Background: the early identification of developmental disabilities allows intervention at the earliest possible point to
improve the developmental potential.
Objective: Identify the scope of knowledge of nurses toward signs of gross motor delay for children and its relation to
their demographic characteristics.
Methodology: A descriptive study design was conducted at (18) primary health care centers in first of the primary
health care sector of Alhawija District in Kirkuk Governorate. This study started from September 2010 to the end of
January 2011, in order to identify the level of nurses' knowledge toward signs of gross motor delay for children in
primary health care centers. Non probability (purposive) sample of

The fingerprinting DNA method which depends on the unique pattern in this study was employed to detect the hydatid cyst of Echinococcus granulosus and to determine the genetic variation among their strains in different intermediate hosts (cows and sheep).  The unique pattern represents the number of amplified bands and their molecular weights with specialized sequences to one sample which different from the other samples.   Five hydatitd cysts samples  from cows and sheep were  collected, genetic analysis for  isolated DNA was done using PCR technique and  Random Amplified Polymorphic DNA reaction(RAPD) depending on (4) random primers, and the results showed:      

A total of 60 cotton swabs are collected from patients suffering from burn wound and surgical site infections admitted to Baghdad Teaching Hospital and Burn Specialist Hospital in Baghdad city during 9/2013 to 11/2013. All cotton swabs are cultured initially on blood agar and MacConkey agar and subjected for standard bacteriological procedures for bacteriological diagnosis. Twenty samples out of sixty are identified as Pseudomonas aeruginosa by conventional methods. The results of antibiotic susceptibility test illustrate that the antibiotics resistance rate of Pseudomonas aeruginosa isolates is as follows:100% (2020) for ceftriaxone, cefepime and carbencillin, 70% (14/20) for amikacin, 65%(13/20) for tobramycin, ceftazidim and gentamycin,

This research was aimed to the purification and characterization of cytosine deaminase as a medically important enzyme from locally isolated Escherichia coli; then studying its cytotoxic anticancer effects against colon cancer cell line. Cytosine deaminase was subjected to three purification steps including precipitation with 90% ammonium sulfate saturation, ion exchange chromatography on DEAE-cellulose column, and gel filtration chromatography throughout Sephadex G-200 column. Specific activity of the purified enzyme was increased up to 9 U/mg with 12.85 folds of purification and 30.85% enzyme recovery. Characterization study of purified enzyme revealed that the molecular weight of cytosine deaminase produced by E. coli was about 48 KDa,

Introduction: Melanin is a high-molecular weight pigment produced through the oxidative polymerization of phenolic or indolic compounds and plays a perfect role in UV-light shielding, as well as in photoprotection. Among biopolymers, melanin is unique in many aspects. This study is designed to screen Production, extraction and characterizes of an extracellular melanin pigment from clinically isolated P. aeruginosa. Objective: The aim of the current study is isolation and diagnosis of P.aeruginosa using vitek-2 compact system and screening the ability to produce melanin and characterization of extracted melanin by UV-vis, FTIR, XRD and SEM. Materials and methods: the samples swab inoculated on cetrimide agar as selective media and incubated

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p