BackgroundIn southeast Ethiopia, people locally use the roots of Gnidia stenophylla Gilg (Thymelaeaceae) to cure malaria and other diseases with no literature evidence substantiating its safety. The aim of this study was, therefore, to investigate the safety of the aqueous root extract of G. stenophylla after acute (single dose) and repeated sub chronic oral administration in mice.ResultsA single oral administration of the extract at 500, 1000, 2000 and 4000 mg/kg body weight did not induce any behavioral change and mortality in both sexes. The oral LD50 of the extract was found to be above 6000 mg/kg body weight in mice. Chronic treatment with the extract for 13 weeks did not induce any sign of illness and/or death and had no adverse effect on the body weight. Dose-related elevations of erythrocytes, hematocrit, hemoglobin, platelets and neutrophils differential and significant decrease in the number of lymphocyte were observed. Liver sections of mice treated with 800 mg/kg body weight, revealed mild inflammations around the portal triads and central veins; whereas the spleen and kidneys appeared normal with no detectable gross morphological and histopathological alteration at both doses.ConclusionsThe results of this study revealed that aqueous root extract of G. stenophylla Gilg at antimalarial dose is safe even when taken for a longer period. At a higher dose, the extract may have a potential to increase some hematological indices but may induce mild hepatotoxicity as a side effect.Electronic supplementary materialThe online version of this article (10.1186/s13104-017-2964-3) contains supplementary material, which is available to authorized users.

BACKGROUND: Non-alcoholic Fatty Liver Disease (NAFLD) among type 2 diabetic patients is completely ignored in developing regions like Africa paving the way for public health and economic burden in the region. Therefore, the main objective of this research was to evaluate non-alcoholic fatty liver disease and associated factors among type 2 diabetic patients in Southwestern Ethiopia attending Diabetic Clinic of Jimma University Specialized Hospital (JUSH). METHODS: Facility based cross-sectional study design was used. Anthropometry, fatty liver (using utrasonography), liver enzymes, and lipid profiles were measured among type 2 diabetic patients who fulfilled the inclusion criteria. Socio-demographic and clinical characteristics were assessed using standard questionnaires. RESULTS: Ninety-six (96) type 2 diabetic patients were enrolled and non-alcoholic fatty liver disease prevalence was 73%. Of nonalcoholic fatty Liver disease documented patients, 35.4%, 31.3% and 6.3% exhibited mild, moderate and severe fatty liver diseases, respectively. Alanine aminotransferase (p ≤0.001), Triacyglycerol (p ≤0.001), total bilirubin (p ≤0.05), direct bilirubin (p ≤0.05) and diabetic duration (p ≤0.01) were significantly associated with nonalcoholic fatty liver disease among type 2 diabetic patients. The Aspartate aminotransferase/ Alanine aminotransferase ratio among non alcoholic fatty liver disease patients was greater than one. CONCLUSIONS: The magnitude of non-alcoholic fatty liver disease is high among study groups and it needs urgent action by healthcare systems. Therefore, targeted treatment approach inclusive of non-alcoholic fatty liver disease should be designed.

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Objectives Now a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts’ experience which may lead to misdiagnosis and mistreatment plans. This study aims to develop a system for the automatic diagnosis of skin cancer margin for squamous cell carcinoma from histopathology microscopic images by applying deep learning techniques. Methods The system was trained, validated, and tested using histopathology images of SCC cancer locally acquired from Jimma Medical Center Pathology Department from seven different skin sites using an Olympus digital microscope. All images were preprocessed and trained with transfer learning pre-trained models by fine-tuning the hyper-parameter of the selected models. Results The overall best training accuracy of the models become 95.3%, 97.1%, 89.8%, and 89.9% on EffecientNetB0, MobileNetv2, ResNet50, VGG16 respectively. In addition to this, the best validation accuracy of the models was 94.7%, 91.8%, 87.8%, and 86.7% respectively. The best testing accuracy of the models at the same epoch was 95.2%, 91.5%, 87%, and 85.5% respectively. From these models, EfficientNetB0 showed the best average training and testing accuracy than the other models. Conclusions The system assists the pathologist during the margin assessment of SCC by decreasing the diagnosis time from an average of 25 minutes to less than a minute.

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