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Shuai Zhang

Bio: Shuai Zhang is an academic researcher from Aalborg University. The author has contributed to research in topics: Antenna (radio) & Medicine. The author has an hindex of 66, co-authored 616 publications receiving 20710 citations. Previous affiliations of Shuai Zhang include Tianjin University & Hong Kong University of Science and Technology.


Papers
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Journal ArticleDOI
TL;DR: This work is the first success of using carbon nanomaterials for efficient in vivo photothermal therapy by intravenous administration and suggests the great promise of graphene in biomedical applications, such as cancer treatment.
Abstract: Although biomedical applications of carbon nanotubes have been intensively studied in recent years, its sister, graphene, has been rarely explored in biomedicine. In this work, for the first time we study the in vivo behaviors of nanographene sheets (NGS) with polyethylene glycol (PEG) coating by a fluorescent labeling method. In vivo fluorescence imaging reveals surprisingly high tumor uptake of NGS in several xenograft tumor mouse models. Distinctive from PEGylated carbon nanotubes, PEGylated NGS shows several interesting in vivo behaviors including highly efficient tumor passive targeting and relatively low retention in reticuloendothelial systems. We then utilize the strong optical absorbance of NGS in the near-infrared (NIR) region for in vivo photothermal therapy, achieving ultraefficient tumor ablation after intravenous administration of NGS and low-power NIR laser irradiation on the tumor. Furthermore, no obvious side effect of PEGylated NGS is noted for the injected mice by histology, blood chemi...

2,151 citations

Journal ArticleDOI
TL;DR: A comprehensive review of recent research efforts on deep learning-based recommender systems is provided in this paper, along with a comprehensive summary of the state-of-the-art.
Abstract: With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.

1,070 citations

Journal ArticleDOI
Bo Tian1, Chao Wang1, Shuai Zhang1, Liangzhu Feng1, Zhuang Liu1 
09 Aug 2011-ACS Nano
TL;DR: It is shown that the photothermal effect of graphene can be utilized to promote the delivery of Ce6 molecules by mild local heating when exposed to a near-infrared laser at a low power density, further enhancing the PDT efficacy against cancer cells.
Abstract: Graphene with unique physical and chemical properties has shown various potential applications in biomedicine. In this work, a photosensitizer molecule, Chlorin e6 (Ce6), is loaded on polyethylene glycol (PEG)-functionalized graphene oxide (GO) via supramolecular π–π stacking. The obtained GO-PEG-Ce6 complex shows excellent water solubility and is able to generate cytotoxic singlet oxygen under light excitation for photodynamic therapy (PDT). Owing to the significantly enhanced intracellular trafficking of photosensitizers, our GO-PEG-Ce6 complex offers a remarkably improved cancer cell photodynamic destruction effect compared to free Ce6. More importantly, we show that the photothermal effect of graphene can be utilized to promote the delivery of Ce6 molecules by mild local heating when exposed to a near-infrared laser at a low power density, further enhancing the PDT efficacy against cancer cells. Our work highlights the promise of using graphene for potential multifunctional cancer therapies.

929 citations

Journal ArticleDOI
25 Jan 2011-ACS Nano
TL;DR: Results show that PEGylated NGS mainly accumulate in the reticuloendothelial system (RES) including liver and spleen after intravenous administration and can be gradually cleared, likely by both renal and fecal excretion.
Abstract: Graphene has emerged as interesting nanomaterials with promising applications in a range of fields including biomedicine. In this work, for the first time we study the long-term in vivo biodistribution of 125I-labeled nanographene sheets (NGS) functionalized with polyethylene glycol (PEG) and systematically examine the potential toxicity of graphene over time. Our results show that PEGylated NGS mainly accumulate in the reticuloendothelial system (RES) including liver and spleen after intravenous administration and can be gradually cleared, likely by both renal and fecal excretion. PEGylated NGS do not cause appreciable toxicity at our tested dose (20 mg/kg) to the treated mice in a period of 3 months as evidenced by blood biochemistry, hematological analysis, and histological examinations. Our work greatly encourages further studies of graphene for biomedical applications.

751 citations

Journal ArticleDOI
Kai Yang1, Jianmei Wan1, Shuai Zhang1, Bo Tian1, Youjiu Zhang1, Zhuang Liu1 
TL;DR: The results highlight that both surface chemistry and sizes are critical to the in vivo performance of graphene, and show the promise of using optimized nano-graphene for ultra-effective photothermal treatment, which may potentially be combined with other therapeutic approaches to assist the fight against cancer.

674 citations


Cited by
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations