GelMA-Carbopol bioinks with low total solids content for high-fidelity extrusion 3D bioprinting of dynamic tissue biomimetics
Bibliographic record
Abstract
Extrusion 3D bioprinting is a technology that allows the deposition of cells within hydrogels in well-defined spatial patterns, facilitating the fabrication of tissue biomimetics. Gelatin methacrylate (GelMA)-based hydrogels are biocompatible, biodegradable, and promote cell adhesion and proliferation, which makes them a common choice to formulate bioinks for extrusion 3D bioprinting. However, due to the low viscosity of GelMA-based inks, it is very difficult to achieve high printability and shape fidelity of complex constructs at physiological temperatures. To overcome this issue, high concentrations of GelMA or rheological modifiers and low temperatures are typically used, with both approaches negatively impacting the printing process and cell viability. The present work develops new GelMA-based bioinks using low concentrations of Carbopol (CBP) (0.1-0.5 wt%) as a rheology modifier. GelMA-CBP inks exhibit excellent rheological properties and outstanding printability at physiological temperatures (37 ºC) and at low GelMA concentrations (1–7 wt%). Complex constructs, including hollow structures with overhangs, were printed at 37 ºC with high shape fidelity using 0.5 wt% CBP. 3T3 fibroblasts embedded within structures 3D printed using GelMA-CBP bioinks exhibited > 90% cell viability for up to 14 days. GelMA-CBP bioinks also enabled the fabrication of a stretchable lung tissue model incorporating primary human lung fibroblasts to study fibroblast-to-myofibroblast transition. This work solves the three key issues of GelMA-based bioinks for extrusion 3D bioprinting, by allowing the printing of complex constructs using i) low concentrations of GelMA, ii) low concentrations of a rheological modifier, 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 | 0.001 |
| 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".