PPARγ signaling is required for mediating EETs protective effects in neonatal cardiomyocytes exposed to LPS
Bibliographic record
Abstract
Lipopolysaccharide (LPS) is a bacterial wall endotoxin producing many pathophysiological conditions including myocardial inflammation leading to cardiotoxicity. Epoxyeicosatrienoic acids (EETs) are biologically active metabolites of arachidonic acids capable of activating protective cellular pathways in response to stress stimuli. EETs evoke a plethora of pathways limiting impairments of cellular structures, reducing cell death, and promoting anti-inflammatory reactions in various cell types. Considering EETs are capable of producing various biological protective effects, we hypothesized that EETs would protect rat neonatal cardiomyocytes (NCM) against LPS-induced cytotoxicity. In this study, we used a dual-acting, synthetic analog of EETs, UA-8 [13-(3-propylureido)tridec-8-enoic acid], possessing both EET-mimetic and soluble epoxide hydrolase selective inhibitory properties and 14,15-EET as a model of canonical EET molecules. We found that both UA-8 and 14,15-EET significantly improved cell viability and mitochondrial function of cardiomyocytes exposed to LPS. Furthermore, treatment with UA-8 or 14,15-EET resulted in significant attenuation of LPS-triggered pro-inflammatory response, caspase-3 activation and reduction in the total antioxidant capacity in cardiomyocytes. Importantly, EET-mediated effects were significantly reduced by pharmacological inhibition of peroxisome proliferator-activated receptors γ (PPARγ) suggesting that PPARγ signaling was required for EETs exerted protective effects. Data presented in the current study demonstrate that activation of PPARγ signaling plays a crucial role in EET-mediated protection against LPS-cytotoxicity in cardiomyocytes.
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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".