The intake of high fat diet with different trans fatty acid levels differentially induces oxidative stress and non alcoholic fatty liver disease (NAFLD) in rats

<p>Abstract</p> <p>Background</p> <p><it>Trans</it>-fatty acids (TFA) are known as a risk factor for coronary artery diseases, insulin resistance and obesity accompanied by systemic inflammation, the features of metabolic syndrome. Little is known about the effects on the liver induced by lipids and also few studies are focused on the effect of foods rich in TFAs on hepatic functions and oxidative stress. This study investigates whether high-fat diets with different TFA levels induce oxidative stress and liver dysfunction in rats.</p> <p>Methods</p> <p>Male Wistar rats were divided randomly into four groups (n = 12/group): C receiving standard-chow; Experimental groups that were fed high-fat diet included 20% fresh soybean oil diet (FSO), 20% oxidized soybean oil diet (OSO) and 20% margarine diet (MG). Each group was kept on the treatment for 4 weeks.</p> <p>Results</p> <p>A liver damage was observed in rats fed with high-fat diet via increase of liver lipid peroxidation and decreased hepatic antioxidant enzyme activities (superoxide dismutase, catalase and glutathione peroxidase). The intake of oxidized oil led to higher levels of lipid peroxidation and a lower concentration of plasma antioxidants in comparison to rats fed with FSO. The higher inflammatory response in the liver was induced by MG diet. Liver histopathology from OSO and MG groups showed respectively moderate to severe cytoplasm vacuolation, hypatocyte hypertrophy, hepatocyte ballooning, and necroinflammation.</p> <p>Conclusion</p> <p>It seems that a strong relationship exists between the consumption of TFA in the oxidized oils and lipid peroxidation and non alcoholic fatty liver disease (NAFLD). The extent of the peroxidative events in liver was also different depending on the fat source suggesting that feeding margarine with higher TFA levels may represent a direct source of oxidative stress for the organism. The present study provides evidence for a direct effect of TFA on NAFLD.</p

North African Countries and Agricultural Trade Liberalization Under the Doha Round: Does a Top-Down Analysis Matters?

The countries of North Africa are characterized by a relatively high contribution of agriculture sector in their economies. At the same time, all the countries in the region are net agricultural importers. In this context, any potential agreement on agricultural trade liberalization under the Doha Round multilateral negotiations will raises world agricultural prices and could adversely affect the region. Although there are numerous studies on the impact of multilateral agricultural trade liberalization on North African countries, few studies have examined the impact of these global changes on the agricultural sector and on income distribution. Moreover, all the past studies use either global or country CGE models. This study attempts to address this gap in the literature. First, it combines the advantages of global and country models by linking the MIRAGE model to two countries dynamic CGE models built specially for this study. Second, we examine the distributional impact of agricultural trade liberalization in the two countries by integrating individually various household categories in both models. Our results show that drawing policy implications from global models for a specific country is completely misleading. In fact, while the results of the global model show that Tunisia will be winner and Morocco a loser from agricultural trade liberalization, the country models show a completely different picture. For both countries, results show that while the macroeconomic effects are relatively modest, all categories of households lose

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks

The incidence of skin cancer is rising globally, posing a significant public health threat. An early and accurate diagnosis is crucial for patient prognoses. However, discriminating between malignant melanoma and benign lesions, such as nevi and keratoses, remains a challenging task due to their visual similarities. Image-based recognition systems offer a promising solution to aid dermatologists and potentially reduce unnecessary biopsies. This research investigated the performance of four unified convolutional neural networks, namely, YOLOv3, YOLOv4, YOLOv5, and YOLOv7, in classifying skin lesions. Each model was trained on a benchmark dataset, and the obtained performances were compared based on lesion localization, classification accuracy, and inference time. In particular, YOLOv7 achieved superior performance with an Intersection over Union (IoU) of 86.3%, a mean Average Precision (mAP) of 75.4%, an F1-measure of 80%, and an inference time of 0.32 s per image. These findings demonstrated the potential of YOLOv7 as a valuable tool for aiding dermatologists in early skin cancer diagnosis and potentially reducing unnecessary biopsies