Bibliographic record
Abstract
Three-dimensional (3D) bioprinting is increasingly explored as a strategy for myocardial repair and regenerative medicine. Conventional 3D casting often yields heterogeneous cellularization, slow electromechanical maturation, and inadequate vascularization; by contrast, bioprinting places cells and biomaterials in predefined architectures to program alignment, stiffness, vascular pathways, and electrical coupling that better recapitulate native myocardium. This review focuses on cardiac-specific advances in 3D bioprinting. We compare major platforms (jetting, light-based, extrusion, and volumetric) and their trade-offs for cardiac applications; distill bioink design principles trending toward natural-synthetic hybrids, including conductive and shape-morphing components; and outline practical characterization readouts spanning rheology, print fidelity, swelling/degradation, and cardiac function. We also summarize cell sources and co-culture strategies. Applications surveyed include cardiac patches, engineered tissues, chambered constructs, and organoids. Finally, we discuss current limitations and potential future directions for 3D bioprinting cardiac tissues. Collectively, recent advances position 3D bioprinting to accelerate the realization of in vivo-like engineered heart tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".