A Novel Single-Step Self-Assembly Approach for the Fabrication of Tissue-Engineered Vascular Constructs
Bibliographic record
Abstract
There is a clinical need for a functional tissue-engineered blood vessel because small-caliber arterial graft (<5 mm) applications are limited by the availability of suitable autologous vessels and suboptimal performances of synthetic grafts. This study presents an analysis of the mechanical properties of tissue-engineered vascular constructs produced using a novel single-step self-assembly approach. Briefly, the tissue-engineered vascular media were produced by culturing smooth muscle cell in the presence of sodium l-ascorbate until the formation of a cohesive tissue sheet. This sheet was then rolled around a tubular support to create a media construct. Alternatively, the tissue-engineered vascular adventitia was produced by rolling a tissue sheet obtained from dermal fibroblasts or saphenous vein fibroblasts. The standard self-assembly approach to obtain the two-layer tissue-engineered vascular constructs comprising both media and adventitia constructs consists of two steps in which tissue-engineered vascular media were first rolled on a tubular support and a tissue-engineered vascular adventitia was then rolled on top of the first layer. This study reports an original alternative method for assembling tissue-engineered vascular constructs comprising both media and an adventitia in a single step by rolling a continuous tissue sheet containing both cell types contiguously. This tissue sheet was produced by growing smooth muscle cells alongside fibroblasts (saphenous vein fibroblasts or dermal fibroblasts) in the same culture dish separated by a spacer, which is removed later in the culture period. The mechanical strength assessed by uniaxial tensile testing, burst pressure measurements, and viscoelastic behavior evaluated by stepwise stress relaxation tests reveals that the new single-step fabrication method significantly improves the mechanical properties of tissue-engineered vascular construct for both ultimate tensile strength and all the viscoelastic moduli.
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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) | 0.001 | 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".