The role of infection in the development of non-valvular atrial fibrillation: Up-regulation of Toll-like receptor 2 expression levels on monocytes

SummaryMany studies have suggested that inflammation may participate in the pathogenesis of non-valvular atrial fibrillation (AF). However, it has been unknown by exposure to what the inflammation is caused. Recently, we reported that Toll-like receptor 2 (TLR2) level on monocytes was significantly up-regulated in viral and bacterial infections, but not in non-infectious inflammatory states. Our purpose was to test the hypothesis that expression of TLR2 levels may be up-regulated in patients with non-valvular AF. A total of 48 consecutive patients with non-valvular AF who were hospitalized for catheter ablation were enrolled in this study. TLR2 levels were assayed by using flow-cytometric analysis and compared with volunteers in sinus rhythm (control group, n=24). Additionally, C-reactive protein (CRP) and interleukin-6 (IL-6) levels were assayed, and the left atrial volume indexes (LAVI) in the non-valvular AF group were measured. The results demonstrated that TLR2 levels in the non-valvular AF group were significantly higher than in the control group (median, 4682 vs. 3866 sites/cell; P<0.01). Moreover, non-valvular AF patients had significantly higher IL-6 levels than controls. However, there was no significant difference in CRP levels between the two groups. It was observed in 44 AF patients, in whom pulmonary vein isolation was confirmed to be successful, that the LAVI significantly diminished 1 month after ablation (median, 33.6 vs. 29.5ml/m2; P<0.001), but not the TLR2 and IL-6 levels. Our results implied that an infectious inflammation may participate in the pathogenesis of non-valvular AF

Increased S1P expression in osteoclasts enhances bone formation in an animal model of Paget's disease

Paget's disease (PD) is characterized by increased numbers of abnormal osteoclasts (OCLs) that drive exuberant bone formation, but the mechanisms responsible for the increased bone formation remain unclear. We previously reported that OCLs from 70% of PD patients express measles virus nucleocapsid protein (MVNP), and that transgenic mice with targeted expression of MVNP in OCLs (MVNP mice) develop bone lesions and abnormal OCLs characteristic of PD. In this report, we examined if OCL-derived sphingosine-1-phosphate (S1P) contributed to the abnormal bone formation in PD, since OCL-derived S1P can act as a coupling factor to increase normal bone formation via binding S1P-receptor-3 (S1PR3) on osteoblasts (OBs). We report that OCLs from MVNP mice and PD patients expressed high levels of sphingosine kinase-1 (SphK-1) compared with wild-type (WT) mouse and normal donor OCLs. SphK-1 production by MVNP-OCLs was interleukin-6 (IL-6)-dependent since OCLs from MVNP/IL-6-/- mice expressed lower levels of SphK-1. Immunohistochemistry of bone biopsies from a normal donor, a PD patient, WT and MVNP mice confirmed increased expression levels of SphK-1 in OCLs and S1PR3 in OBs of the PD patient and MVNP mice compared with normal donor and WT mice. Further, MVNP-OCLs cocultured with OBs from MVNP or WT mice increased OB-S1PR3 expression and enhanced expression of OB differentiation markers in MVNP-OBs precursors compared with WT-OBs, which was mediated by IL-6 and insulin-like growth factor 1 secreted by MVNP-OCLs. Finally, the addition of an S1PR3 antagonist (VPC23019) to WT or MVNP-OBs treated with WT and MVNP-OCL-conditioned media (CM) blocked enhanced OB differentiation of MVNP-OBs treated with MVNP-OCL-CM. In contrast, the addition of the SIPR3 agonist, VPC24191, to the cultures enhanced osterix and Col-1A expression in MVNP-OBs treated with MVNP-OCL-CM compared with WT-OBs treated with WT-OCL-CM. These results suggest that IL-6 produced by PD-OCLs increases S1P in OCLs and S1PR3 on OBs, to increase bone formation in PD

Tax Farm Register of Damascus Province in the Seventeenth Century : Archival and Historical Studies

boo

Transfer Learning for Multiple-Domain Sentiment Analysis — Identifying Domain Dependent/Independent Word Polarity

Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. In this model, each word is associated with three factors: Domain label, domain dependence/independence and word polarity. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether each word's polarity is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain