Microfluidic platforms for monitoring cardiomyocyte electromechanical activity
Bibliographic record
Abstract
Cardiovascular diseases account for ~40% of global deaths annually. This situation has revealed the urgent need for the investigation and development of corresponding drugs for pathogenesis due to the complexity of research methods and detection techniques. An in vitro cardiomyocyte model is commonly used for cardiac drug screening and disease modeling since it can respond to microphysiological environmental variations through mechanoelectric feedback. Microfluidic platforms are capable of accurate fluid control and integration with analysis and detection techniques. Therefore, various microfluidic platforms (i.e., heart-on-a-chip) have been applied for the reconstruction of the physiological environment and detection of signals from cardiomyocytes. They have demonstrated advantages in mimicking the cardiovascular structure and function in vitro and in monitoring electromechanical signals. This review presents a summary of the methods and technologies used to monitor the contractility and electrophysiological signals of cardiomyocytes within microfluidic platforms. Then, applications in common cardiac drug screening and cardiovascular disease modeling are presented, followed by design strategies for enhancing physiology studies. Finally, we discuss prospects in the tissue engineering and sensing techniques of microfluidic platforms.
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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 | 0.001 |
| 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".