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The purpose of this study is to determine the correlation between the distribution of nanoparticles in the vitreous and retina and their surface properties after intravitreal injection. For this purpose, we synthesized seven kinds of nanoparticles through self-assembly of amphiphilic polymer conjugates in aqueous condition. They showed similar size but(More)
Since computed tomography (CT) was developed and its resolution, sensitivity, and scan speed rapidly improved, the use of CT in the diagnosis of hepatic disease has been evaluated by various investigators. In particular, liver-specific X-ray CT imaging has attracted much attention in cancer diagnosis and cancer treatment because liver metastases are a(More)
The application of gold nanoparticles (AuNPs) in biomedical field was limited due to the low stability in the biological condition. Herein, to enhance stability and tumor targeting ability of AuNPs, their surface was modified with biocompatible glycol chitosan (GC) and the in vivo biodistribution of GC coated AuNPs (GC-AuNPs) were studied through computed(More)
An efficient and straightforward method for radiolabeling nanoparticles is urgently needed to understand the in vivo biodistribution of nanoparticles. Herein, we investigated a facile and highly efficient strategy to prepare radiolabeled glycol chitosan nanoparticles with (64)Cu via a strain-promoted azide-alkyne cycloaddition strategy, which is often(More)
Cell labeling and tracking are important processes in understanding biologic mechanisms and the therapeutic effect of inoculated cells in vivo. Numerous attempts have been made to label and track inoculated cells in vivo; however, these methods have limitations as a result of their biological effects, including secondary phagocytosis of macrophages and(More)
In this paper, we propose a scheme to improve the performance of subspace learning by using a pattern(data) selection method as preprocessing. Generally, a training set for subspace learning contains irrelevant or unreliable samples, and removing these samples can improve the learning performance. For this purpose, we use pattern selection preprocessing(More)