Black Tea Extract and Its Theaflavin Derivatives Inhibit the Growth of Periodontopathogens and Modulate Interleukin-8 and β-Defensin Secretion in Oral Epithelial Cells
Bibliographic record
Abstract
Over the years, several studies have brought evidence suggesting that tea polyphenols, mostly from green tea, may have oral health benefits. Since few data are available concerning the beneficial properties of black tea and its theaflavin derivatives against periodontal disease, the objective of this study was to investigate their antibacterial activity as well as their ability to modulate interleukin-8 and human β-defensin (hBD) secretion in oral epithelial cells. Among the periodontopathogenic bacteria tested, Porphyromonas gingivalis was found to be highly susceptible to the black tea extract and theaflavins. Moreover, our data indicated that the black tea extract, theaflavin and theaflavin-3,3'-digallate can potentiate the antibacterial effect of metronidazole and tetracycline against P. gingivalis. Using lipopolysaccharide-stimulated oral epithelial cells, the black tea extract (100 μg/ml), as well as theaflavin and theaflavin-3,3'-digallate (50 μg/ml) reduced interleukin-8 (IL-8) secretion by 85%, 79%, and 86%, respectively, thus suggesting an anti-inflammatory property. The ability of the black tea extract and its theaflavin derivatives to induce the secretion of the antimicrobial peptides hBD-1, hBD-2 and hBD-4 by oral epithelial cells was then evaluated. Our results showed that the black tea extract as well as theaflavin-3,3'-digallate were able to increase the secretion of the three hBDs. In conclusion, the ability of a black tea extract and theaflavins to exert antibacterial activity against major periodontopathogens, to attenuate the secretion of IL-8, and to induce hBD secretion in oral epithelial cells suggest that these components may have a beneficial effect against periodontal disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".