Spatial Distribution of Fibrosis Governs Fibrillation Wave Dynamics in the Posterior Left Atrium During Heart Failure
Bibliographic record
Abstract
Heart failure (HF) commonly results in atrial fibrillation (AF) and fibrosis, but how the distribution of fibrosis impacts AF dynamics has not been studied. HF was induced in sheep by ventricular tachypacing (220 bpm, 6 to 7 weeks). Optical mapping (Di-4-ANEPPS, 300 frames/sec) of the posterior left atrial (PLA) endocardium was performed during sustained AF (burst pacing) in Langendorff-perfused HF (n=7, 4 micromol/L acetylcholine; n=3, no acetylcholine) and control (n=6) hearts. PLA breakthroughs were the most frequent activation pattern in both groups (72.0+/-4.6 and 90.2+/-2.7%, HF and control, respectively). However, unlike control, HF breakthroughs preferentially occurred at the PLAs periphery near the pulmonary vein ostia, and their beat-to-beat variability was greater than control (1.93+/-0.14 versus 1.47+/-0.07 changes/[beats/sec], respectively, P<0.05). On histological analysis (picrosirius red), the area of diffuse fibrosis was larger in HF (23.4+/-0.4%) than control (14.1+/-0.6%; P<0.001, n=4). Also the number and size of fibrous patches were significantly larger and their location was more peripheral in HF than control. Computer simulations using 2-dimensional human atrial models with structural and ionic remodeling as in HF demonstrated that changes in AF activation frequency and dynamics were controlled by the interaction of electrical waves with clusters of fibrotic patches of various sizes and individual pulmonary vein ostia. During AF in failing hearts, heterogeneous spatial distribution of fibrosis at the PLA governs AF dynamics and fractionation.
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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) | 0.000 | 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) | 0.000 | 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".