Hematopoietic malignancies associated with viral and alcoholic hepatitis

Hepatitis C virus (HCV) and hepatitis B virus (HBV) have been associated with hematopoietic malignancies, but data for many subtypes are limited. From the U.S. Surveillance, Epidemiology, and End Results-Medicare database, we selected 61,464 cases (=67 years) with hematopoietic malignancies and 122,531 population-based controls, frequency-matched by gender, age, and year (1993-2002). Logistic regression was used to compare the prevalence of HCV, HBV, and alcoholic hepatitis in cases and controls, adjusted for matching factors, race, duration of Medicare coverage, and number of physician claims. HCV, HBV, and alcoholic hepatitis were reported in 195 (0.3%), 111 (0.2%), and 404 (0.7%) cases and 264 (0.2%), 242 (0.2%), and 798 (0.7%) controls, respectively. HCV was associated with increased risk of diffuse large B-cell lymphoma [odds ratio (OR) 1.52, 95% confidence interval (95% CI) 1.05-2.18], Burkitt lymphoma (OR 5.21, 95% CI 1.62-16.8), follicular lymphoma (OR 1.88, 95% CI 1.17-3.02), marginal zone lymphoma (OR 2.20, 95% CI 1.22-3.95), and acute myeloid leukemia (OR 1.54, 95% CI 1.00-2.37). In contrast, HBV was unrelated to any hematopoietic malignancies. Alcoholic hepatitis was associated with decreased risk of non-Hodgkin lymphoma overall, but increased risk of Burkitt lymphoma. In summary, HCV, but not other causes of hepatitis, was associated with the elevated risk of non-Hodgkin lymphoma and acute myeloid leukemia. HCV may induce lymphoproliferative malignancies through chronic immune stimulation. Copyright © 2008 American Association for Cancer Research.


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Respiratory viral infection in lower airways of asymptomatic children.

Aim: The aim of this study was to determine if asthmatic children have viruses more commonly detected in lower airways during asymptomatic periods than normal children. Methods: Fifty-five asymptomatic children attending elective surgical procedures (14 with stable asthma, 41 normal controls) underwent non-bronchoscopic bronchoalveolar lavage. Differential cell count and PCR for 13 common viruses were performed. Results: Nineteen (35%) children were positive for at least one virus, with adenovirus being most common. No differences in the proportion of viruses detected were seen between asthmatic and normal ‘control’ children. Viruses other than adenovirus were associated with higher neutrophil counts, suggesting that they caused an inflammatory response in both asthmatics and controls (median BAL neutrophil count, 6.9% for virus detected vs. 1.5% for virus not detected, p = 0.03). Conclusions: Over one-third of asymptomatic children have a detectable virus (most commonly adenovirus) in the lower airway; however, this was not more common in asthmatics. Viruses other than adenovirus were associated with elevated neutrophils suggesting that viral infection can be present during relatively asymptomatic periods in asthmatic children.

Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.

De novo generation of a non-segmented negative strand RNA virus with a bicistronic gene

Reverse genetics has facilitated the use of non-segmented negative strand RNA viruses (NNSV) as vectors. Currently, heterologous gene expression necessitates insertion of extra-numeral transcription units (ENTUs), which may alter the NNSV polar transcription gradient and attenuate growth relative to wildtype (Wt). We hypothesized that rescuing recombinant Sendai Virus (rSeV) with a bicistronic gene might circumvent this attenuation but still allow heterologous open reading frame (ORF) expression. Therefore, we used a 9-nucleotide sequence previously described with internal ribosome entry site (IRES) activity, which, when constructed as several repeats, synergistically increased the level of expression of the second cistron [Chappell, S.A., Edelman, G.M., Mauro, V.P., 2000. A 9-nt segment of a cellular mRNA can function as an internal ribosome entry site (IRES) and when present in linked multiple copies greatly enhances IRES activity. Proc. Natl. Acad. Sci. U.S.A. 97, 1536-1541]. We inserted the Renilla luciferase (rLuc) ORF, preceded by 1, 3 or 7 IRES copies, downstream of the SeV N ORF in an infectious clone. Corresponding rSeVs were successfully rescued. Interestingly, bicistronic rSeVs grew as fast as or faster than Wt rSeV. Furthermore, SeV gene transcription downstream of the N/rLuc gene was either equivalent to, or slightly enhanced, compared to Wt rSeV. Importantly, all rSeV/rLuc viruses efficiently expressed rLuc. IRES repetition increased rLuc expression at a multiplicity of infection of 0.1, although without evidence of synergistic enhancement. In conclusion, our approach provides a novel way of insertion and expression of foreign genes in NNSVs. (C) 2008 Elsevier B.V. All rights reserved.

Ribose 2'-O-methylation provides a molecular signature for the distinction of self and non-self mRNA dependent on the RNA sensor Mda5.

The 5' cap structures of higher eukaryote mRNAs have ribose 2'-O-methylation. Likewise, many viruses that replicate in the cytoplasm of eukaryotes have evolved 2'-O-methyltransferases to autonomously modify their mRNAs. However, a defined biological role for 2'-O-methylation of mRNA remains elusive. Here we show that 2'-O-methylation of viral mRNA was critically involved in subverting the induction of type I interferon. We demonstrate that human and mouse coronavirus mutants lacking 2'-O-methyltransferase activity induced higher expression of type I interferon and were highly sensitive to type I interferon. Notably, the induction of type I interferon by viruses deficient in 2'-O-methyltransferase was dependent on the cytoplasmic RNA sensor Mda5. This link between Mda5-mediated sensing of viral RNA and 2'-O-methylation of mRNA suggests that RNA modifications such as 2'-O-methylation provide a molecular signature for the discrimination of self and non-self mRNA.

Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding

In this paper, we introduce an application of matrix factorization to produce corpus-derived, distributional
models of semantics that demonstrate cognitive plausibility. We find that word representations
learned by Non-Negative Sparse Embedding (NNSE), a variant of matrix factorization, are sparse,
effective, and highly interpretable. To the best of our knowledge, this is the first approach which
yields semantic representation of words satisfying these three desirable properties. Though extensive
experimental evaluations on multiple real-world tasks and datasets, we demonstrate the superiority
of semantic models learned by NNSE over other state-of-the-art baselines.

Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew’s correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.