Advances in cardiac tissue engineering and heart‐on‐a‐chip
Bibliographic record
Abstract
Recent advances in both cardiac tissue engineering and hearts-on-a-chip are grounded in new biomaterial development as well as the employment of innovative fabrication techniques that enable precise control of the mechanical, electrical, and structural properties of the cardiac tissues being modelled. The elongated structure of cardiomyocytes requires tuning of substrate properties and application of biophysical stimuli to drive its mature phenotype. Landmark advances have already been achieved with induced pluripotent stem cell-derived cardiac patches that advanced to human testing. Heart-on-a-chip platforms are now commonly used by a number of pharmaceutical and biotechnology companies. Here, we provide an overview of cardiac physiology in order to better define the requirements for functional tissue recapitulation. We then discuss the biomaterials most commonly used in both cardiac tissue engineering and heart-on-a-chip, followed by the discussion of recent representative studies in both fields. We outline significant challenges common to both fields, specifically: scalable tissue fabrication and platform standardization, improving cellular fidelity through effective tissue vascularization, achieving adult tissue maturation, and ultimately developing cryopreservation protocols so that the tissues are available off the shelf.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".