Device for determining relation between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ interferences

Abstract

The invention relates to a device for determining an anti-double-stranded DNAIgG glycosylation and systemic lupus erythematosus organ encumbent relationship, which comprises the following steps: constructing coarse-grained classification models, training the coarse-grained classification models by adopting pairwise combined anti-double-stranded DNAIgG glycoforms, evaluating a plurality of trained coarse-grained classification models, and determining the relationship between the anti-double-stranded DNAIgG glycosylation and systemic lupus erythematosus organ encumbent relationship and the systemic lupus erythematosus organ encumbent relationship. Finding out a plurality of trained coarse-grained classification models ranked in the top from the evaluation result; constructing a fine-grained classification model, training the fine-grained classification model by adopting input corresponding to a plurality of trained coarse-grained classification models ranked in the top, evaluating the plurality of trained fine-grained classification models, and finding out the trained fine-grained classification model with the best evaluation result; and inputting the trained fine-grained classification model with the best evaluation result as a main index for predicting systemic lupus erythematosus organ fatigue. According to the invention, the relationship between anti-double-stranded DNAIgG glycosylation and SLE clinical manifestation can be found.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence-assisted medical technology, and in particular to a device for determining the relationship between anti-double-stranded DNA IgG glycosylation level and systemic lupus erythematosus. Background Art

[0002] Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease with diverse clinical manifestations and involvement of multiple organ systems. The heterogeneity of the disease presents significant challenges for assessment and treatment, requiring a nuanced understanding of the underlying pathophysiological mechanisms. Recent advances in the field of immunology have revealed a critical role for immunoglobulin G (IgG) glycosylation in the pathogenesis of SLE, providing a potential avenue for the development of more precise diagnostic and prognostic tools. Glycosylation is a post-translational modification that can significantly affect the biological properties of antibodies, including their effector functions, stability, and interactions with immune cells. In SLE, alterations in IgG glycosylation patterns have been observed that may be relevant to disease pathogenesis, for example, changes in IgG Fc glycosylation, specifically decreased galactosylation and increased focalization, have been found to be associated with disease activity and progression in SLE. In the broader context of rheumatic diseases, glycosylation has been studied in rheumatoid arthritis (RA), where it is known to affect the function of antibodies and immune cells. For example, the glycosylation signature of the IgG Fc domain in RA is a shift toward galactosylation, which is associated with increased inflammation and disease severity. This finding suggests that glycosylation changes may play a common role in the pathogenesis of different rheumatic diseases and may affect disease progression and organ-specific manifestations.

[0003] The relationship between IgG glycosylation and organ involvement in SLE is an area of ​​active research. It has been hypothesized that alterations in glycosylation may contribute to the deposition of immune complexes in target organs, leading to inflammation and tissue damage. Fucosylation, galactosylation, dichotomization, and sialylation of IgG subclasses play an important role in this process. For example, the glycosylation pattern of IgG has been associated with the severity of nephritis in patients with SLE. In addition, the glycosylation profile of IgG has emerged as a promising biomarker for SLE, with different glycosylation patterns associated with different disease outcomes. Anti-dsDNA IgG is a specific antibody for SLE, and its glycosylation may affect its pathogenic potential in SLE. Abnormal glycosylation can modulate the ability of these antibodies to activate the complement system, bind to Fcγ receptors on immune cells, and form immune complexes, which are associated with the development of lupus nephritis. In addition, the glycosylation status of anti-dsDNA IgG can be used as a biomarker for disease activity and organ involvement in SLE.

[0004] However, the relationship between anti-dsDNA antibody glycosylation and SLE organ involvement is still unclear, and there is currently no effective method to evaluate and predict SLE organ involvement by anti-dsDNA antibody glycosylation levels. The complexity of these glycosylation patterns and their interaction with clinical symptoms require sophisticated analytical methods to fully elucidate their significance. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement, which can find the relationship between anti-double-stranded DNA IgG glycosylation and SLE clinical manifestations.

[0006] The technical solution adopted by the present invention to solve the technical problem is: to provide a device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement, comprising:

[0007] The first building block is used to build a coarse-grained classification model, wherein the input of the coarse-grained classification model is a combination of different anti-double-stranded DNA IgG glycoforms, and the output is the degree of organ system involvement;

[0008] The first training module is used to train the coarse-grained classification model using anti-double-stranded DNA IgG glycoforms in pairs to obtain multiple trained coarse-grained classification models, and evaluate the multiple trained coarse-grained classification models, and find out several trained coarse-grained classification models with top rankings from the evaluation results;

[0009] A second construction module is used to construct a fine-grained classification model, the input of the fine-grained classification model is the input corresponding to the top-ranked trained coarse-grained classification models, and the output is the organ system involvement score;

[0010] The second training module is used to train the fine-grained classification model using the inputs corresponding to the top-ranked trained coarse-grained classification models to obtain several trained fine-grained classification models, and evaluate the several trained fine-grained classification models to find the trained fine-grained classification model with the best evaluation result;

[0011] The indicator determination module is used to use the input of the trained fine-grained classification model with the best evaluation result as the main indicator for predicting organ involvement of systemic lupus erythematosus.

[0012] The coarse-grained classification model is a random forest model.

[0013] The anti-double-stranded DNA IgG glycoforms include IgG3 / 4Bis, IgG1Gal, IgG1Fuc, IgG2Sia, IgG2Gal, IgG3 / 4Sia, IgG3 / 4Gal, IgG2Fuc, IgG1Sia, IgG3 / 4Fuc, IgG2Bis and IgG1Bis.

[0014] When the first training module uses anti-double-stranded DNA IgG glycoforms in pairs to train the coarse-grained classification model, each combination trains the coarse-grained classification model K times, and the average AUC of the coarse-grained classification model trained K times is used as an evaluation indicator of the trained coarse-grained classification model.

[0015] The fine-grained classification model is an artificial neural network model.

[0016] When the second training module evaluates a number of trained fine-grained classification models, mean square error is used as an evaluation indicator of the number of trained fine-grained classification models.

[0017] The device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement also includes:

[0018] A third training module, wherein the third training module is used to perform three and four combinations of the anti-double-stranded DNA IgG glycoforms in the input corresponding to the top-ranked trained coarse-grained classification models, and use the combined anti-double-stranded DNA IgG glycoforms to train the coarse-grained classification models to obtain N trained coarse-grained classification models, and evaluate the N trained coarse-grained classification models;

[0019] A judgment module, used to judge whether the coarse-grained classification model with the best evaluation result among N trained coarse-grained classification models is better than the coarse-grained classification model with the best evaluation result among several trained coarse-grained classification models;

[0020] A first determination module is used to use the coarse-grained classification model with the best evaluation result among the N trained coarse-grained classification models as the final classification model when the coarse-grained classification model with the best evaluation result among the N trained coarse-grained classification models is better than the coarse-grained classification model with the best evaluation result among several trained coarse-grained classification models;

[0021] The second determination module is used to use the coarse-grained classification model with the best evaluation results among N trained coarse-grained classification models as the final classification model when the coarse-grained classification model with the best evaluation results among several trained coarse-grained classification models is not better than the coarse-grained classification model with the best evaluation results among several trained coarse-grained classification models.

[0022] Beneficial Effects

[0023] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention finds out the relationship between anti-double-stranded DNA IgG glycosylation and SLE clinical manifestations through relatively non-invasive examination, so as to correctly evaluate the systemic involvement of SLE patients, which helps doctors to evaluate the patient's condition at an early stage, intervene in time, formulate appropriate treatment plans, and improve the patient's prognosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic diagram of a device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement according to an embodiment of the present invention;

[0025] Figure 2 Schematic diagram of the AUC value of the coarse-grained model corresponding to each anti-double-stranded DNA IgG glycoform in an embodiment of the present invention;

[0026] Figure 3 Schematic diagram of the results of the combination of anti-double-stranded DNA IgG glycoforms in the random forest classification model according to the embodiment of the present invention;

[0027] Figure 4 It is a diagram of the prediction result of the organ involvement degree of the artificial neural network in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0029] The embodiments of the present invention relate to a device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement, such as Figure 1 As shown, it includes: a first construction module, a first training module, a second construction module, a second training module and an indicator determination module.

[0030] The first construction module is used to construct a coarse-grained classification model, the input of which is a combination of different anti-double-stranded DNA IgG glycoforms, and the output is the degree of organ system involvement. The first training module is used to train the coarse-grained classification model using anti-double-stranded DNA IgG glycoforms in pairs to obtain multiple trained coarse-grained classification models, and evaluate the multiple trained coarse-grained classification models, and find out several trained coarse-grained classification models with high rankings from the evaluation results.

[0031] In this embodiment, the coarse-grained classification model adopts a random forest model. The random forest model is a machine learning algorithm widely used in classification tasks. It trains multiple decision trees and combines their integrated outputs to determine which class the sample should be assigned to. In this embodiment, the random forest algorithm is executed by the scikit-learn module on python 3.10. In order to make the results more convincing, the samples are divided into 2 groups in this embodiment, 70% are used for the training set, and the remaining 30% are used for the test set. In order to improve the performance of the random forest model, the bootstrap technology is also used when sampling the data set. Different types of glycosylation of anti-double-stranded DNA IgG subclasses, including fucosylation (Fuc), galactosylation (Gal), dichotomization (Bis) and sialylation (Sia) are used as classification criteria, and the degree of organ involvement (HIGH or LOW) is set as the output result of the random forest model. Among them, the HIGH group represents patients with 3 or more organ systems involved, and the LOW group represents patients with 2 or less organ systems involved.

[0032] In order to verify the effectiveness of the model and compare the performance of different anti-dsDNA IgG glycoforms, the present embodiment applies the model to the samples in the test set. Then, the area under the receiver operating characteristic (ROC) curve (AUC) and the classification accuracy of the test set are calculated as evaluation means. It is worth noting that the present embodiment does not use a single anti-dsDNA IgG glycoform of a single IgG subclass as input each time, but uses a pair of anti-dsDNA IgG glycoforms each time to improve the coarse-grained classification model. A pair of different anti-dsDNA IgG glycoforms usually obtains a higher AUC value than using one of the anti-dsDNA IgG glycoforms alone, which indicates that the combination of two anti-dsDNA IgG glycoforms is better than using one anti-dsDNA IgG glycoform alone, because a pair of anti-dsDNA IgG glycoforms carries more information than using one anti-dsDNA IgG glycoform alone.

[0033] In this embodiment, a coarse-grained classification model is trained 100 times for each anti-double-stranded DNA IgG glycoform, and the average AUC of the 100 coarse-grained classification models is used as the AUC of the coarse-grained classification model corresponding to the anti-double-stranded DNA IgG glycoform. The anti-double-stranded DNA IgG glycoforms are arranged in descending order according to the average AUC value obtained by combining them with all 12 anti-double-stranded DNA IgG glycoforms. Figure 2 As shown, the classification abilities from front to back are: IgG3 / 4Bis, IgG1Gal, IgG1Fuc, IgG2Sia, IgG2Gal, IgG3 / 4Sia, IgG3 / 4Gal, IgG2Fuc, IgG1Sia, IgG3 / 4Fuc, IgG2Bis, IgG1Bis. Figure 2 A trend can be clearly seen in the data that the higher-ranked anti-dsDNA IgG glycoform combinations have better AUC values ​​overall than the lower-ranked anti-dsDNA IgG glycoform combinations. In addition, some anti-dsDNA IgG glycoforms have higher AUC values ​​when combined with most other anti-dsDNA IgG glycoforms, indicating that they do have a significant impact on the severity of organ system involvement in patients. The average AUC of the anti-dsDNA IgG glycoform pair IgG1Gal&IgG3 / 4Bis reached 0.6300, exceeding other anti-dsDNA IgG glycoform pairs, indicating that the level of the IgG1Gal&IgG3 / 4Bis glycoform pair is best correlated with SLE organ involvement. At the same time, the AUC values ​​of several other low-ranked anti-dsDNA IgG glycoform pairs (such as IgG1Bis&IgG3 / 4Fuc, IgG1Bis&IgG2Bis) were lower than 0.5, indicating that they could not classify the degree of organ involvement.

[0034] The apparatus for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement in this embodiment may further include: a third training module, the third training module being used to train the anti-double-stranded DNA in the input corresponding to the top-ranked several trained coarse-grained classification models; The IgG glycoforms are combined into three or four combinations, and the combined anti-double-stranded DNA IgG glycoforms are used to train the coarse-grained classification model to obtain N trained coarse-grained classification models, and the N trained coarse-grained classification models are evaluated; a judgment module is used to judge whether the coarse-grained classification model with the best evaluation result among the N trained coarse-grained classification models is better than the coarse-grained classification model with the best evaluation result among several trained coarse-grained classification models; a first determination module is used to use the coarse-grained classification model with the best evaluation result among the N trained coarse-grained classification models as the final classification model when the coarse-grained classification model with the best evaluation result among the N trained coarse-grained classification models is better than the coarse-grained classification model with the best evaluation result among several trained coarse-grained classification models; a second determination module is used to use the coarse-grained classification model with the best evaluation result among several trained coarse-grained classification models as the final classification model when the coarse-grained classification model with the best evaluation result among the N trained coarse-grained classification models is not better than the coarse-grained classification model with the best evaluation result among several trained coarse-grained classification models.

[0035] After obtaining the classification ability ranking of each anti-double-stranded DNA IgG glycoform, select several top-ranked anti-double-stranded DNA IgG glycoforms, and re-combine them in groups of three and four, and implement the RF algorithm again to generate an RF model with the best classification performance. After the above operation, N trained new coarse-grained classification models can be obtained, which are evaluated by the classification accuracy and AUC of the model, and provide evidence of the sugar combination that best characterizes the degree of organ involvement in SLE. This embodiment tries a variety of combinations, and the classification accuracy of 3 anti-double-stranded DNA IgG glycoform combinations and 4 anti-double-stranded DNA IgG glycoform combinations is not much different from the classification accuracy of sugar pairs. In addition, the combination results of more than 3 glycoforms lack significance. Therefore, this embodiment can use the coarse-grained classification model with the best evaluation result among the coarse-grained classification models trained in the first training module as the final classification model.

[0036] This embodiment studies and compares the classification ability of sugar pairs by implementing the RF algorithm. Among a few sugar pairs, when the RF model is applied to the test set, the anti-double-stranded DNA IgG1Gal&IgG3 / 4Bis and IgG1Fuc&IgG3 / 4Bis combinations (see Figure 3 a and c) in the above table have the highest classification accuracy for the degree of organ involvement, followed by the IgG1Fuc&IgG2Sia and IgG2Sia&IgG3 / 4Bis combinations (see Figure 3 d and f in Figure 3). However, due to the limited size of the test set, the accuracy was unable to distinguish certain anti-dsDNA IgG glycoform combinations. To further validate the results, the AUC was calculated to make the classification performance more reliable. Figure 3 In g and l, the corresponding ROC curves with the highest AUC values ​​among all trained RF models are plotted, and the 95% CI is given to demonstrate the effectiveness of each anti-dsDNA IgG glycoform pair. Figure 3 g) also has the highest AUC value (0.8187) and 95% confidence interval (0.6111, 0.6388). Anti-double-stranded DNA IgG1Fuc&IgG3 / 4Bis( Figure 2 i) and IgG1Gal&IgG3 / 4Bis( Figure 2 a) has the same highest accuracy ( Figure 3 c), but the AUC value was lower (0.7820), 95% CI (0.5204, 0.5673). Other combinations such as IgG1Gal&IgG2Gal and IgG2Sia&IgG3 / 4Bis ( Figure 3k and 1) in the classification of organ system involvement were also slightly weaker. Among the two evaluation indicators, anti-dsDNA IgG1Gal&IgG3 / 4Bis performed best in the classification task. Moreover, these two anti-dsDNA IgG glycoforms Figure 2 This indicates that they are indeed strongly correlated with the degree of involvement and their combination can serve as an important indicator of SLE organ involvement.

[0037] The second construction module in this embodiment is used to construct a fine-grained classification model, the input of the fine-grained classification model is the input corresponding to the top-ranked several trained coarse-grained classification models, and the output is the organ system involvement score; the second training module is used to train the fine-grained classification model using the input corresponding to the top-ranked several trained coarse-grained classification models to obtain several trained fine-grained classification models, and evaluate the several trained fine-grained classification models to find the trained fine-grained classification model with the best evaluation result; the indicator determination module is used to use the input of the trained fine-grained classification model with the best evaluation result as the main indicator for predicting organ involvement of systemic lupus erythematosus.

[0038] The fine-grained classification model of this embodiment can be an artificial neural network (ANN) model, which can define the degree of involvement as a continuous value between 0 and 1. A patient with an involved organ number x is assigned an involvement rate of x / 7. Since the number of organ systems involved in the actual sample can only be an integer, there are only a few possible values ​​for the actual involvement rate. However, the predicted involvement rate can be any value between 0 and 1, reflecting the severity of the impact on multiple organ systems. It was finally found that the ANN model of anti-double-stranded DNA IgG1Gal&IgG3 / 4Bis had the lowest MSE value among all sugar combinations (see Figure 4 ). Compared with other sugar combinations, the trends of the red and blue lines are more similar. The results show that when the anti-double-stranded DNA IgG1Gal&IgG3 / 4Bis is used to train the model, the difference between the predicted value and the actual value is the smallest, and it can better predict the degree of organ involvement, which is consistent with the previous classification results. Therefore, anti-double-stranded DNA IgG1Gal&IgG3 / 4Bis is used as the main indicator for predicting systemic SLE organ involvement.

[0039] It is not difficult to find that the present invention finds out the relationship between anti-double-stranded DNA IgG glycosylation and SLE clinical manifestations through relatively non-invasive examination, so as to correctly evaluate the systemic involvement of SLE patients, which helps doctors to evaluate the patient's condition early, intervene in time, formulate appropriate treatment plans, and improve the patient's prognosis.

Claims

1. A device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement, characterized in that: include: The first construction module is used to construct a coarse-grained classification model, wherein the input of the coarse-grained classification model is a combination of different anti-double-stranded DNA IgG glycoforms, and the output is the degree of organ system involvement; The first training module is used to train the coarse-grained classification model using anti-double-stranded DNA IgG glycoforms in pairs to obtain multiple trained coarse-grained classification models, and evaluate the multiple trained coarse-grained classification models, and find out several trained coarse-grained classification models with top rankings from the evaluation results; A second construction module is used to construct a fine-grained classification model, the input of the fine-grained classification model is the input corresponding to the top-ranked trained coarse-grained classification models, and the output is the organ system involvement score; The second training module is used to train the fine-grained classification model using the inputs corresponding to the top-ranked trained coarse-grained classification models to obtain several trained fine-grained classification models, and evaluate the several trained fine-grained classification models to find the trained fine-grained classification model with the best evaluation result; The indicator determination module is used to use the input of the trained fine-grained classification model with the best evaluation result as the main indicator for predicting organ involvement of systemic lupus erythematosus.

2. The device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement according to claim 1, characterized in that: The coarse-grained classification model is a random forest model.

3. The device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement according to claim 1, characterized in that: The anti-double-stranded DNA antibody IgG glycoforms include IgG3 / 4Bis, IgG1Gal, IgG1Fuc, IgG2Sia, IgG2Gal, IgG3 / 4Sia, IgG3 / 4Gal, IgG2Fuc, IgG1Sia, IgG3 / 4Fuc, IgG2Bis and IgG1Bis.

4. The device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement according to claim 1, characterized in that: When the first training module uses anti-double-stranded DNA IgG glycoforms in pairs to train the coarse-grained classification model, each combination trains the coarse-grained classification model K times, and the average AUC of the coarse-grained classification model trained K times is used as an evaluation indicator of the trained coarse-grained classification model.

5. The device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement according to claim 1, characterized in that: The fine-grained classification model is an artificial neural network model.

6. The device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement according to claim 1, characterized in that: When the second training module evaluates a number of trained fine-grained classification models, mean square error is used as an evaluation indicator of the number of trained fine-grained classification models.

7. The device for determining the relationship between anti-double-stranded DNA IgG glycosylation and systemic lupus erythematosus organ involvement according to claim 1, characterized in that: Also includes: A third training module, wherein the third training module is used to perform three and four combinations of the anti-double-stranded DNA IgG glycoforms in the input corresponding to the top-ranked trained coarse-grained classification models, and use the combined anti-double-stranded DNA IgG glycoforms to train the coarse-grained classification models to obtain N trained coarse-grained classification models, and evaluate the N trained coarse-grained classification models; A judgment module, used to judge whether the coarse-grained classification model with the best evaluation result among N trained coarse-grained classification models is better than the coarse-grained classification model with the best evaluation result among several trained coarse-grained classification models; A first determination module is used to use the coarse-grained classification model with the best evaluation result among the N trained coarse-grained classification models as the final classification model when the coarse-grained classification model with the best evaluation result among the N trained coarse-grained classification models is better than the coarse-grained classification model with the best evaluation result among several trained coarse-grained classification models; The second determination module is used to use the coarse-grained classification model with the best evaluation results among N trained coarse-grained classification models as the final classification model when the coarse-grained classification model with the best evaluation results among several trained coarse-grained classification models is not better than the coarse-grained classification model with the best evaluation results among several trained coarse-grained classification models.

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