Epsilon Haemoglobin Specific Antibodies with Applications in Noninvasive Prenatal Diagnosis

Invasive procedures for prenatal diagnosis are associated with increased risk of abortion; thus, development of noninvasive procedures would be beneficial. Based on the observation that embryonic nucleated red blood cell (NRBC) crosses the placenta and enters the circulation of pregnant women, the ability to identify such cell would allow development of such procedures. Identification of NRBCs in blood samples would be possible provided that specific antibodies are available. Here we have isolated recombinant antibodies using phage display. From the panel of antibody fragments specifically recognising ε-Hb, one was chosen for further characterization, DAb1. DAb1 binds to ε-Hb both in Western blots and immunocytochemistry. Several ε-Hb positive cells were detected in a blood sample taken as postchorionic villus sampling (CVS). To evaluate the sensitivity of the method, K562 cells (which express ε-Hb) were spiked in a blood sample followed by staining in solution and FACS analysis

Effectiveness and Safety of Ticagrelor Implementation in Patients with Acute Coronary Syndrome undergoing Percutaneous Coronary Intervention:A Cohort Study in Western Denmark

BACKGROUND: Ticagrelor was introduced in Denmark in 2011 after randomised data showed its superiority over clopidogrel for patients with acute coronary syndrome (ACS). We assessed the effectiveness and safety of ticagrelor implementation in ACS patients undergoing percutaneous coronary intervention (PCI). METHODS: We identified PCI-treated ACS patients in Western Denmark who redeemed a P2Y12 inhibitor prescription within 14 days. Using Danish health registries, 1-year outcomes were compared before (2007-2010) and after (2012-2015) introduction of ticagrelor. Outcomes were MACE (death, myocardial infarction, and ischaemic stroke) and hospitalisation for bleeding. Inverse probability of treatment weights were used to estimate weighted incidence rate ratios (wIRRs). FINDINGS: We included 14,450 patients; 7,102 were treated in the earlier time period (99·9% clopidogrel) and 7,348 in the later time period (87·8% ticagrelor). Ticagrelor implementation was not associated with a clinically relevant difference in 1-year risk of MACE with 413 events in the ticagrelor period vs. 424 events in the clopidogrel period (cumulative incidence percentage [CIP] 5·6% vs. 6·0%; wIRR 1·06, 95% CI 0·92-1·22). The 1-year risk of bleeding was also similar between groups with 335 bleedings requiring hospitalisation in the ticagrelor period vs. 309 events in the clopidogrel period (CIP 4·6% vs. 4·4%; wIRR 1·05, 95% CI 0·89-1·23). Results were robust in patients above and below 70 years of age. INTERPRETATION: Implementation of ticagrelor was not associated with changes in risks of ischaemic or bleeding events in Danish PCI-treated ACS patients

Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

BACKGROUND: There is an immense scientific interest in the human microbiome and its effects on human physiology, health, and disease. A common approach for examining bacterial communities is high-throughput sequencing of 16S rRNA gene hypervariable regions, aggregating sequence-similar amplicons into operational taxonomic units (OTUs). Strategies for detecting differential relative abundance of OTUs between sample conditions include classical statistical approaches as well as a plethora of newer methods, many borrowing from the related field of RNA-seq analysis. This effort is complicated by unique data characteristics, including sparsity, sequencing depth variation, and nonconformity of read counts to theoretical distributions, which is often exacerbated by exploratory and/or unbalanced study designs. Here, we assess the robustness of available methods for (1) inference in differential relative abundance analysis and (2) beta-diversity-based sample separation, using a rigorous benchmarking framework based on large clinical 16S microbiome datasets from different sources. RESULTS: Running more than 380,000 full differential relative abundance tests on real datasets with permuted case/control assignments and in silico-spiked OTUs, we identify large differences in method performance on a range of parameters, including false positive rates, sensitivity to sparsity and case/control balances, and spike-in retrieval rate. In large datasets, methods with the highest false positive rates also tend to have the best detection power. For beta-diversity-based sample separation, we show that library size normalization has very little effect and that the distance metric is the most important factor in terms of separation power. CONCLUSIONS: Our results, generalizable to datasets from different sequencing platforms, demonstrate how the choice of method considerably affects analysis outcome. Here, we give recommendations for tools that exhibit low false positive rates, have good retrieval power across effect sizes and case/control proportions, and have low sparsity bias. Result output from some commonly used methods should be interpreted with caution. We provide an easily extensible framework for benchmarking of new methods and future microbiome datasets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-016-0208-8) contains supplementary material, which is available to authorized users