In‐Foam Bioprinting: An Embedded Bioprinting Technique with Self‐Removable Support Bath
Bibliographic record
Abstract
The emergence of embedded three-dimensional (3D) bioprinting has revolutionized the biofabrication of free-form constructs out of low-viscosity and slow-crosslinking hydrogels. Using gel-based support baths has limitations including lack of proper oxygenation and nutrition and complications with bath removal. Herein, a novel-embedded 3D bioprinting technique is developed with an albumin foam support bath as a promising substitute. The proposed technique, in-foam bioprinting, offers excellent printability and convenience in bath removal while providing cells with easy access to oxygen and nutrients. The foam-based support bath is characterized through foam stability and rheological tests. The bubble size in the foam is measured to study the change in the structure of the bath due to the coalescence of the bubbles over time. Free-form structures are successfully 3D printed with thermoresponsive chitosan-based bioinks to demonstrate the capability of the in-foam bioprinting technique. The viability of bioprinted fibroblast L929 cells is studied over a seven-day period, showing high cell viability of over 97%, which is attributed to the abundance of oxygen and nutrition in the foam support bath. Importantly, in-foam bioprinting is beneficial for biofabricating large samples with a long printing time without jeopardizing cell viability.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".