Construction method of mouse kidney organoid

Abstract

The invention discloses a construction method of a murine kidney organoid, and relates to the technical field of murine kidney organoid construction, and the method specifically comprises the following steps: analyzing temperature response information, obtained in real time, of each monitoring area, predicting whether temperature fluctuation in each monitoring area can cause nonuniform differentiation rate of organoid cells or not, and if yes, judging whether the temperature fluctuation in each monitoring area can cause nonuniform differentiation rate of organoid cells; dividing each monitoring area into a high fluctuation area, a medium fluctuation area and a low fluctuation area according to the prediction result; constructing a temperature regulation mechanism based on the division result of each monitoring area; in the process that each monitoring area is adjusted by the temperature adjusting mechanism, temperature feedback information of each monitoring area is obtained in real time, analysis is carried out after obtaining, whether the adjusting effect of the temperature adjusting mechanism in each monitoring area can meet uniform differentiation of organoid cells or not is evaluated, and the adjusting mechanism is optimized according to the evaluation result. According to the invention, the problem of non-uniform differentiation of organoid cells caused by temperature regulation lag is solved, and accurate temperature regulation and uniform cell differentiation are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of constructing murine kidney organoids, and particularly relates to a method for constructing murine kidney organoids. Background Art

[0002] Murine kidney organoids are formed by differentiating and culturing mouse-derived stem cells or progenitor cells in a specific three-dimensional culture environment to form a microtissue model that mimics the structure and function of the kidney. The purpose of its construction is to provide a model system for studying kidney development, disease mechanisms, and drug screening in vitro. Using such organoids, researchers can deeply explore the development process of the kidney and understand the effects of different cell signaling pathways and genes on organ formation. In addition, murine kidney organoids can be used to simulate various kidney diseases, such as renal failure or renal fibrosis, to help accelerate the development of related treatment methods. At the same time, by combining software for data collection and analysis, the organoids can be precisely environmentally controlled and monitored in real time during the culture process to ensure the stability and repeatability of the construction process, thereby improving experimental efficiency and reducing the use of experimental animals. This is not only of great significance in the field of basic research but also provides a new technical platform for clinical drug screening and personalized medicine.

[0003] In the existing technology for constructing murine kidney organoids, software plays a key role in optimizing and managing the construction process. Specifically, the software is used to monitor and analyze key parameters of the culture environment in real time, such as temperature, nutrient solution concentration, oxygen supply, etc. After these data are collected by sensors and input into the software system, the software will analyze these data to determine whether the culture conditions need to be adjusted. The software can also simulate the development process of kidney organoids based on algorithms, predict cell behavior under different conditions, and provide precise optimization suggestions to ensure that the organoids develop as expected. In addition, the software can be used for three-dimensional simulation to simulate the structural development of organoids through digital modeling, helping researchers better understand the changes of cell tissues under different conditions. Throughout the process, the software not only realizes the automatic control of the experimental process but also provides more efficient and precise regulation means through data analysis and simulation, significantly improving the success rate and research efficiency of organoid construction.

[0004] The existing technology has the following deficiencies:

[0005] During the construction of mouse-derived kidney organoids, especially during the accelerated cell differentiation period (from day 3 to day 5), when the temperature fluctuates in a local area inside the incubator, the problem of uneven cell differentiation speed will occur. This is because cells are extremely sensitive to temperature changes, and the temperature control systems of existing technologies cannot monitor and adjust the temperature of different regions of organoids in real time and accurately. Due to the lack of analysis and prediction based on advanced mathematical models, existing technologies cannot effectively collect and process the temperature data of each region, resulting in a lag in temperature regulation. Too low temperature in a local area will slow down cell proliferation and prevent normal differentiation, while too high local temperature may cause cell metabolic disorders, resulting in cell damage or death. Eventually, the lag in temperature regulation will lead to inconsistent differentiation speeds in different regions of the organoid, abnormal structures, and even possible differentiation failure. This situation will not only seriously affect the development quality and functional expression of the organoid, but also increase the failure rate of the experiment, thereby affecting the usability of the organoid in related research and applications.

[0006] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0007] The object of the present invention is to provide a method for constructing mouse-derived kidney organoids to solve the problems in the above background art.

[0008] To achieve the above object, the present invention provides the following technical solution: A method for constructing mouse-derived kidney organoids, specifically including the following steps:

[0009] Divide the area inside the incubator used for constructing mouse-derived kidney organoids into several monitoring areas, and ensure that the organoid cells in each monitoring area are evenly distributed. Deploy a sensor network in each monitoring area respectively to obtain the temperature response information of each monitoring area in real time;

[0010] Analyze the temperature response information of each monitoring area obtained in real time, predict whether the temperature fluctuations in each monitoring area will cause uneven cell differentiation speed of the organoids, and divide each monitoring area into a high-fluctuation area, a medium-fluctuation area, and a low-fluctuation area according to the prediction results;

[0011] Based on the division results of each monitoring area, construct a temperature regulation mechanism to perform different temperature regulations on the high-fluctuation area, the medium-fluctuation area, and the low-fluctuation area respectively;

[0012] During the process of the temperature regulation mechanism regulating each monitoring area, obtain the temperature feedback information of each monitoring area in real time, and analyze it after obtaining, evaluate whether the regulation effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of the organoid cells, and optimize the regulation mechanism according to the evaluation results;

[0013] Record and comprehensively analyze the temperature response information, adjustment records, and cell differentiation effects during the entire construction process in real time, continuously optimize the temperature regulation mechanism, and dynamically adapt to the developmental needs of the organoids.

[0014] Preferably, analyze the temperature response information of each monitoring area obtained in real time, predict whether the temperature fluctuations in each monitoring area will cause uneven cell differentiation rates of the organoids, and divide each monitoring area into a high-fluctuation area, a medium-fluctuation area, and a low-fluctuation area according to the prediction results. The specific steps are as follows:

[0015] After obtaining the temperature response information of each monitoring area in real time, preprocess it;

[0016] Extract the temperature fluctuation information and cell differentiation dynamic information from the temperature response information of each preprocessed monitoring area, and analyze them after extraction to generate the temperature fluctuation influence coefficient and differentiation rate fluctuation index of each monitoring area respectively;

[0017] Construct a fluctuation influence model for the generated temperature fluctuation influence coefficient and differentiation rate fluctuation index of each monitoring area, generate the fluctuation influence coefficient of each monitoring area, calculate the standard deviation between the generated fluctuation influence coefficients of each monitoring area after generation, and compare the calculated standard deviation with a preset standard deviation threshold. Predict whether the temperature fluctuations in each monitoring area will cause uneven cell differentiation rates of the organoids according to the comparison result;

[0018] In the case where it is predicted that the temperature fluctuations in each monitoring area will cause uneven cell differentiation rates of the organoids, determine the preset fluctuation influence coefficient threshold interval, and compare it with the generated fluctuation influence coefficients of each monitoring area after determination. Divide each monitoring area into a high-fluctuation area, a medium-fluctuation area, and a low-fluctuation area according to the comparison result.

[0019] Preferably, the acquisition logic of the temperature fluctuation influence coefficient and differentiation rate fluctuation index of each monitoring area is as follows:

[0020] Extract the temperature fluctuation information from the temperature response information of each preprocessed monitoring area, specifically including the average temperature, preset temperature, and temperature fluctuation frequency at different times within a period of time in each monitoring area, and respectively label them as and represents the average temperature at the m-th moment within a period of time in the i-th monitoring area, represents the preset temperature at the m-th moment within a period of time in the i-th monitoring area, represents the temperature fluctuation frequency at the m-th moment within a period of time in the i-th monitoring area, m = 1, 2, 3,..., k, i = 1, 2, 3,..., d, and both k and d are positive integers;

[0021] Calculate the temperature fluctuation influence coefficient for each monitoring area. The specific calculation formula is as follows:

[0022]

[0023] In the formula, TFIC i is the temperature fluctuation influence coefficient of the i-th monitoring area;

[0024] Extract the cell differentiation dynamic information from the temperature response information of each preprocessed monitoring area, specifically including the differentiation speed and energy consumption rate of the organoid cells in each monitoring area at different times within a period, and label them respectively as and represents the differentiation speed of the organoid cells in the i-th monitoring area at the m-th moment within a period, represents the energy consumption rate of the organoid cells in the i-th monitoring area at the m-th moment within a period;

[0025] Calculate the fluctuation amplitude of the differentiation speed of the organoid cells in each monitoring area at different times within a period. The specific calculation formula is as follows:

[0026]

[0027] In the formula, is the fluctuation amplitude of the differentiation speed of the organoid cells in the i-th monitoring area at the m-th moment within a period;

[0028] Calculate the temperature fluctuation amplitude of each monitoring area at different times within a period. The specific calculation formula is as follows:

[0029]

[0030] In the formula, is the temperature fluctuation amplitude of the i-th monitoring area at the m-th moment within a period;

[0031] Calculate the differentiation speed fluctuation index of each monitoring area. The specific calculation formula is as follows:

[0032]

[0033] In the formula, DSFI i is the differentiation speed fluctuation index of the i-th monitoring area.

[0034] Preferably, for the generated temperature fluctuation influence coefficient TFIC i and differentiation speed fluctuation index DSFI i of each monitoring area, construct a fluctuation influence model, and generate the fluctuation influence coefficient BD i, and calculate the fluctuation influence coefficient BD of each monitoring area after generation i The standard deviation BD between σ , according to the formula: And the calculated standard deviation BD σ Compare with the pre-set standard deviation threshold , and predict whether the temperature fluctuation in each monitoring area will cause uneven differentiation speed of organoid cells according to the comparison result. The specific comparison and analysis are as follows:

[0035] If The temperature fluctuation in each monitoring area will not cause uneven differentiation speed of organoid cells;

[0036] If The temperature fluctuation in each monitoring area will cause uneven differentiation speed of organoid cells.

[0037] Preferably, when it is predicted that the temperature fluctuation in each monitoring area will cause uneven differentiation speed of organoid cells, determine the pre-set fluctuation influence coefficient threshold interval [BD min , BD max , and compare with the fluctuation influence coefficient BD of each generated monitoring area after determination i , and divide each monitoring area into a high fluctuation area, a medium fluctuation area and a low fluctuation area according to the comparison result. The specific division is as follows:

[0038] If BD i < BD min , divide this monitoring area into a low fluctuation area;

[0039] If BD min ≤ BD i ≤ BD max , divide this monitoring area into a medium fluctuation area;

[0040] If BD i > BD max , divide this monitoring area into a high fluctuation area.

[0041] Preferably, based on the division results of each monitoring area, construct a temperature regulation mechanism, specifically: according to the division results of the high fluctuation area, the medium fluctuation area and the low fluctuation area, set different temperature regulation parameters respectively to form a temperature regulation mechanism; this temperature regulation mechanism is based on the temperature fluctuation influence coefficient and the differentiation speed fluctuation index of each monitoring area, and automatically determines the amplitude and method of temperature regulation through pre-set rules;

[0042] Different temperature regulations are carried out for the high - fluctuation area, medium - fluctuation area, and low - fluctuation area respectively, specifically as follows: In the high - fluctuation area, the forced cooling parameter in the temperature regulation mechanism is used to quickly reduce the temperature fluctuation amplitude; in the medium - fluctuation area, the standard temperature parameter in the temperature regulation mechanism is maintained to keep the temperature stable; in the low - fluctuation area, the gentle heating parameter in the temperature regulation mechanism is used to increase the temperature and reduce the differentiation fluctuation.

[0043] Preferably, during the process of the temperature regulation mechanism regulating each monitoring area, the temperature feedback information of each monitoring area is obtained in real time, analyzed after being obtained, and the regulation effect of the temperature regulation mechanism in each monitoring area is evaluated to determine whether it can meet the uniform differentiation of organoid cells, and the regulation mechanism is optimized according to the evaluation results. The specific steps are as follows:

[0044] During the process of the temperature regulation mechanism regulating each monitoring area, the temperature feedback information of each monitoring area is obtained in real time and pre - processed after being obtained;

[0045] The temperature feedback information of each monitoring area after pre - processing is extracted and analyzed after being extracted, and the temperature regulation response coefficient and cell differentiation uniformity coefficient of each monitoring area are generated respectively;

[0046] A regulation evaluation model is constructed for the generated temperature regulation response coefficient and cell differentiation uniformity coefficient of each monitoring area to generate the regulation coefficient of each monitoring area, and the generated regulation coefficient of each monitoring area is compared with the pre - set regulation coefficient threshold of each monitoring area. According to the comparison result, the regulation effect of the temperature regulation mechanism in each monitoring area is evaluated to determine whether it can meet the uniform differentiation of organoid cells, and the regulation mechanism is optimized according to the evaluation result.

[0047] Preferably, the acquisition logic of the temperature regulation response coefficient and cell differentiation uniformity coefficient of each monitoring area is as follows:

[0048] The temperature feedback information of each monitoring area after pre - processing is extracted, specifically including the average temperature of each monitoring area at different times within a period of time after the temperature regulation mechanism makes adjustments, the differentiation speed of the cells in each monitoring area at different times within a period of time, and the corresponding time points, and are respectively represented by the functions PWD i (t) and FSD i (t) according to the time series, where t is the time point, PWD i (t) represents the average temperature of the i - th monitoring area at time t within a period of time after the temperature regulation mechanism makes adjustments, FSD i (t) represents the differentiation speed of the cells in the i - th monitoring area at time t within a period of time after the temperature regulation mechanism makes adjustments, and the defined time period is [t 1 , t 2, where \(i = 1, 2, 3, \cdots, d\) and \(d\) is a positive integer;

[0049] Calculate the temperature regulation response coefficient and cell differentiation uniformity coefficient for each monitoring area. The specific calculation formulas are as follows:

[0050]

[0051] In the formula, \(TRC\) i is the temperature regulation response coefficient of the \(i\)-th monitoring area, and \(CUDC\) i is the cell differentiation uniformity coefficient of the \(i\)-th monitoring area.

[0052] Preferably, construct a regulation evaluation model for the generated temperature regulation response coefficient \(TRC\) i and cell differentiation uniformity coefficient \(CUDC\) i of each monitoring area, and generate a regulation coefficient \(TJ\) for each monitoring area through weighted summation i , and compare the generated regulation coefficient \(TJ\) i of each monitoring area with the pre-set regulation coefficient threshold of each monitoring area, and evaluate whether the regulation effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of organoid cells according to the comparison result, and optimize the regulation mechanism according to the evaluation result. The specific comparison and analysis are as follows:

[0053] If the regulation effect of the temperature regulation mechanism in this monitoring area can meet the uniform differentiation of organoid cells, there is no need to optimize the regulation mechanism;

[0054] If the regulation effect of the temperature regulation mechanism in this monitoring area cannot meet the uniform differentiation of organoid cells, it is necessary to optimize the regulation mechanism, specifically including: for this monitoring area, reduce the amplitude of temperature regulation and adjust the time interval to reduce the amplitude of temperature fluctuation; increase the frequency of temperature regulation and the duration of regulation to accurately control the temperature.

[0055] In the above technical solution, the technical effects and advantages provided by the present invention:

[0056] 1. The present invention divides each monitoring area in the incubator and deploys a sensor network to collect temperature response information in real time, enabling precise and timely capture of temperature fluctuations in each monitoring area. This real-time data collection method greatly improves the accuracy of temperature control, ensuring that any local temperature change during the accelerated cell differentiation period can be quickly identified and processed, avoiding the problem of abnormal differentiation caused by lagging temperature monitoring in the prior art. In addition, the system also forms a quantitative adjustment basis by calculating the temperature adjustment response coefficient and the cell differentiation uniformity coefficient based on an advanced mathematical model, solving the problem that traditional temperature control systems cannot precisely adjust the temperature.

[0057] 2. The present invention constructs an adjustment evaluation model, generates an adjustment coefficient by combining the weighted sum of the temperature response coefficient and the cell differentiation uniformity coefficient, and ensures the accuracy and pertinence of the adjustment mechanism by comparing it with a preset threshold. This method effectively avoids the shortcomings of overly general and lack of pertinence in the adjustment methods of existing temperature control systems, realizing the function of personalized temperature adjustment for each monitoring area. By dividing different fluctuation areas (high fluctuation area, medium fluctuation area, low fluctuation area), the system can formulate differentiated adjustment strategies for different temperature fluctuation regions, thus ensuring the uniform differentiation of organoid cells and avoiding cell metabolic disorders or damage caused by too low or too high local temperature.

[0058] 3. The present invention can continuously optimize the temperature adjustment mechanism and dynamically adapt to the development needs of organoids by recording and comprehensively analyzing temperature feedback information, adjustment records, and cell differentiation effects in real time. This dynamic adjustment mechanism ensures that the adjustment mechanism can maintain efficient operation at different experimental stages and under different environmental conditions, avoiding the defects of rigid adjustment mechanisms and inability to adapt to changes in traditional systems. This is not only reflected in improving the success rate of experiments, but also in saving experimental resources and reducing the experimental failure rate caused by adjustment errors, thereby improving the overall experimental efficiency and the quality of organoid construction. This software-driven automated and intelligent adjustment mechanism significantly enhances the operability and reliability of organoid research in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0060] Figure 1 It is a schematic flow chart of a method for constructing a murine kidney organoid of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.

[0062] The present invention provides a method for constructing a murine kidney organoid as shown in Figure 1 the following, which specifically includes the following steps:

[0063] Divide the area inside the incubator used for constructing the murine kidney organoid into several monitoring areas, and ensure that the organoid cells in each monitoring area are evenly distributed. Deploy a sensor network in each monitoring area respectively to obtain the temperature response information of each monitoring area in real time;

[0064] Dividing the area inside the incubator used for constructing the murine kidney organoid into several monitoring areas can be precisely planned through a software system combined with physical layout design. First, based on the size of the incubator and the number of organoid cells, use a geometric algorithm to divide the inside of the incubator into several equally spaced monitoring areas. The system automatically divides the area according to the distribution rules of these monitoring areas to ensure that the area or volume of each area is the same. Next, through an automated cell distribution software, use a random distribution algorithm or a uniform distribution model to control the uniform distribution of cells in each area, avoiding too high or too low cell density, so as to ensure the stability of experimental conditions. The realization of this step can solve the local temperature change caused by uneven cell distribution and fundamentally improve the accuracy of temperature control.

[0065] The deployment of the sensor network can be controlled through software optimization design. First, the software system will calculate the optimal deployment position of the sensors according to the location of the monitoring area division and generate a layout plan. Through algorithm simulation, one or more temperature sensor nodes will be deployed in each monitoring area to ensure that the temperature changes in the entire area can be covered. The deployed sensors are connected to the central control system through a wireless or wired network to collect the temperature response information of each area in real time. These sensors transmit the temperature information to the central system in the form of a data stream through the sensor network, and the software performs real-time processing and analysis to ensure that the temperature data of all monitoring areas can be accurately reflected in real time. In this way, through the real-time monitoring of the sensors, temperature fluctuations can be quickly identified and adjusted in a timely manner to avoid uneven differentiation caused by local temperature lag.

[0066] The core purpose of dividing the monitoring area and deploying the sensor network in this way is to solve the problem of temperature control lag caused by the inability to accurately monitor the regional temperature in the existing technology. By dividing the incubator into several monitoring areas and precisely deploying sensors in each area, it is possible to ensure real-time acquisition of the temperature response information of each area, realizing accurate monitoring and prediction of temperature changes. These operations are automatically controlled by the software system, which can dynamically adapt to the needs of cell differentiation, avoid uneven cell differentiation speed caused by local temperature out of control, and thus ensure uniform differentiation of the organoids during the construction process, improving the success rate and quality stability of the construction.

[0067] Analyze the temperature response information of each monitoring area obtained in real time, predict whether the temperature fluctuations in each monitoring area will cause uneven cell differentiation speed of the organoids, and divide each monitoring area into a high-fluctuation area, a medium-fluctuation area, and a low-fluctuation area according to the prediction results;

[0068] In this embodiment, analyzing the temperature response information of each monitoring area obtained in real time, predicting whether the temperature fluctuations in each monitoring area will cause uneven cell differentiation speed of the organoids, and dividing each monitoring area into a high-fluctuation area, a medium-fluctuation area, and a low-fluctuation area according to the prediction results specifically include the following steps:

[0069] After obtaining the temperature response information of each monitoring area in real time, preprocess it;

[0070] Regarding the method of obtaining the temperature response information of each monitoring area in real time: The temperature response information can be obtained in real time through the sensor network deployed in each monitoring area of the incubator. The sensors in each monitoring area will regularly collect the temperature data of that area, and the sensor network will transmit these data to the central control system through wireless communication or wired connection. The software system obtains the real-time data from the sensor network through the interface and automatically records it in the database to ensure that the temperature response information of each monitoring area can be continuously and synchronously obtained. This method ensures that the temperature changes in each monitoring area are accurately captured and then provided to the subsequent analysis steps.

[0071] Regarding the necessity and specific implementation of preprocessing: The purpose of preprocessing is to ensure that the temperature response information is cleaned and normalized before analysis, avoiding noise data or outliers from affecting the prediction accuracy. The steps of preprocessing include data denoising, outlier detection and correction, data smoothing, etc. First, the software denoises the temperature data through a noise filtering algorithm to eliminate the short-term fluctuations that may be generated by the sensors. Secondly, an outlier detection algorithm is used to identify the abnormal data in the temperature response information and correct it through methods such as interpolation to ensure data continuity. Finally, the software smooths the temperature data to make the data more stable and smooth, facilitating subsequent parameter calculation and model analysis. Preprocessing is a key link to ensure data quality and improve the accuracy of analysis results.

[0072] Extract the temperature fluctuation information and cell differentiation dynamic information from the temperature response information of each pre-processed monitoring area, and perform analysis after extraction to generate the temperature fluctuation influence coefficient and differentiation speed fluctuation index of each monitoring area respectively;

[0073] Construct a fluctuation influence model for the temperature fluctuation influence coefficient and differentiation speed fluctuation index of each generated monitoring area to generate the fluctuation influence coefficient of each monitoring area, and calculate the standard deviation between the fluctuation influence coefficients of each monitoring area after generation, and compare the calculated standard deviation with a pre-set standard deviation threshold, and predict whether the temperature fluctuation in each monitoring area will cause uneven cell differentiation speed of the organoids according to the comparison result;

[0074] The "pre-set standard deviation threshold" can be determined through statistical analysis of historical data and experimental regression models. First, the software system needs to collect historical data on the temperature fluctuation influence coefficients of different monitoring areas in a large number of experiments, especially in the case where the temperature fluctuation causes uneven cell differentiation speed. Then, the software calculates the difference in temperature fluctuation coefficients under different experimental conditions through standard deviation analysis of these historical data. Next, using the regression analysis method, the software system can find the correlation between the standard deviation of the temperature fluctuation coefficient and uneven cell differentiation speed, so as to obtain an optimal standard deviation threshold range. This threshold will be used to predict whether the temperature fluctuation will cause uneven cell differentiation, and can be dynamically adjusted according to the system operation situation. Through this method based on historical data and regression analysis, the software can automatically generate the "preset standard deviation threshold" that meets the current experimental or application requirements.

[0075] In the case where it is predicted that the temperature fluctuation in each monitoring area will cause uneven cell differentiation speed of the organoids, determine the pre-set threshold interval of the fluctuation influence coefficient, and compare it with the generated fluctuation influence coefficient of each monitoring area after determination, and divide each monitoring area into a high fluctuation area, a medium fluctuation area, and a low fluctuation area according to the comparison result.

[0076] The pre-set threshold interval of the fluctuation influence coefficient can be determined through historical data analysis and experimental result regression analysis. First, the software system records the changes in cell differentiation speed under different temperature fluctuation conditions through a large number of historical experimental data. Through statistical analysis and regression modeling of these data, the software can generate a correlation curve between the temperature fluctuation influence coefficient and uneven cell differentiation speed. According to the specific requirements of the experiment, the software can set a critical interval value (such as the allowable range of deviation of cell differentiation speed), and then determine the optimal threshold interval of the fluctuation influence coefficient through an optimization algorithm. This threshold interval can most reflect the negative impact of temperature fluctuation on cell differentiation speed, so as to provide an accurate reference during analysis.

[0077] In this embodiment, the acquisition logic of the temperature fluctuation influence coefficient and the differentiation speed fluctuation index for each monitoring area is as follows:

[0078] Extract the temperature fluctuation information from the temperature response information of each preprocessed monitoring area, specifically including the average temperature, preset temperature, and temperature fluctuation frequency at different times within a period of time for each monitoring area, and label them respectively as and represents the average temperature of the i-th monitoring area at the m-th moment within a period of time, represents the preset temperature of the i-th monitoring area at the m-th moment within a period of time, represents the temperature fluctuation frequency of the i-th monitoring area at the m-th moment within a period of time, where m = 1, 2, 3, …, k, i = 1, 2, 3, …, d, and both k and d are positive integers;

[0079] To extract the temperature fluctuation information from the temperature response information of each monitoring area, it is first necessary to collect temperature data in real time through the sensor network deployed in each monitoring area. The sensor records the actual temperature at each moment at fixed time intervals and transmits this data to the central control system. Through the software system, the temperature data is first processed and filtered to exclude noise and outliers. Next, the system calculates the average temperature at each moment. By statistically averaging the temperature data at different time points, the temperature fluctuation trend for a specific period is obtained. The preset temperature can be automatically provided by the ideal temperature benchmark set in the system as the reference value for each monitoring area. As for the temperature fluctuation frequency, the software calculates the number of times the temperature data exceeds the preset temperature range to obtain the temperature fluctuation frequency of each monitoring area. All these data are automatically collected by the sensor and, after being preprocessed by the software, key information such as the average temperature, preset temperature, and temperature fluctuation frequency is extracted, and then the analysis and prediction of the temperature fluctuation impact are carried out.

[0080] Calculate the temperature fluctuation influence coefficient for each monitoring area. The specific calculation formula is as follows:

[0081]

[0082] In the formula, TFIC i is the temperature fluctuation influence coefficient of the i-th monitoring area;

[0083] This calculation formula evaluates the impact of temperature fluctuations on the monitoring area through multiple steps. First, in the formula measures the deviation between the average temperature at the current moment m and the preset temperature, reflecting the relative difference between the temperature fluctuation and the reference temperature. Through this ratio, the actual temperature fluctuation degree of each monitoring area can be calculated. Then, the formula calculates the dynamic change of the temperature fluctuation That is, the amplitude of temperature fluctuation change between two consecutive moments, which is used to capture the trend and intensity of temperature fluctuation. Finally, combined with the temperature fluctuation frequency Higher weights are assigned to the moments with higher fluctuation frequencies to reflect the comprehensive impact of the persistence of temperature fluctuations on the system. The entire formula sums up the impacts at each moment and normalizes them to obtain the overall temperature fluctuation impact coefficient for each monitoring area. This calculation method comprehensively considers the temperature deviation, fluctuation amplitude, and frequency, making the result more comprehensive and capable of accurately reflecting the actual impact of temperature fluctuations on the system.

[0084] The temperature fluctuation impact coefficient TFIC of the i-th monitoring area i directly reflects the amplitude and frequency of temperature fluctuations in this monitoring area, and this is closely related to predicting whether temperature fluctuations will lead to uneven differentiation rates of organoid cells. The larger the temperature fluctuation impact coefficient, the greater the deviation of the temperature from the reference temperature and the more frequent the fluctuations in this monitoring area. Such drastic temperature fluctuations may interfere with the normal differentiation process of cells, resulting in uneven differentiation rates. On the contrary, a smaller temperature fluctuation impact coefficient indicates that the temperature is more stable, with a smaller amplitude and lower frequency of fluctuations, so the impact on the cell differentiation rate is smaller, and the cell differentiation rate is more likely to remain balanced. Therefore, by analyzing the temperature fluctuation impact coefficient of each monitoring area, it is possible to effectively predict whether temperature fluctuations in this area will lead to uneven cell differentiation rates and provide a basis for subsequent adjustments and optimizations.

[0085] Extract the cell differentiation dynamic information from the temperature response information of each preprocessed monitoring area, specifically including the differentiation rate and energy consumption rate of organoid cells in each monitoring area at different moments within a period of time, and label them respectively as and indicating the differentiation rate of organoid cells in the i-th monitoring area at the m-th moment within a period of time, indicating the energy consumption rate of organoid cells in the i-th monitoring area at the m-th moment within a period of time;

[0086] To extract the dynamic information of cell differentiation in each monitoring area, it is first necessary to deploy high-precision cell monitoring devices and energy metabolism detection sensors within each monitoring area. Through these devices, the cell differentiation rate at each moment is monitored in real time. Usually, an automatic imaging analysis system or microfluidic chip technology is adopted, combined with microscopic image analysis, and the software automatically calculates the number of cell divisions and the differentiation rate within a specific time period. These image data are processed in real time by the software to automatically extract the cell differentiation rate information. In addition, the energy consumption rate can be calculated by monitoring the metabolic activity of organoid cells, such as changes in ATP (adenosine triphosphate) levels or oxygen consumption rates, and these data can be collected through cell metabolism sensors. The software system will preprocess the raw data collected by these sensors, including noise removal, outlier correction, and data smoothing, and then use advanced mathematical models to calculate the energy consumption rate of cells. In this way, the software can not only extract the real-time cell differentiation rate but also the energy consumption rate of cells, providing accurate quantitative data support for further analysis of cell differentiation dynamics.

[0087] Calculate the fluctuation amplitude of the differentiation rate of organoid cells in each monitoring area at different moments within a period of time. The specific calculation formula is as follows:

[0088]

[0089] In the formula, is the fluctuation amplitude of the differentiation rate of organoid cells in the i-th monitoring area at the m-th moment within a period of time;

[0090] Calculate the fluctuation amplitude of the temperature in each monitoring area at different moments within a period of time. The specific calculation formula is as follows:

[0091]

[0092] In the formula, is the fluctuation amplitude of the temperature in the i-th monitoring area at the m-th moment within a period of time;

[0093] Calculate the differentiation rate fluctuation index of each monitoring area. The specific calculation formula is as follows:

[0094]

[0095] In the formula, DSFI i is the differentiation rate fluctuation index of the i-th monitoring area.

[0096] This calculation formula calculates the differentiation rate fluctuation index of each monitoring area through multiple key steps, aiming to evaluate the impact of temperature fluctuations on cell differentiation. First, in the formula Represents the cell differentiation rate, reflecting the differentiation rate of cells within each moment \(m\). The higher the differentiation rate, the more active the cell activities are and the more susceptible they are to temperature fluctuations. Subsequently, the amplitude of the differentiation rate fluctuation is introduced into the formula. It measures the degree of fluctuation of the cell differentiation rate between different time points. Through the amplitude of the fluctuation, the stability of the cell differentiation process can be judged. Subsequently, the cell energy consumption rate is used to reflect the metabolic activity of cells. The greater the energy consumption, the more dependent the cell differentiation is on the stability of the external environment, and the more significant the impact of temperature fluctuations. Next, the in the denominator represents the amplitude of temperature fluctuation, capturing the severity of temperature fluctuations in the monitoring area, and together with the temperature fluctuation frequency indicates the intensity of temperature fluctuations and the frequency at which they occur. By combining these factors, the formula can dynamically evaluate the impact of temperature fluctuations on the cell differentiation rate. The differentiation rate fluctuation index at each moment is calculated through , while the inhibitory effect of the amplitude and frequency of temperature fluctuations on cell differentiation is reflected through . Finally, the data at each moment are added up and standardized to comprehensively obtain the overall differentiation rate fluctuation index of the monitoring area. The reason for doing this is to comprehensively reflect the changes in the cell differentiation rate and the degree of influence of temperature fluctuations on the cell differentiation process through different parameters, thereby providing a strong basis for monitoring and controlling temperature.

[0097] The differentiation rate fluctuation index DSFI of the \(i\)-th monitoring area i directly reflects the degree of influence of temperature fluctuations on the cell differentiation rate in this area. The larger the differentiation rate fluctuation index, the greater the interference of temperature fluctuations on the cell differentiation rate in the monitoring area, the more unstable the cell differentiation process, and there may be a significant unevenness in the differentiation rate. The cell differentiation rate, the amplitude of the differentiation rate fluctuation, and the energy consumption rate are integrated into the formula. These factors can reflect the differentiation dynamics of cells under different environmental conditions, and at the same time measure the inhibitory effect of temperature changes on cell differentiation through the amplitude and frequency of temperature fluctuations. Therefore, when the differentiation rate fluctuation index is high, it can be predicted that the temperature fluctuations in the monitoring area may seriously interfere with cell differentiation, resulting in uneven differentiation rates. When the index is small, it indicates that the impact of temperature fluctuations on cell differentiation is small, the cell differentiation process is relatively stable, and the differentiation rate tends to be uniform. By analyzing the magnitude of DSFI i , it is possible to accurately predict whether the temperature fluctuations in each monitoring area will cause uneven cell differentiation rates of organoids.

[0098] In this embodiment, the temperature fluctuation influence coefficient TFIC i and the differentiation rate fluctuation index DSFI iBuild a fluctuation impact model and generate the fluctuation impact coefficient BD for each monitoring area through weighted summation i After generation, calculate the fluctuation impact coefficient BD for each monitoring area i and the standard deviation BD between them σ According to the formula: And compare the calculated standard deviation BD σ with the pre-set standard deviation threshold to predict whether the temperature fluctuations in each monitoring area will cause uneven differentiation rates of organoid cells. The specific comparison and analysis are as follows:

[0099] If the temperature fluctuations in each monitoring area do not cause uneven differentiation rates of organoid cells;

[0100] This situation means that the temperature fluctuation amplitudes in each monitoring area are small and relatively consistent, and the differences in temperature fluctuations between monitoring areas are not obvious. In this case, the temperature fluctuations will not significantly interfere with the organoid cell differentiation process, the cell differentiation rate can remain relatively uniform, and the organoid development is stable. As a result, the structure and function expression of the organoids in the experiment are relatively normal, the cell differentiation success rate is high, and the expected effect of the experiment can be maintained.

[0101] If the temperature fluctuations in each monitoring area will cause uneven differentiation rates of organoid cells.

[0102] This situation means that the temperature fluctuation differences between monitoring areas are large, and the temperature fluctuations in some monitoring areas deviate significantly from the reference temperature. Such large temperature fluctuation differences may cause uneven cell differentiation rates, and the cell differentiation in some areas is greatly interfered by temperature fluctuations, and there may be incomplete or failed differentiation phenomena. As a result, the organoid development may be abnormal, the cell proliferation is uneven, the experimental failure rate increases, and it may affect the functionality of the organoids, unable to meet the expected research or application requirements.

[0103] The process of building the fluctuation impact model is to generate the fluctuation impact coefficient BD for each monitoring area by weighted combination of the temperature fluctuation impact coefficient TFIC i and the differentiation speed fluctuation index DSFI i . Specifically, first, appropriate weights need to be assigned to TFIC i i and DSFI i i i These weight coefficients reflect the relative importance of these two factors in predicting uneven cell differentiation rates. Generally, the weight coefficients can be determined through experimental data or historical data analysis. For example, if the experiment shows that the impact of temperature fluctuations on cell differentiation is more significant, the temperature fluctuation impact coefficient TFICi may have a higher weight, while the differentiation speed fluctuation index DSFI i has a relatively lower weight. The formula for weighted summation is: BD i = ω 1 *TFIC i + ω 2 *DSFI i , where ω 1 and ω 2 are the weight coefficients of the temperature fluctuation influence coefficient TFIC i and the differentiation speed fluctuation index DSFI i respectively, and ω 1 + ω 2 = 1. Through such weighted summation, the fluctuation influence coefficient of each monitoring area can comprehensively reflect the comprehensive influence of temperature fluctuation and differentiation speed fluctuation on cell differentiation, thus providing a basis for subsequent standard deviation calculation and analysis.

[0104] In this embodiment, when it is predicted that the temperature fluctuation in each monitoring area will cause uneven differentiation speed of organoid cells, a preset fluctuation influence coefficient threshold interval [BD min , BD max is determined, and after determination, it is compared with the generated fluctuation influence coefficient BD i of each monitoring area. According to the comparison result, each monitoring area is divided into a high fluctuation area, a medium fluctuation area and a low fluctuation area, and the specific division is as follows:

[0105] If BD i < BD min , this monitoring area is divided into a low fluctuation area;

[0106] If BD min ≤ BD i ≤ BD max , this monitoring area is divided into a medium fluctuation area;

[0107] If BD i > BD max , this monitoring area is divided into a high fluctuation area.

[0108] The significance of this partitioning method lies in that by dividing each monitoring area into a high-fluctuation area, a medium-fluctuation area, and a low-fluctuation area according to the magnitude of the fluctuation influence coefficient, it is possible to accurately identify different areas where temperature fluctuations have uneven effects on the differentiation speed of organoid cells. Based on the temperature fluctuation influence coefficient and the differentiation speed fluctuation index in the technical solution, the fluctuation influence coefficient generated by weighted summation can reflect the comprehensive influence of temperature fluctuations and differentiation speed fluctuations in each monitoring area. Through these three types of partitioning, when the technical solution predicts that certain monitoring areas may cause uneven differentiation, targeted temperature adjustment measures can be taken to achieve precise control of temperature fluctuations, ensure the stability and uniformity of the organoid cell differentiation process, and solve the problem of uneven cell differentiation speed caused by temperature fluctuations. This partitioning method effectively improves the flexibility and pertinence of temperature control, and ensures the reliability and consistency of experimental results.

[0109] Based on the partitioning results of each monitoring area, a temperature adjustment mechanism is constructed to perform different temperature adjustments on the high-fluctuation area, the medium-fluctuation area, and the low-fluctuation area respectively;

[0110] In this embodiment, based on the partitioning results of each monitoring area, a temperature adjustment mechanism is constructed as follows: according to the partitioning results of the high-fluctuation area, the medium-fluctuation area, and the low-fluctuation area, different temperature adjustment parameters are set respectively to form a temperature adjustment mechanism; this temperature adjustment mechanism is based on the temperature fluctuation influence coefficient and the differentiation speed fluctuation index of each monitoring area, and automatically determines the amplitude and method of temperature adjustment through preset rules;

[0111] To implement the construction of a temperature adjustment mechanism based on the partitioning results of each monitoring area, it can be achieved through the following methods. First, the temperature data of each monitoring area is monitored in real time through a sensor network, and the temperature fluctuation influence coefficient and the differentiation speed fluctuation index of each monitoring area are calculated in combination with the algorithms in the system. These data will be automatically transmitted to the central control system. After the software system analyzes these data, each monitoring area is divided into a high-fluctuation area, a medium-fluctuation area, and a low-fluctuation area according to preset rules and algorithms. Then, based on these partitioning results, the software applies specific temperature adjustment parameters, and the adjustment mechanism will determine the appropriate temperature adjustment amplitude for each monitoring area by learning and analyzing the historical temperature fluctuation conditions of each monitoring area. Specifically, when implemented, the system will dynamically adjust the operating frequency of heating or cooling equipment to control the temperature change in real time. For example, for the high-fluctuation area, the software will automatically reduce the heating power or increase the cooling power to quickly reduce temperature fluctuations; for the low-fluctuation area, the software will correspondingly increase the heating power or reduce the cooling power to gradually raise the temperature. In this way, the software can automatically adjust the method and amplitude of temperature adjustment according to the actual temperature conditions, historical data, and preset adjustment rules of different monitoring areas, achieve precise control of temperature, and ensure uniform cell differentiation speed of organoids.

[0112] Different temperature regulations are carried out for the high - fluctuation area, medium - fluctuation area, and low - fluctuation area respectively, specifically as follows: In the high - fluctuation area, the forced cooling parameter in the temperature regulation mechanism is used to quickly reduce the amplitude of temperature fluctuation; in the medium - fluctuation area, the standard temperature parameter in the temperature regulation mechanism is maintained to keep the temperature stable; in the low - fluctuation area, the gentle heating parameter in the temperature regulation mechanism is used to increase the temperature and reduce the differentiation fluctuation.

[0113] To achieve different temperature regulations for the high - fluctuation area, medium - fluctuation area, and low - fluctuation area, first, it is necessary to monitor the temperature fluctuation situation of each monitoring area in real - time through a sensor network to obtain the temperature - fluctuation influence coefficient and the differentiation - speed fluctuation index. The software system automatically classifies according to these coefficients and divides each monitoring area into a high - fluctuation area, a medium - fluctuation area, and a low - fluctuation area. Then, the temperature regulation mechanism implements corresponding adjustment measures according to the specific situation of each area. In the high - fluctuation area, the software will automatically activate the cooling equipment according to the severe temperature fluctuation situation, for example, by increasing the power of the cooling system or reducing the heating power to quickly reduce the amplitude of temperature fluctuation. This process is achieved by adjusting the frequency and time of the cooling system to ensure that the temperature quickly returns to the stable range. For the medium - fluctuation area, the system will maintain the standard temperature parameter, that is, by maintaining the existing heating or cooling frequency, preventing the temperature fluctuation from further intensifying and ensuring that the temperature remains stable. In the low - fluctuation area, the system implements a gentle heating strategy by slowly increasing the heating power, gradually increasing the temperature, ensuring that the cell differentiation process is more stable and reducing the impact of fluctuation on differentiation. The reason for this is that the temperature fluctuation situations in different areas are different. The greater the temperature fluctuation, the more likely it is to interfere with the cell differentiation process. Especially in the high - fluctuation area, the temperature must be quickly controlled to prevent uneven cell differentiation. In the low - fluctuation area, the temperature is relatively stable, but appropriate heating helps to further optimize the cell differentiation conditions and ensure that the differentiation speeds in each area tend to be uniform. In this way, the software can dynamically adjust the temperature regulation strategy according to the actual temperature fluctuation situation in each area to ensure the stable development of the organoid.

[0114] During the process of the temperature regulation mechanism adjusting each monitoring area, the temperature feedback information of each monitoring area is obtained in real - time and analyzed after acquisition to evaluate whether the adjustment effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of organoid cells, and the regulation mechanism is optimized according to the evaluation result;

[0115] In this embodiment, during the process of the temperature regulation mechanism adjusting each monitoring area, the temperature feedback information of each monitoring area is obtained in real - time and analyzed after acquisition to evaluate whether the adjustment effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of organoid cells, and the regulation mechanism is optimized according to the evaluation result, which specifically includes the following steps:

[0116] During the process of the temperature regulation mechanism adjusting each monitoring area, the temperature feedback information of each monitoring area is obtained in real time and preprocessed after being obtained.

[0117] During the process of the temperature regulation mechanism adjusting each monitoring area, the real-time acquisition of temperature feedback information can be achieved through a sensor network deployed in each monitoring area. Each sensor monitors the current temperature at a set time interval (such as every second or shorter), and transmits the data wirelessly or wired to the central control system. After the software system receives these temperature feedback data, the data is classified and stored according to the monitoring area. Since the data collected by the sensors may contain noise, instantaneous fluctuations or outliers, preprocessing is a crucial step. Preprocessing includes denoising (eliminating random noise caused by environmental interference or device errors), smoothing (using algorithms such as moving average to reduce instantaneous fluctuations and ensure the data is smoother), and outlier detection and correction (identifying and correcting temperature data outside the reasonable range). For example, if the temperature data at a certain moment is abnormally high or low, the system will compare it with the previous and subsequent data to determine whether it is an outlier, and use adjacent data for correction. The purpose of preprocessing is to ensure that the temperature data feedback by the sensors is accurate, which is convenient for subsequent analysis and calculation of the temperature regulation response coefficient, thereby providing reliable data support for optimizing the regulation mechanism.

[0118] Extract the temperature feedback information of each preprocessed monitoring area, and perform analysis after extraction to generate the temperature regulation response coefficient and cell differentiation uniformity coefficient of each monitoring area respectively.

[0119] Construct a regulation evaluation model for the generated temperature regulation response coefficient and cell differentiation uniformity coefficient of each monitoring area, generate the regulation coefficient of each monitoring area, and compare the generated regulation coefficient of each monitoring area with the preset regulation coefficient threshold of each monitoring area. According to the comparison result, evaluate whether the regulation effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of organoid cells, and optimize the regulation mechanism according to the evaluation result.

[0120] The adjustment coefficient thresholds for each preset monitoring area can be determined through historical experimental data, machine learning algorithms, and model regression analysis. First, the software system can collect temperature adjustment data and cell differentiation rate data from a large number of experiments, which can be obtained under different experimental conditions and environments. Then, using data analysis tools, the software performs regression analysis on these data to find the correlations between the temperature adjustment response coefficient, the cell differentiation uniformity coefficient, and the cell differentiation effect. In this way, the software can establish an empirical model for predicting the uniformity performance of cell differentiation under different coefficient values. Based on this model, the software can automatically generate the adjustment coefficient thresholds for different monitoring areas to ensure that these thresholds meet the expected cell differentiation requirements. In addition, the software can continuously optimize the adjustment coefficient thresholds through machine learning algorithms. By learning new experimental data, it dynamically adjusts the thresholds to make them more suitable for the current experimental environment and goals. Finally, the adjustment coefficient thresholds are automatically set according to the historical data and differentiation requirements of a specific monitoring area to ensure that the temperature adjustment mechanism meets the uniformity standard of organoid cell differentiation.

[0121] In this embodiment, the acquisition logic of the temperature adjustment response coefficient and the cell differentiation uniformity coefficient for each monitoring area is as follows:

[0122] Extract the temperature feedback information of each preprocessed monitoring area, specifically including the average temperature of each monitoring area at different times within a period after the temperature adjustment mechanism makes an adjustment, the differentiation rate of the cell-like in each monitoring area at different times within a period, and the corresponding time points, and represent them respectively by functions PWD i (t) and FSD i (t) according to the time series, where t is the time point, PWD i (t) represents the average temperature of the i-th monitoring area at time t within a period after the temperature adjustment mechanism makes an adjustment, and FSD i (t) represents the differentiation rate of the cell-like in the i-th monitoring area at time t within a period after the temperature adjustment mechanism makes an adjustment. Define the time period as [t 1 , t 2 , i = 1, 2, 3,..., d, where d is a positive integer;

[0123] To extract the temperature feedback information of each preprocessed monitoring area, it can be achieved through a sensor network deployed in each monitoring area. First, temperature sensors collect the average temperature at different time points during the adjustment process in the monitoring area in real-time, and the data is transmitted to the central control system wirelessly or wiredly. At the same time, biosensors can also be deployed in each monitoring area to monitor the differentiation rate of organoid cells. The differentiation rate is monitored by monitoring cell metabolic activities, energy consumption rates, or related biochemical reactions and recorded at different time points. All sensing data will go through the preprocessing process of the software system, mainly including denoising (eliminating random interference in sensor acquisition), smoothing (reducing the impact of short-term fluctuations), and outlier correction (excluding sudden extreme values). In this way, the system can extract the average temperature, differentiation rate, and corresponding timestamps of each monitoring area at different time points. After being preprocessed, these quantitative data can provide accurate support for the subsequent evaluation of temperature regulation effects and optimization of regulation mechanisms.

[0124] Calculate the temperature regulation response coefficient and cell differentiation uniformity coefficient for each monitoring area. The specific calculation formulas are as follows:

[0125]

[0126] In the formula, TRC i is the temperature regulation response coefficient of the i-th monitoring area, and CUDC i is the cell differentiation uniformity coefficient of the i-th monitoring area.

[0127] The temperature regulation response coefficient TRC i is calculated by integration to measure the response effect of the temperature regulation mechanism on the temperature change in each monitoring area within a specific time period. In the formula, PWD i (t represents the temperature regulation of the i-th monitoring area by the temperature regulation mechanism at time t, and PWD i (t - 1) represents the temperature state at the previous moment. By calculating the temperature difference between these two time points (i.e., the temperature change rate), we can understand the immediate effect of the temperature regulation mechanism. Then, by performing definite integration on these temperature changes within the time interval [t 1 , t 2 , the regulation effect over the entire time period can be cumulatively evaluated. The use of integration ensures that the temperature regulation effect is not limited to the performance at a certain instant, but comprehensively considers the changes within a continuous time period. This method can effectively capture the dynamic effect of the regulation mechanism in the time dimension rather than a static instantaneous evaluation. At the same time, the use of absolute values ensures that regardless of whether the temperature increases or decreases, the regulation amplitude can be quantified to provide a comprehensive regulation evaluation.

[0128] The cell differentiation uniformity coefficient CUDCi The calculation logic of i is similar to that of TRC, but focuses on the change in the cell differentiation rate. The FSD i (t) in the formula represents the differentiation rates of the i-th monitoring area at times t and t - 1 respectively. By calculating the difference in the differentiation rates at these two times, the change amplitude of the cell differentiation process can be evaluated. The integration operation is carried out within the time interval [[t 1 , t 2 , accumulating the change trend of the differentiation rate. The key to this method is that it not only considers the difference in the differentiation rate at a single moment but also can capture the fluctuations during the differentiation process, comprehensively reflecting the stability or uniformity of cell differentiation. The CUDC i calculated in this way can accurately quantify the impact of the regulatory mechanism on the uniformity of cell differentiation, thus ensuring the normal differentiation and development of organoid cells.

[0129] The temperature regulation response coefficient TRC i of the i-th monitoring area directly reflects the regulation effect of the temperature regulation mechanism in this monitoring area. The smaller the TRC i , the smaller the temperature fluctuation within the time period, indicating that the regulation mechanism can quickly and stably adjust the temperature to the preset range, reducing temperature instability, and thus maintaining the optimal environment required for organoid cell differentiation. On the contrary, if the value of TRC i is large, it means that the temperature fluctuation amplitude is large, and the regulation mechanism cannot control the temperature in a timely or effective manner, which may lead to the instability of the cell differentiation environment, thus affecting the uniformity of differentiation. When the value of TRC i is too large, the system needs to further optimize the temperature regulation strategy to ensure that the temperature regulation in each monitoring area can meet the requirements of cell differentiation uniformity.

[0130] The cell differentiation uniformity coefficient CUDC i of the i-th monitoring area is directly related to evaluating the cell differentiation effect of the regulation mechanism in this monitoring area. The smaller the CUDC i , the more uniform the cell differentiation rate is maintained throughout the time period, indicating that the regulation mechanism effectively maintains the stability of differentiation, ensuring the healthy growth of organoid cells and the normal progress of the differentiation process. If the value of CUDC i is large, it means that there are obvious fluctuations in the cell differentiation rate, indicating that the regulation mechanism fails to well control the differentiation environment, which may lead to uneven cell differentiation and affect the structure and function of the organoid. Therefore, when the value of CUDC i is large, the regulation mechanism needs to be adjusted to optimize the uniformity and stability of cell differentiation.

[0131] In this embodiment, for the temperature regulation response coefficient TRC iand the cell differentiation uniformity coefficient CUDC i Construct a regulation evaluation model, and generate the regulation coefficient TJ of each monitoring area through weighted summation i and the generated regulation coefficient TJ of each monitoring area i is compared with the pre-set regulation coefficient threshold of each monitoring area to evaluate whether the regulation effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of organoid cells, and optimize the regulation mechanism according to the evaluation results. The specific comparison and analysis are as follows:

[0132] If the regulation effect of the temperature regulation mechanism in this monitoring area can meet the uniform differentiation of organoid cells, there is no need to optimize the regulation mechanism;

[0133] This situation means that the regulation effect of the temperature regulation mechanism in this monitoring area has met the requirements of organoid cell differentiation, and both the temperature fluctuation range and the cell differentiation speed fluctuation during the regulation process are kept within a reasonable range. This indicates that the existing temperature regulation mechanism is operating well, and the system does not require additional adjustment or optimization. Therefore, in this case, the temperature and cell differentiation speed can be continuously monitored in real time through software, and their data can be stored and archived for reference in subsequent experiments. At the same time, the monitoring system will maintain the current regulation parameters and strategies to ensure a stable and consistent temperature environment in each monitoring area.

[0134] If the regulation effect of the temperature regulation mechanism in this monitoring area cannot meet the uniform differentiation of organoid cells, the regulation mechanism needs to be optimized, specifically including: for this monitoring area, reducing the amplitude of temperature regulation and adjusting the time interval to reduce the temperature fluctuation range; increasing the frequency and duration of temperature regulation to accurately control the temperature.

[0135] This situation shows that the regulation effect of the temperature regulation mechanism in this monitoring area fails to meet the requirements of organoid cell differentiation, and there may be problems such as excessive temperature fluctuation, slow regulation response, or uneven cell differentiation. At this time, it is necessary to optimize the regulation mechanism to improve the effect. The optimization methods include: analyzing the real-time data of each monitoring area through software, reducing the amplitude of temperature regulation (to reduce the fluctuation caused by over-adjustment), shortening the regulation time interval (to make the regulation response faster), and increasing the regulation frequency and duration (to adjust the temperature more frequently to ensure stability). These adjustments can be achieved through an automated control system. The software will automatically generate new regulation strategies according to the pre-set rules and dynamically adjust the control parameters to achieve more precise temperature control and ultimately promote the uniform differentiation of organoid cells.

[0136] When constructing the regulation evaluation model, first, the temperature regulation response coefficient TRC of each monitoring area needs to bei and the coefficient of uniformity of cell differentiation CUDC i as the core parameters. To accurately evaluate the temperature regulation effect, the software will comprehensively evaluate each monitoring area based on these two parameters. By the method of weighted summation, the regulation coefficient TJ of each monitoring area is generated i . In this process, the weight coefficient is used to balance the relative importance of temperature regulation and cell differentiation. For example, when the impact of temperature fluctuations on cell differentiation is relatively large, the weight can be more inclined to CUDC i , so that the regulation evaluation model pays more attention to the uniformity of cell differentiation. The weight coefficient can be dynamically adjusted according to the historical performance of experimental data, or the weight allocation can be automatically optimized by machine learning algorithms according to different environments and experimental requirements. Finally, the regulation coefficient TJ i is the comprehensive result after weighting TRC i and CUDC i . The formula can be expressed as: TJ i = ω 3 *TRC i + ω 4 *CUDC i , where ω 3 and ω 4 are the weight coefficients of TRC i and CUDC i respectively, and ω 3 + ω 4 = 1.

[0137] Real-time record and comprehensive analysis of the temperature response information, regulation records and cell differentiation effects during the entire construction process, continuously optimize the temperature regulation mechanism, and dynamically adapt to the development needs of the organoids.

[0138] The real-time record of temperature response information can be achieved through a temperature sensor network deployed in each monitoring area. Each sensor will obtain the current temperature at a set frequency (such as per second or per minute) and transmit it to the central control system wirelessly or wiredly. The software system classifies and stores the received temperature data, and preprocesses these data (such as denoising, outlier detection, etc.) to ensure the accuracy of the temperature information. Through the data visualization module in the software, the system can generate a temperature curve graph to monitor the temperature fluctuations in each monitoring area in real time and promptly discover problems in temperature regulation. The reason for this is that the impact of temperature on the cell differentiation of organoids is very sensitive, and real-time monitoring can ensure the stability of temperature during the experiment and avoid negative impacts on cell growth.

[0139] The storage and retrieval of adjustment records can be achieved through the logging module in the software. Whenever the temperature adjustment mechanism is adjusted, the system automatically records the adjustment parameters (such as adjustment frequency, adjustment time, adjustment amplitude, etc.) of each monitoring area and the relevant temperature feedback data. These adjustment records are stored in the database, and the software can retrieve these records at any time for retrieval and analysis. The purpose of doing this is to facilitate researchers to understand the specific operations of each adjustment and their corresponding adjustment effects, so as to optimize the adjustment mechanism subsequently. By analyzing the adjustment records, researchers can identify the differences in adjustment effects at different times and in different monitoring areas, and adjust the experimental strategy in a timely manner.

[0140] The cell differentiation effect can be monitored through biosensors or image processing techniques. Biosensors can monitor biological parameters such as the metabolic activities and differentiation rates of cells, while image processing techniques can evaluate the state of cell differentiation by performing real-time analysis on cell images. The software system combines these differentiation data with the temperature response information for comprehensive analysis to determine whether the temperature adjustment mechanism meets the cell differentiation requirements. The purpose of doing this is to ensure that the adjustment mechanism can not only maintain temperature stability but also promote the uniform differentiation of cells to achieve the goals of the experiment.

[0141] The continuous optimization of the temperature adjustment mechanism can be achieved through the machine learning or adaptive control algorithms of the software. The system automatically analyzes the real-time recorded temperature response information, adjustment records, and cell differentiation effects, and adjusts the adjustment parameters based on these data feedbacks. By comparing historical data with current experimental data, the system can identify problems in temperature adjustment and dynamically adjust the adjustment strategy (such as adjusting the adjustment frequency, time, or amplitude). This automated optimization mechanism enables the temperature adjustment process to continuously adapt to the development needs of organoids and improves the success rate of the experiment. By dynamically adjusting the adjustment mechanism, the experiment can more flexibly respond to environmental changes and ensure the healthy growth of organoids at different developmental stages.

[0142] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the real situation. The preset parameters in the formulas are set by technicians in this field according to the actual situation.

[0143] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0144] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0145] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0146] In several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the above-described embodiments are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can be in an electrical, mechanical, or other form.

[0147] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional unit in various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

[0149] As mentioned above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for constructing mouse kidney organoids, characterized in that: The specific steps include: The area inside the incubator used to construct mouse kidney organoids was divided into several monitoring zones, and the organoid cells in each monitoring zone were ensured to be evenly distributed. A sensor network was deployed in each monitoring zone to obtain the temperature response information of each monitoring zone in real time. Analyze the temperature response information of each monitoring area obtained in real time, predict whether the temperature fluctuation in each monitoring area will lead to uneven differentiation rate of organoid cells, and divide each monitoring area into high fluctuation area, medium fluctuation area and low fluctuation area according to the prediction results; Based on the division results of each monitoring area, a temperature adjustment mechanism is constructed to adjust the temperature of high fluctuation area, medium fluctuation area and low fluctuation area respectively; In the process of the temperature regulation mechanism regulating each monitoring area, the temperature feedback information of each monitoring area is obtained in real time, and analyzed after acquisition to evaluate whether the regulation effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of organoid cells, and optimize the regulation mechanism according to the evaluation results; The temperature response information, regulation records and cell differentiation effects during the entire construction process are recorded and comprehensively analyzed in real time to continuously optimize the temperature regulation mechanism and dynamically adapt to the developmental needs of organoids.

2. The method for constructing a mouse kidney organoid according to claim 1, characterized in that: The temperature response information of each monitoring area obtained in real time is analyzed to predict whether the temperature fluctuation in each monitoring area will lead to uneven differentiation rate of organoid cells, and each monitoring area is divided into a high fluctuation area, a medium fluctuation area and a low fluctuation area according to the prediction results, which specifically includes the following steps: After obtaining the temperature response information of each monitoring area in real time, pre-process it; Extracting the temperature fluctuation information and cell differentiation dynamic information from the pre-processed temperature response information of each monitoring area, and analyzing them after extraction to generate the temperature fluctuation influence coefficient and differentiation speed fluctuation index of each monitoring area respectively; A fluctuation influence model is constructed for the temperature fluctuation influence coefficient and differentiation speed fluctuation index of each monitoring area, and the fluctuation influence coefficient of each monitoring area is generated. After the generation, the standard deviation between the fluctuation influence coefficients of each monitoring area is calculated, and the calculated standard deviation is compared with the pre-set standard deviation threshold. According to the comparison results, it is predicted whether the temperature fluctuation in each monitoring area will lead to uneven differentiation speed of organoid cells; When it is predicted that temperature fluctuations in each monitoring area will lead to uneven differentiation rates of organoid cells, a pre-set fluctuation influence coefficient threshold interval is determined, and after determination, it is compared with the generated fluctuation influence coefficients of each monitoring area. According to the comparison results, each monitoring area is divided into high fluctuation area, medium fluctuation area and low fluctuation area.

3. The method for constructing a mouse kidney organoid according to claim 2, characterized in that: The logic for obtaining the temperature fluctuation influence coefficient and differentiation speed fluctuation index of each monitoring area is as follows: The temperature fluctuation information in the preprocessed temperature response information of each monitoring area is extracted, including the average temperature, preset temperature and temperature fluctuation frequency of each monitoring area at different times within a period of time, and calibrated as and represents the average temperature of the ith monitoring area at time m within a period of time, It represents the preset temperature of the ith monitoring area at time m within a period of time. represents the temperature fluctuation frequency of the i-th monitoring area at time m within a period of time, m = 1, 2, 3, ..., k, i = 1, 2, 3, ..., d, k and d are both positive integers; Calculate the temperature fluctuation influence coefficient of each monitoring area. The specific calculation formula is as follows: Where TFIC i is the temperature fluctuation influence coefficient of the i-th monitoring area; The cell differentiation dynamic information is extracted from the pre-processed temperature response information of each monitoring area, including the differentiation speed and energy consumption rate of the organoid cells in each monitoring area at different times within a period of time, and calibrated as and represents the differentiation rate of organoid cells in the ith monitoring area at time m within a period of time, represents the energy consumption rate of the organoid cells in the ith monitoring area at time m within a period of time; Calculate the fluctuation range of the differentiation rate of organoid cells in each monitoring area at different times over a period of time. The specific calculation formula is as follows: In the formula, is the fluctuation amplitude of the differentiation speed of the organoid cells in the ith monitoring area at time m within a period of time; Calculate the temperature fluctuation range of each monitoring area at different times within a period of time. The specific calculation formula is as follows: In the formula, is the temperature fluctuation amplitude of the ith monitoring area at time m within a period of time; Calculate the differentiation speed fluctuation index of each monitoring area. The specific calculation formula is as follows: In the formula, DSFI i is the differentiation speed fluctuation index of the ith monitoring area.

4. The method for constructing a mouse kidney organoid according to claim 3, characterized in that: The temperature fluctuation influence coefficient TFIC of each monitoring area generated i and Differentiation Speed ​​Fluctuation Index DSFI i Construct a fluctuation impact model and generate the fluctuation impact coefficient BD of each monitoring area through weighted summation i , and calculate the fluctuation influence coefficient BD of each monitoring area after generation i The standard deviation between BD σ , according to the formula: The calculated standard deviation BD σ With the pre-set standard deviation threshold A comparison was performed to predict whether the temperature fluctuations in each monitoring area would lead to uneven differentiation rates of organoid cells based on the comparison results. The specific comparison analysis is as follows: like Temperature fluctuations within each monitoring zone will not lead to uneven differentiation rates of organoid cells; like Temperature fluctuations within each monitoring zone can lead to uneven differentiation rates of organoid cells.

5. The method for constructing a mouse kidney organoid according to claim 4, characterized in that: When it is predicted that the temperature fluctuation in each monitoring area will lead to uneven differentiation rate of organoid cells, a pre-set threshold interval of the fluctuation influence coefficient [BD min , B.D. max ], and after determination, the fluctuation influence coefficient BD of each monitoring area generated i Comparison is carried out, and each monitoring area is divided into high volatility area, medium volatility area and low volatility area according to the comparison results. The specific division is as follows: If BD i <BD min , the monitoring area is divided into a low volatility area; If BD min ≤BD i ≤BD max , the monitoring area is divided into the medium fluctuation area; If BD i >BD max , dividing the monitoring area into a high volatility area.

6. The method for constructing a mouse kidney organoid according to claim 5, characterized in that: Based on the division results of each monitoring area, a temperature adjustment mechanism is constructed. Specifically, different temperature adjustment parameters are set according to the division results of high fluctuation area, medium fluctuation area and low fluctuation area to form a temperature adjustment mechanism. The temperature adjustment mechanism is based on the temperature fluctuation influence coefficient and differentiation speed fluctuation index of each monitoring area, and automatically determines the amplitude and method of temperature adjustment through pre-set rules. Different temperature adjustments are performed for high fluctuation areas, medium fluctuation areas and low fluctuation areas respectively. Specifically: in the high fluctuation area, the forced cooling parameters in the temperature adjustment mechanism are used to quickly reduce the temperature fluctuation amplitude; in the medium fluctuation area, the standard temperature parameters in the temperature adjustment mechanism are maintained to maintain temperature stability; in the low fluctuation area, the mild heating parameters in the temperature adjustment mechanism are used to increase the temperature and reduce differentiation fluctuations.

7. The method for constructing a mouse kidney organoid according to claim 6, characterized in that: In the process of the temperature regulation mechanism regulating each monitoring area, the temperature feedback information of each monitoring area is obtained in real time, and analyzed after acquisition to evaluate whether the regulation effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of organoid cells, and optimize the regulation mechanism according to the evaluation results, which specifically includes the following steps: In the process of adjusting each monitoring area by the temperature adjustment mechanism, the temperature feedback information of each monitoring area is obtained in real time and preprocessed after acquisition; Extracting the pre-processed temperature feedback information of each monitoring area, and analyzing it after extraction to generate the temperature regulation response coefficient and cell differentiation uniformity coefficient of each monitoring area respectively; A regulation evaluation model is constructed for the temperature regulation response coefficient and cell differentiation uniformity coefficient generated for each monitoring zone, and the regulation coefficient of each monitoring zone is generated. The generated regulation coefficient of each monitoring zone is compared with the pre-set regulation coefficient threshold of each monitoring zone. According to the comparison results, it is evaluated whether the regulation effect of the temperature regulation mechanism in each monitoring zone can meet the uniform differentiation of organoid cells, and the regulation mechanism is optimized according to the evaluation results.

8. The method for constructing a mouse kidney organoid according to claim 7, characterized in that: The acquisition logic of the temperature regulation response coefficient and the cell differentiation uniformity coefficient of each monitoring area is as follows: The temperature feedback information of each monitoring area after preprocessing is extracted, including the average temperature of each monitoring area at different times within a period of time after the temperature regulation mechanism is adjusted, the differentiation speed of the cell-like cells in each monitoring area at different times within a period of time, and the corresponding time points, and the function PWD is used according to the time series. i (t) and FSD i (t), t is the time point, PWD i (t) represents the average temperature of the ith monitoring area at time t within a period of time after the temperature adjustment mechanism is adjusted, FSD i (t) represents the differentiation rate of the cell-like cells in the ith monitoring area at time t within a period of time after the temperature regulation mechanism is regulated, and the time period is defined as [t1, t2], i = 1, 2, 3, ..., d, d is a positive integer; Calculate the temperature regulation response coefficient and cell differentiation uniformity coefficient of each monitoring area. The specific calculation formula is as follows: Where, TRC i is the temperature adjustment response coefficient of the ith monitoring area, CUDC i is the cell differentiation uniformity coefficient of the ith monitoring area.

9. The method for constructing a mouse kidney organoid according to claim 8, characterized in that: The temperature adjustment response coefficient TRC for each monitoring area generated i and cell differentiation uniformity coefficient CUDC i Construct a regulation evaluation model and generate the regulation coefficient TJ of each monitoring area through weighted summation i , and the adjustment coefficient TJ of each monitoring area is generated i The adjustment coefficient thresholds of each monitoring area are set in advance Compare and evaluate whether the regulation effect of the temperature regulation mechanism in each monitoring area can meet the uniform differentiation of organoid cells based on the comparison results, and optimize the regulation mechanism based on the evaluation results. The specific comparison analysis is as follows: like The regulation effect of the temperature regulation mechanism in this monitoring area can satisfy the uniform differentiation of organoid cells, and there is no need to optimize the regulation mechanism; like The regulation effect of the temperature regulation mechanism in this monitoring area cannot meet the uniform differentiation of organoid cells, and the regulation mechanism needs to be optimized, including: for this monitoring area, reducing the temperature regulation amplitude and adjusting the time interval to reduce the temperature fluctuation amplitude; increasing the temperature regulation frequency and duration to accurately control the temperature.

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