Staphylococcus aureus is one of the common causative agents of infections, from asymptomatic carriers to healthy individuals. It can colonize anterior nares of carriers with a high capability to resist different antibiotics. Students are susceptible to bacterial infection due to some factors, including poor health habits and surrounding school conditions. This study screened the rate of vancomycin- and methicillin- resistant Staphylococcus aureus nose carriers among secondary students in rural and urban schools and its association with some sociodemographic factors. The study sample included 300 male/female students aged 15-20 years from 12 schools of rural and urban areas during the period from November 2020 till May 2021. It was fo

Salmonella is approved as a common foodborne pathogen, causing major health problems throughout the world particularly in low‐ and middle‐income countries. Low-level fluoroquinolone resistance is conferred by both chromosomal and plasmid-encoded resistance, this research was carried out look into the occurrence rate of qnrA,qnrB and qnrS genes  in  Salmonella enterica serotype Typhi Cipr  ofloxacin-resistant insulate from blood samples of patients with typhoid fever. Fifteen Salmonella enterica serotype Typhi isolated previously from patients with typhoid fever were included in this study. All bacterial isolates were confirmed to have ciprofloxacin

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

Background: Acetaminophen (N-acetyl-para-aminophenol, or APAP) poisoning, whether intentional or accidental, is a major general health problem, with its toxicity prevalence significantly increasing in many countries. Currently, acetaminophen is considered one of the main causes of acute liver failure globally.

Aim: The aim of this study was to evaluate the possible hepatoprotective effect of Omega-3,6,9 against acetaminophen-induced hepatotoxicity in albino male mice.

Methods: Thirty-five albino male mice were randomly divided into five groups: Group 1 (the negative control) received liquid paraffin orally at a dose of 10 ml/kg for t

The preliminary test of the compounds N [2– (3,4–dimethoxy nitrobenzene oxazepine– 2,3–dihydro–4,7–dione]–5–mercupto–2–amino–1,3,4–thiadiazol [A] and N [ 2–anthralidene– 5– ( 2–nitrophenyl ) –1,3–oxazepine–4,7–dione–2–d](5–mercapto–1,3,4–thiadiazole–2–amin) [B] , showed that they possess high activity against some positive and negative bacteria , like pseudomonas aeruginosa (pseudo.), Escherichia coli (E-coli), staphylococcus aureus (sta.) and Bacillus subtilis (Ba.) and finally there is a study of the effect of some antibiotics like streptomycin (S), gentamycin (GN), chloramphenicol (C) and Nalitixic acid (NA) in order to compare the differences in effects. In the present study, results

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi