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Record W4403052995 · doi:10.1007/s13239-024-00754-x

Finite Element Simulation of Opening Angle Response of Porcine Aortas Using Layer Specific GAG Distributions in One and Two Layered Solid Matrices

2024· article· en· W4403052995 on OpenAlexafffund
Noor M. Ghadie, Jean-Philippe St-Pierre, Michel R. Labrosse

Bibliographic record

VenueCardiovascular Engineering and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAdventitiaComposite materialMatrix (chemical analysis)Biomedical engineeringThoracic aortaStiffnessGlycosaminoglycanSwellingUltimate tensile strengthDescending aortaAortaAnatomyChemistrySurgeryBiochemistry

Abstract

fetched live from OpenAlex

PURPOSE: Recent studies have identified an effect of glycosaminoglycans (GAG) on residual stresses in the aorta, underscoring the need to better understand their biomechanical roles. METHODS: Aortic ring models for each of the ascending, arch and descending thoracic regions of the porcine thoracic aorta were created in FEBioStudio, using a framework that incorporates the Donnan osmotic swelling in a porous solid matrix. The distribution of fixed charge densities (FCD) through the thickness of the tissue was prescribed as calculated from experimentally quantified sulfated GAG mural distributions. Material parameters for the solid matrix, modeled using a Holmes-Mow constitutive law, were optimized using data from biaxial tensile tests. In addition to modelling the solid matrix as one layer, two layers were considered to capture the differences between the intima-media and the adventitia, for which various stiffness ratios were explored. RESULTS: As the stiffness of the adventitia with respect to that of the media increased, the simulated opening angle increased. The opening angle also decreased from the ascending to the descending thoracic region in both one- and two-layered solid matrices models. The simulated results were compared against the experimental contribution of GAG to the opening angle, as previously quantified via enzymatic GAG-depletion. When using one layer for the solid matrix, the errors between the simulated opening angles and the experimental contribution of GAG to the opening angle were respectively 28%, 15% and 23% in the ascending, arch and descending thoracic regions. When using two layers for the solid matrix, the smallest errors in the ascending and arch regions were 21% and 5% when the intima-media was modelled as 10 times stiffer, and as twice stiffer than the adventitia, respectively, and 23% in the descending thoracic regions when the intima-media and adventitia shared similar mechanical properties. CONCLUSIONS: Overall, this study demonstrates that GAG partially contribute to circumferential residual stress, and that GAG swelling is one of several regulators of the opening angle. The minor discrepancies between simulated and experimental opening angles imply that the contribution of GAG extends beyond mere swelling, aligning with previous experimental indications of their interaction with ECM fibers in determining the opening angle.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.239
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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