CN113447601B

CN113447601B - Biomarker for diagnosing cerebral infarction and leukoencephalopathy and application thereof

Info

Publication number: CN113447601B
Application number: CN202110999863.4A
Authority: CN
Inventors: 陈显扬; 宋王婷; 张珂; 韩佳睿; 薛腾
Original assignee: Baofeng Biotech Beijing Co ltd
Current assignee: Baofeng Biotech Beijing Co ltd
Priority date: 2021-08-27
Filing date: 2021-08-27
Publication date: 2021-11-30
Anticipated expiration: 2041-08-27
Also published as: CN113447601A

Abstract

The biomarker is the application of galactosylceramide in preparing a detection reagent for diagnosing cerebral infarction and leukoencephalopathy, and the risk of cerebral infarction and leukoencephalopathy is judged by combining the biomarker of galactosylceramide with glucose ceramide, beta-erythroid, thujene, 1,3Z,6Z, 9Z-nonane tetraene, p-cymene, oxalic acid and 3- (stearoyloxy) -4- (trimethyl ammonium) butyrate, so that the cerebral infarction and leukoencephalopathy can be prevented and prevented in advance.

Description

Biomarker for diagnosing cerebral infarction and leukoencephalopathy and application thereof

Technical Field

The invention belongs to the technical field of biological detection, and particularly relates to a biomarker for diagnosing cerebral infarction and leukoencephalopathy and application thereof.

Background

At present, with the aging of society, the incidence of cerebral apoplexy is increased sharply, wherein ischemic stroke (cerebral infarction) is ischemic necrosis of brain tissue caused by stenosis or occlusion of cerebral artery, and accounts for more than 70% of all cerebral apoplexy. White matter disease (WML) is a common neurodegenerative disease, the most typical pathology being the destruction of white matter integrity or demyelination, which may be secondary to neurological diseases such as infection, intoxication, degeneration, post-traumatic injury, cerebral infarction, etc. The disease is commonly seen in various diseases such as cerebral apoplexy, Alzheimer disease, Parkinson disease, multiple sclerosis, schizophrenia and the like. The leukoencephalopathy mainly causes symptoms of cognitive disorder, speech disorder, mental behavior disorder, gait disorder, urination disorder and the like of a patient. Cerebral infarction combined with leukoencephalopathy can seriously increase family burden of patients, reduce life quality of the patients and bring heavy burden to families of the patients and the whole society, and the cerebral infarction combined with leukoencephalopathy is a key point and a difficult point of research in the field of medicine and health at present.

The existing cerebral infarction diagnosis mainly depends on medical history and physical examination and is assisted by imaging examination, but the imaging examination has the defects of long time consumption, high cost and the like, so that a valuable biomarker is required to be developed to predict risks, the cerebral infarction can be quickly and accurately diagnosed, and different types of cerebral apoplexy and other diseases can be distinguished. In general, the condition of most patients with leukoencephalopathy is reversible, so that the symptoms can be obviously improved by taking proper preventive measures for the patients with leukoencephalopathy, wherein early screening is a crucial means. At present, no peripheral blood biomarker with high accuracy and strong specificity is used for detecting cerebral infarction combined with leukoencephalopathy. Imaging methods such as CT and MRI are widely used for diagnosing cerebral infarction and leukoencephalopathy, but have the defects of complex operation, patient moving, high cost and the like, and the method finds a blood biological marker with an indicating function on cerebral infarction diagnosis and disease evolution, and has important clinical value for guiding the treatment of cerebral apoplexy and improving the prognosis of patients. As a promising tool for finding novel biomarkers of cerebral infarction combined with white brain disease, metabolomics can reflect the state of the organism by analyzing the changes of endogenous metabolites, and further identify specific biomarkers or marker groups.

How to find an easy-to-detect biomarker for predicting and diagnosing the cerebral infarction combined with the leukoencephalopathy is an urgent technical problem to be solved.

Disclosure of Invention

In order to solve the technical problems, the invention provides application of galactosylceramide combined with beta-hydroxyethyl ethanolamine, tomatidine, phosphatidylserine or decanoyl carnitine in preparing a detection reagent for diagnosing cerebral infarction of a patient with leukoencephalopathy.

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

application of a biomarker galactosylceramide in preparing a detection reagent for diagnosing cerebral infarction and leukoencephalopathy.

The application of the biomarker galactose ceramide combined with glucose ceramide, beta-erythrol, thujene, 1,3Z,6Z, 9Z-nonane tetraene, p-cymene, oxalic acid or 3- (stearoyloxy) -4- (trimethyl ammonium) butyrate in preparing a detection reagent for diagnosing cerebral infarction and leukoencephalopathy.

The use as described above, preferably, which is carried out by combining galactosylceramide with glucosylceramide, β -erythrol, thujene, 1,3Z,6Z, 9Z-nonanetetraene, p-cymene, oxalic acid or 3- (stearoyloxy) -4- (trimethylammonium) butyrate, for determining the presence or absence of a risk of cerebral infarction and leukosis.

For the above applications, preferably, the content of galactosylceramide is F12, the content of glucosylceramide is F1, the TC value is calculated according to TC = -3.2447 +1.5308 × F12+ 5.6658 × F1, and if TC is greater than or equal to 0.707, the cerebral infarction and the white brain lesion are determined; if TC < 0.707, it is normal.

Preferably, the content of galactosyl ceramide is represented as F12, the content of beta-picropitanol is represented as F2, the TC value is calculated according to TC =0.2374 +1.1922 XF 12-3.2681 XF 2, and if the TC is more than or equal to 0.394, the cerebral infarction and the white brain lesion are judged; if TC < 0.394, it is normal.

Preferably, the content of galactosyl ceramide is represented as F12, the content of thujene is represented as F3, the TC value is calculated according to TC =1.6272 +1.2751 XF 12-3.7494 XF 3, and if the TC is more than or equal to 0.481, the cerebral infarction and the leukoderma are judged; if TC < 0.481, it is normal.

The application is preferably characterized in that the content of galactosyl ceramide is represented as F12, the content of 1,3Z,6Z, 9Z-nonane tetraene is represented as F4, the TC value is calculated according to TC =1.5368 + 1.2667 XF 12-3.5964 XF 4, and if the TC is more than or equal to 0.456, the cerebral infarction and the white brain lesion are judged; if TC is less than 0.456, the test is normal.

For the above applications, preferably, the content of galactosylceramide is represented as F12, the content of p-cymene is represented as F5, the TC value is calculated according to TC =1.826 +1.255 xf 12-4.036 xf 5, and if TC ≧ 0.486, the cerebral infarction and the white brain lesion are determined; if TC is less than 0.486, the test result is normal.

For the above applications, preferably, the content of galactosylceramide is represented as F12, the content of oxalic acid is represented as F6, the TC value is calculated according to TC = -5.5473+1.9863 × F12+ 7.0067 × F6, and if TC is greater than or equal to 0.500, the cerebral infarction and the white brain lesion are determined; if TC is less than 0.500, the test result is normal.

In the above application, preferably, the content of galactosylceramide is represented as F12, the content of 3- (stearoyloxy) -4- (trimethylammonium) butyrate is represented as F10, the TC value is calculated according to TC = -3.4525+1.4840 × F12+ 1.6207 × F10, and if TC is greater than or equal to 0.498, the cerebral infarction and the white brain lesion are determined; if TC < 0.498, it is normal.

The invention has the beneficial effects that:

the invention provides a novel molecular marker galactosylceramide and a model for distinguishing cerebral infarction and leukoencephalopathy, which can be applied to a detection kit for early detection, diagnosis and prediction of cerebral infarction combined leukoencephalopathy.

The biomarker for diagnosing the cerebral infarction and the leukoencephalopathy provided by the invention comprises galactosylceramide, glucosylceramide, beta-piceopentyl alcohol, thujene, 1,3Z,6Z, 9Z-nonanetetraene, p-cymene, oxalic acid or 3- (stearoyloxy) -4- (trimethylammonium) butyrate, and the TC value is calculated according to the content of the detected substances in serum to predict the risk of the cerebral infarction and the leukoencephalopathy, so that the biomarker is helpful for diagnosing whether the tendency of the cerebral infarction and the leukoencephalopathy exists or not, and can be used for preventing in advance.

Drawings

FIG. 1 is a sample of VIP >1 in positive ion mode;

FIG. 2 is a sample of VIP >1 in negative ion mode;

FIG. 3 is a score plot of (O) PLS-DA in positive ion mode;

FIG. 4 is a score plot of (O) PLS-DA in negative ion mode;

FIG. 5 is a diagram of S-plot in positive ion mode;

FIG. 6 is a diagram of S-plot in negative ion mode;

FIG. 7 is a ROC curve based on a logistic regression model (variables F12+ F1);

FIG. 8 is a ROC curve based on a logistic regression model (variables F12+ F2);

FIG. 9 is a ROC curve based on a logistic regression model (variables F12+ F3);

FIG. 10 is a ROC curve based on a logistic regression model (variables F12+ F4);

FIG. 11 is a ROC curve based on a logistic regression model (variables F12+ F5);

FIG. 12 is a ROC curve based on a logistic regression model (variables F12+ F6);

FIG. 13 is a ROC curve based on a logistic regression model (variables F12+ F10).

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 indicated, the technical means used in the examples are conventional means well known to those skilled in the art, and unless otherwise specified, the reagents used in the present invention are of analytical purity or above specification, and the ultra performance liquid chromatography: UPLC; model number ACQUITY UPLC I-Class System, manufacturer: waters, Manchester, UK; analysis and identification software: prognesis QI, manufacturer: waters.

Example 1

1. Model building sample group 116 people (internal group)

Selecting a control population: the history of the cerebral infarction and MRI nuclear magnetic detection are not abnormal, and the clinical evaluation shows that the human body is normal 66 persons, and the proportion of men and women is as follows: 1: 1, age range: 45 or more.

The patient population is as follows: the history of cerebral infarction exists, and MRI nuclear magnetic detection shows that high signals appear in white matter, and the proportion of 50 people is as follows: 1: 1, age range: 45 or more.

Collected serum samples of the above population were thawed on ice, 200 μ L of serum was extracted with 600 μ L of pre-cooled isopropanol, vortexed with a vortex shaker (model MX-S, Scilogex, usa) for 1min, incubated at room temperature for 10min, the extraction mixture was then stored overnight at-20C, centrifuged at 4000r in a refrigerated centrifuge (model D3024R, Scilogex, usa) for 20min, the supernatant was transferred to a new centrifuge tube, diluted to 1: 10. the samples were stored at-80C prior to LC-MS analysis. In addition, 10. mu.L of each extraction mixture was combined together to prepare a mixed serum as a quality control.

Wherein the reagents used in the present invention: isopropanol, acetonitrile, formic acid, ammonium formate, leucine enkephalin and sodium formate, wherein the manufacturers are Fisher.

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

The samples were analyzed by ACQUITY UPLC (Waters, USA) connected to an ESI-bearing Xevo-G2XS high-resolution time of flight (QTOF) mass spectrometer (ESI-QTOF/MS; model: Xevo G2-S Q-TOF; manufacturer: Waters, Manchester, UK). Using a CQUITY UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm, Waters), mobile phase a: the solute comprises 10 mM ammonium formate and 0.1% formic acid, and the solvent comprises acetonitrile in a volume ratio of 60: 40: water; mobile phase B: the solute comprises 10 mM ammonium formate and 0.1% formic acid, and the solvent comprises isopropanol in a volume ratio of 90: 10: and (3) acetonitrile. Prior to large scale studies, pilot experiments including 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 PIM, abundant lipid precursor ions and fragments are separated in the same order, with similar peak shapes and ionic strengths. In addition, the mixed QC samples with 10 minute elution periods also showed similar basal peak intensities of precursors and debris as the test samples. 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, and a Xevo-G2XS QTOF mass spectrometer is used for detecting the lipid under positive and negative modes, wherein 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.

Data acquisition was performed using data acquisition software (MassLynx4.1; manufacturer: Waters), results analysis:

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

Orthogonal partial least squares discriminant analysis (OPLS-DA) combines Orthogonal Signal Correction (OSC) and PLS-DA (partial minimum discriminant analysis) methods to screen for differential variables by removing irrelevant differences. As shown in figure 1 and figure 2, VIP value is a variable importance projection of a PLS-DA first main component, VIP >1 is generally taken as a metabonomic common judgment standard and is taken as one of the standards for differential metabolite screening; fig. 3 and 4 are scoring graphs obtained by dimension reduction of the first principal component and the second principal component in two groups, namely, a cerebral infarction combined white brain lesion group (represented by Disease) and a blank Control group (represented by Control), wherein the abscissa represents the difference between groups, the ordinate represents the difference in groups, and the results of the two groups are better separated. Fig. 5 and 6 are S-plot diagrams, in which the abscissa represents the co-correlation coefficient between the principal component and the metabolite, and the ordinate represents the correlation coefficient between the principal component and the metabolite, and p <0.05, VIP >1 is satisfied, and 230 difference impurities are present in the positive ion mode and 234 difference impurities are present in the negative ion mode.

4. Jode index analysis

To further narrow the range, VIP threshold was increased to 2, while showing a fold difference between normal and patient of less than 0.83 fold, or more than 1.2 fold, with P values less than 0.01, to finally give the following 6 compounds, as detailed in table 1.

They were then subjected to the calculation of youden joden index to reflect the diagnosis and prediction effect of individual indices on the whole, and the area under the curve (AUC), specificity and sensitivity results of individual metabolites predicting white brain lesions are shown in table 1.

TABLE 1 analysis of Johnson index of lipid associated with cerebral infarction complicated by leukoencephalopathy

Numbering	Name of Compound	AUC value	Sensitivity of the composition	Specificity of
					F1	Glucose ceramide (d15:2(4E,6E)/20:0)	0.847	0.740	0.815
F2	Beta-erythrol alcohol	0.804	0.754	0.720
					F3	Arborvitae flower	0.802	0.815	0.720
F4	1,3Z,6Z, 9Z-nonanetetraene	0.798	0.754	0.780
					F5	P-cymene	0.797	0.831	0.700
F6	Oxalic acid	0.769	0.700	0.738
					F7	N-stearoylserine	0.742	0.720	0.692
F8	4-hydroxy-8 cis-sphingosine	0.733	0.620	0.769
					F9	Oleyl arachidonic acid	0.732	0.646	0.740
F10	3- (stearoyloxy) -4- (trimethylammonium) butanoic acid ester	0.727	0.620	0.785
					F11	Trans-hexadecyl-2-enoyl carnitine	0.720	0.420	0.923
F12	Galactosylceramide (d18:1/24:1(15Z))	0.698	0.6	0.723
					F13	Cis-5-tetradecenoyl carnitine	0.697	0.420	0.908

5. Ten-fold cross validation result of internal 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.

And randomly dividing the internal population into 10 parts, selecting 1 part as a verification set, and selecting the others as training sets, repeating the steps for ten times, and investigating the optimal variable combination. The secondary results, 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
				F12+F1	0.874	1	1
F12+F2	0.898	1	1
				F12+F3	0.930	1	1
F12+F4	0.905	1	1
				F12+F5	0.923	1	1
F12+F6	0.937	1	1
				F12+F10	0.902	1	1

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

The logistic regression model A, B, C, D, E, F was built based on the above as follows:

the "model a" variable was F12+ F1, the TC value was calculated by the formula TC = -3.2447 +1.5308 × F12+ 5.6658 × F1, where F12 is galactosylceramide (d18:1/24:1(15Z)), and F1 is glucosylceramide (d15:2(4E,6E)/20:0), and the cerebral infarction and the white brain lesion were predicted from the TC value: if TC is more than or equal to 0.707, judging the cerebral infarction and the leukoencephalopathy; if TC < 0.707, it is normal.

The "model B" variable was F12+ F2, the TC value was calculated by the formula TC =0.2374 +1.1922 × F12-3.2681 × F2, where F12 is galactosylceramide (d18:1/24:1(15Z)), F2 is β -erythrol, and cerebral infarction and leukosis were predicted from the TC value: if TC is more than or equal to 0.394, judging that the cerebral infarction is combined with the leukoencephalopathy; if TC < 0.394, it is normal.

The "model C" variable was F12+ F3, the TC value was calculated by the formula TC =1.6272 +1.2751 × F12-3.7494 × F3, F12 was galactosyl ceramide (d18:1/24:1(15Z)), F3 was thujene, and cerebral infarction complicated with leukoencephalopathy was predicted from the TC value: if TC is greater than or equal to 0.481, judging the cerebral albinism lesion and the cerebral infarction; if TC < 0.481, it is normal.

The "model D" variable was F12+ F4, the TC value was calculated by the formula TC =1.5368 + 1.2667 × F12-3.5964 × F4, F12 was galactosylceramide (D18:1/24:1(15Z)), F4 was 1,3Z,6Z, 9Z-nonanetetraene, and cerebral infarction with leukosis was predicted from the TC value: if TC is more than or equal to 0.456, judging the cerebral infarction and the leukoencephalopathy; if TC is less than 0.456, the test is normal.

The "model E" variable was F12+ F5, the TC value was calculated by the formula TC =1.826 +1.255 xf 12-4.036 xf 5, where F12 is galactosyl ceramide (d18:1/24:1(15Z)), F5 is p-cymene, and cerebral infarction combined leukosis was predicted from the TC value: if TC is more than or equal to 0.486, judging the cerebral infarction and the leukoencephalopathy; if TC is less than 0.486, the test result is normal.

The "model F" variable was F12+ F6, the TC value was calculated by the formula TC = -5.5473+1.9863 × F12+ 7.0067 × F6, where F12 is galactosylceramide (d18:1/24:1(15Z)), F6 is oxalic acid, and the cerebral infarction with leukosis was predicted from the TC value: if TC is more than or equal to 0.500, judging the cerebral infarction and the leukoencephalopathy; if TC is less than 0.500, the test result is normal.

The "model G" variable was F12+ F10, the TC value was calculated by the formula TC = -3.4525+1.4840 × F12+ 1.6207 × F10, where F12 is galactosylceramide (d18:1/24:1(15Z)), and F10 is 3- (stearoyloxy) -4- (trimethylammonium) butyrate, and cerebral infarction combined with leukosis was predicted from the TC value: if TC is more than or equal to 0.498, judging that the cerebral infarction is combined with the leukoencephalopathy; if TC < 0.498, it is normal.

Example 2 external data set, logistic regression model validation

The model verifies sample population 158 persons (outside population), where normal: history of cerebral infarction, MRI nuclear magnetic detection has no abnormality, 58 people are counted, and male and female proportion is as follows: 1: age range 1: 45 or more.

The patient population is as follows: there is a history of cerebral infarction, MRI nuclear magnetic resonance examination shows white matter and high signal, 100 patients, male and female proportion: 1: age range 1: 45 or more.

The contents of galactosylceramide, glucosylceramide,. beta. -isopinopentadiol, thujene, 1,3Z,6Z, 9Z-nonanetetraene, p-cymene, oxalic acid, and 3- (stearoyloxy) -4- (trimethylammonium) butyrate were determined as in example 1, and according to and plotted in a corresponding ROC diagram, the results were as follows:

the variables of the "model a" are F12+ F1, and the ROC graph results are shown in fig. 7, Sensitivity =1, Specificity =1, and Accuracy =1.

The "model B" variables were F12+ F2, and the ROC graph results are shown in fig. 8, Sensitivity =1, Specificity =1, and Accuracy =1.

The "model C" variable is F12+ F3, and the ROC graph results are shown in fig. 9, Sensitivity =1, Specificity =1, and Accuracy =1.

The "model D" variable is F12+ F4, and the ROC graph results are shown in fig. 10, Sensitivity =1, Specificity =1, and Accuracy =1.

The "model E" variable is F12+ F5, and the ROC graph results are shown in fig. 11, Sensitivity =1, Specificity =1, and Accuracy =1.

The "model F" variable is F12+ F6, and the ROC graph results are shown in fig. 12, Sensitivity =1, Specificity =1, and Accuracy =1.

The "model G" variable is F12+ F10, and the ROC graph results are shown in fig. 13, Sensitivity =1, Specificity =1, and Accuracy =1.

And (3) displaying data: galactose ceramide (d18:1/24:1(15Z)) by itself, and glucose ceramide (d15:2(4E,6E)/20:0), β -erythrol, thujene, 1,3Z,6Z, 9Z-nonanetetraene, p-cymene, oxalic acid, and 3- (stearoyloxy) -4- (trimethylammonium) butyrate, which are seven other biomarkers, all exhibit very high diagnostic ability, and can be used as a clinical kit.

Through comparative analysis on sample information, the following results are obtained: compared with the normal group, the 8 biomarkers have a downward trend in the groups of cerebral infarction and leukoencephalopathy of F2, F3, F4 and F5, and the opposite is true for F1, F6 and F10.

Claims

1. The application of the biomarker galactosylceramide in preparing a detection reagent for diagnosing cerebral infarction and leukoencephalopathy is characterized in that the galactosylceramide is combined with glucose ceramide, beta-erythrol, thujene, 1,3Z,6Z, 9Z-nonane tetraene, p-cymene, oxalic acid or 3- (stearoyloxy) -4- (trimethyl ammonium) butyrate to judge whether the cerebral infarction and the leukoencephalopathy risk exist;

the content of galactosylceramide is recorded as F12, the content of glucosylceramide is recorded as F1, TC value is calculated according to TC = -3.2447 +1.5308 XF 12+ 5.6658 XF 1, and if TC is larger than or equal to 0.707, cerebral infarction and white brain lesion are judged; if TC is less than 0.707, the test is normal;

the content of beta-picropriol is recorded as F2, a TC value is calculated according to TC =0.2374 +1.1922 XF 12-3.2681 XF 2, and if TC is more than or equal to 0.394, cerebral infarction and leukoencephalopathy are judged; if TC is less than 0.394, the test result is normal;

recording the content of the platycladi-derived arborvitae as F3, calculating a TC value according to TC =1.6272 +1.2751 xF 12-3.7494 xF 3, and judging the cerebral infarction and the leukoencephalopathy if the TC is more than or equal to 0.481; if TC is less than 0.481, the result is normal;

recording the content of 1,3Z,6Z, 9Z-nonane tetraene as F4, calculating a TC value according to TC =1.5368 + 1.2667 XF 12-3.5964 XF 4, and if the TC is more than or equal to 0.456, judging the cerebral infarction and the leukoencephalopathy; if TC is less than 0.456, the test is normal;

marking the content of the cymene as F5, calculating a TC value according to TC =1.826 +1.255 XF 12-4.036 XF 5, and if TC is more than or equal to 0.486, judging the cerebral infarction and the leukoencephalopathy; if TC is less than 0.486, the test is normal;

the content of oxalic acid is marked as F6, a TC value is calculated according to TC = -5.5473+1.9863 XF 12+ 7.0067 XF 6, and if the TC is more than or equal to 0.500, the cerebral infarction and the white brain lesion are judged; if TC is less than 0.500, the test is normal;

the content of 3- (stearoyloxy) -4- (trimethylammonium) butyrate is recorded as F10, a TC value is calculated according to TC = -3.4525+1.4840 XF 12+ 1.6207 XF 10, and if the TC is more than or equal to 0.498, the cerebral infarction and the white brain lesion are judged; if TC < 0.498, it is normal.