ALBERT

Description: A (model) composite system for drug delivery was developed based on a thermoresponsive hydrogel loaded with microparticles. We used Pluronic F127 hydrogel as the continuous phase and alginate microparticles as the dispersed phase of this composite system. It is well known that Pluronic F127 forms a gel when added to water in an appropriate concentration and in a certain temperature range. Pluronic F127 hydrogel may be loaded with drug and injected, in its sol state, to act as a drug delivery system in physiological environment. A rheological characterization allowed the most appropriate concentration of Pluronic F127 (15.5 wt%) and appropriate alginate microparticles contents (5 and 10 wt%) to be determined. Methylene blue (MB) was used as model drug to perform drug release studies in MB loaded Pluronic hydrogel and in MB loaded alginate microparticles/Pluronic hydrogel composite system. The latter showed a significantly slower MB release than the former (10 times), suggesting its potential in the development of dual cargo release systems either for drug delivery or tissue engineering.

Description: Development and progression of many human diseases, such as systemic lupus erythematosus (SLE), are hypothesized to result from interactions between genetic and environmental factors. Current approaches to identify and evaluate interactions are limited, most often focusing on main effects and two-way interactions. While higher order interactions associated with disease are documented, they are difficult to detect since expanding the search space to all possible interactions of p predictors means evaluating 2p − 1 terms. For example, data with 150 candidate predictors requires considering over 1045 main effects and interactions. In this study, we present an analytical approach involving selection of candidate single nucleotide polymorphisms (SNPs) and environmental and/or clinical factors and use of Logic Forest to identify predictors of disease, including higher order interactions, followed by confirmation of the association between those predictors and interactions identified with disease outcome using logistic regression. We applied this approach to a study investigating whether smoking and/or secondhand smoke exposure interacts with candidate SNPs resulting in elevated risk of SLE. The approach identified both genetic and environmental risk factors, with evidence suggesting potential interactions between exposure to secondhand smoke as a child and genetic variation in the ITGAM gene associated with increased risk of SLE.