Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics.

Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental haemodynamic models. In this work, we focus on the use of Reduced Order Models (ROMs) to reconstruct velocity fields in a patient-specific dissected aorta, with the objective being to compare the ROMs obtained from Robust Proper Orthogonal Decomposition (RPOD) to those obtained from the traditional Proper Orthogonal Decomposition (POD). POD and RPOD are applied to in vitro, haemodynamic data acquired by Particle Image Velocimetry and compare the decomposed flows to those derived from Computational Fluid Dynamics (CFD) data for the same geometry and flow conditions.

Experimental evaluation of the patient-specific haemodynamics of an aortic dissection model using particle image velocimetry.

Aortic Dissection (AD) is a complex pathology that affects the aorta. Diagnosis, management and treatment remain a challenge as it is a highly patient-specific pathology and there is still a limited understanding of the fluid-mechanics phenomena underlying clinical outcomes. Although in vitro models can allow the accurate study of AD flow fields in physical phantoms, they are currently scarce and almost exclusively rely on over simplifying assumptions.

A Computational Framework for Pre-Interventional Planning of Peripheral Arteriovenous Malformations.

Purpose: Peripheral arteriovenous malformations (pAVMs) are congenital lesions characterised by abnormal high-flow, low-resistance vascular connections-the so-called nidus-between arteries and veins. The mainstay treatment typically involves the embolisation of the nidus, however the complexity of pAVMs often leads to uncertain outcomes. This study aims at developing a simple, yet effective computational framework to aid the clinical decision making around the treatment of pAVMs using routinely acquired clinical data.

A Combined In Vivo, In Vitro, In Silico Approach for Patient-Specific Haemodynamic Studies of Aortic Dissection.

The optimal treatment of Type-B aortic dissection (AD) is still a subject of debate, with up to 50% of the cases developing late-term complications requiring invasive intervention. A better understanding of the patient-specific haemodynamic features of AD can provide useful insights on disease progression and support clinical management. In this work, a novel in vitro and in silico framework to perform personalised studies of AD, informed by non-invasive clinical data, is presented.

Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer's disease and aortic dissection.

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.

Patient-specific haemodynamic simulations of complex aortic dissections informed by commonly available clinical datasets.

Patient-specific computational fluid-dynamics (CFD) can assist the clinical decision-making process for Type-B aortic dissection (AD) by providing detailed information on the complex intra-aortic haemodynamics. This study presents a new approach for the implementation of personalised CFD models using non-invasive, and oftentimes minimal, datasets commonly collected for AD monitoring. An innovative way to account for arterial compliance in rigid-wall simulations using a lumped capacitor is introduced, and a parameter estimation strategy for boundary conditions calibration is proposed.

Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment.

The use of in silico tools for the interventional planning of complex vascular conditions, such as Aortic Dissections has been often limited by high computational cost, involving long timescales for accurate results to be produced and low numbers of patients, precluding the use of statistical analyses to inform individual-level models. In the paper [ 2018; 8(20):5758-5771. doi:10.

Cell-to-cell heterogeneity of EWSR1-FLI1 activity determines proliferation/migration choices in Ewing sarcoma cells.

Ewing sarcoma is characterized by the expression of the chimeric EWSR1-FLI1 transcription factor. Proteomic analyses indicate that the decrease of EWSR1-FLI1 expression leads to major changes in effectors of the dynamics of the actin cytoskeleton and the adhesion processes with a shift from cell-to-cell to cell-matrix adhesion. These changes are associated with a dramatic increase of in vivo cell migration and invasion potential.

MiR-30a-5p connects EWS-FLI1 and CD99, two major therapeutic targets in Ewing tumor.

Ewing sarcoma is a pediatric bone tumor characterized in 85% of cases by the fusion between EWS and FLI1 genes that results in the expression of the EWS-FLI1 aberrant transcription factor. Histologically, the Ewing tumor expresses high levels of the CD99 membrane glycoprotein. It has been recently described that CD99 expression contributes to the Ewing tumor oncogenesis by modulating growth and differentiation of tumor cells.