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Introduction: The true nature of the population spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in populations is often not fully known as most cases, particularly in Africa, are asymptomatic. Finding the true magnitude of SARS-CoV-2 spread is crucial to provide actionable data about the epidemiological progress of the disease for researchers and policymakers. This study developed and optimized an antibody enzyme-linked immunosorbent assay (ELISA) using recombinant nucleocapsid antigen expressed in-house using a simple bacterial expression system.
Methods: Nucleocapsid protein from SARS-CoV-2 was expressed and purified from Escherichia coli. Plasma samples used for the assay development were obtained from Ghanaian SARS-CoV-2 seropositive individuals during the pandemic, while seronegative controls were plasma samples collected from blood donors before the coronavirus disease 2019 (COVID-19) pandemic. Another set of seronegative controls was collected during the COVID-19 pandemic. Antibody detection and levels within the samples were validated using commercial kits and Luminex. Analyses were performed using GraphPad Prism, and the sensitivity, specificity and background cut-off were calculated.
Results And Discussion: This low-cost ELISA (£0.96/test) assay has a high prediction of 98.9%, and sensitivity and specificity of 97% and 99%, respectively. The assay was subsequently used to screen plasma from SARS-CoV-2 RT-PCR-positive Ghanaians. The assay showed no significant difference in nucleocapsid antibody levels between symptomatic and asymptomatic, with an increase of the levels over time. This is in line with our previous publication.
Conclusion: This study developed a low-cost and transferable assay that enables highly sensitive and specific detection of human anti-SARS-CoV-2 IgG antibodies. This assay can be modified to include additional antigens and used for continuous monitoring of sero-exposure to SARS-CoV-2 in West Africa.
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http://dx.doi.org/10.1007/s40291-023-00655-0 | DOI Listing |
Spectrochim Acta A Mol Biomol Spectrosc
September 2025
College of Chemistry, Chemical Engineering and Material Science, Soochow University, No. 199 Ren'Ai Road, Suzhou 215123, China; Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China. Electronic address: g
The dynamic monitoring of cell death processes remains a significant challenge due to the scarcity of highly sensitive molecular tools. In this study, two hemicyanine-based probes (5a-5b) with D-π-A structures were developed for organelle-specific viscosity monitoring. Both probes exhibited correlation with the Förster-Hoffmann viscosity-dependent relationship (R > 0.
View Article and Find Full Text PDFThromb Res
September 2025
Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg University, Mainz, Germany. Electronic address:
Warfarin is a widely used vitamin K antagonist (VKA) with known pleiotropic effects beyond anticoagulation. Preclinical and case-control evidence suggests that warfarin may affect hematopoiesis, but longitudinal human evidence is lacking. To explore this potential effect, we conducted a post-hoc analysis of participants in the Hokusai-VTE and ENGAGE AF-TIMI 48 trials, which randomized patients to warfarin or the direct oral anticoagulant edoxaban with routine laboratory testing at predefined follow-up visits.
View Article and Find Full Text PDFDriven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.