汽枪子弹击中右心室及心电图改变一例

汽枪子弹击中右心室及心电图改变一例史训凡，夏斌赞（湖南医科大学湘雅医院－心内科·长沙·４１０００５）ＴＨＥＣＨＡＮＧＥＯＧＥＣＧＡＦＴＥＲＲＩＧＨＴＶＥＮＴＲＩＣＬＥＳＨＯＯＴＥＤＢＹＧＡＳＧＵＮＳｈｉＸｕｎｆａｎ；ＸｉａＢｉｎｚａｎ（Ｄｅｐａｒｔｍ...     

DDPH对猪肺动脉平滑肌细胞PDGF·BB和bFGF表达的影响：免疫细胞化学研究

ＤＤＰＨ［１－（２．６－二甲基苯乙氧基）－２－（３．４二甲氧基苯乙胺基）丙烷盐酸盐］是南京药科大学合成的降压新化合物,也具有降低肺动脉高压和抑制肺动脉平滑肌细胞增殖作用。本实验用细胞培养、免疫细胞化学、图像分析、３Ｈ－ＴｄＲ、细胞周期测定等方法,进一步探讨ＤＤＰＨ对缺氧性肺动脉平滑肌细胞（ＰＡＳＭＣＳ）增殖的抑制机制。结果：缺氧促进肺动脉内皮细胞（ＰＡＥＣｓ）的ＰＤＧＦ·ＢＢ和ｂＦＧＦ两种生长因子的表达（积分光密度ＯＤ值）增高。缺氧内皮细胞条件培养液（ＨＥＣＣＭ）能促进ＰＡＳＭＣＳ的ＰＤＧＦ·ＢＢ的ＯＤ值增高,ｂＦＧＦ的ＯＤ值无明显改变。加药组（ＨＥＣ－ＣＭ＋ＤＤＰＨ）的ＰＤＧＦ·ＢＢ和ｂＦＧＦ的ＯＤ值均显著降低,尤以ＰＤＧＦ·ＢＢ的ＯＤ值减少最多．提示：ＤＤＰＨ能抑制ＨＥＣＣＭ引起ＰＡＳＭＣＳ的ＰＤＧＦ·ＢＢ和ｂＦＧＦ表达增多和细胞增殖。结果与大鼠实验观察相符。     

低氧对肺动脉内皮细胞PDGF—B链释放及平滑肌细胞生长的影响

应用蛋白ｄｏｔｂｌｏｔ技术检测了低氧内皮细胞条件培养液（ＨＥＣＣＭ）和常氧内皮细胞条件培养液（ＮＥＣＣＭ）内ＰＤＧＦ相对含量,并利用［３Ｈ］－ＴｄＲ掺入法和流式细胞术观察了ＨＥＣＣＭ和ＮＥＣＣＭ及加入特异ＰＤＧＦ抗体对肺动脉平滑肌细胞（ＰＡＳＭＣ）生长的影响。结果表明,ＨＥＣＣＭ中的ＰＤＧＦ含量明显高于ＮＥＣＣＭ;ＨＥＣＣＭ能明显增强ＰＡＳＭＣ内ＤＮＡ合成,促进ＰＡＳＭＣ从Ｇｏ／Ｇ１期进入Ｓ期;当预先加入ＰＤＧＦ－Ｂ链抗体时,则会明显地抑制ＨＥＣＣＭ对ＰＡＳＭＣ的ＤＮＡ合成,阻止ＰＡＳＭＣ从Ｇｏ／Ｇ１期进入Ｓ期。结果提示,低氧时ＰＡＳＭＣ增殖与肺动脉内皮细胞分泌释放ＰＤＧＦ增加有关     

炎症介质对离体大鼠肠系膜动脉降钙素基因相关肽释放的影响

本工作目的是在离体大鼠肠系膜动脉床灌流模型上,观察几种常见炎症介质：前列腺素Ｅ２（ＰＧＥ２）、缓激肽（ＢＫ）、组胺（ＨＩＳ）、血小板活化因子（ＰＡＦ）及５－羟色胺（５－ＨＴ）对血管周围感觉神经介质ＣＧＲＰ释放的直接影响。结果显示：ＰＧＥ２（１－１００μｍｏｌ／Ｌ）和ＢＫ（５－１０μｍｏｌ／Ｌ）能引起大鼠肠系膜动脉床时间和浓度依赖性地释放ＣＧＲＰ。ＨＩＳ,ＰＡＦ和５－ＨＴ则未见明显作用。结果提示,ＰＧＥ２与ＢＫ可能是引起血管周围感觉神经兴奋和ＣＧＲＰ释放的主要炎症介质。     

提高外淋巴钙浓度对耳蜗电位的影响

本实验以人工外淋巴灌流方式,提高豚鼠耳蜗外淋巴液钙离子浓度（［Ｃａ２＋］ＰＬ）,观察蜗内直流电位（ＥＰ）和耳蜗电图（ＥＣｏｃｈＧ）的变化,ＥＣｏｃｈＧ包括听神经复合动作电位（ＣＡＰ）、耳蜗微音电位（ＣＭ）。结果可见：高钙灌流明显抑制ＣＡＰ幅值,延长同一声强下（９０ｄＢＳＰＬ）Ｎ１－峰潜伏期,但不改变ＣＭ的幅值及总和ＥＰ（Ｇ－ＥＰ）。高钙灌流降低了ＥＰ对噪声的给－撤声反应（ＥＰ－ＯＮ,ＥＰ－ＯＦＦ）和缺氧所得到的最大负ＥＰ（Ｎ－ＥＰ）绝对值。本文分析了外淋巴高钙影响耳蜗电位的可能机制。     

日本血吸虫26kD抗原基因在BCG中的表达

研究了外源基因日本血吸虫２６ｋＤ抗原（Ｓｊ２６ＧＳＴ）在卡介苗（ｂａｃｉｌｕｓＣａｌｍｅｔｅ－Ｇｕｅｒｉｎ,ＢＣＧ）、耻垢分枝杆菌（Ｍ．ｓｍｅｇｍａｔｉｓ）和大肠杆菌（Ｅ．ｃｏｌｉ）中的表达．运用重组ＤＮＡ和聚合酶链反应（ＰＣＲ）等分子生物学技术,以表达Ｓｊ２６ＧＳＴ的Ｅ．ｃｏｌｉｐＧＥＸ衍生质粒为模板,经ＰＣＲ得到编码Ｓｊ２６ＧＳＴ的全长ｃＤＮＡ片段．将其按正确的阅读框顺序,克隆到人结核杆菌热休克蛋白（ｈｅａｔｓｈｏｃｋｐｒｏｔｅｉｎ,ＨＳＰ）７０的启动子下游,再将ＨＳＰ７０启动子和Ｓｊ２６ＧＳＴ基因一起亚克隆到Ｅ．ｃｏｌｉ－分枝杆菌穿梭质粒ｐＢＣＧ－２０００中,得到Ｅ．ｃｏｌｉ－分枝杆菌穿梭表达质粒ｐＢＣＧ－Ｓｊ２６．ｐＢＣＧ－Ｓｊ２６电转化入ＢＣＧ和Ｍ．ｓｍｅｇｍａｔｉｓｍｃ２１５５中表达Ｓｊ２６ＧＳＴ抗原,所表达的天然重组Ｓｊ２６ＧＳＴ（ｒＳｊ２６ＧＳＴ）为可溶性蛋白,在ＳＤＳ－ＰＡＧＥ上分子量为２６ｋＤ处可见明显的表达蛋白带．其表达量分别占ＢＣＧ和Ｍ．ｓｍｅｇｍａｔｉｓ菌体总蛋白的１５％和１０％．可见,Ｓｊ２６ＧＳＴ基因能在ＢＣＧ中高效表达．     

多叶猴耳环──猴耳环属一新种

多叶猴耳环──猴耳环属一新种文和群（广西植物研究所分类研究室,桂林５４１００６）ＰＩＴＨＥＣＥＬＬＯＢＩＵＭＭＵＬＴＩＦＯＬＩＯＬＡＴＵＭ－ＡＮＥＷＳＰＥＣＩＥＳＯＦＰＩＴＨＥＣＥＬＬＯＢＩＵＭＦＲＯＭＧＵＡＮＧＸＩ,ＣＨＩＮＡＷｅｎＨｅｑｕｎ（Ｇｕ...     

过氧化心肌线粒体产生的脂类自由基及（－）－EGCG的抑制作用

本文利用ＥＳＲ技术研究了心肌线粒体酶修饰下的脂质过氧化和脂类自由基,以及（－）－ＥＧＣＧ的抑制作用。结果表明：４－ＰＯＢＮ能捕集ｌｉｐｏｘｙｇｅｎｇｓｅ诱发心肌线粒体产生的自由基,得到６条线谱的脂类自由基和４条线谱捕集物,（－）－ＥＧＣＧ对该体系中使用的１ｉｐｏｘｙｅｎａｓｅ活性无影响,对所产生的自由基有明显的清除作用,并呈量效关系。对脂质过氧化的抑制作用,在本试验浓度范围内随浓度增加变化不大,最大抑制率约２０％。     

过氧化心肌线粒体产生的脂类自由基及（—）—EGCG的抑制…

本文利用ＥＳＲ技术研究了心肌线粒体酶修饰下的脂过氧化和脂类自由基,以及（－）－ＥＧＣＧ的抑制作用,结果表明：４－ＰＯＢＮ能捕集ｌｉｐｏｘｙｇｅｎａｓｅ诱发心肌线粒体产生的自由基,得到６条线谱的脂类自由基和４条线谱捕集物,（－）－ＥＧＣＧ对该本系中使用的ｌｉｐｏｘｙｇｅｎａｓｅ活性无影响,对所产生有明显的消除作用,并呈量效关系,对脂质过氧化的抑制作用,在本试验浓度范围内随浓度增加变化不大,最大抑制率     

异裂菊属两新种

异裂菊属两新种梁健英（广西植物研究所,桂林５４１００６）关键词菊科;异裂菊属;新种ＴＷＯＮＥＷＳＰＥＣＩＥＳＯＦＨＥＴＥＲＯＰＬＥＸＩＳＣＨＡＮＧ（ＣＯＭＰＯＳＩＴＡＥ）￥ＬｉａｎｇＪｉａｎｇｙｉｎｇ（ＧｕａｎｇｘｉＩｎｓｔｉｔｕｔｅｏｆＢｏｔａｎｙ...     

Early diagnosis of systemic lupus erythmatosus using ANN models of dsDNA binding antibody sequence data

In this paper a new method based on artificial neural networks (ANN), is introduced for identifying pathogenic antibodies in Systemic Lupus Erythmatosus (SLE). dsDNA binding antibodies have been implicated in the pathogenesis of this autoimmune disease. In order to identify these dsDNA binding antibodies, the protein sequences of 42 dsDNA binding and 608 non-dsDNA binding antibodies were extracted from Kabat database and encoded using a physicochemical property of their amino acids namely Hydrophilicity. Encoded antibodies were used as the training patterns of a general regression neural network (GRNN). Simulation results show that the accuracy of proposed method in recognizing dsDNA binding antibodies is 83.2%. We have also investigated the roles of the light and heavy chains of anti-dsDNA antibodies in binding to DNA. Simulation results concur with the published experimental findings that in binding to DNA, the heavy chain of anti-dsDNA is more important than their light chain.     

Adaptive Neural Network-Based ECG Data Compression According to the Morphology

1IntroductionElbo（ECG）offersalotofilllcorralltinfondionforthediagnosisOfheartdis－eases．Berz1，seahaormalEChcax［lrins＞llleu＂knownsituations，thcycanbeCSUghtinthelongti1Ylecontin～11xHlltoriDg．ndterrnoultonngsystemwhichcanrecord24－hoUrECGdataisoneofeffectiveme～toprovidethefun～．AlthOUghthehag6scaleICmorestohavebeeddevelopepbedeavailabletostorelOngtboeECGdsta，itisveqdifficultyandtioublesomethatalopnUmbeOfdstaisprasersandstoredortiallsillltted．InthedigitedECGdata，thereare…     

Software reliability prediction using recurrent neural network with Bayesian regularization

A recurrent neural network modeling approach for software reliability prediction with respect to cumulative failure time is proposed. Our proposed network structure has the capability of learning and recognizing the inherent internal temporal property of cumulative failure time sequence. Further, by adding a penalty term of sum of network connection weights, Bayesian regularization is applied to our network training scheme to improve the generalization capability and lower the susceptibility of overfitting. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to both goodness-of-fit and next-step-predictability compared to existing neural network models for failure time prediction.     

混沌理论及其在建立神经网络模型中的应用

随着许多学科的相互紧密交叉以及混沌理论的日益深入的研究,人们从生物现象中提出了许多与混沌有关的神经网络模型,本文对混沌理论的基本原理做了简要概述,并着重介绍了四种有代表性的混沌神经网络模型及其应用．同时指出这一研究方向无论在理论还是在应用方面都具有十分诱人的前景．     

ECG beat classification using PCA,LDA, ICA and Discrete Wavelet Transform

Electrocardiogram (ECG) is the P-QRS-T wave, representing the cardiac function. The information concealed in the ECG signal is useful in detecting the disease afflicting the heart. It is very difficult to identify the subtle changes in the ECG in time and frequency domains. The Discrete Wavelet Transform (DWT) can provide good time and frequency resolutions and is able to decipher the hidden complexities in the ECG. In this study, five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed namely: non-ectopic beats, supra-ventricular ectopic beats, ventricular ectopic beats, fusion betas and unclassifiable and paced beats. Three dimensionality reduction algorithms; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were independently applied on DWT sub bands for dimensionality reduction. These dimensionality reduced features were fed to the Support Vector Machine (SVM), neural network (NN) and probabilistic neural network (PNN) classifiers for automated diagnosis. ICA features in combination with PNN with spread value (σ) of 0.03 performed better than the PCA and LDA. It has yielded an average sensitivity, specificity, positive predictive value (PPV) and accuracy of 99.97%, 99.83%, 99.21% and 99.28% respectively using ten-fold cross validation scheme.     

Toll-like Receptors 3, 4, and 7 Are Expressed in the Enteric Nervous System and Dorsal Root Ganglia

The aim of the present study was to evaluate the expression of innate immunity receptors belonging to the Toll-like family in the neural plexuses of the different tracts of murine intestine, of the human ileum, and in lower dorsal root ganglia (DRGs) from where extrinsic afferents to these plexuses originate. Results obtained by immunohistochemistry and immunofluorescence on paraffin-embedded tissue and whole-mount preparations show that Toll-like receptors (TLRs) -3 and -7, recognizing viral RNA, and TLR4, recognizing lipopolysaccharide (membrane component of Gram-negative bacteria), are expressed in the myenteric and submucous plexuses of murine intestine and human ileum, and in DRGs primary sensory neurons. They also show that TLR4 immunostaining is stronger in murine distal large bowel. In murine tissue, expression of TLRs was present in both neurons and glial cells. These observations indicate that the enteric neural network might be directly activated by bacterial and viral components and is therefore more in the forefront than previously envisaged in defense responses of the intestinal wall and in the cross-talk with intestinal microbiota. They also highlight the presence of a peripheral neural network that by way of hardwired neurotransmission could potentially convey to the central nervous system specific information on our microbial counterpart and invading or potentially invading pathogens. (J Histochem Cytochem 57:1013–1023, 2009)     

A modified Zeeman model for producing HRV signals and its application to ECG signal generation

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)--all of which are important indexes of human heart functions. In this paper we present a comprehensive model for generating such artificial ECG signals. We incorporate into our model the effects of respiratory sinus arrhythmia, Mayer waves and the important very low-frequency component in the power spectrum of HRV. We use a new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced by using a simple neural network. The importance of the work is the model's ability to produce artificial ECG signals that resemble experimental recordings under various physiological conditions. As such the model provides a useful tool to simulate and analyse the main characteristics of ECG, such as its power spectrum and HRV under different conditions. Potential applications of this model include using the generated ECG as a flexible signal source to assess the effectiveness of a diagnostic ECG signal-processing device.     

基于BP神经网络与ETM+遥感数据的盐城滨海自然湿地覆被分类

高效而精确的湿地遥感分类是大范围湿地资源动态监测与管理的必要保障。本研究使用ETM 遥感数据,借助Matlab神经网络工具箱,构建了基于BP神经网络的滨海湿地覆被分类模型,并将其应用于江苏盐城沿海湿地珍禽国家级自然保护区的核心区的自然湿地覆被分类研究中。本研究选择3、4、7、8波段作为输入层变量,单隐藏层设为10个节点,输出层变量对应待划分的8种覆被类型,构建三层式BP神经网络滨海湿地覆被分类模型。结果显示,BP分类总精度为85.91%,Kappa系数为0.8328,与最小距离法和极大似然法的分类总精度相比,分别提高了7.99%和6.08%,Kappa系数也相比提高。研究结果表明,BP神经网络分类法是一种较为有效的湿地遥感影像分类技术,能够提高分类精度。     

Nestin-expressing neural stem cells identified in the scar following myocardial infarction

Nerve fiber innervation of the scar following myocardial damage may have occurred either via the growth of pre-existing fibers and/or the mobilization of neural stem cells. The present study examined whether neural stem cells were recruited to the infarct region of the rat heart following coronary artery ligation. The neural stem cell marker nestin was detected in the infarct region of 1-week post-myocardial infarct (MI) male rats and cultured scar-derived neural-like cells. By contrast, nestin staining was undetected in either scar myofibroblasts or cardiac myocytes residing in the non-infarcted left ventricle. Reactive astrocytes were isolated from the infarct region and characterized by the co-expression of nestin, glial fibrillary acidic protein, and vimentin. Specific staining of oligodendrocytes and neurons was also detected in the infarct region and cultured scar-derived neural-like cells. Furthermore, neurofilament-M positive fibers were identified in the scar and tyrosine hydroxylase immunoreactivity was observed in peripherin-positive neurons. Neurite formation was induced in PC12 cells treated with the conditioned-media of primary passage scar-derived cells, highlighting the synthesis and secretion of neurotrophic factors. Nerve growth factor (NGF) and brain-derived neurotrophic factor were detected in myofibroblasts and neural cells, and both cell types expressed the NGF receptors trkA and p75. These data highlight the novel observation that neural stem cells were recruited to the infarct region of the damaged rat heart and may contribute in part to nerve fiber growth and subsequent innervation of the scar.     

A model of neural network for spatiotemporal pattern recognition

A model of neural network to recognize spatiotemporal patterns is presented. The network consists of two kinds of neural cells: P-cells and B-cells. A P-cell generates an impulse responding to more than one impulse and embodies two special functions: short term storage (STS) and heterosynaptic facilitation (HSF). A B-cell generates several impulses with high frequency as soon as it receives an impulse. In recognizing process, an impulse generated by a P-cell represents a recognition of stimulus pattern, and triggers the generation of impulses of a B-cell. Inhibitory impulses with high frequency generated by a B-cell reset the activities of all P-cells in the network.Two examples of spatiotemporal pattern recognition are presented. They are achieved by giving different values to the parameters of the network. In one example, the network recognizes both directional and non-directional patterns. The selectivities to directional and non-directional patterns are realized by only adjusting excitatory synaptic weights of P-cells. In the other example, the network recognizes time series of spatial patterns, where the lengths of the series are not necessarily the same and the transitional speeds of spatial patterns are not always the same. In both examples, the HSF signal controls the total activity of the network, which contributes to exact recognition and error recovery. In the latter example, it plays a role to trigger and execute the recognizing process. Finally, we discuss the correspondence between the model and physiological findings.