Information processing device, information processing method, and computer program

Abstract

This invention provides an information processing device, an information processing method, and a computer program that can determine an individual's immune response to probiotics. [Solution] In an information provision system in which a terminal device and an information provision device are connected to communicate via a network, and a judgment model generation device and an information provision device are also connected to communicate via a network, the judgment model generation device 20, which is an information processing device, includes a control unit 23 that acquires the immune information of a target person based on intake information including the items of food consumed by the target person, and an immune judgment model in which the intake information is used as an explanatory variable and immune information indicating the immune response of the target person to probiotics is used as the objective variable.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a computer program.

Background Art

[0002] In recent years, probiotics have attracted attention. For example, Patent Document 1 discloses a technique aimed at providing an immunostimulatory composition using lactic acid bacteria. Thus, an immunostimulatory effect by probiotics that contributes to elimination of pathogens and the like is expected. On the other hand, since there is a risk of causing an inflammatory state if the effect becomes excessive, it is also important to be tolerant to probiotics. As described above, there are immunostimulation and tolerance in the immune responsiveness to probiotics, and it has been found that the immune responsiveness to probiotics varies greatly among individuals. Therefore, it is important to accurately grasp the immune responsiveness of each individual to probiotics.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the factors for the difference in the immune responsiveness to probiotics among individuals have not been clearly identified. Therefore, it has been difficult to obtain information indicating the immune responsiveness (for example, stimulation or tolerance) to probiotics for each individual.

[0005] The present invention has been made in view of the above circumstances, and provides a technique that enables knowing the immune responsiveness of an individual to probiotics.

Means for Solving the Problems

[0006] One aspect of the present invention is an information processing device comprising a control unit that acquires immune information of a target person based on intake information including the items of food consumed by the target person, and an immune determination model in which the intake information is used as an explanatory variable and immune information indicating the target person's immune response to probiotics is used as an objective variable.

[0007] One aspect of the present invention is the above-described information processing device, wherein the control unit acquires the immune information for each type of probiotic.

[0008] One aspect of the present invention is an information processing device comprising a control unit that provides a user with at least one of the following: information indicating that the food items consumed by a subject person are probiotics that have a positive correlation with immune effects, and information indicating that the food items consumed by a subject person are probiotics that have a negative correlation with immune effects.

[0009] One aspect of the present invention is an information processing device comprising a control unit that provides the user with information indicating foods that have a positive correlation with the immune effect of probiotics or foods that have a negative correlation with the immune effect, for each type of probiotic.

[0010] One aspect of the present invention is an information processing device comprising a control unit that provides a user with information indicating foods that have a positive correlation with the immune effect related to probiotics or foods that have a negative correlation with the immune effect, from among intake information including the items of food consumed by a target person.

[0011] One aspect of the present invention is an information processing method in which a computer obtains immune information of a subject based on intake information including the types of food consumed by the subject, and an immune determination model in which the intake information is used as an explanatory variable and immune information indicating the subject's immune response to probiotics is used as an objective variable.

[0012] One aspect of the present invention is a computer program for causing a computer to function as an information processing device, which includes an information processing device that acquires immune information of a target person based on intake information including the items of food consumed by the target person, and an immune determination model in which the intake information is used as an explanatory variable and immune information indicating the target person's immune response to probiotics is used as an objective variable. [Effects of the Invention]

[0013] This invention makes it possible to understand an individual's immune response to probiotics and to modulate their immune response accordingly. [Brief explanation of the drawing]

[0014] [Figure 1] This is a schematic block diagram showing the system configuration of the information provision system 100 of the present invention. [Figure 2] This is a schematic block diagram showing a specific example of the functional configuration of terminal device 10. [Figure 3] This is a schematic block diagram showing a specific example of the functional configuration of the judgment model generation device 20. [Figure 4] This figure shows a specific example of the integrated value of cytokine production. [Figure 5] This figure shows a specific example of the integrated value of cytokine production. [Figure 6] This is a diagram showing the probiotic bacteria used in the experiment. [Figure 7] This is correlation information regarding immune activity for each cytokine obtained using item information (nutrient name) from the three days prior to blood collection as an explanatory factor for BB536. [Figure 8] This is correlation information regarding immune activity for each cytokine obtained using each nutrient (nutrient name) from the BDHQ information for BB536 as an explanatory factor. [Figure 9] This is correlation information on immune activity related to each cytokine obtained using the names of each food item (dish name, ingredient name, product name, etc.) from the BDHQ information for BB536 as explanatory factors. [Figure 10]This is the correlation information of immune activity regarding each cytokine obtained by using the item information (nutritional component names) in the three days before blood sampling for MCC1274 as explanatory factors. [Figure 11] This is the correlation information of immune activity regarding each cytokine obtained by using each nutrient (nutritional component names) in the information of BDHQ for MCC1274 as explanatory factors. [Figure 12] This is the correlation information of immune activity regarding each cytokine obtained by using each food name (cooking name, ingredient name, product name, etc.) in the information of BDHQ for MCC1274 as explanatory factors. [Figure 13] This is the correlation information of immune activity regarding each cytokine obtained by using the item information (nutritional component names) in the three days before blood sampling for MCC1849 as explanatory factors. [Figure 14] This is the correlation information of immune activity regarding each cytokine obtained by using each nutrient (nutritional component names) in the information of BDHQ for MCC1849 as explanatory factors. [Figure 15] This is the correlation information of immune activity regarding each cytokine obtained by using each food name (cooking name, ingredient name, product name, etc.) in the information of BDHQ for MCC1849 as explanatory factors. [Figure 16] This is a figure showing the result of clustering. [Figure 17] This is a schematic block diagram showing a specific example of the functional configuration of the information providing device 30. [Figure 18] This is a figure showing an overview of the hardware configuration example of the information processing device 90 applied to this embodiment.

Mode for Carrying Out the Invention

[0015] In the following description, "probiotics" generally refers to live microorganisms that bring beneficial effects to humans by improving the balance of the intestinal flora. As major probiotics, bacteria of the genus Bifidobacterium (so-called bifidus bacteria) and lactic acid bacteria are known.

[0016] While not particularly limited, the genus Bifidobacterium includes Bifidobacterium longum, Bifidobacterium breve, Bifidobacterium infantis (which has been reclassified as Bifidobacterium longum subspecies infantis), Bifidobacterium bifidum, Bifidobacterium adolescentis, Bifidobacterium catenulatum, Bifidobacterium pseudocatenulatum, and Bifidobacterium animalis. Examples include Bifidobacterium animalis, Bifidobacterium lactis, and Bifidobacterium pseudolongum.

[0017] More specifically, Bifidobacterium longum BB536 is a relevant example. Bifidobacterium longum BB536 was internationally deposited with the National Institute of Technology and Evaluation (NPMD) Patent Microorganism Depositary Center (NPMD) (Room 122, 2-5-8 Kazusa-Kamatari, Kisarazu City, Chiba Prefecture 292-0818, Japan) on January 26, 2018, under the accession number NITE BP-02621, in accordance with the Budapest Convention.

[0018] Furthermore, a more specific example of Bifidobacterium breve is Bifidobacterium breve MCC1274. Bifidobacterium breve MCC1274 was internationally deposited under the Budapest Convention on August 25, 2009, with the Patent Organism Depository Center of the National Institute of Advanced Industrial Science and Technology (now the Patent Organism Depository Center of the National Institute of Technology and Evaluation (IPOD) (Room 120, 2-5-8 Kazusa Kamatari, Kisarazu City, Chiba Prefecture 292-0818), under accession number FERM BP-11175.

[0019] Among these Bifidobacterium bacteria, Bifidobacterium longum or Bifidobacterium breve are more preferred, and of these, Bifidobacterium longum BB536 (accession number: NITE BP-02621) or Bifidobacterium breve MCC1274 (accession number: FERM BP-11175) are particularly preferred.

[0020] While there are no particular limitations on the lactic acid bacteria used, Lactobacillus paracasei is preferred. More specifically, Lactobacillus paracasei MCC1849 (NITE BP-01633) is a relevant example. Lactobacillus paracasei MCC1849 (NITE BP-01633) was deposited on June 6, 2013, at the National Institute of Technology and Evaluation Biotechnology Center Patent Microorganism Depository Center (NPMD) (Room 122, 2-5-8 Kazusa Kamatari, Kisarazu City, Chiba Prefecture 292-0818, Japan) with the accession number NITE BP-01633. On January 31, 2014, it was transferred to international deposit under the Budapest Convention and was assigned the same accession number NITE BP-01633. Among the Lactobacillus paracasei species, Lactobacillus paracasei MCC1849 (accession number: NITE BP-01633) is particularly preferred.

[0021] Figure 1 is a schematic block diagram showing the system configuration of the information provision system 100 of the present invention. First, the outline of the information provision system 100 will be explained. The information provision system 100 determines information indicating an individual's immune response to probiotics (hereinafter referred to as "response information") and provides the responsiveness information to the person being determined (hereinafter referred to as "target person") or to a person who will take action based on the responsiveness information for the target person (hereinafter referred to as "responder"). In the following explanation, the target person and the responder will be collectively referred to as the user.

[0022] The information provision system 100 accepts input of information regarding the oral intake of a target person (hereinafter referred to as "intake information"). The intake information includes, for example, information indicating the type of food consumed by the target person (hereinafter referred to as "item information"). The item information may be expressed, for example, as the name of the dish, the name of the ingredients, the product name, or the name of the nutritional component. The intake information may further include, for each item of information, information indicating the time when each target person consumed the food (hereinafter referred to as "time information").

[0023] The information provision system 100 generates responsiveness information for a target person who is taking probiotics, based on the input intake information. Responsiveness information may include, for example, information indicating the level of immune effect the target person is receiving from probiotics (hereinafter referred to as "immune information"). Responsiveness information may also include, for example, information on diet to obtain a higher immune effect from probiotics (hereinafter referred to as "intake suggestion information"). Intake suggestion information may include, for example, information suggesting foods that should be consumed, or information suggesting foods that should be avoided.

[0024] The information provision system 100 provides responsive information to the user. If the user is a target person, the target person can learn about the effectiveness they can obtain from probiotics (immune information) and the types of foods they should consume to enhance those effects (suggested intake information). If the user is a responder, the responder can provide information to the target person, receive consultations about diet, and offer advice based on the responsive information obtained.

[0025] The following describes a specific example of the information provision system 100. First, we will describe an information provision system 100 in which the device operated by the user when inputting intake information and the device that generates responsive information based on the intake information have different configurations.

[0026] The information provision system 100 includes a terminal device 10, a judgment model generation device 20, and an information provision device 30. The terminal device 10 and the information provision device 30 are connected to each other via a network 70. The judgment model generation device 20 and the information provision device 30 may also be connected to each other via the network 70. The network 70 may be a wireless communication network or a wired communication network. The network 70 may be configured using, for example, the Internet or a local area network (LAN). The network 70 may be configured by combining multiple networks.

[0027] Figure 2 is a schematic block diagram showing a specific example of the functional configuration of the terminal device 10. The terminal device 10 is configured using information devices such as a smartphone, tablet, personal computer, or dedicated device. The terminal device 10 is operated by a user. The terminal device 10 comprises a communication unit 11, an input unit 12, an output unit 13, a storage unit 14, and a control unit 15.

[0028] The communication unit 11 is a communication device. The communication unit 11 may be configured, for example, as a network interface. The communication unit 11 communicates data with other devices via the network 70 in accordance with the control of the control unit 15. The communication unit 11 may be a wireless communication device or a wired communication device.

[0029] The input unit 12 is configured using existing input devices such as a keyboard, pointing device (mouse, tablet, etc.), buttons, or touch panel. The input unit 12 is operated by the user when inputting user instructions to the terminal device 10. The input unit 12 is used, for example, when the user inputs intake information of a target person. The input unit 12 may also be an interface for connecting the input device to the terminal device 10. In this case, the input unit 12 inputs the input signal generated in the input device in response to the user's input to the terminal device 10. The input unit 12 may also be configured using a microphone and a speech recognition device. In this case, the input unit 12 acquires the acoustic signal generated by the user's speech, performs speech recognition on the words spoken by the user, and inputs the recognized string information to the terminal device 10. The speech recognition process may be performed by the control unit 15. The input unit 12 can be configured in any way that allows user instructions to be input to the terminal device 10.

[0030] The output unit 13 outputs information in a format that the user can recognize. For example, the output unit 13 outputs responsive information provided by the information providing device 30 to the user. The output unit 13 may be an image display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display. The output unit 13 may also be an interface for connecting an image display device to the terminal device 10. In this case, the output unit 13 generates a video signal for displaying image data and outputs the video signal to the image display device connected to it. The output unit 13 may also be a device that outputs sound, such as a speaker. The output unit 13 may also be an interface for connecting an audio output device such as a speaker or headphones to the terminal device 10. In this case, the output unit 13 generates an audio signal for playing audio data and outputs the audio signal to the audio output device connected to it. The output unit 13 may also be configured as a touch panel integrated with the input unit 12.

[0031] The storage unit 14 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The storage unit 14 stores data used by the control unit 15. The storage unit 14 stores data necessary when the control unit 15 performs processing.

[0032] The control unit 15 is composed of a processor such as a CPU (Central Processing Unit) and memory (main memory). The control unit 15 functions when the processor executes a program. Note that all or part of the functions of the control unit 15 may be implemented using hardware such as an ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), or FPGA (Field Programmable Gate Array). The above program may be recorded on a computer-readable recording medium. Computer-readable recording media include, for example, portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, and semiconductor memory devices (e.g., SSDs: Solid State Drives), as well as storage devices such as hard disks and semiconductor memory devices built into computer systems. The above program may be transmitted via a telecommunications line.

[0033] The control unit 15 may, for example, execute an application installed on its own device (terminal device 10). A specific example of such an application is an application provided to the terminal device 10 as a dedicated application for the information provision system 100. Another specific example of such an application is a web browser application. Such an application may be pre-installed on the terminal device 10, or it may be downloaded each time the user performs a process to receive information. For example, if it is implemented as a web browser application, the terminal device 10 may download and execute the application from a device specified by the web server (for example, the web server itself or another server) when the terminal device 10 connects to a specific web server. The control unit 15 operates according to the program of the application being executed.

[0034] The control unit 15 controls the terminal device 10 in accordance with user operations and information received from the information providing device 30. For example, the control unit 15 transmits intake information entered by the target person or user operating the input unit 12 to the information providing device 30 using the communication unit 11. For example, when the control unit 15 receives responsive information transmitted from the information providing device 30 via the network 70 to the communication unit 11, it generates screen data based on the received responsive information and displays the screen data on the output unit 13. Such screen data includes images and characters that represent the responsive information transmitted from the information providing device 30. For example, when the control unit 15 receives responsive information transmitted from the information providing device 30 via the network 70 to the communication unit 11, it generates audio data based on the received responsive information and outputs the audio data from the output unit 13.

[0035] Figure 3 is a schematic block diagram showing a specific example of the functional configuration of the judgment model generation device 20. The judgment model generation device 20 is configured using information processing equipment such as a personal computer or a server device. The judgment model generation device 20 generates an immune judgment model and an intake judgment model by performing a model generation process based on experimental data obtained in advance from multiple subjects (for example, a combination of intake information and immune information). The immune judgment model is a judgment model in which intake information is the explanatory variable and immune information is the objective variable. The intake judgment model is information that defines the immune effect for each food item. Next, the details of the judgment model generation device 20 will be described. The judgment model generation device 20 includes a communication unit 21, a storage unit 22, and a control unit 23.

[0036] The communication unit 21 is a communication device. The communication unit 21 may be configured, for example, as a network interface. The communication unit 21 communicates data with other devices via the network 70 in accordance with the control of the control unit 23. The communication unit 21 may be a wireless communication device or a wired communication device.

[0037] The storage unit 22 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The storage unit 22 stores data used by the control unit 23. The storage unit 22 may function, for example, as an experimental data storage unit 221, an immunoassay model storage unit 222, and an intake judgment model storage unit 223.

[0038] The experimental data storage unit 221 stores experimental data used in the judgment model generation process performed in the judgment model generation device 20. The experimental data stored in the experimental data storage unit 221 includes intake information related to the oral intake of subjects and immune information obtained from subjects.

[0039] Intake information includes, for example, food item information such as dish name, ingredient name, product name, and nutritional component name. Specific examples of dish names include ramen, stir-fry, grilled fish, tempura, and hamburger. Specific examples of ingredient names include chicken, sugar, soy sauce, ham, lettuce, and daikon radish. Specific examples of product names include wine, canned tuna, cola, and potato chips. Specific examples of nutritional component names include lipids, vitamin B1, carbohydrates, calcium, iron, zinc, folic acid, and n-3 fatty acids. Intake information may also include timing information indicating the timing of intake for each item. Timing information may be defined, for example, as information indicating that the food was consumed within a specified period (e.g., within 3 days or 1 month) from the time immune information was obtained through an experiment.

[0040] In this specification, immunomodulation refers to balancing the immune system so that it functions properly, and includes "immunostimulation" and "immune tolerance." In this specification, "immunostimulation" refers to an action that enhances the immune response, and specifically includes an improvement effect from a state of reduced immune response, and an enhancement effect that further enhances an immune response from a normal or good state. Furthermore, actions that enhance the immune response may include actions that protect against viral infection and suppress viral replication (collectively referred to as antiviral effects) caused by immunostimulation, as well as actions that treat or prevent infectious diseases or improve or alleviate the symptoms of such diseases. Here, "prevention" refers to the prevention or delay of the occurrence of a disease or symptom in the target area, or a reduction in the risk of the disease or symptom in the target area. Infectious diseases include diseases caused by influenza virus, norovirus, RSV, etc., and COVID-19, etc. In this specification, "immune tolerance" includes actions that suppress inflammation (anti-inflammatory effects) so that the immune response does not become excessive.

[0041] In this specification, immune responsiveness includes "immunostimulation" and "immune tolerance." Immune effect includes "immunostimulation."

[0042] Immune information may be defined, for example, as a value indicating the degree of responsiveness to probiotics. A more specific example of such a value is a value indicating immune function activity obtained based on a specific probiotic and blood collected from the subject. A more specific example of a value indicating immune function activity is a value indicating the amount of cytokine production. A value indicating the amount of cytokine production may be defined, for example, as the amount of a specific cytokine (e.g., IL-10) produced, or as a value (hereinafter referred to as the "integrated value") that uses the ranking of the production levels of a given set of cytokines among multiple individuals, including the subject.

[0043] In this specification, immune function includes innate immunity and adaptive immunity. Improved immune function specifically includes activation and appropriate suppression of immune cells (including macrophages, dendritic cells, neutrophils, T cells, B cells, and NK cells), control of immune cell differentiation, control of cytokine production (including promotion and suppression of production), suppression of viral infections, suppression of bacterial infections, suppression of fungal infections, elimination of parasites, and promotion of antibody production. Cytokines include inflammatory cytokines and anti-inflammatory cytokines.

[0044] Figures 4 and 5 illustrate specific examples of the combined value of cytokine production. Figure 4 shows the amounts of two types of cytokines obtained from the blood of four subjects. Figure 5 shows the ranking of cytokine production for each of the four subjects based on the amounts shown in Figure 4. For example, subject 1 produced the most cytokine 1 (ranked 1st) and also the most cytokine 2 (ranked 1st). The combined value represents the sum of the rankings of a predetermined number of cytokines (two cytokines in this example). For example, the combined value for subject 1 is 1 + 1 = 2. Combined values ​​can be obtained similarly for the other subjects. The smaller the combined value obtained in this way, the more cytokine production is indicated, and the larger the value, the less cytokine production is indicated. In the examples of Figures 4 and 5, the combined value is obtained based on the production amounts of two types of cytokines, but the combined value may also be obtained based on the production amounts of more types of cytokines. For example, an integrated value may be obtained based on the ranking of the production levels of 15 types of cytokines: BAFF, IL-12, IL-23, CXCL10, CXCL9, CCL2, CCL3, CCL4, CCL7, IL-1ra, IL-10, IL-1β, TNFα, IL-6, and IL-8.

[0045] Such experimental data may be obtained through experiments like the following. Below, we will describe specific examples of experiments for obtaining immune and intake information from subjects.

[0046] Blood was collected from 60 subjects using mononuclear cell isolation tubes, and peripheral blood mononuclear cells (PBMCs) were isolated by centrifugation. Next, monocytes were isolated from the PBMCs by negative screening using the EasySep Human monocyte isolation kit. The monocytes were then cultured in RPMI-1640 medium, supplemented with 10% heat-inactivated FBS, antibiotics, and IL-4 and GM-CSF at a final concentration of 25 ng / ml, to a cell concentration of 5 × 10^5 cells / ml. The cultures were then incubated in a CO2 incubator at 37°C and 5% CO2 for 6 days, with the medium changed every 2-3 days, to produce monocyte-derived dendritic cells (moDCs).

[0047] Figure 6 shows the probiotic bacteria used in the experiment. The Bifidobacterium or Lactobacillus strains shown in Figure 6 were cultured statically in MRS medium at 37°C for 16 hours. After collection, the cells were washed twice with sterile water and sterilized by autoclaving at 90°C for 15 minutes. Subsequently, the concentration of the bacterial strains was adjusted with sterile water to 1 x 10^8 or 3 x 10^8 / mL to obtain heat-sterilized cells for use in the test.

[0048] The prepared monocyte-derived dendritic cells were collected, washed once with RPMI-1640 medium containing 10% heat-inactivated FBS and antibiotics, and then adjusted to a concentration of 5 x 10^5 / mL in the same medium. Sterilized cells of various bacterial strains were added to the monocyte-derived dendritic cells in a 10-fold proportion ratio, and cultured in a CO2 incubator at 37°C and 5% CO2. The culture supernatant was collected 24 hours after the start of culture, and BAFF, CCL2, CCL3, CCL4, CCL7, CXCL9, CXCL10, IL1b, IL1ra, IL6, IL8, IL10, IL12, IL23, and TNFa were measured using CBA or Luminex Discovery assay.

[0049] Participants who underwent blood sampling were asked to complete a "Simplified Self-Report Dietary History Questionnaire (BDHQ)" regarding their eating habits over the past month. The results for the following 51 nutrient intakes and 70 food intakes, which have been considered reliable in nutritional epidemiological studies, were used for analysis.

[0050] Of the 51 nutrients analyzed, 48 were: energy, weight, water, protein, animal protein, plant protein, lipids, animal lipids, plant lipids, carbohydrates, ash, sodium, potassium, calcium, magnesium, phosphorus, iron, zinc, copper, manganese, retinol, β-carotene equivalent, retinol equivalent, vitamin D, α-tocopherol, vitamin K, vitamin B1, vitamin B2, niacin, vitamin B6, vitamin B12, folic acid, pantothenic acid, vitamin C, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, cholesterol, soluble dietary fiber, insoluble dietary fiber, total dietary fiber, salt equivalent, sucrose, alcohol, daidzein, genistein, n-3 fatty acids, and n-6 ​​fatty acids. All values ​​except energy were divided by energy. Furthermore, the three values ​​obtained by adding protein (P), lipids (F), and carbohydrates (C) to the above items and then dividing each of them are also subjected to analysis.

[0051] In the following explanation, items marked with (*) are detailed descriptions of the items listed immediately before them.The 70 food items included in the analysis were: low-fat milk (*milk / yogurt low-fat), regular milk (*milk / yogurt regular / high-fat), chicken (*chicken), pork / beef (*pork / beef), ham (*ham / sausage / bacon), liver (*liver), squid / octopus / shrimp / shellfish (*squid / octopus / shrimp / shellfish), fish with bones (*fish eaten with bones), canned tuna (*canned tuna), dried fish (*dried fish / salted fish), fatty fish (*fatty fish), lean fish (*fish with less fat), eggs (*eggs), tofu / fried tofu (*tofu / fried tofu (soy milk, etc.)), natto (*fermented soybeans), potatoes (*potatoes), pickles (green leafy vegetables) (*pickles dark green leafy vegetables), pickles (other) (*pickles (All other items), raw (lettuce, cabbage) (*raw vegetables) Lettuce, shredded cabbage, green leafy vegetables (*dark green leafy vegetables), cabbage (*cabbage, Chinese cabbage), carrots, pumpkins (*carrots, pumpkins), radishes, turnips (*radishes, turnips), root vegetables (*all other root vegetables), tomatoes (*tomatoes, tomato ketchup, etc.), mushrooms (*mushrooms), seaweed (*seaweed), Western-style sweets (*Western-style sweets, cookies, biscuits), Japanese sweets (*Japanese sweets), rice crackers (*rice crackers, mochi, okonomiyaki), ice cream (*ice cream), citrus fruits (*citrus fruits such as mandarins), persimmons, strawberries (*persimmons, strawberries, kiwis), others (*all other fruits), mayonnaise (*mayonnaise, dressings), bread (*bread), soba noodles (*soba), udon noodles (*udon, hiyamugi, somen), ramen (*ramen, udon The following are examples of foods that are not suitable for consumption: ramen, pasta (*spaghetti, macaroni, etc.), green tea (*green tea (tea)), black tea / oolong tea (*black tea / oolong tea), coffee (*coffee), cola (*cola / juice), 100% juice (*100% fruit juice / vegetable juice), sugar (*sugar for coffee / tea), rice (*rice), miso soup (*miso soup), sake (*sake), beer (*beer), shochu (*shochu / chuhai / awamori), whiskey (*whiskey), wine (*wine), raw fish, grilled fish, boiled fish, tempura / fried fish, grilled meat, hamburger steak, fried food, stir-fried food, simmered food, noodle soup (*soup / broth for noodles), amount of soy sauce (*amount of soy sauce / sauce consumed), citrus fruits (seasonal), persimmons (seasonal), strawberries (seasonal), cooking salt, cooking oil, and cooking sugar.

[0052] In addition to completing the BDHQ, subjects who underwent blood sampling were also asked to record their meals for the three days prior to blood sampling. The intake amounts for the following 22 nutrients were calculated based on the recorded meals, taking into account the amount of nutrients generally obtained from each meal.

[0053] Of the 22 items used in the analysis, 19 were calories, protein, lipids, carbohydrates, salt, sugars, dietary fiber, potassium, calcium, magnesium, iron, vitamin A, vitamin D, vitamin E, vitamin B1, vitamin B2, vitamin B6, vitamin B12, and vitamin C. All items except calories were divided by the calorie value. Three items obtained by adding protein (P), lipids (F), and carbohydrates (C) to the above items and then dividing by each were also used in the analysis.

[0054] Through such experiments, the types of food consumed by each subject in the three days prior to blood sampling, the types of food consumed in the past month, and the subject's immune information may be obtained.

[0055] Returning to the explanation of Figure 3, the immune determination model storage unit 222 stores the immune determination model. The immune determination model is a determination model for obtaining immune information, which is the target variable, based on intake information given as an explanatory variable. The immune determination model may be configured, for example, as a table that associates intake information with immune information. The immune determination model may also be configured as a trained model obtained by a learning process using multiple training data including intake information and immune information.

[0056] The intake determination model storage unit 223 stores intake determination models. An intake determination model is a determination model that associates food items with information on the effect each food has on immune effects. The intake determination model may, for example, be information that defines foods that have a positive correlation with immune effects and foods that have a negative correlation. By using the intake determination model, it is possible to determine foods that have a positive correlation with immune effects (foods that can enhance immune effects by being actively consumed) and foods that have a negative correlation (foods that can enhance immune effects by being avoided). The intake determination model may be defined for each type of probiotic. By using such an intake determination model, it is possible to determine foods that have a positive correlation with a particular probiotic and foods that have a negative correlation.

[0057] The control unit 23 is configured using a processor such as a CPU and memory. The control unit 23 functions as an information control unit 231, an immunoassay model generation unit 232, and an intake determination model generation unit 233 when the processor executes a program. Note that all or part of the functions of the control unit 23 may be implemented using hardware such as an ASIC, PLD, or FPGA. The above program may be recorded on a computer-readable recording medium. Computer-readable recording media include, for example, portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, and semiconductor storage devices (e.g., SSDs), as well as storage devices such as hard disks and semiconductor storage devices built into computer systems. The above program may be transmitted via a telecommunications line.

[0058] The information control unit 231 controls the input and output of information. For example, the information control unit 231 acquires experimental data from other devices (information processing devices and storage media) and records it in the experimental data storage unit 221. For example, the information control unit 231 transmits the immunoassay model stored in the immunoassay model storage unit 222 to another device (e.g., the information providing device 30). For example, the information control unit 231 transmits the intake determination model stored in the intake determination model storage unit 223 to another device (e.g., the information providing device 30).

[0059] The immune determination model generation unit 232 performs model generation processing using experimental data stored in the experimental data storage unit 221. A specific example of such model generation processing is learning processing. For example, supervised learning for classification such as support vector machines, random forests, or neural networks may be used for learning processing. The immune determination model generation unit 232 generates a trained model for outputting immune information based on the input intake information, for example by performing supervised learning. The immune determination model generation unit 232 records the generated trained model in the immune determination model storage unit 222. The immune determination model generation unit 232 may operate in response to the operation of the operator of the determination model generation device 20 and generate a table as an immune determination model that associates intake information with immune information by analyzing experimental data. The immune determination model obtained by the immune determination model generation unit 232 may be transmitted to the information providing device 30 and recorded in the immune determination model storage unit 321 of the information providing device 30.

[0060] The intake determination model generation unit 233 generates an intake determination model by analyzing the experimental data stored in the experimental data storage unit 221. The processing of the intake determination model generation unit 233 may be performed automatically according to the execution command of the processing, or it may be performed in response to the operation of the determination model generation device 20. A specific example of the processing of the intake determination model generation unit 233 will be described below. In the following description, the example of using the specific experimental data described above will be used in the generation of the intake determination model.

[0061] In generating the intake determination model, the relationship between the 15 types of cytokines measured during experimental data generation and the ingested nutrients and foods is calculated. Spearman's correlation coefficient may be used in this calculation. To extract dietary components and factors that correlate with the amount of cytokines produced by monocyte-derived dendritic cells stimulated by bactericidal agents, values ​​calculated using Spearman's correlation coefficient during stimulation with BB536 are calculated.

[0062] Figure 7 shows the correlation information of immune activity for each cytokine obtained using item information (nutrient name) from the three days prior to blood collection as an explanatory factor for BB536. Figure 8 shows the correlation information of immune activity for each cytokine obtained using each nutrient (nutrient name) from BDHQ information as an explanatory factor for BB536. Figure 9 shows the correlation information of immune activity for each cytokine obtained using each food name (dish name, ingredient name, product name, etc.) from BDHQ information as an explanatory factor for BB536. In Figures 7 to 9, only groups that include foods (nutrients) in which more than half (out of 15 types) of cytokines have an absolute correlation coefficient of 0.15 or higher are shown. The conditions for the subjects shown in the figures are the same for Figures 10 to 15 shown below.

[0063] Figure 10 shows the correlation information of immune activity for each cytokine obtained using item information (nutrient name) from the three days prior to blood collection as an explanatory factor for MCC1274. Figure 11 shows the correlation information of immune activity for each cytokine obtained using each nutrient (nutrient name) from BDHQ information as an explanatory factor for MCC1274. Figure 12 shows the correlation information of immune activity for each cytokine obtained using each food name (dish name, ingredient name, product name, etc.) from BDHQ information as an explanatory factor for MCC1274.

[0064] Figure 13 shows the correlation information of immune activity for each cytokine obtained using item information (nutrient name) from the three days prior to blood collection as an explanatory factor for MCC1849. Figure 14 shows the correlation information of immune activity for each cytokine obtained using each nutrient (nutrient name) from BDHQ information as an explanatory factor for MCC1849. Figure 15 shows the correlation information of immune activity for each cytokine obtained using each food name (dish name, ingredient name, product name, etc.) from BDHQ information as an explanatory factor for MCC1849.

[0065] Through the above processing, correlation information between each item and each cytokine for the three probiotic strains (BB536, MCC1274, and MCC1849) is obtained. Based on this correlation information, clustering is performed using the Manhattan or Euclid method to extract item information from groups containing foods (nutrients) where more than half (out of 15 types) of cytokines have a correlation coefficient of 0.15 or higher, or from groups containing foods (nutrients) where more than half (out of 15 types) of cytokines have a correlation coefficient of -0.15 or lower. Figure 16 shows the clustering results. Figure 16 shows item information (ingested nutrients or foods) with a positive correlation and item information (ingested nutrients or foods) with a negative correlation for each of the three strains. Furthermore, the item information common to both positive and negative correlations for all heat-sterilized products was as follows.

[0066] Positive correlation: Vitamin B6, Vitamin B12, mushrooms, simmered dishes, cooking sugar, 100% juice, Western-style confectionery Negative correlation: Lipids (PFC balance), lipids / calories

[0067] The model for classifying cytokine production from ingested nutrients and foods was created as follows: The first objective factor was to predict individuals whose IL10 production was higher or lower than the average of the subject population when stimulated with BB536. The second objective factor was to predict individuals whose production of 15 cytokines when stimulated with BB536 was ranked from 1 to 59 according to the amount produced, and the ranks of all 15 cytokines were summed up. The model aimed to predict individuals whose rank was below or above the median of this sum. For explanatory factors, ingested nutrients analyzed by BDHQ and ingested nutrients (22 items) obtained from the diet for 3 days prior to blood collection were used. For BDHQ, the ingested nutrients used were limited to 10 specific items.

[0068] When item information from the three days prior to blood collection was used as an explanatory factor, the primary objective factor was predicted using the Light Gradient Boosted Trees Classifier (4 leaves), resulting in a cross-validation of 0.8 and a hold-out of 0.72. The secondary objective factor was predicted using the Eureqa Classifier (Quick Search: 250 Generations), resulting in a cross-validation of 0.76 and a hold-out of 0.91.

[0069] When BDHQ information was used as an explanatory factor, the first objective factor was predicted using the Light Gradient Boosted Trees Classifier (4 leaves), resulting in a cross-validation of 0.88 and a hold-out of 0.61. The second objective factor was predicted using the Generalized Additive2 Model, resulting in a cross-validation of 0.82 and a hold-out of 0.72.

[0070] Figure 17 is a schematic block diagram showing a specific example of the functional configuration of the information providing device 30. The information providing device 30 is configured using information processing equipment such as a personal computer or a server device. The information providing device 30 includes a communication unit 31, a storage unit 32, and a control unit 33.

[0071] The communication unit 31 is a communication device. The communication unit 31 may be configured, for example, as a network interface. The communication unit 31 communicates data with other devices via the network 70 in accordance with the control of the control unit 33. The communication unit 31 may be a device that performs wireless communication or a device that performs wired communication.

[0072] The storage unit 32 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The storage unit 32 stores data used by the control unit 33. The storage unit 32 may function, for example, as an immunoassay model storage unit 321 and an ingestion model storage unit 322. The immunoassay model storage unit 321 stores immunoassay models. The ingestion model storage unit 322 stores ingestion models.

[0073] The control unit 33 is configured using a processor such as a CPU and memory. The control unit 33 functions as an information control unit 331, an immunoassay unit 332, an intake determination unit 333, and an information provision unit 334 when the processor executes a program. Note that all or part of the functions of the control unit 33 may be implemented using hardware such as an ASIC, PLD, or FPGA. The above program may be recorded on a computer-readable recording medium. Computer-readable recording media include, for example, portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, and semiconductor memory devices (e.g., SSDs), as well as storage devices such as hard disks and semiconductor memory devices built into computer systems. The above program may be transmitted via a telecommunications line.

[0074] The information control unit 331 acquires an immunity assessment model and an intake assessment model from the assessment model generation device 20. The information control unit 331 also acquires intake information from other devices such as the terminal device 10. Such information exchange between the information control unit 331 and other devices may be performed, for example, by communication via the communication unit 31.

[0075] The immune determination unit 332 performs immune determination processing using the immune determination model stored in the immune determination model memory unit 321 and the intake information. Through immune determination processing, immune information of a person who has consumed the foods indicated by the intake information is generated. By obtaining this information, the subject can learn about their immune information in relation to their current diet. By knowing their immune information, the subject can increase their motivation to improve their current diet and gain a sense of security that it is okay to continue their current diet. In addition, by knowing the subject's immune information, the person providing care can advise the subject on improving or maintaining their diet.

[0076] If an immune assessment model is stored in the immune assessment model memory unit 321 for each type of probiotic, the immune assessment unit 332 may generate immune information for each type of probiotic based on the intake information. By obtaining such information, the subject can learn about their immune response to each type of probiotic. Based on this information, the subject can learn which type of probiotic is suitable for their current diet (which can provide appropriate immune effects with their current diet). Therefore, instead of taking probiotics indiscriminately, they can selectively take probiotics that are suitable for their diet, and thus obtain more appropriate immune effects. In addition, the caregiver can advise the subject on which type of probiotic is suitable.

[0077] The intake determination unit 333 performs intake determination processing using the intake determination model stored in the intake determination model storage unit 322. Intake suggestion information is generated by the intake determination processing. The intake suggestion information obtained by the intake determination processing may include, for example, information indicating foods (including nutrients) that have a positive correlation with multiple probiotics, or foods that have a negative correlation with multiple probiotics, without limiting the type of probiotic. In this case, for example, information indicating foods that have a common positive or negative correlation with multiple probiotics, as shown in Figure 16 above, may be generated as intake suggestion information. By obtaining such information, the target person can learn about the foods they should consume to obtain an appropriate immune response. For example, a target person requiring a higher immune effect (activation) can learn about foods they should actively consume (foods with a positive correlation) and foods they should limit their consumption of (foods with a negative correlation) to obtain an immune effect. By doing so, the target person can obtain an appropriate immune response. Furthermore, the caregiver can provide the target person with dietary advice regarding probiotics to obtain a more appropriate immune response.

[0078] In the intake determination process, information indicating foods with a positive or negative correlation may be generated as intake suggestion information for each type of probiotic. If a specific type of probiotic is specified by the user, information indicating foods with a positive or negative correlation to that specified probiotic may also be generated as intake suggestion information. By obtaining such information, the individual can learn about the relationship between obtaining an appropriate immune response and the foods they consume. For example, an individual requiring a higher immune effect (activation) can learn about foods that should be actively consumed (foods with a positive correlation) and foods that should be avoided (foods with a negative correlation) to obtain the immune effect of a specific probiotic. By following this advice, the individual can obtain an appropriate immune response. Furthermore, the caregiver can advise on foods suitable for specific probiotics.

[0079] In the intake determination process, if the user's (target person's) intake information is obtained, intake suggestion information may be generated according to the obtained intake information. For example, if the foods the user is consuming include foods that have a positive correlation with one or more probiotics, information indicating that those foods have a positive correlation may be generated as intake suggestion information. For example, if the foods the user is consuming include foods that have a negative correlation with one or more probiotics, information indicating that those foods have a negative correlation may be generated as intake suggestion information. By obtaining such information, the target person can learn about the relationship between obtaining an appropriate immune response and foods. For example, a target person who needs a higher immune effect (activation) can learn which foods they should actively consume (foods with a positive correlation) and which foods they should limit (foods with a negative correlation) from their past diet. By putting this into practice, the target person can obtain an appropriate immune response. In addition, the person in charge can advise the target person on foods.

[0080] For example, for users who should achieve a higher immune effect (activation), the following measures can be taken: If the food consumed by the user contains foods that have a positive correlation with one or more probiotics, information suggesting the intake of that one or more probiotics may be generated as intake suggestion information. By obtaining such information, the individual can learn which types of probiotics are effective in their diet. Therefore, by consuming such types of probiotics, the individual can improve their immunity without significantly changing their eating habits.

[0081] If a user specifies a particular probiotic, information may be generated as intake suggestion information regarding foods that should be consumed and foods that should be avoided in order to obtain a higher immune effect from that specified probiotic. Based on this information, the individual can decide which foods to actively consume and which to avoid in their future diet. Furthermore, a caregiver can advise the individual based on the intake suggestion information.

[0082] For example, for users who should achieve a lower immune response (tolerance), the following approach may be taken: If the food consumed by the user contains foods that have a negative correlation with one or more probiotics, information suggesting the intake of those one or more probiotics may be generated as intake suggestion information. By obtaining such information, the individual can learn which types of probiotics are effective in their diet. Therefore, by consuming such types of probiotics, the individual can avoid excessive immune activation without significantly changing their dietary habits.

[0083] If a user specifies a particular probiotic, information may be generated as intake suggestion information regarding foods that should be consumed or avoided to obtain a lower immune effect from that specified probiotic. Based on this information, the individual can decide which foods to actively consume and which to avoid in their future diet. Furthermore, a caregiver can provide advice to the individual based on the intake suggestion information.

[0084] The information provision unit 334 transmits responsive information, including immune information generated by the immune determination unit 332 and intake suggestion information generated by the intake determination unit 333, to the terminal device 10.

[0085] The information provision system 100, configured in this way, makes it possible to learn about a target individual's immune response to probiotics. Furthermore, it becomes possible to adjust the immune response based on the learned immune response. Specifically, it becomes possible to obtain immune information and suggested intake information for the target individual. For example, by inputting intake information for a target individual, the information provision system 100 makes it possible to learn about the immune response to probiotics (immune information) for that individual who is consuming the foods indicated by that intake information. The information provision system 100 also makes it possible to obtain information (suggested intake information) about foods that should be consumed and foods that should be avoided in order to obtain a more appropriate immune response to probiotics. The detailed effects obtained from this information are as described above.

[0086] Figure 18 is a schematic diagram of an example hardware configuration of an information processing device 90 applied to this embodiment. The information processing device 90 comprises a processor 91, main memory 92, communication interface 93, auxiliary storage device 94, input / output interface 95, and internal bus 96. The processor 91, main memory 92, communication interface 93, auxiliary storage device 94, and input / output interface 95 are connected to each other via the internal bus 96 so as to be able to communicate with each other. The information processing device 90 may be applied to, for example, a terminal device 10, a decision model generation device 20, and an information providing device 30. In this case, for example, the communication unit 11, communication unit 21, and communication unit 31 may be configured using the communication interface 93. For example, the storage unit 14, storage unit 22, and storage unit 32 may be configured using the auxiliary storage device 94. Also, the control unit 15, control unit 23, and control unit 33 may be configured using the processor 91 and main memory 92.

[0087] (modified version) In this embodiment, the terminal device 10 and the information providing device 30 are configured as separate devices, but they may be configured as a single device. In this case, for example, the information providing device 30 may further include configurations corresponding to the input unit 12 and the output unit 13, and be operated by the user. By configuring in this way, the device operated by the user when inputting intake information and the device that generates responsive information based on the intake information can be configured as the same device.

[0088] In this embodiment, the judgment model generation device 20 and the information provision device 30 are configured as separate devices, but they may be configured as a single integrated device. In this case, for example, the information provision device 30 may further include configurations corresponding to the experimental data storage unit 221, the immunojudgment model generation unit 232, and the intake judgment model generation unit 233.

[0089] The judgment model generation device 20 may be implemented using multiple information processing devices. For example, the judgment model generation device 20 may be implemented using a cloud or other device. For example, in the judgment model generation device 20, the storage unit 22 and the control unit 23 may be implemented on different information processing devices. For example, the storage unit 22 of the judgment model generation device 20 may be distributed and implemented across multiple information processing devices. The information provision device 30 may be implemented using multiple information processing devices. For example, the information provision device 30 may be implemented using a cloud or other device. For example, in the information provision device 30, the storage unit 32 and the control unit 33 may be implemented on different information processing devices. For example, the storage unit 32 of the information provision device 30 may be distributed and implemented across multiple information processing devices.

[0090] The application running on the terminal device 10 may not be intended to determine the responsiveness information (immune information or suggested intake information) itself, but may be an application that provides the user with information obtained by processing the result of the determination of the responsiveness information. Such an application may be an application that processes using an API (Programming Interface) provided by the information providing device 30, for example. In this case, the output unit 13 may output to the terminal device 10 other information obtained by processing using the responsiveness information, instead of information indicating the information itself (responsiveness information) obtained from the information providing device 30.

[0091] While embodiments of this invention have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and includes designs and the like that do not depart from the spirit of this invention. [Explanation of symbols]

[0092] 100…Information provision system, 10…Terminal device, 11…Communication unit, 12…Input unit, 13…Output unit, 14…Storage unit, 15…Control unit, 20…Judgment model generation device, 21…Communication unit, 22…Storage unit, 221…Experimental data storage unit, 222…Immunity judgment model storage unit, 223…Ingestion judgment model storage unit, 23…Control unit, 231…Information control unit, 232…Immunity judgment model generation unit, 233…Ingestion judgment model generation unit, 30…Information provision device, 31…Communication unit, 32…Storage unit, 321…Immunity judgment model storage unit, 322…Ingestion judgment model storage unit, 33…Control unit, 331…Information control unit, 332…Immunity judgment unit, 333…Ingestion judgment unit, 334…Information provision unit

Claims

1. An information processing device comprising: an information processing device that acquires immune information of a subject based on intake information including the items of food consumed by the subject; and an immune determination model in which the intake information is used as an explanatory variable and immune information indicating the subject's immune response to probiotics is used as an objective variable.

2. The information processing apparatus according to claim 1, wherein the control unit acquires the immune information for each type of probiotic.

3. An information processing device comprising a control unit that provides the user with at least one of the following: information indicating that the food items consumed by the subject have a positive correlation with the probiotic effect; and information indicating that the food items consumed by the subject have a negative correlation with the probiotic effect.

4. An information processing device comprising a control unit that provides the user with information indicating, for each type of probiotic, foods that have a positive correlation with the immune effect of probiotics or foods that have a negative correlation with the immune effect of probiotics.

5. An information processing device comprising a control unit that provides the user with information indicating foods that have a positive correlation with the immune effect of probiotics or foods that have a negative correlation with the immune effect, from among the intake information including the items of food consumed by the target person.

6. An information processing method in which a computer obtains immune information of a subject based on intake information including the types of food consumed by the subject, and an immune determination model in which the intake information is used as an explanatory variable and immune information indicating the subject's immune response to probiotics is used as the dependent variable.

7. A computer program for causing a computer to function as an information processing device, comprising a control unit that acquires immune information of a target person based on intake information including the items of food consumed by the target person, and an immune determination model in which the intake information is used as an explanatory variable and immune information indicating the target person's immune response to probiotics is used as an objective variable.

Images