To assess the molecular epidemiology of human immunodeficiency virus type 1 (HIV-1), a screening method was developed for identification of non-B subtypes from sequence data obtained for resistance testing. The method is based on the evaluation of the percentage of divergence of a given sequence from the reference B subtype HXB2. Analysis of 1720 reverse-transcriptase (RT) and 1824 protease sequences stored in a database allowed for the determination of a threshold level of divergence from HXB2 above which a non-B subtype could be unambiguously characterized regardless of the pattern of resistance mutations (>8.6% for RT; >10.8% for protease). This conclusion was validated by phylogenetic analysis of RT, protease, and env genes. Overall, 72 (4.2%) and 73 (4.0%) non-B sequences were identified in the RT and protease coding regions, respectively. This method allows for the rapid detection of non-B subtypes among retrospective, recent, and future RT and/or protease sequence databases.
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http://dx.doi.org/10.1086/319859 | DOI Listing |
Biol Direct
January 2025
National Key Laboratory for Innovation and Transformation of Luobing Theory; The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Jinan, China.
Background: Carotid atherosclerotic plaque is the primary cause of cardiovascular and cerebrovascular diseases. It is closely related to oxidative stress and immune inflammation. This bioinformatic study was conducted to identify key oxidative stress-related genes and key immune cell infiltration involved in the formation, progression, and stabilization of plaques and investigate the relationship between them.
View Article and Find Full Text PDFClin Epigenetics
January 2025
Center of Oncocytogenomics, Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and 1st Faculty of Medicine of Charles University in Prague, U Nemocnice 499/2, 128 00, Prague, Czech Republic.
Background: Glioblastoma is the commonest malignant brain tumor and has a very poor prognosis. Reduced expression of the MGMT gene (10q26.3), influenced primarily by the methylation of two differentially methylated regions (DMR1 and DMR2), is associated with a good response to temozolomide treatment.
View Article and Find Full Text PDFAnn Clin Microbiol Antimicrob
January 2025
Laboratoire de Bactériologie, CHU Félix Guyon, Allée des Topazes, 97400, Saint-Denis, La Réunion, France.
Aim: Located in the Southwest Indian Ocean area (SIOA), the two French overseas territories (FOTs) of Reunion and Mayotte islands are heavily impacted by antimicrobial resistance. The aim of this study was to investigate all cases of NDM-5 and OXA-181 carbapenemase-producing Escherichia coli (CPEc) in these two FOTs between 2015 and 2020, to better understand the regional spread of these last-line treatment resistant bacteria.
Methods: All E.
BMC Genomics
January 2025
Department of Virology, Norwegian Institute of Public Health, Oslo, 0456, Norway.
The COVID-19 pandemic has underscored the importance of virus surveillance in public health and wastewater-based epidemiology (WBE) has emerged as a non-invasive, cost-effective method for monitoring SARS-CoV-2 and its variants at the community level. Unfortunately, current variant surveillance methods depend heavily on updated genomic databases with data derived from clinical samples, which can become less sensitive and representative as clinical testing and sequencing efforts decline.In this paper, we introduce HERCULES (High-throughput Epidemiological Reconstruction and Clustering for Uncovering Lineages from Environmental SARS-CoV-2), an unsupervised method that uses long-read sequencing of a single 1 Kb fragment of the Spike gene.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.
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