The study conducted to investigate the association between Helicobacter pylori infection and eye diseases (Glaucoma, Cataract, CSR and Uveitis). One hundred and four patients with multiple eye disorders (10-80) years were observed from 10/9/2020 to 18/11/2020 and compared to thirty-one healthy people (19 female and 12 male). Each participant was tested for H. pylori CagAAbs and TNF-α using an enzyme-linked immunosorbent assay (ELISA). The results have shown that there was a non-significant difference (p≥0.05) in the concentration of CagAantibodies in sera of patients with eye diseases except in the case of CSR (central serous chorioretinopathy), which was a significant difference (P≤0.05) compared to the control group. Also, the result

ABSTRACT

            The controversy is currently revolving around industrial additives, including antioxidants, their negative effects on consumer health and the emergence of various and various diseases, which led scientists and researchers to intensify most studies on natural antioxidants and their synthesis from medicinal plants mentioned in ancient medicine and in divine books as potential antioxidants of increasing importance. Therefore, this study was designed to synthesize silver nitrate particles from plant leaf extracts (Figs, Olives, and Moringa) and study their effect on bacterial inhibition of each of the undesirable Coliform bacteria (E-Coli,

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Polymethylmethacrylate film (PMMA) of thickness 75 μm was evaluated Spectrophotometrically for using it as a low-doses gamma radiation dosimeter. The doses were examined in the range 0.1 mrad-10 krad. Within an absorption band of 200-400 nm, the irradiated films showed an increase in the absorption intensity with increasing the absorbed doses. Calibration curves for the changes in the absorption differences were obtained at 218, 301, and 343 nm. At 218 nm the response for the absorbed doses is a linear in the range 10 mrad- 10 krad. Hence it is recommended to be adopted as an environmental low doses dosimeter

Maximum likelihood estimation method, uniformly minimum variance unbiased estimation method and minimum mean square error estimation, as classical estimation procedures, are frequently used for parameter estimation in statistics, which assuming the parameter is constant , while Bayes method assuming the parameter is random variable and hence the Bayes estimator is an estimator which minimize the Bayes risk for each value the random observable and for square error lose function the Bayes estimator is the posterior mean. It is well known that the Bayesian estimation is hardly used as a parameter estimation technique due to some difficulties to finding a prior distribution.

The interest of this paper is that

Bioaccumulation of heavy metals in the terrestrial invertebrates in Al-Jadriyia district Baghdad- Iraq were investigated. Forth terrestrial invertebrates snails, slug, isopods, and diplopods , were selected for this study. The results showed that all invertebrate groups have the ability in accumulate considerable amounts of heavy metals. Higher levels of zinc and copper were observed in the isopods specimens, it's about ( 60.50±0.58 ) and ( 96.00±0.58 ) ppm respectively , while higher levels of lead were observed in the diplopods specimens ,it's about ( 23.00±1.15 ) ppm ,but the higher levels of both iron and cadmium were observed in snail specimens , it's about ( 590.00±1.15 ) and ( 9.50±1.15 ) ppm respectively .but the