Light-sheet fluorescence microscopy (LSFM) enables gentle and efficient volumetric imaging. Through axially swept light-sheet microscopy (ASLM), the resolution in the third dimension can be made equal to the lateral resolution, simplifying viewing and analysis of three-dimensional samples.
We recently developed a graph deep learning method that considers contextual histopathological features from the whole-slide images. We show that the proposed method can provide interpretable prognostic biomarkers in a semi-supervised manner. We believe it will aid prognostic tasks in the future.
In this paper, we designed an electrochemical diagnostic device that bridges biology and electronics to simultaneously detect viral RNA and human antibodies for diagnosing and tracking the course of COVID-19 and potentially other infections.
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