Background: The emergence and spread of multidrug-resistant Gram-negative bacilliin burn wound infections related to biofilm formation, which lend to challenge in treatment with conventional antibiotics andprompting to search for novel antimicrobial agents to control the infections.Silver nanoparticles (AgNPs) have wide spectrum biological properties with different mechanisms of action and less toxicity towards human cells.

Objective:The goal of this study was to evaluated the anti-bacterial and anti-biofilm activities of AgNPs alone and in combination with aminoglycoside (Amikacin) and β-lactam (Ampicillin) antibiotics against multidrug resistant Gram-negative bacilli (Pseudomonas aeruginos

Atopic dermatitis (atopic eczema), is a common familial chronic inflammatory skin disease, determined by xerosis, itching, scaly and erythematous skin lesions, and high serum levels of IgE. Between 10 to 20% of children and 1 to 3% of adults worldwide affected by it and has negative medical and social effect on patients and their families. To evaluate the effectiveness of Phytosterol Fraction of Chenopodium Murale on induced atopic dermatitis (AD) of mice; Forty mice were included in the study, divided in to four groups (10 mice/group): apparently healthy, induced AD without treatment, induced AD treated with Tacrolimus 0.1% ointment, and induced AD treated with Phytosterol Fraction of Chenopodium Murale cream 3% topica

The present study was conducted to investigate the antimicrobial activity of the hot water and the hot ethanolic extracts of Thuja orientalis against some pathogenic microorganisms (Staphylococcus aureus, Pseudomonas aeruginosa, Eschericha coli, Proteus mirrablis, Salmonilla typhi, Klebsiella pneumoniae, Bacillus cereus, Bacillus subtilus, Acinobacter, Staphylococcus epidermidis and Candida albicans). Results showed that both the water and alcoholic extracts of this plant exert marked inhibitory effect against all the bacterial isolates and yeast and at different ratio, and it was shown that ethanolic extract was more effective in microbial inhibition than the water extract. Maximum inhibition (16 mm) was recorded against Staphylococcus aur

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi