Drivers of Sinoatrial Node Automaticity in Zebrafish: Comparison With Mechanisms of Mammalian Pacemaker Function
Bibliographic record
Abstract
Cardiac excitation originates in the sinoatrial node (SAN), due to the automaticity of this distinct region of the heart. SAN automaticity is the result of a gradual depolarisation of the membrane potential in diastole, driven by a coupled system of transarcolemmal ion currents and intracellular Ca2+ cycling. The frequency of SAN excitation determines heart rate and is under the control of extra- and intracardiac (extrinsic and intrinsic) factors, including neural inputs and responses to tissue stretch. While the structure, function, and control of the SAN have been extensively studied in mammals, and some critical aspects have been shown to be similar in zebrafish, the specific drivers of zebrafish SAN automaticity and the response of its excitation to vagal nerve stimulation and mechanical preload remain incompletely understood. As the zebrafish represents an important alternative experimental model for the study of cardiac (patho-) physiology, we sought to determine its drivers of SAN automaticity and the response to nerve stimulation and baseline stretch. Using a pharmacological approach mirroring classic mammalian experiments, along with electrical stimulation of intact cardiac vagal nerves and the application of mechanical preload to the SAN, we demonstrate that the principal components of the coupled membrane- Ca2+ pacemaker system that drives automaticity in mammals are also active in the zebrafish, and that the effects of extra- and intracardiac control of heart rate seen in mammals are also present. Overall, these results, combined with previously published work, support the utility of the zebrafish as a novel experimental model for studies of SAN (patho-) physiological function.
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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 | 0.001 |
| 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".