Neutralizing effect of green tea epigallocatechin‐3‐gallate on nicotine‐induced toxicity and chemokine (C‐C motif) ligand 5 secretion in human oral epithelial cells and fibroblasts
Bibliographic record
Abstract
AIM: Tobacco use has been identified as the most important environmental risk factor for periodontitis. The aim of this study was to investigate the effect of green tea epigallocatechin-3-gallate on the nicotine-induced toxic and inflammatory responses in oral epithelial cells and gingival fibroblasts. METHODS: The effect of nicotine, alone and in combination with the lipopolysaccharide of Aggregatibacter actinomycetemcomitans, on the viability of oral epithelial cells and fibroblasts was evaluated using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide colorimetric assay. The ability of epigallocatechin-3-gallate to neutralize the nicotine-induced cytotoxicity was then investigated. The nicotine-induced cytokine secretion in epithelial cells and the inhibitory effect of epigallocatechin-3-gallate were determined by enzyme-linked immunosorbent assay. RESULTS: Our results indicated that nicotine caused a dose-dependent loss of viability in both epithelial cells and fibroblasts. A mixture of nicotine and A. actinomycetemcomitans lipopolysaccharide demonstrated additive instead of synergistic effects on loss of cell viability. Pretreatment of cells with epigallocatechin-3-gallate efficiently neutralized the nicotine-induced toxic effects in epithelial cells and fibroblasts. It also dose dependently inhibited the nicotine-induced secretion of chemokine (C-C motif) ligand 5 by epithelial cells. CONCLUSIONS: The present study suggests that epigallocatechin-3-gallate, the major polyphenol in green tea, may represent a novel preventive/therapeutic agent for smoking-related periodontitis.
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 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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".