The rapid and accurate detection of antimicrobial resistance is critical to limiting the spread of infections and delivering effective treatments. Here, we developed a rapid, sensitive, and simple colorimetric nanodiagnostic platform to identify disease-causing pathogens and their associated antibiotic resistance genes within 2 h. The platform can detect bacteria from different biological samples (i.e., blood, wound swabs) with or without culturing. We validated the multicomponent nucleic acid enzyme–gold nanoparticle (MNAzyme-GNP) platform by screening patients with central line associated bloodstream infections and achieved a clinical sensitivity and specificity of 86% and 100%, respectively. We detected antibiotic resistance in methicillin-resistant Staphylococcus aureus (MRSA) in patient swabs with 90% clinical sensitivity and 95% clinical specificity. Finally, we identified mecA resistance genes in uncultured nasal, groin, axilla, and wound swabs from patients with 90% clinical sensitivity and 95% clinical specificity. The simplicity and versatility for detecting bacteria and antibiotic resistance markers make our platform attractive for the broad screening of microbial pathogens.
Infectious diseases are a major global threat that accounts for one of the leading causes of global mortality and morbidity. Prompt diagnosis is a crucial first step in the management of infectious threats, which aims to quarantine infected patients to avoid contacts with healthy individuals and deliver effective treatments prior to further spread of diseases. This review article discusses current advances of diagnostic systems using colloidal nanomaterials (e.g., gold nanoparticles, quantum dots, magnetic nanoparticles) for identifying and differentiating infectious pathogens. The challenges involved in the clinical translation of these emerging nanotechnology based diagnostic devices will also be discussed.
There is a tremendous focus on the application of nanomaterials for the treatment of cancer. Nonprimate models are conventionally used to assess the biomedical utility of nanomaterials. However, these animals often lack an intact immunological background, and the tumors in these animals do not develop spontaneously. We introduce a preclinical woodchuck hepatitis virus-induced liver cancer model as a platform for nanoparticle (NP)-based in vivo experiments. Liver cancer development in these out-bred animals occurs as a result of persistent viral infection, mimicking human hepatitis B virus-induced HCC development. We highlight how this model addresses key gaps associated with other commonly used tumor models. We employed this model to (1) track organ biodistribution of gold NPs after intravenous administration, (2) examine their subcellular localization in the liver, (3) determine clearance kinetics, and (4) characterize the identity of hepatic macrophages that take up NPs using RNA-sequencing (RNA-seq). We found that the liver and spleen were the primary sites of NP accumulation. Subcellular analyses revealed accumulation of NPs in the lysosomes of CD14+ cells. Through RNA-seq, we uncovered that immunosuppressive macrophages within the woodchuck liver are the major cell type that take up injected NPs. The woodchuck-HCC model has the potential to be an invaluable tool to examine NP-based immune modifiers that promote host anti-tumor immunity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.