Urinary tract infection (UTI) is a considerable problem aecting the health of people each year. It is caused by various Gram-positive (G+ve) and Gram-negative (G-ve) pathogens. It is an important illness in the world aecting all age groups across their life span. Objectives: To identify the most common aerobic bacteria that cause UTIs and their antibiotic susceptibility and antimicrobial activity of plant extracts of the males' patients. Materials and methods: The study involved 35 midstream urine samples from the male students (University of Baghdad, Baghdad, Iraq) with suspicious symptoms of UTI, during the period from January-March 2018. Each urine sample was cultured rst on Mannitol Salt Agar and MacConkey agar plates to dierentiate

Uropathogenic E. coli (UPEC) is problematic and still the leading cause of urinary tract infections worldwide. It is developed resistance against most antibiotics. The investigation, surveillance system, and efficient strategy will facilitate selecting an appropriate treatment that could control the bacterial distribution. The present study aims to investigate the epidemiology and associated risk factors of uropathogenic E. coli and to study their antibiotic resistance patterns. 1585 midstream urine specimens were collected from symptomatic urinary tract infections (UTI) patients (225 males and 1360 females) admitted to Zakho emergency hospital, Zakho, Kurdistan Region, Iraq from January 2016 until the end of December 2

Background: Pleomorphic adenoma is the most common benign salivary gland tumor and shows a pronounced morphological complexity and diversity; for this The immunoprofiles and clinical course of PA differed according to cellular differentiation. Therefore, it is important to assess potential biomarkers in diagnostic and therapeutic trials. This study evaluates the immunohistochemical expression of D2-40, VEGF and PCNA as markers of lymphangiogenesis, angiogenesis and proliferation of PA and their correlation with clinicpathological parameters and with each other. Materials and Methods: Twenty five formalin – fixed, paraffin – embedded tissue blocks were included in this study. After histopathological reassessment of haematoxylin & eosin

Extension of bandwidth for high reflectance zone for the spectral region (8-14pm) was studied adapting the concept of contiguous and overlapping high reflectance stacks. Computations was carried out using the modified characteristic matrix theory restricted to near-normal incidence of light on dielectric , homogenous and isotropic symmetrical stack. Certain precautions must be taken in the choice of stacks to avoid deep —reflectance minima from developing within the extended high reflectance region. Results illustrate that the techniques of extending the high reflectance regions are applicable not only to mirrors , but also to short-and long-edge filter and to narrow band pass filters.

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D