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| http://dx.doi.org/10.1016/j.ijsu.2022.106823 | DOI Listing |
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A Global Collaborative Comparison of SARS-CoV-2 Antigenicity Across 15 Laboratories.
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December 2024
World Health Organization, 1202 Geneva, Switzerland.
Setting up a global SARS-CoV-2 surveillance system requires an understanding of how virus isolation and propagation practices, use of animal or human sera, and different neutralisation assay platforms influence assessment of SARS-CoV-2 antigenicity. In this study, with the contribution of 15 independent laboratories across all WHO regions, we carried out a controlled analysis of neutralisation assay platforms using the first WHO International Standard for antibodies to SARS-CoV-2 variants of concern (source: NIBSC). Live virus isolates (source: WHO BioHub or individual labs) or spike plasmids (individual labs) for pseudovirus production were used to perform neutralisation assays using the same serum panels.
View Article and Find Full Text PDFDevelopment and Evaluation of a Newcastle Disease Virus-like Particle Vaccine Expressing SARS-CoV-2 Spike Protein with Protease-Resistant and Stability-Enhanced Modifications.
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December 2024
Animal Infectious Disease Laboratory, College of Veterinary Medicine, Yangzhou University, Yangzhou 225012, China.
The ongoing global health crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates the continuous development of innovative vaccine strategies, especially in light of emerging viral variants that could undermine the effectiveness of existing vaccines. In this study, we developed a recombinant virus-like particle (VLP) vaccine based on the Newcastle Disease Virus (NDV) platform, displaying a stabilized prefusion form of the SARS-CoV-2 spike (S) protein. This engineered S protein includes two proline substitutions (K986P, V987P) and a mutation at the cleavage site (RRAR to QQAQ), aimed at enhancing both its stability and immunogenicity.
View Article and Find Full Text PDFPemphigus and Bullous Pemphigoid Following COVID-19 Vaccination: A Systematic Review.
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December 2024
Section of Dermatology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy.
The COVID-19 pandemic has encouraged the rapid development and licensing of vaccines against SARS-CoV-2. Currently, numerous vaccines are available on a global scale and are based on different mechanisms of action, including mRNA technology, viral vectors, inactive viruses, and subunit particles. Mass vaccination conducted worldwide has highlighted the potential development of side effects, including ones with skin involvement.
View Article and Find Full Text PDFGenomic Evolution of the SARS-CoV-2 Omicron Variant in Córdoba, Argentina (2021-2022): Analysis of Uncommon and Prevalent Spike Mutations.
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December 2024
Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba X5000HUA, Argentina.
Understanding the evolutionary patterns and geographic spread of SARS-CoV-2 variants, particularly Omicron, is essential for effective public health responses. This study focused on the genomic analysis of the Omicron variant in Cordoba, Argentina from 2021 to 2022. Phylogenetic analysis revealed the dominant presence of BA.
View Article and Find Full Text PDFIntegrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.
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November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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