This study was done to evaluate a new technique to determine the presence of methamphetamine in the hair using nano bentonite-based adsorbent as the filler of extraction column. The state of the art of this study was based on the presence of silica in the nano bentonite that was assumed can interact with methamphetamine. The hair used was treated using methanol to extract the presence of methamphetamine, then it was continued by sonicating the hair sample. Qualitative analysis using Marquish reagent was performed to confirm the presence of methamphetamine in the isolate.The hair sample that has been taken in a different period confirmed that this current developing method can be used to analyzed methamphetamine. This m

The current study aims to determine the prevalence of Trichomonas vaginalis and Candida spp., and also to identify Candida parapsilosis and some virulence genes. It was conducted in Bint Al-Hoda Hospital of Maternity and Children in Thi-Qar province, south of Iraq for the period from the beginning of January to the end of December 2020. Two hundred and fifty samples were collected from the female genital tract for women whose age ranged between 17-50 years. Microscopic, traditional and molecular tests were used in the sample examination. The results recorded 12 (4.8%) samples infected with T. vaginalis parasite, whereas 130 (52%) samples showed Candida yeast distributed as follows: 75 (30 %) <

Introduction: A Pap test can detect pre-cancerous and cancerous cells in the vagina and uterine cervix. Cervical cancer is the easiest gynecologic cancer to be prevented and diagnosed using regular screening tests and follow-up. This study aimed to estimate the cytological changes and the precancerous lesions using Pap smear test and visual inspection of the cervices of Iraqi women, and also to determine the possible relationship of this cancer with patients’ demographic characteristics. Methods: The study included 140 women aged (18-67) years old referred to the National Cancer Research Center (NCRC), Baghdad, Iraq, during the period 2011-2016. Both visual inspections of the uterine cervix and Papanicolaou smear screening were performed

Single mode-no core-single mode fiber structure with a section of tuned no-core fiber diameter to sense changes in relative humidity has been experimentally demonstrated. The sensor performance with tuned NCF diameter was investigated to maximize the evanescent fields. Different tuned diameters of of (100, 80, and 60)μm were obtained by chemical etching process based on hydrofluoric acid immersion. The highest wavelength sensitivity was obtained 184.57 pm/RH% in the RH range of 30% –100% when the no-core fiber diameter diameter was 60 μm and the sensor response was in real-time measurements

Zerumbone (ZER), a natural compound has been extracted from Zingiber zerumbet with known pharmacological activities. The aim was to determine the anti-human Burkitt’s lymphoma (Raji) cell effect of ZER. The 3-(4,5-dimethylthiazol-2-yl)-2,5,-diphenyltetrazolium bromide (MTT) assay was used to determine cytotoxic effect while the Annexin-V-fluorescein isothiocyanate/propidium iodide-PI flow cytometric assays was used to determine apoptotic effect of ZER on the human Burkitt’s lymphoma (Raji) cell (ATCC CCL-86) cell line. The expressions of Bax, Bcl-2, and c-Myc genes were determined via real-time PCR. ZER suppressed the proliferation of Raji cells with a 48 h IC50 value of 5.1 μg/mL. Treated Raji cells also underwen

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor