Simulation analysis system for heart de-loading state

Abstract

The invention discloses a simulation analysis system for a heart load removal state, and belongs to the technical field of medicine. The problem that simulation cannot be carried out on a small animal body in the prior art is solved, the left ventricle unloading model is creatively constructed by combining the myocardial infarction model and the heart transplantation model, the limitation that the heart unloading state cannot be simulated on the small animal body in the prior art is overcome, and the experimental group and the control group are set, so that the simulation accuracy is improved. The method can clearly compare the difference between the heart de-loading state and the normal state, is beneficial to accurately evaluating the heart de-loading effect, avoids simulation analysis deviation caused by individual difference factors, and can accurately evaluate the heart de-loading effect by collecting the physiological and pathological data of the heart of the experimental group and the control group and combining quantitative analysis and quantitative analysis methods. The heart load removal state can be comprehensively evaluated, and a more accurate basis is provided for research and treatment of heart diseases.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a simulation analysis system for a heart unload state. Background Art

[0002] Myocardial infarction (MI) is a life-threatening condition caused by acute coronary artery obstruction, leading to insufficient blood supply to the affected myocardial region and subsequent myocardial necrosis. Even after MI, even with recanalization achieved through PCI (percutaneous coronary intervention) or thrombolysis, some patients still fail to regain effective pumping function due to myocardial stunning or extensive necrosis. Veno-arterial extracorporeal membrane oxygenation (ECMO) bypasses the heart and lungs by directing blood from the veins, oxygenating it, and then returning it to the arteries, providing direct circulatory support and reducing cardiac workload while ensuring systemic oxygen supply. ECMO not only rapidly improves end-organ perfusion (such as the brain and kidneys), but also buys time for myocardial recovery or subsequent treatment. ECMO unloads the heart of patients with MI, reducing cardiac work and providing an opportunity for myocardial recovery. However, this model can only be used in humans and cannot be simulated in small animals, significantly hindering in-depth research on cardiac unloaded states. Therefore, it does not meet current needs. Therefore, we propose a system for simulating and analyzing cardiac unloaded states. Summary of the Invention

[0003] The purpose of the present invention is to provide a simulation and analysis system for the cardiac unloading state. By combining the mouse myocardial infarction model and the mouse heterotopic heart transplantation model, the infarcted heart can be transplanted into the abdominal cavity of another mouse to construct a left ventricular unloading model, thereby solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: a simulation and analysis system for cardiac unload state, the system comprising a model building unit and a data analysis unit; The model building unit is configured to set up an experimental group and a control group, specifically: Experimental group: The mouse myocardial infarction model and the mouse heterotopic heart transplantation model were combined to construct a left ventricular unloading model; Control group: The myocardial infarction model of mice was retained but no heart transplantation was performed to construct the control group model; The data analysis unit is configured to collect physiological and pathological data of the hearts of the experimental group and the control group, and to evaluate the effect of the cardiac unloading state by combining quantitative analysis and quantitative analysis methods; At the same time, the effect of the model is verified after the experiment. By analyzing the changing patterns and abnormal characteristics of the verification data, the model adjustment parameters and strategies are determined to achieve optimal adjustment of the model.

[0005] Furthermore, the model building unit includes: The experimental group construction module is configured to complete the construction of the left ventricular unloading model by inducing myocardial infarction in donor mice and then transplanting the infarcted heart into the abdominal cavity of recipient mice; The control group construction module is configured to select mice of the same strain, age, and weight as the experimental group, induce myocardial infarction in the mice, but do not perform heart transplantation to complete the construction of the control group model; A verification module is constructed and configured to verify whether the construction of the left ventricular unloading model and the control group model is successful.

[0006] Furthermore, the model verification module is specifically: Left ventricular unloading model: The effect of left ventricular unloading is verified by echocardiography and pressure-volume loop technology, and the systolic and diastolic function indicators of the heart are recorded to evaluate whether the unloading state is successful; Control group model: The heart load state of the control group was verified by echocardiography and histological staining, and the systolic and diastolic functions of the heart were recorded to ensure that the heart of the control group maintained a normal load state.

[0007] Furthermore, the data analysis unit includes: The data acquisition module is configured to respectively acquire physiological data and pathological data of the heart of the experimental group and the control group, wherein: Pathological data: used to slice heart tissue and evaluate the degree of myocardial fibrosis and myocardial cell apoptosis using Masson trichrome staining and HE staining, and record myocardial infarction area and collagen deposition; Physiological data include: Echocardiography: used to collect cardiac systolic and diastolic functions, as well as left ventricular end-diastolic volume and end-systolic volume; Pressure-volume loop: used to measure changes in left ventricular pressure and volume, and to calculate the heart's systolic and diastolic functions; Hemodynamic monitoring: used to measure heart rate and mean arterial pressure indicators to assess the heart's pumping function and overall hemodynamic status; a processing and analysis module configured to perform quantitative and quantitative analysis on the collected physiological data and pathological data, and to evaluate the overall effect of the cardiac unloading state by combining the physiological data and the pathological data; The experimental verification module is configured to verify the effect of the model after the experiment and optimize and adjust the model according to the verification results.

[0008] Furthermore, the processing and analysis module includes: a data processing module configured to perform quantitative analysis on the echocardiogram and the pressure-volume loop, analyze cardiac function indicators, and compare differences between the experimental group and the control group using statistical methods, wherein the statistical methods include t-test and analysis of variance; Quantitative analysis of cardiac tissue slice images was performed to assess the area of myocardial fibrosis and infarct size; The data analysis module is configured to analyze the cardiac function and pathological changes of the experimental group and the control group, and to evaluate the overall effect of the cardiac unloading state by combining physiological and pathological data.

[0009] Furthermore, the data analysis module is specifically: Cardiac function assessment: used to compare the cardiac systolic and diastolic functions of the experimental group and the control group, and to evaluate the improvement effect of left ventricular unloading on cardiac function; Pathological change analysis: used to compare the degree of myocardial fibrosis and myocardial cell apoptosis between the experimental group and the control group, and to analyze the protective effect of unloading on myocardial tissue; Comprehensive analysis and evaluation: It is used to combine physiological and pathological data, quantify and analyze the improvement effect of left ventricular unloading on cardiac function and the protective effect on myocardial tissue, obtain the unloading effect characterization coefficient, evaluate the overall effect of the cardiac unloading state based on the unloading effect characterization coefficient, and put forward suggestions for optimizing the model.

[0010] Furthermore, the experimental verification module includes: A model validation module is configured to perform short-term and long-term validation on the models of the experimental group and the control group, respectively, and determine the validity of the models of the experimental group and the control group through validation; A model optimization module is configured to adjust model parameters and optimize the experimental process based on the validation results.

[0011] Furthermore, the model verification module is specifically: The contents of the validation experimental group model include: Short-term validation: used to verify the short-term effects of the unloading state through echocardiography and pressure-volume loop analysis within 1 week after the experiment to ensure the successful construction of the left ventricular unloading model; Long-term validation: used to evaluate the long-term effects of the unloading state 4-8 weeks after the experiment and verify the stability of the left ventricular unloading model in combination with tissue slice analysis; The content of the validation control group model includes: Short-term verification: used to verify the heart load status of the control group by echocardiography and histological staining within 1 week after the experiment to ensure that the heart of the control group maintains a normal load status; Long-term validation: used to evaluate the stability of the control group by tissue section analysis 4-8 weeks after the experiment.

[0012] Furthermore, the model optimization module specifically includes: Curve establishment: used to establish short-term indicator change difference curves and long-term indicator change difference curves for different verification analysis indicators using all data obtained from the experimental group and the control group during the short-term verification and long-term verification processes; Abnormal point confirmation: used to determine the short-term change boundary value based on the short-term indicator change difference curve of the current verification analysis indicator, and regard the point in the curve where the difference between the short-term change boundary value and the short-term change boundary value exceeds the set short-term boundary benchmark threshold as a short-term change abnormal point; According to the long-term indicator change difference curve of the current verification analysis indicator, the long-term change boundary value is determined, and the point in the curve where the difference with the long-term change boundary value exceeds the set long-term boundary benchmark threshold is regarded as a long-term change abnormal point; Change stability analysis: It is used to put the corresponding short-term change boundary point of the same verification analysis indicator as the first, and arrange the long-term change boundary points in the order of acquisition to obtain the indicator change boundary series; Performing a change stability analysis on the indicator change boundary series to obtain an indicator change stability score; Determine the stability level of the verification analysis indicator based on the indicator change stability score; Abnormal trend analysis: used to obtain the short-term abnormal distribution characteristics of the short-term change abnormal points and the long-term abnormal distribution characteristics of the long-term change abnormal points of the current verification analysis indicators; By comprehensively considering the short-term abnormal distribution characteristics and the long-term abnormal distribution characteristics, the abnormal change pattern of the current verification analysis indicators is determined; Abnormal impact analysis: used to analyze the comprehensive similarity between the short-term abnormal distribution characteristics and long-term abnormal distribution characteristics of the current verification analysis indicator and other verification analysis indicators with which there is an evaluation correlation, and obtain the abnormal characteristic similarity coefficient; Based on the causal relationship between the current verification analysis indicator and other verification analysis indicators with which there is an evaluation correlation, the corresponding abnormal feature similarity coefficient is assigned a corresponding cause / effect relationship weight, and then summarized and processed to obtain the abnormal impact representation value of the current verification analysis indicator; Determine the abnormal impact level of the current verification analysis indicator based on the abnormal impact characterization value; Adjustment parameter determination: used to mark the verification analysis indicators with poor change stability level as adjustment reference indicators; The indicator category and abnormal change pattern of the adjustment reference indicator are used as screening conditions to determine the model adjustment parameters and the corresponding parameter adjustment strategy; Summarize all model adjustment parameters and corresponding parameter adjustment strategies. If there are overlapping model adjustment parameters and the corresponding parameter adjustment strategies are different, comprehensively consider the historical adjustment effects of each parameter adjustment strategy and the abnormal impact representation value of the corresponding adjustment reference indicator to determine the strategy priority coefficient of each parameter adjustment strategy; The parameter adjustment strategy with the highest strategy priority coefficient is used as the actual parameter adjustment strategy for the corresponding overlap model adjustment parameters.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention creatively constructs a left ventricular unloading model by combining a myocardial infarction model and a heart transplant model, overcoming the limitation of the existing technology that it is impossible to simulate the cardiac unloading state on small animals, and provides a new method for the study of the cardiac unloading state. Setting up an experimental group and a control group can clearly compare the difference between the cardiac unloading state and the normal state, which helps to accurately evaluate the effect of cardiac unloading and avoid deviations in simulation analysis due to individual differences. By collecting physiological and pathological data of the hearts of the experimental group and the control group, and combining quantitative analysis and quantitative analysis methods, the cardiac unloading state can be comprehensively evaluated, providing a more accurate basis for the research and treatment of heart diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a structural schematic diagram of the simulation and analysis system for cardiac unloading state of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] In order to solve the technical problem that the existing model can only be simulated in humans and cannot be simulated in small animals, which seriously hinders the in-depth study of cardiac unloading state, please refer to Figure 1 , this embodiment provides the following technical solutions: A simulation and analysis system for a cardiac unload state, the system comprising a model building unit and a data analysis unit; The model building unit is configured to set up an experimental group and a control group, specifically: Experimental group: The mouse myocardial infarction model and the mouse heterotopic heart transplantation model were combined to construct a left ventricular unloading model; Control group: The myocardial infarction model of mice was retained but no heart transplantation was performed to construct the control group model; The data analysis unit is configured to collect physiological and pathological data of the hearts of the experimental group and the control group, and to evaluate the effect of the cardiac unloading state by combining quantitative analysis and quantitative analysis methods; At the same time, the effect of the model is verified after the experiment. By analyzing the changing patterns and abnormal characteristics of the verification data, the model adjustment parameters and strategies are determined to achieve optimal adjustment of the model.

[0017] The technical effect of the above content is: by combining the mouse myocardial infarction model with the mouse heterotopic heart transplantation model, a left ventricular unloading model is constructed, which can accurately simulate the physiological and pathological changes of the heart in the unloaded state, overcome the limitation of the existing technology that it is impossible to simulate the cardiac unloaded state in small animals, set up reasonable controls, and more clearly observe the impact of the cardiac unloaded state on cardiac function and structure, eliminate the interference of other factors, improve the scientificity and credibility of the research results, collect the physiological and pathological data of the hearts of the experimental group and the control group, and combine quantitative analysis and quantitative analysis methods to more accurately evaluate the effect of the cardiac unloaded state, providing a strong basis for clinical application and further research.

[0018] Model building unit, including: The experimental group construction module is configured to complete the construction of the left ventricular unloading model by inducing myocardial infarction in donor mice and then transplanting the infarcted heart into the abdominal cavity of recipient mice; The control group construction module is configured to select mice of the same strain, age, and weight as the experimental group, induce myocardial infarction in the mice, but do not perform heart transplantation to complete the construction of the control group model; A verification module is constructed and configured to verify whether the construction of the left ventricular unloading model and the control group model is successful.

[0019] The technical effects of the above content are as follows: the experimental group construction module established a left ventricular unloading model by inducing myocardial infarction in donor mice and then performing heart transplantation, thereby simulating the effect of unloading on cardiac function after myocardial infarction. The design of the control group model ensured the consistency of the experimental and control groups in terms of mouse strain, age, and weight, eliminating the interference of the transplantation operation itself on the experimental results. The construction verification module ensured the successful construction of the experimental and control group models, providing a reliable experimental platform for subsequent simulation analysis.

[0020] It should be noted that if there may be other interfering factors in the simulation analysis (such as the impact of the surgical operation itself), it is recommended to set up multiple control groups to ensure the rigor and reliability of the experimental results.

[0021] Model validation module, specifically: Left ventricular unloading model: Echocardiography and pressure-volume loop technology can accurately evaluate the effectiveness of the left ventricular unloading model, record the heart's systolic function (such as ejection fraction EF) and diastolic function (such as E / A ratio) indicators, and evaluate whether the unloading state is successful; Control group model: Echocardiography and histological staining were used to verify whether the heart load state of the control group was normal, and the heart's systolic and diastolic functions were recorded to ensure that the heart of the control group maintained a normal load state.

[0022] The technical effect of the above content is: the model verification module ensures the successful construction of the experimental and control group models through multi-dimensional technical means (such as echocardiography, pressure-volume loops, and histological staining), thereby ensuring the accuracy of subsequent simulation analysis results.

[0023] Data Analysis Unit, including: The data acquisition module is configured to respectively acquire physiological data and pathological data of the heart of the experimental group and the control group, wherein: Pathological data: used to slice heart tissue and evaluate the degree of myocardial fibrosis and myocardial cell apoptosis using Masson trichrome staining and HE staining, and record myocardial infarction area and collagen deposition; Physiological data include: Echocardiography: used to collect cardiac systolic function (such as left ventricular ejection fraction (EF) and fractional shortening (FS)) and diastolic function (such as E / A ratio, E peak velocity, A peak velocity), as well as left ventricular end-diastolic volume (LVEDV) and end-systolic volume (LVESV); Pressure-volume loop: used to measure changes in left ventricular pressure (LVP) and volume (LVV), and calculate the heart's systolic function (such as the maximum rate of pressure rise dp / dt max) and diastolic function (such as the maximum rate of pressure decrease dp / dt min); Hemodynamic monitoring: used to measure indicators such as heart rate (HR) and mean arterial pressure (MAP) to evaluate the heart's pumping function and overall hemodynamic status; a processing and analysis module configured to perform quantitative and quantitative analysis on the collected physiological data and pathological data, and to evaluate the overall effect of the cardiac unloading state by combining the physiological data and the pathological data; The experimental verification module is configured to verify the effect of the model after the experiment and optimize and adjust the model according to the verification results.

[0024] The technical effects of the above content are: the data acquisition module can collect physiological and pathological data of the heart of the experimental group and the control group, including cardiac function indicators, tissue structure changes, changes at the cellular level and other information, providing rich data support for a comprehensive evaluation of the cardiac unloading state. The processing and analysis module combines physiological and pathological data to perform quantitative and quantitative analysis, and comprehensively evaluates the overall effect of the left ventricular unloading state, so as to conduct in-depth research on the mechanism of unloading on the recovery of cardiac function after myocardial infarction, thereby providing a scientific basis. The experimental verification module verifies the effect of the model after the experiment to ensure the scientificity and reliability of the experimental design.

[0025] Processing and analysis modules, including: a data processing module configured to perform quantitative analysis on the echocardiogram and the pressure-volume loop, analyze cardiac function indicators, and compare differences between the experimental group and the control group using statistical methods, wherein the statistical methods include t-test and analysis of variance; Quantitative analysis of cardiac tissue slice images was performed to assess the area of myocardial fibrosis and infarct size; The data analysis module is configured to analyze the cardiac function and pathological changes of the experimental group and the control group, and to evaluate the overall effect of the cardiac unloading state by combining physiological and pathological data.

[0026] The technical effects of the above content are as follows: the data processing module accurately evaluates cardiac function indicators by quantitatively analyzing echocardiography and pressure-volume loop data, uses statistical methods to compare the differences between the experimental group and the control group, provides quantitative data support, and ensures the scientific nature and reliability of the experimental results. It then performs quantitative analysis on the cardiac tissue slice images to evaluate the myocardial fibrosis area and infarction area, and provides intuitive pathological change data. The data analysis module combines physiological and pathological data, and by comparing the physiological functions and pathological changes of the experimental group and the control group, it can comprehensively evaluate the overall effect of the cardiac unloading state. Finally, the experimental verification module can verify the effect of the model after the experiment to ensure the scientific nature and reliability of the model design.

[0027] Data analysis module, specifically: Cardiac function assessment: used to compare cardiac systolic function (such as EF, dp / dt max) and diastolic function (such as E / A ratio, dp / dt min) between the experimental group and the control group, and to evaluate the effect of left ventricular unloading on improving cardiac function; Pathological change analysis: used to compare the degree of myocardial fibrosis and myocardial cell apoptosis between the experimental group and the control group, and to analyze the protective effect of unloading on myocardial tissue; Comprehensive analysis and evaluation: It is used to combine physiological and pathological data, quantify and analyze the improvement effect of left ventricular unloading on cardiac function and the protective effect on myocardial tissue, obtain the unloading effect characterization coefficient, evaluate the overall effect of the cardiac unloading state based on the unloading effect characterization coefficient, and put forward suggestions for optimizing the model.

[0028] In this embodiment, the calculation formula of the load shedding effect characterization coefficient is as follows: Where, Expressed as the unloading effect characterization coefficient; It is represented as the value of the cardiac function-related index of the experimental group i, where i=1, 2, , n; n represents the total number of cardiac function-related indicators; represents the value of the i-th cardiac function-related index in the control group; It is represented as the influence weight of the i-th cardiac function-related index on the analysis of the improvement effect of left ventricular unloading on cardiac function; It is expressed as the total number of corresponding cardiac function-related indices whose index values of the experimental group are higher than those of the control group; It is expressed as the weight of the effect of left ventricular unloading on improving cardiac function to the overall effect of evaluating cardiac unloading status; It is expressed as the jth pathological index value of the control group, where j = 1, 2, , m; m represents the total number of pathology-related indicators; represents the jth pathology-related index value of the experimental group; It is expressed as the influence weight of the jth pathological-related index on the analysis of the protective effect of left ventricular unloading on myocardial tissue; It is expressed as the total number of corresponding pathology-related indicators whose index values of the control group are higher than those of the experimental group; It is expressed as the weight of the protective effect of left ventricular unloading on myocardial tissue in evaluating the overall effect of cardiac unloading state.

[0029] In this embodiment, cardiac function-related indicators refer to quantitative parameters used to evaluate cardiac systolic and diastolic function, including left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), the ratio of left ventricular early diastolic filling peak velocity (E peak) to atrial systolic filling peak velocity (A peak) (E / A ratio), etc.; pathology-related indicators refer to quantitative parameters used to evaluate the degree of pathological changes in myocardial tissue, that is, to analyze the protective effect of unloading on myocardial tissue, including the proportion of myocardial fibrosis area, myocardial cell apoptosis rate, the proportion of myocardial infarction area, and collagen deposition, etc.

[0030] In this embodiment, the weights appearing in the calculation formula of the load-removal effect characterization coefficient are obtained by using the hierarchical analysis method (the specific core steps include: establishing a hierarchical structure model, constructing a judgment matrix, hierarchical single sorting and consistency test, hierarchical total sorting and consistency test) to compare two by two and solve the matrix constructed after scoring. The value range is ,and , , ; Among them, the establishment of the hierarchical model specifically refers to: calculating When calculating the left ventricular unloading effect on cardiac function, the evaluation target (improvement effect of left ventricular unloading on cardiac function) is used as the target layer, cardiac function-related indicators are used as the criterion layer, and specific indicators (such as LVEF, LVFS, E / A ratio, etc.) are used as the indicator layer. When calculating the protective effect of left ventricular unloading on myocardial tissue, the evaluation target (protective effect of left ventricular unloading on myocardial tissue) is used as the target layer, the pathological related indicators are used as the criterion layer, and the specific indicators (such as the proportion of myocardial fibrosis area, myocardial cell apoptosis rate, and myocardial infarction area) are used as the indicator layer. 、 When evaluating the cardiac function, the assessment target (the overall effect of cardiac unloading) was used as the target layer, and the improvement effect of left ventricular unloading on cardiac function and the protective effect of left ventricular unloading on myocardial tissue were used as the criterion layer).

[0031] In this embodiment, the specific content of evaluating the overall effect of the cardiac unload state is: comparing the calculated unload effect characterization coefficient with the preset effect coefficient threshold range; when the unload effect characterization coefficient exceeds the preset effect coefficient threshold range, the overall effect of the cardiac unload state is evaluated as excellent; when the unload effect characterization coefficient falls within the preset effect coefficient threshold range, the overall effect of the cardiac unload state is evaluated as good; when the unload effect characterization coefficient is less than the preset effect coefficient threshold range, the overall effect of the cardiac unload state is evaluated as poor; wherein the preset effect coefficient threshold range is obtained by collecting multiple groups of unload effect tables under different experimental conditions. The characteristic coefficient and the corresponding cardiac unload status assessment result (such as excellent, good, and poor assessed by experts); for samples with excellent, good, and poor assessment results, the corresponding mean of the unload effect characterization coefficient is calculated respectively; the mean of the unload effect characterization coefficient of the samples with excellent assessment results and the mean of the unload effect characterization coefficient of the samples with good assessment results are averaged as the upper threshold limit of the preset effect coefficient threshold range, and the mean of the unload effect characterization coefficient of the samples with poor assessment results and the mean of the unload effect characterization coefficient of the samples with good assessment results are averaged as the lower threshold limit of the preset effect coefficient threshold range.

[0032] In this embodiment, for example, the current load shedding effect characterization coefficient is 0.55, and the preset effect coefficient threshold range is , the overall effect of the current cardiac unloading state is assessed to be good.

[0033] The beneficial effect of the above technical solution is: by combining physiological and pathological data, comprehensively considering cardiac function-related indicators and pathology-related indicators, quantifying and combining the improvement effect of left ventricular unloading on cardiac function and the protective effect on myocardial tissue, and obtaining the unloading effect characterization coefficient as a quantitative indicator, a comprehensive and accurate evaluation of the overall effect of the cardiac unloading state is achieved.

[0034] The technical effects of the above content are: through the evaluation of cardiac function indicators, the improvement effect of left ventricular unloading on cardiac function can be scientifically evaluated. In terms of pathological change analysis, the changes in myocardial fibrosis area and infarct area can be intuitively displayed, which not only reveals the protective effect of unloading on myocardial tissue, but also provides key clues for understanding its potential molecular and cellular mechanisms. Comprehensive analysis and evaluation comprehensively evaluates the overall effect of the cardiac unloading state by combining physiological and pathological data. By integrating the improvement of cardiac function and the pathological changes of myocardial tissue, it can provide a comprehensive perspective on the therapeutic effect of left ventricular unloading. In addition, based on the analysis results, suggestions for optimizing the model are put forward to provide scientific guidance for subsequent experimental design and clinical application, which helps to improve the accuracy and repeatability of the model.

[0035] Experimental verification module, including: A model validation module is configured to perform short-term and long-term validation on the models of the experimental group and the control group, respectively, and determine the validity of the models of the experimental group and the control group through validation; A model optimization module, configured to adjust model parameters (such as infarct size, transplantation location, etc.) and optimize the experimental process based on the validation results; Among them, the model verification module is specifically: The contents of the validation experimental group model include: Short-term validation: used to verify the short-term effects of the unloading state through echocardiography and pressure-volume loop analysis within 1 week after the experiment to ensure the successful construction of the left ventricular unloading model; Long-term validation: used to evaluate the long-term effects of the unloading state 4-8 weeks after the experiment and verify the stability of the left ventricular unloading model in combination with tissue slice analysis; The content of the validation control group model includes: Short-term verification: used to verify the heart load status of the control group by echocardiography and histological staining within 1 week after the experiment to ensure that the heart of the control group maintains a normal load status; Long-term validation: used to evaluate the stability of the control group by tissue section analysis 4-8 weeks after the experiment.

[0036] The technical effects of the above content are as follows: the experimental verification module comprehensively evaluates the effectiveness and stability of the experimental and control group models through short-term and long-term verification. Short-term verification is carried out within 1 week after the experiment to ensure the immediate effectiveness of the model construction. Long-term verification is carried out 4-8 weeks after the experiment to verify the continued stability and biological significance of the model. The reliability of the simulation analysis results is ensured through a systematic verification method, thereby improving the accuracy and repeatability of the model and providing a solid foundation for subsequent research.

[0037] Working principle: By combining the mouse myocardial infarction model and the mouse heterotopic heart transplantation model to construct a left ventricular unloading model, the limitation of existing technology that cannot simulate the cardiac unloading state in small animals is overcome, and a new method is provided for the study of cardiac unloading state. The control group model is constructed by retaining the mouse myocardial infarction model but not performing a heart transplant operation, which can clearly compare the difference between the cardiac unloading state and the normal state, and help to accurately evaluate the effect of cardiac unloading, avoid experimental deviations caused by individual differences and other factors, and improve the reliability of simulation analysis results. Combined with quantitative analysis and quantitative analysis methods, the collected data can be deeply processed and analyzed, which can not only determine the existence and extent of the cardiac unloading state, but also further explore its long-term effects on cardiac structure and function, providing a more accurate basis for the research and treatment of heart disease.

[0038] The model optimization module specifically includes: Curve establishment: used to establish short-term indicator change difference curves and long-term indicator change difference curves for different verification analysis indicators using all data obtained from the experimental group and the control group during the short-term verification and long-term verification processes; Abnormal point confirmation: used to determine the short-term change boundary value based on the short-term indicator change difference curve of the current verification analysis indicator, and regard the point in the curve where the difference between the short-term change boundary value and the short-term change boundary value exceeds the set short-term boundary benchmark threshold as a short-term change abnormal point; According to the long-term indicator change difference curve of the current verification analysis indicator, the long-term change boundary value is determined, and the point in the curve where the difference with the long-term change boundary value exceeds the set long-term boundary benchmark threshold is regarded as a long-term change abnormal point; Change stability analysis: It is used to put the corresponding short-term change boundary point of the same verification analysis indicator as the first, and arrange the long-term change boundary points in the order of acquisition to obtain the indicator change boundary series; Performing a change stability analysis on the indicator change boundary series to obtain an indicator change stability score; Determine the stability level of the verification analysis indicator based on the indicator change stability score; Abnormal trend analysis: used to obtain the short-term abnormal distribution characteristics of the short-term change abnormal points and the long-term abnormal distribution characteristics of the long-term change abnormal points of the current verification analysis indicators; By comprehensively considering the short-term abnormal distribution characteristics and the long-term abnormal distribution characteristics, the abnormal change pattern of the current verification analysis indicators is determined; Abnormal impact analysis: used to analyze the comprehensive similarity between the short-term abnormal distribution characteristics and long-term abnormal distribution characteristics of the current verification analysis indicator and other verification analysis indicators with which there is an evaluation correlation, and obtain the abnormal characteristic similarity coefficient; Based on the causal relationship between the current verification analysis indicator and other verification analysis indicators with which there is an evaluation correlation, the corresponding abnormal feature similarity coefficient is assigned a corresponding cause / effect relationship weight, and then summarized and processed to obtain the abnormal impact representation value of the current verification analysis indicator; Determine the abnormal impact level of the current verification analysis indicator based on the abnormal impact characterization value; Adjustment parameter determination: used to mark the verification analysis indicators with poor change stability level as adjustment reference indicators; The indicator category and abnormal change pattern of the adjustment reference indicator are used as screening conditions to determine the model adjustment parameters and the corresponding parameter adjustment strategy; Summarize all model adjustment parameters and corresponding parameter adjustment strategies. If there are overlapping model adjustment parameters and the corresponding parameter adjustment strategies are different, comprehensively consider the historical adjustment effects of each parameter adjustment strategy and the abnormal impact representation value of the corresponding adjustment reference indicator to determine the strategy priority coefficient of each parameter adjustment strategy; The parameter adjustment strategy with the highest strategy priority coefficient is used as the actual parameter adjustment strategy for the corresponding overlap model adjustment parameters.

[0039] In this embodiment, the short-term indicator change difference curve is used to reflect the differences in the changes of different verification analysis indicators over time between the experimental group and the control group during the short-term verification process (within one week after the experiment). The specific steps for establishing the curve are: first, all data obtained by the experimental group and the control group during the short-term verification process (within one week after the experiment) are collected and marked as verification analysis indicator data; then, for different verification analysis indicators, the indicator difference of the experimental group's indicator value minus the control group's indicator value is calculated; finally, the indicator difference is used to construct the short-term indicator change difference curve of different verification analysis indicators in time series.

[0040] In this embodiment, the long-term indicator change difference curve is used to reflect the differences in the changes of different verification analysis indicators over time between the experimental group and the control group during the long-term verification process (within 4-8 weeks after the experiment). The specific steps for establishing the curve are: first, all data obtained from the experimental group and the control group during the long-term verification process (within 4-8 weeks after the experiment) are collected and labeled as verification analysis indicator data; then, for different verification analysis indicators, the indicator difference is calculated by subtracting the indicator value of the control group from the indicator value of the experimental group; finally, the indicator difference is used to construct the long-term indicator change difference curve according to the time series (including five types of time series curves within the 4th week after the experiment, the 5th week after the experiment, the 6th week after the experiment, the 7th week after the experiment, and the 8th week after the experiment).

[0041] In this embodiment, the validation analysis indicators refer to specific parameters used to measure and evaluate the effectiveness of the model, including quantitative parameters for evaluating cardiac systolic and diastolic function (such as left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), the ratio of left ventricular early diastolic filling peak velocity (E peak) to atrial systolic filling peak velocity (A peak) (E / A ratio), etc.), and quantitative parameters for evaluating the degree of pathological changes in myocardial tissue, that is, analyzing the protective effect of unloading on myocardial tissue (including the proportion of myocardial fibrosis area, myocardial cell apoptosis rate, the proportion of myocardial infarction area, and collagen deposition, etc.).

[0042] In this embodiment, the short-term change boundary value is used to define the values of the normal change range and the abnormal change range. The specific acquisition steps are: first, the maximum and minimum points are extracted from the short-term indicator change difference curve; then, all the maximum points or minimum points obtained are averaged to obtain the maximum mean or minimum mean; then, the extreme mean is obtained by calculating the average of the maximum mean and the minimum mean; finally, the maximum value is selected from the change difference average value determined according to the short-term indicator change difference curve and the extreme mean, and output as the short-term change boundary value; setting the short-term boundary benchmark threshold refers to the threshold used to judge whether the short-term indicator change is abnormal, generally refers to 30% of the short-term change boundary value; the short-term change abnormal point refers to the corresponding point in the short-term indicator change difference curve where the absolute difference between the change difference and the short-term change boundary value exceeds the set short-term boundary benchmark threshold.

[0043] In this embodiment, the difference value refers to the absolute difference between the change difference and the short-term change boundary value in the short-term indicator change difference curve, or the absolute difference between the change difference and the long-term change boundary value in the long-term indicator change difference curve.

[0044] In this embodiment, the long-term change boundary value is used to define the values of the normal change range and the abnormal change range. The specific acquisition steps are: first, the maximum and minimum points are extracted from the long-term indicator change difference curve; then, all the maximum points or minimum points obtained are averaged to obtain the maximum mean or minimum mean; then, the extreme mean is obtained by calculating the average of the maximum mean and the minimum mean; finally, the maximum value is selected from the change difference average value determined according to the long-term indicator change difference curve and the extreme mean, and output as the long-term change boundary value; setting the long-term boundary benchmark threshold refers to the threshold used to judge whether the long-term indicator change is abnormal, generally refers to 30% of the long-term change boundary value; the long-term change abnormal point refers to the corresponding point in the long-term indicator change difference curve where the absolute difference between the change difference and the long-term change boundary value exceeds the set long-term boundary benchmark threshold.

[0045] In this embodiment, the indicator change boundary series is obtained by taking the corresponding short-term change boundary value of the same verification analysis indicator as the first and arranging the long-term change boundary values in the order of acquisition; the indicator change stability score is obtained by analyzing the change boundary ratio between two adjacent change boundary values in the order of arrangement of the change boundary values in the indicator change boundary series, and then calculating the variance value of all the obtained change boundary ratios, and the value range is The change stability level is the stability level obtained by screening from the preset indicator change stability level mapping table based on the indicator change stability score, wherein the preset indicator change stability level mapping table is composed of the indicator change stability score value range and the corresponding stability level (including excellent, good and poor levels).

[0046] In this embodiment, the short-term anomaly distribution characteristics refer to the temporal distribution characteristics of short-term change anomaly points (including the frequency of occurrence of anomaly points, the clustering of anomaly points on the time axis, and the continuity of the occurrence of anomaly points), which are obtained by counting the distribution number of anomaly points in different time periods, calculating the time intervals between anomaly points, and analyzing the continuity of the occurrence of anomaly points; the long-term anomaly distribution characteristics refer to the temporal distribution characteristics of long-term change anomaly points (including the frequency of occurrence of anomaly points, the clustering of anomaly points on the time axis, and the continuity of the occurrence of anomaly points); the abnormal change pattern is obtained by summarizing and statistically analyzing the short-term anomaly distribution characteristics and the long-term anomaly distribution characteristics, and is used to represent the abnormal change law of the current verification analysis indicator.

[0047] In this embodiment, for example, if verification analysis indicator 1 is left ventricular ejection fraction (LVEF), analysis of the corresponding short-term indicator change difference curve reveals that abnormal points mainly appear on the third and fifth days after the experiment, with a higher frequency of abnormal points on these two days. Furthermore, the abnormal points exhibit a certain clustering pattern on the time axis, i.e., the abnormal points are relatively concentrated on these two days. Furthermore, the abnormal points do not appear continuously, but rather intermittently. The short-term abnormal distribution characteristics of verification analysis indicator 1 are as follows: the abnormal points on the third and fifth days after the experiment have a high frequency and concentration, and the abnormal points on the third and fifth days of the experiment are continuous, while the abnormal points in other time periods are intermittent; By analyzing the corresponding long-term indicator change difference curves, we found that abnormal points still appeared in the 6th and 8th weeks after the experiment, and the distribution of abnormal points on the time axis was relatively uniform, with no obvious clustering or continuous occurrence; The long-term abnormal distribution characteristics of validation analysis indicator 1 are as follows: abnormal points appeared in the 6th and 8th weeks after the experiment, with low frequency of abnormal points and no obvious clustering or continuous appearance; The abnormal change pattern of verification analysis indicator 1 is as follows: in the short term, abnormal points appeared on the 3rd and 5th day after the experiment, and the frequency of abnormal points was high and relatively concentrated, and they appeared intermittently in the rest of the time periods; in the long term, abnormal points appeared on the 6th and 8th week after the experiment, and the frequency of abnormal points was relatively low and evenly distributed, without obvious clustering or continuity.

[0048] In this embodiment, the abnormal feature similarity coefficient is a coefficient obtained by analyzing the comprehensive similarity between the short-term abnormal distribution characteristics and the long-term abnormal distribution characteristics of the current verification analysis indicator and other verification analysis indicators that have an evaluation correlation relationship; wherein, whether there is an evaluation correlation relationship between the verification analysis indicators is determined by whether the evaluation targets of the analysis indicators are consistent. For example, the indicator evaluation targets of the verification analysis indicators left ventricular ejection fraction (LVEF) and left ventricular short-axis shortening (LVFS) are both to evaluate cardiac systolic and diastolic functions, then the verification analysis indicators left ventricular ejection fraction (LVEF) and left ventricular short-axis shortening (LVFS) have an evaluation correlation relationship; the specific steps for obtaining the abnormal feature similarity coefficient are as follows: : First, use the set similarity algorithm (such as cosine similarity algorithm, Pearson correlation coefficient) to obtain the current verification analysis indicator, and the corresponding short-term feature similarity and long-term feature similarity of the short-term abnormal distribution characteristics and long-term abnormal distribution characteristics of each other verification analysis indicator that has an evaluation correlation relationship. By calculating the average similarity of the short-term feature similarity and the long-term feature similarity, the corresponding single feature similarity of the current verification analysis indicator and each other verification analysis indicator that has an evaluation correlation relationship is obtained; finally, the current verification analysis indicator is added to the single feature similarities of all other verification analysis indicators that have an evaluation correlation relationship to obtain the comprehensive feature similarity, and the comprehensive feature similarity is normalized to obtain it.

[0049] In this embodiment, the cause / effect relationship weight refers to the pre-set cause weight and result weight (wherein the cause weight is greater than the result weight, and the sum of the cause weight and the result weight is equal to 1), which is a preset weight assigned based on the causal relationship between the current verification analysis indicator and other verification analysis indicators that have an evaluation correlation relationship. The causal relationship is determined by referring to the Granger causality test method, and the specific steps include: obtaining long-term series data of the verification analysis indicator and other related indicators, data stationarity test, determination of the lag order and Granger causality test, for example, left ventricular pressure is the cause of myocardial contractility.

[0050] In this embodiment, the abnormal impact characterization value is obtained by normalizing the abnormal characteristic similarity coefficient of the current verification analysis indicator and the comprehensive characterization value calculated after considering the causal relationship with other indicators. It is used to quantitatively evaluate the impact of the abnormal indicator on other indicators. The calculation formula of the comprehensive characterization value is expressed as follows: Where, It is expressed as the comprehensive characterization value of the current validation analysis indicators; It is expressed as the abnormal characteristic similarity coefficient of the current validation analysis indicator; It is expressed as the weight value assigned based on the causal relationship between the verification analysis indicator and the dth other verification analysis indicator with an evaluation correlation relationship (the value range is ).

[0051] In this embodiment, for example, according to the Granger causality test results, there is currently a verification analysis indicator - Left ventricular ejection fraction (LVEF) is the confirmatory analysis indicator - Left ventricular fractional shortening (LVFS) is a validation analysis indicator - Results of left ventricular early diastolic filling peak velocity (E peak) and validation analysis indicators - The similarity coefficient of abnormal characteristics of left ventricular ejection fraction (LVEF) is p; The current verification analysis index - The composite representation of left ventricular ejection fraction (LVEF) is expressed as Where, Expressed as verification analysis indicators - Comprehensive representation of left ventricular ejection fraction (LVEF); p represents the validation analysis indicator - Abnormal characteristic similarity coefficient of left ventricular ejection fraction (LVEF); Expressed as validation analysis indicators - Left ventricular ejection fraction (LVEF) is the confirmatory analysis indicator - Causes of left ventricular fractional shortening (LVFS), and the weights assigned to these causes; Expressed as validation analysis indicators - Left ventricular ejection fraction (LVEF) is the confirmatory analysis indicator -Left ventricular early diastolic peak filling velocity (E peak) results, and the weights assigned to the results.

[0052] In this embodiment, the abnormal impact level is the abnormal impact level obtained by screening from a preset abnormal impact level mapping table based on the abnormal impact characterization value, wherein the preset abnormal impact level mapping table is composed of the abnormal impact characterization value value range and the corresponding abnormal impact level (including three levels: excellent, good, and poor).

[0053] In this embodiment, adjusting the reference indicator refers to the verification analysis indicator with a poor change stability level; the model adjustment parameter is the model parameter that needs to be adjusted, such as the type of inducer, inducer dose, induction method and transplantation location, etc., determined by using the indicator category and abnormal change pattern of the adjustment reference indicator as matching conditions; the parameter adjustment strategy refers to the specific adjustment method and steps formulated for the model adjustment parameter, obtained by using the indicator category and abnormal change pattern of the adjustment reference indicator as matching conditions, such as changing the coronary artery ligation method to the drug injection induction method, adding the inducer dose and adjusting the transplantation location from the apex to the base of the heart, etc.

[0054] In this embodiment, overlapping model adjustment parameters refer to the situation where, when summarizing all model adjustment parameters and corresponding parameter adjustment strategies, it is found that there are multiple parameter adjustment strategies targeting the same model adjustment parameter; the strategy priority coefficient refers to the priority coefficient determined by comprehensively considering the historical adjustment effect of the parameter adjustment strategy and the abnormal impact characterization value of the corresponding adjustment reference indicator. The specific acquisition steps are to first collect the historical adjustment effect data of the parameter adjustment strategy, and use the effect evaluation indicators (such as the historical average accuracy and the historical average recall rate) to evaluate and obtain the effect evaluation results; then introduce the hierarchical analysis method to weight each effect evaluation result and add them up to obtain the historical adjustment effect score (for example, there is The historical adjustment of parameter adjustment strategy 1 has increased the historical average accuracy of the model by 5% and the historical average recall by 3%. The weights of accuracy and recall are 0.6 and 0.4 respectively. The historical adjustment effect score of parameter adjustment strategy 1 is 5%×0.6+3%×0.4=4.2%. Then, the priority score is obtained by performing a weighted average calculation on the historical adjustment effect score and the abnormal impact representation value of the adjustment reference indicator corresponding to the current strategy (for example, the historical adjustment effect is currently considered more important, the weight assigned to the historical adjustment effect score is 0.7, the weight assigned to the abnormal impact representation value is 0.3, and there is an abnormal impact representation value of adjustment reference indicator 1). , the historical adjustment effect score of the corresponding parameter adjustment strategy is , then the priority score of the parameter adjustment strategy for adjusting reference indicator 1 is 0.7× +0.3× ); Finally, the obtained priority value is normalized to obtain the strategy priority coefficient.

[0055] The working principle of the above technical solution is: first, using the data obtained from the experimental group and the control group during the short-term and long-term verification process, construct the short-term and long-term indicator change difference curves of different verification analysis indicators, and determine the short-term or long-term change difference constant points based on the curves; then, construct the indicator change boundary series of each verification analysis indicator, and perform a change stability analysis on the series to obtain the indicator change stability score and determine the change stability level, so as to evaluate the indicator change stability; at the same time, obtain the distribution characteristics of the short-term and long-term change abnormal points, and comprehensively consider the two to determine the abnormal change pattern; then, analyze the short-term and long-term abnormalities between the current verification analysis indicator and the related indicators. The similarity of normal distribution characteristics is comprehensively calculated to obtain the abnormal characteristic similarity coefficient, and then the corresponding weight is assigned according to the cause / effect role of the related indicators. After summary processing, the abnormal impact representation value is obtained, and then the abnormal impact level is determined, and the degree of correlation between the abnormal impacts of each indicator is clarified; finally, the indicators with different change stability levels are marked as adjustment reference indicators, and the indicator category and abnormal change pattern are used as screening conditions to determine the model adjustment parameters and strategies. If there are overlapping adjustment parameters and the corresponding strategies are different, the historical adjustment effects of each strategy and the abnormal impact representation value of the adjustment reference indicator are comprehensively considered to calculate the strategy priority coefficient, and finally the strategy with the highest coefficient is selected as the actual adjustment strategy.

[0056] The beneficial effects of the above technical solution are: by utilizing the verification data of the experimental group and the control group, establishing the short-term and long-term indicator change difference curves and confirming the abnormal points, it can accurately capture the abnormal fluctuations of the verification analysis indicators in different time dimensions; comprehensively considering the short-term and long-term abnormal distribution characteristics, it can deeply analyze the abnormal change pattern and provide an effective basis for obtaining model adjustment parameters and parameter adjustment strategies; by calculating the causal relationship between the abnormal feature similarity coefficient and the analysis indicator, the abnormal impact characterization value and level are obtained, which can clearly quantify the abnormal impact relationship between each indicator; it realizes the accurate screening of model adjustment parameters and strategies, and for the overlapping model adjustment parameters, by comprehensively considering the historical adjustment effect and the abnormal impact characterization value to determine the strategy priority coefficient and select the optimal parameter adjustment strategy, it effectively improves the efficiency and accuracy of model optimization, and ensures that the model always maintains good performance and stability in a constantly changing environment.

[0057] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0058] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A simulation and analysis system for cardiac unloading state, characterized in that: The system includes a model building unit and a data analysis unit; The model building unit is configured to set up an experimental group and a control group, specifically: Experimental group: The mouse myocardial infarction model and the mouse heterotopic heart transplantation model were combined to construct a left ventricular unloading model; Control group: The myocardial infarction model of mice was retained but no heart transplantation was performed to construct the control group model; The data analysis unit is configured to collect physiological and pathological data of the hearts of the experimental group and the control group, and to evaluate the effect of the cardiac unloading state by combining quantitative analysis and quantitative analysis methods; At the same time, the effect of the model is verified after the experiment. By analyzing the changing patterns and abnormal characteristics of the verification data, the model adjustment parameters and strategies are determined to achieve optimal adjustment of the model.

2. A cardiac unload state simulation and analysis system according to claim 1, characterized in that: The model building unit includes: The experimental group construction module is configured to complete the construction of the left ventricular unloading model by inducing myocardial infarction in donor mice and then transplanting the infarcted heart into the abdominal cavity of recipient mice; The control group construction module is configured to select mice of the same strain, age, and weight as the experimental group, induce myocardial infarction in the mice, but do not perform heart transplantation to complete the construction of the control group model; A verification module is constructed and configured to verify whether the construction of the left ventricular unloading model and the control group model is successful.

3. The system for simulating and analyzing cardiac unload state according to claim 2, characterized in that: The model verification module is specifically: Left ventricular unloading model: The effect of left ventricular unloading is verified by echocardiography and pressure-volume loop technology, and the systolic and diastolic function indicators of the heart are recorded to evaluate whether the unloading state is successful; Control group model: The heart load state of the control group was verified by echocardiography and histological staining, and the systolic and diastolic functions of the heart were recorded to ensure that the heart of the control group maintained a normal load state.

4. The cardiac unload state simulation and analysis system according to claim 1, characterized in that: The data analysis unit comprises: The data acquisition module is configured to respectively acquire physiological data and pathological data of the heart of the experimental group and the control group, wherein: Pathological data: used to slice heart tissue and evaluate the degree of myocardial fibrosis and myocardial cell apoptosis using Masson trichrome staining and HE staining, and record myocardial infarction area and collagen deposition; Physiological data include: Echocardiography: used to collect cardiac systolic and diastolic functions, as well as left ventricular end-diastolic volume and end-systolic volume; Pressure-volume loop: used to measure changes in left ventricular pressure and volume, and to calculate the heart's systolic and diastolic functions; Hemodynamic monitoring: used to measure heart rate and mean arterial pressure indicators to assess the heart's pumping function and overall hemodynamic status; a processing and analysis module configured to perform quantitative and quantitative analysis on the collected physiological data and pathological data, and to evaluate the overall effect of the cardiac unloading state by combining the physiological data and the pathological data; The experimental verification module is configured to verify the effect of the model after the experiment and optimize and adjust the model according to the verification results.

5. The system for simulating and analyzing cardiac unloading state according to claim 4, characterized in that: The processing and analysis module includes: a data processing module configured to perform quantitative analysis on the echocardiogram and the pressure-volume loop, analyze cardiac function indicators, and compare differences between the experimental group and the control group using statistical methods, wherein the statistical methods include t-test and analysis of variance; Quantitative analysis of cardiac tissue slice images was performed to assess the area of myocardial fibrosis and infarct size; The data analysis module is configured to analyze the cardiac function and pathological changes of the experimental group and the control group, and to evaluate the overall effect of the cardiac unloading state by combining physiological and pathological data.

6. The system for simulating and analyzing cardiac unloading state according to claim 5, characterized in that: The data analysis module is specifically: Cardiac function assessment: used to compare the cardiac systolic and diastolic functions of the experimental group and the control group, and to evaluate the improvement effect of left ventricular unloading on cardiac function; Pathological change analysis: used to compare the degree of myocardial fibrosis and myocardial cell apoptosis between the experimental group and the control group, and to analyze the protective effect of unloading on myocardial tissue; Comprehensive analysis and evaluation: It is used to combine physiological and pathological data, quantify and analyze the improvement effect of left ventricular unloading on cardiac function and the protective effect on myocardial tissue, obtain the unloading effect characterization coefficient, evaluate the overall effect of the cardiac unloading state based on the unloading effect characterization coefficient, and put forward suggestions for optimizing the model.

7. The system for simulating and analyzing cardiac unloading state according to claim 4, characterized in that: The experimental verification module includes: A model validation module is configured to perform short-term and long-term validation on the models of the experimental group and the control group, respectively, and determine the validity of the models of the experimental group and the control group through validation; A model optimization module is configured to adjust model parameters and optimize the experimental process based on the validation results.

8. The system for simulating and analyzing cardiac unloading state according to claim 7, characterized in that: The model verification module is specifically: The contents of the validation experimental group model include: Short-term validation: used to verify the short-term effects of the unloading state through echocardiography and pressure-volume loop analysis within 1 week after the experiment to ensure the successful construction of the left ventricular unloading model; Long-term validation: used to evaluate the long-term effects of the unloading state 4-8 weeks after the experiment and verify the stability of the left ventricular unloading model in combination with tissue slice analysis; The content of the validation control group model includes: Short-term verification: used to verify the heart load status of the control group by echocardiography and histological staining within 1 week after the experiment to ensure that the heart of the control group maintains a normal load status; Long-term validation: used to evaluate the stability of the control group by tissue section analysis 4-8 weeks after the experiment.

9. The system for simulating and analyzing cardiac unloading state according to claim 7, characterized in that: The model optimization module specifically includes: Curve establishment: used to establish short-term indicator change difference curves and long-term indicator change difference curves for different verification analysis indicators using all data obtained from the experimental group and the control group during the short-term verification and long-term verification processes; Abnormal point confirmation: used to determine the short-term change boundary value based on the short-term indicator change difference curve of the current verification analysis indicator, and regard the point in the curve where the difference between the short-term change boundary value and the short-term change boundary value exceeds the set short-term boundary benchmark threshold as a short-term change abnormal point; According to the long-term indicator change difference curve of the current verification analysis indicator, the long-term change boundary value is determined, and the point in the curve where the difference with the long-term change boundary value exceeds the set long-term boundary benchmark threshold is regarded as a long-term change abnormal point; Change stability analysis: It is used to put the corresponding short-term change boundary point of the same verification analysis indicator as the first, and arrange the long-term change boundary points in the order of acquisition to obtain the indicator change boundary series; Performing a change stability analysis on the indicator change boundary series to obtain an indicator change stability score; Determine the stability level of the verification analysis indicator based on the indicator change stability score; Abnormal trend analysis: used to obtain the short-term abnormal distribution characteristics of the short-term change abnormal points and the long-term abnormal distribution characteristics of the long-term change abnormal points of the current verification analysis indicators; By comprehensively considering the short-term abnormal distribution characteristics and the long-term abnormal distribution characteristics, the abnormal change pattern of the current verification analysis indicators is determined; Abnormal impact analysis: used to analyze the comprehensive similarity between the short-term abnormal distribution characteristics and long-term abnormal distribution characteristics of the current verification analysis indicator and other verification analysis indicators with which there is an evaluation correlation, and obtain the abnormal characteristic similarity coefficient; Based on the causal relationship between the current verification analysis indicator and other verification analysis indicators with which there is an evaluation correlation, the corresponding abnormal feature similarity coefficient is assigned a corresponding cause / effect relationship weight, and then summarized and processed to obtain the abnormal impact representation value of the current verification analysis indicator; Determine the abnormal impact level of the current verification analysis indicator based on the abnormal impact characterization value; Adjustment parameter determination: used to mark the verification analysis indicators with poor change stability level as adjustment reference indicators; The indicator category and abnormal change pattern of the adjustment reference indicator are used as screening conditions to determine the model adjustment parameters and the corresponding parameter adjustment strategy; Summarize all model adjustment parameters and corresponding parameter adjustment strategies. If there are overlapping model adjustment parameters and the corresponding parameter adjustment strategies are different, comprehensively consider the historical adjustment effects of each parameter adjustment strategy and the abnormal impact representation value of the corresponding adjustment reference indicator to determine the strategy priority coefficient of each parameter adjustment strategy; The parameter adjustment strategy with the highest strategy priority coefficient is used as the actual parameter adjustment strategy for the corresponding overlap model adjustment parameters.

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