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A virtual staining using convolutional neural networks was used to facilitate label-free quantification of NETs trapped in a microfluidic device based on morphological features in phase-contrast images.
Neutrophils, Microfluidics, 610, 600, DNA, Extracellular Traps, Antibodies, Diabetes Mellitus, Type 2, Engineering::Mechanical engineering, :Mechanical engineering [Engineering], Humans, Tetradecanoylphorbol Acetate, Chemical Detection
Neutrophils, Microfluidics, 610, 600, DNA, Extracellular Traps, Antibodies, Diabetes Mellitus, Type 2, Engineering::Mechanical engineering, :Mechanical engineering [Engineering], Humans, Tetradecanoylphorbol Acetate, Chemical Detection
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |