Structural Model for Viscoelastic Properties of Pericardial Bioprosthetic Valves
Bibliographic record
Abstract
The benefit of bioprosthetic aortic valve over mechanical valve replacements is the release of thromboembolism and digression of long-term anticoagulation treatment. The function of bioprostheses and their efficiency is known to depend on the mechanical properties of the leaflet tissue. So it is necessary to select a suitable tissue for the bioprosthesis. The purpose of the present study is to clarify the viscoelastic behavior of bovine, equine, and porcine pericardium. In this study, pericardiums were compared mechanically from the viscoelastic aspect. After fixation of the tissues in glutaraldehyde, first uniaxial tests with different extension rates in the fiber direction were performed. Then, the stress relaxation tests in the fiber direction were done on these pericardial tissues by exerting 20, 30,40, and 50% strains. After evaluation of viscoelastic linearity, the Prony series, quasilinear viscoelastic (QLV) and modified superposition theory were applied to the stress relaxation data. Finally, the parameters of these constitutive models were extracted for each pericardium tissue. All three tissues exhibited a decrease in relaxation rate with elevating strain, indicating the nonlinear viscoelastic behavior of these tissues. The three-term Prony model was selected for describing the linear viscoelasticity. Among different models, the QLV model was best able to capture the relaxation behavior of the pericardium tissues. More stiffness of porcine pericardium was observed in comparison to the two other pericardium tissues. The relaxation percentage of porcine pericardium was less than the two others. It can be concluded that porcine pericardium behaves more as an elastic and less like a viscous tissue in comparison to the bovine and equine pericardium.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".