Embedded 3D Printing of Ultrasound‐Compatible Arterial Phantoms with Biomimetic Elasticity
Bibliographic record
Abstract
Abstract Here, a class of ink materials and an embedded 3D printing strategy for the fabrication of macroscale elastic tissue‐mimetic constructs are presented. Novel inks composed of 10 wt% glycidyl methacrylated poly(vinyl alcohol) (PVAGMA) with different degrees of substitution (DOS) and 4 wt% cellulose nanocrystals (CNCs) (PVAGMA(DOS)/CNC) with strong shear‐thinning property are developed. By controlling the DOS of PVAGMA, hydrogels with desired mechanical stiffness mimicking that of healthy and diseased artery are designed to construct vascular phantoms. Cyclic tensile tests and in vitro hemodynamic study performed on phantoms demonstrate their excellent mechanical stability and low hysteresis. The burst pressure is found to be about 273 mmHg for PVAGMA2/CNC and 102 mmHg for PVAGMA4/CNC and the printed vascular phantoms are able to withstand 863K cycles over 10 days. Further, their suitability for ultrasonic imaging is demonstrated. Via B‐mode imaging, it is found that the vessel strains under a pulsatile flow are 11.7 ± 1.0% and 6.5 ± 1.5% for PVAGMA2/CNC and PVAGMA4/CNC vessels, respectively, perfectly matching the behaviors of healthy and atherosclerotic arteries. It exemplifies that the ink materials and printing strategy can have broad applications in biomedical research, especially for the fabrication of complex elastic tissue phantoms.
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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".