A Multi-Parameter Feedback-Based Intelligent Regulation Method for Chir99021-Induced Differentiation of Dental Pulp Stem Cells

Abstract

This invention relates to the field of cell regulation technology and discloses a multi-parameter feedback-based intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells. The method includes: acquiring a dataset of the differentiation process of dental pulp stem cells and performing structured analysis to obtain a multi-parameter monitoring dataset; performing multi-dimensional assessment of cell differentiation status to obtain a differentiation status assessment characterization; obtaining drug release regulation parameters based on the differentiation status assessment characterization and historical differentiation trend data; performing multi-dimensional planning on the differentiation status assessment characterization and drug release regulation parameters to obtain a regulation strategy, and encoding and reconstructing the regulation strategy to obtain microenvironment regulation instructions; applying the microenvironment regulation instructions to obtain an optimized culture microenvironment and monitoring cell state information in the optimized culture microenvironment; and iteratively regulating the drug release regulation parameters based on the cell state information to obtain the final regulation instructions. This invention can improve the accuracy of Chir99021-induced differentiation regulation of dental pulp stem cells.

Description

Technical Field

[0001] This invention relates to the field of cell regulation technology, and in particular to a multi-parameter feedback-based intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells. Background Technology

[0002] In the field of Chir99021-induced differentiation of dental pulp stem cells, existing regulatory methods have significant technical limitations. Traditional methods lack systematic planning in data collection for the differentiation process of dental pulp stem cells, only collecting scattered, single-type data. They fail to perform time-series alignment of the collected differentiation process datasets to eliminate data asynchrony interference, do not conduct effective noise filtering, and do not achieve multimodal data fusion of cell morphology characteristics and biochemical indicators. This results in insufficient accuracy and completeness of the acquired data, failing to accurately reflect the dynamic changes in cell differentiation, and consequently making it difficult to comprehensively and objectively assess the cell differentiation status from multiple dimensions.

[0003] Meanwhile, existing regulatory strategies lack dynamic adaptability and rely heavily on fixed drug release parameters. They neither combine real-time cell differentiation status assessment and characterization with historical differentiation trend data for targeted calibration, nor construct a closed-loop regulatory mechanism. They cannot iteratively optimize drug release parameters based on real-time feedback of cell status in the culture microenvironment, resulting in a disconnect between the release concentration and timing of the target compound and the actual needs of the cells. This not only reduces the efficacy and stability of dental pulp stem cell differentiation but also leads to low compound utilization. Therefore, how to improve the regulatory precision of Chir99021-induced dental pulp stem cell differentiation has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a multi-parameter feedback-based intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a multi-parameter feedback-based intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells, comprising:

[0006] S1. Obtain a dataset of the differentiation process of dental pulp stem cells, and perform structured parsing on the dataset to obtain a multi-parameter monitoring dataset of the dental pulp stem cells;

[0007] S2. Based on the multi-parameter monitoring dataset, the cell differentiation status of the dental pulp stem cells is evaluated in multiple dimensions to obtain the differentiation status assessment characterization of the dental pulp stem cells.

[0008] S3. Based on the differentiation status assessment and characterization and the historical differentiation trend data of the dental pulp stem cells, the release requirements of the target compound are targeted and calibrated to obtain the drug release regulation parameters of the dental pulp stem cells.

[0009] S4. Perform multi-dimensional planning on the differentiation state assessment and characterization and the drug release regulation parameters to obtain the regulation strategy of the dental pulp stem cells, and encode and reconstruct the regulation strategy to obtain the microenvironment regulation instructions of the dental pulp stem cells.

[0010] S5. Apply the microenvironment regulation command to obtain an optimized culture microenvironment for the dental pulp stem cells, and monitor the cell state information in the optimized culture microenvironment.

[0011] S6. Based on the cell state information, the drug release regulation parameters are iteratively adjusted to obtain the final regulatory instructions for the dental pulp stem cells.

[0012] In a preferred embodiment, the step of acquiring a dataset of the differentiation process of dental pulp stem cells and performing structured parsing on the dataset to obtain a multi-parameter monitoring dataset of the dental pulp stem cells includes:

[0013] Collect a dataset of the differentiation process of dental pulp stem cells;

[0014] The differentiation process dataset is time-aligned to obtain aligned differentiation data of the dental pulp stem cells;

[0015] The alignment differentiation data is subjected to noise filtering to obtain the purified differentiation data of the dental pulp stem cells;

[0016] The purified differentiation data were subjected to morphological analysis to obtain the cell morphology characteristics of the dental pulp stem cells;

[0017] Biochemical indicators were mapped onto the purified differentiation data to obtain the biochemical indicators of the dental pulp stem cells;

[0018] Multimodal data fusion of the cell morphology characteristics and biochemical indicators is performed to obtain a multi-parameter monitoring dataset of the dental pulp stem cells.

[0019] In a preferred embodiment, the step of performing a multi-dimensional assessment of the cell differentiation status of the dental pulp stem cells based on the multi-parameter monitoring dataset to obtain a characterization of the differentiation status of the dental pulp stem cells includes:

[0020] Trend analysis was performed on the multi-parameter monitoring dataset to obtain the temporal evolution characteristics of the dental pulp stem cells;

[0021] Trajectory fitting is performed on the temporal evolution characteristics to obtain the differentiation path of the dental pulp stem cells;

[0022] The morphological features and the biochemical indicators are combined using tensor synthesis to obtain the feature vector of the dental pulp stem cells.

[0023] The feature vectors are subjected to correlation mining to obtain the cross-modal correlation feature vectors of the dental pulp stem cells;

[0024] The differentiation path and the cross-modal correlation feature vector are used to determine the state of the dental pulp stem cells, thereby obtaining a characterization of their differentiation state.

[0025] In a preferred embodiment, the step of performing correlation mining on the feature vector to obtain the cross-modal correlation feature vector of the dental pulp stem cells includes:

[0026] The feature vector is dimensionality reduced to obtain the dimensionality-reduced core features of the dental pulp stem cells;

[0027] Correlation analysis was performed on the dimensionality-reduced core features to obtain the cross-modal association mapping relationship of the dental pulp stem cells;

[0028] Based on the cross-modal association mapping relationship, feature mapping is performed on the dimensionality-reduced core features to obtain the cross-modal association feature vector of the dental pulp stem cells.

[0029] In a preferred embodiment, the step of targeting and calibrating the release requirements of the target compound based on the differentiation state assessment characterization and the historical differentiation trend data of the dental pulp stem cells to obtain drug release regulation parameters for the dental pulp stem cells includes:

[0030] Pattern mining was performed on the historical differentiation trend data of the dental pulp stem cells to obtain the differentiation rules of the dental pulp stem cells;

[0031] The differentiation state assessment characterization and the differentiation law are threshold-anchored to obtain the action threshold of the target compound;

[0032] Based on the action threshold and the differentiation state assessment characterization, the release demand of the target compound is synergistically fitted to obtain the preliminary release demand curve of the target compound;

[0033] The preliminary release demand curve was analyzed parametrically to obtain the drug release regulation parameters of the dental pulp stem cells.

[0034] In a preferred embodiment, the step of co-fitting the release demand of the target compound based on the action threshold and the differentiation state assessment characterization to obtain a preliminary release demand curve of the target compound includes:

[0035] The effective threshold is used as the safe concentration boundary for the target compound;

[0036] The safe concentration boundary is offset and corrected based on the differentiation state assessment characterization to obtain the real-time effective concentration range of the target compound;

[0037] Discrete sampling is performed on the real-time effective concentration range to obtain the time-series discrete concentration of the target compound;

[0038] The concentration requirement value is interpolated and smoothed to obtain the preliminary release requirement curve of the target compound.

[0039] In a preferred embodiment, the multi-dimensional planning of the differentiation state assessment and characterization and the drug release regulation parameters to obtain the regulatory strategy for the dental pulp stem cells, and the encoding and reconstruction of the regulatory strategy to obtain the microenvironment regulation instructions for the dental pulp stem cells, includes:

[0040] The differentiation state assessment characterization and the drug release regulation parameters were scaled and normalized to obtain the input dataset of the dental pulp stem cells;

[0041] Perform association analysis on the input dataset to obtain potential associations in the input dataset;

[0042] Based on the potential correlation, the differentiation process of the dental pulp stem cells and drug requirements are comprehensively matched to obtain the target regulation strategy for the dental pulp stem cells.

[0043] The target regulation strategy is encapsulated as a microenvironment regulation instruction for the dental pulp stem cells.

[0044] In a preferred embodiment, the step of comprehensively matching the differentiation process of the dental pulp stem cells with drug requirements based on the potential correlation to obtain a targeted regulatory strategy for the dental pulp stem cells includes:

[0045] Based on the potential correlation, and with the differentiation efficiency and compound utilization rate of the dental pulp stem cells as the synergistic optimization objectives, a synergistic guidance framework for the differentiation of dental pulp stem cells is constructed.

[0046] Based on the aforementioned guiding framework, a boundary intersection operation is performed on the differentiation process of the dental pulp stem cells and the release of compounds to obtain the decision space of the dental pulp stem cells.

[0047] Based on the decision space, the differentiation pathway of the dental pulp stem cells and drug requirements are matched with strategies to obtain candidate regulatory strategies for the dental pulp stem cells.

[0048] The candidate regulatory strategies are optimized and selected to obtain the target regulatory strategy for the dental pulp stem cells.

[0049] In a preferred embodiment, the step of applying the microenvironment regulation command to obtain an optimized culture microenvironment for the dental pulp stem cells and monitoring cell state information within the optimized culture microenvironment includes:

[0050] The microenvironment regulation command is parsed to obtain the environmental regulation parameter set of the microenvironment regulation command;

[0051] Based on the aforementioned set of environmental regulation parameters, the culture environment and biochemical components of the dental pulp stem cells are adapted and optimized to obtain the optimized culture microenvironment for the dental pulp stem cells.

[0052] Cell morphology, metabolite concentration, and physicochemical indicators of the culture medium in the optimized culture microenvironment were collected simultaneously to obtain the original environmental monitoring data of the optimized culture microenvironment.

[0053] Feature extraction is performed on the original environmental monitoring data to obtain cell state information in the optimized culture microenvironment.

[0054] In a preferred embodiment, the iterative adjustment of the drug release regulation parameters based on the cell state information to obtain the final regulatory instructions for the dental pulp stem cells includes:

[0055] The cell state information is decoded to obtain the real-time response characteristics of the dental pulp stem cells to the current culture conditions;

[0056] Based on the real-time response characteristics, a closed-loop evaluation of the execution effect of the drug release regulation parameters is performed to obtain the parameter adaptability evaluation of the drug release regulation parameters.

[0057] Based on the parameter adaptability evaluation, the drug release regulation parameters are adaptively corrected to obtain the calibrated release regulation parameters;

[0058] The calibrated release regulation parameters are encoded as the final regulatory instructions for the dental pulp stem cells.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. This invention utilizes a multi-parameter feedback intelligent regulation method to accurately acquire multi-parameter monitoring datasets by performing structured analysis and multimodal data fusion on a dataset of dental pulp stem cell differentiation process. Combined with temporal evolution feature analysis and cross-modal association mining, it achieves multi-dimensional and accurate assessment of cell differentiation status, providing a comprehensive and reliable basis for the formulation of regulation strategies. This effectively improves the accuracy and comprehensiveness of dental pulp stem cell differentiation status assessment and ensures the targeted nature of subsequent regulation.

[0061] 2. This invention utilizes differentiation status assessment results and historical trend data for targeted calibration, constructs a collaborative optimization framework to form a suitable regulatory strategy and microenvironment adjustment instructions, and continuously optimizes drug release parameters through closed-loop iterative regulation. This achieves both precise adaptation and efficient utilization of target compound release and dynamic optimization of the culture microenvironment, significantly improving the efficiency and stability of dental pulp stem cell differentiation, and providing strong support for the efficient induction and differentiation of dental pulp stem cells. Attached Figure Description

[0062] Figure 1 A flowchart illustrating a multi-parameter feedback-based intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells, provided in an embodiment of the present invention;

[0063] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0064] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0065] This application provides a multi-parameter feedback method for the intelligent regulation of Chir99021-induced differentiation of dental pulp stem cells. The executing entity of this multi-parameter feedback method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the multi-parameter feedback method for the intelligent regulation of Chir99021-induced differentiation of dental pulp stem cells can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0066] Reference Figure 1 The diagram shown is a flowchart illustrating a multi-parameter feedback method for intelligent regulation of Chir99021-induced differentiation of dental pulp stem cells according to an embodiment of the present invention. In this embodiment, the multi-parameter feedback method for intelligent regulation of Chir99021-induced differentiation of dental pulp stem cells includes:

[0067] S1. Obtain a dataset of the differentiation process of dental pulp stem cells, and perform structured parsing on the dataset to obtain a multi-parameter monitoring dataset of the dental pulp stem cells;

[0068] In this embodiment of the invention, obtaining a dataset of the differentiation process of dental pulp stem cells and performing structured parsing on the dataset to obtain a multi-parameter monitoring dataset of the dental pulp stem cells includes:

[0069] Collect a dataset of the differentiation process of dental pulp stem cells;

[0070] The differentiation process dataset is time-aligned to obtain aligned differentiation data of the dental pulp stem cells;

[0071] The alignment differentiation data is subjected to noise filtering to obtain the purified differentiation data of the dental pulp stem cells;

[0072] The purified differentiation data were subjected to morphological analysis to obtain the cell morphology characteristics of the dental pulp stem cells;

[0073] Biochemical indicators were mapped onto the purified differentiation data to obtain the biochemical indicators of the dental pulp stem cells;

[0074] Multimodal data fusion of the cell morphology characteristics and biochemical indicators is performed to obtain a multi-parameter monitoring dataset of the dental pulp stem cells.

[0075] With the help of an intelligent monitoring system equipped with an image sensing module and a biochemical sensing unit, various raw information during the differentiation process of dental pulp stem cells is continuously captured, including cell microscopic images at different time points, records of nutrient consumption in the culture medium, and data on the generation of cell secreted metabolites. All the scattered raw data collected by the sensing devices are systematically collected and summarized to form a dataset of the differentiation process of dental pulp stem cells covering the entire cell differentiation cycle.

[0076] Using high-precision timestamps as a unified reference benchmark, time calibration is performed on asynchronous data from different sensing modules and acquisition channels in the differentiation process dataset. Each data entry is precisely mapped to the same time coordinate, ensuring that various information such as cell image data and biochemical reaction data at the same time point can be matched and correspond one-to-one, ultimately forming aligned differentiation data of dental pulp stem cells that are coherent and unified in the time dimension.

[0077] Based on the intelligent data verification model, each data point in the alignment differentiation data is screened one by one to identify and mark outliers that exceed the normal data distribution range. These outliers may be caused by non-specific factors such as temporary equipment failure or environmental electromagnetic interference. By completely removing these marked outlier data points from the alignment differentiation data, valid data that conforms to the normal differentiation law of dental pulp stem cells is retained, resulting in interference-free and highly reliable purified differentiation data of dental pulp stem cells.

[0078] Intelligent image analysis technology is used to refine cell microscopic images in the purified differentiation data frame by frame. Through contour recognition and detail extraction logic, morphological details such as cell shape contour, size, arrangement density, protrusion growth length and number are captured. These morphological details are classified, described and quantified, and key information that can accurately reflect cell morphological changes is extracted to obtain the cell morphological characteristics of dental pulp stem cells.

[0079] Based on a pre-constructed intelligent association database of biochemical indicators related to dental pulp stem cell differentiation, the data such as changes in nutrient composition and metabolite production recorded in the purified differentiation data are precisely matched with the known differentiation-related biochemical indicators in the database. This clarifies the specific biochemical indicator type and value corresponding to each data entry, forming clear and standardized biochemical indicators for dental pulp stem cells.

[0080] By employing intelligent multi-source data integration technology, a time-dimensional correlation mechanism between cell morphology characteristics and biochemical indicators is established. This organically integrates morphological characteristic data and biochemical indicator data corresponding to the same cell sample at the same time point, eliminating data redundancy and enhancing the complementarity of the two types of information. This forms a comprehensive dataset that can fully and three-dimensionally reflect the differentiation status of dental pulp stem cells, namely, a multi-parameter monitoring dataset for dental pulp stem cells.

[0081] The beneficial effects are that through intelligent data processing of the entire process, including intelligent monitoring, data calibration, noise removal, feature extraction and multimodal fusion, the accurate collection and efficient integration of data on the differentiation process of dental pulp stem cells are achieved. This ensures the integrity, accuracy and consistency of the multi-parameter monitoring dataset, provides high-quality and comprehensive data support for the subsequent multi-dimensional intelligent assessment of cell differentiation status, effectively improves the accuracy and efficiency of the assessment process, and lays a solid data foundation for the entire intelligent regulation method.

[0082] S2. Based on the multi-parameter monitoring dataset, the cell differentiation status of the dental pulp stem cells is evaluated in multiple dimensions to obtain the differentiation status assessment characterization of the dental pulp stem cells.

[0083] In this embodiment of the invention, the step of performing a multi-dimensional assessment of the cell differentiation status of the dental pulp stem cells based on the multi-parameter monitoring dataset to obtain a characterization of the differentiation status of the dental pulp stem cells includes:

[0084] Trend analysis was performed on the multi-parameter monitoring dataset to obtain the temporal evolution characteristics of the dental pulp stem cells;

[0085] Trajectory fitting is performed on the temporal evolution characteristics to obtain the differentiation path of the dental pulp stem cells;

[0086] The morphological features and the biochemical indicators are combined using tensor synthesis to obtain the feature vector of the dental pulp stem cells.

[0087] The feature vectors are subjected to correlation mining to obtain the cross-modal correlation feature vectors of the dental pulp stem cells;

[0088] The differentiation path and the cross-modal correlation feature vector are used to determine the state of the dental pulp stem cells, thereby obtaining a characterization of their differentiation state.

[0089] The process of performing correlation mining on the feature vector to obtain the cross-modal correlation feature vector of the dental pulp stem cells includes:

[0090] The feature vector is dimensionality reduced to obtain the dimensionality-reduced core features of the dental pulp stem cells;

[0091] Correlation analysis was performed on the dimensionality-reduced core features to obtain the cross-modal association mapping relationship of the dental pulp stem cells;

[0092] Based on the cross-modal association mapping relationship, feature mapping is performed on the dimensionality-reduced core features to obtain the cross-modal association feature vector of the dental pulp stem cells.

[0093] The cell morphology and biochemical index data contained in the multi-parameter monitoring dataset were systematically sorted out in chronological order. The subtle changes in cell morphology at different time points were compared, such as the regularity of cell outlines and the changes in the length and number of protrusions. At the same time, the numerical fluctuations of biochemical indexes, such as the changes in the strength of specific enzyme activities and the fluctuations in the expression levels of marker proteins, were analyzed. Through continuous tracking and summarization of these changes, the continuous and regular changes of two types of data over time were extracted. These changes, which can comprehensively reflect the dynamic process of cell differentiation, are the temporal evolution characteristics of dental pulp stem cells.

[0094] Using the timeline as the core support, the cell state data corresponding to each time node in the temporal evolution characteristics are taken as key nodes. All nodes are connected in an orderly manner according to the time progression, constructing a continuous path that can intuitively show the evolution of dental pulp stem cells from the initial state to the target differentiation state. This path clearly presents the overall direction of cell differentiation, the transition of each stage, and the key turning points, which is the differentiation path of dental pulp stem cells.

[0095] Morphological features and biochemical indicators are regarded as two independent and complementary information dimensions. Based on the strict correspondence of the same cell sample and the same time point, the two types of data are systematically integrated. The specific description of cell morphological features and the specific values ​​of biochemical indicators are uniformly transformed into standardized information units. Then, according to the preset dimension sorting rules, these information units are orderly combined to form a high-dimensional data set containing comprehensive information on cell morphology and biochemical status. This set is the feature vector of dental pulp stem cells.

[0096] The information contained in the feature vector is thoroughly sorted and filtered to remove secondary information that is not highly correlated with the differentiation status of dental pulp stem cells and is redundant. The key information that plays a decisive role in the cell differentiation process is retained, such as the core morphological features and key biochemical indicators that can clearly distinguish different differentiation stages. Through this targeted information simplification and refinement, the core features of dental pulp stem cells after dimensionality reduction are obtained, which are more representative and effective.

[0097] We conducted a comparative analysis of the morphological and biochemical core features in the dimensionality-reduced core features, continuously tracking the synchronous changes of the two types of features during cell differentiation. For example, when a certain core morphological feature appears, we track the specific numerical change pattern of the corresponding biochemical core feature, or when a certain biochemical core feature fluctuates, we track the response state of the morphological core feature. By summarizing these stable correspondences, we clarified the association pattern between the morphological and biochemical features, which is the cross-modal association mapping relationship of dental pulp stem cells.

[0098] According to the feature correspondence rules established by the cross-modal association mapping relationship, the core features after dimensionality reduction are subjected to bidirectional information fusion processing. The information of the morphological core features is integrated into the corresponding biochemical core features through the mapping relationship. At the same time, the information of the biochemical core features is back-integrated into the corresponding morphological core features. This ensures that the integrated features not only fully retain the key information of the original core features, but also carry the correlation information between the two types of features. Finally, a new feature set that can comprehensively reflect the synergistic changes of morphology and biochemistry is formed, which is the cross-modal association feature vector of dental pulp stem cells.

[0099] A standard system for judging the differentiation status of dental pulp stem cells was constructed, covering key dimensions such as differentiation stage, degree of differentiation activity, and consistency of differentiation direction. The overall direction of cell differentiation reflected by the differentiation path and the morphological and biochemical synergistic information contained in the cross-modal correlation feature vector were substituted into the discrimination system. By comparing and matching one by one, the specific position, development status and potential evolution trend of dental pulp stem cells in the differentiation process were clarified. Finally, a differentiation status characterization of dental pulp stem cells that can comprehensively and accurately characterize the cell differentiation status was formed.

[0100] The beneficial effects are that by analyzing trends, integrating features, mining correlations, and identifying states in multi-parameter data, a differentiation path and cross-modal correlation feature vector that accurately reflects the dynamics of cell differentiation are gradually constructed. This enables a multi-dimensional and in-depth accurate characterization of the differentiation state of dental pulp stem cells. The obtained differentiation state characterization comprehensively covers the dynamic trend and core correlation information of cell differentiation, providing a high-precision and high-reliability decision-making basis for subsequent target compound release requirement calibration and regulation strategy formulation, effectively improving the scientificity and pertinence of the entire intelligent regulation process.

[0101] S3. Based on the differentiation status assessment and characterization and the historical differentiation trend data of the dental pulp stem cells, the release requirements of the target compound are targeted and calibrated to obtain the drug release regulation parameters of the dental pulp stem cells.

[0102] In this embodiment of the invention, the step of targeting and calibrating the release requirements of the target compound based on the differentiation state assessment characterization and the historical differentiation trend data of the dental pulp stem cells to obtain the drug release regulation parameters of the dental pulp stem cells includes:

[0103] Pattern mining was performed on the historical differentiation trend data of the dental pulp stem cells to obtain the differentiation rules of the dental pulp stem cells;

[0104] The differentiation state assessment characterization and the differentiation law are threshold-anchored to obtain the action threshold of the target compound;

[0105] Based on the action threshold and the differentiation state assessment characterization, the release demand of the target compound is synergistically fitted to obtain the preliminary release demand curve of the target compound;

[0106] The preliminary release demand curve was analyzed parametrically to obtain the drug release regulation parameters of the dental pulp stem cells.

[0107] The method of co-fitting the release demand of the target compound based on the action threshold and the differentiation state assessment to obtain a preliminary release demand curve of the target compound includes:

[0108] The effective threshold is used as the safe concentration boundary for the target compound;

[0109] The safe concentration boundary is offset and corrected based on the differentiation state assessment characterization to obtain the real-time effective concentration range of the target compound;

[0110] Discrete sampling is performed on the real-time effective concentration range to obtain the time-series discrete concentration of the target compound;

[0111] The concentration requirement value is interpolated and smoothed to obtain the preliminary release requirement curve of the target compound.

[0112] We compiled historical differentiation trend data for dental pulp stem cells, covering the entire process from initial state to completion of differentiation under different batches and culture conditions. This included cell morphology characteristics, biochemical indicators, and the use of corresponding target compounds at each time point. The data were categorized and sorted according to the chronological order of differentiation stages. We compared the correspondence between cell state changes and the effects of target compounds in different batches of data, and screened out recurring and consistent state evolution patterns. For example, specific changes in cell morphology when certain biochemical indicators reach a certain level, and common characteristics of responses to target compounds at different differentiation stages. By extracting and summarizing these common patterns, we obtained the differentiation pattern of dental pulp stem cells.

[0113] By comparing the current differentiation state assessment characterization with the extracted differentiation rules one by one, the corresponding stage and degree of matching of the current cell differentiation state in the differentiation rules are clarified. Referring to the concentration critical standard in the differentiation rules that the target compound can effectively promote differentiation without producing adverse effects at this stage, and combining the specific situation such as cell activity and differentiation progress reflected by the current differentiation state assessment characterization, a clear concentration critical value is determined. This critical value is the action threshold of the target compound.

[0114] The effective threshold of the target compound is directly set as the safe concentration boundary of the target compound. The upper limit of the effective threshold is used as the highest limit of the safe concentration, and the lower limit of the effective threshold is used as the lowest limit of the safe concentration, forming a clear concentration range boundary that will not damage dental pulp stem cells and can ensure basic efficacy.

[0115] By combining specific information such as the differentiation activity, cell density, and biochemical index levels of the current dental pulp stem cells as reflected by the differentiation status assessment, the set safe concentration boundary is fine-tuned. If the current cell differentiation is relatively slow, the upper limit of the concentration can be appropriately adjusted upward within the safe boundary range. If the current cell differentiation is too active, the upper limit of the concentration can be appropriately adjusted downward. Through this targeted offset correction, the real-time effective concentration range of the target compound that meets both safety requirements and adapts to the current cell state is obtained.

[0116] At fixed time intervals, several concentration points are selected within the obtained real-time concentration range. The time intervals must cover key stages of cell differentiation to ensure corresponding concentration data for each key time point. Each selected concentration point corresponds to a specific time moment. These concentration points, arranged in chronological order, are then organized to obtain the time-series discrete concentration of the target compound. The formula for calculating the time-series discrete concentration is as follows:

[0117] ;

[0118] in, For the first The concentration of the target compound at each sampling time. This represents the lower limit of the real-time effective concentration range. This represents the upper limit of the real-time effective concentration range. This represents the total number of samples.

[0119] No. The lower and upper limits of the target compound concentration corresponding to each sampling time are derived from the real-time concentration range obtained based on the target compound's action threshold and the differentiation status assessment of dental pulp stem cells. The lower limit is the lowest value within this range, and the upper limit is the highest value. The total number of samples is a fixed number of samples pre-set before discrete sampling of the real-time concentration range. Each sampling time is a specific time node determined sequentially according to the order of sampling.

[0120] The significance of this calculation process is that, according to the set total number of sampling times, the concentration is evenly distributed between the lower and upper limits of the real-time effective concentration range. This allows for the precise determination of the target compound concentration at each sampling moment, ultimately yielding a time-series discrete concentration that reflects the compound concentration requirements at different time points. This provides accurate basic data for subsequent interpolation and smoothing to obtain the initial release demand curve. As the sampling moments progress, the corresponding target compound concentration gradually increases from the lower limit of the real-time effective concentration range at a fixed rate until it reaches the upper limit of that range. The entire change process exhibits a uniform increasing trend, with the concentration difference between each adjacent sampling moment remaining consistent.

[0121] For the obtained discrete time-series concentrations, a continuous connection method is adopted to fill the gaps between the concentration values ​​corresponding to two adjacent time points, so that the originally discrete concentration data can form a continuous and smooth curve. During the filling process, the rationality of concentration changes is maintained and abrupt concentration fluctuations are avoided, and finally the preliminary release demand curve of the target compound is obtained.

[0122] A comprehensive analysis of the initial release demand curve is conducted to extract key information reflecting the release of the target compound, including the release start time, peak concentration during the release process, time to reach the peak concentration, duration of concentration within the effective range, and rate of concentration decrease. These key elements are then organized and summarized to form a set of specific parameters that can be directly used to regulate the release of the target compound. These parameters are the drug release regulation parameters for dental pulp stem cells.

[0123] The beneficial effects are that, relying on artificial intelligence data processing and analysis logic, patterns are extracted by deeply mining historical differentiation data, and the action threshold and concentration range are accurately anchored by combining the current cell differentiation state. Through discrete sampling, smoothing and parameter extraction, suitable drug release regulation parameters are formed. The entire process achieves dynamic matching between the target compound release demand and the differentiation state of dental pulp stem cells. This not only ensures the safety and effectiveness of the target compound, but also maximizes the compound utilization efficiency. It provides scientific and reliable parameter support for subsequent precise regulation of drug release and promotion of efficient differentiation of dental pulp stem cells, and significantly enhances the accuracy and scientific nature of the entire regulation process.

[0124] S4. Perform multi-dimensional planning on the differentiation state assessment and characterization and the drug release regulation parameters to obtain the regulation strategy of the dental pulp stem cells, and encode and reconstruct the regulation strategy to obtain the microenvironment regulation instructions of the dental pulp stem cells.

[0125] In this embodiment of the invention, the multi-dimensional planning of the differentiation state assessment and characterization and the drug release regulation parameters to obtain the regulation strategy of the dental pulp stem cells, and the encoding and reconstruction of the regulation strategy to obtain the microenvironment regulation instructions of the dental pulp stem cells, includes:

[0126] The differentiation state assessment characterization and the drug release regulation parameters were scaled and normalized to obtain the input dataset of the dental pulp stem cells;

[0127] Perform association analysis on the input dataset to obtain potential associations in the input dataset;

[0128] Based on the potential correlation, the differentiation process of the dental pulp stem cells and drug requirements are comprehensively matched to obtain the target regulation strategy for the dental pulp stem cells.

[0129] The target regulation strategy is encapsulated as a microenvironment regulation instruction for the dental pulp stem cells.

[0130] Based on the potential correlation, the differentiation process of the dental pulp stem cells and drug requirements are comprehensively matched to obtain the target regulation strategy for the dental pulp stem cells, including:

[0131] Based on the potential correlation, and with the differentiation efficiency and compound utilization rate of the dental pulp stem cells as the synergistic optimization objectives, a synergistic guidance framework for the differentiation of dental pulp stem cells is constructed.

[0132] Based on the aforementioned guiding framework, a boundary intersection operation is performed on the differentiation process of the dental pulp stem cells and the release of compounds to obtain the decision space of the dental pulp stem cells.

[0133] Based on the decision space, the differentiation pathway of the dental pulp stem cells and drug requirements are matched with strategies to obtain candidate regulatory strategies for the dental pulp stem cells.

[0134] The candidate regulatory strategies are optimized and selected to obtain the target regulatory strategy for the dental pulp stem cells.

[0135] To address the multi-dimensional content of differentiation status assessment, including cell morphology characteristics, biochemical correlation information, and differentiation stage identifiers, as well as the different types of data covered by drug release regulation parameters such as release concentration, release sequence, and duration, we first clarify the original numerical range and dimensional attributes of each type of data. Through a unified numerical conversion standard, we map all data to the same preset numerical range. For example, we adjust the concentration data and morphological characteristic quantification values ​​of different magnitudes to the range of 0-1, completely eliminating the interference of dimensional differences and numerical spans caused by different data types. This makes the differentiation status-related information and drug release parameters directly comparable under the same measurement standard. Then, we systematically integrate all these standardized data according to time series and functional categories to form a well-structured, dimensionally unified input dataset for dental pulp stem cells that can be directly used for subsequent analysis.

[0136] A comprehensive and detailed cross-comparison analysis was conducted on each data item in the input dataset, focusing on the corresponding changes in key dimensions of differentiation status assessment and drug release regulation parameters. For example, the adaptation relationship between cell differentiation activity and target compound release concentration was explored in depth, the matching effect between differentiation stage progression and drug release timing was analyzed, and the interaction between cell morphology regularity and compound release duration was observed. At the same time, implicit correlations not directly presented between data were captured, such as the optimal adjustment direction and range of drug release parameters when a certain biochemical correlation feature appears. Through systematic sorting and summarizing of these explicit correspondences and implicit influence patterns, the potential correlations in the input dataset were accurately extracted.

[0137] Using the identified potential correlations as the core basis for constructing the framework, the differentiation efficacy and compound utilization rate of dental pulp stem cells are identified as the two core synergistic optimization objectives. Differentiation efficacy is specifically reflected in key indicators such as cell differentiation success rate, differentiation maturity, and consistency of differentiation direction, while compound utilization rate is specifically reflected in indicators such as the effective action ratio of target compounds, the proportion of no waste or loss, and the efficiency of unit compound in promoting differentiation. Through data analysis of past regulatory cases, a reasonable weight allocation for the two objectives is determined. For example, when cell differentiation is difficult, the weight of differentiation efficacy is appropriately increased, and when target compounds are scarce, the weight of compound utilization rate is increased accordingly. At the same time, based on the potential correlations, basic rules such as data screening criteria and strategy adjustment principles are formulated to construct a synergistic guidance framework for dental pulp stem cell differentiation that can simultaneously take into account the two optimization objectives and provide clear directional guidance for subsequent regulation.

[0138] Based on the established collaborative guidance framework, the boundary conditions of dental pulp stem cell differentiation are first systematically defined, clarifying the time thresholds for different differentiation stages, such as the start and end time standards for the initial, proliferation, and maturation stages, as well as the cellular state requirements for each stage, such as the cell density threshold for the proliferation stage and the acceptable range of biochemical indicators for the maturation stage. Simultaneously, the boundary conditions for compound release are clearly defined, including the upper limit of the safe concentration of the target compound, the minimum effective concentration standard, the reasonable range of release rates, and the longest and shortest duration of a single release. By comparing the fit range of the differentiation process boundary conditions with the compound release boundary conditions, the overlapping region that simultaneously satisfies the requirements for advancing the differentiation process and the safe and effective specifications for compound release is calculated. This overlapping region represents the boundary of all feasible regulatory schemes, which is the decision space for dental pulp stem cells.

[0139] Within the feasible scope defined by the decision space, this study fully integrates the well-established differentiation pathways of dental pulp stem cells, including key inflection points, overall evolutionary direction, duration of each stage, and specific drug requirements for different differentiation stages, such as the optimal concentration, appropriate release frequency, and ideal release timing of target compounds for a specific stage. Each key node in the differentiation pathway is precisely matched with its corresponding drug requirements, and suitable combinations of drug release parameters are designed for each node. Simultaneously, combined with auxiliary control requirements of the culture environment, such as adjustments to temperature, humidity, and pH of the culture medium, multiple control schemes are designed to fully cover the entire differentiation process, meet the constraints of the decision space, and adapt to the needs of cell differentiation. These schemes collectively constitute candidate regulatory strategies for dental pulp stem cells.

[0140] A quantitative evaluation method was used to comprehensively analyze all candidate regulatory strategies. An evaluation index system was established that includes two major objectives: differentiation efficiency and compound utilization rate. For each candidate strategy, its performance score was calculated on specific indicators such as differentiation success rate, differentiation maturity, effective effect ratio, and no waste ratio. The scores of each indicator were weighted according to the target weights set in the synergistic guidance framework to obtain the comprehensive evaluation result of each candidate strategy. At the same time, the constraints of the actual application scenario, such as culture cost and operational complexity, were further verified for the candidate strategies with the highest comprehensive scores. Finally, the optimal solution that can maximize the differentiation efficiency of dental pulp stem cells, achieve efficient utilization of target compounds, and meet the requirements of practical application was selected as the target regulatory strategy for dental pulp stem cells.

[0141] Following the standardized instruction coding specifications that are identifiable and executable by the equipment, all regulatory information involved in the target regulation strategy is structurally transformed, including specific adjustment parameters of the culture environment, such as temperature setpoints, humidity control ranges, gas concentration standards, and pH adaptation values ​​of the culture medium; detailed release instructions for the target compound, such as release start time, duration of continuous release, specific release concentration, and release frequency; and triggering conditions for status monitoring, such as initiating concentration adjustment monitoring when a certain cell density is reached, or triggering environmental parameter verification when a certain biochemical indicator fluctuates. Through data encapsulation technology, all the transformed structured information is integrated into a complete instruction package, ensuring that the instruction content is comprehensive, the format is standardized, and the logic is clear, ultimately forming microenvironment regulation instructions for dental pulp stem cells that can be directly issued to the execution equipment.

[0142] The beneficial effects are as follows: through systematic scale normalization and correlation analysis, potential correlations between data are accurately captured and a collaborative guidance framework is constructed. Based on boundary intersection operations, feasible control ranges are locked. Through strategy adaptation, optimization selection and instruction encapsulation, precise microenvironment regulation instructions are formed. The entire process realizes the deep integration and collaborative optimization of differentiation state assessment and characterization and drug release regulation parameters. This ensures both the scientificity and feasibility of the regulation strategy and the accuracy and executability of the microenvironment regulation instructions. It effectively improves the targeting and efficiency of dental pulp stem cell differentiation, while maximizing the utilization efficiency of target compounds. This provides solid and reliable execution support for subsequent optimization of the culture microenvironment and promotion of stable and efficient cell differentiation.

[0143] S5. Apply the microenvironment regulation command to obtain an optimized culture microenvironment for the dental pulp stem cells, and monitor the cell state information in the optimized culture microenvironment.

[0144] In this embodiment of the invention, applying the microenvironment regulation command to obtain an optimized culture microenvironment for the dental pulp stem cells and monitoring cell state information within the optimized culture microenvironment includes:

[0145] The microenvironment regulation command is parsed to obtain the environmental regulation parameter set of the microenvironment regulation command;

[0146] Based on the aforementioned set of environmental regulation parameters, the culture environment and biochemical components of the dental pulp stem cells are adapted and optimized to obtain the optimized culture microenvironment for the dental pulp stem cells.

[0147] Cell morphology, metabolite concentration, and physicochemical indicators of the culture medium in the optimized culture microenvironment were collected simultaneously to obtain the original environmental monitoring data of the optimized culture microenvironment.

[0148] Feature extraction is performed on the original environmental monitoring data to obtain cell state information in the optimized culture microenvironment.

[0149] The microenvironment regulation instructions were comprehensively and meticulously broken down, identifying each regulatory requirement contained within the instructions and clarifying the specific control standards related to the culture environment, such as specific temperature values, humidity control ranges, and the concentration ratio of oxygen and carbon dioxide in the culture environment. At the same time, the regulatory requirements related to biochemical components were extracted, including the pH standard of the culture medium, the ratio of various nutrients, and the concentration and method of adding target compounds. All the decomposed regulatory requirements were systematically classified into two major categories: "culture environment parameters" and "biochemical component parameters," ensuring that each parameter item clearly corresponds to the regulatory intent in the instructions. Finally, a well-structured and complete set of environmental regulation parameters for dental pulp stem cells was formed.

[0150] Based on a set of environmental control parameters, a comprehensive adaptation and optimization of the existing culture environment and biochemical components for dental pulp stem cells was carried out. The operating parameters of the incubator were adjusted according to the temperature and humidity standards set in the parameter set to ensure that the temperature and humidity of the culture environment remained stable within the specified range. Based on pH requirements, the acid-base balance of the culture medium was adjusted to the optimal state by precisely adding acid-base adjusting reagents. Referring to the nutrient composition ratios, missing amino acids, vitamins, minerals, and other nutrients were supplemented into the culture medium, while adjusting the content ratio of each component to ensure that the nutrient supply met the needs of cell differentiation. The required concentration and method of addition of target compounds were strictly followed, and they were precisely added to the culture medium. Through the synergistic adjustment of the culture environment and biochemical components, an optimized culture microenvironment for dental pulp stem cells that perfectly met the regulatory requirements was ultimately constructed.

[0151] High-resolution optical microscopy was used to continuously observe dental pulp stem cells in an optimized culture microenvironment, capturing real-time microscopic images of the cells and recording morphological details such as cell outline, arrangement, and protrusion growth. High-precision concentration detection equipment was used to periodically extract culture medium samples and measure the specific content of cell metabolites to ensure the accuracy of the results. Professional physicochemical analysis instruments were used to simultaneously monitor the pH, osmotic pressure, conductivity, and other physicochemical indicators of the culture medium, recording real-time changes in these indicators. The captured cell morphology images, detected metabolite concentration data, and monitored physicochemical indicators of the culture medium were simultaneously summarized to form raw environmental monitoring data of the optimized culture microenvironment, encompassing multi-dimensional information.

[0152] The raw environmental monitoring data underwent in-depth review and screening, eliminating invalid data caused by equipment errors and environmental interference, as well as redundant information unrelated to cell state. The focus was on key information directly reflecting the growth and differentiation status of dental pulp stem cells. Core features such as cell morphology regularity, protrusion length and number, and cell density were extracted from cell morphology images. Key information such as the generation rate and concentration variation of various metabolites were analyzed from metabolite concentration data. Important information such as pH stability and osmotic pressure fluctuation range were extracted from culture medium physicochemical index data. These key information extracted from different dimensions were systematically integrated and refined to ultimately obtain cell state information of dental pulp stem cells that accurately reflects the true growth and differentiation status of cells in the optimized culture microenvironment.

[0153] The beneficial effects include the precise implementation of systematic command parsing, which enables targeted optimization of the culture environment and biochemical components, constructing an optimized culture microenvironment suitable for the differentiation needs of dental pulp stem cells; relying on multi-dimensional synchronous acquisition and targeted feature extraction, comprehensive and accurate cell state information is efficiently obtained; the entire process is data-driven, which not only ensures the optimization quality of the culture microenvironment, but also provides high-quality and reliable data support for the iterative optimization of subsequent drug release regulation parameters, effectively improving the response accuracy and operating efficiency of the entire intelligent regulation system.

[0154] S6. Based on the cell state information, the drug release regulation parameters are iteratively adjusted to obtain the final regulatory instructions for the dental pulp stem cells.

[0155] In this embodiment of the invention, the iterative adjustment of the drug release regulation parameters based on the cell state information to obtain the final regulatory instruction for the dental pulp stem cells includes:

[0156] The cell state information is decoded to obtain the real-time response characteristics of the dental pulp stem cells to the current culture conditions;

[0157] Based on the real-time response characteristics, a closed-loop evaluation of the execution effect of the drug release regulation parameters is performed to obtain the parameter adaptability evaluation of the drug release regulation parameters.

[0158] Based on the parameter adaptability evaluation, the drug release regulation parameters are adaptively corrected to obtain the calibrated release regulation parameters;

[0159] The calibrated release regulation parameters are encoded as the final regulatory instructions for the dental pulp stem cells.

[0160] A comprehensive and detailed interpretation of cell state information is conducted. This information includes morphological characteristics such as cell regularity, protrusion growth, and cell density, as well as key data such as the production rate and concentration variation of metabolites, the stability of culture medium pH, and the range of osmotic pressure fluctuations. The biological significance of each data point is analyzed. For example, high cell morphological regularity and an increased number of protrusions indicate strong cell differentiation activity; a steady increase in metabolite concentration indicates normal cell metabolism and ongoing differentiation. The interpretation results of these individual data points are systematically integrated to clarify the growth rate, differentiation activity, tolerance to target compounds, and adaptation to environmental parameters of dental pulp stem cells under current culture conditions. Ultimately, this results in a comprehensive real-time response profile of dental pulp stem cells that fully reflects the true cellular response.

[0161] Based on real-time response characteristics, a complete closed-loop evaluation system is constructed. This system includes preset differentiation target parameters, such as expected differentiation rate, differentiation maturity, cell viability threshold, and compound utilization efficiency standards. The actual differentiation state and growth status in the real-time response characteristics are compared one by one with the preset target parameters. The effect of the current drug release regulation parameters on the differentiation of dental pulp stem cells during execution is analyzed to determine whether the expected differentiation progress has been achieved. The impact of the parameters on cell viability is assessed to confirm whether there is cell damage due to inappropriate compound concentration. The effective percentage of the compound is calculated to determine whether there is any waste. Based on these comparison results, the fit between the drug release regulation parameters and the current cell state is comprehensively evaluated, resulting in a clear parameter adaptability evaluation of the drug release regulation parameters for dental pulp stem cells. The evaluation results include specific conclusions such as complete parameter fit, parameters requiring minor adjustment, and parameters requiring significant correction.

[0162] Based on the specific conclusions of the parameter adaptability evaluation, the original drug release regulation parameters are specifically modified and adjusted. If the evaluation result indicates that the parameters are perfectly adapted, it means that the current parameters are highly consistent with the cell state, and the original drug release regulation parameters can remain unchanged. If the evaluation result indicates that the parameters need minor adjustments, such as a low concentration of the target compound leading to slow differentiation, the release concentration of the compound is appropriately increased; if the concentration is too high, leading to decreased activity in some cells, the release concentration is moderately decreased. If the evaluation result indicates that the parameters need significant correction, such as a mismatch between the release timing and key cell differentiation nodes, the start time and duration of drug release are readjusted; if the release frequency is unreasonable, causing fluctuations in the differentiation process, the release frequency is optimized. During the correction process, the adjustment range is repeatedly verified by combining the cell dynamic changes reflected by the real-time response characteristics to ensure that the corrected parameters can accurately adapt to the current growth and differentiation state of dental pulp stem cells, ultimately obtaining the calibrated release regulation parameters for dental pulp stem cells.

[0163] Following standardized instruction coding specifications, the calibrated release regulation parameters are converted into machine-readable and directly executable instruction formats. The instructions explicitly include core information such as the final release concentration of the target compound, the release start time, the duration of release, and the release frequency. They also specify the corresponding auxiliary control requirements for the culture environment, such as the temperature, humidity, and pH of the culture medium to be maintained during drug release, ensuring that all regulatory intentions are clearly and accurately conveyed to the execution device. The encoded instructions undergo integrity verification to confirm no information omissions or logical conflicts, ultimately forming a well-structured, clearly defined, and directly executable final regulation instruction for dental pulp stem cells.

[0164] The beneficial effects are that by accurately decoding cell state information and extracting real-time response features, and relying on a closed-loop evaluation system, the dynamic adaptability of drug release regulation parameters is judged. Then, through targeted correction and standardized coding, the final regulatory instructions are formed. A dynamic regulation closed loop of "monitoring-evaluation-correction-execution" is constructed throughout the process. This not only ensures the real-time matching of drug release parameters with the differentiation state of dental pulp stem cells, but also maximizes the cell differentiation efficiency and the utilization rate of target compounds. It significantly enhances the flexibility, accuracy and reliability of the entire regulation system, and provides a solid guarantee for the high-quality and stable differentiation of dental pulp stem cells.

[0165] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0166] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0167] Finally, it should be noted that the above 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-parameter feedback-based intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells, characterized in that, The method includes: S1. Obtain a dataset of the differentiation process of dental pulp stem cells, and perform structured parsing on the dataset to obtain a multi-parameter monitoring dataset of the dental pulp stem cells; S2. Based on the multi-parameter monitoring dataset, the cell differentiation status of the dental pulp stem cells is evaluated in multiple dimensions to obtain the differentiation status assessment characterization of the dental pulp stem cells. S3. Based on the differentiation status assessment and characterization and the historical differentiation trend data of the dental pulp stem cells, the release requirements of the target compound are targeted and calibrated to obtain the drug release regulation parameters of the dental pulp stem cells. S4. Perform multi-dimensional planning on the differentiation state assessment and characterization and the drug release regulation parameters to obtain the regulation strategy of the dental pulp stem cells, and encode and reconstruct the regulation strategy to obtain the microenvironment regulation instructions of the dental pulp stem cells. S5. Apply the microenvironment regulation command to obtain an optimized culture microenvironment for the dental pulp stem cells, and monitor the cell state information in the optimized culture microenvironment. S6. Based on the cell state information, the drug release regulation parameters are iteratively adjusted to obtain the final regulatory instructions for the dental pulp stem cells.

2. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 1, characterized in that, The process of obtaining a dataset of the differentiation process of dental pulp stem cells, and performing structured parsing on the dataset to obtain a multi-parameter monitoring dataset of the dental pulp stem cells, includes: Collect a dataset of the differentiation process of dental pulp stem cells; The differentiation process dataset is time-aligned to obtain aligned differentiation data of the dental pulp stem cells; The alignment differentiation data is subjected to noise filtering to obtain the purified differentiation data of the dental pulp stem cells; The purified differentiation data were subjected to morphological analysis to obtain the cell morphology characteristics of the dental pulp stem cells; Biochemical indicators were mapped onto the purified differentiation data to obtain the biochemical indicators of the dental pulp stem cells; Multimodal data fusion of the cell morphology characteristics and biochemical indicators is performed to obtain a multi-parameter monitoring dataset of the dental pulp stem cells.

3. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 1, characterized in that, The process involves a multi-dimensional assessment of the cell differentiation status of the dental pulp stem cells based on the multi-parameter monitoring dataset, yielding a characterization of the differentiation status of the dental pulp stem cells, including: Trend analysis was performed on the multi-parameter monitoring dataset to obtain the temporal evolution characteristics of the dental pulp stem cells; Trajectory fitting is performed on the temporal evolution characteristics to obtain the differentiation path of the dental pulp stem cells; The morphological features and the biochemical indicators are combined using tensor synthesis to obtain the feature vector of the dental pulp stem cells. The feature vectors are subjected to correlation mining to obtain the cross-modal correlation feature vectors of the dental pulp stem cells; The differentiation path and the cross-modal correlation feature vector are used to determine the state of the dental pulp stem cells, thereby obtaining a characterization of their differentiation state.

4. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 3, characterized in that, The process of performing correlation mining on the feature vector to obtain the cross-modal correlation feature vector of the dental pulp stem cells includes: The feature vector is dimensionality reduced to obtain the dimensionality-reduced core features of the dental pulp stem cells; Correlation analysis was performed on the dimensionality-reduced core features to obtain the cross-modal association mapping relationship of the dental pulp stem cells; Based on the cross-modal association mapping relationship, feature mapping is performed on the dimensionality-reduced core features to obtain the cross-modal association feature vector of the dental pulp stem cells.

5. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 1, characterized in that, The release requirements of the target compound are targeted and calibrated based on the differentiation state assessment and the historical differentiation trend data of the dental pulp stem cells to obtain the drug release regulation parameters of the dental pulp stem cells, including: Pattern mining was performed on the historical differentiation trend data of the dental pulp stem cells to obtain the differentiation rules of the dental pulp stem cells; The differentiation state assessment characterization and the differentiation law are threshold-anchored to obtain the action threshold of the target compound; Based on the action threshold and the differentiation state assessment characterization, the release demand of the target compound is synergistically fitted to obtain the preliminary release demand curve of the target compound; The preliminary release demand curve was analyzed parametrically to obtain the drug release regulation parameters of the dental pulp stem cells.

6. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 5, characterized in that, The method of co-fitting the release demand of the target compound based on the action threshold and the differentiation state assessment to obtain a preliminary release demand curve of the target compound includes: The effective threshold is used as the safe concentration boundary for the target compound; The safe concentration boundary is offset and corrected based on the differentiation state assessment characterization to obtain the real-time effective concentration range of the target compound; Discrete sampling is performed on the real-time effective concentration range to obtain the time-series discrete concentration of the target compound; The concentration requirement value is interpolated and smoothed to obtain the preliminary release requirement curve of the target compound.

7. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 1, characterized in that, The differentiation state assessment and characterization, along with the drug release regulation parameters, are used for multi-dimensional planning to obtain a regulatory strategy for the dental pulp stem cells. This regulatory strategy is then encoded and reconstructed to obtain microenvironmental regulation instructions for the dental pulp stem cells, including: The differentiation state assessment characterization and the drug release regulation parameters were scaled and normalized to obtain the input dataset of the dental pulp stem cells; Perform association analysis on the input dataset to obtain potential associations in the input dataset; Based on the potential correlation, the differentiation process of the dental pulp stem cells and drug requirements are comprehensively matched to obtain the target regulation strategy for the dental pulp stem cells. The target regulation strategy is encapsulated as a microenvironment regulation instruction for the dental pulp stem cells.

8. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 7, characterized in that, Based on the potential correlation, the differentiation process of the dental pulp stem cells and drug requirements are comprehensively matched to obtain the target regulation strategy for the dental pulp stem cells, including: Based on the potential correlation, and with the differentiation efficiency and compound utilization rate of the dental pulp stem cells as the synergistic optimization objectives, a synergistic guidance framework for the differentiation of dental pulp stem cells is constructed. Based on the aforementioned guiding framework, a boundary intersection operation is performed on the differentiation process of the dental pulp stem cells and the release of compounds to obtain the decision space of the dental pulp stem cells. Based on the decision space, the differentiation pathway of the dental pulp stem cells and drug requirements are matched with strategies to obtain candidate regulatory strategies for the dental pulp stem cells. The candidate regulatory strategies are optimized and selected to obtain the target regulatory strategy for the dental pulp stem cells.

9. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 1, characterized in that, The application of the microenvironment regulation instructions to obtain an optimized culture microenvironment for the dental pulp stem cells, and the monitoring of cell state information within the optimized culture microenvironment, includes: The microenvironment regulation command is parsed to obtain the set of environmental regulation parameters for the microenvironment regulation command; Based on the aforementioned set of environmental regulation parameters, the culture environment and biochemical components of the dental pulp stem cells are adapted and optimized to obtain the optimized culture microenvironment for the dental pulp stem cells. Cell morphology, metabolite concentration, and physicochemical indicators of the culture medium in the optimized culture microenvironment were collected simultaneously to obtain the original environmental monitoring data of the optimized culture microenvironment. Feature extraction is performed on the original environmental monitoring data to obtain cell state information in the optimized culture microenvironment.

10. The multi-parameter feedback intelligent regulation method for Chir99021-induced differentiation of dental pulp stem cells as described in claim 1, characterized in that, The iterative adjustment of the drug release regulation parameters based on the cell state information to obtain the final regulatory instructions for the dental pulp stem cells includes: The cell state information is decoded to obtain the real-time response characteristics of the dental pulp stem cells to the current culture conditions; Based on the real-time response characteristics, a closed-loop evaluation of the execution effect of the drug release regulation parameters is performed to obtain the parameter adaptability evaluation of the drug release regulation parameters. Based on the parameter adaptability evaluation, the drug release regulation parameters are adaptively corrected to obtain the calibrated release regulation parameters; The calibrated release regulation parameters are encoded as the final regulatory instructions for the dental pulp stem cells.

Images