Feedback regulation system for electric field stimulation based on organoid micro-reaction observation

Abstract

The present application relates to the technical field of feedback regulation, and particularly relates to an electric field stimulation feedback regulation system based on organoid micro-reaction observation, comprising: obtaining an electric stimulation period between two adjacent electric stimulation parameter adjustment time points, determining fatigue influence sensitivity according to the numerical change of the electric stimulation parameter and the morphological similarity analysis of the action potential; determining the fatigue superposition performance degree of the organoid in each electric stimulation period according to the lactic acid concentration distribution and the change of the action potential amplitude, combining the fatigue superposition performance degree of the current electric stimulation period and the fatigue influence sensitivity of the time period set to obtain a fatigue early warning coefficient; and adjusting and warning the electric field stimulation according to the fatigue early warning coefficient of the current electric stimulation period. The present application can accurately judge the fatigue state and timely perform fatigue early warning by combining the change of the electric stimulation parameter and the superposition performance, thereby reducing the possibility of irreversible damage of the organoid caused by continuous electric stimulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of feedback regulation, and particularly relates to an electric field stimulation feedback regulation system based on micro-reaction observation of organoids. BACKGROUND

[0002] In recent years, organoid technology has made significant progress. Researchers construct organoids with tissue structure and function by simulating the in-vivo microenvironment, providing a new platform for disease modeling, drug screening and regenerative medicine. By applying controllable electric field stimulation to organoids in vitro (culture dish / chip), physiological responses (such as electrical signals, morphological changes) of organoids are monitored, and electric field parameters (intensity, frequency, direction) are dynamically adjusted based on feedback data to simulate in-vivo physiological environment or pathological state, in order to improve the functional maturity of organoids and further explore the dynamic behavior of cells and tissues at microscale.

[0003] When performing long-term continuous electric stimulation, the muscle sensitivity of organoids may gradually decrease (similar to "fatigue"), and the current adaptive ability of organoid electric stimulation system is insufficient, usually relying on manual adjustment to cope with such changes. Due to the lack of effective physiological response prediction model, such manual adjustment has a certain delay, which cannot accurately judge the fatigue state, timely intervene in the critical window period (5-30 seconds) of muscle recovery, and may cause irreversible damage to cells. SUMMARY

[0004] In order to solve the technical problem that in the related art, the fatigue state cannot be accurately judged, timely intervention is not performed in the critical window period (5-30 seconds) of muscle recovery, and cells may suffer irreversible damage, the present application provides an electric field stimulation feedback regulation system based on micro-reaction observation of organoids, and the technical scheme adopted is as follows:

[0005] The present application provides an electric field stimulation feedback regulation system based on micro-reaction observation of organoids, which comprises:

[0006] The acquisition module is configured to acquire an electric stimulation period between adjacent two electric stimulation parameter adjustment time points, and determine the electric stimulation parameters of the electric stimulation period on the organoids.

[0007] The sensitivity analysis module is configured to acquire an action potential of each electric stimulation period, cluster the electric stimulation periods to obtain a period set according to the numerical changes of the electric stimulation parameters under different electric stimulation periods, and determine the fatigue influence sensitivity of the period set according to the similarity analysis of the morphologies of the action potentials of different electric stimulation periods in the same period set.

[0008] The performance analysis module is configured to obtain the lactic acid concentration at different sampling time points in each electrical stimulation period, determine the fatigue superposition performance of the organoid in each electrical stimulation period according to the lactic acid concentration distribution at all sampling time points in each electrical stimulation period and the change in the action potential amplitude of each electrical stimulation period and a preset number of electrical stimulation periods before the timing.

[0009] The adjustment module is configured to obtain a fatigue warning coefficient by combining the fatigue superposition performance of the current electrical stimulation period and the fatigue influence sensitivity of the timing set, and adjust and warn the electrical stimulation according to the fatigue warning coefficient of the current electrical stimulation period.

[0010] Further, the clustering of the electrical stimulation periods according to the numerical changes in the electrical stimulation parameters in different electrical stimulation periods comprises:

[0011] The absolute value of the difference between the same type of electrical stimulation parameters in any two electrical stimulation periods is normalized as the numerical difference index of the corresponding type of electrical stimulation parameters.

[0012] The mean value of the numerical difference index of all types of electrical stimulation parameters is taken as the parameter distance between the two electrical stimulation periods.

[0013] The DBSCAN clustering method is used to perform density clustering on all electrical stimulation periods according to the parameter distance, and a clustering cluster containing different electrical stimulation periods is obtained as the timing set.

[0014] Further, the similarity analysis of the action potential morphology of different electrical stimulation periods in the same timing set to determine the fatigue influence sensitivity of the timing set comprises:

[0015] The similarity analysis of the action potential morphology of any two electrical stimulation periods in the same timing set is performed to obtain the potential similarity degree of the two electrical stimulation periods.

[0016] The mean value of the potential similarity degree between all electrical stimulation periods in the same timing set is normalized as the fatigue influence sensitivity of the timing set.

[0017] Further, the similarity analysis of the action potential morphology of different electrical stimulation periods in the same timing set to determine the fatigue influence sensitivity of the timing set comprises:

[0018] A two-dimensional coordinate system is constructed with time as the horizontal coordinate and intracellular potential as the vertical coordinate, and the change curve of the action potential of each electrical stimulation period in the two-dimensional coordinate system is determined.

[0019] The starting points of any two change curves are aligned, and the area ratio of the overlapping area to the total coverage area of the two change curves is calculated by integration as the potential similarity degree of the two electrical stimulation periods.

[0020] Further, the preset number is 5, the fatigue superposition performance degree of the organoid of each electric stimulation period is determined according to the lactate concentration distribution of all sampling time points of each electric stimulation period and the change of the action potential amplitude of each electric stimulation period and the preset number of electric stimulation periods before the timing, comprising:

[0021] The electric stimulation period before the timing is taken as the preceding period;

[0022] The lactate concentration distribution of all sampling time points of each preceding period is analyzed to determine the metabolic imbalance degree of the preceding period;

[0023] The electric signal attenuation rate of each preceding period is determined according to the change of the action potential amplitude of each electric stimulation period and the preceding period;

[0024] The fatigue superposition performance degree of the organoid of each electric stimulation period is determined by combining the metabolic imbalance degree and the electric signal attenuation rate of all preceding periods.

[0025] Further, the metabolic imbalance degree of the preceding period is determined by analyzing the lactate concentration distribution of all sampling time points of each preceding period, comprising:

[0026] The least square linear fitting is performed on the lactate concentration of all sampling time points in the preceding period to obtain a fitting straight line, and the slope normalized value of the fitting straight line is determined to obtain the metabolic imbalance degree.

[0027] Further, the electric signal attenuation rate of each preceding period is determined according to the change of the action potential amplitude of each electric stimulation period and the preceding period, comprising:

[0028] The maximum value of the action potential of any electric stimulation period is taken as the corresponding action potential amplitude;

[0029] The difference value between the action potential amplitude of the preceding period and the action potential amplitude of the corresponding electric stimulation period is calculated, and the difference value is normalized to be the electric signal attenuation rate of the preceding period.

[0030] Further, the fatigue superposition performance degree of the organoid of each electric stimulation period is determined by combining the metabolic imbalance degree and the electric signal attenuation rate of all preceding periods, comprising:

[0031] The product value of the metabolic imbalance degree and the electric signal attenuation rate of each preceding period is calculated as the superposition value;

[0032] The sum value of the superposition values of all preceding periods is normalized as the fatigue superposition performance degree.

[0033] Further, the fatigue early warning coefficient is obtained by combining the fatigue superposition performance degree of the current electric stimulation period and the fatigue influence sensitivity of the period set.

[0034] The product value of the fatigue superposition performance of the current electric stimulation period and the fatigue influence sensitivity of the period set is normalized as a fatigue early warning coefficient.

[0035] Further, the fatigue early warning coefficient of the current electric stimulation period is used to adjust and warn the electric field stimulation, including:

[0036] When the fatigue early warning coefficient is greater than a preset early warning threshold, an early warning function is started to remind to adjust the electric field stimulation.

[0037] The present application has the following advantages:

[0038] In the embodiment of the present application, the similarity analysis is performed through the numerical change of the electric stimulation parameters, so as to determine the fatigue influence sensitivity of different periods according to the similarity analysis, the fatigue influence sensitivity can quantize the sensitivity of the period set to the fatigue state, and the influence of the fatigue on the organoid performance is accurately analyzed; then, the lactic acid concentration at different sampling time points in each electric stimulation period is obtained, the fatigue superposition performance of the organoid in each electric stimulation period is determined according to the lactic acid concentration distribution of all sampling time points in each electric stimulation period and the change of the action potential amplitude of each electric stimulation period and a preset number of electric stimulation periods before the time sequence, the fatigue superposition performance represents the fatigue performance of the organoid under high load state, and the fatigue cumulative effect is dynamically quantized through the superposition analysis, so as to avoid the problem of poor reliability when only the current time is analyzed; then, the fatigue early warning coefficient is obtained by combining the fatigue superposition performance of the current electric stimulation period and the fatigue influence sensitivity of the period set; and the electric field stimulation is adjusted and warned according to the fatigue early warning coefficient of the current electric stimulation period. The present application can accurately determine the fatigue state and timely perform the fatigue early warning by combining the change and superposition performance of the electric stimulation parameters, so as to reduce the possibility of irreversible damage of the organoid caused by continuous electric stimulation. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0040] Figure 1 A structure diagram of an electric field stimulation feedback adjustment system based on organoid micro-reaction observation is provided for an embodiment of the present application.

[0041] Figure 2 A complete action potential curve schematic diagram is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0042] To further clarify the technical means and effects taken by the present application to achieve the intended purpose, the specific embodiments, structure, features and effects of a kind of electric field stimulation feedback regulation system based on organoid micro-reaction observation according to the present application are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0044] The specific scheme of the electric field stimulation feedback regulation system based on organoid micro-reaction observation provided by the present application is described in detail below in conjunction with the drawings.

[0045] Please refer to Figure 1 which shows the structure diagram of an electric field stimulation feedback regulation system based on organoid micro-reaction observation provided by one embodiment of the present application, which includes:

[0046] The acquisition module 101 is configured to acquire an electric stimulation period between adjacent two electric stimulation parameter adjustment time points, and determine the electric stimulation parameters of the electric stimulation period to the organoid.

[0047] In recent years, organoid technology has made significant progress. Researchers construct organoids with tissue structure and function by simulating the in vivo microenvironment, providing a new platform for disease models, drug screening and regenerative medicine. By applying controllable electric field stimulation to organoids in vitro (culture dish / chip), the physiological responses of organoids (such as electrical signals and morphological changes) are monitored, and the electric field parameters (intensity, frequency, direction) are dynamically adjusted based on feedback data to simulate in vivo physiological environment or pathological state, so as to improve the functional maturity of organoids and further explore the dynamic behavior of cells and tissues at microscale.

[0048] When performing long-term continuous electric stimulation, the muscle sensitivity of organoids may gradually decrease (similar to "fatigue"), and the current adaptive ability of organoid electric stimulation system is insufficient, usually relying on manual adjustment to cope with such changes. Due to the lack of effective physiological response prediction model, this manual adjustment has a certain delay, which cannot timely intervene in the critical window period (5-30 seconds) of muscle recovery, which may cause irreversible damage to cells (such as permanent inactivation of ion channels, calcium overload destroying structure), and in severe cases, it may affect the accuracy (such as misjudging "temporary fatigue" as "permanent loss of function") and reliability of experimental results.

[0049] In observational studies of organoid microreactions, electric field stimulation can significantly influence the biological behavior of organoids. Systematic research on organoid performance under different electric field conditions can provide a deeper understanding of the regulatory mechanisms by which electric fields affect organoid development and function.

[0050] The observation of organoid microresponses under electric field stimulation typically involves multiple procedures, as follows:

[0051] (1) Organoid Culture and Fixation: This provides an environment for organoid survival and allows for precise positioning to receive electric field stimulation. Specifically, it includes a 3D culture medium, a microchamber array, and a perfusion system. The 3D culture medium is a hydrogel (such as Matrigel) that encapsulates the organoid, mimicking the in vivo microenvironment. The microchamber array is designed with a micron-scale pore array to fix individual organoids (avoiding signal crosstalk). The perfusion system uses microfluidic channels to continuously deliver nutrient solution and maintain cell viability (requires integration of a peristaltic pump and flow sensor).

[0052] (2) Electric field stimulation function: Generates a controllable electric field and applies it precisely to the organoid. It mainly consists of an electrode array, using biocompatible electrodes (gold / platinum / ITO conductive glass); the organoid is surrounded by two or more electrodes; and the stimulation is modulated by voltage / current sources (±10V, adjustable from 0.1 to 100Hz). Among them, the constant current source circuit avoids the influence of tissue impedance changes on the stimulation intensity; the H-bridge circuit realizes bidirectional electric field stimulation.

[0053] (3) Microresponse observation: used to capture organoid responses to electrical stimulation in real time. Electrophysiological monitoring was used, with action potentials / field potentials recorded by microelectrodes (sampling rate >10kHz), impedance spectroscopy analysis was used to detect changes in cell membrane integrity, and a microfluidic pH sensor was used for real-time data feedback.

[0054] (4) Feedback control: used to dynamically adjust electric field parameters based on observation data.

[0055] Traditionally, studies on organoid electric field stimulation have mostly employed continuous stimulation methods. However, the physiological responses of organoids are dynamic and complex. During actual stimulation, the influence of various factors, such as the intensity, frequency, and duration of the electric field stimulation, which are artificially regulated, causes organoids to exhibit different micro-responses and undergo different biological responses during the electrical stimulation process. Therefore, to more accurately analyze the physiological responses of organoids, the entire stimulation process can be divided into multiple stages. Based on each complete organoid electrical stimulation record, the adjustment of electrical stimulation parameters (i.e., stimulation intensity, frequency, etc.) at any given moment is marked as the marked moment, and the time interval between two adjacent marked moments is defined as an electrical stimulation period.

[0056] Since no change in electrical stimulation parameters occurs during the electrical stimulation period, the electrical stimulation parameters of the organoid can be directly determined, enabling state analysis of the organoid under these electrical stimulation parameters and providing early warning for the adjustment of electrical stimulation parameters.

[0057] Sensitivity analysis module 102 is used to acquire the action potential of each electrical stimulation period, and to cluster the electrical stimulation periods to obtain a period set based on the changes in the values ​​of electrical stimulation parameters under different electrical stimulation periods; and to perform similarity analysis on the shape of the action potential of different electrical stimulation periods in the same period set to determine the fatigue influence sensitivity of the period set.

[0058] The similarity of electrical stimulation parameters between any two electrical stimulation periods is calculated using Euclidean distance. This method determines how to measure the similarity of electrical stimulation parameters between any two electrical stimulation periods.

[0059] Furthermore, in some embodiments of the present invention, the electrical stimulation periods are clustered to obtain a time period set based on the numerical changes of electrical stimulation parameters under different electrical stimulation periods. This includes: normalizing the absolute value of the difference between the same type of electrical stimulation parameters under any two electrical stimulation periods as a numerical difference index of the corresponding type of electrical stimulation parameters; taking the mean of the numerical difference indices of all types of electrical stimulation parameters as the parameter distance between the two electrical stimulation periods; and using the DBSCAN clustering method to perform density clustering on all electrical stimulation periods based on the parameter distance to obtain clusters containing different electrical stimulation periods as time period sets.

[0060] Since electrical stimulation parameters include different types, such as electrical stimulation intensity and frequency, a measurement analysis is performed for each type. First, the absolute value of the difference between the same type of electrical stimulation parameters under any two electrical stimulation periods is normalized and used as a numerical difference index for the corresponding type of electrical stimulation parameters. The numerical difference index represents the difference between two electrical stimulation periods under the same type of electrical stimulation parameters. After normalization, the influence of dimensions can be eliminated, which is convenient for subsequent analysis.

[0061] By calculating the mean of the numerical difference index of all types of electrical stimulation parameters, the parameter distance between two electrical stimulation periods is obtained. The larger the parameter distance, the greater the difference between the corresponding electrical stimulation parameters, that is, the lower the similarity between the two electrical stimulation periods. Therefore, it is used as the distance value for density clustering. The DBSCAN clustering method is used to perform density clustering on all electrical stimulation periods based on the parameter distance, and the clusters containing different electrical stimulation periods are obtained as the period set.

[0062] It should be noted that the DBSCAN clustering method is an unsupervised clustering algorithm well-known to those skilled in the art. Using DBSCAN clustering, it is possible to perform similarity clustering on all electrical stimulation time periods to obtain a time period set, which will not be elaborated further.

[0063] Within the same time period set, organoid stimulation segments representing similar electrical stimuli are identified. Based on this, a similarity analysis is performed to determine the fatigue sensitivity of the time period set. In the specific analysis process, embodiments of the present invention further analyze the action potentials.

[0064] First, we consider the impact of organoid fatigue on the performance of each organoid with similar time-series stimulation. This means that even if an organoid is fatigued, it may not exhibit significant changes due to fatigue if the intensity or parameters of electrical stimulation are insufficient. Therefore, by analyzing the relative consistency of stimulation performance across each organoid with similar time-series stimulation parameters, we can analyze the sensitivity of the current organoid to the influence of organoid fatigue on its corresponding time-series stimulation parameters, i.e., the sensitivity to fatigue effects.

[0065] Furthermore, in some embodiments of the present invention, the fatigue sensitivity of the time period set is determined by performing similarity analysis on the morphology of action potentials in different electrical stimulation periods within the same time period set. This includes: performing similarity analysis on the morphology of action potentials in any two electrical stimulation periods within the same time period set to obtain the potential similarity between the two electrical stimulation periods; and normalizing the mean of the potential similarity between all electrical stimulation periods within the same time period set using the negative of the mean as the fatigue sensitivity of the time period set.

[0066] See Figure 2 , Figure 2 This is a complete action potential curve diagram provided in one embodiment of the present invention, including overshoot potential, threshold potential and resting potential; if the overall shape of the action potentials (action potential curve morphology) of several electrical stimulation periods contained in the time period set is more similar, it indicates that the similar action potential curves in the time period set are more likely to be caused by the influence of their own electrical stimulation, and the sensitivity to fatigue is lower. Conversely, it indicates that the influence of electrical stimulation in the time period set is smaller, and the sensitivity to fatigue is higher.

[0067] Furthermore, in some embodiments of the present invention, similarity analysis is performed on the morphology of action potentials from any two electrical stimulation periods within the same time period to obtain the potential similarity between the two electrical stimulation periods. This includes: constructing a two-dimensional coordinate system with time as the abscissa and intracellular potential as the ordinate, determining the change curve of the action potential in the two-dimensional coordinate system for each electrical stimulation period; aligning the starting points of any two change curves, and calculating the area ratio of the overlapping area of ​​the two change curves to the total coverage area by integration, as the potential similarity between the two electrical stimulation periods.

[0068] In this embodiment of the invention, potential similarity analysis is performed by the similarity of the change curves. Taking the morphological comparison of any two action potentials as an example, the overlapping area and the total coverage area of ​​the two in the time series can be obtained by integration. The area of ​​the overlapping area and the total coverage area are compared to determine the morphological similarity of any two action potentials. That is, the smaller the ratio, the higher the overlap rate, which means that the fluctuation similarity of the action potentials corresponding to the two electrical stimulation periods is higher.

[0069] Then, pairwise analysis was performed. The mean of the potential similarity among all electrical stimulation periods in the same time period was normalized by taking the negative of the mean as the fatigue influence sensitivity of the time period set. That is, the higher the fatigue influence sensitivity value, the lower the similarity of the action potential fluctuations among all electrical stimulation periods in the time period set. In other words, under the electrical stimulation parameters of the corresponding time period set, its overall distribution fluctuates more and is more sensitive. Further more sensitive numerical detection and analysis are needed.

[0070] The performance analysis module 103 is used to obtain the lactate concentration at different sampling times within each electrical stimulation period. Based on the lactate concentration distribution at all sampling times of each electrical stimulation period, and the change in action potential amplitude between each electrical stimulation period and a preset number of electrical stimulation periods before the time sequence, the fatigue superposition performance of organoids in each electrical stimulation period is determined.

[0071] The above process analyzed the fatigue sensitivity of organoids in time periods with similar stages. Here, we can focus on the cumulative effect of previous electrical stimulation on the current fatigue state of organoids under different stages of stimulation.

[0072] After organoids are subjected to continuous electrical stimulation, their cell membrane responsiveness, metabolic level, and electrophysiological properties may change, leading to different intensity and manner of response to the same electrical stimulation. Therefore, even if several electrical stimulation periods within a set of similar time periods of organoids maintain similar electrical stimulation parameters, the fatigue level of each organoid in the previous period will be different, and its performance of phased stimulation will also vary.

[0073] This difference is not only a matter of parameter control in electrical stimulation, but also a comprehensive reflection of organoid adaptability and fatigue state. Therefore, when analyzing the process of organoid electrical stimulation, it is necessary to comprehensively consider the fatigue effect of the organoid and the gradual cumulative effect of electrical stimulation.

[0074] In this embodiment of the invention, lactic acid concentration is simultaneously collected based on a microfluidic lactic acid sensor.

[0075] Furthermore, in some embodiments of the present invention, the fatigue superposition performance of organoids in each electrical stimulation period is determined based on the lactate concentration distribution at all sampling times of each electrical stimulation period and the change in action potential amplitude between each electrical stimulation period and a preset number of electrical stimulation periods prior to the time sequence. This includes: using a preset number of electrical stimulation periods prior to the time sequence as a pre-stressing period; analyzing the lactate concentration distribution at all sampling times of each pre-stressing period to determine the metabolic imbalance of the pre-stressing period; determining the electrical signal attenuation rate of each pre-stressing period based on the change in action potential amplitude between each electrical stimulation period and the pre-stressing period; and combining the metabolic imbalance and electrical signal attenuation rate of all pre-stressing periods to determine the fatigue superposition performance of organoids in each electrical stimulation period.

[0076] The preset quantity is the quantity value used for pre-overlay analysis. The overlay analysis is performed on the five electrical stimulation periods preceding each electrical stimulation period, under the dimension of fatigue overlay.

[0077] From different physiological perspectives, the attenuation of electrical signals and the increase in lactate reveal the electrophysiological and metabolic characteristics of organoid fatigue, respectively. The attenuation of electrical signals indicates that after continuous stimulation, organoids experience reduced cellular excitability and decreased conduction efficiency, affecting their normal physiological functions. Meanwhile, the accumulation of lactate indicates an increase in anaerobic metabolism, usually caused by insufficient oxygen supply or excessive cellular metabolic demands. This change suggests that when faced with sustained stress, organoids have shifted from aerobic metabolism to anaerobic metabolism to meet their energy needs.

[0078] Therefore, we first analyze lactic acid accumulation by analyzing the lactic acid concentration distribution at all sampling times in each pre-segment to determine the metabolic imbalance in the pre-segment. This includes fitting the lactic acid concentration at all sampling times in the pre-segment to a straight line using the least squares method, obtaining the fitted straight line, determining the normalized slope of the fitted straight line, and thus obtaining the metabolic imbalance.

[0079] During the accumulation process, lactic acid gradually increases over time. Therefore, the greater the slope, the greater the metabolic imbalance, indicating that anaerobic metabolism occurs due to excessive load. The more intense the anaerobic metabolism, the stronger the increase, revealing the fatigue manifestation of organoids under high load conditions.

[0080] Furthermore, in some embodiments of the present invention, determining the electrical signal attenuation rate of each pre-stimulation segment based on the change in action potential amplitude between each electrical stimulation period and the pre-stimulation segment includes: taking the maximum value of the action potential during any electrical stimulation period as the corresponding action potential amplitude; calculating the difference between the action potential amplitude of the pre-stimulation segment and the action potential amplitude of the corresponding electrical stimulation period; normalizing the difference as the electrical signal attenuation rate of the pre-stimulation segment.

[0081] When an electrical signal is fatigued, it exhibits a decaying state, which is reflected in the action potential as the highest point of the action potential curve, gradually decreasing as fatigue accumulates.

[0082] Therefore, in this embodiment of the invention, by determining the action potential amplitude of each electromagnetic technician end, and then calculating the difference between the action potential amplitude of the pre-stimulation phase and the action potential amplitude of the corresponding electrical stimulation phase, the larger the difference, the greater the attenuation of the electrical signal, indicating a greater degree of amplitude reduction and a greater fatigue accumulation effect. Therefore, in this embodiment of the invention, the difference is normalized and used as the electrical signal attenuation rate of the pre-stimulation phase.

[0083] After determining the metabolic imbalance and electrical signal attenuation rate, fatigue superposition analysis was performed on the electrical stimulation period by combining the parameter changes in these two dimensions for each pre-stimulation segment.

[0084] Furthermore, in some embodiments of the present invention, the fatigue superposition performance of organoids for each electrical stimulation period is determined by combining the metabolic imbalance degree and electrical signal attenuation rate of all pre-stimulation segments, including: calculating the product of the metabolic imbalance degree and electrical signal attenuation rate of each pre-stimulation segment as the superposition value; and normalizing the sum of the superposition values ​​of all pre-stimulation segments as the fatigue superposition performance.

[0085] Since a larger value of metabolic imbalance indicates anaerobic metabolism due to excessive load, and the more intense the anaerobic metabolism, the stronger the increase, revealing the fatigue performance of organoids under high load, and the greater the amplitude of electrical signal attenuation, indicating a greater reduction in amplitude and a greater fatigue accumulation effect, the product of metabolic imbalance and electrical signal attenuation rate of each pre-stage is directly calculated as the superposition value, and the sum of the superposition values ​​of all pre-stages is normalized to obtain the fatigue superposition performance degree.

[0086] The fatigue superposition performance represents the fatigue performance of organoids under high load conditions. Combining the superposition analysis of the preceding segment improves the accuracy of the overall data.

[0087] The adjustment module 104 is used to combine the fatigue superposition performance of the current electrical stimulation period and the fatigue influence sensitivity of the time period set to obtain a fatigue warning coefficient; and to adjust and warn the electric field stimulation according to the fatigue warning coefficient of the current electrical stimulation period.

[0088] Furthermore, in some embodiments of the present invention, a fatigue warning coefficient is obtained by combining the fatigue superposition performance of the current electrical stimulation period and the fatigue influence sensitivity of the time period set, including: calculating the product of the fatigue superposition performance of the current electrical stimulation period and the fatigue influence sensitivity of the time period set, and normalizing it as the fatigue warning coefficient.

[0089] In this embodiment of the invention, the product of fatigue superposition performance and fatigue impact sensitivity of the time period set is directly calculated and normalized to serve as the fatigue early warning coefficient.

[0090] Since fatigue superposition performance represents the fatigue performance of organoids under high load, the higher the sensitivity to fatigue impact, the more sensitive the time period set of that stage is to the fatigue state. This quantifies the impact of fatigue on organoid performance at each stage and reflects whether the data analysis level can more accurately capture the actual state of fatigue, thus obtaining a fatigue early warning coefficient.

[0091] The higher the fatigue warning coefficient, the more sensitive the organoid's electrical stimulation state is to fatigue, and the stronger the fatigue accumulation. In this case, in order to avoid irreversible damage to organoid cells due to excessive fatigue, timely warning and adjustment are required.

[0092] Furthermore, in some embodiments of the present invention, the electric field stimulation is adjusted and warned according to the fatigue warning coefficient of the current electric stimulation period, including: when the fatigue warning coefficient is greater than a preset warning threshold, the warning function is activated to remind the user to adjust the electric field stimulation.

[0093] The preset warning threshold is the threshold value of the fatigue warning coefficient. In this embodiment of the invention, the fatigue warning coefficient can be specifically 0.65, that is, when the fatigue warning coefficient is greater than 0.65, the warning function is activated to remind the user to adjust the electric field stimulation.

[0094] In this embodiment of the invention, similarity analysis is performed by analyzing the numerical changes of electrical stimulation parameters. Based on this similarity analysis, the fatigue impact sensitivity at different time periods is determined. The fatigue impact sensitivity quantifies the sensitivity of a time period set to the response to fatigue, accurately analyzing the impact of fatigue on organoid performance. Then, lactate concentrations at different sampling times within each electrical stimulation period are obtained. Based on the lactate concentration distribution at all sampling times for each electrical stimulation period, and the changes in action potential amplitude between each electrical stimulation period and a preset number of previous electrical stimulation periods, the fatigue superposition performance of the organoid in each electrical stimulation period is determined. The fatigue superposition performance represents the fatigue performance of the organoid under high load. Through superposition analysis, the cumulative fatigue effect can be dynamically quantified, avoiding the problem of poor reliability when analyzing only the current moment. Subsequently, by combining the fatigue superposition performance of the current electrical stimulation period and the fatigue impact sensitivity of the time period set, a fatigue warning coefficient is obtained. Based on the fatigue warning coefficient of the current electrical stimulation period, the electric field stimulation is adjusted for early warning. This invention, by combining changes in electrical stimulation parameters and superposition performance, can accurately determine the fatigue state, provide timely fatigue warnings, and reduce irreversible organoid damage caused by continuous electrical stimulation.

[0095] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An electric field stimulation feedback modulation system based on organoid microresponse observation, characterized in that, The system includes: The acquisition module is used to acquire the electrical stimulation period between two adjacent electrical stimulation parameter adjustment times and determine the electrical stimulation parameters of the organoid during the electrical stimulation period. The sensitivity analysis module is used to acquire the action potential for each electrical stimulation period. Based on the changes in the values ​​of electrical stimulation parameters under different electrical stimulation periods, the electrical stimulation periods are clustered to obtain a period set. Based on the similarity analysis of the action potential morphology of different electrical stimulation periods in the same period set, the fatigue impact sensitivity of the period set is determined. The performance analysis module is used to obtain the lactate concentration at different sampling times within each electrical stimulation period. Based on the lactate concentration distribution at all sampling times of each electrical stimulation period, and the change in action potential amplitude between each electrical stimulation period and a preset number of electrical stimulation periods before the time sequence, the fatigue superposition performance of organoids in each electrical stimulation period is determined. The adjustment module is used to combine the fatigue superposition performance of the current electrical stimulation period with the fatigue impact sensitivity of the current time period set to obtain a fatigue warning coefficient; and to adjust the electric field stimulation according to the fatigue warning coefficient of the current electrical stimulation period. Methods for determining time period sets include: The absolute value of the difference between the same type of electrical stimulation parameters under any two electrical stimulation periods is normalized and used as a numerical difference index of the corresponding type of electrical stimulation parameters. The mean of the numerical differences of all types of electrical stimulation parameters is used as the parameter distance between two electrical stimulation periods. The DBSCAN clustering method was used to perform density clustering on all electrical stimulation periods based on parameter distance, resulting in clusters containing different electrical stimulation periods as time period sets. Methods for determining the sensitivity to fatigue effects over time periods include: The similarity analysis of the action potentials of any two electrical stimulation periods within the same time period is used to obtain the degree of potential similarity between the two electrical stimulation periods. The mean of the potential similarity among all electrical stimulation time periods in the same time period set is normalized by taking the negative of the mean and used as the fatigue sensitivity of the time period set. The methods for determining the degree of fatigue superposition include: Pre-set a number of electrical stimulation periods before the time sequence as a pre-sequence segment; Analyze the lactate concentration distribution at all sampling times in each pre-treatment segment to determine the metabolic imbalance in the pre-treatment segment; The electrical signal attenuation rate for each pre-stimulation segment is determined based on the change in action potential amplitude during each electrical stimulation period and the pre-stimulation period. By combining the metabolic imbalance and electrical signal attenuation rate of all pre-stimulation segments, the fatigue superposition performance of organoids at each electrical stimulation period was determined. Methods for determining the fatigue warning coefficient include: The product of the fatigue superposition performance during the current electrical stimulation period and the fatigue impact sensitivity of the time period set is calculated and normalized to serve as the fatigue early warning coefficient. Methods for regulating and providing early warning of electric field stimulation include: When the fatigue warning coefficient exceeds the preset warning threshold, the warning function is activated to remind the user to adjust the electric field stimulation.

2. The electric field stimulation feedback modulation system based on organoid microresponse observation as described in claim 1, characterized in that, The similarity analysis of the action potential morphology of any two electrical stimulation periods within the same time frame is performed to obtain the degree of potential similarity between the two electrical stimulation periods, including: A two-dimensional coordinate system was constructed with time as the horizontal axis and intracellular potential as the vertical axis to determine the change curve of action potential in the two-dimensional coordinate system for each electrical stimulation period. Align the starting points of any two variation curves, and calculate the ratio of the overlapping area of ​​the two variation curves to the total coverage area by integration. This ratio is used as the potential similarity between the two electrical stimulation periods.

3. The electric field stimulation feedback modulation system based on organoid microresponse observation as described in claim 1, characterized in that, The analysis of lactate concentration distribution at all sampling times in each pre-treatment segment to determine the metabolic imbalance in the pre-treatment segment includes: The lactate concentration at all sampling times within the pre-test period is fitted with a straight line using the least squares method to obtain the fitted straight line. The normalized slope of the fitted straight line is then determined to obtain the metabolic imbalance.

4. The electric field stimulation feedback modulation system based on organoid microresponse observation as described in claim 1, characterized in that, The determination of the electrical signal attenuation rate for each pre-stimulation segment based on the change in action potential amplitude during each electrical stimulation period and the pre-stimulation segment includes: The maximum value of the action potential during any electrical stimulation period is taken as the corresponding action potential amplitude. Calculate the difference between the action potential amplitude of the pre-stimulation phase and the action potential amplitude of the corresponding electrical stimulation phase, normalize the difference, and use it as the electrical signal attenuation rate of the pre-stimulation phase.

5. The electric field stimulation feedback modulation system based on organoid microresponse observation as described in claim 1, characterized in that, The method combines the metabolic imbalance and electrical signal attenuation rate of all pre-stimulation segments to determine the fatigue superposition performance of organoids at each electrical stimulation period, including: The product of the metabolic imbalance and the electrical signal attenuation rate of each pre-stage is calculated as the summation value. The sum of all preceding segment values ​​is normalized and used as the fatigue superposition performance.

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