Contribution of estrogen to the pregnancy-induced increase in cardiac automaticity
Bibliographic record
Abstract
Background The heart rate progressively increases throughout pregnancy, reaching a maximum in the third trimester. This elevated heart rate is also present in pregnant mice and is associated with accelerated automaticity, higher density of the pacemaker current I f and changes in Ca 2+ homeostasis in sinoatrial node (SAN) cells. Strong evidence has also been provided showing that 17β-estradiol (E 2 ) and estrogen receptor α (ERα) regulate heart rate. Accordingly, we sought to determine whether E 2 levels found in late pregnancy cause the increased cardiac automaticity associated with pregnancy. Methods and results Voltage- and current-clamp experiments were carried out on SAN cells isolated from female mice lacking estrogen receptor alpha (ERKOα) or beta (ERKOβ) receiving chronic E 2 treatment mimicking late pregnancy concentrations. E 2 treatment significantly increased the action potential rate (284 ± 24 bpm, +E 2 354 ± 23 bpm, p = 0.040) and the density of I f (+52%) in SAN cells from ERKOβ mice. However, I f density remains unchanged in SAN cells from E 2 -treated ERKOα mice. Additionally, E 2 also increased I f density (+67%) in nodal-like human-induced pluripotent stem cell-derived cardiomyocytes (N-hiPSC-CM), recapitulating in a human SAN cell model the effect produced in mice. However, the L-type calcium current (I CaL ) and Ca 2+ transients, examined using N-hiPSC-CM and SAN cells respectively, were not affected by E 2 , indicating that other mechanisms contribute to changes observed in these parameters during pregnancy. Conclusion The accelerated SAN automaticity observed in E 2 -treated ERKOβ mice is explained by an increased I f density mediated by ERα, demonstrating that E 2 plays a major role in regulating SAN function during pregnancy.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".