Background:The development of accurate, non-invasive markers to diagnose and stage non-alcoholic fatty liver disease (NAFLD) is critical to reduce the need for an invasive liver biopsy and to identify patients who are at the highest risk of hepatic and cardio-metabolic complications. Disruption of steroid hormone metabolic pathways has been described in patients with NAFLD. Aim(s):To assess the hypothesis that assessment of the urinary steroid metabolome may provide a novel, non-invasive biomarker strategy to stage NAFLD. Methods:We analysed the urinary steroid metabolome in 275 subjects (121 with biopsy-proven NAFLD, 48 with alcohol-related cirrhosis and 106 controls), using gas chromatography-mass spectrometry (GC-MS) coupled with machine learning-based Generalised Matrix Learning Vector Quantisation (GMLVQ) analysis. Results: Generalised Matrix Learning Vector Quantisation analysis achieved excellent separation of early (F0-F2) from advanced (F3-F4) fibrosis (AUC receiver operating characteristics [ROC]: 0.92 [0.91-0.94]). Furthermore, there was near perfect separation of controls from patients with advanced fibrotic NAFLD (AUC ROC = 0.99 [0.98-0.99]) and from those with NAFLD cirrhosis (AUC ROC = 1.0 [1.0-1.0]). This approach was also able to distinguish patients with NAFLD cirrhosis from those with alcoholrelated cirrhosis (AUC ROC = 0.83 [0.81-0.85]). Conclusions:Unbiased GMLVQ analysis of the urinary steroid metabolome offers excellent potential as a non-invasive biomarker approach to stage NAFLD fibrosis as well as to screen for NAFLD. A highly sensitive and specific urinary biomarker is likely to have clinical utility both in secondary care and in the broader general population within primary care and could significantly decrease the need for liver biopsy. S U PP O RTI N G I N FO R M ATI O N Additional supporting information will be found online in the Supporting Information section. How to cite this article: Moolla A, de Boer J, Pavlov D, et al. Accurate non-invasive diagnosis and staging of non-alcoholic fatty liver disease using the urinary steroid metabolome.
Background Human T-Cell Lymphotropic Viruses (HTLV) type 1 and type 2 account for an estimated 5 to 10 million infections worldwide and are transmitted through breast feeding, sexual contacts and contaminated cellular blood components. HTLV-associated syndromes are considered as neglected diseases for which there are no vaccines or therapies available, making it particularly important to ensure the best possible diagnosis to enable proper counselling of infected persons and avoid secondary transmission. Although high quality antibody screening assays are available, currently available confirmatory tests are costly and have variable performance, with high rates of indeterminate and non-typable results reported in many regions of the world. The objective of this project was to develop and validate a new high-performance multiplex immunoassay for confirmation and discrimination of HTLV-1 and HTLV-2 strains. Methodology/Principal findings The multiplex platform was used first as a tool to identify suitable antigens and in a second step for assay development. With data generated on over 400 HTLV-positive blood donors sourced from USA and French blood banks, we developed and validated a high-precision interpretation algorithm. The Multi-HTLV assay demonstrated very high performance for confirmation and strain discrimination with 100% sensitivity, 98.1% specificity and 100% of typing accuracy in validation samples. The assay can be interpreted either visually or automatically with a colorimetric image reader and custom algorithm, providing highly reliable results. Conclusions/Significance The newly developed Multi-HTLV is very competitive with currently used confirmatory assays and reduces considerably the number of indeterminate results. The multiparametric nature of the assay opens new avenues to study specific serological signatures of each patient, follow the evolution of infection, and explore utility for HTLV disease prognosis. Improving HTLV diagnostic testing will be critical to reduce transmission and to improve monitoring of seropositive patients.
Background: It is crucial for medical decision-making and vaccination strategies to collect information on sustainability of immune responses after infection or vaccination, and how long-lasting antibodies against SARS-COV-2 could provide a humoral and protective immunity, preventing reinfection with SARS-CoV-2 or its variants. The aim of this study is to present a novel method to quantitatively measure and monitor the diversity of SARS-CoV-2 specific antibody profiles over time. Methods: Two collections of serum samples were used in this study: A collection from 20 naturally-infected subjects (follow-ups to 1 year) and a collection from 83 subjects vaccinated with one or two doses of Pfizer BioNtech vaccine (BNT162b2/BNT162b2) (follow-ups to 6 months). The Multi-SARS-CoV-2 assay, a multiparameter serology test, developed for the serological confirmation of past-infections was used to determine the reactivity of six different SARS-CoV-2 antigens. For each patient sample, 3 dilutions (1/50, 1/400 and 1/3200) were defined as an optimal set over the six antigens and their respective linear ranges, allowing accurate quantitation of the corresponding six specific antibodies. Nonlinear mixed-effects modelling was applied to convert intensity readings from 3 determined dilutions to a single quantification value for each antibody. Results: Median half-life for the 20 naturally infected vs 74 vaccinated subjects (two doses) was respectively 120 vs 50 days for RBD, 127 vs 53 days for S1 and 187 vs 86 days for S2 antibodies. Respectively, 90% of the antibody concentration wanes after 158 vs 398 days for RBD, 171 vs 420 days for S1, and 225 vs 620 days for S2 after the second vaccine shot. Conclusion: The newly proposed method, based on a series of a limited number of dilutions, can convert a conventional qualitative assay into a quantitative assay. This conversion helps define the sustainability of specific immune responses against each relevant viral antigen and can help in defining the protection characteristics after an infection or a vaccination.
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