Image-based agglomerated particle detection and fitting formula acquisition method and device

Abstract

In the image-based agglomerated particle detection and fitting formula acquisition method and device, detection particles are added into a sample to form a detection sample, and the detection particles and a target object are combined and aggregated to form agglomerated particles; obtaining a microscopic image of the agglomerated microparticles or the detected microparticles; comprising the following steps: respectively selecting N groups of target objects with different concentrations P, respectively adding detection particles to prepare N groups of detection samples, and detecting the concentration T of the detection particles in the detection samples; n is greater than or equal to 3; the method comprises the following steps: respectively obtaining corresponding N groups of microscopic images, selecting a test image area Sn, carrying out image analysis on the microscopic images to obtain the concentration P and the total area of agglomerated microparticles or the number of non-agglomerated detection particles corresponding to the N groups of microscopic images, and calculating according to the concentration P and the total area ST of agglomerated microparticles or the number of non-agglomerated detection particles to obtain an empirical formula, the empirical formula is used for detecting and calculating the content of the target object.

Description

Technical Field

[0001] This application belongs to the technical field of in vitro diagnostic immunology, and particularly relates to an image-based agglomerated particle detection and a method and device for obtaining its fitting formula. 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 diagnoses. 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 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 patent with Application No. "CN201410197209.1" integrates immunomagnetic enrichment and visualization detection, with simple operation. However, this patent only reveals that "the degree of aggregation of aggregates is positively correlated with the concentration of biomacromolecules or microorganisms", and uses "determining the concentration of biomacromolecules or microorganisms in the sample to be detected according to the degree of aggregation of aggregates, specifically: obtaining a photo of the aggregates and determining the concentration of biomacromolecules or microorganisms 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 tests.

[0006] The Chinese patent application number is "CN202180041737.6" and the application name is "Aggregation-Induced Assay for Improving Sensitivity". In this application, a system, device, and method for rapidly and accurately measuring analyte particle-binding-induced reporter particle aggregation are provided. In the presence of analyte particles of interest, reporter particles form aggregates, and the average particle size increases as the analyte concentration increases. 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. A "calibration curve" is proposed, where the horizontal axis = SARS-CoV-2 antibody (mg / dL) and the vertical axis = aggregation size (pixels), establishing the relationship between the average aggregation area and the target analyte. After extensive experimental verification, when the content of the target substance fluctuates by more than 20%, the measurement deviation is huge, and it has no application value in medical measurement. Facts have proved that there is no direct correlation between the average aggregated particle size and the content of the target substance. It is affected by various factors such as the length of time for sample preparation, the magnitude of the vibration force, and the temperature during preparation. It is not scientific and has no application value to measure the content of the target substance using the average aggregated particle size.

[0007] With the development of image analysis technology, especially the development of AI technology, the recognition and classification of images are becoming more and more accurate. It has become possible to detect antigen-antibody or affinity reactions based on the image recognition technology of aggregated microparticles. To measure accurately, the accuracy of the fitting formula is the key.

[0008] Technical name:

[0009] 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 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 membrane surface of its B cells. An antibody can recognize a unique feature of a specific foreign substance, and this foreign target is called an antigen.

[0010] Affinity reaction: The normal function of the immune system is crucial because it enables the immune system to recognize and eliminate pathogens, foreign substances, and abnormal cells in the body. Affinity reactions are widely used in laboratory techniques and clinical diagnostics. For example, techniques such as ELISA (enzyme-linked immunosorbent assay), immunofluorescence, chemiluminescence, and immunohistochemistry utilize the affinity reaction between antibodies and antigens to detect specific molecules or cells.

[0011] Latex turbidimetry: It is a common experimental method used to detect the reaction between antibodies and antigens.

[0012] Chemiluminescence and magnetic bead technology: In immunoassay, specific antibodies can be immobilized on the surface of magnetic beads, and then the presence and content of the analyte can be detected through specific chemiluminescence reactions.

[0013] Avidin is a glycoprotein that can be extracted from egg white. It has a molecular weight of 60 kD and each molecule consists of 4 subunits, which can tightly bind to 4 biotin molecules. More commonly used is streptavidin extracted from Streptomyces.

[0014] Biotin, also known as vitamin H, has a molecular weight of 244.31 and is present in egg yolk. The chemically synthesized derivative, biotin-hydroxysuccinimide (BNHS), can form biotinylated products with various types of macromolecules and small molecules such as proteins, carbohydrates, and enzymes. The binding of avidin and biotin, although not an immune reaction, is highly specific and has a large affinity. Once they bind, they are extremely stable. Since one avidin molecule has 4 binding sites for biotin molecules, it can link more biotinylated molecules to form a complex similar to a lattice. Therefore, coupling avidin and biotin with ELISA can greatly improve the sensitivity of ELISA. Summary of the Invention

[0015] The detection of antigens and antibodies, or affinity reactions, based on the image recognition technology of aggregated particles uses the aggregation phenomenon of micron-sized detection particles to quantitatively measure the content of molecular-level target substances. It is like using balloons to detect the amount of oil stain in a room. It relies on the oil stain adsorbing to the balloons and then aggregating with each other. This is very difficult, and the accuracy of modeling is the key.

[0016] The applicant proposes that when fitting, a measurement space with the same volume as that during detection needs to be established, just like a standard measurement room.

[0017] An objective evaluation parameter is obtained by measuring the total area of aggregated particulate bodies or the number of unaggregated detection particles. If measuring the total number of balloons aggregated due to oil stain in a room or the individual unaggregated balloons, a fitting formula is established based on the objective and stable evaluation parameter.

[0018] A method for obtaining an empirical formula of aggregated particles. Detection particles are added to a sample to form a detection sample. The detection particles bind and aggregate with the target substance to form aggregated particle bodies. Microscopic images of the aggregated particle bodies or the detection particles are obtained. N groups of target substances with different concentrations P are respectively selected and detection particles are added to them to prepare N groups of detection samples. In the detection samples, the concentration T of the detection particles in the detection samples is measured; N is greater than or equal to 3. The corresponding N groups of microscopic images are respectively obtained. The test image area Sn is selected, and image analysis is performed on the microscopic images to obtain the concentration P corresponding to the N groups of microscopic images and the total area of the aggregated particle bodies or the number of unaggregated detection particles. An empirical formula is calculated based on the concentration P and the total area ST of the aggregated particle bodies or the number of unaggregated detection particles. The empirical formula is used for the detection and calculation of the content of the target substance.

[0019] It can be that the height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, and V = Sn×H; the target substance content M corresponding to the microscopic image is M = P×V. The empirical formula for the aggregated particles is M = A1×ST - B1, where A1 and B1 are empirical parameters of the empirical formula; A1 and B1 are obtained by fitting based on N groups of different concentrations P and the corresponding ST measured for N groups.

[0020] It can be a multi-segment empirical formula for the aggregated particles:

[0021] When ST corresponds to the interval M1 - M2: M = A1×ST - B1;

[0022] When ST corresponds to the interval M3 - M4: M = A2×ST - B2;

[0023] When ST corresponds to the interval M5 - M6: M = A3×ST - B3;

[0024] The intervals M1 - M2, M3 - M4, and M5 - M6 correspond to different target substance contents. For the target substances of different projects, the empirical formulas for the aggregated particles of their respective projects are carried out separately. For the target substance of a single project, it can be segmented and fitted in concentration intervals to make the fitting more accurate. The finer the concentration interval is divided, the more accurate the fitting is.

[0025] It can be that the height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, and V = Sn×H; the target substance content M corresponding to the microscopic image is M = P×V; the number of unaggregated detection particles is NUM_T2, and the total number of aggregated particle bodies NT = T×V - NUM_T2; the empirical formula for the aggregated particles is M = A2×NT - B2, where A2 and B2 are empirical parameters of the empirical formula, and M is the target substance content; A2 and B2 are obtained by fitting based on N groups of different concentrations P and the corresponding NT calculated and measured for N groups.

[0026] It may be that the empirical formula for agglomerated particles is M = A3 × Gn - B3, where A3 and B3 are empirical parameters of the empirical formula, M is the known content of the target substance, and Gn is the agglomeration rate = total number of agglomerated particles NT / (T × V); A3 and B3 are obtained by fitting NT calculated from N groups of different concentrations P and corresponding N groups of measurements.

[0027] An image-based detection method for agglomerated particles, comprising:

[0028] Step A: Detection particles are added to the sample to form a detection sample, and the detection particles bind and aggregate with the target substance to form agglomerated particles. The concentration T of the detection particles in the detection sample is equal to the set value.

[0029] Step B: Obtain a microscopic image of the agglomerated particles or the detection particles.

[0030] Step C: Select a test image area Sn, perform image analysis on the microscopic image. The total area ST of the agglomerated particles or the number of unagglomerated detection particles NUM_T1 within the area Sn is determined, and the content M of the target substance corresponding to the microscopic image is calculated according to the empirical formula for agglomerated particles.

[0031] It may be that the height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, and V = Sn × H; the content of the target substance per unit volume P = M / V. The empirical formula for agglomerated particles is M = A1 × ST - B1, where A1 and B1 are empirical parameters of the empirical formula, and M is the content of the target substance.

[0032] It may be that the height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, and V = Sn × H; the content of the target substance per unit volume P = M / V; the total number of detection particles NUM_T1 corresponding to the sample volume = V × T, the number of unagglomerated detection particles is NUM_T2, and the total number of agglomerated particles NT = total number of detection particles NUM_T1 - NUM_T2.

[0033] It may be that the empirical formula for agglomerated particles is M = A2 × NT - B2, where A2 and B2 are empirical parameters of the empirical formula, and M is the content of the target substance.

[0034] It may be that the empirical formula for agglomerated particles is M = A3 × Gn - B3, where A3 and B3 are empirical parameters of the empirical formula, M is the content of the target substance, Gn is the agglomeration rate, and the total number of agglomerated particles NT / the total number of detection particles NUM_T1 corresponding to the sample volume.

[0035] It may be that a microscopic image of the sample is taken under a microscope; the magnification of the microscope is the set value, and the sample area corresponding to the microscopic image is equal to the pixel area of the microscopic image × the magnification; the sample height corresponding to the microscopic image is H, and the sample volume corresponding to the image is V = pixel area of the microscopic image × magnification × H.

[0036] It can be that the target includes any one of proteins, carbohydrates or enzymes; the surface of the detection particle is labeled with avidin, and the binding and aggregation are affinity reactions.

[0037] It can be that the target includes avidin; the surface of the detection particle includes any one of proteins, carbohydrates or enzymes, and the binding and aggregation are affinity reactions.

[0038] It can 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.

[0039] It can be that the surface of the detection particle is labeled with a specific antibody, and the binding and aggregation are immune reactions. It can be that the agglomerated microsomes are the binding of the cell surface antigen in the sample to the antibody on the particle surface.

[0040] It can be that the agglomerated microsomes are the binding of the cell surface antibody in the sample to the antigen on the particle surface;

[0041] It can 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;

[0042] It can be that the surface of the detection particle is labeled with an antibody / antigen modified with a fluorescent group. When the detection particle is attracted and aggregated by the corresponding antigen / antibody, the fluorescent group emits fluorescence. The more the aggregation, the more the fluorescence.

[0043] It can be that the microscopic magnification of the microscopic image is a set value;

[0044] It can be that the detection particle is a polymer particle or a magnetic bead particle;

[0045] It can be that the diameter of the detection particle is greater than 0.1 micron;

[0046] It can be that the number of detection particles in the agglomerated microsomes is greater than a set value, and the set value 2 is less than 5.

[0047] It can be that the sample includes any one of body fluids or excreta;

[0048] It can be that the sample includes any one of serum, plasma, whole blood, saliva, body cavity effusion, tissue fluid;

[0049] It can be that the sample includes any one of urine, feces; the feces is diluted feces;

[0050] It can be that the sample is from humans, animals or plants;

[0051] It can be that the sample includes any one of the blood or serum samples of humans, cats or dogs;

[0052] It is possible that the target includes any one of immunoproteins, inflammatory factors, viruses, and pathogenic microorganisms in body fluids or excreta.

[0053] A detection device for all or part of the above method.

[0054] It is possible that the memory of the computing and processing device includes all or part of the data of all or part of the above method.

[0055] A data storage device that stores all or part of the program code for executing the above method.

[0056] It is possible that all or part of the data of the above method is stored.

[0057] The technical effects of the above technical solution include: in the fitting stage, the concentration of the same detection particles used in the measurement stage is used to obtain accurate measurement results.

[0058] The technical effects of the above technical solution include: during the fitting process, the volume of the sample is accurately measured to ensure that it is consistent with the volume detected during measurement, which can increase the accuracy of the fitting formula.

[0059] The technical effects of the above technical solution include: selecting the total number NT of aggregated microparticles can better reflect the overall trend of the binding of antibodies to detection particles. It can not only perform qualitative detection but also relatively accurate quantitative detection.

[0060] The technical effects of the above technical solution include: selecting Gn as the aggregation rate, the formula is simple, and both calculation and fitting are convenient.

[0061] The technical effects of the above technical solution include: based on AI to identify the characteristics of aggregated microparticles, such as the identification and counting of unaggregated detection particles, the detection microparticles that have aggregated can be measured, and accurate measurement values can be obtained.

[0062] The technical effects of the above technical solution include: using traditional image processing algorithms and combining black-and-white binary images can distinguish larger aggregated microparticles from individual detection microparticles, and can achieve good accuracy in qualitative detection.

[0063] The technical effects of the above technical solution include: the number of detection microparticles in the aggregated microparticles is greater than a set value, the set value 2 is less than 5, and the lower limit of the set number can reduce errors and improve measurement accuracy.

[0064] The technical effects of the above technical solution include: the detection microparticles can use the fluorescence mechanism to combine with images for component analysis, without the need for complex fluorescence metrology, only fluorescence imaging is required.

[0065] The technical effects of the above technical solution include: a variety of light sources, adapting to different detection requirements.

[0066] The technical effects of the above technical solution include: binarizing the image, obtaining the area of the aggregated particulate matter from the binarized image, facilitating efficient image processing, and saving the image computation amount.

[0067] The technical effects of the above technical solution include: calculating the content M of the target substance corresponding to the image according to the empirical formula of aggregated particulate matter, which is an efficient component quantitative analysis method, eliminating the complex optical hardware design of the quantitative analysis system based on the degree of optical absorption, and greatly reducing the detection cost of this type of component.

[0068] The technical effects of the above technical solution include: the volume of the sample corresponding to the captured microscopic image is V, and the volume of the sample corresponding to the microscopic image is V; the content of the target substance per unit volume = M / V; quantitative analysis of the sample is performed through the image area, and the method is simple and reliable.

[0069] The technical effects of the above technical solution include: the volume of the sample corresponding to the image is V = the pixel area of the microscopic image × magnification × H, calculating the corresponding sample volume according to the pixel area and magnification of the captured image, and the quantitative acquisition of the volume is simple and efficient.

[0070] The technical effects of the above technical solution include: the magnification of the microscope is a set value, with a fixed magnification, facilitating conversion.

[0071] The technical effects of the above technical solution include: the diameter of the detected particulate matter is greater than 0.1 micrometer, with a suitable size design, making the size of the aggregated particulate matter appropriate.

[0072] The technical effects of the above technical solution include: the empirical formula of aggregated particulate matter is simple and efficient, with high operation efficiency, and can quickly output the detection result.

[0073] The technical effects of the above technical solution include: the method for obtaining the empirical formula of aggregated particulate matter is reliable, and an empirical formula of aggregated particulate matter with very good accuracy can be obtained, improving the subsequent calculation efficiency. Description of the Drawings

[0074] Figure 1 It is a schematic diagram of the steps of a target substance detection method based on particulate aggregation;

[0075] Figure 2 It is a schematic diagram of the substance to be detected that is invisible in the distribution in the sample;

[0076] Figure 3 It is a schematic diagram of the structures of traditional monoclonal antibodies, heavy chain antibodies, and nanobodies;

[0077] Figure 4 It is a schematic diagram of antibodies or antigens coated on microparticles such as latex or magnetic beads;

[0078] Figure 5 It is a schematic diagram of the mutual adsorption and aggregation of coated microparticles;

[0079] Figure 6 It is a micrograph showing that the detection microparticles are relatively uniform without the target substance;

[0080] Figure 7 It is a micrograph showing that the detection microparticles are aggregated with the target substance;

[0081] Figure 8 It is the aggregated microparticles recognized by AI;

[0082] Figure 9 It is a schematic diagram of the detection area corresponding to one image when taking an array image;

[0083] Figure 10 It is a top view schematic diagram of a detection chip;

[0084] Figure 11 It is a cross-sectional schematic diagram of the detection cavity and the sample injection channel inside the detection chip;

[0085] Figure 12 It is a cross-sectional schematic diagram of the detection cavity, the sample injection channel, and the exhaust channel inside the detection chip;

[0086] Figure 13 It is a calculation method for detecting the content of the target substance based on the microparticle aggregation image;

[0087] Figure 14 It is a schematic diagram of obtaining the number or area or volume of aggregated microparticles through AI image recognition;

[0088] Figure 15 It is a schematic diagram of obtaining the number or area or volume of aggregated microparticles by binarizing the test image area;

[0089] Figure 16 It is a schematic diagram of obtaining an image by binarizing the test image area;

[0090] Figure 17 It is a schematic diagram of the fitting steps based on AI recognition of aggregated microparticles;

[0091] Figure 18 It is a schematic diagram of the fitting steps for recognizing aggregated microparticles based on image analysis;

[0092] Figure 19 It is a schematic diagram of the target substance detection process. Specific implementation manners

[0093] The following further elaborates on 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 principles 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 order relationship in time or space. The combinations of letters and numbers "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.

[0094] For example Figure 7 , a method for obtaining an empirical formula of agglomerated particles. Detection particles are added to a sample to form a detection sample. The detection particles bind and aggregate with the target substance to form an agglomerated particle body; a microscopic image of the agglomerated particle body or the detection particles is obtained; N groups of target substances with different concentrations P are respectively selected and detection particles are respectively added to prepare N groups of detection samples. In the detection samples, the concentration T of the detection particles in the detection samples; N is greater than or equal to 3; the corresponding N groups of microscopic images are respectively obtained. The test image area Sn is selected, and the microscopic images are subjected to image analysis to obtain the concentration P corresponding to the N groups of microscopic images and the total area of the agglomerated particle bodies or the number of unagglomerated detection particles. An empirical formula is calculated based on the concentration P and the total area ST of the agglomerated particle bodies or the number of unagglomerated detection particles. The empirical formula is used for the detection and calculation of the target substance content.

[0095] Generally, 3 points can fit a set of basic curves. If an accurate empirical formula is required, the value of N can be increased. To improve the fitting accuracy, formulas can be established separately for different concentration ranges for piecewise fitting.

[0096] For accurate measurement, in the fitting stage, the same concentration of the detection particles used in the measurement stage is required. If the concentration of the detection particles used in the measurement stage is different from the concentration of the detection particles in the fitting stage, accurate measurement results cannot be obtained.

[0097] It can be that the height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, V = Sn×H; the target substance content M corresponding to the microscopic image = P×V, and the empirical formula for the agglomerated particles is M = A1×ST - B1, where A1 and B1 are the empirical parameters of the empirical formula; A1 and B1 are obtained by fitting based on N groups of different concentrations P and the corresponding N groups of measured ST.

[0098] During the fitting process, if the volume of the sample is accurately measured and ensured to be consistent with the volume detected during measurement, the accuracy of the fitting formula can be increased. Although fitting the target substance content M based on the total area of the agglomerated particle bodies does not require strict consistency between the volume during fitting and the detected volume, it is necessary to ensure that the volume during fitting and the detected volume are roughly within a certain range.

[0099] It can be that the height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, and V = Sn×H; the content M of the target substance corresponding to the microscopic image is M = P×V; the number of unagglomerated detection particles is NUM_T2, and the total number NT of agglomerated particles is NT = T×V - NUM_T2; the empirical formula for agglomerated particles is M = A2×NT - B2, where A2 and B2 are empirical parameters of the empirical formula, and M is the content of the target substance; A2 and B2 are obtained by fitting NT calculated from N groups of different concentrations P and the corresponding N groups of measurements.

[0100] Agglomerated particles. There are various evaluation parameters, such as the maximum volume and the proportion of different volumes. However, the monomer volume size or the average volume size of agglomerated particles is greatly affected by the environment.

[0101] Selecting the total number NT of agglomerated particles can better reflect the overall trend of the binding of antibodies and detection particles. It can not only perform qualitative detection but also relatively accurate quantitative detection.

[0102] It can be that the empirical formula for agglomerated particles is M = A3×Gn - B3, where A3 and B3 are empirical parameters of the empirical formula, M is the known content of the target substance, and Gn is the agglomeration rate = the total number NT of agglomerated particles / (T×V); A3 and B3 are obtained by fitting NT calculated from N groups of different concentrations P and the corresponding N groups of measurements.

[0103] Selecting Gn as the agglomeration rate, the formula is simple, and both calculation and fitting are convenient. For the concentration of agglomerated particles, it needs to be accurately configured. When preparing detection particles, the particle size should be made as consistent as possible.

[0104] Figure 17 In it, the characteristics of agglomerated particles can be recognized based on AI, such as the recognition and counting of unagglomerated detection particles. With the development of AI technology, the recognition accuracy and counting accuracy have met the requirements of precise measurement.

[0105] Figure 18 In it, by using traditional image processing algorithms and combining black-and-white binary images, larger agglomerated particles and individual detection particles can be distinguished, and a very high accuracy can be achieved in qualitative detection.

[0106] Such as Figure 19 , an image-based method for detecting agglomerated particles, includes:

[0107] Step A: Detection particles are added to the sample to form a detection sample. The detection particles bind and aggregate with the target substance to form agglomerated particles. The concentration T of the detection particles in the detection sample is equal to the set value;

[0108] Step B: Obtain a microscopic image of the agglomerated particles or the detection particles;

[0109] Step C: Select the test image area Sn, perform image analysis on the microscopic image. The total area ST of the aggregated particulate matter or the number of unaggregated detected particles NUM_T1 within the area Sn is used to calculate the content M of the target object corresponding to the microscopic image according to the empirical formula for aggregated particles.

[0110] It can be that the height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, V = Sn×H; the content P of the target object per unit volume = M / V, and the empirical formula for aggregated particles is M = A1×ST - B1, where A1 and B1 are empirical parameters of the empirical formula, and M is the content of the target object.

[0111] In the quantitative test, based on obtaining the content M of the target object, the volume of the test sample is obtained, and the content of the target object per unit volume can be calculated.

[0112] It can be that the height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, V = Sn×H; the content P of the target object per unit volume = M / V; the total number NUM_T1 of detected particles corresponding to the sample volume = V×T, the number of unaggregated detected particles is NUM_T2, and the total number NT of aggregated particulate matter = the total number NUM_T1 of detected particles - NUM_T2; it can be that the empirical formula for aggregated particles is M = A2×NT - B2, where A2 and B2 are empirical parameters of the empirical formula, and M is the content of the target object.

[0113] It can be that the empirical formula for aggregated particles is M = A3×Gn - B3, where A3 and B3 are empirical parameters of the empirical formula, M is the content of the target object, Gn is the aggregation rate, and Gn = the total number NT of aggregated particulate matter / the total number NUM_T1 of detected particles corresponding to the sample volume.

[0114] It can be that a microscopic image of the sample is taken under a microscope; the magnification of the microscope is a set value, and the sample area corresponding to the microscopic image = the pixel area of the microscopic image × the magnification; the sample height corresponding to the microscopic image is H, and the sample volume corresponding to the image is V = the pixel area of the microscopic image × the magnification × H.

[0115] It can be that the target object includes any one of proteins, carbohydrates, or enzymes; the surface of the detected particles is labeled with avidin, and the binding and aggregation is an affinity reaction.

[0116] It can be that the target object includes avidin; the surface of the detected particles includes any one of proteins, carbohydrates, or enzymes, and the binding and aggregation is an affinity reaction.

[0117] It can be that the surface of the detected particles is labeled with a specific antigen or antibody, and the binding and aggregation is a specific antigen-antibody binding.

[0118] It may be to detect specific antibodies on the surface of the microparticles, and the binding and aggregation are immune reactions. It may be that the aggregated microsomes are due to the binding of cell surface antigens in the sample to the antibodies on the surface of the microparticles.

[0119] It may be that the aggregated microsomes are due to the binding of cell surface antibodies in the sample to the antigens on the surface of the microparticles;

[0120] It may be to detect antibodies / antigens with modified quenching groups on the surface markers of the microparticles. When the detected microparticles are attracted and aggregated by the corresponding antigens / antibodies, the fluorescent groups are quenched and no longer emit fluorescence. The more aggregated, the less fluorescence;

[0121] It may be to detect antibodies / antigens with modified fluorescent groups on the surface markers of the microparticles. When the detected microparticles are attracted and aggregated by the corresponding antigens / antibodies, the fluorescent groups are excited to emit fluorescence. The more aggregated, the more fluorescence.

[0122] It may be that the microscopic magnification of the microscopic image is a set value;

[0123] It may be that the detected microparticles are polymer microparticles or magnetic bead microparticles;

[0124] It may be that the diameter of the detected microparticles is greater than 0.1 micron;

[0125] It may be that the number of detected microparticles in the aggregated microsomes is greater than a set value, and the set value 2 is less than 5.

[0126] It may be that the sample includes any one of body fluids or excreta;

[0127] It may be that the sample includes any one of serum, plasma, whole blood, saliva, body cavity effusion, tissue fluid;

[0128] It may be that the sample includes any one of urine, feces; the feces is diluted feces;

[0129] It may be that the sample is from humans, animals or plants;

[0130] It may be that the sample includes any one of blood or serum samples of humans, cats or dogs;

[0131] It may be that the target includes any one of immune proteins, inflammatory factors, viruses, pathogenic microorganisms in body fluids or excreta.

[0132] A detection device for all or part of the above method.

[0133] It may be that the memory of the computing and processing device includes all or part of the data of all or part of the above method.

[0134] A data storage device that stores all or part of the program code for executing the above method.

[0135] It can be data storing all or part of the above method.

[0136] Such as Figure 1 , a detection method for detecting a target substance based on particle aggregation, including adding detection particles to a sample; the target substance in the sample binds to the detection particles to form a particle conjugate; the particle conjugates aggregate to form aggregated particulate bodies; taking an image of the sample under a microscope; analyzing the image to obtain the number, area or volume of the aggregated particulate bodies, and obtaining the number or content of the target substance according to the binding degree between the target substance and the detection particles.

[0137] 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.

[0138] 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.

[0139] Such as Figure 4 , coating antibodies or antigens 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.

[0140] Such as Figure 5 , the coated particles adsorb or aggregate with each other to form large aggregated particulate bodies. By observing the number, area or volume of the aggregated particulate bodies under a microscope, the number or content information of the target substance can be obtained.

[0141] Such as Figure 6 , for the actually captured 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-particle-size 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.

[0142] Such as Figure 7 , for the actually captured 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 form aggregated detection particles, and the aggregated detection particles are clearly distinguishable after aggregation.

[0143] Such as Figure 8, when detecting, add detection particles with a fixed concentration, identify the number of aggregated detection particles, and quantify the antigen concentration. By identifying and counting the aggregated particles, and based on the binding degree between the particles and the target substance, the quantity or content of the target substance can be calculated. The binding degree between the particles and the target substance is calculated according to the particle size, the amount of coated antibody, and the degree of aggregation.

[0144] The above detection method; the particles can be polymer particles or magnetic bead particles. The polymer particles can be polystyrene microspheres, i.e., latex particles. The polystyrene microspheres, i.e., latex particles, adsorb proteins. Through adsorption, the invisible particles can expand into visible particles. By detecting the number and size of the visible particles, the amount of protein can be detected.

[0145] Various modifications can be carried out on the surface of polystyrene, such as hydrophilic modification. The main surface modification groups of the polymer particles are functional groups such as polysaccharides, acrylamides, polyvinyl alcohols, and polyamines. After modification, the 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.

[0146] The above detection method; the detection particles adsorb or couple 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 particles in this application. The modified or coated particles can selectively aggregate when encountering the target to be detected.

[0147] The above detection method; 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.

[0148] The above detection method; according to the corresponding sample area and sample height of the captured image, obtain the sample volume, and calculate the unit volume content of the aggregated particulate matter based on the number of aggregated particulate matter and the sample volume.

[0149] 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 sample area and sample height corresponding to the area of the image, 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.

[0150] The above detection method; according to the binding degree between the target to be detected and the detection particles, and the unit volume content of the aggregated particulate matter, calculate the unit volume content of the target to be detected.

[0151] Such as Figure 9, the aggregated microsomes 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 then be converted into measurement indicators in the prior art, such as virus content, the content of specific proteins per unit volume, etc.

[0152] The above detection method; irradiate the aggregated microsomes with excitation light, and the aggregated microsomes are excited by the excitation light to emit fluorescence, and the number, area, volume, or fluorescence intensity of the aggregated microsomes emitting fluorescence is obtained.

[0153] Such 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 accommodate 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 are adsorbed or coupled with antibodies or antigens; the target analyte in the sample binds to the detection microparticles to form a microparticle conjugate; the microparticle conjugates aggregate to form aggregated microsomes; through the transparent windows, an image of the sample can be taken; the number, area, or volume of the aggregated microsomes is obtained.

[0154] Such as Figure 11 , a cross-sectional schematic diagram of the detection cavity and the sample injection channel of the detection chip, the detection cavity 1120 internally accommodates the sample, 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, and at the same time, due to the internal pressure of the liquid, air will automatically be discharged.

[0155] The upper and lower surfaces of the detection cavity are transparent windows, which can introduce light to obtain an image of the internal aggregated microsomes.

[0156] The above detection chip; the detection microparticles are polymer microparticles or magnetic bead microparticles; such 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.

[0157] The above detection chip; the polymer microparticles include polystyrene microspheres, i.e., latex microparticles.

[0158] According to the sample area corresponding to the captured image and the height of the detection cavity, the sample volume is obtained. The height H of the detection cavity is used to calculate the sample volume, and the sample volume = the sample area corresponding to the image × the height H of the detection cavity.

[0159] The above detection chip; according to the binding degree of the target analyte to the microparticles and the unit volume content of the aggregated microparticles, calculate the unit volume content of the target analyte. Calculate the unit volume content of the aggregated microparticles according to the number of aggregated microparticles and the sample volume. Obtain the number of aggregated microparticles from the image; the unit volume content of the aggregated microparticles = the number of aggregated microparticles ÷ the sample volume; the unit volume content of the target analyte = the unit volume content of the aggregated microparticles × the binding degree of the target analyte to the microparticles.

[0160] Such as Figure 9 , during the process of taking pictures, take multiple pictures to increase the sample volume. In the taken images, select all or part, such as Figure 9 , the area of the selected image corresponds to an area inside the chip that is 0.3 mm long, 0.2 mm wide, and 0.4 mm high, and the height is determined by the height of the internal cavity of the chip.

[0161] Furthermore, according to the binding degree of the target analyte to the microparticles and the unit volume content of the aggregated microparticles, calculate the unit volume content of the target analyte.

[0162] Within a certain concentration range, the binding degree of the target analyte to the microparticles is relatively fixed. With the unit volume content of the aggregated microparticles, multiplying by the binding degree gives the unit volume content of the target analyte. The binding degree of the microparticles is obtained through a calibration experiment.

[0163] The sample can be serum, and the target analyte can be various viruses, bacteria, antibodies caused by various pathogens, and the microparticles are coated with the corresponding antigens.

[0164] A reagent for antibody-antigen detection, including detection microparticles, where the detection microparticles adsorb or couple with antibodies or antigens; during the detection process, the reagent is mixed with the sample; the target analyte in the sample binds to the detection microparticles to form microparticle conjugates; the microparticle conjugates aggregate to form aggregated microparticles; obtain an image of the aggregated microparticles, and analyze the amount of aggregated microparticles through the image to obtain the amount of antibodies or antigens in the sample.

[0165] Add the detection microparticles to various detection liquids. The concentration of the detection microparticles can be pre-prepared so that the detection microparticles are dispersed in the detection liquid. During the test, add according to the volume ratio for convenient detection. There are detection microparticles in the reagent, which can serve as the target for focusing of the optical microscopy system to assist in focusing. The detection microparticles can be polymer microparticles or magnetic bead microparticles. The polymer microparticles can be polystyrene microspheres, i.e., latex microparticles. The diameter of the detection microparticles can be greater than 0.1 μm. The diameter of the detection microparticles can be 0.3 - 3 μm. If the diameter is too small, such as less than 0.1 μm, it cannot be seen under a conventional microscope with a magnification of 40 times. If it is greater than 3 μm, the antigen-antibody binding force cannot cause it to aggregate, and it is difficult to form aggregates and thus impossible to perform detection.

[0166] As Figure 13 , a calculation method for detecting the content of a target substance based on particulate aggregation images, includes selecting one or more microscopic test pictures as calculation selection pictures; selecting all or part of the calculation selection pictures as test image areas; obtaining the test sample volume according to the test image area and the sample height; obtaining the number, area, or volume of aggregated particulate bodies in the test image area; obtaining the number or content of the target substance according to the number, area, or volume information of the aggregated particulate bodies; and obtaining the content or number per unit volume of the target substance by dividing the number or content of the target substance by the test sample volume.

[0167] The method for calculating the sample volume can be to obtain the test image area by multiplying the pixel area of the image sensor by the number of pixel points in the test image area; obtain the test sample area by dividing the test image area by the microscope magnification; and obtain the test sample volume by multiplying the test sample area by the test sample height.

[0168] As Figure 14 , obtaining the number, area, or volume of aggregated particulate bodies in the test image area can be to obtain the number, area, or volume of aggregated particulate bodies through AI image recognition.

[0169] As Figure 15 , obtaining the number, area, or volume of aggregated particulate bodies in the test image area can be to binarize the test image area, calculate the area of the binarized image, and obtain the area of the large mass.

[0170] As Figure 16 , photos of the target substance at 3 concentrations were taken using the detection device of "Shenzhen Anlv Medical Technology Co., Ltd.". 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 high concentration of the target analyte. The pictures were binarized to obtain pictures A1, B1, and C1, and it can be clearly seen that the area of the aggregated particulate bodies changes with the content of the target analyte.

[0171] By using AI image recognition to obtain the number, area, or volume of non-aggregated particulate bodies in the test image area, the more the number, area, or volume of the recognized non-aggregated particulate bodies, the less the content of the target substance.

[0172] 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.

[0173] The target substance can also be a substance with cell structure, and the substance with cell structure includes any one of platelets, blood parasites, and red blood cells.

[0174] Although the present invention has been described and illustrated with reference to preferred embodiments and several alternatives, the invention is not limited to the specific description in this specification. Other additional alternatives or equivalent components may also be used to practice the present invention.

Claims

1. A method for obtaining an empirical formula of agglomerated particles. Detection particles are added to a sample to form a detection sample. The detection particles bind and aggregate with the target substance to form an agglomerated particle body; Obtain aggregated microsomes or microscopic images of the detected particles; it is characterized in that it includes: Select N groups of target substances with different concentrations P respectively, add the detected particles respectively to prepare N groups of detection samples, and in the detection samples, the concentration T of the detected 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, obtain the concentration P corresponding to the N groups of microscopic images and the total area of the aggregated microsomes or the number of non-aggregated detected particles, and calculate an empirical formula based on the concentration P and the total area ST of the aggregated microsomes or the number of non-aggregated detected particles, and the empirical formula is used for the detection calculation of the target substance content.

2. The method for obtaining the aggregated particle empirical formula according to claim 1, characterized in that The height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, and V = Sn×H; the target substance content M corresponding to the microscopic image = P×V, The aggregated particle empirical formula is M = A1×ST - B1, where A1 and B1 are empirical parameters of the empirical formula; A1 and B1 are obtained by fitting based on the N groups of different concentrations P and the corresponding N groups of measured ST.

3. The method for obtaining the aggregated particle empirical formula according to claim 1, characterized in that The height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, and V = Sn×H; the target substance content M corresponding to the microscopic image = P×V; the number of non-aggregated detected particles is NUM_T2, and the total number of aggregated microsomes NT = T×V - NUM_T2; including any one or more of the following features; Feature TE20: The aggregated particle empirical formula is M = A2×NT - B2, where A2 and B2 are empirical parameters of the empirical formula, and M is the target substance content; A2 and B2 are obtained by fitting based on the N groups of different concentrations P and the corresponding N groups of measured and calculated NT. Feature TE30: The aggregated particle empirical formula is M = A3×Gn - B3, where A3 and B3 are empirical parameters of the empirical formula, M is the known target substance content, and Gn is the aggregation rate = the total number of aggregated microsomes NT / (T×V); A3 and B3 are obtained by fitting based on the N groups of different concentrations P and the corresponding N groups of measured and calculated NT.

4. An image-based detection method for agglomerated particles, characterized in that, Including Step A: Add the detected particles to the sample to form a detection sample, the detected particles combine and aggregate with the target substance to form aggregated microsomes, the concentration T of the detected particles in the detection sample, and the concentration T is equal to the set value; Step B: Obtain the microscopic image of the aggregated microsomes or the detected particles; Step C: Select the test image area Sn, perform image analysis on the microscopic image, the total area ST of the aggregated microsomes or the number of non-aggregated detected particles NUM_T1 inside the area Sn, and calculate the target substance content M corresponding to the microscopic image according to the aggregated particle empirical formula.

5. The method for detecting agglomerated particles based on an image according to claim 4, wherein The height of the detection cavity is H; the sample volume corresponding to the microscopic image is V, and V = Sn×H; the target substance content per unit volume P = M / V, The aggregated particle empirical formula is M = A1×ST - B1, where A1 and B1 are empirical parameters of the empirical formula, and M is the target substance content.

6. The method for detecting agglomerated particles based on an image according to claim 4, wherein The height of the detection cavity is H; the corresponding sample volume of the microscopic image is V, and V = Sn×H; the content P of the target substance per unit volume = M / V; the total number NUM_T1 of detected particles corresponding to the sample volume = V×T, the number of undetected aggregated particles is NUM_T2, and the total number NT of aggregated particle bodies = the total number NUM_T1 of detected particles - NUM_T2; any one or more of the following features are included; Feature TE20: The empirical formula for the aggregated particles is M = A2×NT - B2, where A2 and B2 are empirical parameters of the empirical formula, and M is the content of the target substance; Feature TE30: The empirical formula for the aggregated particles is M = A3×Gn - B3, where A3 and B3 are empirical parameters of the empirical formula, M is the content of the target substance, Gn is the aggregation rate, and the total number NT of aggregated particle bodies / the total number NUM_T1 of detected particles corresponding to the sample volume.

7. The method for detecting aggregated particles based on an image according to claim 4, wherein A microscopic image of the sample is taken under a microscope; the magnification of the microscope is a set value, and the sample area corresponding to the microscopic image is equal to the pixel area of the microscopic image × the magnification; the height of the sample corresponding to the microscopic image is H, and the sample volume corresponding to the image is V = the pixel area of the microscopic image × the magnification × H.

8. The method according to any one of claims 1 to 7, characterized in that, Any one or more of the following features are included; Feature TA11: The target substance includes any one of proteins, sugars, or enzymes; the surface of the detected particles is labeled with avidin, and the binding and aggregation are avidin reactions; Feature TA12: The target substance includes avidin; the surface of the detected particles includes any one of proteins, sugars, or enzymes, and the binding and aggregation are avidin reactions; Feature TA21: The surface of the detected particles is labeled with a specific antigen or antibody, and the binding and aggregation are specific antigen-antibody binding; Feature TA22: The surface of the detected particles is labeled with a specific antibody, and the binding and aggregation are immune reactions; Feature TA51: The aggregated particle body is the binding of the cell surface antigen in the sample to the antibody on the surface of the particle; Feature TA52: The aggregated particle body is the binding of the cell surface antibody in the sample to the antigen on the surface of the particle; Feature TA31: The surface of the detected particles is labeled with an antibody / antigen modified with a quenching group. When the detected particles are attracted and aggregated by the corresponding antigen / antibody, the fluorescent group is quenched and no longer emits fluorescence. The more aggregated, the less fluorescence; Feature TA32: The surface of the detected particles is labeled with an antibody / antigen modified with a fluorescent group. When the detected particles are attracted and aggregated by the corresponding antigen / antibody, the fluorescent group emits fluorescence. The more aggregated, the more fluorescence.

9. The method according to any one of claims 1 to 7, characterized in that Any one or more of the following features are included; Feature TB10: The microscopic magnification of the microscopic image is a set value; Feature TB20: The detected particles are polymer particles or magnetic bead particles; Feature TB30: The diameter of the detected particles is greater than 0.1 micron; Feature TB40: The number of detected particles in the aggregated particle body 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 fluid, 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 a human, an animal, or a plant; Feature TC50: The sample includes any one of human, cat, or dog blood or serum samples; Feature TC60: The target includes any one of immunoproteins, inflammatory factors, viruses, and pathogenic microorganisms in body fluids or excreta.

10. A detection device, characterized in that, It includes any one of the following technical features: TJ1: Used to run all or part of the method described in any one of claims 1 to 7; TJ2: The memory of the computing and processing device includes all or part of the data of the method described in any one of claims 1 to 7.

11. A data storage device, characterized in that, It includes any one of the following technical features: TK1: Stores all or part of the program code for executing the method described in any one of claims 1 to 7; TK2: Stores all or part of the data of the method described in any one of claims 1 to 7.

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