SM5-1 is a humanized mouse monoclonal antibody, targeting an over-expressed membrane protein of approximately 230 kDa in hepatocellular carcinoma (HCC). SM5-1 can be used for target therapy in hepatocellular carinoma due to its ability of inhibiting cell growth and inducing apoptosis. However, the tumor inhibition efficacy of SM5-1 in HCC cancer treatment remains low. In this study, we synthesized SM5-1-conjugated gold nanoparticles (Au-SM5-1 NPs) and investigated their anticancer efficacy in HCC both in vitro and in vivo. The tumor inhibition rates of Au-SM5-1 NPs for subcutaneous tumor mice were 40.10% ± 4.34%, 31.37% ± 5.12%, and 30.63% ± 4.87% on day 12, 18, and 24 post-treatment as determined by bioluminescent intensity. In addition, we investigated the antitumor efficacy of Au-SM5-1 NPs in orthotopic HCC tumor models. The results showed that the inhibition rates of Au-SM5-1 NPs can reach up to 39.64% ± 4.87% on day 31 post-treatment determined by the bioluminescent intensity of the abdomen in tumor-bearing mice. Furthermore, three-dimensional reconstruction results of the orthotopic tumor revealed that Au-SM5-1 NPs significantly inhibited tumor growth compared with SM5-1 alone. Our results suggested that the developed Au-SM5-1 NPs has great potential as an antibody-based nano-drug for HCC therapy.

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Deconvolution provides an efficient technology to implement angular super-resolution for scanning radar forward-looking imaging. However, deconvolution is an ill-posed problem, of which the solution is not only sensitive to noise, but also would be easily deteriorate by the noise amplification when excessive iterations are conducted. In this paper, a penalized maximum likelihood angular super-resolution method is proposed to tackle these problems. Firstly, a new likelihood function is deduced by separately considering the noise in I and Q channels to enhance the accuracy of the noise modeling for radar imaging system. Afterwards, to conquer the noise amplification and maintain the resolving ability of the proposed method, a joint square-Laplace penalty is particularly formulated by making use of the outlier sensitivity property of square constraint as well as the sparse expression ability of Laplace distribution. Finally, in order to facilitate the engineering application of the proposed method, an accelerated iterative solution strategy is adopted to solve the obtained convex optimal problem. Experiments based on both synthetic data and real data demonstrate the effectiveness and superior performance of the proposed method.

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This paper describes a numerical procedure for determining geodesies on a surface. The surface is defined by a mesh of planar triangular finite elements. Examples are presented to illustrate the relevance of this procedure in the development of cutting patterns for membrane structures.