The objective of this research was to estimate the dose distribution delivered by radioactive gold nanoparticles (198 AuNPs or 199 AuNPs) to the tumor inside the human prostate as well as to normal tissues surrounding the tumor using the Monte-Carlo N-Particle code (MCNP-6.1. 1 code). Background Radioactive gold nanoparticles are emerging as promising agents for cancer therapy and are being investigated to treat prostate cancer in animals. In order to use them as a new therapeutic modality to treat human prostate cancer, accurate radiation dosimetry simulations are required to estimate the energy deposition in the tumor and surrounding tissue and to establish the course of therapy for the patient. Materials and methods A simple geometrical

In this research, we tackled the idea of absence and what companies it of interpretations and human, textual, philosophical and explanatory concerns. We also tackled the features and drawing them and identifying and lighting them by Ali Abdunnabi Az-Zaidi and how he read them as an Iraqi who writes in order to express a social, intellectual, political and religious reality in some of its aspects. The idea of absence and what accompanies it of pain or heartbreak or human change was a rich subject for all the writers and authors in the Iraqi theatre, and Ali Abdunnabi Az-Zaidi was one of them and the closest and most affected by it, who deserves discussion, explanation and briefing. The research problem was looking for the nature of absenc

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Proteus mirabilis isolates have been intensively researched for their capacity to cause urinary tract infections (UTIs) and their swarming motility, although little is known about this phenomenon. Probiotic Lactobacillus species, which are beneficial bacteria, are being studied worldwide as therapeutic and preventative agents against bacterial infections. This study investigated Lactobacillus supernatants as a potential new treatment against Proteus mirabilis. In addition to testing their antimicrobial and anti-swarming activities, the research also aimed to understand the genetic mechanisms behind the observed phenotypic changes. Methods. A total of 150 urine specimens were collected from UTI patients at various hospitals in Baghdad. Dire

Abstract
Objective: To assess pregnant women Knowledge toward Urinary Tract Infection at Kirkuk City.

Methodology: A descriptive and analytical study was conducted from 1st of November 2013 and up to the 19 th of August 2014 in five typical primary health care centers at Kirkuk City. A Probability (randomly sample) was used to select the sample of 180 women aged (15-44) years. A questionnaire format was used as a tool for data collection , content validity of the questionnaire achieved through reviewing it by (24) experts in numerous scientific fields and reliability of the questionnaire was determined through a pilot study. Descriptive and inferential statistics were used to analyze the data.

Resul

Cerebellum is the most important and critical part of the central nervous system, cerebellum is very sensitive to the abnormal changes during the embryological development in its histological structure, the exposure to any infection during embryogenesis produce abnormalities in the cerebellum and behavioral of offspring. In this study we tried to study the ontogenesis of the cerebellum in the embryos of the albino rats and detection the effect of the AgNPs on the ontogenesis of the rat cerebellum after exposure of AgNPs during pregnancy. we used 60 female pregnant rats divided in to three group, each contain 20 female, (G1) treated with 2mg/kg /day suspension of silver nanoparticles (Ag NPs) (G2) treated with 20mg/kg/day AgNPs from first da