Persistence of Human Herpesvirus 7 in Normal Tissues Detected by Expression of a Structural Antigen

Human herpesvirus 7 (HHV-7) infection in histologically normal human tissues was investigated by immunohistochemical detection of the 85-kDa tegument phosphoprotein (pp85) encoded by the U14 gene. So far, two cell types were recognized as sites of HHV-7 infection in vivo: CD4+ T lymphocytes, believed to be the site of latent infection, and epithelial cells of salivary glands, the site of productive infection and viral shedding. Unexpectedly, cells expressing the HHV-7 structural antigen were detectable in lungs, skin, and mammary glands. Morphologically and phenotypically, they were distinct from lymphocytes. Liver, kidney, and tonsils were positive, although the number of HHV-7-positive cells was low. Large intestine, spleen, and brain were negative. Different from the current notion of the state of HHV-7 in humans, the results show that a variety of tissues harbor cells at a late stage of infection and suggest that HHV-7 causes a persistent rather than a true latent infectio

scROSHI: robust supervised hierarchical identification of single cells

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

A Service of zbw Input Price Discrimination (Bans), Entry and Welfare Input Price Discrimination (Bans), Entry and Welfare

Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The working papers published in the Series constitute work in progress circulated to stimulate discussion and critical comments. Views expressed represent exclusively the authors' own opinions and do not necessarily reflect those of the editor. Terms of use: Documents in Input Price Discrimination (Bans), Entry and Welfare Markus Dertwinkel-Kalt JEL Classi…cation: L13, D43, K31

Die Medizin der Zukunft: Digitale Pathologie

Future Medicine: Digital Pathology Abstract. Pathology is facing a paradigm shift. Digitization enables highly efficient, networked diagnostics and the simplified exchange of expert knowledge. Algorithms for image analysis and artificial intelligence have the potential to further increase the quality of diagnostics in pathology. Structured electronic reporting enables the continuous development of digital diagnostics and improves the communication between clinical disciplines. Here we identify and discuss the main trends that will shape digital pathology. In der Pathologie steht ein Paradigmenwechsel bevor. Die Digitalisierung ermöglicht eine hocheffiziente, vernetzte Diagnostik und den vereinfachten Austausch von Expertenwissen. Algorithmen zur Bildanalyse und artifizielle Intelligenz besitzen das Potential, die Qualität der Diagnostik in der Pathologie weiter zu erhöhen. Wichtige Neuerungen ergeben sich auch in der Befunderstellung. Strukturierte Befundungssysteme ermöglichen die kontinuierliche Weiterentwicklung der digitalen Diagnostik und verbessern die Kommunikation zwischen den Spezialdisziplinen

Persistence of human herpesvirus 7 in normal tissues detected by expression of a structural antigen

Human herpesvirus 7 (HHV-7) infection in histologically normal human tissues was investigated by immunohistochemical detection of the 85-kDa tegument phosphoprotein (pp85) encoded by the U14 gene. So far, two cell types were recognized as sites of HHV-7 infection in vivo: CD4+ T lymphocytes, believed to be the site of latent infection, and epithelial cells of salivary glands, the site of productive infection and viral shedding. Unexpectedly, cells expressing the HHV-7 structural antigen were detectable in lungs, skin, and mammary glands. Morphologically and phenotypically, they were distinct from lymphocytes. Liver, kidney, and tonsils were positive, although the number of HHV-7-positive cells was low. Large intestine, spleen, and brain were negative. Different from the current notion of the state of HHV-7 in humans, the results show that a variety of tissues harbor cells at a late stage of infection and suggest that HHV-7 causes a persistent rather than a true latent infectio

Author Correction / 2019: Automated Gleason grading of prostate cancer tissue microarrays via deep learning

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper

Correction: Automated Gleason grading of prostate cancer tissue microarrays via deep learning (vol 8, 12054, 2018)

ISSN:2045-232