Device, method, and program

Abstract

SOLUTION: The present invention comprises: a detection unit for detecting the number of predetermined types of microorganism per unit volume of a fluid sample sampled from a living body; and a diagnosis unit for diagnosing the state of the living body on the basis of detection results from the detection unit. The diagnosis unit diagnoses the state of the living body on the basis of a change trend of detection results from the detection unit, the microorganism to be detected being pathogenic. The diagnosis unit diagnoses that the living body is sick when the number of microorganisms is greater than or equal to a first reference value, or when the number of microorganisms is less than the first reference value and a temporal change rate of the number of microorganisms exceeds a reference change rate.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an apparatus, a method, and a program. [Background technology]

[0002] Patent Document 1 and other documents state that "the most commonly used method for diagnosing the presence or absence of mastitis is to measure the somatic cell count (SCC)" (paragraph 0003 of Patent Document 1). [Prior art document] [Patent documents] [Patent Document 1] JP 2000-41696 A [Patent Document 2] JP 2010-112799 A [Patent Document 3] Japanese Patent Publication No. 2002-543826 [Patent Document 4] Japanese Patent Application Laid-Open No. 5-317343 [Patent Document 5] JP 2010-256231 A [Patent Document 6] JP ​​2007-271331 A [Patent Document 7] JP 2022-190700 A Summary of the Invention

[0003] In a first aspect of the present invention, there is provided an apparatus comprising a detection unit that detects the number of predetermined types of microorganisms per unit volume of a fluid sample collected from a living organism, and a diagnosis unit that diagnoses the condition of the living organism based on the detection results by the detection unit.

[0004] In the above device, the diagnosing section may diagnose the state of the living body based on a trend in fluctuations in the detection results obtained by the detecting section.

[0005] In the above device, the microorganisms are pathogens, and the diagnostic unit may diagnose the living body as sick when the number of the microorganisms is equal to or greater than a first reference value, or when the number of the microorganisms is less than the first reference value and the time change rate of the number of the microorganisms exceeds a reference change rate.

[0006] In any of the above devices, the detection unit may further detect the number of somatic cells of a predetermined type per unit volume of the fluid sample, and the diagnosis unit may diagnose the condition of the living body based on the number of microorganisms and the number of somatic cells detected by the detection unit.

[0007] In the above device, the microorganisms are pathogens, and the diagnostic unit may diagnose the living body as sick when the number of somatic cells is equal to or greater than a second reference value, or when the number of somatic cells is less than the second reference value, the number of microorganisms is less than a third reference value, and the time rate of change of the number of microorganisms is greater than the time rate of change of the number of somatic cells.

[0008] In any of the above devices in which the detection unit further detects the number of somatic cells, the microorganisms may be pathogens, and the somatic cells may be white blood cells or epithelial cells.

[0009] Any of the above devices may further include an acquisition unit that acquires the collection time from the living body for each fluid sample, and the diagnosis unit may calculate the time rate of change of the detection results by the detection unit from the collection times of the multiple fluid samples, and perform diagnosis using the time rate of change.

[0010] In any of the above devices, the diagnosis unit makes a diagnosis by comparing the detection result by the detection unit with a reference value, and the device may further include a setting unit that sets the reference value to a different value depending on the age of the living organism.

[0011] In any of the above devices, the detection section may include an imaging section that captures an image of the fluid sample, and an image recognition section that performs image recognition of a subject in the captured image.

[0012] In the above-described device, the imaging section may image the fluid sample while allowing it to flow.

[0013] In any of the above devices, the living body may be a human.

[0014] In any of the above devices, the living organism may be a livestock animal.

[0015] In the above apparatus, the living body may be a dairy cow and the fluid sample may be raw milk.

[0016] In the above device, the diagnosis unit may diagnose whether or not the living body is suffering from mastitis.

[0017] In a second aspect of the present invention, there is provided a computer-implemented method comprising a detection step of detecting the number of predetermined types of microorganisms per unit volume of a fluid sample collected from a living organism, and a diagnosis step of diagnosing the condition of the living organism based on the detection results from the detection step.

[0018] In a third aspect of the present invention, there is provided a program that, when executed by a computer, causes the computer to function as a detection unit that detects the number of predetermined types of microorganisms per unit volume of a fluid sample collected from a living organism, and a diagnosis unit that diagnoses the condition of the living organism based on the detection results by the detection unit.

[0019] The above summary of the invention does not list all of the necessary features of the present invention, and subcombinations of these features may also constitute inventions. [Brief explanation of the drawings]

[0020] [Figure 1] 1 shows a device 1 according to an embodiment. [Figure 2] The operation of the device 1 is shown. [Figure 3] This shows the trend of fluctuation in the number of Staphylococcus aureus contained in raw milk when a living organism suffers from mastitis. [Figure 4] 12 illustrates an example computer 1200 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE INVENTION

[0021] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0022] (Configuration of Device 1) FIG. 1 shows an apparatus 1 according to this embodiment. The apparatus 1 diagnoses the condition of a living organism using a fluid sample collected from the living organism. The apparatus 1 includes a detection unit 11, an acquisition unit 12, a diagnosis unit 13, and a setting unit 14. In this specification, a fluid sample collected from a living organism is described as an example, but the sample collected from the living organism is not limited to a fluid, and may be a gas. Furthermore, the apparatus 1 according to this embodiment may assist in the diagnosis of a disease in a living organism, such as a human or animal.

[0023] ((Detection unit 11)) The detection unit 11 detects the number of predetermined types of microorganisms (also referred to as target microorganisms) per unit volume of a fluid sample collected from a living body. The detection unit 11 includes an imaging unit 110 and an image recognition unit 111.

[0024] Here, the living body may be a living thing or a living body, and may be a human (person) or a non-human animal (for example, dairy livestock). In this embodiment, as an example, the living body may be a dairy cow.

[0025] The fluid sample may be a liquid sample, and in this embodiment, may be raw milk as an example. However, the fluid sample may also be other bodily fluids such as blood, lymph, cerebrospinal fluid, or urine. The fluid sample collected from a living body may be pre-treated to remove impurities such as fat particles before imaging by the imaging unit 110. The unit volume of the fluid sample is, for example, 1 cm 3 (i.e., 1 mL), or any other volume that is set arbitrarily.

[0026] At least one type of microorganism that can affect the state of a living organism may be dispersed in the fluid sample. At least one type of somatic cell may be dispersed in the fluid sample.

[0027] The microorganisms may be minute organisms that cannot be observed with the naked eye. The microorganisms may be any of eukaryotic algae, protozoa, fungi, prokaryotic bacteria, cyanobacteria, viruses, etc. The microorganisms to be detected may be pathogens, and in this embodiment, as an example, they may be Staphylococcus aureus, which causes mastitis, environmental Staphylococcus, or Escherichia coli. When there are multiple microorganisms to be detected, the number of microorganisms may be the number of individual microorganisms.

[0028] Somatic cells may be any cell that constitutes a living organism, other than a germ cell. Somatic cells may increase or decrease in number due to the presence of microorganisms, and the increase or decrease in somatic cells may occur later than the increase or decrease in the microorganisms. For example, somatic cells may be white blood cells (e.g., lymphocytes and neutrophils) that increase in number due to the presence of pathogenic microorganisms.

[0029] A fluid sample may be collected from a single living organism multiple times, and the collection time interval may be set arbitrarily. The fluid samples according to this embodiment may be different fluid samples even when obtained from the same living organism, provided that the collection times from the living organism are different. Each fluid sample may be assigned identification information (also referred to as a sample ID) by the user. The identification information (sample ID) may be stored in a memory unit (not shown) within the device 1 together with information identifying each fluid sample, the collection time, the type of living organism, the age of the living organism, and the gender of the living organism.

[0030] (((imaging unit 110))) The imaging unit 110 images the fluid sample. The imaging unit 110 may image each fluid sample, for example, each time a sample ID is input by the user. The imaging unit 110 may image the fluid sample while it is flowing. For example, the imaging unit 110 may image the fluid sample flowing in a channel (e.g., a microchannel) having a light-transmitting window through the window. The flow rate of the fluid sample may be controlled to any speed. The captured image may include microorganisms and somatic cells contained in the fluid sample as subjects. The imaging unit 110 may be capable of detecting visible light, infrared light, ultraviolet light, or the like.

[0031] The imaging unit 110 may include any type of microscope for magnifying and imaging a fluid sample. As an example, the imaging unit 110 may be a flow imaging microscope manufactured by Yokogawa Electric Corporation.

[0032] The imaging section 110 may supply the captured image for each fluid sample to the image recognition section 111. For example, the imaging section 110 may supply the captured image to the image recognition section 111 in association with the sample ID of the captured fluid sample.

[0033] (((Image recognition unit 111))) The image recognition unit 111 performs image recognition of a subject in a captured image. For example, the image recognition unit 111 may be generated by a deep learning technique and perform image recognition.

[0034] The image recognition unit 111 may count the number of microorganisms to be detected (Staphylococcus aureus in this embodiment as an example) among the recognized subjects for each image. The image recognition unit 111 may detect the number of microorganisms to be detected per unit volume of the fluid sample from the ratio between the volume of the fluid sample captured in one image (i.e., the volume of the fluid sample located within the field of view of the imaging unit 110) and the count number of microorganisms to be detected in the image. As an example, one image may capture 0.1 cm 3 When an image of a fluid sample is captured and the number of counts in the image is 5, the unit volume (1 cm in this embodiment as an example) 3) may be 50. When images of separate fluid samples are supplied from the imaging unit 110, the image recognition unit 111 may detect the number of target microorganisms per unit volume for each fluid sample.

[0035] The image recognition unit 111 may supply the number of microorganisms to be detected for each fluid sample to the diagnosis unit 13. For example, the image recognition unit 111 may supply the number of microorganisms to be detected to the diagnosis unit 13 in association with the sample ID.

[0036] ((Acquisition part 12)) The acquiring unit 12 acquires the collection time from the living body for each fluid sample. For example, the acquiring unit 12 may acquire the collection time input by the user in association with each sample ID.

[0037] The acquiring unit 12 may supply the collection time of each fluid sample to the diagnosing unit 13. For example, the acquiring unit 12 may supply the collection time of each fluid sample to the diagnosing unit 13 in association with the sample ID.

[0038] ((Diagnosis unit 13)) The diagnosis unit 13 diagnoses the condition of the living organism based on the detection result by the detection unit 11 (in this embodiment, as an example, the number of Staphylococcus aureus per unit volume). The diagnosis unit 13 may diagnose whether the living organism is suffering from mastitis. The diagnosis unit 13 may output the diagnosis result to a display device (not shown) or the like.

[0039] The diagnostic unit 13 may diagnose the state of the living body based on the trend of fluctuations in the detection results obtained by the detection unit 11. For example, the diagnostic unit 13 may calculate the time rate of change of the detection results obtained by the detection unit 11 from the collection times of multiple fluid samples and perform diagnosis using the time rate of change. The diagnostic unit 13 may acquire the collection time of each fluid sample from the acquisition unit 12. The time rate of change of the detection results may be calculated by dividing the difference between the values ​​indicated by the detection results by the interval between the collection times. When the detection unit 11 detects three or more fluid samples, the diagnostic unit 13 may use the time rate of change calculated for the two most recent fluid samples for diagnosis, or may average multiple time rate of change calculated for two fluid samples adjacent to each other on the time axis for diagnosis. The diagnostic unit 13 may calculate an approximate line or approximate curve for multiple detection results within an arbitrary time range and use the differential value (i.e., slope) of the calculated approximate line or approximate curve at an arbitrary time as the time rate of change for diagnosis.

[0040] The diagnosing unit 13 may make a diagnosis by comparing the detection result by the detecting unit 11 with a reference value. In the present embodiment, as an example, the diagnosing unit 13 may diagnose the living body as having a disease when the number of microorganisms to be detected is equal to or greater than a first reference value, or when the number of the microorganisms is less than the first reference value and the time rate of change in the number of the microorganisms exceeds a reference rate of change. The first reference value and the reference rate of change may be set arbitrarily. As an example, the first reference value is several thousand microorganisms / cm. 3 and the reference rate of change may be a value calculated by the following formula (1): The reference time in the formula may be a time set according to the transition time from subclinical (also called subclinical) mastitis to clinical mastitis, and may be set within the range of 1 to 72 hours, for example.

[0041] Reference change rate = {first reference value - (current number of target microorganisms contained in the fluid sample per unit volume)} / reference time (1)

[0042] Note that a living organism's resistance to diseases such as mastitis (immunity, for example) can change with age. For example, during the rearing stage (for example, from 0 months to 16 months), resistance increases with age, is strongest during the prime of life (for example, from 16 months to 5 years), and decreases with age during old age (over 5 years). Therefore, in the device 1 according to this embodiment, the setting unit 14, described below, sets the first reference value to a value according to age so that an appropriate diagnosis can be made according to resistance to disease.

[0043] ((Setting unit 14)) The setting unit 14 sets the first reference value to a different value depending on the age of the living organism. The age may be expressed in months. The setting unit 14 may read out the age of the living organism stored in the storage unit in association with the sample ID, or may obtain the age of the living organism through user input.

[0044] The setting unit 14 may set the first reference value to be larger as the age increases during the growing stage. For example, the setting unit 14 may set the first reference value to be larger as the age increases. 3 The setting unit 14 may set the first reference value to a constant value regardless of age in the prime of life, for example, by setting the first reference value to several thousand cells / cm 3 In the elderly age, the setting unit 14 may decrease the first reference value as the age increases. For example, the setting unit 14 may decrease the first reference value as the age increases. 3 It can be made smaller from

[0045] According to the above-described device 1, the number of target microorganisms contained in a fluid sample collected from a living body is detected, and the state of the living body is diagnosed based on the detection results. Therefore, the state of the living body can be diagnosed earlier than when the state is diagnosed based on the number of somatic cells.

[0046] Furthermore, the state of the living organism is diagnosed based on the trend of fluctuations in the detection results by the detection unit 11. Therefore, unlike when diagnosis is made by comparing the number of microorganisms at a given point in time with a reference value, diagnosis can be made even when the number of microorganisms does not reach the reference value, allowing for earlier diagnosis of the state of the living organism.

[0047] Furthermore, microorganisms are pathogens, and a living body is diagnosed with a disease when the number of microorganisms is equal to or greater than a first reference value, or when the number of microorganisms is less than the first reference value and the time rate of change in the number of the microorganisms exceeds a reference change rate. Therefore, when the number of microorganisms is small, diagnosis can be made based on the time rate of change in the number of microorganisms, and when the number of microorganisms is large, diagnosis can be made based on the number of microorganisms. Therefore, a disease can be reliably diagnosed regardless of the number of microorganisms.

[0048] Furthermore, the collection time of each fluid sample from the living body is acquired, and the time rate of change of the detection result by the detection unit 11 is calculated from the collection times of the multiple fluid samples, and diagnosis is performed using the time rate of change. Therefore, the time rate of change can be accurately calculated, and highly accurate diagnosis can be performed.

[0049] Furthermore, diagnosis is performed by comparing the detection result by the detection unit 11 with the first reference value, and the first reference value is set to a different value depending on the age of the living body. Therefore, highly accurate diagnosis can be performed regardless of age.

[0050] Furthermore, since the fluid sample is imaged and the subject is recognized in the image, microorganisms in the fluid sample can be detected with high accuracy.

[0051] Furthermore, since the imaging is performed while the fluid sample is flowing, microorganisms in the fluid sample can be detected with high accuracy even if the microorganisms are unevenly distributed in the fluid sample.

[0052] Furthermore, since the living body is a livestock, the condition of the livestock can be diagnosed. Furthermore, since the living body is a dairy cow and the fluid sample is raw milk, the condition of the dairy cow can be diagnosed using the raw milk. Furthermore, since it is determined whether the living body is suffering from mastitis, mastitis can be detected early.

[0053] (Device 1 operation) 2 shows the operation of the device 1. The device 1 diagnoses the living body by performing the processes of steps S11 to S15. In this embodiment, as an example, a first reference value corresponding to the age of the living body is set in the diagnosing unit 13 by the setting unit 14 at the start of the operation.

[0054] In step S11, the imaging unit 110 images the fluid sample. The imaging unit 110 may image each of the multiple fluid samples discretely multiple times. As an example in the present embodiment, the imaging unit 110 may image each fluid sample at each reference interval while flowing the fluid sample through the flow channel. The multiple images thus captured may depict different subjects.

[0055] In step S13, the image recognition unit 111 performs image recognition of the subject in the captured image. The image recognition unit 111 may count the number of microorganisms to be detected (Staphylococcus aureus as an example in this embodiment) among the recognized subjects for each image. The image recognition unit 111 may detect the number of microorganisms to be detected per unit volume of the fluid sample from the ratio between the volume of the fluid sample captured in one image and the count number of the microorganisms to be detected in the image. When multiple images are captured of one fluid sample, the count number of the microorganisms to be detected in the image may be the average value of the count numbers in the multiple images.

[0056] In step S15, the diagnosing unit 13 diagnoses the state of the living organism (whether or not the living organism suffers from mastitis, as an example, in this embodiment) based on the detection result (the number of Staphylococcus aureus per unit volume, as an example, in this embodiment) by the detecting unit 11. The diagnosing unit 13 may diagnose the living organism as sick when the number of microorganisms to be detected is equal to or greater than a first reference value, or when the number of the microorganisms is less than the first reference value and the time rate of change in the number of the microorganisms exceeds a reference change rate.

[0057] (Example of operation) Figure 3 shows the trend in the number of Staphylococcus aureus contained in raw milk when a living organism develops mastitis. In the figure, the vertical axis represents the number of Staphylococcus aureus per unit volume of raw milk, and the horizontal axis represents time. Note that this figure explains the case where a living organism is healthy up to time t1, goes through an incubation period from time t1 to time t2, and then develops mastitis at time t2. As shown in this figure, Staphylococcus aureus in raw milk increases rapidly during the incubation period, and the time rate of change in the number of bacteria exceeds the reference rate of change before the onset of symptoms.

[0058] According to the device 1 of this embodiment, a living organism is diagnosed with mastitis when the bacterial count is equal to or greater than a first reference value, or when the bacterial count is less than the first reference value and the time rate of change in the bacterial count exceeds a reference change rate. That is, a living organism is diagnosed with mastitis when the detection result of the detection unit 11 for the fluid sample falls within the shaded area in the figure. Therefore, even when the living organism has not yet developed mastitis (for example, at the time plotted in the figure), it can be diagnosed that the living organism has mastitis.

[0059] (Variation) In the above embodiment, the detection unit 11 has been described as detecting the number of target microorganisms. However, the detection unit 11 may further detect the number of predetermined types of somatic cells (also referred to as target somatic cells) per unit volume. In this case, the image recognition unit 111 of the detection unit 11 may detect the target microorganisms and target somatic cells contained in the fluid sample by image recognition. The target somatic cells may be cells other than germ cells that constitute a living organism, and may increase or decrease in number due to the presence of microorganisms. In this modification, as an example, the microorganisms may be pathogens, and the somatic cells may be white blood cells (e.g., lymphocytes and neutrophils) or epithelial cells that increase in number due to the presence of the microorganisms. Epithelial cells may increase in number within the fluid sample by detaching from a living organism damaged by pathogens.

[0060] When the detection unit 11 further detects the number of somatic cells, the diagnosis unit 13 may diagnose the state of the living organism based on the number of microorganisms to be detected and the number of somatic cells to be detected detected by the detection unit 11. For example, the diagnosis unit 13 may diagnose the living organism as having a disease when the number of detected microorganisms is equal to or greater than the above-mentioned first reference value and when the number of somatic cells is equal to or greater than a second reference value. The second reference value may be set arbitrarily, for example, to 200,000 cells / cm. 3 ~300,000 pieces / cm 3 may be set within the range.

[0061] The diagnostic unit 13 may diagnose the state of the living organism based on the trend of fluctuations in the detection results by the detection unit 11. For example, the diagnostic unit 13 may diagnose the living organism as healthy if the number of microorganisms to be detected increases, then the number of somatic cells to be detected increases, then the number of microorganisms to be detected decreases, and the number of somatic cells to be detected increases, i.e., if it is indicated that the living organism's resistance is superior to the infectivity of the microorganisms. The diagnostic unit 13 may diagnose the living organism as sick if the number of microorganisms to be detected increases, then the number of somatic cells to be detected increases, then the number of microorganisms to be detected increases, and the number of somatic cells to be detected increases, i.e., if it is indicated that the infectivity of the microorganisms is superior to the living organism's resistance.

[0062] The diagnostic unit 13 may diagnose the living body as having a disease when the number of somatic cells to be detected is equal to or greater than a second reference value, or when the number of somatic cells to be detected is less than the second reference value, the number of microorganisms to be detected is less than a third reference value, and the time rate of change in the number of microorganisms to be detected is greater than the time rate of change in the number of somatic cells to be detected. When the time rate of change in the number of microorganisms to be detected is greater than the time rate of change in the number of somatic cells to be detected, the infectivity of the microorganisms may be greater than the living body's resistance. The third reference value may be the same as or different from the first reference value described above.

[0063] In this modification, at least one of the first reference value, the second reference value, and the third reference value may be set to a different value depending on the age of the living organism by the setting unit 14. The setting unit 14 may set at least one reference value such that the higher the age is during the growing age, the larger the first reference value is, the higher the age is, the constant the first reference value is during the prime of life, and the lower the higher the age is during the elderly age.

[0064] According to the above-described modified example, the number of somatic cells of the detection target contained in the fluid sample is further detected, and the condition of the living body is diagnosed based on the number of microorganisms and the number of somatic cells detected by the detection unit. Therefore, the condition of the living body can be diagnosed more accurately than when the number of somatic cells is not used.

[0065] Furthermore, microorganisms are pathogens, and a living organism is diagnosed with a disease when the number of somatic cells is equal to or greater than a second standard value, or when the number of somatic cells is less than the second standard value, the number of microorganisms is less than a third standard value, and the time rate of change of the number of microorganisms is greater than the time rate of change of the number of somatic cells. Therefore, when the number of somatic cells is low, a diagnosis can be made based on the number of somatic cells, the number of microorganisms, and their time rates of change, and when the number of somatic cells is high, a diagnosis can be made based on the somatic cell count. Therefore, a disease can be reliably diagnosed regardless of the somatic cell count.

[0066] Furthermore, since the microorganisms to be detected are pathogens and the somatic cells to be detected are leukocytes or epithelial cells, it is possible to diagnose whether or not a living body is suffering from a disease.

[0067] (Other variations) In the above embodiment and modified examples, the device 1 has been described as including the acquisition unit 12 and the setting unit 14, but either of these may be omitted. For example, if the device 1 does not include the acquisition unit 12, a fluid sample may be collected from a living body at predetermined intervals, and the diagnosis unit 13 may calculate the time rate of change of the detection result by the detection unit 11 based on the intervals and use the calculated time rate of change for diagnosis, or the diagnosis may be performed without using the time rate of change. If the diagnosis unit 13 performs diagnosis without using the time rate of change, the diagnosis unit 13 may perform diagnosis based on whether the number of microorganisms to be detected exceeds a first reference value. If the device 1 does not include the setting unit 14, the diagnosis unit 13 may perform diagnosis using a fixed reference value regardless of age.

[0068] Although the imaging unit 110 has been described as imaging a fluid sample while it is flowing, it may also image a stationary fluid sample. For example, the imaging unit 110 may image a fluid sample dropped onto a slide glass, or a fluid sample contained in a well provided in a plate.

[0069] Although the fluid sample has been described as a liquid such as raw milk, it may also be a gas sample collected from a living body. For example, the fluid sample may be exhaled air expelled from the mouth or nose (for example, burps expelled from the internal organs), or farts expelled from the anus.

[0070] Furthermore, although the living body has been described as an animal other than a human, it may also be a human. In this case, the device 1 can diagnose the human's condition at an early stage. When the living body is a human, for example, the fluid sample may be breast milk, the microorganisms to be detected may be Staphylococcus aureus, environmental Staphylococcus aureus, or Escherichia coli, and the diagnosis unit 13 may diagnose whether the human is suffering from mastitis.

[0071] Although the microorganisms to be detected are described as pathogens, they may also be benign microorganisms that have a positive effect on living organisms. For example, the microorganisms to be detected may be beneficial bacteria such as lactic acid bacteria and bifidobacteria.

[0072] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of an apparatus responsible for performing the operations. Particular stages and sections may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable medium, and / or a processor provided with computer-readable instructions stored on a computer-readable medium. Dedicated circuitry may include digital and / or analog hardware circuitry, and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuitry may include reconfigurable hardware circuitry, including logical AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, memory elements such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like.

[0073] A computer-readable medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that the computer-readable medium having instructions stored thereon comprises an article of manufacture containing instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable media may include electronic, magnetic, optical, electromagnetic, and semiconductor storage media. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray disc, memory stick, integrated circuit card, and the like.

[0074] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0075] The computer-readable instructions may be provided to a processor or programmable circuit of a programmable data processing device, such as a computer, locally or over a wide area network (WAN) such as a local area network (LAN) or the Internet, and the computer-readable instructions may be executed to create means for performing the operations specified in the flowcharts or block diagrams. Here, the computer may be a personal computer (PC), a tablet computer, a smartphone, a workstation, a server computer, a general-purpose computer, a special-purpose computer, or the like, or may be a computer system in which multiple computers are connected. Such a computer system in which multiple computers are connected is also called a distributed computing system, and is a broad definition of computer. In a distributed computing system, the multiple computers collectively execute a program by each executing a portion of the program and passing data between the computers as needed during program execution.

[0076] Examples of processors include a computer processor, a central processing unit (CPU), a processing unit, a microprocessor, a digital signal processor, a controller, a microcontroller, etc. A computer may have one or more processors. In a multiprocessor system with multiple processors, each processor executes a portion of a program and passes data between processors as needed during program execution, allowing the multiple processors to collectively execute a program. For example, in multitasking, each of the multiple processors may execute a portion of each task in small chunks by switching tasks at time slice intervals. In this case, which portion of a program each processor executes changes dynamically. Which portion of a program each of the multiple processors executes may also be statically determined by multiprocessor-aware programming.

[0077] 4 illustrates an example of a computer 1200 in which aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 1200 may cause the computer 1200 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to embodiments of the present invention, and / or to perform a process or steps of a process according to embodiments of the present invention. Such programs may be executed by the CPU 1212 to cause the computer 1200 to perform specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0078] A computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, a graphics controller 1216, and a display device 1218, which are interconnected by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224 such as a hard disk drive, a DVD-ROM drive 1226, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The computer also includes legacy input / output units such as a ROM 1230 and a keyboard 1242, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0079] The CPU 1212 operates according to programs stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into the graphics controller 1216 itself, and causes the image data to be displayed on the display device 1218.

[0080] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD-ROM drive 1226 reads programs or data from a DVD-ROM 1227 and provides the programs or data to the storage device 1224 via the RAM 1214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0081] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or programs that depend on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0082] The programs are provided by a computer-readable medium such as a DVD-ROM 1227 or an IC card. The programs are read from the computer-readable medium, installed in the storage device 1224, RAM 1214, or ROM 1230, which are also examples of computer-readable media, and executed by the CPU 1212. Information processing described in these programs is read by the computer 1200, and causes cooperation between the programs and the various types of hardware resources described above. An apparatus or a method may be configured by implementing information manipulation or processing in accordance with the use of the computer 1200.

[0083] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 may execute a communication program loaded into the RAM 1214 and instruct the communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer processing area provided in the RAM 1214, the storage device 1224, the DVD-ROM 1227, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0084] Furthermore, the CPU 1212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, the DVD-ROM drive 1226 (DVD-ROM 1227), an IC card, etc. to be read into the RAM 1214, and perform various types of processing on the data in the RAM 1214. The CPU 1212 then writes back the processed data to the external recording medium.

[0085] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. CPU 1212 may perform various types of processing on data read from RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to RAM 1214. CPU 1212 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored on the recording medium, CPU 1212 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0086] The above-described programs or software modules may be stored in a computer-readable medium on or near the computer 1200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable medium, thereby providing the programs to the computer 1200 via the network.

[0087] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0088] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0089] 1 device 11 Detection unit 12 Acquisition Department 13 Diagnostic Department 14 Setting section 110 Imaging unit 111 Image Recognition Unit 1200 Computer 1210 host controller 1212 CPU 1214 RAM 1216 Graphics Controller 1218 Display Devices 1220 Input / Output Controller 1222 communication interface 1224 Storage device 1226 DVD-ROM drive 1227 DVD-ROM 1230 ROM 1240 Input / Output Chip 1242 keyboard

Claims

1. a detection unit that detects the number of predetermined types of microorganisms per unit volume of a fluid sample collected from a living body; a diagnosis unit that diagnoses a state of the living body based on a detection result by the detection unit; An apparatus comprising:

2. The device according to claim 1 , wherein the diagnosing unit diagnoses the state of the living body based on a trend of fluctuations in the detection results obtained by the detecting unit.

3. the microorganism is a pathogen; The diagnostic unit the number of microorganisms is equal to or greater than a first reference value; The number of microorganisms is less than a first reference value and the time change rate of the number of microorganisms exceeds a reference change rate; The device of claim 2 , further comprising: a diagnostic device for diagnosing a disease in the living body;

4. the detection unit further detects the number of somatic cells of a predetermined type per unit volume of the fluid sample; The device according to claim 1 , wherein the diagnostic unit diagnoses the state of the living body based on the number of the microorganisms and the number of the somatic cells detected by the detection unit.

5. the microorganism is a pathogen; The diagnostic unit the number of somatic cells is equal to or greater than a second reference value; the number of somatic cells is less than a second reference value, the number of microorganisms is less than a third reference value, and the time rate of change of the number of microorganisms is greater than the time rate of change of the number of somatic cells; The device of claim 4 , further comprising: a diagnostic device for diagnosing the living body as having a disease;

6. the microorganism is a pathogen; The device of claim 4 , wherein the somatic cells are white blood cells or epithelial cells.

7. an acquisition unit that acquires a collection time from the living body for each of the fluid samples; The device according to claim 1 , wherein the diagnostic unit calculates a time rate of change of the detection result by the detection unit from the collection times of the plurality of fluid samples, and performs diagnosis using the time rate of change.

8. the diagnosing unit performs a diagnosis by comparing a detection result by the detecting unit with a reference value; The device according to claim 1 , further comprising a setting unit that sets the reference value to a different value depending on the age of the living organism.

9. The detection unit an imaging unit that images the fluid sample; an image recognition unit that recognizes a subject in a captured image; 10. The apparatus of claim 1, comprising:

10. The device according to claim 9 , wherein the imaging unit images the fluid sample while the fluid sample is flowing.

11. The apparatus of claim 1 , wherein the living body is a human.

12. The apparatus of claim 1 , wherein the living organism is a livestock animal.

13. the living organism is a dairy cow, The device of claim 12 , wherein the fluid sample is raw milk.

14. The apparatus according to claim 13 , wherein the diagnostic unit diagnoses whether the living body is suffering from mastitis.

15. 1. A computer-implemented method comprising: a detection step of detecting the number of predetermined types of microorganisms per unit volume of a fluid sample taken from a living body; a diagnosis step of diagnosing a state of the living body based on the detection result of the detection step; A method for providing the above.

16. When executed by a computer, the computer a detection unit that detects the number of predetermined types of microorganisms per unit volume of a fluid sample collected from a living body; a diagnosis unit that diagnoses the state of the living body based on the detection result by the detection unit; A program that functions as a

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