Dynamics of Intramural and Transmural Reentry During Ventricular Fibrillation in Isolated Swine Ventricles
Bibliographic record
Abstract
The intramural dynamics of ventricular fibrillation (VF) remain poorly understood. Recent investigations have suggested that stable intramural reentry may underlie the mechanisms of VF. We performed optical mapping studies of VF in isolated swine right ventricles (RVs) and left ventricles (LVs). Nine RV walls were cut obliquely in their distal edge exposing the transmural surface. Six LV wedge preparations were also studied. Results showed that intramural reentry was present. In RV, 28 of 44 VF episodes showed reentry; 15% of the activation pathways were reentrant. Except for 4 episodes, reentry was transmural, involving subendocardial structures as the papillary muscle (PM) or trabeculae. In LV, reentry was observed in 27 of 27 VF episodes; 23% of the activations were part of reentrant pathways (P<0.05 compared with RV). All LV reentrant pathways were truly intramural (confined to the wall) and were frequently located at the PM insertion. In both ventricles, reentry was spatially and temporally unstable. Histological studies showed abrupt changes in fiber orientation at sites of reentry and wave splitting. Connexin 40 immunostaining demonstrated intramyocardial Purkinje fibers at sites of reentry in the PM root and around endocardial trabeculae. Our results confirm that reentry is frequent-but unstable-in the myocardial wall during VF. In RV, reentry is mostly transmural and requires participation of subendocardial structures. The LV has a greater incidence of reentry and is intramural. Anisotropic anatomic structures played key roles in the generation of wave splitting and in the maintenance of reentry.
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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 | 0.000 |
| 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) | 0.000 | 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".