Olmesartan medoxomil (OM) has low bioavailability and limited solubility. To enhance bioavailability, fast dissolving films (FDF) with mixed micelles of soluplus (SPL) and solutol HS15 (STL H15) were developed using solvent casting. The optimised formula, FM2, used polyvinyl alcohol (PVA) and showed high entrapment efficiency, rapid disintegration, and significant improvement in OM bioavailability compared to the market tablet (Olmetec®). FM2 also demonstrated stability and potential for enhanced drug delivery.

The aims of study is to detect the inhibitory effect of Saccharomyces boulardii and Lactobacillus acidophilus on Escherichia coli that has been isolated from recurrent urinary tract infection in women. The sensitivity of E.coli isolates to antibiotics had been studied and the most resistant E.coli isolate to antibiotics had been studied .The cup assay was used on nutrient agar and Muller-Hinton agar to detect the inhibitory activity for each S.boulardii yeast grown on YEGP media and L.acidophilus grown on MRS media in which the result showed a high inhibition activity for each of them .Also in this study the adhesion property of E.coli had been evaluated in the presence of S.boulardii at concentration of 1×109 and L.acidophilus at conc

Blastocystis is a ubiquitous human and animal protozoa that inhabit the gastrointestinal tract. Metronidazole is considered the standard drug for the treatment of Blastocystis infection; however, there is growing evidence of treatment failure, hazardous side effects, and appearance of strains resistant to metronidazole. In the last era, many studies have been implicated in the quest for new treatments for Blastocystis infection, especially natural products. Attention has been focused on the effect of Amygdalin (B17) and pumpkin seed on eradicating parasitic infections. The current work was built up to explore the in vitro efficacy of two natural compounds, Amygdalin (B17) and pumpkin seeds against

Background: The aim of this study was to determine phototoxic effect of visible blue light on anaerobic periodontal pathogens namely Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis. Materials and methods: Strains of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis were isolated from pockets of systemically healthy patients aged between 35-55 years old with pocket depths of 5-6 mm, the bacteria cultured on special blood Agar plates solid media, then subjected to visible blue light emitted from commercially available light cure devise (LED curing light); that emits blue light (400-500nm) of 1000mw energy at different periods of time exposures, then the CFU of each plate was measured by direct colony count

Background: Although radiological diagnostic studies (RDS) are an important and acceptable part of medical practice, it is not without hazards. It is associated with increased risk of cancer. Unfortunately the typical and safe dose of each radiological examination is not known. Most of our knowledge of cancer risk comes from studies of survivors of those exposed to whole body radiation from atomic bomb in Hiroshima & Nagasaki, jobs associated with radiation exposure, Chernobyl survivors & patients treated with radiation therapy for cancer and other diseases.

 Objectives   To estimate radiation dose received by patients from diagnostic radiological examinations and lifetime

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

Background: Breast cancer is the most common cancer in Iraq and the United Kingdom. While the disease is frequently diagnosed among middleaged Iraqi women at advanced stages accounting for the second cause of cancer-related deaths, breast cancer often affects elderly British women yielding the highest survival of all registered malignancies in the UK. Objective: To compare the clinical and pathological profiles of breast cancer among Iraqi and British women; correlating age at diagnosis with the tumor characteristics, receptor-defined biomarkers and phenotype patterns. Methods: This comparative retrospective study included the clinical and pathological characteristics of (1,940) consecutive female patients who were diagnosed with invasive b