A method for constructing a systemic lupus erythematosus evaluation model based on flow cytometry and a systemic lupus erythematosus evaluation method

Abstract

The application relates to the technical field and discloses a construction method and system of a systemic lupus erythematosus evaluation model based on flow cytometry and a systemic lupus erythematosus evaluation method. The model is directly trained by specific cell expression data of a target group obtained by flow cytometry to learn specific cell expression data of a systemic lupus erythematosus patient group, a healthy group and a disease patient group other than systemic lupus erythematosus, so that a high-precision systemic lupus erythematosus evaluation model is obtained. The model focuses on the unique changes of peripheral blood of SLE, selects the expression amounts of monocyte CD169, neutrophil CD177 and B lymphocyte CD317, and realizes accurate and efficient early clinical identification of SLE through the high-precision systemic lupus erythematosus evaluation model, thereby providing reliable data guidance for diagnosis and treatment of systemic lupus erythematosus patients.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, and in particular to a method for constructing a systemic lupus erythematosus (SLE) assessment model based on flow cytometry and a method for assessing SLE. Background Technology

[0002] Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by dysregulation of immune tolerance, production of autoantibodies, and formation and deposition of immune complexes, leading to tissue and organ damage. SLE presents with diverse clinical phenotypes and involves multiple and severe organs, often affecting the kidneys, cardiovascular system, skin, central nervous system, hematologic system, and respiratory system.

[0003] Currently, the pathogenesis of SLE is believed to be related to factors such as genetics, environment, infection, and hormone levels. Its core pathological mechanism is immune imbalance, with type I interferon (IFN-I) and its pathway playing a decisive role in this immune imbalance. SLE patients exhibit significantly elevated circulating IFN-I levels, and their peripheral blood mononuclear cells show characteristic IFN gene expression. However, directly measuring type I interferon presents challenges. Firstly, its low concentration is limited by the instability of different ligand quantities and properties in serum, making it difficult to obtain good biological correlations. Secondly, although the latest single-molecule arrays can accurately measure it, they are expensive and have limited availability. Currently, clinical diagnosis of SLE mainly relies on symptom assessment and serum autoantibody measurement, which has low sensitivity and specificity, leading to frequent misdiagnosis.

[0004] Therefore, there is an urgent need for a method to construct a systemic lupus erythematosus (SLE) assessment model based on flow cytometry, so as to assist in the accurate and efficient diagnosis of SLE patients. Summary of the Invention

[0005] This invention provides a method for constructing a systemic lupus erythematosus (SLE) assessment model based on flow cytometry and a method for assessing SLE, in order to address the shortcomings of current SLE diagnostic methods in terms of poor precision and low accuracy.

[0006] This invention provides a method for constructing a systemic lupus erythematosus assessment model based on flow cytometry, comprising:

[0007] The study aimed to obtain specific cell expression data of the target population through flow cytometry. The target population included an experimental group and a control group, which consisted of patients with systemic lupus erythematosus (SLE) and a control group, which included healthy individuals and patients with diseases other than SLE.

[0008] Based on specific cell expression data of the target population, a systemic lupus erythematosus (SLE) assessment model is obtained by learning specific cell expression data of patients with SLE, healthy individuals, and patients with diseases other than SLE through model learning.

[0009] According to the present invention, a method for constructing a systemic lupus erythematosus (SLE) assessment model based on flow cytometry is provided. The method involves learning specific cell expression data from a target population, a SLE patient population, a healthy population, and a patient population with diseases other than SLE through a model, to obtain the SLE assessment model. The method includes:

[0010] When the specific cell expression data of the target population represents the expression data of only one specific cell, the ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group is obtained based on the specific cell expression data of the experimental group and the control group, thereby obtaining the preset individual index threshold of the specific cell expression data;

[0011] When the specific cell expression data of the target population represents the expression data of more than two specific cells, the joint index value of the experimental group and the joint index value of the control group are obtained based on the specific cell expression data of the experimental group and the control group. The ROC curve of the joint index value of the experimental group versus the joint index value of the control group is obtained, thereby obtaining the preset joint index threshold of the specific cell expression data.

[0012] According to the present invention, a method for constructing a systemic lupus erythematosus assessment model based on flow cytometry is provided, wherein specific cell expression data include any one or any combination of the following: monocyte CD169 expression data, neutrophil CD177 expression data, and B lymphocyte CD317 expression data.

[0013] According to the present invention, a method for constructing a systemic lupus erythematosus assessment model based on flow cytometry is provided, wherein the preset individual indicator threshold includes any one of the following or any combination thereof: preset individual indicator threshold for monocyte CD169 expression data, preset individual indicator threshold for neutrophil CD177 expression data, and preset individual indicator threshold for B lymphocyte CD317 expression data.

[0014] This invention provides a method for assessing systemic lupus erythematosus, comprising:

[0015] Receive specific cell expression data from the subject obtained by flow cytometry from at least one terminal;

[0016] Based on the specific cell expression data of the subject, the systemic lupus erythematosus assessment model obtained by the method of constructing the systemic lupus erythematosus assessment model based on flow cytometry described above can be used to obtain the assessment result of whether the subject is or is a candidate for systemic lupus erythematosus.

[0017] The assessment results regarding whether the subject is or is a candidate for systemic lupus erythematosus are output to at least one terminal.

[0018] According to a systemic lupus erythematosus (SLE) assessment method provided by the present invention, the method involves obtaining an assessment result regarding whether the subject is or is a candidate for SLE based on specific cell expression data of the subject using a SLE assessment model, including:

[0019] When the specific cell expression data of the test subject indicates that there is only one specific cell expression data, the systemic lupus erythematosus (SLE) assessment model compares the specific cell expression data of the test subject with the corresponding preset individual indicator threshold. When the specific cell expression data of the test subject is greater than the preset individual indicator threshold, the test subject is determined to be or a candidate for systemic lupus erythematosus (SLE) patient. When the specific cell expression data of the test subject is less than or equal to the preset individual indicator threshold, the test subject is determined not to be or a candidate for systemic lupus erythematosus (SLE) patient.

[0020] When the specific cell expression data of the test subject indicates the presence of expression data of more than two specific cells, the systemic lupus erythematosus (SLE) assessment model is used to determine whether the test subject is or is a candidate for SLE.

[0021] According to a systemic lupus erythematosus (SLE) assessment method provided by the present invention, when the specific cell expression data of the test subject indicates the presence of expression data of two or more specific cells, the method uses a systemic lupus erythematosus assessment model and a joint assessment expression to determine whether the test subject is or is a candidate for systemic lupus erythematosus, including:

[0022] When the specific cell expression data of the test subject indicates the expression data of more than two specific cells, the joint index value of the test subject is obtained by using the joint assessment expression through the systemic lupus erythematosus assessment model.

[0023] The combined indicator value of the test subject is compared with the preset combined indicator threshold. When the combined indicator value of the test subject is greater than the preset combined indicator threshold, the test subject is determined to be or a candidate for systemic lupus erythematosus (SLE) patient. When the combined indicator value of the test subject is less than or equal to the preset combined indicator threshold, the test subject is determined not to be or a candidate for systemic lupus erythematosus (SLE) patient.

[0024] According to the systemic lupus erythematosus assessment method provided by the present invention, the combined assessment expression is as follows:

[0025] C = Q*a + W*b + E*c - 2.811

[0026] In the joint assessment expression, C represents the joint index value of the subject, a represents the subject's neutrophil CD177 expression data, b represents the subject's monocyte CD169 expression data, c represents the subject's B lymphocyte CD317 expression data, Q represents the weight of the neutrophil CD177 expression data, W represents the weight of the monocyte CD169 expression data, and E represents the weight of the B lymphocyte CD317 expression data.

[0027] According to the systemic lupus erythematosus assessment method provided by the present invention, the subject can be a suspected systemic lupus erythematosus patient, or more specifically, a suspected systemic lupus erythematosus patient who is negative for anti-dsDNA antibodies.

[0028] The present invention also provides a systemic lupus erythematosus assessment system, comprising:

[0029] A data receiving module is used to: receive specific cell expression data obtained from a subject by flow cytometry from at least one terminal;

[0030] The assessment module is used to: based on the specific cell expression data of the subject, and through the systemic lupus erythematosus assessment model obtained by the method of constructing the systemic lupus erythematosus assessment model based on flow cytometry described above, obtain the assessment result of whether the subject is or is a candidate for systemic lupus erythematosus.

[0031] The data output module is used to output the assessment results of whether the subject is or is a candidate for systemic lupus erythematosus to at least one terminal.

[0032] It should be noted that a terminal refers to an input / output device connected to a computer system. Depending on the function, terminals can be divided into various types: smart terminals or intelligent terminals, dumb terminals, interactive terminals or online terminals. Specifically, a terminal can be various mobile communication devices, such as mobile phones and tablets. This article aims to provide users with the function of inputting data and outputting data.

[0033] According to the present invention, a systemic lupus erythematosus assessment system includes an assessment module comprising:

[0034] A separate evaluation submodule is used to: when the specific cell expression data of the target population represents the expression data of only one specific cell, obtain the ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group based on the specific cell expression data of the experimental group and the control group, thereby obtaining the preset individual index threshold of the specific cell expression data;

[0035] The joint evaluation submodule is used to: when the specific cell expression data of the target population represents the expression data of more than two specific cells, obtain the joint index value of the experimental group and the joint index value of the control group based on the specific cell expression data of the experimental group and the control group, and obtain the ROC curve of the joint index value of the experimental group versus the joint index value of the control group, thereby obtaining the preset joint index threshold of the specific cell expression data.

[0036] According to the present invention, a systemic lupus erythematosus assessment system includes a combined assessment submodule comprising:

[0037] The joint indicator value calculation submodule is used to: when the specific cell expression data of the test subject represents the expression data of more than two specific cells, obtain the joint indicator value of the test subject through the systemic lupus erythematosus assessment model and the joint assessment expression;

[0038] The comparison submodule is used to: compare the combined indicator value of the test subject with the preset combined indicator threshold; when the combined indicator value of the test subject is greater than the preset combined indicator threshold, the test subject is determined to be or a candidate of systemic lupus erythematosus (SLE) patient; when the combined indicator value of the test subject is less than or equal to the preset combined indicator threshold, the test subject is determined not to be or a candidate of systemic lupus erythematosus (SLE) patient.

[0039] The present invention also provides an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the computer program to implement the method for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or the systemic lupus erythematosus assessment method described above.

[0040] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a flow cytometry-based systemic lupus erythematosus assessment model and / or the systemic lupus erythematosus assessment method described above.

[0041] The present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing any of the above-described methods for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or for assessing systemic lupus erythematosus.

[0042] This invention provides a method for constructing a systemic lupus erythematosus (SLE) assessment model based on flow cytometry and a method for assessing SLE. The method directly uses specific cell expression data obtained by flow cytometry from the target population to train the model to learn specific cell expression data from SLE patients, healthy individuals, and patients with diseases other than SLE, thereby obtaining a high-precision SLE assessment model.

[0043] This invention provides a method for constructing a systemic lupus erythematosus (SLE) assessment model based on flow cytometry and an SLE assessment method. It focuses on the unique changes in peripheral blood in SLE patients, selects the expression levels of CD169 on monocytes, CD177 on neutrophils, and CD317 on B lymphocytes, and uses the SLE assessment model to assist in accurate and efficient early clinical identification of SLE, providing reliable data guidance for the diagnosis and treatment of SLE patients. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a method for constructing a systemic lupus erythematosus assessment model provided by the present invention.

[0046] Figure 2 Gene expression in the mononuclear cell (PBMC) transcriptome and neutrophil transcriptome is shown, where SLE represents the systemic lupus erythematosus patient population, RA represents the rheumatoid arthritis patient population, and HC represents the healthy population.

[0047] Figure 3 The gene expression of CD169 (SIGLEC1) and CD317 (BST2) in a public database is shown.

[0048] Figure 4 This demonstrates a flow cytometry gating strategy for detecting the expression levels of CD169 / CD177 / CD317 in peripheral blood.

[0049] Figure 5 The MFI expression levels and ROC curves of CD169 / CD177 / CD317 in Example 1 are shown.

[0050] Figure 6 The MFI expression levels of CD169 / CD177 / CD317 in the training and validation sets are shown in Example 2.

[0051] Figure 7 The evaluation performance of the combined evaluation of CD169 / CD177 / CD317 is shown.

[0052] Figure 8 The MFI expression levels and ROC curves of CD169 / CD177 / CD317 in Example 4 are shown.

[0053] Figure 9 This is a schematic diagram of the structure of a systemic lupus erythematosus assessment system provided by the present invention.

[0054] Figure 10 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0056] This invention argues that flow cytometry is a simple, rapid, reproducible method requiring minimal sample volume. It can quickly and accurately detect and analyze the composition, quantity, state, and differentiation stage of cells, thus overcoming the limitations of simple blood tests. By characterizing the protein expression of immune cells with different phenotypes and functions in peripheral blood, important details can be provided for better disease diagnosis and prediction. Immune cell imbalance and functional abnormalities are important pathological mechanisms of SLE. Clinically, direct detection of immune cell surface and functional protein expression using flow cytometry can assist in the precise diagnosis and treatment of SLE.

[0057] Figure 1 This is a flowchart illustrating a method for constructing a systemic lupus erythematosus (SLE) assessment model provided by the present invention. The execution entity of this method can be any applicable terminal-side device or network-side device, such as a device for constructing a SLE assessment model.

[0058] See Figure 1 The present invention provides a method for constructing a systemic lupus erythematosus assessment model, which may include:

[0059] S110. Obtain specific cell expression data of the target population by flow cytometry. The target population includes an experimental group and a control group, which is a patient group of systemic lupus erythematosus. The control group includes a healthy group and a patient group of diseases other than systemic lupus erythematosus. The specific cell expression data may include any one of the following or any combination thereof: monocyte CD169 expression data, neutrophil CD177 expression data, and B lymphocyte CD317 expression data.

[0060] S120. Based on the specific cell expression data of the target population, the model learns the specific cell expression data of the systemic lupus erythematosus (SLE) patient group, the healthy group, and the patient group with diseases other than SLE, to obtain the SLE assessment model.

[0061] In one embodiment, S120 may include:

[0062] When the specific cell expression data of the target population represents the expression data of only one specific cell, the ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group is obtained based on the specific cell expression data of the experimental group and the control group, thereby obtaining the preset individual index threshold of the specific cell expression data. The preset individual index threshold may include any one of the following or any combination thereof: the preset individual index threshold of monocyte CD169 expression data, the preset individual index threshold of neutrophil CD177 expression data, and the preset individual index threshold of B lymphocyte CD317 expression data.

[0063] When the specific cell expression data of the target population represents the expression data of more than two specific cells, the joint index value of the experimental group and the joint index value of the control group are obtained based on the specific cell expression data of the experimental group and the control group. The ROC curve of the joint index value of the experimental group versus the joint index value of the control group is obtained, thereby obtaining the preset joint index threshold of the specific cell expression data.

[0064] After obtaining the systemic lupus erythematosus (SLE) assessment model, it can be applied to SLE assessment to form a SLE assessment method. It should be noted that the implementation of the SLE assessment method provided by this invention can be realized by programming software, and its execution subject can be any applicable terminal-side device or network-side device, such as a systemic lupus erythematosus assessment device.

[0065] The present invention provides a method for assessing systemic lupus erythematosus, which may include:

[0066] S210. Receive specific cell expression data of the subject obtained by flow cytometry from at least one terminal;

[0067] S220. Based on the specific cell expression data of the test subject, the systemic lupus erythematosus assessment model is used to obtain the assessment results of whether the test subject is or is a candidate for systemic lupus erythematosus.

[0068] S230, Output the assessment results of whether the subject is or is a candidate for systemic lupus erythematosus to at least one terminal.

[0069] In one embodiment, S220 may include:

[0070] When the specific cell expression data of the test subject indicates that there is only one specific cell expression data, the systemic lupus erythematosus (SLE) assessment model compares the specific cell expression data of the test subject with the corresponding preset individual indicator threshold. When the specific cell expression data of the test subject is greater than the preset individual indicator threshold, the test subject is determined to be or a candidate for systemic lupus erythematosus (SLE) patient. When the specific cell expression data of the test subject is less than or equal to the preset individual indicator threshold, the test subject is determined not to be or a candidate for systemic lupus erythematosus (SLE) patient.

[0071] When the specific cell expression data of the test subject indicates the expression data of more than two specific cells, the joint index value of the test subject is obtained by using the joint assessment expression through the systemic lupus erythematosus assessment model.

[0072] The combined indicator value of the test subject is compared with the preset combined indicator threshold. When the combined indicator value of the test subject is greater than the preset combined indicator threshold, the test subject is determined to be or a candidate for systemic lupus erythematosus (SLE) patient. When the combined indicator value of the test subject is less than or equal to the preset combined indicator threshold, the test subject is determined not to be or a candidate for systemic lupus erythematosus (SLE) patient.

[0073] The joint evaluation expression is as follows:

[0074] C = Q*a + W*b + E*c - 2.811

[0075] In the joint assessment expression, C represents the joint index value of the subject, a represents the subject's neutrophil CD177 expression data, b represents the subject's monocyte CD169 expression data, c represents the subject's B lymphocyte CD317 expression data, Q represents the weight of the neutrophil CD177 expression data, W represents the weight of the monocyte CD169 expression data, and E represents the weight of the B lymphocyte CD317 expression data.

[0076] The following examples will be used to verify the construction method of the systemic lupus erythematosus assessment model and the systemic lupus erythematosus assessment method provided by the present invention.

[0077] The characteristics of the case samples used in the following examples are shown in Table 1. Table 1 is a table of basic characteristics of the disease group (systemic lupus erythematosus group), healthy control group and disease control group (patients have diseases other than systemic lupus erythematosus) included in the examples.

[0078] Table 1. Basic characteristics of systemic lupus erythematosus (SLE) control groups and healthy controls.

[0079]

[0080] Note: The systemic lupus erythematosus (SLE) group refers to patients clinically diagnosed according to the diagnostic classification criteria for systemic lupus erythematosus (SLE) jointly published by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR) in 2019; the healthy control group (HC) includes healthy volunteers with normal indicators in the physical examination center; the disease control group (DC) includes patients diagnosed according to the diagnostic criteria for various diseases, including 26 cases of rheumatoid arthritis, 14 cases of Sjögren's syndrome, 6 cases of adult Still's disease, 5 cases of systemic vasculitis, 3 cases of multiple sclerosis, and 2 cases of connective tissue disease.

[0081] Example 1

[0082] I. Discovery of flow cytometry-based biomarkers for diagnosing systemic lupus erythematosus—CD169 / CD177 / CD317 on the surface of immune cells

[0083] The preliminary analysis of peripheral blood mononuclear cell transcriptomes and neutrophil transcriptomes of 14 patients with systemic lupus erythematosus, 15 patients with rheumatoid arthritis, and 13 healthy individuals (population information is shown in Table 2).

[0084] Table 2. Basic characteristics of systemic lupus erythematosus, rheumatoid arthritis, and healthy controls.

[0085]

[0086] *p-values ​​compare the differences between systemic lupus erythematosus (SLE) and healthy controls.

[0087] #p-value comparison between patients with systemic lupus erythematosus and rheumatoid arthritis

[0088] Differential analysis revealed that the gene expression of CD169 (SIGLEC1) and CD317 (BST2) was significantly upregulated in the transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with systemic lupus erythematosus (SLEP). Figure 2 A, 2B), CD177 gene expression was significantly upregulated in the neutrophil transcriptome ( Figure 2 C) suggests that immune cell markers have diagnostic value for systemic lupus erythematosus.

[0089] To verify the diagnostic value of immune cell markers for systemic lupus erythematosus (SLE), transcriptome data of immune cell subsets in SLE were downloaded from a public database (GSE148601). The study found that CD169 (SIGLEC1) expression was significantly upregulated in the monocyte transcriptome of SLE patients and significantly downregulated after clinical treatment. Figure 3 A); CD317 (BST2) expression was significantly upregulated in the transcriptomes of memory B cells and naive B cells. Figure 3 B) This study revealed that the expression of CD169 on the surface of monocytes and CD317 on the surface of B cells has diagnostic value for systemic lupus erythematosus.

[0090] As can be seen from the above, the expression of CD169 molecules on the surface of monocytes, CD317 molecules on the surface of B cells, and CD177 molecules on the surface of neutrophils in patients with systemic lupus erythematosus (SLE) showed significant differences among healthy individuals, disease control groups, and SLE patients, suggesting that these differences have clinical application value in the diagnosis of SLE.

[0091] II. Establishment of a method for diagnosing systemic lupus erythematosus based on flow cytometry detection of immune cell markers 1. Use peripheral blood whole blood from the subject, lyse and remove red blood cells before flow cytometry staining.

[0092] Specifically as follows:

[0093] 1) Collect 400 μL of peripheral EDTA-anticoagulated whole blood from subjects (including clinically diagnosed systemic lupus erythematosus patients, healthy controls, and disease controls) and aspirate it into a 15 mL clean centrifuge tube;

[0094] 2) Add 8 ml of 1X erythrocyte lysis buffer (BDBiosciences, 10X, diluted in ultrapure water) to 2 ml of lysis buffer per 100 μL of whole blood. Tighten the cap, gently invert and mix for 1 minute, then let stand at room temperature for 10 minutes to lyse the erythrocytes.

[0095] 3) Fill the container with PBS (Gibco), mix well, centrifuge at 1500 rpm (rapid rise and fall) at room temperature for 5 minutes, and discard the supernatant;

[0096] 4) Add 1 ml of PBS, blow it to mix well, then fill the PBS container with the mixture, mix well, and centrifuge under the conditions described above.

[0097] 5) After centrifugation, discard the supernatant, add 200 μL of PBS to resuspend the supernatant, transfer it to a 96-well plate, centrifuge (under the same conditions as above), and quickly shake the plate once to discard the supernatant.

[0098] 6) Add 100 μL of Fc blocking buffer (Biolegend, 1:50 diluted in PBS) per well, mix well and block on ice at 4°C for 10 minutes;

[0099] 7) After adding 100 μL of PBS, centrifuge under the conditions described above;

[0100] 8) Discard the supernatant after swirl test, add 100 μL of flow cytometry antibody per well, mix well, and incubate at 4°C in the dark for 30 minutes. Antibodies were prepared as follows: APC / Cy7-labeled anti-CD19 antibody (Biolegend, 1:100), PerCP-Cyanine 5.5-labeled anti-CD15 antibody (Biolegend, 1:100), PE-labeled anti-CD14 antibody (Biolegend, 1:100), PE-Cyanine 7-labeled anti-CD317 antibody or PE-Cyanine 7-labeled anti-mouse IgG1, κ chain antibody (Biolegend, 1:50), FITC-labeled anti-CD177 antibody or FITC-labeled anti-mouse IgG1, κ chain antibody (Biolegend, 1:50), APC-labeled anti-CD169 antibody or APC-labeled anti-mouse IgG1, κ chain antibody (Biolegend, 1:50). All antibodies were diluted in PBS.

[0101] 9) After staining, add 100 μL of PBS to each well and centrifuge under the above conditions;

[0102] 10) Discard the supernatant by swiping the plate, add 200 μL of PBS to each well, and centrifuge again;

[0103] 11) Discard the supernatant by swiping the plate, add 2% PFA 200 μL, blow well and transfer to the marked flow cytometer tube.

[0104] 2. Detection of CD169 / CD177 / CD317 expression levels

[0105] The data was analyzed using a FACSCanto™ II (BDBiosciences) flow cytometer, following the instructions. Figure 4 The gate strategy shown separates monocytes (CD14+), B cells (CD19+), and neutrophils (CD15+). FlowJo software (BDBiosciences) was used to record the median fluorescence intensity (MFI) values ​​of CD169 on monocytes, CD177 on neutrophils, CD317 on B lymphocytes, and their respective isotype control antibodies. The expression intensity of the immune cell surface markers was defined as the MFI value of the analyte minus the MFI value of the corresponding channel's isotype antibody. The gate strategy is as follows: Figure 4 As shown.

[0106] 3. Determination of threshold and judgment criteria

[0107] The subjects shown in Table 1 were used as the test sample, including 100 clinically diagnosed patients with systemic lupus erythematosus (also known as the test sample) and 116 control patients (including 60 healthy controls and 56 disease controls).

[0108] All samples were randomly divided into a training set and a validation set in a 7:3 ratio. The training set included 72 patients with systemic lupus erythematosus (SLE) and 79 control patients (38 healthy and 41 disease controls). The validation set included 28 patients with SLE and 37 control patients (22 healthy and 15 disease controls).

[0109] The threshold was determined based on the CD169 / CD177 / CD317 MFI expression levels detected by flow cytometry for each sample in the training set. The threshold was determined based on the CD169 / CD177 / CD317 MFI expression levels in the systemic lupus erythematosus group compared to the control group in the training set. Figure 5 Plot an ROC curve for (A, 5B, 5C) and take the critical value where the Youden exponent reaches its maximum. Figure 5 As shown in Figures D, 5E, and 5F, the threshold values ​​for CD169 expression are 501.6, CD177 expression is 6651, and CD317 expression is 874.5. If the CD169 / CD177 / CD317 expression levels of the test subject are greater than the corresponding thresholds, the test subject is determined to be or a candidate for systemic lupus erythematosus (SLE). If the CD169 / CD177 / CD317 expression levels of the test subject are less than or equal to the thresholds, the test subject is determined not to be or a candidate for SLE.

[0110] Example 2: Application in the diagnosis of systemic lupus erythematosus

[0111] 1. Testing

[0112] The subjects shown in Table 1 were used as the test sample, including 100 clinically diagnosed patients with systemic lupus erythematosus (also known as the test sample) and 116 control patients (including 60 healthy controls and 56 disease controls).

[0113] All samples were randomly divided into a training set and a validation set in a 7:3 ratio. The training set included 72 patients with systemic lupus erythematosus (SLE) and 79 control patients (38 healthy and 41 disease controls). The validation set included 28 patients with SLE and 37 control patients (22 healthy and 15 disease controls).

[0114] The detection was performed according to the method of Example 1, Part 2.

[0115] Table 1 shows the training and validation set results in the samples. Figure 6As shown, there were significant differences in the expression levels of CD169 / CD177 / CD317 between the systemic lupus erythematosus group and the control group, further confirming that these three molecular proteins can serve as diagnostic markers for systemic lupus erythematosus patients.

[0116] 2. Sensitivity and Specificity Detection

[0117] ROC curves were plotted on the expression levels of CD169 / CD177 / CD317 in various systemic lupus erythematosus (SLE) patients and controls in the training set. The threshold value was set at the maximum Yoden index. Diagnostic predictions were then performed on both the training and validation sets, and their sensitivity, specificity, positive predictive value, and negative predictive value were calculated. The results are shown in Tables 3, 4, and 5, listing the diagnostic sensitivity, specificity, positive predictive value, and negative predictive value. It can be seen that the expression levels of CD169 / CD177 / CD317 alone can be used as biomarkers to assist in the diagnosis of SLE patients, with high sensitivity and / or specificity.

[0118] Table 3. Sensitivity, specificity, positive predictive value, and negative predictive value of CD169

[0119]

[0120] Table 4. Sensitivity, specificity, positive predictive value, and negative predictive value of CD177

[0121]

[0122] Table 5. Sensitivity, specificity, positive predictive value, and negative predictive value of CD317.

[0123]

[0124] Example 3: Combined diagnosis of systemic lupus erythematosus

[0125] I. Joint Indicator Evaluation Formula

[0126] As can be seen from Examples 1 and 2 above, the expression levels of CD169, CD177, and CD317 alone can be used to assist in the diagnosis of systemic lupus erythematosus (SLE) patients. To further investigate whether the combined diagnosis of these three indicators can improve diagnostic efficacy, this example constructs a combined indicator evaluation formula based on the training set results:

[0127] C=1.959*a+2.328*b+2.194*c-2.811

[0128] Where C is the joint index value, a is the MFI expression level of CD177, b is the MFI expression level of CD169, and c is the MFI expression level of CD317.

[0129] II. Establishment of a method for the combined diagnosis of systemic lupus erythematosus using immune cell surface markers.

[0130] 1. Same as 1 in Example 1, Part 2;

[0131] 2. Same as 2 in Example 1;

[0132] 3. Substitute the MFI expression levels of the target molecules CD169 / CD177 / CD317 obtained in step 2 above into the following joint index evaluation formula to calculate the joint index value.

[0133] Based on the ROC curves of the combined index values ​​of the systemic lupus erythematosus group and the control group in the training set, as well as the ROC curves of individual indexes (… Figure 8 The threshold value of the joint index value is 0.4965, obtained by taking the critical value at the maximum value of the Youden index on the ROC curve of the joint index value.

[0134] If the combined indicator value of the test subject is greater than the combined indicator value threshold, the test subject is determined to be or a candidate for systemic lupus erythematosus (SLE). If the combined indicator value of the test subject is less than or equal to the combined indicator value threshold, the test subject is determined not to be or a candidate for systemic lupus erythematosus (SLE).

[0135] III. Application of combined diagnosis of systemic lupus erythematosus using immune cell surface markers

[0136] The subjects shown in Table 1 were used as the test sample, including 100 clinically diagnosed patients with systemic lupus erythematosus (also known as the test sample) and 116 control patients (including 60 healthy controls and 56 disease controls).

[0137] All samples were randomly divided into a training set and a validation set in a 7:3 ratio. The training set included 72 patients with systemic lupus erythematosus (SLE) and 79 control patients (38 healthy and 41 disease controls). The validation set included 28 patients with SLE and 37 control patients (22 healthy and 15 disease controls).

[0138] The combined index values ​​of CD169 / CD177 / CD317 expression levels of each systemic lupus erythematosus patient and control group in the training and validation sets were substituted into the above combined index assessment formula to calculate the combined index value for each subject.

[0139] ROC curves were plotted on the combined index values ​​of each systemic lupus erythematosus patient and control group in the training set, and the threshold value was taken as the value at the highest Yoden index. Figure 5 (D, 5E, 5F) are used to make diagnostic predictions on the training set and validation set samples respectively, and their sensitivity, specificity, positive predictive value and negative predictive value are calculated.

[0140] The results are shown in Table 6. It can be seen that the combined expression levels of CD169 / CD177 / CD317 MFI can be used to assist in the diagnosis of patients with systemic lupus erythematosus, and the sensitivity and specificity are high.

[0141] Table 6. Sensitivity, specificity, positive predictive value, and negative predictive value of the combined diagnostic C-value.

[0142]

[0143] Example 4: The use of CD169 / CD177 / CD317, alone and in combination, as markers in the auxiliary diagnosis of anti-dsDNA negative systemic lupus erythematosus.

[0144] 1. Testing

[0145] The subjects shown in Table 1 were used as the test sample, consisting of 100 clinically diagnosed systemic lupus erythematosus (SLE) patients (also referred to as the test sample) and 116 control patients (including 60 healthy controls and 56 disease controls). It is worth noting that anti-dsDNA, as a classic diagnostic marker for SLE, has the clinical problem of low diagnostic sensitivity. In this example, 36 patients in the included study population were negative for anti-dsDNA antibodies; their basic information is shown in Table 7.

[0146] The anti-dsDNA antibody-negative samples and the control group were randomly divided into a training set and a validation set in a 7:3 ratio. The training set included 25 patients with anti-dsDNA antibody-negative systemic lupus erythematosus and 79 control patients (38 healthy and 41 disease controls). The validation set included 11 patients with anti-dsDNA antibody-negative systemic lupus erythematosus and 37 control patients (22 healthy and 15 disease controls), as shown in Table 7.

[0147] Table 7. Basic characteristics of anti-dsDNA-negative systemic lupus erythematosus control groups and healthy controls.

[0148]

[0149] The detection was performed according to the method of Example 1, Part 2.

[0150] Table 7 shows the training and validation set results in the samples. Figure 8 As shown, there were significant differences in the expression levels of CD169 / CD177 / CD317 between the anti-dsDNA antibody-negative systemic lupus erythematosus group and the control group. This further confirms that these three molecular proteins can serve as diagnostic markers for patients with anti-dsDNA antibody-negative systemic lupus erythematosus.

[0151] 2. Sensitivity and specificity detection of CD169 / CD177 / CD317

[0152] ROC curves were plotted on the expression levels of CD169 / CD177 / CD317 in anti-dsDNA antibody-negative systemic lupus erythematosus (SLE) patients and controls in the training set. The threshold value at the highest Yangen index was used to perform diagnostic predictions on both the training and validation sets, calculating their sensitivity, specificity, positive predictive value, and negative predictive value. The results are shown in Tables 8, 9, and 10, listing the diagnostic sensitivity, specificity, positive predictive value, and negative predictive value. It can be seen that the expression levels of CD169 / CD177 / CD317 alone can serve as biomarkers to aid in the diagnosis of anti-dsDNA antibody-negative SLE patients, significantly improving diagnostic sensitivity while maintaining good diagnostic specificity.

[0153] Table 8. Sensitivity, specificity, positive predictive value, and negative predictive value of CD169 for the diagnosis of anti-dsDNA-SLE.

[0154]

[0155] Table 9. Sensitivity, specificity, positive predictive value, and negative predictive value of CD177 for the diagnosis of anti-dsDNA-SLE.

[0156]

[0157]

[0158] Table 10. Sensitivity, specificity, positive predictive value, and negative predictive value of CD317 for the diagnosis of anti-dsDNA-SLE.

[0159]

[0160] 3. Sensitivity and specificity detection of the CD169 / CD177 / CD317 combined diagnostic model

[0161] As can be seen from Example 3 above, the combined indicator assessment model constructed based on the results of the overall systemic lupus erythematosus (SLE) patient training set exhibits good diagnostic performance and can be used to assist in the diagnosis of SLE patients. The next step is to further investigate whether the combined diagnostic model can improve diagnostic efficacy in anti-dsDNA antibody-negative SLE.

[0162] The formula for evaluating the joint indicators is C = 1.959*a + 2.328*b + 2.194*c - 2.811

[0163] Where C is the combined indicator C value, a is the MFI expression level of CD177, b is the MFI expression level of CD169, and c is the MFI expression level of CD317. The critical value at the maximum value of the Youden index of the combined diagnostic ROC curve is taken, and the combined indicator value threshold is obtained as 0.4965. If the combined indicator value of the subject is greater than the combined indicator value threshold, the subject is determined to be or a candidate for systemic lupus erythematosus (SLE). If the combined indicator value of the subject is less than or equal to the combined indicator value threshold, the subject is determined not to be or a candidate for systemic lupus erythematosus (SLE).

[0164] The ROC curve and diagnostic performance of this joint indicator evaluation model for diagnosing systemic lupus erythematosus (SLE) in the training set with negative anti-dsDNA antibodies are as follows: Figure 9 As shown.

[0165] The above embodiments used flow cytometry to detect CD169 / CD177 / CD317, and the results showed that CD169 / CD177 / CD317 were significantly upregulated in the systemic lupus erythematosus group compared to the control group (e.g., Figure 5 A, as shown in the figure. ROC curves were plotted based on the expression levels of CD169 / CD177 / CD317 in patients with systemic lupus erythematosus and in the control group (e.g., Figure A). Figure 5 As shown in D, 5E, and 5F, the threshold value at the maximum Yoden index was used to make diagnostic predictions on the training and validation sets. The sensitivity, specificity, positive predictive value, and negative predictive value of each and the combination of the three were calculated. The diagnostic sensitivity of the CD169 / CD177 / CD317 combined evaluation model was 82.76%, and the specificity was 87.80% (as shown in Table 11). Compared with the traditional classic anti-dsDNA, it greatly improves the sensitivity of disease diagnosis while ensuring specificity. It can make up for the problem of high clinical missed diagnosis rate to a certain extent and help in the differential diagnosis of systemic lupus erythematosus.

[0166] Table 11 Diagnostic efficacy of various biomarkers for systemic lupus erythematosus

[0167]

[0168] This invention provides the application of substances for detecting the expression levels of CD169 / CD177 / CD317MFI in the preparation of products for the auxiliary diagnosis of systemic lupus erythematosus (SLE). It also provides the application of substances for detecting the expression levels of CD169 / CD177 / CD317MFI by flow cytometry and vectors loaded with the following combined indicator C-value model formula in the preparation of products for the auxiliary diagnosis of SLE. This invention focuses on the unique changes in peripheral blood of specific populations with SLE, selecting the expression levels of CD169 (CD14+MoCD169) specifically expressed on CD14-positive monocytes, CD177 (CD15+NECD177) specifically expressed on CD15-positive neutrophils, and CD317 (CD19+BCD317) specifically expressed on CD19-positive B lymphocytes to assist in the clinical differential diagnosis of SLE. A model for the combined diagnosis of CD169 / CD177 / CD317 is constructed, enabling the diagnosis or screening of SLE; it has high sensitivity and specificity.

[0169] This invention provides a method for constructing a systemic lupus erythematosus (SLE) assessment model based on flow cytometry and a method for assessing SLE. The method directly uses specific cell expression data obtained by flow cytometry from the target population to train the model to learn specific cell expression data from SLE patients, healthy individuals, and patients with diseases other than SLE, thereby obtaining a high-precision SLE assessment model.

[0170] This invention provides a method for constructing a systemic lupus erythematosus (SLE) assessment model based on flow cytometry and an SLE assessment method. It focuses on the unique changes in peripheral blood in SLE patients, selects the expression levels of CD169 on monocytes, CD177 on neutrophils, and CD317 on B lymphocytes, and uses the SLE assessment model to assist in accurate and efficient early clinical identification of SLE, providing reliable data guidance for the diagnosis and treatment of SLE patients.

[0171] The systemic lupus erythematosus (SLE) assessment system provided by this invention will be described below. The SLE assessment system described below can be referred to in correspondence with the SLE assessment method described above.

[0172] See Figure 9 The present invention provides a systemic lupus erythematosus assessment system, which may include:

[0173] A data receiving module is used to: receive specific cell expression data obtained from a subject by flow cytometry from at least one terminal;

[0174] The assessment module is used to: based on the specific cell expression data of the subject, and through the systemic lupus erythematosus assessment model obtained by the method of constructing the systemic lupus erythematosus assessment model based on flow cytometry described above, obtain the assessment result of whether the subject is or is a candidate for systemic lupus erythematosus.

[0175] The data output module is used to output the assessment results of whether the subject is or is a candidate for systemic lupus erythematosus to at least one terminal.

[0176] According to the present invention, a systemic lupus erythematosus assessment system includes an assessment module comprising:

[0177] A separate evaluation submodule is used to: when the specific cell expression data of the target population represents the expression data of only one specific cell, obtain the ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group based on the specific cell expression data of the experimental group and the control group, thereby obtaining the preset individual index threshold of the specific cell expression data;

[0178] The joint evaluation submodule is used to: when the specific cell expression data of the target population represents the expression data of more than two specific cells, obtain the joint index value of the experimental group and the joint index value of the control group based on the specific cell expression data of the experimental group and the control group, and obtain the ROC curve of the joint index value of the experimental group versus the joint index value of the control group, thereby obtaining the preset joint index threshold of the specific cell expression data.

[0179] According to the present invention, a systemic lupus erythematosus assessment system includes a combined assessment submodule comprising:

[0180] The joint indicator value calculation submodule is used to: when the specific cell expression data of the test subject represents the expression data of more than two specific cells, obtain the joint indicator value of the test subject through the systemic lupus erythematosus assessment model and the joint assessment expression;

[0181] The comparison submodule is used to: compare the combined indicator value of the test subject with the preset combined indicator threshold; when the combined indicator value of the test subject is greater than the preset combined indicator threshold, the test subject is determined to be or a candidate of systemic lupus erythematosus (SLE) patient; when the combined indicator value of the test subject is less than or equal to the preset combined indicator threshold, the test subject is determined not to be or a candidate of systemic lupus erythematosus (SLE) patient.

[0182] Figure 10 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 10As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call logical instructions in the memory 830 to execute the method for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or the systemic lupus erythematosus assessment method described above.

[0183] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0184] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing the method for constructing a systemic lupus erythematosus assessment model based on flow cytometry and / or the systemic lupus erythematosus assessment method described above.

[0185] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a flow cytometry-based systemic lupus erythematosus assessment model and / or the systemic lupus erythematosus assessment method as described in any of the preceding claims.

[0186] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0187] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a systemic lupus erythematosus assessment model based on flow cytometry, characterized in that, include: Specific cell expression data were obtained from the target population using flow cytometry. The target population included an experimental group and a control group. The experimental group consisted of patients with systemic lupus erythematosus (SLE), while the control group included healthy individuals and patients with diseases other than SLE. The SLE patient group included SLE patients who were negative for anti-dsDNA antibodies. Specific cell expression data included the following combinations: CD169 expression data from monocytes, CD177 expression data from neutrophils, and CD317 expression data from B lymphocytes. Based on specific cell expression data of the target population, a systemic lupus erythematosus (SLE) assessment model is obtained by learning specific cell expression data of patients with SLE, healthy individuals, and patients with diseases other than SLE through model learning.

2. The method for constructing a systemic lupus erythematosus assessment model based on flow cytometry according to claim 1, characterized in that, The process involves using specific cell expression data from the target population to learn specific cell expression data from patients with systemic lupus erythematosus (SLE), healthy individuals, and patients with diseases other than SLE, thereby obtaining a SLE assessment model, including: When the specific cell expression data of the target population represents the expression data of only one specific cell, the ROC curve of the specific cell expression data of the experimental group versus the specific cell expression data of the control group is obtained based on the specific cell expression data of the experimental group and the control group, thereby obtaining the preset individual index threshold of the specific cell expression data; When the specific cell expression data of the target population represents the expression data of more than two specific cells, the joint index value of the experimental group and the joint index value of the control group are obtained based on the specific cell expression data of the experimental group and the control group. The ROC curve of the joint index value of the experimental group versus the joint index value of the control group is obtained, thereby obtaining the preset joint index threshold of the specific cell expression data.

3. The method for constructing a systemic lupus erythematosus assessment model based on flow cytometry according to claim 2, characterized in that, The preset individual indicator thresholds include the following combinations: preset individual indicator thresholds for monocyte CD169 expression data, preset individual indicator thresholds for neutrophil CD177 expression data, and preset individual indicator thresholds for B lymphocyte CD317 expression data.

4. A method for assessing systemic lupus erythematosus with negative anti-dsDNA antibodies, characterized in that, include: Receive specific cell expression data of a subject obtained by flow cytometry from at least one terminal, wherein the subject is negative for anti-dsDNA antibodies; Based on the specific cell expression data of the subject, the systemic lupus erythematosus assessment model obtained by the method of constructing the systemic lupus erythematosus assessment model based on flow cytometry as described in any one of claims 1-3 yields the assessment result of whether the subject is or is a candidate for anti-dsDNA antibody-negative systemic lupus erythematosus patients. The assessment results regarding whether the subject is or is a candidate for being a negative anti-dsDNA antibody patient with systemic lupus erythematosus are output to at least one terminal.

5. The method for assessing systemic lupus erythematosus negative for anti-dsDNA antibodies according to claim 4, characterized in that, The systemic lupus erythematosus (SLE) assessment model, obtained by constructing the flow cytometry-based SLE assessment model according to any one of claims 1-3 based on the specific cell expression data of the subject, provides an assessment result on whether the subject is or is a candidate for anti-dsDNA antibody-negative SLE patients, including: When the specific cell expression data of the test subject indicates that there is only one specific cell type of expression data, the systemic lupus erythematosus (SLE) assessment model is used to compare the specific cell expression data of the test subject with the corresponding preset individual indicator threshold. When the specific cell expression data of the test subject is greater than the preset individual indicator threshold, the test subject is determined to be or a candidate for anti-dsDNA antibody-negative SLE patient. When the specific cell expression data of the test subject is less than or equal to the preset individual indicator threshold, the test subject is determined not to be or a candidate for anti-dsDNA antibody-negative SLE patient. When the specific cell expression data of the test subject indicates the presence of expression data of more than two specific cells, the systemic lupus erythematosus assessment model is used to determine whether the test subject is or is a candidate for anti-dsDNA antibody-negative systemic lupus erythematosus patients through a combined assessment expression.

6. The method for assessing systemic lupus erythematosus negative for anti-dsDNA antibodies according to claim 5, characterized in that, When the specific cell expression data of the test subject indicates the presence of expression data for two or more specific cell types, the systemic lupus erythematosus (SLE) assessment model, using a combined assessment expression, determines whether the test subject is, or is a candidate for, a negative anti-dsDNA antibody SLE patient, including: When the specific cell expression data of the test subject indicates the expression data of more than two specific cells, the joint index value of the test subject is obtained by using the joint assessment expression through the systemic lupus erythematosus assessment model. The combined indicator value of the test subject is compared with the preset combined indicator threshold. When the combined indicator value of the test subject is greater than the preset combined indicator threshold, the test subject is determined to be or a candidate of anti-dsDNA antibody negative systemic lupus erythematosus (SLE) patient. When the combined indicator value of the test subject is less than or equal to the preset combined indicator threshold, the test subject is determined not to be or a candidate of anti-dsDNA antibody negative SLE patient.

7. The method for assessing systemic lupus erythematosus negative for anti-dsDNA antibodies according to claim 6, characterized in that, The joint evaluation expression is: C = Q*a + W*b + E*c - 2.811 In the joint assessment expression, C represents the joint index value of the subject, a represents the subject's neutrophil CD177 expression data, b represents the subject's monocyte CD169 expression data, c represents the subject's B lymphocyte CD317 expression data, Q represents the weight of the neutrophil CD177 expression data, W represents the weight of the monocyte CD169 expression data, and E represents the weight of the B lymphocyte CD317 expression data.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for constructing a systemic lupus erythematosus assessment model based on flow cytometry as described in any one of claims 1 to 3 and / or the method for assessing systemic lupus erythematosus negative for anti-dsDNA antibodies as described in any one of claims 4 to 7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for constructing a flow cytometry-based systemic lupus erythematosus assessment model as described in any one of claims 1 to 3 and / or the method for assessing anti-dsDNA antibody-negative systemic lupus erythematosus as described in any one of claims 4 to 7.

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