Abstract 519: Endothelin-1-Induced Oxidative Stress and Inflammatory Cell Infiltration Play a Role in High-Fat Diet Induced-Atherosclerosis in Apolipoprotein E Knockout Mice
Bibliographic record
Abstract
Background: Endothelin (ET)-1 plays an important role in generation of reactive oxygen species (ROS) and inflammation in the vasculature. ET-1has been implicated in the pathogenesis of atherosclerosis since plasma and tissue ET-1 are increased in human and animal models of atherosclerosis. We observed that ET-1 overexpression exacerbates high-fat diet (HFD)-induced atherosclerosis in apolipoprotein E knockout ( apoE -/- ) mice. We hypothesized that ET-1-induced ROS and inflammation contribute to the development of atherosclerosis. Design and methods: Eight-week-old male transgenic mice overexpressing preproET-1 in the endothelium (eET-1), apoE -/- , eET-1/ apoE -/- and wild type mice were fed a HFD for 8 weeks. Aortic atherosclerotic lesions were quantified using Oil Red O staining. ROS production using dihydroethidium staining and monocyte/macrophage and T cell infiltration using immunofluorescence with MOMA-2 and anti-CD4 antibodies, respectively, were determined in perivascular fat, media and plaque in ascending aortic sections. Results: eET-1/ apoE -/- presented 3.8-fold more atherosclerotic lesions in whole aorta compared to apoE -/- ( P <0.01). ET-1 overexpression caused 2.6-, 1.9- and 1.9-fold increase in ROS production in perivascular fat, media and plaque of apoE -/- , respectively ( P <0.05). ET-1 overexpression increased monocyte/macrophage infiltration by 5- and 8-fold in perivascular fat and media, respectively ( P <0.05). CD4 + T cell infiltration was observed in perivascular fat and plaque of 3 and 5 of 6 eET-1/ apoE -/- compared to 0 and 1 of 6 apoE -/- , respectively ( P <0.05). Conclusions: These results suggest that ET-1 play an important role in progression of atherosclerotic lesions by increasing the oxidative stress and monocyte/macrophage and T cell infiltration in the atherosclerotic aorta, including the perivascular fat.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".