A Complete and Low-Cost Cardiac Optical Mapping System in Translational Animal Models
Bibliographic record
Abstract
Clinicians, biologists, physicists, engineers, and computer scientists are coming together to better understand heart disease, which is currently the leading cause of death globally. Optical mapping, a high-speed fluorescence imaging technique that visualizes and measures key cardiac parameters such as action potentials, cytosolic calcium transients, and fibrillation dynamics, is a core research tool that has arisen from such interdisciplinary collaborations. In an effort to broaden its use, especially among clinical scientists and students, we developed a complete and low-cost optical mapping system, including a constant-flow Langendorff perfusion system, which minimizes the economic threshold to widespread use of this powerful tool in cardiac electrophysiology research. The system described here provides high spatiotemporal resolution data about action potentials, intracellular calcium transients and fibrillation wave dynamics in isolated Langendorff-perfused hearts (pigs and rabbits), relevant for translational research. All system components and software elements are fully disclosed with the aim of increasing the use of this affordable and highly versatile tool among clinicians, basic scientists and students wishing to tackle their own research questions with their own customizable systems.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".