A Software Architecture to Mimic a Ventricular Tachycardia in Intact Murine Hearts by Means of an All-Optical Platform
Bibliographic record
Abstract
Optogenetics is an emerging method that uses light to manipulate electrical activity in excitable cells exploiting the interaction between light and light-sensitive depolarizing ion channels, such as channelrhodopsin-2 (ChR2). Initially used in the neuroscience, it has been adopted in cardiac research where the expression of ChR2 in cardiac preparations allows optical pacing, resynchronization and defibrillation. Recently, optogenetics has been leveraged to manipulate cardiac electrical activity in the intact heart in real-time. This new approach was applied to simulate a re-entrant circuit across the ventricle. In this technical note, we describe the development and the implementation of a new software package for real-time optogenetic intervention. The package consists of a single LabVIEW program that simultaneously captures images at very high frame rates and delivers precisely timed optogenetic stimuli based on the content of the images. The software implementation guarantees closed-loop optical manipulation at high temporal resolution by processing the raw data in workstation memory. We demonstrate that this strategy allows the simulation of a ventricular tachycardia with high stability and with a negligible loss of data with a temporal resolution of up to 1 ms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".