Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Graphene. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.

Abstract: The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.

Tumor Targeting Strategies of Smart Fluorescent Nanoparticles and Their Applications in Cancer Diagnosis and Treatment

Abstract: Advantages such as strong signal strength, resistance to photobleaching, tunable fluorescence emissions, high sensitivity, and biocompatibility are the driving forces for the application of fluorescent nanoparticles (FNPs) in cancer diagnosis and therapy. In addition, the large surface area and easy modification of FNPs provide a platform for the design of multifunctional nanoparticles (MFNPs) for tumor targeting, diagnosis, and treatment. In order to obtain better targeting and therapeutic effects, it is necessary to understand the properties and targeting mechanisms of FNPs, which are the foundation and play a key role in the targeting design of nanoparticles (NPs). Widely accepted and applied targeting mechanisms such as enhanced permeability and retention (EPR) effect, active targeting, and tumor microenvironment (TME) targeting are summarized here. Additionally, a freshly discovered targeting mechanism is introduced, termed cell membrane permeability targeting (CMPT), which improves the tumor-targeting rate from less than 5% of the EPR effect to more than 50%. A new design strategy is also summarized, which is promising for future clinical targeting NPs/nanomedicines design. The targeting mechanism and design strategy will inspire new insights and thoughts on targeting design and will speed up precision medicine and contribute to cancer therapy and early diagnosis.

Co-Saliency Detection Based on Hierarchical Segmentation

Abstract: Co-saliency detection, an emerging and interesting issue in saliency detection, aims to discover the common salient objects in a set of images. This letter proposes a hierarchical segmentation based co-saliency model. On the basis of fine segmentation, regional histograms are used to measure regional similarities between region pairs in the image set, and regional contrasts within each image are exploited to evaluate the intra-saliency of each region. On the basis of coarse segmentation, an object prior for each region is measured based on the connectivity with image borders. Finally, the global similarity of each region is derived based on regional similarity measures, and then effectively integrated with intra-saliency map and object prior map to generate the co-saliency map for each image. Experimental results on two benchmark datasets demonstrate the better co-saliency detection performance of the proposed model compared to the state-of-the-art co-saliency models.