Comparison of Ca<sup>2+</sup>-handling properties of canine pulmonary vein and left atrial cardiomyocytes
Bibliographic record
Abstract
Cardiac tissue in the pulmonary vein sleeves plays an important role in clinical atrial fibrillation. Mechanisms leading to pulmonary vein activity in atrial fibrillation remain unclear. Indirect experimental evidence points to pulmonary vein Ca(2+) handling as a potential culprit, but there are no direct studies of pulmonary vein cardiomyocyte Ca(2+) handling in the literature. We used the Ca(2+)-sensitive dye indo-1 AM to study Ca(2+) handling in isolated canine pulmonary vein and left atrial myocytes. Results were obtained at 35 degrees C and room temperature in cells from control dogs and in cardiomyocytes from dogs subjected to 7-day rapid atrial pacing. We found that basic Ca(2+)-transient properties (amplitude: 186 +/- 28 vs. 216 +/- 25 nM; stimulus to half-decay time: 192 +/- 9 vs. 192 +/- 9 ms; atria vs. pulmonary vein, respectively, at 1 Hz), beat-to-beat regularity, propensity to alternans, beta-adrenergic response (amplitude increase at 0.4 Hz: 96 +/- 52 vs. 129 +/- 61%), number of spontaneous Ca(2+)-transient events after Ca(2+) loading (in normal Tyrode: 0.9 +/- 0.2 vs. 1.3 +/- 0.2; with 1 microM isoproterenol: 7.6 +/- 0.3 vs. 5.1 +/- 1.8 events/min), and caffeine-induced Ca(2+)-transient amplitudes were not significantly different between atrial and pulmonary vein cardiomyocytes. In an arrhythmia-promoting model (dogs subjected to 7-day atrial tachypacing), Ca(2+)-transient amplitude and kinetics were the same in cells from both pulmonary veins and atrium. In conclusion, the similar Ca(2+)-handling properties of canine pulmonary vein and left atrial cardiomyocytes that we observed do not support the hypothesis that intrinsic Ca(2+)-handling differences account for the role of pulmonary veins in atrial fibrillation.
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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".