Blood cell analyzer and blood cell analysis method

Abstract

The present application relates to a blood cell analyzer and a blood cell analysis method. The method comprises: drawing a blood sample to be tested; mixing part of said blood sample with a hemolytic agent to prepare a first test sample, and detecting first optical information of particles in the first test sample; mixing the other part of said blood sample, a diluent and a fluorescent stain to prepare a second test sample, and detecting second optical information of particles in the second test sample; acquiring first white blood cell classification information on the basis of the first optical information; and when it is determined that there is a classification abnormality in a first white blood cell classification scatter diagram, acquiring second white blood cell classification information on the basis of the second optical information, and using the second white blood cell classification information to correct the first white blood cell classification information. The present application can obtain an accurate white blood cell classification result.

Description

Blood cell analyzer and blood cell analysis method Technical Field

[0001] The present application relates to the field of blood analysis, and in particular to a blood cell analyzer and a blood cell analysis method. Background Art

[0002] A routine blood test is a basic clinical examination item. The test results generally include white blood cell count, red blood cell count, platelet count, hemoglobin concentration, reticulocyte count, etc. It also includes scatter plots or histograms of white blood cells, red blood cells, platelets and reticulocytes obtained during the test to assist doctors in making clinical diagnoses for patients.

[0003] The blood cell analyzer performs optical measurement of white blood cells based on chemical dye laser scattering flow cytometry and optical measurement of reticulocytes (or optical measurement of platelets) based on fluorescent dye flow cytometry. The optical measurement of white blood cells obtains the count value and classification of white blood cell groups, and the optical measurement of reticulocytes obtains parameters such as red blood cell count value, platelet count value, and reticulocyte count value.

[0004] However, when performing optical leukocyte measurement on certain abnormal samples, accurate leukocyte classification results may not be obtained due to overlap between some leukocyte clusters or between leukocyte clusters and other particle clusters, such as blood ghost particle clusters, in the scattergram.

[0005] Summary of the Invention

[0006] In order to at least partially solve the above technical problems, the task of the present application is to provide a blood cell analyzer and a blood cell analysis method, which can improve the accuracy of white blood cell classification.

[0007] In order to achieve the above-mentioned tasks of the present application, the first aspect of the present application provides a blood cell analyzer, comprising:

[0008] A sample suction device, used for sucking a blood sample to be tested;

[0009] a sample preparation device for mixing a portion of the blood sample to be tested with a hemolytic agent to prepare a first measurement sample for leukocyte differentiation, and for mixing another portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes;

[0010] an optical detection device comprising a flow cell, a light source, and a light detector, wherein the flow cell is configured to allow the first and second measurement samples to pass through the flow cell, respectively; the light source is configured to illuminate the first and second measurement samples, respectively passing through the flow cell, with light; and the light detector is configured to detect first and second optical information generated by the first and second measurement samples being illuminated by the light while respectively passing through the flow cell; and

[0011] A data processing device configured to:

[0012] generating a first leukocyte classification scattergram based on the first optical information and acquiring first leukocyte classification information of the blood sample to be tested based on the first leukocyte classification scattergram, wherein the first leukocyte classification information includes at least a lymphocyte percentage, a monocyte percentage, a neutrophil percentage, and an eosinophil percentage;

[0013] When it is determined that there is a classification abnormality in the first white blood cell classification scatter plot in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population, a second white blood cell classification scatter plot is generated based on the second optical information and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, the second white blood cell classification information including the percentage of lymphocytes, the percentage of monocytes and the percentage of granulocytes, the granulocytes including neutrophils and eosinophils, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0014] A second aspect of the present application provides a blood cell analysis method, comprising:

[0015] Draw a blood sample to be tested;

[0016] mixing a portion of the blood sample to be tested with a hemolytic agent to prepare a first measurement sample for leukocyte differentiation, and allowing particles in the first measurement sample to pass through an optical detection area irradiated with light one by one to obtain first optical information generated by the particles in the first measurement sample after being irradiated with light;

[0017] Mixing another portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes, and allowing particles in the second measurement sample to pass through an optical detection area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light;

[0018] generating a first leukocyte classification scattergram based on the first optical information and acquiring first leukocyte classification information of the blood sample to be tested based on the first leukocyte classification scattergram, wherein the first leukocyte classification information at least includes a lymphocyte percentage, a monocyte percentage, a neutrophil percentage, and an eosinophil percentage; and

[0019] When it is determined that there is a classification abnormality in the first white blood cell classification scatter plot in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population, a second white blood cell classification scatter plot is generated based on the second optical information and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, the second white blood cell classification information including the percentage of lymphocytes, the percentage of monocytes and the percentage of granulocytes, the granulocytes including neutrophils and eosinophils, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0020] In the technical solutions provided in the first and second aspects of the present application, when it is determined that there is a classification abnormality in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population in the blood sample to be tested, the first white blood cell classification information obtained based on the first optical information is corrected using the second white blood cell classification information obtained based on the second optical information, thereby obtaining accurate white blood cell classification results without increasing the detection cost and reducing the detection efficiency.

[0021] A third aspect of the present application provides a blood cell analyzer, comprising:

[0022] A sample suction device, used for sucking a blood sample to be tested;

[0023] a sample preparation device for mixing a portion of the blood sample to be tested with a hemolytic agent to prepare a first measurement sample for leukocyte differentiation, and for mixing another portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes;

[0024] an optical detection device comprising a flow cell, a light source, and a light detector, wherein the flow cell is configured to allow the first and second measurement samples to pass through the flow cell, respectively; the light source is configured to illuminate the first and second measurement samples, respectively passing through the flow cell, with light; and the light detector is configured to detect first and second optical information generated by the first and second measurement samples being illuminated by the light while respectively passing through the flow cell; and

[0025] The data processing device is configured to: obtain the out-of-body time of the blood sample to be tested, and when the out-of-body time is greater than a preset time period, perform white blood cell classification on the blood sample to be tested using the first optical information and the second optical information.

[0026] A fourth aspect of the present application provides a blood cell analysis method, comprising:

[0027] Draw a blood sample to be tested;

[0028] mixing a portion of the blood sample to be tested with a hemolytic agent to prepare a first measurement sample for leukocyte differentiation, and allowing particles in the first measurement sample to pass through an optical detection area irradiated with light one by one to obtain first optical information generated by the particles in the first measurement sample after being irradiated with light;

[0029] Mixing another portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes, and allowing particles in the second measurement sample to pass through an optical detection area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light; and

[0030] The ex vivo time of the blood sample to be tested is obtained, and when the ex vivo time is greater than a preset time period, white blood cell classification is performed on the blood sample to be tested using the first optical information and the second optical information.

[0031] In the technical solutions provided in the third and fourth aspects of the present application, when the blood sample to be tested has been out of body for a long time, the white blood cell classification of the blood sample to be tested is performed in combination with the first optical information and the second optical information, thereby obtaining accurate white blood cell classification results without increasing the detection cost and reducing the detection efficiency.

[0032] A fifth aspect of the present application provides a blood cell analyzer, comprising:

[0033] A sample suction device, used for sucking a blood sample to be tested;

[0034] a sample preparation device for mixing a portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes;

[0035] an optical detection device comprising a flow cell for allowing the second measurement sample to pass through, a light source for irradiating the first measurement sample passing through the flow cell with light, and a light detector for detecting second optical information generated by the first measurement sample being irradiated with light while passing through the flow cell; and

[0036] A data processing device configured to:

[0037] generating a reticulocyte scattergram based on the second optical information and identifying platelets and / or reticulocytes of the blood sample to be tested based on the reticulocyte scattergram; and

[0038] A second white blood cell classification scatter plot is generated based on the second optical information, and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, wherein the second white blood cell classification information includes at least the percentage of lymphocytes, the percentage of monocytes, and the percentage of granulocytes, and the granulocytes include neutrophils and eosinophils.

[0039] A sixth aspect of the present application provides a blood cell analysis method, comprising:

[0040] Draw a blood sample to be tested;

[0041] Mixing a portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes, and allowing particles in the second measurement sample to pass through an optical detection area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light;

[0042] generating a reticulocyte scattergram based on the second optical information and identifying platelets and / or reticulocytes of the blood sample to be tested based on the reticulocyte scattergram; and

[0043] A second white blood cell classification scatter plot is generated based on the second optical information, and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, wherein the second white blood cell classification information includes at least the percentage of lymphocytes, the percentage of monocytes, and the percentage of granulocytes, and the granulocytes include neutrophils and eosinophils.

[0044] In the technical solutions provided in the fifth aspect and the second aspect of the present application, the white blood cell classification results are obtained based on the second optical information used to identify platelets and / or reticulocytes. The white blood cell classification results can be obtained without increasing the detection cost and reducing the detection efficiency. That is, the reticulocyte detection results and the white blood cell classification results are obtained simultaneously through the same non-hemolytic optical detection channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] FIG1 is a schematic external view of a blood cell analyzer according to some embodiments of the present application.

[0046] FIG2 is a schematic block diagram of an optical detection device according to some embodiments of the present application.

[0047] FIG3 is a schematic block diagram of an optical detection device according to other embodiments of the present application.

[0048] FIG4 is a first leukocyte scattergram of a normal human sample according to some embodiments of the present application.

[0049] FIG5 is a first leukocyte scattergram of a dog sample according to some embodiments of the present application.

[0050] FIG6 is a first leukocyte scattergram of a cat sample according to some embodiments of the present application.

[0051] FIG. 7 is a FL-LAS reticulocyte scattergram of a blood sample according to some embodiments of the present application.

[0052] FIG8 is a scatter plot of the WAS-LAS ​​second leukocyte classification of the blood sample in FIG7 .

[0053] FIG9 is a scattergram of FL-FS reticulocytes of blood samples according to other embodiments of the present application.

[0054] FIG. 10 is a scatter plot of the SS-FS second leukocyte classification of the blood sample in FIG. 9 .

[0055] FIG11 is a scatter plot of the second leukocyte classification of SS-FS blood samples according to other embodiments of the present application.

[0056] FIG12 is a LAS-MAS first leukocyte scattergram obtained by testing a fresh blood sample within a preset time period according to some embodiments of the present application.

[0057] FIG13 is a LAS-MAS first leukocyte scattergram obtained after the fresh sample in FIG12 was stored for 24 hours.

[0058] FIG14 is a LAS-MAS first leukocyte scattergram of abnormal samples according to some embodiments of the present application.

[0059] FIG15 is a schematic flow chart of a blood cell analysis method according to some embodiments of the present application.

[0060] FIG16 is a schematic flow chart of a blood cell analysis method according to some other embodiments of the present application.

[0061] FIG17 is a schematic flow chart of a blood cell analysis method according to some further embodiments of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0063] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence when permitted.

[0064] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs.

[0065] To facilitate the subsequent explanation, here is a brief explanation of some of the terms involved below:

[0066] 1) Scatterplot: A 2D or 3D graph generated by a hematology analyzer that displays the 2D or 3D characteristic information of multiple particles. The X, Y, and Z axes of the scatterplot each represent a characteristic of each particle. For example, in a scatterplot, the X axis represents forward scattered light intensity, the Y axis represents fluorescence intensity, and the Z axis represents side scattered light intensity.

[0067] 2) Cell swarm: A particle cluster formed by multiple particles with the same characteristics distributed in a certain area of ​​the scatter plot, such as a leukocyte swarm, and a neutrophil swarm, lymphocyte swarm, monocyte swarm, eosinophil swarm, or basophil swarm among the leukocytes.

[0068] 3) Blood ghosts: fragments obtained by dissolving red blood cells and platelets in the blood with hemolytic reagents.

[0069] Currently, hematology analyzers can test human blood, blood from other mammals (such as dogs, cats, and horses), poultry, and fish. Typically, hematology analyzers use the DIFF channel (white blood cell differential channel) to count and classify white blood cells, for example, into five categories: lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), eosinophils (Eos), and eosinophils (Eos). Furthermore, hematology analyzers use the RET channel (reticulocyte detection channel, also known as the platelet optical detection channel) to obtain reticulocyte counts, red blood cell counts, and platelet counts.

[0070] The blood cell analyzer used in the embodiments of the present application uses flow cytometry technology based on laser scattering and optional fluorescent staining to classify and count particles in a sample. For example, the principle of reticulocyte or platelet detection in a blood cell analyzer is as follows: first, a blood sample is drawn and treated with a diluent and a fluorescent dye. Next, the particles in the blood sample pass through a detection aperture illuminated by a laser beam. When the laser beam hits the particles, the particle's own characteristics (such as volume, staining level, size and content of cellular contents, cell nuclear density, etc.) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to its characteristics. This scattered light is received by a signal detector, which can obtain information related to the particle structure and composition. The scattered light reflects the number and volume of particles and the complexity of the cell's internal structure (such as intracellular granules or cell nuclei), while fluorescence (FL) reflects the content of nucleic acid substances in the cell. This optical information can be used to classify and count the particles in the sample.

[0071] FIG1 is a schematic diagram of the structure of a hematology analyzer according to some embodiments of the present application. The hematology analyzer 100 includes a sample aspirator 110, a sample preparation device 120, an optical detection device 130, and a data processing device 140. The hematology analyzer 100 also includes a fluidic system (not shown) for connecting the sample aspirator 110, the sample preparation device 120, and the optical detection device 130 to facilitate fluid transfer between these devices.

[0072] The sample aspirating device 110 is used to aspirate a blood sample to be tested.

[0073] In some embodiments, the sample aspirating device 110 includes a sampling needle (not shown) for aspirating a blood sample to be tested. Furthermore, the sample aspirating device 110 may further include a drive mechanism for driving the sampling needle to quantitatively aspirate the blood sample to be tested through the needle tip of the sampling needle. The sample aspirating device 110 may transport the collected blood sample to the sample preparation device 120.

[0074] The sample preparation device 120 is used to mix a portion of the blood sample to be tested with a hemolytic agent to prepare a first measurement sample for white blood cell classification, and to mix another portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes. The second measurement sample can be used, in particular, to distinguish platelets, mature red blood cells, reticulocytes, and white blood cells from one another.

[0075] In the embodiment of the present application, the hemolytic agent is used to dissolve the red blood cells in the blood, breaking the red blood cells into fragments, but being able to keep the morphology of the white blood cells basically unchanged.

[0076] In some embodiments, the sample preparation device 120 may include at least one reaction pool and a reagent supply device (not shown). The at least one reaction pool is used to receive the blood sample to be tested drawn by the sample aspirating device 110, and the reagent supply device provides a processing reagent (including a hemolytic agent, a fluorescent dye, etc.) to the at least one reaction pool, so that the blood sample to be tested drawn by the sample aspirating device 110 and the processing reagent provided by the reagent supply device are mixed in the reaction pool to prepare a measurement sample (including a first measurement sample and a second measurement sample).

[0077] For example, the at least one reaction pool may include a first reaction pool and a second reaction pool, and the reagent supply device may include a first reagent supply unit and a second reagent supply unit. The sample suction device 110 is used to partially distribute the collected blood sample to be tested to the first reaction pool and the second reaction pool respectively. The first reagent supply unit is used to provide a hemolytic agent to the first reaction pool, so that the part of the blood sample to be tested distributed to the first reaction pool is mixed and reacted with the hemolytic agent to prepare a first measurement sample. The second reagent supply unit is used to provide a fluorescent dye and a diluent, such as a low osmotic pressure diluent (for example, for sphering red blood cells) to the second reaction pool, so that the part of the blood sample to be tested distributed to the second reaction pool is mixed and reacted with the fluorescent dye and the optional diluent to prepare a second measurement sample.

[0078] The optical detection device 130 includes a flow chamber, a light source, and a light detector. The flow chamber is used for allowing the first measurement sample and the second measurement sample to pass through respectively, the light source is used to irradiate the first measurement sample and the second measurement sample passing through the flow chamber respectively with light, and the light detector is used to detect the first optical information and the second optical information generated after the first measurement sample and the second measurement sample are irradiated with light when passing through the flow chamber respectively.

[0079] It can be understood here that the first detection channel for white blood cell classification (also called DIFF channel) refers to the detection of the first measurement sample prepared by the sample preparation device 120 by the optical detection device 130, and the second detection channel for identifying platelets and / or reticulocytes (also called RET channel) refers to the detection of the second measurement sample prepared by the sample preparation device 120 by the optical detection device 130.

[0080] As used herein, a flow cell refers to a chamber containing a focused fluid flow suitable for detecting light scattering and fluorescence signals. When a particle, such as a blood cell, passes through the detection aperture of the flow cell, it scatters an incident light beam directed from a light source into all directions. A light detector can be positioned at one or more different angles relative to the incident light beam to detect light scattered by the particle, thereby generating a light scattering signal. Because different particles have different light scattering properties, light scattering signals can be used to distinguish between different particle populations. Specifically, a light scattering signal detected at approximately a first angle to the incident light beam is typically referred to as a forward light scattering signal or low-angle light scattering signal (LAS). The first angle is, for example, 0-4 degrees. A light scattering signal detected at a second angle relative to the incident light beam, which is approximately greater than the first angle, is typically referred to as a mid-angle light scattering signal (MAS). The second angle is, for example, 5-10 degrees. A light scattering signal detected at a third angle relative to the incident light beam, which is approximately greater than the second angle, is typically referred to as a high-angle light scattering signal (WAS). The third angle is, for example, 35-40 degrees. Typically, the fluorescent signal FL emitted from blood cells stained with a fluorescent dye is also detected in a direction of approximately 90° to the incident light beam.

[0081] In some embodiments, the light detector may include a first detector for detecting low-angle scattered light signals, a second detector for detecting medium-angle scattered light signals, a third detector for detecting high-angle scattered light signals, and a fluorescence detector for detecting fluorescence signals. Accordingly, the first optical information may include the low-angle scattered light signals, medium-angle scattered light signals, and high-angle scattered light signals of particles in the first measurement sample, and the second optical information may include the low-angle scattered light signals, medium-angle scattered light signals, high-angle scattered light signals, and fluorescence signals of particles in the second measurement sample.

[0082] FIG2 shows a specific example of an optical detection device 130. The optical detection device 130 includes a light source 131 (e.g., a laser light source), a flow chamber 132, a first detector 133, a second detector 134, a third detector 135, a spectroscope 136, and a fluorescence detector 137. The first detector 133, the second detector 134, and the third detector 135 are used to detect low-angle scattered light signals LAS, medium-angle scattered light signals MAS, and high-angle scattered light signals WAS, respectively. The spectroscope 136 is arranged on one side of the flow chamber 132. A portion of the side light emitted by the particles in the flow chamber 132 passes through the spectroscope 136 and is captured by the fluorescence detector 137 arranged behind the spectroscope 136.

[0083] In other embodiments, the light detector may include a forward scattered light detector for detecting a forward scattered light signal, a side scattered light detector for detecting a side scattered light signal, and a fluorescence detector for detecting a fluorescent signal. Specifically, the light scattering signal detected near the incident light beam is generally referred to as a forward light scattering signal. In some embodiments, the forward light scattering signal can be detected at an angle of about 1° to about 4° from the incident light beam. In other embodiments, the forward light scattering signal can be detected at an angle of about 2° to about 6° from the incident light beam. The light scattering signal detected at a direction of about 90° 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 at an angle of about 65° to about 115° from the incident light beam. Typically, the fluorescent signal emitted by blood cells stained with fluorescent dyes is also generally detected at a direction of about 90° to the incident light beam.

[0084] FIG3 illustrates another specific example of an optical detection device 130. This optical detection device 130 comprises a light source 101, a beam shaping assembly 102, a flow cell 103, and a forward scattered light detector 104, arranged sequentially in a straight line. A dichroic mirror 106 is positioned on one side of the flow cell 103 at a 45° angle to the straight line. A portion of the side light emitted by particles in the flow cell 103 passes through the dichroic mirror 106 and is captured by a fluorescence detector 105, which is positioned behind the dichroic mirror 106 at a 45° angle. Another portion of the side light is reflected by the dichroic mirror 106 and captured by a side scattered light detector 107, which is positioned in front of the dichroic mirror 106 at a 45° angle.

[0085] The data processing device 140 is used to process and calculate data to obtain the required results. For example, it can generate a two-dimensional or three-dimensional scatter plot based on the various collected optical signals and perform particle analysis on the scatter plot using gating methods. The data processing device 140 can also visualize intermediate or final calculation results and then display them on the display device 150. In the embodiment of the present application, the data processing device 140 is configured to implement the method steps described in detail below.

[0086] In the embodiments of the application, the data processing device includes, but is not limited to, a central processing unit (CPU), a microcontroller unit (MCU), a field-programmable gate array (FPGA), a digital signal processing device (DSP), and other devices used to interpret computer instructions and process data in computer software. For example, the data processing device is used to execute various computer applications stored in a computer-readable storage medium, thereby causing the hematology analyzer 100 to perform the corresponding detection process and analyze the optical information or optical signals detected by the optical detection device 130 in real time.

[0087] The hematology analyzer 100 may further include a first housing 160 and a second housing 170. The display device 150 may be, for example, a user interface. The optical detection device 130 and the data processing device 140 are disposed within the second housing 170. The sample preparation device 120 is, for example, disposed within the first housing 160. The display device 150 is, for example, disposed on the exterior of the first housing 160 and is used to display the test results of the hematology analyzer.

[0088] As mentioned in the background art, when measuring certain abnormal samples using the DIFF channel, there may be anomalies in the white blood cell scatter plot obtained from the DIFF channel where there is no clear boundary between different white blood cell populations, resulting in the inability to obtain accurate white blood cell classification results from the DIFF channel. As shown in Figure 4, for normal human samples, in the white blood cell scatter plot obtained from the DIFF channel, there is a clear boundary between monocytes and lymphocytes, and there is a clear boundary between monocytes and neutrophils; as shown in Figure 5, for some dog samples, in the white blood cell scatter plot obtained from the DIFF channel, there is a clear boundary between monocytes and lymphocytes, but no clear boundary between monocytes and neutrophils; as shown in Figure 6, for some cat samples, in the white blood cell scatter plot obtained from the DIFF channel, there is a clear boundary between monocytes and lymphocytes, but no clear boundary between monocytes and neutrophils. When the boundary between monocytes and neutrophils is unclear, it will affect the accuracy of monocyte and neutrophil classification.

[0089] In addition, the applicant has found through research that, whether for abnormal samples or normal samples, the second optical information obtained from the RET channel can at least accurately identify lymphocytes RET_Lym, monocytes RET_Mon, and granulocytes RET_Gran, where granulocytes include neutrophils and eosinophils.

[0090] Based on this, an embodiment of the present application proposes that, for a blood sample to be tested with classification abnormalities, a more accurate white blood cell classification result is obtained by combining the first optical information obtained in the DIFF channel and the second optical information obtained in the RET channel.

[0091] According to the first embodiment of the present application, the data processing device 140 is configured to:

[0092] generating a reticulocyte scattergram based on the second optical information and identifying platelets and / or reticulocytes of the blood sample to be tested based on the reticulocyte scattergram; and

[0093] A second white blood cell classification scatter plot is generated based on the second optical information, and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, wherein the second white blood cell classification information includes at least lymphocyte percentage RET_Lym%, monocyte percentage RET_Mon%, and granulocyte percentage RET_Gran%, and the granulocytes include neutrophils and eosinophils.

[0094] Here, preferably, the data processing device 140 is further configured to output the second leukocyte differentiation scattergram to the display device 150, such as a user interface, so as to display the second leukocyte differentiation scattergram on the display device 150 for user reference.

[0095] In some embodiments, the data processing device 140 generating a reticulocyte scattergram based on the second optical information may include: the data processing device 140 generating the reticulocyte scattergram based on at least the low-angle scattered light signal LAS and the side fluorescence signal FL in the second optical information, as shown in FIG7 . Accordingly, the data processing device 140 generating a second leukocyte differential scattergram based on the second optical information may include: the data processing device 140 generating a second leukocyte differential scattergram based on at least the low-angle scattered light signal LAS and the high-angle scattered light signal WAS in the second optical information, as shown in FIG8 .

[0096] In other embodiments, the data processing device 140 generating a reticulocyte scattergram based on the second optical information may include: the data processing device 140 generating the reticulocyte scattergram based on at least the forward scattered light signal FS and the side fluorescence signal FL in the second optical information, as shown in FIG9 . Accordingly, the data processing device 140 generating a second leukocyte differential scattergram based on the second optical information may include: the data processing device 140 generating a second leukocyte differential scattergram based on at least the forward scattered light signal FS and the side scattered light signal SS in the second optical information, as shown in FIG10 .

[0097] In some embodiments, the sample preparation device 120 is further configured to additionally add a chemical stain when preparing the second measurement sample to further distinguish neutrophils from eosinophils in the granulocytes. In this manner, the data processing device 140 can distinguish eosinophils from the granulocytes based on the second white blood cell classification scatter plot, so that the second white blood cell classification information includes at least the percentage of lymphocytes, the percentage of monocytes, the percentage of eosinophils, and the percentage of neutrophils. As shown in FIG11 , if a chemical stain is additionally added when preparing the second measurement sample, four detailed classifications of white blood cells can be achieved based on the second white blood cell classification scatter plot, namely: lymphocytes RET_Lym, monocytes RET_Mon, neutrophils RET_Neu, and eosinophils RET_Eos.

[0098] For example, the sample preparation device 120 is further configured to add a chemical dye in a diluent or a fluorescent dye, or separately, when preparing the second measurement sample.

[0099] In some embodiments, the chemical dye is selected from acidic organic pigments. Alternatively, the chemical dye is selected from the acid blue series; alternatively, the chemical dye is selected from the group consisting of direct blue, acid green, acid yellow, acid orange, methyl red, methyl orange, aniline blue, alizarin yellow, reactive black, Sudan black B, and azo black E.

[0100] Optionally, the data processing device is further configured to identify basophils in the second measurement sample based on the second optical information, so that the second white blood cell detection result also includes a basophil percentage. As shown in FIG11 , if a chemical dye is additionally added when preparing the second measurement sample, a detailed five-way white blood cell classification can be achieved based on the second white blood cell classification scatter plot, namely, lymphocytes RET_Lym, monocytes RET_Mon, neutrophils RET_Neu, eosinophils RET_Eos, and basophils RET_Baso.

[0101] In some embodiments, the data processing device 140 is further configured to obtain at least one, and preferably all, of a red blood cell count, a platelet count, a reticulocyte count, and a reticulocyte classification based on the second optical information.

[0102] For example, as shown in Figures 7 and 9 , the data processing device 140 can obtain red blood cell counts, platelet counts, reticulocyte counts, and reticulocyte classification based on reticulocyte scatter plots. In the reticulocyte scatter plots shown in Figures 7 and 9 , from left to right in the FL direction are mature red blood cells, low-fluorescent reticulocytes, medium-fluorescent reticulocytes, high-fluorescent reticulocytes, and white blood cells. By adjusting the staining time of blood samples from different species to be tested, a clear distinction can be achieved between white blood cells, reticulocytes, and platelets while ensuring the accuracy of the test results for various reticulocyte parameters. Using the FL-LAS scatter plot or the FL-FS scatter plot, mature red blood cells, reticulocytes, platelets, and white blood cells can be accurately distinguished.

[0103] According to the second embodiment of the present application, the data processing device 140 is configured to:

[0104] A first white blood cell classification scatter plot is generated based on the first optical information, and first white blood cell classification information of the blood sample to be tested is obtained based on the first white blood cell classification scatter plot, wherein the first white blood cell classification information at least includes a lymphocyte percentage (DIFF_Lym%), a monocyte percentage (DIFF_Mon%), a neutrophil percentage (DIFF_Neu%), and an eosinophil percentage (DIFF_Eos%). Optionally, the first white blood cell classification information also includes a basophil percentage (DIFF_Baso%).

[0105] When it is determined that there is a classification abnormality in the first white blood cell classification scatter plot in which the lymphocyte population and / or the monocyte population overlaps with at least one other particle population, a second white blood cell classification scatter plot is generated based on the second optical information and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, the second white blood cell classification information includes at least the lymphocyte percentage (RET_Lym%), the monocyte percentage (RET_Mon%) and the granulocyte percentage (RET_Gran%), the granulocytes including neutrophils and eosinophils, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0106] Thus, accurate leukocyte classification results can be obtained for abnormal samples with classification abnormalities in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population.

[0107] In some embodiments, the data processing device 140 generates a first white blood cell classification scattergram based on the first optical information, including: the data processing device 140 generates the first white blood cell classification scattergram based on the low-angle scattered light signals, the medium-angle scattered light signals, and the high-angle scattered light signals in the first optical information. Accordingly, the data processing device 140 generates a second white blood cell classification scattergram based on the second optical information, including: the data processing device 140 generates the second white blood cell classification scattergram based on the low-angle scattered light signals and the high-angle scattered light signals in the second optical information.

[0108] The following describes some implementations of how the data processing device 140 determines whether there is a classification abnormality in the blood sample to be tested.

[0109] In some embodiments, the data processing device 140 may be further configured to determine whether the classification abnormality exists for the blood sample to be tested based on the first optical information, in particular based on the first white blood cell differential scatter plot. For example, the classification abnormality exists when the data processing device 140 cannot clearly separate the lymphocyte population (DIFF_Lym), the monocyte population (DIFF_Mon), and the neutrophil population (DIFF_Neu) from each other based on the first white blood cell differential scatter plot.

[0110] In other embodiments, the data processing device 140 may be further configured to determine whether the classification abnormality exists based on disease information of the subject from whom the blood sample to be tested is collected. For example, when the subject is determined to have a tumor based on the subject's disease information, the classification abnormality may be determined to exist for the blood sample to be tested from the subject.

[0111] The applicant has found through research that for a blood cell analyzer that only uses scattered light information in the DIFF channel to realize white blood cell classification, when the blood sample to be tested leaves the subject for a certain period of time (called the in vitro time), it becomes an aging sample. For aging samples, there is overlap between monocytes and neutrophils in their first white blood cell classification scatter plot, which affects the accuracy of the monocyte and neutrophil classification results. Figure 12 shows a LAS-MAS scatter plot obtained by detecting a fresh blood sample within a preset time, and Figure 13 shows a LAS-MAS scatter plot obtained by detecting the fresh sample after 24 hours of storage. It can be seen from Figures 12 and 13 that the longer the sample is in vitro, the less clear the boundary between monocytes and neutrophils is, and the more difficult it is to distinguish. Therefore, the in vitro time can be used to determine whether there is a classification abnormality.

[0112] Therefore, in yet other embodiments, the data processing device 140 is further configured to: determine whether the classification abnormality exists for the blood sample to be tested based on the time the blood sample to be tested is isolated from the body, preferably determine whether the classification abnormality exists for the blood sample to be tested based on the time the blood sample to be tested is isolated from the body and the species category corresponding to the blood sample to be tested.

[0113] For example, the data processing device 140 is further configured to: when the out-of-body time of the blood sample to be tested is greater than a preset time period, determine that the classification abnormality exists for the blood sample to be tested.

[0114] Furthermore, the data processing device 140 is further configured to determine the preset time duration based on the species category of the blood sample to be tested. For example, for human, dog, and cat samples, the preset time duration is selected as 4 hours, while for horse samples, the preset time duration is selected as 2 hours.

[0115] In some embodiments, the ex-vivo time is input into the data processing device 140 by the user through the user interface.

[0116] In some embodiments, the species category is also input into the data processing device 140 by the user through the user interface.

[0117] In some embodiments, the data processing device 140 is further configured to, when determining that the blood sample has been out of the body for no longer than the predetermined time period, acquire and output the first white blood cell classification information based solely on the first optical information. In other words, the first white blood cell classification information is accurate and does not need to be corrected using the second white blood cell classification information.

[0118] The following describes some implementations of the data processing device 140 correcting the first leukocyte classification information using the second leukocyte classification information.

[0119] In some embodiments, the data processing device 140 uses the second white blood cell classification information to modify the first white blood cell classification information to obtain and output modified first white blood cell classification information, including: the data processing device 140

[0120] When it is determined that there is a classification abnormality in which the monocyte population and the neutrophil population overlap in the first white blood cell classification scatter plot, and the lymphocyte population is clearly demarcated from the other particle populations, the monocyte percentage in the second white blood cell classification information is used to correct the monocyte percentage and the neutrophil percentage in the first white blood cell classification information. For example, the monocyte percentage and the neutrophil percentage in the first white blood cell classification information are corrected using the following formula:

[0121] DIFF_Neu_per2=DIFF_Neu_per1-Ret_Mon_per+DIFF_Mon_per1,

[0122] DIFF_Mon_per2=Ret_Mon_per,

[0123] Among them, DIFF_Mon_per1 is the monocyte percentage in the first white blood cell classification information before correction, DIFF_Neu_per1 is the neutrophil percentage in the first white blood cell classification information before correction, Ret_Mon_per is the monocyte percentage in the second white blood cell classification information, DIFF_Neu_per2 and DIFF_Mon_per2 are the neutrophil percentage and monocyte percentage in the first white blood cell classification information after correction.

[0124] Here, the first leukocyte classification scattergram includes, for example, a scattergram obtained based on the low-angle scattered light signal and the medium-angle scattered light signal in the first optical information and a scattergram obtained based on the medium-angle scattered light signal and the high-angle scattered light signal in the first optical information.

[0125] Preferably, the data processing device 140 outputs the corrected neutrophil percentage and the corrected monocyte percentage and outputs the lymphocyte percentage and the eosinophil percentage in the first white blood cell information before correction.

[0126] In other embodiments, the data processing device 140 uses the second white blood cell classification information to modify the first white blood cell classification information to obtain and output modified first white blood cell classification information, including: the data processing device 140

[0127] When it is determined that there is a classification abnormality in which the monocyte population and the neutrophil population overlap in the first white blood cell classification scatter plot, and the lymphocyte population is clearly demarcated from the other particle populations, the monocyte percentage in the second white blood cell classification information is used to correct the monocyte percentage and the neutrophil percentage in the first white blood cell classification information. For example, the monocyte percentage and the neutrophil percentage in the first white blood cell classification information are corrected by the following formula:

[0128] DIFF_Mon_per=Ret_Mon_per,

[0129] DIFF_Neu_per=1-DIFF_Lym_per-DIFF_Mon_per-DIFF_Eos_per,

[0130] Wherein, DIFF_Mon_per is the percentage of monocytes in the corrected first white blood cell classification information, Ret_Mon_per is the percentage of monocytes in the second white blood cell classification information, DIFF_Neu_per is the percentage of neutrophils in the corrected first white blood cell classification information, DIFF_Lym_per and DIFF_Eos_per are the percentage of lymphocytes and the percentage of eosinophils in the first white blood cell classification information before correction, respectively.

[0131] Here, the first leukocyte classification scattergram includes, for example, a scattergram obtained based on the low-angle scattered light signal and the medium-angle scattered light signal in the first optical information and a scattergram obtained based on the medium-angle scattered light signal and the high-angle scattered light signal in the first optical information.

[0132] Preferably, the data processing device 140 outputs the corrected neutrophil percentage and the corrected monocyte percentage and outputs the lymphocyte percentage and the eosinophil percentage in the first white blood cell information before correction.

[0133] Furthermore, for some abnormal samples, the WBC scatter plots obtained from the DIFF channel lack a clear boundary between red blood cell fragments and lymphocytes, making it impossible to distinguish lymphocytes from red blood cell fragments, which affects the WBC classification results. As shown in Figure 14, for some abnormal samples, the WBC scatter plots obtained from the DIFF channel show that red blood cell fragments (blood ghosts) overlap with the lymphocyte population.

[0134] Therefore, in yet other embodiments, the sample preparation device 120 is further configured to mix yet another portion of the blood sample to be tested with another hemolytic agent to prepare a third measurement sample for identifying basophils; the flow cell is further configured to allow the third measurement sample to pass through the flow cell; the light source is further configured to illuminate the third measurement sample passing through the flow cell with light; and the light detector is further configured to detect third optical information generated by the third measurement sample being illuminated by light as it passes through the flow cell. In this case, the data processing device 140 is further configured to obtain white blood cell count information of the blood sample to be tested based on the first white blood cell differential scattergram, the white blood cell count information including lymphocyte count, monocyte count, neutrophil count, and eosinophil count, and to obtain the basophil percentage and basophil count of the blood sample to be tested based on the third optical information.

[0135] Here, the data processing device 140 uses the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information, including: the data processing device 140

[0136] When it is determined that there is a classification abnormality in which the lymphocyte population and the red blood cell fragments overlap in the first white blood cell classification scatter plot, and the monocyte population is clearly separated from other particle populations, the first white blood cell classification information is corrected using the lymphocyte percentage in the second white blood cell classification information. For example, the lymphocyte percentage, monocyte percentage, neutrophil percentage, and eosinophil percentage in the first white blood cell classification information are corrected using the following formula:

[0137] Diff_Lym_per=Ret_Lym_per,

[0138] Diff_Mon_per=Diff_Num_Mon / (Diff_Num_Mon+Diff_Num_Other)*(100%-Diff_Lym_per),

[0139] Diff_Eos_per=Diff_Num_Eos / (Diff_Num_Eos+Diff_Num_Neu_Bas)*(100%-Diff_Lym_per-Diff_Mon_per,

[0140] Diff_Neu_per=100%-Diff_Lym_per-Diff_Mon_per-Diff_Eos_per-Diff_Bas_per,

[0141] Wherein, Diff_Lym_per is the percentage of lymphocytes in the corrected first white blood cell classification information, Ret_Lym_per is the percentage of lymphocytes in the second white blood cell classification information, Diff_Mon_per is the percentage of monocytes in the corrected first white blood cell classification information, Diff_Num_Mon is the monocyte count, Diff_Num_Other is the sum of the neutrophil count, the eosinophil count, and the basophil count, Diff_Eos_per is the percentage of eosinophils in the corrected first white blood cell classification information, Diff_Num_Eos is the eosinophil count, Diff_Num_Neu_Bas is the sum of the neutrophil count and the basophil count, DIFF_Neu_per is the percentage of neutrophils in the corrected first white blood cell classification information, and Diff_Bas_per is the percentage of basophils.

[0142] Here, the first leukocyte classification scattergram includes, for example, a scattergram obtained based on the low-angle scattered light signal and the medium-angle scattered light signal in the first optical information and a scattergram obtained based on the medium-angle scattered light signal and the high-angle scattered light signal in the first optical information.

[0143] Preferably, the data processing device 140 outputs the corrected neutrophil percentage, monocyte percentage, lymphocyte percentage, and eosinophil percentage, and outputs the basophil percentage.

[0144] According to the third embodiment of the present application, the data processing device 140 is configured to: obtain the ex-vivo time of the blood sample to be tested, and determine a white blood cell classification algorithm according to the ex-vivo time.

[0145] In some embodiments, the data processing device 140 determines the white blood cell classification algorithm according to the ex-vivo time, including: when the ex-vivo time is greater than a preset time, the data processing device 140 performs white blood cell classification on the blood sample to be tested using the first optical information and the second optical information.

[0146] Alternatively or additionally, the data processing device 140 determines the leukocyte classification algorithm according to the ex-vivo time, including: when the ex-vivo time is not greater than the preset time period, the data processing device 140 only uses the first optical information and performs leukocyte classification on the blood sample to be tested.

[0147] Specifically, the data processing device 140 is further configured to, when the ex vivo time is not greater than the preset time length, generate a first white blood cell classification scatter plot based on the first optical information and obtain and output first white blood cell classification information of the blood sample to be tested based on the first white blood cell classification scatter plot, wherein the first white blood cell classification information includes at least the percentage of lymphocytes, the percentage of monocytes, the percentage of neutrophils and the percentage of eosinophils.

[0148] In some embodiments, the data processing device 140 uses the first optical information and the second optical information to perform white blood cell classification on the blood sample to be tested, including: the data processing device 140

[0149] A first white blood cell classification scatter plot is generated based on the first optical information, and first white blood cell classification information of the blood sample to be tested is obtained based on the first white blood cell classification scatter plot, wherein the first white blood cell classification information includes at least the percentage of lymphocytes, the percentage of monocytes, the percentage of neutrophils, and the percentage of eosinophils. A second white blood cell classification scatter plot is generated based on the second optical information, and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, wherein the second white blood cell classification information includes the percentage of lymphocytes, the percentage of monocytes, and the percentage of granulocytes, wherein the granulocytes include neutrophils and eosinophils. The first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0150] In a specific example, the data processing device 140 uses the first optical information and the second optical information to perform white blood cell classification on the blood sample to be tested, including: the data processing device 140

[0151] The monocyte percentage in the second leukocyte classification information is used to correct the monocyte percentage and the neutrophil percentage in the first leukocyte classification information. For example, the monocyte percentage and the neutrophil percentage in the first leukocyte classification information are corrected using the following formula:

[0152] DIFF_Mon_per=Ret_Mon_per,

[0153] DIFF_Neu_per=1-DIFF_Lym_per-DIFF_Mon_per-DIFF_Eos_per,

[0154] Wherein, DIFF_Mon_per is the percentage of monocytes in the corrected first white blood cell classification information, Ret_Mon_per is the percentage of monocytes in the second white blood cell classification information, DIFF_Neu_per is the percentage of neutrophils in the corrected first white blood cell classification information, DIFF_Lym_per and DIFF_Eos_per are the percentage of lymphocytes and the percentage of eosinophils in the first white blood cell classification information before correction, respectively.

[0155] Here, the first leukocyte classification scattergram includes, for example, a scattergram obtained based on the low-angle scattered light signal and the medium-angle scattered light signal in the first optical information and a scattergram obtained based on the medium-angle scattered light signal and the high-angle scattered light signal in the first optical information.

[0156] Preferably, the data processing device 140 outputs the corrected neutrophil percentage and the corrected monocyte percentage and outputs the lymphocyte percentage and the eosinophil percentage in the first white blood cell information before correction.

[0157] In some embodiments, the data processing device 140 generates a first leukocyte classification scattergram based on the first optical information, including: the data processing device 140 generates the first leukocyte classification scattergram based on the low-angle scattered light signal, the medium-angle scattered light signal, and the high-angle scattered light signal in the first optical information.

[0158] The data processing device 140 generates a second leukocyte classification scattergram based on the second optical information, including: the data processing device 140 generates the second leukocyte classification scattergram based on the low-angle scattered light signal and the high-angle scattered light signal in the second optical information.

[0159] In some embodiments, the ex-vivo time is input into the data processing device 140 by the user through a user interface.

[0160] In some embodiments, the data processing device 140 is further configured to: obtain the species category corresponding to the blood sample to be tested, preferably, the ex vivo time and the species category are input into the data processing device by the user through the user interface; and

[0161] The preset time duration is determined according to the species category.

[0162] For example, the user may select the in vitro time and species category of the blood sample to be tested through the user interface. The data processing device 140 receives the user's input and determines a white blood cell classification algorithm based on the user's input.

[0163] According to a fourth embodiment of the present application, the blood cell analysis further includes a mode selection device, such as a user interface, for selecting a first mode or a second mode, wherein the first mode is a mode for fresh samples, and the second mode is a second mode for aged samples. In this case, the data processing device 140 is configured to:

[0164] obtaining a selection of the first mode or the second mode by a mode selection device;

[0165] When the mode selection device selects the first mode, acquiring and outputting first white blood cell classification information based only on the first optical information; and

[0166] When the mode selection device selects the second mode, first white blood cell classification information is acquired based on the first optical information, second white blood cell classification information is acquired based on the second optical information, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0167] For more embodiments and advantages of the fourth embodiment of the present application, reference may be made to the above descriptions of the first to third embodiments of the present application.

[0168] As shown in FIG15 , the fifth embodiment of the present application provides a blood cell analysis method 200 , comprising:

[0169] S210, drawing a blood sample to be tested;

[0170] S220, mixing a portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes, and allowing particles in the second measurement sample to pass through an optical detection area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light;

[0171] S230, generating a reticulocyte scattergram based on the second optical information and identifying platelets and / or reticulocytes of the blood sample to be tested based on the reticulocyte scattergram; and

[0172] S240, generating a second white blood cell classification scatter plot based on the second optical information and obtaining second white blood cell classification information of the blood sample to be tested based on the second white blood cell classification scatter plot, wherein the second white blood cell classification information includes at least a lymphocyte percentage, a monocyte percentage, and a granulocyte percentage, and the granulocytes include neutrophils and eosinophils.

[0173] In some embodiments, generating a reticulocyte scattergram based on the second optical information may include generating the reticulocyte scattergram based on at least the low-angle scattered light signal LAS and the side fluorescence signal FL in the second optical information. Accordingly, generating a second white blood cell differential scattergram based on the second optical information may include generating the second white blood cell differential scattergram based on at least the low-angle scattered light signal LAS and the high-angle scattered light signal WAS in the second optical information.

[0174] In other embodiments, generating a reticulocyte scattergram based on the second optical information may include generating the reticulocyte scattergram based on at least the forward scattered light signal FS and the side fluorescence signal FL in the second optical information. Accordingly, generating a second leukocyte differential scattergram based on the second optical information may include generating the second leukocyte differential scattergram based on at least the forward scattered light signal FS and the side scattered light signal SS in the second optical information.

[0175] In some embodiments, a chemical stain is additionally added during the preparation of the second measurement sample to further differentiate neutrophils from eosinophils within the granulocytes. Thus, eosinophils within the granulocytes can be differentiated based on the second white blood cell differential scatter plot, such that the second white blood cell differential information includes at least the percentage of lymphocytes, the percentage of monocytes, the percentage of eosinophils, and the percentage of neutrophils.

[0176] For example, when preparing the second measurement sample, a chemical dye is mixed in a diluent or a fluorescent dye or is added separately.

[0177] Optionally, the method 200 further includes identifying basophils in the second measurement sample based on the second optical information, so that the second white blood cell detection result further includes a basophil percentage.

[0178] In some embodiments, the method 200 further includes: obtaining at least one, and preferably all, of a red blood cell count, a platelet count, a reticulocyte count, and a reticulocyte classification based on the second optical information.

[0179] As shown in FIG16 , the sixth embodiment of the present application provides a blood cell analysis method 300 , comprising:

[0180] S310, drawing a blood sample to be tested;

[0181] S320, mixing a portion of the blood sample to be tested with a hemolytic agent to prepare a first measurement sample for white blood cell differentiation, and allowing particles in the first measurement sample to pass through an optical detection area irradiated with light one by one to obtain first optical information generated by the particles in the first measurement sample after being irradiated with light;

[0182] S330, mixing another portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes, and allowing particles in the second measurement sample to pass through an optical detection area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light;

[0183] S340, generating a first white blood cell classification scattergram based on the first optical information and acquiring first white blood cell classification information of the blood sample to be tested based on the first white blood cell classification scattergram, wherein the first white blood cell classification information at least includes a lymphocyte percentage, a monocyte percentage, a neutrophil percentage, and an eosinophil percentage; and

[0184] S350, when it is determined that there is a classification anomaly in the first white blood cell classification scatter plot in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population, a second white blood cell classification scatter plot is generated based on the second optical information and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, the second white blood cell classification information including the percentage of lymphocytes, the percentage of monocytes and the percentage of granulocytes, the granulocytes including neutrophils and eosinophils, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0185] Optionally, the blood cell analysis method 300 further includes step S360: when there is no classification abnormality, directly outputting the first white blood cell classification information.

[0186] For more embodiments and advantages of the method 300 according to the sixth embodiment of the present application, reference may be made to the above description of the blood cell analyzers according to the first to fourth embodiments of the present application.

[0187] As shown in FIG17 , the seventh embodiment of the present application provides a blood cell analysis method 400 , comprising:

[0188] S410, drawing a blood sample to be tested;

[0189] S420, mixing a portion of the blood sample to be tested with a hemolytic agent to prepare a first measurement sample for white blood cell differentiation, and allowing particles in the first measurement sample to pass through an optical detection area irradiated with light one by one to obtain first optical information generated by the particles in the first measurement sample after being irradiated with light;

[0190] S430, mixing another portion of the blood sample to be tested, a diluent, and a fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes, and allowing particles in the second measurement sample to pass through an optical detection area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light; and

[0191] S440 , obtaining the ex-vivo time of the blood sample to be tested, and determining a white blood cell classification algorithm according to the ex-vivo time.

[0192] In some embodiments, determining a white blood cell classification algorithm based on the ex-vivo time includes: when the ex-vivo time is greater than a preset time period, performing white blood cell classification on the blood sample to be tested using the first optical information and the second optical information.

[0193] Alternatively or additionally, determining the leukocyte classification algorithm according to the ex-vivo time includes: when the ex-vivo time is not greater than the preset time period, the data processing device 140 only uses the first optical information and performs leukocyte classification on the blood sample to be tested.

[0194] For more embodiments and advantages of the method 400 according to the seventh embodiment of the present application, reference may be made to the above description of the blood cell analyzers according to the first to fourth embodiments of the present application.

[0195] The features or feature combinations mentioned above in the specification, drawings, and claims may be used in any combination or individually, as long as they are meaningful and not mutually inconsistent within the scope of this application. The advantages and features described with reference to the blood cell analyzer provided in the embodiments of this application apply in a corresponding manner to the blood cell analysis method provided in the embodiments of this application, and vice versa.

[0196] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. All equivalent transformation schemes made by using the contents of the present application description and drawings under the inventive concept of the present application, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A blood cell analyzer, comprising: A sampling device for aspirating a blood sample to be tested; A sample preparation device for mixing a part of the blood sample to be tested with a hemolytic agent to prepare a first measurement sample for white blood cell classification, and for mixing another part of the blood sample to be tested, a diluent, and a fluorescent stain to prepare a second measurement sample for identifying platelets and / or reticulocytes; An optical detection device, including a flow cell, a light source, and a light detector, wherein the flow cell is used for the first measurement sample and the second measurement sample to pass through respectively, the light source is used for irradiating the first measurement sample and the second measurement sample passing through the flow cell with light, and the light detector is used for detecting first optical information and second optical information generated after the first measurement sample and the second measurement sample are irradiated by light when passing through the flow cell respectively; And A data processing device configured to: Generate a first white blood cell classification scatter plot based on the first optical information and obtain first white blood cell classification information of the blood sample to be tested based on the first white blood cell classification scatter plot, where the first white blood cell classification information at least includes lymphocyte percentage, monocyte percentage, neutrophil percentage, and eosinophil percentage; When it is determined that there is a classification abnormality in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population in the first white blood cell classification scatter plot, generate a second white blood cell classification scatter plot based on the second optical information and obtain second white blood cell classification information of the blood sample to be tested based on the second white blood cell classification scatter plot, where the second white blood cell classification information includes lymphocyte percentage, monocyte percentage, and granulocyte percentage, and the granulocytes include neutrophils and eosinophils, and use the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information.

2. The blood cell analyzer according to claim 1, characterized in that, The data processing device is further configured to: determine whether there is such a classification abnormality for the blood sample to be tested according to the first optical information, preferably based on the first white blood cell classification scatter plot.

3. The blood cell analyzer according to claim 1, wherein The data processing device is further configured to: determine whether there is such a classification abnormality for the blood sample to be tested according to the ex vivo time of the blood sample to be tested; Preferably, the ex vivo time is input by the user into the data processing device through a user interface.

4. The blood cell analyzer according to claim 1, wherein The data processing device is further configured to: determine whether there is such a classification abnormality for the blood sample to be tested according to the ex vivo time of the blood sample to be tested and the species category corresponding to the blood sample to be tested; Preferably, the ex vivo time and the species category are input by the user into the data processing device through a user interface.

5. The blood cell analyzer according to claim 3 or 4, characterized in that, The data processing device is further configured to: When the ex vivo time of the blood sample to be tested is greater than a preset duration, determine that there is Such a classification abnormality.

6. The blood cell analyzer according to claim 5, characterized in that, The data processing device is further configured to: determine the preset duration according to the species category corresponding to the blood sample to be tested.

7. The blood cell analyzer according to claim 5 or 6, characterized in that, The data processing device is further configured to: When it is determined that the ex vivo time of the blood sample to be tested is not greater than the preset duration, the first white blood cell classification information is obtained and output only based on the first optical information.

8. The blood cell analyzer according to any one of claims 1 to 7, characterized in that, The data processing device uses the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information, including: the data processing device When it is determined that there is a classification abnormality where the monocyte population and the neutrophil population overlap in the first white blood cell classification scatter plot, and the lymphocyte population and other particle populations are clearly demarcated, the data processing device uses the monocyte percentage in the second white blood cell classification information to correct the monocyte percentage and the neutrophil percentage in the first white blood cell classification information. For example, the monocyte percentage and the neutrophil percentage in the first white blood cell classification information are corrected by the following formula DIFF_Neu_per2 = DIFF_Neu_per1 - Ret_Mon_per + DIFF_Mon_per1, DIFF_Mon_per2 = Ret_Mon_per, where DIFF_Mon_per1 is the monocyte percentage in the first white blood cell classification information before correction, DIFF_Neu_per1 is the neutrophil percentage in the first white blood cell classification information before correction, Ret_Mon_per is the monocyte percentage in the second white blood cell classification information, and DIFF_Neu_per2 and DIFF_Mon_per2 are the neutrophil percentage and the monocyte percentage in the first white blood cell classification information after correction.

9. The blood cell analyzer according to any one of claims 1 to 7, characterized in that, The data processing device uses the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information, including: the data processing device When it is determined that there is a classification abnormality where the monocyte population and the neutrophil population overlap in the first white blood cell classification scatter plot, and the lymphocyte population and other particle populations are clearly demarcated, the data processing device uses the monocyte percentage in the second white blood cell classification information to correct the monocyte percentage and the neutrophil percentage in the first white blood cell classification information. For example, the monocyte percentage and the neutrophil percentage in the first white blood cell classification information are corrected by the following formula DIFF_Mon_per = Ret_Mon_per, DIFF_Neu_per = 1 - DIFF_Lym_per - DIFF_Mon_per - DIFF_Eos_per, Among them, DIFF_Mon_per is the percentage of monocytes in the corrected first white blood cell classification information, Ret_Mon_per is the percentage of monocytes in the second white blood cell classification information, DIFF_Neu_per is the percentage of neutrophils in the corrected first white blood cell classification information, and DIFF_Lym_per and DIFF_Eos_per are the percentages of lymphocytes and eosinophils in the first white blood cell classification information before correction, respectively.

10. The blood cell analyzer according to any one of claims 1 to 7, characterized in that, The sample preparation device is further configured to mix yet another portion of the blood sample to be tested with another hemolytic agent to prepare a third measurement sample for identifying basophils; The flow cell is further configured to allow the third measurement sample to pass through, the light source is further configured to irradiate the third measurement sample passing through the flow cell with light, and the light detector is further configured to detect third optical information generated after the third measurement sample is irradiated with light when passing through the flow cell; The data processing device is further configured to obtain white blood cell count information of the blood sample to be tested based on the first white blood cell classification scatter plot, the white blood cell count information including lymphocyte count, monocyte count, neutrophil count, and eosinophil count, and obtain the percentage of basophils and basophil count of the blood sample to be tested based on the third optical information; Among them, the data processing device uses the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information, including: the data processing device When it is determined that there is a classification abnormality in which the lymphocyte population coincides with red blood cell fragments in the first white blood cell classification scatter plot, and the monocyte population is clearly demarcated from other particle populations, the data processing device uses the lymphocyte percentage in the second white blood cell classification information to correct the first white blood cell classification information. For example, the lymphocyte percentage, monocyte percentage, neutrophil percentage, and eosinophil percentage in the first white blood cell classification information are corrected by the following formula Diff_Lym_per = Ret_Lym_per, Diff_Mon_per = Diff_Num_Mon / (Diff_Num_Mon + Diff_Num_Other) * (100% - Diff_Lym_per), Diff_Eos_per = Diff_Num_Eos / (Diff_Num_Eos + Diff_Num_Neu_Bas) * (100% - Diff_Lym_per - Diff_Mon_per, Diff_Neu_per = 100% - Diff_Lym_per - Diff_Mon_per - Diff_Eos_per - Diff_Bas_per, Wherein, Diff_Lym_per is the lymphocyte percentage in the corrected first white blood cell classification information, Ret_Lym_per is the lymphocyte percentage in the second white blood cell classification information, Diff_Mon_per is the monocyte percentage in the corrected first white blood cell classification information, Diff_Num_Mon is the monocyte count, Diff_Num_Other is the sum of the neutrophil count, eosinophil count, and basophil count, Diff_Eos_per is the eosinophil percentage in the corrected first white blood cell classification information, Diff_Num_Eos is the eosinophil count, Diff_Num_Neu_Bas is the sum of the neutrophil count and basophil count, DIFF_Neu_per is the neutrophil percentage in the corrected first white blood cell classification information, and Diff_Bas_per is the basophil percentage.

11. The blood cell analyzer according to any one of claims 1 to 10, characterized in that, The data processing device generates a first white blood cell classification scatter plot based on the first optical information, including: the data processing device generates the first white blood cell classification scatter plot based on the low-angle scattered light signal, medium-angle scattered light signal, and high-angle scattered light signal in the first optical information; The data processing device generates a second white blood cell classification scatter plot based on the second optical information, including: the data processing device generates the second white blood cell classification scatter plot based on the low-angle scattered light signal and high-angle scattered light signal in the second optical information.

12. The blood cell analyzer according to any one of claims 1 to 11, characterized in that, The data processing device is further configured to obtain at least one, preferably all, of the red blood cell count, platelet count, reticulocyte count, and reticulocyte classification based on the second optical information.

13. A blood cell analyzer, comprising: A sampling device for aspirating a blood sample to be tested; A sample preparation device for mixing a part of the blood sample to be tested with a lysing agent to prepare a first test sample for white blood cell classification, and for mixing another part of the blood sample to be tested, a diluent, and a fluorescent staining agent to prepare a second test sample for identifying platelets and / or reticulocytes; An optical detection device including a flow cell, a light source, and a light detector, wherein the flow cell is used for the first test sample and the second test sample to pass through respectively, the light source is used for irradiating the first test sample and the second test sample passing through the flow cell respectively with light, and the light detector is used for detecting the first optical information and the second optical information generated after the first test sample and the second test sample are irradiated with light when passing through the flow cell respectively; And A data processing device configured to: obtain the ex vivo time of the blood sample to be tested, and when the ex vivo time is greater than a preset duration, perform white blood cell classification on the blood sample to be tested using the first optical information and the second optical information.

14. The blood cell analyzer according to claim 13, wherein, The data processing device performs white blood cell classification on the blood sample to be tested using the first optical information and the second optical information, including: the data processing device Generate a first white blood cell classification scatter plot based on the first optical information and obtain first white blood cell classification information of the blood sample to be tested based on the first white blood cell classification scatter plot. The first white blood cell classification information includes at least lymphocyte percentage, monocyte percentage, neutrophil percentage, and eosinophil percentage. Generate a second white blood cell classification scatter plot based on the second optical information and obtain second white blood cell classification information of the blood sample to be tested based on the second white blood cell classification scatter plot. The second white blood cell classification information includes lymphocyte percentage, monocyte percentage, and granulocyte percentage. The granulocytes include neutrophils and eosinophils. And use the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information.

15. The blood cell analyzer according to claim 14, characterized in that, The data processing device performs white blood cell classification on the blood sample to be tested using the first optical information and the second optical information, including: the data processing device uses the monocyte percentage in the second white blood cell classification information to correct the monocyte percentage and neutrophil percentage in the first white blood cell classification information. For example, correct the monocyte percentage and neutrophil percentage in the first white blood cell classification information through the following formula DIFF_Mon_per = Ret_Mon_per, DIFF_Neu_per = 1 - DIFF_Lym_per - DIFF_Mon_per - DIFF_Eos_per, where DIFF_Mon_per is the monocyte percentage in the corrected first white blood cell classification information, Ret_Mon_per is the monocyte percentage in the second white blood cell classification information, DIFF_Neu_per is the neutrophil percentage in the corrected first white blood cell classification information, and DIFF_Lym_per and DIFF_Eos_per are respectively the lymphocyte percentage and eosinophil percentage in the first white blood cell classification information before correction.

16. The blood cell analyzer according to claim 14 or 15, characterized in that, The data processing device generates a first white blood cell classification scatter plot based on the first optical information, including: the data processing device generates the first white blood cell classification scatter plot based on the low-angle scattered light signal, medium-angle scattered light signal, and high-angle scattered light signal in the first optical information; The data processing device generates a second white blood cell classification scatter plot based on the second optical information, including: the data processing device generates the second white blood cell classification scatter plot based on the low-angle scattered light signal and high-angle scattered light signal in the second optical information.

17. The blood cell analyzer according to any one of claims 13 to 16, characterized in that The data processing device is further configured to: When the ex vivo time is not greater than the preset duration, a first white blood cell classification scatter plot is generated based on the first optical information, and first white blood cell classification information of the blood sample to be tested is obtained and output based on the first white blood cell classification scatter plot. The first white blood cell classification information at least includes lymphocyte percentage, monocyte percentage, neutrophil percentage, and eosinophil percentage.

18. The blood cell analyzer according to any one of claims 13 to 17, characterized in that, The ex vivo time is input by the user into the data processing device through a user interface.

19. The blood cell analyzer according to any one of claims 13 to 18, characterized in that, The data processing device is further configured to: Obtain the species category corresponding to the blood sample to be tested. Preferably, the ex vivo time and the species category are input by the user into the data processing device through a user interface; and Determine the preset duration according to the species category.

20. A blood cell analysis method, comprising: Aspirate a blood sample to be tested; Mix a part of the blood sample to be tested with a lysing agent to prepare a first test sample for white blood cell classification, and make the particles in the first test sample pass through an optically detected area irradiated by light one by one to obtain first optical information generated after the particles in the first test sample are irradiated by light; Mix another part of the blood sample to be tested, a diluent, and a fluorescent stain to prepare a second test sample for identifying platelets and / or reticulocytes, and make the particles in the second test sample pass through an optically detected area irradiated by light one by one to obtain second optical information generated after the particles in the second test sample are irradiated by light; Generate a first white blood cell classification scatter plot based on the first optical information and obtain the first white blood cell classification information of the blood sample to be tested based on the first white blood cell classification scatter plot. The first white blood cell classification information at least includes lymphocyte percentage, monocyte percentage, neutrophil percentage, and eosinophil percentage; And When it is determined that there is a classification abnormality in which a lymphocyte population and / or a monocyte population coincides with at least one other particle population in the first white blood cell classification scatter plot, a second white blood cell classification scatter plot is generated based on the second optical information, and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot. The second white blood cell classification information includes lymphocyte percentage, monocyte percentage, and granulocyte percentage. The granulocytes include neutrophils and eosinophils, and the second white blood cell classification information is used to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information.

21. A blood cell analysis method, comprising: Aspirate a blood sample to be tested; Mix a part of the blood sample to be tested with a lysing agent to prepare a first test sample for white blood cell classification, and make the particles in the first test sample pass through an optically detected area irradiated by light one by one to obtain first optical information generated after the particles in the first test sample are irradiated by light; Mix another part of the blood sample to be tested, a diluent, and a fluorescent stain to prepare a second measurement sample for identifying platelets and / or reticulocytes, and cause the particles in the second measurement sample to pass one by one through an optically detected area irradiated with light to obtain second optical information generated after the particles in the second measurement sample are irradiated with light; And Obtain the ex vivo time of the blood sample to be tested, and when the ex vivo time is greater than a preset duration, perform white blood cell classification on the blood sample to be tested using the first optical information and the second optical information.

22. A blood cell analyzer, comprising: A sampling device for aspirating a blood sample to be tested; A sample preparation device for mixing a part of the blood sample to be tested, a diluent, and a fluorescent stain to prepare a second measurement sample for identifying platelets and / or reticulocytes; An optical detection device, including a flow cell, a light source, and a light detector, wherein the flow cell is for the second measurement sample to pass through, the light source is for irradiating a first measurement sample passing through the flow cell with light, and the light detector is for detecting second optical information generated after the first measurement sample is irradiated with light when passing through the flow cell; And A data processing device configured to: Generate a reticulocyte scatter plot based on the second optical information and identify platelets and / or reticulocytes of the blood sample to be tested based on the reticulocyte scatter plot; And Generate a second white blood cell classification scatter plot based on the second optical information and obtain second white blood cell classification information of the blood sample to be tested based on the second white blood cell classification scatter plot, where the second white blood cell classification information at least includes lymphocyte percentage, monocyte percentage, and granulocyte percentage, and the granulocytes include neutrophils and eosinophils.

23. The blood cell analyzer according to claim 22, wherein, The sample preparation device is further configured to additionally add a chemical stain when preparing the second measurement sample, so that the data processing device can distinguish eosinophils in the granulocytes based on the second white blood cell classification scatter plot, such that the second white blood cell classification information at least includes lymphocyte percentage, monocyte percentage, eosinophil percentage, and neutrophil percentage.

24. The blood cell analyzer according to claim 23, characterized in that, The sample preparation device is further configured to add the chemical stain in a manner of being mixed in the diluent or the fluorescent stain or separately when preparing the second measurement sample.

25. The blood cell analyzer according to any one of claims 22 to 24, characterized in that, The data processing device generating a reticulocyte scatter plot based on the second optical information includes: the data processing device generating the reticulocyte scatter plot based on at least a low-angle scattered light signal and a lateral fluorescence signal in the second optical information; and The data processing device generating a second white blood cell classification scatter plot based on the second optical information includes: the data processing device generating the second white blood cell classification scatter plot based on at least a low-angle scattered light signal and a high-angle scattered light signal in the second optical information; Or, The data processing device generates a reticulocyte scatter plot based on the second optical information, including: the data processing device generates the reticulocyte scatter plot based on at least the forward scatter light signal and the side fluorescence signal in the second optical information; and The data processing device generates a second white blood cell classification scatter plot based on the second optical information, including: the data processing device generates the second white blood cell classification scatter plot based on at least the forward scatter light signal and the side scatter light signal in the second optical information.

26. A blood cell analysis method, including: Aspirating a blood sample to be tested; Mixing a part of the blood sample to be tested, a diluent, and a fluorescent stain to prepare a second measurement sample for identifying platelets and / or reticulocytes, and causing the particles in the second measurement sample to pass through an optically detected area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light; Generating a reticulocyte scatter plot based on the second optical information and identifying platelets and / or reticulocytes in the blood sample to be tested based on the reticulocyte scatter plot; And Generating a second white blood cell classification scatter plot based on the second optical information and obtaining second white blood cell classification information of the blood sample to be tested based on the second white blood cell classification scatter plot, the second white blood cell classification information at least including lymphocyte percentage, monocyte percentage, and granulocyte percentage, and the granulocytes including neutrophils and eosinophils.

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