Estrogen is a modulator of vascular inflammation
Bibliographic record
Abstract
Vascular inflammation underlies the pathogenesis of atherosclerosis. Atherosclerotic changes in the vasculature lead to conditions such as coronary artery disease and stroke, which are the major causes of morbidity and mortality worldwide. Epidemiological studies in premenopausal women suggest a beneficial role for estrogen in preventing vascular inflammation and consequent atherosclerosis. However, the benefits of estrogen areabsent or even reversed in older postmenopausal subjects. The modulation of inflammation by estrogen under different conditions might explain this discrepancy. Estrogen exerts its antiinflammatory effects on the vasculature through different mechanisms such as direct antioxidant effect, generation of nitric oxide, prevention of apoptosis in vascular cells and suppression of cytokines and the renin-angiotensin system. On the other hand, estrogen also elicits proinflammatory changes under certain conditions, which are less completely understood. Some of the mechanisms underlying a possible proinflammatory role for estrogen include increased expression of the proinflammatory receptor for advanced glycation end products, increased tyrosine nitration of cellular proteins, and generation of reactive oxygen species through an uncoupled eNOS. In this review, we have presented evidence for both antiinflammatory and proinflammatory pathways modulated by estrogen and how interactions among such pathways might determine the effects of estrogen on the vascular system.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".