20240044904. SYSTEM, METHOD, AND ARTICLE FOR DETECTING ABNORMAL CELLS USING MULTI-DIMENSIONAL ANALYSIS simplified abstract (Hematologics, Inc.)

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SYSTEM, METHOD, AND ARTICLE FOR DETECTING ABNORMAL CELLS USING MULTI-DIMENSIONAL ANALYSIS

Organization Name

Hematologics, Inc.

Inventor(s)

Michael R. Loken of Mercer Island WA (US)

Andrew P. Voigt of Seattle WA (US)

SYSTEM, METHOD, AND ARTICLE FOR DETECTING ABNORMAL CELLS USING MULTI-DIMENSIONAL ANALYSIS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240044904 titled 'SYSTEM, METHOD, AND ARTICLE FOR DETECTING ABNORMAL CELLS USING MULTI-DIMENSIONAL ANALYSIS

Simplified Explanation

The abstract of this patent application describes a method for characterizing and comparing sets of cells using flow cytometry. It involves defining a centroid and radius for clusters of cells in an n-dimensional space, representing normal cell maturation. Support vector machine (SVM) subroutines are used to identify reference populations and generate multidimensional boundary definitions. These boundary definitions can be used to define or refine the centroid line or radius of normal clusters, and to compare test sets of cells to the defined normal clusters.

  • The patent describes a method for characterizing and comparing sets of cells using flow cytometry.
  • It involves defining a centroid and radius for clusters of cells in an n-dimensional space.
  • The centroid and radius represent normal cell maturation for a cell lineage in the normal set of cells.
  • Support vector machine (SVM) subroutines are used to identify reference populations of interest.
  • The SVM subroutines generate multidimensional boundary definitions.
  • These boundary definitions can be used to identify reference populations and refine the centroid line or radius defining a set of normal clusters.
  • The method can be used to characterize and compare a test set of cells to the defined set of normal clusters.

Potential Applications:

  • Medical research and diagnostics: This technology can be used in medical research and diagnostics to characterize and compare sets of cells, which can help in understanding diseases and developing targeted treatments.
  • Drug discovery: The method can be used in drug discovery to evaluate the effects of drugs on cell populations and identify potential therapeutic targets.
  • Quality control in cell manufacturing: This technology can be used in the manufacturing of cells for therapeutic purposes to ensure the quality and consistency of cell populations.

Problems Solved:

  • Cell characterization: The method provides a way to characterize and compare sets of cells, which can be challenging using traditional methods.
  • Identification of reference populations: The SVM subroutines help in identifying reference populations of interest, which can be useful in various research and diagnostic applications.

Benefits:

  • Improved understanding of diseases: By characterizing and comparing sets of cells, this technology can contribute to a better understanding of diseases and their underlying mechanisms.
  • Targeted treatments: The method can help in identifying specific cell populations that are relevant to a disease, enabling the development of targeted treatments.
  • Efficient drug discovery: By evaluating the effects of drugs on cell populations, this technology can aid in the discovery of new drugs and therapeutic targets.
  • Quality control in cell manufacturing: The method can ensure the quality and consistency of cell populations used in cell manufacturing for therapeutic purposes.


Original Abstract Submitted

a normal set of cells is characterized using flow cytometry. a centroid and radius are defined for a set of clusters in an n-dimensional space corresponding to a normal maturation for a cell lineage in the normal set of cells. a test set of cells is characterized using flow cytometry and the characterization is compared to the defined set of clusters. support vector machine (svm) subroutines are employed to identify reference populations of interest by generating multidimensional boundary definitions. these boundary definitions may be used to identify reference populations to use in defining or refining a centroid line or a radius or radii defining a set of normal clusters, and to characterize and compare a test set of cells to the defined set of normal clusters.

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