The mechanical characterization of blood vessels and their substitutes in the continuous quest for physiological-relevant performances. A critical review
Bibliographic record
Abstract
During the last 50 years, novel biomaterials and tissue engineering techniques have been investigated to produce alternative vascular substitutes that recapitulate the unique elastic mechanical features of blood vessels. A large variation in mechanical characterization, including the test type, protocol, and data analysis, is present in literature which complicates the comparison among studies and prevents the blooming and the advancement of this field. In addition, a limited mechanical assessment of the substitute for the intended application is often provided. In this light, this review presents the mechanical environment of blood vessels, discusses their mechanical behavior responsible for the suited blood flow into the body (non-linearity, anisotropy, hysteresis, and compliance), and compares the mechanical properties reported in literature (obtained with compression, tensile, stress-relaxation, creep, dynamic mechanical analysis, burst pressure, and dynamic compliance tests). This perspective highlights that the mechanical properties extracted through conventional tests are not always suitable indicators of the mechanical performance during the working life of a vascular substitute. The available tests can be then strategically used at different stages of the substitute development, prioritizing the simplicity of the method at early stages, and the physiological pertinence at later stages, following as much as possible ISO standards in the field. A consistent mechanical characterization focused on the behavior to which they will be subdued during real life is one key and missing element in the quest for physiological-like mechanical performance of vascular substitutes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".