A simple and high-resolution stereolithography-based 3D bioprinting system using visible light crosslinkable bioinks
Bibliographic record
Abstract
Bioprinting is a rapidly developing technique for biofabrication. Because of its high resolution and the ability to print living cells, bioprinting has been widely used in artificial tissue and organ generation as well as microscale living cell deposition. In this paper, we present a low-cost stereolithography-based bioprinting system that uses visible light crosslinkable bioinks. This low-cost stereolithography system was built around a commercial projector with a simple water filter to prevent harmful infrared radiation from the projector. The visible light crosslinking was achieved by using a mixture of polyethylene glycol diacrylate (PEGDA) and gelatin methacrylate (GelMA) hydrogel with eosin Y based photoinitiator. Three different concentrations of hydrogel mixtures (10% PEG, 5% PEG + 5% GelMA, and 2.5% PEG + 7.5% GelMA, all w/v) were studied with the presented systems. The mechanical properties and microstructure of the developed bioink were measured and discussed in detail. Several cell-free hydrogel patterns were generated to demonstrate the resolution of the solution. Experimental results with NIH 3T3 fibroblast cells show that this system can produce a highly vertical 3D structure with 50 μm resolution and 85% cell viability for at least five days. The developed system provides a low-cost visible light stereolithography solution and has the potential to be widely used in tissue engineering and bioengineering for microscale cell patterning.
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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".