Gene therapy with caspase-3 small interfering RNA-nanoparticles is neuroprotective after optic nerve damage

Apoptosis,a key mechanism of programmed cell death,is triggered by caspase-3 protein and lowering its levels with gene therapy may rescue cell death after central nervous system damage.We developed a novel,non-viral gene therapy to block caspase-3 gene expression using small interfering RNA(siRNA)delivered by polybutylcyanoacrylate nanoparticles(CaspNPs).In vitro CaspNPs significantly blocked caspase-3 protein expression in C6 cells,and when injected intraocularly in vivo,CaspNPs lowered retinal capsase-3 immunofluorescence by 57.9%in rats with optic nerve crush.Longitudinal,repeated retinal ganglion cell counts using confocal neuroimaging showed that post-traumatic cell loss after intraocular CaspNPs injection was only 36.1%versus 63.4%in lesioned controls.Because non-viral gene therapy with siRNA-nanoparticles can selectively silence caspace-3 gene expression and block apoptosis in post-mitotic neurons,siRNA delivery with nanoparticles may be promising for neuroprotection or restoration of central visual system damage and other neurological disorders.The animal study procedures were approved by the German National Act on the use of experimental animals(Ethic Committee Referat Verbraucherschutz,Veterinärangelegenheiten;Landesverwaltungsamt Sachsen-Anhalt,Halle,Germany,#IMP/G/01-1150/12 and#IMP/G/01-1469/17).

Analog Module Placement Design Using Genetic Algorithm

This paper presents a novel genetic algorithm for analog module placement based on a generalization of the two-dimensional bin packing problem. The genetic encoding and operators assure that all problem constraints are always satisfied. Thus the potential problems of adding penalty terms to the cost function are eliminated so that the search configuration space is drastically decreased. The dedicated cost function is based on the special requirements of analog integrated circuits. A fractional factorial experiment was conducted using an orthogonal array to study the algorithm parameters. A meta-GA was applied to determine the optimal parameter values. The algorithm was tested with several local benchmark circuits. The experimental results show that the algorithm has better performance than the simulated annealing approach with satisfactory results comparable to manual placement. This study demonstrates the effectiveness of the genetic algorithm in the analog module placement problem. The algorithm has been successfully used in a layout synthesis tool.