Extrusion bioprinting of soft materials: An emerging technique for biological model fabrication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it