A brief review of extrusion‐based tissue scaffold bio‐printing
Bibliographic record
Abstract
Extrusion-based bio-printing has great potential as a technique for manipulating biomaterials and living cells to create three-dimensional (3D) scaffolds for damaged tissue repair and function restoration. Over the last two decades, advances in both engineering techniques and life sciences have evolved extrusion-based bio-printing from a simple technique to one able to create diverse tissue scaffolds from a wide range of biomaterials and cell types. However, the complexities associated with synthesis of materials for bio-printing and manipulation of multiple materials and cells in bio-printing pose many challenges for scaffold fabrication. This paper presents an overview of extrusion-based bio-printing for scaffold fabrication, focusing on the prior-printing considerations (such as scaffold design and materials/cell synthesis), working principles, comparison to other techniques, and to-date achievements. This paper also briefly reviews the recent development of strategies with regard to hydrogel synthesis, multi-materials/cells manipulation, and process-induced cell damage in extrusion-based bio-printing. The key issue and challenges for extrusion-based bio-printing are also identified and discussed along with recommendations for future, aimed at developing novel biomaterials and bio-printing systems, creating patterned vascular networks within scaffolds, and preserving the cell viability and functions in scaffold bio-printing. The address of these challenges will significantly enhance the capability of extrusion-based bio-printing.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".