Bionic tear sensing device and deep learning tear intelligent test system

Abstract

The invention relates to a bionic tear sensing device. The bionic tear sensing device comprises a base, a cover body, a tear collecting unit and an immunochromatography test strip, the tear collection unit designed by the invention has a bionic flow channel design simulating a lizard horn structure, tears of eyes can be directionally collected and conveyed to the tear collection chamber through the bionic flow channel structure, and the collection efficiency of the tears is effectively improved. The invention further provides an intelligent deep learning tear testing system which comprises the bionic tear sensing device, an image collecting and processing module, an intelligent image data analysis module and a terminal, and the system can convert a concentration signal into a color change signal and can detect the concentration of the tear biomarker based on the color change signal. The system further comprises a machine learning classification module, and the technical problem of improving the test precision based on a small number of samples is solved through the machine learning classification module.

Description

Technical Field

[0001] The present invention relates to the field of tear detection technology, and in particular to a bionic tear sensor device and a deep learning tear intelligent testing system. Background Art

[0002] In the clinical setting, high-sensitivity dry eye disease (DED) testing faces many challenges, including high equipment costs, complex operating procedures, and long testing time. Although the commercially available InflammaDry LFA test kit facilitates DED diagnosis by detecting the biomarker MMP-9, its tear collection process is still difficult and has limited sensitivity, providing only qualitative results.

[0003] In view of this, it is particularly urgent to design a new device that can effectively collect tears and ensure detection accuracy. The device needs to overcome the limitations of existing technologies, optimize the tear collection process, and improve detection sensitivity to meet the clinical needs for rapid and accurate diagnosis of DED. Summary of the invention

[0004] In order to solve the above technical problems, this application provides a bionic tear sensor device and a deep learning tear intelligent testing system

[0005] To achieve the above objectives, this application is implemented through the following technical solutions:

[0006] The present application provides a bionic tear sensor device, comprising

[0007] Base;

[0008] A cover body, the cover body is detachably buckled on the base, the bottom of the cover body and the base together form a receiving cavity, the upper part of the cover body is provided with a column, a penetrating test channel is provided in the column, and the test channel is communicated with the receiving cavity;

[0009] A tear collection unit, which is detachably mounted in the accommodating chamber, and comprises a bionic flow channel imitating a lizard horn structure and a collection chamber connected to the bionic flow channel, wherein the collection chamber is connected to the test channel, and the tear collection unit collects tears at the corner of the eye by using the bionic flow channel and the collection chamber;

[0010] An immunochromatographic test strip is installed in the test channel and is used to display color after being in contact with the collection chamber to absorb tears.

[0011] As a further improvement of the present application, the base is provided with a positioning protrusion, and the cover is provided with a positioning groove matching the positioning protrusion, or,

[0012] The base is provided with a positioning groove, and the cover is provided with a positioning protrusion matching the positioning protrusion.

[0013] As a further improvement of the present application, the bionic flow channel includes a flow channel and structural protrusions imitating lizard horns that are arranged in parallel and staggered along the chromatography direction, wherein the gullies are the flow channels, and the direction in which the collection chamber is located is the chromatography direction;

[0014] Along the tomographic direction, the spacing between every two adjacent parallel-arranged structural protrusions that mimic the lizard's horns is 0.3 to 0.5 mm;

[0015] Along the tomographic direction, the ratio of the axial length of a single structural protrusion imitating a lizard horn to the axial length of the bionic flow channel is 1:(6-10);

[0016] Perpendicular to the tomographic direction, the ratio of the longitudinal width of a single structural protrusion imitating a lizard horn to the longitudinal width of the bionic flow channel is 1:(1.5-2).

[0017] As a further improvement of the present application, the tear collection unit is prepared by the following steps:

[0018] S1. Use Blender software to create a mold containing a structure that imitates the lizard's horns;

[0019] S2, using 3D printing equipment to print the mold using high temperature resistant resin;

[0020] S3, sequentially soaking the mold in isopropanol, ultrasonically cleaning, rinsing with deionized water, vacuum drying, ultraviolet curing, and drying to obtain a mold containing a lizard horn-mimicking structure;

[0021] S4, pouring a first mixture of PDMS prepolymer and curing agent in a ratio of 10:1 by weight into a mold containing a lizard horn-mimicking structure, vacuum degassing, and curing to obtain a PDMS bottom layer;

[0022] S5, sequentially washing the PDMS bottom layer with ethanol, washing with deionized water, vacuum drying, and plasma activation, and then immersing it in a polyvinyl pyrrolidone solution with a mass volume ratio of 8%, washing with deionized water, and vacuum drying to obtain a pretreated PDMS bottom layer;

[0023] S6. Rotate-cast a second mixture of a PDMS prepolymer and a curing agent in a ratio of 20:1 on a smooth culture dish at a speed of 2000 r / min to 4000 r / min to obtain a PDMS top layer;

[0024] S7. Cover the pretreated PDMS bottom layer with a top PDMS layer, wherein the top PDMS layer and the bottom PDMS layer together form the collection chamber, and the portion of the PDMS bottom layer containing the lizard horn-mimicking structure is exposed to the outside of the top PDMS layer. Allow the mixture to stand at room temperature to obtain a tear collection unit.

[0025] As a further improvement of the present application, a connection hole for connecting the test channel with the collection chamber is provided on the PDMS top layer.

[0026] As a further improvement of the present application, a first adding hole for adding a buffer solution is provided on the PDMS top layer, and a second adding hole for adding a buffer solution is provided on the cover body, and the first adding hole matches the second adding hole to facilitate adding a buffer solution to the collection chamber from the outside of the device.

[0027] As a further improvement of the present application, the immunochromatographic test strip includes a bottom plate, on which a sample pad layer, a binding pad layer, a detection membrane layer and a water absorbent pad layer are overlapped from left to right in sequence, the binding pad layer is coated with AuNPs-labeled mouse anti-MMP-9 antibodies, the detection membrane layer is coated with mouse anti-MMP-9 antibodies as a detection line, and is also coated with anti-mouse IgG as a quality control line.

[0028] To achieve the above objectives, the present application also provides a deep learning tear intelligent testing system, comprising

[0029] A bionic tear sensor device, which is the bionic tear sensor device described above, and is used to collect and detect the concentration of biomarkers in tears and convert the concentration signal into a color change signal;

[0030] An image acquisition and processing module, the image acquisition and processing module is used to collect pictures showing color change signals, and to input the collected pictures showing color change signals into the network model using the "Everything" mode of the SAM model, to segment the pictures so as to obtain pictures showing effective color change signals, and to upload the pictures showing effective color change signals to the cloud server;

[0031] An image data intelligent analysis module, which is used to analyze and process the images received by the cloud server and output the biomarker concentration based on the analysis and processing results;

[0032] A terminal, the terminal is used to load the image data intelligent analysis module and display the biomarker concentration detection result;

[0033] Wherein: the image data intelligent analysis module includes a data processing unit, a data analysis unit and an output unit,

[0034] The data processing unit is used to extract the RGB value corresponding to the color change signal in the picture and convert it into the required feature signal, including Rm, Gm, Bm, Rm / RGBn, Gm / RGBn, Bm / RGBn, RGBn, weighted intensity, unweighted intensity, Cbm, Crm, Hm, Sm, Vm, Xm, Ym, Zm, Lm, am, bm, CT, and pH;

[0035] The data analysis unit is used to obtain the biomarker concentration by analyzing and processing the characteristic signal based on the SAM model;

[0036] The output unit is used to output the biomarker concentration.

[0037] As a further improvement of the present application, the image acquisition and processing module can also be used to allocate pictures that effectively display color change signals after being processed using the SAM model to training sets and test sets for deep learning classification models.

[0038] As a further improvement of the present application, it also includes a machine learning classification module, which is used to learn the characteristics of the display color change signal based on the training set, and test the trained machine learning classification model based on the test set to evaluate the model's classification and prediction ability for the display color change signal.

[0039] The beneficial effect of the present application is that the present application provides a bionic tear sensor device, including a base, a cover, a tear collection unit and an immunochromatographic test strip. The tear collection unit designed in the present application has a bionic flow channel design that imitates the structure of a lizard horn. The tears of the eye can be collected and transported to the tear collection chamber in a direction through the bionic flow channel structure, which effectively improves the collection efficiency of tears. At the same time, by designing the base and the cover to enclose a receiving chamber, and the test channel in the column is connected to the receiving chamber, convenient contact and testing of the immunochromatographic test strip and tears is achieved.

[0040] The present application also provides a deep learning tear intelligent testing system, including a bionic tear sensor device, an image acquisition and processing module, an image data intelligent analysis module, and a terminal. The system can convert concentration signals into color change signals and detect the concentration of tear biomarkers based on color change signals. The system also includes a machine learning classification module, which solves the technical problem of improving test accuracy based on a small number of samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic structural diagram of a tear collection unit of Example 1;

[0042] Figure 2Schematic diagram of the bionic flow channel structure of the tear collection unit of Example 2 and a comparison diagram of the liquid flow rate with that of an empty channel;

[0043] Figure 3 Schematic diagram of the structure of the immunochromatographic test strip of Example 3.

[0044] In the figure: 101, immunochromatographic test strip; 102, cover; 103, second addition hole; 104, tear collection unit; 105, base; 106, positioning protrusion; 201, second addition hole; 202, connecting hole; 203, part containing structure imitating lizard horn; 301, bottom plate; 302, sample pad; 303, binding pad; 304, detection membrane layer; 305, water-absorbing pad; 306, detection line; 307 quality control line. DETAILED DESCRIPTION

[0045] The following will be combined with the embodiments of the present application to clearly and completely describe the technical solution of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with specific implementation methods.

[0047] In order to solve the technical problems of low tear collection efficiency and low detection sensitivity, the present application provides a bionic tear sensor device, including a base, a cover, a tear collection unit and an immunochromatographic test strip, wherein: the cover can be detachably snapped onto the base, the bottom of the cover and the base together form a accommodating cavity, the upper part of the cover is provided with a column, a penetrating test channel is provided in the column, and the test channel is connected to the accommodating cavity; the tear collection unit is detachably installed in the accommodating cavity, including a bionic flow channel imitating a lizard horn structure and a collection chamber connected to the bionic flow channel, the collection chamber is connected to the test channel, and the tear collection unit uses the bionic flow channel and the collection chamber to collect tears at the corner of the eye; the immunochromatographic test strip is installed in the test channel, and after contacting with the collection chamber to absorb tears, it is used to display color.

[0048] The tear collection unit designed in this application has a bionic flow channel design that imitates the structure of a lizard's horn. Tears in the eyes can be collected and transported to the tear collection chamber in a direction through the bionic flow channel structure, effectively improving the collection efficiency of tears. At the same time, by designing the base and the cover to form a receiving chamber, and the test channel in the column is connected to the receiving chamber, convenient contact and testing of the immunochromatographic test strip and tears is achieved.

[0049] In an optional embodiment, the base is provided with a positioning protrusion, and the cover is provided with a positioning groove matching the positioning protrusion, or the base is provided with a positioning groove, and the cover is provided with a positioning protrusion matching the positioning protrusion. By providing mutually matching positioning protrusions and positioning grooves on the base and the cover, on the one hand, quick installation between the cover and the base is ensured, and on the other hand, a secure fit between the cover and the base is ensured, thereby improving the stability and reliability of the device and avoiding problems such as tear leakage or inaccurate testing due to loose cover during use.

[0050] In an optional embodiment, the bionic flow channel includes a flow channel and structural protrusions that imitate lizard horns and are arranged in parallel and staggered along the chromatographic direction, wherein the gullies are flow channels, and the direction in which the collection chamber is located is the chromatographic direction; along the chromatographic direction, the spacing between every two adjacent structural protrusions that imitate lizard horns is 0.3 to 0.5 mm; along the chromatographic direction, the ratio of the axial length of a single structural protrusion that imitates lizard horns to the axial length of the bionic flow channel is 1:(6 to 10); perpendicular to the chromatographic direction, the ratio of the longitudinal width of a single structural protrusion that imitates lizard horns to the longitudinal width of the bionic flow channel is 1:(1.5 to 2). The arrangement ratio and size relationship between the flow channel designed in the present application and the structural protrusion that imitates lizard horns optimize the flow path of tears and improve the collection efficiency.

[0051] In an optional embodiment, the tear collection unit is prepared by the following steps: S1. Using Blender software to create a mold containing a structure that imitates a lizard horn; S2. Using a 3D printing device to print the mold using a high-temperature resistant resin; S3. Soaking the mold in isopropyl alcohol, ultrasonic cleaning, rinsing with deionized water, vacuum drying, ultraviolet curing, and drying in sequence to obtain a mold containing a structure that imitates a lizard horn; S4. Pour a first mixture of a PDMS prepolymer and a curing agent in a ratio of 10:1 by weight into the mold containing the structure that imitates a lizard horn, vacuum degassing, curing at 60°C for 3h, and obtaining a PDMS bottom layer; S5. Cleaning the PDMS bottom layer with ethanol, deionized water, vacuum drying, plasma activation, and then Immerse in a polyvinyl pyrrolidone solution with a mass volume ratio of 8% for 3 minutes, wash with deionized water, and vacuum dry to obtain a pretreated PDMS bottom layer; S6, spin cast a second mixture of PDMS prepolymer and curing agent in a ratio of 20:1 on a smooth culture dish at a speed of 3000r / min to obtain a PDMS top layer, and set a connecting hole on the PDMS top layer to connect the test channel with the collection chamber; S7, cover the PDMS top layer on the pretreated PDMS bottom layer, the PDMS top layer and the PDMS bottom layer together form the collection chamber, and the part of the PDMS bottom layer containing the lizard horn-mimicking structure is exposed to the outside of the PDMS top layer, and stand at room temperature for 24 hours to obtain a tear collection unit. This application uses 3D printing technology combined with PDMS materials to accurately replicate the structure of the lizard horn mimicking structure, and improves the collection efficiency of the tear collection unit through a series of pretreatment steps.

[0052] In an optional embodiment, a first addition hole for adding a buffer solution is provided on the top layer of the PDMS, and a second addition hole for adding a buffer solution is provided on the cover body, and the first addition hole matches the second addition hole, so as to facilitate adding a buffer solution to the collection chamber from the outside of the device. By providing the first addition hole and the second addition hole, it is convenient to add a buffer solution to the collection chamber from the outside of the device. The function of the buffer solution is mainly to dilute the tear fluid, so as to facilitate the full contact between the immunochromatographic test strip and the tear fluid, improve the convenience of operation, and avoid the problem of tear contamination or loss caused by opening the device, and further ensure the accuracy of the test.

[0053] In an optional embodiment, the immunochromatographic test strip comprises a bottom plate, on which a sample pad layer, a binding pad layer, a detection membrane layer and a water absorbent pad layer are overlapped in sequence from left to right, the binding pad layer is coated with AuNPs-labeled mouse anti-MMP-9 antibodies, the detection membrane layer is coated with mouse anti-MMP-9 antibodies as a detection line, and is also coated with anti-mouse IgG as a quality control line, and the distance between the detection line and the quality control line is 3 mm.

[0054] The present application also provides a deep learning tear intelligent testing system, including a bionic tear sensor device, an image acquisition and processing module, an image data intelligent analysis module and a terminal, wherein: the bionic tear sensor device is the bionic tear sensor device described above, which is used to collect and detect the concentration of biomarkers in tears, and convert the concentration signal into a color change signal; the image acquisition and processing module is used to collect pictures showing color change signals, and is used to input the collected pictures showing color change signals into a network model using the "Everything" mode of the SAM model, segment the pictures, so as to obtain pictures that effectively display color change signals, and upload the pictures that effectively display color change signals to a cloud server; the image data intelligent analysis module is used to analyze and process the pictures received by the cloud server, and output the biomarker concentration based on the analysis and processing results; the terminal is used to load the image data intelligent analysis module to display the biomarker concentration detection results.

[0055] The image data intelligent analysis module includes a data processing unit, a data analysis unit and an output unit, wherein: the data processing unit is used to extract the RGB value corresponding to the color change signal in the picture, and convert it into the required characteristic signal, including Rm, Gm, Bm, Rm / RGBn, Gm / RGBn, Bm / RGBn, RGBn, weighted intensity, unweighted intensity, Cbm, Crm, Hm, Sm, Vm, Xm, Ym, Zm, Lm, am, bm, CT, pH; the data analysis unit is used to use the characteristic signal to analyze and process based on the SAM model to obtain the biomarker concentration; the output unit is used to output the biomarker concentration.

[0056] In an optional embodiment, the image acquisition and processing module can also be used to allocate pictures that effectively display color change signals after being processed using the SAM model to training sets and test sets for deep learning classification models.

[0057] In an optional embodiment, a machine learning classification module is also included, wherein the machine learning classification module is used to learn the characteristics of the display color change signal based on the training set, and to test the trained machine learning classification model based on the test set to evaluate the model's classification and prediction ability for the display color change signal.

[0058] In an optional embodiment, the machine learning classification module is also used to learn features showing color change signals based on a large ImageNet dataset containing 1,000 categories.

[0059] In an optional embodiment, the machine learning classification module includes a ResNet machine learning model or a MobileNet machine learning model.

[0060] Example 1

[0061] This embodiment provides a bionic tear sensor device, such as Figure 1 and Figure 2 As shown in (a), it includes a base 105, a cover 102, a tear collection unit 104 and an immunochromatographic test strip 101, wherein: the cover 102 can be detachably fastened to the base 105, the bottom of the cover 102 and the base 105 together form a receiving chamber, the upper part of the cover 102 is provided with a column, a penetrating test channel is provided in the column, and the test channel is connected to the receiving chamber; the tear collection unit 104 is detachably installed in the receiving chamber, including a bionic flow channel that imitates the structure of a lizard horn and a collection chamber connected to the bionic flow channel, the collection chamber is connected to the test channel, and the tear collection unit 104 uses the bionic flow channel and the collection chamber to collect tears at the corner of the eye; the immunochromatographic test strip 101 is installed in the test channel, and after contacting with the collection chamber to absorb tears, it is used to display color.

[0062] In this embodiment, a positioning protrusion 106 is provided on the base 105 , and a positioning groove matching the positioning protrusion 106 is provided on the cover 102 .

[0063] Example 2

[0064] The preparation method of the tear collection unit 104 of Example 1 is as follows: S1, using Blender software to create a mold containing a structure imitating a lizard horn; S2, using a 3D printing device to print the mold using a high temperature resistant resin; S3, sequentially soaking the mold in isopropanol, ultrasonically cleaning for 10 minutes, rinsing with deionized water, vacuum drying for 20 minutes, ultraviolet curing for 5 hours, and drying at 120°C for 3 hours to obtain a mold containing a structure imitating a lizard horn, wherein the isopropanol is AR grade and has a purity of ≥99.5%; S4, by weight, mixing the PDMS prepolymer and the curing The first mixture of the PDMS substrate and the PDMS agent in a ratio of 10:1 is poured into a mold containing a lizard horn structure, vacuum dried and degassed, and cured at 60°C for 3h to obtain a PDMS bottom layer; S5, the PDMS bottom layer is sequentially cleaned with ethanol, cleaned with deionized water, vacuum dried, and plasma activated, and then immersed in a polyvinyl pyrrolidone solution with a mass volume ratio of 8% for 3min, cleaned with deionized water, and vacuum dried to obtain a pretreated PDMS bottom layer; wherein: the plasma treatment time is 40s, and the equipment used for plasma treatment is VP-R series, ShanZhun Co., Ltd., Guangzhou, China, the power mode adopts high power; S6, rotating casting a second mixture of PDMS prepolymer and curing agent in a ratio of 20:1 on a smooth culture dish at a speed of 3000r / min to obtain a PDMS top layer, and setting a connecting hole 202 on the PDMS top layer to connect the test channel with the collection chamber; S7, covering the PDMS top layer on the pretreated PDMS bottom layer, the PDMS top layer and the PDMS bottom layer together form the collection chamber, and the part 203 on the PDMS bottom layer containing the lizard horn structure is exposed to the outside of the PDMS top layer, and standing at room temperature for 24 hours to obtain a tear collection unit 104.

[0065] The tear collection unit 104 in the present embodiment includes a bionic flow channel that imitates a lizard horn structure, wherein the bionic flow channel includes a flow channel and structural protrusions that imitate lizard horns and are arranged in parallel and staggered along the chromatographic direction, wherein the grooves are the flow channels, and the direction in which the collection chamber is located is the chromatographic direction; along the chromatographic direction, the spacing between every two adjacent structural protrusions that imitate lizard horns is 0.4 mm; along the chromatographic direction, the ratio of the axial length of a single structural protrusion that imitates lizard horns to the axial length of the bionic flow channel is 1:8; perpendicular to the chromatographic direction, the ratio of the longitudinal width of a single structural protrusion that imitates lizard horns to the longitudinal width of the bionic flow channel is 1:1.8. The structure of the bionic flow channel containing structural protrusions that imitate lizard horns prepared in the present embodiment is as follows: Figure 2 (a) Figure 2 (b) and Figure 2As shown in (c), the axial length of the structural protrusion imitating the lizard horn along the tomographic direction is 0.5 mm, and the lengths of the two side edges of the structural protrusion imitating the lizard horn perpendicular to the axial length are 0.48 mm and 1.8 mm respectively. Figure 2 In (c), 1.2 mm means that the thickness of the bionic flow channel as a whole is 1.2 mm, the thickness of the upper bionic flow channel is 0.6 mm, and the thickness of the lower substrate is 0.6 mm.

[0066] This example also compares the liquid flow rate of the bionic flow channel with the structural protrusions imitating lizard horns prepared in this example and the empty flow channel without the structural protrusions imitating lizard horns. Figure 2 (d)

[0067] Example 3

[0068] This embodiment provides an immunochromatographic test strip 101, which is applied to the bionic tear sensor device described in Example 1. Figure 3 As shown, it includes a bottom plate 301, on which a sample pad layer 302, a binding pad layer 303, a detection membrane layer 304 and a water-absorbing pad layer 305 are overlapped from left to right in sequence, the binding pad layer 303 is coated with AuNPs (gold nanoparticles)-labeled mouse anti-MMP-9 antibodies, the detection membrane layer 304 is coated with mouse anti-MMP-9 antibodies as a detection line 306, and is also coated with anti-mouse IgG as a quality control line, and the distance between the detection line 306 and the quality control line is 3 mm.

[0069] Preparation of immunochromatographic test strip 101: A1. Preparation of sample pad 302: pre-treat the polyester fiber membrane, soak the polyester fiber membrane in a 0.01 mol / L PBS buffer solution containing 0.5% BSA (bovine serum albumin), 2% NaCl, 1% sucrose, 0.5% Tritonx-100 (Triton), and 0.5% sodium caseinate for 30 minutes, then dry overnight for later use; A2. Preparation of binding pad 303: first, select the polyester fiber membrane as the base of the binding pad 303, soak the binding pad 303 in a buffer solution containing 2% BSA (bovine serum albumin) and 5% sucrose for a preset time, which can be 30 minutes, take it out and place it in an oven to dry, and then seal it for later use; secondly, prepare the AuNPs solution, add 0.0927 grams of tetrachloroauric acid to 188.5 ml of deionized water and heat it to boiling, then slowly add 11.5 ml of 1% sodium citrate, continue boiling for 15 minutes, cool, store at room temperature away from light for later use; again, use AuNPs to label mouse anti-MMP-9 antibody; A3, preparation of detection membrane layer 304: select polyester fiber membrane as the substrate of detection membrane layer 304, coat mouse anti-MMP-9 antibody as detection line 306 on detection membrane layer 304, and coat anti-mouse IgG antibody as quality control line, specifically: 30 microliters of mouse anti-MMP-9 antibody and 30 microliters of anti-mouse IgG antibody (1 mg / mL) are evenly applied on detection membrane layer 304 at a speed of 1 μL / mm, and the spacing between membrane scribed lines is 3 mm to form detection line 306 (T) and control line (C) required for detecting MMP-9;

[0070] A4. After preparing the components, cut them into appropriate sizes, and then assemble them to the base plate 301 in sequence, with each component overlapping by 2 mm.

[0071] In this embodiment, the specific process of labeling mouse anti-MMP-9 antibody with AuNPs is as follows: 2500 microliters of the prepared AuNPs solution are divided into five centrifuge tubes, and then the pH value is adjusted to 8.5, 9.0, 9.5, 10.0 and 10.5 with K2CO3. Then, 5 microliters of 1 mg / ml mouse anti-MMP-9 antibody are added to each centrifuge tube to form a concentration of 10 μg / mL. In order to bind AuNPs to mouse anti-MMP-9 antibody, the mixed solution is incubated at room temperature for 10 minutes. After incubation, 1 / 100 volume of 5% casein sodium is added and incubated at room temperature for 10 minutes. The optimal pH value is selected by observing the color of the mixed solution and measuring its absorption peak using ultraviolet visible spectroscopy (UV-Vis). The peak value is the highest, and the pH value is the best. Then, the mixed solution is frozen and centrifuged at 4°C at a speed of 10,000 rpm for 10 minutes. The supernatant is removed and 200 microliters of 50mM Tris solution (pH=8) are added. Shake well by hand, then transfer to the pre-treated conjugate pad with a pipette and leave until dry for use.

[0072] Example 4

[0073] The present embodiment provides a deep learning tear intelligent testing system, including a bionic tear sensor device, an image acquisition and processing module, an image data intelligent analysis module and a terminal, wherein: the bionic tear sensor device is the bionic tear sensor device described in the above embodiment 1, which is used to collect and detect the concentration of biomarkers in tears and convert the concentration signal into a color change signal; the image acquisition and processing module is used to collect pictures showing color change signals, and is used to input the collected pictures showing color change signals into a network model using the "Everything" mode of the SAM model, segment the pictures, so as to obtain pictures that effectively show color change signals, and upload the pictures that effectively show color change signals to a cloud server; the image data intelligent analysis module is used to analyze and process the pictures received by the cloud server, and output the biomarker concentration based on the analysis and processing results; the terminal is used to load the image data intelligent analysis module and display the biomarker concentration detection results.

[0074] The image data intelligent analysis module includes a data processing unit, a data analysis unit and an output unit, wherein: the data processing unit is used to extract the RGB value corresponding to the color change signal in the picture, and convert it into the required characteristic signal, including Rm, Gm, Bm, Rm / RGBn, Gm / RGBn, Bm / RGBn, RGBn, weighted intensity, unweighted intensity, Cbm, Crm, Hm, Sm, Vm, Xm, Ym, Zm, Lm, am, bm, CT, pH; the data analysis unit is used to use the characteristic signal to analyze and process based on the SAM model to obtain the biomarker concentration; the output unit is used to output the biomarker concentration.

[0075] The image acquisition and processing module of this embodiment can also be used to distribute pictures that effectively display color change signals after being processed using the SAM model to training sets and test sets for deep learning classification models.

[0076] The deep learning tear intelligent testing system of this embodiment also includes a machine learning classification module, which is used to learn the characteristics of the display color change signal based on the training set, and test the trained machine learning classification model based on the test set to evaluate the model's classification prediction ability for the display color change signal. The machine learning classification module is also used to learn the characteristics of the display color change signal based on a large ImageNet data set containing 1,000 categories. The machine learning classification module of this embodiment is a ResNet machine learning model or a MobileNet machine learning model.

[0077] This embodiment provides an operation method of a deep learning tear intelligent testing system, comprising the following steps:

[0078] B1. Train a machine learning classification model based on the large ImageNet dataset containing 1,000 categories. The machine learning classification module is a ResNet machine learning model or a MobileNet machine learning model.

[0079] B2. Based on the image acquisition and processing module, collect pictures showing color change signals under different backgrounds, the number of pictures is at least 864, and based on the SAM model "Everything" mode in the image acquisition and processing module, the collected pictures showing color change signals are input into the network model, and the pictures are segmented to obtain pictures showing color change signals effectively, and the pictures showing color change signals effectively are uploaded to the cloud server;

[0080] B3. Based on the SAM model, the pictures of the effective display color change signals in the cloud server are assigned to the training set and test set of the deep learning classification model; the machine learning classification module learns the characteristics of the display color change signals based on the training set, and tests the trained machine learning classification model based on the test set to evaluate the model's classification and prediction ability for the display color change signals;

[0081] B4. Collecting tears based on the tear collection unit in the bionic tear sensor device, and converting the concentration signal of the tear biomarker into a color change signal based on the immunochromatographic test strip in the bionic tear sensor device;

[0082] B5. Collect pictures showing color change signals based on the image acquisition and processing module, and input the collected pictures showing color change signals into the network model based on the "Everything" mode of the SAM model in the image acquisition and processing module, segment the pictures, so as to obtain pictures showing color change signals effectively, and upload the pictures showing color change signals effectively to the cloud server;

[0083] B6. The data processing unit in the image data intelligent analysis module extracts the RGB value corresponding to the color change signal in the image and converts it into the required feature signal. Based on the feature signal and the SAM model, the data analysis unit in the image data intelligent analysis module analyzes and processes to obtain the biomarker concentration; the output unit in the image data intelligent analysis module outputs the biomarker concentration.

[0084] Based on the above steps, the detection accuracy using the ResNet machine learning model or the MobileNet machine learning model is 99% and 97% respectively.

[0085] Although this specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each implementation mode may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

[0086] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present application. They are not intended to limit the scope of protection of the present application. Any equivalent implementation methods or changes that do not deviate from the technical spirit of the present application should be included in the scope of protection of the present application.

Claims

1. A bionic tear sensor device, characterized in that: include Base; A cover body, the cover body is detachably buckled on the base, the bottom of the cover body and the base together form a receiving cavity, the upper part of the cover body is provided with a column, a penetrating test channel is provided in the column, and the test channel is communicated with the receiving cavity; A tear collection unit, which is detachably mounted in the accommodating chamber, and comprises a bionic flow channel imitating a lizard horn structure and a collection chamber connected to the bionic flow channel, wherein the collection chamber is connected to the test channel, and the tear collection unit collects tears at the corner of the eye by using the bionic flow channel and the collection chamber; An immunochromatographic test strip is installed in the test channel and is used to display color after being in contact with the collection chamber to absorb tears.

2. The bionic tear sensor device according to claim 1, characterized in that: The base is provided with a positioning protrusion, and the cover is provided with a positioning groove matching the positioning protrusion, or, The base is provided with a positioning groove, and the cover is provided with a positioning protrusion matching the positioning protrusion.

3. The bionic tear sensor device according to claim 1, characterized in that: The bionic flow channel includes a flow channel and structural protrusions imitating lizard horns that are arranged in parallel and staggered along the chromatography direction, wherein the gullies are the flow channels, and the direction in which the collection chamber is located is the chromatography direction; Along the tomographic direction, the spacing between every two adjacent parallel-arranged structural protrusions that mimic the lizard's horns is 0.3 to 0.5 mm; Along the tomographic direction, the ratio of the axial length of a single structural protrusion imitating a lizard horn to the axial length of the bionic flow channel is 1:(6-10); Perpendicular to the tomographic direction, the ratio of the longitudinal width of a single structural protrusion imitating a lizard horn to the longitudinal width of the bionic flow channel is 1:(1.5-2).

4. The bionic tear sensor device according to claim 3, characterized in that: The tear collection unit is prepared by the following steps: S1. Use Blender software to create a mold containing a structure that imitates the lizard's horns; S2, using 3D printing equipment to print the mold using high temperature resistant resin; S3, sequentially soaking the mold in isopropanol, ultrasonically cleaning, rinsing with deionized water, vacuum drying, ultraviolet curing, and drying to obtain a mold containing a lizard horn-mimicking structure; S4, pouring a first mixture of PDMS prepolymer and curing agent in a ratio of 10:1 by weight into a mold containing a lizard horn-mimicking structure, vacuum degassing, and curing to obtain a PDMS bottom layer; S5, sequentially washing the PDMS bottom layer with ethanol, washing with deionized water, vacuum drying, and plasma activation, and then immersing it in a polyvinyl pyrrolidone solution with a mass volume ratio of 8%, washing with deionized water, and vacuum drying to obtain a pretreated PDMS bottom layer; S6. Rotate-cast a second mixture of a PDMS prepolymer and a curing agent in a ratio of 20:1 on a smooth culture dish at a speed of 2000 r / min to 4000 r / min to obtain a PDMS top layer; S7. Cover the pretreated PDMS bottom layer with a top PDMS layer, wherein the top PDMS layer and the bottom PDMS layer together form the collection chamber, and the portion of the PDMS bottom layer containing the lizard horn-mimicking structure is exposed to the outside of the top PDMS layer. Allow the mixture to stand at room temperature to obtain a tear collection unit.

5. The bionic tear sensor device according to claim 4, characterized in that: The PDMS top layer is provided with a connecting hole that connects the test channel with the collection chamber.

6. The bionic tear sensor device according to claim 5, characterized in that: The PDMS top layer is provided with a first adding hole for adding a buffer solution, and the cover body is provided with a second adding hole for adding a buffer solution. The first adding hole matches the second adding hole to facilitate adding a buffer solution into the collection chamber from the outside of the device.

7. The bionic tear sensor device according to claim 1, characterized in that: The immunochromatographic test strip comprises a bottom plate, on which a sample pad layer, a binding pad layer, a detection membrane layer and a water-absorbing pad layer are overlapped in sequence from left to right, the binding pad layer is coated with AuNPs-labeled mouse anti-MMP-9 antibodies, the detection membrane layer is coated with mouse anti-MMP-9 antibodies as a detection line, and is also coated with anti-mouse IgG as a quality control line.

8. A deep learning tear intelligent testing system, characterized in that: include A bionic tear sensor device, wherein the bionic tear sensor device is the bionic tear sensor device according to any one of claims 1 to 7, and is used to collect and detect the concentration of biomarkers in tears, and convert the concentration signal into a color change signal; An image acquisition processing module, the image acquisition processing module is used to collect pictures showing color change signals, and is used to input the collected pictures showing color change signals into the network model using the "Everything" mode of the SAM model, segment the pictures so as to obtain pictures showing effective color change signals, and upload the pictures showing effective color change signals to the cloud server; An image data intelligent analysis module, which is used to analyze and process the images received by the cloud server and output the biomarker concentration based on the analysis and processing results; A terminal, the terminal is used to load the image data intelligent analysis module and display the biomarker concentration detection result; Wherein: the image data intelligent analysis module includes a data processing unit, a data analysis unit and an output unit, The data processing unit is used to extract the RGB value corresponding to the color change signal in the picture and convert it into the required feature signal, including Rm, Gm, Bm, Rm / RGBn, Gm / RGBn, Bm / RGBn, RGBn, weighted intensity, unweighted intensity, Cbm, Crm, Hm, Sm, Vm, Xm, Ym, Zm, Lm, am, bm, CT, and pH; The data analysis unit is used to obtain the biomarker concentration by analyzing and processing the characteristic signal based on the SAM model; The output unit is used to output the biomarker concentration.

9. The deep learning tear intelligent testing system according to claim 8, characterized in that: The image acquisition and processing module can also be used to distribute pictures that effectively display color change signals after being processed using the SAM model to training sets and test sets of deep learning classification models.

10. The deep learning tear intelligent testing system according to claim 9, characterized in that: It also includes a machine learning classification module, which is used to learn the characteristics of the display color change signal based on the training set, and test the trained machine learning classification model based on the test set to evaluate the model's classification and prediction ability for the display color change signal.

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