3D Bioprinted Cardiac Tissues and Devices for Tissue Maturation
Bibliographic record
Abstract
Cardiovascular diseases are the leading cause of mortality worldwide. Given the limited endogenous regenerative capabilities of cardiac tissue, patient-specific anatomy, challenges in treatment options, and shortage of donor tissues for transplantation, there is an urgent need for novel approaches in cardiac tissue repair. 3D bioprinting is a technology based on additive manufacturing which allows for the design of precisely controlled and spatially organized structures, which could possibly lead to solutions in cardiac tissue repair. In this review, we describe the basic morphological and physiological specifics of the heart and cardiac tissues and introduce the readers to the fundamental principles underlying 3D printing technology and some of the materials/approaches which have been used to date for cardiac repair. By summarizing recent progress in 3D printing of cardiac tissue and valves with respect to the key features of cardiovascular tissue (such as contractility, conductivity, and vascularization), we highlight how 3D printing can facilitate surgical planning and provide custom-fit implants and properties that match those from the native heart. Finally, we also discuss the suitability of this technology in the design and fabrication of custom-made devices intended for the maturation of the cardiac tissue, a process that has been shown to increase the viability of implants. Altogether this review shows that 3D printing and bioprinting are versatile and highly modulative technologies with wide applications in cardiac regeneration and beyond.
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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 | 0.002 |
| 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) | 0.004 | 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".