Practical Aspects of Cardiac Tissue Engineering With Electrical Stimulation
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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How this classification was reachedexpand
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".