Nanostructured Chitosan-Based Biomaterials for Sustained and Colon-Specific Resveratrol Release

In the present work, we demonstrate the preparation of chitosan-based composites as vehicles of the natural occurring multi-drug resveratrol (RES). Such systems are endowed with potential therapeutic effects on inflammatory bowel diseases (IBD), such as Crohn’s disease (CD) and ulcerative colitis, through the sustained colonic release of RES from long-lasting mucoadhesive drug depots. The loading of RES into nanoparticles (NPs) was optimized regarding two independent variables: RES/polymer ratio, and temperature. Twenty experiments were carried out and a Box–Behnken experimental design was used to evaluate the significance of these independent variables related to encapsulation efficiency (EE). The enhanced RES EE values were achieved in 24 h at 39 °C and at RES/polymer ratio of 0.75:1 w/w. Sizes and polydispersities of the optimized NPs were studied by dynamic light scattering (DLS). Chitosan (CTS) dispersions containing the RES-loaded NPs were ionically gelled with tricarballylic acid to yield CTS-NPs composites. Macro- and microscopic features (morphology and porosity studied by SEM and spreadability), thermal stability (studied by TGA), and release kinetics of the RES-loaded CTS-NPs were investigated. Release patterns in simulated colon conditions for 48 h displayed significant differences between the NPs (final cumulative drug release: 79–81%), and the CTS-NPs composites (29–34%)

Attribute-Guided Face Generation Using Conditional CycleGAN

We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the input attributes. We demonstrate impressive results on the attribute-guided conditional CycleGAN, which can synthesize realistic face images with appearance easily controlled by user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network, the attribute-guided network becomes the identity-guided conditional CycleGAN which produces impressive and interesting results on identity transfer. We demonstrate three applications on identity-guided conditional CycleGAN: identity-preserving face superresolution, face swapping, and frontal face generation, which consistently show the advantage of our new method.Comment: ECCV 201

Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI.

Identification of optimal ablation sites in hearts with infarct-related ventricular tachycardia (VT) remains difficult to achieve with the current catheter-based mapping techniques. Limitations arise from the ambiguities in determining the reentrant pathways location(s). The goal of this study was to develop experimentally validated, individualized computer models of infarcted swine hearts, reconstructed from high-resolution ex-vivo MRI and to examine the accuracy of the reentrant circuit location prediction when models of the same hearts are instead reconstructed from low clinical-resolution MRI scans. To achieve this goal, we utilized retrospective data obtained from four pigs ~10 weeks post infarction that underwent VT induction via programmed stimulation and epicardial activation mapping via a multielectrode epicardial sock. After the experiment, high-resolution ex-vivo MRI with late gadolinium enhancement was acquired. The Hi-res images were downsampled into two lower resolutions (Med-res and Low-res) in order to replicate image quality obtainable in the clinic. The images were segmented and models were reconstructed from the three image stacks for each pig heart. VT induction similar to what was performed in the experiment was simulated. Results of the reconstructions showed that the geometry of the ventricles including the infarct could be accurately obtained from Med-res and Low-res images. Simulation results demonstrated that induced VTs in the Med-res and Low-res models were located close to those in Hi-res models. Importantly, all models, regardless of image resolution, accurately predicted the VT morphology and circuit location induced in the experiment. These results demonstrate that MRI-based computer models of hearts with ischemic cardiomyopathy could provide a unique opportunity to predict and analyze VT resulting for from specific infarct architecture, and thus may assist in clinical decisions to identify and ablate the reentrant circuit(s)