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Flow Cytometry01:23

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
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On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
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Thermodynamics of a Redox Reaction
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The energy stored by a structure and location of matter in space is called potential energy. For instance, raising a kettlebell changes its spatial location and increases its potential energy. Similarly, a stretched rubber band contains potential energy which, under certain conditions, can be converted into other forms of energy, such as kinetic energy.
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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Diagnostic Potential of Imaging Flow Cytometry.

Minh Doan1, Ivan Vorobjev2, Paul Rees3

  • 1Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.

Trends in Biotechnology
|February 4, 2018
PubMed
Summary

Imaging flow cytometry (IFC) offers rapid, high-content cell analysis using deep learning. This technology is advancing to new applications with potential clinical uses.

Keywords:
deep learningdisease diagnosticshigh-content analysisimaging flow cytometrytranslational medicine

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Area of Science:

  • Biotechnology
  • Cell Biology
  • Computational Biology

Background:

  • Imaging flow cytometry (IFC) rapidly captures multichannel images of numerous single cells.
  • Current IFC analysis is shifting towards higher information content.
  • Deep learning algorithms are a key driver of this analytical shift.

Purpose of the Study:

  • To highlight the paradigm shift in Imaging Flow Cytometry (IFC) analysis.
  • To discuss the impact of deep learning on IFC data interpretation.
  • To explore the future applications and clinical potential of advanced IFC.

Main Methods:

  • Utilizing Imaging Flow Cytometry (IFC) for high-throughput cellular imaging.
  • Applying deep learning algorithms for advanced image analysis.
  • Analyzing multichannel image data from hundreds of thousands of single cells.

Main Results:

  • IFC enables the capture of detailed cellular images at scale.
  • Deep learning enhances the transition from low- to high-information-content analysis in IFC.
  • The study identifies a growing number of potential applications for IFC.

Conclusions:

  • IFC technology is evolving towards sophisticated, high-content analysis.
  • Deep learning is crucial for unlocking the full potential of IFC data.
  • Advanced IFC methods show promise for future clinical applications.