JP5901689B2 - System and method for tentative identification of microbial species in culture - Google Patents

Description

  An improved system and method for identifying the type of microorganism in a culture is disclosed.

  Rapid and reliable detection of microorganisms in cultures such as blood cultures is one of the most important roles of clinical microbiology laboratories. Currently, the presence of biologically active agents such as patient body fluids, especially bacteria in the blood, is determined using culture vials. A small amount of patient bodily fluid is injected through a sealed rubber septum into a sterile vial containing media, and the vial is then incubated and monitored for microbial growth.

  A general visual observation of the culture vial then monitors the turbidity of the liquid suspension in the vial or observes the final change in its color. Known instrumental methods can also be used to detect changes in the carbon dioxide content of the culture vessel, which is a metabolic byproduct of bacterial growth. Monitoring the carbon dioxide content can be accomplished by methods well established in the art.

  In some cases, a non-invasive infrared microbe detection instrument that uses a special vial with an infrared transmission window is used. In some cases, the glass vial is transferred to an infrared spectrometer by an automated operating arm and measured through the glass vial. In some cases, a chemical sensor is placed in the vial. These sensors respond to changes in the carbon dioxide concentration in the liquid phase by changing their color or by changing their fluorescence intensity. These techniques are based on light intensity measurements and require spectral filtering on the excitation and / or emission signals.

As mentioned above, several different culture systems and methods are available in the laboratory. For example, BACTEC® radiometric and non-radiometric analysis systems (Becton Dickenson Diagnostic Instrument Systems, Sparks, Maryland) are often used for this task. The BACTEC® 9240 instrument accommodates, for example, up to 240 culture vessels and serves as an incubator, stirrer, and detection system. Each vessel includes a fluorescent CO ₂ sensor that is continuously monitored (eg, every 10 minutes). A culture is identified as positive by a computer algorithm for growth detection based on a change in rate of increase in CO ₂ production or a sustained increase, rather than using a growth index threshold or delta value. BACTEC® 9240 is fully automatic once the container is installed.

US Pat. No. 6,432,697 US Pat. No. 6,617,127 US Pat. No. 6,096,272 US Pat. No. 4,889,992 International Publication No. 20066071800 Pamphlet US Pat. No. 6,900,030

Stanier et al., 1986, The Microbial World, 5th edition, Prentice-Hall, Englewood Cliffs, New Jersey, pages 10-20, 33-37, and 190-195 Stanier et al, 1986, The Microbial World, 5th edition, Prentice-Hall, Englewood Cliffs, New Jersey, Chapter 5 Oberoi et al. 2004, "Comparison of rapid colorimetric method with conventional method in the isolation of mycobacterium tuberculosis," Indian J Med Microbiol 22: 44-46

  The disadvantage of these microorganism detection methods is that they do not necessarily detect the type of microorganism in such a culture. Each of the above systems requires a non-invasive and automatic method for determining what kind of microorganisms are present in the culture.

  In order to meet the requirements found in the prior art, provisional for any culture system designed to incubate a sample for the presence of an unknown type of microorganism used to determine the presence or presence and identification of the type of microorganism. Systems, methods, and apparatus for microbial identification are provided.

  Advantageously, using the novel systems, methods and apparatus of the present invention, incubation systems such as BACTEC® blood culture systems can be cultured before manual tests such as Gram staining or subculture are performed. It can be programmed to determine the type of microorganism in the object. Indeed, in some cases, the novel systems, methods, and devices of the present invention eliminate the need for gram testing or subculture to identify the type of microorganism that infects the culture.

  Briefly, the novel parameters disclosed herein, such as maximum metabolic rate and degree of growth, are used to determine the type of microorganism that infects the culture. It was unexpectedly found that any microbial species (eg, any microbial species) had unique values for these new parameters. Based on this unexpected discovery, the type of microorganism that infects a biological sample can be directly determined by an automated incubator before manual testing such as Gram staining or subculture is performed by an experimental technician. . In fact, in some cases, such manual testing is no longer needed to determine the type of microorganism that infects the culture.

  In the present invention, a value for a novel parameter disclosed herein is calculated from each biological sample infected with a known type of microorganism and used to identify the type of microorganism. Populate an optional lookup table that can be used, or train a classifier or other form of expression. Typically, the lookup table is created using a known main microorganism. The value of each of the types of microorganisms (eg, microbial species) that can potentially infect unknown biological samples is calculated and used to populate the lookup table. Advantageously, new parameters such as maximum metabolic rate and degree of proliferation are calculated using observations already recorded in conventional laboratories handling biological samples, so such lookup tables are Can be constructed without the need for any additional experiments. For example, in many laboratories, over time, several infected samples have been identified using conventional Gram staining and / or subculture. In addition, in such laboratories, growth curve data that traces metabolism as a function of time was recorded for such samples. In fact, it is often this growth curve data that leads to the determination that such a sample is infected with a microorganism. For these infected samples, new parameters can be calculated from growth curve data, matched to microbial types from Gram stain and / or subculture, and used to populate the lookup table. The type of microorganism infecting the new culture (unknown) then calculates the values for the new parameters of this new culture and, for example, for a given microorganism type, these values are It can be determined by comparing with the value.

  A number of exemplary values for the novel parameters disclosed herein are given in the data presented herein for many different microbial types for a given medium, but any It should be understood that these values for the new parameters for a given microbial species may change as the medium used to support the growth of the culture is changed. Thus, for any given look-up table, the biological medium used to determine the growth curve of the known culture is the growth curve of the known sample used to populate the look-up table. Care must be taken to be the same or similar to the biological medium used to determine the. Furthermore, the value of the new parameter can change when different incubators are used. Thus, preferably the same incubator used to generate the data used to populate the lookup table is also used for unknown cultures. Alternatively, several different look-up tables can be created, each look-up table for a different media type and / or incubator.

  Exemplary values for novel parameters such as maximum metabolic rate and degree of growth from several different microbial types for a given growth medium are disclosed herein. The data indicates that each microbial species has a unique characteristic value that differs with respect to the novel parameters disclosed herein. Furthermore, since the values for the new parameters were calculated from the growth data already recorded for the samples, no additional experiments were required to calculate the values shown here. As discussed above, the values disclosed herein for new parameters may change when different media are used. However, the data disclosed herein show that even for a given medium, the value of the new parameter will be different for each type of microorganism, and thus It clearly demonstrates that it can be used as a reference for identifying the type of microorganism in an unknown sample that has been temporarily identified. Thus, the systems and methods of the present invention can provide a number of useful applications in microbiology and related fields and meet specific applications in cell culture sterilization test methods.

  In one aspect, the present invention takes advantage of differences in growth rate and degree of growth to provide information about the types of microorganisms present and growing in a culture that can provide or contribute to the identification of the type of microorganism. provide. This aspect of the invention utilizes data transformation that can be applied to metabolic or cell growth data to provide values for parameters to the type of microorganism present in the culture. This aspect of the invention can be used to determine the identification of the type of microorganism.

  In another aspect, the present invention provides a method for identifying the type of microorganism in a culture in a container. In this method, for each individual measurement in a plurality of measurements of the biological state of the culture in the vessel, (i) the individual measurement and (ii) the initial culture of the culture measured at the initial time point A normalized relative value is calculated between the biological states, thereby obtaining a plurality of normalized relative values.

  The plurality of normalized relative values can be divided into predetermined constant intervals at a plurality of time points between the first time point and the second time point, with respect to time. For example, the first predetermined constant interval can include a first ten normalized relative value, the second predetermined constant interval can include a next ten normalized relative value, This is repeated until time point 2 is reached. For each of these individual predetermined fixed intervals at a plurality of time points between the first time point and the second time point, a first derivative of the normalized relative value at each predetermined fixed interval is determined. This gives a plurality of rate transformation values.

  There is a rate transformation value for each of the predetermined constant intervals at a plurality of time points. These multiple rate transformation values can be considered to include multiple sets of rate transformation values. Each of the individual sets of rate transformation values is for a different set of consecutive time points between the first time point and the second time point. For example, the first set of rate transformation values can be the first seven rate transformation values in a plurality of rate transformation values, and the second set of rate transformation values can be a plurality of rate transformation values. It can be the next seven rate transformation values in the value and this is repeated. For each individual set of rate transformation values in multiple sets of rate transformation values, calculate the average relative transformation value as a representative value for each rate transformation value in the individual set of rate transformation values . In this way, a plurality of average relative transformation values are calculated.

  Maximum metabolic rate and degree of proliferation are determined from multiple normalized relative values and multiple average relative transformation values. In some embodiments, the maximum metabolic rate and degree of growth are compared to an optional look-up table that matches the maximum metabolic rate and degree of growth to the type of microorganism, whereby the type of microorganism in the culture in the container To decide. In some embodiments, the maximum metabolic rate and degree of growth are used in a formula or other form of trained classifier to determine the type of microorganism in the culture in the container.

  In some embodiments, the identification of the type of microorganism in the culture in the container is output to a user interface device, monitor, computer readable storage medium, computer readable memory, or a local or remote computer system. In some embodiments, the identification of the type of microorganism in the culture is displayed.

  In some embodiments, the first time point is more than 5 minutes after the initial time point, and the second time point is more than 30 hours after the initial time point. In some embodiments, the first time point is 0.5 hours to 3 hours after the initial time point and the second time point is 4.5 hours to 20 hours after the initial time point. In some embodiments, the representative value of the rate transformation value in the first set of rate transformation values in the plurality of sets of rate transformation values is each rate transformation value in the first set of rate transformation values. Including geometric mean, arithmetic mean, median, or mode.

  In some embodiments, the measurements in the plurality of measurements of the biological state of the culture are each performed on the culture at periodic time intervals between the first time point and the second time point. In some embodiments, the periodic time interval is an amount of time between 1 to 20 minutes, 5 to 15 minutes, or 0.5 to 120 minutes.

In some embodiments, the initial biological state of the culture is determined by the fluorescence output of a sensor in contact with the culture. For example, in some embodiments, the amount of sensor fluorescence output is affected by CO ₂ concentration, O ₂ concentration, or pH.

  In some embodiments, 10 to 50,000 measurements, 100 to 10,000 measurements, or 150 to 5,000 measurements of the biological state of the culture in the container, Among the multiple measurements of the biological state of the culture. In some embodiments, each of the individual predetermined regular intervals of the plurality of time points includes a respective rate transformation value for the time point in the time window between the first time point and the second time point, or Consists of rate transformation values and the time window is 20 minutes to 10 hours, 20 minutes to 2 hours, or 30 minutes to 90 minutes.

  In some embodiments, each set of rate transformation values in the plurality of rate transformation values includes 4 to 20, 5 to 15, or 2 to 100 consecutive rate transformation values, or these rates. Consists of transformation values. In some embodiments, there are 5 to 500 or 20 to 100 average relative transformation values in the plurality of average relative transformation values. In some embodiments, the volume of the culture is 1 ml to 40 ml, 2 ml to 10 ml, less than 100 ml, or more than 100 ml.

  In some embodiments, the container includes a sensor composition in fluid communication with the culture, and the sensor composition is irradiated with light that includes a wavelength that causes the fluorescent compound to fluoresce when exposed to oxygen. The presence of the sensor composition is non-destructive to the culture, and (i) causes the fluorescent compound to fluoresce. The sensor composition is irradiated with light containing a wavelength, and (ii) measurement is performed by a method of observing the intensity of fluorescent light from the fluorescent compound while irradiating the sensor composition with light. In some embodiments, the fluorescent compound is included in a substrate that is relatively impermeable to water and non-gaseous solutes but highly permeable to oxygen. In some embodiments, the substrate comprises rubber or plastic.

In some embodiments, the maximum metabolic rate is considered to be the maximum average relative transformation value in a plurality of average relative transformation values. In some embodiments, the degree of proliferation is determined by the following formula:
NR _{after_growth} −NR _{minimum_growth
Where} NR _{after_growth} is (i) a first average relative transformation value after a maximum average relative transformation value in a plurality of average relative transformation values, (ii) a maximum average relative transformation value, or (iii) The normalized relative value in the plurality of normalized relative values used to calculate the first average relative transformation value before the maximum average relative transformation value, NR _{minimum_growth} is the first average to obtain a threshold This is a normalized relative value among a plurality of normalized relative values used for calculation of the relative transformation value. In some embodiments, the threshold is a value from 5 to 100 or 25 to 75.

In some embodiments, the degree of proliferation is determined by the following formula:
ARTmax ^* (time _ARTmax -time _initial )
Where ARTmax is the maximum average relative transformation value at multiple average relative transformation values, and time _ARTmax is (i) the maximum average at (a) the initial time point and (b) multiple average relative transformation values. For calculating the first average relative transformation value after the relative transformation value, (ii) the maximum average relative transformation value, or (iii) the first average relative transformation value before the maximum average relative transformation value The time between the normalized relative values of the plurality of normalized relative values used was measured, and time _initial is the first average to obtain (i) the initial time point and (ii) the threshold value Multiple normalizations used to calculate relative transformation values Is the time between the time the normalization relative value in the relative value was determined. In some embodiments, the threshold is a value from 5 to 100 or 25 to 75.

In some embodiments, the degree of proliferation is determined by the following formula:
[ARTmax ^* (time _ARTmax- time _initial )] / time _initial
Where ARTmax is the maximum average relative transformation value at multiple average relative transformation values, and time _ARTmax is (i) the maximum average at (a) the initial time point and (b) multiple average relative transformation values. For calculating the first average relative transformation value after the relative transformation value, (ii) the maximum average relative transformation value, or (iii) the first average relative transformation value before the maximum average relative transformation value The time between the normalized relative values of the plurality of normalized relative values used was measured, and time _initial is the first average to obtain (i) the initial time point and (ii) the threshold value Multiple normalizations used to calculate relative transformation values Is the time between the time the normalization relative value in the relative value was determined.

  In some embodiments, the determining step is performed in the container as (i) Enterobacteriaceae bacteria or (ii) Enterobacteriaceae bacteria based on maximum metabolic rate and degree of growth. Identify the type of microorganism in the culture. In some embodiments, the determining step identifies the type of microorganism as a bacterium based on the maximum metabolic rate and degree of growth. In some embodiments, the determining step is based on (i) Enterobacteriaceae, (ii) Staphylococcusae, (iii) Streptococcus based on maximum metabolic rate and degree of proliferation. ) Or (iv) identifying the type of the microorganism as Acinetobacter. In some embodiments, the determining step comprises: Arishwanella, Alterococcus, Aquamonas, Aranicola, Arsenophonus, AzotivirganB, Azotivirmana , Brenneria, Buchnera, Budvicia, Buttiauxella, Cedecea, Citrobacter, Dickeya, Edwer Near (Erwinia), Escherichia coli (Escherichia), Ewingella (Ewingella), Grimontella (Grimontella), Hafnia (Hafnia), Klebsiella (Klebsiella), Kleibera (Kleiver) Morganella, Obesbacterium, Pantoea, Peptobacterium, Candidus Phlomobacter, Photolabdas Phortohabdus omonas, Pragia, Proteus, Providencia, Rahnella, Raoulella, Salmonella, Samsonia, Sera, Sera, Sera, Sera, Sera ) Teraceae a teraceae a teraceae selected from the group consisting of Tatumella, Trabulsiella, Wigglesworthia, Xenorhabdus, Yersinia, and Yokkenella As one genus of To identify the types of microorganisms.

  In some embodiments, the determining step comprises Staphylococcus aureus, Staphylococcus caprae, Staphylococcus epidermidis, Staphylococcus staphylococcus staphylococcus staphylococci (Staphylococcus epidermidis). Staphylococcus hominis, Staphylococcus rugdunensis, Staphylococcus petencoferi, Staphylococcus saficus saprophysica Prophyticus, Staphylococcus warneri, and Staphylococcus xylosus, which identify one species of the family Staphylococcus as a species of the family Staphylococcus. In some embodiments, the determining step includes S. agalactiae, S. aureus. S. bovis, S. S. mutans, S. pneumoniae, S. pyogenes, S. salivarius, S. pneumoniae. Microorganisms as one species of Streptococcus selected from the group consisting of S. sanguinis, Streptococcus pneumoniae (S. suis), Streptococcus viridans, and Streptococcus uberis Identify the type.

  In some embodiments, the determining step identifies the type of microorganism as an aerobic bacterium based on the maximum metabolic rate and degree of growth. In some embodiments, the determining step identifies the type of microorganism as an anaerobic bacterium based on maximum metabolic rate and degree of growth.

  In some embodiments, the initial biological state of the culture is measured by colorimetric, fluorescence, turbidimetric, or infrared methods. In some embodiments, each biological state in the plurality of biological state measurements is determined by a colorimetric method, a fluorometric method, a turbidimetric method, or an infrared method. In some embodiments, the culture is a blood culture from a subject (eg, a human subject, a mammalian subject, etc.).

  In another aspect, the present invention is an apparatus for identifying the type of microorganism in a culture in a container, the processor for performing any of the methods disclosed herein and connected to the processor There is provided an apparatus comprising a configured memory. In yet another aspect, the present invention is a computer readable medium storing a computer program product that identifies a type of microorganism in a culture in a container, the computer program product being executable by a computer. I will provide a. The computer program product includes instructions for performing any of the methods disclosed herein.

  Yet another aspect of the invention provides a method for identifying the type of microorganism in a culture in a container. Obtain multiple measurements of the biological state of the culture in the container. Each measurement in the plurality of measurements is performed at a different time point between the first time point and the second time point. Determining the first derivative of the biological state measurement at each of the predetermined time intervals of the plurality of time points, with respect to each of the predetermined time intervals of the plurality of time points between the first time point and the second time point Thus, a plurality of rate transformation values are obtained. The plurality of rate transformation values includes a plurality of sets of rate transformation values, each of the individual sets of rate transformation values in the plurality of sets of rate transformation values being a first time point and a second time point. For a different set of consecutive time points between. For each individual set of rate transformation values in multiple sets of rate transformation values, calculate an average relative transformation value as a representative value for each rate transformation value in the individual set of rate transformation values; This calculates a plurality of average relative transformation values. The maximum metabolic rate and degree of proliferation are determined from multiple normalized relative values and multiple average relative transformation values. The maximum metabolic rate and degree of growth are compared with a look-up table that matches the maximum metabolic rate value and degree of growth to the type of microorganism, thereby determining the type of microorganism in the culture in the container.

FIG. 2 illustrates an apparatus for identifying the type of microorganism in a culture in a container, comprising a processor and a memory connected to the processor, in accordance with an embodiment of the present invention. 1 is a schematic diagram of a culture vessel and a CO ₂ detection system according to an embodiment of the present invention. FIG. 4 illustrates a method for identifying the type of microorganism in a culture in a container according to an embodiment of the present invention. FIG. 4 illustrates a method for identifying the type of microorganism in a culture in a container according to an embodiment of the present invention. FIG. 4 is a plot of normalized relative values measured from blood cultures in a container according to an embodiment of the invention. FIG. 5 is a plot of average relative transformation values over time based on the average rate of change of the rate transformation values of FIG. 4 over time, in accordance with an embodiment of the invention. FIG. 5 is a plot of the second derivative of the normalized relative value of FIG. 4, showing the metabolic rate over time, according to an embodiment of the present invention. FIG. 6 is a plot of the ratio of growth to maximum metabolic rate for a reference culture containing known types of microorganisms.

  In some scenes of each drawing, like reference numerals refer to corresponding parts.

  Systems, methods, and apparatus are provided for identifying the type of microorganism in a culture. In one aspect, for each measurement of the biological state of the culture, a normalized relative value is calculated between (i) the individual measurement and (ii) the initial biological state. For each fixed interval of multiple time points, the first derivative of the normalized relative value for the measurement of the biological state at the individual interval of time points is calculated, thereby obtaining multiple rate transformation values. For each set of rate transformation values in a plurality of rate transformation values, a representative value of the rate transformation values in the set is calculated, thereby obtaining a plurality of average relative transformation values. In some embodiments, the maximum metabolic rate and degree of growth determined from the normalized relative value and the average relative transformation value are compared against an optional lookup table that matches these values to the type of microorganism. .

5.1 Definitions As used herein, the term “Acinetobacter” refers to a gram-negative genus of bacteria belonging to the phylum Proteobacteria. Non-motile Acinetobacter species are oxidase negative and occur in pairs under magnification.

As used herein, the term “biological state” refers to, for example, CO ₂ concentration, O ₂ concentration, pH, rate of change of CO ₂ concentration, rate of change of O ₂ concentration, or rate of change of pH in a culture. Refers to a measurement of the metabolic activity of a culture determined by

  As used herein, the term “blood” refers to whole blood or one from the group of cell types composed of red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, and monocytes. Means two, three, four, five, six or seven cell types. The blood can be from any species including, but not limited to, humans, any laboratory animal (eg, rat, mouse, dog, chimpanzee), or any mammal.

  The term “blood culture” as used herein refers to any amount of blood mixed with a blood medium. Examples of media include, but are not limited to, broth supplemented with soy casein, digested soy casein, hemin, menadione, sodium bicarbonate, sodium polyanitol sulfonate, sucrose, pyridoxal HCK1, yeast extract, And L-cysteine. One or more reagents that can be used as a blood medium are described in, for example, Non-Patent Document 1, which is incorporated herein by reference in its entirety for such purposes. In some cases, a blood culture is obtained when a subject has symptoms of blood infection or bacteremia. Blood is drawn from the subject and placed directly into a container containing nutrient broth. In some embodiments, each container requires 10 ml of blood.

  As used herein, the term “culture” refers to any biological from a subject that is separated from or mixed with one or more reagents designed to culture cells. Refers to the sample. Biological samples from subjects include, for example, blood, cells, cell extracts, cerebrospinal fluid, plasma, serum, saliva, sputum, tissue specimens, tissue biopsies, urine, wound secretions, from indwelling line catheter surfaces It can be a sample or a stool specimen. A subject can be a member of any species including, but not limited to, a human, any laboratory animal (eg, rat, mouse, dog, chimpanzee), or any mammal. One or more reagents that can be mixed with a biological sample are described, for example, in Non-Patent Document 1, which is incorporated herein by reference in its entirety for such purposes. A blood culture is an example of a culture. In some embodiments, the biological sample is in liquid form and the amount of biological sample in the culture is 1 ml to 150 ml, 2 ml to 100 ml, 0.5 ml to 90 ml, 0.5 ml to 75 ml. Or 0.25 ml to 100,000 ml. In some embodiments, the biological sample is in liquid form, 1% to 99% of the culture volume, 5% to 80% of the culture volume, 10% to 75% of the culture volume. %, Less than 80% of the culture volume, or 10% or more of the culture volume. In some embodiments, the biological sample is 1% to 99% of the total weight of the culture, 5% to 80% of the total weight of the culture, 10% to 75% of the total weight of the culture, Less than 80% of the total weight of the product, or more than 10% of the total weight of the culture.

  The term “Enterobacteriaceae” as used herein refers to a large family of bacteria including Salmonella and Escherichia coli. The Enterobacteriaceae family is also referred to herein as the Enteric group. According to genetic studies, the Enterobacteriaceae are included in Proteobacteria, which bring their own order (Enterobacteriaes). The members of the Enterobacteriaceae family are rod-shaped, typically 1 to 5 μm long. Like other proteobacteria, Enterobacteriaceae has Gram-negative staining and is a facultative anaerobe, fermenting sugar to produce lactic acid and various other end products. Enterobacteriaceae reduces nitrate to nitrite. Unlike most similar bacteria, the Enterobacteriaceae family is generally deficient in cytochrome C oxidase, although there are exceptions (e.g., Plesiomonas). Most have a large number of flagella, but a few genera are non-motile. The Enterobacteriaceae family is non-spore forming, except for Shigella dysenteriae strains that are catalase positive. Many members of this family are normal parts of the intestinal flora found in the intestines of humans and other animals, while other members are found in water or in the soil, or a variety of different animals and It is a plant parasite. Most members of the Enterobacteriaceae family have pericynic type I pili that are responsible for the attachment of bacterial cells to their host. The genus Enterobacteriaceae includes, but is not limited to, Arishwanella, Alterococcus, Aquamonas, Aranicola, Arsenophovir, Arsenophovir Brochmannia, Brenneria, Buchnera, Budvicia, Buttiaxella, Cedea, Citrobacter E, cetrobacter Enterobacter, Erwinia (eg, Erwinia amylovora), Escherichia (eg, Escherichia coli), Ewingella (N), Grimontera (G) (Klebsiella) (for example, Klebsiella pneumoniae), Kluyvera, Leclercia, Leminorella, Moellerella, Morganella, Morganella, Pantoea, Pectobacterium, Candidatus Phromobacter, Photohabdus (eg, Photolabdas luminescens), Pregiomonas (le) Ides (Plesiomonas shigelloides), Pragia, Proteus (eg, Proteus vulgaris), Providencia, Rahnella, Laoulterra (RaSulella) onella), Samsonia, Serratia (eg, Serratia marcescens), Shigella, Sodalis, Tatumella, Trobulsialia, Trabulsiala (Wigglesworthia), Xenolabdas, Yersinia (eg, Yersinia pestis), and Yokkenella. Further information about Enterobacteriaceae is described in Non-Patent Document 2, which is incorporated herein by reference for such purposes.

  As used herein, the term “instance” refers to the execution of an algorithmic step. Some steps of the algorithm execute multiple times and each iteration of the step is called an instance of the step.

  As used herein, the term “microorganism” refers to organisms with a diameter of 1 mm or less excluding viruses.

  As used herein, the term “microorganism type” refers to any subclassification of the bacterial kingdom, such as a gate, class, eye, family, genus, or species in the bacterial kingdom.

  As used herein, the term “part” refers to at least 1%, at least 2%, at least 10%, at least 20%, at least 30%, at least 50%, at least 75%, at least 90%, or at least 99 of the set. %. Thus, in non-limiting examples, at least a portion of the plurality of subjects is at least 1%, at least 2%, at least 10%, at least 20%, at least 30%, at least 50%, at least 75% of the plurality of subjects. , At least 90%, or at least 99%.

  As used herein, the term “Staphylococcusaceae” includes, but is not limited to, Staphylococcus aureus, Staphylococcus caprae, Staphylococcus staphylococcus (Staphylococcus). Staphylococcus haemolyticus, staphylococcus hominis, staphylococcus rugdunensis, staphylococcal sp Staphylococcus saprophyticus (Staphylococcus saprophyticus), Staphylococcus word Neri (Staphylococcus warneri), and a Staphylococcus xylo over suspension (Staphylococcus xylosus) bacteria, refers to the family of Bacillus (Bacillales) eyes of the bacteria.

  As used herein, the term “Streptococcus” refers to a genus of globular gram-positive bacteria belonging to the family Firmictes and the group of lactic acid bacteria. Because cell division occurs along the single axis of these bacteria, they grow in chains or pairs, thereby the name comes from the Greek streptos, which means that the name bends or twists easily like a chain To do. This is in contrast to staphylococci that divide along multiple axes to form a cluster of grape-like cells. Streptococcus species include, but are not limited to, S. agalactiae, S. S. bovis, S. S. mutans, S. pneumoniae, S. pyogenes, S. salivarius, S. pneumoniae. S. sanguinis, S. suis, green streptococci (Streptococcus viridans), and Streptococcus uberis are included.

  The term “subject” as used herein is an animal, preferably a mammal, more preferably a non-human primate, most preferably a human. The terms “subject”, “individual”, and “patient” are used interchangeably herein.

The term “container” as used herein refers to any container that can hold a culture, such as a blood culture. For example, in one embodiment, the container is a container having a side wall, a bottom wall, and an open upper end for placing culture inside, which container can visually check turbidity in glass and samples. Formed from a material such as a transparent plastic (eg, a cyclic olefin copolymer) that is sufficiently transparent to be observed, the container preferably withstands heating at a temperature of at least 250 ° C. In some embodiments, the container has a sufficient wall thickness to withstand an internal pressure of at least 25 psi, and a lid attached to the open upper end of the container, and the culture is stored in the container. Not substantially contaminated over long periods of time under ambient conditions. An exemplary container is described in U.S. Patent No. 6,057,028, which is incorporated herein by reference. In some embodiments, the long term under ambient conditions is at about 40 ° C. for at least about 1 year. In some embodiments, the container further comprises a fluorescent sensor compound immobilized on the inner surface of the container that, when exposed to oxygen, exhibits a decrease in fluorescence intensity when exposed to fluorescent light. In some embodiments, the container is substantially transparent to the fluorescent light. In some embodiments, the fluorescent sensor compound is tris-4,7-diphenyl-1,10-phenanthroline ruthenium (II) salt, tris-2,2′-bipyridyl ruthenium (II) salt, 9,10-diphenyl. At least one compound selected from the group consisting of anthracene and mixtures thereof. In some embodiments, the container is a Blood Culture BACTEC® LYTIC / 10 Anaerobic / F culture vial, a BBL® SEPTI-CHEK® vial, a BBL® SEPTI-CHEK® (Trademark) blood culture bottle, Becton Dickinson BACTEC (registered trademark) vial, Plus Aerobic / F ^* and Plus Anaerobic / F ^* culture vial, Becton Dickinson BACTEC (registered trademark) Standard / 10 Aerobic / F culture vial, BectonDEC TM Myco / F Lytic culture vial, Becton Dickinson BACTEC® PEDS P US (registered trademark) / F culture vial or Becton Dickinson BACTEC (registered trademark) Standard Anaerobic / F culture vials (Becton Dickinson, Franklin Lakes, New Jersey), it is.

5.2 Exemplary Apparatus FIG. 1 is a detailed view of an apparatus 11 for identifying the type of microorganism in a culture in a container, including a processor and a memory connected to the processor. In some embodiments, the type of microorganism identified by the device 11 or any of the methods of the present invention is a gate in the bacterial kingdom. In some embodiments, the type of microorganism identified by the device 11 or any of the methods of the present invention is a class within the kingdom of the bacterial kingdom. In some embodiments, the type of microorganism identified by the device 11 or any of the methods of the present invention is an eye in the Bacteria genus. In some embodiments, the type of microorganism identified by the device 11 or any of the methods of the present invention is a family within the kingdom of the kingdom of the bacterial kingdom. In some embodiments, the type of microorganism identified by the device 11 or any of the methods of the present invention is a genus in the family of the kingdom of the kingdom of the bacterial kingdom. In some embodiments, the type of microorganism identified by the device 11 or any of the methods of the present invention is a species within the genus of the family of the genus Bacteriophage.

The processor and memory illustrated in FIG. 1 can be part of, for example, an automated or semi-automated radiometric or non-radiometric microbial culture system. The device 11 is preferably:
A central processing unit 22;
An optional non-volatile main memory 14 such as a hard disk drive, for example, which is controlled by the storage controller 12 and stores software and data;
A system memory 36, preferably high-speed random access memory (RAM), that stores system control programs, data, and application programs, and programs and data (optionally loaded from non-volatile storage 14) A system memory 36, which may include a read only storage element (ROM);
A user interface 32 including one or more input devices (eg, keyboard 28, mouse) and a display 26 or other output device;
A sensor 34 for measuring the biological state of the culture in the container;
A network interface card 20 (communication circuit) connected to the sensor 34;
An internal bus 30 interconnecting the above elements of the system;
Includes a power supply 24 that provides power to the elements.

The operation of the central processing unit 22 is mainly controlled by the operating system 40. The operating system 40 can be stored in the system memory 36. In a typical implementation, the system memory 36 is:
A file system 42 that controls access to the various files and data structures used by the present invention;
A culture type determination module 44 that identifies the type of microorganism in the culture in the container;
A biological data structure 46 storing a plurality of measurements of the initial biological state 48 of the culture and the biological state of the culture, wherein each measurement 50 in the plurality of measurements is a first (initial) A biological data structure 46 performed at a different time between the current time point and the second (final) time point;
(I) multiple sets of values, wherein each set 56 of values in the multiple sets of values includes a maximum metabolic rate value 57 and a degree of growth 58, and (ii) the type of microorganism An optional look-up table 54 including matches between sets, for each set 56 of values in the plurality of sets of values, there is a corresponding microorganism type 59 in the set of microorganism types A lookup table 54;
A set of rate transformation values 60, wherein each set of rate transformation values includes a plurality of rate transformation values 62, each rate transformation value 62 being a normal associated with a predetermined constant interval of time A set 60 of rate transformation values that are first derivatives of the normalized relative values;
Also includes a data structure that stores an average relative transformation value 66 for each set 60 of rate transformation values 60; and an identification 68 of the type of microorganism in the culture in the vessel.

  As illustrated in FIG. 1, device 11 includes a biological state data structure 46, an optional lookup table 54, a set of rate transformation values 60, an average relative transformation value 66, and microorganisms in culture. Data such as type identification 68 may be included. In some embodiments, memory 36 or data store 14 also stores representative values of average relative transformation values 66. The above data may be in any form of data storage, including but not limited to flat files, relational databases (SQL), or online analytical processing (OLAP) databases (MDX and / or variants thereof). it can. In some embodiments, such data structures are not stored as cubes, but are stored in a database that includes a star scheme with dimension tables that define a hierarchy. Further, in some embodiments, such data structures are stored in a database (eg, a dimension table that is not hierarchically arranged) that has a hierarchy that is not clearly classified in the underlying database or database scheme. . In some embodiments, such a data structure is stored in device 11. In other embodiments, all or part of these data structures are stored (stored) in one or more computers addressable by the device 11 over the Internet / network not shown in FIG. In some embodiments, all or some of the one or more program modules shown in the apparatus 11 of FIG. 1, such as the culture type determination module 44, are actually Internet not shown in FIG. / It exists in a device (for example, a computer) other than the device 11 that can be addressed by the device 11 via the network.

The apparatus 11 determines the metabolic activity value of the culture based on, for example, the CO ₂ concentration, O ₂ concentration, pH, CO ₂ concentration change rate, O ₂ concentration change rate, or pH change rate in the culture. By determining this metabolic activity, the device 11 can identify the type of microorganism in the culture. In some embodiments, the device 11 contains multiple culture vessels and serves as an incubator, agitator, and detection system. These elements of the device 11 are not shown in FIG. 1 because the nature of such components varies greatly depending on the actual configuration of the device 11. For example, the number of culture containers accommodated by the apparatus can range from one container to 1000 or more containers. There may be a sensor attached to each container to measure the biological state of the culture contained in the container. The sensors can be placed anywhere on the container and there are a wide range of possible sensors that can be used.

FIG. 2 illustrates one exemplary sensor that can measure the biological state of the culture. In FIG. 2, a CO ₂ sensor 204 is coupled to the bottom of the culture vessel 202, on which a certain amount of culture has been introduced. The CO ₂ sensor 204 is not permeable to ions, medium components, and cultures, but CO ₂ is freely permeable. Carbon dioxide produced by the cells in the culture diffuses in the sensor 204 and dissolves in the water present in the sensor substrate, producing hydrogen ions. As the hydrogen ion concentration increases (pH decreases), the fluorescence output of the sensor 204 increases, thereby changing the signal sent from the excitation filter 206 to the emission filter 208. The device 11 repeatedly measures the signal transmitted through the emission filter 208 over a period of time and uses this data to identify the type of microorganism in the culture using the algorithm disclosed herein.

  In some embodiments, the device 11 is an incubator, shaker, and fluorescence detector that houses from 1 to 1,000 culture vessels (eg, 96, 240, or 384 culture vessels). It is. In some embodiments, the containers are arranged in a rack (eg, a circular or linear rack), and each container has multiple container stations. For example, in one particular embodiment, apparatus 11 holds 240 culture vessels arranged in 6 racks, each rack having 40 vessel stations. In some embodiments, each vessel station of apparatus 11 includes a light emitting diode and a photodiode detector with appropriate excitation and emission filters (eg, as illustrated in FIG. 2). In some embodiments, the culture vessel is shaken and heated at 35 ± 1 ° C.

5.3 Exemplary Method Now that an exemplary apparatus according to the present invention has been described, an exemplary method according to the present invention will be described in detail. In some embodiments, such methods can be implemented by the culture type determination module 44 of FIG. Referring to step 302 of FIG. 3, the initial biological state of the culture is measured. For example, referring to FIG. 2, in some embodiments, the initial value of detector 204 is read to determine the CO ₂ concentration of the sensor. In an alternative embodiment, the initial O ₂ concentration, pH, or other indicator of the biological state of the culture is read (measured) at step 302. In some embodiments, the initial biological state of the culture is determined by the fluorescence output of a sensor (eg, sensor 204) that is in contact with the culture. In some embodiments, the amount of fluorescence output of the sensor is affected by the CO ₂ concentration as described above in conjunction with FIG. In some embodiments, the amount of sensor fluorescence output is affected by O ₂ concentration, pH, or other indicators of metabolic status known in the art. In general, any physical observable that indicates the metabolic rate of the culture can be measured and stored as the initial state. In some embodiments, this physically observable is the accumulation of molecular products (eg, lipopolypolysaccharide with gram-negative bacteria), non-molecular physical / chemical changes (pressure) related to growth. Change), and / or the production of carbon dioxide or other metabolites that accumulate or the consumption of substances such as oxygen) or the accumulation of cellular substances.

In some embodiments, the initial biological state of the blood culture is measured at step 302 using a colorimetric, fluorescent, turbidimetric, or infrared method. Examples of colorimetric methods include, but are not limited to, resazurin / colorimetric redox indices such as methylene blue and tetrazolium chloride, or described in US Pat. Use of p-iodonitrotetrazolium violet compounds that are included. Another example of a colorimetric method includes the colorimetric assay used in Non-Patent Document 3, which is incorporated herein by reference in its entirety. In Oberoi et al. (Non-patent Document 3), a culture vessel is attached to the MB / Bact240 system (Organon Teknika). The operating principle of this system is based on the detection of mycobacterial growth by a colorimetric sensor. In the presence of the organism, CO ₂ is produced when the organism metabolizes the substrate glycerol. The color of the gas permeability sensor at the bottom of each culture vessel increases the reflectivity of the device monitored by the system using infrared light. Examples of colorimetric method, further includes any monitoring color change of the sensor composition due to changes of the gas components such as CO ₂ concentration in the container resulting from the metabolism of microorganisms.

  Examples of fluorometric and colorimetric methods are incorporated herein by reference in their entirety, there are multiple light sources that each emit light of a different wavelength for colorimetric and fluorescent detection, and incubation Patent Document 3 discloses an equipment system including a rotating rack for an index. As used herein, turbidimetric analysis refers to measuring the turbidity of a culture using a nephelometer. A nephelometer is an instrument that measures suspended particles in a liquid or gaseous colloid. This measurement is performed by using a light beam (light source beam) and a photodetector set on one side of the light source beam (usually 90 degrees). Thus, the particle density is a function of the light reflected from the particles into the detector. To some extent, how much light is reflected by a given density of particles depends on the properties of the particles, such as the shape, color, and reflectivity of the particles. Therefore, establishing an operational correlation between turbidity and suspended solids (more useful but typically more difficult to quantify particulates) must be done individually for each situation .

  Infrared methods for measuring the biological state of blood cultures as used herein are not limited, but are described in US Pat. Any infrared microbial detection system or method known in the art, including those disclosed.

  In some embodiments, the container 202 that holds the culture includes a sensor composition 204 that is in fluid communication with the culture. The sensor composition 204 includes a fluorescent compound that, when exposed to oxygen, changes its fluorescence characteristics when irradiated with light that includes a wavelength that causes the fluorescent compound to fluoresce. The presence of sensor composition 204 is non-destructive to the culture. In such an embodiment, measuring step 302 (and each instance of measuring step 308) includes irradiating sensor composition 202 with light that includes a wavelength that causes the fluorescent compound to fluoresce, and irradiating the sensor composition with this light. While observing the intensity of the fluorescent light from the fluorescent compound. In some embodiments, the fluorescent compound is included in a substrate that is relatively impermeable to water and non-gaseous solutes but highly permeable to oxygen. In some embodiments, the substrate comprises rubber or plastic. Further details of the sensor according to this embodiment of the invention are disclosed in US Pat.

  At step 304, the initial biological state of the culture measured at the start of step 302 is normalized and stored as the initial biological state 48 of the culture (eg, for 100 percent or other predetermined value). And). This initial biological fluid state, stored as data element 48 in FIG. 1, serves as a reference value for subsequent measurements of the biological state of the culture. In some embodiments, without performing step 304, the absolute measurement of step 302 is used in the algorithms disclosed herein.

  The device 11 incubates the culture for a predetermined time after the initial biological state measurement is performed. Then, after a predetermined time has elapsed, the device 11 makes another measurement of the biological state of the culture. This process is illustrated by steps 306 and 308 in FIG. In FIG. 3A, this process is illustrated as proceeding to time step t of step 306. The biological state during step 306, waiting for the device to progress by time step t, is not used in the next processing step to identify the type of microorganism in the culture. In step 308, as time progresses by time step t, the measurement of the biological state of the culture in the container is performed again in the same manner as the initial measurement of the biological state was made (eg, FIG. 2). In some embodiments, the predetermined period (the length of time step t) is 10 minutes. In some embodiments, the predetermined period (the length of time step t) is less than 5 minutes, less than 10 minutes, less than 15 minutes, less than 20 minutes, 1 to 30 minutes, A period of 1 to 20 minutes, a period of 5 to 15 minutes, or a period of more than 5 minutes. Standardizing the measurement of the biological state of the culture in the vessel made in step 308 with the measurement of step 308 relative to the initial measurement of step 302 in an embodiment where the initial measurement of step 302 is used for normalization. To convert to a normalized relative value. In one embodiment, the biological state measurement of the culture in the container made in step 308 is converted to a normalized relative value by taking the ratio of the measurement in step 308 to the initial measurement in step 302. In some embodiments, this calculated normalized relative value is stored as data element 50 of FIG. In some embodiments, the biological state measurement measured in step 308 is stored as data element 50 in FIG. 1 and the normalized relative value corresponding to the biological state measurement measured in step 308. Is calculated in subsequent processing steps as necessary.

  At step 310, a determination is made as to whether a first predetermined time interval has elapsed. For example, in some embodiments, the predetermined constant time interval is 70 minutes. In this example, if the time step t of step 306 is 10 minutes, the time step t needs to advance seven times before state 310-Yes is achieved. In some embodiments, the predetermined fixed time interval is a 5 minute to 5 hour time interval, a 20 minute to 10 hour time interval, a 20 minute to 2 hour time interval, a 30 minute to 90 minute time interval, less than 24 hours. A time interval or a time interval exceeding 24 hours. When the first predetermined time interval has elapsed (310-Yes), process control proceeds to step 312 where an additional step of the algorithm is performed. If the first predetermined time interval has not elapsed (310-No), process control returns to step 306 where the algorithm waits for time to advance by the amount of time t, and then again in step 308 the new Take measurements of the biological state of the culture in the instance.

  The final result of steps 306-310 is a plurality of measurements of the biological state of the culture in the container, each measurement of which is a first (initial) time point and an end (final) time point. Are at different points in time. Further, in an exemplary embodiment where time step t is the same time in each instance of step 306, each measurement of the plurality of measurements is measured at periodic time intervals for the culture. In some embodiments, the periodic time interval is an amount of time from 1 minute to 20 minutes, an amount of time from 5 minutes to 15 minutes, an amount of time from 30 seconds to 5 hours, or more than 1 minute. It is a certain amount of time.

  When the predetermined fixed intervals have passed (310-Yes), the first derivative of the normalized relative value at each predetermined fixed interval (or, in an embodiment where normalization has not been performed, the step at each predetermined fixed interval). (Absolute value of 302) is calculated in step 312 to obtain a rate transformation value 62. In other words, the change in normalized relative value during a predetermined fixed interval is determined in step 312. In embodiments that normalize the measurement data, the rate transformation value is the first derivative of the normalized relative value, and in embodiments that do not normalize the measurement data, the rate transformation value is the first order of the absolute measurement in step 302. Note that it is a derivative. In some embodiments, the predetermined constant time interval for calculating the first derivative is all measurements at the previous time that is between 20 minutes and 2 hours. For example, in some embodiments, the predetermined constant time interval in step 310 is 70 minutes, and in step 312 the change over all normalized relative values of the measurements in this 70 minute time interval (past 70 minutes). The rate is determined at step 312 and stored as a rate transformation value 62. In some embodiments, the predetermined fixed time interval (time window) for calculating the first derivative is 5 minutes to 2 hours, 30 minutes to 10 hours, 20 minutes to 2 hours, 20 minutes to 10 hours, or 30 minutes to 90 minutes. All measurements in the immediately preceding time interval between.

  At step 314, a determination is made as to whether a predetermined number of rate transformation values have been measured since the last time condition 314-Yes. If measured (314-Yes), process control proceeds to step 316. If not measured (314-No), the process returns to step 306, where process control waits until time step t has elapsed, and then continues to step 308 where the normalized relative value of the culture is recalculated. Each condition (314-Yes) indicates the completion of the set 60 of rate transformation values 62. For example, in some embodiments, condition 314-Yes is achieved when seven new rate transformation values 62 are measured. In this example, the set of rate transformation values 60 includes or consists of seven rate transformation values 62. In some embodiments, each set 60 of rate transformation values 62 includes or consists of 4 to 20 consecutive rate transformation values. The continuous rate transformation value 62 is the rate transformation value in the same set 60. Such a rate transformation value 62 is calculated and stored, for example, in successive instances of step 312. In some embodiments, each set 60 of rate transformation values 62 in a plurality of rate transformation values is 5 to 15 consecutive rate transformation values 62 and 1 to 100 consecutive rate transformations. It comprises or consists of the value 62, 5 to 15 consecutive rate transformation values 62, 5 or more rate transformation values 62, or less than 10 rate transformation values 62.

  If condition 314-Yes is achieved, step 316 is performed. In step 316, an average relative transformation (average rate of change) value 66 is calculated from the newly formed set of rate transformation values 62. Thus, there is an average relative transformation value 66 for each set 60 of rate transformation values 62. In some embodiments, the average relative transformation (average rate of change) value 66 is newly formed by taking a representative value of the rate transformation value 62 in the set 60 of the newly formed rate transformation value 62. Calculate from a set 60 of rate transformation values 62. In some embodiments, this representative value is the geometric mean, arithmetic mean, median, or mode of all or some of the rate transformation values 62 in the newly formed set of rate transformation values 62. is there.

  At step 318, a determination is made as to whether a predetermined point has been reached in the protocol. This predetermined point is the final time point, and is also called the end point or the second time point. In some embodiments, the second time point is reached 1 hour or more, 2 hours or more, 10 hours or more, 3 hours to 100 hours, less than 20 hours after the initial measurement in step 302 (318). -Yes). In some embodiments, the second time point is a measurement of the biological state of the culture in the vessel 10 to 50,000 times, 100 to 10,000 times, or 150 in the instance of step 308. 5,000 times, over 10 times, over 50 times, over 100 times (318-Yes). If the predetermined point in the protocol has not been reached (318-No), process control returns to step 306, where process control waits for time step t to proceed, after which another instance of step 308 is started, of which The biological state of the culture is again measured and used to calculate the normalized relative value. If the predetermined point in the protocol has been reached (318-Yes), process control proceeds to step 320.

  At step 320, the maximum metabolic rate value 57 reached by the culture is determined. In some embodiments, the maximum metabolic rate value 57 is considered to be the maximum average relative rate transformation value 66 calculated in any instance of step 316 for the culture. For example, if the maximum average relative transformation value 66 calculated for the culture during one instance of step 316 is 250, then the maximum metabolic rate value 57 for the culture would be considered 250.

In step 322, the degree of growth 58 of the culture is determined. In some embodiments, the degree of proliferation (EG) 58 is determined by the following equation:
EG = NR _{after_growth} -NR _{minimum_growth} formula 1
In some embodiments, the NR _{after_growth} in Equation 1 is a normalized relative value among a plurality of normalized relative values used to calculate the maximum average relative transformation value 66 that the culture has reached so far. Therefore, when the maximum average relative transformation value 66 is calculated using normalized relative values 135 to 144, NR _{after_growth} is normalized relative values {135,. . . , 144} is one of the normalized relative values in the set.

In some embodiments, the NR _{after_growth} of Equation 1 is a plurality of normalized relative values used to calculate the average relative transformation value 66 after the maximum average relative transformation value 66 that the culture has reached so far. Is the normalized relative value at. Therefore, when the average relative transformation value 66 after the maximum average relative transformation value 66 is calculated using the normalized relative values 145 to 154, the NR _{after_growth} is the normalized relative value {145,. . . 154} is one of the normalized relative values in the set.

In some embodiments, the NR _{after_growth} of Equation 1 is a plurality of normalized relative values used to calculate the average relative transformation value 66 before the maximum average relative transformation value 66 that the culture has reached so far. Is the normalized relative value at. Therefore, when the average relative transformation value 66 immediately before the maximum average relative transformation value 66 is calculated using the normalized relative values 125 to 134, the NR _{after_growth} is the normalized relative value {125,. . . , 134} is one of the normalized relative values in the set.

In some embodiments, the NR _{after_growth} in Equation 1 is a representative value of all or a portion of the normalized relative value that was used to calculate the maximum average relative transformation value 66 that the culture has reached so far. Therefore, when the maximum average relative transformation value 66 is calculated using normalized relative values 135 to 144, NR _{after_growth} is normalized relative values {135,. . . , 134} are representative values (geometric mean, arithmetic mean, median, or mode) of all or part of the normalized relative values.

In some embodiments, the NR _{after_growth} of Equation 1 is all of the normalized relative values used to calculate the average relative transformation value 66 after the maximum average relative transformation value 66 that the culture has reached so far. Or some representative values. Therefore, when the average relative transformation value 66 after the maximum average relative transformation value 66 is calculated using the normalized relative values 145 to 154, the NR _{after_growth} is the normalized relative value {145,. . . 154} are representative values (geometric mean, arithmetic mean, median, or mode) of all or some of the normalized relative values in the set.

In some embodiments, the NR _{after_growth} of Equation 1 is all of the normalized relative values used to calculate the average relative transformation value 66 before the maximum average relative transformation value 66 that the culture has reached so far. Or some representative values. Therefore, when the average relative transformation value 66 immediately before the maximum average relative transformation value 66 is calculated using the normalized relative values 125 to 134, the NR _{after_growth} is the normalized relative value {125,. . . , 134} are representative values (geometric mean, arithmetic mean, median, or mode) of all or part of the normalized relative values.

In some embodiments, NR _{minimum_growth} is a normalized relative value among a plurality of normalized relative values used to calculate the first average relative transformation value 66 to obtain a threshold. Thus, if the first average relative transformation value 66 is calculated to obtain a threshold using normalized relative values 20 to 29, NR _{minimum_growth} is normalized relative values {20,. . . , 29} is one of the normalized relative values in the set.

In some embodiments, NR _{minimum_growth} is a representative relative normalized value used to calculate the first average relative transformation value 66 to obtain a threshold. Thus, if the first average relative transformation value 66 is calculated to obtain a threshold using normalized relative values 20 to 29, NR _{minimum_growth} is normalized relative values {20,. . . , 29} are all or part of the normalized relative values.

  In some embodiments using Equation 1 to calculate the degree of growth 58, the threshold is a value between 5 and 100, a value between 25 and 75, and between 1 and 1000 in a non-limiting example. Value, or a value less than 50.

In some embodiments, the degree of proliferation (EG) 58 is determined by the following equation:
EG = ARTmax ^* (time _ARTmax− time _initial ) Equation 2
Where ARTmax is the maximum average relative transformation value 66 reached by the culture, and time _ARTmax is (a) the initial time when the biological state of the culture was measured at time step 302, and (b) (I) the first average relative transformation value after the maximum average relative transformation value, (ii) the maximum average relative transformation value, or (iii) maximum determined for the culture by the instance of step 316 Time between when the normalized relative value used to calculate the first average relative transformation value before the average relative transformation value was measured. In addition, time _initial was used to calculate the first average relative transformation value to obtain (i) the initial time point at which the biological state of the culture was measured in time step 302 and (ii) the threshold. This is the time between when the normalized relative value was measured.

  In some embodiments for calculating the degree of growth 58 using Equation 2, the threshold value is a value between 5 and 100, a value between 25 and 75, and between 1 and 1000 in a non-limiting example. Value, or a value less than 50.

In some embodiments, the degree of proliferation (EG) 58 is determined by the following equation:
EG = [ARTmax ^* (time _ARTmax− time _initial )] / time _initial formula 3
In the formula, the values of ARTmax, time _ARTmax , and time _initial are the same as those in formula 2. In some embodiments using Equation 3 to calculate the degree of growth 58, the threshold is a value between 5 and 100, a value between 25 and 75, and between 1 and 1000 in a non-limiting example. Value, or a value less than 50.

The values of ARTmax, time _ARTmax , time _initial , NR _{after_growth} , and NR _{minimum_growth} used in Equations 1, 2, or 3 are of the same form used to create the reference growth value 58 of the optional lookup table 54. If so, they can be multiplied individually by any real number. More generally, any of Equations 1, 2, or 3 can include additional mathematical operations, both linear and non-linear, in creating a reference growth value 58 for the optional lookup table 54. Can still be used to calculate the degree of growth 58. A reference growth value 58 and a maximum metabolic rate value 57 are calculated for the look-up table 54 using cultures containing known types of microorganisms.

  At step 324, the maximum metabolic rate value 57 of the culture determined at step 320 and the degree of growth 58 of the culture determined at step 322 can be used to identify the infecting microorganism. In some embodiments of step 342, the maximum metabolic rate value 57 of the culture determined in step 320 and the growth rate 58 of the culture determined in step 322 are set to the maximum metabolic rate value 57 and the growth rate 58 of the microorganism. And the value of the look-up table 54 that matches the type 59, thereby determining the type of microorganism in the culture in the container. To illustrate, considering the case where the maximum metabolic rate value 57 of the culture is “200” and the degree of growth 58 of the culture is “450”, the values in the lookup table 54 have the following values: .

In this example, the microbial species 59 of the culture has the look-up table 54 values (200, 455) most closely matching the observed maximum metabolic rate and growth values (200, 450). Therefore, it is regarded as the type Y. This example shows the set of values that match the set 56 of the lookup table 54 (maximum metabolic rate 57, growth 58) that most closely matches the observed maximum metabolic rate 57 and growth 58 of the test culture. Thus, a preferred embodiment of the present invention is illustrated in which the test culture is considered to be microbial type 59 in the lookup table 54 corresponding to this set of matching values. In some embodiments, a lookup table is not used. In such an embodiment, the maximum metabolic rate value 57 of the culture determined in step 320 and the growth rate 58 of the culture determined in step 332 are based on the maximum metabolic rate value 57 and the growth rate 58 of the culture. Used in one or more trained classifiers or other forms of equations (eg, regression equations) that can identify microorganisms.

  In some embodiments, step 324 determines the microbial species 59 in the culture in the container based on the maximum metabolic rate value 57 and the degree of growth 58 of the test culture: (i) Enterobacteriaceae ) Or bacteria that are not included in (ii) Enterobacteriaceae. For example, in some embodiments, this is done by comparing the maximum metabolic rate value 57 and degree of growth 58 of the test culture to the values in the lookup table 54. In other embodiments, this may result in one or more trained classifiers and / or other forms of formulas that take into account the values of maximum metabolic rate values 57 and growth rates 58 of known microorganisms in the culture. This is done by using the maximum metabolic rate value 57 and the degree of growth 58. In some embodiments, step 324 identifies microbial species 59 as a bacterium based on the maximum metabolic rate value 57 and degree of growth 58 of the test culture. In some embodiments, step 324 identifies the microbial species 59 as a bacterium based on comparing the maximum metabolic rate value 57 and degree of growth 58 of the test culture to the values in the lookup table 54. In some embodiments, step 324 may comprise (i) Enterobacteriaceae, (ii) Staphylococcusaceae, based on the maximum metabolic rate value 56 and degree of growth 58 of the test culture. iii) Identify the type of microorganism as Streptococcus, or (iv) Acinetobacter. In some embodiments, step 324 is based on comparing the maximum metabolic rate value 56 and growth rate 58 of the test culture to the values in the lookup table 54 (i) Enterobacteriaceae, ( ii) Identify the type of microorganism as Staphylococcusaceae, (iii) Streptococcus, or (iv) Acinetobacter. In some embodiments, step 324 is based on the maximum metabolic rate value 56 and the degree of growth 58 of the test culture, based on Alishwanella, Alterococcus, Aquamonas, Aranicola, Arsenophonus, Azotivirga, Brochmannia, Brenneria, Buchneria, Budoceria, Buttiaceta (Buttiaceta) Dickyea, Edwardsiella (Edwardsiell) ), Enterobacter, Erwinia, Escherichia, Ewingella, Grimontella, Hafnia, Klebsiella, er L Leminorella, Moellerella, Morganella, Obesumbacterium, Pantoea, Peptobacteria, Candidas phromobacter Phantalobda (Phototorhabdus), Plesiomonas (Plesiomonas), Pragia (Pragia), Proteus (Proteus), Providencia (Rahnella), Laoultella (Rauultella), Salmonella (Salmonia, Salmonella (Salmonia) From Shigella, Sodalis, Tatumella, Trabulsiella, Wigglesworthia, Xenorhabdus, Yersinia, and Yokeneella. Intestine To identify the microorganisms of the type 59 as one of the genus of bacteria family (Enterobacteriaceae). In some embodiments, step 324 is based on comparing the maximum metabolic rate value 56 and growth rate 58 of the test culture to the values in the look-up table 54, Alishwanella, Alterococcus. , Aquamonas, Aranicola, Arsenophonus, Azotivirga, Blochmannia, Breneria, Buchneria, Buchneria, Buchneria, Buchneria, Buchneria, Buchneria Cedecea, Citrobacter, Dickey ), Edwardsiella, Enterobacter, Erwinia, Escherichia, Ewingella, Grimontella lu, Hafnia K, Lech Leclercia, Leminorella, Moellerella, Morganella, Obesbacterium, Pantoea, Peptobacterid and Candacterid Candida s Phlomobacter, Photorhabdus, Presiomonas, Pragia, Proteus, Providencia, Rahnella, Raulella, Raulella ), Serratia, Shigella, Sodalis, Tatumella, Trobulsiella, Wigglesworthia, Xenorhabdus, Y Identifying a microorganism type 59 as a single genus Enterobacteriaceae selected from the group consisting of enella) (Enterobacteriaceae).

  In some embodiments, step 324 may include staphylococcus aureus, staphylococcus caprae, staphylococcus epidermidis, staphylococcus staphylococcus staphylococcus staphylococci Staphylococcus hominis, staphylococcus rugdunensis, staphylococcus petencoferi, staphylococcus sapophytophthria Rophyticus, a species of the bacterium belonging to the family Staphylococcusaceae as a species of microorganisms selected from the group consisting of Staphylococcus warneri and Staphylococcus xylosus . In some embodiments, the determining step is based on the maximum metabolic rate value 56 and the degree of growth 58 of the test culture, based on S. agalactiae, S. cerevisiae. S. bovis, S. S. mutans, S. pneumoniae, S. pyogenes, S. salivarius, S. pneumoniae. Microorganisms as one species of Streptococcus selected from the group consisting of S. sanguinis, Streptococcus pneumoniae (S. suis), Streptococcus viridans, and Streptococcus uberis Identify the type. For example, in some embodiments, step 324 is based on comparing the maximum metabolic rate value 56 and growth rate 58 of the test culture to the values in the look-up table 54, based on S. agalactiae. ), S. S. bovis, S. S. mutans, S. pneumoniae, S. pyogenes, S. salivarius, S. pneumoniae. Microorganisms as one species of Streptococcus selected from the group consisting of S. sanguinis, Streptococcus pneumoniae (S. suis), Streptococcus viridans, and Streptococcus uberis Identify the type.

  In some embodiments, step 324 identifies the type of microorganism as an aerobic bacterium based on the maximum metabolic rate value 56 and the degree of growth 58. In some embodiments, step 324 identifies microbial species 59 as anaerobic bacteria based on the maximum metabolic rate value 57 and degree of growth 58 of the test culture. In some embodiments, step 324 identifies the type of microorganism as an aerobic bacterium based on comparing the maximum metabolic rate value 56 and the degree of growth 58 to the values in the lookup table 54. In some embodiments, step 324 identifies microbial species 59 as anaerobic bacteria based on comparing the maximum metabolic rate value 57 and degree of growth 58 of the test culture to the values in the lookup table 54.

  In some embodiments, the method includes identifying the type of microorganism in the culture 68 as a user interface device (eg, 32), a monitor (eg, 26), a computer readable storage medium (eg, 14 or 36), It further includes outputting to a computer readable memory (eg, 14 or 36), or a local or remote computer system. In some embodiments, the identification of the type of microorganism in culture 68 is displayed. As used herein, the term local computer system means a computer system directly connected to the device 11. As used herein, the term remote computer system means a computer system connected to the device 11 via a network such as the Internet.

5.4 Exemplary Computer Program Product and Computer The present invention can be implemented as a computer program product that includes a computer program mechanism embedded in a computer readable storage medium. Further, any method of the present invention can be implemented on one or more computers. Further, any method of the present invention may be implemented in one or more computer program products. Some embodiments of the present invention provide a computer program product that encodes any or all of the methods disclosed herein. Such methods can be stored on a CD-ROM, DVD, magnetic disk storage product, or any other computer readable data or program storage product. Such a method can also be incorporated into a permanent storage device such as a ROM, one or more programmable chips, or one or more application specific integrated circuits (ASICs). Such permanent storage devices can be localized to servers, 802.11 access points, 802.11 wireless bridges / stations, repeaters, routers, cell phones, or other electronic devices. Such a method encoded in a computer program product can also be distributed electronically via the Internet or other methods.

  Some embodiments of the present invention provide a computer program product that includes all of the program modules and data structures shown in FIG. These program modules can be stored on a CD-ROM, DVD, magnetic disk storage product, or any other computer readable data or program storage product. Program modules can also be incorporated into permanent storage devices such as ROM, one or more programmable chips, or one or more application specific integrated circuits (ASICs). Such permanent storage devices can be localized to servers, 802.11 access points, 802.11 wireless bridges / stations, repeaters, routers, cell phones, or other electronic devices. The software modules of the computer program product can also be distributed electronically via the Internet or other methods.

5.5 Kits Some embodiments of the invention can include kits for performing any of the methods disclosed herein. By way of non-limiting example, the kit can include containers, sample cultures, additional agents, and software for performing any combination of the methods disclosed herein. Thus, the kit includes one or more of these reagents placed in a suitable container means.

  Software, containers, and kit components other than radiometric or non-radioactive systems can be packaged in aqueous media or in lyophilized form. Suitable container means of the kit generally include at least one vial, test tube, flask, bottle, syringe, or other container means that can contain the components and preferably be appropriately aliquoted. If more than one component is present in the kit, the kit will generally also include a second, third, or other additional container in which additional components can be placed separately. However, various combinations of components are possible in the vial. The kits of the present invention also typically include means for tightly enclosing the reagent container for sale. Such containers may include injection molded or blow molded plastic containers in which the desired vials are held.

6 Examples Any culture system designed to culture biological samples (hereinafter “cultures”) is intended for use in determining the presence and identification of microorganisms in the culture. A provisional organism identification test for the presence of unknown microorganisms has been developed. The principle is that the culture is inoculated into one or more culture vials and these vials are inserted into the device 11, which is present in the presence of metabolic activity (production of accumulated carbon dioxide or other metabolites). Or consumption of substances such as oxygen) or vials are monitored for cellular material accumulation (by measuring microbial cell accumulation). A BACTEC® blood culture system is an example of such a device 11. The BACTEC® blood culture system uses a fluorescent sensor that reports changes to this system when microbial metabolism occurs. Next, the algorithm shown in FIG. 3 is applied to the series of signal data. This algorithm was designed to recognize signal changes over time suggesting the presence of growing microorganisms. The user is notified when the system confirms evidence of growth (changes in status relative to positive vials), and then the container is processed to determine the presence of microorganisms (gram staining and passage for plated medium). Culture). The algorithm of the present invention exploited differences in growth rate and degree of growth to provide information about the presence and growth of microorganisms in the culture. A look-up table that records this information and similar information about known microorganisms identifies the type of microorganism in the culture.

  The data collected by the BACTEC® blood culture system is used as an example of the application of the data medium conversion of the present invention illustrated in FIG. The BACTEC® system described above uses a fluorescent sensor to detect changes in metabolic activity in the culture using a series of corrected fluorescent signal data collected at 10-minute intervals from sensors placed in the culture reagent. Monitor. The data used in this example was collected from the BACTEC® device used in the internal inoculation culture study or during clinical evaluation of this system. The data include the identification of the container (by serial number or accession number), a record of the inoculation date, the amount of blood in the sample (which is a blood culture system), and the results of identification of microorganisms found in the container (yes Aircraft identification was stored and stored in a Becton Dickinson database, including external data provided by the clinical site. Subsequently, the algorithm illustrated in FIG. 3 was applied for analysis. The usefulness of this information is as a tentative identification to closely related organism types. In this example, the microorganisms in the clinical blood culture are four different microorganism types: Enterobacteriaceae, (ii) Staphylococcusae, (iii) Streptococcus, or ( iv) Characterized by Acinetobacter. Closely related species of microorganisms exhibiting these general classifications of microorganisms were distinguished from each other (Escherichia coli and Klebsiella pneumoniae) from one another, Staphylococcus aureus and coagulase negative Distinguished from coagulase negative staphylococci). In some cases, individual species of microorganisms can be reliably identified. The basic principle of using these data for tentative identification is to determine the types of metabolic processes that these microorganisms have and the relative ecological energy of these different types of microorganisms, the various rate functions associated with their growth under limited conditions. Can be distinguished by monitoring.

  The data transformation of the present invention began with an initial normalization of the container signal for a particular output (the initial state when entering the system), as described above with steps 302 and 304 of FIG. All subsequent data was expressed as a percentage of the initial signal normalized to 100 percent in these analyses, as outlined in steps 306 and 308 of FIG. The data measurement normalized by the initial signal was called the normalized relative value. Under ideal theoretical conditions, a normalized relative value of 125 means that microbial metabolism increased the fluorescence measured by the BACTEC® sensor by 25 percent relative to the initial measurement. The calculated next value was the first derivative of the NR value that changed over time as outlined in steps 310 and 312 of FIG. This value is a rate transformation (RT) value, and the reference RT value used in this example uses a periodic limit of 70 minutes. Every RT value represented the percent change rate of the fluorescent signal over 70 minutes before calculation. The next value calculated was the ART or average rate change value as outlined in steps 314 and 316 of FIG. This was calculated as the average of the previous seven ART values calculated and served as a smoothing function for the RT values.

  Examples of parameters calculated to determine the identification of the type of microorganism infecting the culture are shown in FIGS. 4, 5, and 6. FIG. Escherichia coli cultures were analyzed using quantitative criteria (normalized relative value 50, rate transformation value 62, and average relative transformation value 66. The cultures contained 3 ml of human blood from the subject. And was inoculated with a suspension of E. coli (55 CFU) and placed in a BACTEC® 9000. The identifier 4942 is the culture reported in FIGS. The product can be uniquely identified and the data of this culture can be used to link to the R & D BACTEC® database, and Figure 4 shows a plot of normalized relative values over time. The container was placed in the apparatus and the temperature effect related to container equilibration was generally observed for the first hour, during which the signal was When stabilized and the background was observed, the initial signal increased from 94 percent to 95 percent (this rate was due to blood activity) .In a normalized relative plot (Figure 4), the growth was 8 Initially visible after time, continued until 15 hours, and the final NR value approached 126. Average relative transformation value over time based on the average rate of change over time in the rate transformation value 62 of FIG. A plot of 66 is shown in Figure 5. Each average relative transformation (ART) value 66 is a measure of the average rate of change, and the maximum ART for this culture is 1158 after 12.8 hours in culture. This shows the average maximum reached sensor change rate of this culture over a period of 1 hour, FIG. A plot of the second derivative of 0, showing the rate change over time, which is the critical point: the first acceleration point 602 (movement from 0), the acceleration reaches its maximum (crossing zero point) FIG. 6 is a graphical illustration showing a maximum acceleration point 604 (maximum), a maximum deceleration point (minimum) 606, and an end point 608 of the growth curve (rate change returns to 0).

  The characteristics of growth using the above confirmed data transformations (rate transformation value 60, average relative transformation value 66, and normalized relative value) for tentative identification of the type of microorganism are the maximum metabolic rate and the degree of growth. is there. In this example, the maximum metabolic rate (ARTmax) was defined as the maximum ART value reached 66 (in this example, the maximum metabolic rate was 1158).

In this example, the growth rate (EG) was determined as follows.
EG = NR _{after_growth} -NR _{minimum_growth} formula 1
This is the difference between the normalized relative (NR) values before growth (NR _{minimum_growth} ) and after growth (NR _{after_growth} ). To standardize the definitions before and after growth, the following format was used to determine the points representing before growth (NR _{minimum_growth} ) and after growth (NR _{after_growth} ). NR _{minimum_growth} was defined as the point where growth could be distinguished from the background signal. The time when NR _{minimum_growth} was measured was called time _initial . An ART value of 66 with a value of 50 established the onset of growth (this time strongly correlates with the time to detection of most cultures in the BACTEC® system). The point of comparison for NR _{after_growth} growth was determined using the determined point after maximum ART. Of course, other formats can be used to determine the points representing before and after growth for the determination of the degree of proliferation, and all such formats are within the scope of the present invention. In the example of E. coli illustrated in FIG. 4, the time to NR = 50 is 9.6 hours, which corresponds to an NR value of 95.7. Therefore, NR _{minimum_growth} was 95.7. FIG. 5 shows that in this example, the time to the first point after the ART maximum called time _ARTmax is 12.3 hours. From FIG. 4, 12.3 hours corresponds to NR value = 119.5. Therefore, NR _{after_growth} was 119.5. Thus, the degree of proliferation determined for this culture is 119.5-95.7, which is equal to 23.8 NR (equal to the percent change in signal over the established time). Furthermore, it was found from FIG. 5 that the maximum growth rate was 1158 ART. These values for maximum growth rate and degree of growth were compared to a look-up table 54 that includes a reference set 56 of values for maximum metabolic rate 57 and degree of growth 58. The microbial species 59 corresponding to the set of values 56 that most closely matched empirically determined values for maximum growth rate and degree of growth was considered to be the microbial type of the culture.

Another value used to determine the degree of proliferation was as follows:
EG = ARTmax ^* (time _ARTmax− time _initial ) Equation 2
In the formula, ARTmax is multiplied by a time difference (time _ARTmax− time _initial ), and time _ARTmax is defined as a time until the first point after ARTmax, and time _initial is a point at which NR reaches a value of 50. Defined. This value for the degree of proliferation can be used in conjunction with or as an alternative to the degree of proliferation calculated using Equation 1. In this example, the degree of proliferation determined by Equation 2 was a value of 3281.

In some embodiments, the degree of proliferation (EG) 58 was determined by the following formula:
EG = [ARTmax ^* (time _ARTmax− time _initial )] / time _initial formula 3
In the equation, the values of ARTmax, time _ARTmax , and time _initial are the same as those in Equation 1 and Equation 2. In this example, the degree of proliferation determined by Equation 3 was a value of 353.

  Table 1 is a list of 96 clinically significant isolates detected in the recent clinical evaluation of the BACTEC® system. Instrument data was collected and sent to Becton Dickinson for data analysis. Table 1 includes rate data that includes the important descriptive calculations described above (eg, degree of growth, maximum metabolic rate). These data are representative of microorganisms that are routinely detected in blood. In Table 1, column 1 is the unique culture identification number, column 2 is the abbreviation for the microorganism identified in the culture (ENTFAl is Enterococcus faecalis), and ENTCFAA is Enterococcus faecalis. Streptococcus sanguis group (STReptococcus sanguiusus group), STRAHE is Streptococcus alpha morito, ST Yes, ESCCOL is Escherichia E. coli), SAUR is Staphylococcus aureus, STACNEEG is Coagulase negative Staphylococcus, PROTMIR is Mirabilis EP, Proteis L EP Klebsiella pneumoniae, NEIMEN is Neisseria meningitidis, ACINBAU is Acinetobacter baumannii), column 3 is start time (unit), time unit (hours) Is the value for the maximum ART value reached by the culture, column 5 is the maximum ART value Column 6 is the time required to reach 50 ART values (time unit), and column 7 is the time required to reach 20 ART values (time unit). Column 8 is a value obtained by subtracting the time (time unit) required to reach the ART value of 50 from the time required to reach the maximum ART value (time unit). , The ratio of the maximum ART value to the maximum rate transformation value, column 10 is (i) the time required to reach ARTmax and (ii) the time required to reach 50 ART values. ARTmaxInterval50 calculated by multiplying the difference by ARTmax, column 11 is the value of ARTmaxInterval50 divided by the time (in hours) required for the culture to reach an ART value of 50 (50 AR Column 10 value divided by the time (in hours) required to reach the T value).

  FIG. 7 is a plot of the ratio of the degree of growth to the maximum metabolic rate (ARTmax Interval50 / TTART50, defined as ARTmax to plot FIG. 7) versus a reference culture containing known types of microorganisms. In FIG. 7, the bacteria of the group Enterobacteriaceae (eg, E. coli and K. pneumoniae) are gathered in the upper right corner of the figure. Enterobacteriaceae was characterized as having a high ARTmax and a high ratio of Integral 50 to TTART 50 (degree of growth determined by Equation 3). There were also some streptococci (STRAGA) that fit into this category. These can be distinguished by relative NR changes compared to Enterobacteriaceae bacteria.

  The next distinguishable group in FIG. 7 was Staphylococcus aureus with an ARTmax value greater than 600 and a ratio of Integral 50 to TTART 50 greater than 80 (proliferation determined by Equation 3). This helped to distinguish Staphylococcus aureus from coagulase negative staphylococci, which typically have lower ARTmax and Integral 50 to TTART 50 ratios.

  The next distinguishable group in FIG. 7 was Acinetobacter (represented by Acinetobacter baumannii in this example). This type of microorganism had a low ARTmax due to its obligatory respiratory properties and performance against non-sugar substrates. These NR curves are markedly characterized by an initial rapid and rapid growth, followed by an early rapid growth arrest and as a result never reach a sustained ARTmax.

7 References All references cited herein are to the same extent as if each individual publication or patent or patent document was incorporated herein by reference in its entirety for all purposes, Which is incorporated herein by reference in its entirety for all purposes.

8. Modifications Many modifications and variations of the present invention are possible without departing from the spirit and scope of the invention, as will be apparent to those skilled in the art. The specific embodiments disclosed herein are merely examples, and the present invention is limited only by the language of the appended claims, and all such equivalents are included in such claims. And

Claims (18)

A method of identifying the type of microorganism in a culture in a container,
(A) for each individual measurement in a plurality of measurements of metabolic activity in the culture in the vessel performed at a different time between the first time point and the second time point, (i) the individual Calculating a normalized relative value between the measurement and (ii) an initial metabolic activity in the culture measured at an initial time point, thereby obtaining a plurality of normalized relative values;
(B) for each predetermined fixed interval of a plurality of time points between the first time point and the second time point, the metabolic activity at each predetermined fixed interval of the plurality of time points; Determining a first derivative of a normalized relative value for the measured value, thereby obtaining a plurality of rate transformation values, wherein the plurality of rate transformation values includes a plurality of sets of rate transformation values; Each of the individual sets of rate transformation values in the plurality of sets of rate transformation values for a different set of successive time points between the first time point and the second time point;
(C) For each individual set of rate transformation values in the plurality of sets of rate transformation values, an average relative transformation as a representative value for each rate transformation value in the individual set of rate transformation values. Calculating a value, thereby calculating a plurality of average relative transformation values;
(D) determining a maximum metabolic rate and degree of proliferation from the plurality of normalized relative values and the plurality of average relative transformation values;
(E) seen including a step of determining the type of microorganisms in the culture in the vessel from the maximum metabolic rate and the degree of proliferation,
The initial metabolic activity in the culture is determined by a sensor that detects the CO ₂ concentration, O ₂ concentration, or pH of the culture ;
The determining step (E) comprises: (i) Enterobacteriaceae, (ii) Staphylococcusae, (iii) Streptococcus, (iv) Acinetobacter, A method characterized in that said microorganism is identified by a kind as selected from the group consisting of those species or genera .   2. The method of claim 1, wherein the maximum metabolic rate is a maximum average relative transformation value in the plurality of average relative transformation values. (F) outputting an identification of the type of microorganism in the culture in the vessel to a user interface device, monitor, computer readable storage medium, computer readable memory, or a local or remote computer system; or in the culture The method of claim 1, further comprising displaying an identification of the type of microorganism.   The first time point is either 5 minutes or more after the initial time point or between 0.5 hours and 3 hours from the initial time point, and the second time point is the initial time point. The method according to claim 1, characterized in that it is at least 30 hours after the time point or between 4.5 hours and 20 hours from the initial time point. A representative value of the rate transformation value in the first set of rate transformation values in the plurality of sets of rate transformation values is:
The geometric mean of each rate transformation value in the first set of rate transformation values;
An arithmetic average of each rate transformation value in the first set of rate transformation values;
The median value of each rate transformation value in the first set of rate transformation values, or
The method of claim 1, comprising a mode value of each rate transformation value in the first set of rate transformation values.   The measurements in the plurality of measurements of the metabolic activity in the culture are each performed on the culture at periodic time intervals between the first time point and the second time point. The method of claim 1. The initial metabolic activity in the culture is determined by the fluorescence output of a sensor in contact with the culture;
The method of claim 1, wherein the amount of fluorescence output of the sensor is affected by CO ₂ concentration, O ₂ concentration, or pH.   The method of claim 1, wherein 10 to 50,000 measurements of the metabolic activity in the culture in the container are among the plurality of measurements of the metabolic activity in the culture. Method. Each of the individual predetermined regular intervals at the plurality of times of step (B) is:
(A) consisting of each rate transformation value for a time point in the time window between the first time point and the second time point, the time window being a time of 20 minutes to 10 hours;
(B) consisting of rate transformation values for all time points in the time window between the first time point and the second time point at which metabolic activity in the culture in the container was measured, The time is from 20 minutes to 2 hours; and (c) in the time window between the first time point and the second time point at which metabolic activity in the culture in the vessel is measured. It consists of rate transformation values for all time points, the time window time is 30 minutes to 90 minutes,
Selected from the group consisting of
2. The method of claim 1, wherein each set of rate transformation values in the plurality of rate transformation values comprises 4 to 20 consecutive rate transformation values.   The method according to claim 1, wherein the culture is a blood culture from a subject, and the volume of the culture is 1 ml to 40 ml. The container includes a sensor composition in fluid communication with the culture, the sensor composition having a fluorescent property when exposed to light comprising a wavelength that causes the fluorescent compound to fluoresce when exposed to oxygen. Wherein the presence of the sensor composition is non-destructive to the culture, and the initial metabolic activity in the culture is
Irradiating the sensor composition with light containing a wavelength that causes the fluorescent compound to emit fluorescence, and observing the intensity of fluorescence from the fluorescent compound while irradiating the sensor composition with the light. ,
The fluorescent compound is contained in a substrate that is relatively impermeable to water and non-gaseous solutes but highly permeable to oxygen. the method of. The degree of proliferation (EG) is:
a) EG = NR _{after_growth} -NR _{minimum_growth}
Where
NR _{after_growth} is (i) a first average relative transformation value after the maximum average relative transformation value in the plurality of average relative transformation values, (ii) a maximum average relative transformation value, or (iii) a maximum average A normalized relative value in the plurality of normalized relative values used to calculate the first average relative transformation value before the relative transformation value;
NR _{minimum_growth} is a normalized relative value in the plurality of normalized relative values used to calculate the first average relative transformation value to obtain a threshold; or b) EG = ARTmax ^* (time _ARTmax− time _initial )
Where
ARTmax is the maximum average relative transformation value in the plurality of average relative transformation values;
time _ARTmax is (i) the initial time point, and (b) the first average relative transformation value after the maximum average relative transformation value at the plurality of average relative transformation values, (ii) The maximum average relative transformation value, or (iii) the normalized relative value in the plurality of normalized relative values used to calculate the first average relative transformation value before the maximum average relative transformation value. The time between the measured time points,
time _initial is the normalized relative value of the plurality of normalized relative values used to calculate (i) the initial time point and (ii) the first average relative transformation value to obtain a threshold. C) EG = [ARTmax ^* (time _ARTmax− time _initial )] / time _initial
Where
ARTmax is the maximum average relative transformation value in the plurality of average relative transformation values;
time _ARTma is: (a) a first average relative transformation value after the maximum average relative transformation value, (ii) at the initial time point, and (b) the plurality of average relative transformation values, A maximum average relative transformation value, or (iii) the normalized relative value in the plurality of normalized relative values used to calculate the first average relative transformation value before the maximum average relative transformation value. Is the time between
time _initial is the normalized relative value of the plurality of normalized relative values used to calculate (i) the initial time point and (ii) the first average relative transformation value to obtain a threshold. Is the time between
The method of claim 1, wherein the method is determined by an expression selected from the group consisting of: The determining step (E) is:
a) Bacteria of Enterobacteriaceae family; or b) Arishwanella, Alterococcus, Aquamonas, Aranicola v, Arsenophon v, Arsenophon v Near (Blochmannia), Brenneria (Brenneria), Buchnera (Buchnera), Budvicia (Butvicia), Butiauxella (Cedecea), Citrobacter (Citrobacter) Enterobacter, Erwinia, E. coli (Escherichia), Ewingella, Grimontella, Hafnia, Klebsiella, Kleiber, L Moellerella, Morganella, Obesbacterium, Pantoea, Pectobacterium, Candidas Phromobacter, Photolaboda rhabdus, Presimononas, Pragia, Proteus, Providencia, Rahnella, Raoultella, Salonia, Samia S, Sonia (Shigella) Sodaris, Tatumella, Trabulsiella, Wigglesworthia, Xenorhabdus, Yersinia, and at least one of Yökenella;
c ) Staphylococcus aureus, Staphylococcus caprae, Staphylococcus epidermidis, Staphylococcus staphylococcus staphylococcus staphylococcus staphylococcus Staphylococcus rugdunensis, Staphylococcus sptencoferi, Staphylococcus saprophyticus, Staphylococcus saprophyticus Suwaneri (Staphylococcus warneri), and one species of Staphylococcus xylo over suspension (Staphylococcus xylosus) Staphylococcus family selected from the group consisting of bacteria (Staphylococcaceae); or
d ) Staphylococcus aureus or coagulase negative staphylococci; or
e ) S. agalactiae, S. S. bovis, S. S. mutans, S. pneumoniae, S. pyogenes, S. salivarius, S. pneumoniae. One species of Streptococcus selected from the group consisting of S. sanguinis, Streptococcus pneumoniae (S. suis), Streptococcus viridans, and Streptococcus uberis;
The method of claim 1, wherein the type of microorganism is identified as being selected from one of the group consisting of:   The method according to claim 1, wherein the metabolic activity is measured by a colorimetric method, a fluorescence analysis method, a turbidimetric analysis method, or an infrared method.   The method according to claim 1, wherein the determining step (E) identifies the type of the microorganism based on the maximum metabolic rate and the degree of proliferation. An apparatus for identifying the type of microorganism in a culture in a container, comprising a processor and a memory connected to the processor,
A microorganism type determination module,
(A) For each of the individual measurements in a plurality of measurements taken at different times between the first time and the second time, (i) the individual measurements and (ii) the measurements at the initial time Electronically encoded instructions for calculating a normalized relative value between initial metabolic activity in said cultured and thereby obtaining a plurality of normalized relative values;
(B) for each predetermined fixed interval of a plurality of time points between the first time point and the second time point, the metabolic activity at each predetermined fixed interval of the plurality of time points; An electronically encoded instruction for determining a first derivative of a normalized relative value for measurement and thereby obtaining a plurality of rate transformation values, wherein the plurality of rate transformation values are rate transformations A plurality of sets of values, wherein each set of rate transformation values in the plurality of sets of rate transformation values is for different sets of successive time points between the first time point and the second time point. , Instructions,
(C) For each individual set of rate transformation values in the plurality of sets of rate transformation values, an average relative transformation as a representative value for each rate transformation value in the individual set of rate transformation values. Electronically encoded instructions for calculating a value, thereby calculating a plurality of average relative transformation values;
(D) an electronically encoded instruction for determining a maximum metabolic rate and degree of growth from the plurality of normalized relative values and the plurality of average relative transformation values;
(E) an electronically encoded instruction for determining the type of microorganism in the culture in the vessel from the maximum metabolic rate and the degree of growth, comprising a microorganism type determination module ,
The initial metabolic activity in the culture is determined by a sensor that detects the CO ₂ concentration, O ₂ concentration, or pH of the culture ;
The determining step (E) comprises: (i) Enterobacteriaceae, (ii) Staphylococcusae, (iii) Streptococcus, (iv) Acinetobacter, An apparatus for identifying the microorganism by a kind selected from the group consisting of those species or genera . A computer readable medium storing a computer program product for identifying microorganisms in a culture in a container, executable by a computer, the computer program product comprising:
A microorganism type determination module,
(A) for each individual measurement of a plurality of measurements made at different times between the first time point and the second time point, for (i) said individual measurement and (ii) at an initial time point An electronically encoded instruction for calculating a normalized relative value between the measured initial metabolic activity in the culture and thereby obtaining a plurality of normalized relative values;
(B) The metabolic activity of each of the plurality of time points at each of the predetermined time intervals for each of the plurality of time points between the first time point and the second time point. An electronically encoded instruction for determining a first derivative of a normalized relative value for measurement and thereby obtaining a plurality of rate transformation values, wherein the plurality of rate transformation values are rate transformations Each of the individual sets of rate transformation values in the plurality of sets of rate transformation values is different in successive time points between the first time point and the second time point. Instructions for sets,
(C) For each individual set of rate transformation values in the plurality of sets of rate transformation values, an average relative transformation as a representative value for each rate transformation value in the individual set of rate transformation values. Electronically encoded instructions for calculating a value, thereby calculating a plurality of average relative transformation values;
(D) an electronically encoded instruction for determining a maximum metabolic rate and degree of growth from the plurality of normalized relative values and the plurality of average relative transformation values;
(E) an electronically encoded instruction for determining the type of microorganism in the culture in the vessel from the maximum metabolic rate and the degree of growth, comprising a microorganism type determination module ,
The initial metabolic activity in the culture is determined by a sensor that detects the CO ₂ concentration, O ₂ concentration, or pH of the culture ;
The determining step (E) comprises: (i) Enterobacteriaceae, (ii) Staphylococcusae, (iii) Streptococcus, (iv) Acinetobacter, A computer readable medium, characterized by identifying the microorganism by a type as selected from the group consisting of those species or genera . A method of identifying the type of microorganism in a culture in a container,
(A) obtaining a plurality of measurements of metabolic activity in the culture in the container, wherein each measurement in the plurality of measurements is performed at a different time point between a first time point and a second time point; Steps,
(B) The metabolic activity of each of the plurality of time points at each of the predetermined time intervals for each of the plurality of time points between the first time point and the second time point. Determining a first derivative of a measurement, thereby obtaining a plurality of rate transformation values, the plurality of rate transformation values including a plurality of sets of rate transformation values, Each of the individual sets of rate transformation values in the plurality of sets is for a different set of successive time points between the first time point and the second time point;
(C) For each individual set of rate transformation values in the plurality of sets of rate transformation values, an average relative transformation as a representative value for each rate transformation value in the individual set of rate transformation values. Calculating a value, thereby calculating a plurality of average relative transformation values;
(D) determining a maximum metabolic rate and degree of proliferation from a plurality of normalized relative values and the plurality of average relative transformation values;
(E) seen including a step of determining the type of microorganisms in the culture in the vessel from the maximum metabolic rate and the degree of proliferation,
Metabolic activity in the culture is determined by a sensor that detects the CO ₂ concentration, O ₂ concentration, or pH of the culture ;
The determining step (E) comprises: (i) Enterobacteriaceae, (ii) Staphylococcusae, (iii) Streptococcus, (iv) Acinetobacter, A method characterized in that the microorganism is identified by a kind as selected from the group consisting of those species or genera .

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