Rapid determination of microbial drowth and antimicrobial suseptability

Abstract

This disclosure is related to systems and methods for rapid determination of microorganism growth and antimicrobial agent susceptibility and/or resistance.

1 . A method, comprising:
 determining, by a computer-based system configured to analyze microorganism information and comprising a processor, a tangible, and a non-transitory memory, a first value associated with an attribute of a microorganism, based on first information from a microorganism detection system;   determining, by the computer-based system, a second value associated with the attribute of the microorganism, based on second information from the microorganism detection system   determining, by the computer-based system, a growth rate based on the first value and the second value; and   comparing, by the computer-based system, the growth rate to a control growth rate.   
     
     
         2 . The method of  claim 1 , wherein the second value is determined in response to an event, wherein the microorganism is subjected to a condition, and wherein the condition is associated with the event. 
     
     
         3 . The method of  claim 1 , wherein the control growth rate is at least one of a predetermined growth rate and a dynamically determined growth rate. 
     
     
         4 . The method of  claim 1 , wherein the event is at least one of a predetermined time, a dynamically determined mass, a number of individuated microorganisms, and a number of clones. 
     
     
         5 . The method of  claim 2 , wherein the condition is at least one of a temperature, a growth medium condition, a carbon source, a nitrogen source, an amino acid, a nutrient, a salt, a metal ion, a cofactor, a pH, a trace element, a dissolved gas, an antimicrobial agent, an aerobic condition, and an anaerobic condition. 
     
     
         6 . The method of  claim 1 , wherein the microorganism detection system comprises an optical detector configured to perform at least one of brightfield imaging, darkfield imaging, phase contrast imaging, fluorescence imaging, upconverting phosphor imaging, chemiluminscence imaging, evanescent imaging, near infra-red detection, confocal microscopy with scattering, and optical densitometry. 
     
     
         7 . The method of  claim 1 , wherein the microorganism detection system is configured to perform darkfield imaging and fluorescence imaging. 
     
     
         8 . The method of  claim 1 , wherein the microorganism detection system is configured to perform detection using a method selected from a group consisting of atomic force microscopy, impedance, electrochemical impedance spectroscopy, fluorescence spectroscopy, diffuse reflectance spectroscopy, infrared spectroscopy, terahertz spectroscopy, transmission and absorbance spectroscopy, Raman spectroscopy, including Surface Enhanced Raman Spectroscopy, Spatially Offset Raman spectroscopy, transmission Raman spectroscopy, resonance Raman spectroscopy, MALDI-TOF mass spectrometry, desorption electrospray ionization mass spectrometry, GC mass spectrometry, LC mass spectrometry, electrospray ionization mass spectrometry and Selected Ion Flow Tube spectrometry, surface plasmon resonance, nephelometry, flow cytometry, capillary electrophoresis, molecular diagnostics, quartz crystal microbalance, bioluminescence, microcantilever sensors, and asynchronous magnetic bead rotation. 
     
     
         9 . The method of  claim 1 , further comprising:
 determining, by the computer-based system, a clone signal intensity curve shape likelihood;   determining, by the computer-based system, a tracking error likelihood;   calculating, by the computer-based system, a growth likelihood value based on the clone signal intensity curve shape likelihood and tracking error likelihood;   determining, by the computer-based system, at least one of microorganism susceptibility to an antimicrobial agent, a lack of microorganism susceptibility to an antimicrobial agent, microorganism resistance to an antimicrobial agent, microorganism expression of a virulence factor, microorganism hypervirulence, and a polymicrobial specimen, based on a comparison of the growth likelihood value to a reference range.   
     
     
         10 . The method of  claim 1 , further comprising, rendering, by the computer-based system, a signal associated with the microorganism into a plurality of signal approximations, wherein the plurality of signal approximations are planes comprising a plurality of point amplitudes corresponding to microorganism locations;
 combining, by the computer-based system, the plurality of signal approximations to create a microorganism model;   analyzing, by the computer-based system, the plurality of point amplitudes associated with at least one of background information and noise information;   filtering by the computer-based system, the plurality of signal approximations to eliminate at least one of background information and noise information; and   registering, by the computer-based system, locations associated with point amplitudes corresponding to microorganisms.   
     
     
         11 . The method of  claim 1 , wherein the microorganism is an individuated microorganism. 
     
     
         12 . A method, comprising:
 detecting, by a computer-based system configured to analyze microorganism information and comprising a processor and a tangible, non-transitory memory, first microorganism information from a microorganism detection system;   detecting, by the computer-based system, second microorganism information from the microorganism detection system;   parsing, by the computer-based system, first microorganism information and second microorganism information into a plurality of microorganism information value subsets, wherein a first microorganism information value subset created from first microorganism information and second microorganism information value subset created from second microorganism information are associated with a location;   associating, by the computer-based system, the first microorganism information value subset and the second microorganism information value subset;   determining, by the computer-based system, a first growth rate of a microorganism, based on the first microorganism information value subset and the second microorganism information value subset, in response to subjecting a microorganism to at least one of a first event and a first condition;   obtaining, by the computer-based system, a second value corresponding to a reference growth rate; and   determining, by the computer-based system, a proportional relationship between the first value to second value.   
     
     
         13 . The method of  claim 12 , further comprising evaluating, by the computer-based system, the proportional relationship against a reference range. 
     
     
         14 . The method of  claim 12 , further comprising identifying, by the computer-based system, at least one of microorganism susceptibility to an antimicrobial agent, a lack of microorganism susceptibility to an antimicrobial agent, microorganism resistance to an antimicrobial agent, microorganism expression of a virulence factor, microorganism hypervirulence, and polymicrobial specimens, in response to the proportional relationship falling one of within and outside of the reference range. 
     
     
         15 . An apparatus comprising means for performing the method of any of  claim 1 . 
     
     
         16 . A computer program that, when run on a computer, performs the method of any of  claim 1 .