Optogenetic manipulation of cardiac electrical dynamics using sub-threshold illumination: dissecting the role of cardiac alternans in terminating rapid rhythms
Bibliographic record
Abstract
cycling. Experimental methods for manipulating AP electrical dynamics commonly use ion channel inhibitors that lack spatial and temporal specificity. In this work, we propose an approach based on optogenetics to manipulate cardiac electrical activity employing a light-modulated depolarizing current with intensities that are too low to elicit APs (sub-threshold illumination), but are sufficient to fine-tune AP electrical dynamics. We investigated the effects of sub-threshold illumination in isolated cardiomyocytes and whole hearts by using transgenic mice constitutively expressing a light-gated ion channel (channelrhodopsin-2, ChR2). We find that ChR2-mediated depolarizing current prolongs APs and reduces conduction velocity (CV) in a space-selective and reversible manner. Sub-threshold manipulation also affects the dynamics of cardiac electrical activity, increasing the magnitude of cardiac alternans. We used an optical system that uses real-time feedback control to generate re-entrant circuits with user-defined cycle lengths to explore the role of cardiac alternans in spontaneous termination of ventricular tachycardias (VTs). We demonstrate that VT stability significantly decreases during sub-threshold illumination primarily due to an increase in the amplitude of electrical oscillations, which implies that cardiac alternans may be beneficial in the context of self-termination of VT.
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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 | 0.001 |
| 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".