Towards the Development of an in Silico Model for the Zebrafish Action Potential
Bibliographic record
Abstract
In the last decades, the use of zebrafish in different fields has significantly grown.This increasing interest is related to its characteristics, which make it very similar to humans in many aspects, especially a similar electrophysiology owing large percentage of orthologues of human genes.Thus, zebrafish has been proposed as a pharmacological and genetic screening model.Developing a numerical AP model seems very important to study pathologies and drug administration to understand the ionic mechanisms involved.Thanks to this knowledge, reducing the number of animals used for experimental studies will also be possible.This work represents the first approach toward the development of a numerical model for the adult zebrafish AP.The developed model uses the TP04 formulation of the action potential for human cardiomyocytes as a base model.Starting from this model, the main currents have been reparametrized to adapt them to the zebrafish while extending the model to account for the T-type calcium current present in the zebrafish and deleting the Ito current, which is not found to be present instead.Moreover, AP recordings from the ventricle of adult zebrafish in isolated hearts were collected to validate the numerical model.Preliminary results showed an AP morphology in good agreement with experimental data and correct restitution curves behaviors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".