Dietary interventions to contrast the onset and progression of diabetic nephropathy. a critical survey of new data

This article is a critical overview of recent contributions on the dietary corrections and the foods that have been claimed to delay or hinder the onset of diabetic nephropathy (DN) and its progression to end-stage renal disease. Innovative dietary and behavioral approaches to the prevention and therapy of DN appear the more captivating in consideration of the rather well established protocols for glucose and blood pressure control in use. In addition to restricted caloric intake to contrast obesity and the metabolic syndrome, adjustments in the patient's macronutrients intake, and in particular some degree of reduction in protein, have been long considered in the prevention of DN progression. More recently, the focus has shifted to the source of proteins and the content of glycotoxins in the diet as well as to the role of specific micronutrients. Few clinical trials have specifically addressed the role of those micronutrients associated with diet proteins that show the most protective effect against DN. Research on clinical outcome and mechanisms of action of such micronutrients appears the most promising in order to develop both effective intervention on nutritional education of the patient and selection of functional foods capable of contrasting the onset and progression of DN

Role of galectin-3 in bone cell differentiation, bone pathophysiology and vascular osteogenesis

Galectin-3 is expressed in various tissues, including the bone, where it is considered a marker of chondrogenic and osteogenic cell lineages. Galectin-3 protein was found to be increased in the differentiated chondrocytes of the metaphyseal plate cartilage, where it favors chondrocyte survival and cartilage matrix mineralization. It was also shown to be highly expressed in differentiating osteoblasts and osteoclasts, in concomitance with expression of osteogenic markers and Runt-related transcription factor 2 and with the appearance of a mature phenotype. Galectin-3 is expressed also by osteocytes, though its function in these cells has not been fully elucidated. The effects of galectin-3 on bone cells were also investigated in galectin-3 null mice, further supporting its role in all stages of bone biology, from development to remodeling. Galectin-3 was also shown to act as a receptor for advanced glycation endproducts, which have been implicated in age-dependent and diabetes-associated bone fragility. Moreover, its regulatory role in inflammatory bone and joint disorders entitles galectin-3 as a possible therapeutic target. Finally, galectin-3 capacity to commit mesenchymal stem cells to the osteoblastic lineage and to favor transdifferentiation of vascular smooth muscle cells into an osteoblast-like phenotype open a new area of interest in bone and vascular pathologies

BERToldo, the Historical BERT for Italian

Recent works in historical language processing have shown that transformer-based models can be successfully created using historical corpora, and that using them for analysing and classifying data from the past can be beneficial compared to standard transformer models. This has led to the creation of BERT-like models for different languages trained with digital repositories from the past. In this work we introduce the Italian version of historical BERT, which we call BERToldo. We evaluate the model on the task of PoS-tagging Dante Alighieri’s works, considering not only the tagger performance but also the model size and the time needed to train it. We also address the problem of duplicated data, which is rather common for languages with a limited availability of historical corpora. We show that deduplication reduces training time without affecting performance. The model and its smaller versions are all made available to the research community

Using Semantic Linking to Understand Persons' Networks Extracted from Text

In this work, we describe a methodology to interpret large persons' networks extracted from text by classifying cliques using the DBpedia ontology. The approach relies on a combination of NLP, Semantic web technologies, and network analysis. The classification methodology that first starts from single nodes and then generalizes to cliques is effective in terms of performance and is able to deal also with nodes that are not linked to Wikipedia. The gold standard manually developed for evaluation shows that groups of co-occurring entities share in most of the cases a category that can be automatically assigned. This holds for both languages considered in this study. The outcome of this work may be of interest to enhance the readability of large networks and to provide an additional semantic layer on top of cliques. This would greatly help humanities scholars when dealing with large amounts of textual data that need to be interpreted or categorized. Furthermore, it represents an unsupervised approach to automatically extend DBpedia starting from a corpus

Deficiency of the purinergic receptor 2X7 attenuates nonalcoholic steatohepatitis induced by high-fat diet. possible role of the NLRP3 Inflammasome

Molecular mechanisms driving transition from simple steatosis to nonalcoholic steatohepatitis (NASH), a critical step in the progression of nonalcoholic fatty liver disease (NAFLD) to cirrhosis, are poorly defined. This study aimed at investigating the role of the purinergic receptor 2X7 (PR2X7), through the NLRP3 inflammasome, in the development of NASH. To this end, mice knockout for the Pr2x7 gene (Pr2x7 −/−) and coeval wild-type (WT) mice were fed a high-fat diet (HFD) or normal-fat diet for 16 weeks. NAFLD grade and stage were lower in Pr2x7 −/− than WT mice, and only 1/7 Pr2x7 −/− animals showed evidence of NASH, as compared with 4/7 WT mice. Molecular markers of inflammation, oxidative stress, and fibrosis were markedly increased in WT-HFD mice, whereas no or significantly reduced increments were detected in Pr2x7 −/− animals, which showed also decreased modulation of genes of lipid metabolism. Deletion of Pr2x7 gene was associated with blunted or abolished activation of NLRP3 inflammasome and expression of its components, which were induced in liver sinusoidal endothelial cells challenged with appropriate stimuli. These data show that Pr2x7 gene deletion protects mice from HFD-induced NASH, possibly through blunted activation of NLRP3 inflammasome, suggesting that PR2X7 and NLRP3 may represent novel therapeutic targets

The "sweet" path to cancer. focus on cellular glucose metabolism

The hypoxia-inducible factor-1α (HIF-1α), a key player in the adaptive regulation of energy metabolism, and the M2 isoform of the glycolytic enzyme pyruvate kinase (PKM2), a critical regulator of glucose consumption, are the main drivers of the metabolic rewiring in cancer cells. The use of glycolysis rather than oxidative phosphorylation, even in the presence of oxygen (i.e., Warburg effect or aerobic glycolysis), is a major metabolic hallmark of cancer. Aerobic glycolysis is also important for the immune system, which is involved in both metabolic disorders development and tumorigenesis. More recently, metabolic changes resembling the Warburg effect have been described in diabetes mellitus (DM). Scientists from different disciplines are looking for ways to interfere with these cellular metabolic rearrangements and reverse the pathological processes underlying their disease of interest. As cancer is overtaking cardiovascular disease as the leading cause of excess death in DM, and biological links between DM and cancer are incompletely understood, cellular glucose metabolism may be a promising field to explore in search of connections between cardiometabolic and cancer diseases. In this mini-review, we present the state-of-the-art on the role of the Warburg effect, HIF-1α, and PKM2 in cancer, inflammation, and DM to encourage multidisciplinary research to advance fundamental understanding in biology and pathways implicated in the link between DM and cancer

Never Retreat, Never Retract: Argumentation Analysis for Political Speeches

International audienceIn this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics

Scent Mining: Extracting Olfactory Events, Smell Sources and Qualities

Olfaction is a rather understudied sense compared to the other senses. In NLP, however, there have been recent attempts to develop taxonomies and benchmarks specifically designed to capture smell-related information. In this work, we further extend this research line by presenting a supervised system for olfactory information extraction in English. We cast this problem as a token classification task and build a system that identifies smell words, smell sources and qualities. The classifier is then applied to a set of English historical corpora, covering different domains and written in a time period between the 15th and the 20th Century. A qualitative analysis of the extracted data shows that they can be used to infer interesting information about smelly items such as tea and tobacco from a diachronical perspective, supporting historical investigation with corpus-based evidence

FBK-DH at SemEval-2020 Task 12: Using Multi-channel BERT for Multilingual Offensive Language Detection

In this paper we present our submission to sub-task A at SemEval 2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval2). For Danish, Turkish, Arabic and Greek, we develop an architecture based on transfer learning and relying on a two-channel BERT model, in which the English BERT and the multilingual one are combined after creating a machine-translated parallel corpus for each language in the task. For English, instead, we adopt a more standard, single-channel approach. We find that, in a multilingual scenario, with some languages having small training data, using parallel BERT models with machine translated data can give systems more stability, especially when dealing with noisy data. The fact that machine translation on social media data may not be perfect does not hurt the overall classification performance