SARS-CoV-2 emerged in late 2019 and has since spread around the world, causing a pandemic of the respiratory disease COVID-19. Detecting antibodies against the virus is an essential tool for tracking infections and developing vaccines. Such tests, primarily utilizing the enzyme-linked immunosorbent assay (ELISA) principle, can be either qualitative (reporting positive/negative results) or quantitative (reporting a value representing the quantity of specific antibodies). Quantitation is vital for determining stability or decline of antibody titers in convalescence, efficacy of different vaccination regimens, and detection of asymptomatic infections. Quantitation typically requires two-step ELISA testing, in which samples are first screened in a qualitative assay and positive samples are subsequently analyzed as a dilution series. To overcome the throughput limitations of this approach, we developed a simpler and faster system that is highly automatable and achieves quantitation in a single-dilution screening format with sensitivity and specificity comparable to those of ELISA.
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http://dx.doi.org/10.1038/s41598-021-91300-5 | DOI Listing |
Nat Commun
January 2025
Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK.
A fundamental obstacle to tackling the antimicrobial resistance crisis is identifying mutations that lead to resistance in a given genomic background and environment. We present a high-throughput technique - Quantitative Mutational Scan sequencing (QMS-seq) - that enables quantitative comparison of which genes are under antibiotic selection and captures how genetic background influences resistance evolution. We compare four E.
View Article and Find Full Text PDFAlzheimers Dement (N Y)
January 2025
Indiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis Indiana USA.
Introduction: The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.
View Article and Find Full Text PDFComb Chem High Throughput Screen
January 2025
Department of Orthopedics, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning 530011, China.
Background: Postmenopausal Osteoporosis (PMOP) is characterized by decreased bone mass and deterioration of bone microarchitecture, leading to increased fracture risk. Current treatments often have adverse effects, necessitating safer alternatives. Kaempferol, a flavonoid identified as a key active component of the traditional Chinese medicine Yishen Gushu formula, has shown promise in improving bone health, but its mechanisms in PMOP treatment remain unclear.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
Background: With the advance of next-generation sequencing, various gene-based rare variant association tests have been developed, particularly for binary and continuous phenotypes. In contrast, fewer methods are available for traits not following binomial or normal distributions. To address this, we previously proposed a set of burden- and kernel-based rare variant tests for count data following zero-inflated Poisson (ZIP) distributions, referred to as ZIP-b and ZIP-k tests.
View Article and Find Full Text PDFJ Cheminform
January 2025
National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, MD, 20850, USA.
Traditional best practices for quantitative structure activity relationship (QSAR) modeling recommend dataset balancing and balanced accuracy (BA) as the key desired objective of model development. This study explores the value of the conventional norms in the context of using QSAR models for virtual screening of modern large and ultra-large chemical libraries. For this increasingly common task, we now recommend the use of models with the highest positive predictive value (PPV) built on imbalanced training sets as preferred virtual screening tools.
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