Background: Astrocytic tumors are the most common primary tumors of the central nervous system. Several grading systems are used to grade astrocytomas. The most widely used system is the World Health Organization (WHO) classification (1979, 1993, 2000, and 2007) that grades astrocytomas (I-IV) based on cytological atypia, mitotic activity, vascular proliferation, and necrosis: pilocytic astrocytoma (grade I), diffuse astrocytoma (grade II), anaplastic astrocytoma (grade III), and glioblastoma (grade IV).
Objectives: The aim of this study is to evaluate p53 over expression , Ki-67 expression in astrocytomas and Correlate these two markers with histologic grade of astrocytomas.
Methods: Formalin fixed, paraff

When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat

Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r

Objectives: the study aims to assess nurses' practices toward chemotherapy-induced peripheral neuropathy (CIPN) for children at the hematology center, and to determine the effectiveness of the health education program on nurses' practices toward CIPN, and to find out the relationships between the effectiveness of Health education program and demographic characteristics of nurses.

Methodology: Use quasi-experimental design in the study (a design that divides the sample into two groups, a study group and a control group, with data collection in three stages). This study was conducted at a hematology center in Baghdad city for the period (from December 16^th, 2019 to 8^th May 202

Abstract

Objective: The purpose of this study is to investigate the family-centered care health services of family-provider partnership in Baghdad/ Iraq.

Methods: A descriptive cross-sectional study is conducted in Baghdad Province. A cluster samples of 440 clients who review family centered care for the purpose of health services. The instruments underlying the study phenomenon deals with client's socio-demographic characteristics and family centered care questionnaire which include (partnership related to decision-making team, supporting the family as the constant in the child’s life, family-to-family and peer support and supporting transition to adulthood). The relia

Background: Fibromyalgia (FMS) is characterized by chronic widespread pain and allodynia (pain from stimuli which are not normally painful with pain that may occur other than in the area stimulated) of more than 3 months duration. The current hypothesis of the etiology of FMS includes inflammatory and neuroendocrine disorders. Many features of fibromyalgia and hypothyroidism are virtually the same, and thyroid hormone treatment trials have reduced or eliminated fibromyalgia symptoms. These findings led the authors to test the hypothesis that fibromyalgia patients are hypometabolic compared to matched control .

Patients and Methods: The samples  were taken from the patients attending the out-

Background: The prevalence of spinal cord lesions is high in multiple sclerosis particularly in the cervical cord, and their detection can assist in both the diagnosis and follow-up of the patients. For spinal multiple sclerosis, MRI is considered the first line investigation.

Objective: To evaluate the value of sagittal 1.5 Tesla proton density-fast spin echo (PD-FSE) MRI in the detecting and increasing conspicuity of multiple sclerosis lesions in cervical cord in comparison with sagittal T2 fast spin-echo (T2-FSE) MRI.

Patients and Methods: A cross sectional study carried out from 3^rd of January 2017 to 1^st of January 2018 in the MRI department of

Background: Obesity is imposing a growing threat to world health. The autonomic nervous system (ANS) regulates visceral functions via balance between sympathetic and parasympathetic divisions. In the cardiovascular system (CVS) this non stationary balance results in the fluctuation between intervals of consecutive heart beats, so called heart rate variability (HRV). Obesity is one of the causative co-morbid conditions leading to metabolic and cardiac disorders as it is accompanied with varied combinations of abnormalities in the ANS, one view is that obese people have higher sympathetic tone. HRV measures the effect of autonomic function on the heart alone. Therefore, it could be the most useful method to investigate the

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that.  The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the proce