Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer's disease and aortic dissection.
Morphologie
; 103(343): 148-160, 2019 Dec.
Article
en En
| MEDLINE
| ID: mdl-31786098
ABSTRACT
For precision medicine to be implemented through the lens of in silico technology, it is imperative that biophysical research workflows offer insight into treatments that are specific to a particular illness and to a particular subject. The boundaries of precision medicine can be extended using multiscale, biophysics-centred workflows that consider the fundamental underpinnings of the constituents of cells and tissues and their dynamic environments. Utilising numerical techniques that can capture the broad spectrum of biological flows within complex, deformable and permeable organs and tissues is of paramount importance when considering the core prerequisites of any state-of-the-art precision medicine pipeline. In this work, a succinct breakdown of two precision medicine pipelines developed within two Virtual Physiological Human (VPH) projects are given. The first workflow is targeted on the trajectory of Alzheimer's Disease, and caters for novel hypothesis testing through a multicompartmental poroelastic model which is integrated with a high throughput imaging workflow and subject-specific blood flow variability model. The second workflow gives rise to the patient specific exploration of Aortic Dissections via a multi-scale and compliant model, harnessing imaging, computational fluid-dynamics (CFD) and dynamic boundary conditions. Results relating to the first workflow include some core outputs of the multiporoelastic modelling framework, and the representation of peri-arterial swelling and peri-venous drainage solution fields. The latter solution fields were statistically analysed for a cohort of thirty-five subjects (stratified with respect to disease status, gender and activity level). The second workflow allowed for a better understanding of complex aortic dissection cases utilising both a rigid-wall model informed by minimal and clinically common datasets as well as a moving-wall model informed by rich datasets.
Palabras clave
Alzheimer's Disease; Aortic Dissection; Computational Fluid Dynamics; Dementia; Dissection aortique; Dynamique des fluides computationnelle; Démence; Glymphatic system; Haemodynamics; Hémodynamique; Maladie d'Alzheimer; Multiple-Network Poroelastic Theory; Physiologie humaine virtuelle (VPH); Système lymphatique; Théorie poroélastique à réseaux multiples; Virtual Physiological Human (VPH)
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Flujo Sanguíneo Regional
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Enfermedad de Alzheimer
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Sistema Glinfático
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Disección Aórtica
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Modelos Biológicos
Tipo de estudio:
Etiology_studies
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Incidence_studies
/
Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Aged80
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Female
/
Humans
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Male
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Middle aged
Idioma:
En
Revista:
Morphologie
Asunto de la revista:
ANATOMIA
Año:
2019
Tipo del documento:
Article