Optical mapping of Langendorff-perfused human hearts: establishing a model for the study of ventricular fibrillation in humans
Bibliographic record
Abstract
Our objective was to establish a novel model for the study of ventricular fibrillation (VF) in humans. We adopted the established techniques of optical mapping to human ventricles for the first time to determine whether human VF is the result of wave breaks and singularity point formation and is maintained by high-frequency rotors and fibrillatory conduction. We describe the technique of acquiring optical signals in human hearts during VF, their characteristics, and the feasibility of possible analyses that could be performed to elucidate mechanisms of human VF. We used explanted hearts from five cardiomyopathic patients who underwent transplantation. The hearts were Langendorff perfused with Tyrode solution (95% O(2)-5% CO(2)), and the potentiometric dye di-4-ANEPPS was injected as a bolus into the coronary circulation. Fluorescence was excited at 531 +/- 20 nm with a 150-W halogen light source; the emission signal was long-pass filtered at 610 nm and recorded with a mapping camera. Fractional change of fluorescence varied between 2% and 12%. Average signal-to-noise ratio was 40 dB. The mean velocity of VF wave fronts was 0.25 +/- 0.04 m/s. Submillimetric spatial resolution (0.65-0.85 mm), activation mapping, and transformation of the data to phase-based analysis revealed reentrant, colliding, and fractionating wave fronts in human VF. On many occasions the VF wave fronts were as large as the entire vertical length (8 cm) of the mapping field, suggesting that there are a limited number of wave fronts on the human heart during VF. Phase transformation of the optical signals allowed the first demonstration ever of phase singularity point, wave breaks, and rotor formation in human VF. This method provides opportunities for potential analyses toward elucidation of the mechanisms of VF and defibrillation in humans.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".