Cardiac action potential wavefront tracking using optical mapping
Bibliographic record
Abstract
A wealth of knowledge is available about the effect of diabetes on the heart but very little has been done to quantify the conduction velocity of the diabetic heart. This study intends to develop a technique for tracking the cardiac wavefront across the heart in order to achieve the total activation time as well as the conduction velocity of the heart at any point during its activation, to compare the newly determined activation times with previously determined activation times, and to also compute the average conduction velocity of the heart from diabetic and control rats. The technique developed for tracking the action potential wavefront across the heart extracts the wavefront and provides the activation time as well as the conduction velocity - both instantaneous and average - successfully. The method reproduces previously measured activation times well, with a correlation of R(2) = 0.875, which suggests that this technique is reliable and that its determination of conduction velocity will allow for the examination of healthy and diseased hearts using a new criterion. In addition, the method for determining the conduction velocity of the heart allows direct comparison of the baseline conduction velocity in control and diabetic hearts. The results of this comparison indicated that conduction velocity in the diabetic hearts is slower (0.47 +/- 0.02 m/s) than in control hearts (0.55 +/- 0.02 m/s) (p = 0.001).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".