Mycetoma spatial geographical distribution in the Eastern Sennar locality, Sennar State, Sudan.

BACKGROUND: Mycetoma is a unique neglected tropical disease caused by a substantial number of different fungi or bacteria. Many of the disease's epidemiological characteristics are an enigma. Hence, understanding the spatial geographic distribution of mycetoma may clarify the association between the local environmental indicators, the spatial geographical distribution of mycetoma and its epidemiology. METHODS: This study set out to determine the spatial geographical distribution of mycetoma in the Eastern Sennar locality, Sennar State, one of the highly endemic states in Sudan. It included 594 patients with confirmed mycetoma seen at the Mycetoma Research Centre, University of Khartoum, Khartoum, Sudan, from 1991 to 2020. The spatial geographical distribution of these mycetoma patients was studied. The study area geographic information system data, which included geological, soil, temperature and land cover details, were collected in different geographic information forms. Different geographical analytical techniques were used. RESULTS: The patients' demographic characteristics were similar to those of the general characteristics of mycetoma patients in Sudan. Eumycetoma was the predominant type of mycetoma encountered in the studied patients. The data studied showed that most patients were located in the southern part of the locality along the Blue Nile river. The study showed an association between patients' spatial geographical distribution and soil types. Most patients' localities had light clay soil (475 patients [80%]), followed by sandy loam soil (79 [13%]) then loam soil (40 [6.71%]). Also, 85% of patients' localities had the same land cover and vegetation. There was no significant correlation between patients' localities with temperature or any other geological characteristic. CONCLUSION: The present study showed certain associations between mycetoma spatial geographical distribution and certain environmental indicators. However, a further in-depth study to provide greater insight into the disease's epidemiological characteristics is needed.

A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis.

Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.

The G3057A LEPR polymorphism is associated with obesity in Tunisian women.

OBJECTIVES: The aim of this study was to evaluate the effect of the G3057A (rs62589000) LEPR polymorphism on obesity risk and plasma leptin, insulin, and lipid levels in a sample of the Tunisian population. DESIGN AND METHODS: Three hundred and ninety-three obese patients and 317 controls participated in this study. The G3057A genotype was determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis. RESULTS: In the entire study sample, no significant differences in genotype frequencies were observed between obese patients and controls. However, stratified analysis by gender revealed a quantitative increase in the variant allele (33.3% vs. 25.8%; χ(2)=4.90, p=0.026) in obese women (but not men) compared to controls. When a dominant model of inheritance was assumed, the GA+AA genotypes were more prevalent in these obese female patients than in controls (58.3% vs. 47.8%; χ(2)=4.08, p=0.044). Unconditional logistic regression showed that in women only, obesity risk was significantly higher for homozygotes for the variant allele (OR=2.73, 95% CI 1.03-7.21) and for carriers of GA+AA genotypes (OR=1.53, 95% CI 1.01-2.31) compared with homozygotes for the normal allele. The association between the G3057A LEPR variant and obesity remained statistically significant even after adjustment for age. No relationship was found between the G3057A LEPR polymorphism and leptin and insulin levels. Additionally, this LEPR gene variant had no effect on plasma lipid concentrations. CONCLUSION: There is evidence in this study that the G3057A LEPR polymorphism is associated with obesity in Tunisian women.