Patents by Inventor Gaël KANEKO

Gaël KANEKO has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11649476
    Abstract: A method for quantifying the sensibility of a test microorganism to a concentration of an antimicrobial agent includes: preparing two liquid samples including the microorganism, one having the antimicrobial agent and one without; for each sample acquiring, by a flow cytometer, a digital set values including a fluorescence, forward, or side scatter distribution, and computing: a first coordinate value corresponding to the acquired distribution main mode and an acquired distribution first area for values greater than the first coordinate value, and a second coordinate value, greater than the first, for which an acquired distribution second area between the values equals a first area predefined percentage over 50%; computing a ratio according to: Q = QT ? ( ATB ) - Mode ? ( ATB ) QT ? ( no ? ? ATB ) - Mode ? ( no ? ? ATB ) where Mode(ATB) and QT(ATB) are the first and second coordinate values with the antimicrobial agent concentration, and Mode(no ATB) and QT(no ATB) are r
    Type: Grant
    Filed: July 6, 2017
    Date of Patent: May 16, 2023
    Assignee: BIOMÉRIEUX
    Inventors: Mahendrasingh Ramjeet, Pierre Mahe, Gaël Kaneko, Margaux Chapel
  • Publication number: 20220319716
    Abstract: The method for detecting and monitoring a bacterial outbreak includes predicting that a collected bacterial strain and a bacterial strain from a database belong to the bacterial outbreak if their genomic distance is less than a first predetermined threshold, do not belong to the bacterial outbreak if their genomic distance is greater than a second predetermined threshold strictly greater than the first threshold, or may belong to the bacterial outbreak if their genetic distance is in-between. The first threshold is greater than or equal to a third threshold, such that a prediction that two bacterial strains with a genomic distance less than the third threshold belong to the outbreak has maximum specificity. The second threshold is less than or equal to a fourth threshold, such that a prediction that two bacterial strains with a genomic distance greater than the fourth threshold do not belong to the outbreak has maximum sensitivity.
    Type: Application
    Filed: July 2, 2020
    Publication date: October 6, 2022
    Applicant: BIOMERIEUX
    Inventors: Gaël Kaneko, Ghislaine Guigon
  • Publication number: 20190352693
    Abstract: A method for quantifying the sensibility of a test microorganism to a concentration of an antimicrobial agent includes: preparing two liquid samples including the microorganism, one having the antimicrobial agent and one without; for each sample acquiring, by a flow cytometer, a digital set values including a fluorescence, forward, or side scatter distribution, and computing: a first coordinate value corresponding to the acquired distribution main mode and an acquired distribution first area for values greater than the first coordinate value, and a second coordinate value, greater than the first, for which an acquired distribution second area between the values equals a first area predefined percentage over 50%; computing a ratio according to: Q = QT ? ( ATB ) - Mode ? ( ATB ) QT ? ( no ? ? ATB ) - Mode ? ( no ? ? ATB ) where Mode(ATB) and QT(ATB) are the first and second coordinate values with the antimicrobial agent concentration, and Mode(no ATB) and QT(no ATB) are r
    Type: Application
    Filed: July 6, 2017
    Publication date: November 21, 2019
    Applicant: BIOMÉRIEUX
    Inventors: Mahendrasingh RAMJEET, Pierre MAHE, Gaël KANEKO, Margaux CHAPEL
  • Publication number: 20190355440
    Abstract: A method for predicting the sensibility phenotype of a test microorganism to an antimicrobial agent amongst susceptible, intermediate and resistant phenotypes, including a learning stage and a prediction stage. The learning stage includes selecting a wide set of different strains having different known sensibility phenotypes determined according EUCAST or CLSI method, acquiring FCM distributions for each of the strain alicoted in liquid samples with fluorescent markers and different concentrations of the antibiotic, and performing a learning machine computing on mono or multidimensional spaces derived from the FCM acquisition to derive a prediction model of the sensibility phenotype to the antibiotic.
    Type: Application
    Filed: July 6, 2017
    Publication date: November 21, 2019
    Applicant: BIOMÉRIEUX
    Inventors: Mahendrasingh RAMJEET, Pierre MAHE, Gaël KANEKO, Margaux CHAPEL