Ram Benny Dessau

Bio: Ram Benny Dessau is an academic researcher from Herlev Hospital. The author has contributed to research in topics: Population & Borrelia burgdorferi. The author has an hindex of 20, co-authored 86 publications receiving 3382 citations. Previous affiliations of Ram Benny Dessau include University of Copenhagen & Odense University Hospital.

[''R"--project for statistical computing].

Abstract: An introduction to the R project for statistical computing (www.R-project.org) is presented. The main topics are: 1. To make the professional community aware of "R" as a potent and free software for graphical and statistical analysis of medical data; 2. Simple well-known statistical tests are fairly easy to perform in R, but more complex modelling requires programming skills; 3. R is seen as a tool for teaching statistics and implementing complex modelling of medical data among medical professionals.

The diagnostic accuracy of serological tests for Lyme borreliosis in Europe: a systematic review and meta-analysis

Abstract: Interpretation of serological assays in Lyme borreliosis requires an understanding of the clinical indications and the limitations of the currently available tests. We therefore systematically reviewed the accuracy of serological tests for the diagnosis of Lyme borreliosis in Europe. We searched EMBASE en MEDLINE and contacted experts. Studies evaluating the diagnostic accuracy of serological assays for Lyme borreliosis in Europe were eligible. Study selection and data-extraction were done by two authors independently. We assessed study quality using the QUADAS-2 checklist. We used a hierarchical summary ROC meta-regression method for the meta-analyses. Potential sources of heterogeneity were test-type, commercial or in-house, Ig-type, antigen type and study quality. These were added as covariates to the model, to assess their effect on test accuracy. Seventy-eight studies evaluating an Enzyme-Linked ImmunoSorbent assay (ELISA) or an immunoblot assay against a reference standard of clinical criteria were included. None of the studies had low risk of bias for all QUADAS-2 domains. Sensitivity was highly heterogeneous, with summary estimates: erythema migrans 50 % (95 % CI 40 % to 61 %); neuroborreliosis 77 % (95 % CI 67 % to 85 %); acrodermatitis chronica atrophicans 97 % (95 % CI 94 % to 99 %); unspecified Lyme borreliosis 73 % (95 % CI 53 % to 87 %). Specificity was around 95 % in studies with healthy controls, but around 80 % in cross-sectional studies. Two-tiered algorithms or antibody indices did not outperform single test approaches. The observed heterogeneity and risk of bias complicate the extrapolation of our results to clinical practice. The usefulness of the serological tests for Lyme disease depends on the pre-test probability and subsequent predictive values in the setting where the tests are being used. Future diagnostic accuracy studies should be prospectively planned cross-sectional studies, done in settings where the test will be used in practice.

Comparison of 16 Serological SARS-CoV-2 Immunoassays in 16 Clinical Laboratories.

Abstract: Serological assays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to support clinical diagnosis and epidemiological investigations. Recently, assays for large-scale detection of total antibodies (Ab), immunoglobulin G (IgG), and IgM against SARS-CoV-2 antigens have been developed, but there are limited data on the diagnostic accuracy of these assays. This study was a Danish national collaboration and evaluated 15 commercial and one in-house anti-SARS-CoV-2 assays in 16 laboratories. Sensitivity was evaluated using 150 samples from individuals with asymptomatic, mild, or moderate COVID-19, nonhospitalized or hospitalized, confirmed by nucleic acid amplification tests (NAAT); samples were collected 13 to 73 days either from symptom onset or from positive NAAT (patients without symptoms). Specificity and cross-reactivity were evaluated in samples collected prior to the SARS-CoV-2 epidemic from >586 blood donors and patients with autoimmune diseases, cytomegalovirus or Epstein-Barr virus infections, and acute viral infections. A specificity of ≥99% was achieved by all total-Ab and IgG assays except one, DiaSorin Liaison XL IgG (97.2%). Sensitivities in descending order were Wantai ELISA total Ab (96.7%), CUH-NOVO in-house ELISA total Ab (96.0%), Ortho Vitros total Ab (95.3%), YHLO iFlash IgG (94.0%), Ortho Vitros IgG (93.3%), Siemens Atellica total Ab (93.2%), Roche Elecsys total Ab (92.7%), Abbott Architect IgG (90.0%), Abbott Alinity IgG (median 88.0%), DiaSorin Liaison XL IgG (median 84.6%), Siemens Vista total Ab (81.0%), Euroimmun/ELISA IgG (78.0%), and Snibe Maglumi IgG (median 78.0%). However, confidence intervals overlapped for several assays. The IgM results were variable, with the Wantai IgM ELISA showing the highest sensitivity (82.7%) and specificity (99%). The rate of seropositivity increased with time from symptom onset and symptom severity.

Gene ontology analysis for RNA-seq: accounting for selection bias

Abstract: We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.

Spark: cluster computing with working sets

Abstract: MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.

Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer

Abstract: Introduction In breast cancer, gene expression analyses have defined five tumor subtypes (luminal A, luminal B, HER2-enriched, basal-like and claudin-low), each of which has unique biologic and prognostic features. Here, we comprehensively characterize the recently identified claudin-low tumor subtype.

Legionella and Legionnaires' Disease: 25 Years of Investigation

Abstract: There is still a low level of clinical awareness regarding Legionnaires' disease 25 years after it was first detected. The causative agents, legionellae, are freshwater bacteria with a fascinating ecology. These bacteria are intracellular pathogens of freshwater protozoa and utilize a similar mechanism to infect human phagocytic cells. There have been major advances in delineating the pathogenesis of legionellae through the identification of genes which allow the organism to bypass the endocytic pathways of both protozoan and human cells. Other bacteria that may share this novel infectious process are Coxiella burnetti and Brucella spp. More than 40 species and numerous serogroups of legionellae have been identified. Most diagnostic tests are directed at the species that causes most of the reported human cases of legionellosis, L. pneumophila serogroup 1. For this reason, information on the incidence of human respiratory disease attributable to other species and serogroups of legionellae is lacking. Improvements in diagnostic tests such as the urine antigen assay have inadvertently caused a decrease in the use of culture to detect infection, resulting in incomplete surveillance for legionellosis. Large, focal outbreaks of Legionnaires' disease continue to occur worldwide, and there is a critical need for surveillance for travel-related legionellosis in the United States. There is optimism that newly developed guidelines and water treatment practices can greatly reduce the incidence of this preventable illness.

Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison

Abstract: The pragmatic species concept for Bacteria and Archaea is ultimately based on DNA-DNA hybridization (DDH). While enabling the taxonomist, in principle, to obtain an estimate of the overall similarity between the genomes of two strains, this technique is tedious and error-prone and cannot be used to incrementally build up a comparative database. Recent technological progress in the area of genome sequencing calls for bioinformatics methods to replace the wet-lab DDH by in-silico genome-to-genome comparison. Here we investigate state-of-the-art methods for inferring whole-genome distances in their ability to mimic DDH. Algorithms to efficiently determine high-scoring segment pairs or maximally unique matches perform well as a basis of inferring intergenomic distances. The examined distance functions, which are able to cope with heavily reduced genomes and repetitive sequence regions, outperform previously described ones regarding the correlation with and error ratios in emulating DDH. Simulation of incompletely sequenced genomes indicates that some distance formulas are very robust against missing fractions of genomic information. Digitally derived genome-to-genome distances show a better correlation with 16S rRNA gene sequence distances than DDH values. The future perspectives of genome-informed taxonomy are discussed, and the investigated methods are made available as a web service for genome-based species delineation.