IntroductionMacrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that plays an important role in the pathogenesis of asthma. Polymorphisms associated with inflammatory diseases exist in the promoter region of MIF, which alter its expression. We aimed to study the association of MIF promoter polymorphism –173G/C with childhood asthma.Material and methodsIn this case-control study, we recruited 60 pediatric patients with bronchial asthma and 90 age- and sex-matched healthy controls. MIF-173G/C was genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP).ResultsGenotype distribution between cases and healthy controls was statistically evaluated. Our results revealed that the frequency of the MIF-173C allele was significantly higher in children with asthma than in the control group (p = 0.002, odds ratio [OR] = 3.61, 95% confidence interval [CI] = 1.63-7.97). The frequency of the MIF-173CC genotype was higher in the asthmatic children than in the controls (p = 0.028, OR = 6.24, 95% CI = 1.24-31.29). Comparing carriage of the MIF-173C allele in pediatric patients with asthma with that observed in healthy controls (GC + CC vs. GG) revealed a positive association with the disease (p = 0.019, OR = 3.12, 95% CI = 1.22-7.99).ConclusionsThese results suggest that MIF-173G/C polymorphism confers an increased risk of susceptibility to the development of childhood asthma in an Egyptian population.

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The feasibility of utilizing Thorium-Plutonium Mixed Oxide in the Westinghouse AP1000 Advanced Passive pressurized water reactor is examined under steady-state, beginning of life (BOL) conditions. Initial core loading of the reactor consists of three types of UO 2 fuel assemblies with different enrichment in U235, as follows: 2.35w/o, 3.40w/o and 4.45w/o. In this paper, one-third of the UO 2 fuel assemblies are replaced by (Th-Pu)O 2 fuel assemblies in two arrangements: the first one assumes a blanket of (Th-Pu)O 2 fuel which replaces the 4.45% enriched UO 2 fuel assemblies surrounding the low enriched UO 2 fuel assemblies, and in the second arrangement some of the UO 2 fuel assemblies are replaced in a way creating a ring of (Th-Pu)O 2 fuel in the core. The reactor is modeled using QUARK computer code. The required cross-section data for QUARK calculations have been generated using WIMSD5 lattice cell code. The results of the steady state analysis show that introducing (Th-Pu)O 2 fuel into AP1000 would not negatively impact the reactor's safety as the criteria mentioned in design control document are met. For (Th-Pu)O 2 fuel blanket and ring loading arrangements, the calculated power peaking factor is less or equal to the design limit. Over the length of the hot channel, the Minimum Departure from Nucleate Boiling Ratio (MDNBR) varies with a minimum above the design limit for the considered (Th-Pu)O 2 fuel assemblies loading arrangements. This work provides the basis for studying Th-based fuel behavior and thermal hydraulic analysis of AP1000 using Th-based fuel in order to evaluate the safety aspect of various core loading patterns under anticipated and accidental conditions.

The behavior of the nuclear reactor in response to any sudden change in reactivity is very important for reactor control. Positive reactivity insertions causes power excursion and could have a destructive impact on the reactor core. The aim of the study is to investigate the safety features of a material test reactor (MTR) during reactivity transient with emphasis on the capability of the mathematical modeling using programming language. Therefore a mathematical model using Python3.6; high-level programming language is developed to solve the point kinetic equations taking into account Doppler and moderator feedback effects. The model is validated with AIREKMOD_RR; point kinetic computer code for reactivity transient analysis in nuclear research reactors. The results of the Python model demonstrate the inherent safety features of the MTR reactor. Also, there is good agreement between the results of the Python model and AIREKMOD_RR code, illustrating the efficiency of the Python model in simulating the behavior of the reactor core under reactivity transient.