Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in children worldwide, while human noroviruses (HuNoV) are a leading cause of epidemic and sporadic acute gastroenteritis. Generating full-length genome sequences for these viruses is crucial for understanding viral diversity and tracking emerging variants. However, obtaining high-quality sequencing data is often challenging due to viral strain variability, quality, and low titers. Here, we present a set of comprehensive oligonucleotide probe sets designed from 1,570 RSV and 1,376 HuNoV isolate sequences in GenBank. Using these probe sets and a capture enrichment sequencing workflow, 85 RSV positive nasal swab samples and 55 (49 stool and six human intestinal enteroids) HuNoV positive samples encompassing major subtypes and genotypes were characterized. The Ct values of these samples ranged from 17.0-29.9 for RSV, and from 20.2-34.8 for HuNoV, with some HuNoV having below the detection limit. The mean percentage of post-processing reads mapped to viral genomes was 85.1% for RSV and 40.8% for HuNoV post-capture, compared to 0.08% and 1.15% in pre-capture libraries, respectively. Full-length genomes were>99% complete in all RSV positive samples and >96% complete in 47/55 HuNoV positive samples-a significant improvement over genome recovery from pre-capture libraries. RSV transcriptome (subgenomic mRNAs) sequences were also characterized from this data. Probe-based capture enrichment offers a comprehensive approach for RSV and HuNoV genome sequencing and monitoring emerging variants.
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http://dx.doi.org/10.1101/2024.09.16.613242 | DOI Listing |
Clin Chem Lab Med
January 2025
70777 TUBITAK National Metrology Institute (TUBITAK UME), Kocaeli, Türkiye.
Objectives: An analytical protocol based on isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS), which includes a peptide-based calibration strategy, was developed and validated for the determination of cardiac troponin I (cTnI) levels in clinical samples. Additionally, the developed method was compared with a protein-based calibration strategy, using cTnI serving as a model for low-abundant proteins. The aim is to evaluate new approaches for protein quantification in complex matrices, supporting the metrology community in implementing new methods and developing fit-for-purpose SI- traceable peptide or protein primary calibrators.
View Article and Find Full Text PDFAm J Community Psychol
January 2025
Boston College, Chestnut Hill, Massachusetts, USA.
Prior research has assessed the ways in which neighborhoods promote or inhibit children's development but has paid less attention to delineating the particular processes through which neighborhoods are linked to child outcomes. This study combines geospatial data with survey data from the Early Childhood Longitudinal Study Kindergarten Cohort of 2010-2011, a nationally representative sample of kindergarteners followed through 5th grade (N ~ 12,300), to explore how differences in neighborhood resources (parks and services) and stressors (crime and neighborhood disadvantage) are associated with variations in parental inputs-school involvement and provision of out-of-home enrichment activities. Using multilevel models assessing within- and between-family associations, we found mixed evidence concerning how neighborhood features are linked to parental inputs.
View Article and Find Full Text PDFFEBS Lett
January 2025
Department of Medical Cell Biophysics, TechMed Center, Faculty of Science and Technology, University of Twente, Enschede, The Netherlands.
TIMP-1 (Tissue Inhibitor of Metalloproteinases-1) is a protein involved in regulating extracellular matrix (ECM) degradation. It is recognized as a significant biomarker for cancer diagnosis. This study aimed to develop and characterize a single-stranded DNA (ssDNA) aptamer targeting human TIMP-1 protein with high affinity and specificity.
View Article and Find Full Text PDFSci Rep
December 2024
School of Computer Science and Technology, Xinjiang University, Urumqi, 830017, China.
Event Causality Identification (ECI) aims to predict causal relations between events in a text. Existing research primarily focuses on leveraging external knowledge such as knowledge graphs and dependency trees to construct explicit structured features to enrich event representations. However, this approach underestimates the semantic features of the original input sentences and performs poorly in capturing implicit causal relations.
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