The antioxidant resveratrol down-regulates inflammation in an in-vitro model of<i>Pseudomonas aeruginosa</i>infection of lung epithelial cells
Bibliographic record
Abstract
Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen that can cause severe pulmonary infection in immunocompromized individuals. During the infectious process, P. aeruginosa provokes a potent inflammatory response and induces the release of reactive oxygen species (ROS). Cells undergo oxidative stress when cellular antioxidants are unable to effectively scavenge and detoxify ROS, resulting in lung damage. Resveratrol (3,5,4'-trihydroxystilbene) is a natural polyphenolic compound with recognized antioxidant effects. We hypothesized that owing to its antioxidant activities, resveratrol can attenuate an inflammatory response in P. aeruginosa-infected cells. Lung epithelial A549 cells were pre-treated with 100 μmol/L of resveratrol for 5 h, followed by infection with P. aeruginosa. Intracellular ROS generation was used as an indicator of P. aeruginosa-induced oxidative stress, and cell surface expression of Fas receptor and activation of caspases-3 and -7 as indicators of apoptosis. We also measured the surface expression of intercellular adhesion molecule (ICAM)-1 and enzymes related to inflammation and redox signaling. Resveratrol significantly reduced ROS generation, ICAM-1, and human beta-defensin-2 expression, as well as the markers of apoptosis in A549 cells infected with P. aeruginosa, and up-regulated glutathione peroxidase, suggesting its potential therapeutic role in protecting the lungs against the deleterious effects of P. aeruginosa infection.
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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) | 0.001 | 0.001 |
| 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".