The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

Background: Breast cancer is the culmination of a multi-step process that occurs over a period of several years or decades and as a cause of death, is a salient "free radical" disease. Aim: The present study aims on investigating the possible protective role of antioxidant drugs (vitamins E and C) to cardiac cells against the oxidative stress induced damage during doxorubicin chemotherapy in patients with breast cancer.
Patients and methods: Thirty two patients with different stages of breast carcinoma attending to Baghdad Teaching Hospital and ten healthy control subjects with age range between (29-61) years, mean (43.6±1.37) were included in this study. The patients were randomized into 3 groups, they

Background: Colorectal cancer (CRC) is the third most prevalent cancer worldwide with 1.80 million new
cases and 862,000 deaths in 2018. Depending on the stage, upfront surgery is the main form of treatment,
followed by adjuvant chemotherapy. Many drugs were approved by the U.S. Food and Drug Administration
for the treatment of CRC, one of which is Capecitabine. During cancer treatment, patient-reported symptoms
and quality of life parameters can provide additional information to evaluate and compare the efficacy and
toxicity of the treatments. Despite the importance of this issue, there is no published data that evaluates this
vital parameter in Iraqi patients receiving anti-cancer drugs, in general,

Background: Arterial functional changes reflected by vascular stiffness might occur at early stages of cardiovascular disease before the morphological alterations reflected by increasing the intima media thickness and it is widely used as a very sensitive indicator of functional vascular damage.
Objectives: This study is aimed to correlate ultrasound detected vascular functional changes with the severity and extent of coronary artery disease.
Patients and methods: Sonographic scans were performed on 100 Patients (61males, 39 females) with an age range of (40-65years) for measuring carotid and brachial arteries end diastolic and end systolic diameters to calculate vascular stiffness index . Coronary CT angio

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

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

Breast cancer is the second deadliest disease infected women worldwide. For this
reason the early detection is one of the most essential stop to overcomeit dependingon
automatic devices like artificial intelligent. Medical applications of machine learning
algorithmsare mostly based on their ability to handle classification problems,
including classifications of illnesses or to estimate prognosis. Before machine
learningis applied for diagnosis, it must be trained first. The research methodology
which isdetermines differentofmachine learning algorithms,such as Random tree,
ID3, CART, SMO, C4.5 and Naive Bayesto finds the best training algorithm result.
The contribution of this research is test the data set with mis

     This study was conducted to use the local Ephedra alata plant as a model for extracting and detecting alkaloids in the stem of plant (alkaloids-rich extract and crude extract). Different extraction procedures were adopted for qualitative as well as the quantitative examination of the alkaloid extracts, as well as plant crude extract, the best methods for the extraction of the plant materials were applied. Simple, fast and accurate methods like TLC (thin layer chromatography) and HPLC (High-performance liquid chromatography), were used for the identification of the alkaloids (ephedrine) in different extracts of stems E. alata stems. Ephedrine alkaloid was detected in each alkaloids-rich and crude extrac