Experimental Determination of Layer-Specific Hyperelastic Parameters of Human Descending Thoracic Aortas
Bibliographic record
Abstract
Abstract Heart disease is the second leading cause of death in Canada resulting in $20.9 billion annual healthcare expenditures [1,2]. Understanding the mechanics of the human descending thoracic aorta is fundamental for comprehending the development of pathologies and improving surgical prostheses. This study presents hyperelastic and viscoelastic material characterizations of the human descending thoracic aorta from twelve different donors, with a mean age of 49.4 years. The specimens were dissected into the three constituent layers: intima, media and adventitia. Evaluating the layer-specific opening angles led to the computation of the circumferential residual stresses. Uniaxial tensile tests of each layer, in both the circumferential and axial direction, were used to model the hyperelastic behavior according to the Gasser-Ogden-Holzapfel model (GOH). The storage modulus and loss tangent for the layers were obtained from uniaxial harmonic excitations at varied frequencies, to model the viscoelastic behavior with the generalized Maxwell model. The results showed a positive correlation between age and stiffness for all layers, both axially and circumferentially. Similar loss tangent values were found across the three layers. A large increase in the storage modulus from static to dynamic experiments further corroborates the importance of a viscoelastic model of the aorta, rather than solely hyperelastic.
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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) | 0.001 | 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".