Practical Aspects of Cardiac Tissue Engineering With Electrical Stimulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heart disease is a leading cause of death in western society. Despite the success of heart transplantation, a chronic shortage of donor organs, along with the associated immunological complications of this approach, demands that alternative treatments be found. One such option is to repair, rather than replace, the heart with engineered cardiac tissue. Multiple studies have shown that to attain functional tissue, assembly signaling cues must be recapitulated in vitro. In their native environment, cardiomyocytes are directed to beat in synchrony by propagation of pacing current through the tissue. Recently, we have shown that electrical stimulation directs neonatal cardiomyocytes to assemble into native-like tissue in vitro. This chapter provides detailed methods we have employed in taking this "biomimetic" approach. After an initial discussion on how electric field stimulation can influence cell behavior, we examine the practical aspects of cardiac tissue engineering with electrical stimulation, such as electrode selection and cell seeding protocols, and conclude with what we feel are the remaining challenges to be overcome.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it