AIMC Topic: Flow Cytometry

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Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to...

Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability Cytometry Under Viscoelastic Extensional Flows.

Small (Weinheim an der Bergstrasse, Germany)
Cellular biophysical metrics exhibit systematic alterations during processes, such as metastasis and immune cell activation, which can be used to identify and separate live cell subpopulations for targeting drug screening. Image-based biophysical cyt...

Artificial intelligence in Andrological flow cytometry: The next step?

Animal reproduction science
Since its introduction in animal andrology, flow cytometry (FC) has dramatically evolved. Nowadays, many compartments and functions of the spermatozoa can be analyzed in thousands of spermatozoa, including, but not limited to DNA, acrosome, membrane ...

Parameter optimization for stable clustering using FlowSOM: a case study from CyTOF.

Frontiers in immunology
High-dimensional cell phenotyping is a powerful tool to study molecular and cellular changes in health and diseases. CyTOF enables high-dimensional cell phenotyping using tens of surface and intra-cellular markers. To utilize the full potential of Cy...

Deep learning classification method for boar sperm morphology analysis.

Andrology
BACKGROUND: Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subj...

An AI-based imaging flow cytometry approach to study erythrophagocytosis.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Erythrophagocytosis is a process consisting of recognition, engulfment and digestion by phagocytes of antibody-coated or damaged erythrocytes. Understanding the dynamics that are behind erythrophagocytosis is fundamental to comprehend this cellular p...

Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry...

Evaluation of artificial intelligence-assisted morphological analysis for platelet count estimation.

International journal of laboratory hematology
INTRODUCTION: This study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC-80 digital morphology analyzer.

GateNet: A novel neural network architecture for automated flow cytometry gating.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Flow cytometry is a widely used technique for identifying cell populations in patient-derived fluids, such as peripheral blood (PB) or cerebrospinal fluid (CSF). Despite its ubiquity in research and clinical practice, the pr...

Machine learning assisted microfluidics dual fluorescence flow cytometry for detecting bladder tumor cells based on morphological characteristic parameters.

Analytica chimica acta
BACKGROUND: Bladder cancer (BC) is the most common malignant tumor and has become a major public health problem, leading the causes of death worldwide. The detection of BC cells is of great significance for clinical diagnosis and disease treatment. U...