Drawing Inspiration from Developmental Biology for Cardiac Tissue Engineers
Bibliographic record
Abstract
A sound understanding of developmental biology is part of the foundation of effective stem cell-derived tissue engineering. Here, the key concepts of cardiac development that are successfully applied in a bioinspired approach to growing engineered cardiac tissues, are reviewed. The native cardiac milieu is studied extensively from embryonic to adult phenotypes, as it provides a resource of factors, mechanisms, and protocols to consider when working toward establishing living tissues in vitro. It begins with the various cell types that constitute the cardiac tissue. It is discussed how myocytes interact with other cell types and their microenvironment and how they change over time from the embryonic to the adult states, with a view on how such changes affect the tissue function and may be used in engineered tissue models. Key embryonic signaling pathways that have been leveraged in the design of culture media and differentiation protocols are presented. The cellular microenvironment, from extracellular matrix chemical and physical properties, to the dynamic mechanical and electrical forces that are exerted on tissues is explored. It is shown that how such microenvironmental factors can inform the design of biomaterials, scaffolds, stimulation bioreactors, and maturation readouts, and suggest considerations for ongoing biomimetic advancement of engineered cardiac tissues and regeneration strategies for the future.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".