Exploring the risk-factor association between depression and incident stroke: a systematic review and meta-analysis

There is growing evidence that depression increases the risk of incident stroke. However, few studies have considered possible residual confounding effects by preexistent cerebrovascular and cardiac diseases. Therefore, we synthesized data from cohort studies to explore whether depressed individuals free of cerebrovascular and cardiac diseases are at higher risk of incident stroke. We searched the electronic databases PubMed and Medline for eligible cohort studies that examined the prospective association between depression and first-ever stroke. A random-effects model was used for quantitative data synthesis. Sensitivity analyses comprised cohort studies that considered a lag period with exclusion of incident strokes in the first years of follow-up to minimize residual confounding by preexistent silent strokes and excluded cardiac disease at baseline. Overall, we identified 28 cohort studies with 681,139 participants and 13,436 (1.97%) incident stroke cases. The pooled risk estimate revealed an increased risk of incident stroke for depression (relative risk 1.40, 95% confidence interval [CI] 1.27–1.53; P<0.0001). When we excluded incident strokes that occurred in the first years of follow-up, the prospective association between depression and incident stroke remained significant (relative risk 1.64, 95% CI 1.27–2.11; P<0.0001). This positive association also remained after we considered only studies with individuals with cardiac disease at baseline excluded (relative risk 1.43, 95% CI 1.19–1.72; P<0.0001). The prospective association of depression and increased risk of first-ever stroke demonstrated in this meta-analysis appears to be driven neither by preexistence of clinically apparent cerebrovascular and cardiovascular diseases nor by silent stroke

Modeling the drug release from hydrogel-based matrices

In this work the behavior of hydrogel-based matrices, the most widespread systems for oral controlled release of pharmaceuticals, has been mathematically described. In addition, the calculations of the model have been validated against a rich set of experimental data obtained working with tablets made of hydroxypropyl methylcellulose (a hydrogel) and theophylline (a model drug). The model takes into account water uptake, hydrogel swelling, drug release, and polymer erosion. The model was obtained as an improvement of a previous code, describing the diffusion in concentrated systems, and obtaining the erosion front (which is a moving boundary) from the polymer mass balance (in this way, the number of fitting parameters was also reduced by one). The proposed model was found able to describe all the observed phenomena, and then it can be considered a tool with predictive capabilities, useful in design and testing of new dosage systems based on hydrogels

3D multi-agent models for protein release from PLGA spherical particles with complex inner morphologies

In order to better understand and predict the release of proteins from bioerodible micro- or nanospheres, it is important to know the influences of different initial factors on the release mechanisms. Often though it is difficult to assess what exactly is at the origin of a certain dissolution profile. We propose here a new class of fine-grained multi-agent models built to incorporate increasing complexity, permitting the exploration of the role of different parameters, especially that of the internal morphology of the spheres, in the exhibited release profile. This approach, based on Monte-Carlo (MC) and Cellular Automata (CA) techniques, has permitted the testing of various assumptions and hypotheses about several experimental systems of nanospheres encapsulating proteins. Results have confirmed that this modelling approach has increased the resolution over the complexity involved, opening promising perspectives for future developments, especially complementing in vitro experimentation

Parallel Excluded Volume Tempering for Polymer Melts

We have developed a technique to accelerate the acquisition of effectively uncorrelated configurations for off-lattice models of dense polymer melts which makes use of both parallel tempering and large scale Monte Carlo moves. The method is based upon simulating a set of systems in parallel, each of which has a slightly different repulsive core potential, such that a thermodynamic path from full excluded volume to an ideal gas of random walks is generated. While each system is run with standard stochastic dynamics, resulting in an NVT ensemble, we implement the parallel tempering through stochastic swaps between the configurations of adjacent potentials, and the large scale Monte Carlo moves through attempted pivot and translation moves which reach a realistic acceptance probability as the limit of the ideal gas of random walks is approached. Compared to pure stochastic dynamics, this results in an increased efficiency even for a system of chains as short as $N = 60$ monomers, however at this chain length the large scale Monte Carlo moves were ineffective. For even longer chains the speedup becomes substantial, as observed from preliminary data for $N = 200$

The electric double layer has a life of its own

Using molecular dynamics simulations with recently developed importance sampling methods, we show that the differential capacitance of a model ionic liquid based double-layer capacitor exhibits an anomalous dependence on the applied electrical potential. Such behavior is qualitatively incompatible with standard mean-field theories of the electrical double layer, but is consistent with observations made in experiment. The anomalous response results from structural changes induced in the interfacial region of the ionic liquid as it develops a charge density to screen the charge induced on the electrode surface. These structural changes are strongly influenced by the out-of-plane layering of the electrolyte and are multifaceted, including an abrupt local ordering of the ions adsorbed in the plane of the electrode surface, reorientation of molecular ions, and the spontaneous exchange of ions between different layers of the electrolyte close to the electrode surface. The local ordering exhibits signatures of a first-order phase transition, which would indicate a singular charge-density transition in a macroscopic limit