Influence of the Purkinje-muscle junction on transmural repolarization heterogeneity
Bibliographic record
Abstract
AIMS: To elucidate the properties of the PMJ and myocardium underlying these effects. Transmural heterogeneity of action potential duration (APD) is known to play an important role in arrhythmogenesis. Regions of non-uniformities of APD gradients often overlap considerably with the location of Purkinje-muscle junctions (PMJs). We therefore hypothesized that such junctions are novel sources of local endocardial and transmural heterogeneity of repolarization, and that remodelling due to heart failure modulates this response. METHODS AND RESULTS: Spatial gradients of endocardial APD in left ventricular wedge preparations from healthy sheep (n = 5) were correlated with locations of PMJs identified through Purkinje stimulation under optical mapping. APD prolongation was dependent on proximity of the PMJ to the imaged surface, whereby shallow PMJs significantly modulated local APD when stimulating either Purkinje (P = 0.0116) or endocardium (P = 0.0123). In addition, we model a PMJ in 5 × 5× 10 mm transmural tissue wedges using healthy and novel failing human ventricular and Purkinje ionic models. Short distances of the PMJ to cut surfaces (<0.875 mm) revealed that APD maxima were localized to the PMJ in healthy myocardium, whereas APD minima were observed in failing myocardium. Amplitudes and spatial gradients of APD were prominent at functional PMJs and quiescent PMJs. Furthermore, increasing the extent of Purkinje fibre branching or decreasing tissue conductivity augmented local APD prolongation in both failing and non-failing models. CONCLUSIONS: The Purkinje network has the potential to influence myocardial AP morphology and rate-dependent behaviour, and furthermore to underlie enhanced transmural APD heterogeneities and spatial gradients of APD in non-failing and failing myocardium.
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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) | 0.001 | 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".