Background: Diabetes mellitus a major factor that has adverse effects on the vascular system and the heart. It causes an increase in cardiac muscle thickness, resulting in decreased compliance and increased peripheral arterial stiffness. This study aims to assess the left ventricular mass (LVM) and left ventricular hemodynamic changes in diabetic patients measured by Doppler echocardiography. Patients and Methods: The study included 50 diabetic patients ranging in age between 25 and 80 years, (mean age: 54.1 ± 15.10, 19 males, 31 females) and 50 healthy subjects, aged 25 to 80 years (mean age: 48.52 ± 14.45, 11 males, 39 females). Doppler echocardiography was used to assess left ventricular function. The measurements included posterior wall thickness at diastole (PWTd), interventricular septum thickness at diastole (IVSTd), left ventricular diameter at the end of diastole (LVIDd), left ventricular diameter at the end of systole (LVIDs), peak velocity at atrial contraction (A), early peak velocity (E), left ventricular ejection fraction (LVEF%), left ventricular mass (LVM), and relative wall thickness (RWT). Results: The data showed that changes in E/A differences between diabetic patients and controls for age ranges 25 to 50 and 60 to 80 years were -24.60% and -31.93% (p < 0.05). There were non-significant differences in the LVIDd/LVIDs ratio between diabetic patients and controls for both age groups: 1.31% and 6.25%, respectively. For 25- to 50-year olds, the changes in RWT and LVM were 50% and 74.43%, respectively (p < 0.05), while the differences in RWT and LVM for 60- to 80-year olds were 48.71% and 70.06%, respectively (p < 0.05). Conclusion: The results indicate that diastolic dysfunction may be higher in diabetic patients compared to healthy subjects, which may be due to adverse influence of diabetes on cardiac muscle. These changes in left ventricular structure may include LV hypertrophy, increase in stiffness, and reduction in compliance, with increase in left ventricular mass, relative wall thickness, posterior wall thickness at diastole, and interventricular septum thickness at diastole.
Industrial development has recently increased, including that of plastic industries. Since plastic has a very long analytical life, it will cause environmental pollution, so studies have resorted to reusing recycled waste plastic (sustainable plastic) to produce environmentally friendly concrete (green concrete). In this research, producing environmentally friendly load-bearing concrete masonry units (blocks) was considered where five concrete mixtures were compressed at the blocks producing machine. The cement content reduced from 400 kg/m3 (B-400) to 300 kg/m3 (B-300) then to 200 kg/m3 (B-200). While (B-380) was produced using 380 kg/m3 cement and 20 kg/m3 nano-sil
... Show MoreWe aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
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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