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Intramural Glycosaminoglycans Distribution vs. Residual Stress in Porcine Ascending Aorta: a Computational Study

2020· article· en· W3082421441 on OpenAlexaff
Noor M. Ghadie, Jean-Philippe St-Pierre, Michel R. Labrosse

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsResidual stressGlycosaminoglycanAscending aortaAortaDistribution (mathematics)ResidualMaterials scienceRing (chemistry)Finite element methodBiomedical engineeringAnatomyChemistryComposite materialStructural engineeringMathematicsMathematical analysisInternal medicineMedicineEngineering

Abstract

fetched live from OpenAlex

In this computational modelling work, we explored the mechanical roles that various glycosaminoglycans (GAGs) distributions may play in the porcine ascending aortic wall, by studying both the transmural residual stress as well as the opening angle in aortic ring samples. A finite element (FE) model was first constructed and validated against published data generated from rodent aortic rings. The FE model was then used to simulate the response of porcine ascending aortic rings with different GAG distributions prescribed through the wall of the aorta. The results indicated that a uniform GAG distribution within the aortic wall did not induce residual stresses, allowing the aortic ring to remain closed when subjected to a radial cut. By contrast, a heterogeneous GAG distribution led to the development of residual stresses which could be released by a radial cut, causing the ring to open. The residual stresses and opening angle were shown to be modulated by the GAG content, gradient, and the nature of the transmural distribution.

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 score0.094
Threshold uncertainty score0.487

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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