Background: Polymorphisms in the TNF-α gene affect the development and progression of rheumatoid arthritis. Objective: To investigate the associations between (-806 T/C) and (-857 T/C) SNPs with rheumatoid arthritis severity and susceptibility in a sample of Iraqi patients. Methods: A case-control study was conducted in Baghdad, Iraq. Twenty healthy controls and 63 patients confirmed to be newly diagnosed with rheumatoid arthritis were included. Those are divided into two groups (patients and controls), and the patients were further subdivided into severe and mild-moderate groups. Samples from those participants were analyzed for clinical and inflammatory parameter measurements. Genotyping by the Sanger method was performed to study the SNPs. Results: No associations were demonstrated between rheumatoid arthritis and polymorphisms at positions -806 and -857. Additionally, there were no differences in the distribution of those SNP genotypes and alleles among the severe and mild-moderate groups. Also, the (-806 C/T) SNP was found to be correlated with DAS 28 in all patients and with hs-CRP in the mild-moderate group. Finally, the -857 C/T SNP was found to be correlated with TNF-α within the mild-moderate group. Conclusions: Polymorphisms at positions -806 and -857 were not associated with rheumatoid arthritis susceptibility, and the CT genotype of -806 C/T SNP was associated with disease activity.
The current study presents the simulative study and evaluation of MANET mobility models over UDP traffic pattern to determine the effects of this traffic pattern on mobility models in MANET which is implemented in NS-2.35 according to various performance metri (Throughput, AED (Average End-2-end Delay), drop packets, NRL (Normalize Routing Load) and PDF (Packet Delivery Fraction)) with various parameters such as different velocities, different environment areas, different number of nodes, different traffic rates, different traffic sources, different pause times and different simulation times . A routing protocol.…was exploited AODV(Adhoc On demand Distance Vector) and RWP (Random Waypoint), GMM (Gauss Markov Model), RPGM (Refere
... Show MoreThis study was aimed to investigate the response surface methodology (RSM) to evaluate the effects of various experimental conditions on the removal of levofloxacin (LVX) from the aqueous solution by means of electrocoagulation (EC) technique with stainless steel electrodes. The EC process was achieved successfully with the efficiency of LVX removal of 90%. The results obtained from the regression analysis, showed that the data of experiential are better fitted to the polynomial model of second-order with the predicted correlation coefficient (pred. R2) of 0.723, adjusted correlation coefficient (Adj. R2) of 0.907 and correlation coefficient values (R2) of 0.952. This shows that the predicted models and experimental values are in go
... Show MoreThe importance of the current research lies in the importance of teaching competencies and the ability of the teacher to deal and success in his educational career. The research aimed to identify the degree of teaching competencies according to Hermann model of physical education teachers in Baghdad governorate. The descriptive method using the survey method was used on a randomly selected sample of 462 teachers and 314 school principals. After the completion of the survey, the Hermann scale forms were distributed to the teachers. The forms of the teaching competency scale were distributed to their school principals as the direct supervisors of the teachers' evaluation. After completing the survey, the results of each scale were classified
... Show MoreThis paper is focused on orthogonal function approximation technique FAT-based adaptive backstepping control of a geared DC motor coupled with a rotational mechanical component. It is assumed that all parameters of the actuator are unknown including the torque-current constant (i.e., unknown input coefficient) and hence a control system with three motor control modes is proposed: 1) motor torque control mode, 2) motor current control mode, and 3) motor voltage control mode. The proposed control algorithm is a powerful tool to control a dynamic system with an unknown input coefficient. Each uncertain parameter/term is represented by a linear combination of weighting and orthogonal basis function vectors. Chebyshev polynomial is used
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