Hyperglycemia‐induced reactive oxygen species are differentially regulated by estrogen in human endothelial cells
Bibliographic record
Abstract
Diabetes is a predisposing factor for atherosclerosis. High glucose generates reactive oxygen species such as superoxide and peroxynitrite which lead to pro‐atherogenic changes in the endothelium. Estrogen has a beneficial anti‐oxidant and anti‐inflammatory effect on the vascular endothelium. However, the beneficial effects of estrogen are absent in patients with diabetes. We investigated the effects of estrogen on endothelial cells in the context of pre‐existing hyperglycemia. Human umbilical vein endothelial cells (HUVEC) were used for the studies. Superoxide and peroxynitrite were detected by staining for Dihydroethidium and nitrotyrosine respectively. Enzymes were detected by western blotting. We found that high glucose increased levels of both superoxide and peroxynitrite in the endothelial cells. Estrogen administration had no effect on superoxide levels. Surprisingly, estrogen significantly reduced the peroxynitrite generated under high glucose conditions. On examining the expression of nitric oxide synthase isoforms, we found that estrogen increased eNOS levels under normal glucose, but not under high glucose. The iNOS levels were unchanged under all conditions. Our results show a novel selective anti‐oxidant effect of estrogen on hyperglycemia‐induced peroxynitrite in endothelial cells. This work has been supported by CIHR, HSF Canada and AHFMR.
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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".