Effects of a tart cherry (Prunus cerasus L.) phenolic extract on Porphyromonas gingivalis and its ability to impair the oral epithelial barrier
Bibliographic record
Abstract
Periodontal diseases, including gingivitis and periodontitis, are a global oral health problem. Porphyromonas gingivalis, a key pathogen involved in the onset of periodontitis, is able to colonize the subgingival epithelium and invade the underlying connective tissue due to the contribution of cysteine proteases known as gingipains. In this study, we investigated the effects of a phenolic extract prepared from tart cherry (Prunus cerasus L.) juice on the growth, adherence, and protease activity of P. gingivalis. We also assessed the protective effect of the tart cherry extract on the disruption of the oral epithelial barrier induced by P. gingivalis. The tart cherry extract that contains procyanidins and quercetin and its derivatives (rutinoside, glucoside) as the most important phenolic compounds attenuated P. gingivalis growth, reduced adherence to an experimental basement membrane matrix model, and decreased the protease activities of P. gingivalis. The tart cherry extract also exerted a protective effect on the integrity of the oral epithelial barrier in an in vitro model infected with P. gingivalis. More specifically, the extract prevented a decrease in transepithelial electrical resistance as well as the destruction of tight junction proteins (zonula occludens-1 and occludin). These results suggest that the tart cherry phenolic extract may be a promising natural product for the treatment of periodontitis through its ability to attenuate the virulence properties of P. gingivalis and curtail the ability of this pathogen to impair the oral epithelial barrier.
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) | 0.001 | 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 | 0.001 |
| 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".