Biocasting of an elastin-like recombinamer and collagen bi-layered model of the tunica adventitia and external elastic lamina of the vascular wall
Bibliographic record
Abstract
The development of techniques for fabricating vascular wall models will foster the development of preventive and therapeutic therapies for treating cardiovascular diseases. However, the physical and biological complexity of vascular tissue represents a major challenge, especially for the design and the production of off-the-shelf biomimetic vascular replicas. Herein, we report the development of a biocasting technique that can be used to replicate the tunica adventitia and the external elastic lamina of the vascular wall. Type I collagen embedded with neonatal human dermal fibroblast (HDFn) and an elastic click cross-linkable, cell-adhesive and protease-sensitive elastin-like recombinamer (ELR) hydrogel were investigated as readily accessible and tunable layers to the envisaged model. Mechanical characterization confirmed that the viscous and elastic attributes predominated in the collagen and ELR layers, respectively. In vitro maturation confirmed that the collagen and ELR provided a favorable environment for the HDFn viability, while histology revealed the wavy and homogenous morphology of the ELR and collagen layer respectively, the cell polarization towards the cell-attachment sites encoded on the ELR, and the enhanced expression of glycosaminoglycan-rich extracellular matrix and differentiation of the embedded HDFn into myofibroblasts. As a complementary assay, 30% by weight of the collagen layer was substituted with the ELR. This model proved the possibility to tune the composition and confirm the versatile character of the technology developed, while revealing no significant differences with respect to the original construct. On-demand modification of the model dimensions, number and composition of the layers, as well as the type and density of the seeded cells, can be further envisioned, thus suggesting that this bi-layered model may be a promising platform for the fabrication of biomimetic vascular wall models.
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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) | 0.000 | 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 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".