AIMC Topic: Cell Separation

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Advances in machine learning-enhanced microfluidic cell sorting.

Science advances
Cell sorting, essential for diagnostics and early intervention, has evolved from conventional methods to sophisticated microfluidic approaches. These miniaturized systems leverage precise hydrodynamic control, facilitating major advances in tumor cel...

Vibration-Assisted Magnetic (VibroMag) Cell Separation for Robotic Liquid Handling Platforms.

Analytical chemistry
Cell separation is a critical step in many assays in biomedical research, diagnostics, and drug development. Here, we developed VibroMag, a vibration-assisted magnetic cell separation technology specifically designed for automated processing of stand...

High-speed cell partitioning through reactive machine learning-guided inkjet printing.

Lab on a chip
Partitioning cells in open nanowells permits high confidence in single cell occupancy and enables flexibility in the development of different molecular assays. A challenge for this approach however is to print cells sufficiently quickly to enable exp...

Versatile Image-Assisted Cell Sorting by Selective Trapping with Spatiotemporal Multiparameter Targeting.

ACS sensors
Current cell sorting methods often lack versatility, require complex setups, demand large initial cell numbers for reliable sorting, and impose size limitations on target objects. To address these challenges, we introduce two-dimensional sorting with...

High-throughput microfluidics for precise separation and focusing of circulating tumor cells with optimized triangular microchannel design.

Talanta
The precise separation and focusing of circulating tumor cells (CTCs) from blood cells are crucial for advancing cancer diagnosis, optimizing therapeutic strategies, and fostering progress in cellular research. Inertial microfluidics offers an effici...

Neuromorphic-enabled video-activated cell sorting.

Nature communications
Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition. However, existing IACS frameworks suffer from the challenges of 3D information loss and processing ...

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...

Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry.

Lab on a chip
Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer circulating tumor cell clusters (CTCCs) are potential biomarkers of metastatic growth, with CTCCs r...

COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning.

Communications biology
Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS)...

Research on cell detection method for microfluidic single cell dispensing.

Mathematical biosciences and engineering : MBE
Single cell dispensing techniques mainly include limiting dilution, fluorescent-activated cell sorting (FACS) and microfluidic approaches. Limiting dilution process is complicated by statistical analysis of clonally derived cell lines. Flow cytometry...