Synthesis and characterization of rabies virus glycoprotein-tagged amphiphilic cyclodextrins for siRNA delivery in human glioblastoma cells: in vitro analysis

In man brain cancer is an aggressive, malignant form of tumour, it is highly infiltrative in nature, is associated with cellular heterogeneity and affects cerebral hemispheres of the brain. Current drug therapies are inadequate and an unmet clinical need exists to develop new improved therapeutics. The ability to silence genes associated with disease progression by using short interfering RNA (siRNA) presents the potential to develop safe and effective therapies. In this work, in order to protect the siRNA from degradation, promote cell specific uptake and enhance gene silencing efficiency, a PEGylated cyclodextrin (CD)-based nanoparticle, tagged with a CNS-targeting peptide derived from the rabies virus glycoprotein (RVG) was formulated and characterized. The modified cyclodextrin derivatives were synthesized and co-formulated to form nanoparticles containing siRNA which were analysed for size, surface charge, stability, cellular uptake and gene-knockdown in brain cancer cells. The results identified an optimised co-formulation prototype at a molar ratio of 1:1.5:0.5 (cationic cyclodextrin:PEGylated cyclodextrin:RVG-tagged PEGylated cyclodextrin) with a size of 281±39.72nm, a surface charge of 26.73±3mV, with efficient cellular uptake and a 27% gene-knockdown ability. This CD-based formulation represents a potential nanocomplex for systemic delivery of siRNA targeting brain cancer.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Separação de dados hiperespectrais baseada na análise de vértices de um simplex

Os sensores hiperespectrais que estão a ser desenvolvidos para aplicações em detecção remota, produzem uma elevada quantidade de dados. Tal quantidade de dados obriga a que as ferramentas de análise e processamento sejam eficientes e tenham baixa complexidade computacional. Uma tarefa importante na detecção remota é a determinação das substâncias presentes numa imagem hiperespectral e quais as suas concentrações. Neste contexto, Vertex component analysis (VCA), é um método não-supervisionado recentemente proposto que é eficiente e tem a complexidade computacional mais baixa de todos os métodos conhecidos. Este método baseia-se no facto de os vértices do simplex corresponderem às assinaturas dos elementos presentes nos dados. O VCA projecta os dados em direcções ortogonais ao subespaço gerado pelas assinaturas das substâncias já encontradas, correspondendo o extremo desta projecção à assinatura da nova substância encontrada. Nesta comunicação apresentam-se várias optimizações ao VCA nomeadamente: 1) a introdução de um método de inferência do sub-espaço de sinal que permite para além de reduzir a dimensionalidade dos dados, também permite estimar o número de substâncias presentes. 2) projeção dos dados é executada em várias direcções para garantir maior robustez em situações de baixa relação sinal-ruído. As potencialidades desta técnica são ilustradas num conjunto de experiências com dados simulados e reais, estes últimos adquiridos pela plataforma AVIRIS.