Noise-induced effects on multicellular biopacemaker spontaneous activity: Differences between weak and strong pacemaker cells
Bibliographic record
Abstract
Self-organization of spontaneous activity of a network of active elements is important to the general theory of reaction-diffusion systems as well as for pacemaking activity to initiate beating of the heart. Monolayer cultures of neonatal rat ventricular myocytes, consisting of resting and pacemaker cells, exhibit spontaneous activation of their electrical activity. Similarly, one proposed approach to the development of biopacemakers as an alternative to electronic pacemakers for cardiac therapy is based on heterogeneous cardiac cells with resting and spontaneously beating phenotypes. However, the combined effect of pacemaker characteristics, density, and spatial distribution of the pacemaker cells on spontaneous activity is unknown. Using a simple stochastic pattern formation algorithm, we previously showed a clear nonlinear dependency of spontaneous activity (occurrence and amplitude of spontaneous period) on the spatial patterns of pacemaker cells. In this study, we show that this behavior is dependent on the pacemaker cell characteristics, with weaker pacemaker cells requiring higher density and larger clusters to sustain multicellular activity. These multicellular structures also demonstrated an increased sensitivity to voltage noise that favored spontaneous activity at lower density while increasing temporal variation in the period of activity. This information will help researchers overcome the current limitations of biopacemakers.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".