Blood analyzer and method for counting basophils

Abstract

The application relates to a blood analyzer and a counting method for detecting basophilic granulocytes. A blood sample to be detected is mixed with a hemolytic reagent and a fluorescent reagent to prepare a sample liquid to be detected; particles in the sample liquid to be detected are made to pass through a detection area one by one, and the particles passing through the detection area are irradiated with light to obtain forward scattering light signals and fluorescent signals of the particles; a first scatter plot is generated according to the forward scattering light signals and the fluorescent signals of the particles, and a basophilic granulocyte group area and a blood shadow particle area are identified from the first scatter plot; the particles falling into the basophilic granulocyte group area are counted to obtain a total number of particles in the basophilic granulocyte group area; the number of nucleated red blood cells in the basophilic granulocyte group area is obtained according to the blood shadow particle area; and a basophilic granulocyte counting result is obtained according to the total number of particles in the basophilic granulocyte group area and the number of nucleated red blood cells. The application can accurately identify interfering nucleated red blood cells from the basophilic granulocyte group.

Description

Technical Field

[0001] This invention relates to the field of in vitro diagnostics, and in particular to a blood analyzer and a method for counting basophils. Background Technology

[0002] A complete blood count (CBC) is a clinical diagnostic test that plays a vital role in the diagnosis and treatment of diseases. By detecting blood cells, it provides information on the number, morphology, and distribution of cells, enabling the assessment of a patient's health status, the degree of disease progression, and the effective identification of various blood disorders, thus allowing for the development of more accurate and effective treatment plans.

[0003] White blood cells are a type of cell in the blood. When pathogens invade the body, white blood cells can concentrate at the site of invasion and engulf the pathogens. If the number of white blood cells in the body exceeds the normal range, it may indicate inflammation. Normal mature white blood cells in the human body can be divided into five types: neutrophils, eosinophils, basophils, lymphocytes, and monocytes.

[0004] Basophils (BASO) originate from pluripotent hematopoietic stem cells in the bone marrow and differentiate and mature within the bone marrow before entering the bloodstream. An increase in basophil count is commonly seen in: allergic or inflammatory diseases, such as urticaria and ulcerative colitis; myeloproliferative disorders, such as primary myelofibrosis, chronic myeloid leukemia, and polycythemia vera; and basophilic leukemia. An increase in basophils is of significant clinical importance in indicating these diseases.

[0005] Currently, when using laser scattering combined with fluorescence staining to detect leukocytes, the identification of BASO particle clusters is easily affected by other types of particles, leading to inaccurate BASO counting. Summary of the Invention

[0006] Therefore, the objective of this invention is to provide a blood analyzer and counting method for detecting basophils, which can accurately identify interfering nucleated red blood cells from the basophil population, thereby obtaining accurate basophil count results.

[0007] To achieve the objectives of this invention, a first aspect of this invention provides a blood analyzer, comprising:

[0008] The sampling device has a sampling needle for drawing up the blood sample to be tested;

[0009] A sample preparation apparatus includes a reaction chamber and a reagent supply unit. The reaction chamber is used to receive a portion of the blood sample to be tested drawn by the sampling device. The reagent supply unit provides a hemolytic reagent and a fluorescent reagent to the reaction chamber, thereby mixing the portion of the blood sample to be tested with the hemolytic reagent and the fluorescent reagent in the reaction chamber to prepare a sample solution to be tested.

[0010] An optical detection device includes a light source, a flow chamber, a scattered light detector, and a fluorescence detector. The light source emits a light beam to illuminate the detection area of ​​the flow chamber. The flow chamber is connected to a reaction cell, allowing each particle in the sample solution to pass through the detection area of ​​the flow chamber one by one. The scattered light detector detects the scattered light signal generated by the particles passing through the detection area after being illuminated by light. The scattered light signal includes at least a forward scattered light signal. The fluorescence detector detects the fluorescence signal generated by the particles passing through the flow chamber after being illuminated by light.

[0011] A data processing device, communicatively connected to the optical detection device and configured to:

[0012] The forward scattered light signal and fluorescence signal of the particles in the sample liquid to be tested are obtained from the optical detection device.

[0013] A first scatter plot is generated based at least on the forward scattered light signal and fluorescence signal of the particles in the sample solution to be tested, and the basophil population region and the blood shadow particle region are identified from the first scatter plot.

[0014] The number of particles falling into the basophil population region is counted to obtain the total number of particles in the basophil population region.

[0015] The number of nucleated erythrocytes in the basophil population region is obtained based on the blood shadow particle region.

[0016] The basophil count of the blood sample to be tested is obtained based on the total number of particles in the basophilic granulocyte region and the number of nucleated red blood cells.

[0017] Therefore, in the first scatter plot generated by the forward-scattered light signal and the fluorescence signal, the number of nucleated red blood cells that may exist in the basophil population region can be estimated based on the blood shadow particle region, thereby obtaining accurate basophil count results.

[0018] In some embodiments, the data processing device can be configured to: identify neutrophil, eosinophil, monocyte, and lymphocyte populations in the blood sample based on the forward-scattered light signal and fluorescence signal of particles in the sample solution, and optionally count these cell populations. Here, the basophil population region is the area where the particle clusters are located below the lymphocyte population in a first scatter plot with the forward-scattered light signal as the abscissa and the fluorescence signal as the ordinate. This enables accurate basophil counts to be obtained in a single test (i.e., neutrophil, eosinophil, monocyte, and lymphocyte populations) in addition to the existing four-part differential white blood cell detection channel, without the need for additional detection channels for basophil detection. This allows for accurate five-part differential white blood cell detection with low cost and small blood volume in a single test (i.e., a single test of one blood sample).

[0019] In some embodiments, the data processing device may be further configured to: determine the number of nucleated red blood cells based on the fluorescence signal of shadow particles in the shadow particle region, for example, the number of nucleated red blood cells may be determined solely based on the fluorescence signal of shadow particles in the shadow particle region. This allows for the rapid and simple acquisition of the number of nucleated red blood cells that may be present in the basophilic population region.

[0020] In some embodiments, the data processing device may be further configured to: identify a first characteristic region representing nucleated red blood cells from the blood shadow particle region based on the fluorescence signal of the blood shadow particles in the blood shadow particle region and count the blood shadow particles in the first characteristic region to obtain a first characteristic particle count; and determine the number of nucleated red blood cells based on the first characteristic particle count.

[0021] In some embodiments, the data processing apparatus may be further configured to: determine the number of nucleated red blood cells based on the first characteristic particle count and a preset function, the preset function describing the relationship between the number of shadow particles in the first characteristic region and the number of nucleated red blood cells in the basophil population region. The preset function may, for example, be a monotonically increasing function, particularly a linearly increasing function.

[0022] In some embodiments, the first feature region is the region in the blood shadow particle region where the fluorescence signal of the blood shadow particles is greater than a preset threshold.

[0023] In some embodiments, the data processing device may be further configured to: determine the preset threshold based on the minimum or maximum boundary value of the fluorescence signal of the basophil population region; or identify the leukocyte population region based on the first scatter plot and determine the preset threshold based on the minimum or maximum boundary value of the fluorescence signal of the leukocyte population region.

[0024] In some alternative or additional embodiments, the data processing device may be further configured to: acquire the side-scattered light signal of particles in the sample solution to be tested from the optical detection device; and determine the number of nucleated red blood cells based on the side-scattered light signal and fluorescence signal of the blood shadow particles in the blood shadow particle region and the side-scattered light signal and fluorescence signal of the particles in the basophil population region.

[0025] In some embodiments, the data processing device may be further configured to: generate a second scatter plot based on the lateral scattered light signal and fluorescence signal of particles in the sample solution to be tested; identify the overlapping area of ​​the mapping area of ​​the basophilic granulocyte population region and the blood shadow particle region in the second scatter plot as a second feature region characterizing nucleated red blood cells; and determine the number of nucleated red blood cells based on the second feature region.

[0026] In some embodiments, the data processing device may be further configured to: count the particles in the second feature region to obtain a second feature particle count; and determine the number of nucleated red blood cells based on the second feature particle count, for example, directly determining the second feature particle count as the number of nucleated red blood cells.

[0027] Alternatively or additionally, the data processing device may be further configured to: calculate a first area of ​​the mapped region of the basophil population region in the second scatter plot and a second area of ​​the second feature region; and determine the number of nucleated red blood cells based on the first area and the second area.

[0028] In some embodiments, the data processing device may be further configured to: identify a first characteristic region representing nucleated erythrocytes from the shadow particle region based on the fluorescence signal of the shadow particles in the shadow particle region, and count the shadow particles in the first characteristic region to obtain a first characteristic particle count; and determine the number of nucleated erythrocytes based on the first characteristic particle count and the second characteristic region. Thus, combining the first and second characteristic regions allows for a more accurate estimation of the number of nucleated erythrocytes in the basophilic population region.

[0029] In some embodiments, the data processing apparatus may be further configured to: determine a first number of nucleated erythrocytes in the basophilic granulocyte region from the first number of characteristic particles; determine a second number of nucleated erythrocytes in the basophilic granulocyte region from the second characteristic region; and determine the number of nucleated erythrocytes based on the first number and the second number, for example, determining the maximum value of the first number and the second number as the number of nucleated erythrocytes.

[0030] In some embodiments, the data processing device may be further configured to: count the particles in the second feature region to obtain a second feature particle count, and determine the second quantity based on the second feature particle count; or calculate a first area of ​​the mapping region of the basophil population region in the second scatter plot and a second area of ​​the second feature region, and determine the second quantity based on the first area and the second area.

[0031] In some embodiments, the data processing device may be further configured to: identify and count all particles in the sample solution except for blood shadow particles based on the forward scattered light signal and fluorescence signal of the particles in the sample solution to obtain the total number of particles (or the total number of white blood cell particles) in the sample solution to be tested; calculate a first difference between the total number of particles in the basophilic granulocyte region and the number of nucleated red blood cells; calculate a second difference between the total number of particles in the sample solution to be tested and the number of nucleated red blood cells; and determine the ratio of the first difference to the second difference as the basophil count result.

[0032] A second aspect of the present invention provides a method for counting basophils, comprising:

[0033] The blood sample to be tested is mixed with hemolysis reagent and fluorescent reagent to prepare the test sample solution;

[0034] The particles in the sample solution to be tested are passed through the detection area one by one, and the particles passing through the detection area are irradiated with light to obtain the forward scattered light signal and fluorescence signal generated by the particles after being irradiated with light;

[0035] A first scatter plot is generated based on the forward scattered light signal and fluorescence signal of the particles in the sample solution to be tested, and the basophil population region and the blood shadow particle region are identified from the first scatter plot.

[0036] The number of particles falling into the basophilic granulocyte population region is counted to obtain the total number of particles in the basophilic granulocyte population region.

[0037] The number of nucleated erythrocytes in the basophilic granulocyte population region is obtained based on the blood shadow particle region;

[0038] The basophil count of the blood sample to be tested is obtained based on the total number of particles in the basophilic granulocyte region and the number of nucleated red blood cells.

[0039] In some embodiments, the counting method may further include: identifying neutrophil, eosinophil, monocyte, and lymphocyte populations in the blood sample based on the forward-scattered light and fluorescence signals of particles in the sample solution. This enables accurate five-part differential white blood cell counts with low cost and small blood volume in a single test.

[0040] In some embodiments, the number of nucleated red blood cells is determined based on the fluorescence signal of the blood shadow particles in the blood shadow particle region.

[0041] In some embodiments, based on the fluorescence signal of the blood shadow particles in the blood shadow particle region, a first characteristic region representing nucleated red blood cells is identified from the blood shadow particle region and the blood shadow particles in the first characteristic region are counted to obtain a first characteristic particle count; the number of nucleated red blood cells is determined based on the first characteristic particle count.

[0042] In some embodiments, the number of nucleated red blood cells is determined based on the first characteristic particle count and a preset function, wherein the preset function describes the relationship between the number of blood shadow particles in the first characteristic region and the number of nucleated red blood cells in the basophilic granulocyte population region.

[0043] In some embodiments, the counting method further includes acquiring the side-scattered light signal generated by each particle in the sample solution after being irradiated by light;

[0044] The number of nucleated red blood cells is determined based on the side-scattered light and fluorescence signals of the blood shadow particles in the blood shadow particle region and the side-scattered light and fluorescence signals of the particles in the basophil population region.

[0045] In some embodiments, a second scatter plot is generated based on the lateral scattered light signal and fluorescence signal of the sample solution to be tested; the overlapping area of ​​the mapping area of ​​the basophilic granulocyte population region and the blood shadow particle region in the second scatter plot is identified as a second characteristic region characterizing nucleated red blood cells; and the number of nucleated red blood cells is determined based on the second characteristic region.

[0046] In some embodiments, the number of particles in the second characteristic region is counted to obtain the number of second characteristic particles, and the number of nucleated red blood cells is determined based on the number of second characteristic particles; or

[0047] Calculate the first area of ​​the mapped region of the basophil population region in the second scatter plot and the second area of ​​the second feature region, and determine the number of nucleated red blood cells based on the first area and the second area.

[0048] In some embodiments, based on the fluorescence signal of the blood shadow particles in the blood shadow particle region, a first characteristic region representing nucleated red blood cells is identified from the blood shadow particle region and the blood shadow particles in the first characteristic region are counted to obtain the number of first characteristic particles.

[0049] The number of nucleated red blood cells is determined based on the number of the first characteristic particles and the second characteristic region.

[0050] In some embodiments, a first number of nucleated erythrocytes in the basophilic granulocyte population region is determined from the first number of characteristic particles;

[0051] The second number of nucleated erythrocytes in the basophilic granulocyte population region is determined from the second characteristic region;

[0052] The number of nucleated red blood cells is determined based on the first quantity and the second quantity.

[0053] In some embodiments, all particles in the sample solution to be tested, except for blood shadow particles, are identified and counted based on the forward scattered light signal and fluorescence signal of the particles in the sample solution to be tested, so as to obtain the total number of particles in the sample solution to be tested.

[0054] Calculate the first difference between the total number of particles in the basophilic granulocyte population region and the number of nucleated red blood cells;

[0055] Calculate the second difference between the total number of particles in the sample solution to be tested and the number of nucleated red blood cells;

[0056] The ratio of the first difference to the second difference is determined as the basophil count result.

[0057] The counting method according to the second aspect of the invention is particularly applicable to a blood analyzer according to the first aspect of the invention.

[0058] Other features and advantages of the counting method according to the second aspect of the invention can be found in the above description of the blood analyzer according to the first aspect of the invention.

[0059] A third aspect of the present invention also provides a method for obtaining a five-part differential count of white blood cells, the method comprising:

[0060] In a single test, a sample of the test liquid is obtained from a detection channel, which generates forward scattered light signal, side scattered light signal, and fluorescence signal as particles in the test sample pass through the optical detection area one by one. The test sample liquid is prepared by mixing the test blood sample with a hemolytic reagent and a fluorescent reagent.

[0061] A first scatter plot is generated based on the forward scattered light signal and fluorescence signal of the particles in the sample solution to be tested, and the basophil population region and the blood shadow particle region are identified from the first scatter plot.

[0062] The number of particles falling into the basophilic granulocyte population region is counted to obtain the total number of particles in the basophilic granulocyte population region.

[0063] The number of nucleated erythrocytes in the basophilic granulocyte population region is obtained based on the blood shadow particle region;

[0064] The basophil count of the sample solution to be tested is obtained based on the total number of particles in the basophilic granulocyte region and the number of nucleated red blood cells.

[0065] At least the neutrophil population, eosinophil population, monocyte population and lymphocyte population in the test sample solution are identified based on the side-scattered light signal and fluorescence signal of the particles in the test sample solution, and the total number of particles in each cell population is counted.

[0066] The white blood cell five-part differential method provided in the third aspect of the present invention can achieve accurate white blood cell five-part differential results with low cost and small blood volume through the same detection channel in the same detection channel in the same detection, without the need to add an additional independent detection channel for detecting basophils.

[0067] Other features and advantages of the method according to the third aspect of the invention can be found in the above description of the blood analyzer according to the first aspect of the invention and the counting method according to the second aspect of the invention. Attached Figure Description

[0068] The present invention will now be described more clearly with reference to the embodiments and accompanying drawings. Through the detailed description of the embodiments of the present invention, the above and other advantages will become apparent to those skilled in the art. The accompanying drawings are for illustrative purposes only and should not be considered as limiting the present invention. Throughout the drawings, the same or similar reference numerals denote the same parts. In the drawings:

[0069] Figure 1 This is a schematic appearance diagram of a blood analyzer according to some embodiments of the present invention;

[0070] Figure 2 This is a schematic block diagram of an optical detection apparatus according to some embodiments of the present invention;

[0071] Figure 3 This is a method in the existing technology for obtaining accurate five-part differential white blood cell count using a blood analyzer;

[0072] Figure 4 This is an alternative method for obtaining accurate five-part differential white blood cell counts using a blood analyzer, as is currently available in the technology.

[0073] Figure 5 This is a schematic flowchart of a method according to some embodiments of the present invention;

[0074] Figure 6A A two-dimensional scatter plot of forward-scattered light and fluorescence for a blood sample that does not contain nucleated red blood cells;

[0075] Figure 6B A two-dimensional scatter plot of forward-scattered light and fluorescence in a blood sample containing nucleated red blood cells;

[0076] Figure 7 This is a schematic flowchart of a method according to some embodiments of the present invention;

[0077] Figure 8 To obtain a two-dimensional scatter plot of lateral scattered light-fluorescence for four-part differential leukocyte analysis according to some embodiments of the present invention;

[0078] Figure 9A A two-dimensional scatter plot of forward-scattered light and fluorescence from another blood sample that does not contain nucleated red blood cells;

[0079] Figure 9B A two-dimensional scatter plot of forward-scattered light and fluorescence from another blood sample containing nucleated red blood cells;

[0080] Figure 10 This is a schematic flowchart of a method according to some embodiments of the present invention;

[0081] Figure 11 This is a schematic diagram showing the relationship between the number of first characteristic particles in the first characteristic region and the number of nucleated red blood cells in the BASO characteristic region according to some embodiments of the present invention.

[0082] Figure 12 This is a schematic diagram illustrating the determination of a preset threshold for identifying a first feature region according to some embodiments of the present invention;

[0083] Figure 13A A two-dimensional scatter plot of side-scattered light and fluorescence for a blood sample that does not contain nucleated red blood cells;

[0084] Figure 13B A two-dimensional scatter plot of lateral scattered light and fluorescence for a blood sample containing nucleated red blood cells;

[0085] Figure 14This is a schematic flowchart of a method according to some embodiments of the present invention;

[0086] Figure 15 A two-dimensional scatter plot of lateral scattered light and fluorescence for a blood sample containing nucleated red blood cells;

[0087] Figure 16 This is a schematic flowchart of a method according to some embodiments of the present invention;

[0088] Figure 17 A graph illustrating the correlation between the BASO% obtained from testing multiple blood samples using the method of this invention and the reference BASO% obtained from testing these blood samples using a dedicated detection channel. Detailed Implementation

[0089] The embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] It should be noted that the terms "first, second, and third" used in the embodiments of the present invention are merely to distinguish similar objects and do not represent a specific order of objects. It can be understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted.

[0091] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0092] To facilitate subsequent explanations, a brief explanation of some terms used below will be provided first:

[0093] 1) Scatter plot: A two-dimensional or three-dimensional graph generated by a blood analyzer, displaying two-dimensional or three-dimensional feature information of multiple particles. The X, Y, and Z axes of a scatter plot each represent a characteristic of each particle. For example, in a scatter plot, the X axis represents the intensity of forward scattered light, the Y axis represents the fluorescence intensity, and the Z axis represents the intensity of side scattered light.

[0094] 2) Cell clusters: Clusters of particles with the same characteristics distributed in a certain area of ​​a scatter plot, such as white blood cell clusters, as well as neutrophil clusters, lymphocyte clusters, monocyte clusters, eosinophil clusters, or basophil clusters within white blood cells.

[0095] 3) Blood shadow: Fragment particles obtained by dissolving red blood cells and platelets in blood with a hemolytic reagent.

[0096] The blood analyzer or blood cell analyzer used in this invention classifies and counts particles in blood or body fluid samples using flow cytometry, which combines laser scattering and fluorescence staining methods. The principle of the blood analyzer detecting blood samples can be illustrated as follows: First, a blood sample is drawn and treated with a hemolysin and a fluorescent dye. Red blood cells are destroyed and dissolved by the hemolysin, while white blood cells are not dissolved. However, the fluorescent dye can enter the nucleus of white blood cells with the help of the hemolysin and bind to nucleic acid substances in the nucleus. Next, each particle in the sample passes through a detection aperture irradiated by a laser beam. When the laser beam irradiates the particles, the characteristics of the particles themselves (such as volume, staining degree, size and content of cell contents, nucleus density, etc.) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to their characteristics. This scattered light is received by a signal detector, which can obtain relevant information about the particle structure and composition. Among them, forward scatter (FS) reflects the number and volume of particles, side scatter (SS) reflects the complexity of intracellular structures (such as intracellular particles or the cell nucleus), and fluorescence (FL) reflects the content of nucleic acid material in the cell. This optical information can be used to classify and count particles in a sample.

[0097] Figure 1 This is a schematic diagram of a blood analyzer used in some embodiments of the present invention. The blood analyzer 100 includes a sampling device 110, a sample preparation device 120, an optical detection device 130, and a data processing device 140. The blood analyzer 100 has a fluid system (not shown) for connecting the sampling device 110, the sample preparation device 120, and the optical detection device 130 to facilitate fluid transfer between these devices.

[0098] The sampling device 110 has a sampling needle for drawing up a blood sample to be tested. Additionally, the sampling device 110 may also include a drive mechanism for driving the sampling needle to quantitatively draw up the sample, such as a body fluid sample or a blood sample, through the tip of the sampling needle. The sampling device 110 can then transport the collected sample to a sample preparation device 120.

[0099] The sample preparation apparatus 120 has a reaction chamber and a reagent supply unit. The reaction chamber is used to receive a portion of the blood sample to be tested drawn by the sampling device, and the reagent supply unit is used to supply hemolytic reagent and fluorescent reagent to the reaction chamber, so that the portion of the blood sample to be tested is mixed with the hemolytic reagent and fluorescent reagent in the reaction chamber to prepare a sample solution to be tested.

[0100] The hemolytic agent can be any existing hemolytic reagent used for white blood cell classification in automated blood analyzers, such as any one or a combination of cationic surfactants, nonionic surfactants, anionic surfactants, and amphiphilic surfactants.

[0101] The optical detection device 130 includes a light source, a flow cell, a scattered light detector, and a fluorescence detector. The light source is used to emit a light beam to illuminate the detection area (optical detection area) of the flow cell. The flow cell is connected to the reaction cell and particles in the sample solution to be tested can pass through the flow cell one by one. The scattered light detector is used to detect the scattered light signal generated by the particles passing through the flow cell after being irradiated by light. The fluorescence detector is used to detect the fluorescence signal generated by the particles passing through the flow cell after being irradiated by light.

[0102] In some embodiments, the scattered light detector may include a forward-scattering light detector for detecting forward-scattered light or a side-scattering light detector for detecting side-scattered light. In other embodiments, the optical detection device 130 includes both a forward-scattering light detector and a side-scattering light detector.

[0103] In this document, a flow chamber refers to a chamber in which a focused liquid stream is used to detect light scattering and fluorescence signals. When a particle, such as a blood cell, passes through the detection aperture of the flow chamber, the particle scatters an incident light beam from a light source, directed through the aperture, in various directions. A light scattering signal is obtained by placing a photodetector at one or more different angles relative to the incident light beam and detecting the light scattered by the particle. Since different particles have different light scattering characteristics, the light scattering signal can be used to distinguish different groups of particles. Specifically, the light scattering signal detected near the incident light beam is generally referred to as a forward light scattering signal or a small-angle light scattering signal. In some embodiments, the forward light scattering signal can be detected from an angle of about 1° to about 10° relative to the incident light beam. In other embodiments, the forward light scattering signal can be detected from an angle of about 2° to about 6° relative to the incident light beam. The light scattering signal detected at about 90° relative to the incident light beam is generally referred to as a side light scattering signal. In some embodiments, the side light scattering signal can be detected from an angle of about 65° to about 115° relative to the incident light beam. Typically, the fluorescent signal emitted by blood cells stained with fluorescent dye is also detected in a direction at approximately 90° to the incident light beam.

[0104] Figure 2A specific example of an optical detection device 130 is shown. This optical detection device 130 has a light source 101, a beam shaping assembly 102, a flow chamber 103, and a forward scattering detector 104 arranged sequentially in a straight line. A dichroic mirror 106 is arranged at a 45° angle to the line on one side of the flow chamber 103. A portion of the side light emitted by particles in the flow chamber 103 passes through the dichroic mirror 106 and is captured by a fluorescence detector 105 arranged at a 45° angle behind the dichroic mirror 106; the other portion of the side light is reflected by the dichroic mirror 106 and captured by a side scattering detector 107 arranged at a 45° angle in front of the dichroic mirror 106.

[0105] The data processing device 140 is used to process and calculate data to obtain the required results. For example, it can generate two-dimensional or three-dimensional scatter plots based on various collected optical signals, and perform particle analysis on the scatter plots using a gating method. The data processing device 140 can also visualize intermediate or final calculation results and then display them through the display device 150. In this embodiment of the invention, the data processing device 140 is configured to implement the methods described in further detail below.

[0106] The data processing device 140 may include a processor, including but not limited to a central processing unit (CPU), a microcontroller unit (MCU), a field-programmable gate array (FPGA), a digital signal processor (DSP), and other devices used to interpret computer instructions and process data in computer software. For example, the processor is used to execute various computer applications in a computer-readable storage medium, thereby enabling the blood analyzer 100 to perform corresponding detection procedures and analyze the optical signals detected by the optical detection device 130 in real time.

[0107] In addition, the blood analyzer 100 also includes a first housing 160 and a second housing 170. The display device 150 may be, for example, a user interface. An optical detection device 130 and a data processing device 140 are disposed inside the second housing 170. A sample preparation device 120 is disposed, for example, inside the first housing 160, and the display device 150 is disposed, for example, on the outer surface of the first housing 160 and is used to display the blood analyzer's test results.

[0108] Currently, there are many methods for detecting white blood cell differential and counting, such as methods combining laser scattering and fluorescence staining or chemical staining, or methods combining laser scattering and impedance methods. To obtain the five-part differential white blood cell count (neutrophils, eosinophils, lymphocytes, monocytes, and basophils), it is usually necessary to perform the test in two separate detection channels. One channel obtains the four-part differential and count results (neutrophils Neu, eosinophils Eos, lymphocytes Lym, and monocytes Mon), while the other channel separately obtains the differential and count results for basophils Baso. For example, as... Figure 3 As shown, white blood cell differential is performed in a detection channel using laser scattering combined with fluorescence staining (e.g., the DIFF optical detection channel of the BC6800 blood analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd.), while BASO differential and white blood cell count are performed in another detection channel using laser scattering combined with fluorescence staining (e.g., the BASO optical detection channel of the BC6800 blood analyzer). In another example, as... Figure 4 As shown, white blood cell differential is achieved in a detection channel using laser scattering combined with fluorescence staining (e.g., the DIFF optical detection channel of the BC5390CRP blood analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd.), and Baso differential and white blood cell count are achieved in a detection channel using impedance method (e.g., the BASO impedance detection channel of the BC5390CRP blood analyzer).

[0109] This is because, in a four-part differential leukocyte assay using laser scattering combined with fluorescence staining, the identification of Baso clumps is easily affected by other particle types, leading to inaccurate Baso counts. Therefore, an additional impedance or optical detection channel is typically required to obtain accurate Baso counts. However, adding a separate detection channel increases reagent costs and blood usage, as additional reagents are needed to process additional blood samples to obtain a suitable sample solution for the added detection channel.

[0110] The inventors unexpectedly discovered that in a hemolysis detection channel, such as the aforementioned DIFF optical detection channel, when the blood sample to be tested contains nucleated red blood cells, blood shadow particle clusters and Baso particle clusters can be identified based on the forward scattered light signal and fluorescence signal obtained from the hemolysis detection channel. A specific region of the blood shadow particle cluster (hereinafter referred to as the characteristic region representing nucleated red blood cells) is related to the number of nucleated red blood cells that may be present in the Baso particle cluster. In other words, the number of nucleated red blood cells present in the Baso particle cluster can be estimated based on this specific region of the blood shadow particle cluster, thereby obtaining an accurate Baso count.

[0111] Based on this, embodiments of the present invention propose a method for detecting white blood cells. This method can obtain accurate basophil counts, and in particular, can obtain accurate five-part differential white blood cell results in a single detection channel. Here, the method proposed in these embodiments of the present invention is particularly based on the above-described combination... Figure 1 and Figure 2 The blood analyzer described is used for implementation.

[0112] like Figure 5 As shown in the figure, an embodiment of the present invention proposes a counting method 200 for counting basophils, which includes the following method steps.

[0113] Step S202: Mix the blood sample to be tested with hemolysis reagent and fluorescent reagent to prepare the sample solution to be tested.

[0114] For example, in step S202, a test tube containing the blood sample to be tested is first provided. The sampling device 110 draws the blood sample through a sampling needle and delivers it to the reaction chamber of the sample preparation device 120. The reagent supply unit of the sample preparation device 120 supplies hemolysis reagent and fluorescent reagent to the reaction chamber, so that the blood sample to be tested is mixed with the hemolysis reagent and fluorescent reagent in the reaction chamber of the sample preparation device 120 and incubated for a period of time to form the sample solution to be tested.

[0115] Step S204: Allow the particles in the sample solution to pass through the detection area one by one, and irradiate the particles passing through the detection area with light to obtain the forward scattered light signal and fluorescence signal generated by the particles after being irradiated with light.

[0116] In step S204, for example, the sample liquid to be tested is transported from the reaction cell to the flow chamber of the optical detection device 130 through the liquid circuit system, and each particle in the sample liquid to be tested passes through the detection hole of the flow chamber one by one. The scattering light detector and the fluorescence detector respectively detect the forward scattering light signal FS and the fluorescence signal FL generated by the particles passing through the flow chamber after being irradiated by light.

[0117] Step S206: Generate a first scatter plot based on the forward scattered light signal FS and fluorescence signal FL of the particles in the sample solution to be tested, and identify the basophil population region (hereinafter referred to as the Baso feature region) and the blood shadow particle region (hereinafter referred to as the Ghost region) from the first scatter plot.

[0118] In step S206, for example, using gating techniques well known to those skilled in the art, the Baso characteristic region and the Ghost region are divided in the first scatter plot. In the first scatter plot with the forward scattered light signal FS as the abscissa and the fluorescence signal FL as the ordinate, the region located below the lymphocyte population Lym is identified as the Baso characteristic region, or the characteristic region where the basophil population appears.

[0119] Step S208: Count the particles that fall into the basophil population region to obtain the total number of particles TargetNum in the basophil population region.

[0120] Step S210: Obtain the number of nucleated erythrocytes NrbcNum in the basophil population region based on the blood shadow particle region.

[0121] Here, interfering particles, such as nucleated red blood cells, may be present in the Baso characteristic region. For example, such as... Figure 6A As shown, when nucleated red blood cells are absent in the blood sample, the particles falling into the Baso characteristic region do not include nucleated red blood cells (NRBCs) and are almost entirely basophils (Baso). Although the distribution area of ​​the Baso particle swarm is relatively clear in the first scatter plot, other types of particle swarms, such as NRBCs, may also exist in the characteristic region where Baso appears below the lymphocyte swarm (Lym) and neutrophil swarm (Neu). For example, when nucleated red blood cells are present in the blood sample, such as... Figure 6B As shown, particles falling into the Baso characteristic region include basophils (Baso cells) and nucleated erythrocytes (NRBCs). This is compared with... Figure 6A and Figure 6B It can be concluded that the characteristic region below the lymphocyte population (Lym) in the first scatter plot is the distribution area of ​​the Baso particle swarm, but the NRBC particle swarm may also exist within this distribution area. Directly classifying the particle swarm in this distribution area as the Baso particle swarm may result in inaccurate Baso classification and counting results. Therefore, it is necessary to remove the interference of NRBC on Baso classification and counting.

[0122] In step S210, for example, at least one feature region representing nucleated red blood cells or related to the number of nucleated red blood cells is identified from the Ghost region, and the number of nucleated red blood cells that may exist in the Baso feature region is estimated based on the feature region in the Ghost region.

[0123] Here, steps S208 and S210 can be implemented simultaneously or sequentially, and the present invention does not specifically limit this.

[0124] Step S212: Obtain the basophil count result of the blood sample to be tested based on the total number of particles in the basophilic granulocyte region TargetNum and the number of nucleated red blood cells NrbcNum.

[0125] Therefore, the data processing device 140 is configured, for example, to acquire forward scattered light signals and fluorescence signals of particles in the sample liquid to be tested from the optical detection device and to perform steps S206 to S212.

[0126] In some embodiments, such as Figure 7 As shown, in step 204, the side-scattered light signal SS generated by each particle in the sample solution to be tested after being irradiated by light is also acquired. Further, the method 200 also includes step S214: identifying the neutrophil population (Neu), eosinophil population (Eos), monocyte population (Mon), and lymphocyte population (Lym) in the blood sample to be tested based at least on the side-scattered light signal and fluorescence signal of the particles in the sample solution to be tested, and optionally counting these cell populations to obtain a four-part differential white blood cell count.

[0127] like Figure 8 As shown, for example, a second scatter plot is generated based on the side-scattered light signal SS and fluorescence signal FL of the particles in the sample solution to be tested, and gating technology is used to identify the neutrophil population (Neu), eosinophil population (Eos), monocyte population (Mon), and lymphocyte population (Lym) in the second scatter plot. Optionally, immature granulocytes (IMG) in the blood sample to be tested can also be identified based on the second scatter plot.

[0128] A particular advantage here is that more accurate four-part differential diagnosis of white blood cells can be obtained based on the forward scattered light signal, side scattered light signal, and fluorescence signal of the particles in the sample solution to be tested.

[0129] exist Figure 7In the illustrated embodiment, method 200 further includes step S216: obtaining a five-part differential white blood cell count based on the basophil count obtained in step S212 and the four-part differential white blood cell count obtained in step S214. This enables accurate five-part differential white blood cell counts to be obtained in a single test within the same detection channel with low cost and minimal blood volume.

[0130] Therefore, the aforementioned data processing device 140 is further configured, for example, to acquire the lateral scattered light signal of the particles in the sample liquid to be tested from the optical detection device and to implement steps S214 and S216.

[0131] In some embodiments, the inventors, through observation of the detection results of a large number of blood samples, discovered a certain relationship between the particle swarm pattern in the Ghost region along the FL direction and the number of NRBC particles in the Baso characteristic region below the Lym lymphocyte swarm. As shown in Figure 9, Figure 9A The FS-FL scatter plot is shown for blood samples that do not contain NRBCs. Figure 9B The FS-FL scatter plot of a blood sample containing NRBCs is shown. By comparing the detection results of a large number of these two blood samples, it can be concluded that the larger the blood shadow particle swarm is displayed in the FL direction, the more NRBC particles are located below the Lym.

[0132] Therefore, in step S210, the number of nucleated red blood cells NrbcNum in the Baso feature region can be determined or estimated based on, for example, only based on the fluorescence signal of the blood shadow particles in the Ghost region, so as to quickly and easily estimate the number of nucleated red blood cells that may be present in the Baso feature region.

[0133] In a specific example, such as Figure 10 As shown, step S210 may include:

[0134] Sub-step S210a: Based on the fluorescence signal of the blood shadow particles in the blood shadow particle region, identify the first characteristic region F1 representing nucleated red blood cells from the blood shadow particle region and count the blood shadow particles in the first characteristic region F1 to obtain the number of first characteristic particles GhostNum_1.

[0135] Sub-step S210b: Determine the number of nucleated red blood cells NrbcNum based on the first characteristic particle number GhostNum_1.

[0136] Therefore, the aforementioned data processing device 140 is further configured to implement sub-steps S210a and S210b.

[0137] Here, the blood shadow particles in the first feature region F1 are associated with nucleated red blood cells appearing in the Baso feature region. For example... Figure 9A As shown, if the first feature region F1 cannot be identified from the Ghost region, it indicates that nucleated red blood cells are essentially absent in the Baso feature region, i.e., NrbcNum equals zero. However, if the first feature region F1 can be identified from the Ghost region, as shown... Figure 9B As shown, this indicates the presence of nucleated red blood cells in the Baso feature region and that NrbcNum can be estimated based on the number of first feature particles, GhostNum_1.

[0138] It has been verified through the testing results of a large number of blood samples that there is a certain correlation between the number of first characteristic particles, GhostNum_1, and the number of NRBC particles, NrbcNum, i.e., NrbcNum = f(GhostNum_1). Therefore, in some embodiments, in sub-step S210b, the number of nucleated red blood cells, NrbcNum, is determined based on the first characteristic particle number, GhostNum_1, and a preset function f. The preset function f describes the relationship between the number of blood shadow particles in the first characteristic region F1 and the number of nucleated red blood cells in the Baso characteristic region.

[0139] The preset function can be, for example, a monotonically increasing function, especially a linearly increasing function. Of course, in other embodiments, the preset function can also be a non-linearly increasing function.

[0140] In some embodiments, the first feature region F1 is the region in the blood shadow particle region where the fluorescence signal of the blood shadow particles is greater than a preset threshold. That is, as... Figure 9B As shown, the first feature region F1 is a large fluorescent region in the Ghost region, and the blood shadow particles in this large fluorescent region have a large fluorescent signal.

[0141] In some embodiments, such as Figure 11 As shown, the results of testing a large number of blood samples have verified that there is a linear relationship between the number of blood shadow particles GhostNum_1 in the large fluorescent region and the number of NRBC particles NrbcNum below the lymphocyte population Lym.

[0142] In some embodiments, such as Figure 12As shown, the preset threshold T can be determined based on the minimum boundary value FL_MIN or the maximum boundary value FL_MAX of the fluorescence signal in the basophil population region. For example, T = FL_MIN / FL_MAX ± c, where c is a preset constant value. Alternatively, the leukocyte population region (i.e., the region containing all leukocytes) can be identified based on a first scatter plot, and the preset threshold can be determined based on the minimum or maximum boundary value of the fluorescence signal in the leukocyte population region. This allows for the adaptive determination of corresponding preset thresholds for different blood samples; that is, the preset threshold varies with different blood samples to accurately identify the first feature region.

[0143] Of course, in other embodiments, the preset threshold can also be determined based on experience or experimental data, for example, by setting a fixed preset threshold.

[0144] Alternatively, the number of nucleated erythrocytes can be estimated based on the fluorescence signal of the shadow particle region. In some embodiments, step S204 further acquires the side-scattered light signal generated by each particle in the sample solution after being irradiated with light. Accordingly, in step S210, the number of nucleated erythrocytes (NrbcNum) is determined based on the side-scattered light signal and fluorescence signal of the shadow particles in the shadow particle region and the side-scattered light signal and fluorescence signal of the particles in the basophil population region.

[0145] In a specific example, by observing the test results of a large number of blood samples, it was found that for blood samples that do not contain nucleated red blood cells, such as... Figure 13A As shown, the mapping regions M1 and M2 of the Baso feature region and Ghost region identified by the first scatter plot in the scatter plots of the side-scattered light signal SS and the fluorescence signal FL basically do not overlap. However, for blood samples containing nucleated red blood cells, such as... Figure 13B As shown, the Baso feature region identified by the first scatter plot has a large overlap with the Ghost region in the mapping regions M1 and M2 of the scatter plots of the side-scattered light signal SS and the fluorescence signal FL.

[0146] Therefore, as Figure 14 As shown, step S210 may include:

[0147] Sub-step S210c: Generate a second scatter plot based on the lateral scattered light signal and fluorescence signal of the sample liquid to be tested;

[0148] Sub-step S210d: Identify the overlapping region of the mapping regions M1 and M2 of the basophilic granulocyte population region and the blood shadow particle region in the second scatter plot as the second feature region F2 characterizing nucleated erythrocytes;

[0149] Sub-step S210e: Determine the number of nucleated red blood cells NrbcNum based on the second feature region F2.

[0150] Therefore, the aforementioned data processing device 140 is further configured to implement sub-steps S210c, S210d, and S210e.

[0151] In some embodiments, the number of nucleated red blood cells (NrbcNum) in the Baso feature region can be estimated by calculating the degree of overlap between the Baso feature region and the Ghost region in the second scatter plot.

[0152] In a specific example of calculating the degree of overlap, the number of nucleated red blood cells, NrbcNum, can be estimated based on the number of particles falling into the overlapping region. That is, in sub-step S210e, the number of particles in the second feature region F2 is counted to obtain the second feature particle number GhostNum_2, and the number of nucleated red blood cells, NrbcNum, is determined based on the second feature particle number GhostNum_2. For example, the second feature particle number GhostNum_2 can be directly determined as the number of nucleated red blood cells, NrbcNum, i.e., NrbcNum = GhostNum_2.

[0153] In alternative or additional examples for calculating the degree of overlap, the number of nucleated red blood cells (NrbcNum) can be estimated based on the area of ​​the overlapping region. For example... Figure 15 As shown, in sub-step S210e, the first area S1 of the mapping region M1 and the second area S2 of the second feature region F2 in the second scatter plot of the Baso feature region are calculated, and the number of nucleated red blood cells NrbcNum is determined based on the first area S1 and the second area S2. For example, NrbcNum = (S2 / S1) * TargetNum.

[0154] In some embodiments, the number of nucleated red blood cells, NrbcNum, can be estimated more accurately by combining the first feature region F1 and the second feature region F2. For example... Figure 16 As shown, step 210 includes:

[0155] Sub-step S210a: Based on the fluorescence signal of the blood shadow particles in the blood shadow particle region, identify the first characteristic region F1 representing nucleated red blood cells from the blood shadow particle region and count the blood shadow particles in the first characteristic region F1 to obtain the number of first characteristic particles GhostNum_1.

[0156] Sub-step S210c: Generate a second scatter plot based on the lateral scattered light signal and fluorescence signal of the sample liquid to be tested;

[0157] Sub-step S210d: Identify the overlapping region of the mapping regions M1 and M2 of the basophilic granulocyte population region and the blood shadow particle region in the second scatter plot as the second feature region F2 characterizing nucleated erythrocytes;

[0158] Sub-step S210e: Determine the number of nucleated red blood cells NrbcNum based on the first characteristic particle number GhostNum_1 and the second characteristic region F2.

[0159] In some embodiments, in sub-step S210e, a first number NrbcNum_1 of nucleated erythrocytes in the basophilic granulocyte region is determined from the first characteristic particle number GhostNum_1, a second number NrbcNum_2 of nucleated erythrocytes in the basophilic granulocyte region is determined from the second characteristic region F2, and the number NrbcNum of nucleated erythrocytes is determined based on the first number NrbcNum_1 and the second number NrbcNum_2. For example, the maximum value of the first number NrbcNum_1 and the second number NrbcNum_2 is determined as the number NrbcNum of nucleated erythrocytes, i.e., NrbcNum = max(NrbcNum_1, NrbcNum_2). Of course, in other embodiments, the number NrbcNum of nucleated erythrocytes can also be estimated in other ways, for example, the number NrbcNum of nucleated erythrocytes can be the average of the first number NrbcNum_1 and the second number NrbcNum_2.

[0160] Here, the second quantity NrbcNum_2 can be determined using the method described above for calculating the degree of overlap. That is, in sub-step S210e, the number of particles in the second feature region F2 is counted to obtain the number of second feature particles, and the second quantity NrbcNum_2 is determined based on the number of second feature particles; or the first area S1 of the mapping region of the basophil population region in the second scatter plot and the second area S2 of the second feature region are calculated, and the second quantity NrbcNum_2 is determined based on the first area S1 and the second area S2.

[0161] In some embodiments, the basophil count result is output as a percentage of the Baso count to the white blood cell count (Baso%). Here, method 200 further includes:

[0162] Based on the forward scattering light signal and fluorescence signal of the particles in the sample solution to be tested, all particles in the sample solution to be tested except for blood shadow particles are identified and counted to obtain the total number of particles in the sample solution to be tested, TotalNum.

[0163] Calculate the first difference between the total number of particles in the basophilic granulocyte population region, TargetNum, and the number of nucleated erythrocytes, NrbcNum;

[0164] Calculate the second difference between the total number of particles in the sample solution to be tested (TotalNum) and the number of nucleated red blood cells (NrbcNum);

[0165] The ratio of the first difference to the second difference is determined as the basophil count result, i.e., Baso% = (TargetNum - NrbcNum) / (TotalNum - NrbcNum) * 100%.

[0166] In a specific example, the DIFF optical detection channel of a blood analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd. is used... Figure 16 The Baso counting method shown calculates the Baso% of multiple blood samples to be tested, and compares the Baso% of the multiple blood samples to a reference Baso% (a reference value measured through a dedicated Baso detection channel). Figure 17 As shown. From this Figure 17 It can be seen that the Baso% obtained by using the present invention has a good correlation with the reference Baso%; therefore, the method provided by the embodiments of the present invention can obtain accurate Baso classification and counting results.

[0167] This invention also provides a computer-readable storage medium storing executable instructions thereon, which, when executed by a computer, cause the computer to perform the following method steps:

[0168] In a single test, forward-scattered light and fluorescence signals were obtained from particles in a blood sample that had undergone hemolysis and fluorescence staining, as detected by an optical detection device.

[0169] A first scatter plot is generated based on the forward scattered light signal and fluorescence signal of the particles in the blood sample to be tested, and the basophil population region and the blood shadow particle region are identified from the first scatter plot.

[0170] The number of particles falling into the basophilic granulocyte population region is counted to obtain the total number of particles in the basophilic granulocyte population region.

[0171] The number of nucleated erythrocytes in the basophilic granulocyte population region is obtained based on the blood shadow particle region;

[0172] The basophil count of the blood sample to be tested is obtained based on the total number of particles in the basophilic granulocyte region and the number of nucleated red blood cells.

[0173] The executable instructions stored on the computer-readable storage medium provided in the embodiments of the present invention cause the computer to implement some or all of the steps of the above-described method when executed by the computer.

[0174] The aforementioned computer-readable storage media may include volatile memory and / or non-volatile memory. Non-volatile memory may include read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, magnetic random access memory, flash memory, magnetic surface memory, optical disc, or read-only optical disc. Volatile memory may include random access memory used as an external cache.

[0175] All features or combinations of features mentioned above in the specification, drawings, and claims, as long as they are meaningful within the scope of this invention and do not contradict each other, can be used in any combination or individually. The advantages and features described for the blood analyzer provided by this invention are applied accordingly to the counting method provided by this invention, and vice versa.

[0176] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent transformations made based on the inventive concept of the present invention and the contents of the specification and drawings of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A blood analyzer, comprising: The sampling device has a sampling needle for drawing up the blood sample to be tested; A sample preparation apparatus includes a reaction chamber and a reagent supply unit. The reaction chamber is used to receive a portion of the blood sample to be tested drawn by the sampling device. The reagent supply unit provides a hemolytic reagent and a fluorescent reagent to the reaction chamber, thereby mixing the portion of the blood sample to be tested with the hemolytic reagent and the fluorescent reagent in the reaction chamber to prepare a sample solution to be tested. An optical detection device includes a light source, a flow chamber, a scattered light detector, and a fluorescence detector. The light source emits a light beam to illuminate the detection area of ​​the flow chamber. The flow chamber is connected to a reaction cell, allowing each particle in the sample solution to pass through the detection area of ​​the flow chamber one by one. The scattered light detector detects the scattered light signal generated by the particles passing through the detection area after being illuminated by light. The scattered light signal includes at least a forward scattered light signal. The fluorescence detector detects the fluorescence signal generated by the particles passing through the flow chamber after being illuminated by light. A data processing device, communicatively connected to the optical detection device and configured to: The forward scattered light signal and fluorescence signal of the particles in the sample liquid to be tested are obtained from the optical detection device. A first scatter plot is generated based at least on the forward scattered light signal and fluorescence signal of the particles in the sample solution to be tested, and the basophil population region and the blood shadow particle region are identified from the first scatter plot. The number of particles falling into the basophil population region is counted to obtain the total number of particles in the basophil population region. The number of nucleated erythrocytes in the basophil population region is obtained based on the blood shadow particle region. The basophil count of the blood sample to be tested is obtained based on the total number of particles in the basophilic granulocyte region and the number of nucleated red blood cells.

2. The blood analyzer according to claim 1, characterized in that, The data processing device is further configured to: determine the number of nucleated red blood cells based on the fluorescence signal of the blood shadow particles in the blood shadow particle region.

3. The blood analyzer according to claim 2, characterized in that, The data processing device is further configured to: identify a first characteristic region representing nucleated red blood cells from the blood shadow particle region based on the fluorescence signal of the blood shadow particles in the blood shadow particle region and count the blood shadow particles in the first characteristic region to obtain a first characteristic particle count; and determine the number of nucleated red blood cells based on the first characteristic particle count.

4. The blood analyzer according to claim 3, characterized in that, The data processing device is further configured to: determine the number of nucleated red blood cells based on the first characteristic particle number and a preset function, wherein the preset function describes the relationship between the number of blood shadow particles in the first characteristic region and the number of nucleated red blood cells in the basophil population region.

5. The blood analyzer according to claim 4, characterized in that, The preset function is a monotonically increasing function.

6. The blood analyzer according to claim 5, characterized in that, The preset function is a linearly increasing function.

7. The blood analyzer according to any one of claims 3 to 6, characterized in that, The first feature region is the region in the blood shadow particle region where the fluorescence signal of the blood shadow particles is greater than a preset threshold.

8. The blood analyzer according to claim 7, characterized in that, The data processing device is further configured to: determine the preset threshold based on the minimum or maximum boundary value of the fluorescence signal of the basophil population region; or identify the leukocyte population region based on the first scatter plot and determine the preset threshold based on the minimum or maximum boundary value of the fluorescence signal of the leukocyte population region.

9. The blood analyzer according to claim 1, characterized in that, The data processing device is further configured to: The side-scattered light signal of the particles in the sample liquid to be tested is obtained from the optical detection device; The number of nucleated red blood cells is determined based on the side-scattered light and fluorescence signals of the blood shadow particles in the blood shadow particle region and the side-scattered light and fluorescence signals of the particles in the basophil population region.

10. The blood analyzer according to claim 9, characterized in that, The data processing device is further configured to: A second scatter plot is generated based on the lateral scattered light signal and fluorescence signal of the particles in the sample solution to be tested; The overlapping region of the mapping areas of the basophilic granulocyte population region and the blood shadow particle region in the second scatter plot is identified as a second characteristic region characterizing nucleated erythrocytes; and The number of nucleated red blood cells is determined based on the second feature region.

11. The blood analyzer according to claim 10, characterized in that, The data processing device is further configured to: Count the particles in the second feature region to obtain the number of second feature particles; The number of nucleated red blood cells is determined based on the number of the second characteristic particles.

12. The blood analyzer according to claim 11, characterized in that, The data processing device is further configured to: The number of the second characteristic particles is determined as the number of nucleated red blood cells.

13. The blood analyzer according to claim 10, characterized in that, The data processing device is further configured to: Calculate the first area of ​​the mapped region of the basophil population region in the second scatter plot and the second area of ​​the second feature region; The number of nucleated red blood cells is determined based on the first area and the second area.

14. The blood analyzer according to claim 10, characterized in that, The data processing device is further configured to: Based on the fluorescence signal of the blood shadow particles in the blood shadow particle region, a first characteristic region representing nucleated red blood cells is identified from the blood shadow particle region and the blood shadow particles in the first characteristic region are counted to obtain the number of first characteristic particles. The number of nucleated red blood cells is determined based on the number of the first characteristic particles and the second characteristic region.

15. The blood analyzer according to claim 14, characterized in that, The data processing device is further configured to: The first number of nucleated erythrocytes in the basophilic granulocyte population region is determined from the first number of characteristic particles; The second number of nucleated erythrocytes in the basophilic granulocyte population region is determined from the second characteristic region; The number of nucleated red blood cells is determined based on the first quantity and the second quantity.

16. The blood analyzer according to claim 15, characterized in that, The data processing device is further configured to: Count the particles in the second feature region to obtain the number of second feature particles, and determine the second quantity based on the number of second feature particles; or Calculate the first area of ​​the mapped region of the basophil population region in the second scatter plot and the second area of ​​the second feature region, and determine the second quantity based on the first area and the second area.

17. The blood analyzer according to claim 15 or 16, characterized in that, The data processing device is further configured to: determine the maximum value of the first quantity and the second quantity as the number of nucleated red blood cells.

18. The blood analyzer according to any one of claims 1 to 6, characterized in that, The data processing device is further configured to: Based on the forward scattering light signal and fluorescence signal of the particles in the sample solution to be tested, all particles in the sample solution to be tested, except for blood shadow particles, are identified and counted to obtain the total number of particles in the sample solution to be tested. Calculate the first difference between the total number of particles in the basophilic granulocyte population region and the number of nucleated red blood cells; Calculate the second difference between the total number of particles in the sample solution to be tested and the number of nucleated red blood cells; The ratio of the first difference to the second difference is determined as the basophil count result.

19. The blood analyzer according to any one of claims 1 to 6, characterized in that, The data processing device is further configured to: The side-scattered light signal of the particles in the sample liquid to be tested is obtained from the optical detection device; Neutrophil population, eosinophil population, monocyte population and lymphocyte population in the blood sample to be tested can be identified at least based on the lateral scattered light signal and fluorescence signal of the particles in the sample solution to be tested.

20. A method for counting basophils, comprising: The blood sample to be tested is mixed with hemolysis reagent and fluorescent reagent to prepare the test sample solution; The particles in the sample solution to be tested are passed through the detection area one by one, and the particles passing through the detection area are irradiated with light to obtain the forward scattered light signal and fluorescence signal generated by the particles after being irradiated with light; A first scatter plot is generated based on the forward scattered light signal and fluorescence signal of the particles in the sample solution to be tested, and the basophil population region and the blood shadow particle region are identified from the first scatter plot. The number of particles falling into the basophilic granulocyte population region is counted to obtain the total number of particles in the basophilic granulocyte population region. The number of nucleated erythrocytes in the basophilic granulocyte population region is obtained based on the blood shadow particle region; The basophil count of the blood sample to be tested is obtained based on the total number of particles in the basophilic granulocyte region and the number of nucleated red blood cells.

21. The counting method according to claim 20, characterized in that, The number of nucleated red blood cells is determined based on the fluorescence signal of the blood shadow particles in the blood shadow particle region.

22. The counting method according to claim 21, characterized in that, Based on the fluorescence signal of the blood shadow particles in the blood shadow particle region, a first characteristic region representing nucleated red blood cells is identified from the blood shadow particle region and the blood shadow particles in the first characteristic region are counted to obtain the number of first characteristic particles; the number of nucleated red blood cells is determined based on the number of first characteristic particles.

23. The counting method according to claim 22, characterized in that, The number of nucleated red blood cells is determined based on the first characteristic particle count and a preset function, wherein the preset function describes the relationship between the number of blood shadow particles in the first characteristic region and the number of nucleated red blood cells in the basophilic granulocyte population region.

24. The counting method according to claim 20, characterized in that, The counting method further includes acquiring the side-scattered light signal generated by each particle in the sample solution after being irradiated by light; The number of nucleated red blood cells is determined based on the side-scattered light and fluorescence signals of the blood shadow particles in the blood shadow particle region and the side-scattered light and fluorescence signals of the particles in the basophil population region.

25. The counting method according to claim 24, characterized in that, A second scatter plot is generated based on the lateral scattered light signal and fluorescence signal of the sample liquid to be tested; The overlapping region of the mapping areas of the basophilic granulocyte population region and the blood shadow particle region in the second scatter plot is identified as a second characteristic region characterizing nucleated erythrocytes; and The number of nucleated red blood cells is determined based on the second feature region.

26. The counting method according to claim 25, characterized in that, Count the particles in the second characteristic region to obtain the number of second characteristic particles, and determine the number of nucleated red blood cells based on the number of second characteristic particles; or Calculate the first area of ​​the mapped region of the basophil population region in the second scatter plot and the second area of ​​the second feature region, and determine the number of nucleated red blood cells based on the first area and the second area.

27. The counting method according to claim 25, characterized in that, Based on the fluorescence signal of the blood shadow particles in the blood shadow particle region, a first characteristic region representing nucleated red blood cells is identified from the blood shadow particle region and the blood shadow particles in the first characteristic region are counted to obtain the number of first characteristic particles. The number of nucleated red blood cells is determined based on the number of the first characteristic particles and the second characteristic region.

28. The counting method according to claim 27, characterized in that, The first number of nucleated erythrocytes in the basophilic granulocyte population region is determined from the first number of characteristic particles; The second number of nucleated erythrocytes in the basophilic granulocyte population region is determined from the second characteristic region; The number of nucleated red blood cells is determined based on the first quantity and the second quantity.

29. The counting method according to any one of claims 20 to 28, characterized in that, Based on the forward scattering light signal and fluorescence signal of the particles in the sample solution to be tested, all particles in the sample solution to be tested, except for blood shadow particles, are identified and counted to obtain the total number of particles in the sample solution to be tested. Calculate the first difference between the total number of particles in the basophilic granulocyte population region and the number of nucleated red blood cells; Calculate the second difference between the total number of particles in the sample solution to be tested and the number of nucleated red blood cells; The ratio of the first difference to the second difference is determined as the basophil count result.

30. The counting method according to any one of claims 20 to 28, characterized in that, The counting method further includes: Acquire the side-scattered light signal generated by each particle in the sample liquid after being irradiated by light; Neutrophil population, eosinophil population, monocyte population and lymphocyte population in the blood sample to be tested can be identified at least based on the lateral scattered light signal and fluorescence signal of the particles in the sample solution to be tested.

31. A method for obtaining a five-part differential count of white blood cells, comprising: In a single test, a sample of the test liquid is obtained from a detection channel, which generates forward scattered light signal, side scattered light signal, and fluorescence signal as particles in the test sample pass through the optical detection area one by one. The test sample liquid is prepared by mixing the test blood sample with a hemolytic reagent and a fluorescent reagent. A first scatter plot is generated based on the forward scattered light signal and fluorescence signal of the particles in the sample solution to be tested, and the basophil population region and the blood shadow particle region are identified from the first scatter plot. The number of particles falling into the basophilic granulocyte population region is counted to obtain the total number of particles in the basophilic granulocyte population region. The number of nucleated erythrocytes in the basophilic granulocyte population region is obtained based on the blood shadow particle region; The basophil count of the sample solution to be tested is obtained based on the total number of particles in the basophilic granulocyte region and the number of nucleated red blood cells. At least the neutrophil population, eosinophil population, monocyte population and lymphocyte population in the test sample solution are identified based on the side-scattered light signal and fluorescence signal of the particles in the test sample solution, and the total number of particles in each cell population is counted.

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