Visible light-based stereolithography bioprinting of cell-adhesive gelatin hydrogels
Bibliographic record
Abstract
Stereolithography-based bioprinting offers advantages in resolution and rapid printing time, and thus has received major attention in recent years. However, traditional stereolithography-based bioprinting utilizes an ultraviolet light which may cause mutagenesis and carcinogenesis of cells. In this paper, we present a new visible light crosslinkable bioink that is based on cell-adhesive gelatin. The bioink consists of Eosin Y (EY) based photoinitiator and gelatin methacrylate (GelMA) pre-polymer solution. We examined the feasibility of using visible light from a commercial beam projector to pattern the EY-GelMA bioink. We measured the absorbance of bioink to characterize its sensitivity to visible light and performed bioprinting to test its ability to promote cell adhesion. It is found that the EY-GelMA bioink has an absorption peak at roughly 522 nm, and that it can be successfully crosslinked by visible light from the commercial projector. We performed the bioprinting experiments and visualized the cell morphology using nuclei/F-actin staining. Experimental results show that most of the cells attached to the EY-GelMA bioink after five days' culturing. Ultimately, the EY-GelMA bioink can support both visible light crosslinking and cell adhesion, offering great potential in bioprinting and tissue engineering.
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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) | 0.001 | 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".