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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Objectives: To determine the impact of an educational program on nurses’ knowledge
and practices concerning neurogenic bladder rehabilitation for spinal cord injured persons
through a follow-up approach each two months post program implementation for six
months.
Methodology: "Follow-up" longitudinal design by using time series approach of data
analysis and the application of pre-post tests approach for the study and the control
groups. The study was carried out at Ibn Al-Kuff hospital for (SCI) in Baghdad governorate
from 5th of July 2010 to 15th of October 2011. To achieve the objectives of the study, a
non-probability (purposive) sample of (60) nurses (males and females) were working in SCI
units were selec
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
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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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