A biomarker for traumatic brain injury detection and uses thereof

Abstract

This invention belongs to the field of molecular biology technology, specifically providing a biomarker for the detection of traumatic brain injury (TBI). The biomarker includes NSE and GFAP, and preferably also includes at least one of S100B, UCH-L1, P-tau, and IL-6. Based on the above biomarkers, reagent cards, kits, and systems for TBI assessment are provided, offering a rapid and non-invasive assessment alternative for TBI and aiding in early intervention. This reduces the risk of radiation exposure for patients and accelerates the diagnostic process, enabling a more comprehensive assessment of the condition. The biomarker, combined with the clinical severity and treatment pathway of TBI, allows for better identification and stratification of mild and severe patients. In specific patient groups requiring hospitalization or at different clinical severities, the biomarker of this invention can provide highly significant and reliable test results.

Description

Technical Field

[0001] This invention belongs to the field of molecular biology technology, specifically relating to a biomarker for detecting traumatic brain injury and its application in diagnosis and assessment. Background Technology

[0002] Traumatic brain injury (TBI) is a serious neurological disorder characterized by rapid progression, high mortality and disability rates, and poor prognosis. It has a high global incidence rate, imposes a significant medical burden, and seriously threatens human life and health. Currently, the diagnosis of TBI mainly relies on imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI).

[0003] The severity of traumatic brain injury (TBI) is typically assessed using computed tomography (CT), magnetic resonance imaging (MRI), or the Glasgow Coma Scale (GCS), a scoring system that determines the severity of injury by evaluating the patient's level of consciousness. According to the GCS scoring criteria, patients with a GCS score of 13-15 are defined as having mild TBI, 9-12 as moderate TBI, and 3-8 as severe TBI. However, for most patients with mild and moderate TBI, even if the score indicates damage, traditional imaging techniques such as CT or MRI often fail to detect significant abnormalities. For the detection of mild and moderate TBI, diffusion tensor imaging (DTI), as a novel brain structural imaging technique, uses computer-aided tracing of axonal networks to construct a 3D model of the brain and is considered one of the more promising detection techniques. However, existing research shows that DTI results contradict the clinical presentation of acute mild and moderate TBI, limiting its widespread application.

[0004] Due to the limitations and delays in imaging diagnosis, many patients with mild traumatic brain injury (mTBI) may miss the optimal treatment window. In contrast, biomarkers in body fluids can reflect the occurrence, extent, location, and severity of brain injury in a timely and dynamic manner, while also indicating clinical prognosis and responsiveness to treatment. Biomarker testing has the advantages of being minimally invasive, simple to operate, and highly sensitive, making it particularly suitable for the early diagnosis of mTBI. However, effectively identifying patients with mTBI and determining whether they require further treatment remains a major challenge in the medical field. Summary of the Invention

[0005] This invention aims to provide an efficient and accurate biomarker for the detection of traumatic brain injury (TBI) and its application, addressing the inaccuracies and lags of existing diagnostic methods in detecting moderate and mild brain injuries, thereby providing a more effective tool for the early diagnosis, severity assessment, treatment selection, and prognosis prediction of TBI.

[0006] The core of this invention lies in identifying one or more biomarkers in bodily fluids that can reflect the occurrence, severity, and pathological changes of TBI in a timely and accurate manner, especially in mild to moderate brain injury, providing earlier diagnostic evidence than traditional imaging examinations. These biomarkers are mainly present in blood, cerebrospinal fluid, or other bodily fluids and can be detected through non-invasive or minimally invasive methods.

[0007] Therefore, the present invention provides a combination of biomarkers for the detection of 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 aforementioned biomarkers also include at least one of S100B, UCH-L1, P-tau, and IL-6.

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

[0010] Furthermore, this invention also provides a traumatic brain injury detection kit, which includes a biomarker detection card; the biomarker detection card includes 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. It can simultaneously or separately detect biomarkers such as S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6.

[0011] Specifically, the aforementioned biomarker detection card includes a sample pad, a conjugate pad, an analytical membrane, and an absorbent pad sequentially stacked on a base plate; the conjugate pad is coated with a corresponding biomarker antibody coupled with fluorescent microspheres, and the analytical membrane is provided with a detection line, which is drawn by a mouse antibody of the biomarker.

[0012] Specifically, the analytical membrane also has a reference line, which is drawn with goat anti-mouse IgG.

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

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

[0015] The input module is used to input the content of biomarkers in the 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, using the content of the biomarkers as a variable, and obtain the analysis results.

[0017] Specifically, the above-mentioned 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] Where 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 The biomarker content refers to the mass concentrations of S100-β, NSE, GFAP, UCH-L1, P-tau, and IL-6 in the sample, respectively; the response value is the analytical result.

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

[0020] The detection device is used to detect the content of biomarkers in a sample;

[0021] Output device, used to output analysis results.

[0022] Specifically, the detection device can be the aforementioned traumatic brain injury detection reagent card, traumatic brain injury detection kit, or other devices capable of detecting corresponding biomarkers.

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

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

[0025] 2. By combining specific biomarkers such as NSE and GFAP, this invention demonstrates extremely high prognostic value in predicting patient mortality and adverse outcomes, providing crucial reference for disease stratification and personalized treatment. Furthermore, the combination of biomarkers such as S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6 significantly enhances the versatility of the diagnostic tool, ensuring high sensitivity across different GCS scores, particularly achieving a sensitivity of 97.9% at GCS15.

[0026] 3. The traumatic brain injury (TBI) diagnostic system of this invention can finely stratify the severity of a patient's condition through biomarker detection results, helping healthcare providers to allocate medical resources more effectively, improve treatment outcomes, and enhance patients' quality of life. This system is of great significance in the diagnosis and management of TBI and can significantly improve treatment success rates.

[0027] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0028] Figure 1 This is a flowchart of the patient screening process for the TBI study in an embodiment of the present invention.

[0029] Figure 2A-2N Different levels of medical services in the embodiments of the present invention ( Figure 2A-2G ) and clinical severity ( Figure 2H-2N The expression levels of each biomarker.

[0030] Figure 3 This is the incremental C-statistic for predicting CT positive results using various biomarkers in the embodiments of the present invention.

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

[0032] Figure 5A-5G This is a graph showing the correlation between biomarker levels and hospitalization levels in an embodiment of the present invention.

[0033] Figure 6A-6G This is a graph showing the correlation between biomarker levels and damage severity in an embodiment of the present invention.

[0034] Figure 7 This is a flowchart illustrating the use of the traumatic brain injury assessment device provided in this embodiment of the invention. Detailed Implementation

[0035] The technical solutions of the present invention will be described in detail below with reference to embodiments, so that those skilled in the art 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, and not all embodiments. Based on this, those skilled in the art can make various modifications and adjustments without departing from the core idea of ​​the present invention, and these modifications and adjustments should be considered to fall within the scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims and their equivalent technical features, and should not be limited to these specific embodiments.

[0036] The following specific embodiments will detail and validate the biomarkers for the detection of traumatic brain injury (TBI) provided by this invention and their application effects. The experimental and analytical results of these embodiments further demonstrate the effectiveness of this invention in the early diagnosis, prognostic assessment, and monitoring of treatment efficacy for TBI.

[0037] Example 1:

[0038] This embodiment included individuals aged 19 years and older who underwent blood sampling and CT scans within 24 hours of head injury. Outcomes were assessed at 6 months using the Glasgow Outcome Scale-Extended (GOSE). The biomarker cohort process is as follows: Figure 1 As shown, the initial screening and inclusion process for the TBI study involved 3,764 patients. Firstly, 475 patients were excluded, including 182 whose blood samples were collected more than 24 hours prior and 293 who did not undergo CT scans. Ultimately, 3,289 patients were included. Of these, 657 patients presented to the emergency department (242 were CT positive); 822 patients were hospitalized (312 were CT positive); and 1,810 patients received treatment in the ICU (1,050 were CT positive).

[0039] The clinical characteristics of the study cohort, categorized hierarchically, are summarized in Table 1, which also lists the frequency 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 details the basic characteristics of patients in different medical settings (emergency department, admission, ICU), including age, sex, Glasgow Coma Scale (GCS) score, pupillary response, CT positivity rate, subarachnoid hemorrhage status, serum marker levels within 24 hours (S100B, NSE, GFAP, UCH-L1, P-tau, IL-6), blood draw time, and prognostic outcomes (GOSE score) at 6 months. At 6 months, 340 out of 3289 patients (10.3%) died, 1034 had poor outcomes (58.3%), and 1915 (31.4%) had incomplete recovery.

[0045] In these experiments, this invention developed a combination of six biomarkers (S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6), which demonstrates significant practicality in clinical settings. The results show that each member provides a unique diagnostic perspective, and combining them enables more accurate predictions. These members meet certain criteria: first, they must possess predictive ability, distinguishing different disease types to a certain extent; second, their measurement methods are simple, easy to perform, and highly reproducible; and finally, they play an important role in biological network analysis, participating in the regulation of key biological processes.

[0046] Based on a combination of biomarkers, this invention provides a traumatic brain injury assessment system for acquiring and analyzing data, comprising:

[0047] The input module is used to input the content of biomarkers in the 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, using the content of the biomarkers as a variable, and obtain the 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] Where ε is the mean square error, β0 is the intercept, and β1, β2, β3, β4, β5, and β6 are regression coefficients obtained by performing multiple regression analysis on a certain number of damage samples; P S100-β P NSE P GFAP P UCH-L1P P-tau P IL-6 The values ​​represent the biomarker concentrations, specifically the mass concentrations of S100-β, NSE, GFAP, UCH-L1, P-tau, and IL-6 in the sample. The response value represents the obtained analytical result.

[0051] In one example, 1000 samples were randomly selected from the aforementioned 3289 samples, and a multiple 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 biomarker were calculated and standardized. Multiple regression analysis was performed using statistical software R to obtain estimated values ​​of the regression coefficients. Based on the estimated values ​​of the regression coefficients, the influence of different biomarker concentrations on the severity of traumatic brain injury was explained, and the regression coefficients composed of the six biomarkers in the example were finally 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 example above, 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) A response value of 0-20 indicates no obvious damage or mild brain damage;

[0057] GCS score: Commonly seen in GCS15 (Mild); CT results: Mostly negative; GOSE prognosis: GOSE 7-8.

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

[0059] GCS score: Commonly seen in GCS 13-14; CT results: May be CT 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 injury.

[0061] GCS score: Commonly seen in GCS 9-12; CT results: Mostly positive on CT, showing brain tissue damage; GOSE prognosis: GOSE 4-5.

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

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

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

[0065] GCS score: Commonly seen in GCS 3-8

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

[0067] A response value below 20 generally rules out severe brain injury and indicates a good prognosis; a response value between 20 and 60 suggests a mild to moderate risk of injury, requiring further observation and management; a response value above 60 indicates severe brain injury requiring immediate intervention, usually accompanied by a positive CT scan; a response value above 80 indicates an extremely high risk, often a severe or critical condition requiring ICU monitoring. The response values ​​output by this model can be used for clinical reference. By rapidly obtaining blood biomarker response values ​​and matching them with the above-mentioned ranges, the degree of brain injury can be preliminarily assessed, and personalized treatment and prognosis plans can be developed.

[0068] R 2 The coefficient of determination (RCD) measures a regression model's ability to explain data variability; it represents the proportion of total variance explained by the model relative to the total variance. Using an internal model as an example, the RCD of the above model in the training queue... 2 The value is as high as 0.82.

[0069] When building the system, the regression coefficients can be adjusted based on the actual sample data used to obtain the corresponding regression coefficients, intercepts, and mean squared error data.

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

[0071] The detection device is used to detect the content of biomarkers in a sample;

[0072] Output device, used to output analysis results.

[0073] Reference Figure 7 When in use, the detection device acquires the content of each biomarker in the blood sample to be tested. Then, the input module of the evaluation system inputs the content of the biomarkers in the sample. The evaluation module performs traumatic brain injury analysis based on a preset formula, using the biomarker content as a variable, and obtains the analysis results. The output device outputs the analysis results for subsequent management of the patient's stratified treatment plan.

[0074] Example 2:

[0075] In statistical analysis, validating the quality of a linear regression model is a crucial step in ensuring its applicability and predictive accuracy. To ensure the model maintains high accuracy and sensitivity on unknown or new datasets, its stability and reliability were validated using the remaining 2289 cases from Example 1. This process aims to avoid overfitting issues where the model performs well only on specific datasets, ensuring that the model works effectively and impartially in real-world applications, especially in critical decision-making scenarios such as healthcare and wellness services. This increases confidence in the model's performance and supports broader clinical and practical applications.

[0076] Further analysis revealed a significant positive correlation between biomarker levels and the intensity of care and the severity of traumatic brain injury (TBI). High levels of biomarkers were strongly associated with adverse outcomes, and this association was more pronounced across different levels of care and severity of injury. Compared with NSE monitoring alone, combined monitoring of UCH-L1, IL-6, S100B, P-tau, and GFAP was more accurate in predicting adverse outcomes. The intensity of care increased with increasing biomarker levels, and the increase in biomarker levels was greater in patients with moderate to severe TBI (GCS 3-12) than in patients with mild concussion (GCS 13-15). Figure 4A-4G , Figure 5A-5G , Figure 6A-6G This trend was demonstrated, showing a significant increase in biomarkers such as S100B, NSE, and GFAP in moderate to severe traumatic brain injury. This positive correlation indicates that biomarkers can not only be used to predict adverse outcomes but also serve as important indicators for assessing injury severity and care needs. Combined detection of multiple biomarkers (UCH-L1, IL-6, S100B, P-tau, GFAP) showed higher sensitivity in predicting the prognosis of moderate to severe traumatic brain injury, outperforming single biomarkers.

[0077] All biomarker results were analyzed according to clinical severity, different strata, and CT examination results (CT+ and CT-). Descriptive statistics are presented as mean (standard deviation), median (interquartile range), or frequency. Differences in biomarker values ​​across different strata (emergency department, admission, ICU) and injury severity were compared using an independent samples t-test. Results are as follows: Figure 2A-2NAs shown, the levels of the six biomarkers detected in combination (S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6) remained consistent across groups, and the levels of biomarkers in CT+ patients were significantly higher than those in CT- patients. The combined detection of multiple biomarkers is significantly superior to 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 predicted increment and adjusted by optimism correction. Subgroup analyses of different care pathways and severity of injury were also conducted to further validate the incremental value of biomarkers in distinguishing between high and low risk. Figure 3 The incremental C-statistic of biomarkers in predicting positive CT results is presented. The combined detection effect is most significant when multiple biomarkers are combined into a six-molecule module. Compared with single serum biomarkers, combined detection of biomarkers significantly improves discriminative ability, demonstrating the greatest incremental value. Biomarker levels are positively correlated with nursing intensity and the severity of traumatic brain injury, indicating that biomarkers can not only predict adverse outcomes but also be used to assess the severity of injury and corresponding nursing needs.

[0079] Table 2 further presents the biomarker values ​​for predicting 6-month prognosis at different injury severity levels (GCS 3-15). The ability of GFAP and NSE on day 1 to discriminate mortality, adverse outcomes, and incomplete recovery within 6 months post-injury was assessed using ROC curves. Combined with the analysis results of different GCS subgroups (GCS 3-12 and GCS 13-15), the results show that GFAP and NSE have high predictive ability in these situations. During external validation, the combined detection values ​​showed the highest sensitivity across all GCS scores, especially approaching 98% at GCS 15. This demonstrates the powerful effect of combined multi-marker detection. The combined detection of multiple markers showed better overall sensitivity, particularly in high GCS score scenarios, 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 subgroups with different injury severity.

[0081]

[0082] To validate the model's broad applicability, a linear regression model was applied to this dataset, focusing on the model's sensitivity—its ability to correctly identify positive cases—to assess its overall performance and application value. External validation results showed that the combined detection value had the highest sensitivity, especially in patients with GCS15, where the sensitivity reached 97.9%, indicating a significant advantage of multi-biomarker combined detection. Table 3 shows that the combined detection was superior to single biomarkers in sensitivity across all GCS scores, particularly GFAP and S100-β at 91.2% and 92.6% respectively at GCS15, while P-Tau remained stable across all scores, with a sensitivity consistently above 73.5%. Furthermore, this invention combines biomarkers with clinical severity (e.g., GCS scores) and care pathways, making it particularly suitable for hospitalized patient populations. In different clinical severity groups (e.g., GCS 3-8, 9-12, 13-14), this labeling method provides significant and reliable test results and achieves accurate detection with fewer molecular markers, offering both technical advantages and economic benefits. This combined testing strategy significantly improves testing efficiency and accuracy, and helps to better guide clinical decision-making.

[0083] Table 3 Sensitivity of Biomarker Combinations as Alternatives to Positive CT Imaging Results

[0084]

[0085] Table 4 compares the sensitivity of different combinations of biomarker detection in prognostic outcomes of incomplete recovery, adverse outcomes, and mortality. The combined detection values ​​showed the best sensitivity across all three prognostic levels, while the NSE+GFAP and S100-β+P-tau combinations showed some effectiveness at specific prognostic levels, but their overall sensitivity was slightly lower. In conclusion, the combined use of biomarkers with clinical severity (e.g., GCS score) and care pathways can significantly improve the prognostic prediction of patients with traumatic brain injury. Compared to single biomarkers, combined detection significantly improves sensitivity and accuracy, especially at high GCS scores. Therefore, it is recommended to prioritize a multi-biomarker detection strategy to more accurately assess patient prognosis and develop personalized treatment plans. This finding not only aids clinical decision-making but also reduces the waste of medical resources and provides patients with more timely and accurate diagnoses.

[0086] Table 4. Sensitivity of Biomarker Combinations in Predicting Prognostic Grades 6 Months Later

[0087]

[0088] This embodiment summarizes the significant advantages of combined biomarker testing in predicting the prognosis of traumatic brain injury. External validation results show that combined testing of multiple biomarkers, including UCH-L1, IL-6, S100B, P-tau, and GFAP, more accurately predicts adverse outcomes compared to single biomarkers, and is significantly positively correlated with nursing intensity and injury severity. Combined testing exhibits high sensitivity across different Glasgow Coma Scale (GCS) scores, reaching 97.9% at GCS15. Compared to single biomarker testing, GFAP, S100-β, and P-Tau show particularly strong performance at high GCS scores, further validating the reliability and accuracy of combined testing in prognostic assessment. Combined biomarker testing not only helps predict patient prognosis more accurately but also optimizes resource allocation and reduces unnecessary medical testing and costs. In practical applications, it is recommended to prioritize combined testing strategies to improve the scientific rigor and efficiency of clinical decision-making and maximize patient care outcomes.

[0089] Example 3:

[0090] This embodiment provides a traumatic brain injury detection kit, comprising a casing and six test cards for detecting S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6, respectively; the six test cards are arranged separately within the casing. The six test cards have similar structures, each including a sample pad, a conjugate pad, an analytical membrane, and an absorbent pad sequentially overlapped on a base plate. The sample pad is a blood filtration pad, the conjugate pad is a gold-sprayed pad, and the analytical membrane is a nitrocellulose membrane.

[0091] A gold-sprayed pad is used to coat a fluorescent microsphere complex solution. The fluorescent microsphere complex solution includes fluorescent microspheres and biomarker antibodies conjugated to the fluorescent microspheres. The biomarker antibody conjugated to the fluorescent microspheres on the S100B test card is the S100B antibody; on the NSE test card, it is the NSE antibody; on the GFAP test card, it is the GFAP antibody; on the UCH-L1 test card, it is the UCH-L1 antibody; on the P-tau test card, it is the P-tau antibody; and on the IL-6 test card, it is the IL-6 antibody.

[0092] The analytical membrane has a detection line and a reference line. The detection line is drawn with a mouse antibody containing a biomarker, and the reference line is drawn with goat anti-mouse IgG.

[0093] The kits and ELISA provided in this embodiment were used to detect samples from the same source, and the results are shown in Table 5.

[0094] Table 5 compares the detection results using ELISA and fluorescence immunoassay kits.

[0095] detection indicators ELISA The reagent kit in this embodiment 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 this invention can quickly and easily detect biomarkers S100-β, NSE, GFAP, UCH-L1, P-tau, and IL-6, with a detection time of 5-10 minutes, and can provide results for 6 indicators at once. As shown in Table 5, the difference between the detection results and the ELISA results is within 10%, indicating that the test results are reliable and can be used as a rapid detection device for TBI assessment.

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

Claims

1. A combination of blood biomarkers for detecting traumatic brain injury, characterized in that: The blood biomarker combination consists of S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6.

2. The use of the combination of blood biomarkers as described in claim 1 in the preparation of diagnostic products for traumatic brain injury.

3. A test kit for detecting traumatic brain injury, characterized in that: It includes a blood biomarker detection card; the blood biomarker detection card consists of an S100B detection card, an NSE detection card, a GFAP detection card, a UCH-L1 detection card, a P-tau detection card, and an IL-6 detection card.

4. The traumatic brain injury detection reagent card as described in claim 3, characterized in that: The blood biomarker detection card includes a sample pad, a conjugate pad, an analytical membrane, and an absorbent pad sequentially overlapped on a base plate; the conjugate pad is coated with blood biomarker antibodies coupled to fluorescent microspheres; the analytical membrane is provided with a detection line; the detection line is drawn with a mouse antibody of the blood biomarker.

5. A traumatic brain injury detection kit, characterized in that: Includes the traumatic brain injury detection reagent card as described in claim 3 or 4.

6. A traumatic brain injury assessment system, characterized in that, include: The input module is used to input the levels of biomarkers in the blood sample; the biomarkers consist of S100B, NSE, GFAP, UCH-L1, P-tau, and IL-6. The assessment module is used to analyze traumatic brain injury based on a preset formula, using biomarker content as a variable, and obtain the analysis results.

7. The traumatic brain injury assessment system as described in claim 6, 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; Where 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 The biomarker content refers to the mass concentrations of S100-β, NSE, GFAP, UCH-L1, P-tau, and IL-6 in the sample, respectively; the response value is the analytical result.

8. A traumatic brain injury assessment device, characterized in that: The evaluation system comprising any one of claims 6-7 further comprises: The detection device is used to detect the content of biomarkers in a sample; Output device, used to output analysis results.

Images