A data analysis system for optimizing stem cell therapy for myocardial infarction

Abstract

The present application relates to the technical field of medical data processing, in particular to a data analysis system for optimizing stem cell therapy for myocardial infarction. The system provides information processing and storage support services for stem cell efficacy prediction, and performs the following steps: obtaining the functional index of several associated efficacy factors during stem cell therapy; determining the main effect index according to each functional index and its corresponding pathway activation sensitivity; determining the correction coefficient of the main effect index, correcting the main effect index, and obtaining the total efficacy prediction value. The system generates the total efficacy prediction value as a storable data object, providing data support for subsequent treatment parameter optimization. Through systematic information processing and data storage support, the present application integrates multi-dimensional influencing factors, improves the accuracy and reliability of efficacy prediction, effectively avoids the risk of ineffective treatment, and provides an efficient data analysis basis for the optimization of stem cell treatment programs.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, and more specifically to a data analysis system for optimizing stem cell therapy for myocardial infarction. Background Technology

[0002] Currently, the ultimate efficacy of stem cell-based therapies is influenced by a complex, multidimensional system of factors, primarily summarized into four dimensions: patient genetic background, stem cell quality, infusion regimen, and in vivo microenvironment. Individual differences in stem cell therapy efficacy are significant, and current analyses of stem cell therapy effects largely rely on limited clinical experience or focus only on data from one or a few of the aforementioned dimensions. This failure to systematically integrate and fuse all these crucial data dimensions may result in an inability to comprehensively assess the true risks and benefits of treatment, leading to low accuracy in efficacy predictions. Furthermore, this leaves some patients at high risk of treatment ineffectiveness due to mismatched regimens. Summary of the Invention

[0003] To address the technical problem of inaccurate efficacy prediction caused by the lack of systematic integration of multi-dimensional influencing factors in existing technologies, the present invention aims to provide a data analysis system for optimizing stem cell therapy for myocardial infarction. The specific technical solution adopted is as follows: One embodiment of the present invention provides a data analysis system for optimizing stem cell therapy for myocardial infarction, including a memory and a processor, wherein the processor is used to process instructions stored in the memory to implement the following process: The functional index of several associated efficacy factors in stem cell therapy for myocardial infarction was obtained; the associated efficacy factors included in vivo microenvironment characteristics and stem cell quality, and the functional index was used to characterize at least the interaction between the microenvironment and stem cells. Based on the functional index of each associated therapeutic factor and the sensitivity of the pathway activation corresponding to the associated therapeutic factor, the main effect index is determined; the main effect index is used to characterize the overall contribution of the microenvironment characteristics and stem cell quality. The correction coefficients of the main effect indicators are determined, and the main effect indicators are corrected using the correction coefficients to obtain the predicted value of the overall efficacy; the correction coefficients include gene correction coefficients and infusion correction coefficients.

[0004] Furthermore, functional indices for each associated efficacy factor were obtained, including: To obtain the concentrations of anti-inflammatory factors, inflammatory factors, angiogenesis factors, and local blood flow velocity in the myocardial infarction area; The ratio of anti-inflammatory factor concentration to inflammatory factor concentration, the concentration of angiogenesis factor, and the local blood flow velocity in the myocardial infarction area were integrated and processed, and the integrated results were determined as a functional index of microenvironment characteristics. The positive rate of stem cell surface activity markers, the concentrations of anti-inflammatory factors and angiogenesis factors secreted by stem cells, and the actual fold increase of stem cells were obtained; the markers included at least CD44, CD73, and CD90. The functional index of stem cell quality is determined based on the positive rate of stem cell surface activity markers, the concentration of anti-inflammatory factors and angiogenesis factors secreted by stem cells, and the actual proliferation fold of stem cells.

[0005] Furthermore, the functional index for determining stem cell quality includes: The survival potential index value is determined based on the ratio of the positive rate of each biomarker to the preset activity threshold of the corresponding biomarker. The baseline value of paracrine capacity is determined based on the concentrations of anti-inflammatory factors and angiogenesis factors secreted by stem cells. The relative proliferation potential value is determined based on the ratio of the actual proliferation fold of stem cells to the preset proliferation fold. The survival potential index, baseline paracrine capacity, and relative proliferation potential were integrated and processed, and the integrated results were determined as the functional index of stem cell quality.

[0006] Furthermore, the determination of the main effect index based on the functional index of each associated efficacy factor, combined with the pathway activation sensitivity of the associated efficacy factor, includes: The concentration sequences of inflammatory factors and the activation fold ratio of the first pathway were obtained; the activation fold ratio of the first pathway was obtained by the protein expression levels of intracellular p-Akt and total Akt. The contribution value of microenvironment support was determined based on the functional index, inflammatory factor concentration sequence, and first pathway activation fold sequence of each associated therapeutic factor. The intensity sequence of paracrine VEGF from stem cells and the activation fold ratio sequence of the second pathway were obtained; the activation fold ratio of the second pathway was obtained by the protein expression levels of p-VEGFR2 and total VEGFR2 in target cells. The contribution value of stem cell remodeling was determined based on the functional index of each associated therapeutic factor, the intensity sequence of the stem cell paracrine VEGF, and the activation fold sequence of the second pathway. The contribution values ​​of the microenvironment support and the stem cell remodeling were fused to determine the main effect index.

[0007] Furthermore, determining the microenvironment support contribution value includes: The concentration of inflammatory factors was used as the independent variable and the activation factor of the first pathway was used as the dependent variable to construct the first linear regression equation, and the first slope of the first linear regression equation was obtained. The functional index of each associated efficacy factor is multiplied by the first slope, and the resulting product is determined as the first adjustment factor. The functional index of the microenvironment characteristics is adjusted using the first adjustment factor, and the adjusted functional index of the microenvironment characteristics is determined as the microenvironment support contribution value.

[0008] Furthermore, determining the contribution value of stem cell remodeling includes: The intensity of VEGF paracrine secretion from stem cells was used as the independent variable and the activation fold of the second pathway was used as the dependent variable to construct a second linear regression equation, and the second slope of the second linear regression equation was obtained. The functional index of each associated efficacy factor is multiplied by the second slope, and the resulting product is determined as the second adjustment factor. The functional index of stem cell quality was adjusted using the second adjustment factor, and the adjusted functional index of stem cell quality was determined as the contribution value of stem cell remodeling.

[0009] Further, determining the correction coefficient for the main effect index includes: Determine the effect values ​​of genes related to the microenvironment and those related to stem cells; obtain gene correction coefficients based on the Euclidean norm of the two related gene effect values; The infusion efficiency and relative dose of the target infusion regimen are determined; the infusion correction coefficient is obtained based on the Euclidean norm of the infusion efficiency and relative dose; the target infusion regimen is the current infusion regimen for treating myocardial infarction using stem cells.

[0010] Furthermore, determining the effect values ​​of microenvironment-related genes and stem cell-related genes includes: The first relative functional strength of several genes related to the regulation of inflammation in the microenvironment was obtained, and the average value of all the first relative functional strengths was determined as the effect value of the microenvironment-related genes. The second relative functional strength of several genes related to the paracrine function of stem cells was obtained, and the average value of all the second relative functional strengths was determined as the effect value of stem cell-related genes. The undetermined relative functional strength is equal to the ratio of the functional strength of the mutant heterozygote or homozygote of the gene to the functional strength of the wild homozygote; the undetermined relative functional strength is either the first relative functional strength or the second relative functional strength.

[0011] Further, determining the infusion efficiency and relative dose values ​​of the target infusion regimen includes: The number of stem cells in the infarcted area and the total number of infusions corresponding to the target infusion protocol are obtained, and the ratio of the number of stem cells in the infarcted area to the total number of infusions is determined as the infusion efficiency. Obtain the actual infusion dose and the minimum effective dose corresponding to the infusion plan. Input the ratio of the actual infusion dose to the minimum effective dose into the S function for conversion and output the relative dose value.

[0012] Furthermore, after obtaining the overall efficacy prediction value, it also includes: Acquire paired data from several clinical samples and the predicted overall efficacy value for the current patient; the paired data consists of the predicted overall efficacy value and the actual efficacy improvement value. A predictive mapping function is constructed based on each of the paired data, and the predicted total efficacy value of the current patient is input into the predictive mapping function to obtain the predicted efficacy improvement value of the current patient; the predicted efficacy improvement value is the basis for determining whether to optimize the treatment-related parameters of the current patient.

[0013] The present invention has the following beneficial effects: This invention provides a data analysis system for optimizing stem cell therapy for myocardial infarction. The system first acquires functional indices related to efficacy factors, unifying core dimensions that may be considered in isolation, such as in vivo microenvironment characteristics and stem cell quality, into quantifiable functional indices to characterize the dynamic interaction between the microenvironment and stem cells. It transforms biological data from different sources and with different dimensions into unified and comparable numerical indicators, laying a solid foundation for subsequent comprehensive analysis. At the same time, by quantifying the interaction between the microenvironment and stem cells, it helps to reveal the potential reasons for differences in efficacy from the data.

[0014] Then, the main effect index is determined. By comprehensively analyzing various functional indices and pathway activation sensitivity, all influencing factors of all dimensions are systematically integrated into a single main effect index to quantify the overall contribution. This overcomes the shortcomings of existing methods that lack systematic integration of multi-dimensional influencing factors, generating a more biologically meaningful and predictive comprehensive index. It can more accurately predict the potential effect of treatment than analyzing any single factor alone, and can predict the trend of efficacy before or early in the treatment process. This is beneficial for providing prospective reference for clinical decision-making and identifying high-risk individuals who may experience ineffectiveness.

[0015] Finally, correction coefficients are used to refine the main effect indicators. Specifically, the introduction of gene correction coefficients and infusion correction coefficients for personalized correction of the main effect indicators is key to further improving prediction accuracy. The gene correction coefficient takes into account the influence of the patient's inherent and unchangeable genetic background on the efficacy, while the infusion correction coefficient takes into account the influence of controllable operational parameters such as infusion route and dosage on cell delivery efficiency, providing a basis for dynamic adjustment. The correction of the main effect indicators involves incorporating more key variables to calibrate the preliminary main effect prediction, making the final total efficacy prediction value more consistent with the actual situation and indicating the direction of optimization. For example, if the infusion correction coefficient is low, the system can prompt to change the infusion route or adjust the dosage.

[0016] In summary, this invention achieves high-precision prediction of the efficacy of stem cell therapy for myocardial infarction. This high-precision efficacy prediction can guide clinical practice to make personalized adjustments to the treatment plan, thereby effectively reducing the risk of ineffective treatment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an execution flowchart of a data analysis system for optimizing stem cell therapy for myocardial infarction, as an embodiment of the present invention. Figure 2 This is a flowchart illustrating the steps for determining the main effect index in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the steps for determining the correction coefficients of the main effect index in an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solution proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The application scenarios corresponding to this invention can be: Myocardial infarction, a highly fatal acute cardiovascular disease, is characterized by myocardial ischemia and necrosis caused by coronary artery occlusion. Stem cell therapy, with its differentiation potential and paracrine effects, offers a breakthrough direction for myocardial regeneration. However, the efficacy of stem cell therapy varies significantly among individuals, with many myocardial infarction patients failing to achieve the expected therapeutic effect after treatment. Furthermore, the long-term survival rate of transplanted cells is extremely low, severely hindering its clinical translation. Therefore, it is necessary to analyze the predictive efficacy based on relevant data on stem cell therapy for myocardial infarction, and use this predictive efficacy as a reference for adjusting subsequent treatment parameters.

[0022] refer to Figure 1 The diagram illustrates an embodiment of the present invention: an execution flowchart of a data analysis system for optimizing stem cell therapy for myocardial infarction, comprising: S101, to obtain the functional index of several related efficacy factors in stem cell therapy for myocardial infarction.

[0023] The efficacy of stem cell therapy for myocardial infarction is essentially the result of the dynamic coupling of four dimensions: genetic background, stem cell quality, infusion protocol, and in vivo microenvironment. The in vivo microenvironment creates the fertile ground for stem cell function through the inhibition of inflammatory factors and support for angiogenesis, while stem cells can also promote angiogenesis and remodel the microenvironment through paracrine function. The synergistic effect of microenvironment characteristics and stem cell quality manifests as a positive additive effect, while the antagonistic effect manifests as a negative offsetting effect, both following concentration-dependent and time-series correlation rules. Therefore, the efficacy-related factors in this embodiment include at least in vivo microenvironment characteristics and stem cell quality, and functional indices are used to characterize the interaction between the microenvironment and stem cells.

[0024] As an exemplary implementation, the functional index of each associated efficacy factor is obtained, including: The first step is to obtain the concentrations of anti-inflammatory factors, inflammatory factors, angiogenesis factors, and local blood flow velocity in the myocardial infarction area.

[0025] The second step involves integrating the data on the ratio of anti-inflammatory factor concentration to inflammatory factor concentration, angiogenesis factor concentration, and local myocardial blood flow velocity in the myocardial infarction area, and determining the integration results as a functional index of microenvironment characteristics.

[0026] The microenvironment, as the foundation for stem cell survival and function, depends on three key conditions: inflammatory balance, angiogenesis support, and blood flow delivery efficiency. Among these, inflammatory balance determines whether stem cells are protected from apoptosis, angiogenesis factors are the material basis for functional development, and blood flow velocity affects the efficiency of stem cell targeted colonization. Therefore, the functional indices of the microenvironment characteristics in this embodiment can demonstrate the impact of the infarcted region on the survival and function of stem cells.

[0027] In this embodiment, the ratio of anti-inflammatory factor concentration to inflammatory factor concentration can clearly indicate the inflammatory balance tendency. When the ratio is greater than 1, it is pro-inflammatory; when it is less than 1, it is anti-inflammatory; and when it is equal to 1, it is in a balanced state. The concentration of angiogenesis factor, also known as vascular endothelial growth factor, indicates that the higher the concentration, the higher the microenvironment's support for angiogenesis. Through hemodynamic monitoring, the local blood flow velocity of the myocardium is recorded. The higher the velocity, the better the environment for stem cells to deliver to the infarct area.

[0028] In one specific implementation, the ratio of anti-inflammatory factor concentration to inflammatory factor concentration, angiogenesis factor concentration, and local myocardial blood flow velocity in the myocardial infarction area are first standardized to eliminate the influence of dimensions. Then, the standardized ratio, angiogenesis factor concentration, and local myocardial blood flow velocity are multiplied together, and the resulting product is used as a functional index of microenvironment characteristics.

[0029] As an example, the formula for calculating the functional index of microenvironment characteristics can be: In the formula, The functional index represents the characteristics of the microenvironment, B represents the ratio of the concentration of anti-inflammatory factors to the concentration of inflammatory factors after standardization, X represents the concentration of angiogenesis factors after standardization, and V represents the local blood flow velocity of the myocardium after standardization.

[0030] In the formula for calculating the functional index, the standardization process employs a normalization method that maps the values ​​to a non-negative interval, such as Min-Max normalization. The implementation process of this standardization is based on existing technology and will not be elaborated upon here. Functional index of microenvironmental characteristics. It can directly reflect the microenvironment's ability to support stem cells. The milder the inflammation, the more abundant the angiogenesis factors, and the more suitable the blood flow, the higher the functional index of the microenvironment characteristics, and vice versa. Updated dynamically as the disease progresses.

[0031] It should be noted that this embodiment uses a product form to construct the functional index. Mathematically, this does not mean a weighted summation of the three factors, but rather the construction of a synergistic inhibition or promotion model. When any of B, X, or V approaches 0, their product rapidly decays, objectively characterizing the decisive limiting effect of the bottleneck factor on the microenvironment's support capacity, consistent with the pathophysiological characteristics of multi-factor interactions in the myocardial infarction region. Furthermore, since all three are normalized to the same order of magnitude, the product operation does not introduce biased weights mathematically.

[0032] The third step is to obtain the positive rate of stem cell surface activity markers, the concentration of anti-inflammatory factors and angiogenesis factors secreted by stem cells, and the actual proliferation fold of stem cells.

[0033] As the seeds of therapy, the degree of compatibility between stem cells and the microenvironment directly determines the upper limit of therapeutic efficacy. Compatibility is mainly reflected in three dimensions: survival potential, paracrine function, and proliferation potential. Among them, survival potential is a prerequisite for exerting its effects, paracrine function is key to reshaping the microenvironment, and proliferation potential determines the duration of the effect.

[0034] In one specific implementation, flow cytometry is used to detect the expression of stem cell surface activity markers. Key markers include at least CD44 (Cluster of Differentiation 44), CD73 (Cluster of Differentiation 73), and CD90 (Cluster of Differentiation 90), which are used to quantify the survival potential of stem cells. Cytokine detection technology is used to measure the concentrations of anti-inflammatory factors (Interleukin-10, IL-10) and angiogenesis factors secreted by stem cells, which are used to quantify the paracrine ability of stem cells to remodel the microenvironment. Through short-term culture observation, the mitotic activity of stem cells is recorded to obtain the actual proliferation fold of stem cells, which is used to quantify the proliferation potential of stem cells in the microenvironment.

[0035] The fourth step is to determine the functional index of stem cell quality based on the positive rate of stem cell surface activity markers, the concentration of anti-inflammatory factors and angiogenesis factors secreted by stem cells, and the actual proliferation fold of stem cells.

[0036] As an exemplary implementation, determining the functional index of stem cell quality includes: The first sub-step involves determining the survival potential index value based on the ratio of the positive rate of each marker to the preset activity threshold of the corresponding marker.

[0037] In one specific implementation, for each biomarker, the ratio of the biomarker's positive rate to a preset activity threshold is calculated, and the average of all ratios is used as the survival potential index value. The preset activity threshold varies for different biomarkers; for example, the preset activity threshold can be a positive rate of 95%.

[0038] The second sub-step involves determining the baseline value of paracrine capacity based on the concentrations of anti-inflammatory factors and angiogenesis factors secreted by stem cells.

[0039] In one specific implementation, the product of the concentrations of anti-inflammatory factors and angiogenesis factors is calculated, and the product of the two concentrations is determined as the baseline value of paracrine capacity.

[0040] The third sub-step involves determining the relative proliferation potential value based on the ratio of the actual proliferation fold of stem cells to the preset proliferation fold.

[0041] In one specific implementation, a preset multiplication factor is set to ≥3 times within 72 hours, and the relative multiplication potential value is obtained by dividing the actual multiplication factor by the preset multiplication factor.

[0042] The fourth sub-step involves integrating the data on survival potential, paracrine capacity, and relative proliferation potential, and determining the integrated results as the functional index of stem cell quality.

[0043] In one specific implementation, the survival potential index value, the baseline value of paracrine capacity, and the relative proliferation potential value are first standardized to eliminate the influence of dimensions. Then, the product of the standardized survival potential index value, the baseline value of paracrine capacity, and the relative proliferation potential value is calculated as the functional index of stem cell quality.

[0044] As an example, the formula for calculating the functional index of stem cell quality can be: In the formula, The functional index representing stem cell quality, also known as the stem cell fit index, is denoted by C, which represents the standardized survival potential index value. Z represents the standardized baseline value of paracrine capacity, and Z represents the standardized relative proliferative potential value.

[0045] In the formula for calculating the stem cell aptability index, the standardization process employs a normalization method that maps the values ​​to a non-negative interval, such as Min-Max normalization. The implementation process of this standardization is based on existing technology and will not be elaborated upon here. Stem cell aptability index This directly reflects the compatibility of stem cells with their microenvironment. Higher stem cell activity, stronger paracrine function, and better proliferation potential indicate a higher functional index of stem cell quality, and vice versa. It changes dynamically with culture conditions and in vivo colonization time.

[0046] Thus, this embodiment has obtained the functional index of each associated efficacy factor in stem cell therapy for myocardial infarction.

[0047] S102. Based on the functional index of each associated therapeutic factor and the pathway activation sensitivity of the associated therapeutic factor, determine the main effect index.

[0048] The condition of the microenvironment, especially its inflammatory balance, is a key factor determining the survival of transplanted stem cells. This inflammatory balance can be achieved by directly regulating the intracellular PI3K / Akt signaling pathway. Specifically: a high microenvironment functional index (i.e., a functional index of microenvironment characteristics), indicating an anti-inflammatory tendency and adequate blood flow, weakens the activation of stem cell apoptosis signals by inflammatory factors, making the PI3K / Akt pathway more easily activated and increasing the survival probability of stem cells—this contribution is positive. Conversely, a low microenvironment functional index, indicating a pro-inflammatory tendency and turbulent blood flow, exacerbates stem cell apoptosis by inflammatory factors, inhibits PI3K / Akt pathway activation, and decreases the survival probability of stem cells—this contribution is negative.

[0049] When stem cells are transplanted into damaged tissue, they release key signaling factors, including VEGF (Vascular Endothelial Growth Factor), through their paracrine function. These factors act as powerful instructions, directly activating and promoting the existing vascular endothelial cells in the local tissue, initiating angiogenesis, and thus building a new vascular network in ischemic and hypoxic areas. Specifically: if some cells have a high fit index, i.e., strong paracrine function and high activity, the angiogenesis factors secreted by the stem cells can supplement the deficiency of this factor in the microenvironment, activate the VEGF pathway, promote angiogenesis, and at the same time, the secreted anti-inflammatory factors can improve the inflammatory balance of the microenvironment, which is a positive contribution; if some cells have a low fit index, i.e., weak paracrine function and low activity, they cannot effectively supplement angiogenesis factors, and may even exacerbate microenvironment disorder due to insufficient survival of their own cells, which is a negative contribution.

[0050] Therefore, the main effect index here is used to characterize the overall contribution of microenvironment characteristics and stem cell quality.

[0051] As an exemplary implementation, the main effect indicators are determined as follows: Figure 2 As shown, it includes: S201, obtain the inflammatory factor concentration sequence and the first pathway activation fold sequence.

[0052] Here, the inflammatory factor concentration sequence and the first pathway activation fold sequence are both arranged in a certain order, such as in ascending or descending order of values; the first pathway activation fold is obtained by the protein expression levels of intracellular p-Akt and total Akt.

[0053] In one specific implementation, the protein expression levels of p-Akt (Ser473) and total Akt in cells were detected, the grayscale ratio was calculated using ImageJ software, the grayscale ratio was normalized, and the normalized result was used as the activation factor of the first pathway.

[0054] It should be noted that the elements in the inflammatory factor concentration sequence and the first pathway activation fold sequence are all normalized data. Normalization can be achieved by using maximum and minimum value normalization, which can be achieved by using the maximum and minimum values ​​in the sequence. The normalization process is an existing technology and will not be described in detail here.

[0055] S202, based on the functional index, inflammatory factor concentration sequence, and first pathway activation fold sequence of each associated therapeutic factor, the contribution value of microenvironment support is determined.

[0056] As an exemplary implementation, determining the microenvironment support contribution value includes: The first step is to construct the first linear regression equation with the concentration of inflammatory factors as the independent variable and the activation factor of the first pathway as the dependent variable, and obtain the first slope of the first linear regression equation.

[0057] The second step is to multiply the functional index and the first slope of each associated efficacy factor, and determine the product as the first adjustment factor.

[0058] The third step is to adjust the functional index of the microenvironment characteristics using the first adjustment factor, and then determine the adjusted functional index of the microenvironment characteristics as the microenvironment support contribution value.

[0059] In this embodiment, the first slope is the slope of the first linear regression equation, which can represent the activation sensitivity of the PI3K / Akt pathway. The microenvironmental support contribution value is calculated by correlating the microenvironment functional index with the PI3K / Akt pathway activation sensitivity.

[0060] As an example, the formula for calculating the contribution value of the microenvironment can be: ; This indicates the contribution value of the microenvironment. Functional indices representing the characteristics of the microenvironment. Indicates the first slope, Functional index indicating stem cell quality.

[0061] In the formula for calculating the contribution value of the microenvironment, Indicates the first adjustment factor. Functional index representing the adjusted microenvironment characteristics, microenvironment support contribution value The larger the value, the greater the positive contribution of the microenvironment to stem cell support.

[0062] S203, obtain the VEGF intensity sequence and the second pathway activation fold sequence from stem cell paracrine VEGF.

[0063] Here, the VEGF intensity sequence and the second pathway activation fold sequence of stem cell paracrine VEGF are both arranged in a certain order, such as in ascending or descending order of numerical values; the second pathway activation fold is obtained by the protein expression levels of p-VEGFR2 and total VEGFR2 in target cells.

[0064] In one specific implementation, the protein expression levels of p-VEGFR2 (a key phosphorylation site for the initiation of the VEGF pathway) and total VEGFR2 in target cells are detected, the grayscale ratio is calculated using ImageJ software, the grayscale ratio is normalized, and the normalized result is used as the activation factor of the second pathway.

[0065] It should be noted that the elements in the stem cell paracrine VEGF intensity sequence and the second pathway activation fold sequence are all normalized data. Normalization is achieved by using maximum and minimum value normalization. The normalization process is an existing technology and will not be described in detail here.

[0066] It should also be noted that the data collection subjects for the inflammatory factor concentration sequence, the first pathway activation fold sequence, the stem cell paracrine VEGF intensity sequence, and the second pathway activation fold sequence are the same, and the number of elements in the sequences is the same. Specifically, they are several clinical samples, which refer to clinical myocardial infarction patients. The number of elements in the sequences can be set by the implementer according to the specific actual requirements, without specific limitations.

[0067] S204, the contribution value of stem cell remodeling is determined based on the functional index of each associated therapeutic factor, the stem cell paracrine VEGF intensity sequence, and the second pathway activation fold sequence.

[0068] As an exemplary implementation, determining the stem cell remodeling contribution value includes: The first step is to construct a second linear regression equation by using the intensity of VEGF paracrine secretion from stem cells as the independent variable and the activation fold of the second pathway as the dependent variable, and then obtain the second slope of the second linear regression equation.

[0069] The second step is to multiply the functional index and the second slope of each associated efficacy factor, and determine the product as the second adjustment factor.

[0070] The third step involves adjusting the functional index of stem cell quality using a second adjustment factor, and then determining the adjusted functional index of stem cell quality as the contribution value for stem cell remodeling.

[0071] In this embodiment, the second slope is the slope of the second linear regression equation, which can reflect the VEGF pathway activation sensitivity. The stem cell remodeling contribution value is calculated by correlating the stem cell fit index with the VEGF pathway activation sensitivity.

[0072] As an example, the formula for calculating the contribution value of stem cell remodeling can be: ; Indicates the contribution value of stem cell remodeling. Functional indices representing the characteristics of the microenvironment. Indicates the second slope. Functional index indicating stem cell quality.

[0073] In the formula for calculating the contribution value of stem cell remodeling, Indicates the second adjustment factor. Functional index representing adjusted stem cell quality, stem cell remodeling contribution value The larger the value, the greater the positive contribution of stem cells to the remodeling of the microenvironment.

[0074] S205, data fusion processing was performed on the contribution values ​​of microenvironment support and stem cell remodeling to determine the main effect indicators.

[0075] In one specific implementation, the overall synergistic or antagonistic effects of stem cell therapy for myocardial infarction are calculated to obtain the main effect index.

[0076] As an example, the formula for calculating the main effect index can be: In the formula, Q represents the main effect index. This indicates the contribution value of the microenvironment. This indicates the contribution value of stem cell remodeling.

[0077] In the formula for calculating the main effect index, a positive value of the main effect index Q indicates that the core interaction promotes the overall therapeutic effect, that is, the microenvironment provides strong support and the stem cell remodeling ability is strong; a negative value of the main effect index Q indicates that the overall therapeutic effect is inhibited, that is, the microenvironment provides weak support and the stem cell remodeling ability is weak; the larger the absolute value of the main effect index Q, the more significant the impact of the core interaction on the therapeutic effect.

[0078] Thus, the main effect index was obtained in this embodiment.

[0079] S103, determine the correction coefficient of the main effect index, use the correction coefficient to correct the main effect index, and obtain the predicted value of the total efficacy.

[0080] Here, the overall efficacy predictive value is used as a reference indicator to characterize the current patient's treatment-related parameters.

[0081] The efficacy of stem cell therapy is built upon a multidimensional framework: First, the patient's inherent genetic background lays the intrinsic foundation for the treatment response by regulating tissue repair potential and setting the tone of the inflammatory response; second, stem cell quality, as a core functional unit, determines its post-transplant survival ability, lineage differentiation orientation, and the upper limit of paracrine function; finally, the infusion protocol, as the execution link, precisely regulates the effective concentration at the lesion site by influencing the cell's targeted colonization efficiency, thereby translating the therapeutic potential into actual efficacy. Therefore, the correction coefficients in this embodiment include gene correction coefficients and infusion correction coefficients.

[0082] As an exemplary implementation, the correction coefficient for the main effect index is determined as follows: Figure 3 As shown, it includes: S301, determine the effect values ​​of microenvironment-related genes and stem cell-related genes; obtain the gene correction coefficient based on the Euclidean norm of the two related gene effect values.

[0083] Here, the effect values ​​of microenvironment-related genes and stem cell-related genes are determined by relative functional strength. The undetermined relative functional strength is equal to the ratio of the functional strength of the gene's mutant heterozygote or homozygote to the functional strength of the wild-type homozygote. The undetermined relative functional strength is either the first relative functional strength or the second relative functional strength.

[0084] As an exemplary implementation, determining the effect values ​​of microenvironment-related genes and stem cell-related genes includes: The first step is to obtain the first relative functional strength of several genes related to the regulation of inflammation in the microenvironment, and to determine the average value of all first relative functional strengths as the effect value of microenvironment-related genes.

[0085] Several genes are associated with the regulation of inflammation in the microenvironment, such as the IL-10 gene (rs1800896, rs1800871) and the VEGF gene (rs699947, rs1570360). It is worth noting that polymorphisms in these genes have been shown to affect protein expression levels or pathway activity.

[0086] In one specific implementation, through meta-analysis, using wild-type homozygotes as a reference group, the change in functional strength of each gene mutation heterozygote or homozygote related to microenvironment inflammation regulation relative to the reference group is detected, i.e., the ratio between the two. The ratio is used as the relative functional strength of the corresponding gene and is denoted as the first relative functional strength.

[0087] The second step is to obtain the second relative functional strength of several genes related to the paracrine function of stem cells, and to determine the average value of all the second relative functional strengths as the effect value of stem cell-related genes.

[0088] Several genes are associated with paracrine function in stem cells, such as the PI3KCA gene (rs11013860) and the CD44 promoter region SNP. It is worth noting that polymorphisms in these genes have also been shown to affect protein expression levels or pathway activity.

[0089] In one specific implementation, through meta-analysis, using wild-type homozygotes as a reference group, the change in functional strength of each gene mutation heterozygote or homozygote related to stem cell paracrine function relative to the reference group is detected, i.e., the ratio between the two. The ratio is used as the relative functional strength of the corresponding gene and is denoted as the second relative functional strength.

[0090] Gene correction coefficients are obtained based on the Euclidean norm of the effect values ​​of two related genes.

[0091] In one specific implementation, the Euclidean norm of the two related gene effect values ​​is standardized, and the standardized Euclidean norm is used as the gene correction coefficient. For example, the Euclidean norm is divided by a baseline value. Here, the baseline value refers to the Euclidean norm of the standard infusion protocol, denoted as [missing value]. The size of the benchmark value can be set by the implementer according to the specific actual situation, without specific limitations.

[0092] As an example, the formula for calculating the gene correction factor can be: In the formula, j represents the gene correction coefficient. This indicates the gene effect value related to the microenvironment. Indicates the effect value of stem cell-related genes. This represents a baseline value, such as an empirical value. .

[0093] In the formula for calculating the gene correction coefficient, the value of the gene correction coefficient j ranges from 0 to positive infinity, and it is a non-independent exponent used only to fine-tune the association strength between the microenvironment functional index and the stem cell adaptation index. If the gene correction coefficient j is greater than 1, it indicates that the genotype tends to enhance the anti-inflammatory capacity of the microenvironment, which can slightly amplify the core interaction effect; if the gene correction coefficient j is less than 1, it indicates a tendency to inhibit, slightly weakening the core interaction effect; if the gene correction coefficient j is equal to 1, the core interaction effect is not corrected based on the gene correction coefficient.

[0094] S302, determine the infusion efficiency and relative dose of the target infusion protocol; obtain the infusion correction coefficient based on the Euclidean norm of the infusion efficiency and relative dose.

[0095] Here, the target infusion protocol is the current infusion protocol for treating myocardial infarction using stem cells.

[0096] The efficiency of different infusion routes varies greatly. For example, intramyocardial injection has high targeting but is highly invasive; intravenous infusion is simple to perform but has poor targeting. Quantifying infusion correction factors can help quantify this difference in models or evaluations, making treatment outcome predictions more accurate. Furthermore, when analyzing current infusion regimens, introducing infusion correction factors allows for a fairer assessment of the effects of core interactions, avoiding biased conclusions based solely on biological potential while ignoring delivery efficiency.

[0097] As an exemplary implementation, determining the infusion efficiency and dose relative value of the target infusion regimen includes: The first step is to obtain the number of stem cells in the infarcted area and the total number of infusions corresponding to the target infusion protocol, and to determine the ratio of the number of stem cells in the infarcted area to the total number of infusions as the infusion efficiency.

[0098] In one specific implementation, the number of stem cells in the infarcted area and the total number of stem cells corresponding to the target infusion protocol are obtained, and the ratio of the two is used as the infusion efficiency of the target infusion protocol. Here, the infusion number refers to the total number of stem cells introduced through the infusion pathway in the current infusion protocol during a specific intervention.

[0099] The second step is to obtain the actual infusion dose and the minimum effective dose corresponding to the infusion plan, input the ratio of the actual infusion dose to the minimum effective dose into the S function for conversion, and output the relative dose value.

[0100] In this embodiment, the S-function is used because the colonization rate gradually approaches saturation as the actual infusion dose increases, exhibiting a marginal effect. The implementation process of the S-function is existing technology and will not be elaborated here.

[0101] The infusion correction factor is obtained based on the Euclidean norm of the infusion efficiency and the relative dose.

[0102] In one specific implementation, the Euclidean norm of the infusion efficiency and dose relative value is standardized, and the standardized Euclidean norm is used as the infusion correction factor. For example, the Euclidean norm is divided by a reference value.

[0103] As an example, the formula for calculating the infusion correction factor can be: In the formula, s represents the infusion correction coefficient. Indicates infusion efficiency. Indicates the relative value of the dose. This represents a baseline value, such as an empirical value. .

[0104] The infusion correction factor is calculated using a formula where its value ranges from 0 to positive infinity. It is a non-independent exponent and is used only to fine-tune the actual effect of the core interaction. If the infusion correction factor is greater than 1, it indicates that the infusion route is local delivery (e.g., intramyocardial injection), which facilitates stem cell aggregation in the microenvironment and slightly enhances the actual contribution of the core interaction. If the infusion correction factor is less than 1, it indicates that the infusion route is systemic delivery, slightly reducing the actual contribution of the core interaction. If the infusion correction factor is equal to 1, the core interaction effect cannot be corrected based on the infusion correction factor.

[0105] The main effect index was corrected using a correction coefficient to obtain the predicted value of the overall therapeutic effect.

[0106] In one specific implementation, the product of the gene correction coefficient, the infusion correction coefficient, and the main effect index is calculated, and the resulting product is used as the predicted value of the overall efficacy.

[0107] As an example, the formula for calculating the overall predictive value of therapeutic efficacy can be: In the formula, Y represents the total efficacy prediction value, Q represents the main effect index, j represents the gene correction coefficient, and s represents the infusion correction coefficient.

[0108] In calculating the overall efficacy predictive value, if both the gene correction coefficient and the infusion correction coefficient are greater than 1, the main effect index is slightly amplified; if both correction coefficients are less than 1, the main effect index is slightly reduced; if one correction coefficient is greater than 1 and the other is less than 1, the product is... It may be close to 1, indicating that the effects cancel each other out.

[0109] It is worth noting that the final result of the overall efficacy prediction value is still mainly based on the trend of the main effect indicators, while the auxiliary dimensions (genes or infusion) only play a fine-tuning role and do not change the essential trend of the core interaction.

[0110] Thus, this embodiment, by optimizing the data analysis method for stem cell therapy for myocardial infarction, has completed the systematic integration of multi-dimensional influencing factors and obtained efficacy prediction results with higher numerical accuracy.

[0111] In one exemplary implementation, after obtaining the overall efficacy prediction value, the data analysis system may further include: The first step is to obtain paired data from several clinical samples and the overall efficacy prediction value for current patients.

[0112] Using a large amount of clinical sample data, a dimensional mapping was performed between the predicted efficacy values ​​and the actual clinical efficacy indicators to ensure that the dimensions of the predicted efficacy values ​​and the actual efficacy values ​​are consistent, such as both being percentages. Among them, the actual clinical efficacy indicators can be set based on human experience.

[0113] In one specific implementation, paired data consisting of the total efficacy prediction value and the actual LVEF improvement value (i.e., actual efficacy improvement value) of several clinical samples are collected. Each clinical sample has its corresponding total efficacy prediction value and actual efficacy improvement value.

[0114] The process of determining the overall efficacy prediction value for current patients can refer to steps S1 to S3 above.

[0115] The second step involves constructing a predictive mapping function based on each paired data set, and inputting the predicted total efficacy value for the current patient into the predictive mapping function to obtain the predicted efficacy improvement value for the current patient.

[0116] Here, the predicted value of therapeutic effect improvement can be used as a basis for judging whether to optimize the current patient's treatment-related parameters.

[0117] In one specific implementation, based on each paired data point, a linear regression model is used to fit the mapping equation, denoted as the predicted actual mapping function, whose expression can be: In the formula, T represents the predicted value of therapeutic effect improvement, Y represents the predicted value of total therapeutic effect, k represents the regression coefficient, and b represents the intercept.

[0118] In the fitting process, T represents the actual improvement in therapeutic effect, and the regression coefficient k and intercept b are automatically generated through data fitting. The dimensions of the predicted improvement in therapeutic effect and the predicted total therapeutic effect are also consistent.

[0119] In one exemplary implementation, after obtaining the predicted value of the current patient's therapeutic effect improvement, the data analysis system may further include: Obtain the efficacy improvement threshold, and based on the efficacy improvement threshold and the current patient's predicted efficacy improvement value, determine whether to optimize the current patient's treatment-related parameters.

[0120] One method for determining the efficacy improvement threshold is as follows: the lower limit of the 95% reference interval calculated based on the actual efficacy improvement values ​​of all clinical sample patients. Of course, implementers can also use other methods to determine the magnitude of the efficacy improvement threshold.

[0121] Based on the efficacy improvement threshold and the current patient's predicted efficacy improvement value, determine whether to optimize the current patient's treatment-related parameters, including: The first step is to calculate the difference between the efficacy improvement threshold and the predicted efficacy improvement value for the current patient, which is used as the treatment expectation bias for the current patient sample.

[0122] The second step is to determine the treatment parameters for the current patient by checking that the expected deviation is less than or equal to 0. If the expected deviation is less than or equal to 0, it means that the treatment for the current patient does not meet the clinically effective criteria. In this case, the treatment parameters for the current patient will be optimized.

[0123] In one specific implementation, the treatment-related parameters for the current patient are optimized based on the functional index of each associated efficacy factor, including: The first sub-step involves calculating the sensitivity of each associated efficacy factor to the improvement in efficacy using partial derivatives, based on the functional index of each associated efficacy factor.

[0124] It should be noted that the method of calculating the sensitivity of the influence of each associated efficacy factor by using partial derivatives is existing technology and will not be elaborated here.

[0125] The second sub-step involves determining the adjustment amount of the treatment-related parameters for each associated efficacy factor based on the sensitivity and expected deviation of the influence of each associated efficacy factor, thereby obtaining the adjusted treatment-related parameters for each associated efficacy factor.

[0126] In this embodiment, the treatment-related parameters of the efficacy-related factors that have the highest impact on sensitivity are adjusted first. The adjustment amount is equal to the product of the normalized impact sensitivity and the expected deviation. The normalization method for impact sensitivity can be range standardization, where the extreme values ​​can be the maximum and minimum values ​​among the impact sensitivities of all efficacy-related factors.

[0127] For example, if the sensitivity of the influence of normalized microenvironment characteristics is 60%, then the adjustment amount is equal to the product of the expected deviation and 60%. Based on this adjustment amount, corresponding intervention strategy suggestions can be generated (e.g., suggesting that the microenvironment function index can be improved through anti-inflammatory means) for clinicians to refer to.

[0128] The third sub-step involves recalculating the total efficacy prediction value based on the adjusted treatment-related parameters corresponding to each associated efficacy factor, repeating the mapping and expectation deviation calculation process until the expectation deviation is less than or equal to 0. At this point, the stem cell quality parameters and infusion protocol are the optimized protocol.

[0129] In summary, this invention integrates four core dimensions—patient genetic background, stem cell quality, infusion regimen, and in vivo microenvironment—to construct a multi-factor synergistic antagonistic dynamic analysis model. This includes extracting microenvironmental functional indices and stem cell compatibility indices, quantifying core interaction effects by combining PI3K / Akt and VEGF pathway activation sensitivity, and using gene and infusion correction coefficients to achieve accurate efficacy prediction. Subsequently, large-sample data dimensional mapping ensures consistency between predicted and actual efficacy dimensions, and data-driven parameter dynamic optimization is implemented based on deviations from expected efficacy, prioritizing adjustments to key influencing factors. This dynamically matches changes in the patient's in vivo microenvironment, effectively mitigating the risk of treatment ineffectiveness.

[0130] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A data analysis system for optimizing stem cell therapy for myocardial infarction, characterized in that, Includes a memory and a processor, the processor being used to process instructions stored in the memory to implement the following process: The functional index of several associated efficacy factors in stem cell therapy for myocardial infarction was obtained; the associated efficacy factors included in vivo microenvironment characteristics and stem cell quality, and the functional index was used to characterize at least the interaction between the microenvironment and stem cells. Based on the functional index of each associated therapeutic factor and the sensitivity of the pathway activation corresponding to the associated therapeutic factor, the main effect index is determined; the main effect index is used to characterize the overall contribution of the microenvironment characteristics and stem cell quality. The correction coefficients of the main effect indicators are determined, and the main effect indicators are corrected using the correction coefficients to obtain the predicted value of the overall efficacy; the correction coefficients include gene correction coefficients and infusion correction coefficients.

2. The data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 1, characterized in that, Obtain the functional index for each associated efficacy factor, including: To obtain the concentrations of anti-inflammatory factors, inflammatory factors, angiogenesis factors, and local blood flow velocity in the myocardial infarction area; The ratio of anti-inflammatory factor concentration to inflammatory factor concentration, the concentration of angiogenesis factor, and the local blood flow velocity in the myocardial infarction area were integrated and processed, and the integrated results were determined as a functional index of microenvironment characteristics. The positive rate of stem cell surface activity markers, the concentrations of anti-inflammatory factors and angiogenesis factors secreted by stem cells, and the actual fold increase of stem cells were obtained; the markers included at least CD44, CD73, and CD90. The functional index of stem cell quality is determined based on the positive rate of stem cell surface activity markers, the concentration of anti-inflammatory factors and angiogenesis factors secreted by stem cells, and the actual proliferation fold of stem cells.

3. The data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 2, characterized in that, The functional indices used to determine stem cell quality include: The survival potential index value is determined based on the ratio of the positive rate of each biomarker to the preset activity threshold of the corresponding biomarker. The baseline value of paracrine capacity is determined based on the concentrations of anti-inflammatory factors and angiogenesis factors secreted by stem cells. The relative proliferation potential value is determined based on the ratio of the actual proliferation fold of stem cells to the preset proliferation fold. The survival potential index, baseline paracrine capacity, and relative proliferation potential were integrated and processed, and the integrated results were determined as the functional index of stem cell quality.

4. The data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 2, characterized in that, The main effect indicators are determined based on the functional index of each associated efficacy factor and the pathway activation sensitivity of the corresponding efficacy factor, including: The concentration sequences of inflammatory factors and the activation fold ratio of the first pathway were obtained; the activation fold ratio of the first pathway was obtained by the protein expression levels of intracellular p-Akt and total Akt. The contribution value of microenvironment support was determined based on the functional index, inflammatory factor concentration sequence, and first pathway activation fold sequence of each associated therapeutic factor. The intensity sequence of paracrine VEGF from stem cells and the activation fold ratio sequence of the second pathway were obtained; the activation fold ratio of the second pathway was obtained by the protein expression levels of p-VEGFR2 and total VEGFR2 in target cells. The contribution value of stem cell remodeling was determined based on the functional index of each associated therapeutic factor, the intensity sequence of the stem cell paracrine VEGF, and the activation fold sequence of the second pathway. The contribution values ​​of the microenvironment support and the stem cell remodeling were fused to determine the main effect index.

5. A data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 4, characterized in that, The determination of the microenvironment support contribution value includes: The concentration of inflammatory factors was used as the independent variable and the activation factor of the first pathway was used as the dependent variable to construct the first linear regression equation, and the first slope of the first linear regression equation was obtained. The functional index of each associated efficacy factor is multiplied by the first slope, and the resulting product is determined as the first adjustment factor. The functional index of the microenvironment characteristics is adjusted using the first adjustment factor, and the adjusted functional index of the microenvironment characteristics is determined as the microenvironment support contribution value.

6. A data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 4, characterized in that, The determination of the stem cell remodeling contribution value includes: The intensity of VEGF paracrine secretion from stem cells was used as the independent variable and the activation fold of the second pathway was used as the dependent variable to construct a second linear regression equation, and the second slope of the second linear regression equation was obtained. The functional index of each associated efficacy factor is multiplied by the second slope, and the resulting product is determined as the second adjustment factor. The functional index of stem cell quality was adjusted using the second adjustment factor, and the adjusted functional index of stem cell quality was determined as the contribution value of stem cell remodeling.

7. A data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 1, characterized in that, Determining the correction coefficient for the main effect index includes: Determine the effect values ​​of genes related to the microenvironment and those related to stem cells; obtain gene correction coefficients based on the Euclidean norm of the two related gene effect values; The infusion efficiency and relative dose of the target infusion regimen are determined; the infusion correction coefficient is obtained based on the Euclidean norm of the infusion efficiency and relative dose; the target infusion regimen is the current infusion regimen for treating myocardial infarction using stem cells.

8. A data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 7, characterized in that, The determination of microenvironment-related gene effect values ​​and stem cell-related gene effect values ​​includes: The first relative functional strength of several genes related to the regulation of inflammation in the microenvironment was obtained, and the average value of all the first relative functional strengths was determined as the effect value of the microenvironment-related genes. The second relative functional strength of several genes related to the paracrine function of stem cells was obtained, and the average value of all the second relative functional strengths was determined as the effect value of stem cell-related genes. The undetermined relative functional strength is equal to the ratio of the functional strength of the mutant heterozygote or homozygote of the gene to the functional strength of the wild homozygote; the undetermined relative functional strength is either the first relative functional strength or the second relative functional strength.

9. A data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 7, characterized in that, The determination of the infusion efficiency and relative dose values ​​of the target infusion regimen includes: The number of stem cells in the infarcted area and the total number of infusions corresponding to the target infusion protocol are obtained, and the ratio of the number of stem cells in the infarcted area to the total number of infusions is determined as the infusion efficiency. Obtain the actual infusion dose and the minimum effective dose corresponding to the infusion plan. Input the ratio of the actual infusion dose to the minimum effective dose into the S function for conversion and output the relative dose value.

10. A data analysis system for optimizing stem cell therapy for myocardial infarction according to claim 1, characterized in that, After obtaining the overall efficacy prediction, the following is also included: Acquire paired data from several clinical samples and the predicted overall efficacy value for the current patient; the paired data consists of the predicted overall efficacy value and the actual efficacy improvement value. A predictive mapping function is constructed based on each of the paired data, and the predicted total efficacy value of the current patient is input into the predictive mapping function to obtain the predicted efficacy improvement value of the current patient; the predicted efficacy improvement value is the basis for determining whether to optimize the treatment-related parameters of the current patient.

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