CN117451998A - Novel immunodetection method based on micro-nano robot cluster vision sensing - Google Patents
Novel immunodetection method based on micro-nano robot cluster vision sensing Download PDFInfo
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Abstract
The invention relates to the technical field of medical detection, in particular to a novel immunodetection method based on micro-nano robot cluster vision sensing. Which comprises the following steps: s1, preparing a magnetic control micro-nano robot monomer: selection of Fe 3 O 4 Nano particles are taken as basic particles, and mesoporous SiO is coated 2 Modifying amino after the shell, modifying carboxyl by transferring carboxyl, and activating carboxyl to modify an antibody to prepare an antibody magnetic ball; s2, forming a detection cluster by using an antibody magnetic ball under the magnetic field condition: dispersing the antibody magnetic balls into a buffer solution for ultrasonic treatment to form an antibody magnetic ball dispersion liquid, adjusting magnetic field parameters, and searching an optimal balance state of interaction of a flow field and a magnetic field to form a vortex cluster; s3, introducing a biological sample to be detected into the detection cluster to carry out specific binding of antigen and antibody, and extracting front and rear vortex of the biological sample to be detectedFeature vectors of the rotating cluster motion video are used for drawing a scatter diagram of data through a support vector machine model. The method breaks through the limitation of the traditional ELISA immunodetection means with the advantages of rapidness, accuracy and automation.
Description
Technical Field
The invention relates to the technical field of medical detection, in particular to a novel immunodetection method based on micro-nano robot cluster vision sensing.
Background
The immunodetection is an important branch in the medical field, plays a key role in disease diagnosis and monitoring, helps doctors to determine disease states of infection, autoimmune diseases, allergic reactions and the like of patients, and is also beneficial to epidemic monitoring and epidemiological research. At the same time, immunodetection also plays a key role in drug development and patient treatment protocols, and can evaluate the efficacy and safety of the drug to be selected, providing an individualized treatment protocol for the patient. In addition, immunodetection is widely used in bioscience research and basic science, revealing the mechanism and regulation of the immune system, facilitating understanding of the disease development mechanism and development of new therapeutic methods. In conclusion, the immunodetection provides an indispensable tool and information for preventing, diagnosing and treating diseases, and has a key meaning for guaranteeing public health and improving life quality.
The principle of immunodetection based on antibody-antigen specific binding plays a vital role in the biomedical sensing field. The principle provides an accurate and highly specific method for immunodetection, and is used for detecting and quantifying molecular markers in biological samples, thereby being beneficial to key applications such as early disease diagnosis, drug development, disease monitoring and the like. Among them, enzyme-linked immunosorbent assay (ELISA) plays an important role as one of the methods based on this principle.
However, although ELISA is not a negligible feature in the field of biochemical analysis, it still has some inconvenient problems. One of the most obvious problems is its cumbersome manual operation, particularly the multiple washing steps. Conventional ELISA requires multiple washes to ensure removal of non-specifically bound substances, which not only increases the complexity of the procedure, but also is prone to human error. Furthermore, multiple washes also extend the execution time of the experiment, and in some cases time is critical, for example in emergency clinical diagnostics.
Accordingly, researchers have sought to improve ELISA and other similar techniques to simplify the process flow, increase the degree of automation, and reduce the time of operation. How to realize high-efficiency, accurate, convenient and automatic detection has great breakthrough significance for the current immunodetection field.
Disclosure of Invention
The invention provides a novel immunodetection method based on micro-nano robot cluster vision sensing, and aims to solve the problems that the manual operation process of the traditional ELISA technology is complicated, the experimental time is long, human errors are easy to introduce, and the like.
The invention provides a novel immunodetection method based on micro-nano robot cluster vision sensing, which comprises the following steps:
s1, preparing a magnetic control micro-nano robot monomer: selection of Fe 3 O 4 Nano particles are taken as basic particles, and mesoporous SiO is coated 2 Modifying amino after the shell, further modifying carboxyl, and activating carboxyl to modify the antibody to prepare an antibody magnetic ball;
s2, forming a detection cluster by using an antibody magnetic ball under the magnetic field condition: dispersing the antibody magnetic balls into a buffer solution for ultrasonic treatment to form an antibody magnetic ball dispersion liquid, adjusting magnetic field parameters, and searching an optimal balance state of interaction of a flow field and a magnetic field to form a stable vortex cluster which can be used for detection;
s3, introducing a biological sample to be detected into the detection cluster to carry out specific combination of antigen and antibody, extracting feature vectors of the vortex cluster motion video before and after the biological sample to be detected is added, drawing a scatter diagram of data through the obtained feature vectors by using a support vector machine model, and carrying out visual data distribution.
As a further improvement of the present invention, the step S2 includes the steps of:
s21, adding the magnetic ball dispersion liquid into the groove;
s22, placing the grooves in a rotating magnetic field of a triaxial Helmholtz coil, and establishing a cluster to form a phase diagram by adjusting the rotating frequency and the intensity of the magnetic field;
s23, determining the rotation frequency and the magnetic field intensity of the magnetic field formed by the clusters to form a compact and smooth vortex-shaped detection cluster.
As a further improvement of the present invention, in the step S22, the process of establishing a cluster-forming phase diagram includes the steps of:
s221, trying the movement of antibody magnetic ball particles under the weak magnetic field, the low frequency and the high frequency and the strong magnetic field, and locking the parameter interval of the phase diagram;
s222, fixing the magnetic field intensity, adjusting the magnetic field rotation frequency and searching the critical rotation frequency;
s223, fixing the rotation frequency, adjusting the magnetic field intensity and searching the critical magnetic field intensity.
As a further improvement of the present invention, in the step S3, the deep learning for the support vector machine model includes the steps of:
a1. extracting feature vectors of the cluster motion videos before and after adding the biological sample to be detected, and respectively adding different labels to the obtained feature vector data according to the number of the videos;
a2. dividing the feature vector data set into a training set and a test set, and performing feature scaling;
a3. and training the support vector machine model by adopting a training set, evaluating the performance of the support vector machine model by using a testing set, and finally outputting the accuracy and confusion matrix of the support vector machine model.
As a further improvement of the present invention, in the step S3, extracting a feature vector of the vortex cluster motion video includes the steps of:
all the extracted videos are preprocessed through a VGG16 neural network, ROI condition limitation is added, and feature extraction is carried out on the contour and texture of the cluster moving image through a cascade classifier based on Haar features in an OpenCV library.
As a further improvement of the present invention, in the step S3, a scatter diagram of data is drawn according to the obtained feature vector by using a support vector machine model, which specifically includes:
and carrying out principal component analysis on the obtained feature vector through a support vector machine model, reducing the high-dimensional feature vector to two dimensions, and drawing a scatter diagram of the feature vector on a two-dimensional plane.
As a further improvement of the present invention, in the step S1, the Fe 3 O 4 The nano particles are magnetic particles prepared by adopting a hydrothermal method reaction and separated by a magnet.
As a further improvement of the present invention, in the step S1, the carboxyl group is activated by NHS and EDC to modify the antibody.
The beneficial effects of the invention are as follows: the invention takes antibody modified ferroferric oxide particles as a basis, forms clusters under the action of a magnetic field, adds a biological sample to be detected after the clusters are stabilized under the magnetic field, and directly changes the movement form of the clusters, such as the rotation speed, the gathering area and the like of the clusters by utilizing the specific combination of the antigen in the biological sample and the antibody on the magnetic sphere. The characteristic vector of the movement behaviors of the front and rear clusters of the added biological sample is extracted by a computer algorithm, the data are classified and analyzed, the information of the biological sample is fed back, a brand-new immunity detection means is established, the detection means does not need to carry out complex early preparation on the sample, the detection sensitivity is high, and the computer algorithm is used for directly producing a data result, so that the limitation of the traditional ELISA immunity detection means is broken through with the advantages of rapidness, accuracy and automation.
Drawings
FIG. 1 is a flow chart of a novel immunodetection method based on micro-nano robot cluster vision sensing of the invention;
FIG. 2 is a schematic diagram of the underlying logic of the computer algorithm processing of the present invention;
FIG. 3 is a flowchart showing a specific implementation of the computer algorithm process of the present invention;
FIG. 4 is a graph comparing cluster morphology before and after adding 10ul of antigen solution to a cluster system in accordance with the present invention;
FIG. 5 is a graph comparing cluster morphology before and after adding 20ul of antigen solution to a cluster system in accordance with the present invention;
FIG. 6 is a graph comparing cluster morphology before and after 50ul of antigen solution is added to the cluster system in accordance with the invention;
FIG. 7 is a graph comparing cluster morphology before and after adding 100ul of antigen solution to a cluster system in accordance with the present invention;
FIG. 8 is a diagram of an confusion matrix for a support vector machine model in the present invention;
FIG. 9 is a scattergram drawn after principal component analysis of data in the present invention;
FIG. 10 is a graph comparing cluster morphology before and after adding 0.1mg/ml antigen solution to a cluster system in accordance with the invention;
FIG. 11 is a graph comparing cluster morphology before and after adding 0.01mg/ml antigen solution to a cluster system in accordance with the invention;
FIG. 12 is a graph comparing cluster morphology before and after adding 0.001mg/ml antigen solution to a cluster system in accordance with the invention;
FIG. 13 is a graph comparing cluster morphology before and after adding 0.0001mg/ml of antigen solution to a cluster system in accordance with the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
The invention provides a brand-new high-sensitivity and high-specificity antigen-antibody specific binding-based immunodetection method under the condition of ensuring the existing biomedical function, has the advantages of simple equipment, easy operation and capability of realizing quantitative and dynamic detection of samples, and provides a new thought for immunodetection.
As shown in fig. 1, the novel immunodetection method based on micro-nano robot cluster vision sensing comprises the following steps:
s1, preparing a magnetic control micro-nano robot monomer: selection of Fe 3 O 4 Nano particles are taken as basic particles, and mesoporous SiO is coated 2 Modifying amino after the shell, further modifying carboxyl, and activating carboxyl by using NHS and EDC to modify the antibody to prepare the antibody magnetic sphere.
Preparation of core-shell structure magnetic microspheres and surface antibody modification: and selecting ferroferric oxide particles as basic magnetic microspheres, and modifying different antibodies on the surfaces to prepare the antibody magnetic microspheres. The preferred structure, materials and preparation process are as follows:
by hydrothermal reactionSynthesis of Fe 3 O 4 Magnetic particles. First, feCl is treated by ultrasonic treatment 3 ·6H 2 O (1.35 g) was dissolved in ethylene glycol (40 ml), and then sodium acetate (1.925 g) and polyethylene glycol (3.6 g) having a molecular weight of 2000 were put into the reaction solution, and dissolved to complete dissolution by magnetic stirring. After stirring to dissolve, the reaction product was transferred to a reaction kettle and heated at 200 ℃ for 12 hours. Cooling to room temperature, separating supernatant by magnet, adding ultrapure water, and cleaning twice to obtain synthesized Fe 3 O 4 And (3) particles.
Coating the surface of the obtained magnetic particles with a mesoporous silica layer (mSiO 2 ) Dry Fe in experiments 3 O 4 Magnetic particles (50 mg) were ultrasonically dispersed into ultrapure water (50 ml), and 100mgCTAB and 100mgTEOA were added. After complete dissolution, nitrogen was introduced into the reaction vessel, and the internal air was completely discharged. After stopping the nitrogen gas, stirring was performed at 135℃for 300min (350 r/min), and TEOS (200 uL) was added to the mixture ten minutes after the start of the reaction. After the reaction was cooled to room temperature, the supernatant was separated by a magnet, and absolute ethanol (20 ml) and HCL (ph=1, 20 ml) were added thereto and shaken for 5 hours. After the completion, separating the supernatant by using a magnet, adding ultrapure water for washing twice to obtain Fe 3 O 4 @mSiO 2 And (3) particles.
At Fe 3 O 4 @mSiO 2 Extra-granular modification of carboxyl: in the experiment Fe 3 O 4 @SiO 2 (20 mg) nanoparticles were dispersed in ethylene glycol (20 mL) and APTES (200 uL) was added and reacted at room temperature for 24 hours. The supernatant was collected by magnet separation and washed 3 times with absolute ethanol. The washed product (20 mg) was dispersed in DMF (10 mL). Then, SAA (50 mg) and TEA (52.1 uL) were added to the above solution, and stirred at room temperature for 24 hours. Collecting silica-coated Fe having carboxyl group by magnet separation supernatant 3 O 4 The nanoparticles were washed 3 times with absolute ethanol.
At Fe 3 O 4 @SiO 2 -COOH surface modified antibodies: fe (Fe) 3 O 4 @SiO 2 (5 mg) of the nanoparticle having carboxyl group was dispersed in 5mL MES (50 mM, pH=6), followed by addition of 0.5mgEDS and 1mg NHS were mixed for 1 hour. The reaction product was then washed 3 times with PBS and dispersed in 5mL of PBS. 0.5mg of the capture antibody was added to the above solution and reacted with shaking for 2 hours. After 2 hours, 0.5ml of ethanolamine (0.1 mol/L) and 10mg of BSA were added thereto, and the mixture was reacted with shaking for 1 hour. Finally, the reaction product was washed 3 times with PBS and dispersed in 5mL of PBS and stored at 4 ℃ for future use.
S2, forming a detection cluster by using an antibody magnetic ball under the magnetic field condition of a triaxial Helmholtz coil: dispersing antibody magnetic balls into PBS buffer solution for ultrasonic treatment to obtain antibody magnetic ball dispersion liquid, adding the antibody magnetic ball dispersion liquid into a groove with the length of 10mm multiplied by 10mm, adjusting magnetic field related parameters, and searching for the optimal balance state of the interaction of a flow field and a magnetic field to form a stable vortex cluster which can be used for detection.
The groove is a square limitation cut by the acrylic plate, and aims to limit the size of a detection area and prevent the detection area from being too large, and the target cluster cannot effectively extract sample information. Too small a detection area can limit the volume of biological sample added (volume to be detected). The 10mm by 10mm size grooves tested are the preferred model.
The specific method comprises the following steps: 0.1mg/ml of magnetic sphere dispersion (300 ul) and 1wt% of Tween 20 (30 ul) are added into a 10×10mm groove, and Tween 20 is used as a surfactant to prevent the magnetic sphere from adhering to a substrate; the grooves are placed in a rotating magnetic field of a triaxial Helmholtz coil, and a cluster is established to form a phase diagram by adjusting the rotating frequency and the intensity of the magnetic field. Firstly, the movement of magnetic particles under the conditions of a weak magnetic field, a low frequency (1 Hz, 1 mT) and a high frequency and strong magnetic field (20 Hz, 12 mT) is tried, the parameter interval of a phase diagram is locked, then the magnetic field intensity is fixed, the magnetic field rotation frequency is adjusted and the critical rotation frequency is searched, then the rotation frequency is fixed, the magnetic field intensity is adjusted and the critical magnetic field intensity is searched. The magnetic field strength and magnetic field rotation frequency parameters (8 mT, 17 Hz) formed by the clusters are determined to form a compact and smooth vortex-like detection cluster. It should be noted that the optimal magnetic field parameters for forming clusters differ for the amount of antibody magnetic spheres added each time, and therefore, the test should be performed around the optimal value before and after each test to achieve the optimal cluster state.
S3, introducing a biological sample to be detected into the detection cluster to carry out specific combination of antigen and antibody, extracting feature vectors of the vortex cluster motion video before and after the biological sample to be detected is added, drawing a scatter diagram of data through the obtained feature vectors by using a support vector machine model, and carrying out visual data distribution.
The monomer module in the cluster movement process is changed by utilizing the specific combination of the antigen and the antibody, so that the movement behavior of the vortex cluster is changed under the condition that the external parameters are not changed, and finally, the detection result is fed back in real time and automatically by utilizing a computer related algorithm.
The specific method comprises the following steps: the method comprises the steps of introducing a biological sample to be detected in situ by using a pipette, and taking antigen to the periphery of a single magnetic particle in the cluster under the action of a flow field, wherein the behavior monomers in the cluster are changed due to the specific combination of antigen and antibody, so that the movement behavior of the whole cluster is obviously changed. And extracting feature vectors of the cluster motion videos before and after the biological sample to be detected is added by using a computer related algorithm, a model and the like, and respectively adding different labels to the obtained feature vector data according to the number of the videos. The feature vector dataset is then divided into a training set and a test set, and feature scaling is performed by calculating the mean and variance of the data, and performing normalization operations accordingly. Training by adopting a Support Vector Machine (SVM) model, evaluating the performance by using a test set, and outputting the accuracy and confusion matrix of the model for displaying the classification performance of the model. And finally, performing principal component analysis on the obtained feature vector data, reducing the high-dimensional data to two dimensions, and drawing a scatter diagram of the feature vector data on a two-dimensional plane for visualized data distribution.
As shown in fig. 2, after a training result of a Support Vector Machine (SVM) model is obtained, a computer correlation algorithm is used to extract relevant feature vectors of a moving individual in a video, and after the data is subjected to dimension reduction processing, the similarity/different degrees of the video are output in a scatter diagram form. The specific implementation is as shown in fig. 3:
the method comprises the steps of extracting vortex cluster motion videos, scaling the videos, preprocessing all videos through a VGG16 neural network, adding a ROI (region of interest) condition limit, and extracting features of a cascading classifier based on Haar features in an OpenCV library aiming at the contours and textures of cluster motion images so as to prevent the background or other positions from being extracted as features when feature vectors are extracted by using a model, thereby reducing the accuracy of result output.
The extracted feature vectors are divided into a training set (80%) and a testing set (20%), a Support Vector Machine (SVM) model is trained by the training set, and performance results are evaluated by the testing set, so that the accuracy of the model is obtained.
And performing principal component analysis on the obtained feature vector through the trained support vector machine model, decentralizing data, constructing a covariance matrix, labeling coordinate axis directions, projecting the feature vector to the corresponding position of the coordinate axis to form a feature vector after dimension reduction, reducing the high-dimension feature vector to two dimensions, and drawing a scatter diagram of the feature vector on a two-dimensional plane.
The novel immune detection method based on micro-nano robot cluster vision sensing utilizes the influence of the specific combination of the micro-nano robot cluster and the antigen-antibody on the cluster movement effect, breaks through the limit of the existing detection method, realizes a high-efficiency, accurate and convenient detection mode, and provides a new idea for immune detection, disease screening and the like.
Based on the successful establishment method, the change condition of the cluster movement morphology is realized under the condition of the presence/absence of rabbit Igg. The experiment and computer vision sensing results of the following examples show that the invention can simultaneously realize qualitative detection of ultralow concentration and quantitative detection of different concentrations.
Embodiment one:
preparation of Fe according to the description of the operation of the method 3 O 4 @SiO 2 -COOH-sheep anti-rabbit Igg magnetic sphere.
0.1mg/ml of magnetic sphere dispersion (300 ul) and 1wt% of Tween 20 (30 ul) are added into a groove with the length of 10mm multiplied by 10mm, and the mixture is placed into a rotating magnetic field of a triaxial Helmholtz coil for testing, so that stable clusters can be formed under the magnetic field condition of 8mT and 17 Hz.
Different volumes of antigen solution (0.1 mg/ml rabbit Igg dispersed in PBS) were placed in situ in the clustered system. Because the added antigen solution is different in volume and contains a certain amount of PBS solvent in the original volume, each specific detection concentration should be calculated as: c= (volume of added antigen solution x concentration of antigen solution)/total solution volume in the system, where c is the final detection concentration.
After a period of time, extracting feature vectors of the cluster motion video before and after adding the biological sample to be detected, and dividing the data into a plurality of different feature labels according to the video types. The data set is then divided into a training set and a test set, and feature scaling is performed. The correlation detection result and the data output are shown in fig. 4 to 7.
The images (a) and (b) in FIGS. 4 to 7 show the cluster morphology before and (b) after the addition of the antigen, and the volumes of the antigen solutions added in FIGS. 10 to 13 were 10ul, 20ul, 50ul and 100ul, respectively, to obtain final detection concentrations of 3ug/ml, 6ug/ml, 15ug/ml and 30ug/ml, respectively.
From the video screenshot, the clusters are uniformly vortex under the balance action of the magnetic field and the fluid field before the antigen is added, and the original movement mode is broken through due to the specific combination of the antigen and the antibody after the antigen is added, so that the cluster agglomeration is tightened, the movement form of the whole block is presented, and meanwhile, the movement rate is obviously slowed down.
All videos are preprocessed by the VGG16 neural network, then training is carried out by adopting a Support Vector Machine (SVM) model, and the performance of the videos is evaluated by using a test set. In the code, ROI condition limitation (a cascade classifier based on Haar features in an OpenCV library is used for identifying relevant features such as the outline of a moving object in an image) is added, so that the background or other positions are prevented from being extracted as features when a model is used for extracting feature vectors, and the accuracy of result output is reduced. Finally, as shown in fig. 8, the accuracy of the model and the confusion matrix are output for displaying the classification performance of the model. The accuracy of the experimental result model is shown to be 1.0 at present.
The calculation process of the accuracy rate comprises the following steps: 20% of all data were taken as test set (not involved in training); the rest 80% of data are training sets, the training SVM model is used for predicting the test set, and the ratio of the predicted result to the actual value is 100%. Therefore, the accuracy of the result was 1.0.
And carrying out principal component analysis on the obtained data, reducing the high-dimensional data to two dimensions, and drawing a scatter diagram of the data on a two-dimensional plane for visualizing data distribution. As shown in fig. 9, the top right corner of the video is its tag code.
From the above results, it can be seen that the clusters were characterized as similar and scattered as several times before antigen addition. The characteristics of the added antigen solutions with different volumes are greatly different, and scattered points are dispersed. Therefore, the experimental result shows that the method can be developed and closed towards the quantitative detection later.
Embodiment two:
preparation of Fe according to the description of the operation of the method 3 O 4 @SiO 2 -COOH-sheep anti-rabbit Igg magnetic sphere.
0.1mg/ml of magnetic sphere dispersion (300 ul) and 1wt% of Tween 20 (30 ul) are added into a groove with the length of 10mm multiplied by 10mm, and the mixture is placed into a rotating magnetic field of a triaxial Helmholtz coil for testing, so that stable clusters can be formed under the magnetic field condition of 8mT and 17 Hz.
Antigen solutions of different concentrations (10 ul, rabbit Igg dispersed in PBS) were added to the clustered system.
Because the added antigen solution concentration is different and contains a certain amount of PBS solvent in the original volume, each specific detection concentration should be calculated as c= (added antigen solution volume x antigen solution concentration)/total solution volume in the system, where c is the final detection concentration.
After a period of time, extracting feature vectors of the cluster motion video before and after adding the biological sample to be detected, and dividing the data into a plurality of different feature labels according to the video types. The data set is then divided into a training set and a test set, and feature scaling is performed. The correlation detection result and the data output are shown in fig. 10 to 13.
Fig. 10 to 13 (a) show the cluster morphology before the addition of the antigen, and (b) show the cluster morphology after the addition of the antigen, wherein the concentrations of the added antigen solutions were 0.1mg/ml, 0.01mg/ml, 0.001mg/ml, and 0.0001mg/ml, respectively, and the final detected concentrations were 3ug/ml, 0.3ug/ml, 0.03ug/ml, and 3ng/ml, respectively, in fig. 10 to 13.
As can be seen from the above results, the current minimum limit of detection has a significantly varying grade of ng/ml with visual inspection, and the current clinical blood detection standard is 16.3ng/ml, which is already standard. That is, when the detection concentration is 30ng/ml, obvious change is visible to naked eyes, at the moment, the clusters are tightly gathered, the rotation speed is reduced, and when the detection concentration is 3ng/ml, the difference is not large by naked eyes. Low concentration detection is currently possible. Experimental results show that the method can be developed and closed in the direction of the qualitative detection of ultralow concentration.
In summary, the experimental operation process and the experimental result can be easily seen, and the interdisciplinary linkage of the micro-nano robot cluster and the computer algorithm provides a new direction for solving the problems of complexity and wider error range of the traditional ELISA. The novel detection mode greatly simplifies the operation flow to a certain extent, improves the automation degree and reduces the operation time. Meanwhile, the micro-nano robot cluster has excellent sensitivity, and can detect the biomarker with extremely low concentration, so that the accurate diagnosis of early diseases is realized. Their synergistic advantage is that it also reduces the risk of false positives to some extent.
The appendix code of the computer algorithm is as follows, and it should be noted that the underlying logic is shown in the flow chart of FIG. 2, and is analyzed below by way of example only with one code, and the required model in the code can be replaced as desired.
Extracting video feature vectors:
after extracting the feature vector, training by adopting a vector machine (SVM) model, and evaluating the performance of the feature vector by using a test set; performing principal component analysis on the obtained data, reducing the high-dimensional data to two dimensions, and drawing a scatter diagram of the data on a two-dimensional plane for visualizing data distribution:
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the foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (8)
1. The novel immunodetection method based on micro-nano robot cluster vision sensing is characterized by comprising the following steps of:
s1, preparing a magnetic control micro-nano robot monomer: selection of Fe 3 O 4 Nano particles are taken as basic particles, and mesoporous SiO is coated 2 Modifying amino after the shell, further modifying carboxyl, and activating carboxyl to modify the antibody to prepare an antibody magnetic ball;
s2, forming a detection cluster by using an antibody magnetic ball under the magnetic field condition: dispersing the antibody magnetic balls into a buffer solution for ultrasonic treatment to form an antibody magnetic ball dispersion liquid, adjusting magnetic field parameters, and searching an optimal balance state of interaction of a flow field and a magnetic field to form a stable vortex cluster which can be used for detection;
s3, introducing a biological sample to be detected into the detection cluster to carry out specific combination of antigen and antibody, extracting feature vectors of the vortex cluster motion video before and after the biological sample to be detected is added, drawing a scatter diagram of data through the obtained feature vectors by using a support vector machine model, and carrying out visual data distribution.
2. The novel immunodetection method based on micro-nano robot cluster vision sensing according to claim 1, wherein the step S2 comprises the steps of:
s21, adding the magnetic ball dispersion liquid into the groove;
s22, placing the grooves in a rotating magnetic field of a triaxial Helmholtz coil, and establishing a cluster to form a phase diagram by adjusting the rotating frequency and the intensity of the magnetic field;
s23, determining the rotation frequency and the magnetic field intensity of the magnetic field formed by the clusters to form a compact and smooth vortex-shaped detection cluster.
3. The novel immunodetection method based on micro-nano robot cluster vision sensing according to claim 2, wherein in step S22, the process of establishing a cluster-formed phase diagram includes the steps of:
s221, trying the movement of antibody magnetic ball particles under the weak magnetic field, the low frequency and the high frequency and the strong magnetic field, and locking the parameter interval of the phase diagram;
s222, fixing the magnetic field intensity, adjusting the magnetic field rotation frequency and searching the critical rotation frequency;
s223, fixing the rotation frequency, adjusting the magnetic field intensity and searching the critical magnetic field intensity.
4. The novel immunodetection method based on micro-nano robot cluster vision sensing according to claim 1, wherein in the step S3, the deep learning for the support vector machine model comprises the steps of:
a1. extracting feature vectors of the cluster motion videos before and after adding the biological sample to be detected, and respectively adding different labels to the obtained feature vector data according to the number of the videos;
a2. dividing the feature vector data set into a training set and a test set, and performing feature scaling;
a3. and training the support vector machine model by adopting a training set, evaluating the performance of the support vector machine model by using a testing set, and finally outputting the accuracy and confusion matrix of the support vector machine model.
5. The novel immunodetection method based on micro-nano robot cluster vision sensing according to claim 1, wherein in the step S3, the feature vector of the vortex cluster motion video is extracted, and the method comprises the steps of:
all the extracted videos are preprocessed through a VGG16 neural network, ROI condition limitation is added, and feature extraction is carried out on the contour and texture of the cluster moving image through a cascade classifier based on Haar features in an OpenCV library.
6. The novel immunodetection method based on micro-nano robot cluster vision sensing according to claim 1, wherein in the step S3, a scatter diagram of data is drawn according to the obtained feature vector by a support vector machine model, and the method specifically comprises the following steps:
and carrying out principal component analysis on the obtained feature vector through a support vector machine model, reducing the high-dimensional feature vector to two dimensions, and drawing a scatter diagram of the feature vector on a two-dimensional plane.
7. The novel immunodetection method based on micro-nano robot cluster vision sensing according to claim 1, wherein in the step S1, the Fe 3 O 4 The nano particles are magnetic particles prepared by adopting a hydrothermal method reaction and separated by a magnet.
8. The novel immunodetection method based on micro-nano robot cluster vision sensing according to claim 1, wherein in the step S1, carboxyl groups are activated by NHS and EDC to modify antibodies.
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