المستودع الرقمي العراقي. مركز المعلومات الرقمية التابع لمكتبة العتبة العباسية المقدسة

This study focuses on diagnosis of Candida species causing Vulvovaginal Candidiasis using phenotype and genotype analyzing methods, and frequencies of candida species also using Vulvovaginal Candidiasis patients. 130 samples (100 from patients and 30 from non infected women) were collected and cultured on biological media. Identifying the yeasts, initially some phenotypic experiments were carried out such as germ tube, from motion of pseudohyphae and clamydospores in CMA+TW80 medium, API20 candida and CHROMagar Candida. Genomic DNA of all species were extracted and analyzed with PCR and subsequent Polymerase Chain Reaction - Restriction Fragments Length Polymorphism (PCR-RFLP) methods. Frequency of C. albicans, C. krusei, C. tropicalis , C.

Biological activity substances was investigated in watery extract of lentil which found to contain phenols, tannin, saponins and resins while, flavons, terpens and steroids were not exist in the extract details explained that 5%, 10% of lentil extract largly inhibited the growth of Psedumonas aeruginosa then Escherichia coli and Bacillus subtilis. The growth of both Staphylococcus aureus and Salmonella typhimurium were slightly affected by all extract concentration. Extracellular protease were screened in all bacterial species under study. Complete inhibition was achieved for extracellular protease while different percentage of protease inhibition were seen for intracellular proteases.

Candida Berkh. (1923) occurs naturally in the body. But it becomes opportunistic fungi, meaning that it infects humans when there is any weakening of the immune system, such as exposure to chemotherapy, diabetes, or organ transplantation. Most species of Candida grow at a temperature between 20-40 °C and have a pH of 3-8. Human pathogens of Candida species include C. albicans, C. glabrata, C. lusitaniae, C. parapsilosis, C. tropicalis and C. utili. C. albicans has many virulence factors that facilitate injury process. Virulence factors are considered as a measure of pathogenicity, and it is in the form of fungal toxins, enzymes, or cell structures that facilitate infection, as well as pathogen resistance in different conditions. This study

The levels of lead (pb), copper (cu), cobalt (co) and cadmium (cd) were determined in different kinds of milk and the health risks were evaluated. The mean levels were 0.73±0.21, 0.06±0.01, 0.12±0.01 and 0.14±0.01 ppm for these metals respectively. The levels of pb and cu were found to be insignificant differences (p<0.05), whereas the levels of co and cd, were no significant differences (p>0.05). The dry and liquid kinds of milk were different significantly (p<0.05), whereas the original, was no significant differences (p>0.05). The values for all metals were more than one. The metals pb and cd were detected at highest concentrations in most dry and liquid milk samples.

Bacteria strain H8, which produces high amount of exopolysaccharide (EPS), was isolated from soil, and identified as strain of Azotobacter chrococcum by its biochemical /physiological characteristics, EPS was extracted, partially purified and used as bioflocculant. The biochemical analysis of the partially purified EPS revealed that it was an alginate. analysis of EPS by Fourier transform infrared spectrometry (FTIR) show that the -OH groups present in bioflocculant are clearly seen at 3433.06 cm-1, the peaks attributed to the -CH3 groups present at 2916.17 cm-1 , and some distinct peaks such as carboxyl group showed strong absorption bands at 1604.66 cm-1, 1411.80 cm-1 and 1303.79 cm-1 indicate the chemical structure of alginate. The effe

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

Introduction and Aims: Job burnout such as occupational hazards that have been considered in recent years. This research aimed to investigate the relationship between religious beliefs and job burnout among nurses working in hospitals in Gonabad city in 2017. Materials and Methods: This study is cross-sectional and correlational study. The sample consisted of 100 nurses in Gonabad city who were selected using stratified randomized method. Using the Maslach job burnout and Alport religious beliefs Inventories, data were collected and were analyzed with SPSS version 16 and Pearson, Spearman and independent sample T tests were analyzed. Significant level was considered less than 0.05. Results: Average job burnout in nurses working in hospit