Extracellular vesicles (EVs), including nanoscale exosomes and ectosomes, hold promise as biomarkers that provide information about the cell of origin through their cargo of nucleic acids and proteins, both on their surface and within. Here, we develop a detection method of EVs based on light-induced acceleration of specific binding between their surface and antibody-modified microparticles, using a controlled microflow with three-dimensional analysis by confocal microscopy. Our method successfully detected 10-10 nanoscale EVs in liquid samples as small as a 500 nanoliters within 5 minutes, with the ability to distinguish multiple membrane proteins. Remarkably, we achieved the specific detection of EVs secreted from living cancer cell lines with high linearity, without the need for a time-consuming ultracentrifugation process that can take several hours. Furthermore, the detection range can be controlled by adjusting the action range of optical force using a defocused laser, consistent with the theoretical calculations. These findings demonstrate an ultrafast, sensitive, and quantitative approach for measuring biological nanoparticles, enabling innovative analyses of cell-to-cell communication and early diagnosis of various diseases, including cancer.
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http://dx.doi.org/10.1039/d2nh00576j | DOI Listing |
Biomed Phys Eng Express
January 2025
Shandong Normal University, Jinan, Jinan, Shandong, 250014, CHINA.
In the medical field, endoscopic video analysis is crucial for disease diagnosis and minimally invasive surgery. The Endoscopic Foundation Models (Endo- FM) utilize large-scale self-supervised pre-training on endoscopic video data and leverage video transformer models to capture long-range spatiotemporal dependencies. However, detecting complex lesions such as gastrointestinal metaplasia (GIM) in endoscopic videos remains challenging due to unclear boundaries and indistinct features, and Endo-FM has not demonstrated good performance.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
View Article and Find Full Text PDFJMIR Public Health Surveill
January 2025
Center for Global Health, University of New Mexico Health Sciences Center, Albuquerque, NM, United States.
Background: Numerous studies have assessed the risk of SARS-CoV-2 exposure and infection among health care workers during the pandemic. However, far fewer studies have investigated the impact of SARS-CoV-2 on essential workers in other sectors. Moreover, guidance for maintaining a safely operating workplace in sectors outside of health care remains limited.
View Article and Find Full Text PDFBlood
January 2025
State Key Laboratory of Experimental Hematology, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College; Center for Stem Cell Medicine,, Tianjin, China.
Adenosine-to-inosine (A-to-I) RNA editing is a prevalent RNA modification essential for cell survival. The process is catalyzed by the Adenosine Deaminase Acting on RNA (ADAR) enzyme family that converts adenosines in double-stranded RNAs (dsRNAs) into inosines, which are read as guanosines during translation. Deep sequencing has helped to reveal that A-to-I editing occurs across various types of RNAs to affect their functions.
View Article and Find Full Text PDFJ Am Soc Mass Spectrom
January 2025
Department of Chemistry, Center for Innovative Technology, Vanderbilt University, Nashville, Tennessee 37235, United States.
Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) provides direct analytical readouts of small molecules that can be used to characterize the metabolic phenotypes of genetically engineered bacteria. In an effort to accelerate the time frame associated with the screening of mutant libraries, we have developed a high-throughput DESI-MSI analytical workflow implementing a single raster line-scan strategy that facilitates the collection of location-resolved molecular information from engineered strains on a subminute time scale. Evaluation of this "Fast-Pass" DESI-MSI phenotyping workflow on analytical standards demonstrated the capability of acquiring full metabolic profiling information with a throughput of ∼40 s per sample.
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