Estradiol Modulates Tumor Necrosis Factor-Induced Endothelial Inflammation: Role of Tumor Necrosis Factor Receptor 2
Bibliographic record
Abstract
The sex hormone estradiol (E(2)) appears to mediate both anti-atherogenic and pro-inflammatory effects in premenopausal women, suggesting a complex immunomodulatory role. Tumor necrosis factor (TNF) is a key pro-inflammatory cytokine involved in the pathogenesis of atherosclerosis and other inflammatory diseases. Alterations at the TNF receptors (TNFRs) and their downstream signaling/transcriptional pathways can affect inflammatory responses. Given this background, we hypothesized that chronic E(2) exposure would alter endothelial inflammatory response involving modulation at the levels of TNFRs and signaling pathways. HUVECs were used as the model system. Pre-treatment with E(2) did not significantly alter TNF-induced upregulation of pro-inflammatory molecules ICAM-1 (3-6 times) and VCAM-1 (5-7 times). However, pharmacological inhibition of transcriptional pathways suggested a partial shift from NF-ĸB (from 97 to 64%) towards the JNK/AP-1 pathway in ICAM-1 upregulation on E(2) treatment. In contrast, VCAM-1 expression remained NF-ĸB dependent in both control (∼96%) and E(2) treated (∼85%) cells. The pro-inflammatory TNF effects were mediated by TNFR1. Interestingly, E(2) pre-treatment increased TNFR2 levels in these cells. Concomitant TNFR2 activation (but not TNFR1 activation alone) led to the shift towards JNK/AP-1-mediated ICAM-1 upregulation in E(2)-treated cells, suggesting the effects of chronic E(2) to be dependent on TNFR2 signaling.
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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".