Cardiac Tissue Engineering: A Journey from Scaffold Fabrication to In Vitro Characterization
Bibliographic record
Abstract
Cardiac tissue engineering has been rapidly evolving with diverse applications, ranging from the repair of fibrotic tissue caused by "adverse remodeling," to the replacement of specific segments of heart tissue, and ultimately to the creation of a whole heart. The repair or replacement of cardiac tissue often involves the development of tissue scaffolds or constructs and the subsequent assessment of their performance and functionality. For this, the design and/or selection of biomaterials, and cell types, scaffold fabrication, and in vitro characterizations are the first starting points, yet critical, to ensure success in subsequent implantation in vivo. This highlights the importance of scaffold fabrication and in vitro experiments/characterization with protocols for cardiac tissue engineering. Yet, a comprehensive and critical review of these has not been established and documented. As inspired, herein, the latest development and advances in scaffold fabrication and in vitro characterization for cardiac tissue engineering are critically reviewed, with focus on biomaterials, cell types, additive manufacturing techniques for scaffold fabrication, and common in vitro characterization techniques or methods. This article would be of benefit to the ones who are working on cardiac tissue engineering by providing insights into the scaffold fabrication and in vitro investigations.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".