Multicentre Performance Evaluation of the Elecsys Anti-SARS-CoV-2 Immunoassay as an Aid in Determining Previous Exposure to SARS-CoV-2

Introduction We performed a multicentre evaluation of the Elecsys® Anti-SARS-CoV-2 immunoassay (Roche Diagnostics), an assay utilising a recombinant protein representing the nucleocapsid (N) antigen, for the in vitro qualitative detection of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods Specificity was evaluated using serum/plasma samples from blood donors and routine diagnostic specimens collected before September 2019 (i.e., presumed negative for SARS-CoV-2-specific antibodies); sensitivity was evaluated using samples from patients with polymerase chain reaction (PCR)-confirmed SARS-CoV-2 infection. Point estimates and 95% confidence intervals (CIs) were calculated. Method comparison was performed versus commercially available assays. Results Overall specificity for the Elecsys Anti-SARS-CoV-2 immunoassay (n = 9575) was 99.85% (95% CI 99.75–99.92): blood donors (n = 6714; 99.82%), routine diagnostic specimens (n = 2861; 99.93%), pregnant women (n = 2256; 99.91%), paediatric samples (n = 205; 100.00%). The Elecsys Anti-SARS-CoV-2 immunoassay demonstrated significantly higher specificity versus LIAISON SARS-CoV-2 S1/S2 IgG (99.71% vs. 98.48%), EUROIMMUN Anti-SARS-CoV-2 IgG (100.00% vs. 94.87%), ADVIA Centaur SARS-CoV-2 Total (100.00% vs. 87.32%) and iFlash SARS-CoV-2 IgM (100.00% vs. 99.58%) assays, and comparable specificity to ARCHITECT SARS-CoV-2 IgG (99.75% vs. 99.65%) and iFlash SARS-CoV-2 IgG (100.00% vs. 100.00%) assays. Overall sensitivity for Elecsys Anti-SARS-CoV-2 immunoassay samples drawn at least 14 days post-PCR confirmation (n = 219) was 93.61% (95% CI 89.51–96.46). No statistically significant differences in sensitivity were observed between the Elecsys Anti-SARS-CoV-2 immunoassay versus EUROIMMUN Anti-SARS-CoV-2 IgG (90.32% vs. 95.16%) and ARCHITECT SARS-CoV-2 IgG (84.81% vs. 87.34%) assays. The Elecsys Anti-SARS-CoV-2 immunoassay showed significantly lower sensitivity versus ADVIA Centaur SARS-CoV-2 Total (85.19% vs. 95.06%) and iFlash SARS-CoV-2 IgG (86.25% vs. 93.75%) assays, but significantly higher sensitivity versus the iFlash SARS-CoV-2 IgM assay (86.25% vs. 33.75%). Conclusion The Elecsys Anti-SARS-CoV-2 immunoassay demonstrated very high specificity and high sensitivity in samples collected at least 14 days post-PCR confirmation of SARS-CoV-2 infection, supporting its use to aid in determination of previous exposure to SARS-CoV-2

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children.

Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.This article is freely available via Open Access. Click on the Additional Link above to access the full-text via the publisher's site

Unbiased prediction and feature selection in high-dimensional survival regression.

With widespread availability of omics profiling techniques, the analysis and interpretation of high-dimensional omics data, for example, for biomarkers, is becoming an increasingly important part of clinical medicine because such datasets constitute a promising resource for predicting survival outcomes. However, early experience has shown that biomarkers often generalize poorly. Thus, it is crucial that models are not overfitted and give accurate results with new data. In addition, reliable detection of multivariate biomarkers with high predictive power (feature selection) is of particular interest in clinical settings. We present an approach that addresses both aspects in high-dimensional survival models. Within a nested cross-validation (CV), we fit a survival model, evaluate a dataset in an unbiased fashion, and select features with the best predictive power by applying a weighted combination of CV runs. We evaluate our approach using simulated toy data, as well as three breast cancer datasets, to predict the survival of breast cancer patients after treatment. In all datasets, we achieve more reliable estimation of predictive power for unseen cases and better predictive performance compared to the standard CoxLasso model. Taken together, we present a comprehensive and flexible framework for survival models, including performance estimation, final feature selection, and final model construction. The proposed algorithm is implemented in an open source R package (SurvRank) available on CRAN

Common patterns of gene regulation associated with Cesarean section and the development of islet autoimmunity - indications of immune cell activation.

Birth by Cesarean section increases the risk of developing type 1 diabetes later in life. We aimed to elucidate common regulatory processes observed after Cesarean section and the development of islet autoimmunity, which precedes type 1 diabetes, by investigating the transcriptome of blood cells in the developing immune system. To investigate Cesarean section effects, we analyzed longitudinal gene expression profiles from peripheral blood mononuclear cells taken at several time points from children with increased familial and genetic risk for type 1 diabetes. For islet autoimmunity, we compared gene expression differences between children after initiation of islet autoimmunity and age-matched children who did not develop islet autoantibodies. Finally, we compared both results to identify common regulatory patterns. We identified the pentose phosphate pathway and pyrimidine metabolism - both involved in nucleotide synthesis and cell proliferation - to be differentially expressed in children born by Cesarean section and after islet autoimmunity. Comparison of global gene expression signatures showed that transcriptomic changes were systematically and significantly correlated between Cesarean section and islet autoimmunity. Moreover, signatures of both Cesarean section and islet autoimmunity correlated with transcriptional changes observed during activation of isolated CD4+ T lymphocytes. In conclusion, we identified shared molecular changes relating to immune cell activation in children born by Cesarean section and children who developed autoimmunity. Our results serve as a starting point for further investigations on how a type 1 diabetes risk factor impacts the young immune system at a molecular level

A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological, and environmental factors.

Background Associations between childhood asthma phenotypes and genetic, immunological, and environmental factors have been previously established. Yet, strategies to integrate high-dimensional risk factors from multiple distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a preponderance of missing data and lack of methods to accommodate them. Methods We assembled questionnaire, diagnostic, genotype, microarray, RT-qPCR, flow cytometry, and cytokine data (referred to as data modalities) to use as input factors for a classifier that could distinguish healthy children, mild-to-moderate allergic asthmatics, and nonallergic asthmatics. Based on data from 260 German children aged 4-14 from our university outpatient clinic, we built a novel multilevel prediction approach for asthma outcome which could deal with a present complex missing data structure. Results The optimal learning method was boosting based on all data sets, achieving an area underneath the receiver operating characteristic curve (AUC) for three classes of phenotypes of 0.81 (95%-confidence interval (CI): 0.65-0.94) using leave-one-out cross-validation. Besides improving the AUC, our integrative multilevel learning approach led to tighter CIs than using smaller complete predictor data sets (AUC = 0.82 [0.66-0.94] for boosting). The most important variables for classifying childhood asthma phenotypes comprised novel identified genes, namely PKN2 (protein kinase N2), PTK2 (protein tyrosine kinase 2), and ALPP (alkaline phosphatase, placental). Conclusion Our combination of several data modalities using a novel strategy improved classification of childhood asthma phenotypes but requires validation in external populations. The generic approach is applicable to other multilevel data-based risk prediction settings, which typically suffer from incomplete data

Peptide serum markers in islet autoantibody-positive children.

Aims/hypothesis We sought to identify minimal sets of serum peptide signatures as markers for islet autoimmunity and predictors of progression rates to clinical type 1 diabetes in a case–control study. Methods A double cross-validation approach was applied to first prioritise peptides from a shotgun proteomic approach in 45 islet autoantibody-positive and -negative children from the BABYDIAB/BABYDIET birth cohorts. Targeted proteomics for 82 discriminating peptides were then applied to samples from another 140 children from these cohorts. Results A total of 41 peptides (26 proteins) enriched for the functional category lipid metabolism were significantly different between islet autoantibody-positive and autoantibody-negative children. Two peptides (from apolipoprotein M and apolipoprotein C-IV) were sufficient to discriminate autoantibody-positive from autoantibody-negative children. Hepatocyte growth factor activator, complement factor H, ceruloplasmin and age predicted progression time to type 1 diabetes with a significant improvement compared with age alone. Conclusion/interpretation Distinct peptide signatures indicate islet autoimmunity prior to the clinical manifestation of type 1 diabetes and enable refined staging of the presymptomatic disease period. &nbsp

Allele-specific methylation of type 1 diabetes susceptibility genes

The susceptibility to autoimmune diseases is influenced by genes encoding major histocompatibility complex (MHC) proteins. By examining the epigenetic methylation maps of cord blood samples, we found marked differences in the methylation status of CpG sites within the MHC genes (cis-metQTLs) between carriers of the type 1 diabetes risk haplotypes HLA-DRB1*03-DQA1*0501-DQB1*0201 (DR3-DQ2) and HLA-DRB1*04-DQA1*0301-DQB1*0302 (DR4-DQ8). These differences were found in children and adults, and were accompanied by reduced HLA-DR protein expression in immune cells with the HLA-DR3-DQ2 haplotype. Extensive cis-metQTLs were identified in all 45 immune and non-immune type 1 diabetes susceptibility genes analyzed in this study. We observed and validated a novel association between the methylation status of CpG sites within the LDHC gene and the development of insulin autoantibodies in early childhood in children who are carriers of the highest type 1 diabetes risk genotype. Functionally relevant epigenetic changes in susceptibility genes may represent therapeutic targets for type 1 diabetes

Thermal conditions during heat waves of a mid-European metropolis under consideration of climate change, urban development scenarios and resilience measures for the mid-21st century

In this study we produce two urban development scenarios estimating potential urban sprawl and optimized development concerning building construction, and we simulate their influence on air temperature, surface temperatures and human thermal comfort. We select two heat waves representative for present and future conditions of the mid 21st century and simulations are run with the Town Energy Balance Model (TEB) coupled online and offline to the Weather Research and Forecasting Model (WRF). Global and regional climate change under the RCP8.5 scenario causes an increase of daily maximum air temperature in Vienna by 7 K. The daily minimum air temperature will increase by 2–4 K. Changes caused by urban growth or densification mainly affect air temperature and human thermal comfort locally where new urbanisation takes place and does not occur significantly in the central districts. A combination of near zero-energy standards and increasing albedo of building materials on the city scale accomplishes a maximum reduction of urban canyon temperature achieved by changes in urban parameters of 0.9 K for the minima and 0.2 K for the maxima. Local scale changes of different adaptation measures show that insulation of buildings alone increases the maximum wall surface temperatures by more than 10 K or the maximum mean radiant temperature (MRT) in the canyon by 5 K. Therefore, measures to reduce MRT within the urban canyons like tree shade are needed to complement the proposed measures. This study concludes that the rising air temperatures expected by climate change puts an unprecedented heat burden on Viennese inhabitants, which cannot easily be reduced by measures concerning buildings within the city itself. Additionally, measures such as planting trees to provide shade, regional water sensitive planning and global reduction of greenhouse gas emissions in order to reduce temperature extremes are required