Biaxial biomechanical properties of self-assembly tissue-engineered blood vessels
Bibliographic record
Abstract
Along with insights into the potential for graft success, knowledge of biomechanical properties of small diameter tissue-engineered blood vessel (TEBV) will enable designers to tailor the vessels' mechanical response to closer resemble that of native tissue. Composed of two layers that closely mimic the native media and adventitia, a tissue-engineered vascular adventitia (TEVA) is wrapped around a tissue-engineered vascular media (TEVM) to produce a self-assembled tissue-engineered media/adventia (TEVMA). The current study was undertaken to characterize the biaxial biomechanical properties of TEVM, TEVA and TEVMA under physiological pressures as well as characterize the stress-free reference configuration. It was shown that the TEVA had the greatest compliance over the physiological loading range while the TEVM had the lowest compliance. As expected, compliance of the SA-TEBV fell in between with an average compliance of 2.73 MPa(-1). Data were used to identify material parameters for a microstructurally motivated constitutive model. Identified material parameters for the TEVA and TEVM provided a good fit to experimental data with an average coefficient of determination of 0.918 and 0.868, respectively. These material parameters were used to develop a two-layer predictive model for the response of a TEVMA which fit well with experimental data.
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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 | 0.001 |
| 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".