谈医院局域网的安全管理

随着医院业务的发展，科技手段的进步，为了解决医院信息分散和数据高度共享之间的矛盾，越来越多的医院使用了计算机网络。但在医院局域网中，由于计算机系统本身存在着一些固有的脆弱性，若不采取相应措施就可能对医院局域网安全构成威胁。本文分析了威胁医院局域网安全的因素，并针对性地提出应对措施。     

Toll样受体与乙型病毒性肝炎的研究进展

Toll样受体在抗病原微生物感染和调节固有免疫和获得性免疫中起着重要的作用,是连接固有免疫与获得性免疫的重要桥梁。TLRs在乙型病毒性肝炎中的作用复杂,以其特异的模式识别方式和相互调节的网络反应参与慢性乙型肝炎、慢性重型肝炎的发病。     

自然杀伤(NK)细胞固有免疫记忆的研究进展

自然杀伤(NK)细胞等固有免疫细胞中的特定亚群经抗原初次刺激诱导,可分化为记忆性免疫细胞,并对抗原的再刺激产生更加快速强烈的免疫应答,这种免疫记忆被称为固有免疫记忆。大量研究表明,半抗原诱导的接触性超敏反应、巨细胞病毒(CMV)感染、卡介苗接种等途径均可以诱导NK细胞产生固有免疫记忆。表观遗传学改变和免疫微环境调控是NK细胞产生固有免疫记忆的重要原因。我们主要总结了上述途径中NK细胞产生固有免疫记忆的表观遗传学调控机制及细胞因子与NK细胞表面受体互相作用网络的研究现状。目前不同研究模型中记忆性NK细胞的表面分子标志尚存差异,固有免疫记忆产生的分子机制仍不明确,对记忆性NK细胞的研究将为深入了解其生物学功能及临床应用提供新的理论依据。     

肝总动脉分支变异1例

肝固有动脉起点变异的情况临床比较常见，多见于肝固有动脉与胃左动脉共干或肝固有动脉起自于肠系膜上动脉，或有起源不同的两支肝固有动脉。作者应用解剖制作标本过程中发现一例肝总动脉末端呈三叉型发出肝左动脉、肝右动脉和胃十二指肠动脉，并无肝固有动脉，同时肝左动脉在近起点处发出细小肝副左动脉与肝左动脉平行进入肝左叶。     

小肠固有免疫与糖尿病的研究进展

糖尿病（DM）作为一种常见病与多发病，已在全球范围内成为巨大的慢性疾病负担，其发病机制非常复杂，尚未完全阐明。小肠是体内重要的免疫器官之一，其固有免疫功能与DM的关系已成为医疗领域研究前沿。本文综述小肠固有免疫在DM发病机制中的作用，包括小肠组织屏障功能障碍、小肠固有免疫细胞功能失调与小肠固有免疫分子比例失衡，为相关机制研究提供参考。     

C型凝集素受体对固有免疫应答的双向调节作用

C型凝集素受体（CLR）是模式识别受体家族中有别于TLR的新家族，在机体免疫应答中发挥着重要作用。近年来，人们对CLR对固有免疫应答的调节作用进行了较为广泛的研究，越来越多的研究发现CLRs对固有免疫应答具有双向调节作用：不同的CLR在固有免疫应答中既可以发挥正性调节作用，也能够发挥负性调节作用；在针对不同的病原体或在抗原提呈细胞不同的成熟状态等条件下，同-CLR在固有免疫应答中也可以发挥正负调节两方面的作用。深入探讨CLR对固有免疫应答的双向调节作用，对于进一步理解免疫应答的精细调节及疾病发病机制的复杂性等都有重要的理论意义，同时也为许多疾病的预防与治疗提供了一种新的策略。     

指动脉皮支与指掌侧固有神经比邻关系及其临床意义

目的：为不带指固有神经的指动脉岛状皮瓣手术方式提供解剖学基础。方法：解剖10侧尸体手标本，观测指掌侧固有动脉各皮支的起点、走行、外径、分布，皮支与指掌侧固有神经相对位置关系。结果：（1）在手指近节，指动脉皮支按与指固有神经的比邻关系分为2型：①外侧优势型：皮支多行经神经外侧；②均势型：行经神经内、外侧的皮支数量均等。（2）在手指中节，指动脉皮支多从指固有神经外侧穿出。（3）在手指末节，两侧指固有动脉向中央靠拢并吻合成弓，发出的皮支多经神经内侧至皮肤。结论：（1）手指中节皮支多经神经外侧发出，在此处切取皮瓣时，应将神经向内侧分出，以免损伤皮支导致皮瓣血管障碍；（2）在手指近节切取指动脉皮瓣时，指固有神经从皮瓣内侧或外侧分离出皆可，但从内侧分离相对更安全一此。     

变异肝动脉的解剖学特点及其临床意义

目的　观察变异肝动脉的解剖学特点，为临床相关血管影像学检查、手术及介入治疗等提供形态学资料。 方法　按常规方法解剖25例成人尸体标本，沿肝总动脉追踪、寻找肝固有动脉的分支并观察分布情况。 结果　本组肝固有动脉及其分支变异率为28%。其中有2例属Hitta分型外的变异肝动脉，肝固有动脉右侧壁未见胃右动脉，而是在左侧壁发出1支动脉主干，远端分支分布于肝和胃，本文称为“肝胃动脉（HGA）”；其中标本2从HGA主干后壁发出1分支，远端也分布于肝和胃，本文称为“肝胃副动脉”。 结论　本组2例起于肝固有动脉左侧壁的HGA，是胃小弯右侧的主要动脉来源，并有分支营养肝和胃的其他部位。此种变异补充了变异肝动脉的形态学资料，对临床有一定的指导价值。     

MDSC对免疫系统的抑制机制

机体的免疫系统是一个复杂的网络调控系统,由固有免疫和适应性免疫组成,其中包括了固有免疫细胞、抗原递呈细胞、适应性免疫细胞等之间的相助作用和调节。越来越多的证据表明一群骨髓来源的具有CD11b和Gr-1标志的抑制性细胞在各种感染、肿瘤、急慢性炎症等疾病中大量存在,并且负向调节机体的免疫功能。这就是具有负向调节机体免疫反应的髓系抑制性细胞(MDSC),近年来被广泛研究。MDSC是免疫系统调节机体免疫反应的一群独特的细胞群体,可以对多种免疫细胞通过多种不同的机制发挥免疫抑制功能,从而导致机体固有免疫和适应性免疫功能的低下,促进疾病的发展和恶化。本文着重就MDSC对参与机体免疫反应的几种炎性细胞的抑制     

人鼻粘膜毛细血管的超微结构观察

在透射电镜下观察了死亡1小时内的5具成年尸体和3具32－36周胎儿尸体鼻粘膜毛细血管的超微结构。结果为：（1）鼻粘膜内毛细血管的内皮细胞之间紧密相连，未见不连续毛细血管；（2）呼吸粘膜内见到的连续毛细血管和有孔毛细血管，后主要位于固有膜浅部和腺泡周围的固有膜中，而且有孔毛细血管的有孔区域位于邻近上皮侧，其相对侧的内皮是连续的，3）在嗅粘膜内未见到有孔毛细血管；（4）在32－36周的胎儿下鼻甲粘膜内观察到连续毛细血管和有孔毛细血管，后也主要位于固有膜浅部和腺泡周围的固有膜中，但内皮孔的数量较成人的为少，中还就上述结果结合鼻生理功能进行了讨论。     

广义Hopfield神经网络模型

本文在Ｈｏｐｆｉｅｌｄ神经网络模型中通过充分考虑稳定性条件，引入节点偏置和引入高阶相互作用项使其存储容量远高于传统的Ｈｏｐｆｉｅｌｄ神经网络模型，并能存储相关图样。给出了理论推导和分析，对由Ｎ个神经元组成的神经网络系统进行计算机仿真，仿真结果证实了上述结论。新模型的上述特点使其具有良好的应用前景。     

Analyze of hospital computer networks

Methods for the analysis of hospital computer networks have been developed without the need for a large-scale computer simulation of the queueing events in the problem. A table-driven methodology and a simple computer program are provided for an analysis of the computer utilization and channel utilization for a network of computers supporting hospital tasks. An example, using a DEC 11/780 VAX and six DEC 11/44's is given. The example network is also analyzed by large-scale computer simulation techniques, and the results are compared.     

Predicting RNA secondary structure based on the class information and Hopfield network

One of the models for RNA secondary structure prediction is to view it as maximum independent set problem, which can be approximately solved by Hopfield network. However, when predicting native molecules, the model is not always accurate and the heuristic method of Hopfield network is not always stable. It is because that the class information is lost and the accuracy is not determined by the number of base pairs only. Secondary structures of non-coding RNAs are believed conservative on the same class. However, software and web servers nowadays for RNA secondary structure prediction do not consider the class information. In this paper, we involve class information in the initialization of Hopfield network. When the initialization is improved, the promising experimental result shows the efficacy and superiority of our proposed methods.     

离散型非对称HOPFIELD模型的运行特性及其在加密技术中的应用

作者给出了动力学系统中的链、直链、环链及广义混沌的概念，分析了离散型ＨＯＰＦＩＥＬＤ网络权重为非对称情况下的一些可能结果，并证明了离散的非对称ＨＯＰＦＩＥＬＤ模型的运行结果不可能出现混沌（对称型ＨＯＰＦＩＬＤ模型只有稳定解），作者还用仿真方法研究了非对称ＨＯＰＦＩＥＬＤ模型的运行结果：直链或环链，并分析了几种权重的设置方法对运行结果的影响，讨论了在数据加密中应用的可能性。     

Picture archiving and communication system design issues: The importance of modelling and simulation

Picture archiving communication system (PACS) development turns out to be very complex. Due to both the vast amount of data and the complexity of hospital organizations, currently only small-scale systems have been realized. And although the experiences obtained with these systems are essential, there is a risk for underestimating the complexity and requirements inherent in hospital-wide PACS systems. In this paper, it is advocated that modelling and simulation be used as tools to obtain insight into the behavior and structure of future PACS systems. Modelling and simulation can also be used to actively support the design of PACS, especially its software. In order to capture the full complexity of PACS in a simulation model, and to take full advantage of simulation as a design tool, the development of a new modelling method has begun. This method is based on semantic data models and decision processes, and can be used for both system analysis and design. The first systems modelled with this method were imaging procedures in a hospital and a computer network. The resulting simulation models are a direct reflection of reality, and have a high degree of modularity. Consequently, in spite of the complexity of the systems, their models are easy to understand and maintain.     

Spatiotemporal Hopfield neural cube for diagnosing recurrent nasal papilloma

Gadolinium-enhanced magnetic resonance imaging (MRI) is widely used to detect recurrent nasal tumours. A specifically designed two-layer Hopfield neural network, called the spatiotemporal Hopfield neural cube (SHNC), is presented, to be used for detecting recurrent nasal papilloma. Differing from conventional, two-dimensional Hopfield neural networks, the SHNC extends the one-layer, two-dimensional Hopfield network in the original image plane into a two-layer, three-dimensional (3D) Hopfield network with pixel classification implemented in its third dimension. With extended 3D architecture, the network is able to use each pixel's spatial information in a pixel labelling procedure. Because the SHNC takes pixel spatial information into consideration, the effects of tiny detail or noise are removed. As a result, the drawback of disconnected fractions can be avoided. Furthermore, owing to the incorporation of competitive learning rules to update neuron states, to avoid the problem of having to satisfy strong constraints, the convergence of the network was improved. In addition, a more accurate signal-time curve, the relative intensity change (RIC), was adopted to represent the gadolinium-enhanced MRI temporal information, and the RIC curves of recurrent nasal papilloma were incorporated into the SHNC. The experimental results showed that the SHNC could obtain a more appropriate, precise position of recurrent nasal papilloma than the k-means, principal components analysis (PCA) or Eigenimagefiltering methods. The average sensitivity and specificity of the 26 cases were 0.9998 and 0.9961, respectively. These values demonstrate the effectiveness of the proposed technique.     

Hopfield network applied to blood vessel detection in angiograms

A neural network classifier for detecting vascular structures in angiograms is developed. The classifier consists of a Hopfield network applied to a square window in which the centre pixel is classified from binary information within the window. Tests are performed using a binary test image corrupted by inverting a percentage of the image pixels. The resulting noisy images simulate the output of a detector using a simple threshold derived from local image statistics. The factors affecting the size of window and the choice of stored patterns are discussed. The results are compared with those obtained from a multi-layer perceptron using a similar approach. The Hopfield network is found to be effective at rejecting the high levels of noise that would result from low-contrast source imagery. Another important feature is that the processed image retains an accurate representation of blood vessel diameter.     

Nonlinear closed-loop control system for intracranial pressure regulation

A nonlinear closed-loop control system with flat pressure-versus-flow characteristics that is aimed at regulating intracranial pressure (ICP) by adjusting the volume of cerebral spinal fluid (CSF) was designed, built, and tested. The control system design allows both the pressure setpoint and hysteresis to be adjusted to overcome the difficulties inherent in differential pressure-activated, fixed resistance, open-loop shunts. A dynamic six-compartment bench-top fluid system, which mimics the cerebral spinal fluid system, was designed, built, and tested. A computer simulation was developed which included the nonlinear on-off controller with hysteresis and a sixth-order, linear, multicompartmental model of the CSF system. The computer model andin vitro system results showed the ability of the system to track and compensate for pressure variations above and below normal as well as for spurious outputs that mimic suchin vivo problems as blood pressure changes, sneezing, or coughing. There was one discrepancy between the simulated andin vitro results. Thein vitro system had a higher rate of increase in pressure due to the more rigid compliance of the materials used, whereas the computer model compliance, based on the basalin vivo compliance of the CSF system, was less rigid. Based on these findings, the controller was modified to account for shortduration, extremely elevated pressures.     

Robust Nonlinear Autoregressive Moving Average Model Parameter Estimation Using Stochastic Recurrent Artificial Neural Networks

In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate model predictions. Furthermore, we compare the performance of the new approach to that of the deterministic recurrent neural network approach. Using this simple two-step procedure, we obtain more robust model predictions than with the deterministic recurrent neural network approach despite the presence of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals. © 1999 Biomedical Engineering Society. PAC99: 8710+e, 8719Uv, 0705Mh     

A computer model of the lymphatic system

A mathematical model of the whole body lymphatic network, which simulates the lymph propulsion along the network from the periphery through the thoracic duct into the venous system, is developed using the fundamental conservation laws and current notions of lymphology. Only the major lymphatic vessels are considered. The set of mathematical equations are then translated into a series of FORTRAN statements for the purpose of digital computer simulation of the pressure and flow patterns along the network. The model analysis revealed interesting characteristics of lymphatic contractility at various points along the network.