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Enregistrement W7062505791

Wave propagation methods for the experimental characterization of soft biomaterials and tissues

2014· dissertation· en· W7062505791 sur OpenAlexfundno aff

Notice bibliographique

RevueeScholarship@McGill (McGill) · 2014
Typedissertation
Langueen
DomaineEngineering
ThématiqueParticle accelerators and beam dynamics
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health ResearchMcGill University
Mots-clésViscoelasticitySelf-healing hydrogelsShear modulusTissue engineeringPhonationVibrationSilicone rubberCharacterization (materials science)Vocal foldsGelatin
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

A good understanding of the viscoelasticity of soft biomaterials and tissue is needed to predict their mechanical response under loads in biological conditions. For example, the viscoelastic properties of hydrogels used in cell culturing and tissue engineering should approximately match those of the tissue they replace. These properties are strongly dependent on the excitation frequency. This has important implication for voice production. The human vocal folds oscillate at a fundamental frequency around 125, 200, 300 and 500 Hz in normal phonation for male, female, children and infants, respectively. Vibration frequencies can reach up to a few kHz, specially in singing. Vocal fold oscillations, essential to voice production, are significantly affected by the mechanical (viscoelastic) properties of the vocal fold mucosa. A novel characterization method based on Rayleigh wave propagation was developed for the quantification of frequency-dependent viscoelastic properties of soft biomaterials over a broad frequency range; i.e., up to 4 kHz. Synthetic silicone rubber and gelatin samples were fabricated and tested to evaluate the proposed method. The shear and elastic moduli and the loss factor obtained from the Rayleigh wave propagation method were compared with results from two other methods, as well as results from an independent study. The proposed method was found to be accurate and cost effective for the measurement of viscoelastic properties of soft biomaterials, such as phonosurgical biomaterials and hydrogels, over a wide frequency range. A non-invasive method was developed to measure the shear modulus of human vocal fold tissue in vivo during phonation. This is needed for the development of injectable biomaterials for vocal fold augmentation and repair, and to evaluate voice treatment procedures. The mucosal wave propagation speed was measured for four human subjects at different phonation frequencies using high speed endoscopic images of the larynx and image processing methods. The transverse shear modulus of the vocal fold mucosa was then calculated from a surface wave propagation dispersion equation using the measured wave speeds. The results were found to be in good agreement with those from other studies obtained via in vitro measurements, thereby supporting the validity of the proposed measurement method. Hyaluronic acid-gelatin hydrogels with varying concentrations of cross-linker are other constituents were fabricated. These biomaterials are for use as synthetic extracellular matrix in vocal fold tissue engineering. The Rayleigh wave propagation method was used to quantify the frequency-dependent viscoelastic properties of these hydrogels, including shear and viscous moduli, over a broad frequency range; i.e., from 40 to 4000 Hz. The viscoelastic properties of the designed hydrogels were similar to those of human vocal fold tissue obtained from in vivo and in vitro measurements. It was shown that the cross-linker concentration is the most common parameter to tune the viscoelastic properties of designed hyaluronan-based hydrogels. The hyaluronic acid and gelatin contents of these hydrogels are the main parameters to adjust their biochemical and biological properties, considering the changes in the viscoelastic properties of the hydrogels.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,121
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
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 Opus0,021
Tête enseignante GPT0,285
Écart entre enseignants0,264 · 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.

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
Publié2014
Routes d'admission1
Résumé présentoui

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