The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of

Abstract Background: The human epidermal growth factor receptor 2(HER2) proto-oncogene is overexpressed or amplified in approximately 15%-25% of invasive breast cancers. Approximately 35% of HER2-amplified breast cancers have coamplification of the topoisomerase II-alpha (TOP2A) gene encoding an enzyme that is a major target of anthracyclines. Hence, the determination of genetic alteration (amplification or deletion) of both genes is considered as an important predictive factor that determines the response of breast cancer patients to treatment. The aims of this study are to determinate TOP2A status gene amplification in a set of Iraqi patients with breast cancer that have had an equivocal (2+) and positive HER2/neu by immunohistochemistry

The current study is aimed to identify personality types of the lecturers of Baghdad University as well as identify the prevailing types among the lecturers. In addition, the study aimed to identify the significant difference among personality types (The Enneagram) according to the variables of academic title and scientific major . One hundred and ninety four lecturers were involved in this study. In order to achieve the study's aims, the scale of (Baron& Wagele 1994) was used to measure the nine personality types (The Enneagram) that was translated by( Anwar Omar Ibrahim 2012). Some statistical analysis were used and the result demonstrated that Romantic types was found to be the most prevailing types among the lecturers of Baghdad

Colorectal cancer is a malignant condition that can arise from multiple causative factors. It ranks second, behind lung cancer, as a leading cause of cancer-related deaths worldwide. Extensive research has been conducted to unravel the genetic underpinnings and molecular mechanisms underlying the development of colorectal cancer (CRC). However, epigenetic modifications of histones at the DNA level have become significantly involved in several malignant diseases such as CRC. Hence, this research sought to assess, for the first time locally, the immunoexpression of HDAC-1 and 3 in a group of colorectal patients. Additionally, we explored potential correlations between the expression of HDAC-1, 3 and VEGF. This retrospective study enco

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

<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

This study aimed to evaluate oral health (OH) and periodontal diseases (PD) awareness in the Iraqi population.