A method and device for parameter adjustment of a combined liver and kidney chip

Abstract

The present application provides a method and device for adjusting parameters of a liver-kidney combined chip, relating to the technical field of microfluidic chips. The key points of its technical solution are: obtaining an electrochemical signal collected by an electrode array in the liver-kidney combined chip, where the electrochemical signal reflects the pH value change during the cell metabolism process; calculating the pH value change trend of each region in the liver-kidney combined chip according to the electrochemical signal; calculating an electrode activation scheme based on the pH value change trend, where the electrode activation scheme is used to determine the electrodes to be activated and their corresponding voltages; and controlling specific electrodes in the electrode array to generate a local electric field according to the electrode activation scheme to affect ion migration and thereby adjust the pH value in the liver-kidney combined chip. The method and device for adjusting parameters of a liver-kidney combined chip provided by the present application have the advantage 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.

Description

Technical Field

[0001] This application relates to the technical field of microfluidic chips, and more specifically, to a method and device for adjusting the parameters of a combined liver and kidney chip. Background Art

[0002] In laboratories specializing in drug metabolism and toxicity research, researchers widely use combined liver and kidney chips for long-term drug studies. This chip adopts a three-dimensional structure of microfluidic channels, aiming to simulate the complex liver and kidney metabolic processes in the human body. However, as the experimental duration extends, the accumulation of cell metabolites in the chip leads to a significant change in pH value. This change in pH value not only affects the cell activity but also interferes with the metabolic processes of some pH-sensitive drugs, thus affecting the accuracy and reliability of experimental results.

[0003] To maintain the accuracy of the experiment, researchers need to monitor and adjust the pH value in the chip in real time. However, traditional pH adjustment methods have many problems. First, introducing additional buffer solutions 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 introduced liquid may change the hydrodynamic characteristics in the chip, which is disadvantageous for microfluidic chips that precisely simulate the human body environment.

[0004] In view of the above problems, there is an urgent need for improvement in the prior art. Summary of the Invention

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

[0006] This application provides a method for adjusting the parameters of a combined liver and kidney chip, and the technical solution is as follows:

[0007] The steps of this method include: obtaining the electrochemical signals collected by the electrode array in the combined liver and kidney chip, where the electrochemical signals reflect the pH value changes during the cell metabolic process; calculating the pH value change trend of each region in the combined liver and kidney chip according to the electrochemical signals; calculating an electrode activation scheme based on the pH value change trend, where the electrode activation scheme is used to determine the electrodes that need to be activated and their corresponding voltages; and controlling 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 combined liver and kidney chip.

[0008] Furthermore, the present application also proposes that the steps of calculating the electrode activation scheme based on the pH value change trend include: 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, where each scheme includes the electrode combination to be activated and its corresponding voltage parameters; evaluating the candidate electrode activation schemes to obtain a multi-dimensional evaluation result, and the evaluation considers the pH adjustment effect, energy consumption, and the impact on cell activity; based on the multi-dimensional evaluation result, selecting the scheme with the optimal comprehensive performance as the final electrode activation scheme for subsequent precise pH value adjustment.

[0009] Furthermore, the present application also proposes that the steps of evaluating the candidate electrode activation schemes to obtain a multi-dimensional evaluation result include: establishing a multi-dimensional evaluation model that comprehensively considers the pH adjustment effect, energy consumption, and the impact on cell activity, where: the pH adjustment effect is characterized by a comprehensive index of the pH value correction speed and stability in the target area; the energy consumption is quantified by the functional relationship between the electrode activation parameters and the power consumption; the impact on cell activity is evaluated by the degree of influence of the electric field on the cell physiological state; when inputting the candidate scheme parameters, dynamically adjust the weight coefficients of each evaluation dimension according to the current experimental stage, increasing the weight coefficient of the pH adjustment effect in the initial stage of drug metabolism and increasing the weight coefficient of cell activity protection in the long-term stability stage.

[0010] Furthermore, the present application also proposes that the steps of dynamically adjusting the weight coefficients of each evaluation dimension include: obtaining the cell metabolite concentration, cell proliferation rate, and pH value fluctuation amplitude as experimental stage judgment indicators; based on a preset judgment rule, determining the current experimental stage according to the judgment indicators, where when the change rate of the cell metabolite concentration and the pH value fluctuation amplitude are lower than their respective preset thresholds and the cell proliferation rate is stable, it is determined as the long-term stability stage, otherwise it is determined as the initial stage of drug metabolism; according to the determined experimental stage, based on a preset weight coefficient mapping relationship, determining the weight coefficients of the three evaluation dimensions of the pH adjustment effect, energy consumption, and the impact on cell activity.

[0011] Furthermore, the present application also proposes that the step of controlling specific electrodes in the electrode array to generate a local electric field for influencing ion migration and thus regulating the pH value in the combined liver and kidney chip according to the electrode activation scheme includes: sequentially activating specific electrodes in the electrode array and applying specific voltages according to the electrode activation sequence and corresponding voltage parameters in the electrode activation scheme to generate a local electric field for influencing ion migration and thus regulating the pH value in the combined liver and kidney chip; monitoring in real time the change in the pH value 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; dynamically optimizing 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 in different regions of the combined liver and kidney chip.

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

[0013] Furthermore, the present application also proposes that the step of determining the priority order of electrode activation according to the multi-dimensional evaluation matrix and calculating the voltage parameters required for each electrode includes: determining the initial activation priority order according to the evaluation scores of each electrode in the multi-dimensional evaluation matrix, and at the same time considering the spatial distance between adjacent electrodes. 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 regions where each electrode is located, and correcting the parameters considering the mutual influence between electrodes to obtain the final voltage parameters.

[0014] Further, the present application also proposes that the steps of calculating the initial voltage parameters based on the adjusted activation order and the pH value change characteristics of the regions where each electrode is located, and performing parameter correction considering the mutual influence between electrodes to obtain the final voltage parameters include: establishing a voltage-pH response model based on the pH value change characteristics of the regions 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 considering 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] Further, the present application also proposes that the steps of calculating the electrode activation scheme based on the pH value change trend include: 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 moment t; based on the current pH value and the change rate, using the pre-established cell metabolism model as the prediction function f to calculate the predicted pH value P_pred(x, y, t+Δt) at the next moment 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 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; 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; calculating the influence 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 influence model; under the constraint conditions of V_min ≤ V(x, y) ≤ V_max, ∑A(x, y) ≤ N_max, and C(x, y, t) ≥ C_threshold, determining the optimal electrode activation scheme A(x, y) and the corresponding voltage parameter V(x, y) 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.

[0016] Furthermore, the present application also proposes a parameter adjustment device for a combined liver and kidney chip, which includes: an acquisition module for acquiring the electrochemical signals collected by the electrode array in the combined liver and kidney chip, where the electrochemical signals reflect the pH value changes during the cell metabolism process; a first calculation module for calculating the pH value change trend of each region in the combined liver and kidney chip according to the electrochemical signals; a second calculation module for calculating an electrode activation scheme based on the pH value change trend, where the electrode activation scheme is used to determine the electrodes to be activated and their corresponding voltages; and a control module for 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 combined liver and kidney chip.

[0017] As can be seen from the above, a parameter adjustment method and device for a combined liver and kidney chip provided by the present application, by acquiring the electrochemical signals collected by the electrode array, calculating the pH value change trend, calculating the electrode activation scheme based on the trend, and controlling specific electrodes to generate a local electric field to adjust the pH value, realizes the precise monitoring and dynamic adjustment of the pH value of the microenvironment in the combined liver and kidney chip without introducing additional substances, while maintaining cell activity and drug stability, and has the advantages of being able to precisely monitor and dynamically adjust the pH value of the microenvironment in the combined liver and kidney chip without introducing additional substances, while maintaining cell activity and drug stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a parameter adjustment method for a combined liver and kidney chip provided by the present application.

[0019] Figure 2 It is a schematic structural diagram of a parameter adjustment device for a combined liver and kidney chip provided by the present application.

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

[0021] Next, the technical solutions in the present application will be clearly and completely described 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. Usually, the components of the present application 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 required to be protected, but only 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 creative efforts fall within the scope of protection of the present application.

[0022] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, 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 the present application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0023] In laboratories specializing in drug metabolism and toxicity, the liver-kidney combined chip is widely used in long-term drug research. The chip simulates the complex liver-kidney metabolic processes in the human body through the three-dimensional structure of microfluidic channels. However, as the experimental duration extends, the metabolites of cells in the chip continuously accumulate, resulting in a significant change in pH value. This pH change not only affects cell activity but also interferes with the metabolic processes of some pH-sensitive drugs, thus affecting the accuracy and reliability of the experiment. Therefore, achieving precise monitoring and dynamic regulation of the pH value in the microenvironment of the liver-kidney combined chip has become a technical problem to be solved urgently.

[0024] In response to this, referring to Figure 1 , the present application proposes a method for adjusting the parameters of a liver-kidney combined chip. The steps of this method include:

[0025] S110. Obtain the electrochemical signals collected by the electrode array in the liver-kidney combined chip. The electrochemical signals reflect the pH value changes during the cell metabolic process;

[0026] S120. Calculate the pH value change trend of each region in the liver-kidney combined chip according to the electrochemical signals;

[0027] S130. Calculate the electrode activation scheme based on the pH value change trend. The electrode activation scheme is used to determine the electrodes that need to be activated and their corresponding voltages;

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

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

[0030] Among them, the electrochemical signal refers to the electrical signal generated at the interface between the electrode and the solution due to electrochemical reactions or ion migration, which reflects the pH value changes during the cell metabolism process. Specifically, these signals can be obtained by measuring the changes in electrode potential, current, or impedance. These signals provide the basic data for real-time monitoring of the pH value, avoiding the interference that may be caused by introducing additional pH sensors.

[0031] Among them, the pH value change trend calculation refers to obtaining the variation law of the pH value in each region within the combined liver and kidney 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 scheme, ensuring the accuracy and timeliness of pH value regulation.

[0032] Among them, the electrode activation scheme refers to the specific plan for determining the electrodes to be activated and their corresponding voltages according to the pH value change trend. This scheme takes into account the spatial distribution and temporal dynamics of the pH value changes. By selectively activating specific electrodes and applying appropriate voltages, precise regulation of the local pH value is achieved.

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

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

[0035] The working principle of this application is as follows:

[0036] First, the electrode array collects electrochemical signals, which reflect the pH value status of each region within the chip. Signal collection can be achieved by measuring the changes in electrode potential, current, or impedance, and the collection frequency can be adjusted according to experimental requirements, such as collecting once every 5 minutes.

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

[0038] Based on the calculated pH value change trend, the system generates an electrode activation scheme. This scheme determines the electrodes to be activated and their corresponding voltage parameters. The generation of the scheme takes into account multiple factors, including the target pH value, the current pH value, the pH value change rate, the electrode position, etc. The optimization algorithm weighs the adjustment 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 corresponding voltages to generate local electric fields. These local electric fields regulate the pH value by affecting the migration of ions in the solution. The intensity and distribution of the electric fields are determined by the positions of the electrodes and the applied voltages, enabling precise regulation of the pH value in specific regions.

[0040] The whole process forms a continuous feedback loop: the change in pH value leads to a change in the electrochemical signal, the signal analysis yields the pH value change trend, the trend analysis generates the electrode activation scheme, the scheme execution produces local electric fields, and the local electric fields affect the pH value. This closed-loop control ensures that the system can continuously and dynamically regulate the pH value within the chip, maintaining the stability of the microenvironment.

[0041] As a preferred implementation, the following specific embodiments can be used to implement the technical solution of this 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 at the bottom of the chip through microfabrication technology.

[0043] The cyclic voltammetry method is used for the acquisition of electrochemical signals. The scanning voltage range is from -0.5V to 0.5V, the scanning rate is 50mV / s, and a complete scan is performed every 5 minutes. The collected current-voltage curve is converted into a pH value through a data processing algorithm.

[0044] The sliding window method is used for the calculation of the pH value change trend, and the window size is 30 minutes. For each electrode position, the linear regression slope of the pH value within 30 minutes is calculated as the pH value change trend at that position.

[0045] The genetic algorithm is used for the generation of the electrode activation scheme, and the objective function comprehensively considers the pH adjustment effect, energy consumption, and the impact on cell activity. The algorithm output includes the electrode numbers to be activated and their corresponding voltage values.

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

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

[0048] In this way, the system can achieve precise monitoring and dynamic adjustment of the pH value in the liver-kidney combined chip without introducing additional substances, providing a stable microenvironment for long-term drug metabolism research.

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

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

[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 the detected pH value change trend exceeds the preset threshold, the system generates multiple candidate electrode activation schemes according to the current pH value change trend. Each scheme includes the electrode combination to be activated and its corresponding voltage parameters, which provides a basis for subsequent evaluation and selection.

[0052] Multiple strategies can be adopted in the process of generating multiple candidate schemes. For example, based on historical data and machine learning algorithms, the impact of different electrode combinations and voltage parameters on the pH value can be predicted, thereby generating a series of potentially effective schemes. Another method is to use genetic algorithms to gradually optimize the electrode combination and voltage parameters by simulating the evolution process, generating multiple potential high-quality schemes.

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

[0054] During the evaluation process, a weighted scoring system can be adopted to assign weights to each factor and comprehensively calculate the total score of each scheme. The weight assignment can be dynamically adjusted according to the experimental stage and specific requirements. For example, in the initial stage of drug metabolism, more attention may be paid to the pH regulation effect, while in the long-term culture stage, more emphasis may be placed on protecting cell viability.

[0055] Based on the multi-dimensional evaluation results, the system will select the scheme with the best comprehensive performance as the final electrode activation scheme. This selection process can be achieved using decision trees or multi-objective optimization algorithms to ensure that the selected scheme achieves the best balance in terms of pH regulation effect, energy efficiency, and cell viability protection.

[0056] Through the above steps, the technical solution of this application realizes that when the detected pH value change trend exceeds the preset threshold, multiple feasible adjustment schemes are quickly generated and evaluated, so as to select the most suitable electrode activation scheme for the current situation. This method not only improves the accuracy and efficiency of pH value regulation, but also minimizes the adverse impact on cell viability while optimizing energy use.

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

[0058] First, set the preset threshold for the pH value change trend. This threshold can be adjusted according to experimental requirements and cell types. For example, it can be set that the pH value changes by more than 0.1 unit per hour. When the system detects that the pH value change trend exceeds this threshold, the generation process of multiple candidate electrode activation schemes will be triggered.

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

[0060] For each candidate solution, the system conducts a multi-dimensional evaluation. The pH regulation effect can be calculated through a prediction model that takes into account factors such as electrode position, voltage parameters, and the current pH distribution. The energy consumption assessment can be calculated based on the number of electrodes, voltage magnitude, and expected activation time. The impact on cell viability can be evaluated through an established electric field strength-cell viability relationship model, which can consider the sensitivity of different cell types to the electric field.

[0061] During the evaluation process, the system uses a dynamic weight assignment mechanism. For example, in the initial stage of the experiment, the weight of the pH regulation effect may be set to 0.5, the energy consumption to 0.3, and the impact on cell viability to 0.2. As the experiment progresses, if a decrease in the cell proliferation rate is detected, the system will automatically increase the weight of the impact on cell viability, perhaps adjusted to 0.4, while correspondingly reducing the weights of other factors.

[0062] Finally, the system uses a multi-objective optimization algorithm, such as NSGA-II (Non-dominated Sorting Genetic Algorithm II), to select the optimal solution. This algorithm can find the best balance among multiple objectives, and the selected solution can effectively regulate the pH value while minimizing energy consumption and the impact on cell viability.

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

[0064] In some of the above embodiments of this application, an evaluation of candidate electrode activation solutions is proposed to select the solution with the optimal comprehensive performance as the final electrode activation solution. However, in this process, there is a lack of comprehensive consideration of multiple evaluation dimensions, making it difficult to dynamically adjust the evaluation focus at different experimental stages, which may lead to the selected solution not being able to meet the different requirements of the experiment.

[0065] In response to this, this application further proposes to establish a multi-dimensional evaluation model that comprehensively considers the pH regulation effect, energy consumption, and the impact on cell viability, where: the pH regulation effect is characterized by a comprehensive index of the pH value correction speed and stability in the target area; the energy consumption is quantified by the functional relationship between electrode activation parameters and power consumption; the impact on cell viability is evaluated by the degree of influence of the electric field on the cell physiological state; when inputting candidate solution parameters, according to the current experimental stage, the weight coefficients of each evaluation dimension are dynamically adjusted, increasing the weight coefficient of the pH regulation effect in the initial stage of drug metabolism and increasing the weight coefficient of cell viability protection in the long-term stability stage.

[0066] The multi-dimensional evaluation model established in this application comprehensively considers three key dimensions: the pH regulation effect, energy consumption, and the impact on cell activity. The pH regulation effect is characterized by a comprehensive index of the pH value correction speed and stability in the target area, which can be achieved by measuring the rate of pH value change 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 the pH value in the stable state, and α and β are weight coefficients.

[0067] The quantification of energy consumption can be achieved by establishing a functional relationship between the electrode activation parameters and the power consumption. Specifically, the power consumption under different combinations of voltage and current can be measured through experiments, and then a function model E = f(V, I) can be established, where V is the voltage and I is the current. This model can be linear or non-linear, depending on the actual electrode characteristics.

[0068] The evaluation of the impact on cell activity can be achieved by observing the degree of influence of the electric field on the cell physiological state. Methods such as cell viability staining or metabolic activity measurement can be used to measure cell activity under different electric field intensities, and a relationship model C = g(E) between the electric field intensity and cell activity can be established, where E is the electric field intensity.

[0069] The mechanism for dynamically adjusting the weight coefficients is an innovation point 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 the pH regulation effect, energy consumption, and the impact on cell activity respectively, and t represents the experimental stage. In the initial stage of drug metabolism, w1(t) can be set to be larger, while in the long-term stability stage, the value of w3(t) is increased. This dynamic adjustment can be triggered by monitoring indicators such as the concentration of cell metabolites and the cell proliferation rate.

[0070] For example, in a practical application, a 10x10 microelectrode array is implanted in a liver-kidney combined chip, the size of each electrode is 50μm x 50μm, and the electrode spacing is 200μm. The system first collects pH value data through an electrochemical sensor and finds that in the initial stage of drug metabolism (within 2 hours after the experiment starts), the pH value in a certain area of the chip rapidly drops 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, this scheme recommends activating four adjacent electrodes (3,3), (3,4), (4,3), and (4,4), and applying a voltage range of 0.5V - 1.5V. The system then uses the multi-dimensional evaluation model to evaluate this scheme:

[0072] pH adjustment effect evaluation: Through simulation calculations, it is predicted that this solution 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 (full score is 1).

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

[0074] Cell viability impact evaluation: Based on the electric field-cell viability impact model, it is predicted that the cell viability retention rate under this solution is 95%, and the cell viability score SC = 0.95 is obtained.

[0075] Considering that it is currently in the initial stage of drug metabolism, the system dynamically adjusts the weight coefficients to W(t) = [0.5, 0.3, 0.2]. Finally, the comprehensive score of this solution is:

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

[0077] The system conducts similar evaluations 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 the pH value changes and cell status and dynamically adjusts the electrode activation parameters as needed.

[0078] In this way, the technical solution of this application can quickly adjust the pH value in the initial stage of drug metabolism, while maintaining high cell viability and low energy consumption throughout the experiment. This method of dynamic adjustment and multi-dimensional evaluation 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 this application, a method of dynamically adjusting the weight coefficients of each evaluation dimension is proposed to optimize the evaluation process of the electrode activation solution. However, in this process, there is a lack of accurate judgment of the experimental stage and corresponding weight adjustment strategies. This may lead to the use of inappropriate evaluation criteria in different experimental stages, affecting the optimization effect of the electrode activation solution. Especially in the initial stage of drug metabolism and the long-term stability stage, the importance evaluation of the pH adjustment effect, energy consumption, and cell viability impact may be biased, thus affecting the overall accuracy and efficiency of the experiment.

[0080] In response to this, the present application further proposes to obtain the concentration of cell metabolites, the cell proliferation rate, and the pH value fluctuation range as the judgment indicators for the experimental stage; based on the preset judgment rules, determine the current experimental stage according to the judgment indicators, where when the change rate of the cell metabolite concentration and the pH value fluctuation range are lower than their respective preset thresholds and the cell proliferation rate is stable, it is determined as the long-term stability stage, otherwise it is determined as the initial 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 impact.

[0081] By obtaining the concentration of cell metabolites, the cell proliferation rate, and the pH value fluctuation range as judgment indicators, the present application provides multi-dimensional data support for the determination of the experimental stage. These indicators can comprehensively reflect the cell metabolic 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 cell metabolites can be monitored in real time through the electrochemical sensors integrated in the microfluidic chip. For example, electrodes modified with glucose oxidase can be used to detect the glucose concentration, or electrodes modified with lactate oxidase can be used to detect the lactate concentration. The concentration changes of these metabolites directly reflect the intensity of cell metabolic activities.

[0083] The cell proliferation rate can be monitored through a real-time cell analyzer or fluorescence labeling technology. For example, a microelectrode array can be integrated in the chip, and the change in electrode impedance can be measured to evaluate the increase in cell number. Or, a fluorescence-labeled cell cycle indicator can be used, and the change in fluorescence intensity can be used to judge the frequency of cell division.

[0084] The pH value fluctuation range can be measured through a micro pH electrode or a pH-sensitive fluorescent dye. For example, a pH-sensitive ion-selective field-effect transistor (ISFET) can be embedded at key positions in the chip to achieve high-precision, real-time pH value monitoring.

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

[0086] Furthermore, the judgment rule can be designed as a multi-level decision tree structure. First, check whether the change rate of the cell metabolite concentration is lower than a preset threshold, for example, the change rate per hour is less than 5%. Second, evaluate whether the fluctuation range of the pH value 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 change in the number of cells does not exceed 10% within 72 consecutive hours. Only when all three conditions are met simultaneously is it determined to enter the long-term stability stage.

[0087] According to the determined experimental stage, the weight coefficients of the evaluation dimensions are determined using a 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 different stages of the experimental process, thereby optimizing the evaluation process of the electrode activation scheme.

[0088] Thus, in the initial stage of drug metabolism, the weight coefficient of the pH adjustment effect can be set to 0.6, the energy consumption to 0.3, and the impact on cell activity to 0.1. This is because in the initial stage, rapid and precise pH adjustment is crucial for maintaining the cell metabolic environment. In the long-term stability stage, the weight coefficients can be adjusted to: pH adjustment effect 0.3, energy consumption 0.2, and impact on cell activity 0.5. This adjustment reflects that protecting cell activity becomes the primary consideration in long-term experiments.

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

[0090] First, integrate a variety of sensors in the liver-kidney combined chip, including an electrochemical sensor for detecting the cell metabolite concentration, a microelectrode array for monitoring the cell proliferation rate, and a pH microelectrode for measuring the local pH value. These sensors are connected to the cell culture area through a microfluidic channel to ensure real-time and non-invasive data collection.

[0091] Second, set the specific parameters of the judgment rule. For example, set the threshold of the change rate of the cell metabolite concentration to 5% per hour, the threshold of the fluctuation range of the pH value to ±0.2 pH units within 24 hours, and the standard for the stability of the cell proliferation rate to that the change in the number of cells does not exceed 10% within 72 hours.

[0092] Third, establish a weight coefficient mapping relationship. In the initial stage of drug metabolism, set the weight coefficients of the pH adjustment effect, energy consumption, and impact on cell activity to 0.6, 0.3, and 0.1 respectively. In the long-term stability stage, adjust these weight coefficients 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 a stage transition is detected, the adjustment of the weight coefficient is immediately triggered. For example, when the system observes that the change rate of the cell metabolite concentration is less than 5% / hour, the pH value fluctuation range is within ±0.2, and the change in the number of cells does not exceed 10% for 24 consecutive hours, 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 extends 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 this application, it is proposed 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 then regulate 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 in a timely manner to the real-time changes in the pH value in the chip; 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 a real-time feedback and optimization mechanism for the electrode activation effect may lead to inaccurate or inefficient pH value regulation.

[0096] In response to this, the steps of this application for further controlling specific electrodes in the electrode array to generate a local electric field according to the electrode activation scheme to affect ion migration and then regulate the pH value in the liver-kidney combined chip include: According to the electrode activation sequence and the corresponding voltage parameters in the electrode activation scheme, sequentially activate the specific electrodes in the electrode array and apply a specific voltage to generate a local electric field to affect ion migration and then regulate the pH value in the liver-kidney combined chip; Real-time monitor the pH value change around the activated electrode, and when the pH value reaches the target range, automatically adjust the activation state of the corresponding electrode; Based on the pH value change trend and the electrode position information, dynamically optimize the electrode activation sequence and voltage parameters to achieve the regulation of the pH value in different regions of the liver-kidney combined chip.

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

[0098] Sequentially activate specific electrodes and apply specific voltages: This step ensures the orderliness and accuracy of electrode activation.

[0099] Real-time monitoring of pH value 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 value changes in different regions.

[0101] Activating the electrodes in sequence provides the basis for the whole 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. Such a system can flexibly adjust the control strategy according to the pH value change characteristics in different regions of the chip, so as to achieve more accurate and efficient pH value regulation.

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

[0103] In actual operation, the system will first calculate an initial electrode activation sequence according to the initial pH value distribution. For example, assuming that the pH value in 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 activating, a voltage of 1V can be applied first for a duration of 100ms. Subsequently, the system will monitor the pH value change 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 will continuously monitor the pH value distribution of the whole chip and update the electrode activation priority every 5 seconds. For example, if it is detected that the pH value in another area suddenly rises to 7.8, the system will immediately adjust the activation sequence and preferentially activate the electrodes in this area. This dynamic adjustment ensures that the system can respond in a timely manner to the pH value changes in each area of the chip.

[0105] Through this precise pH value regulation, the technical solution of the present application can effectively maintain the microenvironment stability in the combined liver and kidney chip. This not only improves the reliability of long-term drug metabolism experiments, but also can better simulate 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 solution is avoided, this method will not dilute the nutrients and the drugs to be tested in the culture medium, nor will it change the hydrodynamic characteristics in the chip, thus 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 values in different regions of the combined liver and kidney chip. However, in this process, how to establish a reasonable evaluation mechanism according to the pH value change characteristics and electrode position information obtained in real time, and accordingly dynamically adjust the electrode activation strategy to achieve precise regulation of the pH values in different regions of the chip is still a technical problem to be solved.

[0107] In response to this, the present application further proposes steps for dynamically optimizing the electrode activation sequence and voltage parameters based on the pH value change trend and electrode position information. The steps include obtaining the pH value change characteristics of each region in the combined liver and kidney chip, including the change rate and gradient distribution; establishing a multi-dimensional evaluation matrix reflecting the adjustment priority of each region based on the pH value change characteristics and electrode position information; determining the priority order of electrode activation according to the multi-dimensional evaluation matrix, and calculating the voltage parameters required for each electrode to generate an adaptive local electric field intensity; when it is detected that the pH value change rate in a certain region exceeds a preset threshold, dynamically adjusting the activation priority and voltage parameters of the corresponding electrode in that region to achieve a 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 region in the combined liver and kidney chip. The change characteristics include not only the change rate of the pH value but also the gradient distribution, which enables the system to comprehensively grasp the dynamic change of the pH value in the chip. Based on these data and electrode position information, a multi-dimensional evaluation matrix reflecting the adjustment priority of each region is established. This step realizes the quantitative evaluation of the pH value regulation requirements in different regions of the chip and provides a scientific basis for subsequent decision-making.

[0109] The multi-dimensional evaluation matrix can be constructed by using the weighted summation method, taking factors such as the pH value change rate, gradient magnitude, and electrode position as evaluation indicators, assigning different weights, and calculating the comprehensive score of each region.

[0110] The process of determining the priority order of electrode activation and calculating the voltage parameters according to the multi-dimensional evaluation matrix reflects the core intelligent decision-making mechanism of this solution. This mechanism can dynamically adjust the electrode activation strategy according to real-time data to generate an adaptive local electric field intensity, thereby achieving precise regulation of the pH values in different regions. The determination of the electrode activation priority order can be based on the score ranking in the evaluation matrix, and the calculation of the 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 worth noting that this solution also includes a rapid response mechanism. When the pH change rate in a certain area is detected to exceed the preset threshold, the system can dynamically adjust the activation priority and voltage parameters of the corresponding electrodes in that area. Specifically, it can be to increase its priority and voltage parameters. This feature greatly improves the response speed and regulation accuracy of the system to rapidly changing areas. The preset threshold can be determined based on the physiological tolerance range of cells and experimental requirements, and the adjustment of priority can be achieved by increasing the weight of that 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 the pH value in different areas of the liver-kidney combined chip. This not only ensures the stability of the microenvironment in 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 rapid changes, this solution also greatly reduces the risk of adverse effects on cell activity and drug stability caused by pH fluctuations. These advantages make this technical solution have broad application prospects in fields such as drug research and development and toxicology research.

[0113] In some of the above embodiments of this application, a method is proposed to determine the priority order of electrode activation based on a multi-dimensional evaluation matrix and calculate the voltage parameters required for each electrode to optimize the electrode activation scheme. However, in this process, the situation where adjacent electrodes are activated simultaneously may occur, resulting in an overly strong local electric field and affecting cell activity. At the same time, simply determining the activation order based on the evaluation score may ignore the spatial relationship between electrodes and cannot fully consider the mutual influence between electrodes, thus affecting the accuracy and efficiency of pH regulation.

[0114] In response to this, this application further proposes to determine the initial activation priority order according to the evaluation scores of each electrode in the multi-dimensional evaluation matrix, and at the same time consider the spatial distance between adjacent electrodes. When it is detected that the distance between adjacent electrodes is less than the preset threshold, adjust the activation order of these electrodes to avoid simultaneous activation; based on the adjusted activation order and the pH change characteristics of the areas where each electrode is located, calculate the initial voltage parameters, and consider the mutual influence between electrodes for parameter correction to obtain the final voltage parameters.

[0115] Specifically, using the multi-dimensional evaluation matrix to determine the initial activation priority order can improve the comprehensive performance of the electrode activation scheme by considering multiple factors (such as pH regulation effect, energy consumption, and impact on cell activity). This step can be achieved by various methods such as the weighted scoring method or the fuzzy comprehensive evaluation method. For example, weights can be assigned to each factor, and then the comprehensive score of each electrode can be calculated to determine the initial activation priority order.

[0116] Considering the spatial distance between adjacent electrodes is an important innovation point of this application. By setting a preset threshold, it is possible to avoid the problem of excessive local electric field caused by simultaneous activation of adjacent electrodes, which helps to protect cell viability. 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 the distance between adjacent electrodes is detected to be less than this threshold, the next adjustment process will be triggered.

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

[0118] Calculating the initial voltage parameter is based on the adjusted activation order and the pH value change characteristics. This step can use a pre-established voltage-pH response model, which can be obtained by fitting 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 parameter required to reach the target can be calculated.

[0119] Considering the mutual influence between electrodes for parameter correction is another innovation point of this application. This step can be achieved by constructing an electric field superposition influence model. For example, the finite element analysis or the analytical solution method can be used to simulate the composite electric field generated by multiple electrodes. By inputting the initial voltage parameter into this model, the electric field interference intensity between adjacent electrodes can be calculated, and the voltage parameter can be dynamically corrected accordingly.

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

[0121] The chip is provided with a 10×10 electrode array, the electrode diameter is 100 microns, and the center distance between adjacent electrodes is 200 microns. The preset threshold is set to 150 microns. The multi-dimensional evaluation matrix includes three dimensions: pH adjustment effect, energy consumption, and cell viability impact, with weights of 0.5, 0.3, and 0.2 respectively.

[0122] First, calculate the comprehensive score of each electrode based on the multi-dimensional 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 order is A>B>C.

[0123] Then, check the spatial distance between adjacent electrodes. It is found that the distance between electrodes A and B is 200 micrometers, which is greater than the preset threshold of 150 micrometers, so no adjustment is required. However, the distance between electrodes B and C is 141 micrometers (√2 × 100 micrometers), which is less than the preset threshold, and the activation order needs to be adjusted. The adjusted order becomes A > C > B.

[0124] Next, use the voltage-pH response model to calculate the initial voltage parameters. Assume 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 and the current pH is 7.2, and the calculated initial voltage is 1.5V.

[0125] Finally, input the initial voltage parameters into the electric field superposition influence model. The model shows that the electric field of electrode A has a 10% influence on the surrounding electrodes. Accordingly, the voltage parameters of electrode C are corrected, and its initial voltage of 1.3V is adjusted to 1.17V.

[0126] Through this method, the technical solution of this application realizes the precise regulation of the pH value in the liver-kidney combined chip, while avoiding the adverse effects of excessive local electric field on cell activity. Compared with the traditional method, the solution of this 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 this application, based on the adjusted activation order and the pH value change characteristics in the regions where each electrode is located, the initial voltage parameters are calculated, and the parameter correction is carried out considering the mutual influence between electrodes to obtain the final voltage parameters for determining the priority order 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 electrodes, resulting in inaccurate final voltage parameters. 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 considered, it may lead to uneven local electric field intensity, affecting the effect and accuracy of pH value regulation.

[0128] In response to this, this application further proposes to establish a voltage-pH response model based on the pH value change characteristics and the target pH value range in the regions where each electrode is located, calculate the initial voltage parameters required to reach the target pH value; construct an electric field superposition influence model considering the geometric layout of the electrode array and the electrode spacing, input the initial voltage parameters into this model, and 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 realizes the 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 change characteristics in the regions where each electrode is located and the target pH value range, 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 influence 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 neglecting the mutual influence between electrodes in traditional methods. The electric field superposition influence model can be implemented by finite element analysis or analytical methods, and the specific implementation method can be selected according to the specific structure of the chip and the computing resources.

[0131] The dynamic correction mechanism adjusts the initial voltage parameters according to the calculated electric field interference intensity, further improving the accuracy of the final voltage parameters. This dynamic adjustment can adapt to complex electrode array environments, ensuring that each electrode can generate the optimal local electric field intensity. The dynamic correction can be implemented by optimization methods such as iterative algorithms or gradient descent methods to minimize the impact of electric field interference on the target pH adjustment.

[0132] The technical solution of this application accurately calculates and optimizes the electrode activation parameters through a comprehensive method of multiple steps and multiple models, taking into account the mutual influence between electrodes. This not only improves the accuracy and efficiency of pH value adjustment, but also ensures the uniformity of the electric field distribution, thus achieving a more precise and controllable pH value 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 pH value changes under different voltages, a functional relationship between the voltage and the pH value change rate is established. This model can be implemented by polynomial fitting or machine learning methods (such as support vector regression) to accurately capture the non-linear relationship between the voltage and the pH value change.

[0134] The construction of the electric field superposition influence model needs to consider the geometric layout and spacing of the electrodes. The principle of electric field superposition can be used to regard the electric field generated by each electrode as a point charge, and then calculate the resultant electric field intensity at any point. For complex electrode arrays, the finite element analysis method can be used for more accurate simulation of the electric field distribution. The output of this model is a function describing the electric field distribution, which can be used to calculate the electric field interference intensity 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 non-linear correction (if the interference is large). The corrected voltage parameters are input into the model again, and this process is repeated until the electric field interference intensity is reduced to an acceptable level.

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

[0137] Furthermore, the electric field superposition influence model considers 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, this application can calculate the electric field interference intensity between adjacent electrodes, which is a key factor ignored by traditional methods. Thus, this application solves the problem of local electric field non-uniformity caused by the mutual influence between electrodes and lays a foundation for subsequent parameter optimization.

[0138] Finally, the application of the dynamic correction mechanism enables this 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 precision of the final voltage parameters and can adapt to the pH adjustment requirements in different regions and at different time points. In this way, this application realizes the fine regulation of the pH value in the entire liver-kidney combined chip and improves the accuracy and repeatability of the experiment.

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

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

[0141] Next, an electric field superposition influence model is constructed. Assume that the electrode array is a 4×4 square matrix with an electrode spacing of 500 μm. Use finite element analysis software (such as COMSOL Multiphysics) to simulate the electric field distribution, and obtain the electric field interference intensity function between any two electrodes: E(i, j) = k * (1 / r_ij^2), 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 parameters are set as V_0 = [1.2V, 1.5V, 1.8V, 2.0V] (corresponding to 4 electrodes to be activated). Input V_0 into the electric field superposition influence model to calculate the electric field interference intensity matrix. Based on this matrix, use the gradient descent method 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 with respect to voltage.

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

[0144] Through this method, the present application can achieve precise pH value regulation in the 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 non-uniformity on cells, and improves energy utilization efficiency at the same time. These improvements are of great significance for the accuracy and reliability of long-term drug metabolism experiments, and help to improve the predictive ability of in vitro experiments in the drug R & D process.

[0145] In some of the above embodiments of the present application, an electrode activation scheme is proposed based on the pH value change trend 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 viability, making it difficult to achieve precise, efficient, and cell-friendly pH value regulation.

[0146] In response to this, 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 the pH value change rate ΔP(x, y, t) at the position (x, y) in the liver-kidney combined chip at the current moment t;

[0148] Based on the current pH value and the rate of change, use the pre-established cell metabolism model as the prediction function f to calculate the predicted pH value P_pred(x, y, t+Δt) at the next moment 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), 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 influence of the electric field on cell viability C(x, y, t) = h(V_eff(x, y), t), where h is the pre-established electric field-cell viability influence model;

[0152] Under the constraint conditions 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) 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 viability threshold.

[0153] The technical solution of this application solves the above problems by introducing multiple key technical features. First, introduce the cell metabolism model prediction function f to accurately predict future pH value changes. This prediction function can be trained based on machine learning algorithms, with inputs including the current pH value, the rate of change of the pH value, and other relevant parameters, and the output is the predicted pH value at the next moment. This prediction mechanism can improve the forward-looking and accuracy of pH adjustment.

[0154] Furthermore, the present application takes into account the mutual influence between electrodes. By calculating the effective voltage V_eff(x, y), the voltage of the electrode itself and the influence of surrounding electrodes are comprehensively considered. The distance attenuation function α(d) can take forms such as exponential attenuation or inverse proportional attenuation 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 value regulation.

[0155] The present application also constructs a multi-objective optimization function O, comprehensively considering the pH regulation effect, energy consumption, and electrode usage. Among them, the term |P_target - P_pred| reflects the accuracy of pH regulation, 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 initial stage of the experiment to improve the pH regulation effect, while the weights of g2(t) and g3(t) can be increased in the long-term stable stage to reduce energy consumption and extend the electrode life.

[0156] To ensure the friendliness of the regulation process to cells, the present application introduces an electric field-cell activity influence model h. This model can be obtained by fitting in vitro experimental data and reflects the influence of different electric field strengths and action times on cell activity. By calculating C(x, y, t) and setting a minimum cell activity threshold C_threshold, cells can be effectively protected from damage by strong electric fields.

[0157] During the optimization process, the present application sets multiple constraint conditions. The voltage range V_min and V_max ensure that the electrodes operate within a safe voltage range; the maximum number of activated electrodes N_max can control energy consumption and avoid interference caused by simultaneous activation of too many electrodes; the minimum cell activity threshold C_threshold further ensures the safety of the regulation process.

[0158] Through the mutual cooperation of these technical features, the present application realizes precise, efficient, and cell-friendly regulation of the pH value in the liver-kidney combined chip. This solution not only considers the pH value change trend 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 predicts the future pH value by obtaining the current pH value and change rate and combining with a pre-established cell metabolism model. This prediction mechanism can identify potential pH abnormalities 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 electrodes in advance for regulation to avoid the adverse effects of drastic pH value fluctuations 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 cancellation 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 needs to be increased in a certain area, the system will not only consider directly activating the electrodes in that area, but also evaluate the influence of the surrounding electrodes and select the optimal combination of electrodes to achieve precise regulation.

[0161] The multi-objective optimization function O constructed in the present application is the core of achieving precise, efficient, and cell-friendly regulation. By dynamically adjusting the weight functions g1(t), g2(t), g3(t), the system can flexibly adjust the optimization strategy according to different requirements in 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; while in the long-term observation stage, 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 electrodes.

[0162] The present application also pays special attention to the influence of the electric field on cell activity. By introducing the electric field-cell activity influence model h, the system can evaluate the potential influence of electrode activation on cells in real time. This feature ensures that while adjusting the pH value, excessive electric field interference will not be caused 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 electrodes in that area, perhaps using milder voltage parameters or reducing the activation time to protect cell activity.

[0163] Finally, the multiple constraint conditions set in the present application further ensure the safety and effectiveness of the regulation process. The setting of the voltage range V_min and V_max avoids damage to cells and electrodes caused by too high or too low voltages; the limitation of the maximum number of activated electrodes N_max not only controls the overall energy consumption, but also avoids the electric field interference that may be caused by the simultaneous activation of too many electrodes; the setting of the minimum cell activity threshold C_threshold provides a safety guarantee for the entire regulation process, ensuring that the cells always maintain a good physiological state.

[0164] As a preferred embodiment, the technical solution of the present application can be applied to the pH value 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 algorithms. For example, the cell metabolism model f can consider the effects of different drug concentrations, temperatures, oxygen concentrations, etc. on pH value 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 value distribution P(x, y, 0) and the initial change rate ΔP(x, y, 0). Suppose the pH value is detected as 7.6 and the change rate is 0.1 / hour at a certain position (x0, y0), 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 after 1 hour. Based on this prediction, the system starts to calculate the optimal electrode activation scheme.

[0166] The system first calculates the effective voltage V_eff of the electrodes at the position (x0, y0) and its surroundings. Suppose there are 4 adjacent electrodes around this position, with distances d1, d2, d3, d4 respectively, and the current voltages are V1, V2, V3, 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. Suppose it is in the initial stage of the experiment and the pH adjustment effect is more important, so 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 influence of the electric field on cell activity C(x, y, t). Suppose the electric field-cell activity influence model h shows that when the effective voltage exceeds 2V, the cell activity begins to decrease significantly. Therefore, the system sets V_max to 2V and V_min to 0.5V to ensure effective pH regulation without damaging the cells.

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

[0171] The system continuously monitors the change in pH value and dynamically adjusts the electrode activation scheme according to the actual effect. For example, if it is found that the pH value is decreasing too slowly, the system may increase the voltage or activate more electrodes; if the pH value is approaching the target value, the system will gradually reduce the voltage or stop the activation of some electrodes.

[0172] Through this precise and dynamic adjustment method, the technical solution of this application can maintain the pH value stability in the liver-kidney combined chip during long-term drug metabolism research, while maximizing the protection of cell viability 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] In a second aspect, with reference to Figure 2 , this application further proposes a parameter adjustment device for a liver-kidney combined chip, which includes:

[0174] An acquisition module 210, configured to acquire the electrochemical signals collected by the electrode array in the liver-kidney combined chip, and the electrochemical signals reflect the change in pH value during the cell metabolism process;

[0175] A first calculation module 220, configured to calculate the pH value change trend of each region in the liver-kidney combined chip according to the electrochemical signals;

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

[0177] A control module 240, configured to control 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.

[0178] By acquiring the electrochemical signals collected by the electrode array, calculating the pH value change trend, calculating the electrode activation scheme based on the trend, and controlling specific electrodes to generate a local electric field to adjust the pH value, the precise monitoring and dynamic regulation of the pH value in the liver-kidney combined chip without introducing additional substances are realized. Meanwhile, the cell activity and drug stability are maintained. It has the advantages of being able to precisely monitor and dynamically regulate the pH value 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 execute any one of the steps in the above method.

[0180] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. 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 combined liver and kidney chip, characterized in that, The steps of the method include: Obtain the electrochemical signals collected by the electrode array in the liver-kidney combined chip, where the electrochemical signals reflect the pH value changes during the cell metabolism process; Calculate the pH value change trend of each region in the liver-kidney combined chip according to the electrochemical signals; Calculate an electrode activation scheme based on the pH value change trend, where the electrode activation scheme is used to determine the electrodes to be activated and their corresponding voltages; According to the electrode activation scheme, control specific electrodes in the electrode array to generate a local electric field to affect ion migration and thus adjust the pH value in the liver-kidney combined chip; The step of calculating the electrode activation scheme based on the pH value change trend includes: Obtain 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 moment t; Based on the current pH value and change rate, use the pre-established cell metabolism model as the prediction function f to calculate the predicted pH value P_pred(x, y, t + Δt) at the next moment 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), 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 influence 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 influence model; Under the constraint conditions 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) 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.

2. The parameter adjustment method of a combined liver and kidney chip according to claim 1, characterized in 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 the preset threshold, generate multiple candidate electrode activation schemes according to the pH value change trend, and each scheme includes the electrode combination to be activated and its corresponding voltage parameter; Evaluate the candidate electrode activation scheme to obtain a multi-dimensional evaluation result, considering the pH adjustment effect, energy consumption, and the impact on cell activity; Based on the multi-dimensional evaluation result, select the scheme with the optimal comprehensive performance as the final electrode activation scheme for subsequent precise pH adjustment.

3. A method for adjusting parameters of a combined liver and kidney chip according to claim 2, characterized in that The step of evaluating the candidate electrode activation scheme to obtain a multi-dimensional evaluation result includes: Establish a multi-dimensional evaluation model that comprehensively considers the pH adjustment effect, energy consumption, and the impact on cell activity, where: The pH adjustment effect is characterized by a comprehensive index of the pH value correction speed and stability in the target area; The energy consumption is quantified by the functional relationship between the electrode activation parameters and the power consumption; The impact on cell activity is evaluated by the degree of influence of the electric field on the cell physiological state; When inputting the candidate scheme parameters, dynamically adjust the weight coefficients of each evaluation dimension according to the current experimental stage, increasing the weight coefficient of the pH adjustment effect in the initial stage of drug metabolism and increasing the weight coefficient of cell activity protection in the long-term stability stage.

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

5. A method for adjusting parameters of a combined liver and kidney 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 includes: According to the electrode activation sequence and the corresponding voltage parameters in the electrode activation scheme, sequentially activate the specific electrodes in the electrode array and apply a specific voltage to generate a local electric field to affect ion migration and thereby adjust the pH value in the liver-kidney combined chip; Real-time monitor the pH value change around the activated electrode. When the detected pH value reaches the target range, automatically adjust the activation state of the corresponding electrode; Based on the pH value change trend and the electrode position information, dynamically optimize the electrode activation sequence and voltage parameters to achieve the regulation of the pH value in different regions of the liver-kidney combined chip.

6. A method for adjusting parameters of a combined liver and kidney 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 the electrode position information includes: Obtain 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 the electrode position information, establish a multi-dimensional evaluation matrix reflecting the adjustment priority of each region; According to the multi-dimensional evaluation matrix, determine the priority order of electrode activation and calculate the voltage parameters required for each electrode to generate an adaptive local electric field intensity; When it is detected that the pH value change rate in a certain region exceeds the preset threshold, dynamically adjust the activation priority and voltage parameters of the corresponding electrode in that region to achieve a rapid response to the rapidly changing region.

7. A method for adjusting parameters of a combined liver and kidney chip according to claim 6, characterized in that The steps of determining the priority order of electrode activation and calculating the voltage parameters required for each electrode based on the multi-dimensional evaluation matrix include: Determine the initial activation priority order according to the evaluation scores of each electrode in the multi-dimensional evaluation matrix, and at the same time consider the spatial distance between adjacent electrodes. When it is detected that the distance between adjacent electrodes is less than the preset threshold, adjust the activation order of these electrodes to avoid simultaneous activation; Based on the adjusted activation order and the pH value change characteristics of the regions where each electrode is located, calculate the initial voltage parameters, and consider the mutual influence between electrodes to correct the parameters to obtain the final voltage parameters.

8. A method for adjusting parameters of a combined liver and kidney chip according to claim 7, characterized in that, The steps of calculating the initial voltage parameters based on the adjusted activation order and the pH value change characteristics of the regions where each electrode is located, and considering the mutual influence between electrodes to correct the parameters to obtain the final voltage parameters include: Based on the pH value change characteristics of the regions where each electrode is located and the target pH value range, establish a voltage-pH response model and calculate the initial voltage parameters required to reach the target pH value; Construct an electric field superposition influence model considering the geometric layout of the electrode array and the electrode spacing, and input the initial voltage parameters into this 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.

9. A parameter adjustment device for a combined liver and kidney chip, characterized in that, The device includes: An acquisition module for acquiring the electrochemical signals collected by the electrode array in the liver-kidney combined chip, and the electrochemical signals reflect the pH value changes during the cell metabolism process; A first calculation module for calculating the pH value change trend of each region in the liver-kidney combined chip according to the electrochemical signals; A second calculation module for calculating an electrode activation scheme based on the pH value change trend, and the electrode activation scheme is used to determine the electrodes that need to be activated and their corresponding voltages; A control module for controlling specific electrodes in the electrode array to generate a local electric field according to the electrode activation scheme to affect ion migration and thereby adjust the pH value in the liver-kidney combined chip; The steps of calculating the electrode activation scheme based on the pH value change trend include: Obtain 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 moment t; Based on the current pH value and the change rate, use the pre-established cell metabolism model as the prediction function f to calculate the predicted pH value P_pred(x, y, t+Δt) at the next moment 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 \(O = w_1*g_1(t)*|P_{target}-P_{pred}| + w_2*g_2(t)*E + w_3*g_3(t)*U\), where \(P_{target}\) is the target pH value, \(E\) is the energy consumption, \(U\) is the electrode usage, \(g_1(t)\), \(g_2(t)\), \(g_3(t)\) are weight functions dynamically adjusted according to the current experimental stage, \(w_1\) is the weight coefficient of the pH adjustment effect, \(w_2\) is the weight coefficient of the energy consumption, and \(w_3\) is the weight coefficient of the electrode usage; Calculate the influence of the electric field on cell viability \(C(x, y, t)=h(V_{eff}(x, y), t)\), where \(h\) is a pre-established electric field-cell viability influence model; Under the constraint conditions of \(V_{min}\leq V(x, y)\leq V_{max}\), \(\sum A(x, y)\leq N_{max}\) and \(C(x, y, t)\geq C_{threshold}\), determine the optimal electrode activation scheme \(A(x, y)\) and the corresponding voltage parameter \(V(x, y)\) 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 viability threshold.

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