Background: Breast cancer (BC) is the most widespread cancer among women worldwide. Its incidence and mortality rates have risen in the previous three decades as a result of changes in risk factor profiles, improved cancer registry, and cancer detection. Objective: The study's goals were to establish if Ki-67 could be used as a potential marker in serum of cancer disease patients as well as their interaction with vascular endothelial growth factor (VEGF) and ES in various stages of breast cancer to assess their function in the progression of BC. Materials and Methods: The levels of Ki-67, VEGF and endostatin (ES) in serum were assessed by commercial enzyme linked immunosorbent assay (ELISA) kits in 60 women diagnosed with breast cancer

Background: CYP1A1 gene polymorphisms and tobacco smoking are among several risk factors for various types of cancers, but their influence on breast cancer remains controversial. We analyzed the possible association of CYP1A1 gene polymorphisms and tobacco smoking-related breast cancer in women from Iraq. Materials and methods: In this case-control study, gene polymorphism of CYP1A1 gene (CYP1A1m1, T6235C and CYP1A1m2, A4889G) of 199 histologically verified breast cancer patients' and 160 cancer-free control women's specimens were performed by using PCR-based restriction fragment length polymorphism. Results: Three genotype frequencies (TT, TC, and CC) of CYP1A1m1T/C appeared in 16.1, 29.6, and 54.3% of women with breast cancer, respectiv

In this research we study a variance component model, Which is the one of the most important models widely used in the analysis of the data, this model is one type of a multilevel models, and it is considered as linear models , there are three types of linear variance component models ,Fixed effect of linear variance component model, Random effect of linear variance component model and Mixed effect of linear variance component model . In this paper we will examine the model of mixed effect of linear variance component model with one –way random effect ,and the mixed model is a mixture of fixed effect and random effect in the same model, where it contains the parameter (μ) and treatment effect (τ_i ) which  has

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

With the increasing prevalence of breast cancer among female internationally, occupies about 25% of all cases of cancer, with a measured 1.57 million up to date cases in 2012. Breast cancer has turn a most warning to health of female in Iraq, where it is the major cause of death among women after cardiovascular diseases, with a mortality rate of 23% related cancer. Recently there is a crucial requirement to include community pharmacists in health elevation activities to support awareness and early diagnosis of cancer, specially breast cancer. The aim of this study is to assess knowledge, attitude and perceived barriers amongst Iraqi community pharmacists towards health promotion of breast cancer. This study is cross sectional research. A

      In this study, different methods were used for estimating location parameter  and scale parameter for extreme value distribution, such as maximum likelihood estimation (MLE) , method of moment  estimation (ME),and approximation  estimators based on percentiles which is called white method in estimation, as the extreme value distribution is one of exponential distributions. Least squares estimation (OLS) was used, weighted least squares estimation (WLS), ridge regression estimation (Rig), and adjusted ridge regression estimation (ARig) were used. Two parameters for expected value to the percentile  as estimation for distribution f

Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin