Enhancement of Naringenin Bioavailability by Complexation with Hydroxypropoyl-β-Cyclodextrin

The abundant flavonoid aglycone, naringenin, which is responsible for the bitter taste in grapefruits, has been shown to possess hypolipidemic and anti-inflammatory effects both in vitro and in vivo. Recently, our group demonstrated that naringenin inhibits hepatitis C virus (HCV) production, while others demonstrated its potential in the treatment of hyperlipidemia and diabetes. However, naringenin suffers from low oral bioavailability critically limiting its clinical potential. In this study, we demonstrate that the solubility of naringenin is enhanced by complexation with β-cyclodextrin, an FDA approved excipient. Hydroxypropoyl-β-cyclodextrin (HPβCD), specifically, increased the solubility of naringenin by over 400-fold, and its transport across a Caco-2 model of the gut epithelium by 11-fold. Complexation of naringenin with HPβCD increased its plasma concentrations when fed to rats, with AUC values increasing by 7.4-fold and Cmax increasing 14.6-fold. Moreover, when the complex was administered just prior to a meal it decreased VLDL levels by 42% and increased the rate of glucose clearance by 64% compared to naringenin alone. These effects correlated with increased expression of the PPAR co-activator, PGC1α in both liver and skeletal muscle. Histology and blood chemistry analysis indicated this route of administration was not associated with damage to the intestine, kidney, or liver. These results suggest that the complexation of naringenin with HPβCD is a viable option for the oral delivery of naringenin as a therapeutic entity with applications in the treatment of dyslipidemia, diabetes, and HCV infection.National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (K01DK080241)Harvard Clinical Nutrition Research Center (P30-DK040561)European Research Council (Starting Grant (TMIHCV 242699))Massachusetts General Hospital (BioMEMS Resource Center (P41 EB-002503))Alexander Silberman Institute of Life Science

A Parallel Computation Algorithm for Image Feature Extraction

 We present a new method for image feature-extraction for learning image classification. An image is represented by a feature vector of distances that measure the dissimilarity between regions of the image and a set of fixed image prototypes. The method uses a text-based representation of images where the texture of an image corresponds to patterns of symbols in the text string. The distance between two images is based on the LZ-complexity of their corresponding strings. Given a set of input images, the algorithm produces cases that can be used by any supervised or unsupervised learning algorithm to learn image classification or clustering. A main advantage in this approach is the lack of need for any image processing or image analysis. A non-expert user can define the image-features by selecting a few small images that serve as prototypes for each class category. The algorithm is designed to run on a parallel processing platform. Results on the classification accuracy and processing speed are reported for several image classification problems including aerial imaging