Biotherapeutics such as peptides possess strong potential for the treatment of intractable neurological disorders. However, because of their low stability and the impermeability of the blood-brain barrier (BBB), biotherapeutics are difficult to transport into brain parenchyma via intravenous injection. Herein, we present a novel poly(ethylene glycol)-poly(d,l-lactic-co-glycolic acid) polymersome-based nanomedicine with self-assembled bilayers, which was functionalized with lactoferrin (Lf-POS) to facilitate the transport of a neuroprotective peptide into the brain. The apparent diffusion coefficient (D*) of H(+) through the polymersome membrane was 5.659 × 10(-26) cm(2) s(-1), while that of liposomes was 1.017 × 10(-24) cm(2) s(-1). The stability of the polymersome membrane was much higher than that of liposomes. The uptake of polymersomes by mouse brain capillary endothelial cells proved that the optimal density of lactoferrin was 101 molecules per polymersome. Fluorescence imaging indicated that Lf101-POS was effectively transferred into the brain. In pharmacokinetics, compared with transferrin-modified polymersomes and cationic bovine serum albumin-modified polymersomes, Lf-POS obtained the greatest BBB permeability surface area and percentage of injected dose per gram (%ID per g). Furthermore, Lf-POS holding S14G-humanin protected against learning and memory impairment induced by amyloid-β25-35 in rats. Western blotting revealed that the nanomedicine provided neuroprotection against over-expression of apoptotic proteins exhibiting neurofibrillary tangle pathology in neurons. The results indicated that polymersomes can be exploited as a promising non-invasive nanomedicine capable of mediating peptide therapeutic delivery and controlling the release of drugs to the central nervous system.
Deep neural network solutions have emerged as a new and powerful paradigm for speech enhancement (SE). The capabilities to capture long context and extract multi-scale patterns are crucial to design effective SE networks. Such capabilities, however, are often in conflict with the goal of maintaining compact networks to ensure good system generalization.In this paper, we explore dilation operations and apply them to fully convolutional networks (FCNs) to address this issue. Dilations equip the networks with greatly expanded receptive fields, without increasing the number of parameters. Different strategies to fuse multi-scale dilations, as well as to install the dilation modules are explored in this work. Using Noisy VCTK and AzBio sentences datasets, we demonstrate that the proposed dilation models significantly improve over the baseline FCN and outperform the state-ofthe-art SE solutions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.