Design of a Collagen/Silk Mechano-Compatible Composite Scaffold for the Vascular Tissue Engineering: Focus on Compliance
Bibliographic record
Abstract
One of the merging methods to produce tissue-engineered vascular substitutes is to process scaffolds to direct the regeneration of vascular tissues. Collagen, as one of the main protein in the vascular extracellular matrix, is one of biopolymers that exhibits a major potential for scaffold technology. However, gels made from reconstituted collagen generally exhibit poor mechanical properties and limited manipulability. Therefore, adding a reinforcement to the scaffold to make the structure resist to the physiological constraints applied during the regeneration represents a valid alternative. Silk fibroin is an interesting reinforcing candidate being a mechanically strong natural fibre, susceptible to proteolytic degradation in vivo and showing acceptable biological performances. Therefore, the aim of this study was to develop a model of a composite scaffold obtained by controlling the filament geometry winding of silk fibroin in the collagen gel. A finite element model taking into account the orthotropic elasticity of arteries has been combined with classic laminate theory applied to the filament winding of a tubular vessel. The design of the small structure susceptible to scaffold the vascular tissue regeneration was optimised by mean of an evolutive algorithm with the imperative to mimic the experimentally measured mechanical properties (compliance) of a native artery.
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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".