An optimized GelMA bioink formulation for high-fidelity extrusion 3D bioprinting of dynamic tissue biomimetics
Bibliographic record
Abstract
Extrusion 3D bioprinting allows depositing cells within hydrogels in well-defined spatial patterns, facilitating the creation of tissue biomimetics. Gelatin methacrylate (GelMA) hydrogels are biocompatible, biodegradable, and promote cell adhesion, making them a common choice to formulate bioinks for bioprinting. However, the low viscosity of GelMA-based inks makes it challenging to print complex constructs at physiological temperatures. Typically, this is overcome by using high concentrations (≥ 10%) of GelMA and rheological modifiers (≥ 1%), as well as low temperatures, which negatively impact the printing process and cell metabolism and viability. This work develops high performance GelMA bioinks using Carbopol (CBP) as a rheology modifier. Inks containing low GelMA and CBP concentrations exhibit excellent printability at physiological temperatures. Complex constructs, including hollow structures with overhangs, were 3D printed with high shape fidelity. The inks show excellent cytocompatibility toward A549 epithelial cells, HUVECs, 3T3 fibroblasts, and primary human lung fibroblasts (HLF). 3T3 fibroblasts and HLF embedded in bioprinted structures exhibited outstanding viability and proliferated over 14 days of continuous culture. As a direct application, GelMA-CBP inks were used to fabricate a stretchable lung tissue model incorporating primary human lung fibroblasts, which was used to study fibroblast-to-myofibroblast transition. This work improves key issues of GelMA bioinks by enabling extrusion 3D bioprinting using i) low GelMA concentrations, ii) low rheological modifier concentrations, and iii) physiological temperatures. Our work lays the foundation for using 3D printable GelMA materials in tissue engineering, regenerative medicine, and implantable medical device applications.
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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) | 0.001 | 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".