环孢素A亲合素—Cyclophilin研究进展

环孢素A亲合素(Cyclophilin,CyP)广泛存在于生物体内,是细胞内环孢素A (Cy-closporin A,CsA)的受体,具有催化加速蛋白质折叠和转运等生物学功能。CyP与CsA结合抑制了钙离子、钙调素(Calmodulin,CaM)依赖的神经钙蛋白(Calcineurin,CN)的磷酸酶活性,阻断IL-2等T细胞增殖必需的一些细胞因子的转录,介导CsA免疫抑制作用。CyP在自身免疫性疾病和器官移植排斥研究中具有应用价值。     

环孢素A研究的新进展(文献综述)

<正> 环孢素 A(Cyclosporin A,CsA)是一种结构为环状11肽化合物。原先是在从多孢木霉菌和柱孢霉菌两种真菌中筛选具有抗菌作用的药物时偶然发现的。最初发现CsA在弗氏佐剂诱导的关节炎中具有和保泰松相似的抗炎作用,后来发现CsA具有强的免疫抑制效应,而且没有明显的骨髓毒性。这就使许多学者从研究其抗炎作用转移到重点研究其免疫抑制作用及其抗移植排斥作用。对     

环孢素A结合蛋白研究进展

环孢素A结合蛋白（CyclophilinCyP）是一个广泛分布、高度保守的新发现的蛋白质家族。它有肽酰脯氨顺反异构酶活性，能催化蛋白质的折叠、装配及转运，调节细胞信号传导过程，介导环抱素A的免疫抑制作用.本文就CyP家族的分布、分类、结构特征以及生物学功能的研究现状作一综述。     

基于BP神经网络预测儿童甲状腺疾病的模型研究

目的:构建儿童甲状腺疾病的预测模型。方法:根据某市疾病预防控制中心2013年~2016年采集的1 400名8~11岁儿童的体检数据及临床初步诊断结果作为研究数据,随机抽取其中的1 000名儿童作为训练样本,剩余的400名儿童作为测试样本,利用 MATLAB R2018b软件编程实现三层BP神经网络模型。结果:当选择log&log组合作为隐含层和输出层的传递函数,隐含层节点数目选择8时,模型的分类正确率达到91.43%。结论:BP神经网络应用于儿童甲状腺疾病的预测,可以为疾病的防治工作提供理论依据。     

基于神经网络的环孢素血药浓度预测

通过研究肾移植病人环孢素A血药浓度的不同影响因子,分别采用广义神经网络、Elman网络、BP神经网络等模型,对环孢素A血药浓度进行预测.结果显示三种网络模型都达到了较好的预测结果,其中BP网络的预测结果最好,平均相对误差为19.94%,Elman网络的平均相对误差为21.39%,广义神经网络的平均相对误差为25.93%.说明将神经网络应用于预测CsA血药浓度是可行的,其结果可以作为临床CsA的个体化给药的参考.     

环孢素A治疗类风湿性关节炎的研究进展

新型免疫抑制剂环孢素Ａ广泛用于防止器官移植排斥反应和治疗自身免疫病如类风湿性关节炎，牛皮癣，多发性硬化，类性肠病等。本文就ＲＡ的免疫病理、ＣｓＡ治疗的作用机理临床效果、毒副反应及应用前景等研究进展作简要介绍。     

环孢素A结合蛋白研究进展

环孢素Ａ结合蛋白（ＣｙＰ）是一个广泛分布、高度保守的新发现的蛋白质家族。它有肽酰脯氨顺反异构酶活性，能催化蛋白质的折叠、装配及转运，调节细胞信号传导过程，介导环孢素Ａ的免疫抑制作用。本文就ＣｙＰ家族的分布、分类、结构特征以及生物学功能的研究现状作一综述。     

环孢素A治疗类风湿性关节炎的研究进展

新型免疫抑制剂环孢素Ａ（ＣｓＡ）广泛用于防止器官移植排斥反应和治疗自身免疫病，如类风湿性关节炎（ＲＡ）、牛皮癣、多发性硬化、炎性肠病等。本文就ＲＡ的免疫病理、ＣｓＡ治疗的作用机理、临床效果、毒副反应及应用前景等研究进展作简要介绍。     

环孢素A延长腺病毒基因治疗载体体内存在时间的研究

目的研究重组腺病毒载体在免疫抑制疗法应用中的安全性并探讨环孢素A对该载体在体内清除的影响。方法采取腹膜后注射纯化重组腺病毒建立动物模型,连续给予环孢素A 14 d,取不同时间点大鼠样本,HE染色观察各主要脏器病理反应,免疫组化方法检测肝、脾、肺、肾、肌肉及淋巴结腺病毒的表达情况。结果①HE染色切片,各脏器呈现一过性可逆性轻微炎性反应。②免疫组化检测,对照组中重组腺病毒逐渐减弱至阴性,实验组中重组腺病毒存在时相性明显延长(P<0.05)。③腺病毒分布呈现肝脾肺高表达而其它组织表达相对较低。结论重组腺病毒作为基因治疗载体在免疫抑制疗法应用中具有一定的安全性,环孢素A能够部分抑制实验动物体内的免疫反应从而延长重组腺病毒在体内存在的时间。     

系统性红斑狼疮病人环孢素A受体mRNA的表达

目的 探讨静止期、活动期系统性红斑狼疮病人（SLE）外周血单个核细胞（PBMC）环孢素A受体（CyP）mRNA表达及糖皮质激素对其表达的影响,为临床应用环孢素A辅助治疗该病提供理论依据。方法 采用逆转录PCR方法,经凝胶图像半定量分析,检测患者外周血单个核细胞CyP mRNA的表达。结果 SLE病人外周血单个核细胞存在有CyPmRNA的表达,静止期较正常人差异无显著性（P＞0．05）意义,但在活动期CyPmRNA的表达较正常人和静止期病人明显降低（P＜0．01）,地塞米松处理后,活动期病人CyPmRNA的表达水平显著上升（P＜0．01）。结论 SLE病人有CyPmRNA的表达,但活动期明显下降,糖皮质激素可改善CyP mRNA的表达。     

基于BP神经网络的CT图像骨皮质分割

目的在CT图像中通过对骨皮质的分割与测量,测定骨量、骨骼的几何形状以及骨强度,并计算相应的组织形态计量学参数。方法通过DCMTK解读CT图像,提取相应的图像信息。利用OpenCV对图像进行预处理,在感兴趣的区域(ROI)设置的基础上,提取图像的纹理特征作为输入向量;以对训练样本手工分割的结果作为导师信号,对BP神经网络进行训练;用训练好的网络对CT图像序列中的骨皮质进行分割,并对分割后的结果进行后处理及显示。结果骨皮质CT图像的神经网络分割效率符合实际应用的需求,分割结果形状明显,与周围组织区分度高,满足临床诊断需求。结论纹理特征明显的情况下,可以达到较为满意的分割效果。分割结果轮廓平滑,分割精度高、成功率高、适应性强;而且图像分割过程人工介入少,可以用于整套CT图像骨皮质的批量分割。不足之处在于此方法神经网络训练时间相对较长。     

基于肌电信号的膝关节跨越障碍角度预测方法

为解决人体跨越障碍物时膝关节角度输出的问题,针对性设计一种穿戴式信号获取实验台,对下肢运动姿态进行运动分析,将肌肉电信号及关节角度信号作为运动数据,对信号进行处理后利用BP神经网络预测跨越障碍时输出角度,提出一种利用BP神经网络算法,根据不同大腿抬起高度,分析膝关节运动主动肌与被动肌发力程度,预测输出人体跨越障碍时膝关节角度的方法,能够有效帮助假肢膝关节或康复机器人实现跨越障碍的复杂动作。     

Improving protein secondary structure prediction using a multi-modal BP method

Methods for predicting protein secondary structures provide information that is useful both in ab initio structure prediction and as additional restraints for fold recognition algorithms. Secondary structure predictions may also be used to guide the design of site directed mutagenesis studies, and to locate potential functionally important residues. In this article, we propose a multi-modal back propagation neural network (MMBP) method for predicting protein secondary structures. Using a Knowledge Discovery Theory based on Inner Cognitive Mechanism (KDTICM) method, we have constructed a compound pyramid model (CPM), which is composed of three layers of intelligent interface that integrate multi-modal back propagation neural network (MMBP), mixed-modal SVM (MMS), modified Knowledge Discovery in Databases (KDD^?) process and so on. The CPM method is both an integrated web server and a standalone application that exploits recent advancements in knowledge discovery and machine learning to perform very accurate protein secondary structure predictions. Using a non-redundant test dataset of 256 proteins from RCASP256, the CPM method achieves an average Q₃ score of 86.13% (SOV99=84.66%). Extensive testing indicates that this is significantly better than any other method currently available. Assessments using RS126 and CB513 datasets indicate that the CPM method can achieve average Q₃ score approaching 83.99% (SOV99=80.25%) and 85.58% (SOV99=81.15%). By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called CPM, which performs these secondary structure predictions, is accessible at http://kdd.ustb.edu.cn/protein_Web/.     

Dipole source localization of MEG by BP neural networks

Summary The purpose of this study was to examine the usefulness of BP neural networks for source localization of MEG. Since the performance of this method does not depend on the complexity of brain parameters and source models, a homogeneous brain model and a single current dipole source are assumed for convenience. Localization accuracy was examined in relation to the configuration and scale of the network. As a result, average error for position and moment estimations was within 2%, while the maximum error was about 5%. It was therefore concluded that the neural network method was useful for MEG source localization, though some improvements are still necessary.     

Use of neural networks for dosage individualisation of erythropoietin in patients with secondary anemia to chronic renal failure

The external administration of recombinant human erythropoietin is the chosen treatment for those patients with secondary anemia due to chronic renal failure undergoing periodic hemodialysis. The goal is to carry out an individualised prediction of the erythropoietin dosage to be administered. It is justified because of the high cost of this medication, its secondary effects and the phenomenon of potential resistance which some individuals suffer.One hundred and ten patients were included in this study and several factors were collected in order to develop the neural models. Since the results obtained were excellent, an easy-to-use decision-aid computer application was implemented.     

BP neural network classification for bleeding detection in wireless capsule endoscopy

Bleeding in the digestive tract is one of the most common gastrointestinal tract (GI) diseases, as well as the complication of some fatal diseases. Wireless capsule endoscopy (WCE) allows physicians to noninvasively examine the entire GI tract. However it is very laborious and time-consuming to inspect large numbers of WCE images, which limits the wider application of WCE. It is therefore important to develop an automatic and intelligent computer-aided bleeding detection technique. In this paper, a new method aimed at bleeding detection in WCE images is proposed. Colour texture features distinguishing the bleeding regions from non-bleeding regions are extracted in RGB and HSI colour spaces; then a neural network using the colour texture features as the feature vector inputs is designed to recognize the bleeding regions. The experiments demonstrate that the bleeding regions can be correctly recognized and clearly marked out. The sensitivity of the algorithm is 93% and the specificity is 96%.     

养心氏片对环孢素A药代动力学参数的影响

目的研究养心氏片(YXS)对环孢素A(CsA)血药浓度及其药动学参数的影响。方法采用反相高效液相色谱法(RP-HPLC)对单用CsA及YXS与CsA合用后家兔体内CsA全血浓度进行测定,并对两组药动学参数进行统计学处理。结果YXS可使CsA的Cmax增大、T1/2β减小,差异有显著性(P<0.05),其余药动学参数无显著变化。结论养心氏片可使CsA的峰浓度增高、消除速率加快。     

The use of artificial neural networks to stratify the length of stay of cardiac patients based on preoperative and initial postoperative factors

BACKGROUND: The limitations of current prognostic models in identifying postoperative cardiac patients at risk of experiencing morbidity and subsequently an extended intensive care unit length of stay (ICU LOS) is well recognized. This coupled with the desire for risk stratification in order to prioritize medical intervention has lead to the need for the development of a system that can accurately predict individual patient outcome based on both preoperative and immediate postoperative clinical factors. The usefulness of artificial neural networks (ANNs) as an outcome prediction tool in the critical care environment has been previously demonstrated for medical intensive care unit (ICU) patients and it is the aim of this study to apply this methodology to postoperative cardiac patients. METHODS: A review of contemporary literature revealed 15 preoperative risk factors and 17 operative and postoperative variables that have a determining effect on LOS. An integrated, multi-functional software package was developed to automate the ANN development process. The efficacy of the resultant individual ANNs as well as groupings or ensembles of ANNs were measured by calculating sensitivity and specificity estimates as well as the area under the receiver operating curve (AUC) when the ANN is applied to an independent test dataset. RESULTS: The individual ANN with the highest discriminating ability produced an AUC of 0.819. The use of the ensembles of networks technique significantly improved the classification accuracy. Consolidating the output of three ANNs improved the AUC to 0.90. CONCLUSIONS: This study demonstrates the suitability of ANNs, in particular ensembles of ANNs, to outcome prediction tasks in postoperative cardiac patients.     

基于B P神经网络的脑肿瘤MRI图像分割

利用BP神经网络技术对MR脑肿瘤图像中的肿瘤区域与正常组织区域进行分割,以辅助医疗诊断与治疗。首先,人工分割出部分影像中的肿瘤组织与正常组织作为已知样本;其次,在BP神经网络模型中输入已知样本中进行训练;最后,用训练好的BP神经网络处理其他脑肿瘤图像。BP神经网络能够有效分割MR脑肿瘤图像,辨别出肿瘤与周围正常组织的差异,但模糊区域也常被误判为肿瘤。因此,本研究提出进一步对模糊区域样本进行针对性训练与特殊的滤波处理,所得结果有较大改进。BP神经网络能有效地进行脑肿瘤MRI图像分割,但在使用时仍需正确选择输入样本的区域和范围并结合特殊的滤波处理。