Therapeutic effect of modified trabeculectomy in treatment of neovascular glaucoma

AIM: To analyze the effect of modified trabeculectomy in the treatment of neovascular glaucoma.<p>METHODS: There were 44 cases of neovascular glaucoma enrolled during June 2008 to December 2012. They were randomly divided into experimental group(22 cases)and control group(22 cases). The experimental group were treated with the modified trabeculectomy, and the control group were treated with cyclocryotherapy. Therapeutic effect and complications between two groups were compared. <p>RESULTS: Introocular pressure in both groups were significantly lower after treatment than before treatment(<i>P</i><0.01), but there was no obvious difference between two groups(<i>P</i>>0.05). The total effective 21 cases(95%)in experimental group was higher than that in the control group 16 cases(73%)and the difference had statistically significance(<i>χ</i>²=7.3789, <i>P</i><0.05). At the same time, the incidence of complications and severity in control group were significantly higher than that in the experimental group(<i>P</i><0.05). <p>CONCLUSION: For patients with neovascular glaucoma, modified trabeculectomy can effectively reduce the intraocular pressure, and significantly improve the treatment success rate, as well as reduce the occurrence of adverse reactions. It is a safe and effective method, so modified trabeculectomy should be applied widely in clinical practice

Identification of Regional Lymph Node Involvement of Colorectal Cancer by Serum SELDI Proteomic Patterns

Background. To explore the application of serum proteomic patterns for the preoperative detection of regional lymph node involvement of colorectal cancer (CRC). Methods. Serum samples were applied to immobilized metal affinity capture ProteinChip to generate mass spectra by Surface-Enhanced Laser Desorption/ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). Proteomic spectra of serum samples from 70 node-positive CRC patients and 75 age- and gender-matched node-negative CRC patients were employed as a training set, and a classification tree was generated by using Biomarker Pattern Software package. The validity of the classification tree was then challenged with a blind test set including another 65 CRC patients. Results. The software identified an average of 46 mass peaks/spectrum and 5 of the identified peaks at m/z 3,104, 3,781, 5,867, 7,970, and 9,290 were used to construct the classification tree. The classification tree separated effectively node-positive CRC patients from node-negative CRC patients, achieving a sensitivity of 94.29% and a specificity of 100.00%. The blind test challenged the model independently with a sensitivity of 91.43% a specificity of 96.67%. Conclusions. The results indicate that SELDI-TOF-MS can correctly distinguish node-positive CRC patients from node-negative ones and show great potential for preoperative screening for regional lymph node involvement of CRC

Luteoloside Inhibits Proliferation of Human Chronic Myeloid Leukemia K562 Cells by Inducing G2/M Phase Cell Cycle Arrest and Apoptosis

Purpose: To investigate the effects of luteoloside on the proliferation of human chronic myeloid leukemia K562 cells and whether luteoloside induces cell cycle arrest and apoptosis in K562 cells.Methods: Luteoloside’s cytotoxicity was assessed using a cell counting kit. Cell cycle distribution was analysed by flow cytometry after propidium iodide (PI) staining. Cell apoptosis was assayed with apoptosis detection kit and Hoechst staining followed by observation under a fluorescence microscope. The expression of cell cycle- and apoptosis-related proteins was examined by Western blot analysis.Results: Luteoloside inhibited the proliferation of K562 cells in a dose- and time- dependent manner (IC50 = 30.7 μM) with less toxicity in a normal human cell line (IC50 = 91.8 μM). Moreover, antiproliferative effect of luteoloside was accompanied with G2/M phase arrest（p ＜ 0.05 or p＜0.01） and apoptosis（p ＜ 0.01 or p ＜ 0.001）. Further studies revealed that the expression level of cyclinB1 was down-regulated by luteoloside treatment. Furthermore, luteoloside treatment also increased proapoptotic protein Bax expression and decreased anti-apoptotic protein Bcl-2 expression.Conclusion: These results suggest that the inhibitory effect of luteoloside on K562 cell proliferation is associated with inducing G2/M phase arrest and apoptosis, and that luteoloside is worth further studying for anticancer potential.Keywords: Luteoloside, Myeloid leukemia, Proliferation, Cell cycle arrest, Apoptosis, Anticance

Learning Semantically Enhanced Feature for Fine-Grained Image Classification

We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we first achieve the sub-feature semantic by arranging feature channels of a CNN into different groups through channel permutation. Meanwhile, to enhance the discriminability of sub-features, the groups are guided to be activated on object parts with strong discriminability by a weighted combination regularization. Our approach is parameter parsimonious and can be easily integrated into the backbone model as a plug-and-play module for end-to-end training with only image-level supervision. Experiments verified the effectiveness of our approach and validated its comparable performance to the state-of-the-art methods. Code is available at https://github.com/cswluo/SEFComment: Accepted by IEEE Signal Processing Letters. 5 pages, 4 figures, 4 table

Molecular docking studies on rocaglamide, a traditional Chinese medicine for periodontitis

Purpose: To undertake an in silico assessment of rocaglamide as a potential drug therapy forperiodontitis (dental arthritis).Method: Lamarckian algorithm-based automated docking approach using AutoDock4.2 tool wasapplied for calculating the best possible binding mode of rocaglamide to IL-23p19 and IL-17, the targets of anti-inflammatory drugs in periodontal disease.Results: The top two interactions of rocaglamide with IL-17 (ΔG = -5.45 and -4.83 kcal/mol) were more spontaneous, and the physical interactions (two hydrogen bonds and one π-πbond) generated in the two IL-17- rocaglamide complexes were higher in number than in IL-23p14-rocaglamide complexes.Conclusion: In silico analysis of rocaglamide, a known antimicrobial and anti-inflammatory agent, is a promising natural candidate for periodontitis therapy, and should be further subjected to in vitro and in vivo anti-periodontitis investigations.Keywords: Periodontitis, Inflammation, Rocaglamide, Molecular docking, Lamarckian algorithm, IL- 23p19, IL-1

4-[(2′-Cyano­biphen­yl-4-yl)meth­yl]morpholin-4-ium tetra­fluoridoborate

In the crystal structure of the title compound, C18H19N2O+·BF4 −, bifurcated N—H⋯(F,F) hydrogen bonds link the protonated 4′-morpholine­methyl­biphenyl-2-carbonitrile cations and slightly distorted tetra­fluoro­borate anions. π–π inter­actions [centroid–centroid distance = 3.805 (3) Å] help to consolidate the packing. The dihedral angle between the benzene rings in the cation is 57.24 (11)°

Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration

The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks. However, almost all GAIL and its extensions only design a kind of reward function of logarithmic form in the adversarial training strategy with the Jensen-Shannon (JS) divergence for all complex environments. The fixed logarithmic type of reward function may be difficult to solve all complex tasks, and the vanishing gradients problem caused by the JS divergence will harm the adversarial learning process. In this paper, we propose a new algorithm named Wasserstein Distance guided Adversarial Imitation Learning (WDAIL) for promoting the performance of imitation learning (IL). There are three improvements in our method: (a) introducing the Wasserstein distance to obtain more appropriate measure in the adversarial training process, (b) using proximal policy optimization (PPO) in the reinforcement learning stage which is much simpler to implement and makes the algorithm more efficient, and (c) exploring different reward function shapes to suit different tasks for improving the performance. The experiment results show that the learning procedure remains remarkably stable, and achieves significant performance in the complex continuous control tasks of MuJoCo.Comment: M. Zhang and Y. Wang contribute equally to this wor