To determine the relationship between Helicobacter pylori infection and skin disorders, sixty six patients who suffering from skin diseases include chronic urticarial (CU) and atopic dermatitis (AD) who attended at Dermatological Clinic/ Al-Numan Teaching Hospital from the beginning of October 2015 to the end of January 2016 with age (6-62) have been investigated and compared to twenty two samples of apparently healthy individuals were studied as control group. All the studied groups were subjected to measurement of antiHelicobacter pylori IgG antibodies by enzyme linked immuno sorbent assay (ELISA) and detection of 16S rRNA and CagA genes by using singleplex and multiplex PCR methods. The results of current study revealed that there was a highly significant elevation (P<0.01) in concentration of H. pylori IgG antibodies in sera of the CU and AD patients compared with control group, also the results revealed that there was a significant elevation (P<0.05) in concentration of H. pylori IgG antibodies in sera of the CU patients compared with control group, and significant elevation (P<0.05) in concentration of H. pylori IgG antibodies in sera of AD patients compared with control group. The results of present study indicated that 26(66.67%) patients out of 39 CU patients were positive for both of 16S rRNA and CagA genes, while 13(33.33%) patients out of 39 CU patients were negative for those genes. Also, the results revealed that 19(70.37%) patients out of 27 AD patients were positive for both of 16S rRNA and CagA genes, whereas only 8(29.62%) were negative for both those genes comparing with control group which showed 1(4.54%) individuals out of 22 apparently healthy individuals were positive for both 16S rRNA and CagA genes, the statistical analysis was highly significant (P<0.01).
The normalized difference vegetation index (NDVI) is an effective graphical indicator that can be used to analyze remote sensing measurements using a space platform, in order to investigate the trend of the live green vegetation in the observed target. In this research, the change detection of vegetation in Babylon city was done by tracing the NDVI factor for temporal Landsat satellite images. These images were used and utilized in two different terms: in March 19th in 2015 and March 5th in 2020. The Arc-GIS program ver. 10.7 was adopted to analyze the collected data. The final results indicate a spatial variation in the (NDVI), where it increases from (1666.91 𝑘𝑚2) in 2015 to (1697.01 𝑘𝑚2)) in 2020 between the t
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The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
... Show MoreBackground: Periodontitis is an inflammatory disease that affects the supporting tissues of the teeth; Smoking is an important risk factor for periodontitis induces alveolar bone loss and cause an imbalance between bone resorption and bone deposition. The purpose of this study is to detect and compare the presence of incipient periodontitis among young smokers and non-smokers by measuring the distance between cement-enamel junction and alveolar crest (CEJ-Ac) using Cone Beam Computed Tomography (CBCT). Material and methods: The total sample composed of fifty two participants, thirty one smokers and twenty one non-smokers (age range 14-22 years). Periodontal parameters: plaque index (PLI), gingival index (GI) were recorded for all teeth exc
... Show MoreThe prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
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The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr
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Omentin (or intelectin) is a main visceral fat secretory adipokine. There is a growing interest to link omentin, obesity and co-morbidity factors. The aim of the present study is to evaluate serum omentin and its association to insulin resistance biomarkers, lipid profile and atherogenic indies. This cross – sectional study was conducted in Obesity Research and Therapy Unit-Alkindy College of Medicine by recruiting (115) individuals; 49 males /66 females. Subjects between (20 to 60) years of age were selected and classified into two groups according to their Body mass index (BMI). Group1 involved healthy lean volunteers (25 male/ 36 female; BMI 18.5 - 24.9). Group2 involved obese subjects; (24 male / 36 female with BMI ≥ 30). The s
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