CN114113046A - Electrochemiluminescence immunosensor for detecting tumor marker and application thereof - Google Patents
Electrochemiluminescence immunosensor for detecting tumor marker and application thereof Download PDFInfo
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Abstract
The invention belongs to the technical field of electrochemiluminescence immunosensors, and discloses an electrochemiluminescence immunosensor for detecting tumor markers and application thereof, wherein a preparation system of the electrochemiluminescence immunosensor for detecting the tumor markers comprises: the device comprises a graphite carbon nitride preparation module, a working electrode pretreatment module, a central control module, an antigen standard solution preparation module, a modified electrode preparation module, an immunosensor preparation module, a tumor marker detection module, a sensor detection curve drawing module, a data storage module and an updating display module. The electrochemiluminescence immunosensor for detecting the tumor marker provided by the invention has the advantages of high analysis sensitivity, strong specificity, convenience in use and low cost. According to the invention, the bovine serum albumin solution and the tumor marker antigen standard solution are dripped on the surface of the graphite carbon nitride working electrode, so that the sensitivity of the electrochemiluminescence immunosensor is improved, no enzyme or mark exists, and the electrode preparation process is greatly simplified.
Description
Technical Field
The invention belongs to the technical field of electrochemiluminescence immunosensors, and particularly relates to an electrochemiluminescence immunosensor for detecting tumor markers and application thereof.
Background
At present, the immunosensor combines a high-sensitivity sensing technology and a high-specificity immunoreaction, and has the advantages of real-time output, high analysis sensitivity, strong specificity, simple and convenient use and low cost. Therefore, immunosensors are widely used for early diagnosis and detection of tumor markers.
In view of the fact that the sensitivity and specificity of the existing tumor markers are not ideal enough, in order to improve the clinical application value of the tumor markers, it is very important to reasonably select multiple markers for simultaneous detection according to the characteristics of different tumors and different markers. Because the analysis efficiency for detecting a single tumor marker is not high, people begin to pay attention to the research of the high-throughput electrochemical immunosensor for realizing the simultaneous detection of a plurality of samples in order to shorten the analysis time, reduce the analysis steps, improve the analysis efficiency, reduce the test cost and the like.
Through the above analysis, the problems and defects of the prior art are as follows: the sensitivity and specificity of the existing tumor markers are not ideal enough, and the analysis efficiency of single tumor marker detection is not high.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an electrochemiluminescence immunosensor for detecting tumor markers and application thereof.
The invention is realized in this way, a system for preparing an electrochemiluminescence immunosensor for detecting a tumor marker, the system for preparing an electrochemiluminescence immunosensor for detecting a tumor marker comprises:
the graphite carbon nitride preparation module is connected with the central control module and used for preparing polyaniline gel by utilizing aniline, phytic acid and an initiator and carbonizing the polyaniline gel to obtain a graphite carbon nitride working electrode;
the working electrode pretreatment module is connected with the central control module and is used for carrying out pretreatment operations of activation, polishing, cleaning and nitrogen blow-drying of the graphite carbon nitride working electrode through pretreatment equipment;
the central control module is connected with the graphite carbon nitride preparation module, the working electrode pretreatment module, the antigen standard solution preparation module, the modified electrode preparation module, the immunosensor preparation module, the tumor marker detection module, the sensor detection curve drawing module, the data storage module and the updating display module, and is used for coordinating and controlling the normal operation of each module of the preparation system of the electrochemiluminescence immunosensor for detecting the tumor marker through the central processing unit;
the antigen standard solution preparation module is connected with the central control module and is used for respectively preparing bovine serum albumin solution and tumor marker antigen standard solution through a standard solution preparation device;
the modified electrode preparation module is connected with the central control module and is used for respectively dripping bovine serum albumin solution and tumor marker antigen standard solution on the surface of the graphite carbon nitride working electrode to obtain a modified electrode;
the immunosensor preparation module is connected with the central control module and used for preparing an electrochemiluminescence immunosensor by utilizing an antigen standard solution and a modified electrode through an electrochemiluminescence technology;
the tumor marker detection module is connected with the central control module and used for detecting the tumor marker through the prepared electrochemiluminescence immunosensor and obtaining a detection report;
the sensor detection curve drawing module is connected with the central control module and used for drawing a working curve according to the electrochemiluminescence intensity of the electrode in the detection report through a curve drawing program;
the data storage module is connected with the central control module and used for storing graphite carbon nitride preparation data, working electrode pretreatment data, antigen standard solution preparation data, modified electrode preparation data, immunosensor preparation data, tumor marker detection reports and working curves through a memory;
and the updating display module is connected with the central control module and is used for updating and displaying the graphite carbon nitride preparation, the working electrode pretreatment, the antigen standard solution preparation, the modified electrode preparation, the immunosensor preparation, the tumor marker detection report and the real-time data of the working curve through a display.
Further, the molar ratio of the hair agent to the aniline to the phytic acid is 1-3: 2-7: 1, the initiator is ammonium sulfate solution or hydrogen peroxide solution.
Further, utilize the preprocessing equipment to carry out the activation of graphite carbon nitride working electrode, polishing, washing and the nitrogen weathered preprocessing operation through working electrode preprocessing module, include:
(1) activating the graphite carbon nitride working electrode by using an activating material to obtain a three-dimensional layered porous graphite carbon nitride working electrode;
(2) using 0.05-1.5 μm Al2O3Polishing the three-dimensional layered porous graphite carbon nitride working electrode by using polishing powder;
(3) and ultrasonically cleaning the three-dimensional layered porous graphite carbon nitride working electrode subjected to polishing treatment in ethanol and ultrapure water for 5-15 min respectively, and then drying by using nitrogen.
Further, the activating material contains NaOH or KOH, and the mass ratio of the activating material to the graphite carbon nitride working electrode is 1: 2-5: 1-2, wherein the activation temperature is 400-800 ℃.
Further, the electrochemiluminescence immunosensor is prepared by the immunosensor preparation module through an electrochemiluminescence technology by using an antigen standard solution and a modified electrode, and the electrochemiluminescence immunosensor preparation module comprises:
(1) dripping 6-10 mu L of tumor marker antibody standard solution on the surface of the modified electrode, airing at room temperature to form a film, and cleaning the electrode by using PBS (phosphate buffer solution) with the pH of 7.4;
(2) after air drying, 10 mu L of bovine serum albumin solution with the mass fraction of 1-3% is dripped on the surface of the electrode, the electrode is incubated for 1-1.5 h at the temperature of 37 ℃, the non-specific binding sites are sealed, and the surface of the electrode is washed;
(3) dripping 10 mu L of 0.5-100 ng/mL tumor marker antigen standard solutions with different concentrations, incubating the antigen and the antibody at room temperature for 30-40 min, washing and drying to obtain the electrochemiluminescence immunosensor.
Further, the drawing a working curve by the sensor detection curve drawing module using a curve drawing program according to the electrochemiluminescence intensity of the electrode in the detection report includes:
(1) correctly connecting the electrochemiluminescence immunosensor to an electrochemical workstation;
(2) testing by using an MPI-B type multi-parameter chemiluminescence analysis test system; the multi-parameter chemiluminescence analysis method comprises the following steps:
firstly, generating a gridded tumor tissue sample model and generating a training tumor tissue sample;
secondly, constructing and training a multilayer perception network of the Cerenkov fluorescence tomography, wherein the multilayer perception network can be divided into a forward network A and a reverse network B;
thirdly, acquiring a Cherenkov fluorescence signal on the surface of the tumor tissue sample, and reconstructing to obtain the three-dimensional distribution information of the Cherenkov fluorescence light source in the tumor tissue sample;
fourthly, mapping the obtained preliminary reconstruction result to the constructed meshed tumor tissue sample model, and inputting the preliminary reconstruction result to a multilayer perception network to obtain an accurate reconstruction result;
the first step of generating a training tumor tissue sample comprises:
(i) constructing a tumor tissue sample model, constructing a tumor tissue sample model with the size of 5 multiplied by 5mm3, and gridding the constructed tumor tissue sample model by using a finite element theory to further obtain a gridded tumor tissue sample model;
(ii) constructing a single Cherenkov fluorescent light source simulation tumor tissue sample, arranging a single spherical Cherenkov fluorescent light source in the tumor tissue sample model after gridding in the step (1), wherein the radius of the light source is 0.1mm, and generating a simulation training tumor tissue sample of the single Cherenkov fluorescent light source by using a Monte Carlo simulation MOSE platform;
(iii) expanding the tumor tissue sample to obtain a multi-Cherenkov fluorescent light source simulation tumor tissue sample, and expanding the tumor tissue sample by using a tumor tissue sample combination method on the basis of the single-Cherenkov fluorescent light source simulation tumor tissue sample set obtained in the step (ii), so as to obtain a multi-Cherenkov fluorescent light source simulation training tumor tissue sample;
the second step of constructing and training the multilayer perception network of the Cerenkov fluorescence tomography comprises the following steps:
1) constructing a forward network A, wherein the forward network A comprises 1 input layer, 4 hidden layers and 1 output layer, the number of nodes of the input layer and the number of nodes of the hidden layers are the same as the number of nodes of a tumor tissue sample model grid, and the number of nodes of the output layer is the same as the number of nodes on the surface of the tumor tissue sample model grid;
2) training a forward network A by using the obtained tumor tissue sample with the multi-Cherenkov fluorescent light source, inputting the distribution data of the Cherenkov fluorescent light source simulating the tumor tissue sample in the tumor tissue sample model into the network, and outputting the predicted distribution data of the tumor tissue sample on the surface of the tumor tissue sample model by the network;
3) constructing a reverse network B, wherein the reverse network B comprises 1 input layer, 4 hidden layers and 1 output layer, the number of nodes of the input layer is the same as the number of nodes on the surface of a tumor tissue sample model grid, and the number of nodes of the output layer and the number of nodes of the hidden layers are the same as the number of nodes of the tumor tissue sample model grid;
4) training a reverse network B by using the obtained tumor tissue sample with the multi-Cherenkov fluorescent light source, inputting the distribution data of the tumor tissue sample simulated by the multi-Cherenkov fluorescent light source on the surface of the tumor tissue sample model by the network, and outputting the predicted distribution data of the Cherenkov fluorescent light source simulated by the multi-Cherenkov fluorescent light source in the tumor tissue sample model by the network;
5) combining the forward network A and the reverse network B, and taking an output layer of the trained forward network A as an input layer of the trained reverse network B to obtain a final multilayer perception network;
the output result of each layer in the steps 2) and 4) is corrected by using a correction function; the negative values in the output results of the linear units of the hidden and output layers are modified by:
wherein X represents the output result of the linear unit of the current layer, and ReLu represents a correction function; when the output result is a negative value or zero, the correction function returns the negative value to zero;
in 2) and 4), the relationship between the current layer and the previous layer is as follows:
Xi＝Dropout0.4(ReLu(WiXi-1+bi)) i≥2；
wherein XiNode value, W, representing the i-th layeriRepresents the weight of the ith layer, biIndicating the bias, Dropout, of the ith layer0.4Is a random function, and indicates that the node of each layer has 40% probability zero clearing;
in the 2) and 4), the multi-layer perception network is subjected to constraint training through the following formula:
wherein | · | purple2Denotes the second order norm, minypredRepresenting y satisfying a minimum second order normpred(ii) a In 2), ytrueFor training known information on the distribution of the Cerenkov fluorescence signal in tumor tissue samples, ypredCorresponding predicted Cerenkov fluorescence signal distribution information output for the network; in 4), ytrueFor training the known three-dimensional distribution information, y, of the Cerenkov fluorescent light source in a tumor tissue samplepredOutputting the three-dimensional distribution information of the corresponding predicted Cerenkov fluorescent light source for the network;
the third step of collecting the Cerenkov fluorescence signal on the surface of the tumor tissue sample, and the reconstructing to obtain the three-dimensional distribution information of the Cerenkov fluorescence light source in the tumor tissue sample specifically comprises the following steps:
collecting a Cerenkov fluorescence signal of a tumor tissue sample table;
reconstructing by using a reconstruction method to obtain a preliminary distribution result of the Cerenkov fluorescent light source in the tumor tissue sample body;
(3) and drawing a working curve according to the relation between the current response obtained by different electron mediators and the concentration of different corresponding tumor marker antigen standard solutions.
The invention also aims to provide the electrochemiluminescence immunosensor for detecting the tumor marker, which is prepared by using the preparation system of the electrochemiluminescence immunosensor for detecting the tumor marker.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for applying said system for preparing an electrochemiluminescence immunosensor for detecting a tumor marker when executed on an electronic device.
It is another object of the present invention to provide a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to apply the system for preparing an electrochemiluminescence immunosensor for detecting tumor markers.
The invention also aims to provide an information data processing terminal, which is characterized in that the information data processing terminal is used for realizing the preparation system of the electrochemiluminescence immunosensor for detecting tumor markers.
By combining all the technical schemes, the invention has the advantages and positive effects that: the electrochemiluminescence immunosensor for detecting the tumor marker provided by the invention has the advantages of high analysis sensitivity, strong specificity, convenience in use and low cost. The bovine serum albumin solution and the tumor marker antigen standard solution are dripped on the surface of the graphite carbon nitride working electrode, so that the sensitivity of the electrochemiluminescence immunosensor is improved; AFP is detected by a mechanism that the protein obstructs electron transfer to change the luminous intensity, and the electrode preparation process is greatly simplified because no enzyme or mark exists. Meanwhile, the graphite carbon nitride is used as the working electrode, so that the photoelectric conversion efficiency and stability are high, and the electrochemiluminescence intensity is effectively enhanced.
The invention adopts a multi-parameter chemiluminescence analysis method: generating a gridded tumor tissue sample model and generating a training tumor tissue sample;
constructing a multilayer perception network of the Cerenkov fluorescence tomography and training, wherein the multilayer perception network can be divided into a forward network A and a reverse network B;
acquiring a Cherenkov fluorescence signal on the surface of the tumor tissue sample, and reconstructing to obtain the three-dimensional distribution information of a Cherenkov fluorescence light source in the tumor tissue sample;
mapping the obtained preliminary reconstruction result to the constructed gridded tumor tissue sample model, and inputting the preliminary reconstruction result to a multilayer perception network to obtain an accurate reconstruction result; accurate detection data can be obtained.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system for preparing an electrochemiluminescence immunosensor for detecting tumor markers according to an embodiment of the present invention;
in the figure: 1. preparing a module by using graphite carbon nitride; 2. a working electrode pretreatment module; 3. a central control module; 4. an antigen standard solution preparation module; 5. a modified electrode preparation module; 6. an immunosensor preparation module; 7. a tumor marker detection module; 8. a sensor detection curve drawing module; 9. a data storage module; 10. and updating the display module.
FIG. 2 is a flowchart of a method for preparing an electrochemiluminescence immunosensor for detecting tumor markers according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for performing the pretreatment operations of activating, polishing, cleaning, and nitrogen blowing-drying the graphite carbon nitride working electrode by the working electrode pretreatment module using the pretreatment apparatus according to the embodiment of the present invention.
Fig. 4 is a flowchart of a method for preparing an electrochemiluminescence immunosensor by an immunosensor preparation module using an electrochemiluminescence technology using an antigen standard solution and a modified electrode according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for drawing a working curve according to the electrochemiluminescence intensity of the electrode in the detection report by using a curve drawing program through a sensor detection curve drawing module according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides an electrochemiluminescence immunosensor for detecting tumor markers and an application thereof, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the system for preparing an electrochemiluminescence immunosensor for detecting a tumor marker according to the embodiment of the present invention includes a graphite carbon nitride preparation module 1, a working electrode pretreatment module 2, a central control module 3, an antigen standard solution preparation module 4, a modified electrode preparation module 5, an immunosensor preparation module 6, a tumor marker detection module 7, a sensor detection curve drawing module 8, a data storage module 9, and an update display module 10.
The graphite carbon nitride preparation module 1 is connected with the central control module 3 and used for preparing polyaniline gel by utilizing aniline, phytic acid and an initiator and carbonizing the polyaniline gel to obtain a graphite carbon nitride working electrode;
the working electrode pretreatment module 2 is connected with the central control module 3 and is used for carrying out pretreatment operations of activation, polishing, cleaning and nitrogen blow-drying of the graphite carbon nitride working electrode through pretreatment equipment;
the central control module 3 is connected with the graphite carbon nitride preparation module 1, the working electrode pretreatment module 2, the antigen standard solution preparation module 4, the modified electrode preparation module 5, the immunosensor preparation module 6, the tumor marker detection module 7, the sensor detection curve drawing module 8, the data storage module 9 and the update display module 10, and is used for coordinating and controlling the normal operation of each module of the preparation system of the electrochemiluminescence immunosensor for detecting the tumor marker through a central processing unit;
the antigen standard solution preparation module 4 is connected with the central control module 3 and is used for respectively preparing bovine serum albumin solution and tumor marker antigen standard solution through a standard solution preparation device;
the modified electrode preparation module 5 is connected with the central control module 3 and is used for respectively dripping bovine serum albumin solution and tumor marker antigen standard solution on the surface of the graphite carbon nitride working electrode to obtain a modified electrode;
the immunosensor preparation module 6 is connected with the central control module 3 and is used for preparing an electrochemiluminescence immunosensor by using an antigen standard solution and a modified electrode through an electrochemiluminescence technology;
the tumor marker detection module 7 is connected with the central control module 3 and used for detecting the tumor marker through the prepared electrochemiluminescence immunosensor and obtaining a detection report;
the sensor detection curve drawing module 8 is connected with the central control module 3 and used for drawing a working curve according to the electrochemiluminescence intensity of the electrode in the detection report through a curve drawing program;
the data storage module 9 is connected with the central control module 3 and used for storing graphite carbon nitride preparation data, working electrode pretreatment data, antigen standard solution preparation data, modified electrode preparation data, immunosensor preparation data, tumor marker detection reports and working curves through a memory;
and the updating display module 10 is connected with the central control module 3 and is used for updating and displaying the graphite carbon nitride preparation, the working electrode pretreatment, the antigen standard solution preparation, the modified electrode preparation, the immunosensor preparation, the tumor marker detection report and the real-time data of the working curve through a display.
As shown in fig. 2, the control method of the electrochemiluminescence immunosensor for detecting tumor markers provided by the embodiment of the present invention includes the following steps:
s101, preparing polyaniline gel by using aniline, phytic acid and an initiator through a graphite carbon nitride preparation module, and carbonizing to obtain a graphite carbon nitride working electrode;
s102, utilizing pretreatment equipment to carry out pretreatment operations of activation, polishing, cleaning and nitrogen blow-drying of the graphite carbon nitride working electrode through a working electrode pretreatment module;
s103, the central control module utilizes a central processing unit to coordinate and control the normal operation of each module of the preparation system of the electrochemiluminescence immunosensor for detecting the tumor markers;
s104, preparing a bovine serum albumin solution and a tumor marker antigen standard solution by an antigen standard solution preparation module through a standard solution preparation device respectively;
s105, respectively dripping bovine serum albumin solution and tumor marker antigen standard solution on the surface of the graphite carbon nitride working electrode through a modified electrode preparation module to obtain a modified electrode;
s106, preparing the electrochemiluminescence immunosensor by the immunosensor preparation module through an electrochemiluminescence technology and by using an antigen standard solution and a modified electrode;
s107, detecting the tumor marker by using the prepared electrochemiluminescence immunosensor through a tumor marker detection module, and obtaining a detection report; drawing a working curve by a sensor detection curve drawing module by using a curve drawing program according to the electrochemiluminescence intensity of the electrode in the detection report;
s108, storing graphite carbon nitride preparation data, working electrode pretreatment data, antigen standard solution preparation data, modified electrode preparation data, immunosensor preparation data, tumor marker detection reports and working curves by a data storage module through a memory;
and S109, updating and displaying the graphite carbon nitride preparation, the working electrode pretreatment, the antigen standard solution preparation, the modified electrode preparation, the immunosensor preparation, the tumor marker detection report and the real-time data of the working curve by the updating and displaying module through the display.
In step S101 provided in the embodiment of the present invention, the molar ratio of the hair agent, the aniline, and the phytic acid is 1 to 3: 2-7: 1, the initiator is ammonium sulfate solution or hydrogen peroxide solution.
As shown in fig. 3, in step S102, the pretreatment operations of activating, polishing, cleaning, and drying the graphite carbon nitride working electrode by the working electrode pretreatment module using a pretreatment apparatus according to the embodiment of the present invention include:
s201, activating the graphite carbon nitride working electrode by using an activating material to obtain a three-dimensional layered porous graphite carbon nitride working electrode;
s202, using 0.05-1.5 μm Al2O3Polishing the three-dimensional layered porous graphite carbon nitride working electrode by using polishing powder;
s203, ultrasonically cleaning the three-dimensional layered porous graphite carbon nitride working electrode subjected to polishing treatment in ethanol and ultrapure water for 5-15 min respectively, and then drying with nitrogen.
The activating material provided by the embodiment of the invention contains NaOH or KOH, and the mass ratio of the activating material to the graphite carbon nitride working electrode is 1: 2-5: 1-2, wherein the activation temperature is 400-800 ℃.
As shown in fig. 4, in step S106, the preparing step of the electrochemiluminescence immunosensor by the immunosensor preparing module using an electrochemiluminescence technology and using an antigen standard solution and a modified electrode includes:
s301, dripping 6-10 mu L of tumor marker antibody standard solution on the surface of a modified electrode, airing at room temperature to form a film, and cleaning the electrode by using PBS (phosphate buffer solution) with the pH of 7.4;
s302, after air drying, 10 mu L of bovine serum albumin solution with the mass fraction of 1-3% is dripped on the surface of the electrode, the electrode is incubated for 1-1.5 h at 37 ℃, the non-specific binding sites are sealed, and the surface of the electrode is washed;
s303, dripping 10 mu L of 0.5-100 ng/mL tumor marker antigen standard solutions with different concentrations, incubating the antigen and the antibody at room temperature for 30-40 min, washing and drying to obtain the electrochemiluminescence immunosensor.
As shown in fig. 5, in step S107, the step of drawing an operation curve by the sensor detection curve drawing module according to the electrochemiluminescence intensity of the electrode in the detection report by using a curve drawing program includes:
s401, correctly connecting the electrochemiluminescence immunosensor to an electrochemical workstation;
s402, testing by using an MPI-B type multi-parameter chemiluminescence analysis testing system;
and S403, drawing a working curve according to the relationship between the current responses obtained by different electron mediators and the concentrations of different corresponding tumor marker antigen standard solutions.
The multi-parameter chemiluminescence analysis method of step S402 comprises:
firstly, generating a gridded tumor tissue sample model and generating a training tumor tissue sample;
secondly, constructing and training a multilayer perception network of the Cerenkov fluorescence tomography, wherein the multilayer perception network can be divided into a forward network A and a reverse network B;
thirdly, acquiring a Cherenkov fluorescence signal on the surface of the tumor tissue sample, and reconstructing to obtain the three-dimensional distribution information of the Cherenkov fluorescence light source in the tumor tissue sample;
fourthly, mapping the obtained preliminary reconstruction result to the constructed meshed tumor tissue sample model, and inputting the preliminary reconstruction result to a multilayer perception network to obtain an accurate reconstruction result;
the first step of generating a training tumor tissue sample comprises:
(i) constructing a tumor tissue sample model with a size of 5 × 5 × 5mm3The constructed tumor tissue sample model is meshed by using a finite element theory, and further the meshed tumor tissue sample model is obtained;
(ii) constructing a single Cherenkov fluorescent light source simulation tumor tissue sample, arranging a single spherical Cherenkov fluorescent light source in the tumor tissue sample model after gridding in the step (1), wherein the radius of the light source is 0.1mm, and generating a simulation training tumor tissue sample of the single Cherenkov fluorescent light source by using a Monte Carlo simulation MOSE platform;
(iii) expanding the tumor tissue sample to obtain a multi-Cherenkov fluorescent light source simulation tumor tissue sample, and expanding the tumor tissue sample by using a tumor tissue sample combination method on the basis of the single-Cherenkov fluorescent light source simulation tumor tissue sample set obtained in the step (ii), so as to obtain a multi-Cherenkov fluorescent light source simulation training tumor tissue sample;
the second step of constructing and training the multilayer perception network of the Cerenkov fluorescence tomography comprises the following steps:
1) constructing a forward network A, wherein the forward network A comprises 1 input layer, 4 hidden layers and 1 output layer, the number of nodes of the input layer and the number of nodes of the hidden layers are the same as the number of nodes of a tumor tissue sample model grid, and the number of nodes of the output layer is the same as the number of nodes on the surface of the tumor tissue sample model grid;
2) training a forward network A by using the obtained tumor tissue sample with the multi-Cherenkov fluorescent light source, inputting the distribution data of the Cherenkov fluorescent light source simulating the tumor tissue sample in the tumor tissue sample model into the network, and outputting the predicted distribution data of the tumor tissue sample on the surface of the tumor tissue sample model by the network;
3) constructing a reverse network B, wherein the reverse network B comprises 1 input layer, 4 hidden layers and 1 output layer, the number of nodes of the input layer is the same as the number of nodes on the surface of a tumor tissue sample model grid, and the number of nodes of the output layer and the number of nodes of the hidden layers are the same as the number of nodes of the tumor tissue sample model grid;
4) training a reverse network B by using the obtained tumor tissue sample with the multi-Cherenkov fluorescent light source, inputting the distribution data of the tumor tissue sample simulated by the multi-Cherenkov fluorescent light source on the surface of the tumor tissue sample model by the network, and outputting the predicted distribution data of the Cherenkov fluorescent light source simulated by the multi-Cherenkov fluorescent light source in the tumor tissue sample model by the network;
5) combining the forward network A and the reverse network B, and taking an output layer of the trained forward network A as an input layer of the trained reverse network B to obtain a final multilayer perception network;
the output result of each layer in the steps 2) and 4) is corrected by using a correction function; the negative values in the output results of the linear units of the hidden and output layers are modified by:
wherein X represents the output result of the linear unit of the current layer, and ReLu represents a correction function; when the output result is a negative value or zero, the correction function returns the negative value to zero;
in 2) and 4), the relationship between the current layer and the previous layer is as follows:
Xi＝Dropout0.4(ReLu(WiXi-1+bi)) i≥2；
wherein XiNode value, W, representing the i-th layeriRepresents the weight of the ith layer, biIndicating the bias, Dropout, of the ith layer0.4Is a random function, and indicates that the node of each layer has 40% probability zero clearing;
in the 2) and 4), the multi-layer perception network is subjected to constraint training through the following formula:
wherein | · | purple2Denotes the second order norm, minypredRepresenting y satisfying a minimum second order normpred(ii) a In 2), ytrueFor training known information on the distribution of the Cerenkov fluorescence signal in tumor tissue samples, ypredCorresponding predicted Cerenkov fluorescence signal distribution information output for the network; in 4), ytrueFor training the known three-dimensional distribution information, y, of the Cerenkov fluorescent light source in a tumor tissue samplepredOutputting the three-dimensional distribution information of the corresponding predicted Cerenkov fluorescent light source for the network;
the third step of collecting the Cerenkov fluorescence signal on the surface of the tumor tissue sample, and the reconstructing to obtain the three-dimensional distribution information of the Cerenkov fluorescence light source in the tumor tissue sample specifically comprises the following steps:
collecting a Cerenkov fluorescence signal of a tumor tissue sample table;
and (4) reconstructing by using a reconstruction method to obtain a preliminary distribution result of the Cerenkov fluorescent light source in the tumor tissue sample body.
In the description of the present invention, "a plurality" means two or more unless otherwise specified; the terms "upper", "lower", "left", "right", "inner", "outer", "front", "rear", "head", "tail", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A system for preparing an electrochemiluminescence immunosensor for detecting tumor markers is characterized in that the system for preparing the electrochemiluminescence immunosensor for detecting tumor markers comprises:
the graphite carbon nitride preparation module is connected with the central control module and used for preparing polyaniline gel by utilizing aniline, phytic acid and an initiator and carbonizing the polyaniline gel to obtain a graphite carbon nitride working electrode;
the working electrode pretreatment module is connected with the central control module and is used for carrying out pretreatment operations of activation, polishing, cleaning and nitrogen blow-drying of the graphite carbon nitride working electrode through pretreatment equipment;
the central control module is connected with the graphite carbon nitride preparation module, the working electrode pretreatment module, the antigen standard solution preparation module, the modified electrode preparation module, the immunosensor preparation module, the tumor marker detection module, the sensor detection curve drawing module, the data storage module and the updating display module, and is used for coordinating and controlling the normal operation of each module of the preparation system of the electrochemiluminescence immunosensor for detecting the tumor marker through the central processing unit;
the antigen standard solution preparation module is connected with the central control module and is used for respectively preparing bovine serum albumin solution and tumor marker antigen standard solution through a standard solution preparation device;
the modified electrode preparation module is connected with the central control module and is used for respectively dripping bovine serum albumin solution and tumor marker antigen standard solution on the surface of the graphite carbon nitride working electrode to obtain a modified electrode;
the immunosensor preparation module is connected with the central control module and used for preparing an electrochemiluminescence immunosensor by utilizing an antigen standard solution and a modified electrode through an electrochemiluminescence technology;
the tumor marker detection module is connected with the central control module and used for detecting the tumor marker through the prepared electrochemiluminescence immunosensor and obtaining a detection report;
and the sensor detection curve drawing module is connected with the central control module and used for drawing a working curve according to the electrochemiluminescence intensity of the electrode in the detection report through a curve drawing program.
2. The system for preparing an electrochemiluminescence immunosensor for detecting tumor markers according to claim 1, further comprising:
the data storage module is connected with the central control module and used for storing graphite carbon nitride preparation data, working electrode pretreatment data, antigen standard solution preparation data, modified electrode preparation data, immunosensor preparation data, tumor marker detection reports and working curves through a memory;
and the updating display module is connected with the central control module and is used for updating and displaying the graphite carbon nitride preparation, the working electrode pretreatment, the antigen standard solution preparation, the modified electrode preparation, the immunosensor preparation, the tumor marker detection report and the real-time data of the working curve through a display.
3. The system for preparing an electrochemiluminescence immunosensor for detecting tumor markers according to claim 1, wherein the molar ratio of the hair agent to the aniline to the phytic acid is 1-3: 2-7: 1, the initiator is ammonium sulfate solution or hydrogen peroxide solution;
utilize preprocessing equipment to carry out the preliminary treatment operation of activation, polishing, washing and nitrogen gas weathering of graphite carbon nitride working electrode through working electrode preliminary treatment module, include:
(1) activating the graphite carbon nitride working electrode by using an activating material to obtain a three-dimensional layered porous graphite carbon nitride working electrode;
(2) using 0.05-1.5 μm Al2O3Polishing the three-dimensional layered porous graphite carbon nitride working electrode by using polishing powder;
(3) and ultrasonically cleaning the three-dimensional layered porous graphite carbon nitride working electrode subjected to polishing treatment in ethanol and ultrapure water for 5-15 min respectively, and then drying by using nitrogen.
4. The system for preparing an electrochemiluminescence immunosensor for detecting tumor markers according to claim 3, wherein the activating material comprises NaOH or KOH, and the mass ratio of the activating material to the graphite carbon nitride working electrode is 1: 2-5: 1-2, wherein the activation temperature is 400-800 ℃.
5. The system for preparing an electrochemiluminescence immunosensor for detecting tumor markers according to claim 1, wherein the electrochemiluminescence immunosensor is prepared by an immunosensor preparation module by using an electrochemiluminescence technology and using an antigen standard solution and a modified electrode, and the system comprises:
(1) dripping 6-10 mu L of tumor marker antibody standard solution on the surface of the modified electrode, airing at room temperature to form a film, and cleaning the electrode by using PBS (phosphate buffer solution) with the pH of 7.4;
(2) after air drying, 10 mu L of bovine serum albumin solution with the mass fraction of 1-3% is dripped on the surface of the electrode, the electrode is incubated for 1-1.5 h at the temperature of 37 ℃, the non-specific binding sites are sealed, and the surface of the electrode is washed;
(3) dripping 10 mu L of 0.5-100 ng/mL tumor marker antigen standard solutions with different concentrations, incubating the antigen and the antibody at room temperature for 30-40 min, washing and drying to obtain the electrochemiluminescence immunosensor.
6. The system for preparing an electrochemiluminescence immunosensor for detecting tumor markers according to claim 1, wherein the step of drawing a working curve according to electrochemiluminescence intensity of electrodes in a detection report by using a sensor detection curve drawing module and a curve drawing program comprises:
(1) correctly connecting the electrochemiluminescence immunosensor to an electrochemical workstation;
(2) testing using a multi-parameter chemiluminescence analysis test system; the multi-parameter chemiluminescence analysis method comprises the following steps:
firstly, generating a gridded tumor tissue sample model and generating a training tumor tissue sample;
secondly, constructing and training a multilayer perception network of the Cerenkov fluorescence tomography, wherein the multilayer perception network can be divided into a forward network A and a reverse network B;
thirdly, acquiring a Cherenkov fluorescence signal on the surface of the tumor tissue sample, and reconstructing to obtain the three-dimensional distribution information of the Cherenkov fluorescence light source in the tumor tissue sample;
fourthly, mapping the obtained preliminary reconstruction result to the constructed meshed tumor tissue sample model, and inputting the preliminary reconstruction result to a multilayer perception network to obtain an accurate reconstruction result;
the first step of generating a training tumor tissue sample comprises:
(i) constructing a tumor tissue sample model with a size of 5 × 5 × 5mm3The constructed tumor tissue sample model is meshed by using a finite element theory, and further the meshed tumor tissue sample model is obtained;
(ii) constructing a single Cherenkov fluorescent light source simulation tumor tissue sample, arranging a single spherical Cherenkov fluorescent light source in the tumor tissue sample model after gridding in the step (1), wherein the radius of the light source is 0.1mm, and generating a simulation training tumor tissue sample of the single Cherenkov fluorescent light source by using a Monte Carlo simulation MOSE platform;
(iii) expanding the tumor tissue sample to obtain a multi-Cherenkov fluorescent light source simulation tumor tissue sample, and expanding the tumor tissue sample by using a tumor tissue sample combination method on the basis of the single-Cherenkov fluorescent light source simulation tumor tissue sample set obtained in the step (ii), so as to obtain a multi-Cherenkov fluorescent light source simulation training tumor tissue sample;
the second step of constructing and training the multilayer perception network of the Cerenkov fluorescence tomography comprises the following steps:
1) constructing a forward network A, wherein the forward network A comprises 1 input layer, 4 hidden layers and 1 output layer, the number of nodes of the input layer and the number of nodes of the hidden layers are the same as the number of nodes of a tumor tissue sample model grid, and the number of nodes of the output layer is the same as the number of nodes on the surface of the tumor tissue sample model grid;
2) training a forward network A by using the obtained tumor tissue sample with the multi-Cherenkov fluorescent light source, inputting the distribution data of the Cherenkov fluorescent light source simulating the tumor tissue sample in the tumor tissue sample model into the network, and outputting the predicted distribution data of the tumor tissue sample on the surface of the tumor tissue sample model by the network;
3) constructing a reverse network B, wherein the reverse network B comprises 1 input layer, 4 hidden layers and 1 output layer, the number of nodes of the input layer is the same as the number of nodes on the surface of a tumor tissue sample model grid, and the number of nodes of the output layer and the number of nodes of the hidden layers are the same as the number of nodes of the tumor tissue sample model grid;
4) training a reverse network B by using the obtained tumor tissue sample with the multi-Cherenkov fluorescent light source, inputting the distribution data of the tumor tissue sample simulated by the multi-Cherenkov fluorescent light source on the surface of the tumor tissue sample model by the network, and outputting the predicted distribution data of the Cherenkov fluorescent light source simulated by the multi-Cherenkov fluorescent light source in the tumor tissue sample model by the network;
5) combining the forward network A and the reverse network B, and taking an output layer of the trained forward network A as an input layer of the trained reverse network B to obtain a final multilayer perception network;
the output result of each layer in the steps 2) and 4) is corrected by using a correction function; the negative values in the output results of the linear units of the hidden and output layers are modified by:
wherein X represents the output result of the linear unit of the current layer, and ReLu represents a correction function; when the output result is a negative value or zero, the correction function returns the negative value to zero;
in 2) and 4), the relationship between the current layer and the previous layer is as follows:
Xi＝Dropout0.4(ReLu(WiXi-1+bi)) i≥2；
wherein XiNode value, W, representing the i-th layeriRepresents the weight of the ith layer, biIndicating the bias, Dropout, of the ith layer0.4Is a random function, and indicates that the node of each layer has 40% probability zero clearing;
in the 2) and 4), the multi-layer perception network is subjected to constraint training through the following formula:
wherein | · | purple2Denotes the second order norm, minypredRepresenting y satisfying a minimum second order normpred(ii) a In 2), ytrueFor training known information on the distribution of the Cerenkov fluorescence signal in tumor tissue samples, ypredCorresponding predicted Cerenkov fluorescence signal distribution information output for the network; in 4), ytrueFor training the known three-dimensional distribution information, y, of the Cerenkov fluorescent light source in a tumor tissue samplepredOutputting the three-dimensional distribution information of the corresponding predicted Cerenkov fluorescent light source for the network;
the third step of collecting the Cerenkov fluorescence signal on the surface of the tumor tissue sample, and the reconstructing to obtain the three-dimensional distribution information of the Cerenkov fluorescence light source in the tumor tissue sample specifically comprises the following steps:
collecting a Cerenkov fluorescence signal of a tumor tissue sample table;
reconstructing by using a reconstruction method to obtain a preliminary distribution result of the Cerenkov fluorescent light source in the tumor tissue sample body;
(3) and drawing a working curve according to the relation between the current response obtained by different electron mediators and the concentration of different corresponding tumor marker antigen standard solutions.
7. An electrochemiluminescence immunosensor for detecting tumor markers, which is prepared by using the preparation system of the electrochemiluminescence immunosensor for detecting tumor markers according to any one of claims 1 to 6, and is characterized in that the electrochemiluminescence immunosensor for detecting tumor markers comprises a working electrode, a bovine serum albumin solution and a tumor marker antigen standard solution.
8. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for applying the system of any one of claims 1 to 6 to the preparation of an electrochemiluminescence immunosensor for detecting tumor markers when the computer program product is executed on an electronic device.
9. A computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to apply the system for preparing an electrochemiluminescence immunosensor for detecting tumor markers according to any one of claims 1 to 6.
10. An information data processing terminal, characterized in that the information data processing terminal is used for realizing the preparation system of the electrochemiluminescence immunosensor for detecting tumor markers according to any one of claims 1-6.
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