Background In rheumatoid arthritis, your immune system attacks the tissue lining the joints on both sides of your body. Other parts of the body may also be affected. Unsure of the exact cause. Two separate genes termed IL12A (p35) and IL12 encode the heterodimeric cytokine known as IL12 (p40). Several different hematopoietic cell types can have several different hematopoietic cell types that can generate antigen-presenting cells (APCs), including DCs and macrophages. Objectives This study aimed to investigate if the interleukin IL-12B gene's common polymorphisms in an Iraqi population were associated with RA. Material and methods Blood samples were taken from 70 Iraqi patients with RA illnesses and 30 Iraqi controls during the periods from April 2022 to June 2022 at Baghdad Teaching Hospital and Typical Rheumatology Unit. IL-12 level was determined by ELISA, and the IL-12B gene SNP was investigated through RT-PCR. Results Between the sick and the healthy group, there was no statistically significant difference in the levels of IL-12. The allele G was more prevalent, and the genotype GG was more noticeable in patients compared to healthy people. As a result, the pattern represents a risk factor for RA (OR (95% CI, 1.55, (0.47 - 5.12), P=0.523). Conclusion We concluded that the IL-12B gene SNP rs3212227 GG was linked to the onset of RA, and that people carrying the G allele had a greater probability of doing so.
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat
... Show MoreIn The Name of Allah Most Gracious Most Merciful
It is no secret to everyone that the endowment is an important nucleus for the prosperity of Islamic civilization, especially in the fields of education, health, economy, and defensive military actions that fall within the door of jihad, and so on. Al-Ashraf, Qom Al-Quds, Cairo, and other parts of the Islamic world. What we will see in the research.
The aim of this paper is to identify Nano-particles that have been used in diagnosis and treatment of leishmaniasis in Iraq. All experiments conducted in this field were based on the following nanoparticles: gold nanoparticles, silver nanoparticles, zinc nanoparticles, and sodium chloride nanoparticles. Most of these experiments were reviewed in terms of differences in the concentrations of nanoparticles and the method that was used in the experiments whether it was in vivo or in vitro. These particles used in most experiments succeeded in inhibiting the growth of Leishmania parasites.