羧基化石墨烯/半胱胺修饰金电极对多巴胺的电化学行为研究

为了基于羧基化石墨烯/半胱胺修饰金电极建立更为先进的多巴胺生物传感器，以定量检测儿茶酚胺类神经递质多巴胺，利用自组装技术将半胱胺修饰于金电极上，再利用1-乙基-\[3-二甲基氨基丙基\]碳酰二亚胺盐酸化物/N-羟基琥珀酰亚胺（EDC/NHS）交联剂将羧基化石墨烯固定在修饰后的金电极上制成多巴胺电化学传感器。先对修饰电极进行表征以检验其灵敏度，再利用循环伏安法研究该电极在多巴胺溶液中的电化学行为，包括检测条件的优化和传感器性能的测定。修饰电极表征结果表明，羧基化石墨烯/半胱胺修饰金电极提高了电极传递电子的能力，具有较高的灵敏度。经单因素实验得出，最佳检测条件为利用pH 6.00的0.30 mol·L-1磷酸盐缓冲溶液（PBS）配制多巴胺溶液，扫描速率设定为200 mV·s-1。在最佳检测条件下，制备的多巴胺电化学传感器电流的大小随着多巴胺浓度的增大而增大，在1.0×10-3~3.5×10-3 mol·L-1范围内呈现良好的线性关系，线性回归方程为I=8.120 6C+7.017，相关系数R2为0.999 5。且该传感器精密度好，稳定性强，具有一定的抗干扰能力。研究结果为药物盐酸多巴胺注射液中多巴胺含量测定提供了支撑。     

基因芯片在衣原体研究中的应用

基因芯片又称为DNA微阵列,是指将大量核酸片段以预先设计的方式固定在载体上组成密集分子阵列,与荧光素或其他方式标记的样品进行杂交,通过检测杂交信号的强弱来判断样品中有无靶分子以及对靶分子进行定量,是一种研究生物大分子功能的新技术。在衣原体研究方面,基因芯片主要应用于衣原体的检测与分型、感染机制的研究、特定基因作用分析、毒力及耐药基因的筛选等。     

Comparison study of microarray meta-analysis methods

Background  

Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods.     

Electrochemical study of mononucleosome

The electrochemical behavior of mononucleosome has been studied in parallel with circular dichroism and trypsin degradation. In mononucleosome, DNA is never adsorbed at the electrode surface. A model of flat adsorption of the mononucleosome via histones is proposed.     

多孔硅适配子生物传感器的电化学研究

构建1种用于快速检测四环素的新型电化学纳米多孔硅(PS)生物传感器。通过脉冲腐蚀法制得多孔硅基片,将适配子固定于其上,这种四环素适配子能够特异性识别四环素分子,并引起阻抗值的变化。利用电化学交流阻抗法比较固定适配子前后硅片表面阻抗值的变化,以及在体系中加入不同浓度四环素后阻抗谱的变化。选择1个合适的等效电路对测得的阻抗数据进行拟合,获得了四环素浓度与阻抗值的变化规律。传感器的线性检测范围为2.079~62.37 nmol/L,检测限为2.079 7 nmol/L。     

Electrochemical study of triscyclopentadienyluranium complexes

The electrochemical behaviour of Cp₃UBH₄ and Cp₃UNEt₂ in THF has been studied using cyclic voltammetry and chronoamperometry. Cp₃UBH₄ undergoes a simple one-electron quasi-reversible reduction, while chemical reactions between the product of the charge-transfer reaction and the starting electroactive species occur both after oxidation and reduction of Cp₃UNEt₂.     

Predicting survival from microarray data--a comparative study

MOTIVATION: Survival prediction from gene expression data and other high-dimensional genomic data has been subject to much research during the last years. These kinds of data are associated with the methodological problem of having many more gene expression values than individuals. In addition, the responses are censored survival times. Most of the proposed methods handle this by using Cox's proportional hazards model and obtain parameter estimates by some dimension reduction or parameter shrinkage estimation technique. Using three well-known microarray gene expression data sets, we compare the prediction performance of seven such methods: univariate selection, forward stepwise selection, principal components regression (PCR), supervised principal components regression, partial least squares regression (PLS), ridge regression and the lasso. RESULTS: Statistical learning from subsets should be repeated several times in order to get a fair comparison between methods. Methods using coefficient shrinkage or linear combinations of the gene expression values have much better performance than the simple variable selection methods. For our data sets, ridge regression has the overall best performance. AVAILABILITY: Matlab and R code for the prediction methods are available at http://www.med.uio.no/imb/stat/bmms/software/microsurv/.     

Tissue microarray study for classification of breast tumors

Clinical and pathological heterogeneity of breast cancer hinders selection of appropriate treatment for individual cases. Molecular profiling at gene or protein levels may elucidate the biological variance of tumors and provide a new classification system that correlates better with biological, clinical and prognostic parameters. We studied the immunohistochemical profile of a panel of seven important biomarkers using tumor tissue arrays. The tumor samples were then classified with a monothetic (binary variables) clustering algorithm. Two distinct groups of tumors are characterized by the estrogen receptor (ER) status and tumor grade (p = 0.0026). Four biomarkers, c-erbB2, Cox-2, p53 and VEGF, were significantly overexpressed in tumors with the ER-negative (ER-) phenotype. Eight subsets of tumors were further identified according to the expression status of VEGF, c-erbB2 and p53. The malignant potential of the ER-/VEGF+ subgroup was associated with the strong correlations of Cox-2 and c-erbB2 with VEGF. Our results indicate that this molecular classification system, based on the statistical analysis of immunohistochemical profiling, is a useful approach for tumor grouping. Some of these subgroups have a relative genetic homogeneity that may allow further study of specific genetically-controlled metabolic pathways. This approach may hold great promise in rationalizing the application of different therapeutic strategies for different subgroups of breast tumors.     

Electrochemical study of d-glucose oxidase autoinactivation

The immobilization of a d-glucose oxidase (β-d-glucose: oxygen 1-oxidoreductase, EC 1.1.3.4) monolayer onto a glassy carbon rotating disc electrode allows the measurement of concentrations in the enzyme's microenvironment and, hence, gives a method of easily following its activity. As it functions, d-glucose oxidase undergoes an autoinactivation which is clearly distinct from inactivation by H₂O₂, and which is more severe as the concentration of the two substrates is increased. It appears that the number of catalytic cycles is fixed at 10⁷, irrespective of the concentrations of the two substrates. Kinetically, it is the enzyme-substrate complex which seems to be inactivated. Scavengers of toxic species of O₂ have no effect on the kinetics of autoinactivation. Identical results were found in solution.     

Electrochemical DNA biosensor for the study of ciprofloxacin-DNA interaction

The interaction of ciprofloxacin with DNA was studied by using an electrochemical DNA biosensor. The binding mechanism of ciprofloxacin was elucidated by using constant current potentiometry and differential pulse voltammetry at DNA-modified glassy carbon electrode. The decrease in the guanine oxidation peak area or peak current at +0.9 V was used as an indicator for the interaction mechanism in 0.2M acetate buffer (pH 5). The binding constant (K) values obtained were 1.33+/-0.02 x 10(4) and 1.32+/-0.08 x 10(4) M(-1) with constant current potentiometry and differential pulse voltammetry, respectively. A linear dependence of the guanine peak area or peak currents was observed in the range of 40-80 microM ciprofloxacin, with a detection limit of 24 microM with r=0.995 and 9 microM with r=0.999 by using constant current potentiometry and differential pulse voltammetry, respectively. Moreover, the influence of sodium and calcium ions was also studied to elucidate the mechanism of ciprofloxacin-DNA interaction at different solution conditions, and this proved to be helpful in understanding the ciprofloxacin-DNA interaction.     

细胞色素c在氨基酸及二肽修饰金电极上的电化学反应

系统地研究了细胞色素ｃ在多种氨基酸和多肽修饰电极上的电化学反应。并对影响加速细胞色素ｃ电化学反应的因素进行了讨论。     

Affinity profiling using the peptide microarray technology: a case study

Little about the reliability of measurements obtained using synthetic peptide microarrays is known. We report results from a study on the quantitative reliability of microarrays manufactured by robot-supported immobilization of presynthesized peptides for different microarray platforms. Technological precision is assessed for inter- and intra-device readout comparisons. Correlations between measured signals and known dissociation constants using a phenomenological model derived from the mass action law are discussed. Special emphasis is on discussing the pitfalls of high-throughput affinity measurements. We show that the quantitative determination of binding affinities is prone to be biased toward a mean affinity of around 10(-7)M, while the classification of peptides into either "binders" or "nonbinders" provides very high prediction accuracy. The experimental requirements needed to obtain reliable binding affinity predictions are discussed.     

The XNA world: progress towards replication and evolution of synthetic genetic polymers

Life's diversity is built on the wide range of properties and functions that can be encoded in natural biopolymers such as polypeptides and nucleic acids. However, despite their versatility, the range of chemical functionalities is limited, particularly in the case of nucleic acids. Chemical modification of nucleic acids can greatly increase their functional diversity but access to the full phenotypic potential of such polymers requires a system of replication. Here we review progress in the chemical and enzymatic synthesis, replication and evolution of unnatural nucleic acid polymers, which promises to enable the exploration of a vast sequence space not accessible to nature and deliver ligands, catalysts and materials based on this new class of biopolymers.     

Effect of classic preconditioning on the gene expression pattern of rat hearts: a DNA microarray study

With the aim to enhance the plant vitamin E content, the barley gene encoding 4-hydroxyphenylpyruvate dioxygenase was overexpressed in tobacco plants under control of the 35S promoter. Transgenic lines have a higher capacity for homogentisate biosynthesis as evident by a more than 10-fold higher resistance towards the bleaching herbicide sulcotrione. Seeds from transgenic lines have an up to two-fold enhanced level of vitamin E without a change in the ratio of γ-tocopherol and γ-tocotrienol. While the vitamin E content is not affected in leaves, the level of plastoquinone is enhanced in leaves of transgenic lines during leaf senescence.     

Rat toxicogenomic study reveals analytical consistency across microarray platforms

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.