Rapid and accurate identification of the bacteria responsible for sepsis is paramount for effective patient care. Molecular diagnostic methods, such as polymerase chain reaction (PCR), encounter challenges in sepsis due to inhibitory compounds in the blood, necessitating their removal for precise analysis. In this study we present an innovative approach that utilizes vancomycin (Van) and allantoin (Al)-conjugated polydopamine (PDA)-coated magnetic nanoparticles (MNPs) for the rapid and automated enrichment of bacteria and their DNA extraction from blood without inducing clumping and aggregation of blood. Al/Van-PDA-MNPs, facilitated by IMS, eliminate the need for preliminary sample treatments, providing a swift and efficient method for bacterial concentration and DNA extraction within an hour. Employing Al/Van-PDA-MNPs within an automated framework has markedly improved our ability to pre-concentrate various Gram-negative and Gram-positive bacteria directly from blood samples. This advancement has effectively reduced the detection threshold to 10 colony-forming unit/mL by both PCR and quantitative PCR. The method's expedited processing time, combined with its precision, positions it as a feasible diagnostic tool for diverse healthcare settings, ranging from small clinics to large hospitals. Furthermore, the innovative application of nanoparticles for DNA extraction holds promising potential for advancing sepsis diagnostics, enabling earlier interventions and improving patient outcomes.
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http://dx.doi.org/10.1038/s41598-024-54236-0 | DOI Listing |
Environmental DNA (eDNA) analysis has become a popular conservation tool for detecting rare and elusive species. eDNA assays typically target mitochondrial DNA (mtDNA) due to its high copy number per cell and its ability to persist in the environment longer than nuclear DNA. Consequently, the development of eDNA assays has relied on mitochondrial reference sequences available in online databases, or in cases where such data are unavailable, de novo DNA extraction and sequencing of mtDNA.
View Article and Find Full Text PDFMol Ecol Resour
January 2025
Institut Systématique Evolution Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, EPHE, Sorbonne Université, Université Des Antilles, Paris, France.
The exon capture approach allows for sequencing a large number of loci to reconstruct phylogenetic relationships at varying taxonomic levels. In order to efficiently recover the targeted loci, the probes designed to capture the exons need to be genetically sufficiently similar to bind to the DNA, with a proposed limit of 10% of divergence. However, this threshold has never been tested with a specific protocol.
View Article and Find Full Text PDFSheng Li Xue Bao
December 2024
College of Life Sciences, Anqing Normal University, Anqing 246133, China.
This study aimed to analyze the impact of single nucleotide polymorphism (SNP) of (encoding adenylate cyclase 3) on the outcome of high-intensity interval training (HIIT) on body composition and screen genetic markers sensitive to HIIT in Chinese Han youth. A total of 237 non-regular exercise Han college students were recruited in a 12-week HIIT program, attending sessions 3 times a week. Before and after the HIIT program, their body composition was measured.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Tumor Biology and Genetics, Medical University of Warsaw, Warsaw, Poland.
Aim: The study was designed to evaluate molecular alterations, relevant to the prognosis and personalized therapy of salivary gland cancers (SGCs).
Materials And Methods: DNA was extracted from archival tissue of 40 patients with various SGCs subtypes. A targeted next-generation sequencing (NGS) panel was used for the identification of small-scale mutations, focal and chromosomal arm-level copy number changes.
Nat Genet
January 2025
Calico Life Sciences LLC, South San Francisco, CA, USA.
Sequence-based machine-learning models trained on genomics data improve genetic variant interpretation by providing functional predictions describing their impact on the cis-regulatory code. However, current tools do not predict RNA-seq expression profiles because of modeling challenges. Here, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence.
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