Estradiol blocks the induction of CD40 and CD40L expression on endothelial cells and prevents neutrophil adhesion: An ERα-mediated pathway
Bibliographic record
Abstract
OBJECTIVE: Interferon gamma (IFN-gamma) was shown to induce CD40 and CD40L expression on endothelial cells (ECs) and consequently to promote neutrophil adhesion. The pro- and anti-inflammatory effects of estrogens are well recognized but their role on the regulation of CD40 and CD40L expression on ECs remains undefined. METHODS AND RESULTS: Treatment of porcine aortic endothelial cells (PAEC) with IFN-gamma for 24 h enhanced CD40 and CD40L expression by 97% and 78%, respectively. Pretreatment of PAEC with 17-beta-estradiol (17betaE) for 24 h prevented the latter expression of CD40/CD40L. Treatment of PAEC with antisense oligomers targeting ERalpha mRNA attenuated the ability of 17betaE to inhibit the IFN-gamma-induced CD40 and CD40L protein expression. The IFN-gamma activation pathway of CD40 is known to involve the phosphorylation of the Janus activated kinase (JAK) and the signal transducer and activator of transcription 1 (Stat1). 17betaE, acting via the estrogen receptor alpha (ERalpha), abrogated IFN-gamma-mediated effects on Stat1 but failed to inhibit Jak1 and Jak2 phosphorylation. Furthermore, 17betaE prevented neutrophil adhesion induced by IFN-gamma. CONCLUSION: In summary, 17betaE binding to ERalpha blocked IFN-gamma-induced Stat1 phosphorylation, CD40 and CD40L protein expression, and neutrophil adhesion onto ECs.
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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) | 0.002 | 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".