Magnetically Targeted Endothelial Cell Localization in Stented Vessels

ObjectivesA novel method to magnetically localize endothelial cells at the site of a stented vessel wall was developed. The application of this strategy in a large animal model is described.BackgroundLocal delivery of blood-derived endothelial cells has been shown to facilitate vascular healing in animal models. Therapeutic utilization has been limited by an inability to retain cells in the presence of blood flow. We hypothesized that a magnetized stent would facilitate local retention of superparamagnetically labeled cells.MethodsCultured porcine endothelial cells were labeled with endocytosed superparamagnetic iron oxide microspheres. A 500:1 microsphere-to-cell ratio was selected for in vivo experiments based on bromo-deoxyuridine incorporation and terminal deoxynucleotidyl transferase mediated dUTP nick end labeling assays. Stents were magnetized and implanted in porcine coronary and femoral arteries using standard interventional equipment. Labeled endothelial cells were delivered locally during transient occlusion of blood flow.ResultsThe delivered cells were found attached to the stent struts and were also distributed within the adjacent denuded vessel wall at 24 h.ConclusionsMagnetic forces can be used to rapidly place endothelial cells at the site of a magnetized intravascular stent. The delivered cells are retained in the presence of blood flow and also spread to the adjacent injured vessel wall. Potential applications include delivering a cell-based therapeutic effect to the local vessel wall as well as downstream tissue

Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into ‘cumulus-type’, i.e., those similar to puffy (white) clouds, and ‘stratus-type’, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an ‘energy transfer’ mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by ‘multi-trajectories’; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach ‘rarely visited’ but functionally-related states. We also show the role of disorder in ‘spatial games’ of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks

Improved crowd psychological model and control

The behavior of human crowd is an interesting phenomenon in which individuals are set as a collection that comprises of a highly dynamic social group. The crowd behaviors have been investigated by researchers over the years. Recent works include the study in modeling and controlling of the dynamic psychological behavior of crowds such as students’ behavior in a classroom or people’s behavior in a one-dimensional queue. In this paper, an improved version of the psychological crowd model has been proposed, where the social interaction between two individuals in a crowd is represented by a weightage, called the weight of social interaction. It has been shown that the inclusion of the social interaction weight has allowed social interactions between individuals to be included and results in a more accurate representation of the crowd’s psychological factors propagations. Since the psychological dynamics of crowd is naturally unstable, this paper also discusses the application of two nonlinear control approaches to stabilise the crowd to make it calm. Results show that for a crowd of n number of agents, the single-agent controller gives similar performance with the n-agent controller but with much less resources. The simulation results also show that it takes less amount of time to stabilise a crowd when the crowd model includes social interaction weights

Characterizing Distances of Networks on the Tensor Manifold

At the core of understanding dynamical systems is the ability to maintain and control the systems behavior that includes notions of robustness, heterogeneity, or regime-shift detection. Recently, to explore such functional properties, a convenient representation has been to model such dynamical systems as a weighted graph consisting of a finite, but very large number of interacting agents. This said, there exists very limited relevant statistical theory that is able cope with real-life data, i.e., how does perform analysis and/or statistics over a family of networks as opposed to a specific network or network-to-network variation. Here, we are interested in the analysis of network families whereby each network represents a point on an underlying statistical manifold. To do so, we explore the Riemannian structure of the tensor manifold developed by Pennec previously applied to Diffusion Tensor Imaging (DTI) towards the problem of network analysis. In particular, while this note focuses on Pennec definition of geodesics amongst a family of networks, we show how it lays the foundation for future work for developing measures of network robustness for regime-shift detection. We conclude with experiments highlighting the proposed distance on synthetic networks and an application towards biological (stem-cell) systems.Comment: This paper is accepted at 8th International Conference on Complex Networks 201

Slice-selective NMR:a non-invasive method for the analysis of separated pyrolysis fuel samples

Pyrolysis oil has been identified as a possible alternative fuel source, however widespread use is hindered by high acidity and water content. These negative characteristics can be mitigated by blending with, for example, biodiesel, marine gas oil and butanol. These blended samples can be unstable and often separate into two distinct phases. NMR spectroscopy is a well-established spectroscopic technique that is finding increasing application in the analysis of pyrolysis oil and blended fuels derived from it. Here, slice-selective NMR, where the NMR spectrum of only a thin slice of the total sample is acquired, is used to study, non-invasively, how the constituent components of blended biofuel samples are partitioned between the two layers. Understanding the outcome of the phase separation is an important step towards understanding why the blended oil samples separate, and may provide answers to mitigating and eventually solving the problem. The NMR method was successfully used to analyse a number of separated biofuel samples - typically separated into an oil layer, containing marine gas oil and biodiesel, above a bio-oil layer with a high water and butanol content

Asymmetric emission of high energy electrons in the two-dimensional hydrodynamic expansion of large xenon clusters irradiated by intense laser fields

Energy spectra and angular distributions have been measured of electrons that are emitted upon disassembly of $Xe_{150000}$ following irradiation by intense (10$^{15}-10^{16}$ W cm$^{-2}$) laser pulses whose durations are varied over the 100-2200 fs range. The cluster explosion dynamics occur in the hydrodynamic regime. Electron emission is found to be unexpectedly asymmetric and exhibits a resonance when the laser pulse duration is $\sim$1 ps. These results are rationalized by extending the hydrodynamic model to also take into account the force that the light field exerts on the polarization charge that is induced on surface of the cluster. We show that the magnitude of this electrostrictive force is comparable to those of Coulombic and the hydrodynamic forces, and it exhibits resonance behavior. Contrary to earlier understanding, we find that low-energy electrons are connected to the resonance in energy absorption by the cluster. The high-energy electrons seem to be produced by a mechanism that is not so strongly influenced by the resonance.Comment: 1 Revtex file, 8 figs. in eps forma

Collagen in Health and Disease

Collagen is the unique, triple helical protein molecule which forms the major part of the extracellular matrix. It is the most abundant protein in the human body, representing 30% of its dry weight and is important to health because it characterizes the structure of skin, connective tissues, tendons, bones and cartilage. As collagen forms building block of body structures, any defect in collagen results in disorders, such as osteogenesis imperfecta, Ehlers-Dalnos syndrome, scurvy, systemic lupus erythematosus, systemic sclerosis, Stickler syndrome, oral submucous fibrosis, Marfan syndrome, epidermolysis bullosa, Alport syndrome. This review discusses the role of collagen in health as well as disease