Estradiol attenuates high glucose-induced endothelial nitrotyrosine: role for neuronal nitric oxide synthase
Bibliographic record
Abstract
Hyperglycemia in diabetes causes increased oxidative stress in the vascular endothelium with generation of free radicals such as superoxide. Peroxynitrite, a highly reactive species generated from superoxide and nitric oxide (NO), induces proinflammatory tyrosine nitration of intracellular proteins under such conditions. The female sex hormone estrogen appears to exert protective effects on the nondiabetic endothelium. However, several studies show reduced vascular protection in women with diabetes, suggesting alterations in estrogen signaling under high glucose. In this study, we examined the endothelial effects of estrogen under increasing glucose levels, focusing on nitrotyrosine and peroxynitrite. Human umbilical vein endothelial cells were incubated with normal (5.5 mM) or high (15.5 or 30.5 mM) glucose before addition of estradiol (E2, 1 or 10 nM). Selective NO synthase (NOS) inhibitors were used to determine the role of specific NOS isoforms. Addition of E2 significantly reduced high glucose-induced increase in peroxynitrite and consequently, nitrotyrosine. The superoxide levels were unchanged, suggesting effects on NO generation. Inhibition of neuronal NOS (nNOS) reduced high glucose-induced nitrotyrosine, demonstrating a critical role for this enzyme. E2 increased nNOS activity under normal glucose while decreasing it under high glucose as determined by its phosphorylation status. These data show that nNOS contributes to endothelial peroxynitrite and subsequent nitrotyrosine generation under high glucose, which can be attenuated by E2 through nNOS inhibition. The altered regulation of nNOS by E2 under high glucose is a potential therapeutic target in women with diabetes.
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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".