3D Printing of Vascular Tubes Using Bioelastomer Prepolymers by Freeform Reversible Embedding
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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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 | 0.001 | 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".