Role of Purkinje‒muscle junction in early ventricular fibrillation in a porcine model: Beyond the trigger concept
Bibliographic record
Abstract
BACKGROUND: The role of the Purkinje network in triggering ventricular fibrillation (VF) has been studied; however, its involvement after onset and in early maintenance of VF is controversial. AIM: We studied the role of the Purkinje-muscle junctions (PMJ) on epicardial-endocardial activation gradients during early VF. METHODS: In a healthy, porcine, beating-heart Langendorff model [control, n = 5; ablation, n = 5], simultaneous epicardial-endocardial dominant frequent mapping was used (224 unipolar electrograms) to calculate activation rate gradients during the onset and early phase of VF. Selective Purkinje ablation was performed using Lugol's solution, followed by VF re-induction and mapping and finally, histological evaluation. RESULTS: Epicardial activation rates were faster than endocardial rates for both onset and early VF. After PMJ ablation, activation rates decreased epicardially and endocardially for both onset and early VF [Epi: 9.7 ± 0.2 to 8.3 ± 0.2 Hz (p <.0001) and 10.9 ± 0.4 to 8.8 ± 0.3 Hz (p < .0001), respectively; Endo: 8.2 ± 0.3 Hz to 7.4 ± 0.2 Hz (p < .0001) and 7.0 ± 0.4 Hz to 6.6 ± 0.3 Hz (p = .0002), respectively]. In controls, epicardial-endocardial activation rate gradients during onset and early VF were 1.7 ± 0.3 Hz and 4.5 ± 0.4 Hz (p < .001), respectively. After endocardial ablation of PMJs, these gradients were reduced to 0.9 ± 0.3 Hz (onset VF, p < .001) and to 2.2 ± 0.3 Hz (early VF, p <.001). Endocardial-epicardial Purkinje fiber arborization and selective Purkinje fiber extinction after only endocardial ablation (not with epicardial ablation) was confirmed on histological analysis. CONCLUSIONS: Beyond the trigger paradigm, PMJs determine activation rate gradients during onset and during early maintenance of VF.
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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 | 0.001 |
| Open science | 0.000 | 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".