Generation of Porous Particle Structures using the Void Expansion Method

The newly developed "void expansion method" allows for an efficient generation of porous packings of spherical particles over a wide range of volume fractions using the discrete element method. Particles are randomly placed under addition of much smaller "void-particles". Then, the void-particle radius is increased repeatedly, thereby rearranging the structural particles until formation of a dense particle packing. The structural particles' mean coordination number was used to characterize the evolving microstructures. At some void radius, a transition from an initially low to a higher mean coordination number is found, which was used to characterize the influence of the various simulation parameters. For structural and void-particle stiffnesses of the same order of magnitude, the transition is found at constant total volume fraction slightly below the random close packing limit. For decreasing void-particle stiffness the transition is shifted towards a smaller void-particle radius and becomes smoother.Comment: 9 pages, 8 figure

The Influence of the Degree of Heterogeneity on the Elastic Properties of Random Sphere Packings

The macroscopic mechanical properties of colloidal particle gels strongly depend on the local arrangement of the powder particles. Experiments have shown that more heterogeneous microstructures exhibit up to one order of magnitude higher elastic properties than their more homogeneous counterparts at equal volume fraction. In this paper, packings of spherical particles are used as model structures to computationally investigate the elastic properties of coagulated particle gels as a function of their degree of heterogeneity. The discrete element model comprises a linear elastic contact law, particle bonding and damping. The simulation parameters were calibrated using a homogeneous and a heterogeneous microstructure originating from earlier Brownian dynamics simulations. A systematic study of the elastic properties as a function of the degree of heterogeneity was performed using two sets of microstructures obtained from Brownian dynamics simulation and from the void expansion method. Both sets cover a broad and to a large extent overlapping range of degrees of heterogeneity. The simulations have shown that the elastic properties as a function of the degree of heterogeneity are independent of the structure generation algorithm and that the relation between the shear modulus and the degree of heterogeneity can be well described by a power law. This suggests the presence of a critical degree of heterogeneity and, therefore, a phase transition between a phase with finite and one with zero elastic properties.Comment: 8 pages, 6 figures; Granular Matter (published online: 11. February 2012

Prediction of response to Certolizumab-Pegol in rheumatoid arthritis (PreCePRA) by functional MRI of the brain – Study protocol for a randomized double-blind controlled study

Background: Tumor necrosis factor inhibitors (TNFi) signify a major advance in the treatment of rheumatoid arthritis (RA). However, treatment success initially remains uncertain as approximately half of the patients do not respond adequately to TNFi. Thus, an unmet need exists to better predict therapeutic outcome of biologicals. Objectives: We investigated whether brain activity associated with arthritis measured by functional magnetic resonance imaging (fMRI) of the brain can serve as a predictor of response to TNFi in RA patients. Methods: PreCePRA is a multi-center, randomized, double-blind, placebo-controlled fMRI trial on patients with RA [1] [2]. Active RA patients failing csDMARDs therapy with a DAS28 > 3.2 and at least three tender and/or swollen joints underwent a brain BOLD (blood-oxygen-level dependent) fMRI scan upon joint compression at screening. Patients were then randomized into a 12-week double-blinded treatment phase with 200 mg Certolizumab Pegol (CZP) every two weeks (arm 1: fMRI BOLD signal activated volume > 2000 voxel, i.e. 2 cm3; arm 2: fMRI BOLD signal activated volume <2000 voxel) or placebo (arm 3). DAS28 low disease activity at 12 weeks was assigned as primary endpoint. A 12-week follow-up phase in which patients were switched from the placebo to the treatment arm followed the blinded phase. fMRI was carried out at screening as well as after 12 and 24 weeks of receiving CZP or placebo. Conclusion: We hypothesize that high-level central nervous representation of pain in patients with rheumatoid arthritis predicts response to the TNFi CZP which we further investigate in the PreCePRA trial