Accurate and timely detection of bacterial pathogens will improve the clinical management of infections. Herein, we demonstrate an electrochemical biosensor that directly detects bacteria in human blood samples, resulting in the rapid diagnosis of a bloodstream infection. The multiplex biosensor detects the species-specific sequences of the 16S ribosomal RNA of bacteria for pathogen identification in physiological samples without preamplification. The analytical performance characteristics of the biosensor, including the limit of detection and probe cross-reactivity, are evaluated systematically. The feasibility of the biosensor for a diagnosis of a bloodstream infection is demonstrated by identifying bacterial clinical isolates spiked in whole blood and blood culture samples that were tested positive for bacteria. The electrochemical biosensor correctly identifies all the species in the samples with 100% concordance to microbiological analysis.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247356 | PMC |
| http://dx.doi.org/10.1177/2211068216651232 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Ultrasensitive and selective impedance paper-based analytical device through Dual-C imprinted sensor for determination of carcinoembryonic antigen and C-reactive protein.
Mikrochim Acta
January 2025
Department of Chemistry and Center of Excellence for Innovation in Chemistry, Faculty of Science, Ubon Ratchathani University, Ubon Ratchathani, 34190, Thailand.
Carcinoembryonic antigen (CEA) and C-reactive protein (CRP) are biomacromolecules known as cancer and inflammatory markers. Thus, they play a crucial role in early cancer diagnosis, post-treatment recurrence detection, and tumor risk assessment. This paper describes the development of an ultrasensitive and selective imprinted paper-based analytical device (PAD) as impedance sensor for determination of CEA and CRP in serum samples for point-of-care testing (POCT).
View Article and Find Full Text PDFRecent progress on nanomaterial-based electrochemical sensors for glucose detection in human body fluids.
Mikrochim Acta
January 2025
Indian Institute of Technology (BHU), Varanasi, 221005, India.
In the modern age, half of the population is facing various chronic illnesses due to glucose maintenance in the body, major causes of fatality and inefficiency. The early identification of glucose plays a crucial role in medical treatment and the food industry, particularly in diabetes diagnosis. In the past few years, non-enzymatic electrochemical glucose sensors have received a lot of interest for their ability to identify glucose levels accurately.
View Article and Find Full Text PDFEnhancing Electrochemical Biosensing of Circulating Nucleic Acids at the Electrode-Biomolecule-Electrolyte Interfaces.
Anal Chem
January 2025
Rural Health Research Institute, Charles Sturt University, Orange, New South Wales 2800, Australia.
The detection and analysis of circulating cell-free nucleic acid (ccfNA) biomolecules are redefining a new era of molecular targeted cancer therapies. However, the clinical translation of electrochemical ccfNA biosensing remains hindered by unresolved challenges in analytical specificity and sensitivity. In this Perspective, we present a novel electrochemical framework for improving ccfNA biosensor performance by optimizing the critical electrode-biomolecules-electrolyte interfaces.
View Article and Find Full Text PDFElectrochemical capacitance-based aptasensor for HER2 detection.
Biomed Microdevices
January 2025
Department of Physics, Faculty of Philosophy, Science and Letter, University of São Paulo, Ribeirão Preto, SP, 14040-901, Brazil.
The overexpression of Human Epidermal Growth Factor Receptor 2 (HER2) protein is specifically related to tumor cell proliferation in breast cancers. Its presence in biological serum samples indicates presence or progression of cancer, becoming a promise biomarker. However, their detection needs a simple and high accuracy platform.
View Article and Find Full Text PDFComparative Analysis of Machine Learning Algorithms Used for Translating Aptamer-Antigen Binding Kinetic Profiles to Diagnostic Decisions.
ACS Sens
January 2025
Department of Engineering Physics, McMaster University, 1280 Main Street West, L8S 4L8 Hamilton, Ontario, Canada.
Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.
View Article and Find Full Text PDFWant 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!