Comparison of Analytical and Finite Element Implementation of Exponential Constitutive Models for Valve Tissue Under Micropipette Aspiration
Bibliographic record
Abstract
Micropipette aspiration (MA) has been widely used to measure the biomechanical properties of cells and biomaterials [1]. Typically a linear elastic half-space model is used to fit the experimental load-deformation data [1]. However, load-deformation relationships for most biological tissues are highly nonlinear, suggesting alternative constitutive models are necessary. In the case of aortic heart valve tissue, exponential-type constitutive models have been found to fit the biaxial stress-strain behavior well [2]. Based on these studies, Butcher et al. used an exponential constitutive model to characterize the response of chicken embryonic valve (atrioventricular cushion) under MA [3]. To do so, they implemented an analytical exponential constitutive model [2] and directly related the stress and strain to the experimentally measured pressure and aspiration length. This allowed the authors to fit the tissue MA data without accounting for the complexities of the boundary conditions and multicomponent strain field inherent in MA. However, it is unclear whether the material parameters estimated using this approach are different from those estimated by solving the more complex boundary value problem, which presumably more faithfully simulates the physical process of tissue aspiration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 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".