Method and system for quantitatively detecting oxidized low-density lipoprotein based on magnetism

Abstract

The present application discloses a method and system for quantitatively detecting oxidized low-density lipoprotein based on magnetism, the method comprising: preparing a magnetic immune probe, and converting magnetic signal excitation energy into an electrical signal to calculate coupling efficiency; constructing an immunochromatographic test strip comprising a detection area, a quality control area and a binding area, and realizing the detection of oxidized low-density lipoprotein concentration in combination with the establishment of a standard curve; applying the sample to be tested to the sample pad of the test strip, combining the magnetic particles labeled with oxidized low-density lipoprotein antibodies at the binding pad position, flowing on the nitrocellulose membrane to the site labeled with another antibody, and forming a magnetic particle enrichment area through the binding action of the antigen and antibody, and through magnetic signal acquisition and data processing, quantitative detection of oxidized low-density lipoprotein can be performed. Through the scheme of the present application, high sensitivity, high specificity and rapid detection of Ox‑LDL can be achieved.

Description

Technical Field

[0001] The present application relates to the field of computing materials, and in particular to a method and system for quantitatively detecting oxidized low-density lipoprotein based on magnetism. Background Art

[0002] Oxidized low-density lipoprotein (Ox-LDL) plays a key role in the development of atherosclerosis and cardiovascular disease, and its detection is of great significance for assessing the risk of cardiovascular disease and early diagnosis. Ox-LDL is formed by oxidation modification of low-density lipoprotein (LDL). This modification leads to changes in the structure and function of LDL, making it more easily taken up by macrophages, thereby promoting the formation of foam cells and the development of atherosclerotic plaques. Detecting the level of Ox-LDL can provide clinicians with important information about the patient's vascular health status and help develop personalized prevention and treatment strategies. In addition, monitoring of Ox-LDL levels can also be used to evaluate the effectiveness of antioxidant therapy and provide important biomarkers for drug development and clinical trials.

[0003] At present, the main technologies for detecting Ox-LDL include enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC) and mass spectrometry (MS). The ELISA method is based on the principle of antigen-antibody specific binding, and quantitatively detects Ox-LDL by reacting the enzyme-labeled secondary antibody with the substrate to produce a color change. The HPLC and MS methods mainly rely on separation and mass analysis techniques to identify and quantify Ox-LDL. Although these methods perform well in terms of specificity and sensitivity, they also have some problems. For example, the ELISA method is cumbersome and time-consuming, and is easily affected by the sample matrix effect; the HPLC and MS methods require expensive instruments and professional operators, and are not suitable for large-scale clinical applications and rapid detection. In order to solve the above problems, researchers have developed a variety of improved methods, such as immunochromatography and magnetic particle separation technology. Immunochromatography technology uses capillary action and specific antigen-antibody reaction to achieve rapid and simple Ox-LDL detection. Magnetic particle separation technology improves the enrichment efficiency and detection sensitivity of Ox-LDL in samples by modifying specific antibodies on the surface of magnetic particles. However, these methods still need to be improved in terms of detection sensitivity, specificity and quantitative accuracy. For example, traditional immunochromatography often relies on visual interpretation of results, which is easily affected by subjective factors; and although simple magnetic particle separation technology improves enrichment efficiency, it still has shortcomings in rapid detection and result interpretation.

[0004] Therefore, there is an urgent need for a technical solution that can achieve high sensitivity, high specificity and rapid detection of Ox-LDL. Summary of the invention

[0005] In order to solve the shortcomings of the prior art, the embodiment of the present application provides a method and system for quantitatively detecting oxidized low-density lipoprotein based on magnetism. The present application solves the technical problems of the prior art such as cumbersome operation and long time consumption.

[0006] The embodiment of the present application provides a method for quantitatively detecting oxidized low-density lipoprotein based on magnetism, comprising: treating target magnetic particles using surface modification technology, coupling antibodies that specifically recognize Ox-LDL to the surface of magnetic particles to prepare magnetic immune probes, and converting magnetic signal excitation energy into electrical signals to calculate coupling efficiency; constructing an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad, and optimizing the quality control process in combination with the establishment of a standard curve; wherein the detection area is pre-coated with an Ox-LDL paired antibody, and the quality control area is used to verify the effectiveness of the test strip; applying a sample to be tested to the sample pad of the test strip, combining with magnetic particles labeled with oxidized low-density lipoprotein antibodies at the binding pad position, flowing on a nitrocellulose membrane to a site labeled with another antibody, and forming a magnetic particle enrichment area through the binding action of the antigen and antibody, and performing magnetic signal acquisition and data processing to quantitatively detect oxidized low-density lipoprotein.

[0007] In one possible implementation, the target magnetic particles are treated with a surface modification technology, and antibodies that specifically recognize Ox-LDL are coupled to the surface of the magnetic particles to prepare a magnetic immunoprobe, and the coupling efficiency is calculated by converting the magnetic signal excitation energy into an electrical signal, including: selecting superparamagnetic iron oxide nanoparticles as the magnetic core, and surface modifying the nanoparticles by silanization and / or pegylation; coupling the antibodies that specifically recognize Ox-LDL to the surface-modified nanoparticles by an EDC / NHS activation method to form a magnetic immunoprobe; and converting the magnetic signal excitation energy into an electrical signal to calculate the coupling efficiency to identify successfully coupled particles.

[0008] In one possible implementation, the coupling efficiency is calculated by converting the magnetic signal excitation energy into an electrical signal to identify successfully coupled particles, including: calculating the coupling efficiency by converting the magnetic signal excitation energy into an electrical signal to evaluate the degree and uniformity of antibody coupling; applying magnetic quantitative detection technology to analyze and evaluate the antibody labeling efficiency, screening out successfully coupled magnetic nanoparticles, and obtaining a magnetic immunoprobe through functional testing.

[0009] In a possible implementation, an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad is constructed, and the quality control process is optimized in combination with the establishment of a standard curve; wherein the detection area is pre-coated with an Ox-LDL paired antibody, and the quality control area is used to verify the effectiveness of the test strip, including: constructing an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad, wherein the detection area is pre-coated with an anti-Ox-LDL polyclonal antibody, and the quality control area is pre-coated with a goat anti-mouse IgG antibody for verifying the effectiveness of the test strip; marking the antibody solution on a nitrocellulose membrane to form a detection area and a quality control area, pasting the nitrocellulose membrane, the sample pad and the absorbent pad on a backing plate according to a design sequence, and cutting them into individual test strips; optimizing the flow rate, magnetic signal strength and specificity of the chromatographic test strip by adjusting the pore size, surface treatment and antibody labeling concentration of the nitrocellulose membrane; using a multi-angle, multi-spectral imaging system to collect chromatographic test strip images, and applying a deep learning model in combination with a standard curve for defect detection and quality scoring, to perform automated quality control on test strip production.

[0010] In one possible implementation, a multi-angle, multi-spectral imaging system is used to capture images of tomographic test strips, and a deep learning model is applied in combination with a standard curve to perform defect detection and quality scoring, and automated quality control is performed on the test strip production, including: using a multi-angle, multi-spectral imaging system to capture images of the test strips to obtain comprehensive image data of the test strips; inputting the comprehensive image data into a pre-trained deep learning model to perform defect detection on the test strips to identify defects including bubbles, cracks and / or scratches; based on the defect detection results, an image quality scoring system is established, and the test strips are automatically quality scored and screened in combination with a standard curve to perform automated quality control on the test strip production.

[0011] In a possible implementation, the sample to be tested is applied to the sample pad of the test strip, combined with the magnetic particles labeled with the oxidized low-density lipoprotein antibody at the binding pad position, and flows on the nitrocellulose membrane to the position labeled with another antibody, and through the binding of the antigen and the antibody, a magnetic particle enrichment area is formed through specific recognition, and the oxidized low-density lipoprotein is quantitatively detected by magnetic signal acquisition and data processing, including: applying the sample to be tested to the sample pad of the test strip, combining with the magnetic particles labeled with the oxidized low-density lipoprotein antibody at the binding pad position, and flowing on the nitrocellulose membrane to the position labeled with another antibody, and through the binding of the antigen and the antibody The method comprises the following steps: a) using a magnetic quantitative detection system to precisely locate the detection line and the quality control line, detecting the magnetic signal intensity, and combining a machine learning model to establish a quantitative relationship between the signal intensity and the concentration of the analyte. b) evaluating the influence of interfering substances on the detection results by calculating one or more indicators.

[0012] In one possible implementation, a magnetic quantitative detection system is used, and a computer software algorithm is used to accurately locate the detection line and the quality control line, detect the magnetic signal intensity, and combine the machine learning model to establish a quantitative relationship between the signal intensity and the concentration of the analyte, including: locating the position of the detection line and the quality control line, and detecting the magnetic signal data; based on the machine learning algorithm, using the acquired signal data to establish a quantitative relationship model of the concentration of the analyte, including constructing a standard curve and applying a four-parameter logistic fitting algorithm.

[0013] In one possible implementation, based on a machine learning algorithm, a quantitative relationship model of the concentration of the analyte is established using the acquired signal data, including constructing a standard curve and applying a four-parameter logistic fitting algorithm, including: ,in, Indicates the signal response, represents the Ox-LDL concentration, and A, B, C, and D represent the fitting parameters.

[0014] The embodiment of the present application also provides a system for quantitatively detecting oxidized low-density lipoprotein based on magnetism, comprising a probe unit, a test paper unit and a detection unit; wherein the probe unit is used to process the target magnetic particles using surface modification technology, and couple the antibody that specifically recognizes Ox-LDL to the surface of the magnetic particles to prepare a magnetic immune probe, and convert the magnetic signal excitation energy into an electrical signal to calculate the coupling efficiency; the test paper unit is used to construct an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad, and optimize the quality control process in combination with the establishment of a standard curve; wherein the detection area is pre-coated with an Ox-LDL paired antibody, and the quality control area is used to verify the effectiveness of the test strip; the detection unit is used to apply the sample to be tested to the sample pad of the test strip, and through the combination pad position and the magnetic microparticles labeled with the oxidized low-density lipoprotein antibody, through the flow on the nitrocellulose membrane to the site labeled with another antibody, through the binding action of the antigen and antibody, a magnetic particle enrichment area is formed through specific recognition, and the oxidized low-density lipoprotein is quantitatively detected through magnetic signal acquisition and data processing.

[0015] An embodiment of the present application also provides a device for quantitatively detecting oxidized low-density lipoprotein based on machine magnetism, comprising: a processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute the method described in the above embodiment.

[0016] In the method, system and equipment for quantitative detection of oxidized low-density lipoprotein based on machine magnetism provided above, the embodiments of the present application can effectively improve the sensitivity and specificity of detection by combining the high separation efficiency of magnetic particles and the rapid detection advantages of immunochromatography technology. Magnetic particles can efficiently enrich Ox-LDL in samples and reduce background interference, while immunochromatography technology ensures the rapidity of detection. Secondly, by introducing machine vision technology, the method can realize automated and objective result interpretation, avoid human errors, and improve the accuracy and repeatability of detection. The machine vision system can accurately capture and analyze the color intensity changes of the test line to achieve more accurate quantitative analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of a method for quantitatively detecting oxidized low-density lipoprotein based on magnetism provided in an embodiment of the present application;

[0019] Figure 2 A schematic block diagram of a system for quantitatively detecting oxidized low-density lipoprotein based on magnetism provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application.

[0021] Those skilled in the art will appreciate that the terms "first", "second" and the like in the embodiments of the present application are only used to distinguish different steps, devices or modules, etc., and do not represent any specific technical meaning, nor do they represent the necessary logical order between them. It should also be understood that in the embodiments of the present application, "multiple" may refer to two or more, and "at least one" may refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present application, in the absence of explicit limitation or contrary revelation given in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present application is only a kind of association relationship describing the associated objects, indicating that there can be three kinds of relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present application generally indicates that the associated objects before and after are an "or" relationship. It should also be understood that the description of each embodiment in the present application emphasizes the differences between the embodiments, and the same or similar parts can refer to each other. For the sake of brevity, they will not be repeated one by one.

[0022] At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use. The techniques, methods and devices known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and devices should be considered as part of the specification. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are 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.

[0024] Figure 1 A schematic diagram of a method for quantitatively detecting oxidized low-density lipoprotein based on magnetism provided in an embodiment of the present application. Figure 1 As shown, in step S101, the target magnetic particles are treated by surface modification technology, and antibodies that specifically recognize Ox-LDL are coupled to the surface of the magnetic particles to prepare magnetic immune probes, and the coupling efficiency is calculated by converting the magnetic signal excitation energy into an electrical signal.

[0025] Specifically, the method includes: selecting superparamagnetic iron oxide nanoparticles as the magnetic core, and surface modifying the nanoparticles by silanization and / or pegylation; coupling antibodies that specifically recognize Ox-LDL to the surface-modified nanoparticles by EDC / NHS activation method to form a magnetic immune probe; and converting the magnetic signal excitation energy into an electrical signal to calculate the coupling efficiency to identify successfully coupled particles.

[0026] Furthermore, the coupling efficiency is calculated by converting the magnetic signal excitation energy into an electrical signal to identify successfully coupled particles, including: calculating the coupling efficiency by converting the magnetic signal excitation energy into an electrical signal to evaluate the degree and uniformity of antibody coupling; applying magnetic quantitative detection technology to analyze and evaluate the antibody labeling efficiency, screen out successfully coupled magnetic nanoparticles, and obtain magnetic immunoprobes through functional testing.

[0027] In one implementation scenario, first of all, the selection of superparamagnetic iron oxide nanoparticles as the magnetic core is the basis. Superparamagnetic iron oxide nanoparticles can refer to Fe3O4 or γ-Fe2O3 nanoparticles with a particle size of less than 20nm. These particles exhibit strong magnetization under the action of an external magnetic field, but do not retain residual magnetization after the external magnetic field is removed. This property makes them very suitable for biomedical applications. In actual operation, Fe3O4 nanoparticles with a particle size of 10-30nm can be selected, and their saturation magnetization intensity is about 60 emu / g.

[0028] For the surface modification of these nanoparticles, silanization and pegylation are two commonly used methods. The silanization process can use 3-aminopropyltriethoxysilane (APTES) as a coupling agent. In this process, the ethoxy groups of APTES will undergo a condensation reaction with the hydroxyl groups on the Fe3O4 surface to form Si-O-Fe bonds and introduce amino groups on the particle surface. This reaction can be carried out in anhydrous ethanol at a temperature of 60°C for 4 hours. Pegylation can use PEG molecules with carboxyl groups at the end, such as mPEG-COOH (molecular weight of about 2000 Da). The PEGylation process can be carried out at room temperature and the reaction time is about 12 hours. These two modification methods can significantly improve the dispersibility and biocompatibility of nanoparticles.

[0029] After surface modification, the next step is to couple antibodies that specifically recognize Ox-LDL to the nanoparticles. The EDC / NHS activation method is a commonly used protein coupling method. In this process, 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) and N-hydroxysuccinimide (NHS) are used to activate the carboxyl groups on the particle surface. Specifically, EDC first reacts with the carboxyl groups to form an unstable O-acylisourea intermediate, and then NHS quickly reacts with this intermediate to form a semi-stable NHS ester. This NHS ester can then react with the amino group on the antibody molecule to form a stable amide bond.

[0030] In practice, a 0.4 M EDC and 0.1 M NHS solution can be used. The modified nanoparticles are dispersed in this solution and reacted at room temperature for 30 minutes. Excess reagents are then removed by magnetic separation, and the antibody solution (concentration is about 1 mg / mL) is immediately added. This coupling reaction can be carried out at 4°C for 4-6 hours.

[0031] After the coupling reaction is completed, the key step is to evaluate the coupling efficiency. Here, the calculation is based on the principle that the excitation energy of the magnetic signal is converted into an electrical signal. This process involves complex physical and mathematical principles. First, it is necessary to understand the behavior of magnetic nanoparticles in an alternating magnetic field. When placed in an alternating magnetic field, magnetic nanoparticles will produce hysteresis losses and Neel relaxation losses, which will cause the temperature of the medium surrounding the particles to increase. The change in temperature can be measured by thermocouples or fiber optic temperature sensors and converted into electrical signals.

[0032] Specifically, a mathematical model can be established to describe this process. Assuming that the intensity of the alternating magnetic field is , the frequency is , then the specific absorption rate (SAR) of magnetic nanoparticles can be expressed as: ,in, is the vacuum permeability, is the imaginary part of the magnetic susceptibility, is the density of the particle. It is related to the size, shape, magnetism and viscosity of the surrounding medium. For the selected 10nm Fe3O4 nanoparticles, in aqueous solution, A typical value is about 0.1.

[0033] The change in temperature over time can be described by the following equation: ,in, is the mass of the magnetic material, is the specific heat capacity, is the sample volume. By measuring the temperature-time curve, the SAR value can be inferred. Then, by comparing the SAR values ​​of the nanoparticles without antibody conjugation and the nanoparticles after antibody conjugation, the coupling efficiency can be estimated. Specifically, the coupling efficiency It can be expressed as: ,in, and Represent the SAR values ​​before and after coupling, respectively. In actual operation, an alternating magnetic field with a frequency of 100 kHz and an intensity of 20 kA / m can be used. The sample volume can be 1 mL, and the mass of the magnetic material is about 1 mg. A high-precision fiber optic temperature sensor (resolution 0.1°C) is used to measure the temperature change. By fitting the temperature-time curve, we can get The value of SAR is then calculated.

[0034] In addition to the SAR method, magnetic quantitative detection technology can also be used to analyze and evaluate the antibody labeling efficiency. Magnetic quantitative detection technology is based on the principle of superconducting quantum interference device (SQUID), which can accurately measure the magnetization intensity of the sample. By comparing the magnetization intensity of the nanoparticles before and after coupling, the coupling amount of the antibody can be estimated. Specifically, assuming that the mass of the nanoparticles before coupling is , the magnetization intensity is , the mass after coupling is , the magnetization intensity is , then the antibody coupling amount can be expressed as: , this formula takes into account the decrease in the magnetization of the nanoparticles after antibody conjugation.

[0035] In actual operation, a SQUID magnetometer (such as Quantum Design MPMS XL-7) can be used for measurement. The measurement temperature can be set to 300K, and the applied magnetic field strength can be set from -70 kOe to 70 kOe. By analyzing the hysteresis loop, the saturation magnetization of the sample can be obtained.

[0036] To further evaluate the uniformity of antibody conjugation, dynamic light scattering (DLS) can be combined. DLS measures the hydrodynamic diameter of the particles, which reflects the size distribution of the particles. A uniformly conjugated sample should exhibit a narrow particle size distribution. DLS measurements can be performed using a Malvern Zetasizer Nano ZS at a measurement angle of 173° and a temperature of 25°C.

[0037] Through the above methods, the coupling efficiency and uniformity of the antibody can be fully evaluated. However, high efficiency and uniformity alone are not enough to guarantee the functionality of the magnetic immunoprobe. Functional testing is also required to ensure that the coupled antibody maintains its ability to recognize Ox-LDL.

[0038] Functional testing can be performed by enzyme-linked immunosorbent assay (ELISA). Ox-LDL can be immobilized on a 96-well plate and then the prepared magnetic immunoprobes can be added. If the antibodies retain their functionality, they will bind to Ox-LDL. After removing unbound probes by washing, hydrogen peroxide and tetramethylbenzidine (TMB) substrate can be added. If the antibodies in the probe bind to Ox-LDL, they will catalyze the oxidation of TMB, producing a blue color. By measuring the absorbance at 450 nm, the functionality of the probe can be quantitatively assessed.

[0039] In this process, several key points need to be noted: First, all reactions and measurements should be carried out in a clean environment to avoid contamination. Second, when performing magnetic measurements, temperature control of the sample is very important because temperature can significantly affect the performance of magnetic materials. Third, when performing ELISA tests, appropriate control groups need to be set up, including blank controls, negative controls, and positive controls to ensure the reliability of the results.

[0040] In addition, you can also consider using a transmission electron microscope (TEM) to visually observe the morphology and size of nanoparticles. TEM can not only provide information on the size of the particles, but also show the shape and aggregation state of the particles. High-resolution TEM (such as FEI Tecnai G2 F20) can be used for observation, with the acceleration voltage set to 200 kV. By analyzing a large number of TEM images, the average size and size distribution of the particles can be obtained.

[0041] Finally, to more fully characterize the performance of the magnetic immunoprobe, magnetic resonance imaging (MRI) experiments can be considered. MRI is a noninvasive imaging technique that can use magnetic nanoparticles as contrast agents to enhance image contrast. A series of magnetic immunoprobe solutions with different concentrations can be prepared and T2-weighted imaging can be performed using a clinical MRI scanner (such as a 3T Siemens Magnetom Trio). By analyzing the signal intensity at different concentrations, the transverse relaxation rate r2 of the probe can be calculated, which is an important parameter for characterizing the performance of MRI contrast agents.

[0042] It can be calculated by the following formula: ,in, is the transverse relaxation time in the presence of the probe, is the transverse relaxation time of pure water, is the concentration of the probe. By plotting and The relationship diagram can be obtained from the slope Typical superparamagnetic iron oxide nanoparticles Values ​​are in the range of 100-200 mM^-1s^-1.

[0043] Through these comprehensive characterization and functional tests, it can be ensured that the prepared magnetic immunoprobe not only has efficient antibody coupling, but also maintains good magnetic properties and biological functions. This multi-angle and multi-scale characterization method provides a solid foundation for the quality control and performance optimization of magnetic immunoprobes.

[0044] In step S102, an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad is constructed, and the quality control process is optimized in combination with the establishment of a standard curve; wherein the detection area is pre-coated with an Ox-LDL paired antibody, and the quality control area is used to verify the validity of the test strip.

[0045] Specifically, it includes: constructing an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad, wherein the detection area is pre-coated with an anti-Ox-LDL polyclonal antibody, and the quality control area is pre-coated with a goat anti-mouse IgG antibody, for verifying the effectiveness of the test strip; marking the antibody solution on a nitrocellulose membrane to form a detection area and a quality control area, pasting the nitrocellulose membrane, the sample pad and the absorbent pad on a backing plate according to a designed sequence, and cutting them into individual test strips; optimizing the flow rate, magnetic signal strength and specificity of the chromatographic test strip by adjusting the pore size, surface treatment and antibody labeling concentration of the nitrocellulose membrane; using a multi-angle, multi-spectral imaging system to collect images of the chromatographic test strip, and applying a deep learning model combined with a standard curve for defect detection and quality scoring, so as to perform automated quality control on the test strip production.

[0046] Furthermore, a multi-angle, multi-spectral imaging system is used to capture images of tomographic test strips, and a deep learning model is applied in combination with a standard curve to perform defect detection and quality scoring, and automated quality control is performed on the test strip production, including: using a multi-angle, multi-spectral imaging system to capture images of the test strips to obtain comprehensive image data of the test strips; inputting the comprehensive image data into a pre-trained deep learning model to perform defect detection on the test strips to identify defects including bubbles, cracks and / or scratches; based on the defect detection results, an image quality scoring system is established, and the test strips are automatically quality scored and screened in combination with a standard curve to perform automated quality control on the test strip production.

[0047] In one embodiment, when constructing an immunochromatographic test strip comprising a detection zone, a quality control zone, and a sample pad, multiple key factors need to be considered to ensure the performance and quality of the test strip. First, let's discuss the construction process and optimization strategy of the test strip in detail.

[0048] When constructing a test strip, you first need to choose the right material. For nitrocellulose membranes, you can choose Millipore's HF135 membrane, which has a pore size of 8μm, which can provide good flow rate and signal strength. In order to optimize the performance of the membrane, it can be surface treated. An effective method is to pretreat the membrane with 0.5% polyvinyl alcohol (PVA) solution, which can reduce nonspecific binding and improve signal stability.

[0049] For antibody labeling, magnetic nanoparticles can be used. For example, Fe3O4 nanoparticles with an average diameter of 50 nm can be used. Particles of this size can provide sufficient magnetic signal strength without significantly affecting the activity of the antibody. The antibody and magnetic nanoparticles can be coupled through an EDC / NHS chemical reaction. Specifically, 10 mg / mL of EDC and 5 mg / mL of NHS can be used to react for 2 hours in a MES buffer at pH 6.0.

[0050] In the test area, anti-Ox-LDL polyclonal antibodies need to be pre-coated. Here, an antibody solution with a concentration of 1 mg / mL can be used and fixed on the nitrocellulose membrane by spraying. The spray speed can be set to 100 nL / s and the spray width is 1 mm. For the quality control area, a sheep anti-mouse IgG antibody with a concentration of 0.5 mg / mL can be used with the same spray parameters.

[0051] In order to optimize the performance of the chromatography test strip, multiple parameters need to be adjusted. First, the pore size of the nitrocellulose membrane has a direct impact on the flow rate. The best balance can be found by experimentally comparing membranes with different pore sizes (such as 5μm, 8μm, and 12μm). Yes, a pore size of 8μm can provide good flow rate and signal strength.

[0052] Surface treatment is another key factor. In addition to the PVA treatment mentioned above, you can also try blocking with 1% BSA solution to further reduce nonspecific binding. The treatment time can be set to 30 minutes at 37°C.

[0053] The antibody labeling concentration also needs to be optimized. A range of antibody concentrations can be prepared (e.g., 0.1 mg / mL, 0.5 mg / mL, 1 mg / mL, 2 mg / mL) and their effects on signal intensity and specificity can be evaluated. Yes, a concentration of 1 mg / mL provides a good balance.

[0054] During the optimization process, the magnetic signal strength needs to be accurately measured and calculated. For a system using Fe3O4 nanoparticles, the following formula can be used to calculate the magnetic moment of a single nanoparticle: ,in, is the radius of the nanoparticle (25 nm), is the saturation magnetization of Fe3O4 (about 480 kA / m). Substituting the value into the formula, we can get: , considering the number of nanoparticles in the detection area, the total magnetic signal strength can be estimated. Assuming that in a typical detection reaction, there are nanoparticles, then the total magnetic moment is: , this magnetic moment value can be used to evaluate the signal strength under different optimization conditions.

[0055] To achieve high-quality image acquisition, an imaging system equipped with multiple cameras can be used. For example, three high-resolution CMOS cameras (such as Sony IMX586, with a resolution of 48MP) can be used to image the test strip from angles of 0°, 45°, and 90°.

[0056] For multispectral imaging, a tunable filter system can be used, such as a liquid crystal tunable filter. This system can select wavelengths in the range of 400-1000nm with a step accuracy of 1nm. Several key wavelengths can be selected for imaging, such as:

[0057] 450nm: corresponds to blue light, which can enhance the contrast of some pigments

[0058] 550nm: corresponds to green light, can be used for imaging of biological samples

[0059] 650nm: corresponds to red light, which can improve the visibility of certain markers

[0060] 850nm: Near infrared light, which can reduce background interference

[0061] To achieve automated defect detection and quality scoring, a convolutional neural network (CNN) can be used. A suitable network structure for this task could be ResNet-50, which has enough depth to capture complex features while not being too large to run on commodity hardware.

[0062] The input of the model can be a combination of multi-angle and multi-spectral images, and the output can be the probability distribution of defect types (such as bubbles, cracks, scratches) and an overall quality score. The following loss function can be used in the model training process: ,in, is the cross entropy loss for defect classification, is the mean square error loss of the quality score, is the regularization term. , and is the weight coefficient, and the optimal value can be determined through experiments.

[0063] In order to establish an accurate standard curve, a series of Ox-LDL samples with known concentrations need to be prepared. For example, samples with 0, 10, 50, 100, 500, and 1000 ng / mL can be prepared. For each concentration, at least three replicates are required.

[0064] Using these data, a four-parameter logistic regression model (4PL model) can be established to fit the standard curve. The formula of the 4PL model is as follows: y = d + (a - d) / (1 + (x / c)^b), where y is the response value (such as magnetic signal intensity), x is the Ox-LDL concentration, a is the minimum asymptotic value, d is the maximum asymptotic value, c is the EC50 (half-maximal effect concentration), and b is the Hill slope.

[0065] The optimal values ​​of these parameters can be determined by nonlinear least squares methods, for example, using Python's scipy library to convert the response values ​​of unknown samples into concentration values.

[0066] Based on the above techniques, an automated quality control process can be designed. Image acquisition: Use a multi-angle, multi-spectral imaging system to image each test strip. Preprocessing: Standardize and remove noise from the acquired images. Gaussian filters can be used for noise reduction, followed by histogram equalization to enhance contrast. Defect detection: The preprocessed image is input into the deep learning model to obtain the probability distribution of defect types. Signal strength measurement: The magnetic signal intensity of the inspection area and the quality control area is measured. Image analysis techniques, such as the integrated optical density (IOD) method, can be used to quantify the signal strength. Quality scoring: Calculate a comprehensive quality score based on the defect detection results and signal strength measurements. The following formula can be used: ,in, is the probability of detecting a defect, is the signal strength in the detection zone, is the ideal maximum signal strength, is the signal intensity of the quality control area, is the reference signal intensity of the quality control area. , and is the weight coefficient, which can be optimized through experiments. Automatic screening: Automatically screen qualified test strips according to the preset quality threshold. For example, you can set > 0.8 as the qualified standard. Through this process, automated quality control of test strip production can be achieved, greatly improving production efficiency and consistency of product quality.

[0067] In practical applications, the following points should also be noted:

[0068] Environmental control: The performance of the test strips may be affected by temperature and humidity. It is recommended to produce and test in a constant temperature and humidity environment, such as a temperature of 25±2°C and a relative humidity of 50±5%.

[0069] Batch consistency: To ensure consistency between different batches, batch comparison tests need to be performed regularly. Standards of known concentrations can be used to ensure that there are no significant differences in the response curves of different batches.

[0070] Stability testing: Accelerated stability testing is required to evaluate the performance changes of the test strips under different storage conditions. For example, the test strips can be stored at 40°C and 75% relative humidity for 3 months and their performance can be tested regularly.

[0071] Cross-reactivity: The cross-reactivity of the test strip to other similar substances needs to be evaluated. For example, the test strip can be tested for its response to native LDL, HDL, etc. to ensure its specificity.

[0072] Magnetic field interference: Due to the use of magnetic nanoparticles, the performance of the test strips may be affected by external magnetic fields. When designing production lines and test equipment, measures to shield against external magnetic fields need to be considered.

[0073] Through the above content, a high-quality and efficient Ox-LDL immunochromatographic test strip production and quality control system can be constructed. This system can not only ensure the quality of the product, but also improve production efficiency through automation and intelligent methods.

[0074] In step S103, the sample to be tested is applied to the sample pad of the test strip, and is combined with the magnetic particles labeled with the oxidized low-density lipoprotein antibody at the binding pad position, and flows on the nitrocellulose membrane to the site labeled with another antibody. Through the binding action of the antigen and antibody, a magnetic particle enrichment area is formed through specific recognition, and the oxidized low-density lipoprotein is quantitatively detected through magnetic signal acquisition and data processing.

[0075] Specifically, the method includes: applying the sample to be tested to the sample pad of the test strip, combining with the magnetic particles labeled with oxidized low-density lipoprotein antibodies at the binding pad position, flowing to the site labeled with another antibody on the nitrocellulose membrane, and forming a magnetic particle enrichment area through the binding action of the antigen and antibody, and specific recognition; measuring the color development of the test line and the quality control line by visual observation, and judging the presence of oxidized low-density lipoprotein in the sample to be tested according to the color development result; measuring the magnetic signal intensity at the test line with a magnetic signal detector, and quantitatively analyzing the content of oxidized low-density lipoprotein in the sample to be tested according to the preset linear relationship between the magnetic signal intensity and the oxidized low-density lipoprotein concentration; using a magnetic quantitative detection system, using computer software algorithms to accurately locate the test line and the quality control line, detecting the magnetic signal intensity, and combining a machine learning model to establish a quantitative relationship between the signal intensity and the concentration of the analyte; and evaluating the influence of interfering substances on the test results by calculating one or more indicators.

[0076] Furthermore, a magnetic quantitative detection system is used, and a computer software algorithm is used to accurately locate the detection line and the quality control line, detect the magnetic signal intensity, and establish a quantitative relationship between the signal intensity and the concentration of the analyte in combination with a machine learning model, including: locating the position of the detection line and the quality control line, and detecting the magnetic signal data; based on the machine learning algorithm, using the acquired signal data to establish a quantitative relationship model of the concentration of the analyte, including constructing a standard curve and applying a four-parameter logistic fitting algorithm. The fitting algorithm includes: ,in, Indicates the signal response, represents the Ox-LDL concentration, and A, B, C, and D represent the fitting parameters.

[0077] In one implementation scenario, during the test, 10 μL of the sample to be tested is added to the sample pad. The sample flows forward driven by capillary force, first binds to the antibody-magnetic particle complex in the binding pad, and continues to flow forward to the detection line position, where it reacts with the immobilized antibody to produce an antigen-antibody reaction. The magnetic particles are captured and enriched to form a visible band. The unbound magnetic particles continue to migrate to the quality control line and are captured to form a quality control band. After 15 minutes, the result is stable, the test strip is taken out, and placed in a magnetic signal detector for signal acquisition.

[0078] The magnetic signal detector can use a high-sensitivity magnetoresistive sensor (such as NVE's AAH002-02, with a sensitivity of up to 5.0mV / V / Oe), and the distance between the sensor surface and the test strip is controlled within 1mm to ensure the consistency of signal acquisition. After amplification, filtering, and AD conversion, the analog signal output by the sensor is processed by a single-chip microcomputer (such as STM32F103) to measure the magnetic signal intensity at the detection line position. By establishing a linear relationship between the magnetic signal intensity and the Ox-LDL concentration (y=kx+b, where y is the magnetic signal intensity, x is the Ox-LDL concentration, k is the slope, and b is the intercept), the quantitative analysis of Ox-LDL can be achieved. The linear range is 0.1-100ng / mL, and the detection limit is 0.05ng / mL, which is equivalent to the ELISA method.

[0079] In order to further improve the accuracy and repeatability of detection, machine learning algorithms can be introduced to optimize the data processing process. First, the collected magnetic signal data is preprocessed, including normalization and denoising, and then characteristic parameters such as peak signal, peak area, half-peak width, etc. are extracted. Based on the support vector machine (SVM) algorithm, an Ox-LDL concentration prediction model is constructed, and the model is trained and optimized with part of the sample data to improve the prediction accuracy. At the same time, a four-parameter Logistic fitting algorithm is used to establish a nonlinear relationship between magnetic signal intensity and Ox-LDL concentration: y=(AD) / (1+(x / C)^B)+D, where A, B, C, and D are fitting parameters obtained by fitting a large amount of standard sample data. This algorithm can effectively expand the dynamic range of detection and improve the sensitivity in the low concentration area.

[0080] In practical applications, it is also necessary to consider the impact of various interfering factors on the test results, such as blood viscosity, hemoglobin concentration, triglyceride level, etc. Interference can be minimized by optimizing the components and structure of the test strips, such as using hydrophilic coatings and barrier layers. If necessary, a correction algorithm can be established to correct the test results according to the concentration of the interfering substances to ensure the accuracy of the test.

[0081] In summary, the present invention uses magnetic particles as markers, combined with immunochromatographic test paper technology, through magnetic signal acquisition and intelligent algorithms, to achieve rapid, sensitive and accurate quantitative detection of Ox-LDL, providing a powerful tool for assessing the risk of atherosclerosis and guiding clinical treatment. Compared with the traditional enzyme-linked immunosorbent assay, this method is simple to operate, has a fast detection speed, and low instrument cost, is more suitable for grassroots and POCT applications, and has good application prospects.

[0082] Figure 2A schematic block diagram of a system for quantitatively detecting oxidized low-density lipoprotein based on magnetism provided in an embodiment of the present application. It should be understood that the system shown in the figure is exemplary and not restrictive. This means that the system architecture involved is not limited to a specific form or design, but is presented as an example. In other words, the architecture shown in the figure can be regarded as a way of expression to clearly describe related concepts and relationships, and does not exclude other forms of architecture. Therefore, when interpreting the architecture in the picture, it should be understood that the model is flexible and diverse, and its purpose is to provide an exemplary description rather than a restrictive provision on a specific form.

[0083] The embodiment of the present application discloses a system for quantitatively detecting oxidized low-density lipoprotein based on magnetism, comprising a probe unit 201, a test paper unit 202 and a detection unit 203; wherein the probe unit 201 is used to process the target magnetic particles by using surface modification technology, and couple the antibody that specifically recognizes Ox-LDL to the surface of the magnetic particles to prepare a magnetic immune probe, and convert the magnetic signal excitation energy into an electrical signal to calculate the coupling efficiency; the test paper unit 202 is used to construct an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad, and in combination with a standard The establishment of the curve optimizes the quality control process; wherein, the detection area is pre-coated with Ox-LDL paired antibodies, and the quality control area is used to verify the validity of the test strip; the detection unit 203 is used to apply the sample to be tested to the sample pad of the test strip, and combine with the magnetic particles marked with oxidized low-density lipoprotein antibodies at the binding pad position, and flow on the nitrocellulose membrane to the site marked with another antibody, through the binding action of the antigen and antibody, a magnetic particle enrichment area is formed through specific recognition, and the oxidized low-density lipoprotein is quantitatively detected through magnetic signal acquisition and data processing.

[0084] Furthermore, an embodiment of the present application also provides a device for quantitatively detecting oxidized low-density lipoprotein based on magnetism, comprising: a processor, a memory, and a system bus; the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute any of the above methods.

[0085] Furthermore, an embodiment of the present application also provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute any of the above methods.

[0086] It can be known from the description of the above implementation mode that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., including several instructions for enabling a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0087] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0088] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0089] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for quantitatively detecting oxidized low-density lipoprotein based on magnetism, characterized in that: include: The target magnetic particles are treated by surface modification technology, and antibodies that specifically recognize Ox-LDL are coupled to the surface of the magnetic particles to prepare magnetic immune probes, and the coupling efficiency is calculated by converting the magnetic signal excitation energy into an electrical signal; The method comprises: selecting superparamagnetic iron oxide nanoparticles as the magnetic core, and modifying the surface of the nanoparticles by silanization and / or pegylation; coupling an antibody that specifically recognizes Ox-LDL to the surface-modified nanoparticles by 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride and N-hydroxysuccinimide to form a magnetic immunoprobe; converting magnetic signal excitation energy into an electrical signal to calculate the coupling efficiency, so as to identify successfully coupled particles; Among them, the coupling efficiency is calculated by converting the magnetic signal excitation energy into an electrical signal to identify the successfully coupled particles, including: placing the coupled nanoparticles in an alternating magnetic field, using the magnetic signal excitation energy to generate temperature changes, measuring the temperature changes through thermocouples or optical fiber temperature sensors, and converting them into electrical signals, and then calculating the coupling efficiency to evaluate the degree and uniformity of antibody coupling; using magnetic quantitative detection technology to analyze and evaluate the antibody labeling efficiency, screen out successfully coupled magnetic nanoparticles, and perform functional testing through enzyme-linked immunosorbent assay to obtain magnetic immune probes; An immunochromatographic test strip comprising a detection area, a quality control area and a sample pad is constructed, and the quality control process is optimized in combination with the establishment of a standard curve; wherein the detection area is pre-coated with an Ox-LDL paired antibody, and the quality control area is used to verify the validity of the test strip; the method comprises: constructing an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad, wherein the detection area is pre-coated with an anti-Ox-LDL polyclonal antibody, and the quality control area is pre-coated with a goat anti-mouse IgG antibody, which is used to verify the validity of the test strip; marking an antibody solution on a nitrocellulose membrane to form a detection area and a quality control area, and sticking the nitrocellulose membrane, the sample pad and the absorbent pad on a backing board according to the design sequence, and cutting into individual test strips; The sample to be tested is applied to the sample pad of the test strip, combined with the magnetic particles labeled with antibodies that specifically recognize Ox-LDL through the position of the binding pad, flows on the nitrocellulose membrane to the position labeled with another antibody, and through the binding of the antigen and antibody, a magnetic particle enrichment area is formed through specific recognition, and the oxidized low-density lipoprotein is quantitatively detected through magnetic signal acquisition and data processing; which includes: applying the sample to be tested to the sample pad of the test strip, combining with the magnetic particles labeled with antibodies that specifically recognize Ox-LDL through the position of the binding pad, flowing on the nitrocellulose membrane to the position labeled with another antibody, and through the binding of the antigen and antibody, A magnetic particle enrichment area is formed through specific recognition; the color development of the detection area and the quality control area is measured by visual observation, and the presence of oxidized low-density lipoprotein in the test sample is judged according to the color development result; the magnetic signal intensity at the detection area is measured by a magnetic signal detector, and the content of oxidized low-density lipoprotein in the test sample is quantitatively analyzed according to the preset linear relationship between the magnetic signal intensity and the concentration of oxidized low-density lipoprotein; a magnetic quantitative detection system is adopted, and a computer software algorithm is used to accurately locate the detection area and the quality control area, detect the magnetic signal intensity, and a quantitative relationship between the signal intensity and the concentration of the test object is established in combination with a machine learning model; the influence of interfering substances on the test results is evaluated by calculating one or more indicators.

2. The method according to claim 1, characterized in that in, An immunochromatographic test strip comprising a detection zone, a quality control zone and a sample pad is constructed, and the quality control process is optimized in combination with the establishment of a standard curve; wherein the detection zone is pre-coated with an Ox-LDL paired antibody, and the quality control zone is used to verify the validity of the test strip, and further comprises: By adjusting the pore size, surface treatment and antibody labeling concentration of the nitrocellulose membrane, the flow rate, magnetic signal strength and specificity of the chromatography test strips were optimized; A multi-angle, multi-spectral imaging system is used to collect images of tomographic test strips, and a deep learning model is applied in combination with a standard curve to perform defect detection and quality scoring, thereby automating the quality control of test strip production.

3. The method according to claim 2, characterized in that in, The multi-angle and multi-spectral imaging system is used to collect tomography test strip images, and the deep learning model is combined with the standard curve for defect detection and quality scoring to automate the quality control of test strip production, including: Using a multi-angle, multi-spectral imaging system to collect images of the test strip to obtain comprehensive image data of the test strip; Inputting the comprehensive image data into a pre-trained deep learning model to perform defect detection on the test strip to identify defects including bubbles, cracks and / or scratches; Based on the results of the defect detection, an image quality scoring system is established, and the test strips are automatically scored and screened in combination with a standard curve, so that automated quality control is performed on the production of test strips.

4. The method according to claim 1, characterized in that: in, The magnetic quantitative detection system is used to accurately locate the detection area and quality control area using computer software algorithms, detect the magnetic signal intensity, and combine the machine learning model to establish a quantitative relationship between the signal intensity and the concentration of the analyte, including: Locating the positions of the detection area and the quality control area, and detecting magnetic signal data; Based on the machine learning algorithm, the acquired signal data is used to establish a quantitative relationship model of the concentration of the analyte, which includes constructing a standard curve and applying a four-parameter logistic fitting algorithm.

5. The method according to claim 4, characterized in that in, Based on the machine learning algorithm, the acquired signal data is used to establish a quantitative relationship model of the concentration of the analyte, which includes constructing a standard curve and applying a four-parameter logistic fitting algorithm, including: ， in, Indicates the signal response, represents the Ox-LDL concentration, and A, B, C, and D represent the fitting parameters.

6. A system for quantitatively detecting oxidized low-density lipoprotein based on magnetism, applied to the method according to any one of claims 1 to 5, characterized in that: It includes a probe unit, a test paper unit and a detection unit; wherein, The probe unit is used to process the target magnetic particles using surface modification technology, and couple the antibody that specifically recognizes Ox-LDL to the surface of the magnetic particles to prepare a magnetic immune probe, and the coupled nanoparticles are placed in an alternating magnetic field, and the magnetic signal excitation energy is used to generate temperature changes. The temperature changes are measured by thermocouples or optical fiber temperature sensors and converted into electrical signals, and then the coupling efficiency is calculated; The test paper unit is used to construct an immunochromatographic test strip comprising a detection area, a quality control area and a sample pad, and the quality control process is optimized in combination with the establishment of a standard curve; wherein the detection area is pre-coated with an Ox-LDL paired antibody, and the quality control area is used to verify the validity of the test strip; The detection unit is used to apply the sample to be tested to the sample pad of the test strip, and combine with the magnetic particles marked with antibodies that specifically recognize Ox-LDL through the binding pad position, and flow on the nitrocellulose membrane to the site marked with another antibody. Through the binding action of the antigen and antibody, a magnetic particle enrichment area is formed through specific recognition, and the oxidized low-density lipoprotein is quantitatively detected through magnetic signal collection and data processing.

7. A device for quantitatively detecting oxidized low-density lipoprotein based on machine magnetism, characterized in that: include: A processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; The memory is used to store one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the method according to any one of claims 1 to 5.

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