Helicobacter pylori (HP) is the etiopathogenic agent of gastric and duodenal disorders ranging from gastritis to malignancy. It is also associated with many extraintestinal diseases, including cardiovascular disease and its associated risk factors. To evaluate the link between HP infection and some cardiovascular risk factors by studying the effects of HP infection on body mass index, blood pressure, and serum lipid profile among patients having gastritis with and without HP infection. A crosssectional study included 1214 patients who had gastritis diagnosed by gastroscopy examination. Those patients were in the age range of 30-65 years and they were divided according to their gender into 725 females and 489 males depending on the 13C urea breath test, they were divided into HP positive ( +) groups (550 female & 300 male ) and HP negative (-) group (175 female & 189 male). The blood pressure and body mass index (BMI) were measured for each patient and following at least 10-h fasting, a lipid profile test was performed. Our study exhibited a significant difference (p > 0.05) in Body Mass Index (BMI) between HP (+) and HP (–) participants. HP (+) participants were obese (34.29 Kg/m2) while HP (-) participants were leanِ. The mean systolic & diastolic blood pressures were non- significantly higher in HP (+) group than those in HP (-) group. The TC, TG, and LDL parameters scored the highest mean value in HP (+) group (212.47±18.35, 117.17±37.14, and 79.30±15.42) respectively. In contrast, HDL scored the lowest mean value in HP (-) group (40.59±2.38). HP infection significantly alters lipid profile test and may be one of the risk factors for obesity, dyslipidemia and hypertension.
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This study presents, for the first time, an innovative Jet Plasma-assisted technique for the green synthesis of TiO₂@Ag core–shell nanoparticles using chard leaf extract as a natural reducing and stabilizing agent. The Jet Plasma provides a highly energetic environment that accelerates nucleation and core–shell formation at low temperatures without toxic precursors. The synthesized nanoparticles exhibited uniform and stable structures, as confirmed by comprehensive characterization techniques including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet–visible (UV–Vis) spectroscopy, transmission electron microscopy (TEM), and zeta potential analysis. XRD patterns confirmed the crystalline anatase
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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In this study, manganese dioxide (MnO₂) nanoparticles (NPs) were synthesized via the hydrothermal method and utilized for the adsorption of Janus green dye (JG) from aqueous solutions. The effects of MnO₂ NPs on kinetics and diffusion were also analyzed. The synthesized NPs were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray analysis (EDX), and Fourier-transform infrared spectroscopy (FT-IR), with XRD confirming the nanoparticle size of 6.23 nm. The adsorption kinetics were investigated using three models: pseudo-first-order (PFO), pseudo-second-order (PSO), and the intraparticle diffusion model. The PSO model provided the best fit (R² = 0.999), indicating that the adsorpti
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