17β-Estradiol Promotes Right Ventricle Angiogenesis via Estrogen Receptor α and Apelin Signaling
Bibliographic record
Abstract
Right ventricular (RV) failure is the major cause of mortality in pulmonary hypertension (PH). Adaptive angiogenesis and RV endothelial cell (RVEC) function are major modifiers of RV adaptation in PH, but the underlying mechanisms and their regulators remain incompletely understood. RV adaptation in PH is sexually dimorphic, and 17β-estradiol (E2) exerts protective effects on RV cardiomyocytes. Whether E2 modifies angiogenesis and RVEC function in RV failure remains unknown. We hypothesized that E2 and estrogen receptor α (ERα) promote RV angiogenesis and RVEC homeostasis in PH and aimed to identify underlying mechanisms. We assessed E2 angiogenic effects using cultured human cardiac microvascular endothelial cells (hCMVECs), RVECs from PH patients with RV failure, and RVECs from sugen/hypoxia (SuHx) and monocrotaline (MCT) rat models. In vivo, we evaluated RV capillary density in PH rats treated with E2 or ERα-selective agonist. Apelin signaling was evaluated via apelin receptor blockade. E2 enhanced angiogenesis in male hCMVECs and RV capillary density in female SuHx- PH rats. E2 reversed angiogenic alterations in RVECs from SuHx-PH rats via apelin receptor signaling. In RVECs from PH patients with RV failure, E2 stimulated vascular network formation. In rat and human PH-RVECs, ERα was necessary and sufficient to mediate E2-induced angiogenesis. Activation of ERα with ERα-specific agonist restored RV capillary density in vivo. ERα-mediated angiogenesis required apelin signaling. These data indicate that E2 promotes RV angiogenesis via ERα and apelin signaling and identify a novel ERα-apelin axis in RVECs as a potential therapeutic target to restore RV vascular integrity in PH.
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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".