A sexy approach to pacemaking: differences in function and molecular make up of the sinoatrial node.
Bibliographic record
Abstract
BACKGROUND: Functional properties of the sinoatrial node (SAN) are known to differ between sexes. Women have higher resting and intrinsic heart rates. Sex determines the risk of developing certain arrhythmias such as sick sinus syndrome, which occur more often in women. We believe that a major contributor to these differences is in gender specific ion channel expression. METHODS: qPCR was used to compare ion channel gene expression in the SAN and right atrium (RA) between male and female rats. Histology, immunohistochemistry and signal intensity analysis were used to locate the SAN and determine abundance of ion channels. The effect of nifedipine on extracellular potential recording was used to determine differences in beating rate between sexes. RESULTS: mRNAs for Cav1.3, Kir3.1, and Nkx2-5, as well as expression of the L-Type Ca²⁺ channel protein, were higher in the female SAN. Females had significantly higher intrinsic heart rates and the effect of nifedipine on isolated SAN preparations was significantly greater in male SAN. Computer modelling using a SAN cell model demonstrated a higher propensity of pacemaker-related arrhythmias in females. CONCLUSION: This study has identified key differences in the expression of Cav1.3, Kir3.1 and Nkx2-5 at mRNA and/or protein levels between male and female SAN. Cav1.3 plays an important role in the pacemaker function of the SAN, therefore the higher intrinsic heart rate of the female SAN could be caused by the higher expression of Cav1.3. The differences identified in this study advance our understanding of sex differences in cardiac electrophysiology and arrhythmias.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".