Dr Roger Liang

© 2017 Future Medicine Ltd. Aim: To develop matrix metalloproteinase-responsive gelatin-albumin hybrid nanoparticles encapsulating a selective tropomyosin receptor kinase A (TrkA) inhibitor GNF-5837 (Gel-Alb-GNF HNPs) and to demonstrate their anticancer effects in breast cancer. Methods: Gel-Alb-GNF HNPs were prepared using a pH-controlled complexation process from cationic gelatin, dextran sulfate and albumin-bound GNF-5837. The anticancer activities of Gel-Alb-GNF HNPs were tested in a panel of subtype-specific breast cancer cell lines. Results: Gel-Alb-GNF HNPs (~130 nm) displayed excellent stability and matrix metalloproteinase-triggered drug release. Compared with GNF-5837 alone, Gel-Alb-GNF HNPs not only significantly enhanced the antiproliferative and anti-invasive effects but also restored the apoptosis of cancer cells. Conclusion: Gel-Alb-GNF HNPs may be adaptable for stand-alone therapies or used in combination with traditional chemotherapies for breast cancer treatment.

© 2016 Future Medicine Ltd. The use of nanotechnology has great potentials to revolutionize the future cancer diagnosis and therapy. In this context, various nanoparticles (NPs) have been developed for targeted delivery of diagnostic/therapeutic agents to the tumor sites, which thus result in greater efficacy and much less side effects. The targeting property of NPs is often achieved by functionalizing their surface with tumor-specific ligands, such as antibodies, peptides, small molecules and oligonucleotides. In this review, we will discuss recent progress in the multifunctional design of antibody-targeted NPs with a special focus on liposomal, polymeric and protein-based delivery systems.

© 2014 SPIE. Although high dynamic range (HDR) images contain large amounts of information, they have weak texture and low contrast. What's more, these images are difficult to be reproduced on low dynamic range displaying mediums. If much more information is to be acquired when these images are displayed on PCs, some specific transforms, such as compressing the dynamic range, enhancing the portions of little difference in original contrast and highlighting the texture details on the premise of keeping the parts of large contrast, are needed. To this ends, a multi-scale guided filter enhancement algorithm which derives from the single-scale guided filter based on the analysis of non-physical model is proposed in this paper. Firstly, this algorithm decomposes the original HDR images into base image and detail images of different scales, and then it adaptively selects a transform function which acts on the enhanced detail images and original images. By comparing the treatment effects of HDR images and low dynamic range (LDR) images of different scene features, it proves that this algorithm, on the basis of maintaining the hierarchy and texture details of images, not only improves the contrast and enhances the details of images, but also adjusts the dynamic range well. Thus, it is much suitable for human observation or analytical processing of machines.