Effect of a Berry Polyphenolic Fraction on Biofilm Formation, Adherence Properties and Gene Expression of Streptococcus mutans and Its Biocompatibility with Oral Epithelial Cells
Bibliographic record
Abstract
The ability of Streptococcus mutans to adhere to oral surfaces and form biofilm is a key step in the tooth decay process. The aim of this study was to investigate a berry (wild blueberry, cranberry, and strawberry) polyphenolic fraction, commercialized as Orophenol®, for its antibacterial, anti-biofilm, and anti-adhesion properties on S. mutans. Moreover, the biocompatibility of the fraction with human oral epithelial cells was assessed. Phenolic acids, flavonoids (flavonols, anthocyanins, flavan-3-ols), and procyanidins made up 10.71%, 19.76%, and 5.29% of the berry polyphenolic fraction, respectively, as determined by chromatography and mass spectrometry. The berry polyphenolic preparation dose-dependently inhibited S. mutans biofilm formation while not reducing bacterial growth. At concentrations ranging from 250 to 1000 µg/mL, the fraction inhibited the adhesion of S. mutans to both saliva-coated hydroxyapatite and saliva-coated nickel–chrome alloy. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis showed that incubating S. mutans with the berry polyphenolic fraction was associated with a reduced expression of luxS gene, which regulates quorum sensing in S. mutans. The berry fraction did not show any significant cytotoxicity in an oral epithelial cell model. In conclusion, Orophenol®, which is a mixture of polyphenols from wild blueberry, cranberry and strawberry, possesses interesting anti-caries properties while being compatible with oral epithelial cells.
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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".