Elastin-like recombinamers in collagen-based tubular gels improve cell-mediated remodeling and viscoelastic properties
Bibliographic record
Abstract
Natural polymers are commonly used as scaffolds for vascular tissue engineering. The recognized biological properties of this class of materials are often counterbalanced by their low mechanical performance. In this work, recombinant elastin-like polypeptides (or elastin-like recombinamers, ELRs) were mixed with collagen gel and cells to produce cellularized tubular constructs in an attempt to recapitulate the mechanical behavior of the vascular extracellular matrix (ECM). The presence of the elastic protein influenced cell-mediated remodeling evaluated in terms of construct compaction, cell proliferation and ECM (collagen, elastin and fibrillin-1) gene expression. The partial substitution of collagen with ELR and the observed differences in cellular behavior synergistically contributed to the superior viscoelastic properties of the constructs containing 30% ELR and 70% of collagen (in mass). This led to the improvement of 40% in the initial elastic modulus, 50% in the equilibrium elastic modulus, and 37% in the tensile strength at break without compromising the strain at break, when compared to a pure collagen scaffold. Suggestions for future research include modifications in the crosslinking technology, ELR composition, polymer concentration, cell seeding density and dynamic stimulation, which have the potential to further improve the mechanical performance of the constructs towards physiological values.
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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".