Estimation device and estimation method

Abstract

To provide an estimation device and a method for estimation that allow an easy estimation of vital data.SOLUTION: An estimation device includes: a container for containing a specimen including a composite particle as a combination of an oral fungus and a labeled substance and a foreign substance; an imaging unit for imaging the composite particle and generating an image; a counting unit for counting the number of oral fungi for each several states on the basis of the generated image; and an estimation unit for estimating the vital data of a subject who provided the specimen on the basis of the number of oral fungi in at least one of the states.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an estimation device and an estimation method.

Background Art

[0002] Conventionally, for the prevention of various diseases and risk judgment, the immunity of the human body has been evaluated in various ways based on vital data of the human body. In addition to being highly accurate, an immunity evaluation method is desired to be simple and cause little invasion to the human body. For example, Patent Document 1 describes a method of measuring the amount of candida mannan antigen contained in saliva collected from the oral cavity using an antigen-antibody reaction and evaluating the magnitude of immunity based on the amount of candida mannan antigen.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, oral fungi may take a plurality of forms with different pathogenicities. For example, candida in the oral cavity first adheres to the oral mucosal epithelium by intermolecular force and then secondarily adheres to the epithelium via proteins in saliva or blood. The secondarily adhered candida changes into a pathogenic pseudohyphal form and invades under the epithelium. By considering such forms of oral fungi, it is considered that vital data for highly accurately evaluating the immunity of a subject can be easily estimated.

[0005] The present invention has been made to solve the above problems, and an object thereof is to provide an estimation device and an estimation method that enable easy estimation of vital data.

Means for Solving the Problems

[0006] The estimation device according to the present invention is characterized by comprising: a container for containing a sample containing composite particles on which a labeling substance is bound to oral fungi and impurities; an imaging unit for imaging the composite particles and generating an image; a counting unit for counting the number of oral fungi for each of several states based on the generated image; and an estimation unit for estimating the vital data of the subject who provided the sample based on the number of oral fungi in at least one of the states of oral fungi.

[0007] Furthermore, in the estimation device according to the present invention, it is preferable that the multiple states are a primary attached yeast state, a secondary attached yeast state, and a pseudohyphae state.

[0008] Furthermore, in the estimation device according to the present invention, the vital data is preferably the number of immune cells in the subject's blood.

[0009] Furthermore, in the estimation device according to the present invention, the number of immune cells in the subject's blood is preferably estimated as the number of white blood cells, based on at least one of the number of oral fungi in a secondary attachment yeast state and the number of oral fungi in a pseudohyphae state.

[0010] Furthermore, in the estimation device according to the present invention, the number of immune cells in the subject's blood is preferably the number of neutrophils, estimated based on at least one of the number of secondary-attached yeast-like oral fungi and the number of pseudohyphae-like oral fungi.

[0011] Furthermore, in the estimation device according to the present invention, the number of immune cells in the subject's blood is preferably the number of lymphocytes, which is estimated based on the total number of oral fungi in each state.

[0012] Furthermore, in the estimation device according to the present invention, the number of immune cells in the subject's blood is preferably the number of T cells.

[0013] Furthermore, in the estimation device according to the present invention, the number of immune cells in the subject's blood is preferably the number of B cells.

[0014] Furthermore, in the estimation device according to the present invention, the number of immune cells in the subject's blood is preferably the number of NK cells.

[0015] Furthermore, in the estimation device according to the present invention, it is preferable that the estimation unit estimates the number of multiple types of immune cells, and the estimation device further includes a determination unit that determines the subject's immune strength based on the estimated number of multiple types of immune cells.

[0016] Furthermore, in the estimation device according to the present invention, it is preferable that the counting unit weights and counts the number of oral fungi in each state based on the brightness values ​​of the pixels included in the generated image.

[0017] The estimation method according to the present invention is characterized by comprising the steps of: placing a sample containing oral fungi and impurities in a container; binding a labeling substance to oral fungi to generate composite particles; imaging the composite particles to generate an image; counting the number of oral fungi for each of several states based on the generated image; and estimating the vital data of the subject who provided the sample based on the number of oral fungi in at least one of the states of oral fungi. [Effects of the Invention]

[0018] The estimation device and estimation method according to the present invention enable the simple estimation of vital data. [Brief explanation of the drawing]

[0019] [Figure 1] This is a schematic diagram showing the configuration of the immunity assessment device 1. [Figure 2] This is a functional block diagram of the immunity assessment device 1. [Figure 3] This is a flowchart illustrating the process for determining immunity levels. [Figure 4] This figure shows the judgment result screen 100. [Figure 5](A) is a diagram showing the relationship between the total protein level in the blood and the number of oral fungi in the primary attachment state, (B) is a diagram showing the relationship between the total protein level in the blood and the number of oral fungi in the secondary attachment state, and (C) is a diagram showing the relationship between the total protein level in the blood and the number of oral fungi in the pseudohyphal state. [Figure 6] (A) is a diagram showing the relationship between the total protein level in the blood and the total number of oral fungi in the primary attachment state and the secondary attachment state, and (B) is a diagram showing the relationship between the total protein level in the blood and the total number of oral fungi in the primary attachment state, the secondary attachment state, and the pseudohyphal state. [Figure 7] (A) is a diagram showing the relationship between the albumin level in the blood and the number of oral fungi in the primary attachment state, (B) is a diagram showing the relationship between the albumin level in the blood and the number of oral fungi in the secondary attachment state, (C) is a diagram showing the relationship between the albumin level in the blood and the number of oral fungi in the pseudohyphal state, and (D) is a diagram showing the relationship between the albumin level in the blood and the total number of oral fungi in each state. [Figure 8] (A) is a diagram showing the relationship between the CRP level in the blood and the number of oral fungi in the primary attachment state, (B) is a diagram showing the relationship between the CRP level in the blood and the number of oral fungi in the secondary attachment state, and (C) is a diagram showing the relationship between the CRP level in the blood and the number of oral fungi in the pseudohyphal state. [Figure 9] (A) is a diagram showing the relationship between the white blood cell count in the blood and the number of oral fungi in the secondary attachment state, (B) is a diagram showing the relationship between the white blood cell count in the blood and the number of oral fungi in the pseudohyphal state, and (C) is a diagram showing the relationship between the white blood cell count in the blood and the total number of oral fungi in the secondary attachment state and the pseudohyphal state. [Figure 10] (A) is a diagram showing the relationship between the neutrophil count in the blood and the number of oral fungi in the secondary attachment state, (B) is a diagram showing the relationship between the neutrophil count in the blood and the number of oral fungi in the pseudohyphal state, and (C) is a diagram showing the relationship between the neutrophil count in the blood and the total number of oral fungi in the secondary attachment state and the pseudohyphal state. [Figure 11](A) is a diagram showing the relationship between the number of lymphocytes in the blood and the number of oral fungi in the primary attachment state, (B) is a diagram showing the relationship between the number of lymphocytes in the blood and the number of oral fungi in the secondary attachment state, (C) is a diagram showing the relationship between the number of lymphocytes in the blood and the number of oral fungi in the pseudohyphae state, and (D) is a diagram showing the relationship between the number of lymphocytes in the blood and the total number of oral fungi in each state. [Figure 12] (A) is a diagram showing the relationship between the number of white blood cells in the blood and the weighted total number of oral fungi in the secondary attachment state and the pseudohyphae state, and (B) is a diagram showing the relationship between the number of neutrophils in the blood and the weighted total number of oral fungi in the secondary attachment state and the pseudohyphae state. [Figure 13] This is a schematic diagram showing the configuration of the immunity assessment device 2. [Figure 14] This is a schematic diagram showing the configuration of the immunity assessment device 3. [Modes for carrying out the invention]

[0020] Various embodiments of the present invention will be described below with reference to the drawings. Please note that the technical scope of the present invention is not limited to these embodiments, but extends to the invention described in the claims and its equivalents.

[0021] Figure 1 is a schematic diagram showing the configuration of the immunotherapy determination device 1 according to an embodiment, and Figure 2 is a functional block diagram of the immunotherapy determination device 1. The immunotherapy determination device 1 is an example of an estimation device that counts oral fungi contained in a sample such as saliva or oral mucosa to estimate the vital data of the subject and determines the subject's immunity based on the estimated vital data. The immunotherapy determination device 1 includes a detection container 11, a magnetic field application unit 12, an irradiation unit 13, an optical system 14, an imaging unit 15, a storage unit 16, a display unit 17, and a processing unit 18.

[0022] The detection container 11 contains a sample containing the target U, which is an oral fungus present in the oral cavity, and contaminants B from the oral cavity. The sample is an unheated sample in which the shape of the target U is maintained and the target U can exist in multiple states with different shapes. In the example shown in Figure 1, the detection container 11 is filled with the sample. The observation surface 111 of the detection container 11, which faces the optical system 14 and the imaging unit 15, is made of a translucent material. Preferably, the observation surface 111 of the detection container 11 is made of one or more translucent materials selected from acrylic, polystyrene, cycloolefin polymer, polyethylene terephthalate, and polyethylene. Preferably, the bottom and sides of the detection container 11, which are surfaces other than the observation surface 111, are made of a material that blocks external light, such as by applying a black coating. Preferably, the bottom and sides of the detection container 11 are made of a material that does not emit autofluorescence due to the excitation light irradiated by the irradiation unit 13.

[0023] The target substance U contained in the sample housed in the detection container 11 is bound to a fluorescent labeling substance E and a magnetic labeling substance M to form a composite particle C. The fluorescent labeling substance E is a labeling substance that specifically binds to the target substance U, which is bound to a fluorescent dye by a staining solution or the like. As the fluorescent labeling substance E, one that is excited at a wavelength of 550 nm or higher is used. Preferably, the fluorescent labeling substance E is one that is excited at a wavelength of 700 nm, and more preferably, one that is excited at a wavelength of 800 nm or higher is used. The magnetic labeling substance M is a labeling substance that specifically binds to the target substance U, which is bound to magnetic particles by chemical bonding, physical adsorption, avidin-biotin bonding, etc. Note that the fluorescent labeling substance E and the magnetic labeling substance M are just examples of labeling substances.

[0024] The labeling substance is, for example, an antibody. If the target U is Candida albicans, for example, an anti-Candida albicans antibody or a β-1,3-glucan antibody is used as the labeling substance. If the target U is Candida tropicalis, for example, a β-1,3-glucan antibody is used as the labeling substance.

[0025] The sample contained in the detection container 11 may have been treated with an impurity-degrading enzyme before the target substance U binds to the fluorescently labeled substance E and the magnetically labeled substance M. This reduces the amount of impurities B contained in the sample. However, since not all impurities are removed by the impurity-degrading enzyme treatment, the sample will still contain a certain amount of impurities B in this case.

[0026] The magnetic field application unit 12 is configured to apply a magnetic field inside the detection container 11 so as to move the composite particles C to the detection region D inside the detection container 11, and includes a pair of magnets 121. The pair of magnets 121 are spaced apart by a predetermined distance and arranged so that the magnetic pole surfaces of the same pole (N pole in the example shown in Figure 1) face each other.

[0027] A pair of magnets 121 are placed, for example, above the detection container 11. In this case, the composite particles C containing the magnetically labeled material M are moved to the detection region D, which is the upper part of the detection container 11, by the magnetic force of the magnets 121. On the other hand, the impurities B contained in the sample are moved to the lower part of the detection container 11 by gravity. Thus, the composite particles C and the impurities B are separated.

[0028] The irradiation unit 13 is configured to irradiate excitation light to excite the fluorescently labeled substance E of the composite particle C, and includes, for example, an LED (Light Emitting Diode) light source. The excitation light irradiated by the irradiation unit 13 is light in the wavelength band of 550 nm or higher to suppress the excitation of impurities B in the sample. The excitation light is preferably light in the wavelength band of 700 nm or higher, and more preferably light in the wavelength band of 800 nm or higher, depending on the excitation wavelength band of the fluorescently labeled substance E.

[0029] The optical system 14 is configured to guide the excitation light emitted from the illumination unit 13 into the detection container 11 and to guide the light emitted from the detection container 11 to the imaging unit 15, and includes an excitation light filter 141, a dichroic mirror 142, an objective lens 143, and a detection filter 144. Note that the detection filter 144 is an example of an optical component.

[0030] The excitation light filter 141 is a band-pass optical filter that transmits light in a predetermined excitation wavelength band and blocks light in other wavelength bands. The excitation wavelength band is the wavelength band that includes the wavelength of the excitation light emitted from the irradiation unit 13. The dichroic mirror 142 reflects the excitation light that has passed through the excitation light filter 141 and guides it into the inside of the detection container 11, and also transmits the fluorescence emitted from the fluorescently labeled material E in the detection container 11 and guides it to the imaging unit 15. The objective lens 143 is a convex lens that faces the observation surface 111 of the detection container 11. The detection filter 144 is a band-pass optical filter that transmits light in a predetermined detection wavelength band from the light traveling from the detection container 11 to the imaging unit 15 and blocks light in other wavelength bands. The detection wavelength band is the wavelength band that includes the wavelength of the fluorescence emitted by the fluorescently labeled material E, for example, a wavelength band of 650 nm or more.

[0031] The imaging unit 15 is configured to capture images of the inside of the container and generate images, and includes, for example, a camera. The imaging unit 15 is positioned above the detection container 11 and the magnets 121 such that the magnets 121 are positioned between the detection container 11 and the imaging unit 15. The imaging unit 15 supplies the generated images to the processing unit 18.

[0032] The storage unit 16 is configured for storing programs or data, and includes, for example, semiconductor memory. The storage unit 16 stores operating system programs, driver programs, application programs, data, etc., used for processing by the processing unit 18. Programs are installed into the storage unit 16 from computer-readable, non-temporary portable storage media such as CD (Compact Disc)-ROM (Read Only Memory) or DVD (Digital Versatile Disc)-ROM.

[0033] The display unit 17 is configured for displaying images and includes, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 17 displays an image based on display data supplied from the processing unit 18.

[0034] The processing unit 18 is configured to comprehensively control the operation of the immunity assessment device 1 and includes, for example, a CPU (Central Processing Unit). The processing unit 18 may also include a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), etc. The processing unit 18 controls the operation of each component and executes various processes so that the various processes of the immunity assessment device 1 are executed in the appropriate procedure based on the program stored in the memory unit 16.

[0035] The processing unit 18 comprises an imaging control unit 181, a counting unit 182, an estimation unit 183, a determination unit 184, and an output unit 185 as functional blocks. Each of these units is a functional module realized based on a program executed by the processing unit 18. Each of these units may be implemented in the immunity assessment device 1 as firmware.

[0036] Figure 3 is a flowchart showing the flow of the immunity assessment process performed by the immunity assessment device 1. The immunity assessment process is realized by the processing unit 18 cooperating with each component of the immunity assessment device 1 based on a program stored in the memory unit 16.

[0037] First, a sample containing the target organism U, which is an oral fungus, is placed in a reaction vessel (step S101). The sample is saliva or oral mucosa, etc., containing oral contaminants B. The sample is collected, for example, by inserting a cotton swab into the subject's oral cavity. If the sample is saliva, it may also be collected by having the subject spit saliva into a sterile tube or centrifuge tube. The sample may also be subjected to treatment with contaminant-degrading enzymes, sonication, filtration, or centrifugation. This reduces contaminants B, such as epithelial cells and bacteria, in the sample, making it possible to detect the target organism U with higher accuracy.

[0038] Next, the fluorescently labeled substance E is bound to the sample U (step S102). The fluorescently labeled substance E is placed in a reaction vessel and stirred, thereby binding to the sample U.

[0039] Next, a magnetically labeled substance M is further bound to the target U to which the fluorescently labeled substance E is bound (step S103). The magnetically labeled substance M is placed in a reaction vessel and stirred, thereby binding to the target U. This generates a composite particle C to which the fluorescently labeled substance E and the magnetically labeled substance M are bound to the target U.

[0040] Steps S102 and S103 may be performed simultaneously, or step S102 may be performed after step S103. Furthermore, after step S103, the sample contained in the reaction vessel may be subjected to magnetic concentration washing. This removes the fluorescently labeled substance E and impurities B that did not bind to the target substance U, thereby enabling more accurate detection of the target substance U.

[0041] Next, the sample containing the composite particles C is placed in the detection container 11 (step S104). The sample is transferred from the reaction vessel to the detection container 11. With the detection container 11 filled with the sample, an observation surface 111 made of a translucent resin material is placed on the top surface of the detection container 11. The detection container 11 containing the sample is placed in a predetermined position on the immunoassay device 1.

[0042] In addition, step S104 may be performed before step S102, instead of step S101. That is, the sample may be placed in the detection container 11 from the beginning without being placed in the reaction vessel, and the target substance U may be bound with the fluorescently labeled substance E and the magnetically labeled substance M inside the detection container 11. In this case, step S104 is omitted.

[0043] Next, the magnetic field application unit 12 applies a magnetic field to move the composite particle C to the detection area D at the top of the detection container 11 (step S105). For example, by arranging a plurality of magnets 121 between the detection container 11 and the imaging unit 15 in a predetermined positional relationship, the magnetic field application unit 12 applies a magnetic field that moves the composite particle C to the detection area D at the top of the detection container 11. This separates the composite particle C from the impurities B.

[0044] Next, the irradiation unit 13 irradiates the inside of the detection container 11 with excitation light to excite the fluorescently labeled substance E (step S106). For example, the irradiation unit 13 irradiates with excitation light. The irradiated excitation light is guided into the inside of the detection container 11 by the optical system 14. This excites the fluorescently labeled substance E.

[0045] Next, the imaging unit 15 images the composite particles C and generates an image (step S107). The imaging control unit 181 supplies a control signal to the imaging unit 15. The imaging unit 15 images the composite particles C within the detection area D of the detection container 11 according to the control signal and generates an image. The imaging unit 15 supplies the generated image to the processing unit 18.

[0046] Next, the counting unit 182 counts the number of oral fungi for each of several states based on the generated image (step S108). For example, the counting unit 182 counts the composite particles C contained in the generated image for each state of oral fungi based on the distribution of brightness values ​​of the pixels that make up the generated image.

[0047] The counting unit 182 extracts pixels from the generated image whose brightness value is equal to or greater than a predetermined value, as pixels corresponding to the excited fluorescently labeled substance E. Before extracting pixels, the counting unit 182 may apply a smoothing process such as a Gaussian filter to the image to remove noise. The counting unit 182 classifies the extracted pixels into multiple groups using known clustering methods or the like. For example, the counting unit 182 classifies the extracted pixels into multiple groups such that pixels that are close to each other belong to the same group. Each group represents a fluorescently labeled substance E bound to the same measurement target U and corresponds to a single composite particle C.

[0048] The counting unit 182 sets the smallest region (e.g., a rectangular region) that encompasses the pixels included in each group, and calculates the size of each region as a distribution of pixel brightness values. The size of each region is, for example, the area of ​​the region or the length of the perimeter of the region. If the region is a rectangular region, the size of each region may be the length of the longer side or the length of the shorter side. Two or more of the area, perimeter length, length of the longer side, and length of the shorter side of the region may be used as the size of the region. The counting unit 182 also calculates, for each group, the number of pixels included in the group whose brightness value is equal to or greater than a predetermined value, and a representative value of the brightness value of the pixels whose brightness value is equal to or greater than the predetermined value, as a distribution of pixel brightness values. The representative value is a value based on the brightness value of each pixel, such as the mean or median. The counting unit 182 counts the composite particles C for each oral fungal state by determining the state of the oral fungi corresponding to each group based on the calculated distribution of pixel brightness values.

[0049] Candida albicans, a type of oral fungus, exists in both a yeast state and a pseudohyphae state. In its yeast state, Candida albicans first attaches to the epithelium of the oral mucosa through intermolecular forces, then secondarily attaches via proteins, and further transforms into a pseudohyphae state, invading the subepithelial layer and exhibiting pathogenicity of oral candidiasis. Therefore, detecting Candida albicans in three different states—primary attachment (yeast state), secondary attachment (yeast state), and pseudohyphae—may be useful in determining the progression of oral candidiasis.

[0050] Since Candida albicans in the primary attachment state is spherical, the fluorescently labeled substance E bound to a single Candida albicans in the primary attachment state is relatively densely concentrated. Therefore, the size of the region and the total number of pixels corresponding to such a group of fluorescently labeled substance E are small, and the representative value of the brightness is large. On the other hand, since Candida albicans in the pseudohyphae state is filamentous, the fluorescently labeled substance E bound to a single Candida albicans in the pseudohyphae state is relatively dispersed. Therefore, the size of the region and the total number of pixels corresponding to such a group of fluorescently labeled substance E are large, and the representative value of the brightness is small. Furthermore, since Candida albicans in the secondary attachment state is adsorbed to epithelial tissue fragments and has a shape different from both spherical and filamentous, the fluorescently labeled substance E bound to a single Candida albicans in the secondary attachment state takes an intermediate distribution between the distribution in the primary attachment state and the distribution in the pseudohyphae state. Therefore, the counting unit 182 can determine whether each group corresponds to a primary attachment state, a secondary attachment state, or a pseudohyphae state of Candida albicans, based on the size of the region of each group, the total number of pixels, or a representative value of the brightness value.

[0051] The counting unit 182 may count the number of oral fungi for each state by weighting them based on the brightness values ​​of the pixels included in the image. For example, the weighting may be based on the brightness values ​​or representative values ​​of each group, or the size of the area corresponding to each group. For example, the counting unit 182 may count the number of oral fungi by weighting them based on the sum of the brightness values ​​as a representative value of the brightness values ​​of each group. In other words, the counting unit 182 may calculate the sum of the brightness values ​​of the groups classified into each state as the number of oral fungi for each state. Alternatively, the counting unit 182 may count the number of oral fungi by weighting them based on the area of ​​each region (i.e., the total number of pixels included in each region) as the size of the area corresponding to each group. In other words, the counting unit 182 may count the total number of pixels included in the groups classified into each state as the number of oral fungi for each state. Alternatively, the counting unit 182 may calculate the sum of the representative value of the brightness values ​​or the size of the area and the number of oral fungi counted without weighting.

[0052] The higher the representative value of the brightness, the more fluorescent labeling substance E is bound to the oral fungi, suggesting that the oral fungi are growing as pseudohyphae or adhering to a wide area of ​​epithelial tissue. Furthermore, the larger the size of the region corresponding to each group, the more likely it is that the oral fungi are growing as pseudohyphae or adhering to a wide area of ​​epithelial tissue. Such oral fungi have a significant impact on vital data. Therefore, by weighting the number of oral fungi in the counting unit 182, vital data can be estimated with higher accuracy.

[0053] Next, the estimation unit 183 estimates the vital data of the subject who provided the sample based on the number of oral fungi in at least one of the states of oral fungi (step S109). The multiple states of oral fungi are, for example, primary attachment state, secondary attachment state, and pseudohyphae state. The estimation unit 183 estimates the subject's vital data based on the relationship between the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state, which are pre-stored in the memory unit 16, and the vital data. The vital data are, for example, the total protein amount, albumin amount, CRP (C-Reactive Protein) amount, or immune cell count in the subject's blood. The immune cell count is, for example, the white blood cell count, neutrophil count, or lymphocyte count. The lymphocyte count is, for example, the T cell count, B cell count, and NK cell count. The relationship between the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state, and vital data, will be described later using Figures 5-12.

[0054] For example, the estimation unit 183 estimates the total protein amount in the subject's blood based on at least one of the following: the number of primary attached oral fungi, the number of secondary attached oral fungi, and the number of pseudohyphae. Preferably, the estimation unit 183 estimates the total protein amount in the subject's blood based on the total number of primary attached oral fungi and secondary attached oral fungi. Preferably, the estimation unit 183 may estimate the total protein amount in the subject's blood based on the total number of primary attached oral fungi, secondary attached oral fungi, and pseudohyphae. The estimation unit 183 also estimates the amount of albumin in the subject's blood based on at least one of the following: the number of primary attached oral fungi, secondary attached oral fungi, and pseudohyphae. Preferably, the estimation unit 183 estimates the amount of albumin in the subject's blood based on the total number of primary attached oral fungi, secondary attached oral fungi, and pseudohyphae. Furthermore, the estimation unit 183 estimates the amount of CRP in the subject's blood based on at least one of the following: the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state.

[0055] For example, the estimation unit 183 estimates the number of white blood cells in the subject's blood based on at least one of the number of oral fungi in the secondary attachment state and the number of oral fungi in the pseudohyphae state. Preferably, the estimation unit 183 estimates the number of white blood cells in the subject's blood based on the total number of oral fungi in the secondary attachment state and the pseudohyphae state. The estimation unit 183 also estimates the number of neutrophils in the subject's blood based on at least one of the total number of oral fungi in the secondary attachment state and the pseudohyphae state. Preferably, the estimation unit 183 estimates the number of neutrophils in the subject's blood based on the total number of oral fungi in the secondary attachment state and the pseudohyphae state. The estimation unit 183 also estimates the number of lymphocytes in the subject's blood based on at least one of the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state and the number of oral fungi in the pseudohyphae state. Preferably, the estimation unit 183 estimates the number of lymphocytes in the subject's blood based on the total number of oral fungi in the primary attachment state, oral fungi in the secondary attachment state, and oral fungi in the pseudohyphae state.

[0056] The estimation unit 183 estimates the number of T cells in the subject's blood based on at least one of the following: the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state. The estimation unit 183 estimates the number of B cells in the subject's blood based on at least one of the following: the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state. The estimation unit 183 estimates the number of NK cells in the subject's blood based on at least one of the following: the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state.

[0057] Next, the determination unit 184 determines the subject's immune system based on the subject's vital data (step S109). For example, the determination unit 184 determines the subject's immune system based on the number of multiple types of immune cells.

[0058] For example, the determination unit 184 determines whether the number of multiple types of immune cells in the estimated vital data falls within a predetermined normal range. Based on the number of types of immune cells determined to fall within the normal range, the determination unit 184 determines an immune grade, which indicates which of several predetermined categories the subject's immune strength belongs to.

[0059] Next, the output unit 185 outputs the result of the assessment of the subject's immune system (step S110). The output unit 185 outputs the assessment result by displaying the assessment result screen on the display unit 17.

[0060] Figure 4 shows the judgment result screen 100 displayed on the display unit 17. The judgment result screen 100 includes a vital data display 110 and an immunity judgment display 120. The vital data display 110 displays the estimated values ​​of vital data estimated by the estimation unit 183 for each item. The vital data display 110 also indicates whether the estimated values ​​of vital data fall within the normal range. In the example shown in Figure 4, estimated values ​​of vital data that do not fall within the normal range are underlined. The immunity judgment display 120 displays the subject's immunity grade as a result of the judgment of the subject's immunity. In the example shown in Figure 4, it is shown that the subject's immunity grade is "C", meaning that the subject's immunity belongs to the third category from the highest.

[0061] The following describes the relationship between the number of oral fungi in each state and vital data, as stored in the memory unit 16. The data used in the following explanation is based on a study of 10 male and female subjects ranging in age from their teens to their 80s. The data shows the relationship between the number of Candida fungi in each state, counted by the immunoassay device 1, and the results of each subject's blood tests, using saliva samples from each subject.

[0062] Figure 5(A) shows the relationship between the total amount of protein in the subject's blood and the number of oral fungi in the primary attachment state. Figure 5(B) shows the relationship between the total amount of protein in the subject's blood and the number of oral fungi in the secondary attachment state. Figure 5(C) shows the relationship between the total amount of protein in the subject's blood and the number of oral fungi in the pseudohyphae state. Figure 6(A) shows the relationship between the total amount of protein in the subject's blood and the total number of oral fungi in the primary attachment state and the total number of oral fungi in the secondary attachment state. Figure 6(B) shows the relationship between the total amount of protein in the subject's blood and the total number of oral fungi in the primary attachment state, the secondary attachment state, and the pseudohyphae state.

[0063] As shown in Figures 5(A) and (B), there is a negative correlation between the total amount of protein in the blood and the number of oral fungi in the primary and secondary attachment states, where a higher total protein amount is associated with a lower number of oral fungi. As shown in Figure 5(C), there is a negative correlation between the total amount of protein in the blood and the number of oral fungi in the pseudohyphae state, where a significantly lower total protein amount outside the normal range is associated with a higher number of pseudohyphae. Therefore, the estimation unit 183 can estimate the total amount of protein in the subject's blood as vital data based on at least one of the following: the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state.

[0064] Furthermore, as shown in Figure 6(A), there is a nearly linear and strong negative correlation between the total amount of protein in the blood and the total number of oral fungi in the primary and secondary attachment states. Therefore, the estimation unit 183 can estimate the total amount of protein in the subject's blood with high accuracy based on the total number of oral fungi in the primary and secondary attachment states. Furthermore, as shown in Figure 6(B), there is a nearly linear and particularly strong negative correlation between the total amount of protein in the blood and the total number of oral fungi in the primary, secondary, and pseudohyphae states. Therefore, the estimation unit 183 can estimate the total amount of protein in the subject's blood with even higher accuracy based on the total number of oral fungi in the primary, secondary, and pseudohyphae states.

[0065] Figure 7(A) shows the relationship between the amount of albumin in the subject's blood and the number of oral fungi in the primary attachment state. Figure 7(B) shows the relationship between the amount of albumin in the subject's blood and the number of oral fungi in the secondary attachment state. Figure 7(C) shows the relationship between the amount of albumin in the subject's blood and the number of oral fungi in the pseudohyphae state. Figure 7(D) shows the relationship between the amount of albumin in the subject's blood and the total number of oral fungi in the primary attachment state, the secondary attachment state, and the pseudohyphae state.

[0066] As shown in Figures 7(A) and (B), there is a negative correlation between the amount of albumin in the blood and the number of oral fungi in the primary and secondary attachment states, where a higher albumin level is associated with a lower number of oral fungi. Therefore, the estimation unit 183 can estimate the amount of albumin in the subject's blood as vital data based on at least one of the number of oral fungi in the primary attachment state and the number of oral fungi in the secondary attachment state. Furthermore, as shown in Figure 7(C), there is a positive correlation between the amount of albumin in the blood and the number of oral fungi in the pseudohyphae state, where a higher albumin level is associated with a higher number of oral fungi. Therefore, the estimation unit 183 can estimate the amount of albumin in the subject's blood as vital data based on the number of oral fungi in the pseudohyphae state. Additionally, as shown in Figure 7(D), there is a strong correlation between the amount of albumin in the blood and the total number of oral fungi in the primary attachment state, the secondary attachment state, and the pseudohyphae state. Therefore, the estimation unit 183 can accurately estimate the amount of albumin in the subject's blood based on the total number of oral fungi in the primary attachment state, oral fungi in the secondary attachment state, and oral fungi in the pseudohyphae state.

[0067] Figure 8(A) shows the relationship between the amount of CRP in the subject's blood and the number of oral fungi in the primary attachment state. Figure 8(B) shows the relationship between the amount of CRP in the subject's blood and the number of oral fungi in the secondary attachment state. Figure 8(C) shows the relationship between the amount of CRP in the subject's blood and the number of oral fungi in the pseudohyphae state. In addition, the data enclosed by dashed circles in Figures 8(A)-(C) are data from subjects who use dentures.

[0068] As shown in Figures 8(A)-(C), for subjects who do not wear dentures, there is a negative correlation between the amount of CRP in the blood and the number of oral fungi in the primary, secondary, and pseudohyphae states, with higher CRP levels associated with lower numbers of oral fungi. Furthermore, for subjects who wear dentures, data was obtained showing low blood CRP levels and particularly high numbers of oral fungi (indicated by dashed circles in Figures 8(A)-(C)). Therefore, when a subject does not wear dentures, the estimation unit 183 can estimate the amount of CRP in the subject's blood as vital data based on at least one of the numbers of oral fungi in the primary, secondary, and pseudohyphae states.

[0069] Figure 9(A) shows the relationship between the number of white blood cells in the subject's blood and the number of oral fungi in a secondary attachment state. Figure 9(B) shows the relationship between the number of white blood cells in the subject's blood and the number of oral fungi in a pseudohyphae state. Figure 9(C) shows the relationship between the number of white blood cells in the subject's blood and the total number of oral fungi in a secondary attachment state and oral fungi in a pseudohyphae state.

[0070] As shown in Figure 9(A), there is a nearly linear negative correlation between the number of white blood cells in the blood and the number of oral fungi in the secondary attachment state, where the number of oral fungi decreases as the number of white blood cells increases. As shown in Figure 9(B), there is a negative correlation between the number of white blood cells in the blood and the number of oral fungi in the pseudohyphal state, where the number of oral fungi in the pseudohyphal state increases when the number of white blood cells is small and outside the normal range. Therefore, the estimation unit 183 can estimate the number of white blood cells in the subject's blood as the number of immune cells in the vital data, based on at least one of the number of oral fungi in the secondary attachment state and the number of oral fungi in the pseudohyphal state. Furthermore, as shown in Figure 9(C), there is a stronger negative correlation between the number of white blood cells in the blood and the total number of oral fungi in the secondary attachment state and oral fungi in the pseudohyphal state. Therefore, the estimation unit 183 can estimate the number of white blood cells in the subject's blood as the number of immune cells in the vital data, based on the number of oral fungi in the secondary attachment state and the number of oral fungi in the pseudohyphal state, with high accuracy.

[0071] Figure 10(A) shows the relationship between the number of neutrophils in the subject's blood and the number of oral fungi in a secondary attachment state. Figure 10(B) shows the relationship between the number of neutrophils in the subject's blood and the number of oral fungi in a pseudohyphae state. Figure 10(C) shows the relationship between the number of neutrophils in the subject's blood and the total number of oral fungi in a secondary attachment state and oral fungi in a pseudohyphae state.

[0072] As shown in Figure 10(A), there is a nearly linear negative correlation between the number of neutrophils in the blood and the number of oral fungi in the secondary attachment state, where the number of oral fungi decreases as the number of neutrophils increases. As shown in Figure 10(B), there is a negative correlation between the number of neutrophils in the blood and the number of oral fungi in the pseudohyphal state, where the number of oral fungi in the pseudohyphal state increases when the number of neutrophils is small and outside the normal range. Therefore, the estimation unit 183 can estimate the number of neutrophils in the blood of the subject as the number of immune cells in the vital data, based on at least one of the number of oral fungi in the secondary attachment state and the number of oral fungi in the pseudohyphal state. Furthermore, as shown in Figure 10(C), there is a stronger negative correlation between the number of neutrophils in the blood and the total number of oral fungi in the secondary attachment state and oral fungi in the pseudohyphal state. Therefore, the estimation unit 183 can accurately estimate the number of neutrophils in the subject's blood as an immune cell count among the vital data, based on the number of oral fungi in a secondary attachment state and the number of oral fungi in a pseudohyphae state.

[0073] Figure 11(A) shows the relationship between the number of lymphocytes in the subject's blood and the number of oral fungi in the primary attachment state. Figure 11(B) shows the relationship between the number of lymphocytes in the subject's blood and the number of oral fungi in the secondary attachment state. Figure 11(C) shows the relationship between the number of lymphocytes in the subject's blood and the number of oral fungi in the pseudohyphae state. Figure 11(D) shows the relationship between the number of lymphocytes in the subject's blood and the total number of oral fungi in the primary attachment state, the secondary attachment state, and the pseudohyphae state.

[0074] As shown in Figure 11(A), there is a positive correlation between the number of lymphocytes in the blood and the number of oral fungi in the primary attachment state, with a higher lymphocyte count associated with a higher number of oral fungi. As shown in Figures 11(B) and (C), there is a negative correlation between the number of lymphocytes in the blood and the number of oral fungi in the secondary attachment and pseudohyphae states, with a higher lymphocyte count associated with a lower number of oral fungi. Therefore, the estimation unit 183 can estimate the number of lymphocytes in the subject's blood as an immune cell count in vital data, based on at least one of the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state. Furthermore, as shown in Figure 11(D), there is a particularly strong correlation between the number of lymphocytes in the blood and the total number of oral fungi in the primary attachment state, the secondary attachment state, and the pseudohyphae state. Therefore, the estimation unit 183 can accurately estimate the number of lymphocytes in the subject's blood based on the total number of oral fungi in the primary attachment state, oral fungi in the secondary attachment state, and oral fungi in the pseudohyphae state.

[0075] The memory unit 16 may also store the relationship between the weighted number of oral fungi in each state and the vital data.

[0076] Figure 12(A) shows the relationship between the white blood cell count in the subject's blood and the weighted total number of oral fungi in the secondary attachment state and pseudohyphal state. The vertical axis of the graph in Figure 12(A) represents the total number of oral fungi in the secondary attachment state and pseudohyphal state, weighted and counted based on the sum of the brightness values ​​of each group determined by the counting unit 182. In other words, the vertical axis represents the sum of the brightness values ​​of pixels included in the group determined to be in the secondary attachment state or pseudohyphal state. As shown in Figure 12(A), there is a near-linear negative correlation between the white blood cell count in the subject's blood and the weighted total number of oral fungi in the secondary attachment state and pseudohyphal state, where the weighted total increases as the white blood cell count decreases. The correlation shown in Figure 12(A) is particularly strong compared to the correlation shown in Figure 9(C). Therefore, the estimation unit 183 can estimate the number of white blood cells in the subject's blood with particularly high accuracy based on the weighted total number of oral fungi in a secondary attachment state and oral fungi in a pseudohyphae state.

[0077] Figure 12(B) shows the relationship between the number of neutrophils in the subject's blood and the weighted total number of oral fungi in the secondary attachment state and pseudohyphal state. The vertical axis of the graph in Figure 12(B), like the vertical axis of the graph in Figure 12(A), represents the total number of oral fungi in the secondary attachment state and pseudohyphal state, weighted and counted based on the sum of the brightness values ​​of each group. As shown in Figure 12(B), there is a negative correlation between the number of neutrophils in the subject's blood and the weighted total number of oral fungi in the secondary attachment state and pseudohyphal state, where the weighted total number becomes significantly larger when the neutrophil count is low and outside the normal range. The correlation shown in Figure 12(A) is particularly strong compared to the correlation shown in Figure 10(C). Therefore, the estimation unit 183 can estimate the number of neutrophils in the subject's blood with particularly high accuracy based on the weighted total number of oral fungi in the secondary attachment state and pseudohyphal state.

[0078] As described above, the immunoassay device 1 includes a detection container 11 for containing a sample containing composite particles C, an imaging unit 15 for capturing images of the composite particles C and generating images, a counting unit 182 for counting the number of oral fungi for each of several states based on the images, and an estimation unit 183 for estimating the subject's vital data based on the number of oral fungi in each state. This makes it possible for the immunoassay device 1 to easily estimate vital data.

[0079] Furthermore, the estimation unit 183 estimates the number of immune cells in the subject's blood as vital data. Since the number of immune cells in the blood has a strong correlation with the subject's immune strength, this allows the immune strength assessment device 1 to easily estimate vital data useful for determining the subject's immune strength.

[0080] Furthermore, the estimation unit 183 estimates the number of white blood cells in the subject's blood as the number of immune cells based on the number of oral fungi in the secondary attachment state. Since there is a strong correlation between the number of oral fungi in the secondary attachment state and the number of white blood cells in the blood, this enables the immunoassay device 1 to estimate vital data useful for determining the subject's immune strength simply and with high accuracy.

[0081] Furthermore, the estimation unit 183 estimates the number of neutrophils in the subject's blood as an immune cell number based on the number of oral fungi in a secondary yeast state. Since there is a strong correlation between the number of oral fungi in a secondary state and the number of neutrophils in the blood, this enables the immunoassay device 1 to estimate vital data useful for determining the subject's immune strength in a simple and highly accurate manner.

[0082] Furthermore, the estimation unit 183 estimates the number of lymphocytes in the subject's blood as the number of immune cells, based on the total number of oral fungi in the primary yeast state, the secondary yeast state, and the pseudohyphae state. Since there is a strong correlation between the total number of oral fungi in each state and the number of lymphocytes in the blood, the immunoassay device 1 enables the estimation of vital data useful for determining the subject's immune strength in a simple and highly accurate manner.

[0083] Furthermore, the estimation unit 183 estimates the number of multiple types of immune cells. The immune strength determination device 1 further includes a determination unit 184 that determines the subject's immune strength based on the number of multiple types of immune cells. This enables the immune strength determination device 1 to determine immune strength with high accuracy.

[0084] Furthermore, the estimation unit 183 estimates the number of white blood cells in the subject's blood as the number of immune cells. White blood cells consist of neutrophils, lymphocytes, monocytes, eosinophils, and basophils. Lymphocytes include NK cells, T cells, and B cells. It is preferable that the estimation unit 183 estimates the number of one or more of the immune cells described above.

[0085] Furthermore, the immunoassay device 1 detects oral microorganisms using unheated samples. This makes it possible to detect oral microorganisms in multiple states with different shapes. Conventionally, a method for detecting oral fungi is known that involves extracting and measuring antigens from oral fungi. However, heat treatment is necessary to extract antigens from oral fungi, and the fungal cells of the oral fungi are not maintained during the heat treatment. Therefore, conventional methods cannot detect oral microorganisms in different states based on their shape. The immunoassay device 1 detects oral microorganisms using unheated samples in which the fungal cells are maintained, thus enabling the detection of oral microorganisms in multiple states.

[0086] The following modifications may be applied to the immunity assessment device 1.

[0087] In the embodiment described above, the counting unit 182 counts the number of oral fungi in the primary attachment state, the number of oral fungi in the secondary attachment state, and the number of oral fungi in the pseudohyphae state, respectively, but it is not limited to this example. For example, the counting unit 182 may count the number of oral fungi in the yeast state and the number of oral fungi in the pseudohyphae state, respectively.

[0088] In the embodiment described above, the determination unit 184 determines the immune grade as the subject's immune strength, but it is not limited to this example. The determination unit 184 may also determine the level of the subject's immune strength using a numerical index. The numerical index may be, for example, the ratio of the level of immunity compared to the average value for the same age group, or the level of immunity converted to age.

[0089] In the embodiment described above, the output unit 185 outputs the judgment result by displaying the judgment result screen 100, but it is not limited to this example. The output unit 185 may also output the judgment result by transmitting information indicating the judgment result to another device. Alternatively, the output unit 185 may output the vital data itself as the judgment result. Since it is assumed that if the vital data is normal, the immune system is also normal, the immune system determination device 1 can determine the subject's immune system with high accuracy even in this manner.

[0090] In the embodiments described above, the composite particle C was assumed to be an oral fungus to which a magnetically labeled substance M and a fluorescently labeled substance E were bound; however, the invention is not limited to such examples. For example, the composite particle C may be an oral fungus to which only the fluorescently labeled substance E is bound. In this case, the immunoassay device 1 does not need to have a magnetic field application unit 12.

[0091] Figure 13 is a schematic diagram showing the configuration of an immunity assessment device 2 according to another embodiment. The immunity assessment device 2 differs from the immunity assessment device 1 in that it does not have a magnetic field application unit 12. The other components of the immunity assessment device 2 are the same as those of the immunity assessment device 1, so they are given the same reference numerals and their description is omitted.

[0092] Since the immunity assessment device 2 does not have a magnetic field application unit, the composite particles C move together with the impurities B to the detection area D below the detection container 11 due to gravity.

[0093] Since the immunoassay device 2 does not have a magnetic field application unit, steps S103 and S105 are omitted in the immunoassay process performed by the immunoassay device 2. Because the immunoassay device 2 does not have a magnetic field application unit, it is possible to estimate vital data more easily. In particular, the immunoassay device 2 facilitates bedside detection in clinical settings and is also useful in home healthcare settings. Furthermore, in the measurement process performed by the immunoassay device 2, similar to the measurement process performed by the immunoassay device 1, the sample contained in the reaction vessel may be subjected to filtration or centrifugation to separate impurities.

[0094] Figure 14 is a schematic diagram showing the configuration of an immunoassay device 3 according to another embodiment. The immunoassay device 3 differs from the immunoassay device 1 in that it does not have an optical system 14 and has a detection container 31 and an irradiation unit 33 instead of the detection container 11 and irradiation unit 13. The other components of the immunoassay device 3 are the same as those of the immunoassay device 1, so they are given the same reference numerals and their description is omitted.

[0095] The detection container 31 differs from the detection container 11 in that it does not have an observation surface 111 and its lower surface is made of a translucent resin. Since the detection container 31 does not have an observation surface 111, the top of the detection container 31 is open. Preferably, the lower surface of the detection container 31 is made of one or more translucent materials selected from acrylic, polystyrene, cycloolefin polymer, polyethylene terephthalate, and polyethylene.

[0096] The irradiation unit 33 differs from the irradiation unit 13 in that it is positioned on the outside of the detection container 31, facing a surface formed of a translucent resin. The excitation light emitted from the irradiation unit 33 is guided into the interior of the detection container 31 by passing through the lower surface of the detection container 31.

[0097] Since the immune system assessment device 3 does not have an optical system, it enables easier estimation of vital data. Furthermore, the immune system assessment device 3 does not necessarily need to have a magnetic field application unit 12. This further enables the immune system assessment device 3 to estimate vital data more easily.

[0098] Those skilled in the art will understand that various changes, substitutions, and modifications can be made without departing from the spirit and scope of the present invention. For example, the embodiments and modifications described above may be combined as appropriate within the scope of the invention. [Explanation of Symbols]

[0099] 1 Immunity determination device 11 Detection container 15 Imaging Unit 182 Counting Unit 183 Estimation Department 184 Judgment Department

Claims

1. A container for containing a sample containing oral fungi bound to a labeling substance and impurities, An imaging unit that captures the composite particles and generates an image, A counting unit that counts the number of oral fungi for each of several states based on the generated image, An estimation unit that estimates the vital data of the subject who provided the sample based on the number of oral fungi in at least one of the oral fungi in each state, An estimation device characterized by having the following features.

2. The aforementioned multiple states are the primary attached yeast state, the secondary attached yeast state, and the pseudohyphae state. The estimation device according to claim 1.

3. The estimation device according to claim 2, wherein the vital data is the number of immune cells in the blood of the subject.

4. The number of immune cells in the subject's blood is the number of white blood cells, which is estimated based on at least one of the number of secondary-attached yeast-like oral fungi and the number of pseudohyphae-like oral fungi. The estimation device according to claim 3.

5. The number of immune cells in the subject's blood is the neutrophil count, which is estimated based on at least one of the number of secondary-attached yeast-like oral fungi and the number of pseudohyphae-like oral fungi. The estimation device according to claim 3 or 4.

6. The estimation device according to claim 3 or 4, wherein the number of immune cells in the blood of the subject is the number of lymphocytes, which is estimated based on the total number of oral fungi in each state.

7. The estimation device according to claim 3, 4, or 6, wherein the number of immune cells in the blood of the subject is the number of T cells.

8. The estimation device according to claim 3, 4, or 6, wherein the number of immune cells in the blood of the subject is the number of B cells.

9. The estimation device according to claim 3, 4, or 6, wherein the number of immune cells in the blood of the subject is the number of NK cells.

10. The estimation unit estimates the number of multiple types of immune cells, The estimation device according to any one of claims 3-9, further comprising a determination unit that determines the subject's immune strength based on the estimated number of multiple types of immune cells.

11. The estimation device according to any one of claims 1 to 10, wherein the counting unit weights and counts the number of oral fungi in each state based on the brightness values ​​of the pixels included in the generated image.

12. The process involves placing a sample containing oral fungi and impurities into a container, A step of forming composite particles by binding a labeling substance to the oral fungus, A step of imaging the composite particles and generating an image, Based on the generated image, the process involves counting the number of oral fungi for each of several states. A step of estimating the vital data of the subject who provided the sample based on the number of oral fungi in at least one of the oral fungi in each state, An estimation method characterized by including the following.

Images