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

Abstract

Background: The novel coronavirus 2 (SARS?CoV?2) pandemic is a pulmonary disease, which leads to cardiac, hematologic, and renal complications. Anticoagulants are used for COVID-19 infected patients because the infection increases the risk of thrombosis. The world health organization (WHO), recommend prophylaxis dose of anticoagulants: (Enoxaparin or unfractionated Heparin for hospitalized patients with COVID-19 disease. This has created an urgent need to identify effective medications for COVID-19 prevention and treatment. The value of COVID-19 treatments is affected by cost-effectiveness analysis (CEA) to inform relative value and how to best maximize social welfare through eviden

Abstract Background: The novel coronavirus 2 (SARS?CoV?2) pandemic is a pulmonary disease, which leads to cardiac, hematologic, and renal complications. Anticoagulants are used for COVID-19 infected patients because the infection increases the risk of thrombosis. The world health organization (WHO), recommend prophylaxis dose of anticoagulants: (Enoxaparin or unfractionated Heparin for hospitalized patients with COVID-19 disease. This has created an urgent need to identify effective medications for COVID-19 prevention and treatment. The value of COVID-19 treatments is affected by cost-effectiveness analysis (CEA) to inform relative value and how to best maximize social welfare through evidence-based pricing decisions. O

Five different bacterial isolates [ Vibrio cholera (Ogawa) , Vibrio cholera (Inaba) , Salmonella typhi , Salmonella paratyphi and ? Salmonella typhimurium ] were obtained from the Central Health Laboratory . Both sensitivity tests (MIC , MBC and wells method ) against these bacteria were performed by using the aqueous of leaves extract of Marjoram plant. The results cleared that the values of MIC for Vibrio cholera serotypes Ogawa and Inaba were 100 mg/ml , while the value of MBC was 200 mg/ml. The value of the Inhibition zone at 100 mg /ml concentration for both Ogawa and Inaba were 13 mm and 9 mm respectively. Our results showed that the three types of Salmonella didn’t show any inhibition zone at 200 mg/ml .

Non-thermal atmospheric pressure plasma has emerged as a
new promising tool in medicine and biology. In this work, A DBD
system was built as a source of atmospheric pressure non-thermal
Plasma suitable for clinical and biological applications. E. coli and
staphylococcus spp bacteria were exposed to the DBD plasma for a
period of time as inactivation (sterilization) process. A series of
experiments were achieved under different operating conditions. The
results showed that the inactivation, of the two kinds of bacteria, was
affected (increasing or decreasing) according to operation conditions
because they affects, as expected, the produced plasma properties
according to those conditions.

Five species of Lactic acid bacteriawere isolated from raw milk, yoghurt, vegetables and pickles, Lactobacillus plantarum, Lactobacillus acidophilus, Lactobacillus brevis, Lactobacillus casei and Lactobacillus bulgaricus isolates were identified by 16S rRNA gene. Evaluate of antimicrobial activity against all the bacterial strains Staphylococcus aureus, Salmonella spp., Pseudomonas fluorescens, Escherichia coli, Bacillus cereus and Bacillus subtilis. It showed that bacteriocin of Lactic acid bacteriamore effective than supernatant of lactic acid bacteria, the results showed that isolatemost efficient isolate belonging to Lactobacillus brevis, the diameter of the inhibition of the bacteriocin of Lactobacillus brevis were 27.7, 26.3 and 25.1