Toward In Vitro Vascular Wall Models: Digital Light Processing of Acrylate‐Endcapped Urethane‐Based Polymers into Tubular Constructs
Bibliographic record
Abstract
Abstract Digital light processing (DLP) is emerging as a powerful tool for fabricating tissue engineering (TE) scaffolds, particularly for vascular TE and the development of representative in vitro vascular wall models. For the latter, biomaterials should mimick the biological and mechanical properties of native blood vessels. To fabricate tubular constructs, the DLP‐printing process is optimized by exploiting acrylate‐endcapped urethane‐based (AUP) polymers as the presence of the acrylate end groups render them suitable for DLP printing and desirable mechanical properties arise from the urethane segments. Four AUP variants are synthesized, exploring polyethylene glycol (PEG) and polypropylene glycol (PPG) backbones with varying acrylate functionalities (di‐acrylate versus hexa‐acrylate), namely UPEG2, UPEG6, UPPG2, and UPPG6. Tubular constructs with precise dimensions and morphology are fabricated. PPG‐based AUP polymers exhibit superior computer‐aided design/manufacturing (CAD/CAM) mimicry compared to PEG‐based derivatives. Construct characterization reveals tunable mechanical properties, with elastic moduli ranging from 45 to 259 kPa, reaching values of the human blood vessels. In particular, UPPG6 shows a two‐fold higher elastic modulus compared to UPPG2. All materials show excellent biocompatibility. Additionally, surface modification with gelatin‐methacryloyl (GELMA) significantly enhances the cytocompatibility of UPPG2 scaffolds. This study demonstrates the feasibility of fabricating tubular constructs with tunable properties using DLP and AUP polymers.
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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".