Search

The central nervous system (CNS) is the most difficult target for drug delivery therapies. Despite datasets available describing physiological, biochemical, cellular, and metabolic properties of the CNS, the development of infusion therapies still faces major delivery challenges. There is a need for the integration of data obtained from different experimental modalities to design molecular therapies. In this paper, we propose a novel mathematical method for the integration of datasets to generate useful dosing criteria for infusion therapies. A case study is used to demonstrate the design of gene silencing therapies to down regulate NMDA receptors in the spinal cord for chronic pain management. Based on experimentally derived kinetics for short interfering RNA (siRNA) and magnetic resonance images, the biodistribution and pharmacokinetics of siRNAs were predicted for different infusion modes. This adaptable, multiscale computational platform enables the prediction of dose–response on an organ-wide level. The quantitative integration of valuable datasets with engineering precision is expected to accelerate the clinical implementation of novel therapeutics. [ABSTRACT FROM PUBLISHER]

Dendritic spines form postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by neurobiologists. Determining the relationship between dendritic spine morphology change and its functional properties such as memory learning is a fundamental yet challenging problem in neurobiology research. In this paper, we propose a novel algorithm to track the morphology change of multiple spines simultaneously in time-lapse neuronal images based on nonrigid registration and integer programming. We also propose a robust scheme to link disappearing-and-reappearing spines. Performance comparisons with other state-of-the-art cell and spine tracking algorithms, and the ground truth show that our approach is more accurate and robust, and it is capable of tracking a large number of neuronal spines in time-lapse confocal microscopy images. [ABSTRACT FROM AUTHOR]