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Clinical use of tumor necrosis factor-alpha inhibitors in Iraq: a review of their documented efficacy, safety, and associated genetics
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This comprehensive review examines the efficacy and safety of tumor necrosis factor-alpha (TNF-α) inhibitors in treating various autoimmune diseases, and focuses on their application in Iraqi patients. Elevated TNF-α levels are linked to autoimmune disorders, leading to the development of anti-TNF-α therapies such as infliximab, etanercept, adalimumab, certolizumab pegol, and golimumab, which have gained FDA approval for conditions like psoriasis, in¬flammatory bowel disease, ankylosing spondylitis, and rheumatoid arthritis. While these therapies demonstrate sig¬nificant therapeutic benefits, including improved quality of life and disease management, they also carry risks, such as increased susceptibility to infections and potential malignancies. The review highlights the variable patient re¬sponses to TNF-α inhibitors, influenced by pharmacokinetic and pharmacodynamic factors as well as genetic varia¬tions. The rise of anti-drug antibodies and inadequate drug concentrations are common challenges observed, empha¬sizing the need for therapeutic drug monitoring. Safety profiles of TNF-α inhibitors are generally favorable, but adverse effects (including infections and infusion reactions) have been reported. Genetic factors, such as polymorphisms in the TNF-α gene, may also play a role in the treatment responsiveness and adverse effects, suggesting the potential for personalized medicine approaches. While TNF-α inhibitors effectively manage autoimmune diseases in Iraqi pa¬tients, further research is warranted in order to optimize treatment strategies, assess long-term safety, and explore genetic influences on therapy outcomes. The findings underscore the importance of individualized treatment plans so as to enhance the efficacy and minimize the risks associated with these biologic therapies.

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Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Economics And Administrative Sciences
Fuzzy Bridge Regression Model Estimating via Simulation
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      The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin

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