硒与白内障

硒与白内障黄佩丽（北京师范大学化学系１００８７５）白内障是眼睛晶状体混浊，失去双透镜作用，使光线不能透过晶状体到达视网膜，致使患者失明的一种眼科疾病。白内障的种类很多，有老年性、外伤性、放射性、代谢性、营养性。其中老年性白内障较普遍，是老年人的常见病...     

神经网络和Eu^2＋离子在复合氟化物基体中跃迁发射的研究

运用人工神经网法对复合氟化物中Ｅｕ＾＋离子光谱结构进行了分类研究，对神经网络的基本特征进行了观察。实验表明，神经网的结构、初始权重以及作为训练集和预测集的随枘发组对最终结果都会产生重要影响。本研究采用测试集来监控训练过程，以避免“过训练”，并提高神经网的预测性能。     

晶状体蛋白识别互作与白内障的研究进展

白内障是全球致盲率最高的眼科疾病, 发病组织为晶状体. 晶状体内纤维细胞含有高浓度的晶状体蛋白, 晶状体蛋白家族分α?, β?和γ?3大亚家族. α-晶状体蛋白具有小分子伴侣功能, 可识别错误折叠蛋白质, 维持晶状体内蛋白质稳态; β?/γ?晶状体蛋白通过分子内或分子间相互作用, 主要发挥结构蛋白功能. 晶状体蛋白在晶状体纤维细胞内呈瞬时有序排列, 精准分子识别及动态相互作用在维持晶状体透明度中发挥关键作用. 晶状体内蛋白质稳态失衡是白内障的主要致病因素. 晶状体蛋白半衰期长, 且翻译合成后不再更新, 广泛受pH值、 金属离子、 辐射损伤和蛋白质翻译后修饰等细胞内外环境因素和化学因素的干扰, 影响晶状体蛋白间的分子识别和相互作用, 诱发白内障. 理清化学调控的晶状体蛋白分子识别及互作调控, 有助于阐明白内障发病机理, 并发掘防治白内障的创新策略. 本文基于晶状体蛋白识别互作与白内障研究进展, 综合评述了晶状体蛋白的分子识别、 相互作用方式、 调控因素及研究技术创新, 并探讨了晶状体蛋白识别互作调控网络在白内障药物研发的应用价值与挑战.     

老年性白内障与微量元素

参阅有关老年性白内障的文献．归纳其病因有四种学说:(1)老化学说;(2)醌体学说;(3)微量元素学说;(4)中医学说．按病因学说不同．用药各异．目前治疗老年性白内障药物有滴眼剂及内服剂,其中有一种新药名为除障灵．它是根据中医学说及微量元素学说，筛选中药制成,它含有丰富的锌、硒、铜、镁等微量元素．应用除障灵治疗老年性白内障患者102例(204只眼)，观察6个月至2年，有效率达95％，开创了治疗老年性白内障的新方法．     

石墨炉原子吸收光谱法测定不同镍环境下人眼晶状体的镍量

利用高灵敏度的石墨炉原子吸收光谱仪对来自不同镍环境和不同状态的40个人眼晶状体中的镍进行了测试。在测试中，采用了方便的HNO，消解法对样品进行预处理，通过实验比较测得普通石墨管对镍的特征质量是长寿命石墨管的4．6倍，然后用长寿命石墨管对各种样品的消解后获得的测试液进行镍的测定，由此得到每个人眼晶状体中的绦量。结果表明：正常人的透明晶状体中的镍量明显低于患有白内障人眼晶状体中的镍量；而与镍矿直接接触的白内障患者的晶状体中的镍量显著高于非直接接触镍矿白内障患者和一般白内障患者晶状体中的镍量。这种关联表明人眼晶状体中的镍元素可能是白内障的致病因素之一。     

Ge—132对半乳糖性白内障的研究

扼要叙述了Ｇｅ－１３２的性质，结构、生物学特性及临床应用等，重点介绍了Ｇｅ－１３２用于半乳糖性白内障的研究方法，结果和讨论，旨在通过Ｇｅ－１３２抑制与晶体混浊有关的糖基化末端产物形成，逆转糖基化蛋白为溶解状态，抑制或延缓白内障的发生和发展，为老年性或糖尿病性白内障开辟新的治疗途径。     

在线的离子交换预富集流动注射—原子吸收光谱法测定铁,锰,…

本文使用大孔弱酸阳离子交换树脂Ｄ－１５２微型柱预富集，将流动注射与原子吸收光谱法相结合测定了Ｆｅ，Ｍｎ，Ｃｕ和Ｃｄ等离子，讨论了溶液酸度，采样速率及洗脱剂等对上述四元素灵敏度的影响，在采样频率４０次／ｈ，采样体种７ｍｌ地Ｆｅ，Ｍｎ，Ｃｕ和Ｃｄ增敏１６－２４倍，检出限分别为０．９、０．４、０．４和０．１ｎｇ／ｍｌ     

IMC—80缓蚀剂的研究和应用

合成了具有炔醇基、氨基和季铵结构的系列化合物，并在此基础上研制出缓蚀剂ＩＭＣ－８０，利用腐蚀失重和电化学方法。地其在ＣＯ２饱和盐水和盐酸体系中的缓蚀性能进行研究，研究结果表明，它们在酸性介质、油田污水介质和含有少量Ｈ２Ｓ和Ｃ煌伴生水介质中具有优良的缓蚀性能，该缓蚀剂在中原油田试用，在控制油井及其相关的集油管线腐蚀上，其效果优于美国同类产品ＣＩ－２０３。     

健康老年人与若干常量及微量元素的相关性研究

用原子吸收光谱法测定了７０例健康老年人、３０例健康成年人和３０例老年高血压病人血清中Ｃｕ、Ｚｎ、Ｆｅ、Ｍｎ、Ｃａ、Ｍｇ６种元素的含量．结果表明，健康老年人组与健康成年人组（对照组）相比，除Ｆｅ以外，Ｃｕ、Ｚｎ、Ｍｎ、Ｃａ、Ｍｇ均有显著性差异；与老年高血压病人组相比，除Ｍｇ以外，Ｃｕ、Ｚｎ、Ｆｅ、Ｍｎ、Ｃａ均有显著性差异．我们认为稳定的Ｃｕ／Ｚｎ比值及较高的血Ｍｇ水平是健康老年人未发生心血管疾病及糖尿病的重要原因之一，而高Ｃｕ、高Ｃａ及显著偏高的Ｃｕ／Ｚｎ比值，则是发生高血压的一个重要原因。     

菊花微量及宏量元素分析

用ＩＣＰ－ＡＥＳ法测定了药用野菊花和抗白菊及６种人工栽培菊花中Ａｓ，Ｂ、Ｂａ、Ｃａ、Ｃｄ、Ｃｏ、Ｃｕ、Ｆｅ、Ｍｇ、Ｍｎ、Ｍｏ、Ｎｉ、Ｐｂ、Ｓｅ、Ｓｉ、Ｖ、Ｚｎ、Ｐ、Ｓ、Ｋ等元素的含量。结果表明：（１）菊花中Ｃａ、Ｍｇ、Ｐ、Ｓ、Ｋ含量很高，Ｂ、Ｆｅ、Ｍｎ、Ｚｎ也有较高的含量；（２）野菊花中Ｂ、Ｃａ、Ｆｅ、Ｍｇ含量较其他品种高、，抗白菊中Ｍｎ、Ｓｅ、Ｓ含量高地其他品种；（３）代栽培的菊花Ｋ、Ｐ含量高于     

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.     

Multivariate classification based on metal contents in human senile cataract lenses

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.     

Study of trace-element metabolism during the formation of rat's cataract

A cataract in the lenses of Wistar rats was induced by injecting Na₂SeO₃ into their bodies; they exhibited various kinds of cataract between 8 and 28 days. Samples were prepared using the method of low temperature ashing. The trace-element analyses of samples of different kinds of lenses were carried out by PIXE method. From the results obtained we have studied the ratio of their concentrations relative to the control values in relation to the formation time of cataract. It was found that the metabolites, of most trace-elements in the control lenses are stable, but in various kinds of cataractous lenses they display certain changes. Elements are clearly divided into two types. Elements of the first type are S, Cl, Ca, Fe and Zn. They appear to accumulate relative to the controls, while the second type, P, K and Rb, shows deficiency. The functions of trace-element metabolism during the formation of cataract are also discussed.     

毛细管电泳-小波神经网络法辅佐诊断乳腺癌的研究

采用毛细管电泳法测定了46个健康人和26个乳腺癌病人尿样中的13种正常核苷和修饰核苷,以小波神经网络作为模式识别工具对健康人和乳腺癌病人的分类作了研究,随机选取的训练集的识别率达到100％,相应的预测集判别率正确性在96％以上,与经典的前向多层神经网络相比,小波神经网络具有更强的信息提取和逼近能力.研究结果还表明,小波神经网络的预测能力强于主成分分析和线性判别分析,毛细管电泳法与小波神经网络的结合有望成为乳腺癌的辅助诊断手段.     

STUDIES ON HUMAN LENSES: II. DISTRIBUTION AND SOLUBILITY OF FLUORESCENT PIGMENTS IN CATARACTOUS AND NON-CATARACTOUS LENSES OF INDIAN ORIGIN

Abstract— Fluorometric studies of cataractous and non-cataractous human lenses were carried out to study the emission characteristics and the distribution and solubility of lenticular pigments. Most of the detected fluorophores were well distributed over the cortical and nuclear portion of the lens. The decrease in solubility of proteins with aging and cataract formation is concomitant with increasing photolysis of tryptophan. However, this is likely a phenomenon independent of the photochemical transformations of the lens proteins. The number of emitting species in the diseased lenses are higher than in the normal mature lenses. A species emitting around 375 or 388 nm is of particular interest (λ_cx, 330 nm) in that the emission characteristics of this fluorophore resemble kynurenic acid which has a high photosensitizing efficiency. The concentration of fluorescent pigments in the lenses of Indian origin is significantly high. The intense pigmentation could be attributed largely to the formation of photoproducts in the absence of normal endogenous antioxidant accumulation that is dependent on nutrition standard. If, indeed, any of these fluorescent pigments, because of their photosensitizing ability, are responsible for lenticular opacity, it is not the abundance of sunlight alone but also malnutrition that could account for the high incidence of cataract in India.     

STUDIES ON HUMAN LENSES: II. DISTRIBUTION AND SOLUBILITY OF FLUORESCENT PIGMENTS IN CATARACTOUS AND NON-CATARACTOUS LENSES OF INDIAN ORIGIN

Fluorometric studies of cataractous and non-cataractous human lenses were carried out to study the emission characteristics and the distribution and solubility of lenticular pigments. Most of the detected fluorophores were well distributed over the cortical and nuclear portion of the lens. The decrease in solubility of proteins with aging and cataract formation is concomitant with increasing photolysis of tryptophan. However, this is likely a phenomenon independent of the photochemical transformations of the lens proteins. The number of emitting species in the diseased lenses are higher than in the normal mature lenses. A species emitting around 375 or 388 nm is of particular interest (lambda cx 330 nm) in that the emission characteristics of this fluorophore resemble kynurenic acid which has a high photosensitizing efficiency. The concentration of fluorescent pigments in the lenses of Indian origin is significantly high. The intense pigmentation could be attributed largely to the formation of photoproducts in the absence of normal endogenous antioxidant accumulation that is dependent on nutrition standard. If, indeed, any of these fluorescent pigments, because of their photosensitizing ability, are responsible for lenticular opacity, it is not the abundance of sunlight alone but also malnutrition that could account for the high incidence of cataract in India.     

Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology

An accurate and generally applicable method for estimating aqueous solubilities for a diverse set of 1297 organic compounds based on multilinear regression and artificial neural network modeling was developed. Molecular connectivity, shape, and atom-type electrotopological state (E-state) indices were used as structural parameters. The data set was divided into a training set of 884 compounds and a randomly chosen test set of 413 compounds. The structural parameters in a 30-12-1 artificial neural network included 24 atom-type E-state indices and six other topological indices, and for the test set, a predictive r2 = 0.92 and s = 0.60 were achieved. With the same parameters the statistics in the multilinear regression were r2 = 0.88 and s = 0.71, respectively.