Tea polyphenols inhibit the activation of NF-κB and the secretion of cytokines and matrix metalloproteinases by macrophages stimulated with Fusobacterium nucleatum
Bibliographic record
Abstract
Fusobacterium nucleatum has been associated with both periodontal disease and inflammatory bowel disease. This Gram-negative bacterium possesses a high inflammatory potential that may contribute to the disease process. We hypothesized that green and black tea polyphenols attenuate the inflammatory response of monocytes/macrophages mediated by F. nucleatum. We first showed that the tea extracts, EGCG and theaflavins reduce the NF-κB activation induced by F. nucleatum in monocytes. Since NF-κB is a key regulator of genes coding for inflammatory mediators, we tested the effects of tea polyphenols on secretion of IL-1β, IL-6, TNF-α, and CXCL8 by macrophages. A pre-treatment of macrophages with the tea extracts, EGCG, or theaflavins prior to a stimulation with F. nucleatum significantly inhibited the secretion of all four cytokines and reduced the secretion of MMP-3 and MMP-9, two tissue destructive enzymes. TREM-1 expressed by macrophages is a cell-surface receptor involved in the propagation of the inflammatory response to bacterial challenges. Interestingly, tea polyphenols inhibited the secretion/shedding of soluble TREM-1 induced by a stimulation of macrophages with F. nucleatum. The anti-inflammatory properties of tea polyphenols identified in the present study suggested that they may be promising agents for the prevention and/or treatment of periodontal disease and inflammatory bowel 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".