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Advancing the Extraction of Mechanical Properties from Biaxial Data

2023· dissertation· en· W6998432057 sur OpenAlexaff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2023
Typedissertation
Langueen
DomaineEngineering
ThématiqueElasticity and Material Modeling
Établissements canadiensBlackberry (Canada)
Organismes subventionnairesnon disponible
Mots-clésDeformation (meteorology)AnisotropyDisplacement (psychology)Consistency (knowledge bases)Characterization (materials science)PlanarIsotropyRange (aeronautics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Mechanical characterization is vital to understand soft tissue behaviour in health and pathology. In aortic aneurysms, for instance, it is used to develop techniques for rupture risk assessment. In skin, for instance, it is used to assess the effects of freezing and anatomic location, the information useful for donor tissue banks. Planar biaxial testing is one of the most common tools for the mechanical characterization of soft tissues. In this experiment, loadings such as displacements or forces are applied to the edges of the square specimens yielding deformations at the center of the specimens that are measured digitally. Biaxial testing is a great tool as it captures anisotropy and nonlinearity of soft tissues’ behaviour and is capable of exploring a wide range of deformation states as it can apply different combinations of loadings. However, because the deformations at the center of the specimens are not controlled, no two mechanical tests are equivalent, complicating the consistency in data extraction and comparison.
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\nIn this study, we propose a new approach for biaxial data analysis. First, a surface is fitted to the biaxial data. Second, the mechanical response is interpolated at the true equi-biaxial stretch deformation state (the state at which the deformations at the center of the sample are equal). Third, the effective mechanical properties such as high/low elastic moduli, and transition stretches/stress are extracted from the interpolated response. Other studies, in contrast, extract properties at the equi-biaxial displacement deformation state (the state at which equal strains are applied at the sample edges), which is due to the anisotropy of soft tissues and experimental setup, varies from specimen to specimen. We argue that our proposed approach of data extraction is more robust from the mechanical point of view.
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\nTo demonstrate that our proposed approach can result in drastically different data sets, we apply it to previously tested aortic tissues from human donors and pigs. We compare effective mechanical properties extracted from the interpolated equi-biaxial stretch deformation state and conventionally used equi-biaxial displacement deformation state. Statistical analysis shows a significant difference between two groups of mechanical measures whether the measures are compared individually within each group or the general comparison of groups is conducted. Particularly, measures related to the transition zones (stress/stretch) linked to collagen fibres’ engagement behaviour were affected the most. Overall, the results indicate that the way the data is extracted can impact the outcome of biaxial studies. This further highlights the advantage of using the proposed approach of biaxial data extraction at equivalent deformation states versus the conventional approach.
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\nThe proposed approach of the data extraction was also applied to human skin samples that came from the same donor’s back. Prior to biaxial testing, the samples were frozen/stored using three different freezing protocols (wet freezing in Phosphate Buffered Salin alone and with the cryoprotectant Glycerol as well as dry freezing using Liquid Nitrogen). Then, after testing, the effective mechanical properties were extracted and the effects of freezing and the anatomic locations were evaluated. We found very little quantitive evidence that the freezing storage approach mattered, although some qualitative observations were made to highlight the distinct behaviour of the samples frozen using Liquid Nitrogen. In the case of heterogeneity analysis, samples closer to the spine were different from samples further away from the spine, with the transition zone properties affected the most, especially for the samples subjected to Liquid Nitrogen freezing protocol. Future studies should assess each of the effect of heterogeneity and the effects of freezing separately, however, the overall approach of data extraction seems promising for intra-patient analysis.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,646
Score d'incertitude au seuil0,968

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,209
Écart entre enseignants0,184 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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