Estrogen is a modulator of Tumor Necrosis Factor responses in the human vascular endothelium
Bibliographic record
Abstract
Premenopausal women are protected against cardiovascular diseases compared to age‐matched men. However, these women are also at an increased risk of inflammatory and autoimmune diseases. Higher circulating levels of estrogen may exert an immunomodulatory effect. Given this background, we hypothesized that chronic estrogen exposure alters the endothelial response to inflammatory stimulation. We treated confluent monolayers of second passage human umbilical vein endothelial cells (HUVECs) for 24 hours with physiologically relevant doses of 17‐beta estradiol (E2) prior to stimulation with the pro‐inflammatory cytokine tumor necrosis factor (TNF). We found that E2 treatment increased TNFR2 levels without affecting TNFR1. There was a trend towards aggravation of TNF induced upregulation of leukocyte adhesion molecules such as intercellular adhesion molecule‐1 (ICAM‐1) and vascular cell adhesion molecule‐1 (VCAM‐1) on E2 pre‐treatment. There was also a change in the activation profile of nuclear factor kappa B on TNF stimulation between E2 treated and control cells. In summary, chronic E2 treatment altered the endothelial responses to TNF stimulation, involving modulation at the levels of receptors, transcriptional pathways and adhesion molecule expression. This work provides a better understanding of the interplay between estrogen and the endothelial inflammatory response in health and disease.
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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".