Elastomeric Polyesters in Cardiovascular Tissue Engineering and Organs-on-a-Chip
Bibliographic record
Abstract
Cardiovascular tissue constructs provide unique design requirements due to their functional responses to substrate mechanical properties and cyclic stretching behavior of cardiac tissue that requires the use of durable elastic materials. Given the diversity of polyester synthesis approaches, an opportunity exists to develop a new class of biocompatible, elastic, and immunomodulatory cardiovascular polymers. Furthermore, elastomeric polyester materials have the capability to provide tailored biomechanical synergy with native tissue and hence reduce inflammatory response in vivo and better support tissue maturation in vitro . In this review, we highlight underlying chemistry and design strategies of polyester elastomers optimized for cardiac tissue scaffolds. The major advantages of these materials such as their tunable elasticity, desirable biodegradation, and potential for incorporation of bioactive compounds are further expanded. Unique fabrication methods using polyester materials such as micromolding, 3D stamping, electrospinning, laser ablation, and 3D printing are discussed. Moreover, applications of these biomaterials in cardiovascular organ-on-a-chip devices and patches are analyzed. Finally, we outline unaddressed challenges in the field that need further study to enable the impactful translation of soft polyesters to clinical applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".