Method for diagnosing rheumatoid arthritis

Abstract

The invention discloses a method for diagnosing rheumatoid arthritis. The method is carried out based on a computer and specifically comprises the following steps: acquiring expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of a subject; the expression level data of the biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 are input into the constructed classification model, and the expression level data of the biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 are input into the constructed classification model; and outputting a classification result based on the classification model, and obtaining a classification result of the subject suffering from rheumatoid arthritis, or obtaining a classification result of the subject not suffering from rheumatoid arthritis.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a method for diagnosing rheumatoid arthritis. Background Technology

[0002] Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that primarily affects peripheral joints, leading to inflammation of the synovial membrane, cartilage destruction, and bone erosion, while also involving multiple organs.

[0003] Current diagnostic methods for rheumatoid arthritis (RA) are mainly based on serological markers, imaging examinations, and clinical symptoms. However, these methods have poor sensitivity and specificity, which makes the diagnosis of RA difficult.

[0004] Currently, the diagnosis of rheumatoid arthritis (RA) does not incorporate intestinal barrier damage and related cell death signals into the diagnostic evaluation system. Furthermore, there is no clear method to effectively explain whether pyroptosis activation directly leads to a decrease in tight junction proteins, and there is a lack of molecular biomarkers for combined diagnosis of RA intestinal damage. Therefore, existing technologies have not yet developed an effective technical solution that can reveal the mechanism of RA intestinal barrier damage from the perspective of pyroptosis signals and changes in tight junction proteins, and can utilize this information for auxiliary disease diagnosis. Summary of the Invention

[0005] In view of this, in order to overcome the shortcomings of the prior art, the present invention is proposed.

[0006] The purpose of this invention is to address the problems of vague research on the intestinal barrier mechanism related to rheumatoid arthritis (RA), lack of clear molecular targets, and lack of combined biomarkers that can be used to assist in diagnosis. This invention proposes an assessment scheme based on the combined indicators of pyroptosis signal activation and tight junction protein decline, thereby solving the shortcomings of existing technologies that cannot accurately reflect the mode of intestinal epithelial cell death and cannot reveal the barrier disruption mechanism at the molecular level.

[0007] The first aspect of this invention provides a method for diagnosing rheumatoid arthritis, the method being computer-based and specifically including: The expression levels of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of the subjects were obtained; The expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 were input into the constructed classification model; Based on the classification model, the classification results are output to obtain the classification result of whether the subject has rheumatoid arthritis or not.

[0008] In some implementations, the classification model is constructed using machine learning methods.

[0009] In some implementations, the machine learning method includes an algorithmic model developed using development tools.

[0010] In some implementations, the development tools include, but are not limited to, TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, VertexAI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

[0011] In some implementations, the algorithm model includes, but is not limited to, linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, Naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, and random forest model.

[0012] In this invention, the subject refers to any animal, including humans and non-human animals. Non-human animals include all vertebrates, such as mammals and non-mammals. Mammals include non-human primates, mice, rats, guinea pigs, goats, sheep, dogs, rabbits, pigs, cats, and cattle. Non-mammals include chickens, amphibians, and reptiles.

[0013] The first aspect of the present invention also provides another method for diagnosing rheumatoid arthritis, the method being computer-based and specifically comprising: To obtain data on the expression level of the biomarker NLRP3 in the colon of the subjects; Based on the NLRP3 expression level data, classification prediction is performed to obtain a classification result of whether the subject has rheumatoid arthritis; if the NLRP3 expression level is higher than the threshold, the subject is classified as having rheumatoid arthritis; if the NLRP3 expression level is lower than the threshold, the subject is classified as not having rheumatoid arthritis. In some implementations, the NLRP3 expression level data are NLRP3 protein expression level data.

[0014] A second aspect of the present invention provides a system for diagnosing rheumatoid arthritis, specifically comprising: Acquisition Unit: Acquires expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of subjects; Prediction unit: Input the expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 into the constructed classification model; Based on the classification model, the classification results are output to obtain the classification result of whether the subject has rheumatoid arthritis or not. Output unit: Outputs the classification results.

[0015] A second aspect of the present invention also provides a system for diagnosing rheumatoid arthritis, specifically comprising: Acquisition Unit: Acquires data on the expression level of the biomarker NLRP3 in the colon of the subjects; Prediction Unit: Based on the expression level data of NLRP3, a classification prediction is performed to obtain a classification result of whether the subject has rheumatoid arthritis; if the expression level of NLRP3 is higher than the threshold, a classification result of whether the subject has rheumatoid arthritis is obtained; if the expression level of NLRP3 is lower than the threshold, a classification result of whether the subject does not have rheumatoid arthritis is obtained. Output unit: Outputs the classification results; In some implementations, the NLRP3 expression level data are NLRP3 protein expression level data.

[0016] A third aspect of the present invention provides a method for constructing a classification model for diagnosing rheumatoid arthritis, the method comprising using the expression levels of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of a subject as input variables for construction.

[0017] In some implementations, the method includes constructing using machine learning methods.

[0018] In some implementations, the machine learning method includes an algorithmic model developed using development tools.

[0019] In some implementations, the development tools include, but are not limited to, TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, VertexAI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

[0020] In some implementations, the algorithm model includes, but is not limited to, linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, Naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, and random forest model.

[0021] A fourth aspect of the present invention provides a computer device / program product, including a memory and a processor, wherein the memory is used to store program instructions, and the processor is used to invoke the program instructions, and when the program instructions are executed, to perform the method described in the first aspect of the present invention.

[0022] The fifth aspect of the present invention provides any of the following methods: (1) A method for producing cells with increased Claudin-1 protein expression levels, the method comprising treating the cells with disulfiram; In some embodiments, the cells are intestinal epithelial cells; (2) A method for constructing a cell model of intestinal barrier damage, the method comprising treating cells with DMB; In some implementations, the cells are intestinal epithelial cells.

[0023] In some implementations, the concentration of DMB used is 10 nM to 10 μM.

[0024] In some embodiments, the concentration of DMB used is 10 nM, 100 nM, 1 μM, 5 μM, or 10 μM.

[0025] In some embodiments, the concentration of the intestinal epithelial cell line used is 1-10x10. 5 / ml.

[0026] In some embodiments, the concentration of the intestinal epithelial cell line used is 5 x 10⁻⁶. 5 / ml.

[0027] The sixth aspect of the present invention provides any of the following applications: (1) Application of reagents for detecting biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of subjects in the preparation of products for diagnosing rheumatoid arthritis; In some embodiments, the reagent includes a reagent for detecting the expression level of a biomarker protein; In some embodiments, the reagent for detecting the expression level of the biomarker protein includes antibodies, antibody fragments, high-affinity polymers, peptides, peptide mimics, and nucleic acid aptamers that specifically bind to the full length of the protein encoded by the biomarker or a fragment thereof. In some embodiments, the reagent further includes a reagent for separating colon tissue from the subject; (2) Application of disulfiram in the preparation of drugs that increase the expression level of Claudin-1 protein; (3) Application of DMB in constructing a cell model of intestinal barrier damage.

[0028] The seventh aspect of the present invention provides the use of a reagent for detecting the biomarker NLRP3 in the colon of a subject in the preparation of a product for diagnosing rheumatoid arthritis.

[0029] The eighth aspect of the present invention provides the application of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in constructing models for diagnosing rheumatoid arthritis.

[0030] In this invention, the reagents for detecting biomarkers in the colon of the subject include reagents for detecting the expression levels of biomarker proteins and gene expression levels.

[0031] In some embodiments, the reagents for detecting the expression level of the biomarker protein include, but are not limited to, antibodies, antibody fragments, high-affinity polymers, peptides, peptide mimics, and nucleic acid aptamers that specifically bind to the full length of the protein encoded by the biomarker or a fragment thereof.

[0032] In some implementations, the methods for detecting the expression level of biomarker proteins include, but are not limited to, enzyme-linked immunosorbent assay (ELISA), Western blotting, immunoelectrophoresis, tissue immunostaining, immunoprecipitation analysis, radioimmunoassay, radioimmunodiffusion, complement fixation analysis, fluorescence-activated cell sorting, quality analysis, and protein microarray methods.

[0033] In some implementations, the reagents for detecting the expression level of biomarker genes include, but are not limited to, probes that specifically recognize the biomarker and primers that specifically amplify the biomarker.

[0034] The probes described in this invention can be prepared by chemical synthesis, by properly designing them with reference to known information using methods well known to those skilled in the art, and by preparing them by chemical synthesis, or by preparing a gene containing the desired nucleic acid sequence from biological material and amplifying it using primers designed for amplifying the desired nucleic acid sequence.

[0035] The primers described in this invention can be prepared by chemical synthesis, appropriately designed using methods well known to those skilled in the art and with reference to known information, and prepared by chemical synthesis.

[0036] In some implementations, the methods for detecting the expression level of biomarker genes include, but are not limited to, polymerase chain reaction (PCR), reverse transcription PCR, competitive PCR, real-time quantitative reverse transcription PCR, nuclease protection assay, in situ hybridization, nucleic acid microarray, RNA blotting, and DNA microarray.

[0037] In some implementations, the reagent is selected from reagents used to detect the expression level of biomarker proteins.

[0038] In some embodiments, the reagent also includes a reagent for separating colon tissue from the subject.

[0039] In some implementations, the reagents used to separate the colon tissue of the subject include, but are not limited to, xylene, ethanol, dewaxing solution, deionized water, PBS buffer, and antigen retrieval reagent.

[0040] In some implementations, the antigen retrieval reagent includes, but is not limited to, EDTA retrieval solution, citrate retrieval solution, or trypsin.

[0041] The ninth aspect of the present invention provides any of the following products: (1) A product for diagnosing rheumatoid arthritis, said product comprising a reagent for detecting the biomarker NLRP3 in the colon of a subject; (2) A product for diagnosing rheumatoid arthritis, said product comprising a reagent for detecting biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of a subject.

[0042] In some embodiments, the reagent also includes a reagent for separating colon tissue from the subject.

[0043] In some implementations, the product includes reagent kits, chips, test strips, and high-throughput sequencing platforms.

[0044] In some implementations, the kits include, but are not limited to, immunohistochemistry detection kits, ELISA kits, Western blot detection kits, immunochromatographic detection kits, electrochemiluminescence detection kits, and flow cytometry analysis kits.

[0045] In some implementations, the kit also includes a container, a positive control, a negative control, a buffer, an adjuvant, a solvent, and an instruction manual that describes how to use the kit for detection and how to interpret the results.

[0046] The advantages and beneficial effects of this invention are as follows: 1. This study is the first to combine pyroptosis signals with tight junction proteins into a diagnostic indicator system, which significantly improves disease discrimination and has high sensitivity and specificity. It can achieve accurate assessment and early identification of RA and can serve clinical screening, disease classification and risk assessment.

[0047] 2. An in vitro model of intestinal barrier damage dominated by pyroptosis is proposed. Based on the NLRP3-GSDMD pathway, this model can stably reproduce RA features such as decreased tight junction proteins, barrier damage and pyroptosis, solving the problem that existing models cannot reflect the real pathological mechanism.

[0048] 3. This demonstrates that the pyroptosis pathway is modifiable, providing a new target for RA treatment.

[0049] 4. Pyroptosis inhibitors can block the decline of tight junction proteins. Therefore, this invention proposes NLRP3 or GSDMD as candidate targets for the development of RA treatment, providing a new mechanistic basis for drug development.

[0050] In summary, this invention forms a closed loop from mechanism, model, detection combination to clinical application, filling many technical gaps in RA intestinal barrier research and has significant theoretical value and application prospects. Attached Figure Description

[0051] Figure 1 This is a schematic flowchart of a method for diagnosing rheumatoid arthritis provided in an embodiment of the present invention.

[0052] Figure 2 This is a schematic flowchart of another method for diagnosing rheumatoid arthritis provided in an embodiment of the present invention.

[0053] Figure 3 This is a schematic diagram of a system for diagnosing rheumatoid arthritis provided in an embodiment of the present invention.

[0054] Figure 4 This is a schematic diagram of another system for diagnosing rheumatoid arthritis provided in an embodiment of the present invention.

[0055] Figure 5 This is a schematic diagram of a computer device / program product provided in an embodiment of the present invention.

[0056] Figure 6 This image shows the immunohistochemical results and H-score of tight junction proteins ZO-1, Claudin-1, and Occludin in colon tissues of healthy individuals and patients with rheumatoid arthritis, illustrating the differences in tight junction protein expression between the two groups.

[0057] Figure 7 This diagram illustrates the process of establishing a mouse model of collagen-induced arthritis (CIA) and shows the swollen appearance of the model mouse's paw, demonstrating the typical inflammatory features of the model.

[0058] Figure 8 This diagram illustrates HE and PAS staining of ileum and colon tissue from CIA mice, used to show changes in intestinal tissue structure and goblet cell count in the model mice.

[0059] Figure 9 The diagram shows the combined ROC curves of NLRP3, ZO-1, Claudin-1, and Occludin in the colon tissue of rheumatoid arthritis patients for training set, to demonstrate the diagnostic performance of this combined index in distinguishing RA patients from healthy individuals.

[0060] Figure 10 This is a schematic diagram of the ROC curve for the combined detection of NLRP3, ZO-1, Claudin-1 and Occludin in the colon tissue of rheumatoid arthritis patients, used to demonstrate the diagnostic performance of this combined index in distinguishing RA patients from healthy individuals.

[0061] Figure 11 This is a schematic diagram of Tunel / EpCAM / DAPI immunofluorescence staining of ileal and colonic tissues from CIA mice, used to show the intestinal epithelial cell death in the model mice.

[0062] Figure 12 This diagram illustrates the immunohistochemical detection of ferroptosis-associated protein GPX4 in colon tissue of patients with rheumatoid arthritis, along with its H-Score, which characterizes the expression level of GPX4.

[0063] Figure 13 This diagram illustrates the immunohistochemical detection of NLRP3, a pyroptosis-related molecule, in the colon tissue of patients with rheumatoid arthritis, along with the H-Score results, to demonstrate the expression changes of NLRP3 in the RA state.

[0064] Figure 14 This diagram illustrates the immunohistochemical detection of GSDMD and GSDME in colon tissue of patients with rheumatoid arthritis (RA) and shows the H-Score results, which are used to demonstrate the expression of pyroptosis executive proteins in RA tissue.

[0065] Figure 15This is a schematic diagram of Western blot analysis of Claudin-1 protein levels after adding different concentrations of DMB to intestinal epithelial cells, illustrating the trend of DMB's influence on tight junction protein expression.

[0066] Figure 16 This is a schematic diagram of a Western blot assay used to reverse the decrease in Claudin-1 protein induced by DMB after the addition of disulfiram, demonstrating the ameliorative effect of the pyroptosis inhibitor on the expression of tight junction proteins. Detailed Implementation

[0067] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0068] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Figure 1 This is a schematic flowchart of a method for diagnosing rheumatoid arthritis provided by an embodiment of the present invention, specifically including: S101: Obtain expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of the subjects; S102: Input the expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 into the constructed classification model; output the classification results based on the classification model to obtain the classification result of whether the subject has rheumatoid arthritis or whether the subject does not have rheumatoid arthritis.

[0071] In this invention, the classification model refers to a mathematical model that associates the expression levels of biomarkers NLRP3, Occludin, Claudin-1, and ZO-1 with the risk or likelihood of developing rheumatoid arthritis. Any suitable mathematical method described in the prior art can be used to associate the combination of biomarkers with the disease. Those skilled in the art will have no problem selecting appropriate statistical methods to evaluate the biomarker combination of this invention and thereby obtaining a suitable mathematical algorithm.

[0072] In some implementations, the classification model is constructed using machine learning methods.

[0073] In some implementations, the machine learning method includes an algorithmic model developed using development tools.

[0074] In some implementations, the development tools include, but are not limited to, TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, VertexAI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

[0075] In some implementations, the algorithm model includes, but is not limited to, linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, Naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, and random forest model.

[0076] In some implementations, the algorithm model is selected from logistic regression models.

[0077] In some implementations, the expression levels of the biomarkers are obtained by comparing changes in tight junction proteins in intestinal epithelial cells between healthy individuals and patients with rheumatoid arthritis (RA), and by comparing the expression of tight junction proteins in colon tissues of healthy individuals and patients with RA. Colon biopsy tissues from both groups of subjects are prepared into paraffin sections, dewaxed, hydrated, and antigen-retrieved. Immunohistochemical staining is then performed with antibodies against ZO-1 (ab221547, Abcam; 1:500), Claudin-1 (ab307692, Abcam; 1:100), and Occludin (ab216327, Abcam; 1:200), respectively. Figure 6 As shown in the figure. All sections were developed with DAB and counterstained with hematoxylin. Positive areas were quantitatively scored using HALO software or image analysis software. Results showed that the staining intensity and positive area of ​​ZO-1, Claudin-1, and Occludin in RA patient tissues were significantly lower than those in the healthy control group, indicating disruption of the tight junction structure of the intestinal epithelium in RA. Statistical methods: Graphpad Prism 8.0 software was used for statistical analysis. The t-test was used for comparison between the two groups. P < 0.05 was considered statistically significant.

[0078] In some implementations, to further verify the correlation between tight junction protein alterations and the pathological state of rheumatoid arthritis, a collagen-induced arthritis model was established in C57 mice. Ten-week-old C57BL / 6J mice were used. On day 0 (primary immunization), 100 μl of an emulsion was injected subcutaneously (sc) into the base of the tail. This emulsion was prepared by mixing 50 μl of type II chicken collagen (CII, 2 mg / ml, Chondrex, catalog number 20012) and 50 μl of complete Freund's adjuvant (CFA, 5 mg / ml, Chondrex, catalog number 7023). On day 21, the same emulsion was used for a booster immunization at the base of the tail via intradermal (id) injection. Mice were continuously monitored from the start of immunization until the experimental endpoint on day 56, at which point typical paw swelling was observed. A schematic diagram of its appearance is shown below. Figure 7The mice were euthanized by CO2 inhalation at the end of the experiment. Ileal and colonic tissues from the model mice were fixed, prepared into Swiss rolls, embedded in paraffin, and cut into continuous tissue sections. The obtained sections were first stained with hematoxylin and eosin (HE). Specifically, the sections were sequentially treated with ethylene glycol ethyl ether acetate I at 37°C for 6 h, then with ethylene glycol ethyl ether acetate II at 37°C overnight, followed by treatment with ethylene glycol ethyl ether acetate III and IV at room temperature for 10–15 min each; then sequentially treated with 100% ethanol I for 10 min, 100% ethanol II for 10 min, 95% ethanol for 10 min, 90% ethanol for 10 min, and 80% ethanol for 10 min, and finally rinsed with tap water. The sections were then placed in high-resolution constant stain pretreatment solution for 1 min, followed by staining with hematoxylin solution for 10 min, and washing with water for 1 min. They were then subjected to constant stain differentiation solution for approximately 10 s, followed by washing with water for 20 s. Next, they were placed in constant stain blue solution for 2 min, followed by washing with water for 20 s. After treatment with 95% ethanol for 60 s, they were stained with eosin Y solution for 5 min. Following staining, the sections were dehydrated and mounted. The sections were then sequentially placed in anhydrous ethanol I, II, and III for 1 min each, followed by xylene I and II for 10 s each to achieve clearing. Finally, they were mounted with neutral resin, examined under a microscope, and images were acquired.

[0079] Simultaneously with the HE staining, serial sections were stained with periodic acid-Schiff (PAS). Sections were sequentially treated with environmentally friendly dewaxing and clearing solutions I and II for 15 min each, followed by 5 min each of anhydrous ethanol I and II, and 5 min of 75% ethanol, then rinsed with running water. The sections were then stained with PAS staining solution B for approximately 15 min, rinsed with tap water and distilled water, and then immersed in PAS staining solution A for approximately 30 min in the dark. Afterward, they were stained with PAS staining solution C for 30 s, rinsed with tap water, briefly differentiated with hydrochloric acid solution, rinsed again, and blued with ammonia before rinsing with running water. After dehydration with a gradient of anhydrous ethanol, the sections were cleared with xylene I and II and mounted with neutral resin.

[0080] Observation after HE and PAS staining ( Figure 8 In collagen-induced arthritis (CIA) mice, the colonic mucosal epithelial structure showed significant disorder, decreased epithelial height, reduced goblet cell count, irregular glandular structure, and focal inflammatory cell infiltration and fibrosis, consistent with the histological changes observed in rheumatoid arthritis (RA) patients. These results confirm that the decrease in tight junction proteins seen in RA patients can be reproduced in the CIA animal model.

[0081] In some implementations, the diagnostic efficacy of the combination of NLRP3, ZO-1, Claudin-1, and Occludin in colonic tissue is assessed by plotting ROC.

[0082] In some implementation schemes, ROC curves are first plotted based on the training set to initially assess model performance, and then its generalization ability is further validated using an independent external validation set. The training set can be derived from diverse samples, including structured electronic data such as electronic health records and genomic databases, as well as prospectively or retrospectively collected clinical samples.

[0083] In some implementation schemes, a combined diagnostic analysis was performed on the immunohistochemical H-score results of NLRP3, ZO-1, Claudin-1, and Occludin in the colon tissue of RA patients. The quantitative results of the aforementioned immunohistochemical analyses were imported into SPSS statistical software to construct an ROC curve model to differentiate between RA patients (19 cases) and healthy subjects (24 cases). Figure 9 The AUC value of the combined detection was significantly higher than that of the single indicator (see Table 1 for specific results), demonstrating high specificity and sensitivity. This example shows that the NLRP3 pyroptosis index combined with tight junction proteins has high diagnostic value for evaluating RA-related intestinal barrier damage.

[0084] Table 1 AUC

[0085] In some implementations, additional clinical samples (8 healthy individuals and 8 RA patients) are collected as a validation set, which are processed in the same way as the training set samples to construct an ROC curve model. Figure 10 The AUC value of the combined detection was significantly higher than that of the single indicator (see Table 2 for specific results), exhibiting higher specificity and sensitivity. This example demonstrates that the combination of the NLRP3 pyroptosis index and tight junction proteins has high diagnostic value for evaluating RA-related intestinal barrier damage.

[0086] Table 2 Validation Set AUC

[0087] In some implementation schemes, further examination of intestinal tissue damage in RA mice is conducted. We intend to examine intestinal cell death to determine the potential mechanism of decreased tight junction proteins. First, TUNEL (apoptotic staining) / EpCAM double staining was performed on the ileum and colon of CIA mice. The specific steps are as follows: After spreading the sections at 55–60 °C, they were dewaxed sequentially by placing them in xylene I and II for 10 min each, followed by immersion in 100% ethanol I, 100% ethanol II, 95% ethanol, 85% ethanol, and 75% ethanol for 5 min each, and then thoroughly washed with distilled water to achieve hydration. After washing with PBS buffer, the sections underwent heat retrieval in preheated antigen retrieval solution, heated at 95–100 °C for 15–20 min, and then naturally cooled to room temperature before washing with PBS.

[0088] TUNEL staining was then performed using the Roche In Situ CellDeath Detection Kit (Fluorescein: Cat. No. 11 684 795 910). Sections were treated with permeation buffer to enhance membrane permeability, then covered with the working solution provided in the kit (TdT enzyme and labeling solution prepared according to the instructions), and incubated in a humidified chamber at 37 °C for 60 min, protected from light. After incubation, sections were washed three times with PBS for 5 min each time. To label intestinal epithelial cells, the same sections were stained with AF647-labeled EpCAM antibody (abcam, ab313669). Pre-diluted (1:100) anti-EpCAM-AF647 antibody was added, and the sections were incubated at room temperature in the dark for 1 h, followed by washing three times with PBS. To further observe the cell nuclear structure, DAPI staining solution was added after the above staining was completed, and the sections were incubated at room temperature in the dark for about 5 minutes, and then rinsed with PBS.

[0089] After staining, the sections were mounted with anti-fluorescence attenuation mounting medium and observed and imaged under a fluorescence microscope. TUNEL positive signal was used to indicate cells with nucleic acid fragmentation, AF647-labeled EpCAM signal was used to locate intestinal epithelial cell regions, and DAPI was used to show the overall distribution of cell nuclei. Double staining revealed (…). Figure 11 The number of TUNEL-positive cells in the colonic epithelium of CIA mice was significantly higher than that in the healthy control group, and their distribution in the colon region was more concentrated, indicating that the degree of cell death was more significant.

[0090] To determine the type of cell death, the immunohistochemical expression of GPX4, a key molecule in colonic ferroptosis, was detected in RA patients. Figure 12 After scoring with HALO software, no significant difference in GPX4 expression levels was found between the RA and healthy groups, suggesting that ferroptosis was not activated.

[0091] Subsequent testing revealed pyroptosis-related molecules. Immunohistochemical results showed elevated levels of NLRP3 (Abcam, 263899, 1:200) in the colon of RA patients. Figure 13 Further examination revealed changes in the pyroptosis executive proteins GSDMD and GSDME; the results are shown in [see attached image]. Figure 14 Both pathways showed positive enhancement in RA patient tissues, suggesting activation of the pyroptosis pathway. The results of this example indicate that pyroptosis, rather than ferroptosis, is activated in the intestinal epithelial cells of RA and CIA, which is more consistent with the molecular background of decreased tight junction proteins.

[0092] In some implementations, to verify whether pyroptosis directly leads to a decrease in tight junction proteins, in the intestinal epithelial cell line Ht29 (5x10⁻¹²) 5 Different concentrations of the small compound DMB (6,7-dichloro-2-methylsulfonyl-3-N-tert-butylaminoquinoxaline, TargetMol) were added to the cells ( / ml) to activate GSDMD. After incubation for a specified time (24h), protein extraction was performed. Specifically, cells were placed in RIPA lysis buffer (Huaxingbio) containing a protease inhibitor (Thermo Fisher, catalog number 78440) and a phosphatase inhibitor (Thermo Fisher, catalog number 78428), lysed on ice, and the supernatant was collected by centrifugation. Protein quantification was then performed using the BCA method (Thermo Fisher, catalog number 23225), and the loading volume was adjusted according to the measured concentration. The protein sample was mixed with loading buffer and boiled at 95 °C for 10 min for denaturation. After separation by polyacrylamide gel electrophoresis, the sample was transferred to a PVDF membrane (Bio-Rad).

[0093] After transfer, the PVDF membrane was treated with blocking buffer and then incubated overnight at 4 °C with the corresponding primary antibody; for the detection of Claudin-1, the corresponding antibody dilution (1:1000) was used. After washing with TBST, horseradish peroxidase-labeled anti-rabbit IgG secondary antibody (1:5000) was added and incubated at room temperature for 1 h. Finally, the bands were developed and the signal intensity was recorded using a Tanon-5200 (Bio-Tanon) Western blot detection system.

[0094] Western blot results showed that the expression level of the tight junction protein Claudin-1 decreased in a dose-dependent manner with increasing DMB concentration. Figure 15 As shown, further observation revealed that after DMB treatment, the cell morphology changed significantly, with decreased cell adhesion, cells gradually becoming rounder from a spread-out state, and a significant increase in the number of floating cells, indicating that the integrity of the cell barrier was disrupted, thus constructing a cell model of intestinal barrier damage.

[0095] Cells were treated with the pyroptosis inhibitor disulfiram (MedChemExpress, HY-B0240) under the same experimental conditions, and the same protein extraction, electrophoresis, transfer, and Western blotting procedures were used for detection. The results showed that disulfiram could inhibit the DMB-induced reduction in Claudin-1 protein, resulting in a partial recovery of its expression level. Figure 15 , Figure 16 The above results indicate that in intestinal epithelial cells, activation of pyroptosis directly leads to a decrease in tight junction proteins, while inhibition of pyroptosis can block this change. This embodiment demonstrates that the pyroptosis pathway has a causal role in the disruption of tight junction proteins.

[0096] Figure 2 This is a schematic flowchart of another method for diagnosing rheumatoid arthritis provided by an embodiment of the present invention, specifically including: S201: Obtain data on the expression level of the biomarker NLRP3 in the colon of the subjects; S202: Based on the expression level data of NLRP3, perform classification prediction to obtain a classification result of whether the subject has rheumatoid arthritis; if the expression level of NLRP3 is higher than the threshold, obtain a classification result of whether the subject has rheumatoid arthritis; if the expression level of NLRP3 is lower than the threshold, obtain a classification result of whether the subject does not have rheumatoid arthritis.

[0097] In some implementations, the NLRP3 expression level data are NLRP3 protein expression level data.

[0098] Figure 3 This invention provides a system for diagnosing rheumatoid arthritis, specifically comprising: 301: Acquisition Unit: Acquires expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of subjects; 302: Prediction Unit: Input the expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 into the constructed classification model; Based on the classification model, the classification results are output to obtain the classification result of whether the subject has rheumatoid arthritis or not. 303: Output Unit: Outputs the classification results.

[0099] Figure 4 This is a schematic diagram of another system for diagnosing rheumatoid arthritis provided in an embodiment of the present invention, specifically including: 401: Acquisition Unit: Acquires data on the expression level of the biomarker NLRP3 in the colon of the subjects; 402: Prediction Unit: Based on the expression level data of NLRP3, perform classification prediction to obtain a classification result of whether the subject has rheumatoid arthritis; if the expression level of NLRP3 is higher than the threshold, obtain a classification result of whether the subject has rheumatoid arthritis; if the expression level of NLRP3 is lower than the threshold, obtain a classification result of whether the subject does not have rheumatoid arthritis. 403: Output Unit: Outputs the classification results.

[0100] In some implementations, the NLRP3 expression level data are NLRP3 protein expression level data.

[0101] Figure 5 This is a schematic diagram of a computer device / program product provided in an embodiment of the present invention.

[0102] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0104] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0107] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0108] For those skilled in the art, based on the ideas of the embodiments of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0109] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. A method for diagnosing rheumatoid arthritis, characterized in that, The method is computer-based and specifically includes: The expression levels of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of the subjects were obtained; The expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 were input into the constructed classification model; Based on the classification model, the classification results are output to obtain the classification result of whether the subject has rheumatoid arthritis or not.

2. The method according to claim 1, characterized in that, The classification model is constructed using machine learning methods.

3. The method according to claim 2, characterized in that, The machine learning methods include algorithmic models developed using development tools.

4. The method according to claim 3, characterized in that, The development tools include TensorFlow, ScikitLearn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

5. The method according to claim 4, characterized in that, The algorithm models include linear regression, logistic regression, Lasso regression, Ridge regression, linear discriminant analysis, nearest neighbor, decision tree, perceptron, neural network, support vector machine, naive Bayes, AdaBoost, GBDT, XGBoost, LightGBM, CatBoost, and random forest.

6. A system for diagnosing rheumatoid arthritis, characterized in that, Specifically, it includes: Acquisition Unit: Acquires expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of subjects; Prediction unit: Input the expression level data of biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 into the constructed classification model; Based on the classification model, the classification results are output to obtain the classification result of whether the subject has rheumatoid arthritis or not. Output unit: Outputs the classification results.

7. A method for constructing a classification model for diagnosing rheumatoid arthritis, characterized in that, The method includes constructing a system using the expression levels of biomarkers NLRP3, Occludin, Claudin-1, and ZO-1 in the colon of the subject as input variables; Optionally, the method includes constructing using machine learning methods; Optionally, the machine learning method includes an algorithmic model developed using development tools; Optionally, the development tools include TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell. Optionally, the algorithm model includes linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, Naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, and random forest model.

8. A computer device / program product, characterized in that, The method includes a memory and a processor, wherein the memory is used to store program instructions and the processor is used to invoke the program instructions, and when the program instructions are executed, they are used to perform the method according to any one of claims 1-5.

9. Any one of the following methods: (1) A method for producing cells with increased Claudin-1 protein expression levels, characterized in that, The method includes treating cells with disulfiram; Optionally, the cells are intestinal epithelial cells; (2) A method for constructing a cell model of intestinal barrier damage, characterized in that the method includes treating cells with DMB; Optionally, the cells are intestinal epithelial cells.

10. Any of the following applications: (1) Application of reagents for detecting biomarkers NLRP3, Occludin, Claudin-1 and ZO-1 in the colon of subjects in the preparation of products for diagnosing rheumatoid arthritis; Optionally, the reagents include reagents for detecting the expression level of biomarker proteins; Optionally, the reagent for detecting the expression level of the biomarker protein includes antibodies, antibody fragments, high-affinity polymers, peptides, peptide mimics, and nucleic acid aptamers that specifically bind to the full length of the protein encoded by the biomarker or a fragment thereof. Optionally, the reagent further includes a reagent for separating the colon tissue of the subject; (2) Application of disulfiram in the preparation of drugs that increase the expression level of Claudin-1 protein; (3) Application of DMB in constructing a cell model of intestinal barrier damage.

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