Inflammation is essential in the initial development and progression of many cardiovascular diseases involving innate and adaptive immune responses. The role of CD4(+)CD25(+)FOXP3(+) regulatory T (TREG) cells in the modulation of inflammation and immunity has received increasing attention. Given the important role of TREG cells in the induction and maintenance of immune homeostasis and tolerance, dysregulation in the generation or function of TREG cells can trigger abnormal immune responses and lead to pathology. A wealth of evidence from experimental and clinical studies has indicated that TREG cells might have an important role in protecting against cardiovascular disease, in particular atherosclerosis and abdominal aortic aneurysm. In this Review, we provide an overview of the roles of TREG cells in the pathogenesis of a number of cardiovascular diseases, including atherosclerosis, hypertension, ischaemic stroke, abdominal aortic aneurysm, Kawasaki disease, pulmonary arterial hypertension, myocardial infarction and remodelling, postischaemic neovascularization, myocarditis and dilated cardiomyopathy, and heart failure. Although the exact molecular mechanisms underlying the cardioprotective effects of TREG cells are still to be elucidated, targeted therapies with TREG cells might provide a promising and novel future approach to the prevention and treatment of cardiovascular diseases.

Abstract. How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.

Considerable evidence supports that the CD4+ T cell-mediated immune response contributes to the development of atherosclerotic plaque. However, the effects of Th17 cells on atherosclerosis are not thoroughly understood. In this study, we evaluated the production and function of Th17 and Th1 cells in atherosclerotic-susceptible ApoE−/− mice. We observed that the proportion of Th17 cells, as well as Th1, increased in atherosclerotic ApoE−/− mice compared with nonatherosclerotic wild-type littermates. In ApoE−/− mice with atherosclerosis, the expression of IL-17 and retinoic acid-related orphan receptor γt was substantially higher in the arterial wall with plaque than in the arterial wall without plaque. Increased Th17 cells were associated with the magnitude of atherosclerotic plaque in ApoE−/− mice. Importantly, treatment of ApoE−/− mice with neutralizing anti–IL-17 Ab dramatically inhibited the development of atherosclerotic plaque, whereas rIL-17 application significantly promoted the formation of atherosclerotic plaque. These data demonstrate that Th17 cells play a critical role in atherosclerotic plaque formation in mice, which may have implications in patients with atherosclerosis.

Abstract. As the most popular microblogging platform, the vast amount of content on Twitter is constantly growing so that the retrieval of relevant information (streams) is becoming more and more difficult every day. Representing the semantics of individual Twitter activities and modeling the interests of Twitter users would allow for personalization and therewith countervail the information overload. Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well. However, automatically inferring the semantic meaning of Twitter posts is a non-trivial problem. In this paper we investigate semantic user modeling based on Twitter posts. We introduce and analyze methods for linking Twitter posts with related news articles in order to contextualize Twitter activities. We then propose and compare strategies that exploit the semantics extracted from both tweets and related news articles to represent individual Twitter activities in a semantically meaningful way. A large-scale evaluation validates the benefits of our approach and shows that our methods relate tweets to news articles with high precision and coverage, enrich the semantics of tweets clearly and have strong impact on the construction of semantic user profiles for the Social Web.