Protective Effects of Grape Seed Proanthocyanidins Against Oxidative Stress Induced by Lipopolysaccharides of Periodontopathogens
Bibliographic record
Abstract
BACKGROUND: During phagocytosis or stimulation with bacterial components, macrophages activate various cell processes, including the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS), which are critical for successful defense against invading organisms. Increased levels of ROS/RNS create oxidative stress that results in tissue and bone destruction. Grape seed proanthocyanidins have been reported to possess a wide range of biologic properties against oxidative stress. In the present study, we investigated the effects of a grape seed proanthocyanidin extract (GSE) and commercial polyphenols on the production of ROS and RNS and on the protein expression of inducible nitric oxide synthase (iNOS) by murine macrophages stimulated with lipopolysaccharides (LPS) of periodontopathogens. METHODS: Macrophages (RAW 264.7) were treated with non-toxic concentrations of either GSE or commercial polyphenols (gallic acid [GA] and [-]-epigallocatechin-3-gallate [EGCG]) and stimulated with LPS of Actinobacillus actinomycetemcomitans or Fusobacterium nucleatum, and iNOS expression was evaluated by immunoblotting. Nitric oxide (NO) production was quantified using the colorimetric Griess assay, whereas ROS production was measured with the fluorescent 123-dihydrorhodamine dye. RESULTS: GSE strongly decreased NO and ROS production and iNOS expression by LPS-stimulated macrophages. GA also revealed a strong inhibitory effect on NO production without affecting iNOS expression but slightly increasing ROS production. EGCG showed an inhibitory effect on NO and ROS production and on iNOS expression by macrophages. CONCLUSION: Our findings demonstrate that proanthocyanidins have potent antioxidant properties and should be considered a potential agent in the prevention of 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) | 0.000 | 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".