Browse Iraqi academic journals and research papers

BACKGROUND: Acute appendicitis (AA) remains a complex case even for experienced surgeons. Rate of negative appendectomy is 5–40% and delayed intervention result in perforated appendicitis in 5–30% of cases. AIM: The aim of the study was to evaluate NPT as a marker for the diagnosis of AA concerning its severity. And compare the diagnostic value of it with the ALV scoring system. METHODS: One hundred twenty patients presented with signs and symptoms of AA and underwent appendectomy, only 84 patients proved to be AA by histopathological examination, were included in the study. Blood samples for neopterin (NPT) estimation and Alvarado (ALV) score was calculated. Control group consists of 45 healthy individual. RESULTS: NPT levels were s

The measurements and tests of the samples conducted in the laboratories of the College of Agriculture included isolating bio-fertilizers and testing the efficiency of isolates that fix atmospheric nitrogen and solubilize phosphorous compounds. Bacteria were isolated and identified from the rhizosphere soils of different plants collected from various agricultural areas. A total of 74 bacterial isolates were obtained based on the phenotypic characteristics of the developing colonies, as well as biochemical and microscopic traits. The results of isolation and identification showed that among the 74 bacterial isolates, there were 15 isolates of A. chroococcum, 13 of Az. lipoferum, 13 of B. megaterium, 10 of P. putida, 10 of Actinomycetes, and n

Objective: The purpose of this study was to determine the association between caries related microorganisms in children’s saliva, such as Streptococcus mutans and lactobacilli, and their demographic factors. Methods: This study involved a sample of 135, both sexes with an age range between 3 and 10 years. Unstimulated saliva was obtained and diluted in normal saline. Saliva was then placed in selective media. Salivaris agar was used for mutans streptococci while Rogosa agar for lactobacilli. After incubation, Streptococcus mutans counting of CFU (colony forming units) with morphology characterization and numbers of CFU per milliliter of saliva for lactobacilli. Demographic factors information was collected using a questionnaire.

Orthodontic tooth movement is characterized by tissue reactions, which consist of an inflammatory response in periodontal ligament and followed by bone remodeling in the periodontium depending on the forces applied. These processes trigger the secretion of various proteins and enzymes into the saliva. The purpose of this study was to evaluate the activity of the lactate dehydrogenase (LDH) in saliva during orthodontic tooth movement using different magnitude of continuous orthodontic forces. Thirty orthodontic patients (12 males and 18 females) with ages 17-23 years with class II division I malocclusion all requiring bilateral maxillary first premolar extractions. Those patients were randomly divided into 3 groups according to the magnitude

In this review of literature, the light will be concentrated on the role of stem cells as an approach in periodontal regeneration.

hemorrhagic fever (VHF), one of which is Filoviridae. The Filoviridae family includes the Ebola virus , is responsible for the current VHF outbreak in West Africa. Viral hemorrhagic fevers (VHFs) occur in various regions around the world, yet traditional diagnostic testing for these diseases has typically been conducted in major reference laboratories located in Europe and the United States. In this review, we explore the current understanding of the mechanisms driving the pathogenesis of viral hemorrhagic fevers (VHFs) and examine the progress in developing preventive and therapeutic strategies for these infections.

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings