Background Type two diabetes (T2DM) is characterized by insufficient insulin production and secretion. Additionally, the body develops insulin resistance which affects 90–95% of diabetics. Complex cytokines, receptors, genetic pathways, and the immune system are involved in T2DM. Interleukin-18 (IL-18) is one of the inflammatory cytokines associated with Type 2 diabetes. Environmental and genetic variables, including genetic polymorphisms, can increase T2DM risk and its consequences. Single nucleotide gene polymorphisms (SNPs) are important risk factors for diabetes that can be used to find the disease early and treat it better. Objective This study aimed to determine the levels of IL-18 in the serum of Iraqi patients with Type 2 diabetes mellitus, as well as the effect of IL-18 SNP rs1946518 (-607 G/T) in the etiology of T2DM. Materials and Methods This study involved 100 T2DM patients (52 males and 48 females) who visited Al-Karamah Teaching Hospital and Baghdad Teaching Hospital. 52 Iraqi control subjects (26 males and 26 females) were included. A sandwich enzyme-linked immunosorbent assay was used to quantify the IL-18 serum levels of 48 patients and 40 healthy controls. The genotype of IL-18 was determined using Real-time (RT) Taqman PCR. Results According to age, the current study revealed a non-significant correlation (p-value > 0.05) among the studied groups. IL-18 levels in the T2DM group were substantially greater than in the healthy control. In addition, the genotyping frequencies revealed that the frequency of TT genotyping was higher in T2DM group than in healthy control (80% versus 66.7%, OR: 2.0), whereas the frequency of GT genotyping was lower in T2DM than in healthy persons (20% versus 33.3%, OR: 0.5). Conclusion: This Iraqi’s novel study indicated that IL-18 and it’s SNP(rs1946518) contributes to the pathophysiology of Type 2 diabetes mellitus.
Synthesis And Studies Of Complexes Of Some Elements With 2-Mercaptohiazole (2-HMBT)
Fusobacterium are compulsory anaerobic gram-negative bacteria, long thin with pointed ends, it causes several illnesses to humans like pocket lesion gingivitis and periodontal disease; therefore our study is constructed on molecular identification and detection of the fadA gene which is responsible for bacterial biofilm formation. In this study, 10.2% Fusobacterium spp. were isolated from pocket lesion gingivitis. The isolates underwent identification depending on several tests under anaerobic conditions and biochemical reactions. All isolates were sensitive to Imipenem (IPM10) 42.7mm/disk, Ciprofloxacin (CIP10) 27.2mm/disk and Erythromycin (E15) 25mm/disk, respectively. 100% of
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Objective(s): To evaluate nurses' practices who work in respiratory intensive care units to control the
complications of patients admitted at this unit and determine the relationship between nurses' sociodemographic
characteristics and their practices.
Methodology: A descriptive study was carried out at Respiratory Care Unit at Baghdad teaching hospitals that
started from February 22th, 2013 to August 30th, 2013. A purposive "non-probability" sample of (70) nurses who
work in Respiratory Care Unit was selected from Baghdad teaching hospitals. The data were collected through the
use of constructed questionnaire that consists of two parts; (l) Demographic data form that consists of 7items and
(2) nurses' practice form
Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B
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