Effect of Hesperidin on Barrier Function and Reactive Oxygen Species Production in an Oral Epithelial Cell Model, and on Se-Cretion of Macrophage-Derived Inflammatory Mediators During Porphyromonas gingivalis Infection
Bibliographic record
Abstract
Porphyromonas gingivalis is a periodontopathogenic bacterium that can adhere and colonize periodontal tissues leading to inflammatory process, and consequently tissue destruction. New therapies using flavonoids such as hesperidin are being studied, and its promising properties have been highlighted. The aim of this study was to evaluate the effect of hesperidin on epithelial barrier function, reactive oxygen species (ROS) production and on inflammatory response caused by P. gingivalis in in vitro models. Integrity of the epithelial tight junctions challenged by P. gingivalis was determined by monitoring the transepithelial electrical resistance (TER). P. gingivalis adherence to a gingival keratinocyte monolayer and a basement membrane model were evaluated by a fluorescence assay. A fluorometric assay was used to determine the ROS production in gingival keratinocytes. The level of pro-inflammatory cytokines and matrix metalloproteinases (MMPs) secretion was evaluated by ELISA, while to assess NF-κB activation, the U937-3xjB-LUC monocyte cell line transfected with a luciferase reporter gene was used. Hesperidin protected against gingival epithelial barrier dysfunction caused by P. gingivalis and reduced the adherence of P. gingivalis to the basement membrane model. Hesperidin dose-dependently inhibited P. gingivalis-mediated ROS production by oral epithelial cells as well as the secretion of IL-1β, TNF- IL-8, MMP-2 and MMP-9 by macrophages challenged with P. gingivalis. Additionally, it was able to attenuate NF-κB activation in macrophages stimulated with P. gingivalis. These findings suggested that hesperidin has a protective effect on the epithelial barrier function, in addition to reduce ROS production and attenuate the inflammatory response associated with periodontal disease.
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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".