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| http://dx.doi.org/10.1369/0022155413500497 | DOI Listing |
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Recent Technologies on 2D and 3D Imaging Flow Cytometry.
Cells
December 2024
Research Center for Advanced Science and Technology, University of Tokyo, Tokyo 153-8904, Japan.
Imaging flow cytometry is a technology that performs microscopy image analysis of cells within flow cytometry and allows high-throughput, high-content cell analysis based on their intracellular molecular distribution and/or cellular morphology. While the technology has been available for a couple of decades, it has recently gained significant attention as technical limitations for higher throughput, sorting capability, and additional imaging dimensions have been overcome with various approaches. These evolutions have enabled imaging flow cytometry to offer a variety of solutions for life science and medicine that are not possible with conventional flow cytometry or microscopy-based screening.
View Article and Find Full Text PDFPotential mutagenicity of aflatoxin B1 in Egyptian spices.
BMC Genomics
January 2025
Botany Department, Faculty of Science, Mansoura University, Mansoura, Egypt.
The current study aimed to detect the mutagenic impacts of aflatoxin B1 (AFB1), which is produced by Aspergillus group fungi, via a high-plant genotoxicity test. Different durations of treatment (3 h, 6 h, and 12 h) were used to treat the Vicia faba root tips with varying concentrations of Aflatoxin B1 (AFB1) following the approved protocol for plant assays published by the International Program on Chemical Safety (IPCS) and the World Health Organization (WHO). The data obtained indicated that AFB1 not only has the ability to induce various alterations in the process of mitosis, ranging from increasing to decreasing mitotic and phase indices but also leads to many mitotic aberrations.
View Article and Find Full Text PDFCytotoxic and regulatory T cell interactions calculated from image mass cytometry predict immunochemotherapy response in triple-negative breast cancer.
Cancer Commun (Lond)
January 2025
Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, USA.
Deep learning-enabled filter-free fluorescence microscope.
Sci Adv
January 2025
Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China.
Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization of optical filtering sets increases the complexity, size, and cost of microscopic systems, making them less suitable for multifluorescence channel, high-speed imaging. Here, we present filter-free fluorescence microscopic imaging enabled with deep learning-based digital spectral filtering.
View Article and Find Full Text PDFNeuromorphic-enabled video-activated cell sorting.
Nat Commun
December 2024
State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition. However, existing IACS frameworks suffer from the challenges of 3D information loss and processing latency dilemma in real-time sorting operation. Herein, we establish a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, designed to achieve high-dimensional spatiotemporal characterization content alongside high-throughput sorting of particles in wide field of view.
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