AI Article Synopsis

  • Rapid identification of respiratory pathogens like FluA, FluB, and RSV can reduce unnecessary antimicrobial use and improve infection control practices.
  • Three molecular testing methods (Aries, Xpert Xpress, and Cobas) were compared using 200 nasopharyngeal swab samples, showing high sensitivity (96.0% to 100.0%) and specificity (99.3% to 100%).
  • The Xpress assay had the fastest turnaround times, making it the most efficient option for testing multiple samples, while the Cobas assay had a 6.5% invalid rate on certain specimens.

Article Abstract

Rapid identification of respiratory pathogens, such as influenza virus A (FluA), influenza virus B (FluB), and respiratory syncytial virus (RSV), reduces unnecessary antimicrobial use and enhances infection control practice. We performed a comparative evaluation of three molecular methods: (i) the Aries Flu A/B & RSV, (ii) the Xpert Xpress Flu/RSV, and (iii) the Cobas Flu A/B & RSV assays. The clinical performances of the three methods were evaluated using 200 remnant nasopharyngeal swab (NPS) specimens against a combined reference standard. The limits of detection (LODs) were determined using FluA, FluB, and RSV control strains with known titers. The 95% LODs were between 1.702 and 0.0003 50% tissue culture infective dose (TCID), with no significant differences revealed among the three assays. Perfect qualitative detection agreement was obtained in the reproducibility study. The Cobas assay failed at the first run on 13 clinical specimens, resulting in an invalid rate of 6.5%. The sensitivities and specificities for all assays were 96.0 to 100.0% and 99.3 to 100% for all three viruses. For on-demand single-specimen and batched 12-specimen workflows, the test turnaround times were 115.5 and 128.8 min for the Aries assay (12 sample capacity), 34.2 and 44.2 min for the Xpress assay (16 sample capacity), and 21.0 and 254.4 min for the Cobas assay (one instrument), respectively. In summary, the Aries, Xpress, and Cobas Liat assays demonstrated excellent sensitivities and specificities for simultaneous detection and identification of FluA, FluB, and RSV from NPS specimens in cancer patients. Test turnaround time was significantly shorter on the Xpress when instrument scalability is unlimited.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5824037PMC
http://dx.doi.org/10.1128/JCM.01691-17DOI Listing

Publication Analysis

Top Keywords

three molecular
8
simultaneous detection
8
detection identification
8
respiratory syncytial
8
influenza virus
8
flu a/b
8
a/b rsv
8
nps specimens
8
flua flub
8
flub rsv
8

Similar Publications

Background: Activated Phosphoinositide 3-Kinase (PI3K) δ Syndrome (APDS), an inborn error of immunity due to upregulation of the PI3K pathway, leads to recurrent infections and immune dysregulation (lymphoproliferation and autoimmunity).

Methods: Clinical and genetic data of 28 APDS patients from 25 unrelated families were collected from fifteen Italian centers.

Results: Patients were genetically confirmed with APDS-1 (n = 20) or APDS-2 (n = 8), with pathogenic mutations in the PIK3CD or PIK3R1 genes.

View Article and Find Full Text PDF

Circularly polarized luminescence (CPL) film attracted considerable attention in information storage and encryption, three-dimensional display, and chiral recognition. However, due to the limited molecular mobility within thin film, achieving a high asymmetry factor and non-contact modulation of CPL remain challenging. In this work, color-switchable homochiral CPL films with high luminescence asymmetry factor (glum~0.

View Article and Find Full Text PDF

PtRu-based catalysts toward hydrogen oxidation reaction (HOR) suffer from low efficiency, CO poisoning and over-oxidation at high potentials. In this work, an amorphization strategy is adopted for preparation of amorphous SrRuPtOxHy nanobelts (a-SrRuPtOxHy NBs). The a-SrRuPtOxHy NBs have optimized adsorption of intermediates (H and OH), increased number of active sites, highly weakened CO poisoning and enhanced anti-oxidation ability owing to the special amorphous structure.

View Article and Find Full Text PDF

A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.

Mol Divers

December 2024

Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.

Protein-ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein-ligand binding affinity is a crucial metric of the strength of the interaction between the two, and accurate prediction of its binding affinity is essential for discovering drugs' new uses. So far, although many predictive models based on machine learning and deep learning have been reported, most of the models mainly focus on one-dimensional sequence and two-dimensional structural characteristics of proteins and ligands, but fail to deeply explore the detailed interaction information between proteins and ligand atoms in the binding pocket region of three-dimensional space.

View Article and Find Full Text PDF

The catalytic efficiency of natural enzymes depends on the precise electronic interactions between active centers and cofactors within a three-dimensional (3D) structure. Single-atom nanozymes (SAzymes) attempt to mimic this structure by modifying metal active sites with molecular ligands. However, SAzymes struggle to match the catalytic efficiency of natural enzymes due to constraints in active site proximity, quantity, and the inability to simulate electron transfer processes driven by internal electronic structures of natural enzymes.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!

A PHP Error was encountered

Severity: Notice

Message: fwrite(): Write of 34 bytes failed with errno=28 No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 272

Backtrace:

A PHP Error was encountered

Severity: Warning

Message: session_write_close(): Failed to write session data using user defined save handler. (session.save_path: /var/lib/php/sessions)

Filename: Unknown

Line Number: 0

Backtrace: