Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

This study was performd on 50 urine specimens of patients with type 2 diabetes, in addition, 50 normal specimens were investigated as control group. The activity rate of maltase in patients (6.40±2.17) I.U/ml and activity rate of maltase in normal (0.44±0.20)I.U/ml. The results of the study reveal that maltase activity of type 2 diabetes patient's urine shows significant increase (P<0.01) compare to normal.

Fever is a common illness in the pediatric age group ,the causes could be viral ,bacterial and fungal , this study was focused on bacterial pathogens as gram positive like Staphylococci, coagulase positive or negative ,Streptococci and gram negative like E-coli , Klebsialla ,Proteus, Pseudomonas, Burkhoderia , Acinetobacter and others like Pusturella ,E-alkalescendiaper, Haemophillus influenza and yeast like candida . Four thousand and seventy eight blood samples (4078)were collected in a period between January 2011 and the end of May 2012 at the child welfare hospital ,all the samples were cultured on suitable culture media and then biochemical tests were done using API-E 20 and sugar fermentation tests ,sensitivity

Abstract: E2F6 is a member of the E2F family of transcription factors involved in regulation of a wide variety of genes through both activation and repression. E2F6 has been reported as overexpressed in breast cancers but whether or not this is important for tumor development is unclear. We first checked E2F6 expression in tumor cDNAs and the protein level in a range of breast cancer cell lines. RNA interference-mediated depletion was then used to assess the importance of E2F6 expression in cell lines with regard to cell cycle profile using fluorescence-activated cell sorting and a cell survival assay using (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). The overexpression of E2F6 was confirmed in breast tumor cDNA samp

Objectives: To assess the relation between breast cancer & blood groups, identify the importance of women
age group and the relation of age with breast cancer.
Methodology: The study was performed on (115) women who were diagnosed with breast cancer in different
stages of disease and different ages. Blood samples were taken from them to demonstrate their blood groups and
(20) fresh tumor tissue samples were obtained; the tumor tissue used as a source of lectin for hemagglutinate
with erythrocyte of different blood groups. The study conducted at Baghdad Teaching Hospital and Radiation &
Nuclear Medicine Hospital from January, 2007 through June 2007.
Results: The study shows that the highest percentage of women

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

Background: Nicotine is the foremost chemical constituent responsible for addiction in tobacco products, in the non-ionized condition can be easily absorbed via epithelial tissue of the lung, the mouth, the nose  and across the skin

Objective:The study examines the harmful effect of   the nicotine which is an important component of cigarette in vitro.

Type of the study: Cross-sectional study.

Methods: Examines the harmful effect of   the nicotine which is an important component of cigarette in vitro by using two types of lung cancer cell lines (H460 TP53+/+, H441 TP53-/-).

The amount of protein in the serum depends on the balance between the rate of its synthesis, and that of its catabolism or loss. Abnormal metabolism may result from nutritional deficiency, enzyme deficiency, abnormal secretion of hormones, or the actions of drugs and toxins. Renal cancer is the third most common malignancy of the genitourinary system, and accounts for 3% of adult malignancies globally. Total serum proteins were measured in malignant kidney tumor, benign kidney tumors, and non tumoral kidney diseases patient groups, as well as in healthy individuals. A significant decrease (p< 0.001) of total serum protein levels in patients with malignant kidney tumors when compared with those of benign tumors, non tumoral diseases, and hea