Preferential association of hepatitis C virus with CD19+ B cells is mediated by complement system

Extrahepatic disease manifestations are common in chronic hepatitis C virus (HCV) infection. The mechanism of HCV-related lymphoproliferative disorders is not fully understood. Recent studies have found that HCV in peripheral blood mononuclear cells (PBMCs) from chronically infected patients is mainly associated with CD19+ B cells. To further elucidate this preferential association of HCV with B cells, we used in vitro cultured virus and uninfected PBMCs from healthy blood donors to investigate the necessary serum components that activate the binding of HCV to B cells. First, we found that the active serum components were present not only in HCV carriers, but also in HCV recovered patients and HCV negative healthy blood donors and that the serum components were heat labile. Second, the preferential binding activity of HCV to B cells could be blocked by anti-complement C3 antibodies. In experiments with complement-depleted serum and purified complement proteins, we demonstrated that complement proteins C1, C2, and C3 were required to activate such binding activity. Complement protein C4 was partially involved in this process. Third, using antibodies against cell surface markers, we showed that the binding complex mainly involved CD21 (complement receptor 2), CD19, CD20, and CD81; CD35 (complement receptor 1) was involved but had lower binding activity. Fourth, both anti-CD21 and anti-CD35 antibodies could block the binding of patient-derived HCV to B cells. Fifth, complement also mediated HCV binding to Raji cells, a cultured B cell line derived from Burkitt´s lymphoma.CONCLUSION:In chronic HCV infection, the preferential association of HCV with B cells is mediated by the complement system, mainly through complement receptor 2 (CD21), in conjunction with the CD19 and CD81 complex. This article is protected by copyright. All rights reserved.Fil: Wang, Richard. National Institutes of Health; Estados UnidosFil: Baré, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; Argentina. National Institutes of Health; Estados UnidosFil: De Giorgi, Valeria. National Institutes of Health; Estados UnidosFil: Matsuura, Kentaro. Nagoya City University Graduate School of Medicine; Japón. National Institutes of Health; Estados UnidosFil: Salam, Kazi Abdus. National Institutes of Health; Estados Unidos. University of Rajshahi; IndiaFil: Grandinetti, Teresa. National Institutes of Health; Estados UnidosFil: Schechterly, Cathy. National Institutes of Health; Estados UnidosFil: Alter, Harvey J.. National Institutes of Health; Estados Unido

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date

Pricing cryptocurrency options

Cryptocurrencies, especially Bitcoin (BTC), which comprise a new digital asset class, have drawn extraordinary worldwide attention. The characteristics of the cryptocurrency/BTC include a high level of speculation, extreme volatility and price discontinuity. We propose a pricing mechanism based on a stochastic volatility with a correlated jump (SVCJ) model and compare it to a flexible co-jump model by \cite{bandi2016price}. The estimation results of both models confirm the impact of jumps and co-jumps on options obtained via simulation and an analysis of the implied volatility curve. We show that a sizeable proportion of price jumps are significantly and contemporaneously anti-correlated with jumps in volatility. Our study comprises pioneering research on pricing BTC options. We show how the proposed pricing mechanism underlines the importance of jumps in cryptocurrency markets

Tapping the Educational Potential of Facebook: Leveraging Social Capital and Knowledge

Facebook is a frequently used Computer Mediated Environment (CME) for college and university students to build social connections and develop as members of their institutions. Facebook functions as a purposed network of identities and deposited self-expression, and is a similar concept to a yearbook, where users can place photos of themselves, their hobbies, interests, movies and music. The value for employing CMEs as a tool for academic purposes is widely accepted. However, whether a social networking site such as Facebook can be used for educational objectives, remains largely unexplored as a research question. This paper discusses studies conducted at the University of Auckland and Manchester Metropolitan University, and explores how students use Facebook, and how it impacts on their social and academic lives. Using theories of social capital, we explore some potential educational uses of Facebook

HSD3B1 is an oxysterol 3β-hydroxysteroid dehydrogenase in human placenta

Most biologically active oxysterols have a 3β-hydroxy-5-ene function in thering system with an additional site of oxidation at C-7 or on the side-chain. In blood plasma oxysterols with a 7α-hydroxy group are also observed with the alternative 3-oxo-4-ene function in the ring system formed by ubiquitously expressed 3β-hydroxy-Δ5-C27-steroid oxidoreduc-taseΔ5-isomerase, HSD3B7. However, oxysterols without a 7α-hydroxy group are not substrates for HSD3B7 and are not usually observed with the 3-oxo-4-ene function. Here we report the unexpected identification of oxysterols in plasma derived from umbilical cord blood and blood from pregnant women taken before delivery at 37+ weeks of gestation, of side-chain oxysterols with a 3-oxo-4-ene function but no 7α-hydroxy group.These 3-oxo-4-ene oxysterols were also identified in placenta, leading to the hypothesis that they may be formed by a previously unrecognized 3β-hydroxy-Δ5-C27-steroid oxidoreductaseΔ5-isomerase activity of HSD3B1,an enzyme which is highly expressed in placenta. Proof-of-principle experiments confirmed that HSD3B1 has this activity. We speculate thatHSD3B1 in placenta is the source of the unexpected 3-oxo-4-ene oxysterols in cord and pregnant women’s plasma and may have a role in controlling the abundance of biologically active oxysterols delivered to the fetus

UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Motivation: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. Results: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (∼7 times shorter hit list before expansion), faster (∼6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation. Availability and implementation: Web access and file download from UniProt website at http://www.uniprot.org/uniref and ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. BLAST searches against UniRef are available at http://www.uniprot.org/blast/ Contact: [email protected]