Latest Advances in 3D Bioprinting of Cardiac Tissues
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are known as the major cause of death worldwide. In spite of tremendous advancements in medical therapy, the gold standard for CVD treatment is still transplantation. Tissue engineering, on the other hand, has emerged as a pioneering field of study with promising results in tissue regeneration using cells, biological cues, and scaffolds. Three-dimensional (3D) bioprinting is a rapidly growing technique in tissue engineering because of its ability to create complex scaffold structures, encapsulate cells, and perform these tasks with precision. More recently, 3D bioprinting has made its debut in cardiac tissue engineering, and scientists are investigating this technique for development of new strategies for cardiac tissue regeneration. In this review, the fundamentals of cardiac tissue biology, available 3D bioprinting techniques and bioinks, and cells implemented for cardiac regeneration are briefly summarized and presented. Afterwards, the pioneering and state-of-the-art works that have utilized 3D bioprinting for cardiac tissue engineering are thoroughly reviewed. Finally, regulatory pathways and their contemporary limitations and challenges for clinical translation are discussed.
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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 | 0.002 | 0.001 |
| 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) | 0.003 | 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".