Upregulation of the Hyperpolarization-Activated Current Increases Pacemaker Activity of the Sinoatrial Node and Heart Rate During Pregnancy in Mice
Bibliographic record
Abstract
BACKGROUND: Pregnancy is associated with a faster heart rate (HR), which is a risk factor for arrhythmias. However, the underlying mechanisms for this increased HR are poorly understood. Therefore, this study was performed to gain mechanistic insight into the pregnancy-induced increase in HR. METHODS AND RESULTS: Using surface ECG we observed that pregnant (P) mice have faster HR (531±14 beats per minute [bpm]) compared with nonpregnant (NP) mice (470±27 bpm; P<0.03). Results obtained with Langendorff-perfused hearts showed that this difference persisted in the absence of autonomic nervous innervation (NP, 327±16 bpm; P, 385±18 bpm; P<0.02). Spontaneous action potentials of sinoatrial node cells from pregnant mice exhibited higher automaticity (NP, 292±13 bpm; P, 330±12 bpm; P=0.047) and steeper diastolic depolarization (NP, 0.20±0.03 V/s; P, 0.40±0.06 V/s; P=0.004). Pregnancy increased the density of the hyperpolarization-activated current (If) (at -90mV: NP, -15.2±1.0 pA/pF; P, -28.6±2.9 pA/pF; P=0.0002) in sinoatrial node cells. Voltage dependence of the If activation curve and the intracellular cAMP levels were unchanged in sinoatrial node cells of pregnant mice. However, there was a significant increase in HCN2 channel protein expression with no change in HCN4 expression. Maximal depolarizing shift of the If activation curve induced by isoproterenol was attenuated in pregnancy. This reduced response to isoproterenol may be attributable to the lower cAMP sensitivity of HCN2 isoform compared with that of HCN4. CONCLUSIONS: This study shows that an increase in If current density contributes to the acceleration of sinoatrial node automaticity and explains, in part, the higher HR observed in 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".