Short-Term Memory and Restitution During Ventricular Fibrillation in Human Hearts
Bibliographic record
Abstract
BACKGROUND: Action potential duration (APD) variation is an important determinant of wave break and reentry. The determinants of APD variability during early ventricular fibrillation (VF) in myopathic human hearts have not been studied. The objective of this study was to study the role of APD restitution and short-term cardiac memory on variation in human VF. METHODS AND RESULTS: The study consisted of 7 patients (67+/-9 years old) with ejection fraction <35%. Monophasic action potentials were recorded from the right and/or left ventricular septum during VF. APD(60/90) was measured in sinus beat preceding induction of VF, and its amplitude was used to define 60%/90% repolarization in VF. The monophasic action potential upstroke (dV/dt(max)) was used to characterize local excitability. Simple linear regression showed that variability in APD(n60) was determined by APD/diastolic interval restitution (R(2)=0.48, P<0.0001) and short-term memory (APD(60) n-1, n-2, n-3, n-4; R(2)=0.55, 0.40, 0.33, and 0.27 respectively; P<0.001). Using multiple stepwise regression, short-term memory and restitution accounted for 62% of variance in APD(60) (P<0.001). Individually, memory effect had the greatest contribution to APD variability (R(2)=0.55, P<0.0001). CONCLUSIONS: In early human VF, short-term memory and APD/diastolic interval restitution explain most of the APD variability, with memory effects predominating. This suggests that in early human VF, short-term cardiac memory may provide a novel therapeutic target to modulate progression of VF in myopathic patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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".