The hemodynamic effects of in-tandem carotid artery stenosis: Implications for carotid endarterectomy

Objectives: It remains controversial whether patients with severe disease of the internal carotid artery and a coexisting stenotic lesion downstream would benefit from a carotid endarterectomy (CEA) of the proximal lesion. The aim of this study was to simulate the hemodynamic and wall shear effects of in-tandem internal carotid artery stenosis using a computational fluid dynamic (CFD) idealized model to give insight into the possible consequences of CEA on these lesions. Methods: A CFD model of steady viscous flow in a rigid tube with two asymmetric stenoses was introduced to simulate blood flow in arteries with multiple constrictions. The effect of varying the distance between the two stenoses, and the severity of the upstream stenosis on the pressure and wall shear stress (WSS) distributions on the second plaque, was investigated. The influence of the relative positions of the two stenoses was also assessed. Results: The distance between the plaques was found to have minimal influence on the overall hemodynamic effect except for the presence of a zone of low WSS (range -20 to 30 dyne/cm2) adjacent to both lesions when the two stenoses were sufficiently close (<4 times the arterial diameter). The upstream stenosis was protective if it was larger than the downstream stenosis. The relative positions of the stenoses were found to influence the WSS but not the pressure distribution. Conclusions: The geometry and positions of the lesions need to be considered when considering the hemodynamic effects of an in-tandem stenosis. Low WSS is thought to cause endothelial dysfunction and initiate atheroma formation. The fact that there was a flow recirculation zone with low WSS in between the two stenoses may demonstrate how two closely positioned plaques may merge into one larger lesion. Decision making for CEA may need to take into account the hemodynamic situation when an in-tandem stenosis is found. CFD may aid in the risk stratification of patients with this problem.

The impact of wall shear stress and pressure drop on the stability of the atherosclerotic plaque

Rupture of vulnerable atheromatous plaque in the carotid and coronary arteries often leads to stroke and heart attack respectively. The mechanism of blood flow and plaque rupture in stenotic arteries is still not fully understood. A three dimensional rigid wall model was solved under steady state conditions and unsteady conditions by assuming a time-varying inlet velocity profile to investigate the relative importance of axial forces and pressure drops in arteries with asymmetric stenosis. Flow-structure interactions were investigated for the same geometry and the results were compared with those retrieved with the corresponding 2D cross-section structural models. The Navier-Stokes equations were used as the governing equations for the fluid. The tube wall was assumed hyperelastic, homogeneous, isotropic and incompressible. The analysis showed that the three dimensional behavior of velocity, pressure and wall shear stress is in general very different from that predicted by cross-section models. Pressure drop across the stenosis was found to be much higher than shear stress. Therefore, pressure may be the more important mechanical trigger for plaque rupture other than shear stress, although shear stress is closely related to plaque formation and progression.

Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests

Selection criteria and misspecification tests for the intra-cluster correlation structure (ICS) in longitudinal data analysis are considered. In particular, the asymptotical distribution of the correlation information criterion (CIC) is derived and a new method for selecting a working ICS is proposed by standardizing the selection criterion as the p-value. The CIC test is found to be powerful in detecting misspecification of the working ICS structures, while with respect to the working ICS selection, the standardized CIC test is also shown to have satisfactory performance. Some simulation studies and applications to two real longitudinal datasets are made to illustrate how these criteria and tests might be useful.

Distributed Robotic Vision as a service

The vision sense of standalone robots is limited by line of sight and onboard camera capabilities, but processing video from remote cameras puts a high computational burden on robots. This paper describes the Distributed Robotic Vision Service, DRVS, which implements an on-demand distributed visual object detection service. Robots specify visual information requirements in terms of regions of interest and object detection algorithms. DRVS dynamically distributes the object detection computation to remote vision systems with processing capabilities, and the robots receive high-level object detection information. DRVS relieves robots of managing sensor discovery and reduces data transmission compared to image sharing models of distributed vision. Navigating a sensorless robot from remote vision systems is demonstrated in simulation as a proof of concept.

A Distributed Robotic Vision Service

Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.

The structure of glycosaminoglycans and their interactions with proteins

Glycosaminoglycans (GAGs) are important complex carbohydrates that participate in many biological processes through the regulation of their various protein partners. Biochemical, structural biology and molecular modelling approaches have assisted in understanding the molecular basis of such interactions, creating an opportunity to capitalize on the large structural diversity of GAGs in the discovery of new drugs. The complexity of GAG–protein interactions is in part due to the conformational flexibility and underlying sulphation patterns of GAGs, the role of metal ions and the effect of pH on the affinity of binding. Current understanding of the structure of GAGs and their interactions with proteins is here reviewed: the basic structures and functions of GAGs and their proteoglycans, their clinical significance, the three-dimensional features of GAGs, their interactions with proteins and the molecular modelling of heparin binding sites and GAG–protein interactions. This review focuses on some key aspects of GAG structure–function relationships using classical examples that illustrate the specificity of GAG–protein interactions, such as growth factors, anti-thrombin, cytokines and cell adhesion molecules. New approaches to the development of GAG mimetics as possible new glycotherapeutics are also briefly covered.