Association of Vascular Endothelial Growth Factor Polymorphisms with Asthma in Tunisian Children

Background: Previous studies demonstrated that the vascular endothelial growth factor (VEGF) was being implicated in the airways inflammation and remodeling process in patients with asthma.Aims: We explored the relationship of three polymorphisms in the VEGF gene with asthma in both case control and family studies.Methods: We Genotyped a total of 210 children with asthma, 224 unrelated controls and 160 parents for the +936 C > T (rs3025039), −634 G > C (rs2010963) and −2549 –2567 del 18 of the VEGF promoter region. The Mutations were identified with polymerase chain reaction followed by restriction fragment length polymorphism (RFLP) analysis for the +936 C > T, and −634 G > C polymorphisms.Results: Of the three polymorphisms studied, a borderline association with asthma was found for the G allele in the −634 G > C polymorphism (p = 0.059). No Statistically significant differences were observed for both +936 C > T, and −2549 –2567 del 18 polymorphisms between asthmatic patients and controls, considering either allelic or genotypic frequencies.The distribution of genotypes according to the severity status revealed a significant differences for the +936 C > T, and −2549 –2567 del 18 polymorphisms. In addition, association was found with the haplotypes inferred by the three polymorphisms and asthma susceptibility.Conclusion: We suggest that VEGF Gene polymorphisms can be implicated in asthma

Étude de l'effet des champs électriques pulsés sur la congélation des produits végétaux

COMPIEGNE-BU (601592101) / SudocSudocFranceF

Bisphenol A removal by the Chlorophyta Picocystis sp.: optimization and kinetic study

Place: Philadelphia Publisher: Taylor & Francis Inc WOS:000600713200001International audienceThe Chlorophyta Picocystis sp. isolated from a Tunisian household sewage pond appears promising for effective removal of Bisphenol A (BPA). Efficient and cost-effective technology for contaminants remediation relies on a tradeoff between several parameters such as removal efficiency, microorganism growth, and its tolerance to contaminant toxicity. This article demonstrates the optimum conditions achieving the highest removal rates and the minimal growth inhibition in batch cultures of Picocystis using response surface methodology. A central composite face-centered (CCF) design was used to determine the effects on removal and growth inhibition of four operating parameters: temperature, inoculum cell density, light intensity, and initial BPA concentration. Results showed that the maximal BPA removal was 91.36%, reached the optimal culture conditions of 30.7 degrees C, 25 x 10(5) cells ml(-1) inoculum density, 80.6 mu mol photons m(-2) s(-1) light intensity, and initial BPA concentration of 10 mg l(-1). Various substrate inhibition models were used to fit the experimental data, and robustness analysis highlighted the Tessier model as more efficient to account for the interaction between Picocystis and BPA and predict removal efficiency. These results revealed how Picocystis respond to BPA contamination and suggest that optimization of experimental conditions can be effectively used to maximize BPA removal in the treatment process

COVID-19 pandemic’s effect on the mental health among the Tunisian general population: Associated factors mining via machine learning

The emergence of COVID-19 pandemic has caused a brutal change in the lifestyle of citizens all around the world and greatly affected the mental health of individuals. In Tunisia, many psychological problems have been triggered during the first peak of the pandemic like anxiety, depression, sleep disturbances, and suicide risk. To overcome such disorders, it is crucial to identify the main factors leading to the mental disorders and then develop preventive strategies if a novel form of pandemic or a traumatic event appears. This paper proposes a novel association rules-based approach to characterize the profiles of citizens highly vulnerable to psychological disorders when confronted to traumatic events. The aim of this work is to use machine learning techniques in order to identify the major factors as well as the clusters of features leading to several psychiatric disorders during the COVID-19 pandemic in Tunisia. Many stressors were found to be associated with some psychiatric disorders. The stronger associations were found between doctor consultation and anxiety, COVID test and depression, quarantine and insomnia, and direct contact with a suspected case and peritraumatic distress and dissociation. In addition, it has been found that some factors, like female gender and regular worker, are not leading to mental disorders when they are treated alone, however, they present a high influence on the mental health when they are associated with other factors. For instance, this work discovered that women who have psychiatric history and who always drink coffee are exposed to depression during the pandemic. Other profile of citizens who are highly vulnerable to peritraumatic dissociation concerns students who are confined and who have recent symptoms. The characterization of such vulnerable profiles can provide considerable decision support for medical staff