Spatial distribution of podoconiosis in relation to environmental factors in Ethiopia: a historical review

BACKGROUND An up-to-date and reliable map of podoconiosis is needed to design geographically targeted and cost-effective intervention in Ethiopia. Identifying the ecological correlates of the distribution of podoconiosis is the first step for distribution and risk maps. The objective of this study was to investigate the spatial distribution and ecological correlates of podoconiosis using historical and contemporary survey data. METHODS Data on the observed prevalence of podoconiosis were abstracted from published and unpublished literature into a standardized database, according to strict inclusion and exclusion criteria. In total, 10 studies conducted between 1969 and 2012 were included, and data were available for 401,674 individuals older than 15 years of age from 229 locations. A range of high resolution environmental factors were investigated to determine their association with podoconiosis prevalence, using logistic regression. RESULTS The prevalence of podoconiosis in Ethiopia was estimated at 3.4% (95% CI 3.3%-3.4%) with marked regional variation. We identified significant associations between mean annual Land Surface Temperature (LST), mean annual precipitation, topography of the land and fine soil texture and high prevalence of podoconiosis. The derived maps indicate both widespread occurrence of podoconiosis and a marked variability in prevalence of podoconiosis, with prevalence typically highest at altitudes >1500 m above sea level (masl), with >1500 mm annual rainfall and mean annual LST of 19-21°C. No (or very little) podoconiosis occurred at altitudes 24°C. CONCLUSION Podoconiosis remains a public health problem in Ethiopia over considerable areas of the country, but exhibits marked geographical variation associated in part with key environmental factors. This is work in progress and the results presented here will be refined in future work

CERTL reduces C16 ceramide, amyloid-β levels, and inflammation in a model of Alzheimer's disease

BACKGROUND: Dysregulation of ceramide and sphingomyelin levels have been suggested to contribute to the pathogenesis of Alzheimer's disease (AD). Ceramide transfer proteins (CERTs) are ceramide carriers which are crucial for ceramide and sphingomyelin balance in cells. Extracellular forms of CERTs co-localize with amyloid-β (Aβ) plaques in AD brains. To date, the significance of these observations for the pathophysiology of AD remains uncertain. METHODS: A plasmid expressing CERTL, the long isoform of CERTs, was used to study the interaction of CERTL with amyloid precursor protein (APP) by co-immunoprecipitation and immunofluorescence in HEK cells. The recombinant CERTL protein was employed to study interaction of CERTL with amyloid-β (Aβ), Aβ aggregation process in presence of CERTL, and the resulting changes in Aβ toxicity in neuroblastoma cells. CERTL was overexpressed in neurons by adeno-associated virus (AAV) in a mouse model of familial AD (5xFAD). Ten weeks after transduction, animals were challenged with behavior tests for memory, anxiety, and locomotion. At week 12, brains were investigated for sphingolipid levels by mass spectrometry, plaques, and neuroinflammation by immunohistochemistry, gene expression, and/or immunoassay. RESULTS: Here, we report that CERTL binds to APP, modifies Aβ aggregation, and reduces Aβ neurotoxicity in vitro. Furthermore, we show that intracortical injection of AAV, mediating the expression of CERTL, decreases levels of ceramide d18:1/16:0 and increases sphingomyelin levels in the brain of male 5xFAD mice. CERTL in vivo over-expression has a mild effect on animal locomotion, decreases Aβ formation, and modulates microglia by decreasing their pro-inflammatory phenotype. CONCLUSION: Our results demonstrate a crucial role of CERTL in regulating ceramide levels in the brain, in amyloid plaque formation and neuroinflammation, thereby opening research avenues for therapeutic targets of AD and other neurodegenerative diseases

Classification of acoustic emission data from buckling test of carbon fibre panel using unsupervised clustering techniques

Acoustic emission is widely used for mechanical diagnostics and to characterise damage in composite materials. Distinction between different damage mechanisms is still one of the major challenges and remains an unresolved issue. The objective of cluster analysis is to separate an acoustic emission data set into multiple classes that reflect different acoustic emission sources. This article is concerned with the implementation of unsupervised clustering techniques to classify acoustic emission transients from a carbon fibre laminate buckling test. A new approach to signal feature extraction was utilised, whereby principal components provide signal features that represent the greatest data variance while remaining linearly uncorrelated with each other; feature selection was undertaken using a hierarchical clustering method and finally a cluster analysis was performed using k-means and Fuzzy C-means techniques. The aim of the work is to reduce the data required in the classification process, thereby reducing the processing time and computational power required, without significantly affecting the classification result. Thus, an approach which is more suited to online processing, allowing fast and efficient processing and storage of data is provided. The proposed unsupervised clustering analysis was able to separate acoustic emission signals into two different clusters that were correlated to the damage mechanisms observed. The results show that the clustering groups have a good fit with ultrasonic C-scan and digital image correlation strain data. The application of a clustering process that uses the most effective acoustic emission features as input data is an objective method, and this investigation shows that it may be a useful complement in the field of non-destructive evaluation