Rapid Biofabrication of Printable Dense Collagen Bioinks of Tunable Properties
Bibliographic record
Abstract
Abstract Recent convergence of the 3D printing of tissue‐like bioinks and regenerative medicine offers promise in the high‐throughput engineering of in vitro tissue models and organoids for drug screening and discovery research, and of potentially implantable neo‐tissues with tailored structural, biological, and mechanical properties. However, the current printing approaches are not compatible with collagen, the native scaffolding material. Herein, a unique biofabrication approach that uses automated gel aspiration‐ejection (GAE) is reported to potentially overcome these challenges. Automated‐GAE generates highly defined, aligned, dense collagen gel bioinks of various geometries (i.e., cylindrical, quadrangular, and tubular), dimensions, as well as tunable microstructural and mechanical properties that modulate seeded cellular responses. By densifying initial naturally derived reconstituted collagen hydrogels incorporating cells, automated‐GAE generates mini‐tissue building blocks with tailored protein fibril density and alignment, as well as cell loading, density and orientation according to the intended use. Surprisingly, a simple mathematical relationship defining the bioink compaction factor is found to be highly effective in predicting the initial and temporal properties of the bioinks in culture. Therefore, automated‐GAE will potentially also enable a fourth dimension to biofabrication, where cell–cell communications and cell‐extracellular matrix interactions as a function of time in culture can be predicted and modeled.
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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 | 0.000 | 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 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".