COVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in order to select the best features that affect the prediction of the proposed model. These are the Recursive Feature Elimination (RFE) as wrapper feature selection and the Extra Tree Classifier (ETC) as embedded feature selection. Two classification methods are applied for classifying the features vectors which include the Naïve Bayesian method and Restricted Boltzmann Machine (RBM) method. The results were 56.181%, 97.906% respectively when classifying all features and 66.329%, 99.924% respectively when classifying the best ten features using features selection techniques.
Irisin is a myokine that controls energy metabolism by making adipose tissue brown. The present goal in doing this research was to determine how irisin concentration relates to other biochemical markers of disease. Hemodialysis (HD) for chronic kidney failure. The study included 30 individuals with end-stage renal disease on HD and 30 healthy subjects as the control group. The ages of all patients and the control group ranged from (25 to 60) years. The excluded criteria included patients with viral hepatitis and diabetes. Serum irisin concentration and the level of fasting serum glucose (FSG), urea, creatinine (Cr), total protein (TP), albumin (Alb), albumin to creatinine ratio (ACR), total cholesterol (TC), alanine aminotransferase (ALT),
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Objective(s): To assess the job satisfaction during of covid-19 among the nurses in respiratory isolation units of coronavirus disease.
Methodology: A descriptive cross-sectional design was carried out in four hospitals at isolation units of coronavirus disease from the period (21th December, 2021 to 27th January, 2022). A non-probability (convenience) sampling method consists of (300) nurse was selected convenience based on the study criteria. The tool used to measure the job satisfaction is Job satisfaction scale for clinical nursing (JSS-CN). This tool consists of two parts, the first part is for demographic information and consists of 8 items, and the second
... Show MoreFour species of insects, Carpophillus obsoletus Er., Carpophilus sp., Bitoma lycnformis Wall and Scatopse sp., were found in association with infected spathes of date palm with Mauginella scaettae Cav. The later fungus was the dominant species isolated in pure cultures both from diseased spathes and from contaminated insects. Bitoma lycriformis is the first record for Iraq.
Background: In the Thermafil as a root canal obturation, system little is known about the effect that varying rates of insertion have on the adaptability of thermoplasticized GP and the amount of apical extrusion. Materials and methods: thirty simulated root canals were obturated with thermafil obturators and Apexit Plus sealer at three different insertion rates. The obturated canals were sectioned at three different levels, the sealer average film thickness for each section was calculated and the amount of apical extrusion for each canal was conducted. Results: the higher adaptability was seen with the faster insertion rate while the slower insertion rate showed fewer tendencies to cause apical extrusion. Conclusions: the intermediate i
... Show MoreNeuron-derived neurotrophic factor [NENF], a human plasma neurotrophic factor, also increases neurotrophic activity in conjunction with Parkinson's disease-related proteins in Neudesin. Although Neudesin (neuron-derived neurotrophic secreted protein) is a member of the membrane-associated progesterone receptor (MAPR) protein subclass, it is not evolutionary related to the other members of the same family. The expression of Neudesin is found in both brain and spinal cord from embryonic stages to adulthood, as w Neudesin levels in Parkinson's patients with osteoporosis disease and Parkinson's patients without osteoporosis disease, as well as the relationship between Neudesin levels, Anthropometric and Clinical Features (Age, Gender, BMI) and
... Show MoreThe objective of this study is to evaluate the level of cytokines IL-1?, IL-10 and IL-17A in the serum of patients with Alzheimer's disease (AD), vascular dementia (VD) and down syndrome (DS). The results showed that Serum level of IL-1? was significantly increased in AD patients (3.79 ± 0.26 pg/ml) as compared with DS patients (2.78 ± 0.39 pg/ml) or controls (2.78 ± 0.22 pg/ml), while no significant difference was observed between AD and VD (3.25 ± 0.20 pg/ml) patients or between VD patients, DS patients and controls. The serum level of IL-10 was approximated in VD and DS patients and controls (3.39 ± 0.24, 2.77 ± 0.39 and 3.41 ± 0.35 pg/ml, respectively), but was significantly (P ? 0.05) increased in AD patients (5.73 ± 0.55 pg/ml
... Show MoreThe purpose of the current study was to explore the standards that teachers take into consideration when selecting and using assistive technology (AT), in addition to their knowledge and skills in this area. A quantitative, descriptive survey design was used and a convenience sample of 79 teachers of students with intellectual disabilities and autism spectrum disorder (ASD) participated in the current study. Based on the four main areas of the SETT Framework—student, environment, tasks, and tools—, teachers reported a lack consideration for most of the standards in each area. Among other findings, statistically significant differences were found between teachers’ standards of the SETT Framework, with teachers who had previous profe
... Show MoreEarly detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med
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