A cocoa (Theobroma cacao L.) extract impairs the growth, virulence properties, and inflammatory potential of Fusobacterium nucleatum and improves oral epithelial barrier function
Bibliographic record
Abstract
Fusobacterium nucleatum is associated with many conditions and diseases, including periodontal diseases that affect tooth-supporting tissues. The aim of the present study was to investigate the effects of a cocoa extract (Theobroma cacao L.) on F. nucleatum with respect to growth, biofilm formation, adherence, and hydrogen sulfide (H2S) production. The anti-inflammatory properties and the effect on epithelial barrier function of the cocoa extract were also assessed. The cocoa extract, whose major phenolic compound is epicatechin, dose-dependently inhibited the growth, biofilm formation, adherence properties (basement membrane matrix, oral epithelial cells), and H2S production of F. nucleatum. It also decreased IL-6 and IL-8 production by F. nucleatum-stimulated oral epithelial cells and inhibited F. nucleatum-induced NF-κB activation in monocytes. Lastly, the cocoa extract enhanced the barrier function of an oral epithelial model by increasing the transepithelial electrical resistance. We provide evidence that the beneficial properties of an epicatechin-rich cocoa extract may be useful for preventing and/or treating periodontal diseases.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".