From Mice to Mainframes: Experimental Models for Investigation of the Intracardiac Nervous System
Bibliographic record
Abstract
The intracardiac nervous system (IcNS), sometimes referred to as the "little brain" of the heart, is involved in modulating many aspects of cardiac physiology. In recent years our fundamental understanding of autonomic control of the heart has drastically improved, and the IcNS is increasingly being viewed as a therapeutic target in cardiovascular disease. However, investigations of the physiology and specific roles of intracardiac neurons within the neural circuitry mediating cardiac control has been hampered by an incomplete knowledge of the anatomical organisation of the IcNS. A more thorough understanding of the IcNS is hoped to promote the development of new, highly targeted therapies to modulate IcNS activity in cardiovascular disease. In this paper, we first provide an overview of IcNS anatomy and function derived from experiments in mammals. We then provide descriptions of alternate experimental models for investigation of the IcNS, focusing on a non-mammalian model (zebrafish), neuron-cardiomyocyte co-cultures, and computational models to demonstrate how the similarity of the relevant processes in each model can help to further our understanding of the IcNS in health and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".