Designing Gelatin Methacryloyl (GelMA)‐Based Bioinks for Visible Light Stereolithographic 3D Biofabrication
Bibliographic record
Abstract
Bioinks play a key role in determining the capability of the biofabricatoin processes and the resolution of the printed constructs. Excellent biocompatibility, tunable physical properties, and ease of chemical or biological modifications of gelatin methacryloyl (GelMA) have made it an attractive choice as bioinks for biomanufacturing of various tissues or organs. However, the current preparation methods for GelMA-based bioinks lack the ability to tailor their physical properties for desired bioprinting methods. Inherently, GelMA prepolymer solution exhibits a fast sol-gel transition at room temperature, which is a hurdle for its use in stereolithography (SLA) bioprinting. Here, synthesis parameters are optimized such as solvents, pH, and reaction time to develop GelMA bioinks which have a slow sol-gel transition at room temperature and visible light crosslinkable functions. A total of eight GelMA combinations are identified as suitable for digital light processing (DLP)-based SLA (DLP-SLA) bioprinting through systematic characterizations of their physical and rheological properties. Out of various types of GelMA, those synthesized in reverse osmosis (RO) purified water (referred to as RO-GelMA) are regarded as most suitable to achieve high DLP-SLA printing resolution. RO-GelMA-based bioinks are also found to be biocompatible showing high survival rates of encapsulated cells in the photocrosslinked gels. Additionally, the astrocytes and fibroblasts are observed to grow and integrate well within the bioprinted constructs. The bioink's superior physical and photocrosslinking properties offer pathways of tuning the scaffold microenvironment and highlight the applicability of developed GelMA bioinks in various tissue engineering and regenerative medicine 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) | 0.000 | 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".