Agglomerated microparticle detection and combination degree acquisition method and device

Abstract

The invention discloses a microaggregate detection and binding degree acquisition method and device. The method comprises the following steps: adding detection particles into a sample; a target substance in the sample is combined with the detection particles to form a particle combination body; aggregating the particle combination to form aggregated particles; shooting an image of the sample under a microscope; and analyzing the image to obtain the number or the total area of the agglomerated detection particles, and obtaining the number or the content of the target substance according to the combination degree of the target substance and the detection particles. Obtaining the combination degree = M / the number of agglomerated detection particles through an agglomerated particle detection combination degree obtaining method; and establishing a combination degree relation corresponding table corresponding to different agglomeration degrees.

Description

Technical Field

[0001] This application belongs to the technical field of immunological analysis based on microscopic magnified images, and particularly relates to a method and device for detecting agglomerated microparticles and obtaining the binding degree based on images. Background Art

[0002] In vitro diagnostic immunology is a method for diagnosing diseases by detecting specific immune markers in body fluids. These immune markers can be antibodies, antigens, immunoglobulins, etc. Detecting their presence or level changes can help doctors diagnose diseases, monitor treatment effects, or predict disease progression.

[0003] The affinity reaction is crucial for the normal function of the immune system because it enables the immune system to recognize and eliminate pathogens, foreign substances, and abnormal cells in the body. In addition, the affinity reaction is also widely used in laboratory techniques and clinical diagnostics. For example, techniques such as ELISA (enzyme-linked immunosorbent assay) and immunohistochemistry utilize the affinity reaction between antibodies and antigens to detect specific molecules or cells.

[0004] In the analytical techniques for quantitatively and qualitatively analyzing trace immune markers, there are multiple different technical paths. The mature techniques include enzyme-linked immunosorbent assay, radioimmunoassay, immunoturbidimetry, immunofluorescence analysis, and chemiluminescence immunoassay.

[0005] Chinese Patent Application No. "CN201410197209.1", "A Method for Detecting Biomacromolecules or Microorganisms", proposes "A method for detecting biomacromolecules or microorganisms. The method includes the following steps: contacting a monodisperse superparamagnetic nanobead coated with an antibody of a biomacromolecule or microorganism with a sample to be detected for an incubation reaction to generate a multi-particle superparamagnetic nanobead cluster; performing magnetic separation after the reaction to obtain an aggregate, and determining the concentration of the biomacromolecule or microorganism in the sample to be detected according to the aggregation degree of the aggregate." The patent with application number "CN201410197209.1" integrates immunomagnetic enrichment and visualization detection, has simple operation, requires a short time for the entire detection process, does not require special instrument equipment, and the method of the present invention has low requirements for the purity of antibodies, which can greatly reduce the detection cost. However, this patent only reveals that "the aggregation degree of the aggregate is positively correlated with the concentration of the biomacromolecule or microorganism", and uses "determining the concentration of the biomacromolecule or microorganism in the sample to be detected according to the aggregation degree of the aggregate, specifically: obtaining a photo of the aggregate and determining the concentration of the biomacromolecule or microorganism in the sample to be detected by quantifying the gray value of the photo", rather than accurately calculating the concentration. The method proposed in this patent application can at most give a qualitative conclusion and cannot give a quantitative measurement result, so it cannot be applied to medical detection.

[0006] Because, during the process of taking photos of the test samples, for the same type of sample, under different thickness conditions, the gray values of the taken photos are different. Therefore, the patent application No. "CN201410197209.1" cannot accurately measure the content of the target substance in the sample. This type of technology is not yet mature and cannot be practically applied in the industry. Especially in the medical application scenarios that require accurate measurement, it cannot meet the 5% measurement accuracy required for medical tests. When applied to human medical tests, it is even more impossible to achieve the 3% measurement accuracy.

[0007] The Chinese patent application No. "CN202180041737.6" has the application name "Aggregation-Inducing Assay for Improving Sensitivity". In this application, it is proposed that "a system, device, and method for rapidly and accurately measuring the aggregation of reporter particles induced by the binding of analyte particles are provided. In the presence of analyte particles of interest, the reporter particles form aggregates, and the average particle size of which increases with the increase in analyte concentration. Based on the analysis of the average particle size determined from the sample frames, the presence and / or concentration of the analyte can be determined", and a "calibration curve" is proposed. The "calibration curve" is "horizontal axis = SARS-CoV-2 antibody (mg / dL), vertical axis = aggregation size (pixels)", establishing the relationship between the total area of the detection microparticles where the average aggregation occurs and the target analyte. After being verified by a large number of experiments, when the change in the content of the target substance fluctuates by more than 20%, the measurement deviation of this method is huge and it has no application value for medical measurement. This patent application is essentially the same as the Chinese patent application No. "CN201410197209.1" with the application name "A Method for Detecting Biological Macromolecules or Microorganisms" and does not give how to accurately calculate the content of the target substance.

[0008] Facts have proved that there is no direct correlation between the average aggregation particle size and the content of the target substance. It is affected by various factors such as the length of time for preparing the test sample, the magnitude of the vibration force, and the temperature during preparation. It is not scientific and has no application value to use the average aggregation particle size to measure the content of the target substance.

[0009] Technical name:

[0010] Antibody: It refers to a protective protein produced by the body due to the stimulation of an antigen. It (immunoglobulin is not just an antibody) is a large Y-shaped protein secreted by plasma cells (effector B cells) and is used by the immune system to identify and neutralize foreign substances such as bacteria and viruses. It is only found in the body fluids such as the blood of vertebrates and on the cell membranes of their B cells. An antibody can recognize a unique feature of a specific foreign substance, and this foreign target is called an antigen.

[0011] In Vitro Diagnostic Immunology: It is a method for diagnosing diseases by detecting specific immune markers in body fluids.

[0012] Affinity reaction: It is crucial for the normal function of the immune system as it enables the immune system to recognize and eliminate pathogens, foreign substances, and abnormal cells in the body.

[0013] Latex turbidimetry: It is a common experimental method used to detect the reaction between antibodies and antigens.

[0014] Chemiluminescence and magnetic bead technology: In immunoassay, specific antibodies can be immobilized on the surface of magnetic beads, and then the presence of the analyte can be detected through a specific chemiluminescence reaction. Summary of the Invention

[0015] Through a large number of experiments, repeated inferences, and demonstrations, the applicant found that the concentration of the target analyte is directly related to the number of detection particles that agglomerate, and is also related to the binding degree of the detection particles, which needs to be obtained through experimental measurement.

[0016] A method for detecting agglomerated microparticles, including adding detection particles to a sample; the target substance in the sample binds to the detection particles to form particle conjugates; the particle conjugates aggregate to form agglomerated microparticles; taking an image of the sample under a microscope; analyzing the image to obtain the number of detection particles that agglomerate or the total area of the detection particles that agglomerate, and obtaining the quantity or content of the target substance based on the binding degree between the target substance and the detection particles.

[0017] It can be that the particles are polymer particles or magnetic bead particles; the detection particles adsorb or conjugate antibodies or antigens. It can be that the number of detection particles that agglomerate is calculated based on the total area of the detection particles that agglomerate.

[0018] It can be that by analyzing the image, the number of detection particles that do not agglomerate is obtained, and based on the concentration of the detection particles added to the sample and the volume of the detection sample, the total number of detection particles is obtained, and the number of detection particles that agglomerate is calculated as = total number of detection particles - number of detection particles that do not agglomerate.

[0019] It can be that the quantity or content of the target substance = number of detection particles that agglomerate × binding degree, and the binding degree is obtained through experimental measurement; binding degree = the number of antigens or antibodies that the microparticles bind on average.

[0020] It can be that the binding degree is related to the degree of agglomeration of the detection particles; degree of agglomeration = number of detection particles that agglomerate / total number of detection particles; the correlation curve between the binding degree and the degree of agglomeration is obtained through experimental measurement.

[0021] It may be that the sample includes body fluids or excreta; the body fluids include serum, plasma, whole blood, saliva, local body fluid accumulation, and the excreta include urine and feces; the feces are diluted feces.

[0022] It may be that the sample volume is obtained based on the total area of the detection particles that agglomerate corresponding to the sample and the sample height in the captured image, and the unit volume content of the agglomerated particulate matter is calculated based on the number of the detection particles that agglomerate and the sample volume.

[0023] It may be that the number of agglomerated particulate matter or the total area or volume of the detection particles that agglomerate in the test image area is obtained by AI image recognition.

[0024] It may be that the number of agglomerated particulate matter or the total area or volume of the detection particles that agglomerate in the test image area is obtained by binarizing the test image area and calculating the total area of the detection particles that agglomerate in the binarized image, so as to obtain the total area of the detection particles that agglomerate of the large substances.

[0025] It may be that the number of non-agglomerated particulate matter or the total area or volume of the detection particles that agglomerate in the test image area is obtained by AI image recognition, and the number of non-agglomerated particulate matter or the total area of the detection particles that agglomerate is recognized.

[0026] It may be that an image of the sample is taken under a microscope after the detection sample is prepared and left standing for a set time T. It may be that the set time T is greater than 10 seconds and less than 120 seconds.

[0027] It may be that the target substance is a substance with antigen activity, and the substance with antigen activity includes any one of protein, nucleic acid, and polysaccharide. It may be that the target includes any one of protein, carbohydrate, or enzyme; the surface of the detection particle is labeled with avidin, and the binding and aggregation are an affinity reaction. It may be that the target includes avidin; the surface of the detection particle includes any one of protein, carbohydrate, or enzyme, and the binding and aggregation are an affinity reaction.

[0028] It may be that the surface of the detection particle is labeled with a specific antigen or antibody, and the binding and aggregation are specific antigen-antibody binding. It may be that the surface of the detection particle is labeled with a specific antibody, and the binding and aggregation are an immune reaction. It may be that the agglomerated particulate matter is the binding of the cell surface antigen in the sample and the antibody on the surface of the particle. It may be that the agglomerated particulate matter is the binding of the cell surface antibody in the sample and the antigen on the surface of the particle.

[0029] It may be that the surface of the detection particle is labeled with an antibody / antigen modified with a quenching group. When the detection particle is attracted and aggregated by the corresponding antigen / antibody, the fluorescent group is quenched and no longer emits fluorescence. The more the aggregation, the less the fluorescence.

[0030] It can be to detect antibodies / antigens modified with fluorescent groups on the surface of microparticles. When the detection microparticles are attracted and aggregated by the corresponding antigens / antibodies, the fluorescent groups are excited to emit fluorescence. The more aggregated they are, the more fluorescence is emitted.

[0031] It can be that the detection microparticles are polymer microparticles or magnetic bead microparticles. It can be that the diameter of the detection microparticles is greater than 0.1 micrometer. It can be that the number of detection microparticles in the aggregated microparticle bodies is greater than a set value, and the set value is less than 5.

[0032] It can be that the sample includes any one of body fluids or excreta. It can be that the sample includes any one of serum, plasma, whole blood, saliva, body cavity effusion, and tissue fluid. It can be that the sample includes any one of urine and feces; the feces is diluted feces.

[0033] It can be that the sample comes from humans, animals, or plants. It can be that the sample includes any one of the blood or serum samples of humans, cats, or dogs. It can be that the target substances include any one of immunoproteins, inflammatory factors, viruses, and pathogenic microorganisms in body fluids or excreta.

[0034] A method for obtaining the binding degree of aggregated microparticle bodies. Select N groups of samples with different target substance concentrations P respectively. The samples are respectively added with detection microparticles to prepare N groups of detection samples. The height of the detection samples is H, and in the detection samples, the concentration of the detection microparticles in the detection samples is T; N is greater than or equal to 3; respectively obtain the corresponding N groups of microscopic images, select the test image area Sn, perform image analysis on the microscopic images, and obtain the concentration P corresponding to the N groups of microscopic images, the total area of the aggregated microparticle bodies, and the number of unaggregated detection microparticles; the volume V of the detection sample = the test image area Sn × H; the total content M of the target substance = P × V; the total number K of detection microparticles = T × V; the aggregation degree = the number of aggregated detection microparticles / the total number K of detection microparticles; the binding degree = M / the number of aggregated detection microparticles; establish a corresponding table of the binding degree relationship corresponding to different aggregation degrees.

[0035] It can be that the microparticles are polymer microparticles or magnetic bead microparticles; the detection microparticles adsorb or couple antibodies or antigens.

[0036] It can be that the number of aggregated detection microparticles is calculated based on the total area of the aggregated detection microparticles.

[0037] It can be that through image analysis, the number of unaggregated detection microparticles is obtained. According to the concentration of the detection microparticles added to the sample and the volume of the detection sample, the total number of detection microparticles is obtained, and the number of aggregated detection microparticles = the total number of detection microparticles - the number of unaggregated detection microparticles is calculated.

[0038] A detection device for detecting target substances based on microparticle aggregation images. The detection device runs part or all of the above methods.

[0039] The technical effects include: Tiny target objects that are invisible under ordinary microscopic magnification are aggregated through particle conjugates to form aggregated particulate bodies. The total area of the detected particles showing aggregation presented in the image by the aggregated particulate bodies is visible under ordinary microscopic magnification. Through such combination, the identification and measurement of smaller target substances can be achieved with the aid of an ordinary microscope. It expands the application depth of an ordinary microscope and enables the detection and analysis of tinier invisible substances with minimized hardware costs.

[0040] The technical effects include: Polymer particles or magnetic bead particles serve as the main body for identification, and their concentration and size are controllable and easily obtainable.

[0041] The technical effects of the above technical solution include that the detected particles adsorb or conjugate with antibodies or antigens, enabling the measurement of immune-related parameters based on the identification of microscopic magnified images.

[0042] The technical effects include: The sample has a wide adaptability range. As long as various body fluids and excretions can be made into suspensions and developed and photographed on the chip.

[0043] The technical effects include: The concentration of the detected particles is known. According to the binding degree between the target analyte and the detected particles and the unit volume content of the aggregated particulate bodies, the unit volume content of the target analyte is calculated, cleverly converting the measurement of tiny target objects into the binding degree, thus enabling measurement.

[0044] The technical effects include: The aggregated particulate bodies are excited by excitation light to emit fluorescence, also expanding the application depth of fluorescence microscopy and enabling the detection and analysis of tinier invisible substances with minimized hardware costs.

[0045] The technical effects include: The detection chip can be used to photograph the images of the aggregated particulate bodies. It is a chip dedicated to this scenario. The height H of the detection cavity in the detection chip is used to calculate the sample volume, thus enabling accurate measurement of volume-related parameters.

[0046] The technical effects include: The detected particles in the reagent adsorb or conjugate with antibodies or antigens to form particle conjugates, and the particle conjugates aggregate to form aggregated particulate bodies, increasing the granularity of the aggregated particulate bodies in the microscopic image and making it easier for identification and measurement calculation.

[0047] The technical effects include: The diameter of the detected particles is greater than 0.1 micrometer and is still visible under an ordinary microscope, with recognizability.

[0048] The technical effects include: The detected particles are non-transparent and can have corresponding features in the microscopic image for subsequent identification and measurement calculation.

[0049] The technical effects include: the detected particles are colored particles, and subsequent identification and measurement calculations can be carried out according to the color characteristics in the microscopic image.

[0050] The technical effects include: a calculation method for detecting the content of the target substance based on the particle aggregation image, which can obtain the content per unit volume or the number per unit volume of the target substance, and deepen the parameter calculation to a more microscopic level.

[0051] The technical effects include: the test sample volume is obtained by calculating the total area or the number of pixel points of the detected particles that agglomerate according to the pixels of the image sensor, making the volume measurement a digital process based on the image, which is very simple and avoids various conventional volume measurement devices and algorithms.

[0052] The technical effects include: there are various image processing methods for identifying and obtaining the number of agglomerated particulate bodies or the total area or volume of the detected particles that agglomerate, which are easy to obtain.

[0053] The technical effects include: the number of non-agglomerated particulate bodies or the total area or volume of the detected particles that agglomerate can also be used for the evaluation of the content of the target substance. Both the non-agglomerated and the agglomerated can be used for the evaluation of the content of the target substance and can be mutually complementary and verified.

[0054] The technical effects include: the target substance is not only a substance with antigen activity; it can also be a substance with a cell structure, and the applicable types of target substances are diverse. Description of the Drawings

[0055] Figure 1 It is a schematic diagram of the steps of a method for detecting agglomerated particulate bodies of a target substance based on particle aggregation;

[0056] Figure 2 It is a schematic diagram of the substance to be detected that is invisible in the sample;

[0057] Figure 3 It is a schematic diagram of the structures of traditional monoclonal antibodies, heavy chain antibodies, and nanobodies;

[0058] Figure 4 It is a schematic diagram of antibodies or antigens coated on particles such as latex or magnetic beads;

[0059] Figure 5 It is a schematic diagram of the coated particles adsorbing and aggregating with each other;

[0060] Figure 6 It is a microscopic magnified view in which the detected particles are relatively uniform without the target substance;

[0061] Figure 7 It is a microscopic magnified view in which the detected particles agglomerate with the target substance;

[0062] Figure 8are the agglomerated particles identified by AI;

[0063] Figure 9 is a schematic diagram of the detection area corresponding to one image in the captured array images;

[0064] Figure 10 is a top view schematic diagram of a detection chip;

[0065] Figure 11 is a cross-sectional schematic diagram of the detection cavity and the sample injection channel inside the detection chip;

[0066] Figure 12 is a cross-sectional schematic diagram of the detection cavity, the sample injection channel, and the exhaust channel inside the detection chip;

[0067] Figure 13 is a calculation method for detecting the content of the target substance based on the particle aggregation image;

[0068] Figure 14 is a schematic diagram for obtaining the number of agglomerated particles or the total area or volume of the detected particles that have agglomerated through AI image recognition;

[0069] Figure 15 is a schematic diagram for obtaining the number of agglomerated particles or the total area or volume of the detected particles that have agglomerated by binarizing the test image area;

[0070] Figure 16 is a schematic diagram for obtaining an image by binarizing the test image area;

[0071] Figure 17 is a correlation curve between the degree of binding and the degree of agglomeration. Detailed implementation manners

[0072] The following further details the content of this application in conjunction with each attached drawing. It should be noted that the following is an illustration of the preferred embodiments of the present invention and does not constitute any limitation to the present invention. The illustration of the preferred embodiments of the present invention is only for the illustration of the general principle of the present invention. The numbers such as "first", "second", "A", and "B" involved in the present invention are only for the convenience of illustration and do not represent the sequence relationship in time or space. The combinations of letters and numbers such as "TA", "TB", and "H" involved in the present invention are only for the convenience of illustration, and the specific meanings are determined by the specific words they represent.

[0073] Refer to, for example, Figure 13 , Figure 14 or Figure 15, Agglomerated microsome detection method, including adding detection microparticles to a sample; the target substance in the sample binds to the detection microparticles to form a microparticle conjugate; the microparticle conjugates aggregate to form agglomerated microsomes; taking an image of the sample under a microscope; analyzing the image to obtain the number of detection microparticles that have agglomerated or the total area of the detection microparticles that have agglomerated, and obtaining the quantity or content of the target substance according to the binding degree between the target substance and the detection microparticles.

[0074] It can be that the microparticles are polymer microparticles or magnetic bead microparticles; the detection microparticles adsorb or couple antibodies or antigens. It can be that the number of detection microparticles that have agglomerated is calculated based on the total area of the detection microparticles that have agglomerated.

[0075] It can be that by analyzing the image, the number of detection microparticles that have not agglomerated is obtained, and the total number of detection microparticles is obtained according to the concentration of the detection microparticles added to the sample and the volume of the detection sample, and the number of detection microparticles that have agglomerated is calculated = total number of detection microparticles - number of detection microparticles that have not agglomerated.

[0076] It can be that the quantity or content of the target substance = number of detection microparticles that have agglomerated × binding degree, and the binding degree is obtained through experimental measurement; the binding degree = the number of antigens or antibodies bound to the average microsome.

[0077] It can be that the binding degree is related to the agglomeration degree of the detection microparticles; the agglomeration degree = number of detection microparticles that have agglomerated / total number of detection microparticles; the correlation curve between the binding degree and the agglomeration degree is obtained through experimental measurement.

[0078] It can be that the sample includes body fluids or excreta; the body fluids include serum, plasma, whole blood, saliva, local body fluid effusions, and the excreta include urine, feces; the feces are diluted feces.

[0079] It can be that according to the total area of the detection microparticles that have agglomerated in the sample corresponding to the captured image and the height of the sample, the volume of the sample is obtained, and the unit volume content of the agglomerated microsomes is calculated according to the number of detection microparticles that have agglomerated and the volume of the sample.

[0080] It can be that obtaining the number of agglomerated microsomes or the total area or volume of the detection microparticles that have agglomerated in the test image area is to obtain the number of agglomerated microsomes or the total area or volume of the detection microparticles that have agglomerated through AI image recognition.

[0081] It can be that obtaining the number of agglomerated microsomes or the total area or volume of the detection microparticles that have agglomerated in the test image area is to binarize the test image area, calculate the total area of the detection microparticles that have agglomerated in the binarized image, and obtain the total area of the detection microparticles that have agglomerated for the large masses.

[0082] It can be the number of unagglomerated microsomes in the test image area detected by AI image recognition, or the total area or volume of the detected microsomes that have agglomerated, or the number of unagglomerated microsomes or the total area of the detected microsomes that have agglomerated identified.

[0083] It can be to take an image of the sample under a microscope, which is taken after the test sample is prepared and left standing for a set time T. It can be that the set time T is greater than 10 seconds and less than 120 seconds.

[0084] A method for obtaining the binding degree of agglomerated microsomes. Respectively select N groups of samples with different target substance concentrations P. The samples are respectively added with detected microsomes to prepare N groups of test samples. The height of the test samples is H, and in the test samples, the concentration of the detected microsomes in the test samples is T; N is greater than or equal to 3; respectively obtain the corresponding N groups of microscopic images, select the test image area Sn, perform image analysis on the microscopic images, and obtain the concentration P corresponding to the N groups of microscopic images and the total area of the agglomerated microsomes and the number of unagglomerated detected microsomes; the volume V of the test sample = the test image area Sn × H; the total content M of the target substance = P × V; the total number K of detected microsomes = T × V; the agglomeration degree = the number of detected microsomes that have agglomerated / the total number K of detected microsomes; the binding degree = M / the number of detected microsomes that have agglomerated; establish a corresponding table of the binding degree relationship corresponding to different agglomeration degrees.

[0085] Such as Figure 17 , the binding degree can be the number or mass of the target substance bound by a detected microsome, or it can also be the percentage at which the binding ability takes effect. Such as Figure 17 In, when the agglomeration degree is 0.4, that is, 40%, in the detected microsomes, 90% of the binding ability. If a detected microsome can bind 1000 antigen - antibodies, at this time, 900 antigen - antibodies are effectively bound.

[0086] When the agglomeration degree is 0.8, in the detected microsomes, the binding ability is about 50%. If a detected microsome can bind 1000 antigen - antibodies, at this time, 500 antigen - antibodies are effectively bound.

[0087] It can be that the microsomes are polymer microsomes or magnetic bead microsomes; the detected microsomes adsorb or couple antibodies or antigens. It can be that the number of detected microsomes that have agglomerated is calculated based on the total area of the detected microsomes that have agglomerated. It can be to perform image analysis to obtain the number of detected microsomes that have not agglomerated, obtain the total number of detected microsomes according to the concentration of the detected microsomes added to the sample and the volume of the test sample, and calculate the number of detected microsomes that have agglomerated = the total number of detected microsomes - the number of detected microsomes that have not agglomerated.

[0088] A detection device for detecting a target substance based on the image of particle aggregation, and the detection device runs part or all of the above - mentioned method.

[0089] Such asFigure 1 , a method for detecting aggregated microsomes, which is based on the detection of target substances by particle aggregation, including adding detection particles to a sample; the target substances in the sample bind to the detection particles to form particle conjugates; the particle conjugates aggregate to form aggregated microsomes; taking an image of the sample under a microscope; analyzing the image to obtain the number of detection particles that have aggregated, or the total area or volume of the detection particles that have aggregated, and obtaining the number or content of the target substances according to the binding degree between the target substances and the detection particles.

[0090] Such as Figure 2 , target substances in the sample, such as proteins, small molecules, amino acids, sugars, enzymes, etc., substances that cannot be observed or are difficult to observe under an optical microscope, cannot be directly observed and detected with an ordinary optical microscope.

[0091] Such as Figure 3 , in an antigen-antibody detection system, specific antibodies react with the substance to be detected, but the antibodies and antigens cannot be directly observed and detected with an ordinary optical microscope. Traditional monoclonal antibodies, heavy chain antibodies, and nanobodies cannot be directly observed under a traditional microscope.

[0092] Such as Figure 4 , antibodies or antigens are coated on particles such as latex or magnetic beads. The diameter of the particles is greater than 0.1 μm, and the particles can be directly observed and detected with an ordinary optical microscope.

[0093] Such as Figure 5 , the coated particles adsorb or aggregate with each other to form large aggregates of aggregated microsomes. By observing the number of detection particles that have aggregated, or the total area or volume of the detection particles that have aggregated under a microscope, the number or content information of the target substances can be obtained.

[0094] Such as Figure 6 , for the actually photographed detection particles, when there is no target substance in the sample, the detection particles are relatively evenly distributed, and only a very small number of particles aggregate into two small clusters. For large-sized detection particles (such as 1000 nm magnetic bead detection particles), when there is no target substance in the sample or the content is extremely low, the detection particles have good dispersibility.

[0095] Such as Figure 7 , for the actually photographed detection particles, when the sample contains the target substance, some particles aggregate into clusters. As the concentration of the corresponding target substance increases, the individually dispersed detection particles aggregate to produce aggregated detection particles, and the aggregated detection particles are clearly distinguishable after aggregation.

[0096] Such as Figure 8, when detecting, fixed-concentration detection particles are added, the number of aggregated detection particles is identified, and the antigen concentration is quantified. By identifying and counting the aggregated particles, the quantity or content of the target substance can be calculated based on the binding degree between the particles and the target substance. The binding degree between the particles and the target substance is calculated based on the particle size, the amount of coated antibody, and the degree of aggregation.

[0097] The above-mentioned method for detecting aggregated microparticles; the microparticles can be polymer microparticles or magnetic bead microparticles. The polymer microparticles can be polystyrene microspheres, i.e., latex microparticles. The polystyrene microspheres, i.e., latex microparticles, adsorb proteins. Through adsorption, the invisible microparticles can expand into visible microparticles. By detecting the number and size of the visible microparticles, the amount of protein can be detected.

[0098] Various modifications can be carried out on the surface of polystyrene. For example, hydrophilic modification. The main surface modification groups of the polymer microparticles include polysaccharides, acrylamide, polyvinyl alcohol, polyamines and other functional groups. After modification, polystyrene can selectively adsorb different target substances. Therefore, the present invention can not only be applied to the detection of antibodies and antigens, but also to the detection of various target substances or non-target substances.

[0099] The above-mentioned method for detecting aggregated microparticles; the detection particles adsorb or conjugate antibodies or antigens. The detection of antibodies and antigens is a very important detection item. The modification methods or coating methods of magnetic bead microspheres in various existing technologies can all be applied to the modification and coating of the microparticles in this application. The modified or coated microparticles can selectively aggregate when encountering the target substance to be detected.

[0100] The above-mentioned method for detecting aggregated microparticles; the sample includes body fluids or excreta; the body fluids include serum, plasma, whole blood, saliva, local body fluid effusions, and the excreta include urine, feces; the feces are diluted feces.

[0101] The above-mentioned method for detecting aggregated microparticles; according to the total area of the detection particles that have aggregated corresponding to the sample and the height of the sample in the captured image, the sample volume is obtained, and the unit volume content of the aggregated microparticles is calculated based on the number of detection particles that have aggregated and the sample volume.

[0102] Such as Figure 9 , during the microscopic imaging process, the field of view is relatively small, and the volume corresponding to each imaging is very small. By selecting the total area of the detection particles that have aggregated corresponding to the sample in the image and the height of the sample, the volume calculated for each imaging can be obtained. By increasing the number of captured images, a larger sample volume can be obtained, improving the accuracy of detection.

[0103] The above-mentioned method for detecting aggregated microparticles; based on the binding degree between the target analyte and the detection particles and the unit volume content of the aggregated microparticles, the unit volume content of the target analyte is calculated.

[0104] As Figure 9 , the aggregated microparticles in the sample volume can be identified, and the content or quantity per unit volume corresponding to the sample volume can be calculated. The test results can be converted into measurement indicators in the prior art, such as virus content, the content of specific proteins per unit volume, etc.

[0105] The above-mentioned method for detecting aggregated microparticles; irradiate the aggregated microparticles with excitation light, and the aggregated microparticles are excited by the excitation light to emit fluorescence, and the number of detected microparticles that agglomerate, the total area, volume or fluorescence intensity of the detected microparticles that agglomerate emitting fluorescence are obtained.

[0106] As Figure 10 , a detection chip for detecting antibodies and antigens, including a sample injection channel 1010, a detection cavity 1020, and a sample injection port 1011; the detection cavity is used to hold the sample; one end of the sample injection channel is communicated with the detection cavity; the other end of the sample injection channel is communicated with the sample injection port, and the sample injection port is communicated with the external atmosphere; the upper and lower parts of the detection cavity include transparent windows, and external illumination light can enter the detection cavity through the transparent windows; when placed horizontally, the sample injection port is higher than the top of the detection cavity; the sample includes detection microparticles, and the detection microparticles adsorb or conjugate antibodies or antigens; the target analyte in the sample binds to the detection microparticles to form microparticle conjugates; the microparticle conjugates aggregate to form aggregated microparticles; through the transparent windows, an image of the sample can be taken; the number of detected microparticles that agglomerate, the total area, volume of the detected microparticles that agglomerate are obtained.

[0107] As Figure 11 , a cross-sectional schematic diagram of the detection cavity and the sample injection channel of the detection chip. The sample is accommodated inside the detection cavity 1120. The sample injection channel 1110 is communicated with the sample injection port 1111. The sample injection port 1111 is higher than the upper surface 1121 of the detection cavity. The sample will not overflow from the sample injection port 1111. At the same time, due to the internal pressure of the liquid, air will automatically be discharged.

[0108] The upper and lower surfaces of the detection cavity are transparent windows, which can introduce light to obtain an image of the internal aggregated microparticles.

[0109] The above-mentioned detection chip; the detection microparticles are polymer microparticles or magnetic bead microparticles; as Figure 12 , it may further include an exhaust channel 1210 and an exhaust port 1211; one end of the exhaust channel is communicated with the detection cavity; the other end of the exhaust channel is communicated with the exhaust port, and the exhaust port is communicated with the external atmosphere; when placed horizontally, the exhaust port is higher than the top of the detection cavity.

[0110] The above-mentioned detection chip; the polymer microparticles include polystyrene microspheres, i.e., latex microparticles.

[0111] The sample volume is obtained based on the total area of the detected particles where agglomeration occurs in the sample corresponding to the captured image and the height of the detection cavity. The height H of the detection cavity is used to calculate the sample volume, and the sample volume = the total area of the detected particles where agglomeration occurs in the image corresponding to the sample × the height H of the detection cavity.

[0112] The above detection chip; the unit volume content of the target analyte is calculated based on the binding degree between the target analyte and the particles and the unit volume content of the agglomerated particulate matter. The unit volume content of the agglomerated particulate matter is calculated based on the number of detected particles where agglomeration occurs and the sample volume. The number of detected particles where agglomeration occurs is obtained from the image; the unit volume content of the agglomerated particulate matter = the number of detected particles where agglomeration occurs ÷ the sample volume; the unit volume content of the target analyte = the unit volume content of the agglomerated particulate matter × the binding degree between the target analyte and the particles.

[0113] Such as Figure 9 , during the process of taking pictures, multiple pictures are taken to improve the sample volume. Among the captured images, all or part are selected, such as Figure 9 , the total area of the detected particles where agglomeration occurs in the selected image corresponds to the total area of the detected particles where agglomeration occurs inside the chip, which is 0.3 mm long, 0.2 mm wide, and the height is 0.4 mm. The height is determined by the height of the internal cavity of the chip.

[0114] Furthermore, the unit volume content of the target analyte is calculated based on the binding degree between the target analyte and the particles and the unit volume content of the agglomerated particulate matter.

[0115] Within a certain concentration range, the binding degree between the target analyte and the particles is relatively fixed. With the unit volume content of the agglomerated particulate matter, multiplying by the binding degree gives the unit volume content of the target analyte. The binding degree of the particles is obtained through calibration experiments.

[0116] The sample can be serum, and the target analyte can be various viruses, bacteria, antibodies caused by various pathogens, and the particles are coated with the corresponding antigens.

[0117] A reagent for antibody-antigen detection, including detection particles, and the detection particles adsorb or conjugate antibodies or antigens; during the detection process, the reagent is mixed with the sample; the target analyte in the sample binds to the detection particles to form particulate conjugates; the particulate conjugates aggregate to form agglomerated particulate matter; an image of the agglomerated particulate matter is obtained, and the amount of antibodies or antigens in the sample is obtained by analyzing the amount of the agglomerated particulate matter through the image.

[0118] The detection particles are added to various detection liquids. The concentration of the detection particles can be pre-prepared so that the detection particles are dispersed in the detection liquid. During the test, they are added according to the volume ratio, which facilitates the detection process. There are detection particles in the reagent, which can serve as the focus target of the optical microscopy system to assist in focusing. The detection particles can be polymer particles or magnetic bead particles. The polymer particles can be polystyrene microspheres, i.e., latex particles. The diameter of the detection particles can be greater than 0.1 micrometer. The diameter of the detection particles can be 0.3 - 3 micrometers. If the diameter is too small, such as less than 0.1 micrometer, it cannot be seen under a conventional microscope with a magnification of 40 times. If it is greater than 3 micrometers, the antigen-antibody binding force cannot cause them to aggregate, and it is difficult to agglomerate, so detection cannot be carried out.

[0119] such as Figure 13 , a calculation method for detecting the content of the target substance based on the particle aggregation image, including selecting one or more microscopic test pictures as the calculation selection pictures; selecting all or part of them as the test image area in the calculation selection pictures; obtaining the test sample volume according to the test image area and the sample height; obtaining the number of aggregated particulate bodies or the total area or volume of the detection particles that have aggregated in the test image area; obtaining the number or content of the target substance according to the number of aggregated particulate bodies or the total area or volume information of the detection particles that have aggregated; dividing the number or content of the target substance by the test sample volume to obtain the content per unit volume or the number per unit volume of the target substance.

[0120] The method for calculating the sample volume can be to obtain the total area of the detection particles that have aggregated in the test image by multiplying the total area of the detection particles that have aggregated in the image sensor pixels by the number of pixel points in the test image area; obtaining the total area of the detection particles that have aggregated in the test sample by dividing the total area of the detection particles that have aggregated in the test image by the microscope magnification; multiplying the total area of the detection particles that have aggregated in the test sample by the height of the test sample to obtain the test sample volume.

[0121] such as Figure 14 , obtaining the number of aggregated particulate bodies or the total area or volume of the detection particles that have aggregated in the test image area can be to obtain the number of aggregated particulate bodies or the total area or volume of the detection particles that have aggregated through AI image recognition.

[0122] such as Figure 15 , obtaining the number of aggregated particulate bodies or the total area or volume of the detection particles that have aggregated in the test image area can be to binarize the test image area and calculate the total area of the detection particles that have aggregated in the binarized image to obtain the total area of the detection particles that have aggregated in the large-mass substances.

[0123] such as Figure 16, photos of the target substance at three concentrations taken with the detection device of "Shenzhen Anlv Medical Technology Co., Ltd." are shown. Picture A corresponds to a very low content of the target analyte in the sample, Picture B contains a small amount of the target analyte, and Picture C contains a relatively high concentration of the target analyte. The pictures are binarized to obtain pictures A1, B1, and C1. It can be clearly seen that the total area of the detection particles where agglomeration occurs for the agglomerated microparticles changes with the content of the target analyte.

[0124] By using AI image recognition to test the number of non-agglomerated microparticles or the total area or volume of the detection particles where agglomeration occurs in the image area, the more the number of non-agglomerated microparticles or the total area or volume of the detection particles where agglomeration occurs is recognized, the less the content of the target substance.

[0125] The target substance can be a substance with antigenic activity, and the substance with antigenic activity includes any one of protein, nucleic acid, and polysaccharide.

[0126] The target substance can also be a substance with a cell structure, and the substance with a cell structure includes any one of platelets, blood parasites, and red blood cells.

[0127] A computing and processing device, the memory of the computing and processing device includes any one of the above computing methods. A data storage device, the data storage includes any one of the above computing methods. A detection device, which performs target substance detection based on the particle aggregation image, and the detection device runs part or all of the above methods.

[0128] Although the present invention is described and illustrated according to preferred embodiments and several alternative schemes, the invention is not limited by the specific description in this specification. Other additional alternatives or equivalent components can also be used to practice the present invention.

Claims

1. A method for detecting aggregated microparticles, characterized in that, it includes adding detection microparticles to a sample; a target substance in the sample binds to the detection microparticles to form a microparticle conjugate; the microparticle conjugates aggregate to form aggregated microparticles; taking an image of the sample under a microscope; analyzing the image to obtain the number of detection microparticles that have aggregated or the total area of the detection microparticles that have aggregated, and obtaining the number or content of the target substance according to the binding degree between the target substance and the detection microparticles.

2. The method for detecting aggregated microparticles according to claim 1, characterized in that, the microparticles are polymer microparticles or magnetic bead microparticles; the detection microparticles adsorb or conjugate antibodies or antigens; it includes any one or more of the following technical features: Feature TA10: calculating the number of detection microparticles that have aggregated based on the total area of the detection microparticles that have aggregated; Feature TA10: analyzing the image to obtain the number of detection microparticles that have not aggregated, obtaining the total number of detection microparticles according to the concentration of the detection microparticles added to the sample and the volume of the test sample, and calculating the number of detection microparticles that have aggregated = total number of detection microparticles - number of detection microparticles that have not aggregated.

3. The method for detecting aggregated microparticles according to claim 2, characterized in that, the number or content of the target substance = the number of detection microparticles that have aggregated × the binding degree, and the binding degree is obtained by experimental measurement; the binding degree = the average number of antigens or antibodies bound by the microparticles.

4. The method for detecting aggregated microparticles according to claim 3, characterized in that, the binding degree is related to the aggregation degree of the detection microparticles; the aggregation degree = the number of detection microparticles that have aggregated / the total number of detection microparticles; the correlation curve between the binding degree and the aggregation degree is obtained by experimental measurement.

5. The method for detecting aggregated microparticles according to claim 1, characterized in that, the sample includes body fluids or excreta; body fluids include serum, plasma, whole blood, saliva, local body fluid effusions, and excreta include urine, feces; the feces are diluted feces.

6. The method for detecting aggregated microparticles according to claim 2, characterized in that, obtaining the sample volume according to the total area of the detection microparticles that have aggregated in the sample corresponding to the captured image and the sample height, and calculating the unit volume content of the aggregated microparticles according to the number of detection microparticles that have aggregated and the sample volume.

7. The method for detecting aggregated microparticles according to claim 1, characterized in that, it includes any one of the following technical features: TB10: obtaining the number of aggregated microparticles or the total area or volume of the detection microparticles that have aggregated in the test image area by AI image recognition; TB20: obtaining the number of aggregated microparticles or the total area or volume of the detection microparticles that have aggregated in the test image area by binarizing the test image area and calculating the total area of the detection microparticles that have aggregated in the binarized image to obtain the total area of the detection microparticles that have aggregated in the large-mass substances.

8. The method for detecting aggregated microparticles according to claim 1, characterized in that, The number of unagglomerated microsomes in the test image area or the total area or volume of the detected microsomes that have agglomerated is detected through AI image recognition, and the number of unagglomerated microsomes or the total area of the detected microsomes that have agglomerated is recognized.

9. The method for detecting agglomerated microsomes according to claim 1, characterized in that it includes any one of the following technical features: TF10: The image of the sample is taken under the microscope after the detection sample is prepared and left stationary for a set time T. TF20: The image of the sample is taken under the microscope after the detection sample is prepared and left stationary for a set time T; the set time T is greater than 10 seconds and less than 120 seconds.

10. The method for detecting agglomerated microparticles according to claim 1, wherein it includes any one of the following technical features: TC10: The target substance is a substance with antigenic activity, and the substance with antigenic activity includes any one of protein, nucleic acid, and polysaccharide; Feature TA11: The target includes any one of protein, carbohydrate, or enzyme; the surface of the detection microsome is labeled with avidin, and the binding and aggregation is an affinity reaction; Feature TA12: The target is avidin; the surface of the detection microsome includes any one of protein, carbohydrate, or enzyme, and the binding and aggregation is an affinity reaction; Feature TA21: The surface of the detection microsome is labeled with a specific antigen or antibody, and the binding and aggregation is a specific antigen-antibody binding; Feature TA22: The surface of the detection microsome is labeled with a specific antibody, and the binding and aggregation is an immune reaction; Feature TA51: The agglomerated microsome is the binding of the cell surface antigen in the sample to the antibody on the surface of the microsome; Feature TA52: The agglomerated microsome is the binding of the cell surface antibody in the sample to the antigen on the surface of the microsome; Feature TA31: The surface of the detection microsome is labeled with an antibody / antigen modified with a quenching group. When the detection microsome is attracted and aggregated by the corresponding antigen / antibody, the fluorescent group is quenched and no longer emits fluorescence. The more the aggregation, the less the fluorescence; Feature TA32: The surface of the detection microsome is labeled with an antibody / antigen modified with a fluorescent group. When the detection microsome is attracted and aggregated by the corresponding antigen / antibody, the fluorescent group is excited to emit fluorescence. The more the aggregation, the more the fluorescence; Feature TB10: The detection microsome is a polymer microsome or a magnetic bead microsome; Feature TB20: The diameter of the detection microsome is greater than 0.1 micrometer; Feature TB30: The number of detection microsomes in the agglomerated microsome is greater than a set value, and the set value is less than 5; Feature TC10: The sample includes any one of body fluids or excreta; Feature TC20: The sample includes any one of serum, plasma, whole blood, saliva, body cavity effusion, and tissue fluid; Feature TC30: The sample includes any one of urine and feces; the feces is diluted feces; Feature TC40: The sample is from humans, animals, or plants; Feature TC50: The sample includes any one of the blood or serum samples of humans, cats, or dogs; Feature TC60: The target includes any one of immune proteins, inflammatory factors, viruses, and pathogenic microorganisms in body fluids or excreta.

11. A method for obtaining the binding degree of agglomerated microsome detection, characterized in that Select N groups of samples with different target substance concentrations P respectively. Detection particles are added to the samples respectively to prepare N groups of detection samples. The height of the detection samples is H, and in the detection samples, the concentration T of the detection particles in the detection samples; N is greater than or equal to 3; Obtain the corresponding N groups of microscopic images respectively. Select the test image area Sn, perform image analysis on the microscopic images, and obtain the concentration P corresponding to the N groups of microscopic images, the total area of the aggregated particulate matter, and the number of non-aggregated detection particles; The volume V of the detection sample = the test image area Sn × H; The total content M of the target substance = P × V; The total number K of detection particles = T × V; Degree of aggregation = the number of detection particles that have aggregated / the total number K of detection particles; Degree of binding = M / the number of detection particles that have aggregated; Establish a correspondence table of the degree of binding corresponding to different degrees of aggregation.

12. The method for obtaining the degree of binding of aggregated particulate matter detection according to claim 11, wherein The particles are polymer particles or magnetic bead particles; the detection particles adsorb or couple antibodies or antigens; It includes any one or more of the following technical features: Feature TA10: Calculate the number of detection particles that have aggregated based on the total area of the detection particles that have aggregated; Feature TA10: Perform image analysis on the image to obtain the number of detection particles that have not aggregated. Based on the concentration of the detection particles added to the sample and the volume of the detection sample, obtain the total number of detection particles, and calculate the number of detection particles that have aggregated = the total number of detection particles - the number of detection particles that have not aggregated.

13. A detection device, characterized in that Based on the particulate aggregation image for target substance detection, the detection device runs part or all of the methods described in any one of claims 1 to 12.

Images