Epoxyeicosatrienoic acids protect rat hearts against tumor necrosis factor-α-induced injury
Bibliographic record
Abstract
Epoxyeicosatrienoic acids (EET), the primary arachidonic acid metabolites of cytochrome P450 2J (CYP2J) epoxygenases, possess potent vasodilatory, anti-inflammatory, antiapoptotic, and mitogenic effects. To date, little is known about the role of CYP2J2 and EETs in tumor necrosis factor (TNF)-α-induced cardiac injury. We utilized cell culture and in vivo models to examine the effects of exogenously applied EETs or CYP2J2 overexpression on TNF-α-induced cardiac apoptosis and cardiac dysfunction. In neonatal rat cardiomyocytes, TNF-α-induced apoptosis was markedly attenuated by EETs or CYP2J2 overexpression, leading to significantly improved cell survival. Further studies showed that TNF-α decreased expression of the antiapoptotic proteins Bcl-2 and Bcl-xL, decreased IκBα and PPARγ, and also inhibited PI3K-dependent Akt and EGFR signaling. Both EETs and CYP2J2 overexpression reversed the effects of TNF-α on these pathways. Furthermore, overexpression of CYP2J2 in rats prevented the decline in cardiac function that is normally observed in TNF-α-challenged animals. These results demonstrate that EETs or CYP2J2 overexpression can prevent TNF-α-induced cardiac cell injury and cardiac dysfunction by inhibiting apoptosis, reducing inflammation, and enhancing PPARγ expression. Targeting the CYP2J2 epoxygenase pathway may represent a novel approach to mitigate cardiac injury in diseases such as heart failure, where increased TNF-α levels are known to occur.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".