Acute ischaemia and gap junction modulation modify propagation patterns across Purkinje-myocardial junctions
Bibliographic record
Abstract
Background: The Purkinje network is essential for normal electrical impulse propagation in the heart but has also been implicated in ventricular arrhythmias. Previous experimental work has suggested that not all Purkinje-myocardial junctions (PMJs) are active at rest due to source-sink mismatch at the PMJs. Objective: We hypothesized that pathological conditions that cause gap junction uncoupling (e.g., acute ischaemia), would increase the number of active PMJs, leading to more complex activation patterns. Methods: We investigated this using a whole-heart intact Purkinje system preparation that allowed direct high-resolution endocardial mapping to interrogate PMJ function. Twelve (7 control, five rotigaptide) Langendorff-perfused hearts from New Zealand white rabbits were subjected to an ischaemia-reperfusion protocol and optically mapped. Computational modelling was performed to determine the effects of gap junction coupling on PMJ function, and on the complexity of endocardial activation. Results: During ischaemia, the percentage of right ventricle area activated within the first 5 ms decreased from baseline 62% ± 7% to 52% ± 8% during early ischaemia (p = 0.04), consistent with slowing of conduction. This was followed by a paradoxical increase in late-ischaemia (60% ± 8%) due to extra regions of early activation. Gap junction enhancement with rotigaptide during ischaemia abolished the aforementioned pattern. Parallel computational experiments replicated experimental findings only when the number of functional PMJs was increased during ischaemia. With more active PMJs, there were more breakthrough sites with increased complexity of activation, as also measured in biological preparations. Conclusion: Normally-quiescent PMJs can become active in the context of gap junction uncoupling during acute ischaemia. Pharmacological gap junction modulation may alter propagation patterns across PMJs and may be used as a therapeutic strategy for Purkinje system associated arrhythmias.
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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".