WALL SHEAR STRESS TOPOLOGICAL SKELETON IDENTIFICATION IN CARDIOVASCULAR FLOWS: A PRACTICAL APPROACH

The observed co-localization of “disturbed” hemodynamics and atherosclerotic lesion prevalence has led to the identification of low and oscillatory Wall Shear Stress (WSS) as a biomechanical localizing factor for vascular dysfunction. However, recent evidences have underlined how consideration of only “low and oscillatory” WSS may oversimplify the complex hemodynamic milieu to which the endothelium is exposed. In this context, recent studies have highlighted the relevance of WSS fixed points, and the stable and unstable manifolds that connect them. These WSS topological features have a strong link with flow features like flow stagnation, separation, and recirculation, which are usually classified as “disturbed” flow. Technically, a fixed point of a vector field is a point where the vector field vanishes, while unstable/stable vector field manifolds identify contraction/expansion regions linking the fixed points. The set of fixed points and their connections form the topological skeleton of a vector field. The presence of WSS fixed points and of WSS contraction/expansion regions, highlighted by WSS manifolds, might induce focal vascular responses relevant for, e.g., early atherosclerosis, or, aneurysm rupture. For these reasons, the topological skeleton analysis of the WSS vector field is of great interest and motivates the study present herein. Lagrangian techniques have been recently proposed to identify WSS manifolds but have certain practical limitations. A Eulerian approach has also been suggested, but only for 2D analytical fields. Here we propose and demonstrate the use of a simple Eulerian approach for identifying WSS topological skeleton on 3D surfaces

Computational hemodynamics & complex networks integrated platform to study intravascular flow in the carotid bifurcation

The well-established role of hemodynamics in cardiovascular disease [1] makes the study of cardiovascular flows of wide interest. Here we apply for the first time a method based on complex networks (CNs) theory [2] to investigate and characterize quantitatively the complexity of cardiovascular flows. The rationale lies in the ability of CNs to explore the complexity of physical systems, such as 4D cardiovascular flows, in a synthetic and effective manner. CN-based approaches have already proven useful for data-driven learning of dynamical processes that are hidden to other analysis techniques. In detail, a dataset of 10 patient-specific computational hemodynamics models of human carotid bifurcation (CB) is considered here. Quantitative metrics derived from CNs theory are applied to two fluid mechanics quantities describing the intricate intravascular hemodynamics. These are (1) the so-called axial velocity, i.e. the blood velocity component aligned with the main flow direction, as identified by the vessels centerline, and (2) the kinetic helicity density, a measure of pitch and torsion of the streaming blood. The obtained results suggest the potency of CNs in unveiling fundamental organization principles in cardiovascular flows

Network-Based Characterization of Blood Large-Scale Coherent Motion in the Healthy Human Aorta With 4D Flow MRI

Objective: The need for distilling the hemodynamic complexity of aortic flows into clinically relevant quantities resulted in a loss of the information hidden in 4D aortic fluid structures. To reduce information loss, this study proposes a network-based approach to identify and characterize in vivo the large-scale coherent motion of blood in the healthy human aorta. Methods: The quantitative paradigm of the aortic flow as a "social network" was applied on 4D flow MRI acquisitions performed on forty-one healthy volunteers. Correlations between the aortic blood flow rate waveform at the proximal ascending aorta (AAo), assumed as one of the drivers of aortic hemodynamics, and the waveforms of the axial velocity in the whole aorta were used to build "one-to-all" networks. The impact of the driving flow rate waveform and of aortic geometric attributes on the transport of large-scale coherent fluid structures was investigated. Results: The anatomical length of persistence of large-scale coherent motion was the 29.6% of the healthy thoracic aorta length (median value, IQR 23.1%-33.9%). Such length is significantly influenced by the average and peak-to-peak AAo blood flow rate values, suggesting a remarkable inertial effect of the AAo flow rate on the transport of large-scale fluid structures in the distal aorta. Aortic geometric attributes such as curvature, torsion and arch shape did not influence the anatomical length of persistence. Conclusion: The proposed in vivo approach allowed to quantitatively characterize the transport of large-scale fluid structures in the healthy aorta, strengthening the definition of coherent hemodynamic structures and identifying flow inertia rather than geometry as one of its main determinants. Significance: The findings on healthy aortas may be used as reference values to investigate the impact of aortic disease or implanted devices in disrupting/restoring the physiological spatiotemporal coherence of large-scale aortic flow

Wall shear stress topological skeleton analysis in cardiovascular flows: Methods and applications

A marked interest has recently emerged regarding the analysis of the wall shear stress (WSS) vector field topological skeleton in cardiovascular flows. Based on dynamical system theory, the WSS topological skeleton is composed of fixed points, i.e., focal points where WSS locally vanishes, and unstable/stable manifolds, consisting of contraction/expansion regions linking fixed points. Such an interest arises from its ability to reflect the presence of near-wall hemodynamic features associated with the onset and progression of vascular diseases. Over the years, Lagrangian-based and Eulerianbased post-processing techniques have been proposed aiming at identifying the topological skeleton features of the WSS. Here, the theoretical and methodological bases supporting the Lagrangian- and Eulerian-based methods currently used in the literature are reported and discussed, highlighting their application to cardiovascular flows. The final aim is to promote the use of WSS topological skeleton analysis in hemodynamic applications and to encourage its application in future mechanobiology studies in order to increase the chance of elucidating the mechanistic links between blood flow disturbances, vascular disease, and clinical observations

Functional selectivity of EM-2 analogs at the mu-opioid receptor

The mu opioid receptor agonists are the most efficacious pain controlling agents but their use is accompanied by severe side effects. More recent developments indicate that some ligands can differentially activate receptor downstream pathways, possibly allowing for dissociation of analgesia mediated through the G protein from the opioid-related side effects mediated by β-arrestin pathway. In an effort to identify such biased ligands, here we present a series of thirteen endomorphin-2 (EM-2) analogs with modifications in positions 1, 2, and/or 3. All obtained analogs behaved as mu receptor selective agonists in calcium mobilization assay carried out on cells expressing opioid receptors and chimeric G proteins. A Bioluminescence Resonance Energy Transfer (BRET) approach was employed to determine the ability of analogs to promote the interaction of the mu opioid receptor with G protein or β-arrestin 2. Nearly half of the developed analogs showed strong bias towards G protein, in addition four compounds were nearly inactive towards β-arrestin 2 recruitment while blocking the propensity of EM-2 to evoke mu-β-arrestin 2 interaction. The data presented here contribute to our understanding of EM-2 interaction with the mu opioid receptor and of the transductional propagation of the signal. In addition, the generation of potent and selective mu receptor agonists strongly biased towards G protein provides the scientific community with novel tools to investigate the in vivo consequences of biased agonism at this receptor