Damage induced arrhythmias: mechanisms and implications
Bibliographic record
Abstract
Little is known about the role played by non-uniform myocardial stress and strain distributions and by non-uniform excitation contraction coupling in mechanisms underlying the premature beats that initiate an arrhythmia. We will review the evidence in support of a mechanism in which both non-uniform contraction and increased Ca2+ load of cells adjacent to acutely damaged cells are essential in the "spontaneous" generation of Ca2+ transients during the relaxation phase of the electrically driven twitch. The putative mechanism of initiation of the propagating Ca2+ waves involves feedback of rapid length (or force) changes to dissociation of Ca2+ from the contractile filaments. A novel aspect of this concept is that these mechanically elicited Ca2+ transients induce propagating Ca2+ waves that travel into the adjacent normal myocardium and cause after-depolarizations, which, in turn, may cause premature action potentials. These premature action potentials will further load the cells with Ca2+, which promotes the subsequent generation of propagating Ca2+ transients and leads to triggered arrhythmias. The damage-induced premature beats may also initiate re-entry arrhythmias in non-uniform myocardium. These observations strongly support the concept that abnormal cellular Ca2+ transport plays a crucial role in the initiation of arrhythmias in damaged and non-uniform 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".