EET Displays Anti-Inflammatory Effects in TNF-α–Stimulated Human Bronchi
Bibliographic record
Abstract
The aim of the present study was to investigate the anti-inflammatory effects of 14,15-epoxyeicosatrienoic acid (EET) on reactivity and Ca(2+) sensitivity in TNF-alpha-stimulated human bronchi. Tension measurements performed on either control, TNF-alpha-, or TNF-alpha + EET-pretreated bronchi revealed that 100 nM 14,15-EET pretreatments significantly reduced the reactivity of TNF-alpha-pretreated tissues to contractile agonists. EET also normalized the relaxing response to isoproterenol in TNF-alpha-treated bronchi. Pretreatment with 100 nM 14,15-EET prevented TNF-alpha-induced IkappaBalpha degradation, as demonstrated by an increase in IkappaBalpha protein levels on Western blot analysis. The anti-inflammatory properties of EET were mediated by the inhibition of IkappaBalpha degradation, suggesting a lower activation of NF-kappaB. The Ca(2+) sensitivity of TNF-alpha-stimulated bronchi was also evaluated on beta-escin-permeabilized preparations. Observed mean responses demonstrated that EET pretreatments abolished Ca(2+) hypersensitivity developed by TNF-alpha-stimulated bronchial explants. Moreover, 14,15-EET significantly reduced PDBu-induced Ca(2+) sensitivity in TNF-alpha-stimulated bronchi. Western blot and RT-PCR analyses revealed that CPI-17 protein and transcript levels were increased in TNF-alpha-treated bronchi, as opposed to being decreased in the presence of 14,15-EET. This eicosanoid also reduced U-46619-induced Ca(2+) sensitivity, which is related to the activation of Rho-kinase pathway. These results were also correlated with an increase in protein staining and transcription level of p116(Rip), a RhoA inhibitory-binding protein. Altogether, these data demonstrate that 14,15-EET is a potent modulator of the hyperreactivity triggered by TNF-alpha in human airway smooth muscle cells.
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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".