A stress index analysis method and system for anti-calcium oxalate damage effect

Abstract

The application relates to the technical field of detection analysis, and specifically discloses a stress index analysis method and system for resisting calcium oxalate damage, which comprises the following steps: in the anti-calcium oxalate damage database, extracting oxidative stress, endoplasmic reticulum stress and cross-node indicators, establishing a stress index database, carrying out interactive analysis through regulation, judging whether there is a cross-regulation relationship, if there is, carrying out specific detection analysis, combining oxidative stress inhibitors and endoplasmic reticulum stress inhibitors, setting up a control experiment to compare the fluctuation difference of response indicators, screening specific response indicators, carrying out correlation analysis, obtaining the correlation between the indicators, combining difference detection, confirming a key regulation path, establishing a stress index prediction model, inputting stress index data in the key regulation path, and predicting the degree of calcium oxalate damage, which is beneficial to improving the accuracy and efficiency of stress index analysis for resisting calcium oxalate damage.

Description

Technical Field

[0001] This invention relates to the field of detection and analysis technology, specifically to a method and system for analyzing stress indicators related to resistance to calcium oxalate damage. Background Technology

[0002] In studies of the mechanisms of resistance to calcium oxalate injury, existing techniques often analyze changes in indices of the two types of stress separately, lacking precise verification of the cross-regulatory relationship between the two types of stress. Furthermore, the screening of stress indices often fails to distinguish specificity, resulting in confounding indices and significant non-specific interference, making it difficult to clearly identify the mediating role of molecules at the cross-regulatory nodes involved in both types of stress. This leads to insufficient accuracy in elucidating the stress mechanisms of resistance to calcium oxalate injury. The cross-regulation of oxidative stress and endoplasmic reticulum stress has been proven to be of great significance in various diseases, such as amygdala neuronal damage caused by epileptic seizures.

[0003] Current technologies for analyzing calcium oxalate damage lack the step of constructing core regulatory pathways based on specific stress indicators, making it difficult to distinguish between regulatory modes dominated by oxidative stress, endoplasmic reticulum stress, and the interaction of the two. Furthermore, they often rely on single indicators for damage assessment without combining data from key regulatory pathways to establish accurate predictive models. This results in poor reliability in predicting the degree and development trend of calcium oxalate damage, failing to provide reliable evidence for early intervention and progress monitoring of calcium oxalate damage, and limiting the practicality and translational value of calcium oxalate damage analysis methods.

[0004] Therefore, the present invention provides a method and system for analyzing stress indicators of resistance to calcium oxalate damage. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for analyzing stress indicators of resistance to calcium oxalate damage, so as to solve the above-mentioned background problems.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for analyzing stress indicators of resistance to calcium oxalate injury, comprising: In the calcium oxalate injury resistance database, oxidative stress, endoplasmic reticulum stress and cross-node indicators were extracted, the detection data of each indicator were integrated, a stress indicator database was established, and through the interaction analysis of regulatory effects, it was determined whether there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress. If a cross-regulatory relationship exists, specific detection and analysis will be performed. Combining oxidative stress inhibitors and endoplasmic reticulum stress inhibitors, specific response indicators will be screened by setting up control experiments to compare the fluctuation differences of response indicators. Based on the obtained specific response indicators, correlation analysis was conducted to obtain the correlation between each specific response indicator, and combined with differential detection, key regulatory pathways were identified. Based on key regulatory pathways, a stress index prediction model for the effect of resisting calcium oxalate damage was established. By inputting stress index data from key regulatory pathways, the degree of calcium oxalate damage was predicted.

[0007] Furthermore, establish a stress indicator database: Oxidative stress, endoplasmic reticulum stress, and cross-node indices were extracted from the calcium oxalate injury resistance database. Oxidative stress, endoplasmic reticulum stress, and cross-node indices under the same detection method were summarized and integrated to obtain a stress index database.

[0008] Furthermore, determine whether there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress: In the stress index database, the detection data of oxidative stress index, endoplasmic reticulum stress index and cross node index are grouped according to sample type, including normal group and calcium oxalate injury group. The correlation between oxidative stress index detection data and endoplasmic reticulum stress index detection data was analyzed using Pearson correlation coefficient to obtain the Pearson correlation coefficient between oxidative stress index and endoplasmic reticulum stress index. If the Pearson correlation coefficient is greater than or equal to the correlation coefficient threshold, then there is a correlation between oxidative stress and endoplasmic reticulum stress. Based on the existence of correlation, the changes in cross-node indices under oxidative stress and endoplasmic reticulum stress conditions were analyzed. By comparing the changes in cross-node indices between the normal group and the calcium oxalate injury group, it was determined whether there was a cross-regulatory relationship.

[0009] Furthermore, by comparing the changes in cross-node index data between the normal group and the calcium oxalate injury group, it was determined whether a cross-regulatory relationship existed: The t-test was used to examine the differences in the crossover node indices. If the cross-node index responds to both oxidative stress and endoplasmic reticulum stress and shows a consistent trend, then the corresponding cross-node index is marked as the response node index. Obtain the detection time point information of oxidative stress, endoplasmic reticulum stress and cross node indicators, and establish a time change curve with the detection time point as the x-axis and the detection data value of the stress indicators as the y-axis. Based on the order of changes in each indicator within the time-varying curve, the cross-regulation relationship is confirmed.

[0010] Furthermore, specific response indicators were screened out: Set up control groups, and conduct index tests on each control group according to the detection time points to obtain the detection values ​​of each index for each group; For the same index detection values ​​in multiple parallel replicate experimental groups within each control experimental group, mean and standard deviation processing were performed to obtain the index detection mean and index detection standard deviation; By comparing the mean values ​​of the same indicators in each group, if the activity or expression level of the indicator is significantly increased or significantly decreased in the calcium oxalate injury group, and the increasing or decreasing trend is suppressed in the oxidative stress inhibitor intervention group, while there is no significant change in the endoplasmic reticulum stress inhibitor intervention group, then the corresponding indicator is marked as an oxidative stress-specific response indicator. If the activity or expression level of an indicator is significantly increased or decreased in the calcium oxalate injury group, the increasing or decreasing trend is significantly suppressed in the endoplasmic reticulum stress inhibitor intervention group, and there is no significant change in the oxidative stress inhibitor intervention group, then the corresponding indicator is marked as an endoplasmic reticulum stress-specific response indicator. If the activity or expression level of an indicator is significantly inhibited in the combined inhibitor intervention group and the inhibition effect is not obvious in the single inhibitor intervention group, the corresponding indicator is marked as a cross-specific response indicator.

[0011] Furthermore, correlation analysis was conducted to obtain the relationships between various specific response indicators: The specific response indicators of oxidative stress, endoplasmic reticulum stress, and cross-specific response indicators were summarized to obtain a specific response indicator summary library. Using the Pearson correlation coefficient, correlation analysis was performed on all specific response indicators in the specific response indicator summary database. The correlation coefficient matrix between specific response indicators was calculated and compared with the correlation coefficient threshold to screen out specific response indicator pairs with strong correlation.

[0012] Furthermore, by combining differential detection, key regulatory pathways were identified: For each regulatory pathway consisting of strongly correlated indices, the expression levels of all indices in the pathway were compared between the calcium oxalate injury group and the normal group using a t-test. If all indices in the pathway showed statistically significant differences in the injury group and the trend of change was consistent, the regulatory pathway was determined to be activated during calcium oxalate injury. If the abnormal changes of all indicators on the regulatory pathway are repaired in the oxidative stress inhibitor intervention group but not in the endoplasmic reticulum stress inhibitor intervention group, then the corresponding regulatory pathway is identified as a key regulatory pathway dominated by oxidative stress. If abnormal changes in pathway indicators are repaired only in the endoplasmic reticulum stress inhibitor intervention group but not in the oxidative stress inhibitor intervention group, it is determined to be a key regulatory pathway dominated by endoplasmic reticulum stress. If abnormal changes in indicators along a regulatory pathway are not repaired in any single inhibitor intervention group but are repaired in the combined inhibitor intervention group, then the corresponding regulatory pathway is identified as a key regulatory pathway of the interaction between oxidative stress and endoplasmic reticulum stress.

[0013] Furthermore, a stress index prediction model for resistance to calcium oxalate injury was established: Extract the detection data of specific response indicators on each path under experimental conditions to form a modeling dataset. Each modeling dataset includes indicator values, detection time points, and corresponding damage degree evaluation data. The stress index data in the modeling dataset is input into the deep learning model for training; The modeling dataset is divided into training, testing, and validation sets according to a certain ratio. The training set is used to train the model, and the parameters are optimized through cross-validation. The model's predictive performance is evaluated on the testing set, and the prediction accuracy is measured using mean squared error and coefficient of determination. After training, the model performance is evaluated using a test set to determine whether the prediction accuracy requirements have been met. If the expected standard is not met, the model parameters are adjusted, and the training and evaluation are repeated. If the expected standard is met, the model parameters are saved to obtain the stress index prediction model.

[0014] Furthermore, predict the extent of calcium oxalate damage: Stress index data from key regulatory pathways are input into a stress index prediction model, which outputs the degree of calcium oxalate damage.

[0015] Furthermore, a stress index analysis system for resisting calcium oxalate damage is characterized by comprising the following modules: Extraction and confirmation module: Extract oxidative stress, endoplasmic reticulum stress and cross-node indicators from the anti-calcium oxalate damage database, integrate the detection data of each indicator, establish a stress indicator database, and determine whether there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress through interactive analysis of regulatory effects. Screening and analysis module: If a cross-regulatory relationship exists, specific detection and analysis will be performed. Combining oxidative stress inhibitors and endoplasmic reticulum stress inhibitors, specific response indicators will be screened by setting up control experiments to compare the fluctuation differences of response indicators. Path analysis module: Based on the obtained specific response indicators, correlation analysis is used to obtain the correlation between each specific response indicator, and combined with difference detection, key regulatory pathways are identified. Model building module: Based on key regulatory pathways, a stress index prediction model for the effect of calcium oxalate damage is established. By inputting stress index data from key regulatory pathways, the degree of calcium oxalate damage is predicted.

[0016] The beneficial effects of this invention are as follows: 1. This invention addresses the problems of unclear cross-regulatory relationships between oxidative stress and endoplasmic reticulum stress, and insufficient specificity of stress indicators in studies of calcium oxalate injury. It extracts oxidative stress, endoplasmic reticulum stress, and cross-node indicators from a calcium oxalate injury database and integrates them to establish a stress indicator database. The cross-regulatory relationship is verified through correlation coefficient analysis and t-tests. Furthermore, it screens oxidative stress-specific, endoplasmic reticulum stress-specific, and cross-specific response indicators, clarifying the cross-regulatory relationship between the two types of stress and eliminating non-specific interference. This lays a precise data foundation for the subsequent identification of key regulatory pathways and improves the relevance and accuracy of the analysis of calcium oxalate injury mechanisms.

[0017] 2. This invention addresses the problems of lacking core regulatory pathways and poor reliability in predicting the degree and trend of damage in anti-calcium oxalate injury research. First, specific response indicators are summarized, and strongly correlated indicator pairs are screened using the Pearson correlation coefficient. Through damage response verification, inhibitor intervention and repair verification, and combined with detection time points, the key regulatory pathways for anti-calcium oxalate injury are integrated. Then, detection data of indicators on the key pathways are extracted to construct a stress indicator prediction model, clarifying the core regulatory modes of two types of stress. This also achieves accurate quantitative prediction of the degree and development trend of calcium oxalate injury, improving the accuracy and efficiency of stress indicator analysis for anti-calcium oxalate injury effects. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart of the steps of a stress index analysis method for resisting calcium oxalate damage according to an embodiment of the present invention; Figure 2 This is a logic diagram of a stress index analysis method for resisting calcium oxalate damage as described in an embodiment of the present invention. Figure 3 This is a flowchart of a stress index analysis system for resisting calcium oxalate damage, as described in an embodiment of the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example 1

[0021] Please see Figure 1 - Figure 2As shown in the embodiments of the present invention, a stress index analysis method for the anti-calcium oxalate damage effect is presented. This invention primarily addresses the problems in anti-calcium oxalate damage research, such as insufficient specificity of stress indexes regarding the cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress, and the lack of damage analysis of core regulatory pathways, leading to low accuracy in analyzing damage mechanisms and poor reliability in predicting damage degree and trends. By integrating stress index data, verifying cross-regulatory relationships, screening specific indicators, identifying key pathways, and constructing a predictive model, the method ultimately achieves accurate analysis of stress mechanisms and damage status during the anti-calcium oxalate damage process. Specifically, the method includes the following steps: Step 1: Extract oxidative stress, endoplasmic reticulum stress, and cross-node indices from the calcium oxalate injury resistance database, integrate the detection data of each index, establish a stress index database, and determine whether there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress through interactive analysis of regulatory effects. The database on resistance to calcium oxalate damage was constructed according to the following steps: Data sources: Data is obtained from three sources: public databases (such as GEO, ArrayExpress), published literature, and data from self-conducted cell or animal experiments.

[0022] Indicator selection: Three types of indicators were included: oxidative stress indicators (such as ROS and SOD), endoplasmic reticulum stress indicators (such as GRP78 and CHOP), and cross-node indicators that appear in both pathways (such as ASK1 and JNK).

[0023] Data structure: Each record includes: indicator name, sample type, detection method, raw data (mean ± standard deviation), group (normal / damaged / inhibitor intervention), detection time point, number of experimental repetitions, and data source.

[0024] Quality control: Self-test experiments are repeated ≥3 times; Literature data are only included if there is clear statistical information; Public data use standardized expression values; Double verification is performed before data entry.

[0025] Update and maintenance: Updated every two years, adding new qualified data and recording the version number.

[0026] It should be noted that the anti-calcium oxalate injury database covers three types of data: cell models (such as renal tubular epithelial cells HK-2 and NRK-52E), animal models (such as rat calcium oxalate kidney stone model), and clinical samples (urine / kidney tissue samples from patients with kidney stones). It includes key information such as indicator name, sample type, detection method, raw data, sample group (normal group / calcium oxalate injury group), detection time point, and number of experimental repetitions. It should be noted that, in this embodiment of the invention, the correlation between oxidative stress, endoplasmic reticulum stress and cross-node indicators is first preliminarily screened in cell models, and then the cross-regulatory relationship is verified through animal models. In step one, oxidative stress, endoplasmic reticulum stress, and crossover node indicators are extracted from the anti-calcium oxalate injury database. Among them, oxidative stress indicators include reactive oxygen species (ROS) and superoxide dismutase (SOD), endoplasmic reticulum stress indicators include glucose-regulated protein 78 (GRP78) and protein kinase R-like endoplasmic reticulum kinase (PERK), and crossover node indicators include apoptosis signal-regulated kinase 1 (ASK1) and c-Jun N-terminal kinase (JNK), which are pivotal molecules that participate in the transduction of both types of stress signals. Oxidative stress, endoplasmic reticulum stress, and cross-node indices under the same detection methods were summarized and integrated to obtain a stress index database; The following process involves interactive analysis of regulatory effects based on a stress index database: In the stress index database, the detection data of oxidative stress index, endoplasmic reticulum stress index and cross node index are grouped according to sample type, including normal group and calcium oxalate injury group. It should be noted that the normal group refers to the control group that was not damaged by calcium oxalate; The correlation between oxidative stress index detection data and endoplasmic reticulum stress index detection data was analyzed using Pearson correlation coefficient to obtain the Pearson correlation coefficient between oxidative stress index and endoplasmic reticulum stress index. If the Pearson correlation coefficient is greater than or equal to the correlation coefficient threshold, it indicates that there is a correlation between oxidative stress and endoplasmic reticulum stress. If the Pearson correlation coefficient is less than the correlation coefficient threshold, it indicates that there is no correlation between oxidative stress and endoplasmic reticulum stress. Based on the existence of correlation, the changes of cross-node indices under oxidative stress and endoplasmic reticulum stress were analyzed. By comparing the changes of cross-node indices in the normal group and the calcium oxalate injury group, it was determined whether there was a cross-regulatory relationship. The t-test was used to test the differences in cross-node indices. If the cross-node index responds to both oxidative stress and endoplasmic reticulum stress, and the trends are consistent (e.g., both show an upward trend or both show a downward trend), then the corresponding cross-node index is marked as the response node index. If the cross-node index only responds to one type of stress, or does not respond to either oxidative stress or endoplasmic reticulum stress, then the corresponding cross-node index is not a response node index. Information on the detection time points of oxidative stress, endoplasmic reticulum stress, and cross-node indices was obtained from the calcium oxalate injury resistance database. A time-varying curve was constructed with the detection time points as the x-axis and the detection data values ​​of the stress indices as the y-axis. Based on the order of change of each indicator in the time-varying curve, confirm the cross-regulation relationship; For example, if ROS increases before GRP78 is upregulated and JNK activity changes synchronously, it can be inferred that oxidative stress triggers endoplasmic reticulum stress and regulates it through cross nodes. It should be noted that the purpose of this step is to determine whether there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress. If a cross-regulatory relationship exists, it further confirms which cross-node indicators oxidative stress affects endoplasmic reticulum stress, providing data for subsequent screening of specific stress indicators and confirmation of key regulatory pathways. This helps to deeply analyze the cross-regulatory mechanism of oxidative stress and endoplasmic reticulum stress in the process of resisting calcium oxalate injury, improve the accuracy of injury mechanism analysis and the reliability of injury degree and trend prediction. Step 2: If a cross-regulatory relationship exists, specific detection and analysis will be performed. Combining oxidative stress inhibitors and endoplasmic reticulum stress inhibitors, a control experiment will be set up to compare the fluctuation differences of response indicators and screen out specific response indicators. Specifically, five control groups were set up, each of which contained multiple parallel replicate control groups with identical conditions, as follows: ① Normal group (control group not damaged by calcium oxalate, consistent with the normal group in step one); ② Calcium oxalate injury group (treated only with calcium oxalate, consistent with the calcium oxalate injury group in step one). ③ Oxidative stress inhibitor intervention group (treated with calcium oxalate first, then oxidative stress inhibitors such as N-acetylcysteine ​​(NAC) and melatonin were added); ④ Endoplasmic reticulum stress inhibitor intervention group (treated with calcium oxalate first, then endoplasmic reticulum stress inhibitors such as 4-phenylbutyrate 4-PBA, tauroursodeoxycholic acid TUDCA, etc.) ⑤ Combined inhibitor intervention group (after treatment with calcium oxalate, oxidative stress inhibitors and endoplasmic reticulum stress inhibitors were added simultaneously). It should be noted that clinical samples cannot be directly treated with inhibitors, and cell models and animal models are the main experimental subjects. Clinical samples can be used to verify the expression differences of the screened specific response indicators in humans and enhance the clinical applicability of the indicators. Inhibitors are set according to actual testing requirements. Based on the detection time points, the indicators of the five control groups were tested respectively, and the test values ​​of each indicator for each group were obtained; For the same index detection values ​​in multiple parallel replicate experimental groups within each control experimental group, mean and standard deviation processing were performed respectively to obtain the index detection mean and index detection standard deviation; By comparing the mean values ​​of the same indicators in each group, if the activity or expression level of the indicator is significantly increased or significantly decreased in the calcium oxalate injury group, and the increasing or decreasing trend is suppressed in the oxidative stress inhibitor intervention group, while there is no significant change in the endoplasmic reticulum stress inhibitor intervention group, then the corresponding indicator is marked as an oxidative stress-specific response indicator. If the activity or expression level of an indicator is significantly increased or decreased in the calcium oxalate injury group, the increasing trend or decreasing trend is significantly suppressed in the endoplasmic reticulum stress inhibitor intervention group, and there is no significant change in the oxidative stress inhibitor intervention group, then the corresponding indicator is marked as an endoplasmic reticulum stress-specific response indicator. If the activity or expression level of an indicator is significantly inhibited in the combined inhibitor intervention group and the inhibition effect is not obvious in the single inhibitor intervention group, the corresponding indicator will be marked as a cross-specific response indicator. It should be noted that in this step, significance refers to the determination of significance through analysis of variance and Tukey post-hoc test. The variance is calculated from the mean and standard deviation of the indicator test, and the Tukey post-hoc test is used to determine whether there is a significant difference in the mean of the indicator test among the groups. For example, taking the oxidative stress index ROS as an example, the test results of each control group are as follows: normal group (100±5), calcium oxalate injury group (280±12), NAC intervention group (130±8), 4-PBA intervention group (275±10), and combined intervention group (110±7). Analysis of variance yielded an intergroup variance of F=186.3, P<0.001, indicating a statistically significant difference. Tukey's post-hoc test showed that the mean ROS value in the calcium oxalate injury group was 180% higher than the normal group (P<0.001), meeting the criteria for a significant increase. The NAC intervention group showed a 53.6% decrease compared to the injury group (P<0.001), indicating that the upward trend was significantly suppressed. The difference between the 4-PBA intervention group and the injury group was only 5±4.2 (P>0.05), showing no significant change. Therefore, ROS was determined to be a specific response indicator to oxidative stress. If the test results of a certain indicator are: normal group (80±4) and damaged group (95±6), Tukey's post-hoc test P=0.08>0.05, although the value increased by 18.7%, it did not reach the statistical significance standard and was judged as not significantly increased, and did not meet the significance condition; It should be noted that the purpose of this step is to accurately screen out specific response indicators directly related to oxidative stress or endoplasmic reticulum stress from a large number of stress indicators through specific detection and analysis, eliminate non-specific interference, lay the foundation for in-depth analysis of the independent and cross-regulatory mechanisms of oxidative stress and endoplasmic reticulum stress in the process of resisting calcium oxalate injury, and improve the pertinence and accuracy of the analysis of injury mechanisms. Example 2

[0027] like Figure 1 - Figure 2 As shown in the embodiment of the present invention, a method for analyzing stress indicators of resistance to calcium oxalate damage includes the following steps: Step 3: Based on the obtained specific response indicators, correlation analysis is used to obtain the correlation between each specific response indicator, and combined with differential detection, key regulatory pathways are identified. Specifically, based on oxidative stress-specific response indicators, endoplasmic reticulum stress-specific response indicators, and cross-specific response indicators, correlation analysis was performed, as follows: The specific response indicators of oxidative stress, endoplasmic reticulum stress, and cross-specific response indicators were summarized to obtain a specific response indicator summary library. Using the Pearson correlation coefficient, correlation analysis was performed on all specific response indicators in the specific response indicator summary database. The correlation coefficient matrix between specific response indicators was calculated and compared with the correlation coefficient threshold to screen out specific response indicator pairs with strong correlation and record the direction of the correlation (positive or negative correlation). For pairs of specific response indicators that are strongly correlated, differential detection was used to analyze the expression differences of specific response indicators in different experimental groups (such as calcium oxalate injury group and normal group, oxidative stress inhibitor intervention group and calcium oxalate injury group, etc.). The specific difference detection and path selection process is as follows: Damage response verification: For each regulatory pathway composed of strongly correlated indicators, the expression levels of all indicators in the pathway in the calcium oxalate injury group and the normal group were compared one by one using the t test. If all indicators in the pathway showed statistically significant differences in the injury group and the trend of change was consistent, it was determined that the regulatory pathway was activated in the process of calcium oxalate injury, and the next step of verification was carried out. Validation of the oxidative stress-dominated pathway: If the abnormal changes (compared to the normal group) of all indicators on the regulatory pathway are repaired in the oxidative stress inhibitor intervention group (i.e., the indicator detection data are restored to no significant difference from the normal group), but are not repaired in the endoplasmic reticulum stress inhibitor intervention group, then the corresponding regulatory pathway is identified as the key regulatory pathway dominated by oxidative stress. Validation of the endoplasmic reticulum stress-dominated pathway: Conversely, if abnormal changes in pathway indicators are repaired only in the endoplasmic reticulum stress inhibitor intervention group but not in the oxidative stress inhibitor intervention group, then it is determined to be a key regulatory pathway dominated by endoplasmic reticulum stress. Cross-regulatory pathway verification: If the abnormal changes of indicators on the regulatory pathway are not repaired in any single inhibitor intervention group, but are repaired in the combined inhibitor intervention group, then the corresponding regulatory pathway is identified as a key regulatory pathway of cross-interaction between oxidative stress and endoplasmic reticulum stress. Path integration and confirmation: The key pathways were organized by combining the detection time points to obtain the key regulatory pathways of the anti-calcium oxalate damage process; It should be noted that the purpose of this step is to construct regulatory pathways through correlation analysis, and then, in combination with the specific repair results of damage activation verification and inhibitor intervention, distinguish key regulatory pathways dominated by oxidative stress, endoplasmic reticulum stress, and the interaction between the two. Step 4: Based on key regulatory pathways, establish a stress index prediction model for the effect of resisting calcium oxalate damage. By inputting stress index data from key regulatory pathways, the degree of calcium oxalate damage can be predicted. In step four, the detection data of specific response indicators on each pathway under experimental conditions (normal group, calcium oxalate injury group, and each inhibitor intervention group) are extracted to form a modeling dataset. Each modeling dataset includes stress indicator data and corresponding damage degree evaluation data (such as cell survival rate, kidney tissue damage score, etc.). The stress indicator data is used as input data and the damage degree evaluation data is used as output data. Stress index data from the modeling dataset are input into a deep learning model (such as an LSTM model) for model training; The modeling dataset is divided into training, test, and validation sets in a 7:2:1 ratio. The training set is used for learning model parameters, the validation set is used for hyperparameter optimization during training (such as the number of hidden layer nodes and learning rate in the LSTM model), and the test set is used to evaluate the final performance of the model. The training data requirements and parameter settings for deep learning models are as follows: minimum sample size (e.g., no less than 50 samples per class); input data format (e.g., time series data must be aligned to time points); model structure (e.g., number of LSTM layers, number of hidden units, activation function, etc.); hyperparameter optimization range (e.g., learning rate 0.0001-0.01, dropout rate 0.2-0.5). The LSTM model is trained by using the feature data of the training set as input and the damage assessment data as the target output. During training, the test set is used to optimize key model parameters (such as the number of LSTM layers, the number of hidden units, the learning rate, the dropout rate, etc.) through cross-validation (such as 5-fold cross-validation) to reduce the risk of model overfitting. Mean squared error (MSE) and coefficient of determination (R²) are used as core evaluation indicators. The smaller the MSE and the closer the R² is to 1, the higher the prediction accuracy. After training, the model performance is evaluated using a test set to determine whether the prediction accuracy requirement has been met. If the expected standard is not met, the model parameters are adjusted, and the training and evaluation are repeated. If the expected standard is met, the model parameters are saved to obtain the stress index prediction model. Input stress index data from key regulatory pathways into the stress index prediction model and output the degree of calcium oxalate damage. It should be noted that this step is based on key regulatory pathways, uses specific response indicators as modeling data, and constructs a stress indicator prediction model through a deep learning model to achieve quantitative assessment of the degree of calcium oxalate damage and accurate prediction of its development trend, thereby improving the practicality and translational value of the analysis method for anti-calcium oxalate damage.

[0028] The technical solution of this invention is as follows: This invention addresses the problems of unclear cross-regulatory relationships between oxidative stress and endoplasmic reticulum (ER) stress, and insufficient specificity of stress indicators in studies of calcium oxalate injury resistance. It extracts oxidative stress, ER stress, and cross-node indicators from a calcium oxalate injury database and integrates them to establish a stress indicator database. The cross-regulatory relationship is verified through correlation coefficient analysis and t-tests. Furthermore, it screens oxidative stress-specific, ER stress-specific, and cross-specific response indicators, clarifying the cross-regulatory relationship between the two types of stress and eliminating non-specific interference. This lays a precise data foundation for the subsequent identification of key regulatory pathways and improves the understanding of calcium oxalate injury resistance mechanisms. The analysis focuses on the specificity and accuracy of the data. Addressing the issue of poor reliability in predicting regulatory pathways, damage levels, and trends, this study first summarizes specific response indicators. Pearson correlation coefficients are used to screen strongly correlated indicator pairs. Through damage response verification, inhibitor intervention and repair verification, and combined with detection time points, key regulatory pathways for resisting calcium oxalate damage are integrated. Then, detection data of indicators along these key pathways are extracted to construct a stress indicator prediction model. This clarifies the core regulatory patterns of two types of stress and achieves accurate quantitative prediction of the degree and development trend of calcium oxalate damage, improving the accuracy and efficiency of stress indicator analysis for resisting calcium oxalate damage. Example 3

[0029] like Figure 3 As shown in the embodiment of the present invention, a stress index analysis system for resisting calcium oxalate damage includes the following modules: Extraction and confirmation module: Extract oxidative stress, endoplasmic reticulum stress and cross-node indicators from the anti-calcium oxalate damage database, integrate the detection data of each indicator, establish a stress indicator database, and determine whether there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress through interactive analysis of regulatory effects. Screening and analysis module: If a cross-regulatory relationship exists, specific detection and analysis will be performed. Combining oxidative stress inhibitors and endoplasmic reticulum stress inhibitors, specific response indicators will be screened by setting up control experiments to compare the fluctuation differences of response indicators. Path analysis module: Based on the obtained specific response indicators, correlation analysis is used to obtain the correlation between each specific response indicator, and combined with difference detection, key regulatory pathways are identified. Model building module: Based on key regulatory pathways, a stress index prediction model for the effect of calcium oxalate damage is established. By inputting stress index data from key regulatory pathways, the degree of calcium oxalate damage is predicted.

[0030] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for analyzing stress indicators of resistance to calcium oxalate damage, characterized in that: Includes the following steps: In the calcium oxalate injury resistance database, oxidative stress, endoplasmic reticulum stress and cross-node indicators were extracted, the detection data of each indicator were integrated, a stress indicator database was established, and through the interaction analysis of regulatory effects, it was determined whether there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress. If a cross-regulatory relationship exists, specific detection and analysis will be performed. Combining oxidative stress inhibitors and endoplasmic reticulum stress inhibitors, specific response indicators will be screened by setting up control experiments to compare the fluctuation differences of response indicators. Based on the obtained specific response indicators, correlation analysis was conducted to obtain the correlation between each specific response indicator, and combined with differential detection, key regulatory pathways were identified. Based on key regulatory pathways, a stress index prediction model for the effect of resisting calcium oxalate damage was established. By inputting stress index data from key regulatory pathways, the degree of calcium oxalate damage was predicted.

2. The method for analyzing stress indicators of resistance to calcium oxalate damage according to claim 1, characterized in that: Establish a stress indicator database: Oxidative stress, endoplasmic reticulum stress, and cross-node indices were extracted from the calcium oxalate injury resistance database. Oxidative stress, endoplasmic reticulum stress, and cross-node indices under the same detection method were summarized and integrated to obtain a stress index database.

3. The method for analyzing stress indicators of resistance to calcium oxalate damage according to claim 1, characterized in that: Determine if there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress: In the stress index database, the detection data of oxidative stress index, endoplasmic reticulum stress index and cross node index are grouped according to sample type, including normal group and calcium oxalate injury group. The correlation between oxidative stress index detection data and endoplasmic reticulum stress index detection data was analyzed using Pearson correlation coefficient to obtain the Pearson correlation coefficient between oxidative stress index and endoplasmic reticulum stress index. If the Pearson correlation coefficient is greater than or equal to the correlation coefficient threshold, then there is a correlation between oxidative stress and endoplasmic reticulum stress. Based on the existence of correlation, the changes in cross-node indices under oxidative stress and endoplasmic reticulum stress conditions were analyzed. By comparing the changes in cross-node indices between the normal group and the calcium oxalate injury group, it was determined whether there was a cross-regulatory relationship.

4. The method for analyzing stress indicators of resistance to calcium oxalate damage according to claim 3, characterized in that: By comparing the changes in cross-node index data between the normal group and the calcium oxalate injury group, it can be determined whether a cross-regulatory relationship exists: The t-test was used to examine the differences in the crossover node indices. If the cross-node index responds to both oxidative stress and endoplasmic reticulum stress and shows a consistent trend, then the corresponding cross-node index is marked as the response node index. Obtain the detection time point information of oxidative stress, endoplasmic reticulum stress and cross node indicators, and establish a time change curve with the detection time point as the x-axis and the detection data value of the stress indicators as the y-axis. Based on the order of changes in each indicator within the time-varying curve, the cross-regulation relationship is confirmed.

5. The method for analyzing stress indicators of resistance to calcium oxalate damage according to claim 1, characterized in that: Screening for specific response indicators: Set up control groups, and conduct index tests on each control group according to the detection time points to obtain the detection values ​​of each index for each group; For the same index detection values ​​in multiple parallel replicate experimental groups within each control experimental group, mean and standard deviation processing were performed to obtain the index detection mean and index detection standard deviation; By comparing the mean values ​​of the same indicators in each group, if the activity or expression level of the indicator is significantly increased or significantly decreased in the calcium oxalate injury group, and the increasing or decreasing trend is suppressed in the oxidative stress inhibitor intervention group, while there is no significant change in the endoplasmic reticulum stress inhibitor intervention group, then the corresponding indicator is marked as an oxidative stress-specific response indicator. If the activity or expression level of an indicator is significantly increased or decreased in the calcium oxalate injury group, the increasing or decreasing trend is significantly suppressed in the endoplasmic reticulum stress inhibitor intervention group, and there is no significant change in the oxidative stress inhibitor intervention group, then the corresponding indicator is marked as an endoplasmic reticulum stress-specific response indicator. If the activity or expression level of an indicator is significantly inhibited in the combined inhibitor intervention group and the inhibition effect is not obvious in the single inhibitor intervention group, the corresponding indicator is marked as a cross-specific response indicator.

6. The method for analyzing stress indicators of resistance to calcium oxalate damage according to claim 1, characterized in that: Correlation analysis was used to obtain the relationships between various specific response indicators: The specific response indicators of oxidative stress, endoplasmic reticulum stress, and cross-specific response indicators were summarized to obtain a specific response indicator summary library. Using the Pearson correlation coefficient, correlation analysis was performed on all specific response indicators in the specific response indicator summary database. The correlation coefficient matrix between specific response indicators was calculated and compared with the correlation coefficient threshold to screen out specific response indicator pairs with strong correlation.

7. The method for analyzing stress indicators of resistance to calcium oxalate damage according to claim 1, characterized in that: By combining differential detection, key regulatory pathways were identified: For each regulatory pathway consisting of strongly correlated indices, the expression levels of all indices in the pathway were compared between the calcium oxalate injury group and the normal group using a t-test. If all indices in the pathway showed statistically significant differences in the injury group and the trend of change was consistent, the regulatory pathway was determined to be activated during calcium oxalate injury. If the abnormal changes of all indicators on the regulatory pathway are repaired in the oxidative stress inhibitor intervention group but not in the endoplasmic reticulum stress inhibitor intervention group, then the corresponding regulatory pathway is identified as a key regulatory pathway dominated by oxidative stress. If abnormal changes in pathway indicators are repaired only in the endoplasmic reticulum stress inhibitor intervention group but not in the oxidative stress inhibitor intervention group, it is determined to be a key regulatory pathway dominated by endoplasmic reticulum stress. If abnormal changes in indicators along a regulatory pathway are not repaired in any single inhibitor intervention group but are repaired in the combined inhibitor intervention group, then the corresponding regulatory pathway is identified as a key regulatory pathway of the interaction between oxidative stress and endoplasmic reticulum stress.

8. The method for analyzing stress indicators of resistance to calcium oxalate damage according to claim 1, characterized in that: Establish a stress index prediction model for resistance to calcium oxalate injury: Extract the detection data of specific response indicators on each path under experimental conditions to form a modeling dataset. Each modeling dataset includes indicator values, detection time points, and corresponding damage degree evaluation data. The stress index data in the modeling dataset is input into the deep learning model for training; The modeling dataset is divided into training, testing, and validation sets according to a certain ratio. The training set is used to train the model, and the parameters are optimized through cross-validation. The model's predictive performance is evaluated on the testing set, and the prediction accuracy is measured using mean squared error and coefficient of determination. After training, the model performance is evaluated using a test set to determine whether the prediction accuracy requirements have been met. If the expected standard is not met, the model parameters are adjusted, and the training and evaluation are repeated. If the expected standard is met, the model parameters are saved to obtain the stress index prediction model.

9. The method for analyzing stress indicators of resistance to calcium oxalate damage according to claim 1, characterized in that: Predicting the degree of calcium oxalate damage: Stress index data from key regulatory pathways are input into a stress index prediction model, which outputs the degree of calcium oxalate damage.

10. A stress index analysis system for resisting calcium oxalate damage, characterized in that: Includes the following modules: Extraction and confirmation module: Extract oxidative stress, endoplasmic reticulum stress and cross-node indicators from the anti-calcium oxalate damage database, integrate the detection data of each indicator, establish a stress indicator database, and determine whether there is a cross-regulatory relationship between oxidative stress and endoplasmic reticulum stress through interactive analysis of regulatory effects. Screening and analysis module: If a cross-regulatory relationship exists, specific detection and analysis will be performed. Combining oxidative stress inhibitors and endoplasmic reticulum stress inhibitors, specific response indicators will be screened by setting up control experiments to compare the fluctuation differences of response indicators. Path analysis module: Based on the obtained specific response indicators, correlation analysis is used to obtain the correlation between each specific response indicator, and combined with difference detection, key regulatory pathways are identified. Model building module: Based on key regulatory pathways, a stress index prediction model for the effect of calcium oxalate damage is established. By inputting stress index data from key regulatory pathways, the degree of calcium oxalate damage is predicted.

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