Extrusion bioprinting of soft materials: An emerging technique for biological model fabrication
Bibliographic record
Abstract
Bioprinting has attracted increasing attention in the tissue engineering field and has been touted to potentially become the leading technology to fabricate, and regenerate, tissues and organs. Bioprinting is derived from well-known additive manufacturing (AM) technology, which features layered deposition of materials into complex three-dimensional geometries that are difficult to fabricate using conventional manufacturing methods. Unlike the conventional thermoplastics used in desktop, AM bioprinting uses cell-laden hydrogel materials, also known as bioinks, to construct complex living biological model systems. Inkjet, stereolithography, laser-induced forward transfer, and extrusion are the four main methods in bioprinting, with extrusion being the most commonly used. In extrusion-based bioprinting, soft materials are loaded into the cartridges and extruded from the nozzle via pneumatic or mechanical actuation. Multiple materials can be printed into the same structure resulting in heterogeneous models. In this focused review, we first review the different methods to describe the physical mechanisms of the extrusion process, followed by the commonly employed bioprintable soft materials with their mechanical and biochemical properties and finally reviewing the up-to-date heterogeneous in vitro models afforded via bioprinting.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".