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Flow Cytometry

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Intelligent image-activated cell sorting 2.0.

Lab on a chip
The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless inte...

Implementing machine learning methods for imaging flow cytometry.

Microscopy (Oxford, England)
In this review, we focus on the applications of machine learning methods for analyzing image data acquired in imaging flow cytometry technologies. We propose that the analysis approaches can be categorized into two groups based on the type of data, r...

Large-scale single-molecule imaging aided by artificial intelligence.

Microscopy (Oxford, England)
Single-molecule imaging analysis has been applied to study the dynamics and kinetics of molecular behaviors and interactions in living cells. In spite of its high potential as a technique to investigate the molecular mechanisms of cellular phenomena,...

Machine Learning Models Improve the Diagnostic Yield of Peripheral Blood Flow Cytometry.

American journal of clinical pathology
OBJECTIVES: Peripheral blood flow cytometry (PBFC) is useful for evaluating circulating hematologic malignancies (HM) but has limited diagnostic value for screening. We used machine learning to evaluate whether clinical history and CBC/differential p...

Label-free optical hemogram of granulocytes enhanced by artificial neural networks.

Optics express
An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to id...

Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery.

Methods in molecular biology (Clifton, N.J.)
CITRUS is a supervised machine learning algorithm designed to analyze single cell data, identify cell populations, and identify changes in the frequencies or functional marker expression patterns of those populations that are significantly associated...

[Expression of follicular helper T cells in peripheral blood of patients with hepatic echinococcosis].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control
OBJECTIVE: To detect the expression of follicuLar helper T cells (Tfh) and interleukin-21 (IL-21) in the peripheral blood of patients with hepatic echinococcosis and healthy controls, so as to explore the associations of Tfh and IL-21 expression with...

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

Apoptosis : an international journal on programmed cell death
This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogen...

Investigating the Generalizability of the MultiFlow ® DNA Damage Assay and Several Companion Machine Learning Models With a Set of 103 Diverse Test Chemicals.

Toxicological sciences : an official journal of the Society of Toxicology
The in vitro MultiFlow DNA Damage assay multiplexes p53, γH2AX, phospho-histone H3, and polyploidization biomarkers into 1 flow cytometric analysis (Bryce, S. M., Bernacki, D. T., Bemis, J. C., and Dertinger, S. D. (2016). Genotoxic mode of action pr...

Gating mass cytometry data by deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve autom...