3D Printing of Vascular Tubes Using Bioelastomer Prepolymers by Freeform Reversible Embedding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bioelastomers have been extensively used in tissue engineering applications because of favorable mechanical stability, tunable properties, and chemical versatility. As these materials generally possess low elastic modulus and relatively long gelation time, it is challenging to 3D print them using traditional techniques. Instead, the field of 3D printing has focused preferentially on hydrogels and rigid polyester materials. To develop a versatile approach for 3D printing of elastomers, we used freeform reversible embedding of suspended prepolymers. A family of novel fast photocrosslinakble bioelastomer prepolymers were synthesized from dimethyl itaconate, 1,8-octanediol, and triethyl citrate. Tensile testing confirmed their elastic properties with Young's moduli in the range of 11-53 kPa. These materials supported cultivation of viable cells and enabled adhesion and proliferation of human umbilical vein endothelial cells. Tubular structures were created by embedding the 3D printed microtubes within a secondary hydrogel that served as a temporary support. Upon photocrosslinking and porogen leaching, the polymers were permeable to small molecules (TRITC-dextran). The polymer microtubes were assembled on the 96-well plates custom made by hot-embossing, as a tool to connect multiple organs-on-a-chip. The endothelialization of the tubes was performed to confirm that these microtubes can be utilized as vascular tubes to support parenchymal tissues seeded on them.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it