzymes, and inflammatory mediator secretion. Such extracts may be incorporated to oral hygiene products or locally delivered into diseased periodontal sites.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it