Objective: To evaluate whether the SOX2 protein could be used as a predictor in patients with GC and to assessment the correlation between the IHC expression of the SOX2 protein and the various clinic pathological Parameters as age, sex, histopathological subtypes, grade and stage of the tumor by immune-histochemical Technique. This is a retrospective study conducted on 60 randomly selected patients (30) normal versus (30) GC, at the pathology department of the Gastroenterology and Hepatology Teaching Hospital & some private hospitals. It were collected and diagnosed during the period between from 2014 to 2018. From each block were stained with H&E and IHC stained for SOX2. The Statistical analysis was done using SPSS system, and the differ

Introduction: With the advent of era of targeted cancer therapy, the serious side effects of chemoradiotherapy have been minimised. Considering the success story of anti-HER2/neu drugs in breast cancer oncology, the present study was conducted. The study evaluates the immunohistochemical expression of HER2/neu in endometrioid cancer among Iraqi patients. Aim: To assess the immunohistochemical expression of HER2/neu in endometrioid carcinoma of uterus and to find the relationship of this expression with FIGO stage and grade and with patient age. Materials and Methods: In this cross sectional study, formalin-fixed, paraffin-embedded tissue blocks of 62 hysterectomy specimens that were diagnosed as endometrioid carcinoma in the teaching labora

Background: Tooth eruption is a more general process, however, which includes certain posteruptive tooth movements. There are two fundamental requirements for both tooth eruption to occur: (1) Require soft tissue, intervening between tooth structure and alveolar bone, which plays an important role in regulating the remodeling of adjacent tissues. (2) Require bone turnover that is temporally and spatially regulated to facilitate specific translocations of teeth through alveolar bone These amniotic stem cells are multipotent and able to differentiate into various tissues, which may be useful for human application and recently it used in many medical branches. CD34 is an endothelial marker that is extensively used in immunohistochemistry a

Background: Oral squamous cell carcinoma (OSCC) is the most prevalent malignant neoplasm of the oral cavity and constitutes a major health problem in developing. In the last 30 years, the 5-year survival rate of patients with oral SCC has not improved despite advance in diagnostic techniques. To improve early diagnosis for this deadly disease, new biological markers are needed. HOX genes encode homeodomain-containing transcription factors involved in the regulation of cellular proliferation and differentiation during embryogenesis. HOX gene expression has been described in several adult tissues, where they performed important roles in maintaining homeostasis. Few studies have suggested that HOXA1 plays a role in tumorigenesis. Besides bein

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

This study has dealt with, the issue of classification of rural road network , in addition to prepare a suggested for the classification for this network in Iraq , this classification account , the specifications and characteristics of rural roads, population, and the range taking of settlements , then this classification was applied on the rural road network in the Najaf province there are four categories of classification ,the first is major arterial rural roads divided into two major arterial and minor arterial roads , while the second category collected roads which was divided into minor arterial roads and main collected roads. The third category was represented by Local Roads , it has been divided into paved roads and unpaved, the f

Scientific development has occupied a prominent place in the field of diagnosis, far from traditional procedures. Scientific progress and the development of cities have imposed diseases that have spread due to this development, perhaps the most prominent of which is diabetes for accurate diagnosis without examining blood samples and using image analysis by comparing two images of the affected person for no less than a period. Less than ten years ago they used artificial intelligence programs to analyze and prove the validity of this study by collecting samples of infected people and healthy people using one of the Python program libraries, which is (Open-CV) specialized in measuring changes to the human face, through which we can infer the