Correlation between fluid-attenuated inversion recovery vascular hyperintensities and prognosis and cognitive dysfunction in patients with internal watershed cerebral infarction / 中华行为医学与脑科学杂志

Objective:To investigate the correlation between fluid-attenuated inversion recovery vascular hyperintensities(FVH) and prognosis and cognitive dysfunction in patients with internal watershed cerebral infarction(IWI).Methods:Totally106 patients with IWI were selected.According to FVH diagnostic criteria, there were 59 cases in FVH (+ ) group and 47 cases in FVH (-) group.Demographic data and risk factors of cerebrovascular disease were collected to assess the clinical neurological function of the patients at admission and discharge, and the short-term outcome was assessed by modified RANKIN scale (mRS) score at 90 days after discharge.The cognitive function of patients was assessed by MMSE scale.Results:There was no significant difference in NIHSS score between the two groups on admission ( P>0.05). The NIHSS score of FVH (+ ) group((3.37±2.33))at discharge was significantly lower than that of the FVH (-) group ((4.43±2.72))( P<0.05). The rate of neural function improvement and mRS score after discharge 90 days in FVH (+ ) group((42.16±12.20)%, (1.75±1.12)) was significantly higher than that in FVH (-) group((37.58±13.64)%, (2.19±1.38))(both P<0.05). The scores of orientation, recall and language ability of MMSE in FVH (+ ) group ((9.26±0.21), (1.66±0.27), (7.69±0.44) respectively)were significantly lower than those of FVH (-) group((9.43±0.36), (1.83±0.34), (7.85±0.28) respectively)(all P<0.05). There was no significant difference in memory, attention and calculation between the two groups ( P>0.05). Conclusion:FVH has no correlation with the severity of IWI patients when they are admitted to hospital.FVH may be used as an imaging sign for prognosis evaluation of patients with IWI .However, IWI patients with FVH may have more severe cognitive impairment.

Research progress of aptamer and organic nanomaterials based tumor targeting drug delivery systems / 国际生物医学工程杂志

Aptamers are DNA or RNA fragments that can specifically bind to target substances.Because of the excellent properties such as strong binding force,high specificity,small physical size,chemical synthesis and modification,good biocompatibility,and low immunogenicity aptamers show wide application propects in biomedical researches.Aptamers can also bind specifically to receptors on the surface of cell membranes,and mediate the endocytosis of nanoparticles into cells,making them ideal drug targeting ligands.Organic nanomaterials have excellent application value in nanodrug delivery system because of their good biocompatibility and degradability.In this paper,the recent research progress of aptamers and organic nanomaterials drug delivery systems was reviewed.

Template Deformation-Based 3-D Reconstruction of Full Human Body Scans From Low-Cost Depth Cameras.

Full human body shape scans provide valuable data for a variety of applications including anthropometric surveying, clothing design, human-factors engineering, health, and entertainment. However, the high price, large volume, and difficulty of operating professional 3-D scanners preclude their use in home entertainment. Recently, portable low-cost red green blue-depth cameras such as the Kinect have become popular for computer vision tasks. However, the infrared mechanism of this type of camera leads to noisy and incomplete depth images. We construct a stereo full-body scanning environment composed of multiple depth cameras and propose a novel registration algorithm. Our algorithm determines a segment constrained correspondence for two neighboring views, integrating them using rigid transformation. Furthermore, it aligns all of the views based on uniform error distribution. The generated 3-D mesh model is typically sparse, noisy, and even with holes, which makes it lose surface details. To address this, we introduce a geometric and topological fitting prior in the form of a professionally designed high-resolution template model. We formulate a template deformation optimization problem to fit the high-resolution model to the low-quality scan. Its solution overcomes the obstacles posed by different poses, varying body details, and surface noise. The entire process is free of body and template markers, fully automatic, and achieves satisfactory reconstruction results.

Construction of human ARF4 lentiviral vector and stable expression in ovarian cancer cell line SKOV3 / 国际生物医学工程杂志

Objective To establish ovarian cancer cell line SKVO3 that can stably express human ADP ribosylation factor-4 (ARF4). Methods A eukaryotic expression vector pCDH-CMV-MCS-EF1-Puro/ARF4 was constructed and transfected into SKOV3 cells after verifying by DNA sequencing. The expression of ARF4 mRNA was verified by real-time quantitative PCR (qRT-PCR). Then, the recombinant plasmid with lentiviral packaging plasmids were co-transfected into SKOV3 cells for packaging. The recombinant lentiviral particles LV-ARF4 were collected and transfected into SKOV3 cells, and the stable transfected SKOV3 cell line was screening by culture with puromycin. The expression of ARF4 gene was detected by qRT-PCR and Western Blot. Results A eukaryotic expression vector pCDH-CMV-MCS-EF1-Puro/ARF4 was successfully constructed. The vector could significantly up-regulate the expression of ARF4 mRNA in SKOV3 cells and be successfully packaged into recombinant lentiviral particles in HEK-293T cells. Compared with the control group, the relative expression level of ARF4 mRNA and protein in SKOV3 cells was significantly increased after the infection with LV-ARF4 (all P<0.001). Conclusion The recombinant plasmid pCDH-CMV-MCS-EF1-Puro/ARF4 and lentiviral vector LV-ARF4 were successfully constructed. The establishment of stably infected SKOV3 cell line with LV-ARF4 provides an experimental foundation for further studies on the biological function of ARF4 in ovarian cancer.

Unsupervised 3D Local Feature Learning by Circle Convolutional Restricted Boltzmann Machine.

Extracting local features from 3D shapes is an important and challenging task that usually requires carefully designed 3D shape descriptors. However, these descriptors are hand-crafted and require intensive human intervention with prior knowledge. To tackle this issue, we propose a novel deep learning model, namely circle convolutional restricted Boltzmann machine (CCRBM), for unsupervised 3D local feature learning. CCRBM is specially designed to learn from raw 3D representations. It effectively overcomes obstacles such as irregular vertex topology, orientation ambiguity on the 3D surface, and rigid or slightly non-rigid transformation invariance in the hierarchical learning of 3D data that cannot be resolved by the existing deep learning models. Specifically, by introducing the novel circle convolution, CCRBM holds a novel ring-like multi-layer structure to learn 3D local features in a structure preserving manner. Circle convolution convolves across 3D local regions via rotating a novel circular sector convolution window in a consistent circular direction. In the process of circle convolution, extra points are sampled in each 3D local region and projected onto the tangent plane of the center of the region. In this way, the projection distances in each sector window are employed to constitute a novel local raw 3D representation called projection distance distribution (PDD). In addition, to eliminate the initial location ambiguity of a sector window, the Fourier transform modulus is used to transform the PDD into the Fourier domain, which is then conveyed to CCRBM. Experiments using the learned local features are conducted on three aspects: global shape retrieval, partial shape retrieval, and shape correspondence. The experimental results show that the learned local features outperform other state-of-the-art 3D shape descriptors.