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

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

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.