Green tea polyphenols enhance gingival keratinocyte integrity and protect against invasion by Porphyromonas gingivalis
Bibliographic record
Abstract
The gingival epithelium, a stratified squamous tissue that acts as an interface between the external environment and the underlying connective tissue, plays an active role in maintaining periodontal health. The aim of the present study was to investigate the ability of green tea catechins to enhance gingival epithelial barrier function and protect against the disruption of epithelial integrity induced by Porphyromonas gingivalis. Both the green tea extract and epigallocatechin-3-gallate (EGCG) dose- and time-dependently increased the transepithelial electrical resistance (TER) of a gingival keratinocyte model and decreased the permeability of the cell monolayer to fluorescein isothyocyanate-conjugated 4.4-kDa dextran. This was associated with the increased expression of zonula occludens-1 (ZO-1) and occludin, two tight junction proteins. Treating the gingival keratinocyte monolayer with P. gingivalis caused a reduction in TER and affected the distribution of ZO-1 and occludin, allowing P. gingivalis to translocate through the cell monolayer. These deleterious effects mediated by P. gingivalis were abolished by the green tea extract and EGCG. This protection may be in part related to the ability of tea catechins to inhibit the protease activities of P. gingivalis. Given the above properties, green tea catechins may represent promising preventive and therapeutic molecules against periodontal disease.
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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".