Biomarker for traumatic brain injury detection and application thereof

Abstract

The invention belongs to the technical field of molecular biology, and particularly provides a biomarker for traumatic brain injury detection, the biomarker comprises NSE and GFAP, and preferably further comprises at least one of S100B, UCH-L1, P-tau and IL-6. On the basis of the biomarker, a reagent card, a kit, a system and other traumatic brain injury assessment products are provided, a rapid and non-invasive assessment alternative scheme is provided for traumatic brain injury, and assistance is provided for early intervention treatment. The radiation exposure risk of the patient is reduced, the diagnosis process is accelerated, and the illness state can be evaluated more comprehensively. Biomarkers are used in combination with the clinical severity of TBI and therapeutic care pathways, enabling better identification and hierarchical management of mild and severe patients. The biomarkers of the invention can provide extremely significant and highly credible test results for specific patient populations requiring admission treatment or at different clinical severity.

Description

Technical Field

[0001] The present invention belongs to the technical field of molecular biology, and in particular relates to a biomarker for detecting traumatic brain injury and its application in diagnosis and evaluation. Background Art

[0002] Traumatic brain injury (TBI) is a serious neurological disease with the characteristics of rapid progression, high mortality and disability rates, and poor prognosis. It has a high incidence rate worldwide and has caused a huge medical burden, seriously threatening human life and health. At present, the diagnosis of TBI mainly relies on imaging technologies such as computed tomography (CT) or magnetic resonance imaging (MRI).

[0003] The severity assessment of traumatic brain injury usually relies on computed tomography (CT), magnetic resonance imaging (MRI) or the Glasgow Coma Scale (GCS), a scoring system that determines the severity of injury by assessing the patient's level of consciousness. According to the GCS scoring criteria, patients with a GCS score of 13-15 are defined as mild TBI, 9-12 points as moderate TBI, and 3-8 points as severe TBI. However, for most patients with mild and moderate TBI, even if the score shows the presence of injury, traditional imaging techniques such as CT or MRI often have difficulty in finding significant abnormalities. For the detection of mild and moderate TBI, magnetic resonance diffusion tensor imaging (DTI), as a new brain structural imaging technology, uses computers to trace axonal networks and construct a 3D model of the brain, and is considered to be one of the more promising detection technologies. However, existing studies have shown that the test results of DTI are inconsistent with the clinical manifestations of acute mild and moderate TBI, limiting its widespread application.

[0004] Due to the limitations and lags of imaging diagnosis, many TBI patients may miss the best treatment window. In contrast, biological markers in body fluids can reflect the occurrence, extent, location and severity of brain injury in a timely and dynamic manner, and can also indicate clinical prognosis and responsiveness to treatment. Biomarker detection has the advantages of minimally invasive, simple operation and high sensitivity, and is particularly suitable for the early diagnosis of mild traumatic brain injury (mTBI). However, how to effectively identify patients with mild brain injury and determine whether they need further treatment is still a major challenge in the current medical field. Summary of the invention

[0005] The present invention aims to provide an efficient and accurate biomarker for the detection of traumatic brain injury (TBI) and its application, so as to solve the problems of inaccuracy and lag in the detection of moderate and mild brain injuries by existing diagnostic methods, thereby providing a more effective tool for the early diagnosis, severity assessment, treatment plan selection and prognosis prediction of TBI.

[0006] The core of the present invention is to identify one or more biomarkers in body fluids, which can timely and accurately reflect the occurrence, severity and pathological changes of TBI, especially mild and moderate brain injury, and provide earlier diagnosis than traditional imaging detection. These biomarkers are mainly present in blood, cerebrospinal fluid or other body fluids, and can be detected by non-invasive or minimally invasive methods.

[0007] To this end, the present invention provides a biomarker combination for detecting traumatic brain injury, including NSE and GFAP, which can be effectively used to detect TBI, assess the severity of injury and predict prognosis.

[0008] Specifically, the above-mentioned biomarkers also include at least one of S100B, UCH-L1, P-tau, and IL-6.

[0009] Specifically, the above-mentioned biomarkers include S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6.

[0010] In addition, the present invention also provides a traumatic brain injury detection reagent card, which includes a biomarker detection card; the biomarker detection card includes at least two of an S100B detection card, an NSE detection card, a GFAP detection card, an UCH-L1 detection card, a P-tau detection card, and an IL-6 detection card. Biomarkers such as S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6 can be detected simultaneously or separately.

[0011] Specifically, the above-mentioned biomarker detection card includes a sample pad, a conjugation pad, an analysis membrane and a water absorbent pad which are sequentially overlapped on the bottom plate; the conjugation pad is coated with a corresponding biomarker antibody coupled with fluorescent microspheres, and the analysis membrane is provided with a detection line, which is drawn by a mouse antibody of the biomarker.

[0012] Specifically, a reference line is also provided on the analysis membrane, and the reference line is drawn by goat anti-mouse IgG.

[0013] The present invention also provides a traumatic brain injury detection kit, comprising the above-mentioned traumatic brain injury detection reagent card.

[0014] The present invention also provides a traumatic brain injury assessment system, comprising:

[0015] An input module, used for inputting the content of biomarkers in a sample; the biomarkers include at least two of S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6;

[0016] The evaluation module is used to perform traumatic brain injury analysis based on a preset formula and taking the biomarker content as a variable to obtain analysis results.

[0017] Specifically, the above preset formula is: response value = β0 + β1 * P S100-β +β2*P NSE +β3*P GFAP +β4*P UCH-L1 +β5*P P-tau +β6*P IL-6 +ε;

[0018] Among them, ε is the mean square error; β0 is the intercept, β1-β6 are the regression coefficients; P S100-β , P NSE , P GFAP , P UCH-L1 , P P-tau , P IL-6 are the biomarker contents, which are the mass concentrations of S100-β, NSE, GFAP, UCH-L1, P-tau and IL-6 in the test samples respectively; and the response value is the analysis result.

[0019] The present invention also provides a traumatic brain injury assessment device, comprising the above-mentioned assessment system, and further comprising:

[0020] A detection device for detecting the content of biomarkers in a sample;

[0021] The output device is used to output the analysis results.

[0022] Specifically, the detection device may be the above-mentioned traumatic brain injury detection reagent card, traumatic brain injury detection kit or other device capable of detecting corresponding biomarkers.

[0023] Compared with the prior art, the present invention has the following significant advantages:

[0024] 1. The present invention provides a rapid, non-invasive TBI detection solution that reduces radiation exposure to patients and accelerates the diagnostic process compared to traditional CT scans. The solution is particularly suitable for the stratified management of mild and severe TBI patients, and can be combined with GCS scores to more accurately formulate personalized treatment plans.

[0025] 2. By combining special biomarkers such as NSE and GFAP, the present invention shows extremely high prognostic value in predicting patient mortality and adverse outcomes, providing a key reference for disease stratification and personalized treatment. In addition, the combination of biomarkers such as S100B, NSE, GFAP, UCH-L1, P-tau, IL-6, etc. greatly enhances the versatility of the diagnostic tool, ensuring high sensitivity under different GCS scores, especially at GCS15, the sensitivity can reach 97.9%.

[0026] 3. The traumatic brain injury diagnostic system of the present invention can finely stratify the severity of the patient's condition through the test results of biomarkers, helping medical service providers to allocate medical resources more effectively, improve treatment effects and patient quality of life. This system is of great significance in TBI diagnosis and management, and can significantly improve the success rate of treatment.

[0027] The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 1 is a flowchart of TBI research patient screening in an embodiment of the present invention.

[0029] Figure 2A-2N is the level of different medical services in the embodiment of the present invention ( Figure 2A-2G ) and clinical severity ( Figure 2H-2N ) is the expression level of each biomarker value under.

[0030] Figure 3 It is the incremental C statistic of each biomarker predicting a positive CT result in the embodiment of the present invention.

[0031] Figures 4A-4G This is a result diagram of the correlation between biomarker levels and adverse outcomes in an embodiment of the present invention.

[0032] Figure 5A-5G This is a result diagram of the correlation between the biomarker level and the hospitalization level in the embodiment of the present invention.

[0033] Figures 6A-6G This is a result diagram of the correlation between the biomarker level and the severity of injury in an embodiment of the present invention.

[0034] Figure 7 The present invention provides a flow chart of the use of a traumatic brain injury assessment device. DETAILED DESCRIPTION

[0035] The technical scheme of the present invention will be described in detail below in conjunction with the embodiments so that those of ordinary skill in the technical field can clearly and completely understand the content of the present invention. It should be noted that the described embodiments are only a part of the present invention, rather than all embodiments. On this basis, those of ordinary skill in the technical field can make various forms of modifications and adjustments to it without departing from the core idea of ​​the present invention, and these modifications and adjustments should be deemed to belong to the scope of the present invention. Therefore, the protection scope of the present invention should be based on the scope defined by the attached claims and their equivalent technical features, and should not be limited to these specific embodiments.

[0036] The following specific examples will be used to study and verify in detail the biomarkers for detecting traumatic brain injury (TBI) provided by the present invention and their application effects. The experimental and analytical results of these examples further demonstrate the effectiveness of the present invention in early diagnosis, prognosis evaluation and monitoring of treatment effects of TBI.

[0037] Embodiment 1:

[0038] This example included patients aged 19 years and above who underwent blood sampling and CT scan within 24 hours after head trauma and underwent outcome assessment according to the Glasgow Outcome Scale-Extended (GOSE) at 6 months. The biomarker cohort process is as follows: Figure 1 As shown in the figure, the initial screening and inclusion process of 3764 patients in the TBI study excluded 475 patients, including 182 patients whose blood samples were collected more than 24 hours ago and 293 patients who did not undergo CT examination, and finally included 3289 patients. Among them, 657 patients visited the emergency department, 242 of them had positive CT; 822 patients were hospitalized, 312 of them had positive CT; 1810 patients were treated in the ICU, and 1050 of them had positive CT.

[0039] The clinical characteristics of the study cohort, by stratum, are summarized in Table 1 , along with the frequencies of specific features included in the clinical decision rule (CDR) for predicting CT abnormalities in patients with mild concussion.

[0040] Table 1 Characteristics of the biomarker cohort (n = 3289) in the study

[0041]

[0042]

[0043]

[0044] Table 1 lists the basic characteristics of the patients in detail according to different medical settings (emergency, hospital admission, ICU), including age, sex, GCS score, pupillary reaction, CT positivity, subarachnoid hemorrhage, serum marker levels within 24 hours (S100B, NSE, GFAP, UCH-L1, P-tau, IL-6), blood draw time, and prognosis after 6 months (GOSE score). At 6 months, 340 (10.3%) of 3289 patients died, 1034 (58.3%) had a poor outcome, and 1915 (31.4%) had incomplete recovery.

[0045] In these experiments, the present invention developed a set of biomarker combinations containing six members (S100B, NSE, GFAP, UCH-L1, P-tau, IL-6), which has significant practicality in clinical settings. The results show that each member of the combination provides a unique diagnostic perspective, and combining them can achieve more accurate prediction results. These members all meet certain criteria: First, they must have predictive capabilities and be able to distinguish different types of diseases to a certain extent. Secondly, their measurement methods are simple and easy, and have high repeatability. Finally, they play an important role in biological network analysis and are involved in regulating key biological processes.

[0046] Based on the combination of biomarkers, the present invention provides a traumatic brain injury assessment system for obtaining analysis data, comprising:

[0047] An input module, used for inputting the content of biomarkers in a sample; the biomarkers include at least two of S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6;

[0048] The evaluation module is used to perform traumatic brain injury analysis based on a preset formula and taking the biomarker content as a variable to obtain analysis results.

[0049] The default formula is: β0+β1*P S100-β +β2*P NSE +β3*P GFAP +β4*P UCH-L1 +β5*P P-tau +β6*P I L-6 +ε.

[0050] Among them, ε is the mean square error, β0 is the intercept, β1, β2, β3, β4, β5, β6 are the regression coefficients obtained by multivariate regression analysis of a certain number of damaged samples; P S100-β , P NSE , P GFAP , P UCH-L1, P P-tau , P IL-6 are the biomarker contents, which are the mass concentrations of S100-β, NSE, GFAP, UCH-L1, P-tau and IL-6 in the test samples respectively; the response value is the obtained analysis result.

[0051] In one example, 1000 samples were randomly selected from the above 3289 samples, and a multivariate linear regression model was used to explore the relationship between the concentrations of six biomarkers and the severity of the disease. Z-score standardization: The mean and standard deviation of each marker were calculated and standardized. Multiple regression analysis was performed using the statistical software R to obtain an estimate of the regression coefficient. Based on the estimated values ​​of the regression coefficients, the degree of influence of different biomarker concentrations on the severity of traumatic brain injury was explained, and finally the regression coefficients of the six biomarkers in the example were obtained: β1 = 4.72612366, β2 = 5.17664773, β3 = 10.63405737, β4 = 4.45013458, β5 = 2.56502399, β6 = 5.15181801;

[0052] Intercept β0 = 37.7311271

[0053] Mean square error ε(MSE)=1.76427778

[0054] In the above example, the response value = 37.7311271 + 4.72612366 * P S100-β +5.17664773*P NSE +10.63405737*P GFAP +4.45013458*P UCH-L1 +2.56502399*P P-tau +5.15181801*P IL-6 +1.76427778

[0055] In this example:

[0056] (1) Response values ​​of 0-20 indicate no obvious injury or mild brain injury;

[0057] GCS score: commonly GCS15 (Mild); CT results: mostly CT negative; GOSE prognosis: GOSE 7-8.

[0058] (2) A response value of 20-40 indicates mild to moderate brain damage

[0059] GCS score: GCS13-14 is common; CT results: CT may be negative, but a few patients may be CT positive; GOSE prognosis: GOSE 5-6.

[0060] (3) A response value of 40-60 indicates moderate brain damage

[0061] GCS score: GCS 9-12 is common; CT results: mostly CT positive, showing brain tissue damage; GOSE prognosis: GOSE4-5.

[0062] (4) A response value of 60-80 indicates moderate to severe brain damage

[0063] GCS score: GCS 9-12 or lower; CT results: most are CT positive; GOSE prognosis: GOSE 2-4.

[0064] (5) A response value of 80-120 indicates severe brain damage

[0065] GCS score: Commonly GCS 3-8

[0066] CT results: Almost all are CT positive, indicating severe brain tissue damage; GOSE prognosis: GOSE 1-2.

[0067] When the response value is less than 20, severe brain injury is basically ruled out and the prognosis is good; a response value between 20-60 indicates the risk of mild to moderate injury, which requires further observation and management; a response value exceeding 60 indicates severe brain injury, which requires immediate intervention, usually accompanied by positive CT findings; a response value exceeding 80 indicates that the patient is at extremely high risk, most of whom are in severe or critical condition and require ICU monitoring. The response value output by the model can be used for clinical reference. By quickly obtaining the blood biomarker response value and matching it with the above segmented range, the patient's degree of brain injury can be preliminarily assessed, and personalized treatment and prognosis plans can be formulated to assist.

[0068] R 2 The coefficient of determination is used to measure the ability of a regression model to explain the variability of the data. It represents the proportion of the total variance explained by the model to the total variance. Using the internal model as an example, the R 2 The value is as high as 0.82.

[0069] When the system is constructed, the regression coefficient can be adjusted according to the sample data actually used to obtain the corresponding regression coefficient, intercept, and mean square error data.

[0070] The present invention also provides a traumatic brain injury assessment device, comprising the above-mentioned assessment system, and further comprising:

[0071] A detection device for detecting the content of biomarkers in a sample;

[0072] The output device is used to output the analysis results.

[0073] Reference Figure 7 When in use, the detection device is used to obtain the content of each biomarker in the blood sample to be tested, and then the input module of the evaluation system inputs the content of the biomarker in the sample. The evaluation module analyzes traumatic brain injury based on a preset formula with the content of the biomarker as a variable to obtain the analysis result. The output device outputs the analysis result for subsequent management of the stratified treatment plan for the patient.

[0074] Embodiment 2:

[0075] In statistical analysis, verifying the quality of the linear regression model is a key step to ensure its applicability and predictive accuracy. In order to ensure that the model can maintain high accuracy and sensitivity on unknown or new data sets, the remaining 2289 models in Example 1 were used to verify its stability and reliability. This process is designed to avoid the problem of overfitting where the model performs well only on a specific data set, and to ensure that the model can work effectively and impartially in practical applications, especially in key decision-making scenarios such as medical and health services, thereby enhancing confidence in model performance and supporting a wider range of clinical or practical applications.

[0076] In further analysis, biomarker levels were significantly positively correlated with care intensity and traumatic brain injury severity. High levels of biomarkers were strongly associated with adverse outcomes, and this association was more significant across levels of care and injury severity. Combined testing of UCH-L1, IL-6, S100B, P-tau, and GFAP more accurately predicted adverse outcomes than monitoring NSE alone. Care intensity increased with biomarker levels, and marker levels were associated with greater increases in patients with moderate to severe traumatic brain injury (GCS 3-12) than in patients with mild concussion (GCS13-15). Figures 4A-4G , Figure 5A-5G , Figures 6A-6G The trend was demonstrated, indicating that markers such as S100B, NSE, and GFAP were significantly elevated in moderate to severe craniocerebral injury. This positive correlation suggests that biomarkers can not only be used to predict adverse outcomes, but also serve as important indicators for assessing injury severity and care needs. The combined detection of multiple biomarkers (UCH-L1, IL-6, S100B, P-tau, GFAP) showed higher sensitivity in the prognosis prediction of moderate to severe craniocerebral injury, and the effect was better than a single marker.

[0077] All biomarker results were analyzed according to clinical severity, different stratifications, and CT findings (CT+ and CT-). Descriptive statistical results are presented as mean (standard deviation), median (interquartile range), or frequency. The differences in biomarker values ​​among different strata (emergency department, hospital admission, ICU) and injury severity were compared using independent sample t-tests. Figure 2A-2NAs shown in the figure, the six biomarkers (S100B, NSE, GFAP, UCH-L1, P-tau, IL-6) detected in combination were consistent among the groups, and the biomarker levels of CT+ patients were significantly higher than those of CT- patients. The combined detection of multiple biomarkers is significantly better than that of a single biomarker and can better identify CT abnormalities.

[0078] In the model evaluation, the incremental C statistic was used to measure the predictive increment and adjusted by optimism correction. Subgroup analyses of different care pathways and injury severity were performed to further verify the incremental value of biomarkers in distinguishing high and low risks. Figure 3 The incremental C statistic of biomarkers in predicting positive CT results was demonstrated. The combined detection was most effective when multiple biomarkers were combined into a module of six molecules. Compared with single serum biomarkers, the combined detection of biomarkers significantly improved the discrimination ability and showed the greatest incremental value. The biomarker levels were positively correlated with the intensity of care and the severity of craniocerebral injury, indicating that biomarkers can not only predict adverse outcomes, but also be used to assess the severity of injury and the corresponding care needs.

[0079] Table 2 further shows the biomarker detection values ​​for predicting 6-month prognosis under different injury severity (GCS 3-15). The ROC curve was used to evaluate the discriminatory ability of GFAP and NSE on day 1 for death, adverse outcomes, and incomplete recovery within 6 months after injury. Combined with the analysis results of different GCS subgroups (GCS 3-12 and GCS13-15), it was shown that the detection of GFAP and NSE had high predictive ability in these cases. When external validation was performed, the combined detection value had the highest sensitivity under all GCS scores, especially at GCS15, the sensitivity was close to 98%. This shows the powerful effect of multi-marker combined detection. The detection of combined multiple markers performed better in overall sensitivity, especially in the high GCS score scenario, the combined detection performance was particularly outstanding, indicating that in some cases combined detection is far superior to single marker detection.

[0080] Table 2 Prognostic value for predicting 6-month outcomes in different injury severity subgroups

[0081]

[0082] In order to verify the wide applicability of the model, a linear regression model was applied to the data set, focusing on the sensitivity of the model, that is, the ability to correctly identify positive examples, to evaluate its overall performance and application value. The external validation results showed that the sensitivity of the combined detection value was the highest, especially in patients with GCS15, the sensitivity reached 97.9%, which shows that the combined detection of multiple markers has significant advantages. Table 3 shows that the sensitivity of the combined detection is better than that of a single marker at all GCS scores, especially GFAP and S100-β at GCS15, with sensitivities of 91.2% and 92.6%, respectively, while P-Tau performed stably at all scores, with a sensitivity of no less than 73.5%. In addition, the invention combines the use of biomarkers with clinical severity (such as GCS score) and care pathways, and is particularly suitable for patient groups admitted to hospital for treatment. In groups with different clinical severity (such as GCS 3-8, 9-12, 13-14), the labeling method can provide significant and reliable test results, and achieve accurate detection with fewer molecular markers, which has both technical advantages and economic benefits. This combined testing strategy significantly improves testing efficiency and accuracy, helping to better guide clinical decision-making.

[0083] Table 3 Sensitivity of biomarker combination to replace positive CT imaging examination

[0084]

[0085] Table 4 compares the sensitivity of different combinations of biomarker tests in the prognosis of incomplete recovery, adverse outcomes, and mortality. The sensitivity of the combined detection value was the best in all three prognostic levels, while the combination of NSE+GFAP and S100-β+P-tau had a certain effect at a specific prognostic level, but the overall sensitivity was slightly lower. In summary, the combination of biomarkers with clinical severity (such as GCS score) and care pathways can significantly improve the prognostic prediction effect of patients with craniocerebral injury. Compared with a single marker, the sensitivity and accuracy of combined detection are significantly improved, especially under high GCS scores. It is recommended to give priority to the detection strategy of combining multiple markers in order to more accurately assess the patient's prognosis and formulate personalized treatment plans. This finding not only helps clinical decision-making, but also reduces the waste of medical resources and provides patients with more timely and accurate diagnosis.

[0086] Table 4 Sensitivity of biomarker combination in predicting prognostic grade after 6 months

[0087]

[0088] This embodiment summarizes the significant advantages of combined detection of biomarkers in the prediction of prognosis of craniocerebral injury. Through external validation, the results show that combined detection of multiple biomarkers such as UCH-L1, IL-6, S100B, P-tau, GFAP, etc. can more accurately predict adverse outcomes than single markers, and is significantly positively correlated with nursing intensity and injury severity. The combined detection showed high sensitivity under different GCS scores, especially at GCS15, the sensitivity reached 97.9%. Compared with single marker detection, GFAP, S100-β and P-Tau performed particularly well under high GCS scores, further verifying the reliability and accuracy of combined detection in prognosis assessment. Combined biomarker detection can not only help predict patient prognosis more accurately, but also optimize resource allocation and reduce unnecessary medical testing and costs. In practical applications, it is recommended to give priority to the combined detection strategy to improve the scientificity and efficiency of clinical decision-making and maximize the effect of patient care.

[0089] Embodiment 3:

[0090] This embodiment provides a traumatic brain injury detection kit, including a housing and six detection cards for detecting S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6 respectively; the six detection cards are separately arranged in the housing. The six detection cards have similar structures, and all include a sample pad, a binding pad, an analysis membrane, and a water absorption pad sequentially overlapped on a bottom plate. The sample pad is a blood filter pad, the binding pad is a gold-sprayed pad, and the analysis membrane is a nitrocellulose membrane.

[0091] The gold spray pad sprays a fluorescent microsphere complex solution, which includes fluorescent microspheres and biomarker antibodies coupled to the fluorescent microspheres. The biomarker antibody coupled to the fluorescent microspheres of the S100B detection card is the S100B antibody; the biomarker antibody coupled to the fluorescent microspheres of the NSE detection card is the NSE antibody; the biomarker antibody coupled to the fluorescent microspheres of the GFAP detection card is the GFAP antibody; the biomarker antibody coupled to the fluorescent microspheres of the UCH-L1 detection card is the UCH-L1 antibody; the biomarker antibody coupled to the fluorescent microspheres of the P-tau detection card is the P-tau antibody; and the biomarker antibody coupled to the fluorescent microspheres of the IL-6 detection card is the IL-6 antibody.

[0092] The analytical membrane is provided with a test line and a reference line. The test line is drawn with mouse antibody of the biomarker, and the reference line is drawn with goat anti-mouse IgG.

[0093] The samples from the same source were detected using the kit and ELISA provided in this example, and the results are shown in Table 5.

[0094] Table 5 Comparison of the results of ELISA and fluorescent immunoassay kits

[0095] Detection indicators ELISA Kit of this Example S100B, mg / L 0.15；0.12；0.13；0.21 0.15；0.11；0.12；0.23 NSE,ng / mL 16.30；14.60；14.90；19.20 16.45；15.30；13.33；20.20 GFAP,ng / mL 7.00；1.60；2.85；12.80 6.88；1.48；3.00；13.10 UCH-L1, pg / mL 120.50；54.29；69.92；196.97 123.40；57.39；72.91；210.00 p-tau, pg / mL 1.27；0.65；0.79；2.42 1.30；0.63；0.73；2.37 IL-6, mg / L 529.50；349.14；395.96；817.59 540.50；327.17；420.64；789.52

[0096] The kit provided by the present invention can quickly and simply detect biomarkers S100-β, NSE, GFAP, UCH-L1, P-tau and IL-6, with a detection time of 5-10 minutes, and can produce results of 6 indicators at one time. As shown in Table 5, the difference between the detection result and the ELISA detection result is within 10%, indicating that the test result is reliable and can be used as a quick detection device for TBI evaluation.

[0097] The above examples are merely illustrative of the present invention and do not constitute a limitation on the protection scope of the present invention. All designs that are the same or similar to the present invention fall within the protection scope of the present invention.

Claims

1. A biomarker for detecting traumatic brain injury, characterized in that: The biomarkers include NSE and GFAP.

2. The biomarker for detecting traumatic brain injury according to claim 1, characterized in that: The biomarkers also include at least one of S100B, UCH-L1, P-tau, and IL-6.

3. The biomarker for detecting traumatic brain injury according to claim 1, characterized in that: The biomarkers include S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6.

4. Use of a substance for detecting a biomarker as claimed in any one of claims 1 to 3 in the preparation of a product for diagnosing traumatic brain injury.

5. A traumatic brain injury detection reagent card, characterized in that: It includes biomarker detection cards; the biomarker detection cards include at least two of the following: S100B detection card, NSE detection card, GFAP detection card, UCH-L1 detection card, P-tau detection card, and IL-6 detection card.

6. The traumatic brain injury detection reagent card as claimed in claim 5, characterized in that: The biomarker detection card comprises a sample pad, a conjugation pad, an analysis membrane and a water absorption pad which are sequentially overlapped on a bottom plate; the conjugation pad is coated with a biomarker antibody coupled to a fluorescent microsphere; the analysis membrane is provided with a detection line; the detection line is marked with a mouse antibody of the biomarker.

7. A traumatic brain injury detection kit, characterized in that: Comprising the traumatic brain injury detection reagent card as described in claim 5 or 6.

8. A traumatic brain injury assessment system, characterized in that: include: An input module, used for inputting the content of biomarkers in a sample; the biomarkers include at least two of S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6; The evaluation module is used to perform traumatic brain injury analysis based on a preset formula and taking the biomarker content as a variable to obtain analysis results.

9. The traumatic brain injury assessment system according to claim 8, characterized in that: The preset formula is: response=β0+β1*P S100-β +β2*P NSE +β3*P GFAP +β4*P UCH-L1 +β5*P P-tau +β6*P IL-6 +e; Among them, ε is the mean square error; β0 is the intercept, β1-β6 are the regression coefficients; P S100-β , P NSE , P GFAP , P UCH-L1 , P P-tau , P IL-6 are the biomarker contents, which are the mass concentrations of S100-β, NSE, GFAP, UCH-L1, P-tau and IL-6 in the test samples respectively; and the response value is the analysis result.

10. A traumatic brain injury assessment device, characterized in that: The evaluation system according to any one of claims 8 to 9 further comprises: A detection device for detecting the content of biomarkers in a sample; The output device is used to output the analysis results.

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