CN112834653A

CN112834653A - Biomarker F3 for diagnosing leukoencephalopathy and application thereof

Info

Publication number: CN112834653A
Application number: CN202110385947.9A
Authority: CN
Inventors: 陈显扬; 宋王婷; 张珂; 薛腾; 韩佳睿
Original assignee: Baofeng Biotech Beijing Co ltd
Current assignee: Baofeng Biotech Beijing Co ltd
Priority date: 2021-04-09
Filing date: 2021-04-09
Publication date: 2021-05-25
Anticipated expiration: 2041-04-09
Also published as: CN112834653B

Abstract

The invention provides a biomarker for diagnosing white brain lesions and application thereof, wherein the biomarker is ceramide (m18:1(4E)/24:1(15Z)) (Cer (m18:1(4E)/24:1 (15Z))). The biomarker ceramide (m18:1(4E)/24:1(15Z)) is combined with Cholesterol-alpha-D-glucoside (Cholesteryl-alpha-D-glucoside) and combines 6Z,9Z, 20-eicosatrienoic acid (6Z, 9Z, 20-Henecositoriene), cannabixanthin A (Cannflavin A), cucurbitacin E (Cucurbitacin E), Cholesteryl ester 22:6 (22: 6 Cholesterol ester) or ceramide (D18:0/24:1(15Z)) (Cer (D18:0/24:1 (15Z))) to judge whether the white brain lesion exists. A biomarker for diagnosing leukoencephalopathy is used for preparing a detection reagent for diagnosing leukoencephalopathy, and is helpful for diagnosing whether the leukoencephalopathy is inclined or not and preventing in advance.

Description

Biomarker F3 for diagnosing leukoencephalopathy and application thereof

Technical Field

The invention belongs to the technical field of biological detection, and particularly relates to a biomarker for diagnosing and recognizing white brain lesions and application thereof.

Background

White matter disease (WML) is a common neurodegenerative disease, the most typical pathology of which is the destruction of white matter integrity or demyelination, and the disease is commonly seen in many diseases such as stroke, alzheimer disease, parkinson disease, multiple sclerosis, schizophrenia, etc. White brain matter is an important component of the central nervous system, where nerve fibers accumulate, and lesions in the myelin sheaths of central nerve cells in white brain matter can cause leukoencephalopathy. The typical response of white brain matter to various noxious stimuli is demyelination, which may be a secondary manifestation of neurological disorders such as infection, intoxication, degeneration, post-traumatic injury, infarction, and the like. The leukoencephalopathy mainly causes symptoms of leukoencephalopathy, speech disorder, abnormal mental behavior, gait disorder, dysuria and the like of a patient, and the healthy life quality of the patient is seriously influenced. It is closely associated with an increased risk of stroke and dementia. With the continuous development of imaging technology, the detection rate of white brain lesions is higher and higher. It is now recognized that age is a clear risk factor for leukoencephalopathy. According to research, the detection rate of the leukoencephalopathy in the population of 60-70 years old reaches 87%; the detection rate of leukoencephalopathy in 80-90-year-old people is as high as 95% -100%, factors such as hypertension, diabetes, dyslipidemia and metabolic disorder are closely related to the onset and progression of leukoencephalopathy, and the metabolic diseases mainly occur in the elderly people. With the advent of the aging society of China, the harm of leukoencephalopathy gradually draws importance to the medical field. Generally, the disease condition of most of the patients with the leukoencephalopathy is reversible, so that the symptoms of the patients with the leukoencephalopathy can be obviously improved by adopting proper preventive measures, wherein early screening is a crucial means.

The diagnosis of white brain lesions comprises mental state examination and imaging examination, and the current common craniocerebral examination means comprise electronic computed tomography and magnetic resonance imaging. The preliminary mental state examination comprises the operations of a test for evaluating inattention, a three-word delayed recall test for identifying dysmnesia, a clock drawing for evaluating visual dysfunction, an alternating motion sequence for evaluating brain function and the like. While leukoencephalopathy can be asymptomatic clinically in the early stage. And the test process involves questions and answers, consumes very large medical resources, and is time-consuming and labor-consuming. With the continuous development of imaging technology, the detection rate of white brain lesions gradually increases. However, the equipment required for detection is expensive and expensive. At present, no peripheral blood biomarker with high accuracy and strong specificity for the white brain lesion exists. The pathogenesis of the leukoencephalopathy is not clear, and a clear and effective treatment target point is lacked clinically, so that the treatment and the recovery of the leukoencephalopathy patient are not facilitated.

Metabolomics is an emerging omics technology that plays an increasingly important role in biological research because it can reveal unique chemical fingerprints of the cellular metabolism of the body. Metabonomics as an unbiased small molecule metabolite research method provides hope for finding more biomarkers of white brain lesions. There is increasing evidence for neurological disorders accompanied by disorders of bile acids, fatty acids and amino acids. And these results demonstrate that metabolic disorders may be predictive of the development of leukoencephalopathy. However, it is not clear which specific substance can be clearly detected as a prediction and diagnosis of the occurrence of leukoencephalopathy.

Disclosure of Invention

In order to effectively predict and diagnose the white brain lesion, the present invention provides a biomarker for diagnosing the white brain lesion.

In order to achieve the purpose, the invention adopts the following technical scheme that:

a biomarker for diagnosing white brain lesions, wherein the biomarker is ceramide (m18:1(4E)/24:1(15Z)) (Cer (m18:1(4E)/24:1 (15Z))).

Use of a biomarker for diagnosing a leukoencephalopathy as described above in the preparation of a test agent.

The use as described above, preferably the biomarker ceramide (m18:1(4E)/24:1(15Z)) combines Cholesterol-alpha-D-glucoside (Cholesterol-alpha-D-glucoside) and binds 6Z,9Z, 20-eicosatrienoic acid (6Z, 9Z, 20-Henecositoriene), cannabixanthin A (Canflavin A), cucurbitacin E (Cucurbitacin E), Cholesteryl ester 22:6 (22: 6 Cholesterol ester) or ceramide (D18:0/24:1(15Z)) (Cer (D18:0/24:1 (15Z))) to determine the presence or absence of white brain disease.

For the above applications, preferably, the ceramide (m18:1(4E)/24:1(15Z)) is denoted as F3, the content of cholesterol- α -D-glucoside is denoted as F1, 6Z,9Z, and the content of 20-eicosatrienoic acid is denoted as F2, when the units of the content are mg/L, the TC value is calculated according to the calculation formula TC = 9.573+2.787 × F3+15.351 × F1-21.578 × F2, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.163, judging the white brain lesion; if TC < 0.163, it is normal.

For the above applications, preferably, the content of ceramide (m18:1(4E)/24:1(15Z)) is represented as F3, the content of cholesterol- α -D-glucoside is represented as F1, the content of cannabixanthin a is represented as F4, when the units of the contents are all mg/L, the TC value is calculated according to the calculation formula TC = 1.6734+1.5739 × F3+7.6078 × F1-6.6884 × F4, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.550, the white brain lesion is judged; if TC is less than 0.550, the test result is normal.

For the above applications, preferably, the content of ceramide (m18:1(4E)/24:1(15Z)) is represented as F3, the content of cholesterol- α -D-glucoside is represented as F1, the content of cucurbitacin E is represented as F5, when the units of the contents are mg/L, the TC value is calculated according to the calculation formula TC = -1.3843+1.1595 × F3+4.6690 × F1-2.1691 × F5, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.336, the brain white lesion is judged; if TC is less than 0.336, the test result is normal.

For the above applications, preferably, the content of ceramide (m18:1(4E)/24:1(15Z)) is represented as F3, the content of cholesterol- α -D-glucoside is represented as F1, the content of cholesterol ester 22:6 is represented as F6, when the units of the contents are mg/L, the TC value is calculated according to the calculation formula TC = -1.5839+0.5951 × F3+5.2904 × F1-2.3305 × F6, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.280, judging the white brain lesion; if TC is less than 0.280, the test result is normal.

For the above applications, preferably, the content of ceramide (m18:1(4E)/24:1(15Z)) is represented as F3, the content of cholesterol- α -D-glucoside is represented as F1, the content of ceramide (D18:0/24:1(15Z)) is represented as F7, when the units of the contents are mg/L, the TC value is calculated according to the calculation formula TC = -1.6537+0.6052 × F3+4.9272 × F1-2.2786 × F7, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.316, judging the white brain lesion; if TC < 0.316, it is normal.

The invention has the beneficial effects that:

the invention provides a biomarker for diagnosing leukoencephalopathy, which is characterized in that ceramide (m18:1(4E)/24:1(15Z)) is combined with cholesterol-alpha-D-glucoside, and is combined with 6Z,9Z, 20-eicosatrienoic acid, cannabixanthin A, cucurbitacin E, cholesterol ester 22:6 or ceramide (D18:0/24:1(15Z)) to judge whether the leukoencephalopathy exists. Can be applied to a diagnostic kit, is helpful for diagnosing whether the tendency of leukoencephalopathy exists, and can be used for early prevention.

Drawings

FIG. 1 is a sample of VIP >1 in positive (A) negative (B) ion mode;

FIG. 2 is a score plot of (O) PLS-DA in positive (A) negative (B) ion mode;

FIG. 3 is a graph of S-plot in positive (A) negative (B) ion mode;

FIG. 4 is a ROC curve based on a logistic regression model (variables F3+ F1+ F2);

FIG. 5 is a ROC curve based on a logistic regression model (variables F3+ F1+ F4);

FIG. 6 is a ROC curve based on a logistic regression model (variables F3+ F1+ F5);

FIG. 7 is a ROC curve based on a logistic regression model (variables F3+ F1+ F6);

FIG. 8 is a ROC curve based on a logistic regression model (variables F3+ F1+ F7).

Detailed Description

The following examples are intended to further illustrate the invention but should not be construed as limiting it. Modifications and substitutions may be made thereto without departing from the spirit and scope of the invention.

Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.

Example 1

Sample(s)

Model a sample group of 112 persons, age range: over the age of 45, with 64 in the control population and 48 in the patient population.

The proportion of males and females in the control population was 1: and 1, magnetic resonance imaging detection shows that no abnormity exists.

The proportion of males and females in the patient population is 1: magnetic resonance imaging detection shows that white matter has infarcted foci.

Laboratory apparatus and reagent

An experimental instrument: 1. a vortex oscillator: model MX-S, Scilogex, USA; high resolution mass spectrometer: ESI-QTOF/MS; the model is as follows: xevo G2-S Q-TOF; the manufacturer: waters, Manchester, UK3. refrigerated centrifuge: model D3024R, Scilogex corporation, usa; 4. ultra-high performance liquid chromatography: UPLC, model: the ACQUITY UPLC I-Class system; the manufacturer: waters, Manchester, UK; 5. data acquisition software: MassLynx4.1, Waters; 6. analysis and identification software: progenetics QI; Waters.

Experimental reagent: isopropanol, formic acid, ammonium formate, acetonitrile, sodium formate leucine enkephalin; the manufacturers are Fisher.

Experimental methods

1. Sample pretreatment

Serum samples from the sample population were collected and thawed on ice, 200 μ L of plasma was extracted with 600 μ L of pre-cooled isopropanol, vortexed for 1min, incubated at room temperature for 10min, the extraction mixture was then stored overnight at-20 ℃, centrifuged at 4000r for 20min, the supernatant was transferred to a new centrifuge tube and diluted to 1: 10. samples were stored at-80 ℃ prior to LC-MS analysis. In addition, a pooled plasma sample was also prepared by combining 10 μ L of each extraction mixture.

2. Ultra-high performance liquid chromatography-mass spectrometry combined method for lipidomics

The samples were analyzed by ACQUITY UPLC coupled to a Xevo-G2XS high resolution time-of-flight mass spectrometer with ESI. A CQUITY UPLC BEH C18 column (2.1X 100 mM, 1.7 μm, Waters) was used with mobile phases of 10 mM ammonium formate-0.1% formic acid-acetonitrile (A, acetonitrile: water ratio 60: 40 by volume) and 10 mM ammonium formate-0.1% formic acid-isopropanol-acetonitrile (B, isopropanol: acetonitrile ratio 90: 10 by volume). Prior to large scale studies, pilot experiments with 10, 15 and 20 minute elution periods were performed to assess the potential impact of mobile phase composition and flow rate on lipid retention time. In Positive Ion Mode (PIM), abundant lipid precursor ions and fragments are separated in the same order, with similar peak shapes and ionic strengths. In addition, the mixed quality control sample with a 10 minute elution period also exhibited similar base peak intensities of the precursor and debris as the test sample. The flow rate of the mobile phase was 0.4 mL/min. The column was initially eluted with 40% B, then a linear gradient to 43% B in 2 minutes, then increasing the percentage of B to 50% in 0.1 min. In the next 3.9 minutes, the gradient further increased to 54% B, then the amount of B increased to 70% in 0.1 minutes. In the final part of the gradient, the amount of B increased to 99% in 1.9 min. Finally, solution B returned to 40% in 0.1min and the column was equilibrated for 1.9 min before the next injection. The sample injection amount is 5 mu L each time, lipid under a positive mode and a negative mode is detected by a QTOF mass spectrometer, the collection range is m/z 50-1200 years, and the collection time is 0.2 s/time. The ion source temperature is 120 ℃, the desolventizing temperature is 600 ℃, the gas flow is 1000L/h, and nitrogen is used as flowing gas. The capillary voltage was 2.0kV (+)/cone voltage was 1.5kV (-), and the cone voltage was 30V. Standard mass measurements were performed with leucine enkephalin, calibrated with sodium formate solution. Samples were randomly ordered. One quality control sample was injected for every 10 samples and analyzed to investigate the reproducibility of the data.

And (4) analyzing results:

1. method for searching serum difference substance by using multivariate statistics

The difference variables were screened by removing irrelevant differences using orthogonal partial least squares discriminant analysis (OPLS-DA) in combination with the Orthogonal Signal Correction (OSC) and PLS-DA methods. The VIP value is a projection of variable importance of a PLS-DA first main component, as shown in FIG. 1, VIP >1 is generally taken as a common judgment standard of metabonomics, and is taken as one of the standards for differential metabolite screening, wherein A is a positive ion mode, and B is a negative ion mode; FIG. 2 is a score chart of the first principal component and the second principal component in two groups of the white brain lesion group and the control group obtained by dimensionality reduction, the abscissa represents the difference between the groups, the ordinate represents the difference within the groups, and the two groups have better results separation, which illustrates that the scheme can be used, wherein A is the score chart of (O) PLS-DA in positive ion mode, and B is the score chart of (O) PLS-DA in negative ion mode. FIG. 3 is an S-plot, in which the abscissa represents the co-correlation coefficient of the main component and the metabolite, the ordinate represents the correlation coefficient of the main component and the metabolite, and p <0.05, VIP >1 is satisfied, and 125 difference impurities exist in the negative ion mode and 174 difference impurities exist in the positive ion mode, wherein A is the S-plot in the positive ion mode and B is the S-plot in the negative ion mode.

2. Jode index analysis

To further narrow the range, the VIP threshold was increased to 2, with a fold difference between normal and patient of less than 0.5 fold, or more than 2.5 fold,Pvalues less than 0.01, the following 7 compounds were obtained, as specified in table 1.

They were then subjected to the calculation of the youden yoden jordan index to reflect the diagnostic and predictive effect of the individual indices on the whole, with the results as given in table 1 below:

TABLE 1 Johnson index analysis of lipid associated with leukoencephalopathy

Numbering	Name of Compound	AUC value	Sensitivity of the composition	Specificity of
					F1	Cholesterol-alpha-D-glucoside	0.851	0.735	0.862
F2	6Z,9Z, 20-eicosatrienoic acid	0.808	0.785	0.735
					F3	Ceramide (m18:1(4E)/24:1(15Z))	0.612	0.408	0.785
F4	Ephedrine A	0.716	0.738	0.673
					F5	Cucurbitacin E	0.684	0.815	0.551
F6	Cholesterol ester 22:6	0.682	0.846	0.510
					F7	Ceramides (d18:0/24:1(15Z))	0.662	0.769	0.510

Table 1 lists the area under the curve (AUC), specificity and sensitivity of individual metabolites for predicting leukoencephalopathy.

3. Ten-fold cross validation result of sample population

In order to improve the biological diagnosis effect of the variable-quantity compound, a proper model needs to be found according to the biomarkers for further analysis. Randomly dividing the sample population into 10 parts, selecting 1 part as a verification set and the others as training sets, repeating the steps for ten times, and investigating the optimal variable combination. Results from ten times, including AUC, sensitivity, specificity, were averaged and statistically significant calculated as shown in table 2 below.

TABLE 2

Combination of	Logistic regression AUC	Sensitivity of the composition	Specificity of
				F3+F1+F2	0.973	1	1
F3+F1+F4	0.948	1	1
				F3+F1+F5	0.952	1	1
F3+F1+F6	0.923	1	1
				F3+F1+F7	0.900	1	1

There was no significant p <0.05 difference in AUC values between combinations.

The logistic regression models A-F are established based on the above as follows:

the variables of the model A are F3+ F1+ F2, and the calculation formula is as follows: TC = 9.573+2.787 xf 3+15.351 xf 1-21.578 xf 2, calculating TC values, where F3 is ceramide (m18:1(4E)/24:1(15Z)), F1 is cholesterol- α -D-glucoside, F2 is 6Z,9Z, 20-eicosatrienoic acid, predicting white brain lesions from TC values: if TC is more than or equal to 0.163, judging the white brain lesion; if TC < 0.163, it is normal.

The variables of the model B are F3+ F1+ F4, and the calculation formula is as follows: TC = 1.6734+1.5739 xf 3+7.6078 xf 1-6.6884 xf 4, the TC value is calculated, where F3 is ceramide (m18:1(4E)/24:1(15Z)), F1 is cholesterol- α -D-glucoside, F4 is cannabixanthin a, and leukoencephalopathy is predicted from the TC value: if TC is more than or equal to 0.550, the white brain lesion is judged; if TC is less than 0.550, the test result is normal.

The variable of the model C is F3+ F1+ F5, and the calculation formula is as follows: TC = -1.3843+1.1595 xf 3+4.6690 xf 1-2.1691 xf 5, calculate TC value, where F3 is ceramide (m18:1(4E)/24:1(15Z)), F1 is cholesterol- α -D-glucoside, F5 is cucurbitacin E, predict white brain disease according to TC value: if TC is more than or equal to 0.336, the brain white lesion is judged; if TC is less than 0.336, the test result is normal.

The variable of the model D is F3+ F1+ F6, and the calculation formula is as follows: TC = -1.5839+0.5951 xf 3+5.2904 xf 1-2.3305 xf 6, calculate TC value, where F3 is ceramide (m18:1(4E)/24:1(15Z)), F1 is cholesterol- α -D-glucoside, F6 is cholesterol ester 22:6, predict white brain lesion according to TC value: if TC is more than or equal to 0.280, judging the white brain lesion; if TC is less than 0.280, the test result is normal.

The variable of the model E is F3+ F1+ F7, and the calculation formula is as follows: TC = -1.6537+0.6052 xf 3+4.9272 xf 1-2.2786 xf 7, calculate TC value, where F3 is ceramide (m18:1(4E)/24:1(15Z)), F1 is cholesterol- α -D-glucoside, F7 is ceramide (D18:0/24:1(15Z)), predict white brain lesion according to TC value: if TC is more than or equal to 0.316, judging the white brain lesion; if TC < 0.316, it is normal.

4. External data set, logistic regression model verification

And verifying the accuracy of the result through a data set of an external crowd, and drawing a corresponding ROC curve graph. The results are as follows:

and (3) verifying the population: 200 people (outside crowd), the sample standard is the same as above the sample crowd, magnetic resonance imaging detects there is not the abnormal 100 people often, and magnetic resonance imaging detects and shows that white matter appears the infarct focus and has 100 people. Performing logistic regression model verification:

the "model a" variables were F3+ F1+ F2 as described above, with results as shown in fig. 4, sensitivity =1, specificity =1, and accuracy = 1.

The "model B" variables were F3+ F1+ F4 as described above, with results as shown in fig. 5, sensitivity =1, specificity =1, and accuracy = 1.

The "model C" variables were F3+ F1+ F5 as described above, with results as shown in fig. 6, sensitivity =1, specificity =1, and accuracy = 1.

The "model D" variables were F3+ F1+ F6 as described above, with results as in fig. 7, sensitivity =1, specificity =1, and accuracy = 1.

The "model E" variables were F3+ F1+ F7 as described above, with results as shown in fig. 8, sensitivity =1, specificity =1, and accuracy = 1.

And (3) displaying data: f3 is ceramide (m18:1(4E)/24:1(15Z)) per se, ceramide (m18:1(4E)/24:1(15Z)) is combined with cholesterol-alpha-D-glucoside, and combined with other five biomarkers, namely 6Z,9Z, 20-eicosatrienoic acid, cannabidin A, cucurbitacin E, cholesterol ester 22:6 and ceramide (D18:0/24:1(15Z)), shows very high diagnostic capability, sensitivity, specificity and accuracy of 100%, and clinical kit application can be carried out in the future.

Through comparative analysis on sample information, the following results are obtained: compared with the normal group, the above 7 biomarkers showed an upward trend in both F1 and F3 in the leukoencephalopathy group, and the opposite was found in F2, F4, F5, F6 and F7.

Claims

1. A biomarker for diagnosing leukoencephalopathy, wherein the biomarker is ceramide (m18:1(4E)/24:1 (15Z)).

2. Use of the biomarker for diagnosing leukoencephalopathy of claim 1 in the preparation of a test agent.

3. The use according to claim 2, wherein the biomarker ceramide (m18:1(4E)/24:1(15Z)) is combined with cholesterol- α -D-glucoside and combines with 6Z,9Z, 20-eicosatrienoic acid, cannabixanthin A, cucurbitacin E, cholesteryl ester 22:6 or ceramide (D18:0/24:1(15Z)) to determine the presence or absence of white brain lesions.

4. The use according to claim 3, wherein the ceramide (m18:1(4E)/24:1(15Z)) is denoted as F3, the content of cholesterol-a-D-glucoside is denoted as F1, 6Z,9Z, the content of 20-eicosatrienoic acid is denoted as F2, and when the units of the content are all mg/L, the TC value is calculated according to the calculation formula TC = 9.573+2.787 XF 3+15.351 XF 1-21.578 XF 2, and the leukoencephalopathy is predicted according to the TC value: if TC is more than or equal to 0.163, judging the white brain lesion; if TC < 0.163, it is normal.

5. The use according to claim 3, wherein the ceramide (m18:1(4E)/24:1(15Z)) is contained in an amount of F3, the cholesterol- α -D-glucoside is contained in an amount of F1, the cannabinoid A is contained in an amount of F4, and when the contents are all in mg/L, a TC value is calculated according to a calculation formula TC = 1.6734+1.5739 XF 3+7.6078 XF 1-6.6884 XF 4, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.550, the white brain lesion is judged; if TC is less than 0.550, the test result is normal.

6. The use according to claim 3, wherein the content of ceramide (m18:1(4E)/24:1(15Z)) is represented by F3, the content of cholesterol-alpha-D-glucoside is represented by F1, the content of cucurbitacin E is represented by F5, when the content units are all mg/L, the TC value is calculated according to the calculation formula TC = -1.3843+1.1595 xF 3+4.6690 xF 1-2.1691 xF 5, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.336, the brain white lesion is judged; if TC is less than 0.336, the test result is normal.

7. The use according to claim 3, wherein the content of ceramide (m18:1(4E)/24:1(15Z)) is represented by F3, the content of cholesterol-a-D-glucoside is represented by F1, the content of cholesterol ester 22:6 is represented by F6, when the units of the content are mg/L, the TC value is calculated according to the calculation formula TC = -1.5839+0.5951 xF 3+5.2904 xF 1-2.3305 xF 6, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.280, judging the white brain lesion; if TC is less than 0.280, the test result is normal.

8. The use according to claim 3, wherein the content of ceramide (m18:1(4E)/24:1(15Z)) is denoted as F3, the content of cholesterol-a-D-glucoside is denoted as F1, the content of ceramide (D18:0/24:1(15Z)) is denoted as F7, when the units of the contents are all mg/L, the TC value is calculated according to the calculation formula TC = -1.6537+0.6052 xF 3+4.9272 xF 1-2.2786 xF 7, and the white brain lesion is predicted according to the TC value: if TC is more than or equal to 0.316, judging the white brain lesion; if TC < 0.316, it is normal.