Microengineered platforms for characterizing the contractile function of in vitro cardiac models
Bibliographic record
Abstract
Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology, disease modeling and drug cardiotoxicity as well as for therapeutic discovery. Challenges still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue models. Changes to contractile strength (contractility) and beating regularity (rhythm) are particularly important to generate accurate, predictive models. Developing new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies, drug-induced inotropic responses, and the mechanisms of cardiac diseases. In this review, we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models, including single cardiomyocytes, 2D monolayers of cardiomyocytes, and 3D cardiac tissues. The characteristics and performance of current platforms are reviewed in terms of sensing principles, measured parameters, performance, cell sources, cell/tissue model configurations, advantages, and limitations. In addition, we highlight applications of these platforms and relevant discoveries in fundamental investigations, drug testing, and disease modeling. Furthermore, challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".