Parameter adjusting method and device for liver-kidney combined chip

Abstract

The invention provides a parameter adjusting method and device of a liver and kidney combined chip, and relates to the technical field of micro-fluidic chips, the key points of the technical scheme are as follows: an electrochemical signal collected by an electrode array in the liver and kidney combined chip is obtained, and the electrochemical signal reflects the pH value change in the cell metabolism process; according to the electrochemical signal, calculating the pH value change trend of each region in the liver-kidney combined chip; calculating an electrode activation scheme based on the pH value change trend, wherein the electrode activation scheme is used for determining an electrode needing to be activated and a voltage corresponding to the electrode; according to the electrode activation scheme, a specific electrode in the electrode array is controlled to generate a local electric field for influencing ion migration so as to adjust the pH value in the liver-kidney combined chip. The parameter adjusting method and device for the liver-kidney combined chip have the advantages that the pH value of the microenvironment in the liver-kidney combined chip can be accurately monitored and dynamically adjusted under the condition that no additional substance is introduced.

Description

Technical Field

[0001] The present application relates to the technical field of microfluidic chips, and in particular to a method and device for adjusting parameters of a liver-kidney combined chip. Background Art

[0002] In laboratories specializing in drug metabolism and toxicity, researchers widely use liver-kidney co-chips for long-term drug research. This chip uses a three-dimensional structure of microfluidic channels to simulate the complex liver and kidney metabolism in the human body. However, as the experiment lasts longer, cell metabolites accumulate in the chip, causing significant changes in pH. This change in pH not only affects the activity of cells, but also interferes with the metabolic process of certain pH-sensitive drugs, thereby affecting the accuracy and reliability of experimental results.

[0003] In order to maintain the accuracy of the experiment, researchers need to monitor and adjust the pH value inside the chip in real time. However, traditional pH adjustment methods have many problems. First, the introduction of additional buffer will dilute the nutrients and drugs to be tested in the culture medium, which may change the experimental conditions and affect the experimental results. Second, the additional liquid may change the fluid dynamics within the chip, which is not conducive to microfluidic chips that accurately simulate the environment inside the human body.

[0004] In view of the above problems, the existing technology needs to be improved urgently. Summary of the invention

[0005] The purpose of the present application is to provide a parameter adjustment method and device for a liver-kidney combined chip, which has the advantages of being able to accurately monitor and dynamically adjust the pH value of the microenvironment in the liver-kidney combined chip without introducing additional substances, while maintaining cell activity and drug stability.

[0006] The present application provides a parameter adjustment method for a liver-kidney combined chip, and the technical solution is as follows:

[0007] The steps of the method include: obtaining electrochemical signals collected by an electrode array in a liver-kidney combined chip, wherein the electrochemical signals reflect changes in pH during cell metabolism; calculating the pH change trend of each region in the liver-kidney combined chip based on the electrochemical signals; calculating an electrode activation scheme based on the pH change trend, wherein the electrode activation scheme is used to determine electrodes that need to be activated and their corresponding voltages; and according to the electrode activation scheme, controlling specific electrodes in the electrode array to generate a local electric field to affect ion migration and thereby adjust the pH value in the liver-kidney combined chip.

[0008] Furthermore, the present application also proposes that the step of calculating the electrode activation scheme based on the pH value change trend includes: when it is detected that the pH value change trend in the liver-kidney combined chip exceeds a preset threshold, generating multiple candidate electrode activation schemes according to the pH value change trend, each scheme includes an electrode combination to be activated and its corresponding voltage parameters; evaluating the candidate electrode activation schemes to obtain a multidimensional evaluation result, the evaluation taking into account the pH adjustment effect, energy consumption and the impact on cell activity; based on the multidimensional evaluation results, selecting the scheme with the best comprehensive performance as the final electrode activation scheme for subsequent precise pH adjustment.

[0009] Furthermore, the present application also proposes that the step of evaluating the candidate electrode activation schemes to obtain a multidimensional evaluation result includes: establishing a multidimensional evaluation model, which comprehensively considers the pH adjustment effect, energy consumption and cell activity effects, wherein: the pH adjustment effect is characterized by a comprehensive indicator of the pH correction speed and stability of the target area; the energy consumption is quantified by the functional relationship between the electrode activation parameters and the power consumption; the cell activity effect is evaluated by the degree of influence of the electric field on the physiological state of the cell; when the candidate scheme parameters are input, the weight coefficients of each evaluation dimension are dynamically adjusted according to the current experimental stage, the weight coefficient of the pH adjustment effect is increased in the early stage of drug metabolism, and the weight coefficient of cell activity protection is increased in the long-term stability stage.

[0010] Furthermore, the present application also proposes that the step of dynamically adjusting the weight coefficients of each evaluation dimension includes: obtaining the concentration of cell metabolites, cell proliferation rate and pH fluctuation amplitude as experimental stage judgment indicators; based on preset judgment rules, determining the current experimental stage according to the judgment indicators, wherein when the cell metabolite concentration change rate and the pH fluctuation amplitude are lower than their respective preset thresholds and the cell proliferation rate is stable, it is judged to be a long-term stability stage, otherwise it is judged to be an early stage of drug metabolism; according to the determined experimental stage, based on the pre-set weight coefficient mapping relationship, determine the weight coefficients of the three evaluation dimensions of pH regulation effect, energy consumption and cell activity influence.

[0011] Furthermore, the present application also proposes that, according to the electrode activation scheme, the step of controlling specific electrodes in the electrode array to generate a local electric field for affecting ion migration and thereby regulating the pH value in the liver-kidney combined chip includes: according to the electrode activation sequence and corresponding voltage parameters in the electrode activation scheme, sequentially activating specific electrodes in the electrode array and applying specific voltages to generate a local electric field to affect ion migration and thereby regulate the pH value in the liver-kidney combined chip; real-time monitoring of pH value changes around the activated electrodes, and automatically adjusting the activation state of the corresponding electrodes when it is detected that the pH value reaches the target range; based on the pH value change trend and electrode position information, dynamically optimizing the electrode activation sequence and voltage parameters to achieve regulation of pH values ​​in different areas of the liver-kidney combined chip.

[0012] Furthermore, the present application also proposes that the step of dynamically optimizing the electrode activation order and voltage parameters based on the pH value change trend and electrode position information includes: obtaining the pH value change characteristics of each region in the liver-kidney combined chip, including the change rate and gradient distribution; based on the pH value change characteristics and electrode position information, establishing a multidimensional evaluation matrix reflecting the regulation priority of each region; according to the multidimensional evaluation matrix, determining the priority of electrode activation, and calculating the voltage parameters required for each electrode to generate an adaptive local electric field strength; when it is detected that the pH value change rate of a certain area exceeds a preset threshold, dynamically adjusting the activation priority and voltage parameters of the electrode corresponding to the area to achieve rapid response to the sharply changing area.

[0013] Furthermore, the present application also proposes that the step of determining the priority of electrode activation based on the multidimensional evaluation matrix and calculating the voltage parameters required for each electrode includes: determining the initial activation priority according to the evaluation score of each electrode in the multidimensional evaluation matrix, while considering the spatial distance between adjacent electrodes, and when it is detected that the distance between adjacent electrodes is less than a preset threshold, adjusting the activation order of these electrodes to avoid simultaneous activation; calculating the initial voltage parameters based on the adjusted activation order and the pH value change characteristics of the area where each electrode is located, and performing parameter correction considering the mutual influence between the electrodes to obtain the final voltage parameters.

[0014] Furthermore, the present application also proposes that the initial voltage parameters are calculated based on the adjusted activation order and the pH value change characteristics of the area where each electrode is located, and the parameters are corrected considering the mutual influence between the electrodes to obtain the final voltage parameters, the steps include: establishing a voltage-pH response model based on the pH value change characteristics of the area where each electrode is located and the target pH value range, and calculating the initial voltage parameters required to reach the target pH value; constructing an electric field superposition influence model that considers the geometric layout of the electrode array and the electrode spacing, and inputting the initial voltage parameters into the model to calculate the electric field interference intensity between adjacent electrodes; dynamically correcting the initial voltage parameters according to the electric field interference intensity to obtain the final voltage parameters.

[0015] Furthermore, the present application also proposes that the step of calculating the electrode activation scheme based on the pH value change trend includes: obtaining the pH value P(x, y, t) and the pH value change rate ΔP(x, y, t) at the position (x, y) in the liver-kidney combined chip at the current time t; based on the current pH value and change rate, using a pre-established cell metabolism model as a prediction function f, calculating the predicted pH value P_pred(x, y, t+Δt) at the next time t+Δt; calculating the effective voltage V_eff(x, y), where V_eff(x,y) = V(x, y) + ∑[i≠(x,y)] α(d_i) * V_i, V(x, y) is the electrode's own voltage, V_i is the voltage parameter of the adjacent electrode i, α(d) is the distance attenuation function, and d_i is the distance between adjacent electrodes; constructing a multi-objective optimization function O = w1*g1(t)*|P_target - P_pred| + w2*g2(t)*E + w3*g3(t)*U, where P_target is the target pH value, E is the energy consumption, U is the electrode usage, g1(t), g2(t), and g3(t) are weight functions dynamically adjusted according to the current experimental stage, w1 is the weight coefficient of the pH adjustment effect, w2 is the weight coefficient of the energy consumption, and w3 is the weight coefficient of the electrode usage; calculate the effect of the electric field on cell activity C(x, y, t) = h(V_eff(x, y), t), where h is the pre-established electric field-cell activity effect model; under the constraints of V_min ≤ V(x, y) ≤ V_max, ∑A(x, y) ≤ N_max, and C(x, y, t) ≥ C_threshold, determine the optimal electrode activation scheme A(x, y) and the corresponding voltage parameter V(x, y), where V_min and V_max are the voltage ranges, N_max is the maximum number of activated electrodes, and C_threshold is the minimum cell activity threshold.

[0016] Furthermore, the present application also proposes a parameter adjustment device for a liver-kidney combined chip, which includes: an acquisition module, used to acquire electrochemical signals collected by an electrode array in the liver-kidney combined chip, wherein the electrochemical signals reflect changes in pH during cell metabolism; a first calculation module, used to calculate the pH change trend of each region in the liver-kidney combined chip based on the electrochemical signals; a second calculation module, used to calculate an electrode activation scheme based on the pH change trend, wherein the electrode activation scheme is used to determine electrodes that need to be activated and their corresponding voltages; a control module, used to control specific electrodes in the electrode array to generate local electric fields to affect ion migration and thereby adjust the pH value in the liver-kidney combined chip according to the electrode activation scheme.

[0017] From the above, it can be seen that the parameter adjustment method and device of the liver-kidney combined chip provided in the present application obtains the electrochemical signals collected by the electrode array, calculates the pH value change trend, calculates the electrode activation scheme based on the trend, and controls the specific electrodes to generate a local electric field to adjust the pH value, thereby realizing the accurate monitoring and dynamic adjustment of the pH value of the microenvironment in the liver-kidney combined chip without introducing additional substances, while maintaining cell activity and drug stability. It has the advantages of being able to accurately monitor and dynamically adjust the pH value of the microenvironment in the liver-kidney combined chip without introducing additional substances, while maintaining cell activity and drug stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a liver-kidney combined chip parameter adjustment method provided in this application.

[0019] Figure 2 A schematic diagram of the structure of a liver-kidney combined chip parameter adjustment device provided in this application.

[0020] In the figure: 210, acquisition module; 220, first calculation module; 230, second calculation module; 240, control module. DETAILED DESCRIPTION

[0021] The technical solutions in the present application will be clearly and completely described below in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0022] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0023] In laboratories specializing in drug metabolism and toxicity, the liver-kidney chip is widely used in long-term drug research. The chip simulates the complex liver and kidney metabolic processes in the human body through the three-dimensional structure of the microfluidic channel. However, as the experiment lasts longer, the cell metabolites in the chip continue to accumulate, resulting in significant changes in pH. This pH change not only affects cell activity, but also interferes with the metabolic process of certain pH-sensitive drugs, thereby affecting the accuracy and reliability of the experiment. Therefore, achieving accurate monitoring and dynamic regulation of the pH value of the microenvironment in the liver-kidney chip has become a technical problem that needs to be solved urgently.

[0024] In this regard, refer to Figure 1 The present application proposes a parameter adjustment method for a liver-kidney combined chip, the steps of which include:

[0025] S110, acquiring electrochemical signals collected by the electrode array in the liver-kidney combined chip, where the electrochemical signals reflect changes in pH during cell metabolism;

[0026] S120, calculating the pH value change trend of each area in the liver-kidney combined chip according to the electrochemical signal;

[0027] S130, calculating an electrode activation scheme based on a pH value change trend, where the electrode activation scheme is used to determine electrodes that need to be activated and their corresponding voltages;

[0028] S140. According to the electrode activation scheme, specific electrodes in the electrode array are controlled to generate a local electric field to affect ion migration and thereby regulate the pH value in the liver-kidney combined chip.

[0029] Among them, the electrode array refers to multiple microelectrodes integrated in the liver-kidney combined chip, which are used to collect electrochemical signals and generate local electric fields. These electrodes can be made of inert metal materials such as gold and platinum, and arranged in an array on the surface or inside the chip through micromachining technology. In this solution, the electrode array serves as both a pH monitoring tool and a pH adjustment tool, realizing integrated monitoring and adjustment functions.

[0030] Among them, electrochemical signals refer to electrical signals generated by electrochemical reactions or ion migration at the interface between the electrode and the solution, reflecting the changes in pH during cell metabolism. These signals can be obtained by measuring changes in electrode potential, current or impedance. These signals provide basic data for real-time monitoring of pH values, avoiding interference that may be caused by the introduction of additional pH sensors.

[0031] The pH value trend calculation refers to the process of obtaining the pH value change pattern of each area in the liver-kidney combined chip over time through data processing and analysis algorithms based on the collected electrochemical signals. This step provides a decision-making basis for the subsequent electrode activation plan, ensuring the accuracy and timeliness of pH value adjustment.

[0032] The electrode activation scheme refers to a specific plan to determine the electrodes that need to be activated and their corresponding voltages based on the pH change trend. This scheme takes into account the spatial distribution and temporal dynamics of pH changes, and achieves precise control of local pH by selectively activating specific electrodes and applying appropriate voltages.

[0033] Among them, the local electric field refers to the electric field within a limited range generated by controlling a specific electrode, which is used to affect ion migration and thus adjust the pH value. This method avoids the introduction of additional substances and directly acts on the ions in the solution through the electric field force, thus achieving non-contact adjustment of the pH value.

[0034] The core innovation of this application is to propose an integrated method that uses an electrode array to monitor and adjust pH. Real-time, non-interference monitoring of pH is achieved by collecting electrochemical signals, and the method of adjusting pH by generating a local electric field to affect ion migration innovatively solves the problem of accurately controlling the pH of the microenvironment without introducing additional substances. This method realizes real-time, accurate and dynamic regulation of the pH of the microenvironment in the liver-kidney combined chip, while minimizing the impact on cell activity and drug stability.

[0035] Here’s how this application works:

[0036] First, the electrode array collects electrochemical signals, which reflect the pH state of each area in the chip. Signal acquisition can be achieved by measuring changes in electrode potential, current or impedance. The acquisition frequency can be adjusted according to experimental requirements, such as once every 5 minutes.

[0037] The collected electrochemical signals are processed and analyzed to calculate the pH change trend of each area. This step involves algorithms such as signal filtering, data calibration, and trend analysis. The calculation of the pH change trend takes into account the two dimensions of time and space, and can reflect the rate and direction of pH change in different areas.

[0038] Based on the calculated pH trend, the system generates an electrode activation plan. This plan determines the electrodes that need to be activated and their corresponding voltage parameters. The generation of the plan takes into account multiple factors, including target pH, current pH, pH change rate, electrode position, etc. The optimization algorithm will weigh the regulation effect and energy consumption to select the optimal electrode combination and voltage parameters.

[0039] According to the generated electrode activation scheme, the control system activates specific electrodes and applies the corresponding voltage to generate local electric fields. These local electric fields regulate the pH value by affecting the migration of ions in the solution. The strength and distribution of the electric field are determined by the position of the electrodes and the applied voltage, which can achieve precise control of the pH value in a specific area.

[0040] The whole process forms a continuous feedback loop: pH changes lead to changes in electrochemical signals, signal analysis yields pH trend, trend analysis generates electrode activation plans, plan execution generates local electric fields, and local electric fields affect pH. This closed-loop control ensures that the system can continuously and dynamically adjust the pH value within the chip and maintain the stability of the microenvironment.

[0041] As a preferred implementation, the following specific embodiments can be used to implement the technical solution of the present application:

[0042] In a liver-kidney combined chip, a 5×5 microelectrode array is integrated. The electrode material is platinum, the diameter of each electrode is 50μm, and the electrode spacing is 500μm. The electrode array is directly integrated on the bottom of the chip through micromachining technology.

[0043] The electrochemical signals were collected by cyclic voltammetry, with a scan voltage range of -0.5 V to 0.5 V, a scan rate of 50 mV / s, and a complete scan every 5 minutes. The collected current-voltage curve was converted into pH value through data processing algorithm.

[0044] The pH value trend was calculated using a sliding window method with a window size of 30 minutes. For each electrode position, the linear regression slope of the pH value within 30 minutes was calculated as the pH value trend at that position.

[0045] Genetic algorithm is used to generate electrode activation scheme, and the objective function comprehensively considers pH adjustment effect, energy consumption and cell activity. The algorithm output includes the number of electrodes to be activated and their corresponding voltage values.

[0046] The generation of local electric field is achieved by a precision power controller with a voltage range of -1V to 1V and an accuracy of 1mV. The electrode activation adopts pulse mode with a pulse width of 100ms and a period of 1s to reduce the electric field stimulation to the cells.

[0047] The system sets the pH target range to 7.35-7.45. When the pH value in a certain area is detected to be outside this range, the adjustment process is triggered. During the adjustment process, the system evaluates the pH change every 30 seconds and adjusts the electrode activation scheme accordingly.

[0048] In this way, the system can achieve accurate monitoring and dynamic regulation of pH values ​​within the liver-kidney combined chip without introducing additional substances, providing a stable microenvironment for long-term drug metabolism studies.

[0049] In some of the above embodiments of the present application, it is proposed to calculate the electrode activation scheme based on the pH value change trend to adjust the pH value in the liver-kidney joint chip. However, in this process, how to generate and evaluate the electrode activation scheme when the pH value change trend exceeds the preset threshold value to select the optimal scheme for accurate pH value adjustment is still a problem that needs to be solved. This involves how to balance multiple factors such as pH adjustment effect, energy consumption and the impact on cell activity.

[0050] In this regard, the present application further proposes that when it is detected that the pH value change trend in the liver-kidney combined chip exceeds a preset threshold, multiple candidate electrode activation schemes are generated according to the pH value change trend, and each scheme includes an electrode combination to be activated and its corresponding voltage parameters; the candidate electrode activation schemes are evaluated to obtain a multidimensional evaluation result, and the evaluation takes into account the pH adjustment effect, energy consumption and the impact on cell activity; based on the multidimensional evaluation results, the scheme with the best comprehensive performance is selected as the final electrode activation scheme for subsequent precise adjustment of the pH value.

[0051] The technical solution of the present application solves the above problems by generating multiple candidate electrode activation schemes and performing multi-dimensional evaluation. Specifically, when it is detected that the pH value change trend exceeds the preset threshold, the system will generate multiple candidate electrode activation schemes based on the current pH value change trend. Each scheme contains the electrode combination that needs to be activated and its corresponding voltage parameters, which provides a basis for subsequent evaluation and selection.

[0052] There are many strategies that can be used to generate multiple candidate solutions. For example, the effects of different electrode combinations and voltage parameters on pH can be predicted based on historical data and machine learning algorithms to generate a series of potentially effective solutions. Another method is to use a genetic algorithm to simulate the evolutionary process, gradually optimize the electrode combination and voltage parameters, and generate multiple potential high-quality solutions.

[0053] The evaluation of candidate electrode activation schemes is a multi-dimensional process that mainly considers three key factors: pH regulation effect, energy consumption, and impact on cell activity. The pH regulation effect can be evaluated by simulation calculations or small-scale experiments, considering the regulation speed and stability. Energy consumption evaluation can calculate the theoretical power consumption based on the electrode activation parameters. The impact on cell activity can be predicted by establishing a relationship model between the electric field strength and the physiological state of the cell.

[0054] During the evaluation process, a weighted scoring system can be used to assign weights to each factor and comprehensively calculate the total score of each solution. The weight allocation can be dynamically adjusted according to the experimental stage and specific needs. For example, in the early stage of drug metabolism, more attention may be paid to the pH adjustment effect, while in the long-term culture stage, more attention may be paid to the protection of cell activity.

[0055] Based on the multi-dimensional evaluation results, the system will select the solution with the best comprehensive performance as the final electrode activation solution. This selection process can be implemented using a decision tree or a multi-objective optimization algorithm to ensure that the selected solution achieves the best balance in terms of pH adjustment effect, energy efficiency, and cell activity protection.

[0056] The technical solution of the present application realizes the rapid generation and evaluation of multiple feasible adjustment schemes through the above steps when the pH value change trend exceeds the preset threshold, thereby selecting the electrode activation scheme that best suits the current situation. This method not only improves the accuracy and efficiency of pH value adjustment, but also minimizes the adverse effects on cell activity and optimizes energy use.

[0057] Furthermore, the technical solution of the present application can be implemented in the following ways in practical application:

[0058] First, a preset threshold for the pH change trend is set. This threshold can be adjusted according to experimental requirements and cell types, for example, it can be set to a pH change of more than 0.1 units per hour. When the system detects that the pH change trend exceeds this threshold, it triggers the generation of multiple candidate electrode activation schemes.

[0059] When generating candidate solutions, a combination of rule-based methods and machine learning strategies can be used. Rule-based methods can quickly generate some basic solutions based on the direction and magnitude of pH changes. For example, if the pH value shows an upward trend, the system will give priority to activating electrodes that can produce acidic ions. Machine learning algorithms, such as reinforcement learning, can learn the most effective electrode combinations and voltage parameters by analyzing historical data, thereby generating more optimized candidate solutions.

[0060] For each candidate solution, the system performs a multi-dimensional evaluation. The pH adjustment effect can be calculated by a predictive model that takes into account factors such as electrode position, voltage parameters, and current pH distribution. Energy consumption evaluation can be calculated based on the number of electrodes, voltage size, and expected activation time. The impact on cell activity can be evaluated by establishing an electric field strength-cell activity relationship model that takes into account the sensitivity of different cell types to the electric field.

[0061] During the evaluation process, the system uses a dynamic weighting mechanism. For example, at the beginning of the experiment, the weight of pH adjustment effect may be set to 0.5, energy consumption to 0.3, and cell activity effect to 0.2. As the experiment progresses, if a decrease in cell proliferation rate is detected, the system will automatically increase the weight of cell activity effect, which may be adjusted to 0.4, while reducing the weights of other factors accordingly.

[0062] Finally, the system uses a multi-objective optimization algorithm, such as NSGA-II (non-dominated sorting genetic algorithm II), to select the best solution. This algorithm can find the best balance between multiple objectives and select a solution that can effectively adjust the pH value while minimizing energy consumption and cell activity.

[0063] Through this method, the technical solution of this application can achieve precise pH control in a complex liver-kidney combined chip environment. It can not only quickly respond to changes in pH, but also dynamically adjust the control strategy according to the experimental stage and cell state, thereby ensuring the stability and reliability of long-term experiments. This is of great significance for long-term drug metabolism and toxicity research, and can significantly improve the accuracy and repeatability of experimental data.

[0064] In some of the above-mentioned embodiments of the present application, it is proposed to evaluate the candidate electrode activation schemes in order to select the scheme with the best overall performance as the final electrode activation scheme. However, in this process, there is a lack of comprehensive consideration of multiple evaluation dimensions, and it is difficult to dynamically adjust the evaluation focus at different experimental stages, which may result in the selected scheme being unable to adapt to the different needs of the experiment.

[0065] In this regard, the present application further proposes to establish a multidimensional evaluation model, which comprehensively considers the pH regulation effect, energy consumption and cell activity influence, wherein: the pH regulation effect is characterized by a comprehensive indicator of the pH correction speed and stability of the target area; the energy consumption is quantified by the functional relationship between the electrode activation parameters and the power consumption; the cell activity influence is evaluated by the degree of influence of the electric field on the physiological state of the cell; when the candidate scheme parameters are input, the weight coefficients of each evaluation dimension are dynamically adjusted according to the current experimental stage, the weight coefficient of the pH regulation effect is increased in the initial stage of drug metabolism, and the weight coefficient of cell activity protection is increased in the long-term stability stage.

[0066] The multidimensional evaluation model established in this application comprehensively considers the three key dimensions of pH adjustment effect, energy consumption and cell activity. The pH adjustment effect is characterized by a comprehensive index of pH correction speed and stability in the target area, which can be achieved by measuring the rate of change of pH value and the stabilization time. For example, a comprehensive index S = α * (1 / T) + β* (1 / ΔpH) can be defined, where T is the time required to reach the target pH value, ΔpH is the fluctuation range of pH value in the stable state, and α and β are weight coefficients.

[0067] Energy consumption can be quantified by establishing a functional relationship between electrode activation parameters and power consumption. Specifically, the power consumption under different voltage and current combinations can be measured experimentally, and then a functional model E = f(V, I) can be established, where V is voltage and I is current. This model can be linear or nonlinear, depending on the actual electrode characteristics.

[0068] The evaluation of the influence of electric field on the physiological state of cells can be achieved by observing the degree of influence of electric field on the physiological state of cells. Cell activity can be measured under different electric field strengths by using methods such as cell activity staining or metabolic activity measurement, and a relationship model between electric field strength and cell activity can be established, C = g(E), where E is the electric field strength.

[0069] The mechanism of dynamically adjusting the weight coefficient is an innovation of this application. A weight adjustment function W(t) = [w1(t), w2(t), w3(t)] can be designed, where w1, w2, and w3 represent the weights of pH regulation effect, energy consumption, and cell activity, respectively, and t represents the experimental stage. In the early stage of drug metabolism, w1(t) can be set larger, and in the long-term stability stage, the value of w3(t) is increased. This dynamic adjustment can be triggered by monitoring indicators such as cell metabolite concentration and cell proliferation rate.

[0070] For example, in a practical application, a 10x10 microelectrode array was implanted in the liver-kidney combined chip, with each electrode measuring 50μm x 50μm and an electrode spacing of 200μm. The system first collected pH data through an electrochemical sensor and found that in the early stage of drug metabolism (within 2 hours after the start of the experiment), the pH value of a certain area of ​​the chip dropped rapidly from 7.4 to 6.8.

[0071] Based on this situation, the system generates multiple candidate electrode activation schemes. Taking one of the schemes as an example, the scheme recommends activating four adjacent electrodes (3,3), (3,4), (4,3), and (4,4) with an applied voltage range of 0.5V-1.5V. The system then evaluates the scheme using a multidimensional evaluation model:

[0072] Evaluation of pH adjustment effect: Through simulation calculation, it is predicted that the scheme can adjust the pH value back to the range of 7.2±0.1 within 10 minutes, and the pH adjustment effect score SpH = 0.85 (the full score is 1).

[0073] Energy consumption evaluation: Based on the electrode parameter-power consumption function model, the expected energy consumption of the scheme is calculated to be 5mW. Compared with the maximum acceptable energy consumption of 10mW, the energy consumption score SE = 0.75 is obtained.

[0074] Evaluation of cell activity impact: Based on the electric field-cell activity impact model, it is predicted that the cell activity retention rate under this scheme is 95%, and the cell activity score SC = 0.95 is obtained.

[0075] Considering that the current stage is the early stage of drug metabolism, the system dynamically adjusts the weight coefficient to W(t) = [0.5,0.3, 0.2]. Finally, the comprehensive score of the scheme is:

[0076] S = 0.5 * 0.85 + 0.3 * 0.75 + 0.2 * 0.95 = 0.84;

[0077] The system performs a similar evaluation on all candidate solutions and selects the solution with the highest comprehensive score as the final electrode activation solution. During the implementation process, the system continuously monitors pH changes and cell status and dynamically adjusts electrode activation parameters as needed.

[0078] In this way, the technical solution of the present application can quickly adjust the pH value in the early stage of drug metabolism, while maintaining high cell activity and low energy consumption throughout the experiment. This dynamic adjustment and multidimensional evaluation method significantly improves the performance and reliability of the liver-kidney combined chip in long-term drug metabolism experiments.

[0079] In some of the above embodiments of the present application, it is proposed to dynamically adjust the weight coefficients of each evaluation dimension to optimize the evaluation process of the electrode activation scheme. However, in this process, there is a lack of accurate judgment of the experimental stage and the corresponding weight adjustment strategy. This may lead to the use of inappropriate evaluation criteria in different experimental stages, affecting the optimization effect of the electrode activation scheme. Especially in the early stage of drug metabolism and the long-term stability stage, there may be deviations in the importance assessment of the pH regulation effect, energy consumption and cell activity, thereby affecting the accuracy and efficiency of the overall experiment.

[0080] In this regard, the present application further proposes to obtain the concentration of cell metabolites, cell proliferation rate and pH fluctuation amplitude as experimental stage judgment indicators; based on the preset judgment rules, the current experimental stage is determined according to the judgment indicators, wherein when the cell metabolite concentration change rate and the pH fluctuation amplitude are lower than their respective preset thresholds and the cell proliferation rate is stable, it is judged to be the long-term stability stage, otherwise it is judged to be the initial stage of drug metabolism; according to the determined experimental stage, based on the pre-set weight coefficient mapping relationship, the weight coefficients of the three evaluation dimensions of pH regulation effect, energy consumption and cell activity influence are determined.

[0081] This application provides multi-dimensional data support for the judgment of the experimental stage by obtaining the concentration of cell metabolites, cell proliferation rate and pH fluctuation range as judgment indicators. These indicators can fully reflect the cell metabolism state and microenvironment changes, and help to accurately distinguish the initial stage of drug metabolism and the long-term stability stage.

[0082] Specifically, the concentration of cellular metabolites can be monitored in real time by electrochemical sensors integrated in microfluidic chips. For example, glucose concentration can be detected using an electrode modified with glucose oxidase, or lactate concentration can be detected using an electrode modified with lactate oxidase. The changes in the concentration of these metabolites directly reflect the intensity of cellular metabolic activity.

[0083] The rate of cell proliferation can be monitored using a real-time cell analyzer or fluorescent labeling technology. For example, a microelectrode array can be integrated into the chip to assess the increase in cell number by measuring changes in electrode impedance. Alternatively, a fluorescently labeled cell cycle indicator can be used to determine the frequency of cell division by changes in fluorescence intensity.

[0084] The pH fluctuation can be measured by micro-pH electrodes or pH-sensitive fluorescent dyes. For example, pH-sensitive ion-selective field-effect transistors (ISFETs) can be embedded in key locations on the chip to achieve high-precision, real-time pH monitoring.

[0085] The judgment of the experimental stage is based on the preset judgment rules, which increases the objectivity and repeatability of the judgment process. In particular, by setting the thresholds of the change rate of cell metabolite concentration and pH fluctuation range, and considering the stability of cell proliferation rate, a clear quantitative standard is provided for the judgment of the long-term stability stage.

[0086] Furthermore, the judgment rules can be designed as a multi-level decision tree structure. First, check whether the rate of change of the concentration of cell metabolites is lower than the preset threshold, for example, the rate of change per hour is less than 5%. Secondly, evaluate whether the pH fluctuation is within an acceptable range, such as the fluctuation within 24 hours does not exceed ±0.2 pH units. Finally, confirm whether the cell proliferation rate remains stable, for example, the cell number change does not exceed 10% within 72 consecutive hours. Only when these three conditions are met at the same time, it is determined to enter the long-term stability stage.

[0087] According to the experimental stage to be determined, the weight coefficients of the evaluation dimensions are determined using the pre-set weight coefficient mapping relationship, realizing the dynamic adjustment of the evaluation criteria. This method can flexibly adjust the importance of the three dimensions of pH adjustment effect, energy consumption and cell activity impact according to the different stages of the experimental process, thereby optimizing the evaluation process of the electrode activation scheme.

[0088] Therefore, in the early stage of drug metabolism, the weight coefficient of pH adjustment effect can be set to 0.6, energy consumption to 0.3, and cell activity effect to 0.1. This is because in the early stage, rapid and accurate pH adjustment is essential to maintain the cell metabolic environment. In the long-term stability stage, the weight coefficient can be adjusted to: pH adjustment effect 0.3, energy consumption 0.2, cell activity effect 0.5. This adjustment reflects that in long-term experiments, protecting cell activity becomes the primary consideration.

[0089] As a preferred implementation, the present application can be implemented through the following steps:

[0090] First, multiple sensors are integrated into the liver-kidney combined chip, including electrochemical sensors for detecting the concentration of cell metabolites, microelectrode arrays for monitoring cell proliferation rate, and pH microelectrodes for measuring local pH. These sensors are connected to the cell culture area through microfluidic channels to ensure real-time, non-invasive data acquisition.

[0091] Secondly, set the specific parameters of the judgment rules. For example, the threshold of the change rate of cell metabolite concentration is set at 5% per hour, the threshold of pH fluctuation is set at ±0.2 pH units within 24 hours, and the standard of cell proliferation rate stability is set at no more than 10% change in cell number within 72 hours.

[0092] Secondly, establish a weight coefficient mapping relationship. In the early stage of drug metabolism, the weight coefficients of pH adjustment effect, energy consumption and cell activity are set to 0.6, 0.3 and 0.1 respectively. In the long-term stability stage, these weight coefficients are adjusted to 0.3, 0.2 and 0.5.

[0093] During the experiment, the system collects data every 15 minutes and automatically determines the experimental stage according to the preset judgment rules. Once the stage transition is detected, the weight coefficient adjustment is triggered immediately. For example, when the system observes that the concentration change rate of cell metabolites is less than 5% / hour for 24 consecutive hours, the pH value fluctuation range is within ±0.2, and the cell number change does not exceed 10%, the experimental stage is automatically determined as the long-term stability stage, and the weight coefficient of the evaluation dimension is adjusted accordingly.

[0094] This implementation method can achieve accurate judgment and timely response to the experimental stage, thereby optimizing the evaluation process of the electrode activation scheme. By dynamically adjusting the evaluation criteria, this application not only improves the accuracy and efficiency of pH regulation, but also significantly prolongs the survival time of cells in the chip, providing reliable technical support for long-term drug metabolism research. At the same time, this automated adjustment mechanism reduces human intervention and improves the repeatability of the experiment and the reliability of the data.

[0095] In some of the above embodiments of the present application, it is proposed to control specific electrodes in the electrode array according to the electrode activation scheme to generate a local electric field for affecting ion migration and thereby regulating the pH value in the liver-kidney combined chip. However, the following problems may exist in this process: First, simply activating the electrodes according to the preset scheme may not be able to respond to the real-time changes in the pH value in the chip in a timely manner; second, the fixed electrode activation sequence and voltage parameters may not be able to adapt to the differences in pH value changes in different regions; finally, the lack of real-time feedback and optimization mechanism for the electrode activation effect may lead to inaccurate or inefficient pH value regulation.

[0096] In this regard, the present application further proposes the steps of controlling specific electrodes in the electrode array to generate a local electric field to affect ion migration and thereby regulate the pH value in the liver-kidney combined chip according to the electrode activation scheme, including: according to the electrode activation sequence and corresponding voltage parameters in the electrode activation scheme, sequentially activating specific electrodes in the electrode array and applying specific voltages to generate a local electric field to affect ion migration and thereby regulate the pH value in the liver-kidney combined chip; real-time monitoring of pH value changes around the activated electrodes, and automatically adjusting the activation state of the corresponding electrodes when it is detected that the pH value reaches the target range; based on the pH value change trend and electrode position information, dynamically optimizing the electrode activation sequence and voltage parameters to achieve regulation of pH values ​​in different areas of the liver-kidney combined chip.

[0097] The technical solution proposed in this application solves the above problems through the following key features:

[0098] Activating specific electrodes in sequence and applying specific voltages: This step ensures the orderly and precise activation of the electrodes.

[0099] Real-time monitoring of pH changes and automatic adjustment of electrode activation status: This feature introduces a real-time feedback mechanism.

[0100] Dynamic optimization of electrode activation sequence and voltage parameters: This feature adapts to the differences in pH changes in different regions.

[0101] The sequential activation of electrodes provides the basis for the entire process, and the real-time monitoring mechanism provides the necessary data support for dynamic optimization. The dynamic optimization process in turn guides the activation sequence and parameter adjustment of the electrodes, forming a closed-loop feedback system. This system can flexibly adjust the control strategy according to the pH value variation characteristics of different areas in the chip, thereby achieving more accurate and efficient pH value regulation.

[0102] As a preferred embodiment, the technical solution of the present application can be implemented on a liver-kidney combined chip that integrates a microfluidic channel, an electrode array, and a pH sensor. The size of the chip can be 50mm×30mm×5mm, including a 100×60 electrode array, and the size of each electrode is 100μm×100μm. The electrode material can be platinum with good biocompatibility. The pH sensor can use an ion-sensitive field-effect transistor (ISFET).

[0103] In actual operation, the system will first calculate an initial electrode activation sequence based on the initial pH value distribution. For example, assuming that the pH value of a certain area of ​​the chip is 7.6 and the target pH value is 7.4, the system will preferentially activate the electrodes near this area. When activated, a voltage of 1V can be applied first for 100ms. Subsequently, the system will monitor the pH value changes in this area in real time. If the pH value drops to 7.5 within 10 seconds, the system will reduce the voltage to 0.5V; if the pH value continues to drop to 7.4, the electrode will be turned off.

[0104] At the same time, the system continuously monitors the pH distribution of the entire chip and updates the electrode activation priority every 5 seconds. For example, if it detects that the pH value of another area suddenly rises to 7.8, the system will immediately adjust the activation sequence to prioritize the electrodes in that area. This dynamic adjustment ensures that the system can respond to pH changes in various areas within the chip in a timely manner.

[0105] Through this precise pH value regulation, the technical solution of the present application can effectively maintain the stability of the microenvironment in the liver-kidney combined chip. This not only improves the reliability of long-term drug metabolism experiments, but also better simulates the physiological environment in the human body, providing a more accurate experimental platform for drug research. At the same time, since the introduction of additional buffer is avoided, this method will not dilute the nutrients and drugs to be tested in the culture medium, nor will it change the fluid dynamics characteristics in the chip, thereby ensuring the accuracy and repeatability of the experimental results.

[0106] In some of the above embodiments of the present application, it is proposed to dynamically optimize the electrode activation sequence and voltage parameters based on the pH value change trend and electrode position information to achieve the regulation of the pH value of different regions of the liver-kidney combined chip. However, in this process, how to establish a reasonable evaluation mechanism based on the real-time acquired pH value change characteristics and electrode position information, and dynamically adjust the electrode activation strategy accordingly to achieve accurate regulation of the pH value of different regions in the chip is still a technical problem that needs to be solved.

[0107] In this regard, the present application further proposes a step of dynamically optimizing the electrode activation sequence and voltage parameters based on the pH value change trend and electrode position information. This step includes obtaining the pH value change characteristics of each region in the liver-kidney joint chip, including the change rate and gradient distribution; based on the pH value change characteristics and electrode position information, establishing a multidimensional evaluation matrix reflecting the regulation priority of each region; according to the multidimensional evaluation matrix, determining the priority of electrode activation, and calculating the voltage parameters required for each electrode to generate an adaptive local electric field strength; when it is detected that the pH value change rate of a certain region exceeds the preset threshold, dynamically adjusting the activation priority and voltage parameters of the corresponding electrode in the region to achieve rapid response to the rapidly changing region.

[0108] This technical solution provides basic data for subsequent optimization strategies by obtaining the pH value change characteristics of each area in the liver-kidney joint chip. The change characteristics include not only the rate of change of pH value, but also the gradient distribution, which enables the system to fully grasp the dynamic changes of pH value in the chip. Based on these data and electrode position information, a multidimensional evaluation matrix reflecting the regulation priority of each area was established. This step realizes the quantitative evaluation of the pH value regulation needs in different areas of the chip, providing a scientific basis for subsequent decision-making.

[0109] The construction of a multidimensional evaluation matrix can adopt the weighted summation method, taking factors such as pH value change rate, gradient size and electrode position as evaluation indicators, assigning different weights, and calculating the comprehensive score of each area.

[0110] The process of determining the priority of electrode activation and calculating voltage parameters based on the multidimensional evaluation matrix reflects the core intelligent decision-making mechanism of the solution. This mechanism can dynamically adjust the electrode activation strategy based on real-time data to generate adaptive local electric field strength, thereby achieving precise regulation of pH values ​​in different regions. The priority of electrode activation can be determined based on the score ranking in the evaluation matrix, while the calculation of voltage parameters needs to consider factors such as the target pH value, the current pH value, and the mutual influence between electrodes.

[0111] It is particularly noteworthy that the solution also includes a rapid response mechanism. When it is detected that the rate of change of pH value in a certain area exceeds the preset threshold, the system can dynamically adjust the activation priority and voltage parameters of the corresponding electrode in the area, specifically, it can increase its priority and increase the voltage parameters. This feature greatly improves the response speed and regulation accuracy of the system to rapidly changing areas. The setting of the preset threshold can be determined based on the physiological tolerance range of the cells and the experimental requirements, and the adjustment of the priority can be achieved by increasing the weight of the area in the evaluation matrix.

[0112] Through this dynamic and refined regulation strategy, the technical solution of this application can achieve high-precision regulation of pH values ​​in different areas of the liver-kidney combined chip. This not only ensures the stability of the microenvironment within the chip, but also provides a more reliable experimental platform for long-term drug metabolism research. Due to the ability to quickly respond to local drastic changes, the solution also greatly reduces the risk of adverse effects on cell activity and drug stability due to pH fluctuations. These advantages make this technical solution have broad application prospects in the fields of drug development, toxicology research, etc.

[0113] In some of the above embodiments of the present application, it is proposed to determine the priority of electrode activation based on a multidimensional evaluation matrix, and calculate the voltage parameters required for each electrode to optimize the electrode activation scheme. However, in this process, adjacent electrodes may be activated at the same time, resulting in excessive local electric fields and affecting cell activity. At the same time, determining the activation order based solely on the evaluation score may ignore the spatial relationship between the electrodes and fail to fully consider the mutual influence between the electrodes, thereby affecting the accuracy and efficiency of pH regulation.

[0114] In this regard, the present application further proposes to determine the initial activation priority order based on the evaluation score of each electrode in the multidimensional evaluation matrix, while taking into account the spatial distance between adjacent electrodes. When it is detected that the distance between adjacent electrodes is less than a preset threshold, the activation order of these electrodes is adjusted to avoid simultaneous activation; based on the adjusted activation order and the pH value change characteristics of the area where each electrode is located, the initial voltage parameters are calculated, and the parameters are corrected considering the mutual influence between the electrodes to obtain the final voltage parameters.

[0115] Specifically, using a multidimensional evaluation matrix to determine the initial activation priority can improve the overall performance of the electrode activation scheme by considering multiple factors such as pH adjustment effect, energy consumption, and cell activity. This step can be achieved using a variety of methods such as weighted scoring or fuzzy comprehensive evaluation. For example, a weight can be assigned to each factor, and then the comprehensive score of each electrode can be calculated to determine the initial activation priority.

[0116] Considering the spatial distance between adjacent electrodes is an important innovation of this application. By setting a preset threshold, the problem of excessive local electric field caused by the simultaneous activation of adjacent electrodes can be avoided, which helps to protect cell activity. This preset threshold can be determined according to the specific structure of the chip and the electrode size, for example, it can be set to 1.5 times the electrode diameter. When it is detected that the distance between adjacent electrodes is less than this threshold, the next adjustment process will be triggered.

[0117] Adjusting the activation order of electrodes is an optimization step based on spatial distance considerations. This step can be implemented using a variety of algorithms, such as greedy algorithms or dynamic programming. For example, starting from the electrode with the highest score, the surrounding electrodes can be checked in turn. If an electrode with a distance less than a threshold is found, its activation order is adjusted backward until the spatial distance requirement is met. This can optimize the spatial distribution of electrode activation and avoid excessive local electric fields.

[0118] The calculation of the initial voltage parameters is based on the adjusted activation sequence and the pH change characteristics. This step can use a pre-established voltage-pH response model that can be fitted by experimental data. For example, a quadratic function or an exponential function can be used to describe the relationship between voltage and pH change. By inputting the target pH value and the current pH value, the initial voltage parameters required to achieve the target can be calculated.

[0119] Another innovative point of the present application is to correct the parameters by considering the mutual influence between electrodes. This step can be achieved by constructing an electric field superposition influence model. For example, finite element analysis or analytical solution methods can be used to simulate the composite electric field generated by multiple electrodes. By inputting the initial voltage parameters into the model, the electric field interference intensity between adjacent electrodes can be calculated, and the voltage parameters can be dynamically corrected accordingly.

[0120] As a preferred implementation, the technical solution of the present application can be implemented in a liver-kidney combined chip as follows:

[0121] The chip is equipped with a 10×10 electrode array with an electrode diameter of 100 microns and a center distance of 200 microns between adjacent electrodes. The preset threshold is set at 150 microns. The multidimensional evaluation matrix includes three dimensions: pH regulation effect, energy consumption, and cell activity impact, with weights of 0.5, 0.3, and 0.2, respectively.

[0122] First, the comprehensive score of each electrode is calculated based on the multidimensional evaluation matrix. For example, the score of electrode A is 8.5, the score of electrode B is 8.2, and the score of electrode C is 7.9. The initial activation priority is A>B>C.

[0123] Then, the spatial distance between adjacent electrodes was checked. It was found that the distance between electrodes A and B was 200 microns, which was greater than the preset threshold of 150 microns and did not require adjustment. However, the distance between electrodes B and C was 141 microns (√2×100 microns), which was less than the preset threshold and required adjustment of the activation order. The adjusted order became A>C>B.

[0124] Next, the voltage-pH response model is used to calculate the initial voltage parameters. Assume that the model is V = a1(pHtarget- pHcurrent)^2 + b1, where a1 and b1 are fitting parameters. For electrode A, the target pH is 7.4, the current pH is 7.2, and the calculated initial voltage is 1.5V.

[0125] Finally, the initial voltage parameters were input into the electric field superposition influence model. The model showed that the electric field of electrode A had a 10% influence on the surrounding electrodes. Based on this, the voltage parameters of electrode C were corrected and its initial voltage of 1.3V was adjusted to 1.17V.

[0126] Through this method, the technical solution of the present application achieves precise control of the pH value in the liver-kidney combined chip, while avoiding the adverse effects of excessive local electric fields on cell activity. Compared with traditional methods, the solution of the present application improves the accuracy and efficiency of pH regulation while maintaining cell activity, providing a more reliable experimental platform for long-term drug metabolism research.

[0127] In some of the above embodiments of the present application, it is proposed to calculate the initial voltage parameters based on the adjusted activation order and the pH value change characteristics of the area where each electrode is located, and to perform parameter correction considering the mutual influence between the electrodes, so as to obtain the final voltage parameters for determining the priority of electrode activation and calculating the voltage parameters required for each electrode. However, in this process, relying solely on the initial voltage parameters may not accurately reflect the interaction between the electrodes, resulting in the final voltage parameters being inaccurate. In addition, the geometric layout of the electrode array and the electrode spacing also have a significant impact on the electric field distribution. If these factors are not taken into account, it may cause uneven local electric field strength, affecting the effect and accuracy of pH value regulation.

[0128] In this regard, the present application further proposes to establish a voltage-pH response model based on the pH value change characteristics and target pH value range of the areas where each electrode is located, and calculate the initial voltage parameters required to reach the target pH value; construct an electric field superposition influence model that takes into account the geometric layout of the electrode array and the electrode spacing, and input the initial voltage parameters into the model to calculate the electric field interference intensity between adjacent electrodes; dynamically correct the initial voltage parameters according to the electric field interference intensity to obtain the final voltage parameters.

[0129] This application achieves accurate calculation and optimization of electrode activation parameters by establishing a voltage-pH response model, an electric field superposition influence model, and a dynamic correction mechanism. The establishment of the voltage-pH response model takes into account the pH value variation characteristics and target pH value range of each electrode area, providing a basis for the calculation of the initial voltage parameters. This ensures the matching of the voltage parameters with the actual pH adjustment requirements.

[0130] The electric field superposition effect model takes into account the geometric layout of the electrode array and the electrode spacing, and can accurately simulate the electric field interaction between adjacent electrodes. This solves the problem of uneven electric field distribution caused by ignoring the mutual influence between electrodes in traditional methods. The electric field superposition effect model can be implemented through finite element analysis or analytical methods, and the specific implementation method can be selected according to the specific structure and computing resources of the chip.

[0131] The dynamic correction mechanism adjusts the initial voltage parameters according to the calculated electric field disturbance intensity, further improving the accuracy of the final voltage parameters. This dynamic adjustment can adapt to the complex electrode array environment and ensure that each electrode can produce the optimal local electric field intensity. Dynamic correction can be achieved using optimization methods such as iterative algorithms or gradient descent methods to minimize the impact of electric field disturbances on target pH regulation.

[0132] The technical solution of this application uses a comprehensive multi-step and multi-model approach to accurately calculate and optimize electrode activation parameters based on the mutual influence between electrodes. This not only improves the accuracy and efficiency of pH adjustment, but also ensures the uniformity of electric field distribution, thereby achieving more precise and controllable pH adjustment in the entire liver-kidney combined chip.

[0133] Specifically, the establishment of the voltage-pH response model can be based on experimental data and theoretical analysis. By measuring the change of pH value under different voltages, the functional relationship between voltage and pH change rate is established. This model can be implemented using polynomial fitting or machine learning methods (such as support vector regression) to accurately capture the nonlinear relationship between voltage and pH change.

[0134] The construction of the electric field superposition effect model needs to consider the geometric layout and spacing of the electrodes. The electric field superposition principle can be used to treat the electric field generated by each electrode as a point charge, and then calculate the composite electric field strength at any point. For complex electrode arrays, finite element analysis can be used to simulate the electric field distribution more accurately. The output of this model is a function that describes the electric field distribution, which can be used to calculate the electric field interference strength between any two electrodes.

[0135] The dynamic correction mechanism can be designed as an iterative process. First, the initial voltage parameters are input into the electric field superposition influence model to calculate the electric field interference intensity. Then, the initial voltage parameters are adjusted according to the interference intensity. The adjustment method can be linear correction (if the interference is small) or nonlinear correction (if the interference is large). The corrected voltage parameters are input into the model again, and the process is repeated until the electric field interference intensity is reduced to an acceptable level.

[0136] The technical solution of the present application fully considers the mutual influence between electrodes and the geometric characteristics of the electrode array in the process of solving the problem of accurate calculation of electrode activation parameters. By establishing a voltage-pH response model, the present application can accurately predict the change of pH value under different voltages, thereby providing a reliable basis for the calculation of initial voltage parameters. This solves the problem of setting voltage parameters in traditional methods based only on experience or simple linear relationships, and improves the accuracy of initial parameters.

[0137] Furthermore, the electric field superposition influence model takes into account the geometric layout of the electrode array and the electrode spacing, and can accurately simulate the electric field distribution in a complex electrode array. Through this model, the present application can calculate the electric field interference intensity between adjacent electrodes, which is a key factor ignored by traditional methods. As a result, the present application solves the problem of local electric field inhomogeneity caused by mutual influence between electrodes, laying the foundation for subsequent parameter optimization.

[0138] Finally, the application of the dynamic correction mechanism enables the application to adjust the initial voltage parameters in real time according to the calculated electric field interference intensity. This adaptive optimization process ensures the high accuracy of the final voltage parameters and can adapt to the pH adjustment requirements in different regions and at different time points. In this way, the application achieves fine control of the pH value in the entire liver-kidney combined chip, improving the accuracy and repeatability of the experiment.

[0139] As a preferred embodiment, the present application can be implemented in a liver-kidney combined chip as follows:

[0140] First, a voltage-pH response model is established based on the experimental data collected in advance. For example, the polynomial regression method can be used to obtain the following relationship: ΔpH = a 2* V^3 + b 2* V^2 + c 2* V + d2, where ΔpH is the change in pH value, V is the applied voltage, and a2, b2, c2, and d2 are fitting coefficients.

[0141] Next, construct an electric field superposition influence model. Assume that the electrode array is a 4×4 square array with an electrode spacing of 500μm. Use finite element analysis software (such as COMSOL Multiphysics) to simulate the electric field distribution and obtain the electric field interference intensity function E(i, j) = k * (1 / r_ij^2) between any two electrodes, where i and j are electrode numbers, r_ij is the distance between electrodes, and k is a constant related to the electrode material and environment.

[0142] Then, dynamic correction is performed. The initial voltage parameter is set to V_0 = [1.2V, 1.5V, 1.8V, 2.0V] (corresponding to the 4 electrodes that need to be activated). V_0 is input into the electric field superposition influence model to calculate the electric field interference intensity matrix. Based on this matrix, the gradient descent method is used to correct V_0: V_n+1 = V_n - α * ∇E(V_n), where α is the learning rate, set to 0.01, and ∇E(V_n) is the gradient of the electric field interference intensity to the voltage.

[0143] After multiple iterations (e.g., 50 times), the optimized voltage parameter V_final = [1.18V, 1.53V, 1.75V, 1.98V] is finally obtained. This set of parameters can achieve precise adjustment of the pH value in the target area while considering the mutual influence between the electrodes.

[0144] Through this method, the present application can achieve precise pH control in a complex liver-kidney combined chip environment. Compared with traditional methods, the technical solution of the present application significantly improves the accuracy of pH regulation, reduces the potential impact of electric field unevenness on cells, and improves energy efficiency. These improvements are of great significance to the accuracy and reliability of long-term drug metabolism experiments, and help improve the predictive ability of in vitro experiments during drug development.

[0145] In some of the above embodiments of the present application, an electrode activation scheme based on the pH value change trend is proposed to adjust the pH value in the liver-kidney combined chip. However, in this process, relying solely on the pH value change trend may not fully consider multiple factors such as cell metabolism, mutual influence between electrodes, energy consumption, and cell activity, making it difficult to achieve accurate, efficient, and cell-friendly pH value regulation.

[0146] In this regard, the present application further proposes the steps of calculating the electrode activation scheme based on the pH value change trend, including:

[0147] Obtain the pH value P(x, y, t) and pH value change rate ΔP(x, y, t) at the position (x, y) in the liver-kidney combined chip at the current time t;

[0148] Based on the current pH value and change rate, the pre-established cell metabolism model is used as the prediction function f to calculate the predicted pH value P_pred(x, y, t+Δt) at the next time t+Δt;

[0149] Calculate the effective voltage V_eff(x, y), where V_eff(x, y) = V(x, y) + ∑[i≠(x,y)] α(d_i) * V_i, V(x, y) is the voltage of the electrode itself, V_i is the voltage parameter of the adjacent electrode i, α(d) is the distance attenuation function, and d_i is the distance between adjacent electrodes;

[0150] Construct a multi-objective optimization function O = w1*g1(t)*|P_target - P_pred| + w2*g2(t)*E + w3*g3(t)*U, where P_target is the target pH value, E is the energy consumption, U is the electrode usage, g1(t), g2(t), and g3(t) are weight functions dynamically adjusted according to the current experimental stage, w1 is the weight coefficient of the pH adjustment effect, w2 is the weight coefficient of the energy consumption, and w3 is the weight coefficient of the electrode usage;

[0151] Calculate the effect of the electric field on cell activity C(x, y, t) = h(V_eff(x, y), t), where h is the pre-established electric field-cell activity effect model;

[0152] Under the constraints of V_min ≤ V(x, y) ≤ V_max, ∑A(x, y) ≤ N_max and C(x, y, t) ≥ C_threshold, the optimal electrode activation scheme A(x, y) and the corresponding voltage parameter V(x, y) are determined by minimizing the optimization function O, where V_min and V_max are the voltage ranges, N_max is the maximum number of activated electrodes, and C_threshold is the minimum cell activity threshold.

[0153] The technical solution of the present application solves the above problems by introducing multiple key technical features. First, a cell metabolism model prediction function f is introduced to accurately predict future pH changes. The prediction function can be obtained based on machine learning algorithm training, with inputs including the current pH value, pH change rate and other related parameters, and output as the predicted pH value at the next moment. This prediction mechanism can improve the foresight and accuracy of pH regulation.

[0154] Furthermore, the present application takes into account the mutual influence between electrodes. By calculating the effective voltage V_eff(x, y), the influence of the electrode's own voltage and the surrounding electrodes is comprehensively considered. The distance decay function α(d) can be in the form of exponential decay or inverse proportional decay to accurately reflect the influence of the distance between electrodes on the electric field strength. This method can more accurately evaluate the actual effect of electrode activation on pH regulation.

[0155] The present application also constructs a multi-objective optimization function O, which comprehensively considers the pH adjustment effect, energy consumption and electrode usage. Among them, the term |P_target - P_pred| reflects the accuracy of pH adjustment, the term E represents energy consumption, and the term U reflects the balance of electrode usage. The weight functions g1(t), g2(t), and g3(t) can be dynamically adjusted according to the experimental stage. For example, the weight of g1(t) can be increased in the early stage of the experiment to improve the pH adjustment effect, and the weights of g2(t) and g3(t) can be increased in the long-term stable stage to reduce energy consumption and extend the life of the electrode.

[0156] In order to ensure that the regulation process is friendly to cells, this application introduces the electric field-cell activity influence model h. This model can be fitted by in vitro experimental data to reflect the effects of different electric field strengths and action times on cell activity. By calculating C(x, y, t) and setting the minimum cell activity threshold C_threshold, cells can be effectively protected from damage by excessively strong electric fields.

[0157] During the optimization process, this application sets multiple constraints. The voltage range V_min and V_max ensure that the electrodes work within the safe voltage range; the maximum number of activated electrodes N_max can control energy consumption and avoid interference caused by too many electrodes being activated at the same time; the minimum cell activity threshold C_threshold further ensures the safety of the regulation process.

[0158] Through the coordination of these technical features, this application achieves accurate, efficient and cell-friendly regulation of pH value in the liver-kidney combined chip. This solution not only takes into account the trend of pH value changes, but also comprehensively considers multiple aspects such as cell metabolism, mutual influence between electrodes, energy consumption optimization and cell activity protection, greatly improving the accuracy and adaptability of pH value regulation.

[0159] Specifically, the technical solution of the present application first obtains the current pH value and change rate, and combines it with a pre-established cell metabolism model to predict the future pH value. This prediction mechanism can identify possible pH anomalies in advance, thereby achieving more proactive and precise regulation. For example, when it is predicted that the pH value in a certain area is about to exceed the safe range, the system can activate the corresponding electrode in advance for adjustment to avoid the adverse effects of drastic fluctuations in pH on cells.

[0160] Furthermore, the present application takes into account the mutual influence between electrodes by calculating the effective voltage V_eff(x, y). In practical applications, the activation of adjacent electrodes may produce superposition or offset effects, affecting the accuracy of pH regulation. By introducing the distance attenuation function α(d), the present application can more accurately simulate the electric field distribution, thereby optimizing the electrode activation strategy. For example, when it is detected that the pH value of a certain area needs to be increased, the system will not only consider directly activating the electrodes in the area, but also evaluate the influence of the surrounding electrodes and select the optimal electrode combination to achieve the purpose of precise regulation.

[0161] The multi-objective optimization function O constructed in this application is the core of achieving accurate, efficient and cell-friendly regulation. By dynamically adjusting the weight functions g1(t), g2(t), and g3(t), the system can flexibly adjust the optimization strategy according to the different needs of the experimental stage. For example, in the initial stage of a drug metabolism experiment, it may be necessary to quickly adjust the pH value to adapt to the initial reaction of the cells. At this time, the weight of g1(t) can be increased; in the long-term observation stage, in order to maintain a stable experimental environment, the weights of g2(t) and g3(t) can be increased to reduce energy consumption and extend the service life of the electrode.

[0162] This application also pays special attention to the effect of electric fields on cell activity. By introducing the electric field-cell activity influence model h, the system can evaluate the potential impact of electrode activation on cells in real time. This feature ensures that while adjusting the pH value, there will be no excessive electric field interference to the cells. For example, when it is detected that the cell activity in a certain area is close to the threshold C_threshold, the system will automatically adjust the activation strategy of the electrode in that area, which may adopt milder voltage parameters or reduce the activation time to protect cell activity.

[0163] Finally, the multiple constraints set in this application further ensure the safety and effectiveness of the regulation process. The voltage range V_min and V_max settings avoid damage to cells and electrodes caused by too high or too low voltage; the maximum number of activated electrodes N_max is limited to control the overall energy consumption, and also avoids the electric field interference that may be caused by too many electrodes being activated at the same time; the setting of the minimum cell activity threshold C_threshold provides a safety guarantee for the entire regulation process, ensuring that the cells always remain in a good physiological state.

[0164] As a preferred embodiment, the technical solution of the present application can be applied to the pH regulation of the liver-kidney combined chip in long-term drug metabolism research. In practical applications, it is first necessary to establish an accurate cell metabolism model f and an electric field-cell activity influence model h. These models can be obtained through a large amount of in vitro experimental data and machine learning algorithm training. For example, the cell metabolism model f can consider the effects of factors such as different drug concentrations, temperature, and oxygen concentration on pH changes, while the electric field-cell activity influence model h needs to consider the effects of different electric field strengths, action times, and cell types on activity.

[0165] At the beginning of the experiment, the system first obtains the initial pH distribution P(x, y, 0) and the initial change rate ΔP(x, y,0). Assume that the pH value detected at a certain position (x0, y0) is 7.6, the change rate is 0.1 / hour, and the target pH value P_target is 7.4. The system uses the prediction function f to calculate that if no adjustment is made, the pH value at this position may reach 7.7 in 1 hour. Based on this prediction, the system begins to calculate the optimal electrode activation plan.

[0166] The system first calculates the effective voltage V_eff of the electrodes at and around the position (x0, y0). Assume that there are 4 adjacent electrodes around the position, with distances of d1, d2, d3, and d4, and the current voltages are V1, V2, V3, and V4, respectively. Using the exponential decay function α(d) = e^(-d / d0) (where d0 is the characteristic distance), the effective voltage can be expressed as:

[0167] V_eff(x0, y0) = V(x0, y0) + e^(-d1 / d0)*V1 + e^(-d2 / d0)*V2 + e^(-d3 / d0)*V3 + e^(-d4 / d0)*V4

[0168] Next, the system constructs a multi-objective optimization function O. Assuming that the experiment is in its early stages and the pH adjustment effect is more important, g1(t) = 0.6, g2(t) = 0.3, g3(t) = 0.1 are set. The energy consumption E can be expressed as a function of the number of activated electrodes and the voltage, and the electrode usage U can be expressed as the standard deviation of the number of electrode activations.

[0169] The system then calculates the effect of the electric field on cell activity, C(x, y, t). The assumed electric field-cell activity effect model h shows that cell activity begins to drop significantly when the effective voltage exceeds 2 V. Therefore, the system sets V_max to 2 V and V_min to 0.5 V to ensure effective pH regulation without damaging the cells.

[0170] Under these parameters and constraints, the system solves the optimal electrode activation scheme A(x, y) and the corresponding voltage parameter V(x, y) through numerical optimization methods (such as gradient descent or genetic algorithm). In the end, the system may come to the following result: activate the electrode at the position (x0, y0) and apply a voltage of 1.5V; at the same time, activate the adjacent electrode to the south and apply a voltage of 0.8V. This combination can effectively reduce the pH value at the position (x0, y0) while keeping the energy consumption within a reasonable range and ensuring that the cell activity is not significantly affected.

[0171] The system will continuously monitor changes in pH and dynamically adjust the electrode activation scheme based on the actual effect. For example, if the pH value is found to be decreasing too slowly, the system may increase the voltage or activate more electrodes; if the pH value is close to the target value, the system will gradually reduce the voltage or stop activating some electrodes.

[0172] Through this precise and dynamic regulation method, the technical solution of this application can maintain the pH value in the liver-kidney combined chip stable in long-term drug metabolism research, while maximally protecting cell activity and reducing energy consumption. This not only improves the reliability of experimental data, but also extends the service life of the chip, providing a more stable and reliable experimental platform for drug metabolism and toxicity research.

[0173] Second, refer to Figure 2 The present application further proposes a parameter adjustment device for a liver-kidney combined chip, the device comprising:

[0174] An acquisition module 210 is used to acquire electrochemical signals collected by the electrode array in the liver-kidney combined chip, where the electrochemical signals reflect changes in pH during cell metabolism;

[0175] The first calculation module 220 is used to calculate the pH value change trend of each area in the liver-kidney combined chip according to the electrochemical signal;

[0176] A second calculation module 230, for calculating an electrode activation scheme based on a pH value change trend, wherein the electrode activation scheme is used to determine electrodes that need to be activated and their corresponding voltages;

[0177] The control module 240 is used to control specific electrodes in the electrode array to generate a local electric field according to the electrode activation scheme to affect ion migration and thus adjust the pH value in the liver-kidney combined chip.

[0178] By acquiring the electrochemical signals collected by the electrode array, calculating the pH change trend, calculating the electrode activation plan based on the trend, and controlling the specific electrodes to generate local electric fields to adjust the pH value, it is possible to accurately monitor and dynamically adjust the pH value of the microenvironment in the liver-kidney combined chip without introducing additional substances, while maintaining cell activity and drug stability. It has the advantages of being able to accurately monitor and dynamically adjust the pH value of the microenvironment in the liver-kidney combined chip without introducing additional substances, while maintaining cell activity and drug stability.

[0179] In addition, in some preferred embodiments, a parameter adjustment device for a liver-kidney combined chip proposed in the present application can perform any step of the above method.

[0180] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for adjusting parameters of a liver-kidney combined chip, characterized in that: The steps of the method include: Acquiring electrochemical signals collected by the electrode array in the liver-kidney combined chip, wherein the electrochemical signals reflect the pH value changes during cell metabolism; Calculating the pH value variation trend of each area in the liver-kidney combined chip according to the electrochemical signal; Calculating an electrode activation scheme based on the pH value change trend, wherein the electrode activation scheme is used to determine the electrodes that need to be activated and their corresponding voltages; According to the electrode activation scheme, specific electrodes in the electrode array are controlled to generate a local electric field for influencing ion migration and thereby regulating the pH value within the liver-kidney combined chip.

2. The parameter adjustment method of a liver-kidney combined chip according to claim 1, characterized in that: The step of calculating the electrode activation scheme based on the pH value change trend comprises: When it is detected that the pH value change trend in the liver-kidney combined chip exceeds a preset threshold, multiple candidate electrode activation schemes are generated according to the pH value change trend, each scheme including an electrode combination to be activated and its corresponding voltage parameters; Evaluating the candidate electrode activation schemes to obtain a multi-dimensional evaluation result, the evaluation taking into account pH adjustment effect, energy consumption and impact on cell activity; Based on the multi-dimensional evaluation results, the scheme with the best comprehensive performance is selected as the final electrode activation scheme for subsequent precise adjustment of the pH value.

3. The parameter adjustment method of a liver-kidney combined chip according to claim 2, characterized in that: The step of evaluating the candidate electrode activation schemes to obtain a multi-dimensional evaluation result comprises: A multidimensional evaluation model was established, which comprehensively considered the pH adjustment effect, energy consumption and cell activity effects, among which: The pH adjustment effect is characterized by a comprehensive index of pH correction speed and stability in the target area; Energy consumption is quantified by the functional relationship between electrode activation parameters and power consumption; The influence of cell activity is assessed by the degree of influence of electric field on the physiological state of cells; When the candidate parameters are input, the weight coefficients of each evaluation dimension are dynamically adjusted according to the current experimental stage. The weight coefficient of the pH regulation effect is increased in the early stage of drug metabolism, and the weight coefficient of cell activity protection is increased in the long-term stability stage.

4. The parameter adjustment method of the liver-kidney combined chip according to claim 3 is characterized in that: The step of dynamically adjusting the weight coefficients of each evaluation dimension includes: Obtain the concentration of cell metabolites, cell proliferation rate and pH fluctuation range as indicators for judging the experimental stage; Based on the preset judgment rules, the current experimental stage is determined according to the judgment indicators, wherein when the rate of change of the concentration of cell metabolites and the fluctuation amplitude of the pH value are lower than the respective preset thresholds and the cell proliferation rate is stable, it is determined to be the long-term stability stage, otherwise it is determined to be the early stage of drug metabolism; According to the determined experimental stage, the weight coefficients of the three evaluation dimensions of pH regulation effect, energy consumption and cell activity impact are determined based on the pre-set weight coefficient mapping relationship.

5. The parameter adjustment method of a liver-kidney combined chip according to claim 1, characterized in that: The step of controlling specific electrodes in the electrode array to generate a local electric field to affect ion migration and thereby adjust the pH value in the liver-kidney combined chip according to the electrode activation scheme comprises: According to the electrode activation sequence and corresponding voltage parameters in the electrode activation scheme, specific electrodes in the electrode array are activated in sequence and specific voltages are applied to generate a local electric field to affect ion migration and thereby regulate the pH value in the liver-kidney combined chip; Monitor the pH value changes around the activated electrode in real time, and automatically adjust the activation state of the corresponding electrode when the pH value reaches the target range; Based on the pH value change trend and electrode position information, the electrode activation sequence and voltage parameters are dynamically optimized to achieve regulation of pH values ​​in different areas of the liver-kidney combined chip.

6. The parameter adjustment method of the liver-kidney combined chip according to claim 5, characterized in that: The step of dynamically optimizing the electrode activation sequence and voltage parameters based on the pH value change trend and electrode position information comprises: Obtain the pH change characteristics of each area in the liver-kidney combined chip, including the change rate and gradient distribution; Based on the pH value variation characteristics and electrode position information, a multidimensional evaluation matrix reflecting the regulation priority of each area is established; Determining the priority of electrode activation according to the multidimensional evaluation matrix, and calculating the voltage parameters required for each electrode to generate an adaptive local electric field strength; When it is detected that the pH value change rate in a certain area exceeds the preset threshold, the activation priority and voltage parameters of the corresponding electrode in the area are dynamically adjusted to achieve rapid response to the sharply changing area.

7. The parameter adjustment method of the liver-kidney combined chip according to claim 6, characterized in that: The step of determining the priority of electrode activation based on the multidimensional evaluation matrix and calculating the voltage parameters required for each electrode comprises: Determine the initial activation priority order according to the evaluation score of each electrode in the multidimensional evaluation matrix, and consider the spatial distance between adjacent electrodes. When it is detected that the distance between adjacent electrodes is less than a preset threshold, adjust the activation order of these electrodes to avoid simultaneous activation; Based on the adjusted activation sequence and the pH value change characteristics of the area where each electrode is located, the initial voltage parameters are calculated, and the parameters are corrected considering the mutual influence between the electrodes to obtain the final voltage parameters.

8. The parameter adjustment method of the liver-kidney combined chip according to claim 7, characterized in that: The steps of calculating the initial voltage parameters based on the adjusted activation sequence and the pH value variation characteristics of the regions where the electrodes are located, and correcting the parameters by considering the mutual influence between the electrodes to obtain the final voltage parameters include: Based on the pH value variation characteristics of each electrode area and the target pH value range, a voltage-pH response model is established to calculate the initial voltage parameters required to achieve the target pH value; Constructing an electric field superposition influence model that takes into account the geometric layout of the electrode array and the electrode spacing, and inputting the initial voltage parameters into the model to calculate the electric field interference intensity between adjacent electrodes; The initial voltage parameters are dynamically corrected according to the electric field interference intensity to obtain final voltage parameters.

9. The parameter adjustment method of the liver-kidney combined chip according to claim 1, characterized in that: The step of calculating the electrode activation scheme based on the pH value change trend comprises: Obtain the pH value P(x, y, t) and pH value change rate ΔP(x, y, t) at the position (x, y) in the liver-kidney combined chip at the current time t; Based on the current pH value and change rate, the pre-established cell metabolism model is used as the prediction function f to calculate the predicted pH value P_pred(x, y, t+Δt) at the next time t+Δt; Calculate the effective voltage V_eff(x, y), where V_eff(x, y) = V(x, y) + ∑[i≠(x,y)] α(d_i) *V_i, V(x, y) is the voltage of the electrode itself, V_i is the voltage parameter of the adjacent electrode i, α(d) is the distance attenuation function, and d_i is the distance between adjacent electrodes; Construct a multi-objective optimization function O = w1*g1(t)*|P_target - P_pred| + w2*g2(t)*E + w3*g3(t)*U, where P_target is the target pH value, E is the energy consumption, U is the electrode usage, g1(t), g2(t), and g3(t) are weight functions dynamically adjusted according to the current experimental stage, w1 is the weight coefficient of the pH adjustment effect, w2 is the weight coefficient of the energy consumption, and w3 is the weight coefficient of the electrode usage; Calculate the effect of the electric field on cell activity C(x, y, t) = h(V_eff(x, y), t), where h is the pre-established electric field-cell activity effect model; Under the constraints of V_min ≤ V(x, y) ≤ V_max, ∑A(x, y) ≤ N_max and C(x, y, t) ≥ C_threshold, the optimal electrode activation scheme A(x, y) and the corresponding voltage parameter V(x, y) are determined by minimizing the optimization function O, where V_min and V_max are the voltage ranges, N_max is the maximum number of activated electrodes, and C_threshold is the minimum cell activity threshold.

10. A parameter adjustment device for a liver-kidney combined chip, characterized in that: The device includes: An acquisition module is used to acquire electrochemical signals collected by the electrode array in the liver-kidney combined chip, wherein the electrochemical signals reflect the pH value changes during cell metabolism; A first calculation module, used for calculating the pH value change trend of each area in the liver-kidney combined chip according to the electrochemical signal; A second calculation module, used for calculating an electrode activation scheme based on the pH value change trend, wherein the electrode activation scheme is used for determining electrodes to be activated and their corresponding voltages; The control module is used to control specific electrodes in the electrode array to generate a local electric field according to the electrode activation scheme to affect ion migration and thus adjust the pH value in the liver-kidney combined chip.

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