Resveratrol suppresses NTHi-induced inflammation via up-regulation of the negative regulator MyD88 short
Bibliographic record
Abstract
Upper respiratory tract inflammatory diseases such as asthma and chronic obstructive pulmonary diseases (COPD) affect more than one-half billion people globally and are characterized by chronic inflammation that is often exacerbated by respiratory pathogens such as nontypeable Haemophilus influenzae (NTHi). The increasing numbers of antibiotic-resistant bacterial strains and the limited success of currently available pharmaceuticals used to manage the symptoms of these diseases present an urgent need for the development of novel anti-inflammatory therapeutic agents. Resveratrol has long been thought as an interesting therapeutic agent for various diseases including inflammatory diseases. However, the molecular mechanisms underlying its anti-inflammatory properties remain largely unknown. Here we show for the first time that resveratrol decreases expression of pro-inflammatory mediators in airway epithelial cells and in the lung of mice by enhancing NTHi-induced MyD88 short, a negative regulator of inflammation, via inhibition of ERK1/2 activation. Furthermore, resveratrol inhibits NTHi-induced ERK1/2 phosphorylation by increasing MKP-1 expression via a cAMP-PKA-dependent signaling pathway. Finally, we show that resveratrol has anti-inflammatory effects post NTHi infection, thereby demonstrating its therapeutic potential. Together these data reveal a novel mechanism by which resveratrol alleviates NTHi-induced inflammation in airway disease by up-regulating the negative regulator of inflammation MyD88s.
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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 | 0.001 |
| 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".