Using Flory-Huggins phase diagrams as a pre-formulation tool for the production of amorphous solid dispersions; a comparison between hot-melt extrusion and spray drying

Objectives: Amorphous drug forms provide a useful method of enhancing the dissolution performance of poorly water-soluble drugs; however, they are inherently unstable. In this article, we have used Flory–Huggins theory to predict drug solubility and miscibility in polymer candidates, and used this information to compare spray drying and melt extrusion as processes to manufacture solid dispersions.
Method:  Solid dispersions were characterised using a combination of thermal (thermogravimetric analysis and differential scanning calorimetry) and spectroscopic (Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction methods. 
Key Findings: Spray drying permitted generation of amorphous solid dispersions to be produced across a wider drug concentration than melt extrusion. Melt extrusion provided sufficient energy for more intimate mixing to be achieved between drug and polymer, which may improve physical stability. It was also confirmed that stronger drug–polymer interactions might be generated through melt extrusion. Remixing and dissolution of recrystallised felodipine into the polymeric matrices did occur during the modulated differential scanning calorimetry analysis, but the complementary information provided from FTIR confirms that all freshly prepared spray-dried samples were amorphous with the existence of amorphous drug domains within high drug-loaded samples. 
Conclusion: Using temperature–composition phase diagrams to probe the relevance of temperature and drug composition in specific polymer candidates facilitates polymer screening for the purpose of formulating solid dispersions.

Mycosis fungoides shows concurrent deregulation of multiple genes involved in the TNF signaling pathway:an expression profile study

Mycosis fungoides (MF) is the most frequent type of cutaneous T-cell lymphoma, whose diagnosis and study is hampered by its morphologic similarity to inflammatory dermatoses (ID) and the low proportion of tumoral cells, which often account for only 5% to 10% of the total tissue cells. cDNA microarray studies using the CNIO OncoChip of 29 MF and 11 ID cases revealed a signature of 27 genes implicated in the tumorigenesis of MF, including tumor necrosis factor receptor (TNFR)-dependent apoptosis regulators, STAT4, CD40L, and other oncogenes and apoptosis inhibitors. Subsequently a 6-gene prediction model was constructed that is capable of distinguishing MF and ID cases with unprecedented accuracy. This model correctly predicted the class of 97% of cases in a blind test validation using 24 MF patients with low clinical stages. Unsupervised hierarchic clustering has revealed 2 major subclasses of MF, one of which tends to include more aggressive-type MF cases including tumoral MF forms. Furthermore, signatures associated with abnormal immunophenotype (11 genes) and tumor stage disease (5 genes) were identified.

Differential power analysis resistance of Camellia and countermeasure strategy on FPGAs

Security devices are vulnerable to Differential Power Analysis (DPA) that reveals the key by monitoring the power consumption of the circuits. In this paper, we present the first DPA attack against an FPGA implementation of the Camellia encryption algorithm with all key sizes and evaluate the DPA resistance of the algorithm. The Camellia cryptographic algorithm involves several different key-dependent intermediate operations including S-Box operations. In previous research, it was believed that the Camellia is stronger than AES due to the additional Whitening phase protecting the S-Box operation. However, we propose an attack that bypasses the Whitening phase and targets the S-Box. In this paper, we also discuss a lowcost countermeasure strategy to protect the Pre-whitening / Post-whitening and FL function of Camellia using Dual-rail Precharged Logic and to protect against attacks of the S-Box using Random Delay Insertion. © 2009 IEEE.

Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

Motivation: To date, Gene Set Analysis (GSA) approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations, or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.

Results: In GSNCA, weight factors are assigned to genes in proportion to the genes' cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA, indeed, captures changes in the structure of genes' cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses.