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Is it Magic? Artificial Intelligence Integration into Clinical Cytometry Workflows
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Cytometry has experienced substantial innovation through the continuous development of novel reagents, new instrumentation, and improved analyses geared towards handling multi-dimensional data of up to 30 to 40 parameters. However, cytometry assays require extensive training and expertise in data acquisition and analysis. The final interpretation relies on integration of all available information...
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Recommendations for Using Artificial Intelligence in Clinical Flow Cytometry
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The authors discuss a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential...
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Unveiling the Epigenetic Landscape of Plants Using Flow Cytometry Approach
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Plants are sessile creatures that have to adapt constantly changing environmental circumstances. Understanding how stress-responsive genes are regulated can help us better understand how plants can adapt to changing environmental conditions. Epigenetic markers that dynamically change in response to stimuli, such as DNA methylation and histone modifications are known to regulate gene expression...
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