Liposomal formulations for enhanced lymphatic drug delivery

AbstractThe lymphatic system that extends throughout the whole body is one of useful targets for efficient drug delivery. The intestinal lymphatic drug delivery has been actively studied to date because administered drugs can avoid the first-pass metabolism in the liver, resulting in improvement of oral bioavailability. Drugs must be hydrophobic in order to be transported into the intestinal lymphatics because the lipid absorption mechanism in the intestine is involved in the lymphatic delivery. Therefore, various lipid-based drug carrier systems have been recently utilized to increase the transport of drug into the intestinal lymphatics. Lipidic molecules of the lipid-based drug delivery systems stimulate production of chylomicrons in the enterocytes, resulting in an increase in drug transport into lymphatic in the enterocytes. This review summarizes recently reported information on development of liposomal carriers for the intestinal lymphatic delivery and covers important determinants for successful lymphatic delivery

N-RPN: Hard Example Learning for Region Proposal Networks

The region proposal task is to generate a set of candidate regions that contain an object. In this task, it is most important to propose as many candidates of ground-truth as possible in a fixed number of proposals. In a typical image, however, there are too few hard negative examples compared to the vast number of easy negatives, so region proposal networks struggle to train on hard negatives. Because of this problem, networks tend to propose hard negatives as candidates, while failing to propose ground-truth candidates, which leads to poor performance. In this paper, we propose a Negative Region Proposal Network(nRPN) to improve Region Proposal Network(RPN). The nRPN learns from the RPN's false positives and provide hard negative examples to the RPN. Our proposed nRPN leads to a reduction in false positives and better RPN performance. An RPN trained with an nRPN achieves performance improvements on the PASCAL VOC 2007 dataset

UGPNet: Universal Generative Prior for Image Restoration

Recent image restoration methods can be broadly categorized into two classes: (1) regression methods that recover the rough structure of the original image without synthesizing high-frequency details and (2) generative methods that synthesize perceptually-realistic high-frequency details even though the resulting image deviates from the original structure of the input. While both directions have been extensively studied in isolation, merging their benefits with a single framework has been rarely studied. In this paper, we propose UGPNet, a universal image restoration framework that can effectively achieve the benefits of both approaches by simply adopting a pair of an existing regression model and a generative model. UGPNet first restores the image structure of a degraded input using a regression model and synthesizes a perceptually-realistic image with a generative model on top of the regressed output. UGPNet then combines the regressed output and the synthesized output, resulting in a final result that faithfully reconstructs the structure of the original image in addition to perceptually-realistic textures. Our extensive experiments on deblurring, denoising, and super-resolution demonstrate that UGPNet can successfully exploit both regression and generative methods for high-fidelity image restoration.Comment: Accepted to WACV 202