Computational modeling of low-density lipoprotein accumulation at the carotid artery bifurcation after stenting

Restenosis typically occurs in regions of low and oscillating wall shear stress, which also favor the accumulation of atherogenic macromolecules such as low-density lipoprotein (LDL). This study aims to evaluate LDL transport and accumulation at the carotid artery bifurcation following carotid artery stenting (CAS) by means of computational simulation. The computational model consists of coupled blood flow and LDL transport, with the latter being modeled as a dilute substance dissolved in the blood and transported by the flow through a convection-diffusion transport equation. The endothelial layer was assumed to be permeable to LDL, and the hydraulic conductivity of LDL was shear-dependent. Anatomically realistic geometric models of the carotid bifurcation were built based on pre- and post-stent computed tomography (CT) scans. The influence of stent design was investigated by virtually deploying two different types of stents (open- and closed-cell stents) into the same carotid bifurcation model. Predicted LDL concentrations were compared between the post-stent carotid models and the relatively normal contralateral model reconstructed from patient-specific CT images. Our results show elevated LDL concentration in the distal section of the stent in all post-stent models, where LDL concentration is 20 times higher than that in the contralateral carotid. Compared with the open-cell stents, the closed-cell stents have larger areas exposed to high LDL concentration, suggesting an increased risk of stent restenosis. This computational approach is readily applicable to multiple patient studies and, once fully validated against follow-up data, it can help elucidate the role of stent strut design in the development of in-stent restenosis after CAS

Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers

Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design

Impact of Patient-Specific Inflow Boundary Conditions and Concentration Initialization on Hemodynamics and Low-Density Lipoproteins Transport in the Thoracic Aorta

The complex hemodynamics observed in the human aorta make this district a site of election for an in depth investigation of the relationship between fluid structures, transport and patho-physiology. In fact, it is well known that hemodynamics play an important role in the mass transport of blood specimen, and in turn, in their transfer to the vascular wall and ultimately in the localization of vascular disease in areas of complex arterial flow. In particular, the accumulation of lipoproteins in the arterial intima is a hallmark of atherosclerosis. Low-density lipoproteins (LDL) are the most abundant atherogenic lipoproteins in plasma and high plasma levels of LDL are causally related to the development of atherosclerosis. Advanced computational fluid dynamics coupled with medical imaging allows to combine the anatomical and hemodynamic inputs to realistic, fully personalized flow simulations to study local hemodynamics in arteries. Such an approach represents an effective way when addressing the still open questions about the role of blood-side LDL transfer to the arterial wall in atherogenesis. In particular, personalized computational models have been proposed to study LDL blood-to-wall transfer in the aorta, a district of election for the study of the relationships between the intricate local hemodynamics, LDL transport and disease. However, computational hemodynamics requires some assumptions that could affect the solutions of the equations governing blood flow and the aortic LDL transport and wall transfer. In previous studies of my research group, has been demonstrated that different strategies in applying boundary conditions (BCs) derived from phase-contrast MRI (PC-MRI) measurements could lead to different results in terms of distribution of near-wall and intravascular flow quantities. Additionally, a paucity of data characterizes the literature concerning the BCs and initial conditions (ICs) adopted to model LDL transport. In this contest, my thesis project have been focalized in analysing the impact that different possible strategies of applying (1) PC-MRI measured data as inflow BCs, and (2) LDL concentration profiles as ICs, have on LDL blood-to-wall transfer modelling in the human aorta. Technically, two different inflow BCs are generated, by imposing at the ascending aorta inflow PC-MRI measured 3D velocity profiles or idealized (flat) velocity profiles. In this way, the sensitivity of blood-to-wall LDL transfer to the inflow BC is explored, as the inflow BC influences LDL advective transport. The impact of applied BC-IC strategies on LDL blood-to-wall transfer has been evaluated in terms of computational costs and LDL polarization profiles at the luminal surface. Moreover, by virtue of the reported high LDL concentration in correspondence of disturbed shear regions, here a co-localization analysis with areas exposed to atheroprone wall shear stress (WSS) phenotype has been conducted as a “physics consistency check”. The results of my research would contribute to clarify which is (1) the level of detail obtained from measured flow data to be used as inflow BC, and (2) the plausibility of hypotheses on LDL concentration to be used as IC strategy, to satisfactorily simulate mass transport/transfer in personalized complex hemodynamic models of human aorta