Biomarker for predicting onset risk of aortic dissection, kit and application

Abstract

The invention discloses a biomarker for predicting the onset risk of aortic dissection, a kit and application. The invention provides application of a reagent for detecting the SERPINE1 level of peripheral blood in preparation of products for aortic dissection attack risk prediction, early screening, diagnosis or curative effect monitoring. The marker and the related detection kit can be used for predicting the occurrence risk of the aortic dissection, and are not limited to diagnosis of the occurred dissection; the method is suitable for screening and dynamic follow-up visit of high-risk groups; the operation is simple and convenient, and a detection sample can be obtained through peripheral blood; the aortic wall pathological remodeling and fibrinolytic / matrix stability change can be reflected. Clinical sample verification shows that the method for predicting the onset risk of the aortic dissection through the SERPINE1 level of the peripheral blood has relatively high sensitivity and specificity, and relatively high clinical application value and application prospect are shown.

Description

Technical Field

[0001] This application relates to a biomarker, reagent kit, and application for predicting the risk of aortic dissection, and belongs to the field of biomedical detection technology. Background Technology

[0002] Aortic dissection (AD) is a critical emergency caused by the rupture of the aortic wall's intima under multiple stimuli, leading to the separation of the true and false lumens and the infiltration of blood into the media. It is characterized by rapid onset, rapid progression, and high mortality. Currently, the identification and risk assessment of AD primarily rely on imaging examinations combined with clinical symptom evaluation. Existing technologies mainly include the following categories: i. Imaging examination methods (mainstream diagnosis) a. CT angiography (CTA) CTA is currently the clinically recognized gold standard for diagnosing aortic dissection. It obtains three-dimensional images of the aorta by injecting contrast agent to observe the true and false lumen structures, intimal tear, and the extent of the dissection. Its disadvantages include: it can only be used for patients who have already experienced dissection or are highly suspected of having it, and it cannot predict the risk of developing the disease; it requires the use of iodine contrast agents, posing a risk of nephrotoxicity; it involves ionizing radiation, making it unsuitable for repeated screening or long-term follow-up of high-risk groups; and it is costly and highly dependent on equipment, making it difficult to use in primary healthcare or large-scale population screening.

[0003] b. Transesophageal echocardiography (TEE) and magnetic resonance angiography (MRA) TEE and MRA, as supplementary methods to CTA, can be used in certain special populations. The disadvantages are: TEE is invasive, has poor patient compliance, and is only suitable for acute and critical illnesses; MRA is time-consuming, expensive, and has poor applicability in emergency settings; similarly, it can only detect existing structural lesions and cannot reflect the molecular or biological risk status before dissection occurs.

[0004] ii. Clinical scoring and symptom assessment methods Clinically, the Aortic Dissection Risk Scale (ADD-RS) is commonly used, which stratifies risk based on patient history, high-risk pain characteristics, and physical signs. Its drawback is that the scoring is highly dependent on physician experience and is inherently subjective. It is only applicable for differential diagnosis in acute cases; it cannot be used for risk prediction in asymptomatic or subclinical populations.

[0005] iii. Laboratory testing methods (non-specific indicators) Currently, clinically available laboratory indicators mainly include D-dimer and inflammatory factors. D-dimer levels are elevated in acute aortic dissection and are primarily used for emergency exclusion diagnosis. However, its drawbacks include: poor specificity, as it can be elevated in various diseases such as infection, tumors, and thrombosis; it only has some indicative value for acute dissection; and it cannot reflect long-term aortic wall remodeling, chronic injury, or the risk of future dissection. Existing studies have also attempted to assess the dissection status using indicators such as CRP, IL-6, and MMPs, but the results are unstable, and a mature predictive method has not yet been developed. Common limitations include: a lack of direct correlation with aortic wall pathological mechanisms; limited repeatability and predictive value; and it has not yet been used for clinical risk stratification or early warning.

[0006] In summary, diagnostic techniques for aortic dissection have at least the following shortcomings: diagnosis heavily relies on imaging, making it a post-hoc identification rather than a pre-hoc prediction; there is a lack of biomarkers that can be detected through peripheral blood and are suitable for repeated monitoring; existing laboratory indicators are mostly non-specific inflammatory or coagulation indicators, which cannot reflect the essential process of aortic wall structural instability; and they cannot meet the long-term, dynamic risk assessment needs of high-risk groups (such as those with aortic aneurysms, hereditary connective tissue diseases, and hypertension). Summary of the Invention

[0007] The purpose of this application is to solve the above-mentioned technical problems by providing a new biomarker, detection reagent and method for predicting the risk of aortic dissection. The biomarker and related detection kits of this application can be used to predict the risk of aortic dissection, rather than being limited to diagnosing dissection that has already occurred. It is suitable for screening and dynamic follow-up of high-risk groups. It is easy to operate and can obtain test samples through peripheral blood. It can reflect the pathological remodeling of the aortic wall and changes in fibrinolysis / matrix stability.

[0008] To achieve the above objectives, this application adopts the following technical solution: This application provides the use of reagents for detecting peripheral blood SERPINE1 levels in the preparation of products for predicting the risk of aortic dissection, early screening, diagnosis, or monitoring treatment efficacy.

[0009] In some embodiments, the detection reagent is a reagent selected from one or more detection techniques or methods chosen from the group consisting of: enzyme-linked immunosorbent assay (ELISA), immunofluorescence assay, radioimmunoassay, immunoprecipitation assay, Western blotting, high performance liquid chromatography (HPLC), capillary gel electrophoresis, near-infrared spectroscopy, mass spectrometry, immunochemiluminescence assay, colloidal gold immunochromatography, fluorescence immunochromatography, surface plasmon resonance (SPR), immuno-PCR, or biotin-avidin assay. In some embodiments, the product includes at least one of reagents, kits, test strips, and chips.

[0010] In some embodiments, the product is a diagnostic device, which includes a sample collection device, a sample detection device, and a diagnostic device; wherein: The sample collection device is configured to collect peripheral blood samples from the subject; The sample detection device is a device capable of detecting the level of SERPINE1 in the peripheral blood sample. The diagnostic device includes a data acquisition module and a diagnostic module. The data acquisition module is configured to acquire data detected by the sample detection device, and the diagnostic module is configured to determine whether the subject has aortic dissection based on the data acquired by the data acquisition module.

[0011] Compared with the prior art, this application has the following beneficial effects: 1) No complex instruments required: The test can be completed with only standard ELISA equipment or other existing testing equipment. It does not rely on large imaging equipment and can be used in primary hospitals and screening scenarios. 2) Reduce the use of imaging resources: By combining the SERPINE1 marker to predict the occurrence of aortic dissection, unnecessary CTA examinations can be reduced by 20-35%, thus reducing radiation exposure and cost burden; 3) When combined with existing aortic dissection risk prediction models, it can improve clinical identification efficiency and significantly enhance prediction capabilities; 4) Achieve early warning: The reagents or kits in this application can identify and screen individuals with abnormally elevated SERPINE1 levels at an early stage, and can be used for risk screening before imaging examinations, significantly increasing the possibility of early identification. Attached Figure Description

[0012] Figure 1 Tissue staining shows the differences between aortic dissection and normal aortic wall. Masson staining; EVG staining; MMP-2 staining; MMP-9 staining; Control staining: aortic wall tissue from normal individuals; AD staining: aortic wall tissue from patients with aortic dissection.

[0013] Figure 2 Spatial transcriptome sequencing revealed that inflammatory stromal cells (SMCs) (InflammSMCs) accumulated near the aortic dissection site in aortic dissection tissue.

[0014] Figure 3 Serum SERPINE1 levels were measured by ELISA in normal individuals (N) and patients with aortic dissection (AD).

[0015] Figure 4Single-cell sequencing analysis of SERPINE1 levels in different SMC subtypes (N: normal individuals; patients with aortic dissection).

[0016] Figure 5 Spatial transcriptome sequencing analysis of SERPINE1 levels in aortic dissection tissues of patients with aortic dissection.

[0017] Figure 6 ROC curve of the SERPINE1 risk prediction model based on ELISA test results. Detailed Implementation

[0018] To make the technical solution of this application clearer and easier to understand, preferred embodiments are described in detail below with reference to the accompanying drawings.

[0019] Unless otherwise specified, the materials and reagents used in the following examples are commercially available.

[0020] Example 1 Numerous basic and clinical studies have shown that aortic dissection is not simply caused by hemodynamic abnormalities, but rather is a disease process centered on the instability of the aortic media structure. Key pathological changes include a decrease in the number and phenotypic abnormalities of vascular smooth muscle cells (SMCs) in the aortic media, a transformation of SMCs from a contractile phenotype to a synthetic / inflammatory phenotype, accompanied by increased extracellular matrix degradation capacity; increased monocyte / macrophage infiltration and elevated levels of inflammatory factors; and an imbalance in the regulation between the fibrinolytic system, matrix remodeling, and inflammatory response, ultimately leading to a decrease in the aortic wall's resistance to tearing.

[0021] These changes are often difficult to identify in the early stages on imaging, but significant abnormalities have already occurred at the molecular and cellular levels. Surgical specimens from inpatients at Zhongshan Hospital affiliated with Fudan University were collected from the arterial wall tissues of one patient with aortic dissection and one without. These tissues were stained with Masson's stain, elastic fiber-Van Gesson stain, metalloproteinase-2 stain, and metalloproteinase-9 stain, respectively. The aorta of the patient with dissection showed abnormal collagen deposition, elastic fiber rupture, and an abnormally increased matrix metalloproteinase. Figure 1 These changes laid the groundwork for the development of interlayers.

[0022] Single-cell transcriptome sequencing was performed on the aortic walls of 5 patients with aortic dissection and 5 healthy individuals. The specific steps are as follows: 1) Sample preparation and library construction After obtaining the target tissue sample, a single-cell suspension was prepared using a tissue dissociation kit (GentleMACS™). After filtration through a 70-μm filter, erythrocyte lysis, and dead cell removal (optional), the cells were resuspended and counted using DPBS containing 0.04% BSA. The cell concentration was precisely adjusted to 800 - 1200 live cells / μL. Subsequently, strictly following the instructions of the 10x Genomics Chromium Next GEM Single Cell 3' kit (v3.1), single-cell sorting, droplet encapsulation, in situ lysis, mRNA capture, and barcode labeling were performed using a Chromium Controller and Next GEM Chip G. The reverse transcription reaction was completed in a thermal cycler (53°C, 45 minutes). The generated barcoded cDNA was purified and amplified using 12 cycles of PCR, and finally a sequencing library with dual-index was constructed. After the library passed the quality control, sequencing was performed on the Illumina NovaSeq 6000 platform, with the sequencing read length set as Read1: 28 bp, i7 / i5 Index: 10 bp, Read2: 90 bp, and the target sequencing depth was 50,000 effective reads per cell.

[0023] 2) Bioinformatics data processing and analysis The data downloaded from the sequencer was processed and analyzed through the following procedures: Data quality control and expression matrix generation: The raw sequencing data (FASTQ files) was aligned to the reference genome (GRCh38 / hg38) using Cell Ranger software (v7.1.0). Valid cell barcodes were identified through the built-in algorithm, and the number of unique molecular identifiers (UMIs) for each gene in each cell was counted to generate the raw gene expression count matrix. At the same time, quality control metrics were output, including the number of detected genes (nGene) per cell, the total number of UMIs (nUMI), and the proportion of mitochondrial genes (percent.mt).

[0024] Cell screening and data preprocessing: The expression matrix was imported into the R language environment (v4.2.0), and the Seurat package (v5.0.0) was used for subsequent analysis. First, low-quality cells and doublets were filtered based on the QC metrics, and the retention criteria were: 200 < nGene < 6000, percent.mt < 15%, and nUMI within a reasonable range of the dataset distribution (usually within three standard deviations of the outliers). Subsequently, the data was normalized (using the LogNormalize method with a scaling factor of 10,000) and logarithmically transformed. To eliminate the technical variation caused by mitochondrial gene content and sequencing depth, the ScaleData function was used to perform a linear regression correction on the expression values.

[0025] Feature selection, dimensionality reduction, and clustering: Genes exhibiting high intercellular variability in the dataset were identified (FindVariableFeatures, selecting the top 2000 highly variable genes). The expression data for these genes were centered and scaled, and principal component analysis (PCA) was performed. Based on the elbow plot and Jack Straw test results from the PCA, the top 15 principal components were selected for downstream analysis. Based on these principal components, a K-nearest neighbor graph (FindNeighbors) was constructed for the cells, and unsupervised clustering of the cells was performed using the Louvain community detection algorithm (FindClusters, with a resolution parameter set to 0.8).

[0026] Cell subpopulation annotation and visualization: High-dimensional data is projected onto a two-dimensional space using a non-linear dimensionality reduction method (UMAP) for visualization, intuitively displaying the clustering results. The FindAllMarkers function (default Wilcoxon rank-sum test) is used to find the specific highly expressed genes (marker genes) of each cell cluster compared to all other clusters. Combining known cell type marker gene databases and biological background, each cell cluster is annotated to identify its cell type or state.

[0027] Differential expression and function analysis: Depending on the research objective, differentially expressed genes were compared between specific cell clusters or experimental groups using the FindMarkers function in Seurat. Significantly differentially expressed genes were selected based on the following criteria: mean logarithmic fold change > 0.25, and a Bonferroni-corrected p-value < 0.05.

[0028] Next, spatial transcriptome sequencing was performed on the aortic wall of the patient with aortic dissection. The specific steps are as follows: 1) Organizing sample processing and library construction OCT-embedded target tissue samples were obtained, and whole tissue sections with a thickness of 10 μm were prepared using a cryostat at -20°C. These sections were then attached to the capture region of a Visium Spatial Gene Expression Slide (10x Genomics) modified with a specific oligonucleotide probe array. After methanol fixation, hematoxylin and eosin (H&E) staining, and imaging, the sections underwent tissue permeation. During permeation, the released intracellular mRNA bound to poly-dT capture probes on the slide carrying spatial barcodes and unique molecular identifiers (UMIs). Subsequently, in situ reverse transcription was performed on the slide to synthesize cDNA with spatial location information. The cDNA was recovered from the slide, purified, and then used for second-strand synthesis. Using this double-stranded cDNA as a template, PCR was performed for 12 cycles, ultimately constructing a sequencing library with a sample index. After the library passed quality control, sequencing was performed on the Illumina NovaSeq 6000 platform. The sequencing read lengths were set as follows: Read1: 28 bp (reading spatial barcodes and UMI), i7Index: 8 bp, i7 Index: 8 bp, Read2: 91 bp (reading cDNA sequences), and the target sequencing depth was 50,000 effective reads per capture point (Spot).

[0029] Bioinformatics Data Processing and Spatial Integration Analysis 2) The offline data is processed and analyzed through the following process: Spatial Expression Matrix Generation and Alignment: Space Ranger software (v2.0.0, 10x Genomics) was used to integrate and analyze the raw sequencing data (FASTQ file) with H&E images of tissue sections and capture region coordinates on the slides (Loupe Browser format). The software aligned the sequencing data to the reference genome (GRCh38 / hg38) and assigned the UMI counting matrix to each capture point on the slide (55 μm in diameter, 100 μm center-to-center distance) based on spatial barcodes, generating the raw gene-spatial coordinate expression matrix. A spatial distribution map of the tissue sections on the capture regions was also output.

[0030] Data quality control, standardization, and preprocessing: The expression matrix and spatial coordinate information output by the Space Ranger were imported into the Python language environment (v3.9.25). The output data was read using scanpy (v1.10.0), and an AnnData object was constructed. Based on the AnnData object, quality control metrics (total count, number of detected genes, and mitochondrial gene proportion for each point) were calculated, and quantile thresholds were used for filtering (retaining points with a total count > 1000 and a gene count between 200 and 6000). The counts for each point were standardized to the same total using the sc.pp.normalize_total function, followed by a logarithmic transformation using sc.pp.log1p. To eliminate technical noise, the influence of mitochondrial gene proportion and total count was regressed using sc.pp.regress_out, and zero-mean unit variance scaling was performed using sc.pp.scale.

[0031] Cell type spatial deconvolution and colocalization analysis: Combining single-cell RNA sequencing data, the cell type deconvolution tool cell2location was used to estimate the proportional distribution of various cell types within each spatial capture point. The deconvolution results were visualized to spatial coordinates to accurately depict the spatial localization, enrichment regions, and adjacency relationships of different cell types in tissue structures. The results showed that inflammatory SMCs aggregated around the dissection incision (…). Figure 2 ).

[0032] Based on these findings, cellular and molecular biomarkers could serve as a key potential entry point for predicting the risk of aortic dissection.

[0033] ii. Abnormally elevated SERPINE1 levels in aortic dissection tissue SERPINE1 is consistently highly expressed during aortic dissection and its early stages, especially as the phenotype of endothelial cells (SMCs) changes from contractile to synthetic / inflammatory, with a synchronous increase in SERPINE1 expression. Inflammatory factors released after inflammatory cell infiltration can further stimulate SMCs and endothelial cells to secrete SERPINE1. These changes can occur before dissection, and we observed a synchronous increase in SERPINE1 levels in the peripheral blood of patients with dissection and its early stages.

[0034] Twenty blood samples were collected from healthy individuals and 20 patients with aortic dissection, respectively. Serum SERPINE1 levels were analyzed using ELISA. The specific steps are as follows: 1) Sample pretreatment and reagent preparation Draw venous blood from the patient into a blood collection tube containing separating gel, centrifuge at 3500 rpm for 15 minutes, collect the supernatant serum and centrifuge at 13000 rpm for 10 minutes, collect the supernatant. The sample can be stored at 4°C on the same day of testing, or stored long-term at -80°C. Before use, centrifuge again at 13000 rpm for 10 minutes and collect the supernatant.

[0035] Before testing, allow serum samples and the standards and controls in the kit to equilibrate at room temperature for 30 minutes. Dilute the serum sample to be tested with pre-cooled sample diluent at a ratio of 1:50. Simultaneously, dilute the concentrated wash buffer to the working concentration with deionized water. Prepare the concentrated biotinylated antibody and the concentrated horseradish peroxidase-labeled streptavidin separately with the corresponding diluents according to the specified ratios. All prepared reagents should be briefly stored at 4°C and used within 1 hour.

[0036] 2) Immune response and signal detection All steps were performed at room temperature (25°C), and the specific procedure is as follows: SERPINE1 antibody was coated onto well plates, patient serum was added, and the plates were incubated at 37°C for 1 hour. The plates were then washed with deionized water 3 times. 5 times, 3 times each time 5 minutes. Add SERPINE1 enzyme-labeled antibody, incubate at 37°C for 30 minutes, and wash the plate with deionized water 3 times. 5 times, 3 times each time 5 minutes. Add developing solution, incubate at 37°C for 15 minutes, add stop solution, and measure absorbance at 450 nm using a microplate reader. A standard curve is plotted using standards for each assay.

[0037] 3) Data collection and quantitative analysis Standard curve fitting and concentration calculation: In the analysis software (GraphPad Prism 9.0), the logarithm of the standard concentration is used as the x-axis, and the corresponding corrected absorbance value (OD450-OD630) is used as the y-axis. A four-parameter logistic curve fitting model is used to fit the standard curve. The coefficient of determination of the fitted curve is required to be greater than 0.99. The software will automatically calculate the diluted concentration of each serum sample based on the fitted standard curve equation, and then multiply it by the dilution factor (50) to obtain the final concentration of human SERPINE1 protein in the sample, in ng / mL.

[0038] Quality control and validity assessment: The absorbance value of the negative control should be lower than the absorbance value of the lowest concentration point of the standard curve. The absorbance value of the test sample should fall within the linear range of the standard curve. For samples outside the range, the dilution ratio should be adjusted and the test repeated. The coefficient of variation between replicates within the same batch should be less than 15%.

[0039] The mean concentration of serum SERPINE1 in healthy control volunteers was (67.6 ± 6.0) ng / ml (median = 66.4 ng / ml, range = 58.4 ng / ml), while the mean concentration of serum SERPINE1 in patients with aortic dissection was (88.3 ± 6.2) ng / ml (median = 90 ng / ml, range = 46.9 ng / ml), which was significantly higher than that in the former group. Figure 3 This reference value was established by this study, and the specific value may vary depending on the testing system and the population.

[0040] The single-cell sequencing results also showed that, in all types of SMCs, the SERPINE1 level in the AD group was higher than that in the N group, and the SERPINE1 level was the highest in inflammatory SMCs. Figure 4 ).

[0041] Spatial transcriptome results showed increased SERPINE1 levels near the interstitial tear. Figure 5 ).

[0042] The SERPINE1 level was quantitatively detected using the ELISA method described above to predict the risk of aortic dissection. ROC curve analysis was used in an external validation cohort to evaluate the predictive performance of SERPINE1 as a biomarker. The AUC was 0.870, the optimal cutoff value was 75.90, corresponding to a sensitivity of 85.00% and a specificity of 85.00%. Figure 6 ).

[0043] Example 2 Based on the analysis results of Example 1, this example provides a kit and method for predicting the risk of aortic dissection based on peripheral blood SERPINE1 levels, and verifies the sensitivity and specificity of predicting AD risk based on peripheral blood SERPINE1 levels. The kit includes the following components (using a 96-well plate as the standard specification): Coating solution and coating antibody (capture antibody): mainly includes anti-human SERPINE1 monoclonal antibody (IgG); concentration range: 1–10 μg / mL, preferably 4–6 μg / mL; coating buffer: 0.05–0.1 mol / L carbonate buffer (pH 9.2–9.8).

[0044] Detection antibody (enzyme-conjugated antibody): anti-human SERPINE1 monoclonal antibody-horseradish peroxidase (HRP) conjugate; working solution concentration: 0.1–1.0 μg / mL, preferably 0.2–0.5 μg / mL.

[0045] Standard: Recombinant human SERPINE1 protein (derived from E. coli or CHO cell expression system); Concentration range: 0, 1, 2.5, 5, 10, 20, 40, 80 ng / mL (can be expanded to pg / mL level according to actual needs).

[0046] Washing solution (20× Concentrated): The main ingredient is Tween-20; dilute 1:20 before use.

[0047] Diluent: Phosphate-buffered saline (PBS) buffer system containing bovine serum albumin (BSA) (pH 7.2–7.6), used for dilution of standards and samples.

[0048] Colorimetric solution: H2O2 and 3,3',5,5'-tetramethylbenzidine (TMB) solution, stored away from light.

[0049] Termination solution: 2 mol / L H2SO4.

[0050] Quality control samples (one tube each for high and low values): SERPINE1 concentration ranges of 10–20 ng / mL and 2–5 ng / mL, respectively.

[0051] Preparation process and operation steps: i. Preparation of the coating plate: Add the capture antibody to the carbonate coating solution at a final concentration of 1–10 μg / mL.

[0052] Add 50–150 μL of coating solution per well to a 96-well plate.

[0053] Place at 4℃ and let stand for 12–24 hours.

[0054] Pour out the coating solution and wash the plate 2–3 times.

[0055] Add blocking solution (containing 1–5% BSA) and block at room temperature for 1–2 h.

[0056] After washing, air dry and store in a sealed container at 2–8℃. Shelf life is 6–12 months.

[0057] Process conditions: Coating temperature: 2–8℃; Encapsulation time: 12–24 h; pH range: 9.0–10.0.

[0058] ii. Preparation of enzyme-labeled antibodies The HRP-NHS enzyme-labeled reagent is used to perform a conjugated reaction with the detection antibody (amine group).

[0059] Incubate for 1–3 h in a reaction system at pH 7.8–8.5.

[0060] Free enzymes are removed by using a gel filtration column (such as Sephadex G-25).

[0061] Aliquot into a preservation solution containing 1% BSA and store at 2–8°C.

[0062] Process conditions: Reaction temperature: 20–28℃; Reaction time: 1–3 h; Storage conditions: Protect from light, 2–8℃.

[0063] iii. Preparation of standards: Recombinant SERPINE1 protein was prepared by lyophilization, and standard points were prepared according to the diluent using a specific ratio. The samples were then aliquoted and packaged. Store at 20℃ The specific steps for using this kit to quantitatively detect the concentration of SERPINE1 protein in human peripheral blood plasma are as follows: 1. Sample Collection and Processing Collect 3-5 mL of peripheral blood from the patient into an EDTA anticoagulant tube, let it stand at room temperature for 30 minutes, then centrifuge at 1500 × g for 15 minutes. Collect the supernatant plasma and freeze it at -80°C until analysis. Thaw at 4°C before testing, avoiding repeated freeze-thaw cycles.

[0064] 2. Testing Procedures 2.1 Reagent Preparation Bring the detection antibodies, standards, and washing solution to room temperature. Dilute the washing solution with distilled water at a ratio of 1:9 for later use.

[0065] 2.2 Sample addition Standard wells: Add 100 μL of different concentrations of standard (0-200 ng / mL) to each well.

[0066] Sample wells: Dilute the plasma sample 10 times with sample diluent and add 100 μL to each well.

[0067] Each sample was prepared in two replicates, and a blank control well was prepared (only sample diluent was added).

[0068] Gently shake the microplate to mix, and incubate at 37°C for 90 minutes.

[0069] 2.3 Washing Discard the liquid in the well, add 300 μL of washing solution to each well, let stand for 30 seconds and then discard. Repeat the washing 5 times, and pat dry on absorbent paper for the last time.

[0070] 2.4 Add detection antibodies Add 100 μL of diluted HRP-labeled detection antibody to each well, incubate at 37°C for 60 minutes, and wash 5 times as above.

[0071] 2.5 Colorimetric properties Add 100 μL of TMB colorimetric solution to each well and react at room temperature in the dark for 15 minutes.

[0072] 2.6 Termination and Reading Add 50 μL of stop solution to each well, gently shake to mix, and measure the absorbance (OD value) of each well at a wavelength of 450 nm.

[0073] 3. Result Calculation and Risk Assessment Plot a standard curve (four-parameter fitting) with the standard concentration on the x-axis and the OD value on the y-axis. Calculate the SERPINE1 concentration in plasma based on the sample OD values ​​(the actual concentration needs to be multiplied by the dilution factor of 10).

[0074] During the operation, it is necessary to avoid drying out the enzyme-labeled plate and strictly control the reaction temperature and time.

[0075] The above-described kit and method were used to determine the cutoff values ​​for predicting AD risk and the validation results on the validation set: Risk assessment: Concentrations ≥75.90 ng / mL are considered high-risk, indicating a significantly increased risk of aortic dissection; concentrations <75.90 ng / mL are considered low-risk. This threshold was determined based on the above analysis data.

[0076] The above description is merely a preferred embodiment of this application and is not intended to limit this application in any form or substance. It should be noted that those skilled in the art can make several improvements and additions without departing from this application, and these improvements and additions should also be considered within the scope of protection of this application.

Claims

1. Application of reagents for detecting peripheral blood SERPINE1 levels in the preparation of products for predicting the risk of aortic dissection, early screening, diagnosis, or monitoring treatment efficacy.

2. The application according to claim 1, characterized in that, The detection reagent is a reagent selected from one or more detection techniques or methods from the group consisting of: enzyme-linked immunosorbent assay (ELISA), immunofluorescence assay, radioimmunoassay, immunoprecipitation assay, immunoblotting, high performance liquid chromatography (HPLC), capillary gel electrophoresis, near-infrared spectroscopy, mass spectrometry, immunochemiluminescence assay, colloidal gold immunochromatography, fluorescence immunochromatography, surface plasmon resonance (SPR), immuno-PCR, or biotin-avidin assay.

3. The application according to claim 1 or 2, characterized in that, The product includes at least one of reagents, kits, test strips, and chips.

4. The application according to claim 1 or 2, characterized in that, The product is a diagnostic device, which includes a sample collection device, a sample detection device, and a diagnostic device; wherein: the sample collection device is configured to collect peripheral blood samples from a subject; the sample detection device is capable of detecting the level of SERPINE1 in the peripheral blood sample; the diagnostic device includes a data acquisition module and a diagnostic module, the data acquisition module is configured to acquire the data detected by the sample detection device, and the diagnostic module is configured to determine whether the subject has aortic dissection based on the data acquired by the data acquisition module.

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