Emerging Biofabrication Strategies for Engineering Complex Tissue Constructs
Bibliographic record
Abstract
The demand for organ transplantation and repair, coupled with a shortage of available donors, poses an urgent clinical need for the development of innovative treatment strategies for long-term repair and regeneration of injured or diseased tissues and organs. Bioengineering organs, by growing patient-derived cells in biomaterial scaffolds in the presence of pertinent physicochemical signals, provides a promising solution to meet this demand. However, recapitulating the structural and cytoarchitectural complexities of native tissues in vitro remains a significant challenge to be addressed. Through tremendous efforts over the past decade, several innovative biofabrication strategies have been developed to overcome these challenges. This review highlights recent work on emerging three-dimensional bioprinting and textile techniques, compares the advantages and shortcomings of these approaches, outlines the use of common biomaterials and advanced hybrid scaffolds, and describes several design considerations including the structural, physical, biological, and economical parameters that are crucial for the fabrication of functional, complex, engineered tissues. Finally, the applications of these biofabrication strategies in neural, skin, connective, and muscle tissue engineering are explored.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".