This study aimed for isolation and identification of Candida glabrata from specimens of blood collected from national center of hematology /Al - mustansiriya University of immunocompromised patients infection of candidemia after diagnosis by doctor. Results showed the morphological features on many media SDA and Corn meal and differential CHROMagar. Colonies appear small and pink pale to dark. Microscopic exam of show that C. glabrata not form pseudohyphae and not produce germ tubes, the cell size 2 - 3 microns, on the other hand, Biochemical test of C. glabrata have high ability for fermentation of glucose within three hours and trehalose given false positive within two hrs., and the Urease test given negative result, on the other hand

CD63 is -one of the tetraspanin family proteins, which are regarded as: hallmark exosomal markers because it is absent from other types of vesicles. It is expressed in the cell membrane of cancer cells, and cytoplasm of stromal cells. Objective: To assess CD63 expression in gastric cancer (GC) patients, and detected if it could be used as a predictive marker. Furthermore, the current study aimed to find the correlation between CD63 expression and clinicopathological parameters as: gender, age, invasion depth, histopathological type, involvement of lymph nodes, grade and stages of GC (TNM). The current study is a retrospective study in the period time from (2018 to-2020); 50 randomly patients formalin-fixed paraffin embedded blocks (FFPE)

To achieve the objectives of the study, a non –probability (purposive) sample of (50) nurses were selected those were working at the oncology wards at the above listed hospitals. The data selected according to the criteria of the study sample. The validity of the questionnaire was determined through an expert panel consists of (11) specialist expert and its reliability was determined through a pilot study by test – retest which was estimated as averages (R=0.89). Data was collected by direct interview technique using the questionnaire formal and data was analyzed by application of descriptive & inferential statistical methods (frequency, percentage, mean of score and Chi-Square). The resul

     Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome called coronavirus 2 (SARS-CoV-2). Due to its concerning rate of transmission and intensity, coronavirus was classified as a pandemic on March 11, 2020. With the continuous evolution of the viral genome and mutations that may alter infectivity, disease severity or interactions with host immunity, SARS-CoV-2 has evolved into many variants: Alpha (B.1.1.7 lineage), Delta (B.1.617.2 lineage), Delta plus (B.1.617.2.1), Omicron (B.1.1.529 lineage) and other variants. Thus, this study aimed to find and provide database for local clinical characteristics of different variants of SARS-COV-2 and severity of infection with viral load compared with the wild

In the early 90s military operations and United Nations Special Commission “UNSCOM” teams have been destroyed the past Iraqi chemical program. Both operations led an extensive number of scattered remnants of contaminated areas. The quantities of hazardous materials, incomplete destructed materials, and toxic chemicals were sealed in two bunkers. Deficiency of appropriate destruction technology led to spreading the contamination around the storage site. This paper aims to introduce the environmental detection of the contamination in the storage site area using geospatial analysis technique. The environmental contamination level of nutrients and major ions such as sulphate (SO₄), potassium (K), sodium (Na), magnesi

One of the most Interesting natural phenomena is clouds that have a very strong effect on the climate, weather and the earth's energy balance. Also clouds consider the key regulator for the average temperature of the plant. In this research monitoring and studying the cloud cover to know the clouds types and whether they are rainy or not rainy using visible and infrared satellite images. In order to interpret and know the types of the clouds visually without using any techniques, by comparing between the brightness and the shape of clouds in the same area for both the visible and infrared satellite images, where the differences in the contrasts of visible image are the albedo differences, while in the infrared images is the temperature d

Change detection is a technology ascertaining the changes of
specific features within a certain time Interval. The use of remotely
sensed image to detect changes in land use and land cover is widely
preferred over other conventional survey techniques because this
method is very efficient for assessing the change or degrading trends
of a region. In this research two remotely sensed image of Baghdad
city gathered by landsat -7and landsat -8 ETM+ for two time period
2000 and 2014 have been used to detect the most important changes.
Registration and rectification the two original images are the first
preprocessing steps was applied in this paper. Change detection using
NDVI subtractive has been computed, subtrac

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.