Evaluation of the nonlinear dynamics of human aortas for material characterization
Bibliographic record
Abstract
Cardiovascular disease is the second leading cause of death in Canada, resulting in $20.9 billion annual healthcare expenditures. Understanding the mechanics of the human aorta is fundamental for studying pathology progression and improving surgical grafts. This work is an experimental evaluation of the dynamic response of the human descending thoracic aorta to pulsatile flow, for hyperelastic and viscoelastic material characterizations. A mock circulatory loop was built to reproduce physiological cardiac pulsatile conditions for the aorta ex-vivo, and obtain the viscoelastic parameters with fluid-structure interactions. Then, specimens were dissected into the three constituent layers: intima, media and adventitia, for layer-specific material characterization. The hyperelastic response was described according to the Gasser-Ogden-Holzapfel model, and a three-spring generalized Maxwell model captured the viscoelastic behavior. The results showed a positive correlation between age and stiffness for all layers, both axially and circumferentially. Similar loss tangent values were found for the three layers independently, but, were larger for the complete aorta in the circulatory loop due to the fluid-structure interaction and grew with pulse frequency. An increase in the storage modulus by 150% from static to dynamic experiments further confirmed the importance of developing a viscoelastic model of the aorta, rather than a solely hyperelastic one. The mock circulatory loop was also effective in simulating in-vivo conditions to obtain viscoelastic parameters at high pulse rates. These are otherwise impossible to determine by MRI or CT scans, without invasive tests. This work is the precursor for designing medical devices and developing innovative biomaterials that better replicate aortic behaviour
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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 | 0.001 | 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) | 0.001 | 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".