Construction method of zebra fish ferric trichloride epilepsy model

Abstract

The invention relates to a construction method of zebra fish ferric trichloride epilepsy, and belongs to the technical field of medical molecular biology. The method comprises the following steps: preparing a microsyringe: drawing the tip of a glass microelectrode by using a needle drawing instrument, connecting the tip with a microsyringe by using a hot melt glue gun, and filling the syringe with a FeCl3 solution; the zebra fish is subjected to 0 DEG C ice-water bath and then lies on the back in the crack of the wet cotton pad; and intracerebral injection: injecting a FeCl3 solution into the left brain of the zebra fish through the skull base. Through the adjustment of a needle pulling instrument, an ultra-thin tip meeting experimental requirements can be produced, and the flow rate control of the solution in the injection process is ensured. The low-temperature anesthesia can reduce the metabolic level of the zebra fish, reduce the movement and physiological reactions of the zebra fish and improve the stability of experimental operation. Through precise intracerebral injection, it can be ensured that the FeCl3 solution only plays a role in a target area, interference on other brain areas of zebra fish is avoided to the maximum extent, and experiment controllability is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of medical molecular biology, and in particular to a method for constructing a zebrafish FeCl3-induced epilepsy model. Background Art

[0002] Epilepsy is one of the most common serious brain disorders, affecting more than 70 million people worldwide. Post-traumatic epileptogenesis is a long-lasting process in which the normal brain exhibits hypersynchronous excitability following a brain injury event. Traumatic epilepsy is refractory to anti-epileptic drug therapy.

[0003] After the pressure wave is generated by the syringe in zebrafish larvae, the larvae show epilepsy-like behavior. However, this model has not been presented in adult zebrafish. There are significant differences in the nervous system components of zebrafish larvae and adult fish, which can lead to different behavioral phenotypes. Therefore, it is necessary to construct a traumatic epilepsy model for adult zebrafish.

[0004] After closed head traumatic brain injury was induced in adult zebrafish using pulsed high-intensity focused ultrasound, the fish showed spontaneous myoclonic-like behavior. However, pulsed high-intensity focused ultrasound is expensive and cannot be used in ordinary behavioral laboratories.

[0005] Traditional zebrafish brain injection uses a 30G needle tip to pierce the zebrafish skull, and then uses a microinjection needle to puncture and inject drugs in the incision on the surface of the zebrafish brain opened by the needle tip. However, this method will damage the zebrafish brain and is not suitable for the construction of a zebrafish traumatic epilepsy model. Summary of the invention

[0006] Based on this, it is necessary to provide a method for constructing a zebrafish FeCl3-induced epilepsy model to address the problems of high cost and damage to zebrafish in constructing an epilepsy model.

[0007] A method for constructing zebrafish FeCl3-induced epilepsy, comprising the following steps:

[0008] S1. Prepare micro-syringe: Use a needle puller to pull the tip of a glass microelectrode and connect it to a micro-injector with a hot-melt glue gun, and fill the syringe with FeCl3 solution;

[0009] S2, place the zebrafish in a supine position in the crack of a moist cotton pad after a 0°C ice water bath;

[0010] S3. Intracerebral injection: FeCl3 solution was injected into the left brain of zebrafish via the skull base.

[0011] The present application discloses a method for constructing a zebrafish FeCl3-induced epilepsy. The production of the glass microelectrode tip can be accurate to the micron level, thereby ensuring the accuracy of the injection. The drawing accuracy and morphology of the microelectrode tip directly affect the meticulousness of the FeCl3 solution injection. Through the adjustment of the needle puller, an extremely fine tip that meets the experimental requirements can be produced to ensure the control of the solution flow rate during the injection process. The connection with the microelectrode is fixed by a hot melt glue gun to ensure the stability of the injector and the injection needle. This reduces the inaccurate injection caused by loose connection or poor contact, avoids the risk of FeCl3 solution leakage, and ensures the consistency and reliability of the experiment. Through precise preparation and connection, contaminants are prevented from entering the syringe, ensuring the purity of the FeCl3 solution, which helps to ensure the effect of zebrafish brain injection. Through the cooling effect of a 0°C ice water bath, the activity of zebrafish can be reduced, so that it enters a state similar to anesthesia, reducing its stress response to the injection process. Hypothermia anesthesia can reduce the metabolic level of zebrafish, reduce its movement and physiological reactions, and improve the stability of experimental operations. Placing zebrafish in the cracks of a moist cotton pad can not only effectively prevent them from twitching, struggling and other unstable behaviors caused by cooling, but also keep them in a supine position. Injection of FeCl3 (ferric chloride solution) into the zebrafish brain can induce local nerve excitement, resulting in symptoms similar to epilepsy. Through precise intracerebral injection, it can be ensured that the FeCl3 solution only works in the target area, minimizing interference with other brain areas of the zebrafish and ensuring the controllability of the experiment.

[0012] In one embodiment, the capacity of the microinjector is 2 μL.

[0013] In one embodiment, the zebrafish are 3 months old and have an average body weight of 300 mg.

[0014] In one embodiment, the FeCl3 solution is prepared by weighing a certain amount of FeCl3 powder with a micro-spoon and dissolving it in physiological saline. The volume of the FeCl3 solution is 1 μL and the concentration is 0.01 mM.

[0015] In one embodiment, the preparation of the micro-syringe comprises the following steps:

[0016] The tip pulled by the needle puller is a glass microelectrode tip with a diameter of 0.2-0.4 μm;

[0017] Control the specific drawing parameters of the needle puller during the drawing process.

[0018] In one embodiment, the specific steps of the needle pulling instrument to pull a glass microelectrode tip with a tip diameter of 0.2-0.4 μm are as follows:

[0019] Select the glass tube for making glass microelectrodes, and adjust the power of the heating source in real time through the temperature control system;

[0020] The power of the heating source is adjusted in real time through the temperature control system to keep the temperature of the glass tube stable. After the glass tube softens, the diameter of the microelectrode tip is monitored in real time by the system.

[0021] A nonlinear feedback control system is used to adjust the tension and stretching speed parameters to ensure the uniformity of the tip. The tension adjustment formula is as follows:

[0022]

[0023] Where Pull is the pulling force, P0 is the initial pulling force, dtarget is the target diameter, d(t) is the glass tip diameter measured in real time, α is the decay constant, and t is the time;

[0024] Using the nonlinear relationship between the stretching rate and the glass tip diameter, the dynamic adjustment formula of the stretching rate is obtained as follows:

[0025]

[0026] Among them, V stretch is the target diameter k1 and k2 are constants set according to the experimental results, d(t) is the glass tip diameter measured in real time, and n is a control parameter representing the dependence of the diameter on the stretching rate;

[0027] After the glass tip reaches the target size, it is quickly cooled by a precisely controlled cooling system to avoid glass deformation. During the cooling process, the heat conduction equation is applied to control the cooling rate:

[0028]

[0029] Where: T is the temperature of the glass tip, T ambient is the ambient temperature, and k is the thermal conductivity coefficient.

[0030] The temperature control system ensures that the temperature of the glass tube is always kept within a precise range during the heating process by adjusting the power of the heating source in real time. The precise control of the temperature control system avoids the potential risks brought by temperature changes, making the manufactured microelectrode tip more uniform and stable. By monitoring the diameter of the glass tip in real time and adjusting the system parameters based on feedback, the diameter of the glass tip can be ensured to be accurate to the target value. During the stretching process, the uniformity of the microelectrode tip directly affects the results of the electrophysiological experiment. The adjustment formula of the tension takes into account the material properties of the glass tube and the change of the tip diameter. The system can adjust the tension according to real-time feedback to ensure uniformity during the stretching process, thereby avoiding irregular shapes or asymmetry of the tip. The stretching process is more refined and stable by adjusting the stretching speed and tension through a nonlinear feedback control system. Controlling the cooling rate not only helps to prevent deformation, but also optimizes the structure of the glass material and ensures the mechanical strength of the glass microelectrode.

[0031] In one embodiment, the specific steps of placing the zebrafish in a supine position in the crack of a wet cotton pad after a 0°C ice water bath are as follows:

[0032] S21. The water bath is equipped with multiple micro temperature sensors. Through data fusion and algorithm processing, the body temperature of zebrafish is kept stable within the range of 0℃±0.05℃ to avoid the difference in anesthesia depth caused by temperature fluctuations.

[0033] S22. Cut a slit in the center of the cotton pad that matches the shape of the zebrafish's back. The depth of the slit is enough to accommodate the zebrafish's body to ensure that it remains in a supine position.

[0034] S23. Flexible support strips are installed on both sides of the crack of the cotton pad. The flexible support strips are made of medical-grade silicone material. The support strips are used to adapt to the changes in the body shape of the zebrafish and provide flexible fixation. The two ends of the flexible support strips are connected to the platform through micro-spring devices to ensure that the flexible support strips can be adaptively adjusted according to slight changes in the body shape of the zebrafish.

[0035] S24. Install a vibration sensor on the platform to monitor external vibrations, arrange a circular microelectrode array around the cotton pad, and adjust the position of the zebrafish in real time by adjusting the voltage and frequency of the electric field.

[0036] Improved temperature control accuracy ensures that zebrafish can maintain a consistent depth of anesthesia during anesthesia, avoiding unnecessary stimulation or over-anesthesia caused by temperature fluctuations. This is very important for the accuracy of subsequent operations (such as injection or behavioral observation), ensuring the stability and repeatability of the anesthesia state. Through precise temperature control, the interference caused by temperature fluctuations can be reduced, ensuring that the zebrafish maintains a stable state and improving repeatability. The slit cut in the center of the cotton pad provides a customized support that matches the shape of the zebrafish's back to ensure the stability of its supine posture. This customized design avoids the subsequent operation of zebrafish due to unstable posture or unnatural movement during anesthesia. The flexible support strip can be adaptively adjusted according to the slight changes in the zebrafish's body shape, ensuring that the zebrafish can maintain a natural posture during the fixation process without unnecessary constraints. This flexible support design not only improves the stability of fixation, but also reduces the movement interference of the zebrafish, allowing it to maintain a relatively stable state during injection, behavioral observation, etc. The introduction of the micro-spring device can adjust the position of the support strip in real time to adapt to the slight changes in the size of the zebrafish in the water bath, thereby ensuring its stability throughout the anesthesia and experimental process. By arranging the circular microelectrode array and adjusting the voltage and frequency of the electric field, the position of the zebrafish can be adjusted in real time to prevent it from shifting due to improper position or slight external interference during the experiment. This precise control can ensure that the zebrafish is always in the most suitable experimental position, improving the controllability of the experiment and the accuracy of the data.

[0037] In one embodiment, the intracerebral injection: the specific steps of injecting the FeCl3 solution into the left brain of the zebrafish through the skull base are as follows:

[0038] S31, scanning the zebrafish to identify the left brain region at the base of the skull;

[0039] S32, inserting a microinjection syringe into the left brain region of the zebrafish's skull base, and determining the insertion depth based on the feedback force received by the microinjection syringe;

[0040] S33, collecting zebrafish brain electrophysiological signals, analyzing the collected signals in real time, and feeding back the analysis results in real time;

[0041] S34, injecting FeCl3 solution through a micro-syringe, and controlling the injection speed through real-time feedback analysis results during the injection process.

[0042] By scanning the zebrafish and identifying the left brain region at the base of the skull, the injection target can be accurately positioned, so that subsequent operations are not affected by position errors, ensuring accurate injection of the FeCl3 solution. After accurately identifying the zebrafish brain region, the syringe can be inserted more accurately, reducing errors and avoiding accidental injuries to other brain regions or body parts of the zebrafish. Ensure high accuracy during the injection process and minimize potential experimental risks. Through real-time adjustment of the feedback force, damage to the zebrafish skull base brain region can be effectively avoided. Too deep an injection may damage brain tissue and cause irreversible damage, while too shallow an injection may not accurately deliver the drug. Accurate depth control ensures the safety and effectiveness of the operation. By analyzing the signal in real time, timely adjustments can be made to the experimental operation. If abnormal fluctuations in brain electrical activity are detected, the injection parameters, injection time or injection location can be adjusted according to real-time feedback to avoid negative effects on the zebrafish and ensure the accuracy and safety of the experiment. Through real-time feedback analysis results, the injection speed of the microsyringe can be adjusted to adapt to the physiological response of the zebrafish. Dynamically adjusting the injection rate can ensure that the drug is injected at the most appropriate speed and dose, avoiding adverse reactions caused by too fast or too slow injection, such as drug leakage, drug overdose or overstimulation. Dynamically adjusting the injection rate can better control the physiological effects of FeCl3 solution, so that the biological effects of the drug are consistent in each experiment. This is crucial for observing the neural effects or electrophysiological changes caused by FeCl3 solution, ensuring the reliability of experimental results.

[0043] In one embodiment, the specific steps of scanning the zebrafish and identifying the left brain region of the skull base of the zebrafish are as follows:

[0044] S311. Construct a 3D model database of zebrafish brain based on different developmental stages, including the size, morphology and distribution characteristics of each layer, anatomical landmarks and each brain region of the zebrafish brain;

[0045] S312. Based on the unique brain structure characteristics of zebrafish, a specially trained deep convolutional neural network is designed to perform automatic anatomical differentiation and labeling. The mathematical model is as follows:

[0046] P left-brain (x, y, z) = f θ (I ct / us (x, y, z)

[0047] Among them, I ct / us (x, y, z) is the input image data of multimodal imaging, f θ is a trained deep neural network;

[0048] S313. After completing the image data input and deep learning model training, automatically identify the various structures and their boundaries in the zebrafish brain, use real-time image processing and edge detection algorithms, combined with morphological analysis to accurately locate the left skull base area;

[0049] S314, using adaptive filters to detect different levels of brain tissue:

[0050]

[0051] where h σ (x) is a Gaussian filter used to remove noise and retain key structures. filtered (x, y, z) is the image data after filtering, I layer (x, y, z) represents the image data of a slice;

[0052] S315. Locate the left brain region at the skull base of zebrafish and provide feedback on the position information.

[0053] By collecting anatomical data of zebrafish brain at different developmental stages, a detailed 3D model database can be constructed to provide rich basic data as a standard reference for zebrafish brain at different developmental stages. With detailed brain landmarks and distribution characteristics, the injection area can be located more accurately, the operation error can be reduced, and the consistency and reliability of the results can be ensured. Automatic anatomical differentiation and labeling using deep convolutional neural networks can greatly improve the analysis efficiency and reduce the errors and deviations caused by manual labeling. The deep learning model can identify complex brain structure features and ensure high-precision brain area calibration. By accurately locating the left skull base area, the microinjector can accurately inject the FeCl3 solution into the target area. Accurate positioning can reduce errors, avoid injection into other areas, reduce experimental errors, and improve the success rate of the experiment. The filtered image data can provide a clearer brain tissue structure, which helps to improve the accuracy of morphological analysis. Ensure that the morphology and distribution of brain structures can be accurately extracted during the analysis, especially the accurate identification of fine parts, to avoid blurring or noise affecting the results. Real-time feedback of position information not only improves the degree of automation of operations, but also enhances accuracy, especially during rapid response and adjustment, effectively ensuring that operations are not affected by external interference or errors.

[0054] In one embodiment, the specific steps of inserting a micro-injection syringe into the left brain region of the zebrafish skull base and determining the insertion depth by the feedback force received by the micro-injection syringe are as follows:

[0055] S321. Install a high-precision micro-force sensor on the tip of the micro-injector or the body of the micro-injector to monitor in real time the feedback force exerted on the syringe when it contacts the zebrafish brain tissue;

[0056] S322. Establish a nonlinear mechanical model to simulate the interaction between brain tissue and syringe. The contact force model is as follows:

[0057] F contact =k brain ΔZ+γ brain ·ΔD+β brain ·(ΔZ) n

[0058] Among them, F contact is the feedback force when the syringe contacts the brain tissue, ΔZ is the insertion depth of the syringe, ΔD is the insertion speed of the syringe, and k brain is the elastic stiffness of brain tissue, γ brain is the damping coefficient of brain tissue, β brain and n are parameters used to fit the nonlinear response of brain tissue;

[0059] S323. Real-time feedback force is obtained through high-precision sensors. Combined with the biomechanical properties of zebrafish brain tissue, a dynamic response model of real-time insertion depth and feedback force is constructed. The dynamic feedback force model is as follows:

[0060] F feedback (t) = k sensor ·(F contact (t)-F threshold )

[0061] Among them, F contact (t) is the real-time feedback force detected by the sensor, K sensor is the sensitivity of the sensor, F threshold The threshold set for the sensor represents the minimum force between the syringe and the brain tissue.

[0062] S324. According to the feedback force data obtained from the micro-force sensor, the insertion depth of the syringe is calculated in real time, and the insertion speed and insertion depth of the syringe are adjusted by an adaptive control algorithm. The relationship between the insertion depth and the feedback force is realized by the following adaptive formula:

[0063]

[0064] Where z(t) is the current insertion depth of the syringe, z0 is the initial depth, e(t) is the error between the feedback force and the target feedback force, and K p , K i and K d are proportional, integral and derivative control gains, F target is the target contact force, which is set according to the mechanical properties of zebrafish brain tissue;

[0065] S325, adjusting the control strategy in real time, and stopping the insertion action when the insertion depth reaches the target range.

[0066] By installing sensors on the syringe, the feedback force generated during the insertion process can be obtained in real time, and real-time adjustments can be made based on the data to ensure that the movement of the syringe is smoother and to avoid discomfort or misoperation caused by too fast insertion. The nonlinear mechanical model takes into account the elasticity, damping and nonlinear response of brain tissue, and can more accurately simulate the contact process between brain tissue and syringe. Through this model, the mechanical behavior during the syringe insertion process can be better predicted and controlled, thereby improving the accuracy and controllability of the experiment. Combining high-precision sensors with the biomechanical properties of zebrafish brain tissue, the feedback force changes during the insertion process can be monitored in real time, and the insertion depth and speed can be adjusted based on the feedback information. This real-time response mechanism makes the injection process smoother and can automatically adjust to the biomechanical properties of brain tissue, reducing errors caused by human operation. The adaptive control algorithm can accurately adjust the insertion speed and depth of the syringe based on real-time feedback force data to ensure the smoothness of the injection process. This control mechanism can cope with different biomechanical properties, so that the insertion process is always kept within a safe and effective range. By dynamically adjusting and accurately controlling the insertion depth, the entire injection process is safer. Especially in operations in sensitive areas of the brain, it can effectively avoid damage caused by excessive insertion, ensure the health of zebrafish, and improve the credibility of experimental results.

[0067] In one embodiment, the specific steps of collecting zebrafish brain electrophysiological signals, analyzing the collected signals in real time, and feeding back the analysis results in real time are as follows:

[0068] S331, Use microelectrode array technology to obtain zebrafish brain electrophysiological signals;

[0069] S332. Perform multi-band bandpass filtering on the EEG signal to remove unnecessary low-frequency noise and high-frequency interference, and use wavelet transform to extract time-frequency features. The filtering model is as follows:

[0070]

[0071] Among them, X raw (t) is the original EEG signal, H(f) is the transfer function of the bandpass filter, and df represents the small frequency change in the frequency domain;

[0072] S333, connecting the EEG signal acquisition system to the computer real-time analysis module, extracting the time-frequency characteristics of the EEG signal by a time-frequency analysis method, and analyzing the time and frequency characteristics of the signal at the same time;

[0073] S334. Decode neural activity information from EEG signals and predict the behavior or response of zebrafish under different EEG states by training deep learning models.

[0074] S335. Based on the analysis results of the real-time EEG signal, the real-time status of the zebrafish is fed back, and the process is stopped when the zebrafish is in an abnormal state to avoid mechanical damage to the zebrafish.

[0075] Microelectrode array technology can efficiently and accurately obtain electrophysiological signals from the zebrafish brain. The advantage of this technology is that it can monitor the electrical activities of multiple brain regions at the same time, with high spatial resolution, thus providing comprehensive EEG information and providing a reliable data source for subsequent signal analysis and decoding. Through the multi-band bandpass filter, low-frequency noise and high-frequency interference in the original EEG signal can be effectively removed to improve the quality of the signal. This helps to improve the accuracy of subsequent analysis and avoid the influence of interference factors on EEG signal processing. After connecting the EEG signal acquisition system to the computer analysis module, real-time analysis of the signal can be achieved to ensure rapid data transmission and timely processing. The experimental process can be adjusted in real time according to the neural activity of the zebrafish to ensure that its state is always in an appropriate range. By decoding the neural activity information in the EEG signal and associating it with behavior or reaction, an accurate zebrafish behavior model can be constructed. The training of deep learning models can help further understand the reaction mechanism of zebrafish under different EEG states and provide a new perspective for behavior prediction. Real-time analysis of EEG signals and feedback of the zebrafish's state based on the results can effectively monitor the physiological state of the zebrafish in the experiment. Once the zebrafish is found to be in an abnormal state, the system can respond immediately to prevent further damage.

[0076] In one embodiment, the specific steps of injecting the FeCl3 solution through a micro-syringe and controlling the injection speed through real-time feedback analysis results during the injection process are as follows:

[0077] S341. Establish the relationship between flow rate and brain tissue pressure. Obtain the reaction force of brain tissue to the injection solution through the pressure applied by the syringe. The relationship between injection flow rate and injection pressure can be expressed as:

[0078]

[0079] V in (t) is the injection flow rate, F in (t) is the feedback force applied during the injection process, μ fluid is the viscosity of the solution, A needle is the cross-sectional area of ​​the injection needle;

[0080] S342. Based on the mechanical model of the injection process, a dynamic feedback control system is designed to adjust the injection speed in real time to ensure that the feedback force during the injection process remains within a reasonable range and to avoid injection that is too fast or too slow. The injection speed is dynamically adjusted based on real-time monitoring of the feedback force. The PID controller formula is:

[0081]

[0082] Where u(t) is the control value of injection speed, e(t) is the error between feedback force and target force, K p is the proportional gain, K i is the integral gain, K d is the differential gain;

[0083] S343. The real-time feedback force analysis results will be used to dynamically adjust the speed of the syringe. During the injection process, if the feedback force exceeds the set threshold, the system will automatically reduce the injection speed; if the feedback force is low, the system may increase the injection speed. The dynamic adjustment formula is as follows:

[0084] V(t)=[K·(F in (t)-F target )]+V base

[0085] Where V(t) is the injection speed after dynamic adjustment, F in (t) is the real-time feedback force, F target is the target feedback force, K is the adjustment coefficient, V base is the initial injection speed;

[0086] S344. Use an optimization algorithm to adjust the parameters during the injection process to ensure the best injection effect. The optimization objective function is as follows:

[0087]

[0088] Among them, α1 and α2 are optimization coefficients used to balance the weight between feedback force and injection speed, F in (t) is the real-time feedback force, F target is the target feedback force, V opatimal The optimal injection speed.

[0089] By establishing a mathematical model between flow rate and brain tissue pressure, it is possible to clarify how the pressure applied during the injection process affects the response of brain tissue. This provides a theoretical basis for optimizing the injection process, and can avoid tissue damage or uneven solution diffusion caused by too fast or too slow injection speeds. By designing a dynamic feedback control system based on a mechanical model, the injection speed can be adjusted in real time during the injection process. This method ensures that the feedback force is always kept within a reasonable range during the injection process, avoiding the risks of too fast or too slow injection speeds. By monitoring the feedback force in real time, the system can dynamically adjust the speed of the syringe. If the feedback force exceeds the set threshold, the injection speed will automatically decrease to avoid excessive pressure; if the feedback force is low, the system will increase the injection speed to ensure smooth injection. This real-time adjustment mechanism makes the entire injection process more intelligent and flexible. Through the optimization algorithm, multiple parameters involved in the injection process can be adjusted to find the optimal injection speed. This optimization process helps achieve maximum efficiency during the injection process and minimizes potential damage to zebrafish brain tissue. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 Flow chart of the constructed method for FeCl3-induced epilepsy in zebrafish;

[0091] Figure 2 A flowchart of the specific steps for placing zebrafish in a supine position in the cracks of a moist cotton pad after a 0°C ice water bath;

[0092] Figure 3 For intracerebral injection: Flow chart of the specific steps of injecting FeCl3 solution into the left brain of zebrafish through the skull base;

[0093] Figure 4 This is the pathological gross appearance of the normal saline group;

[0094] Figure 5 This is the gross pathological appearance of the FeCl3 group. DETAILED DESCRIPTION

[0095] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0096] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0097] The following describes the construction method of zebrafish FeCl3-induced epilepsy according to some embodiments of the present invention with reference to the accompanying drawings.

[0098] Example 1

[0099] like Figure 1 As shown, this embodiment discloses a method for constructing a zebrafish FeCl3-induced epilepsy, comprising the following steps:

[0100] S1. Prepare micro-syringe: Use a needle puller to pull the tip of a glass microelectrode and connect it to a micro-injector with a hot-melt glue gun, and fill the syringe with FeCl3 solution;

[0101] S2, place the zebrafish in a supine position in the crack of a moist cotton pad after a 0°C ice water bath;

[0102] S3. Intracerebral injection: FeCl3 solution was injected into the left brain of zebrafish via the skull base.

[0103] The present application discloses a method for constructing a zebrafish FeCl3-induced epilepsy. The production of the glass microelectrode tip can be accurate to the micron level, thereby ensuring the accuracy of the injection. The drawing accuracy and morphology of the microelectrode tip directly affect the meticulousness of the FeCl3 solution injection. Through the adjustment of the needle puller, an extremely fine tip that meets the experimental requirements can be produced to ensure the control of the solution flow rate during the injection process. The connection with the microelectrode is fixed by a hot melt glue gun to ensure the stability of the injector and the injection needle. This reduces the inaccurate injection caused by loose connection or poor contact, avoids the risk of FeCl3 solution leakage, and ensures the consistency and reliability of the experiment. Through precise preparation and connection, contaminants are prevented from entering the syringe, ensuring the purity of the FeCl3 solution, which helps to ensure the effect of zebrafish brain injection. Through the cooling effect of a 0°C ice water bath, the activity of zebrafish can be reduced, so that it enters a state similar to anesthesia, reducing its stress response to the injection process. Hypothermia anesthesia can reduce the metabolic level of zebrafish, reduce its movement and physiological reactions, and improve the stability of experimental operations. Placing zebrafish in the cracks of a moist cotton pad can not only effectively prevent them from twitching, struggling and other unstable behaviors caused by cooling, but also keep them in a supine position. Injection of FeCl3 (ferric chloride solution) into the zebrafish brain can induce local nerve excitement, resulting in symptoms similar to epilepsy. Through precise intracerebral injection, it can be ensured that the FeCl3 solution only works in the target area, minimizing interference with other brain areas of the zebrafish and ensuring the controllability of the experiment.

[0104] In addition to the features of the above embodiments, this embodiment further defines that: the capacity of the micro-injector is 2 μL.

[0105] In addition to the features of the above embodiments, this embodiment further defines that: the zebrafish is 3 months old and has an average body weight of 300 mg.

[0106] In addition to the features of the above embodiments, this embodiment further defines that: the FeCl3 solution is prepared by weighing a certain amount of FeCl3 powder with a micro-spoon and dissolving it in physiological saline, and the volume of the FeCl3 solution is 1 μL and the concentration is 0.01 mM.

[0107] Example 2

[0108] In addition to the features of the above embodiment, this embodiment further defines that the preparation of the micro-syringe comprises the following steps:

[0109] The tip pulled by the needle puller is a glass microelectrode tip with a diameter of 0.2-0.4 μm;

[0110] Control the specific drawing parameters of the needle puller during the drawing process.

[0111] In addition to the features of the above embodiment, this embodiment further defines that the specific steps of the needle pulling instrument to pull a glass microelectrode tip with a diameter of 0.2-0.4 μm are as follows:

[0112] Select the glass tube for making glass microelectrodes, and adjust the power of the heating source in real time through the temperature control system;

[0113] The power of the heating source is adjusted in real time through the temperature control system to keep the temperature of the glass tube stable. After the glass tube softens, the diameter of the microelectrode tip is monitored in real time by the system.

[0114] A nonlinear feedback control system is used to adjust the tension and stretching speed parameters to ensure the uniformity of the tip. The tension adjustment formula is as follows:

[0115]

[0116] Where P ull is the tension, P0 is the initial tension, d target is the target diameter, d(t) is the glass tip diameter measured in real time, α is the decay constant, and t is the time;

[0117] Using the nonlinear relationship between the stretching rate and the glass tip diameter, the dynamic adjustment formula of the stretching rate is obtained as follows:

[0118]

[0119] Among them, V stretch is the target diameter, k1 and k2 are constants set according to the experimental results, d(t) is the glass tip diameter measured in real time, and n is a control parameter, which represents the dependence of the diameter on the stretching rate;

[0120] After the glass tip reaches the target size, it is quickly cooled by a precisely controlled cooling system to avoid glass deformation. During the cooling process, the heat conduction equation is applied to control the cooling rate:

[0121]

[0122] Where: T is the temperature of the glass tip, T ambient is the ambient temperature, and k is the thermal conductivity coefficient.

[0123] The temperature control system ensures that the temperature of the glass tube is always kept within a precise range during the heating process by adjusting the power of the heating source in real time. The precise control of the temperature control system avoids the potential risks brought by temperature changes, making the manufactured microelectrode tip more uniform and stable. By monitoring the diameter of the glass tip in real time and adjusting the system parameters based on feedback, the diameter of the glass tip can be ensured to be accurate to the target value. During the stretching process, the uniformity of the microelectrode tip directly affects the results of the electrophysiological experiment. The adjustment formula of the tension takes into account the material properties of the glass tube and the change of the tip diameter. The system can adjust the tension according to real-time feedback to ensure uniformity during the stretching process, thereby avoiding irregular shapes or asymmetry of the tip. The stretching process is more refined and stable by adjusting the stretching speed and tension through a nonlinear feedback control system. Controlling the cooling rate not only helps to prevent deformation, but also optimizes the structure of the glass material and ensures the mechanical strength of the glass microelectrode.

[0124] Example 3

[0125] like Figure 2 As shown, in addition to the features of the above embodiment, this embodiment further defines the specific steps of placing the zebrafish in a supine position in the crack of a wet cotton pad after a 0°C ice water bath as follows:

[0126] S21. The water bath is equipped with multiple micro temperature sensors. Through data fusion and algorithm processing, the body temperature of zebrafish is kept stable within the range of 0℃±0.05℃ to avoid the difference in anesthesia depth caused by temperature fluctuations.

[0127] S22. Cut a slit in the center of the cotton pad that matches the shape of the zebrafish's back. The depth of the slit is enough to accommodate the zebrafish's body to ensure that it remains in a supine position.

[0128] S23. Flexible support strips are installed on both sides of the crack of the cotton pad. The flexible support strips are made of medical-grade silicone material. The support strips are used to adapt to the changes in the body shape of the zebrafish and provide flexible fixation. The two ends of the flexible support strips are connected to the platform through micro-spring devices to ensure that the flexible support strips can be adaptively adjusted according to slight changes in the body shape of the zebrafish.

[0129] S24. Install a vibration sensor on the platform to monitor external vibrations, arrange a circular microelectrode array around the cotton pad, and adjust the position of the zebrafish in real time by adjusting the voltage and frequency of the electric field.

[0130] Improved temperature control accuracy ensures that zebrafish can maintain a consistent depth of anesthesia during anesthesia, avoiding unnecessary stimulation or over-anesthesia caused by temperature fluctuations. This is very important for the accuracy of subsequent operations (such as injection or behavioral observation), ensuring the stability and repeatability of the anesthesia state. Through precise temperature control, the interference caused by temperature fluctuations can be reduced, ensuring that the zebrafish maintains a stable state and improving repeatability. The slit cut in the center of the cotton pad provides a customized support that matches the shape of the zebrafish's back to ensure the stability of its supine posture. This customized design avoids the subsequent operation of zebrafish due to unstable posture or unnatural movement during anesthesia. The flexible support strip can be adaptively adjusted according to the slight changes in the zebrafish's body shape, ensuring that the zebrafish can maintain a natural posture during the fixation process without unnecessary constraints. This flexible support design not only improves the stability of fixation, but also reduces the movement interference of the zebrafish, allowing it to maintain a relatively stable state during injection, behavioral observation, etc. The introduction of the micro-spring device can adjust the position of the support strip in real time to adapt to the slight changes in the size of the zebrafish in the water bath, thereby ensuring its stability throughout the anesthesia and experimental process. By arranging the circular microelectrode array and adjusting the voltage and frequency of the electric field, the position of the zebrafish can be adjusted in real time to prevent it from shifting due to improper position or slight external interference during the experiment. This precise control can ensure that the zebrafish is always in the most suitable experimental position, improving the controllability of the experiment and the accuracy of the data.

[0131] Example 4

[0132] like Figure 3 As shown, in addition to the features of the above embodiment, this embodiment further defines: Intracerebral injection: The specific steps of injecting FeCl3 solution into the left brain of zebrafish through the skull base are as follows:

[0133] S31, scanning the zebrafish to identify the left brain region at the base of the skull;

[0134] S32, inserting a microinjection syringe into the left brain region of the zebrafish's skull base, and determining the insertion depth based on the feedback force received by the microinjection syringe;

[0135] S33, collecting zebrafish brain electrophysiological signals, analyzing the collected signals in real time, and feeding back the analysis results in real time;

[0136] S34, injecting FeCl3 solution through a micro-syringe, and controlling the injection speed through real-time feedback analysis results during the injection process.

[0137] By scanning the zebrafish and identifying the left brain region at the base of the skull, the injection target can be accurately positioned, so that subsequent operations are not affected by position errors, ensuring accurate injection of the FeCl3 solution. After accurately identifying the zebrafish brain region, the syringe can be inserted more accurately, reducing errors and avoiding accidental injuries to other brain regions or body parts of the zebrafish. Ensure high accuracy during the injection process and minimize potential experimental risks. Through real-time adjustment of the feedback force, damage to the zebrafish skull base brain region can be effectively avoided. Too deep an injection may damage brain tissue and cause irreversible damage, while too shallow an injection may not accurately deliver the drug. Accurate depth control ensures the safety and effectiveness of the operation. By analyzing the signal in real time, timely adjustments can be made to the experimental operation. If abnormal fluctuations in brain electrical activity are detected, the injection parameters, injection time or injection location can be adjusted according to real-time feedback to avoid negative effects on the zebrafish and ensure the accuracy and safety of the experiment. Through real-time feedback analysis results, the injection speed of the microsyringe can be adjusted to adapt to the physiological response of the zebrafish. Dynamically adjusting the injection rate can ensure that the drug is injected at the most appropriate speed and dose, avoiding adverse reactions caused by too fast or too slow injection, such as drug leakage, drug overdose or overstimulation. Dynamically adjusting the injection rate can better control the physiological effects of FeCl3 solution, so that the biological effects of the drug are consistent in each experiment. This is crucial for observing the neural effects or electrophysiological changes caused by FeCl3 solution, ensuring the reliability of experimental results.

[0138] Embodiment 5:

[0139] In addition to the features of the above embodiment, this embodiment further defines that the specific steps of scanning the zebrafish and identifying the left brain region of the skull base of the zebrafish are as follows:

[0140] S311. Construct a 3D model database of zebrafish brain based on different developmental stages, including the size, morphology and distribution characteristics of each layer, anatomical landmarks and each brain region of the zebrafish brain;

[0141] S312. Based on the unique brain structure characteristics of zebrafish, a specially trained deep convolutional neural network is designed to perform automatic anatomical differentiation and labeling. The mathematical model is as follows:

[0142] P left-brain (x, y, z) = f θ (I ct / us (x, y, z)

[0143] Among them, I ct / us (x, y, z) is the input image data of multimodal imaging, f θ is a trained deep neural network;

[0144] S313. After completing the image data input and deep learning model training, automatically identify the various structures and their boundaries in the zebrafish brain, use real-time image processing and edge detection algorithms, combined with morphological analysis to accurately locate the left skull base area;

[0145] S314, using adaptive filters to detect different levels of brain tissue:

[0146]

[0147] where h σ (x) is a Gaussian filter used to remove noise and retain key structures. filtered (x, y, z) is the image data after filtering, I layer (x, y, z) represents the image data of a slice;

[0148] S315. Locate the left brain region at the skull base of zebrafish and provide feedback on the position information.

[0149] By collecting anatomical data of zebrafish brain at different developmental stages, a detailed 3D model database can be constructed to provide rich basic data as a standard reference for zebrafish brain at different developmental stages. With detailed brain landmarks and distribution characteristics, the injection area can be located more accurately, the operation error can be reduced, and the consistency and reliability of the results can be ensured. Automatic anatomical differentiation and labeling using deep convolutional neural networks can greatly improve the analysis efficiency and reduce the errors and deviations caused by manual labeling. The deep learning model can identify complex brain structure features and ensure high-precision brain area calibration. By accurately locating the left skull base area, the microinjector can accurately inject the FeCl3 solution into the target area. Accurate positioning can reduce errors, avoid injection into other areas, reduce experimental errors, and improve the success rate of the experiment. The filtered image data can provide a clearer brain tissue structure, which helps to improve the accuracy of morphological analysis. Ensure that the morphology and distribution of brain structures can be accurately extracted during the analysis, especially the accurate identification of fine parts, to avoid blurring or noise affecting the results. Real-time feedback of position information not only improves the degree of automation of operations, but also enhances accuracy, especially during rapid response and adjustment, effectively ensuring that operations are not affected by external interference or errors.

[0150] Example 6

[0151] In addition to the features of the above embodiment, this embodiment further defines the following specific steps of inserting a micro-injection syringe into the left brain region of the skull base of zebrafish and determining the insertion depth by the feedback force received by the micro-injection syringe:

[0152] S321. Install a high-precision micro-force sensor on the tip of the micro-injector or the body of the micro-injector to monitor in real time the feedback force exerted on the syringe when it contacts the zebrafish brain tissue;

[0153] S322. Establish a nonlinear mechanical model to simulate the interaction between brain tissue and syringe. The contact force model is as follows:

[0154] F contact =k brain ΔZ+γ brain ·ΔD+β brain ·(ΔZ) n

[0155] Among them, F contact is the feedback force when the syringe contacts the brain tissue, ΔZ is the insertion depth of the syringe, ΔD is the insertion speed of the syringe, and k brain is the elastic stiffness of brain tissue, γ brain is the damping coefficient of brain tissue, β brain and n are parameters used to fit the nonlinear response of brain tissue;

[0156] S323. Real-time feedback force is obtained through high-precision sensors. Combined with the biomechanical properties of zebrafish brain tissue, a dynamic response model of real-time insertion depth and feedback force is constructed. The dynamic feedback force model is as follows:

[0157] F feedback (t) = k sensor ·(F contact (t)-F threshold )

[0158] Among them, F contact (t) is the real-time feedback force detected by the sensor, K sensor is the sensitivity of the sensor, F threshold The threshold set for the sensor represents the minimum force between the syringe and the brain tissue.

[0159] S324. According to the feedback force data obtained from the micro-force sensor, the insertion depth of the syringe is calculated in real time, and the insertion speed and insertion depth of the syringe are adjusted by an adaptive control algorithm. The relationship between the insertion depth and the feedback force is realized by the following adaptive formula:

[0160]

[0161] Where z(t) is the current insertion depth of the syringe, z0 is the initial depth, e(t) is the error between the feedback force and the target feedback force, and K p , K i and K d are proportional, integral and derivative control gains, F targetis the target contact force, which is set according to the mechanical properties of zebrafish brain tissue;

[0162] S325, adjusting the control strategy in real time, and stopping the insertion action when the insertion depth reaches the target range.

[0163] By installing sensors on the syringe, the feedback force generated during the insertion process can be obtained in real time, and real-time adjustments can be made based on the data to ensure that the movement of the syringe is smoother and to avoid discomfort or misoperation caused by too fast insertion. The nonlinear mechanical model takes into account the elasticity, damping and nonlinear response of brain tissue, and can more accurately simulate the contact process between brain tissue and syringe. Through this model, the mechanical behavior during the syringe insertion process can be better predicted and controlled, thereby improving the accuracy and controllability of the experiment. Combining high-precision sensors with the biomechanical properties of zebrafish brain tissue, the feedback force changes during the insertion process can be monitored in real time, and the insertion depth and speed can be adjusted based on the feedback information. This real-time response mechanism makes the injection process smoother and can automatically adjust to the biomechanical properties of brain tissue, reducing errors caused by human operation. The adaptive control algorithm can accurately adjust the insertion speed and depth of the syringe based on real-time feedback force data to ensure the smoothness of the injection process. This control mechanism can cope with different biomechanical properties, so that the insertion process is always kept within a safe and effective range. By dynamically adjusting and accurately controlling the insertion depth, the entire injection process is safer. Especially in operations in sensitive areas of the brain, it can effectively avoid damage caused by excessive insertion, ensure the health of zebrafish, and improve the credibility of experimental results.

[0164] Example 7

[0165] In addition to the features of the above embodiment, this embodiment further defines the following specific steps for collecting zebrafish brain electrophysiological signals, analyzing the collected signals in real time, and providing real-time feedback of the analysis results:

[0166] S331, Use microelectrode array technology to obtain zebrafish brain electrophysiological signals;

[0167] S332. Perform multi-band bandpass filtering on the EEG signal to remove unnecessary low-frequency noise and high-frequency interference, and use wavelet transform to extract time-frequency features. The filtering model is as follows:

[0168]

[0169] Among them, X raw (t) is the original EEG signal, H(f) is the transfer function of the bandpass filter, and df represents the small frequency change in the frequency domain;

[0170] S333, connecting the EEG signal acquisition system to the computer real-time analysis module, extracting the time-frequency characteristics of the EEG signal by a time-frequency analysis method, and analyzing the time and frequency characteristics of the signal at the same time;

[0171] S334. Decode neural activity information from EEG signals and predict the behavior or response of zebrafish under different EEG states by training deep learning models.

[0172] S335. Based on the analysis results of the real-time EEG signal, the real-time status of the zebrafish is fed back, and the process is stopped when the zebrafish is in an abnormal state to avoid mechanical damage to the zebrafish.

[0173] Microelectrode array technology can efficiently and accurately obtain electrophysiological signals from the zebrafish brain. The advantage of this technology is that it can monitor the electrical activities of multiple brain regions at the same time, with high spatial resolution, thus providing comprehensive EEG information and providing a reliable data source for subsequent signal analysis and decoding. Through the multi-band bandpass filter, low-frequency noise and high-frequency interference in the original EEG signal can be effectively removed to improve the quality of the signal. This helps to improve the accuracy of subsequent analysis and avoid the influence of interference factors on EEG signal processing. After connecting the EEG signal acquisition system to the computer analysis module, real-time analysis of the signal can be achieved to ensure rapid data transmission and timely processing. The experimental process can be adjusted in real time according to the neural activity of the zebrafish to ensure that its state is always in an appropriate range. By decoding the neural activity information in the EEG signal and associating it with behavior or reaction, an accurate zebrafish behavior model can be constructed. The training of deep learning models can help further understand the reaction mechanism of zebrafish under different EEG states and provide a new perspective for behavior prediction. Real-time analysis of EEG signals and feedback of the zebrafish's state based on the results can effectively monitor the physiological state of the zebrafish in the experiment. Once the zebrafish is found to be in an abnormal state, the system can respond immediately to prevent further damage.

[0174] Example 8

[0175] In addition to the features of the above embodiment, this embodiment further defines the following specific steps: injecting the FeCl3 solution through a micro-syringe, and controlling the injection speed through real-time feedback analysis results during the injection process:

[0176] S341. Establish the relationship between flow rate and brain tissue pressure. Obtain the reaction force of brain tissue to the injection solution through the pressure applied by the syringe. The relationship between injection flow rate and injection pressure can be expressed as:

[0177]

[0178] V in (t) is the injection flow rate, F in (t) is the feedback force applied during the injection process, μfluid is the viscosity of the solution, A needle is the cross-sectional area of ​​the injection needle;

[0179] S342. Based on the mechanical model of the injection process, a dynamic feedback control system is designed to adjust the injection speed in real time to ensure that the feedback force during the injection process remains within a reasonable range and to avoid injection that is too fast or too slow. The injection speed is dynamically adjusted based on real-time monitoring of the feedback force. The PID controller formula is:

[0180]

[0181] Among them, u(t) is the control amount of injection speed, e(t) is the error between feedback force and target force, Kp is proportional gain, Ki is integral gain, and Kd is differential gain;

[0182] S343. The real-time feedback force analysis results will be used to dynamically adjust the speed of the syringe. During the injection process, if the feedback force exceeds the set threshold, the system will automatically reduce the injection speed; if the feedback force is low, the system may increase the injection speed. The dynamic adjustment formula is as follows:

[0183]

[0184] Where V(t) is the injection speed after dynamic adjustment, F in (t) is the real-time feedback force, F target is the target feedback force, K is the adjustment coefficient, V base is the initial injection speed;

[0185] S344. Use an optimization algorithm to adjust the parameters during the injection process to ensure the best injection effect. The optimization objective function is as follows:

[0186]

[0187] Among them, α1 and α2 are optimization coefficients used to balance the weight between feedback force and injection speed, F in (t) is the real-time feedback force, F target is the target feedback force, V opatimal The optimal injection speed.

[0188] By establishing a mathematical model between flow rate and brain tissue pressure, it is possible to clarify how the pressure applied during the injection process affects the response of brain tissue. This provides a theoretical basis for optimizing the injection process, and can avoid tissue damage or uneven solution diffusion caused by too fast or too slow injection speeds. By designing a dynamic feedback control system based on a mechanical model, the injection speed can be adjusted in real time during the injection process. This method ensures that the feedback force is always kept within a reasonable range during the injection process, avoiding the risks of too fast or too slow injection speeds. By monitoring the feedback force in real time, the system can dynamically adjust the speed of the syringe. If the feedback force exceeds the set threshold, the injection speed will automatically decrease to avoid excessive pressure; if the feedback force is low, the system will increase the injection speed to ensure smooth injection. This real-time adjustment mechanism makes the entire injection process more intelligent and flexible. Through the optimization algorithm, multiple parameters involved in the injection process can be adjusted to find the optimal injection speed. This optimization process helps achieve maximum efficiency during the injection process and minimizes potential damage to zebrafish brain tissue.

[0189] Example 9

[0190] 1. Animal selection: 3-month-old adult AB wild-type zebrafish weighing approximately 450 mg.

[0191] 2. Grouping: FeCl3 group and normal saline group.

[0192] 3. Experimental procedure: 20 3-month-old adult wild-type AB zebrafish were randomly divided into 2 groups. After being placed in a 0°C ice-water mixture to stop swimming, 1 μL of 0.01 mM FeCl3 solution or an equal volume of normal saline was injected through the left midbrain at the skull base.

[0193] n PTEM success number PTEM unsuccessful number Success rate mortality rate <![CDATA[FeCl3 group]]> 10 8 2 80％ 0％ Normal saline group 10 0 10 0％ 0％

[0194] PTEM: Post-tramatic epilepsy model.

[0195] For the gross pathological features of different groups, see Figure 4 and Figure 5 .

[0196] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0197] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for constructing zebrafish FeCl3-induced epilepsy, characterized in that: The method for constructing zebrafish FeCl3-induced epilepsy comprises the following steps: S1. Prepare micro-syringe: Use a needle puller to pull the tip of a glass microelectrode and connect it to a micro-injector with a hot-melt glue gun, and fill the syringe with FeCl3 solution; S2, place the zebrafish in a supine position in the crack of a moist cotton pad after a 0°C ice water bath; S3. Intracerebral injection: FeCl3 solution was injected into the left brain of zebrafish via the skull base.

2. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 1, characterized in that: The capacity of the micro-injector is 2 μL; and / or the zebrafish are 3 months old and have an average body weight of 300 mg; And / or the FeCl3 solution is prepared by weighing a certain amount of FeCl3 powder with a micro-spoon and dissolving it in physiological saline, the volume of the FeCl3 solution is 1 μL and the concentration is 0.01 mM.

3. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 1, characterized in that: The preparation of the micro-syringe comprises the following steps: The tip pulled by the needle puller is a glass microelectrode tip with a diameter of 0.2-0.4 μm; Control the specific drawing parameters of the needle puller during the drawing process.

4. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 3, characterized in that: The specific steps of the needle pulling instrument for pulling a glass microelectrode tip with a tip diameter of 0.2-0.4 μm are as follows: Select the glass tube for making glass microelectrodes, and adjust the power of the heating source in real time through the temperature control system; The power of the heating source is adjusted in real time through the temperature control system to keep the temperature of the glass tube stable. After the glass tube softens, the diameter of the microelectrode tip is monitored in real time by the system. A nonlinear feedback control system is used to adjust the tension and stretching speed parameters to ensure the uniformity of the tip. The tension adjustment formula is as follows: Where P ull is the tension, P0 is the initial tension, d target is the target diameter, d(t) is the glass tip diameter measured in real time, α is the decay constant, and t is the time; Using the nonlinear relationship between the stretching rate and the glass tip diameter, the dynamic adjustment formula of the stretching rate is obtained as follows: Among them, V stretch is the target diameter, k1 and k2 are constants set according to the experimental results, d(t) is the glass tip diameter measured in real time, and n is a control parameter, which represents the dependence of the diameter on the stretching rate; After the glass tip reaches the target size, it is quickly cooled by a precisely controlled cooling system to avoid glass deformation. During the cooling process, the heat conduction equation is used to control the cooling rate: Where: T is the temperature of the glass tip, T ambient is the ambient temperature, and k is the thermal conductivity coefficient.

5. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 1, characterized in that: The specific steps of placing the zebrafish in a supine position in the crack of a wet cotton pad after a 0°C ice water bath are as follows: S21. The water bath is equipped with multiple micro temperature sensors. Through data fusion and algorithm processing, the body temperature of zebrafish is kept stable within the range of 0℃±0.05℃ to avoid the difference in anesthesia depth caused by temperature fluctuations. S22. Cut a slit in the center of the cotton pad that matches the shape of the zebrafish's back. The depth of the slit is enough to accommodate the zebrafish's body to ensure that it remains in a supine position. S23. Flexible support strips are installed on both sides of the crack of the cotton pad. The flexible support strips are made of medical-grade silicone material. The support strips are used to adapt to the changes in the body shape of the zebrafish and provide flexible fixation. The two ends of the flexible support strips are connected to the platform through micro-spring devices to ensure that the flexible support strips can be adaptively adjusted according to slight changes in the body shape of the zebrafish. S24. Install a vibration sensor on the platform to monitor external vibrations, arrange a circular microelectrode array around the cotton pad, and adjust the position of the zebrafish in real time by adjusting the voltage and frequency of the electric field.

6. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 1, characterized in that: The specific steps of intracerebral injection: injecting FeCl3 solution into the left brain of zebrafish through the skull base are as follows: S31, scanning the zebrafish to identify the left brain region at the base of the skull; S32, inserting a microinjection syringe into the left brain region of the zebrafish's skull base, and determining the insertion depth based on the feedback force received by the microinjection syringe; S33, collecting zebrafish brain electrophysiological signals, analyzing the collected signals in real time, and feeding back the analysis results in real time; S34, injecting FeCl3 solution through a micro-syringe, and controlling the injection speed through real-time feedback analysis results during the injection process.

7. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 6, characterized in that: The specific steps of scanning the zebrafish and identifying the left brain region of the zebrafish's skull base are as follows: S311. Construct a 3D model database of zebrafish brain based on different developmental stages, including the size, morphology and distribution characteristics of each layer, anatomical landmarks and each brain region of the zebrafish brain; S312. Based on the unique brain structure characteristics of zebrafish, a specially trained deep convolutional neural network is designed to perform automatic anatomical differentiation and labeling. The mathematical model is as follows: P left-brain (x,y,z)=f θ (I ct / us (x,y,z)) Among them, I ct / us (x, y, z) is the input image data of multimodal imaging, f θ is a trained deep neural network; S313. After completing the image data input and deep learning model training, automatically identify the various structures and their boundaries in the zebrafish brain, use real-time image processing and edge detection algorithms, combined with morphological analysis to accurately locate the left skull base area; S314, using adaptive filters to detect different levels of brain tissue: where h σ (x) is a Gaussian filter used to remove noise and retain key structures. filtered (x, y, z) is the image data after filtering, I layer (x, y, z) represents the image data of a slice; S315. Locate the left brain region at the skull base of zebrafish and provide feedback on the position information.

8. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 6, characterized in that: The specific steps of inserting a micro-injection syringe into the left brain region of the zebrafish skull base and determining the insertion depth by the feedback force received by the micro-injection syringe are as follows: S321. Install a high-precision micro-force sensor on the tip of the micro-injector or the body of the micro-injector to monitor in real time the feedback force exerted on the syringe when it contacts the zebrafish brain tissue; S322. Establish a nonlinear mechanical model to simulate the interaction between brain tissue and syringe. The contact force model is as follows: F contact =k brain ·ΔZ+γ brain ·ΔD+β brain ·(ΔZ) n Among them, F contact is the feedback force when the syringe contacts the brain tissue, ΔZ is the insertion depth of the syringe, ΔD is the insertion speed of the syringe, and k brain is the elastic stiffness of brain tissue, γ brain is the damping coefficient of brain tissue, β brain and n are parameters used to fit the nonlinear response of brain tissue; S323. Real-time feedback force is obtained through high-precision sensors. Combined with the biomechanical properties of zebrafish brain tissue, a dynamic response model of real-time insertion depth and feedback force is constructed. The dynamic feedback force model is as follows: F feedback (t)=k sensor ·(F contact (t)-F threshold ) Among them, F contact (t) is the real-time feedback force detected by the sensor, K sensor is the sensitivity of the sensor, F threshold The threshold set for the sensor represents the minimum force between the syringe and the brain tissue. S324. According to the feedback force data obtained from the micro-force sensor, the insertion depth of the syringe is calculated in real time, and the insertion speed and insertion depth of the syringe are adjusted by an adaptive control algorithm. The relationship between the insertion depth and the feedback force is realized by the following adaptive formula: Where z(t) is the current insertion depth of the syringe, z0 is the initial depth, e(t) is the error between the feedback force and the target feedback force, and K p , K i and K d are proportional, integral and derivative control gains, F target is the target contact force, which is set according to the mechanical properties of zebrafish brain tissue; S325, adjusting the control strategy in real time, and stopping the insertion action when the insertion depth reaches the target range.

9. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 6, characterized in that: The specific steps of collecting zebrafish brain electrophysiological signals, analyzing the collected signals in real time, and feeding back the analysis results in real time are as follows: S331, Use microelectrode array technology to obtain zebrafish brain electrophysiological signals; S332. Perform multi-band bandpass filtering on the EEG signal to remove unnecessary low-frequency noise and high-frequency interference, and use wavelet transform to extract time-frequency features. The filtering model is as follows: Among them, X raw (t) is the original EEG signal, H(f) is the transfer function of the bandpass filter, and df represents the small frequency change in the frequency domain; S333, connecting the EEG signal acquisition system to the computer real-time analysis module, extracting the time-frequency characteristics of the EEG signal by a time-frequency analysis method, and analyzing the time and frequency characteristics of the signal at the same time; S334. Decode neural activity information from EEG signals and predict the behavior or response of zebrafish under different EEG states by training deep learning models. S335. Based on the analysis results of the real-time EEG signal, the real-time status of the zebrafish is fed back, and the process is stopped when the zebrafish is in an abnormal state to avoid mechanical damage to the zebrafish.

10. The method for constructing a zebrafish FeCl3-induced epilepsy model according to claim 6, characterized in that: The specific steps of injecting the FeCl3 solution through a micro-syringe and controlling the injection speed through real-time feedback analysis results during the injection process are as follows: S341. Establish the relationship between flow rate and brain tissue pressure. Obtain the reaction force of brain tissue to the injection solution through the pressure applied by the syringe. The relationship between injection flow rate and injection pressure can be expressed as: V in (t) is the injection flow rate, F in (t) is the feedback force applied during the injection process, μ fluid is the viscosity of the solution, A needle is the cross-sectional area of ​​the injection needle; S342. Based on the mechanical model of the injection process, a dynamic feedback control system is designed to adjust the injection speed in real time to ensure that the feedback force during the injection process remains within a reasonable range and to avoid injection that is too fast or too slow. The injection speed is dynamically adjusted based on real-time monitoring of the feedback force. The PID controller formula is: Where u(t) is the control value of injection speed, e(t) is the error between feedback force and target force, K p is the proportional gain, K i is the integral gain, K d is the differential gain; S343. The real-time feedback force analysis results will be used to dynamically adjust the speed of the syringe. During the injection process, if the feedback force exceeds the set threshold, the system will automatically reduce the injection speed; if the feedback force is low, the system may increase the injection speed. The dynamic adjustment formula is as follows: V(t)=[K·(F in (t)-F target )]+V base Where V(t) is the injection speed after dynamic adjustment, F in (t) is the real-time feedback force, F target is the target feedback force, K is the adjustment coefficient, V base is the initial injection speed; S344. Use an optimization algorithm to adjust the parameters during the injection process to ensure the best injection effect. The optimization objective function is as follows: Among them, α1 and α2 are optimization coefficients used to balance the weight between feedback force and injection speed, F in (t) is the real-time feedback force, F target is the target feedback force, V opatimal The optimal injection speed.

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