Novel small pathological specimen pretreatment system and method

Abstract

The invention provides a novel pathological small specimen pretreatment system and method. The novel pathological small specimen pretreatment system comprises a sample input module, a cleaning module, a fixing module, a dehydration module, an embedding box automatic identification module, a database module and an intelligent control unit, and the sample input module is used for receiving a pathological small specimen and generating a pathological number Si. According to the novel pathological small specimen pretreatment system and method, the docking efficiency with a hospital information system is remarkably improved, so that the risks of specimen exposure and cross confusion are reduced. Through the automatic numbering function of the sample input module, a unique pathological number is generated, the sample type, the sampling time and the sampling position can be recorded, a complete sample information file is formed, the accuracy of sample identification and positioning is ensured in the process, and medical negligence caused by human errors is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of small specimen pre-processing, and in particular to a novel pathological small specimen pre-processing system and method. Background Art

[0002] A new type of pathological small specimen pretreatment system is currently designed to improve the efficiency and accuracy of pathological specimen processing. The system usually includes the following key components: Automated processing: The system uses automated technology to reduce manual intervention and human errors. This includes automatic sampling, fixation, dehydration, transparency and other steps. Modular design: The system adopts a modular design, which is easy to expand and customize according to experimental needs. Users can flexibly configure each module according to the specimen type and experimental process. Temperature control and time control: The system has precise temperature control and time control functions to ensure that the environmental conditions of each step in the processing process are in the best state to improve the quality of the sample. Data recording and analysis: The system is equipped with a data recording function, which can monitor and record the data of each processing link in real time, which is convenient for subsequent analysis and traceability. At the same time, the system can also be integrated with the laboratory information management system (LIMS) to achieve unified data management.

[0003] However, the efficiency of the connection and transmission between the current pathology small specimen preprocessing system and the hospital information system is low, there is a risk of specimen exposure and cross-confusion, and there are problems with the accuracy of specimen identification and positioning. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a novel pathology small specimen preprocessing system and method, which solves the problem that the current pathology small specimen preprocessing system and hospital information system are inefficient in docking and transmitting, resulting in the risk of specimen exposure and cross-confusion, and the problem of low accuracy in specimen identification and positioning.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a new type of pathological small specimen pretreatment system, comprising:

[0006] Sample input module, cleaning module, fixing module, dehydration module, embedding box automatic identification module, database module and intelligent control unit;

[0007] Sample input module: used to receive small pathological specimens and generate pathological numbers S i , and its number generation function is:

[0008] S i =hash(PatientID i +Timestamp i )

[0009] Among them, PatientIDi Indicates the unique identifier of the patient, Timestamp i Indicates the sample entry timestamp;

[0010] Cleaning module: configure a variety of cleaning liquids, cleaning time T 清洗与溶液浓度 C 溶液的关系 for:

[0011]

[0012] Among them, C standard is the standard concentration, and k is a constant coefficient used to dynamically adjust the cleaning process, which is:

[0013]

[0014] Among them, d 样本 represents the sample thickness, k1 is the permeability coefficient of the fixative to ensure uniform fixation of samples of different thicknesses;

[0015] Dehydration module: Dehydration is performed through gradient alcohol, and its concentration increases over time. The concentration calculation formula is:

[0016] C 酒精 (t) = C0 + α·e βt

[0017] Among them, C0 is the initial concentration, α and β are adjustment coefficients to ensure the gradual removal of water;

[0018] Embedding box automatic identification module: Use QR code mark and digital label to identify the specimen. The QR code generation function is:

[0019] Q = hash (specimen ID + timestamp + random number)

[0020] To ensure the uniqueness and traceability of each specimen and reduce the risk of cross-confusion;

[0021] Intelligent control unit: responsible for monitoring and optimizing the operation of each module. Its optimization algorithm is based on historical data learning. By adjusting the processing time of each module, the total processing time T is optimized. 总： :

[0022]

[0023] Among them, T i The processing time of each module is adjusted in real time to ensure efficient operation of the system;

[0024] A fixing module, wherein the fixing module comprises an embedding box, an embedding box storage rack and a transfer box.

[0025] Preferably, when generating the specimen number, the sample input module also records the sample type, sampling time and sampling location to form a complete specimen information file. The cleaning module monitors the effectiveness of the cleaning solution through a centralized management system to ensure timely replacement of expired or contaminated cleaning solution.

[0026] Preferably, the dehydration module adopts a multi-stage alcohol gradient dehydration scheme to ensure that the sample gradually adapts to different concentrations of alcohol and reduce the risk of sample damage. The intelligent control unit can optimize the cleaning and fixing parameters according to the processing historical data through a machine learning algorithm to improve the processing effect. The intelligent control unit analyzes the historical data through data mining technology to optimize the parameter settings of the processing flow.

[0027] Preferably, the database module records the processing status and historical information of each specimen to support subsequent data analysis and quality traceability.

[0028] Preferably, the embedding cassette is disposable and made of biodegradable materials to reduce environmental burden, and is put into the dehydration process immediately after the specimen is fixed to avoid the specimen being affected by delays. The system is equipped with a visual monitoring interface to display the processing status and specimen information in real time, ensuring that the operator can grasp the processing progress at any time and reduce the possibility of operational errors.

[0029] Preferably, the cleaning module uses a combination of multiple cleaning solutions and performs adaptive selection according to the specimen type to improve cleaning efficiency.

[0030] Preferably, the dehydration module has a sensor for real-time monitoring of the water removal rate so as to enable dynamic adjustment.

[0031] A novel method for pre-processing small pathological specimens comprises the following steps:

[0032] a. In the sample input module, receive the small pathological specimen and use the number generation function;

[0033] b. Through the cleaning module, the cleaning module monitors the effectiveness of the cleaning fluid through a centralized management system to ensure timely replacement of expired or contaminated cleaning fluid;

[0034] c. Transfer the cleaned samples into the fixation module for fixation;

[0035] d. In the dehydration module, the samples are dehydrated by gradient alcohol, with the concentration increasing over time to ensure that water is gradually removed and reduce the risk of sample damage;

[0036] e. Use the embedding cassette automatic recognition module to identify the specimens through QR code marking and digital labels to ensure the uniqueness and traceability of each specimen and reduce the risk of cross-confusion;

[0037] f. The intelligent control unit is responsible for monitoring the operation of each module and adjusting the processing time and optimizing the total processing time based on historical data learning to ensure efficient operation of the system;

[0038] g. The processing status and historical information of each specimen are recorded through the database module to support subsequent data analysis and quality traceability.

[0039] h. The system is equipped with a visual monitoring interface that displays the processing status and specimen information in real time, ensuring that operators can keep track of the processing progress at any time and reduce the possibility of operational errors;

[0040] i. The embedding cassettes used are disposable and made of biodegradable materials to reduce environmental burden. After the specimen is fixed, it is immediately put into the dehydration process to avoid the specimen being affected by delays.

[0041] j. The dehydration module has sensors that monitor the water removal rate in real time so that dynamic adjustments can be made to ensure optimal treatment results.

[0042] The present invention provides a novel pathological small specimen pre-processing system and method, which has the following beneficial effects:

[0043] This new pathological small specimen pre-processing system and method significantly improves the efficiency of docking with the hospital information system, thereby reducing the risk of specimen exposure and cross-confusion. Through the automatic numbering function of the sample input module, not only a unique pathological number is generated, but also the sample type, sampling time and location can be recorded to form a complete specimen information file. This process ensures the accuracy of specimen identification and positioning, and reduces medical accidents caused by human errors.

[0044] The system adopts an integrated modular design, including cleaning, fixation, dehydration and intelligent control units. The modules work together to improve processing efficiency. The centralized management system of the cleaning module monitors the effectiveness of the cleaning solution to ensure timely replacement, thereby ensuring the cleaning effect. The intelligent control unit uses machine learning and data mining technology to dynamically optimize the processing parameters based on historical data, improving the processing effect and system stability. The overall design is environmentally friendly and uses embedding boxes made of biodegradable materials to reduce the environmental burden. At the same time, the visual monitoring interface provides real-time status feedback to operators, ensuring the efficiency and accuracy of the processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the structure of the embedding cassette of the present invention;

[0046] Figure 2 This is a schematic diagram of the structure of the embedding cassette storage rack of the present invention;

[0047] Figure 3This is a schematic diagram of the structure of the transfer box of the present invention;

[0048] Figure 4 It is a schematic diagram of the structure of the present invention;

[0049] Figure 5 It is a schematic diagram of another embedding box structure of the present invention;

[0050] Figure 6 This is a structural schematic diagram of another embedding cassette storage rack of the present invention;

[0051] Figure 7 This is another schematic diagram of the transfer box structure of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] like Figure 1-7 As shown, an embodiment of the present invention provides a novel pathological small specimen preprocessing system, including a sample input module, a cleaning module, a fixing module, a dehydration module, an embedding box automatic identification module, a database module and an intelligent control unit. The database module records the processing status and historical information of each specimen to support subsequent data analysis and quality traceability.

[0054] Sample input module: used to receive small pathological specimens and generate pathological numbers S i , and its number generation function is:

[0055] S i =hash(PatientID i +Timestamp i )

[0056] Among them, PatientID i Indicates the unique identifier of the patient, Timestamp i It indicates the sample entry timestamp. When generating the specimen number, the sample input module also records the sample type, sampling time and sampling location to form a complete specimen information file. The cleaning module monitors the effectiveness of the cleaning solution through the centralized management system to ensure timely replacement of expired or contaminated cleaning solution.

[0057] Cleaning module: configure a variety of cleaning liquids, cleaning time T 清洗与溶液浓度 C 溶液的关系 for:

[0058]

[0059] Among them, C standard is the standard concentration, and k is a constant coefficient used to dynamically adjust the cleaning process, which is:

[0060]

[0061] Among them, d 样本 represents the sample thickness, k1 is the permeability coefficient of the fixing solution to ensure uniform fixation of samples of different thicknesses. The cleaning module adopts a combination of various cleaning solutions and makes adaptive selection according to the specimen type to improve the cleaning efficiency. The cleaning module monitors the effectiveness of the cleaning solution through a centralized management system to ensure timely replacement of expired or contaminated cleaning solution. The system is equipped with a cleaning solution sensor to monitor the liquid status in real time and issue a replacement prompt based on the frequency of use and the remaining amount.

[0062] Dehydration module: Dehydration is performed through gradient alcohol, and its concentration increases over time. The concentration calculation formula is:

[0063] C 酒精 (t) = C0 + α·e βt

[0064] Among them, C0 is the initial concentration, α and β are adjustment coefficients to ensure the gradual removal of water. The dehydration module adopts a multi-stage alcohol gradient dehydration scheme to ensure that the sample gradually adapts to different concentrations of alcohol and reduces the risk of sample damage. The intelligent control unit can optimize the cleaning and fixing parameters according to the processing historical data through machine learning algorithms to improve the processing effect. The intelligent control unit analyzes historical data through data mining technology to optimize the parameter settings of the processing flow. The dehydration module has a sensor for real-time monitoring of the water removal rate for dynamic adjustment. The dehydration module adopts a multi-stage alcohol gradient dehydration scheme and is equipped with a sensor to monitor the water content of the sample so as to dynamically adjust the alcohol concentration and dehydration time during the dehydration process to ensure the integrity and effectiveness of the sample.

[0065] Embedding box automatic identification module: Use QR code mark and digital label to identify the specimen. The QR code generation function is:

[0066] Q = hash (specimen ID + timestamp + random number)

[0067] To ensure the uniqueness and traceability of each specimen and reduce the risk of cross-confusion, the QR code mark not only contains the specimen ID, but also integrates the processing history and sample characteristics information to ensure real-time updates throughout the entire processing process and provide comprehensive traceability capabilities.

[0068] Intelligent control unit: responsible for monitoring and optimizing the operation of each module. Its optimization algorithm is based on historical data learning. By adjusting the processing time of each module, the total processing time T is optimized. 总：:

[0069]

[0070] Among them, T i The processing time of each module is adjusted in real time to ensure the efficient operation of the system. The intelligent control unit can actively identify abnormal situations by analyzing the comparison of real-time data and historical data, and automatically adjust the processing parameters to cope with emergencies, thereby ensuring the stability of the processing process. The intelligent control unit uses machine learning algorithms to analyze historical processing data, including cleaning time, fixative usage, dehydration time, etc., to establish a model and identify the best processing parameters under different conditions. By classifying and regressing the processing results of different types of samples, the algorithm can automatically adjust the cleaning and fixation parameters to adapt to the changes and needs of the samples and improve the processing effect. For example, the system can recommend the most suitable cleaning solution concentration and fixative flow rate in real time based on the sample's diameter, material and other characteristics.

[0071] The fixing module includes an embedding cassette, an embedding cassette storage rack and a transfer box. The embedding cassette is disposable and made of biodegradable materials to reduce environmental burden. The specimen is immediately put into the dehydration process after fixation to avoid the specimen being affected by delays. The system is equipped with a visual monitoring interface to display the processing status and specimen information in real time to ensure that the operator can grasp the processing progress at any time and reduce the possibility of operational errors. The design of the fixing module takes into account the diversity of samples and can adaptively adjust the flow rate and immersion time of the fixative according to the characteristics of different specimens to ensure the best fixation effect.

[0072] A novel method for pre-processing small pathological specimens comprises the following steps:

[0073] a. In the sample input module, receive small pathological specimens and use the number generation function.

[0074] b. Through the cleaning module, the cleaning module monitors the effectiveness of the cleaning fluid through a centralized management system to ensure timely replacement of expired or contaminated cleaning fluid.

[0075] c. Transfer the cleaned samples into the fixation module for fixation.

[0076] d. In the dehydration module, the samples are dehydrated through a gradient of alcohol, with the concentration increasing over time to ensure that water is gradually removed and reduce the risk of sample damage.

[0077] e. Use the embedding box automatic recognition module to identify the specimens through QR code marking and digital labels to ensure the uniqueness and traceability of each specimen and reduce the risk of cross confusion.

[0078] f. The intelligent control unit is responsible for monitoring the operation of each module, and based on historical data learning, adjusts the processing time and optimizes the total processing time to ensure efficient operation of the system.

[0079] g. The processing status and historical information of each specimen are recorded through the database module to support subsequent data analysis and quality traceability.

[0080] h. The system is equipped with a visual monitoring interface that displays the processing status and specimen information in real time, ensuring that operators can keep track of the processing progress at any time and reduce the possibility of operational errors.

[0081] i. The embedding cassettes used are disposable and made of biodegradable materials to reduce environmental burden. After the specimen is fixed, it is immediately put into the dehydration process to avoid the specimen being affected by delays.

[0082] j. The dehydration module has sensors that monitor the water removal rate in real time so that dynamic adjustments can be made to ensure optimal treatment results.

[0083] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A new type of pathological small specimen pre-processing system, characterized in that: include: Sample input module, cleaning module, fixing module, dehydration module, embedding box automatic identification module, database module and intelligent control unit; Sample input module: used to receive small pathological specimens and generate pathological numbers S i , and its number generation function is: S i =hash(PatientID i +Timestamp i ) Among them, PatientID i Indicates the unique identifier of the patient, Timestamp i Indicates the sample entry timestamp; Cleaning module: configure a variety of cleaning liquids, cleaning time T 清洗与溶液浓度 C 溶液的关系 for: Among them, C standard is the standard concentration, and k is a constant coefficient used to dynamically adjust the cleaning process, which is: Among them, d 样本 represents the sample thickness, k1 is the permeability coefficient of the fixative to ensure uniform fixation of samples of different thicknesses; Dehydration module: Dehydration is performed through gradient alcohol, and its concentration increases over time. The concentration calculation formula is: C 酒精 (t)=C0+α·e βt Among them, C0 is the initial concentration, α and β are adjustment coefficients to ensure the gradual removal of water; Embedding box automatic identification module: Use QR code mark and digital label to identify the specimen. The QR code generation function is: Q = hash (specimen ID + timestamp + random number) To ensure the uniqueness and traceability of each specimen and reduce the risk of cross-confusion; Intelligent control unit: responsible for monitoring and optimizing the operation of each module. Its optimization algorithm is based on historical data learning. By adjusting the processing time of each module, the total processing time T is optimized. 总： : Among them, T i The processing time of each module is adjusted in real time to ensure efficient operation of the system; A fixing module, wherein the fixing module comprises an embedding box, an embedding box storage rack and a transfer box.

2. A novel pathological small specimen pre-processing system according to claim 1, characterized in that: When generating the specimen number, the sample input module also records the sample type, sampling time and sampling location to form a complete specimen information file. The cleaning module monitors the effectiveness of the cleaning solution through a centralized management system to ensure timely replacement of expired or contaminated cleaning solution.

3. A novel pathological small specimen pre-processing system according to claim 1, characterized in that: The dehydration module adopts a multi-level alcohol gradient dehydration solution to ensure that the sample gradually adapts to different concentrations of alcohol and reduce the risk of sample damage. The intelligent control unit can optimize the cleaning and fixing parameters according to the processing historical data through a machine learning algorithm to improve the processing effect. The intelligent control unit analyzes the historical data through data mining technology to optimize the parameter settings of the processing flow.

4. A novel pathological small specimen pre-processing system according to claim 1, characterized in that: The database module records the processing status and historical information of each specimen to support subsequent data analysis and quality traceability.

5. A novel pathological small specimen pre-processing system according to claim 1, characterized in that: The embedding box is disposable and made of biodegradable materials to reduce environmental burden. It is put into the dehydration process immediately after the specimen is fixed to avoid the specimen being affected by delays. The system is equipped with a visual monitoring interface to display the processing status and specimen information in real time, ensuring that the operator can grasp the processing progress at any time and reduce the possibility of operational errors.

6. A novel pathological small specimen pre-processing system according to claim 1, characterized in that: The cleaning module adopts a combination of multiple cleaning solutions and performs adaptive selection according to the specimen type to improve the cleaning efficiency.

7. A novel pathological small specimen pre-processing system according to claim 1, characterized in that: The dehydration module has a sensor for real-time monitoring of the water removal rate so as to make dynamic adjustments.

8. A novel method for pre-processing small pathological specimens, characterized in that: The following steps are involved: a. In the sample input module, receive the small pathological specimen and use the number generation function; b. Through the cleaning module, the cleaning module monitors the effectiveness of the cleaning fluid through a centralized management system to ensure timely replacement of expired or contaminated cleaning fluid; c. Transfer the cleaned samples into the fixation module for fixation; d. In the dehydration module, the samples are dehydrated by gradient alcohol, with the concentration increasing over time to ensure that water is gradually removed and reduce the risk of sample damage; e. Use the embedding cassette automatic recognition module to identify the specimens through QR code marking and digital labels to ensure the uniqueness and traceability of each specimen and reduce the risk of cross-confusion; f. The intelligent control unit is responsible for monitoring the operation of each module and adjusting the processing time and optimizing the total processing time based on historical data learning to ensure efficient operation of the system; g. The processing status and historical information of each specimen are recorded through the database module to support subsequent data analysis and quality traceability. h. The system is equipped with a visual monitoring interface that displays the processing status and specimen information in real time, ensuring that operators can keep track of the processing progress at any time and reduce the possibility of operational errors; i. The embedding cassettes used are disposable and made of biodegradable materials to reduce environmental burden. After the specimen is fixed, it is immediately put into the dehydration process to avoid the specimen being affected by delays. j. The dehydration module has sensors that monitor the water removal rate in real time so that dynamic adjustments can be made to ensure optimal treatment results.

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