Association of Forkhead Box O3a (FOXO3a) single - nucleotide polymorphism with bronchial asthma in Egyptian population.

Asthma is a common chronic inflammatory condition with a highly complex genetic predisposition and environmental factors which play an important role in its development. Polymorphism in FOXO3a transcription factor has been linked to a number of inflammatory and respiratory diseases such as bronchiolitis and idiopathic pulmonary fibrosis suggesting that it may be implicated in the pathogenesis of asthma. This study aimed to investigate FOXO3a SNP (rs13217795) association with bronchial asthma and its degree of severity in adult Egyptian population. This case control study included 60 asthmatic patients and 40 apparently healthy controls. Peripheral blood samples were collected from all participants. Genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The study revealed high frequency of the mutant TT genotype of FOXO3a gene in asthma patients (51.7%) than controls (12.5%) with OR= 7.48 & 95% CI (2.58-21.71) (P˂0.05) and in severe cases (41.9%) compared to mild and moderate cases (25.8% and12.5%, respectively). T allele frequency showed significant statistical association with asthma, OR= 12.40, 95% CI (5.65-27.19) (P˂0.05). However, there was no association between T allele and disease severity. The high frequency of the mutant TT genotype among patients and sever cases may indicates that FOXO3a rs13217795 C>T single nucleotide polymorphism can be considered as a risk factor in development and severity of asthma.

Hybrid model for ecological vulnerability assessment in Benin.

Identifying ecologically fragile areas by assessing ecosystem vulnerability is an essential task in environmental conservation and management. Benin is considered a vulnerable area, and its coastal zone, which is subject to erosion and flooding effects, is particularly vulnerable. This study assessed terrestrial ecosystems in Benin by establishing a hybrid ecological vulnerability index (EVI) for 2016 that combined a composite model based on principal component analysis (PCA) with an additive model based on exposure, sensitivity and adaptation. Using inverse distance weighted (IDW) interpolation, point data were spatially distributed by their geographic significance. The results revealed that the composite system identified more stable and vulnerable areas than the additive system; the two systems identified 48,600 km2 and 36,450 km2 of stable areas, respectively, for a difference of 12,150 km2, and 3,729 km2 and 3,007 km2 of vulnerable areas, for a difference of 722 km2. Using Moran's I and automatic linear modeling, we improved the accuracy of the established systems. In the composite system, increases of 11,669 km2 in the potentially vulnerable area and 1,083 km2 in the highly vulnerable area were noted in addition to a decrease of 4331 km2 in the potential area; while in the additive system, an increase of 3,970 km2 in the highly vulnerable area was observed. Finally, southern Benin was identified as vulnerable in the composite system, and both northern and southern Benin were identified as vulnerable in the additive system. However, regardless of the system, Littoral Province in southern Benin, was consistently identified as vulnerable, while Donga Province was stable.