Background: Peanut allergy is one of the most severe food allergies in children. The diagnostic gold standard is the oral food challenge (OFC). However, OFC has inherent risks and is time consuming. The measurement of specific immunoglobulin E (sIgE) to peanut components in blood detects peanut sensitization, but the decision point predicting allergy is still unclear. The aim of this study was to determine the diagnostic value of these tests for the evaluation of child peanut allergy. Methods: In this retrospective study, 81 children were referred for peanut allergy. The diagnosis of peanut allergy was based on the clinical context and a positive OFC. Levels of sIgE against whole peanuts or peanut components (Ara h 2 and Ara h 8) were determined by immunoassay. Results: The Ara h 2 sIgE assay has the best negative predictive value (0.93) and positive predictive value (1) at a cutoff of 0.1 kU/l. Ara h 2 sIgE titers can predict the risk of anaphylaxis (<0.44 kU/l, low risk; >14 kU/l, high risk). The Ara h 8 sIgE assay is not able to discriminate peanut-allergic patients but can be used to evaluate possible cross-reactions to birch pollen with a low risk of anaphylaxis. The best diagnostic strategy is to first determine the Ara h 2 sIgE level and, if negative, evaluate Ara h 8 sIgE. Conclusions: We propose an algorithm for a better use of peanut component sIgE immunoassays that should improve their diagnostic value and avoid unnecessary OFC.
Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in many other areas of pathology. To facilitate diagnosis, we have thus developed a gene expression assay able to discriminate the seven most frequent B-cell NHL categories. This assay relies on the combination of ligation-dependent RT-PCR and next-generation sequencing, and addresses the expression of more than 130 genetic markers. It was designed to retrieve the main gene expression signatures of B-NHL cells and their microenvironment. The classification is handled by a random forest algorithm which we trained and validated on a large cohort of more than 400 annotated cases of different histology. Its clinical relevance was verified through its capacity to prevent important misclassification in low grade lymphomas and to retrieve clinically important characteristics in high grade lymphomas including the cell-of-origin signatures and the MYC and BCL2 expression levels. This accurate pan-B-NHL predictor, which allows a systematic evaluation of numerous diagnostic and prognostic markers, could thus be proposed as a complement to conventional histology to guide the management of patients and facilitate their stratification into clinical trials.
Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. It includes three major subtypes termed germinal center B-cell-like, activated B-cell-like, and primary mediastinal B-cell lymphoma. With the emergence of novel targeted therapies, accurate methods capable of interrogating this cell-of-origin classification should soon become essential in the clinics. To address this issue, we developed a novel gene expression profiling DLBCL classifier based on reverse transcriptase multiplex ligation-dependent probe amplification. This assay simultaneously evaluates the expression of 21 markers, to differentiate primary mediastinal B-cell lymphoma, activated B-cell-like, germinal center B-cell-like, and also Epstein-Barr virus-positive DLBCLs. It was trained using 70 paraffin-embedded biopsies and validated using >160 independent samples. Compared with a reference classification established from Affymetrix U133 + 2 data, reverse transcriptase multiplex ligation-dependent probe amplification classified 85.0% samples into the expected subtype, comparing favorably with current diagnostic methods. This assay also proved to be highly efficient in detecting the MYD88 L265P mutation, even in archival paraffin-embedded tissues. This reliable, rapid, and cost-effective method uses common instruments and reagents and could thus easily be implemented into routine diagnosis workflows, to improve the management of these aggressive tumors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.