Efficient isolation and patterning of biomolecules is a vital step within sample preparation for biomolecular analysis, with numerous diagnostic and therapeutic applications. For exosomes, nanoscale lipid-bound biomolecules, efficient isolation is challenging due to their minute size and resultant behavior within biofluids. This study presents a method for the rapid isolation and patterning of magnetically tagged exosomes via rationally designed micromagnets. Micromagnet fabrication utilizes a novel, scalable, and high-throughput laser-based fabrication approach that enables patterning at microscale lateral resolution (<50 µm) without lithographic processing and is agnostic to micromagnet geometry. Laser-based processing allows for flexible and tunable device configurations, and herein magnetophoretic capture within both an open-air microwell and an enclosed microfluidic system is demonstrated. Patterned micromagnets enhance localized gradient fields throughout the fluid medium, resulting in rapid and high efficiency magnetophoretic separation, with capture efficiencies nearing 70% after just 1s within open-air microwells, and throughputs upward of 3 mL h within enclosed microfluidic systems. Using this microchip architecture, immunomagnetic exosome isolation and patterning directly from undiluted plasma samples is further achieved. Lastly, a FEA-based modeling workflow is introduced to characterize and optimize micromagnet unit cells, simulating magnetophoretic capture zones for a given micromagnet geometry.
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http://dx.doi.org/10.1002/smtd.202400388 | DOI Listing |
JACS Au
January 2025
CAS Key Laboratory of Tropical Marine Bio-resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 164 West Xingang Road, Guangzhou 510301, China.
The rapid emergence of antimicrobial-resistant pathogenic microbes has accelerated the search for novel therapeutic agents. Here we report the discovery of antarmycin A (), an antibiotic containing a symmetric 16-membered macrodiolide core with two pendant vancosamine moieties, one of which is glucosylated, from deep-sea-derived SCSIO 07407. The biosynthetic gene cluster of was identified on a giant plasmid featuring transferable elements.
View Article and Find Full Text PDFFront Cell Infect Microbiol
January 2025
Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, China.
Introduction: Brucellosis, a significant zoonotic infectious disease, poses a global health threat. Accurate and efficient diagnosis is crucial for prevention, control, and treatment of brucellosis. VirB proteins, components of the Type IV secretion system (T4SS) in , play a pivotal role in bacterial virulence and pathogenesis but have been understudied for their diagnostic potential.
View Article and Find Full Text PDFVirol J
January 2025
Division of Biological Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand.
Background: Vibrio parahaemolyticus is a marine bacterium causing seafood-associated gastrointestinal illness in humans and acute hepatopancreatic necrosis disease (AHPND) in shrimp. Bacteriophages have emerged as promising biocontrol agents against V. parahaemolyticus.
View Article and Find Full Text PDFAccurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neural networks (CNNs), have significantly improved diagnostic capabilities and accuracy by enabling the automated analysis of medical images.
View Article and Find Full Text PDFSci Rep
January 2025
Electronics and Communication Engineering Department, Mansoura University, Mansoura, 35516, Egypt.
As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified grey wolf optimization model for effective feature selection and weighting. Additionally, the system uses an ensemble of classifiers, incorporating confusion based voting scheme to combine salient data features.
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