Dehydration method for brain tissue neuropathological diagnosis

Abstract

The invention discloses a dehydration method for brain tissue neuropathological diagnosis, which comprises the following steps: adding a nano penetrant into a dehydrating agent to promote the uniform penetration of the dehydrating agent in brain tissue, dehydrating a brain tissue sample by using a full-automatic dehydrator, and monitoring the dehydration state of the brain tissue sample in real time by combining with an intelligent regulation and control system. The parameters of the full-automatic dehydrator and the type and concentration of the dehydrating agent are dynamically adjusted according to the monitoring result, vacuum treatment and low-temperature treatment are combined in the dehydration process to further accelerate permeation of the dehydrating agent and discharge of water, meanwhile, the antigenicity and morphological structure of brain tissue are kept, the dehydration efficiency and uniformity are remarkably improved, and the method is suitable for large-scale popularization and application. The integrity and the quality of the brain tissue sample are ensured; and the dehydration process can be accurately controlled and optimized through the dynamic adjustment capability of the intelligent regulation and control system, so that the flexibility and the adaptability of treatment are improved.

Description

Technical Field

[0001] The invention belongs to the field of pathological diagnosis dehydration, and in particular, relates to a dehydration method for brain tissue neuropathological diagnosis. Background Art

[0002] In the diagnosis of brain tissue neuropathology, dehydration is a key step, which involves removing water from brain tissue samples for subsequent tissue sectioning and staining analysis. Traditional dehydration methods have multiple shortcomings, which affect the processing efficiency and quality of samples.

[0003] Existing technologies usually lack real-time monitoring methods for the dehydration process. Since it is impossible to monitor the status of the dehydration process in real time, it is difficult for researchers to adjust the dehydration parameters in time, resulting in uncontrollable dehydration process. This uncontrollability makes it difficult to maintain consistent processing results, which seriously affects the reliability of samples and the accuracy of experimental results.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide a dehydration method for brain tissue neuropathology diagnosis, and solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0007] A dehydration method for brain tissue neuropathology diagnosis comprises the following steps:

[0008] Step (1): placing the brain tissue sample in a formalin solution for fixation, and then after fixation, moving the tissue sample into water for washing for 30-40 minutes at a temperature of 50-45° C., and finally placing the brain tissue sample in a dehydrating agent;

[0009] Step (2): dehydrating the brain tissue sample using a fully automatic dehydrator;

[0010] Step (3): monitoring the dehydration status of the brain tissue sample in real time through an intelligent control system and dynamically adjusting the parameters of the fully automatic dehydrator and the type and concentration of the dehydrating agent according to the monitoring results;

[0011] Step (4): The dehydration process lasts for 2-68 hours. After the dehydration is completed, the brain tissue samples are transparentized and wax-impregnated.

[0012] Optionally, the dehydrating agent in step (1) includes but is not limited to 70%-100% ethanol, a rapid fixative, or a combination of two or more thereof.

[0013] Optionally, the common initial setting parameters of the fully automatic dehydrator in step (2) include dehydrating agent concentration, temperature, and time.

[0014] Optionally, xylene is used for the transparent treatment in step (4), and paraffin is used for the wax treatment.

[0015] Optionally, after the dehydration process is completed, the dehydrated brain tissue sample needs to be subjected to a quality assessment, which includes morphological assessment, antigenicity assessment, and molecular biology assessment;

[0016] The steps of morphological assessment are:

[0017] The morphological structure of brain tissue samples was observed using optical microscopy and electron microscopy to evaluate the integrity of tissue and changes in cell morphology after dehydration. Routine staining and special staining were performed on brain tissue samples to evaluate tissue structure and cellular components.

[0018] Optionally, the antigenicity assessment step is:

[0019] Dehydrated brain tissue samples were immunohistochemically stained using specific antibodies to evaluate the expression of specific antigens, and the results of antigen staining were observed using a fluorescence microscope to evaluate the distribution and expression intensity of the antigens.

[0020] Optional, molecular biological evaluation steps are:

[0021] RNA was extracted from dehydrated brain tissue samples and quantitative PCR analysis was performed to evaluate the expression levels of specific genes, and DNA was extracted from dehydrated tissue samples and sequencing analysis was performed to evaluate the integrity of the genome and changes in specific genes.

[0022] Optionally, the intelligent control system includes a monitoring module, an analysis module, a visual recognition module, and a control module.

[0023] Monitoring module: The monitoring module is used to continuously collect various data during the dehydration process;

[0024] Analysis module: The analysis module analyzes the data in real time and evaluates the dehydration process and effect;

[0025] A visual recognition module, which is used to recognize the dehydration state of brain tissue nerves;

[0026] The control module is used to dynamically adjust the parameters of the fully automatic dehydrating machine according to the data sent by the analysis module and the visual recognition module, and adjust the type and concentration of the dehydrating agent according to the dehydration state of the brain tissue sample.

[0027] Optionally, the steps for real-time analysis of data and evaluation of dehydration progress and effect are as follows:

[0028] Conduct preliminary analysis on the data collected in real time, calculate basic statistics, understand the basic situation of the dehydration process, and use dynamic monitoring algorithms to track and analyze the data in real time to promptly detect changes in the dehydration process;

[0029] A baseline model of the dehydration process is established as a reference standard to evaluate the deviation between the current dehydration state and the ideal state, and a prediction model is established to predict the future dehydration trend and effect. The expression is: Among them, y t is the dehydration state at time t, c is a constant term, φ i and θ j are the parameters of the autoregressive and moving average parts, ∈ t is the error term;

[0030] Use the prediction model through the formula Calculate the water removal rate during the dehydration process and evaluate the uniform penetration of the dehydrating agent in the brain tissue sample. Finally, the formula Evaluation of the morphological structure of brain tissue samples during dehydration, including R t is the dehydration rate at time t, W t is the sample weight at time t, Δt is the time interval, D is the cell density, N c is the number of cells, and A is the sample area.

[0031] Optionally, the steps for identifying the dehydration status of brain tissue nerves are:

[0032] Use high-resolution light or electron microscopy to capture images of brain tissue samples;

[0033] Extract morphological features of brain tissue samples and extract texture features and color features;

[0034] Use deep learning algorithms to train visual recognition models to learn the correspondence between features in images and dehydration states;

[0035] The real-time collected brain tissue sample images are input into the trained visual recognition model, and the model analyzes the input images, identifies the dehydration status, and outputs the recognition results;

[0036] The parameters in the dehydration process are optimized and adjusted according to the identification and prediction results.

[0037] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0038] The present invention adds nano-permeabilizing agents to dehydrating agents to promote uniform penetration of dehydrating agents in brain tissues, uses a fully automatic dehydrating machine to dehydrate brain tissue samples, combines an intelligent control system to monitor the dehydration state of brain tissue samples in real time, dynamically adjusts parameters of the fully automatic dehydrating machine and the type and concentration of the dehydrating agent according to the monitoring results, combines vacuum treatment and low-temperature treatment during the dehydration process to further accelerate the penetration of the dehydrating agent and the discharge of water, while maintaining the antigenicity and morphological structure of the brain tissue. The method significantly improves the dehydration efficiency and uniformity, ensures the integrity and quality of brain tissue samples, and the dynamic adjustment capability of the intelligent control system enables the dehydration process to be accurately controlled and optimized, thereby improving the flexibility and adaptability of the process.

[0039] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0041] In the figure:

[0042] Figure 1 Flow chart of dehydration methods for neuropathological diagnosis of brain tissue;

[0043] Figure 2 This is a diagram of the intelligent control system.

[0044] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0045] The present invention will now be described in further detail with reference to the accompanying drawings.

[0046] See also Figure 1-2 As shown, in this embodiment, a dehydration method for brain tissue neuropathology diagnosis is provided, comprising the following steps:

[0047] Step (1): placing the brain tissue sample in a formalin solution for fixation, and then after fixation, moving the tissue sample into water for washing for 30-40 minutes at a temperature of 50-45° C., and finally placing the brain tissue sample in a dehydrating agent;

[0048] Step (2): dehydrating the brain tissue sample using a fully automatic dehydrator;

[0049] Step (3): monitoring the dehydration status of the brain tissue sample in real time through an intelligent control system and dynamically adjusting the parameters of the fully automatic dehydrator and the type and concentration of the dehydrating agent according to the monitoring results;

[0050] Step (4): The dehydration process lasts for 2-68 hours. After the dehydration is completed, the brain tissue samples are transparentized and wax-impregnated.

[0051] In this embodiment, the dehydrating agent in step (1) includes but is not limited to 70%-100% ethanol, a rapid fixative, or a combination of two or more thereof.

[0052] In this embodiment, the common initial setting parameters of the fully automatic dehydrating machine in step (2) include dehydrating agent concentration, temperature, and time.

[0053] In this embodiment, xylene is used for the transparent treatment in step (4), and paraffin is used for the wax dipping treatment.

[0054] In this embodiment, the dehydrating agent in step (1) includes, but is not limited to, one or a combination of two or more of an ethanol series dehydrating agent, a rapid fixative, a dehydrating agent based on nanomaterials, or a supercritical fluid dehydrating medium. By utilizing the unique properties of supercritical fluids, efficient dehydration can be achieved at a relatively low temperature, thus avoiding damage to tissue samples caused by high temperatures.

[0055] In this embodiment, the parameters of the fully automatic dehydrator in step (2) include the concentration of the dehydrating agent, the temperature, and the time. The real-time monitoring and feedback mechanism of the fully automatic dehydrator enables the system to dynamically adjust the parameters, while maintaining efficient dehydration, while minimizing the damage to the tissue sample. This precise control ensures that the morphological structure and antigenicity of the brain tissue are protected during the dehydration process, avoiding cell deformation, destruction, and loss of antigenicity that may occur in traditional methods.

[0056] In this embodiment, after the dehydration process is completed, the dehydrated brain tissue sample needs to be subjected to quality assessment, and the quality assessment includes morphological assessment, antigenicity assessment and molecular biology assessment;

[0057] The steps of morphological assessment are:

[0058] Use optical microscopy and electron microscopy to observe the morphology and structure of brain tissue samples, evaluate the integrity of tissues and changes in cell morphology after dehydration, and perform routine and special staining on brain tissue samples to evaluate tissue structure and cell composition;

[0059] The steps for antigenicity assessment are:

[0060] Immunohistochemical staining was performed on dehydrated brain tissue samples using specific antibodies to evaluate the expression of specific antigens, and the results of antigen staining were observed using a fluorescence microscope to evaluate the distribution and expression intensity of the antigens. Multiple evaluation methods complemented each other to ensure the comprehensiveness and reliability of the data, providing high-quality samples and data for subsequent research and diagnosis.

[0061] The steps of molecular biology assessment are:

[0062] RNA was extracted from dehydrated brain tissue samples for quantitative PCR analysis to evaluate the expression level of specific genes, and DNA was extracted from dehydrated tissue samples for sequencing analysis to evaluate the integrity of the genome and changes in specific genes. By combining morphological, antigenic, and molecular biological evaluations, the experimental results can be better interpreted and the changes and effects of the sample during the dehydration process can be revealed. Antigenic evaluation uses specific antibodies and immunohistochemical staining to ensure that the expression of specific antigens in the sample is accurately evaluated, improving the reliability of the experimental results.

[0063] In this embodiment, the intelligent control system includes a monitoring module, an analysis module, a visual recognition module, and a control module.

[0064] Monitoring module: The monitoring module is used to continuously collect various data during the dehydration process; the monitoring module continuously collects various data during the dehydration process to ensure that the dehydration status can be understood in real time and provide accurate real-time information.

[0065] Analysis module: The analysis module performs real-time analysis on the collected data, and can timely evaluate the dehydration process and effect to ensure that the dehydration process is carried out under optimal conditions;

[0066] The visual recognition module is used to identify the dehydration status of brain tissue nerves; according to the dehydration status of the brain tissue sample, the type and concentration of the dehydrating agent are adjusted to improve the efficiency and effect of the use of the dehydrating agent.

[0067] The control module is used to dynamically adjust the parameters of the fully automatic dehydrator according to the data sent by the analysis module and the visual recognition module, and adjust the type and concentration of the dehydrating agent according to the dehydration status of the brain tissue sample. The control module dynamically adjusts the parameters of the fully automatic dehydrator according to the data provided by the analysis module and the visual recognition module to ensure that the dehydration process is always in the optimal state. According to the dehydration status of the brain tissue sample, the type and concentration of the dehydrating agent are adjusted to improve the efficiency and effect of the use of the dehydrating agent.

[0068] In this embodiment, the steps of analyzing the data in real time and evaluating the dehydration progress and effect are as follows:

[0069] Conduct preliminary analysis on the data collected in real time, calculate basic statistics, understand the basic situation of the dehydration process, and use dynamic monitoring algorithms to track and analyze the data in real time to promptly detect changes in the dehydration process; use dynamic monitoring algorithms to track and analyze data in real time to promptly detect changes in the dehydration process and ensure the stability and efficiency of the process.

[0070] A baseline model of the dehydration process is established as a reference standard to evaluate the deviation between the current dehydration state and the ideal state, and a prediction model is established to predict the future dehydration trend and effect. The expression is: Among them, y t is the dehydration state at time t, c is a constant term, φ i and θ j are the parameters of the autoregressive and moving average parts, ∈ t is the error term; by predicting the future dehydration trend and effect through the prediction model, adjustment measures can be taken in advance to optimize the dehydration process and improve efficiency.

[0071] Use the prediction model through the formula Calculate the water removal rate during the dehydration process and evaluate the uniform penetration of the dehydrating agent in the brain tissue sample. Finally, the formula Evaluation of the morphological structure of brain tissue samples during dehydration, including R t is the dehydration rate at time t, W t is the sample weight at time t, Δt is the time interval, D is the cell density, N c is the number of cells, and A is the sample area. Evaluate the uniform penetration of the dehydrating agent in the brain tissue sample to ensure that the dehydrating agent is evenly distributed in the sample to prevent local over- or under-dehydration.

[0072] In this embodiment, the steps of identifying the dehydration state of brain tissue nerves are:

[0073] Use a high-resolution optical microscope or electron microscope to capture images of brain tissue samples; Use a high-resolution optical microscope or electron microscope to capture images to ensure that clear and detailed images of brain tissue samples are obtained, which helps to accurately extract sample features.

[0074] Extract morphological features of brain tissue samples and extract texture features and color features;

[0075] The visual recognition model is trained using a deep learning algorithm to learn the correspondence between the features in the image and the dehydration state. The visual recognition model is trained using a deep learning algorithm to enable the model to automatically learn and recognize the relationship between the features in the image and the dehydration state, thereby improving the recognition accuracy.

[0076] The real-time collected brain tissue sample images are input into the trained visual recognition model. The model analyzes the input images, identifies the dehydration status, and outputs the recognition results. Through automated image analysis and deep learning models, intelligent management of the dehydration process is achieved, improving experimental efficiency and accuracy.

[0077] According to the recognition results and prediction results, the parameters in the dehydration process are optimized and adjusted. According to the recognition results output by the visual recognition model, the parameters in the dehydration process are optimized and adjusted to ensure that the dehydration process is always in the best state.

[0078] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0079] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0080] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to the skilled artisan. The present invention description and examples are exemplary only.

[0081] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0082] In the following examples, the meaning of "ratio" is the proportion of a certain raw material component in the total mass of the raw materials.

[0083] It should be noted that all raw materials used in the examples of the present invention were obtained through commercial channels.

[0084] Example 1: Dehydration procedure of adult brain tissue, totaling 55 hours and 30 minutes

[0085] serial number Reagent name Time (hours: minutes) Temperature (Celsius) 1 Formalin 12：00 37 2 water 00:30 45 3 70% ethanol 04:00 45 4 85% ethanol 04:00 45 5 95% ethanol 04:00 45 6 95% ethanol 04:00 45 7 100% ethanol 04:00 45 8 100% ethanol 04:00 45 9 Xylene 02:00 45 10 Xylene 02:00 45 11 paraffin 01:00 62 12 paraffin 02:00 62 13 paraffin 12:00 62

[0086] Tissue fixation and washing: brain tissue samples were fixed in formalin solution for 12 h, the temperature was set at 37 °C and after fixation, the tissue samples were moved into water for washing for 30 min, the temperature was set at 45 °C;

[0087] Stepwise ethanol dehydration: Place the tissue sample in 70% ethanol (4 hours) and 85% ethanol (4 hours) solutions, both at 45°C. Then, place the tissue sample in 95% ethanol solution twice, each for 4 hours at 45°C. Finally, place the tissue sample in 100% ethanol (absolute ethanol) solution twice, each for 4 hours at 45°C.

[0088] Tissue clearing: Place tissue samples in xylene solution twice, 2 hours each time, at 45°C;

[0089] Paraffin infiltration treatment: The tissue samples were placed in melted paraffin for three times: the first time was 1 hour, the second time was 2 hours, and the third time was 12 hours, all at 62°C.

[0090] Example 2: Dehydration procedure for fetal and neonatal brain tissue, 26 hours and 40 minutes

[0091] serial number Reagent name Time (hours: minutes) Temperature (Celsius) 1 Formalin 12：00 37 2 water 00:30 45 3 70% ethanol 1:30 45 4 85% ethanol 1:30 45 5 95% ethanol 1:30 45 6 95% ethanol 01:30 45 7 100% ethanol 01:00 45 8 100% ethanol 01:00 45 9 Xylene 00:40 45 10 Xylene 00:30 45 11 paraffin 01:00 62 12 paraffin 01:00 62 13 paraffin 03:00 62

[0092] Tissue fixation and washing: The brain tissue samples were fixed in formalin solution for 12 hours at 37°C. Then, after fixation, the tissue samples were washed in water at 45°C for 30 minutes.

[0093] Stepwise ethanol dehydration: the tissue samples were placed in 70% ethanol (1 hour and 30 minutes) and 85% ethanol (1 hour and 30 minutes) solutions, both at 45°C. Then, the tissue samples were placed in 95% ethanol solution twice, the first time for 1 hour and 30 minutes, the second time for 1 hour and 30 minutes, both at 45°C. Finally, the tissue samples were placed in 100% ethanol (absolute ethanol) solution twice, each time for 1 hour, at 45°C.

[0094] Tissue clearing treatment: Place the tissue sample in xylene solution twice, the first time for 40 minutes and the second time for 30 minutes, at a temperature of 45°C;

[0095] Paraffin infiltration treatment: The tissue samples were placed in melted paraffin for three times: 1 hour each time and 3 hours for the last time, all at 62°C.

[0096] After the brain tissue of Example 1-2 is dehydrated and waxed, it is embedded using a fully automatic embedding machine. Note that the brain tissue must be completely immersed in the tissue embedding agent, and the embedding mold filled with liquid paraffin is placed on a freezing table. When the bottom turns white and solidifies, the brain tissue is quickly placed in the center of the embedding mold, and the embedding frame is pressed on the embedding mold until it is completely solidified, demolded, and stored in a pathology cabinet at room temperature in order.

[0097] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. A dehydration method for neuropathological diagnosis of brain tissue, It is characterized in that The following steps are involved: Step (1): placing the brain tissue sample in a formalin solution for fixation, and then after fixation, moving the tissue sample into water for washing for 30-40 minutes at a temperature of 50-45° C., and finally placing the brain tissue sample in a dehydrating agent; Step (2): dehydrating the brain tissue sample using a fully automatic dehydrator; Step (3): monitoring the dehydration status of the brain tissue sample in real time through an intelligent control system and dynamically adjusting the parameters of the fully automatic dehydrator and the type and concentration of the dehydrating agent according to the monitoring results; Step (4): The dehydration process lasts for 2-68 hours. After the dehydration is completed, the brain tissue samples are transparentized and wax-impregnated.

2. A dehydration method for brain tissue neuropathological diagnosis according to claim 1, It is characterized in that The dehydrating agent in step (1) includes, but is not limited to, one or a combination of two or more of 70%-100% ethanol and a rapid fixative.

3. A dehydration method for brain tissue neuropathological diagnosis according to claim 1, It is characterized in that The parameters of the fully automatic dehydrating machine in step (2) include dehydrating agent concentration, temperature and time.

4. A dehydration method for brain tissue neuropathological diagnosis according to claim 1, It is characterized in that In step (4), xylene is used for the transparent treatment, and paraffin is used for the wax dipping treatment.

5. A dehydration method for brain tissue neuropathological diagnosis according to claim 1, It is characterized in that After the dehydration process is completed, the dehydrated brain tissue samples need to be evaluated for quality, including morphological evaluation, antigenicity evaluation, and molecular biology evaluation; The steps of morphological assessment are: The morphological structure of brain tissue samples was observed using optical microscopy and electron microscopy to evaluate the integrity of tissue and changes in cell morphology after dehydration. Routine staining and special staining were performed on brain tissue samples to evaluate tissue structure and cellular components.

6. A dehydration method for brain tissue neuropathological diagnosis according to claim 5, It is characterized in that The steps for antigenicity assessment are: Dehydrated brain tissue samples were immunohistochemically stained using specific antibodies to evaluate the expression of specific antigens, and the results of antigen staining were observed using a fluorescence microscope to evaluate the distribution and expression intensity of the antigens.

7. A dehydration method for brain tissue neuropathological diagnosis according to claim 5, It is characterized in that The steps of molecular biology assessment are: RNA was extracted from dehydrated brain tissue samples and quantitative PCR analysis was performed to evaluate the expression levels of specific genes, and DNA was extracted from dehydrated tissue samples and sequencing analysis was performed to evaluate the integrity of the genome and changes in specific genes.

8. A dehydration method for brain tissue neuropathological diagnosis according to claim 1, It is characterized in that The intelligent control system includes a monitoring module, an analysis module, a visual recognition module, and a control module. Monitoring module: The monitoring module is used to continuously collect various data during the dehydration process; Analysis module: The analysis module analyzes the data in real time and evaluates the dehydration process and effect; A visual recognition module, which is used to recognize the dehydration state of brain tissue nerves; The control module is used to dynamically adjust the parameters of the fully automatic dehydrating machine according to the data sent by the analysis module and the visual recognition module, and adjust the type and concentration of the dehydrating agent according to the dehydration state of the brain tissue sample.

9. A dehydration method for brain tissue neuropathological diagnosis according to claim 8, It is characterized in that The steps to analyze the data in real time and evaluate the dehydration progress and effect are as follows: Conduct preliminary analysis on the data collected in real time, calculate basic statistics, understand the basic situation of the dehydration process, and use dynamic monitoring algorithms to track and analyze the data in real time to promptly detect changes in the dehydration process; A baseline model of the dehydration process is established as a reference standard to evaluate the deviation between the current dehydration state and the ideal state, and a prediction model is established to predict the future dehydration trend and effect. The expression is: Among them, y t is the dehydration state at time t, c is a constant term, φ i and θ j are the parameters of the autoregressive and moving average parts, ∈ t is the error term; Use the prediction model through the formula Calculate the water removal rate during the dehydration process and evaluate the uniform penetration of the dehydrating agent in the brain tissue sample. Finally, the formula Evaluation of the morphological structure of brain tissue samples during dehydration, including R t is the dehydration rate at time t, W t is the sample weight at time t, Δt is the time interval, D is the cell density, N c is the number of cells, and A is the area of ​​the sample region.

10. A dehydration method for brain tissue neuropathological diagnosis according to claim 8, It is characterized in that The steps to identify the dehydration status of brain tissue nerves are: Use high-resolution light or electron microscopy to capture images of brain tissue samples; Extract morphological features of brain tissue samples and extract texture features and color features; Use deep learning algorithms to train visual recognition models to learn the correspondence between features in images and dehydration states; The real-time collected brain tissue sample images are input into the trained visual recognition model, and the model analyzes the input images, identifies the dehydration status, and outputs the recognition results; The parameters in the dehydration process are optimized and adjusted according to the identification and prediction results.

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