[go: up one dir, main page]

WO2025142997A1 - Procédé d'évaluation d'une inflammation après une parturition - Google Patents

Procédé d'évaluation d'une inflammation après une parturition Download PDF

Info

Publication number
WO2025142997A1
WO2025142997A1 PCT/JP2024/045856 JP2024045856W WO2025142997A1 WO 2025142997 A1 WO2025142997 A1 WO 2025142997A1 JP 2024045856 W JP2024045856 W JP 2024045856W WO 2025142997 A1 WO2025142997 A1 WO 2025142997A1
Authority
WO
WIPO (PCT)
Prior art keywords
value
blood
evaluation
formula
roms
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/JP2024/045856
Other languages
English (en)
Japanese (ja)
Inventor
愛実 夏目
一輝 中川
裕介 杉本
明 今泉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ajinomoto Co Inc
Original Assignee
Ajinomoto Co Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ajinomoto Co Inc filed Critical Ajinomoto Co Inc
Publication of WO2025142997A1 publication Critical patent/WO2025142997A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids

Definitions

  • the present invention relates to a method for evaluating postpartum inflammation, a calculation method, an evaluation device, a calculation device, an evaluation program, a calculation program, a recording medium, an evaluation system, and a terminal device.
  • Patent Document 1 describes a method for evaluating postpartum ketosis.
  • Mastitis is diagnosed after calving by looking at the breast during lactation, changes in milk characteristics, bacterial tests and somatic cell counts (SCC).
  • Non-Patent Documents 2-5 It has been reported that differences in concentration were observed in blood, urine, etc. taken before parturition between cows that were normal after parturition and cows that developed mastitis.
  • Metritis and endometritis are diagnosed after delivery through mucosal and cytological examinations and clinical findings.
  • Non-Patent Documents 6-11 It has been reported that when blood samples were taken before parturition, differences in concentration were observed between cows that were normal after parturition and those that developed metritis.
  • Non-Patent Documents 12-13, 19 It has been reported that d-ROMs, an indicator of oxidative stress, and BAP, an indicator of antioxidant capacity, fluctuate 52 weeks before the onset of inflammatory conditions in humans (Non-Patent Documents 12-13, 19). On the other hand, it has been reported that in cows undergoing ketosis, these values fluctuate 14 days before and 15 days after calving (Non-Patent Document 20). Based on these findings, d-ROMs, BAP, and the ratio of d-ROMs to BAP may be effective in predicting diseases before calving and understanding inflammatory conditions after calving.
  • Haptoglobin in the blood is known as an indicator of an inflammatory state.
  • haptoglobin concentrations In dairy cows with high blood haptoglobin concentrations and considered to be in an inflammatory state, reduced milk yields and reduced conception rates have been confirmed (Non-Patent Documents 1, 14-17).
  • Non-Patent Documents 1, 14-17 There are several different standards for the standard value of haptoglobin depending on the disease, but there is no consensus yet.
  • the present invention has been made in consideration of the above, and aims to provide an evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium, evaluation system, and terminal device that can provide highly reliable information regarding the state of inflammation after delivery before delivery.
  • the evaluation method of the present invention involves measuring the concentration values of 25 types of amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) in the blood of ruminants before parturition and 37 types of biochemical parameters (meaning biochemical parameters) in the blood.
  • amino acids Al, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val
  • the method is characterized by including an evaluation step of evaluating the postpartum inflammatory state of the ruminant animal using at least one value of the test values of CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL-1 ⁇ , haptoglobin, serum amyloid A, IL-6, TNF- ⁇ , cortisol, and kynurenine and the measured values of four types of measurement items obtained from the blood (d-ROMs value (oxidative stress value), BAP value (antioxidant power value), BAP value/d-ROMs value, and d-ROMs value/BAP value), or using an equation including a variable into which the at least one value is substituted and a value of the equation
  • the evaluation step may also be performed in a control unit provided in the information processing device.
  • the calculation method according to the present invention is also characterized by including a calculation step of calculating the value of an equation for evaluating the inflammatory state of the ruminant after parturition, the equation including at least one value of the concentration values of the 25 amino acids in the blood of the ruminant before parturition, the test values of the 37 biochemical tests in the blood, and the measured values of the four measurement items obtained from the blood, and a variable into which the at least one value is substituted.
  • the evaluation device may be communicatively connected via a network to a terminal device that provides the at least one value or the value of the formula, and may further include a data receiving unit that receives the at least one value or the value of the formula transmitted from the terminal device, and a result transmitting unit that transmits the evaluation result obtained by the evaluation unit to the terminal device, and the evaluation unit may use the at least one value or the value of the formula received by the data receiving unit.
  • the calculation device is characterized in that it includes a calculation unit that calculates the value of an equation for evaluating the inflammatory state of the ruminant after parturition, using the concentration values of the 25 types of amino acids in the blood of the ruminant before parturition, the test values of the 37 types of biochemical tests in the blood, and at least one value of the measurement values of the four types of measurement items obtained from the blood, and a variable into which the at least one value is substituted.
  • the evaluation program of the present invention causes an information processing device to execute an evaluation step of evaluating the postpartum inflammatory state of the dairy cow using at least one value of the concentration values of the 25 types of amino acids in the blood of a ruminant before parturition, the test values of the 37 types of biochemical properties in the blood, and the measured values of the four types of measurement items obtained from the blood, or using an equation including a variable into which the at least one value is substituted and a value of the equation calculated using the at least one value.
  • the calculation program of the present invention causes an information processing device to execute a calculation step of calculating the value of an equation for evaluating the inflammatory state of the ruminant after parturition, the equation including at least one value of the concentration values of the 25 types of amino acids in the blood of the ruminant before parturition, the test values of the 37 types of biochemical tests in the blood, and the measured values of the four types of measurement items obtained from the blood, and a variable into which the at least one value is substituted.
  • the evaluation system is an evaluation system configured by connecting an evaluation device and a terminal device via a network so that they can communicate with each other, and the terminal device is equipped with a data transmission unit that transmits to the evaluation device the concentration values of the 25 types of amino acids in the blood of the ruminant before parturition, the test values of the 37 types of biochemical tests in the blood, and at least one value of the measured values of the four types of measurement items obtained from the blood, or an equation including a variable into which the at least one value is substituted and the value of the equation calculated using the at least one value, and a result receiving unit that receives the evaluation result regarding the inflammatory state after parturition transmitted from the evaluation device, and the evaluation device is characterized by being equipped with a data receiving unit that receives the at least one value or the value of the equation transmitted from the terminal device, an evaluation unit that evaluates the inflammatory state after parturition of the ruminant using the at least one value or the value of the equation received by the data receiving unit, and a result transmitting unit
  • the terminal device is further characterized in that it includes a result acquisition unit that acquires an evaluation result regarding the state of inflammation after parturition, and the evaluation result is a result of evaluating the state of inflammation after parturition of the ruminant using at least one value of the concentration values of the above 25 types of amino acids in the blood of the ruminant before parturition, the above 37 types of biochemical test values in the blood, and the measured values of the above four types of measurement items obtained from the blood, or using an equation including a variable into which the at least one value is substituted and the value of the equation calculated using the at least one value.
  • the evaluation method of the present invention may further include a suggestion step of proposing preventive treatment to cows that are evaluated in the evaluation step as having a high possibility of suffering from the metabolic disease after calving.
  • the inflammatory state of a ruminant after parturition is evaluated using the concentration values of the above 25 types of amino acids in the blood of the ruminant before parturition, the test values of the above 37 types of biochemistry in the blood, and at least one of the measured values of the above four measurement items obtained from the blood, thereby providing reliable information on the inflammatory state after parturition before parturition.
  • dairy farmers can reduce the onset of inflammation after parturition by implementing preventive nutritional intervention based on the information provided by the present invention before parturition.
  • the present invention contributes to efficient production by dairy farmers.
  • FIG. 1 is a diagram showing the basic principle of the first embodiment.
  • FIG. 2 is a diagram showing the basic principle of the second embodiment.
  • FIG. 3 is a diagram showing an example of the overall configuration of this system.
  • FIG. 4 is a diagram showing another example of the overall configuration of the present system.
  • FIG. 5 is a block diagram showing an example of the configuration of the evaluation device 100 of this system.
  • FIG. 6 is a diagram showing an example of information stored in blood data file 106a.
  • FIG. 7 is a diagram showing an example of information stored in the index state information file 106b.
  • FIG. 8 is a diagram showing an example of information stored in the designated index state information file 106c.
  • FIG. 9 is a diagram showing an example of information stored in the formula file 106d1.
  • FIG. 1 is a diagram showing the basic principle of the first embodiment.
  • FIG. 2 is a diagram showing the basic principle of the second embodiment.
  • FIG. 3 is a diagram showing an example of the overall configuration of this system.
  • FIG. 10 is a diagram showing an example of information stored in the evaluation result file 106e.
  • FIG. 11 is a block diagram showing the configuration of the evaluation unit 102d.
  • FIG. 12 is a block diagram showing an example of the configuration of a client device 200 of this system.
  • FIG. 13 is a block diagram showing an example of the configuration of the database device 400 of this system.
  • FIG. 14 is a diagram showing the linear regression model extracted in Example 4.
  • FIG. 15 is a diagram showing the linear regression model extracted in Example 4.
  • FIG. 16 is a diagram showing the linear regression model extracted in Example 4.
  • FIG. 17 is a diagram showing the linear regression model extracted in Example 4.
  • FIG. 18 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 19 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 20 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 21 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 22 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 23 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 24 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 25 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 26 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 27 is a diagram showing the logistic regression model extracted in Example 4.
  • FIG. 28 is a diagram showing the logistic regression model extracted in Example 5.
  • FIG. 29 is a diagram showing the logistic regression model extracted in Example 5.
  • FIG. 30 is a diagram showing the logistic regression model extracted in Example 5.
  • FIG. 31 is a diagram showing the logistic regression model extracted in Example 5.
  • FIG. 32 is a diagram showing the logistic regression model extracted in Example 5.
  • FIG. 33 is a diagram showing the logistic regression model extracted in Example 5.
  • FIG. 34 is a diagram showing the logistic regression model extracted in Example 5.
  • FIG. 35 is a diagram showing a logistic regression model for discriminating milk fever extracted in Example 6.
  • FIG. 36 shows a logistic regression model for discriminating milk fever and a logistic regression model for discriminating abomasum mutation extracted in Example 6.
  • FIG. 37 is a diagram showing a logistic regression model for discriminating abomasum mutation extracted in Example 6.
  • FIG. 38 is a diagram showing a logistic regression model for discriminating abomasum mutation extracted in Example 6.
  • FIG. 39 is a diagram showing a logistic regression model for discriminating metritis extracted in Example 6.
  • FIG. 40 is a diagram showing a logistic regression model for discriminating metritis extracted in Example 6.
  • FIG. 41 is a diagram showing a logistic regression model for discriminating metritis extracted in Example 6.
  • FIG. 42 is a diagram showing a logistic regression model for discriminating metritis extracted in Example 6.
  • FIG. 43 is a diagram showing a logistic regression model for discriminating metritis extracted in Example 6.
  • Figure 44 is a diagram showing a logistic regression model for discriminating mastitis extracted in Example 6.
  • Figure 45 is a diagram showing a logistic regression model for discriminating mastitis extracted in Example 6.
  • Figure 46 is a diagram showing a logistic regression model for discriminating mastitis extracted in Example 6.
  • Figure 47 is a diagram showing a logistic regression model for discriminating mastitis extracted in Example 6.
  • Figure 48 is a diagram showing a logistic regression model for discriminating mastitis extracted in Example 6.
  • FIG. 49 is a diagram showing a logistic regression model for discriminating lameness extracted in Example 6.
  • FIG. 50 is a diagram showing a logistic regression model for discriminating lameness and a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 51 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 52 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 53 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 54 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 55 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 56 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 57 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 58 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6.
  • FIG. 59 is a diagram showing a logistic regression model for discriminating inflammatory diseases extracted in Example 6 and a linear regression model for predicting serum amyloid A concentration 7 days after delivery extracted in Example 7.
  • FIG. 60 is a diagram showing a linear regression model for predicting serum amyloid A concentrations 7 days after delivery, extracted in Example 7.
  • Fig. 1 is a diagram showing the basic principle of the first embodiment.
  • blood data is obtained that includes the concentration values of the above 25 types of amino acids in blood (including, for example, plasma, serum, etc.) taken from the ruminant animal (e.g., dairy cow) to be evaluated before parturition (e.g., a certain period before the expected date of parturition), the test values of the above 37 types of biochemistry in the blood, and at least one value of the measured values of the above four types of measurement items obtained from the blood (any one or more values selected from the concentration values of the above 25 types of amino acids, the test values of the above 37 types of biochemistry, and the measured values of the above four types of measurement items) (step S11).
  • the concentration values of the above 25 types of amino acids in blood including, for example, plasma, serum, etc.
  • the ruminant animal e.g., dairy cow
  • parturition e.g., a certain period before the expected date of parturition
  • the test values of the above 37 types of biochemistry in the blood e.g., a certain period before the
  • concentration value When the concentration value is measured, 0.02 N hydrochloric acid is added and protein is removed by ultrafiltration, followed by pre-column derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and the concentration value is analyzed by liquid chromatography mass spectrometry (LC/MS) (see WO 2003/069328 and WO 2005/116629).
  • LC/MS liquid chromatography mass spectrometry
  • At least one of the concentration values of the 25 types of amino acids, the 37 types of biochemical test values, and the measured values of the four types of measurement items contained in the blood data acquired in step S11 is used to evaluate (predict/estimate) the state of inflammation (e.g., metritis, mastitis, hypocalcemia, milk fever, abomasal mutation, lameness, etc.) after parturition (e.g., a certain period after the date of parturition) of the subject (step S12).
  • the state of inflammation e.g., metritis, mastitis, hypocalcemia, milk fever, abomasal mutation, lameness, etc.
  • parturition e.g., a certain period after the date of parturition
  • data such as missing values and outliers may be removed from the blood data acquired in step S11 before executing step S12.
  • the postpartum inflammatory state of the subject may be evaluated by calculating the value of a formula using a formula including at least one value of the concentration values of the above 25 types of amino acids, the above 37 types of biochemical test values, and the measurement values of the above four types of measurement items, and a variable into which the at least one value is substituted.
  • the variable into which the concentration value, test value, or measurement value is substituted may be substituted with a value obtained by converting the concentration value, test value, or measurement value, for example, using a method described below.
  • the concentration values or formula values may be converted, for example, by the methods described below, and the converted values may be used to evaluate the postpartum inflammatory state of the subject.
  • a predetermined range e.g., a range of 0.0 to 1.0, a range of 0.0 to 10.0, a range of 0.0 to 100.0, a range of -10.0 to 10.0, etc.
  • the concentration value or the value of the formula may be converted by adding, subtracting, multiplying, or dividing an arbitrary value with respect to the concentration value or the value of the formula, converting the concentration value or the value of the formula using a predetermined conversion method (e.g., exponential transformation, logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation, etc.), or by performing a combination of these calculations on the concentration value or the value of the formula.
  • a predetermined conversion method e.g., exponential transformation, logarithmic transformation, angular transformation, square root transformation, prob
  • the value of an exponential function with the concentration value or the value of the formula as the exponent and the Napier's number as the base (specifically, the value of p/(1-p) when the natural logarithm ln(p/(1-p)) when the probability p of the state of postpartum inflammation being in a predetermined state (for example, a state in which the blood haptoglobin concentration exceeds a reference value) is defined is equal to the concentration value or the value of the formula) may be further calculated, and a value obtained by dividing the calculated exponential function value by the sum of 1 and the value itself (specifically, the value of the probability p) may be further calculated.
  • the concentration values or the values of the formula may be converted so that the converted values under specific conditions are specific values.
  • the concentration values or the values of the formula may be converted so that the converted value is 5.0 when the sensitivity is 95% and the converted value is 8.0 when the sensitivity is 80%.
  • the concentration values may be normalized for each amino acid, and then the concentration distribution may be normalized to have an average of 50 and a standard deviation of 10.
  • the formula values may be normalized to have an average of 50 and a standard deviation of 10. It should be noted that these transformations described above may be applied to test values and measurement values.
  • position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper may be generated using at least one value (if the value is converted, the converted value) or formula value (if the formula value is converted, the converted value) of the concentration values of the 25 types of amino acids, the 37 types of biochemical test values, and the measurement values of the four types of measurement items, and the generated position information may be used as an evaluation result regarding the postpartum inflammatory state of the evaluation subject.
  • the predetermined ruler is for evaluating the postpartum inflammatory state, and is, for example, a ruler with a scale that shows at least scales corresponding to the upper and lower limits of the "range in which the concentration value, test value, measurement value, or formula value, or the converted value can be taken" or "a part of the range".
  • the predetermined mark is a value that corresponds to the concentration value, test value, measurement value, or formula value, or the converted value, and is, for example, a circle or a star.
  • a predetermined value such as the mean value ⁇ 1 SD, 2 SD, 3 SD, N quantile, N percentile, or a cutoff value recognized for clinical significance
  • the subject may be evaluated as having inflammation after delivery.
  • a standard deviation may be used instead of the concentration value, test value, or formula value itself. For example, if the standard deviation is less than the mean value - 2 SD (standard deviation ⁇ 30) or if the standard deviation is higher than the mean value + 2 SD (standard deviation > 70), the subject may be evaluated as having inflammation after delivery.
  • the risk (possibility) of the subject developing inflammation after delivery may be qualitatively evaluated.
  • the subject may be classified into one of a plurality of categories defined by at least considering the degree of risk of developing inflammation after delivery, using at least one value of the concentration values of the 25 kinds of amino acids, the test values of the 37 kinds of biochemistry, and the measured values of the four kinds of measurement items and one or more preset thresholds, or using an equation including the at least one value and a variable into which the at least one value is substituted and one or more preset thresholds.
  • the plurality of categories may include a category for subjects with a high risk of developing inflammation after delivery (e.g., subjects whose blood haptoglobin concentration after delivery is equal to or higher than a reference value (e.g., 800 ⁇ g/mL, etc.)), and a category for subjects with a low risk of developing inflammation after delivery (e.g., subjects whose blood haptoglobin concentration after delivery is less than a reference value (e.g., 800 ⁇ g/mL, etc.)).
  • the multiple categories may also include a category for subjects at high risk of developing inflammation after delivery, a category for subjects at low risk of developing inflammation after delivery, and a category for subjects at medium risk of developing inflammation after delivery.
  • control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, required data, etc., and executes various information processing based on these programs.
  • control unit 102 is broadly equipped with an acquisition unit 102a, a designation unit 102b, a formula creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f.
  • the control unit 102 also performs data processing such as removing data with missing values, removing data with many outliers, and removing variables with many missing values for the index status information transmitted from the database device 400 and the blood data transmitted from the client device 200.
  • the conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1, for example, using the conversion method described above.
  • the conversion unit 102d2 may convert at least one of the concentration values of the 25 types of amino acids, the 37 types of biochemical test values, and the measurement values of the four types of measurement items contained in the blood data, for example, using the conversion method described above.
  • the evaluation unit 102d may store the converted value obtained by the conversion unit 102d2 as the evaluation result in a specified storage area of the evaluation result file 106e.
  • the generating unit 102d3 generates position information regarding the position of a specified mark on a specified ruler that is visibly shown on a display device such as a monitor or a physical medium such as paper, using the value of the formula calculated by the calculating unit 102d1 or the converted value obtained by the converting unit 102d2 (which may be a concentration value or test value, or a converted value of the concentration value or test value).
  • the evaluating unit 102d may store the position information generated by the generating unit 102d3 in a specified storage area of the evaluation result file 106e as the evaluation result.
  • the classification unit 102d4 uses the value of the formula calculated by the calculation unit 102d1 or the converted value obtained by the conversion unit 102d2 (which may be a concentration value, test value, or measurement value, or a converted value of the concentration value, test value, or measurement value) to classify the individual into one of a number of categories defined with at least consideration of the degree of risk of developing inflammation after delivery.
  • the sending unit 102f sends the evaluation results to the client device 200 that sent the individual's blood data, and sends the formula created by the evaluation device 100 and the evaluation results to the database device 400.
  • the sending unit 102f may send the evaluation results to a client device 200 different from the client device 200 that sent the data used in the evaluation.
  • FIG. 12 is a block diagram showing an example of the configuration of the client device 200 of this system, and conceptually shows only the parts of the configuration that are relevant to the present invention.
  • the client device 200 is composed of a control unit 210, a ROM 220, a HD 230, a RAM 240, an input device 250, an output device 260, an input/output IF 270, and a communication IF 280, and these units are connected to each other so that they can communicate with each other via any communication path.
  • the client device 200 may be based on an information processing device (for example, a personal computer, a workstation, a home game device, an Internet TV, a PHS (Personal Handyphone System) terminal, a mobile terminal, a mobile communication terminal, a PDA (Personal Digital Assistant), or other information processing terminal) to which peripheral devices such as a printer, monitor, and image scanner are connected as necessary.
  • an information processing device for example, a personal computer, a workstation, a home game device, an Internet TV, a PHS (Personal Handyphone System) terminal, a mobile terminal, a mobile communication terminal, a PDA (Personal Digital Assistant), or other information processing
  • the input device 250 is a keyboard, mouse, microphone, etc.
  • the monitor 261 which will be described later, also works with the mouse to realize a pointing device function.
  • the output device 260 is an output means that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker, etc.
  • the input/output IF 270 is connected to the input device 250 and the output device 260.
  • the evaluation unit 210a may convert the value of the formula (which may be a concentration value or a test value) in the conversion unit 210a2, generate position information corresponding to the value of the formula or the converted value (which may be a concentration value or a test value, or a value after the conversion of the concentration value or the test value) in the generation unit 210a3, and classify the individual into one of a plurality of categories using the value of the formula or the converted value (which may be a concentration value or a test value, or a value after the conversion of the concentration value or the test value) in the classification unit 210a4, depending on information included in the evaluation result transmitted from the evaluation device 100.
  • the network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 so that they can communicate with each other, and is, for example, the Internet, an intranet, or a LAN (Local Area Network) (including both wired and wireless networks).
  • LAN Local Area Network
  • the network 300 may be a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog and digital), a dedicated line network (including both analog and digital), a CATV (Community Antenna TeleVision) network, a mobile circuit switching network or a mobile packet switching network (IMT (International Mobile Telecommunication) 2000 system, GSM (Registered Trademark) (Global System for Mobile Communications, etc.) r Mobile Communications system or PDC (Personal Digital Cellular)/PDC-P system, etc.), a radio paging network, a local radio network such as Bluetooth (registered trademark), a PHS network, a satellite communication network (including CS (Communication Satellite), BS (Broadcasting Satellite), or ISDB (Integrated Services Digital Broadcasting), etc.), etc.
  • VAN Value-Added Network
  • PHS Personal System for Mobile Communications
  • satellite communication network including CS (Communication Satellite), BS (Broadcasting Satellite), or ISDB (Integrated Services Digital Broadcast
  • FIG. 13 is a block diagram showing an example of the configuration of the database device 400 of this system, and conceptually shows only the parts of the configuration that are related to the present invention.
  • the database device 400 has a function of storing index state information used when creating an equation in the evaluation device 100 or the database device itself, equations created in the evaluation device 100, evaluation results in the evaluation device 100, etc.
  • the database device 400 is composed of a control unit 402 such as a CPU that controls the database device in an overall manner, a communication interface unit 404 that communicatively connects the database device to the network 300 via a communication device such as a router and a wired or wireless communication circuit such as a dedicated line, a storage unit 406 that stores various databases, tables, files (e.g., files for web pages), etc., and an input/output interface unit 408 that connects to an input device 412 and an output device 414, and each of these units is communicatively connected via any communication path.
  • a control unit 402 such as a CPU that controls the database device in an overall manner
  • a communication interface unit 404 that communicatively connects the database device to the network 300 via a communication device such as a router and a
  • the memory unit 406 is a storage means, and may be, for example, a memory device such as a RAM or ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like.
  • the memory unit 406 stores various programs used for various processes.
  • the communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). In other words, the communication interface unit 404 has a function of communicating data with other terminals via a communication line.
  • the input/output interface unit 408 is connected to an input device 412 and an output device 414.
  • the output device 414 may be a monitor (including a home television), a speaker, or a printer.
  • the input device 412 may be a keyboard, a mouse, a microphone, or a monitor that works with a mouse to realize a pointing device function.
  • the control unit 402 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, and required data, and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into a transmission unit 402a and a reception unit 402b.
  • the transmission unit 402a transmits various information such as index state information and equations to the evaluation device 100.
  • the reception unit 402b receives various information such as equations and evaluation results transmitted from the evaluation device 100.
  • the evaluation device 100 receives blood data, calculates the value of a formula, classifies the individual into categories, and transmits the evaluation results, while the client device 200 receives the evaluation results.
  • the client device 200 is equipped with the evaluation unit 210a, it is sufficient for the evaluation device 100 to calculate the value of a formula, and, for example, the conversion of the value of a formula, the generation of position information, and the classification of the individual into categories may be appropriately shared between the evaluation device 100 and the client device 200.
  • the evaluation unit 210a may convert the value of the equation in the conversion unit 210a2, generate location information corresponding to the value of the equation or the converted value in the generation unit 210a3, and classify the individual into one of a plurality of categories using the value of the equation or the converted value in the classification unit 210a4.
  • the evaluation unit 210a may generate position information corresponding to the converted value in the generation unit 210a3, or classify the individual into one of a plurality of categories using the converted value in the classification unit 210a4.
  • the evaluation unit 210a may classify the individual into one of a plurality of categories using the value of the formula or the converted value in the classification unit 210a4.
  • evaluation device calculation device, evaluation method, calculation program, recording medium, evaluation system, and terminal device according to the present invention may be embodied in various different embodiments within the scope of the technical idea described in the claims, in addition to the second embodiment described above.
  • all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method.
  • processing procedures, control procedures, specific names, registered data for each process, information including search conditions and other parameters, screen examples, and database configurations shown in the above documents and drawings may be changed as desired unless otherwise specified.
  • the processing functions of the evaluation device 100 may be realized in whole or in part by a CPU and a program interpreted and executed by the CPU, or may be realized as hardware using wired logic.
  • the program is recorded on a non-transient computer-readable recording medium that includes programmed instructions for causing an information processing device to execute the evaluation method or calculation method of the present invention, and is mechanically read by the evaluation device 100 as necessary. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106, such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM, and cooperates with the CPU to form the control unit.
  • this computer program may be stored in an application program server connected to the evaluation device 100 via any network, and it is also possible to download all or part of it as needed.
  • the evaluation program or calculation program of the present invention may be stored in a non-transitory computer-readable recording medium, and may also be configured as a program product.
  • the term "recording medium” refers to a memory card, a USB (Universal Serial Bus) memory, a SD (Secure Digital) card, a flexible disk, a magneto-optical disk, a ROM, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable and Programmable Read Only Memory), a ...
  • Programmable Read Only Memory registered trademark
  • CD-ROM Compact Disc Read Only Memory
  • MO Magnetic-Optical disk
  • DVD Digital Versatile Disk
  • Blu-ray registered trademark
  • a "program” is a data processing method written in any language or description method, and may be in any format, such as source code or binary code.
  • a "program” is not necessarily limited to a single configuration, but also includes a distributed configuration consisting of multiple modules or libraries, and a program that works in conjunction with a separate program, such as an OS, to achieve its function. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, can use well-known configurations and procedures.
  • the various databases stored in the memory unit 106 are storage devices such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and website provision.
  • storage devices such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and website provision.
  • the evaluation device 100 may also be configured as a known information processing device such as a personal computer or a workstation, or as an information processing device to which any peripheral device is connected.
  • the evaluation device 100 may also be realized by implementing software (including a program or data, etc.) that realizes the evaluation method or calculation method of the present invention in the information processing device.
  • the specific form of distribution/integration of the devices is not limited to that shown in the figures, and all or part of the devices can be functionally or physically distributed/integrated in any unit according to various additions or functional loads.
  • the above-mentioned embodiments can be implemented in any combination, or the above-mentioned embodiments can be implemented selectively.
  • Blood samples taken 21 days before the expected delivery date were used to measure the blood concentrations of 25 amino acids (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and 29 biochemical parameters (ALB (g/dl), ALT (IU/l), AST (IU/l), B The following laboratory values were measured: HBA ( ⁇ mol/l), BUN (mg/dl), Ca (mg/dl), gGTP (IU/l), Glc (mg/dl), Glob (mg/dl), NEFA ( ⁇ Eq/l), T-Bil (mg/dl), TCHO (mg/dl), TG (mg/dl), TP (g/dl), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C
  • mastitis individuals diagnosed with mastitis based on clinical findings were classified as individuals with clinical mastitis, and individuals judged to have mastitis based on the somatic cell count (SCC) in the milk were classified as individuals with SCC mastitis.
  • SCC is a general term for white blood cells and sloughed epithelial cells in milk. It is said that SCC increases when a cow suffers from mastitis. In this example, mastitis is judged to occur when the SCC is 200,000/mL or higher.
  • ROC_AUC of the prediction formula based on the training data 0.70
  • the ROC_AUC of the prediction formula based on the validation data is 0.60
  • the ROC_AUC of the prediction formula based on the validation data was 0.58.
  • Blood samples taken 21 days before the expected delivery date were used to measure the blood concentrations of the 25 amino acids mentioned above (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) and 28 biochemical parameters (ALB (g/dl), ALT (IU/l), AST ...
  • T (IU/l), BHBA ( ⁇ mol/l), BUN (mg/dl), Ca (mg/dl), gGTP (IU/l), Glc (mg/dl), NEFA ( ⁇ Eq/l), T-Bil (mg/dl), TCHO (mg/dl), TG (mg/dl), TP (g/dl), P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE).
  • the blood concentrations of amino acids were measured by the above-mentioned measurement method (A) or (B).
  • the blood levels of haptoglobin, IL-1 ⁇ , and serum albumin A were measured using an enzyme-linked immunosorbent assay (ELISA).
  • the d-ROMs and BAP levels were measured using a colorimetric method.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Immunology (AREA)
  • Molecular Biology (AREA)
  • Urology & Nephrology (AREA)
  • Hematology (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Medicinal Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Food Science & Technology (AREA)
  • Pathology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Biotechnology (AREA)
  • Cell Biology (AREA)
  • Microbiology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

La présente invention aborde le problème de la fourniture, par exemple, d'un procédé d'évaluation avec lequel des informations hautement fiables concernant l'état d'inflammation après la parturition peuvent être fournies avant la parturition. Dans le présent mode de réalisation, l'état d'inflammation chez un ruminant après parturition est évalué à l'aide d'au moins une valeur parmi : des valeurs de concentration de 25 sortes d'acides aminés (Ala, Arg, Asn, Asp, BCAA, Cit, Cys, Glu, Gln, Gly, His, Ile, Leu, Lys, Met, 3 MeHis, Orn, Phe, Pro, Ser, Tau, Thr, Trp, Tyr, Val) dans le sang du ruminant avant la parturition ; des valeurs de test de 37 composants biochimiques (ALB, ALT, AST, BHBA, BUN, Ca, gGTP, Glc, Glob, NEFA, T-Bil, TCHO, TG, TP, P, Na, K, Cl, GGT, ALP, LDH, CPK, LDL-C, HDL-C, Mg, AMY, PA, LA, CRE, LBP, IL -1β, haptoglobine, amyloïde sérique a, IL -6, TNF-α, cortisol, kynurénine) dans le sang ; et des valeurs de mesure (valeur d-ROMs, valeur BAP, valeur BAP/valeur d-ROMs, et valeur d-ROMs/valeur BAP) de quatre éléments de mesure obtenus à partir du sang.
PCT/JP2024/045856 2023-12-28 2024-12-25 Procédé d'évaluation d'une inflammation après une parturition Pending WO2025142997A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2023222934 2023-12-28
JP2023-222934 2023-12-28

Publications (1)

Publication Number Publication Date
WO2025142997A1 true WO2025142997A1 (fr) 2025-07-03

Family

ID=96219158

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2024/045856 Pending WO2025142997A1 (fr) 2023-12-28 2024-12-25 Procédé d'évaluation d'une inflammation après une parturition

Country Status (1)

Country Link
WO (1) WO2025142997A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160209423A1 (en) * 2013-09-04 2016-07-21 Univesity College Dublin, National University Of Ireland, Dublin Methods for predicting, diagnosing or monitoring infections or conditions
WO2019130920A1 (fr) * 2017-12-28 2019-07-04 味の素株式会社 Système de commande d'alimentation et procédé de commande d'alimentation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160209423A1 (en) * 2013-09-04 2016-07-21 Univesity College Dublin, National University Of Ireland, Dublin Methods for predicting, diagnosing or monitoring infections or conditions
WO2019130920A1 (fr) * 2017-12-28 2019-07-04 味の素株式会社 Système de commande d'alimentation et procédé de commande d'alimentation

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
DERVISHI ELDA, ZHANG GUANSHI, DUNN SUZANNA M., MANDAL RUPASRI, WISHART DAVID S., AMETAJ BURIM N.: "GC–MS Metabolomics Identifies Metabolite Alterations That Precede Subclinical Mastitis in the Blood of Transition Dairy Cows", JOURNAL OF PROTEOME RESEARCH, vol. 16, no. 2, 3 February 2017 (2017-02-03), pages 433 - 446, XP093330162, ISSN: 1535-3893, DOI: 10.1021/acs.jproteome.6b00538 *
DERVISHI ELDA; ZHANG GUANSHI; ZWIERZCHOWSKI GRZEGORZ; MANDAL RUPASRI; WISHART DAVID S.; AMETAJ BURIM N.: "Serum metabolic fingerprinting of pre-lameness dairy cows by GC–MS reveals typical profiles that can identify susceptible cows", JOURNAL OF PROTEOMICS, vol. 213, 14 December 2019 (2019-12-14), AMSTERDAM, NL , pages 1 - 12, XP086019379, ISSN: 1874-3919, DOI: 10.1016/j.jprot.2019.103620 *
KAMEMORI YASUYUKI , SHINTAROU OOTANI, HARUJI NIMURA , TATSUYA NISHIKAWA, SACHIKO AKAGI, KIYOSHI OKUDA: "Prediction of perinatal disease development and preventive measures with the use of blood biochemistry profile in dairy cows in a dry period", JOURNAL OF VETERINARY CLINIC, vol. 60, no. 9, 1 January 2013 (2013-01-01), pages 557 - 562, XP093330164 *
ZANDKARIMI F., VANEGAS J., FERN X., MAIER C.S., BOBE G.: "Metabotypes with elevated protein and lipid catabolism and inflammation precede clinical mastitis in prepartal transition dairy cows", JOURNAL OF DAIRY SCIENCE, vol. 101, no. 6, 1 June 2018 (2018-06-01), AMSTERDAM, NL, pages 5531 - 5548, XP093330160, ISSN: 0022-0302, DOI: 10.3168/jds.2017-13977 *
ZHANG GUANSHI, TOBOLSKI DAWID, ZWIERZCHOWSKI GRZEGORZ, MANDAL RUPASRI, WISHART DAVID S., AMETAJ BURIM N.: "Identification of Serum-Predictive Biomarkers for Subclinical Mastitis in Dairy Cows and New Insights into the Pathobiology of the Disease", JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, vol. 70, no. 5, 9 February 2022 (2022-02-09), US , pages 1724 - 1746, XP093330159, ISSN: 0021-8561, DOI: 10.1021/acs.jafc.1c07281 *
ZHANG GUANSHI; ZWIERZCHOWSKI GRZEGORZ; MANDAL RUPASRI; WISHART DAVID S.; AMETAJ BURIM N.: "Serum metabolomics identifies metabolite panels that differentiate lame dairy cows from healthy ones", METABOLOMICS, vol. 16, no. 6, 13 June 2020 (2020-06-13), New York, pages 1 - 22, XP037169808, ISSN: 1573-3882, DOI: 10.1007/s11306-020-01693-z *
小比類巻 正幸 ほか, 乳牛における分娩前後の疾病の発生と血中遊離アミノ酸値の関係, 平成18年度 日本獣医師会学会年次大会（さいたま）講演要旨集, 2007, p. 138, (KOHIRUIMAKI, Masayuki et al.), non-official translation (The relationship between the occurrence of diseases around parturition and plasma free amino acid levels in dairy cows, Lecture Abstracts of 2006 Annual Conference of Japan Veterinary Medical Association (Saitama)) *

Similar Documents

Publication Publication Date Title
US20250087362A1 (en) System and method of generating a model to detect, or predict the risk of, an outcome
JP7501573B2 (ja) 分娩後ケトーシスの評価方法
US20190228844A1 (en) Evaluating method, evaluating apparatus, evaluating program product, evaluating system, and terminal apparatus for colorectal cancer
JP7643598B2 (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、記録媒体、評価システム
Foldager et al. Predicting physiological imbalance in Holstein dairy cows by three different sets of milk biomarkers
WO2025142997A1 (fr) Procédé d'évaluation d'une inflammation après une parturition
JP7595611B2 (ja) 癌モニタリングの方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、および評価システム
JP7506646B2 (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、及び評価システム
US11871728B2 (en) Feeding management system and feeding management method
JP2019015547A (ja) 膵がん患者における骨格筋状態の評価方法、評価装置、評価プログラム、評価システムおよび端末装置
JP7435855B2 (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、記録媒体、及び評価システム
WO2020184660A1 (fr) Procédé d'évaluation de la sarcopénie, procédé de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, support d'enregistrement, système d'évaluation et dispositif terminal
JP7435856B2 (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、記録媒体、及び評価システム
WO2024225424A1 (fr) Procédé d'évaluation d'une maladie métabolique après un vêlage
WO2024143402A1 (fr) Procédé d'évaluation de diversité alimentaire
JP7669695B2 (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、記録媒体および評価システム
WO2019194144A1 (fr) Méthode d'évaluation du cancer du sein, procédé de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, support d'enregistrement, système d'évaluation et dispositif terminal
JP7093162B2 (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、および評価システム
WO2016148155A1 (fr) Méthode, dispositif, programme et système d'évaluation ainsi que dispositif terminal
JPWO2018143369A1 (ja) 糖尿病患者における膵臓癌の評価方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、評価システム、及び端末装置
JP2017122655A (ja) 骨格筋面積の評価方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24912883

Country of ref document: EP

Kind code of ref document: A1