Biomarker for rapidly diagnosing blood-brain barrier destruction of cerebral apoplexy

Abstract

The invention discloses a biomarker for rapidly diagnosing blood-brain barrier destruction of cerebral apoplexy, the biomarker is a peripheral blood biomarker 24 (S)-hydroxycholesterol (24 (S)-OHC) and / or GDNF, and the blood-brain barrier destruction of cerebral apoplexy can be rapidly diagnosed by detecting the expression level of the peripheral blood biomarker in a subject. The method can quickly and accurately diagnose the integrity of the blood brain barrier of middle cerebral artery infarction or cerebral apoplexy, has high diagnosis efficiency, provides a basis for clinical decision-making related to cerebral apoplexy, lays a foundation for subsequent clinical research, and has a wide application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technologies, and specifically, relates to biomarkers for rapidly diagnosing blood-brain barrier disruption in stroke. Background Art

[0002] Middle cerebral artery occlusion (MCAO) refers to the occlusion of cerebral blood vessels, where the blood supply to a certain part of the brain is reduced or lacking (i.e., ischemic stroke), which is usually accompanied by the disruption of the blood-brain barrier (BBB). Blood-brain barrier defect is a basic event after cerebral ischemia, which can lead to more severe damage after cerebral ischemia and affect the spread of brain injury. The blood-brain barrier is a barrier between plasma and brain tissue, formed by endothelial cells of blood vessel walls and various glial cells. The blood-brain barrier has multiple functions, such as restricting the transport of neurotoxic cells, immune and inflammatory cytokines from the blood to the brain, maintaining the balance of ions and water, controlling the secretion of brain metabolites, and the levels of neurotransmitters and hormones. The function and structure of the blood-brain barrier are affected by multiple factors, among which vascular endothelial cells are the central components, and tight junction proteins are important structures for the connections between these cells. The main function of tight junction proteins is to restrict the paracellular and transcellular transport of substances between brain endothelial cells. In this regard, tight junction proteins such as Occludin and Claudin (which strictly control the extracellular diffusion within endothelial cells) and ZO1 play a crucial role in the formation and integrity of the blood-brain barrier. These tight junctions between endothelial cells are essential for maintaining the integrity of the blood-brain barrier because they form extracellular permeability restrictions. The blood-brain barrier is damaged after ischemia, activating matrix metalloproteinases (MMPs) and calpains. MMPs, especially MMP-2 and MMP-9, directly lead to an increase in blood-brain barrier permeability by destroying tight junction proteins. In addition, studies have shown that calpain can cause the destruction of Occludin and ZO1 in tight junctions, thus disrupting the integrity of the blood-brain barrier. Calpain is a Ca 2+ -activated cytoplasmic cysteine protease, which is activated after cerebral ischemia and increases intracellular calcium, leading to the generation of free radicals. This process is related to the loosening of tight junctions and the destruction of these junction structures. The permeability of the blood-brain barrier leads to an increase in the paracellular space, allowing blood to flow into the brain parenchyma, ultimately resulting in brain edema and neuronal death. Therefore, quickly determining the integrity of the blood-brain barrier in the brain plays a crucial role in the treatment and prevention of middle cerebral artery infarction. However, currently, there is a lack of detection methods and biomarkers for rapidly evaluating middle cerebral artery infarction and the integrity of the blood-brain barrier in this field.

[0003] To evaluate the integrity of the blood-brain barrier in the brain, various methods and techniques are usually adopted, including: (1) Evans Blue (EB) staining: This is a commonly used method to evaluate the integrity of the blood-brain barrier. By injecting EB intravenously and observing whether it infiltrates into the brain tissue. If the blood-brain barrier is damaged, EB will infiltrate into the brain tissue and show blue fluorescence. (2) Magnetic resonance imaging (MRI): Especially using T2-weighted or FLAIR sequences, it can detect lesions such as brain edema and hemorrhage, indirectly reflecting the integrity of the blood-brain barrier. (3) Laser speckle contrast imaging (LSCI): It is used to evaluate the changes in cerebral blood flow and microvascular density, thereby indirectly reflecting the state of the blood-brain barrier. (4) Immunohistochemistry and Western Blot: Detect the expression levels of tight junction proteins (such as Occludin, ZO-1) and matrix metalloproteinases (such as MMP-9). The changes in the expression of these proteins can reflect the integrity of the blood-brain barrier. However, these methods all have certain defects, such as the equipment being relatively expensive and time-consuming, and causing secondary harm to the patient's body. Summary of the Invention

[0004] Aiming at the technical problem of the lack of biomarkers for quickly judging the integrity of the blood-brain barrier in stroke in the above-mentioned existing technologies, the present invention provides the biomarkers 24(S)-hydroxycholesterol (24(S)-OHC) and / or GDNF for quickly diagnosing the damage of the blood-brain barrier in stroke. The present invention first proposes and verifies the effectiveness and accuracy of the biomarkers 24(S)-OHC and / or GDNF as biomarkers for quickly judging the integrity of the blood-brain barrier in stroke.

[0005] The present invention adopts the following technical solutions to achieve the above-mentioned invention purpose:

[0006] The first aspect of the present invention provides the application of biomarkers in the preparation of products for diagnosing and / or early predicting middle cerebral artery infarction or for diagnosing and / or early predicting blood-brain barrier defects in stroke.

[0007] Furthermore, the biomarker is 24(S)-hydroxycholesterol and / or GDNF.

[0008] Furthermore, the product includes reagents for detecting the expression level of the biomarker in a sample.

[0009] Furthermore, the reagents include reagents for detecting the expression level of the biomarker in a sample by enzyme-linked immunosorbent assay, electrochemiluminescence immunoassay, chromatography, and / or mass spectrometry.

[0010] Furthermore, the sample is a peripheral blood sample.

[0011] Furthermore, the product includes a detection kit or a detection chip.

[0012] In the present invention, the 24(S)-hydroxycholesterol, the same as 24(S)-OHC, is a substance that plays a key role in brain cholesterol metabolism and is of great significance in the nervous system and whole-body metabolism. 24(S)-OHC is closely associated with nervous system diseases. The changes in its plasma and cerebrospinal fluid levels are related to Alzheimer's disease (AD). Some studies have shown that the plasma and cerebrospinal fluid levels of 24(S)-OHC increase in AD patients, while other studies have reported a decrease in its plasma level. In addition, in multiple sclerosis, the concentration of 24(S)-OHC decreases. Currently, there are no relevant studies or reports on the application of 24(S)-OHC in the diagnosis or early prediction of middle cerebral artery infarction or blood-brain barrier defect in stroke.

[0013] In the present invention, the GDNF refers to Glial Cell Line-Derived Neurotrophic Factor, which is an important neurotrophic factor and plays a key role in the development, maintenance, and repair of the nervous system. Studies have shown that GDNF is related to Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), nerve injury and repair, mental diseases (such as depression), etc. Currently, there are no relevant studies or reports on the application of GDNF in the diagnosis or early prediction of middle cerebral artery infarction or blood-brain barrier defect in stroke.

[0014] In some embodiments, the method for detecting the expression level of the biomarker is not limited to enzyme-linked immunosorbent assay, electrochemiluminescence immunoassay, chromatography, and / or mass spectrometry. Any method disclosed in the prior art that can be used to detect the expression level of the above-mentioned biomarker is within the protection scope of the present invention.

[0015] In some embodiments, the chromatography includes but is not limited to: high-performance liquid chromatography, thin-layer chromatography, gas chromatography, and / or any combination thereof.

[0016] In some embodiments, the mass spectrometry includes but is not limited to: matrix-assisted laser desorption ionization (MALDI)-time of flight (TOF) mass spectrometry, MALDI-TOF-TOF mass spectrometry, MALDI quadrupole-time of flight (Q-TOF) mass spectrometry, electrospray ionization (ESI)-TOF mass spectrometry, tandem mass spectrometry, ESI-Q-TOF, ESI-TOF-TOF, ESI-ion trap mass spectrometry, ESI triple quadrupole mass spectrometry, ESI Fourier transform mass spectrometry (FTMS), and / or any combination thereof.

[0017] In the present invention, the sample refers to a composition obtained from or derived from a target subject, which comprises cellular entities and / or other molecular entities to be characterized and / or identified, for example, based on physical, biochemical, chemical, and / or physiological characteristics.

[0018] In some embodiments, the sample can be obtained from the subject's blood and other fluid samples and tissue samples of biological origin, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as from fresh, frozen, and / or preserved organ or tissue samples, biopsy tissues, or aspirates; blood or any blood component; body fluid; cells at any time during an individual's pregnancy or development; or plasma. The sample includes biological samples that have been processed in any way after their acquisition, such as treated with reagents, stabilized, or enriched for certain components (such as proteins or polynucleotides), or embedded in a semi-solid or solid matrix for sectioning purposes.

[0019] In some embodiments, the sample includes, but is not limited to: blood, plasma, serum, lymph fluid, synovial fluid, sweat, saliva, tears, feces, urine, cerebrospinal fluid, cells, tissues, or organs from a subject, etc. Techniques for obtaining the above different types of biological samples are well known in the art. In a specific embodiment of the present invention, the sample is a peripheral blood sample from a subject.

[0020] In the present invention, the subject refers to any animal, and also refers to humans and non-human animals. The non-human animals include all vertebrates, for example, mammals, such as non-human primates (especially higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cows, and any domestic animal or pet; and non-mammals, such as chickens, amphibians, reptiles, etc. In a specific embodiment of the present invention, the subject is preferably a human.

[0021] In the present invention, the AUC refers to the area under the curve of the receiver operating characteristic (ROC) curve, which is well known in the art. AUC measurement is useful for comparing the accuracy of classifiers across the entire data range. A classifier with a higher AUC has a higher ability to correctly classify unknowns between two or more target groups. The ROC curve is useful for depicting the performance of a specific feature (for example, any biomarker described herein and / or any entry of additional biomedical information) when distinguishing between two populations.

[0022] Typically, feature data is selected across the entire population in ascending order based on the values of a single feature. Then, for each value of the feature, the true positive rate and false positive rate of the data are calculated. The true positive rate is determined by counting the number of cases above the value of the feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls above the value of the feature and dividing by the total number of controls.

[0023] An ROC curve can be generated for an individual feature or for other individual outputs. For example, combinations of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and this single sum value can be plotted in the ROC curve. Additionally, any combination of multiple features whose combinations are derived from individual output values can be plotted in the ROC curve, and the ROC curve can be used to analyze the accuracy of a diagnosis.

[0024] The second aspect of the present invention provides a product for diagnosing and / or early predicting middle cerebral artery infarction or blood-brain barrier defect in stroke.

[0025] Furthermore, the product includes a reagent for detecting the expression levels of biomarker 24(S)-hydroxycholesterol and / or GDNF in a sample;

[0026] Optionally, the reagent includes a reagent for detecting the expression levels of the biomarker in the sample by enzyme-linked immunosorbent assay, electrochemiluminescence immunoassay, chromatography, and / or mass spectrometry;

[0027] Optionally, the product further includes a pretreatment reagent for pretreating the sample;

[0028] Optionally, the product includes a detection kit or a detection chip.

[0029] In some embodiments, the detection kit is used to detect the expression levels of the biomarker; the expression levels of the biomarker are obtained by processing a biological sample from a subject and then quantitatively detecting and calculating the expression levels of the biomarker in the biological sample from the subject by methods well known to those skilled in the art (e.g., enzyme-linked immunosorbent assay, electrochemiluminescence immunoassay, chromatography, and / or mass spectrometry, etc.).

[0030] In some embodiments, the detection kit may include a solid substrate such as a chip, a glass slide, an array, etc., which has reagents capable of detecting and / or quantifying one or more of the biomarkers fixed at predetermined positions on the substrate. As an illustrative example, reagents fixed at discrete predetermined positions can be provided to the chip for detecting and quantifying the expression levels of the biomarker in a sample from a subject.

[0031] In some embodiments, the detection chip has reagents capable of detecting and / or quantifying one or more of the biomarkers fixed at a predetermined position on a substrate. As an illustrative example, reagents fixed at discrete predetermined positions can be provided to the chip for detecting and quantifying the expression levels of the biomarkers in a sample from a subject.

[0032] In the present invention, the diagnosis or early diagnosis refers to the identification or classification of a molecular or pathological state, disease or disorder. For example, through molecular characteristics (such as proteins encoded by specific genes, specific microbial communities, specific genes, specific small molecules, etc.), early differential diagnosis is performed to determine whether a subject has middle cerebral artery infarction or is at risk of middle cerebral artery infarction, or whether the blood-brain barrier of stroke is intact. In a specific embodiment of the present invention, the molecular characteristic is a specific biomarker (24(S)-hydroxycholesterol and / or GDNF).

[0033] In the present invention, the prediction or early prediction refers to assessing whether a subject is or is not at risk of middle cerebral artery infarction or blood-brain barrier defect of stroke. In some embodiments, it should be evaluated whether the subject's risk is at an increased risk or a decreased risk compared to the average risk of the target population. The prediction of middle cerebral artery infarction or blood-brain barrier defect of stroke, or the early prediction of middle cerebral artery infarction or blood-brain barrier defect of stroke means herein that by the method of the present invention, it is analyzed whether a subject is classified into a group of subjects at risk of middle cerebral artery infarction or blood-brain barrier defect of stroke, or into a group of subjects not at risk of middle cerebral artery infarction or blood-brain barrier defect of stroke.

[0034] According to the present invention, having a risk of middle cerebral artery infarction or blood-brain barrier defect of stroke preferably means an increased risk of middle cerebral artery infarction or blood-brain barrier defect of stroke (preferably falling within the prediction window). According to the present invention, a subject not at risk of middle cerebral artery infarction or blood-brain barrier defect of stroke preferably has a reduced risk of middle cerebral artery infarction or blood-brain barrier defect of stroke (preferably falling within the prediction window). A subject at risk of middle cerebral artery infarction or blood-brain barrier defect of stroke preferably has a risk of middle cerebral artery infarction or blood-brain barrier defect of stroke of 10-20% or higher, more preferably 20% or higher. A subject not at risk of middle cerebral artery infarction or blood-brain barrier defect of stroke preferably has a risk of middle cerebral artery infarction or blood-brain barrier defect of stroke of less than 10%, more preferably less than 5% or lower.

[0035] The third aspect of the present invention provides the application of biomarkers in constructing a warning model for middle cerebral artery infarction or blood-brain barrier defect of stroke.

[0036] Furthermore, the early warning model for middle cerebral artery infarction or stroke blood-brain barrier defect uses the expression level values of biomarkers in the sample as input variables;

[0037] The biomarker is 24(S)-hydroxycholesterol and / or GDNF.

[0038] The fourth aspect of the present invention provides an early warning device for middle cerebral artery infarction or stroke blood-brain barrier defect.

[0039] Furthermore, the early warning device for middle cerebral artery infarction or stroke blood-brain barrier defect includes:

[0040] A model loading module, configured to load the early warning model for middle cerebral artery infarction or stroke blood-brain barrier defect constructed by the application described in the third aspect of the present invention;

[0041] An index value acquisition module, configured to obtain the index values of the biomarkers corresponding to the subject to be diagnosed according to the early warning model for middle cerebral artery infarction or stroke blood-brain barrier defect, where the index values of the biomarkers are the expression level data of 24(S)-hydroxycholesterol and / or GDNF;

[0042] An index value detection module, configured to determine whether the index values of the biomarkers corresponding to the subject to be diagnosed exceed the preset normal value range;

[0043] An early warning module for middle cerebral artery infarction or stroke blood-brain barrier defect, configured to output the early warning information of middle cerebral artery infarction or stroke blood-brain barrier defect of the subject to be diagnosed according to the judgment result.

[0044] The fifth aspect of the present invention provides a system for diagnosing and / or early predicting middle cerebral artery infarction or stroke blood-brain barrier defect using biomarkers.

[0045] Furthermore, the biomarker is 24(S)-hydroxycholesterol and / or GDNF, and the system includes:

[0046] A detection device, configured to detect the levels of the biomarkers in the samples of each reference person and the subject to be tested in the reference group; the reference group consists of patients with middle cerebral artery infarction or stroke blood-brain barrier defect and healthy persons without middle cerebral artery infarction or stroke blood-brain barrier defect;

[0047] A reference device, configured to receive the information on the levels of the biomarkers in all the samples of the reference group output by the detection device, and set the information on the levels of the biomarkers in all the samples of the reference persons in the reference group as known grouping information;

[0048] A comparison device, which is used to receive the information on the level of the biomarker in the sample of the subject to be tested output by a detection device and the known grouping information output by a reference device, set the information on the level of the biomarker in the sample of the subject to be tested as unknown information, compare the unknown information with the data set of the known grouping information, and determine whether the subject to be tested belongs to the group of middle cerebral artery infarction or blood-brain barrier defect in stroke or the group of non-middle cerebral artery infarction or non-blood-brain barrier defect in stroke.

[0049] In some embodiments, the device or system of the present invention is a method for distinguishing different components, elements, parts, portions or assemblies of different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions. Those skilled in the art of the present invention are well aware that the present invention can be implemented as a device, a method or a computer program product. Therefore, the content disclosed in the present invention can be specifically implemented in the following forms, that is, it can be completely hardware, can also be completely software (including firmware, resident software, microcode, etc.), and can also be in the form of a combination of hardware and software. In addition, in some specific embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable media contains computer-readable program codes.

[0050] The sixth aspect of the present invention provides the application of the biomarker in constructing a computational model for diagnosing and / or early predicting middle cerebral artery infarction or blood-brain barrier defect in stroke.

[0051] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the system described in the fifth aspect of the present invention is implemented.

[0052] In some embodiments, the computer-readable medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus or device.

[0053] Further, more specific examples of the computer-readable storage medium include but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0054] In addition, the present invention also provides a method for predicting middle cerebral artery infarction or blood-brain barrier defect in stroke, the method comprising:

[0055] Obtaining biomarker expression level data of a sample to be tested;

[0056] Extracting the expression level data of a target biomarker from the biomarker expression level data, the target biomarker being 24(S)-hydroxycholesterol and / or GDNF;

[0057] Performing classification prediction based on the expression level data of the target biomarker to obtain a classification result as to whether the sample to be tested is a sample of middle cerebral artery infarction or blood-brain barrier defect in stroke;

[0058] The classification result is obtained based on a prediction model, and the construction method of the prediction model comprises: obtaining the expression level data of the target biomarker of a training set sample and the corresponding clinical features of the sample, the clinical features including patients with middle cerebral artery infarction or blood-brain barrier defect in stroke and healthy controls, extracting the expression level data of the target biomarker in the training set and inputting it into a machine learning model to construct a prediction model, obtaining the constructed prediction model and a threshold value;

[0059] If the expression level of the target biomarker is higher than the threshold value, a classification result that the sample to be tested is a sample of middle cerebral artery infarction or blood-brain barrier defect in stroke is obtained; if the expression level of the target biomarker is lower than the threshold value, a classification result that the sample to be tested is not a sample of middle cerebral artery infarction or blood-brain barrier defect in stroke is obtained.

[0060] In some embodiments, the machine learning model is a linear regression model, a logistic regression model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a naive Bayes model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model or a CatBoost model.

[0061] In some embodiments, the biomarker expression level data is biomarker expression level data obtained by detecting the sample to be tested by enzyme-linked immunosorbent assay, electrochemiluminescence immunoassay, chromatography and / or mass spectrometry.

[0062] In a specific embodiment of the present invention, the sample to be tested is a peripheral blood sample from a subject, and the subject is a human.

[0063] In some embodiments, the threshold in a machine learning model refers to the critical value used to convert the model output into a classification result. In a binary classification problem, the model typically outputs a real number, which represents the probability of belonging to a certain class. For example, a logistic regression model outputs a probability value between 0 and 1. Usually, the default threshold is 0.5, that is, those with a probability greater than or equal to 0.5 are classified as the positive class, and those less than 0.5 are classified as the negative class.

[0064] In some embodiments, the Precision-Recall Curve can be used to select the optimal threshold. By plotting the Precision-Recall Curve, one can intuitively see the impact of different thresholds on the model performance. Select the point on the curve such that the precision and recall reach the best balance, and this point is the optimal threshold.

[0065] In some embodiments, the F1 score can be used to select the optimal threshold. The F1 score is the harmonic mean of precision and recall, and the optimal threshold is selected by maximizing the F1 score.

[0066] In some embodiments, the ROC curve can be used to select the optimal threshold. The Receiver Operating Characteristic Curve (ROC) can also be used to select the optimal threshold. The performance of the model is evaluated by calculating the Area Under the Curve (AUC), and then the optimal threshold is determined.

[0067] In some embodiments, when the construction method of the above prediction model is determined, the obtained prediction model contains the threshold, that is, when the prediction model is determined, the threshold is also determined. Based on the determined threshold, the classification result of whether the sample to be tested is a middle cerebral artery infarction or a stroke blood-brain barrier defect sample can be predicted.

[0068] The specific judgment result based on the prediction model is: if the content of the target biomarker is higher than the threshold, the classification result that the sample to be tested is a middle cerebral artery infarction or a stroke blood-brain barrier defect sample is obtained; if the content of the target biomarker is lower than the threshold, the classification result that the sample to be tested is not a middle cerebral artery infarction or a stroke blood-brain barrier defect sample is obtained.

[0069] In some embodiments, the efficacy of the constructed prediction model can also be predicted, that is, another dataset containing the expression level data of the target biomarker corresponding to patients with middle cerebral artery infarction or stroke blood-brain barrier defect and healthy controls is taken, and the efficacy of the constructed prediction model is verified in this dataset.

[0070] In addition, the present invention also provides a prediction system for middle cerebral artery infarction or stroke blood-brain barrier defect, and the system includes:

[0071] A data acquisition unit, which acquires the expression level data of the target biomarker of the sample to be tested;

[0072] A data extraction unit, which extracts the expression level data of the target biomarker from the expression level data of the biomarker, and the target biomarker is 24(S)-hydroxycholesterol and / or GDNF;

[0073] A result prediction unit, which performs classification prediction based on the expression level data of the target biomarker to obtain the classification result of whether the sample to be tested is a sample with middle cerebral artery infarction or blood-brain barrier defect in stroke;

[0074] The classification result is obtained based on a prediction model, and the construction method of the prediction model includes: acquiring the expression level data of the target biomarker of the training set sample and the corresponding clinical characteristics of the sample, and the clinical characteristics include patients with middle cerebral artery infarction or blood-brain barrier defect in stroke and healthy controls, extracting the expression level data of the target biomarker in the training set and inputting it into a machine learning model to construct a prediction model, so as to obtain the constructed prediction model and threshold;

[0075] If the content of the target biomarker is higher than the threshold, the classification result that the sample to be tested is a sample with middle cerebral artery infarction or blood-brain barrier defect in stroke is obtained; if the content of the target biomarker is lower than the threshold, the classification result that the sample to be tested is not a sample with middle cerebral artery infarction or blood-brain barrier defect in stroke is obtained.

[0076] In some embodiments, the machine learning model is a linear regression model, a logistic regression model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a random forest model, a nearest neighbor model, a naive Bayes model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model or a CatBoost model.

[0077] In some embodiments, the biomarker expression level data is the biomarker expression level data obtained by detecting the sample to be tested by enzyme-linked immunosorbent assay, electrochemiluminescence immunoassay, chromatography and / or mass spectrometry.

[0078] In a specific embodiment of the present invention, the sample to be tested is a peripheral blood sample from a subject, and the subject is a human.

[0079] In addition, the present invention also provides a computer prediction device for middle cerebral artery infarction or blood-brain barrier defect in stroke. The computer prediction device includes: a memory and a processor. The memory is used for storing program instructions. The processor is used for calling the program instructions, and when the program instructions are executed, the prediction method for middle cerebral artery infarction or blood-brain barrier defect in stroke as described above is implemented.

[0080] In addition, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the prediction method for middle cerebral artery infarction or blood-brain barrier defect in stroke as described above is implemented.

[0081] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the following embodiments and will not be elaborated here. In this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings, direct couplings, or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0082] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment solution. In addition, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0083] Those of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk, or optical disc, etc.

[0084] Those of ordinary skill in the art can understand that all or part of the steps in implementing the above methods can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, or the like.

[0085] In addition, the present invention also provides a method for diagnosing, screening, and / or early predicting middle cerebral artery infarction or blood-brain barrier defect in stroke, the method comprising: detecting the expression levels of the biomarkers 24(S)-hydroxycholesterol and / or GDNF in a sample from a subject in need, and diagnosing, screening, and / or early predicting the risk of whether the subject is a patient with middle cerebral artery infarction or a patient with blood-brain barrier defect in stroke based on the detection result.

[0086] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0087] The present invention for the first time discovers the peripheral blood biomarkers 24(S)-hydroxycholesterol (24(S)-OHC) and / or GDNF related to the diagnosis of blood-brain barrier integrity in stroke. By detecting the expression levels of the above-mentioned peripheral blood biomarkers in a subject, it is possible to quickly and accurately diagnose middle cerebral artery infarction or blood-brain barrier defect in stroke, and has a high diagnostic efficiency, providing a basis for clinical decisions related to stroke, and at the same time laying a foundation for subsequent clinical research, with broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 : Changes in rat brain weight;

[0089] Figure 2 : Detection of the expression levels of markers in rat peripheral blood;

[0090] Figure 3 : Detection of the expression levels of markers in human peripheral blood in the training set;

[0091] Figure 4 : ROC curve corresponding to the markers in human peripheral blood in the training set;

[0092] Figure 5 : Detection of the expression levels of markers in human peripheral blood in the validation set;

[0093] Figure 6 : ROC curve corresponding to the markers in human peripheral blood in the validation set. DETAILED DESCRIPTION OF THE INVENTION

[0094] The present invention will be further described below in conjunction with specific embodiments. The following specific embodiments are only used to explain the present invention and should not be construed as limiting the present invention. Those of ordinary skill in the art can understand that: various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

[0095] The reagents, raw materials, and experimental consumables used in the present invention are easily obtained by those of ordinary skill in the art. Without special instructions, they can all be obtained from commercial channels. The experimental methods without specific conditions described in the present invention are usually carried out under conventional conditions or according to the conditions recommended by the manufacturer. In particular, the following embodiments are only used to illustrate the present invention and should not limit the scope of the present invention in any way. It should be noted that the experimental conditions and results described in the following embodiments are only used to illustrate the present invention and should not and will not limit the present invention described in detail in the claims.

[0096] Example 1 Detection of Blood Indexes in Peripheral Blood of Rat MCAO (Middle Cerebral Artery Occlusion) Model

[0097] 1. Experimental Materials

[0098] Experimental equipment: thread embolism; Experimental animals: 20 SD rats.

[0099] 2. Experimental Methods

[0100] The animals were randomly divided into two groups according to body weight and started modeling after 7 days of adaptive feeding.

[0101] (1) Establishment of MCAO Model

[0102] ① Separate and expose blood vessels: Anesthesia induction was performed using an anesthesia induction box with 3.0% isoflurane. Surgery could only begin after the disappearance of the eyelid reflex and the pain sensation in the limbs and tail. After shaving the surgical area, the rats were placed under a surgical microscope. The skin of the rats was cut along the midline with an ophthalmic scissors, about 2 cm in length. Through the right paracervical approach, the right common carotid artery (CCA) was bluntly dissected with microsurgical forceps and dissected upward along the common carotid artery to expose the external carotid artery (ECA) and the internal carotid artery (ICA);

[0103] ② Ligate the ECA and temporarily clamp the ICA; Threads were passed through the proximal and distal ends of the CCA respectively, the distal end was tightened, and a loose knot was tied at the proximal end for standby. A small incision was made between the two threads;

[0104] ③ Thread embolism insertion: Insert the 3600AAA thread embolism through the incision of the CCA, and then slowly and gently push it into the internal carotid artery. Pause when reaching the artery clip of the ICA, and further tighten the pre-ligated thread (to avoid excessive bleeding when pushing the thread embolism). Remove the artery clip that blocks the blood flow of the ICA, and immediately push the thread embolism into the ICA until it enters the skull;

[0105] ④ Fix the thread embolism and suture the incision: When the insertion depth of the thread embolism is about 18 mm from the bifurcation of the common carotid artery, if there is a slight resistance, it indicates that the tip of the thread embolism has entered the anterior cerebral artery (ACA), and the side wall of the thread embolism has blocked the opening of the middle cerebral artery. At this time, stop inserting and record the time;

[0106] ⑤ Cerebral reperfusion: Place the ischemic rats at room temperature. After 90 min, induce anesthesia, and slowly and gently pull the thread embolism under the maintained anesthesia state to make its tip return to the common carotid artery, that is, to achieve reperfusion of the middle cerebral artery;

[0107] ⑥ Disinfect the incision with iodophor.

[0108] Animals in the sham operation group only underwent vascular dissection without ischemia.

[0109] (2) Blood index detection

[0110] Index selection: 24(S)-OHC (24(S)-hydroxycholesterol), MMP-9, GDNF, PDGFRβ (platelet-derived growth factor receptor β).

[0111] (3) Detection method

[0112] Collect the sera separated from the blood of the rats in the above MCAO model group and the sham operation group, and detect them according to the instructions of the commercial ELISA kits for the above indexes.

[0113] 3. Experimental results

[0114] The results showed that compared with the control group, there was no significant difference in the brain weight of the rats in the MCAO model group ( Figure 1 ), the content of GNDF in the peripheral blood of the MCAO model group increased to varying degrees at 6 h, 24 h, and 48 h; the content of PDGFRβ decreased at 6 h and increased at 48 h; GFAP did not change at each time point; the content of 24(S)-HC increased significantly at 48 h (p<0.05); the content of MMP-9 increased at 48 h, but there was no significance ( Figure 2 ).

[0115] Example 2 Detection of markers in the peripheral blood of patients with middle cerebral artery infarction and normal people and verification of the diagnostic efficacy of markers

[0116] 1. Experimental Materials

[0117] Peripheral blood samples were collected from patients with middle cerebral artery infarction (MCA) and healthy controls (normal). The samples were collected from the Department of Neurology and Department of Neurosurgery at Tiantan Hospital. The inclusion and exclusion criteria for patients with MCA were as follows:

[0118] Inclusion criteria: Imaging confirmed internal jugular territory infarction.

[0119] Exclusion criteria: impaired consciousness (NIHSS score Ia ≥ 2 points).

[0120] The peripheral blood samples were randomly divided into a training set and a validation set, wherein the training set had 30 samples of patients with middle cerebral artery infarction and 30 samples of healthy controls, and the validation set had 30 samples of patients with middle cerebral artery infarction and 30 samples of healthy controls.

[0121] 2. Experimental methods

[0122] As described in Example 1, the ELISA method was used to detect the expression levels of peripheral blood markers in patients with middle cerebral artery infarction and healthy controls.

[0123] The R package “pROC” (version 1.15.0) was used to draw the receiver operating characteristic (ROC) curve, and its sensitivity, specificity, and AUC value were analyzed to determine the diagnostic efficacy of the biomarkers in the training set and the validation set.

[0124] 3. Experimental results

[0125] The results showed that in the training set, compared with the healthy control group, the levels of GDNF, 24(S)-OHC, and PDGFRβ in the peripheral blood of patients with middle cerebral artery infarction were significantly increased, while the levels of GFAP and MMP9 did not change significantly ( Figure 3 The AUC values of the markers were 0.802 (GDNF), 0.540 (GFAP), 0.938 (24(S)-OHC), 0.623 (MMP9), and 0.807 (PDGERβ) ( Figure 4 ). Among them, 24(S)-OHC had a high diagnostic efficacy in the training set, with an AUC value of 0.938, a sensitivity of 93%, and a specificity of 80%. The AUC value of GDNF in the training set was 0.802, a sensitivity of 86.67%, and a specificity of 60%.

[0126] To further verify the accuracy of the detection results, in this embodiment, 60 peripheral blood samples were recollected as a validation set for detection (30 cases in each of the control group and the patient group). The results showed that in the validation set, the levels of GDNF and 24(S)-OHC in the peripheral blood of patients with middle cerebral artery infarction were significantly higher than those of the control group population( Figure 5 ), and the AUC values corresponding to each biomarker were 0.663 (GDNF), 0.502 (GFAP), 0.896 (24(S)-OHC), 0.510 (MMP9), and 0.583 (PDGERβ)( Figure 6 ). Among them, 24(S)-OHC also had a high diagnostic efficacy in the validation set, with an AUC value as high as 0.896, a sensitivity of 83.33%, and a specificity of 76.67%. The AUC value of GDNF in the validation set was 0.663, with a sensitivity of 76.67% and a specificity of 50%.

Claims

1. Use of a biomarker in the preparation of a product for diagnosing and / or early predicting middle cerebral artery infarction or for diagnosing and / or early predicting blood-brain barrier defect in stroke, characterized in that, The biomarker is 24(S)-hydroxycholesterol and / or GDNF.

2. The application according to claim 1, characterized in that, The product includes reagents for detecting the expression level of the biomarker in a sample.

3. The application according to claim 2, characterized in that The reagents include reagents for detecting the expression level of the biomarker in a sample by enzyme-linked immunosorbent assay, electrochemiluminescence immunoassay, chromatography, and / or mass spectrometry.

4. The application according to claim 2, wherein The sample is a peripheral blood sample.

5. The application according to claim 1, characterized in that The product includes a detection kit or a detection chip.

6. A product for diagnosing and / or early predicting middle cerebral artery infarction or blood-brain barrier defect in stroke, characterized in that The product includes reagents for detecting the expression level of the biomarker 24(S)-hydroxycholesterol and / or GDNF in a sample; Optionally, the reagents include reagents for detecting the expression level of the biomarker in a sample by enzyme-linked immunosorbent assay, electrochemiluminescence immunoassay, chromatography, and / or mass spectrometry; Optionally, the product further includes a pretreatment reagent for pretreating the sample; Optionally, the product includes a detection kit or a detection chip.

7. Use of a biomarker in constructing a warning model for blood-brain barrier defect in middle cerebral artery infarction or stroke, characterized in that, The early warning model for middle cerebral artery infarction or stroke blood-brain barrier defect uses the expression level value of the biomarker in the sample as an input variable; The biomarker is 24(S)-hydroxycholesterol and / or GDNF.

8. An early warning device for blood-brain barrier defect in middle cerebral artery infarction or stroke, characterized in that, The early warning device for middle cerebral artery infarction or stroke blood-brain barrier defect includes: A model loading module for loading the early warning model for middle cerebral artery infarction or stroke blood-brain barrier defect constructed by the application according to claim 7; An index value acquisition module for obtaining the index value of the biomarker corresponding to the subject to be diagnosed according to the early warning model for middle cerebral artery infarction or stroke blood-brain barrier defect, where the index value of the biomarker is the expression level data of 24(S)-hydroxycholesterol and / or GDNF; An index value detection module for determining whether the index value of the biomarker corresponding to the subject to be diagnosed exceeds a preset normal value range; An early warning module for middle cerebral artery infarction or stroke blood-brain barrier defect for outputting an early warning message for middle cerebral artery infarction or stroke blood-brain barrier defect of the subject to be diagnosed according to the judgment result.

9. A system for diagnosing and / or early predicting middle cerebral artery infarction or blood-brain barrier defect in stroke by using biomarkers, characterized in that, The biomarker is 24(S)-hydroxycholesterol and / or GDNF, and the system includes: A detection device for detecting the level of the biomarker in the samples of each reference subject and the subject to be tested in the reference group; the reference group consists of patients with middle cerebral artery infarction or stroke blood-brain barrier defect and healthy individuals without middle cerebral artery infarction or stroke blood-brain barrier defect; A reference device for receiving the information on the level of the biomarker in all the samples of the reference group output by the detection device and setting the information on the level of the biomarker in all the samples of the reference subjects in the reference group as known grouping information; A comparison device for receiving the information on the level of the biomarker in the sample of the subject to be tested output by the detection device and the known grouping information output by the reference device, setting the information on the level of the biomarker in the sample of the subject to be tested as unknown information, comparing the unknown information with the known grouping information dataset, and determining whether the subject to be tested belongs to the group with middle cerebral artery infarction or stroke blood-brain barrier defect or the group without middle cerebral artery infarction or stroke blood-brain barrier defect.

10. Use of biomarkers in constructing a computational model for diagnosing and / or early predicting middle cerebral artery infarction or blood-brain barrier defect in stroke.

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