Lack of association between cyclooxygenase 2−765G/C gene polymorphism and breast cancer risk in Ahvaz, west-south Iran

Cyclooxygenases are key enzymes in conversion of arachidonic acid into prostaglandin H2. Cyclooxygenase-2 (COX-2) increases prostaglandins in neoplastic tissue. COX-2 has important roles in cell proliferation cancers, angiogenesis, and alzheimer. COX-2 is up-regulated in several types of cancer, and it is hypothesized that COX-2 expression may be genetically influenced. Our main objective was to evaluated the association of polymorphism COX-2 with risk of breast cancer in khouzestan province, and the second objective of the study was to evaluate the association with biochemistry parameters. This study consisting of 150 patients with breast cancer and 120 normal DNA was extracted from the white blood cells. Polymorphism cox2 gene was detected by polymerase chain reaction according to the standard methods. The profile lipids and estrogen were measured in two groups by standard methods. Chi square analysis showed that there was no association between breast cancer risk and COX-2 ?765G>C genotype and alleles. Also, no association were observed between ?765G>C polymorphism and biochemistry parameters. A multiple logistic regression model with cox2 genotypes and LDL and HDL as covariates revealed that there is no significant association between cox2 genotypes and risk of breast cancer, but higher values of LDL and HDL significantly increase risk of breast cancer.     

An Evidence-Based Combining Classifier for Brain Signal Analysis

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.     

恒河猴群微卫星DNA多态性的分析

目的　确立一种对恒河猴群个体的遗传物质进行准确可靠、快速简便的遗传检测方法。方法　利用聚合酶链反应 (PCR)扩增技术对 2 0只恒河猴群个体间进行了DNA多态性的分析。结果　筛选出 9个微卫星DNA位点具有显著多态性 ,4个微卫星DNA位点没有多态性 ,还有 2个位点等位基因数目较少。结论　利用这些多态性微卫星位点建立一种对恒河猴群个体进行有效、准备可靠、快捷简便的遗传背景监测方法。     

绵羊GDF9和BMP15基因多态性检测

以绵羊GDF9基因FecG^H突变和BMP15基因FecX^B和FecX^G突变为候选基因,采用PCR—RFLP方法研究其在湖羊、夏洛来、陶赛特、萨福克、中国美利奴肉用多胎品系、中国美利奴羊和罗米丽羊7个品种中的多态性．结果发现,湖羊群体中存在GDF和BMP15基因的FecG^H和FecX^B突变,但发生率极低,分别为0．645％(2／310)和0．968％(3／310)：而其它品种中则没有发现GDF9和BMP15基因的相应突变．这一发现对于建立绵羊基因标记辅助选择方法具有重要意义．