Macromolecular Transport Through Porous Arterial Walls

Arteries are heterogeneous, composite structures that undergo large cyclic deformations during blood transport. Presence, build-up and consequent rupture of blockages in blood vessels, called atherosclerotic plaques, lead to disruption in the blood flow that can eventually be fatal. Abnormal lipid profile and hypertension are the main risk factors for plaque progression. Treatments span from pharmacological methods, to minimally invasive balloon angioplasty and stent procedures, and finally to surgical alternatives. There is a need to understand arterial disease progression and devise methods to detect, control, treat and manage arterial disease through early intervention. Local delivery through drug eluting stents also provide an attractive option for maintaining vessel integrity and restoring blood flow while releasing controlled amount of drug to reduce and alleviate symptoms. Development of drug eluting stents is hence interesting albeit challenging because it requires an integration of knowledge of mechanical properties with material transport of drug through the arterial wall to produce a desired biochemical effect. Although experimental models are useful in studying such complex multivariate phenomena, numerical models of mass transport in the vessel have proved immensely useful to understand and delineate complex interactions between chemical species, physical parameters and biological variables. The goals of this review are to summarize literature based on studies of mass transport involving low density lipoproteins in the arterial wall. We also discuss numerical models of drug elution from stents in layered and porous arterial walls that provide a unique platform that can be exploited for the design of novel drug eluting stents.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

Use of Vertex Index in Structure-Activity Analysis and Design of Molecules

The last few decades have witnessed application of graph theory and topological indices derived from molecular graph in structure-activity analysis. Such applications are based on regression and various multivariate analyses. Most of the topological indices are computed for the whole molecule and used as descriptors for explaining properties/activities of chemical compounds. However, some substructural descriptors in the form of topological distance based vertex indices have been found to be useful in identifying activity related substructures and in predicting pharmacological and toxicological activities of bioactive compounds. Another important aspect of drug discovery e. g. designing novel pharmaceutical candidates could also be done from the distance distribution associated with such vertex indices. In this article, we will review the development and applications of this approach both in activity prediction as well as in designing novel compounds.