CN113077885A - Data analysis method and device based on intestinal metabolites - Google Patents
Data analysis method and device based on intestinal metabolites Download PDFInfo
- Publication number
- CN113077885A CN113077885A CN202110270758.7A CN202110270758A CN113077885A CN 113077885 A CN113077885 A CN 113077885A CN 202110270758 A CN202110270758 A CN 202110270758A CN 113077885 A CN113077885 A CN 113077885A
- Authority
- CN
- China
- Prior art keywords
- intestinal
- total ion
- ion abundance
- analysis result
- metabolites
- 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
Links
- 239000002207 metabolite Substances 0.000 title claims abstract description 122
- 230000000968 intestinal effect Effects 0.000 title claims abstract description 84
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000007405 data analysis Methods 0.000 title claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims abstract description 59
- 206010003805 Autism Diseases 0.000 claims abstract description 47
- 208000020706 Autistic disease Diseases 0.000 claims abstract description 47
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 claims description 80
- WHUUTDBJXJRKMK-UHFFFAOYSA-N Glutamic acid Natural products OC(=O)C(N)CCC(O)=O WHUUTDBJXJRKMK-UHFFFAOYSA-N 0.000 claims description 45
- 229960003692 gamma aminobutyric acid Drugs 0.000 claims description 43
- OGNSCSPNOLGXSM-UHFFFAOYSA-N (+/-)-DABA Natural products NCCC(N)C(O)=O OGNSCSPNOLGXSM-UHFFFAOYSA-N 0.000 claims description 37
- 235000013922 glutamic acid Nutrition 0.000 claims description 37
- 239000004220 glutamic acid Substances 0.000 claims description 37
- SFLSHLFXELFNJZ-QMMMGPOBSA-N (-)-norepinephrine Chemical compound NC[C@H](O)C1=CC=C(O)C(O)=C1 SFLSHLFXELFNJZ-QMMMGPOBSA-N 0.000 claims description 16
- 229960002748 norepinephrine Drugs 0.000 claims description 16
- SFLSHLFXELFNJZ-UHFFFAOYSA-N norepinephrine Natural products NCC(O)C1=CC=C(O)C(O)=C1 SFLSHLFXELFNJZ-UHFFFAOYSA-N 0.000 claims description 16
- WHUUTDBJXJRKMK-VKHMYHEASA-N L-glutamic acid Chemical compound OC(=O)[C@@H](N)CCC(O)=O WHUUTDBJXJRKMK-VKHMYHEASA-N 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000003745 diagnosis Methods 0.000 abstract description 7
- 230000007547 defect Effects 0.000 abstract description 3
- 150000002500 ions Chemical class 0.000 description 58
- PXIPVTKHYLBLMZ-UHFFFAOYSA-N Sodium azide Chemical compound [Na+].[N-]=[N+]=[N-] PXIPVTKHYLBLMZ-UHFFFAOYSA-N 0.000 description 8
- WEVYAHXRMPXWCK-UHFFFAOYSA-N Acetonitrile Chemical compound CC#N WEVYAHXRMPXWCK-UHFFFAOYSA-N 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 4
- BDAGIHXWWSANSR-UHFFFAOYSA-N methanoic acid Natural products OC=O BDAGIHXWWSANSR-UHFFFAOYSA-N 0.000 description 4
- 229960002989 glutamic acid Drugs 0.000 description 3
- OSWFIVFLDKOXQC-UHFFFAOYSA-N 4-(3-methoxyphenyl)aniline Chemical compound COC1=CC=CC(C=2C=CC(N)=CC=2)=C1 OSWFIVFLDKOXQC-UHFFFAOYSA-N 0.000 description 2
- 208000029560 autism spectrum disease Diseases 0.000 description 2
- 235000019253 formic acid Nutrition 0.000 description 2
- 230000008014 freezing Effects 0.000 description 2
- 238000007710 freezing Methods 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 238000002156 mixing Methods 0.000 description 2
- 229910052757 nitrogen Inorganic materials 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 239000006228 supernatant Substances 0.000 description 2
- 208000012202 Pervasive developmental disease Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005119 centrifugation Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- SVWLIIFHXFGESG-UHFFFAOYSA-N formic acid;methanol Chemical compound OC.OC=O SVWLIIFHXFGESG-UHFFFAOYSA-N 0.000 description 1
- 210000003736 gastrointestinal content Anatomy 0.000 description 1
- 238000000265 homogenisation Methods 0.000 description 1
- 238000002013 hydrophilic interaction chromatography Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000004949 mass spectrometry Methods 0.000 description 1
- 230000003340 mental effect Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000018102 proteins Nutrition 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000010791 quenching Methods 0.000 description 1
- 230000000171 quenching effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 238000009210 therapy by ultrasound Methods 0.000 description 1
- 229910021642 ultra pure water Inorganic materials 0.000 description 1
- 239000012498 ultrapure water Substances 0.000 description 1
- 238000001291 vacuum drying Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Pathology (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Databases & Information Systems (AREA)
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention discloses a data analysis method and a data analysis device based on intestinal metabolites, wherein the method comprises the following steps: the method comprises the steps of obtaining MRM mode data of a detected sample, obtaining a total ion abundance value of a target metabolite, calculating and obtaining an intestinal metabolite analysis result of the detected sample, and using the intestinal metabolite analysis result for evaluating the risk of autism of a detected patient. The embodiment of the invention can overcome the defect that the traditional autism diagnosis depends on subjective experience judgment, improves the accuracy and objectivity of evaluating the autism risk of a patient to be detected, and provides accurate data support for the early warning of autism.
Description
Technical Field
The invention relates to the field of biomedical diagnosis and prediction, in particular to a data analysis method and device based on intestinal metabolites.
Background
Autism (ASD), also known as Autism or Autism Spectrum disorder, is a subtype of pervasive developmental disorder, and is mostly seen in males who are ill in infants and children, seriously affecting the social function of the children, and bringing great mental stress and heavy economic burden to families and society of the children. If the autistic patients can find early intervention, the autistic patients have a crucial effect on subsequent recovery or symptom relief.
The existing autism diagnosis method is usually evaluated according to a scale, and the diagnosis method has the problems of long time consumption, low efficiency, easy influence of environmental factors, low accuracy and the like, and can not realize quick, accurate and objective autism diagnosis. Meanwhile, effective early warning cannot be provided for the onset of autism.
Disclosure of Invention
The embodiment of the invention provides a data analysis method and device based on intestinal metabolites, which improve the accuracy of evaluating the risk of autism of a patient to be detected and provide accurate data support for early warning of autism.
A first aspect of embodiments of the present application provides a method for analyzing data based on intestinal metabolites, including:
acquiring MRM mode data of a detected sample;
acquiring a total ion abundance value of a target metabolite according to MRM mode data of a detected sample; wherein the target metabolites include glutamic acid and gamma-aminobutyric acid in intestinal metabolites;
calculating and obtaining an intestinal metabolite analysis result of the detected sample according to the total ion abundance value of the target metabolite; wherein the results of the intestinal metabolite analysis are used for assessing the risk of autism in the patient to be tested.
In a possible implementation manner of the first aspect, the intestinal metabolite analysis result of the detected sample is calculated and obtained according to the total ion abundance value of the target metabolite, and specifically:
calculating a first ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid;
and calculating and obtaining a first intestinal metabolite analysis result of the detected sample according to the first ratio and the first formula.
In one possible implementation form of the first aspect, the target metabolite further comprises: norepinephrine.
In a possible implementation manner of the first aspect, the intestinal metabolite analysis result of the detected sample is calculated and obtained according to the total ion abundance value of the target metabolite, and specifically:
calculating a second ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid; obtaining a third numerical value according to the total ion abundance value of the norepinephrine;
and calculating and obtaining a second intestinal metabolite analysis result of the detected sample according to the second ratio and the third value and by combining a second formula.
In a possible implementation manner of the first aspect, the calculating and obtaining a first intestinal metabolite analysis result of the detected sample according to the first ratio and by combining a first formula specifically includes:
B1＝1/(1+e^((4.71-6.88*A1)))
wherein A1 is the first ratio and B1 is the first intestinal metabolite analysis result.
In a possible implementation manner of the first aspect, the second intestinal metabolite analysis result of the detected sample is calculated and obtained according to the second ratio and the third value in combination with the second formula, and specifically:
B2＝1/(1+e^((5.68-2.76*A3*10^(-7)-5.38*A2))
wherein B2 is the second intestinal metabolite analysis result, A2 is the second ratio, and A3 is the third value.
A second aspect of the embodiments of the present application provides an intestinal metabolite-based data analysis apparatus, including a first obtaining module, a second obtaining module, and a calculating module;
the first acquisition module is used for acquiring MRM mode data of a detected sample;
the second acquisition module is used for acquiring the total ion abundance value of the target metabolite according to the MRM mode data of the detected sample; wherein the target metabolites include glutamic acid and gamma-aminobutyric acid in intestinal metabolites;
the calculation module is used for calculating and obtaining an intestinal metabolite analysis result of the detected sample according to the total ion abundance value of the target metabolite; wherein the results of the intestinal metabolite analysis are used for assessing the risk of autism in the patient to be tested.
In a possible implementation manner of the second aspect, the calculating module is configured to calculate and obtain an analysis result of the intestinal metabolite of the detected sample according to the total ion abundance value of the target metabolite, and specifically includes:
calculating a first ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid;
and calculating and obtaining a first intestinal metabolite analysis result of the detected sample according to the first ratio and the first formula.
In one possible implementation of the second aspect, the target metabolite further comprises: norepinephrine.
In a possible implementation manner of the second aspect, the calculating module is configured to calculate and obtain an analysis result of the intestinal metabolite of the detected sample according to the total ion abundance value of the target metabolite, and specifically includes:
calculating a second ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid; obtaining a third numerical value according to the total ion abundance value of the norepinephrine;
and calculating and obtaining a second intestinal metabolite analysis result of the detected sample according to the second ratio and the third value and by combining a second formula.
Compared with the prior art, the data analysis method and device based on the intestinal metabolites provided by the embodiment of the invention have the beneficial effects that: according to the data analysis method based on the intestinal metabolites, disclosed by the embodiment of the invention, the total ion abundance value of the target metabolites is obtained by obtaining the MRM mode data of the detected sample, the intestinal metabolite analysis result of the detected sample is calculated and obtained, and the intestinal metabolite analysis result is used for evaluating the risk of the autism of the detected patient. The method overcomes the defect that the traditional autism diagnosis depends on subjective experience judgment, improves the accuracy and objectivity of evaluating the autism risk of a patient to be detected, and provides accurate data support for the early warning of autism.
Drawings
FIG. 1 is a schematic flow chart of a method for analyzing data based on intestinal metabolites according to an embodiment of the present invention;
fig. 2 is a schematic view of an interface of an autism warning system model 1 according to an embodiment of the present invention;
fig. 3 is a schematic view of an autism warning system model 2 interface according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a positive result interface obtained by the autism warning system according to an embodiment of the invention;
fig. 5 is a schematic diagram of a negative result interface obtained by the autism warning system according to an embodiment of the invention;
fig. 6 is a schematic structural diagram of a data analysis device based on intestinal metabolites according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a schematic flow chart of a method for analyzing data based on intestinal metabolites according to an embodiment of the present invention is shown, including:
s101: MRM mode data of a detected sample is acquired.
In this embodiment, before acquiring MRM mode data of a detected sample, the method further includes the following steps: collecting, preprocessing and subpackaging the detected samples.
In one embodiment, collecting the sample to be tested comprises: the instrument container sterilized by high temperature and high pressure is used, the sample of fresh excrement or intestinal contents is collected as much as possible and is close to the middle part, the action is rapid, and the phenomenon that the sample stays in the air for too long time is avoided.
In one embodiment, pre-processing and dispensing the test sample comprises:
one drop (about 10uL) of Sodium azide (Sodium azide) at a mass to volume ratio of 1/100(w/v) was added and the sample was aliquoted at 200 mg/tube. If no sodium azide is present, quenching can be carried out by rapidly placing the sample in liquid nitrogen after sampling.
After being treated by sodium azide, the mixture is quickly put into liquid nitrogen for freezing treatment for 15min, and then transferred to-80 ℃ for freezing storage.
Weighing a proper amount of each part, adding 200 mu L of 1% formic acid precooled ultrapure water, carrying out tissue homogenization, adding 800 mu L of 1% formic acid methanol/acetonitrile (1:1, v/v), carrying out vortex mixing, carrying out low-temperature ultrasonic treatment for 30 minutes, incubating at-20 ℃ for 1 hour to precipitate protein, collecting 14000g, centrifuging at 4 ℃ for 20 minutes, taking supernatant, and carrying out vacuum drying.
When mass spectrometry is carried out, 100 mu L acetonitrile/water (1:1, v/v) of 1% formic acid is added for redissolution, 14000g is carried out, centrifugation is carried out for 15min at 4 ℃, and the supernatant is taken for sample injection analysis. And taking another appropriate amount of sample, and mixing the samples in equal amount to obtain a QC sample. QC samples were prepared in parallel as described above.
In an embodiment, the acquiring MRM mode data of the detected sample includes: and sequentially separating the detected sample, the standard sample and the QC sample by using a HILIC technology, correcting the ion peak of the detected sample by using the data of the standard sample and the QC sample, and obtaining MRM mode data of the detected sample.
S102: and acquiring the total ion abundance value of the target metabolite according to the MRM mode data of the detected sample.
In this embodiment, when the target metabolites include: glutamic acid and gamma-aminobutyric acid, performing step S103; when the target metabolites include: glutamic acid, gamma-aminobutyric acid, and norepinephrine, step S104 is performed.
S103: when the target metabolites include glutamic acid and gamma-aminobutyric acid, a first intestinal metabolite analysis result is calculated and obtained according to the total ion abundance values of glutamic acid and gamma-aminobutyric acid.
In the present embodiment, a first ratio between the total ion abundance value of γ -aminobutyric acid and the total ion abundance value of glutamic acid is calculated based on the total ion abundance values of glutamic acid and γ -aminobutyric acid;
and calculating and obtaining a first intestinal metabolite analysis result of the detected sample according to the first ratio and a first formula.
In an embodiment, the calculating and obtaining a first intestinal metabolite analysis result of the detected sample according to the first ratio and by combining a first formula specifically includes:
B1＝1/(1+e^((4.71-6.88*A1)))
wherein A1 is the first ratio and B1 is the first intestinal metabolite analysis result.
The first intestinal metabolite analysis result B1 is used for assessing the risk of autism of the patient to be tested, and specifically: when B1 is greater than a first threshold, assessing that the detected patient has a higher probability of being acquired from autism; when B1 is less than the first threshold, the probability of assessing that the detected patient is getting autism is low.
In one embodiment, the first threshold is 0.063.
S104: when the metabolites of interest include glutamic acid, gamma-aminobutyric acid and norepinephrine, a second intestinal metabolite analysis result is calculated and obtained based on the total ion abundance values of glutamic acid, gamma-aminobutyric acid and norepinephrine.
In this embodiment, a second ratio and a third value are obtained according to the total ion abundance values of the glutamic acid, the gamma-aminobutyric acid and the norepinephrine; wherein the third value is the total ion abundance of norepinephrine and the second ratio is the ratio between the total ion abundance of γ -aminobutyric acid and the total ion abundance of glutamic acid;
and calculating and obtaining a second intestinal metabolite analysis result of the detected sample according to the second ratio and the third value and by combining a second formula.
In a specific embodiment, the calculating and obtaining a second intestinal metabolite analysis result of the detected sample according to the second ratio and the third value by combining a second formula specifically includes:
B2＝1/(1+e^((5.68-2.76*A3*10^(-7)-5.38*A2))
wherein B2 is the second intestinal metabolite analysis result, A2 is the second ratio, and A3 is the third value.
The second intestinal metabolite analysis result B2 is used for assessing the risk of autism of the patient to be tested, and specifically: when B2 is greater than a second threshold, the probability of assessing the detected patient for autism is high; when B2 is less than the second threshold, the probability of assessing that the detected patient is getting autism is low.
In an embodiment, the second threshold is: 0.021.
in this embodiment, the model 1 of the autism warning system is formed according to step S104; the model 2 of the autism warning system is formed according to step S103.
To further explain the model 1 of the autism warning system, please refer to fig. 2, where fig. 2 is a schematic view of an interface of the autism warning system model 1 according to an embodiment of the present invention, including: a model selection box, a numerical value input box, a predict button, and a leave button.
As shown in fig. 2, when the model selection box of the autism warning system is selected as model 1, the numerical value input boxes are a GABA/Glu ratio input box and an NE total ion peak input box.
Wherein, GAGB is the total ion abundance value of the gamma-aminobutyric acid, and Glu is the total ion abundance value of the glutamic acid, so that the GABA/Glu ratio is the second ratio; the NE total ion peak is the total ion abundance value of the norepinephrine, so the NE total ion peak is the third value.
And after the second ratio and the third numerical value are respectively filled into a GABA/Glu ratio input box and an NE total ion peak input box, clicking prediction to calculate and obtain a second intestinal metabolite analysis result of the detected sample according to the second formula.
To further explain the model 2 of the autism warning system, please refer to fig. 3, where fig. 3 is a schematic view of an interface of the autism warning system model 2 according to an embodiment of the present invention, including: a model selection box, a numerical value input box, a predict button, and a leave button.
As shown in fig. 3, when the model selection box of the autism warning system is selected as model 2, the numerical value input box is a GABA/Glu ratio input box.
Wherein, GAGB is the total ion abundance value of the gamma-aminobutyric acid, and Glu is the total ion abundance value of the glutamic acid, so that the GABA/Glu ratio is the first ratio.
And after filling the GABA/Glu ratio input box with the first ratio, clicking prediction to calculate and obtain a first intestinal metabolite analysis result of the detected sample according to the first formula.
Both the model 1 and the model 2 of the autism early warning system can be used for evaluating the autism risk of a detected patient, and a user can select one of the models according to the actual situation.
When the second intestinal metabolite analysis result is greater than the second threshold or the first intestinal metabolite analysis result is greater than the first threshold, a positive result is determined, that is, the probability that the patient gets autism is high, a system interface is shown in fig. 4, and fig. 4 is a schematic diagram of a positive result interface obtained by the autism early warning system provided by an embodiment of the present invention.
When the second intestinal metabolite analysis result is smaller than the second threshold or the first intestinal metabolite analysis result is smaller than the first threshold, it is determined as a negative result, that is, the probability that the patient gets autism is higher, a system interface is shown in fig. 5, and fig. 5 is a schematic diagram of a negative result interface obtained by the autism early warning system provided by an embodiment of the present invention.
To further illustrate the data analysis device based on intestinal metabolites, please refer to fig. 6, fig. 6 is a schematic structural diagram of a data analysis device based on intestinal metabolites according to an embodiment of the present invention, including: a first obtaining module 601, a second obtaining module 602 and a calculating module 603;
the first obtaining module 601 is configured to obtain MRM mode data of a detected sample;
the second obtaining module 602 is configured to obtain a total ion abundance value of a target metabolite according to the MRM mode data of the detected sample; wherein the metabolites of interest include glutamic acid and gamma-aminobutyric acid in intestinal metabolites;
the calculation module 603 is configured to calculate and obtain an analysis result of the intestinal metabolite of the detected sample according to the total ion abundance value of the target metabolite; wherein the intestinal metabolite analysis result is used for evaluating the risk of autism of the detected patient.
In this embodiment, the calculating module is configured to calculate and obtain an analysis result of the intestinal metabolite of the detected sample according to the total ion abundance value of the target metabolite, specifically: calculating a first ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid; and calculating and obtaining a first intestinal metabolite analysis result of the detected sample according to the first ratio and a first formula.
In a specific embodiment, the total ion abundance value of the target metabolite acquired by the second acquiring module 602 further includes: when the total ion abundance value of norepinephrine is larger than the first ion abundance value, the calculating module 603 calculates a second ratio between the total ion abundance value of gamma-aminobutyric acid and the total ion abundance value of glutamic acid according to the total ion abundance values of glutamic acid and gamma-aminobutyric acid; obtaining a third numerical value according to the total ion abundance value of the norepinephrine; and calculating and obtaining a second intestinal metabolite analysis result of the detected sample according to the second ratio and the third value and by combining a second formula.
According to the embodiment of the invention, MRM mode data of a detected sample is firstly acquired through a first acquisition module 601, then a second acquisition module 602 acquires a total ion abundance value of a target metabolite according to the MRM mode data of the detected sample, and finally an intestinal metabolite analysis result of the detected sample is calculated and acquired through a calculation module 603 according to the total ion abundance value of the target metabolite. By the embodiment of the invention, the defect that the traditional autism diagnosis depends on subjective experience judgment is overcome, the accuracy and objectivity of evaluating the autism risk of the patient to be detected are improved, and accurate data support is provided for the autism early warning.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for analyzing data based on intestinal metabolites, comprising:
acquiring MRM mode data of a detected sample;
acquiring a total ion abundance value of a target metabolite according to the MRM mode data of the detected sample; wherein the metabolites of interest include glutamic acid and gamma-aminobutyric acid in intestinal metabolites;
calculating and obtaining an intestinal metabolite analysis result of the detected sample according to the total ion abundance value of the target metabolite; wherein the intestinal metabolite analysis result is used for evaluating the risk of autism of the detected patient.
2. The method for analyzing data based on intestinal metabolites according to claim 1, wherein the intestinal metabolite analysis result of the detected sample is calculated and obtained according to the total ion abundance value of the target metabolites, specifically:
calculating a first ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid;
and calculating and obtaining a first intestinal metabolite analysis result of the detected sample according to the first ratio and a first formula.
3. The method for analyzing data based on intestinal metabolites as claimed in claim 1, wherein the target metabolites further comprise: norepinephrine.
4. The method for analyzing data based on intestinal metabolites according to claim 3, wherein the intestinal metabolite analysis result of the detected sample is calculated and obtained according to the total ion abundance value of the target metabolite, specifically:
calculating a second ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid; obtaining a third numerical value according to the total ion abundance value of the norepinephrine;
and calculating and obtaining a second intestinal metabolite analysis result of the detected sample according to the second ratio and the third value and by combining a second formula.
5. The method for analyzing data based on intestinal metabolites according to claim 2, wherein the first intestinal metabolite analysis result of the tested sample is calculated and obtained according to the first ratio and a first formula, specifically:
B1＝1/(1+e^((4.71-6.88*A1)))
wherein A1 is the first ratio and B1 is the first intestinal metabolite analysis result.
6. The method according to claim 4, wherein a second intestinal metabolite analysis result of the detected sample is calculated and obtained according to the second ratio and the third value and by combining a second formula, specifically:
B2＝1/(1+e^((5.68-2.76*A3*10^(-7)-5.38*A2))
wherein B2 is the second intestinal metabolite analysis result, A2 is the second ratio, and A3 is the third value.
7. An intestinal metabolite-based data analysis device, comprising: the device comprises a first acquisition module, a second acquisition module and a calculation module;
the first acquisition module is used for acquiring MRM mode data of a detected sample;
the second acquisition module is used for acquiring a total ion abundance value of a target metabolite according to the MRM mode data of the detected sample; wherein the metabolites of interest include glutamic acid and gamma-aminobutyric acid in intestinal metabolites;
the calculation module is used for calculating and obtaining an intestinal metabolite analysis result of the detected sample according to the total ion abundance value of the target metabolite; wherein the intestinal metabolite analysis result is used for evaluating the risk of autism of the detected patient.
8. The device according to claim 7, wherein the calculating module is configured to calculate and obtain an analysis result of the intestinal metabolites of the detected sample according to the total ion abundance value of the target metabolites, specifically:
calculating a first ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid;
and calculating and obtaining a first intestinal metabolite analysis result of the detected sample according to the first ratio and a first formula.
9. The intestinal metabolite based data analysis device as claimed in claim 7, wherein the target metabolite further comprises: norepinephrine.
10. The device according to claim 9, wherein the calculating module is configured to calculate and obtain an analysis result of the intestinal metabolites of the detected sample according to the total ion abundance value of the target metabolites, specifically:
calculating a second ratio between the total ion abundance value of the gamma-aminobutyric acid and the total ion abundance value of the glutamic acid according to the total ion abundance values of the glutamic acid and the gamma-aminobutyric acid; obtaining a third numerical value according to the total ion abundance value of the norepinephrine;
and calculating and obtaining a second intestinal metabolite analysis result of the detected sample according to the second ratio and the third value and by combining a second formula.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110270758.7A CN113077885A (en) | 2021-03-12 | 2021-03-12 | Data analysis method and device based on intestinal metabolites |
| PCT/CN2021/114740 WO2022188370A1 (en) | 2021-03-12 | 2021-08-26 | Data analysis method and apparatus based on intestinal metabolites |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110270758.7A CN113077885A (en) | 2021-03-12 | 2021-03-12 | Data analysis method and device based on intestinal metabolites |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113077885A true CN113077885A (en) | 2021-07-06 |
Family
ID=76612229
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110270758.7A Pending CN113077885A (en) | 2021-03-12 | 2021-03-12 | Data analysis method and device based on intestinal metabolites |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113077885A (en) |
| WO (1) | WO2022188370A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022188370A1 (en) * | 2021-03-12 | 2022-09-15 | 中山大学附属第七医院(深圳) | Data analysis method and apparatus based on intestinal metabolites |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111197085A (en) * | 2020-03-16 | 2020-05-26 | 中国科学院北京基因组研究所 | Intestinal flora biomarker related to autism and application thereof |
| WO2020206443A1 (en) * | 2019-04-05 | 2020-10-08 | Arizona Board Of Regents On Behalf Of Arizona State University | Metabolites as diagnostics for autism spectrum disorder in children with gastrointestinal symptoms |
| WO2020223197A1 (en) * | 2019-05-02 | 2020-11-05 | Stemina Biomarker Discovery, Inc. | Diagnosis and treatment of autism spectrum disorders associated with altered metabolic pathways |
| CN111983098A (en) * | 2020-08-28 | 2020-11-24 | 中山大学附属第七医院(深圳) | Application of intestinal microorganism metabolite in preparation of autism diagnosis kit |
| WO2021024198A2 (en) * | 2019-08-05 | 2021-02-11 | Tata Consultancy Services Limited | System and method for risk assessment of autism spectrum disorder |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150160241A1 (en) * | 2013-12-09 | 2015-06-11 | Regents Of The University Of Minnesota | Gamma-aminobutyric acid a receptor subunit biomarkers |
| CN109675037B (en) * | 2019-01-25 | 2021-09-14 | 南方医科大学 | Application of ATP and receptor thereof in preparation of drugs for treating autism |
| CN113077885A (en) * | 2021-03-12 | 2021-07-06 | 中山大学附属第七医院(深圳) | Data analysis method and device based on intestinal metabolites |
-
2021
- 2021-03-12 CN CN202110270758.7A patent/CN113077885A/en active Pending
- 2021-08-26 WO PCT/CN2021/114740 patent/WO2022188370A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020206443A1 (en) * | 2019-04-05 | 2020-10-08 | Arizona Board Of Regents On Behalf Of Arizona State University | Metabolites as diagnostics for autism spectrum disorder in children with gastrointestinal symptoms |
| WO2020223197A1 (en) * | 2019-05-02 | 2020-11-05 | Stemina Biomarker Discovery, Inc. | Diagnosis and treatment of autism spectrum disorders associated with altered metabolic pathways |
| WO2021024198A2 (en) * | 2019-08-05 | 2021-02-11 | Tata Consultancy Services Limited | System and method for risk assessment of autism spectrum disorder |
| CN111197085A (en) * | 2020-03-16 | 2020-05-26 | 中国科学院北京基因组研究所 | Intestinal flora biomarker related to autism and application thereof |
| CN111983098A (en) * | 2020-08-28 | 2020-11-24 | 中山大学附属第七医院(深圳) | Application of intestinal microorganism metabolite in preparation of autism diagnosis kit |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022188370A1 (en) * | 2021-03-12 | 2022-09-15 | 中山大学附属第七医院(深圳) | Data analysis method and apparatus based on intestinal metabolites |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022188370A1 (en) | 2022-09-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103543123A (en) | Infrared spectrum recognition method for adulterated milk | |
| Hopker et al. | Reliability of cycling gross efficiency using the Douglas bag method | |
| Baghurst et al. | Lead in the placenta, membranes, and umbilical cord in relation to pregnancy outcome in a lead-smelter community. | |
| CN102339356B (en) | Method for evaluating and predicating toxicity and efficacy of medicament by using metabonomic technology | |
| CN105092528A (en) | Method for identifying honey quality through physical and chemical indexes and electronic nose technology | |
| EP2836832A1 (en) | Early trimester screening for early- and late-onset preeclampsia | |
| CN104251841A (en) | Multi-sample breath analyzer based on cavity ring-down spectroscopy | |
| CN113077885A (en) | Data analysis method and device based on intestinal metabolites | |
| Yudhana et al. | Multi sensor application-based for measuring the quality of human urine on first-void urine | |
| Dasi et al. | Point-of-care Hb measurement in pooled capillary blood by a portable autoanalyser: comparison with venous blood Hb measured by reference methods in cross-sectional and longitudinal studies | |
| CN103115884B (en) | Method for measuring p-hydroxybenzoate in cosmetics | |
| CN105181869B (en) | A kind of application of macrosomia's auxiliary diagnosis mark | |
| Gümüş et al. | Evaluation of the postmortem glucose and glycogen levels in hepatic, renal, muscle, and brain tissues: is it possible to estimate postmortem interval using these parameters? | |
| CN106525765A (en) | Blood testing method | |
| CN102998436B (en) | Diganosis of a renal disease by oxygen and hydrogen isotopes in a biological sample | |
| CN112710636B (en) | Method and device for detecting concentration of specific protein | |
| Ro et al. | Comparison of the bovine blood gas parameters produced with three types of portable blood gas analyzers | |
| CN103483442B (en) | Detect mass spectra model and the construction process of carious tooth albumen | |
| Eberman et al. | Comparison of refractometry, urine color, and urine reagent strips to urine osmolality for measurement of urinary concentration | |
| Stube et al. | A method for the measurement of mercury in human whole blood | |
| JPH01169356A (en) | Method and apparatus for inspecting kidney function | |
| Cihan et al. | Comparison of Glucose Levels in Blood Samples Taken from Coccygeal, Jugular and Mammary Vein of Cows in Lactation | |
| CN113138268B (en) | Semen quality evaluation method, storage medium and electronic equipment | |
| JP2018179565A (en) | Risk estimation method and system of aged related macular degeneration disease | |
| JP2024120779A (en) | BIOMARKER FOR EVALUATING SERIOUSNESS OF ALLERGIC RHINITIS AND METHOD FOR OBTAINING INDEX FOR EVALUATING SERIOUSNESS OF ALLERGIC RHINITIS |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210706 |
|
| RJ01 | Rejection of invention patent application after publication |