Wild Blueberry (<i>Vaccinium angustifolium</i> Ait.) Polyphenols Target <i>Fusobacterium nucleatum</i> and the Host Inflammatory Response: Potential Innovative Molecules for Treating Periodontal Diseases
Bibliographic record
Abstract
Blueberries contain significant amounts of flavonoids to which a number of beneficial health effects in humans have been associated. The present study investigated the effect of a polyphenol-rich lowbush blueberry (Vaccinium angustifolium Ait.) extract on the two main etiologic components of periodontitis, a multifactorial disorder affecting the supporting structures of the teeth. Phenolic acids, flavonoids (flavonols, anthocyanins, flavan-3-ols), and procyanidins made up 16.6, 12.9, and 2.7% of the blueberry extract, respectively. The blueberry extract showed antibacterial activity (MIC = 1 mg/mL) against the periodontopathogenic bacterium Fusobacterium nucleatum. This property may result from the ability of blueberry polyphenols to chelate iron. Moreover, the blueberry extract at 62.5 μg/mL inhibited F. nucleatum biofilm formation by 87.5 ± 2.3%. Subsequently, the ability of the blueberry extract to inhibit the NF-κB signaling pathway in U937-3xκB cells was investigated. The blueberry extract dose-dependently inhibited the activation of NF-κB induced by F. nucleatum. In addition, a pretreatment of macrophages with the blueberry extract (62.5 μg/mL) inhibited the secretion of IL-1β, TNF-α, and IL-6 by 87.3 ± 1.3, 80.7 ± 5.6, and 28.2 ± 9.3%, respectively, following a stimulation with F. nucleatum. Similarly, the secretion of MMP-8 and MMP-9 was also dose-dependently inhibited. This dual antibacterial and anti-inflammatory action of lowbush blueberry polyphenols suggests that they may be promising candidates for novel therapeutic agents.
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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".