The liver is an important site for iron and lipid metabolism and the main site for the interactions between these two metabolic pathways. Although conflicting results have been obtained, most studies support the hypothesis that iron plays a role in hepatic lipogenesis. Iron is an integral part of some enzymes and transporters involved in lipid metabolism and, as such, may exert a direct effect on hepatic lipid load, intrahepatic metabolic pathways and hepatic lipid secretion. On the other hand, iron in its ferrous form may indirectly affect lipid metabolism through its ability to induce oxidative stress and inflammation, a hypothesis which is currently the focus of much research in the field of non-alcoholic fatty liver disease/non-alcoholic steatohepatitis (NAFLD/NASH). The present review will first discuss how iron might directly interact with the metabolism of hepatic lipids and then consider a new perspective on the way in which iron may have a role in the two hit hypothesis for the progression of NAFLD via ferroportin and the iron regulatory molecule hepcidin. The review concludes that iron has important interactions with lipid metabolism in the liver that can impact on the development of NAFLD/NASH. More defined studies are required to improve our understanding of these effects.

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Extending the feeding period of the high fat/ad lib diet for 5 weeks placed our rats with Type I to II NAFLD compared to the more progressed Type III state previously obtained after 3 weeks feeding. The milder condition obtained raised the prospect of genetic modifiers present in our rats that resist disease progression.

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With the increasing prevalence of Internet usage, Internet-Delivered Psychological Treatment (IDPT) has become a valuable tool to develop improved treatments of mental disorders. IDPT becomes complicated and labor intensive because of overlapping emotion in mental health. To create a usable learning application for IDPT requires diverse labeled datasets containing an adequate set of linguistic properties to extract word representations and segmentations of emotions. In medical applications, it is challenging to successfully refine such datasets since emotion-aware labeling is time consuming. Other known issues include vocabulary sizes per class, data source, method of creation, and baseline for the human performance level. This paper focuses on the application of personalized mental health interventions using Natural Language Processing (NLP) and attention-based in-depth entropy active learning. The objective of this research is to increase the trainable instances using a semantic clustering mechanism. For this purpose, we propose a method based on synonym expansion by semantic vectors. Semantic vectors based on semantic information derived from the context in which it appears are clustered. The resulting similarity metrics help to select the subset of unlabeled text by using semantic information. The proposed method separates unlabeled text and includes it in the next active learning mechanism cycle. Our method updates model training by using the new training points. The cycle continues until it reaches an optimal solution, and it converts all the unlabeled text into the training set. Our in-depth experimental results show that the synonym expansion semantic vectors help enhance training accuracy while not harming the results. The bidirectional Long Short-Term Memory (LSTM) architecture with an attention mechanism achieved 0.85 Receiver Operating Characteristic (ROC curve) on the blind test set. The learned embedding is then used to visualize the activated word's contribution to each symptom and find the psychiatrist's qualitative agreement. Our method improves the detection rate of depression symptoms from online forum text using the unlabeled forum texts.