CD317 在肿瘤发生发展及治疗方面的研究进展

CD317(Tetherin、BST-2 或 HM1.24)是由 BST-2 基因编码的一个结构独特的 II 型跨膜糖蛋白, 在多种人体组织中组成型表达,也可被干扰素等细胞因子诱导。近年来,越来越多的研究表明,CD317 在多种肿瘤中表达上调,广泛参与肿瘤细胞增殖、迁移以及凋亡抵抗等生物学过程从而促进肿瘤的发 生发展,是肿瘤治疗的潜在靶点。该文聚焦肿瘤领域,总结 CD317 的表达模式、作用机制以及靶向策 略的相关进展,为肿瘤发病理论研究、治疗策略开发提供新的思考和方向。     

Delineation and diagnosis of brain tumors from post contrast T1-weighted MR images using rough granular computing and random forest

This paper presents a new approach of delineation and characterization of four different types of brain tumors viz. Glioblastoma multiforme (GBM), metastasis (MET), meningioma (MG) and granuloma (GN) from magnetic resonance imaging (MRI) slices of post contrast T1-weighted (T1C) sequence to improve the computer assistive diagnostic accuracy. An integrated framework of identification and extraction of tumor region, quantification of histogram, shape and textural features followed through pattern classification by machine learning algorithm has been proposed. Rough entropy based thresholding in granular computing paradigm has been adopted for delineation of tumor area. After accomplishing quantitative validation and comparison with existing methods, experimental results prove the efficiency and applicability of proposed segmentation approach. In the next stage, the extracted lesions have been quantified with 86 features to develop the training dataset. Random forest (RF), an ensemble learning scheme has been implemented, which learns the training data for accurate prediction of the class label of a given input. The performance of RF has been evaluated by statistical measures from 3 fold cross-validation and compared with five different classifiers. The same experiment has been repeated over the reduced set of features generated by correlation based feature selection strategy. Experimental results show the superiority of RF (Sensitivity achieved in %: GBM-96.7, MET-96.2, MG-98.1 and GN-97.7) with the complete set of features. The comparison of proposed methodology with the existing works signifies its applicability and effectiveness. Additionally a 10 fold cross-validation has been accomplished to justify the statistical significance of the classification accuracy achieved from proposed methodology.     

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow-up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.     

脉冲控制对肿瘤治疗的应用

本文提出将脉冲控制应用到肿瘤的治疗中,通过简单的肿瘤细胞模型,建立脉冲控制肿瘤细胞模型,并且讨论该模型的稳定性,最后通过实例进行仿真.仿真结果表明,脉冲控制系统可以使肿瘤细胞的数目控制在稳定的状态,由此可以得出脉冲控制系统在理论上是可以应用到肿瘤的治疗中的.     

An Effective Diagnosis System for Brain Tumor Detection and Classification

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a Support Vector Machine (SVM). Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.     

基于U-Net改进模型的直肠肿瘤分割方法

诊断直肠癌时,如果能够从CT图像中自动准确分割出直肠肿瘤区域,将有助于医生进行更准确和快速的诊断。针对直肠肿瘤分割问题,提出基于U-Net改进模型的直肠肿瘤自动分割方法。首先在U-Net模型的每级编码器中嵌入子编码模块提升模型特征提取能力;其次通过对比不同优化器的优化性能,获得最适合的优化器用于训练模型;最后对训练集进行数据扩充使模型得到更充分的训练,从而提高分割性能。与U-Net、Y-Net和FocusNetAlpha三种网络模型进行的对比实验表明：所提改进模型得到的分割区域与真实肿瘤区域更接近,对小目标的分割性能更突出,该模型的查准率、查全率和Dice系数三个评价指标都优于对比的模型,能有效分割直肠肿瘤区域。     

多尺度区域增长的肿瘤区域分割方法

数字化的医学图像在早期临床诊断中具有重要的意义。肿瘤形状分析是区分良性肿瘤和恶性肿瘤的重要方法之一。对乳腺肿瘤检测的最终结果很大程度上依赖于对肿瘤区域的分割。为了对肿瘤区域进行特征的提取和识别,首先需要把待处理的肿瘤区域从背景中分离出来。论文使用多尺度区域增长的图像分割技术实现肿瘤分割及肿瘤边缘的准确定位,该方法具有精度高、鲁棒性强等优点。实验结果表明,效果非常理想。     

乳腺超声图像肿瘤全自动定位方法研究*

乳腺超声图像肿瘤的定位是计算机辅助诊断(CAD)系统进行肿瘤分割和良恶性分类处理的前提,为此提出了一种全自动定位肿瘤位置的方法.该方法不依赖初始的固定参考位置和强制性后处理规则,能够较大限度地适应肿瘤在超声图像中相对位置的变化.与目前最好的几种自动定位方法相比,该方法具有更高的定位准确率.     

核酸适体靶向红荧烯有机纳米探针用于肿瘤成像研究

近二十年来,肿瘤在我国的发生率和死亡率呈现不断增高的趋势。若能早期发现癌变位点并及时对其进行靶向治疗,将有助于提高癌症的治愈率。伴随着纳米技术的发展,用于癌症早期诊断成像和治疗的纳米材料的开发也得到了人们的广泛关注。文章介绍了一种基于核酸适体靶向肿瘤细胞的有机荧光纳米探针,其制备过程快速简便,所制得的纳米颗粒不仅表现出优异的靶向能力,而且具有良好的生物相容性和稳定性,该研究为肿瘤的靶向诊疗提供了有力工具。     

WEKA平台在肿瘤分类研究中的应用

采用传统的病理学诊断方法对肿瘤进行分类存在一定的局限性,基因芯片等高通量技术的问世为肿瘤研究带来了革命性的进展,在肿瘤分类中发挥了积极作用。该文以weka数据挖掘平台作为特征基因选择与样本分类模型建立的工具,解决了肿瘤分类理论性强,操作难度高的问题。该文以97名乳腺肿瘤患者的基因表达谱数据进行实验,实验结果表明,weka平台可以有效降低基因表达谱数据的维度,对肿瘤的精确诊断具有较高应用价值。     

病理性肺部粘连肿瘤分割

病理性肺部粘连肿瘤的分割是放射治疗靶区界定的关键。本文利用CT序列图像分割出完整肺区中的肿瘤边界信息,然后应用这些信息对其相邻CT图片中粘连肿瘤作进一步分割。使用重叠率和边界位置评价方法,对分割结果与放射科医生手工勾画的边界作了比较。结果说明该方法是可行的。     

机器学习在乳腺肿瘤分类检测中的应用研究

机器学习算法在医学检测与诊断,尤其是乳腺肿瘤分类检测与诊断中扮演愈发重要的角色。分析比较了几种经典机器学习分类器在乳腺肿瘤分类检测中的性能,并从准确率、灵敏度、特异性及执行效率等方面对各分类器的性能进行了评估比较,根据在不同数据库上的实验结果,总结了各机器学习分类器在乳腺肿瘤分类中的性能特点:线性判别分析和极限学习机两种分类器性能优良且训练效率很高;支持向量机性能较为平均且非常稳定,但训练耗时较长;而人工神经网络分类器虽然可以给出良好的特异性指标,但灵敏度指标不够理想。     

一种简单有效的算法在肿瘤亚型发现中的应用

本文采用了一种简单有效的肿瘤亚型发现的算法。该算法主要包括三个步骤:首先对数据进行预处理;然后采用定义的信息系数进行信息基因的选择;最后将层次聚类算法与t-检验相结合,完成了肿瘤亚型的发现。以白血病和结肠癌的基因表达谱为例,得出了很好的分型结果,表明了该方法的有效性和可行性。     

CD317 功能研究进展

CD317 (Tetherin,BST-2或HM1.24)于1994年被发现并命名,是终末分化B细胞的特异性表面标志.2008年首次被鉴定为干扰素诱导型宿主抗病毒因子,此后越来越多的科学家加入到该领域的探索中.经过近十年的研究,目前已经阐述了CD317结构、抗病毒及免疫特性等问题,也陆续发现了一些诸如参与肿瘤进展、束缚外泌体释放等新功能,研究热度不减当年.因此,文章对近几年CD317功能的研究进展进行一个系统的总结,以期为病毒感染、肿瘤发病以及治疗等方面的理论进步和技术发展提供新的思路.     

基于支持向量机的肿瘤亚型识别

肿瘤亚型的准确判别对肿瘤的治疗具有重要意义。该文依据肿瘤的基因表达谱,研究了利用支持向量机进行肿瘤亚型识别的方法。该方法以基因蕴含的样本分类信息量为依据首先排除噪声基因,然后采用支持向量机作为分类器进行肿瘤亚型的识别。文章以急性白血病的基因表达谱数据为例,进行亚型识别并同已有的研究成果进行了对比。实验结果表明了该方法的可行性和有效性。     

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system in or close to real-time is examined. In this article, a Deep Learning (DL) model is implemented and modified to fit liver CT segmentation, and a semantic pixel classification of road scenes is recommended. The architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers. A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training phase. For the normal class, we obtained 100% precision for chronic cirrhosis hepatitis (73%), offset cirrhosis (59.26%), and offensive cirrhosis (91.67%) for chronic hepatitis or cirrhosis (73,0%). The aim is to develop a Computer-Aided Detection (CAD) screening tool to detect steatosis. The results proved 98.33% exactness, 94.59% sensitivity, and 92.11% case with Convolutional Neural Networks (CNN) classification. Although the classifier’s performance did not differentiate so clearly at this level, it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve (AUC) and accuracy.     

基于改进Marching Cubes算法的乳腺MRI肿块三维重建

核磁共振成像MRI(Magnetic Resonance Imaging)是目前乳腺癌肿块诊断的常用辅助手段,对图像的正确解析是关键,针对传统MC(Marching Cubes)面绘制算法应用于乳腺MRI图像的不足,提出了改进方法。首先利用乳腺MRI序列图相邻帧间图像灰度分布的相似,肿块组织形状相近等相关性,在RSF(Region-Scalable Fitting)模型的基础上利用初始轮廓迭代的方法提取肿块区域。接着将多组参数下获得的结果,依据每一帧与其前后帧的重叠面积越大越好作为条件进行筛选,使提取的等值面最优化。最后采用基于加权二次误差度量的三角形折叠方法,对面绘制产生的大量三角网格进行了简化。将所提出的改进方法应用于30例乳腺MRI序列图,实验结果表明,对于乳腺MRI肿块的三维重建在精度和绘制速度上都比使用传统MC算法有很大提高。     

p53抑癌基因电化学阻抗传感器研究

基于电化学阻抗技术构建了一种检测p53抑癌基因的电化学传感器.首先利用自组装作用将末端带巯基的探针p53抑癌基因(p53-DNA)固定于金电极表面,根据传感器结合前后电子转移阻抗值的变化,对目标p53-DNA进行了测定.以pH =7.4的5mmol/L K3[Fe(CN)6]-5mmol/L K4[Fe(CN)6]平衡电对溶液作为检测液,检测目标p53-DNA的线性浓度范围为1.0×10-8～ 1.0×10-6 mol/L,其检出限为3.0×10-9 mol/L(S/N =3).对5.0×10-8 mol/L的目标p53-DNA进行11次平行测定,其RSD为3.4％.该传感器制作简单、灵敏高、选择性好,且无需标记,易于操作.