Blood cell analyzer

Abstract

The invention relates to a blood cell analyzer, comprising: a sample suction device for sucking a blood sample to be detected; the sample preparation device is used for mixing a part of the blood sample to be detected, a hemolytic agent and a coloring agent to prepare a determination sample; an optical detection device for acquiring optical information generated after the measurement sample is irradiated by light when passing through the flow cell; a processor configured to: generate a scatter diagram based on a scattered light intensity signal and a fluorescence intensity signal in the optical information; determining a feature region from the scatter diagram and acquiring particle feature information of particles falling into the feature region; and obtaining and outputting characteristic parameters according to the particle characteristic information to judge whether the to-be-detected blood sample is from a patient suffering from infectious monocytosis or not, and / or outputting alarm information to represent that the to-be-detected blood sample is from the patient suffering from infectious monocytosis if the particle characteristic information meets a preset condition. Therefore, the clinical effective identification of IM patients is simply assisted.

Description

Technical Field

[0001] This application relates to the field of in vitro diagnosis, and particularly to a blood cell analyzer. Background Art

[0002] Infectious mononucleosis (IM) is an infectious disease caused by Epstein-Barr virus (EBV) infection, resulting in acute hyperplasia of the mononuclear-macrophage system. This disease is mainly transmitted through contact and saliva, and can also be transmitted through blood transfusion and feces. The typical clinical "triad" is fever, pharyngitis, and cervical lymphadenopathy, which may be accompanied by hepatosplenomegaly and increased atypical lymphocytes in the peripheral blood.

[0003] IM is a benign self-limiting disease with an incubation period of about 5-15 days and a course of disease usually lasting 2-3 weeks. The peak age of IM incidence in China is 4-6 years. Most IM patients have a good prognosis, but some patients may have complications during the disease process, affecting the prognosis. According to statistics, about 50% of IM patients have complications such as hepatitis, hepatomegaly, and splenomegaly, and 1%-2% of patients may develop life-threatening serious complications such as splenic rupture, hemophagocytic syndrome, and B-cell lymphoma.

[0004] Based on the characteristics of IM, such as a relatively young average age of onset and diverse complications, it is of great clinical significance to identify IM patients as early as possible and accurately in clinical laboratory examinations, and then carry out targeted treatment for patients to avoid the occurrence of complications and improve the prognosis of patients.

[0005] Currently, the diagnosis of IM patients usually adopts the method of combining clinical manifestations with peripheral blood smear microscopy. If the clinical manifestations of a patient conform to the typical clinical "triad" and the atypical lymphocytes increase under the microscope, specific laboratory tests (such as anti-EBV-VCA antibody detection and heterophil agglutination test, etc.) are performed on the patient. Patients with positive results in specific laboratory tests are diagnosed as IM. Due to the diverse clinical manifestations of IM, which are similar to those of common febrile infections, it is difficult for inexperienced clinicians to identify such patients as possibly having IM based on clinical manifestations. In the absence of identifying such patients as possibly having IM based on clinical manifestations, doctors will not let patients undergo examinations such as peripheral blood smear microscopy, resulting in the easy missed diagnosis and misdiagnosis of IM clinically at the present stage, thus affecting the prognosis and outcome of patients. Summary of the Invention

[0006] Therefore, the task of this application is to provide a blood cell analyzer that can quickly, accurately, and at low cost obtain characteristic parameters for judging whether a blood sample is from a patient with IM and / or output an alarm message indicating that the blood sample is from a patient with IM, so as to make a more accurate diagnosis of patients with IM clinically.

[0007] To achieve the above tasks of the present application, a first aspect of the present application first proposes a blood cell analyzer, including:

[0008] A sampling device for sucking a blood sample to be tested;

[0009] A sample preparation device for mixing at least a part of the blood sample to be tested, a hemolytic agent and a staining agent to prepare a measurement sample;

[0010] An optical detection device, including a flow cell, a light source and a photodetector, wherein the flow cell is used for the measurement sample to pass through, the light source is used to irradiate the measurement sample passing through the flow cell with light, and the photodetector is used to detect the optical information generated after the measurement sample is irradiated by light when passing through the flow cell;

[0011] A processor configured to:

[0012] Generate a scatter plot based on the scattered light intensity signal and the fluorescence intensity signal in the optical information;

[0013] Determine a characteristic region from the scatter plot and obtain particle characteristic information of the particles falling into the characteristic region;

[0014] Obtain and output characteristic parameters according to the particle characteristic information, where the characteristic parameters are used to determine whether the blood sample to be tested is from a patient with infectious mononucleosis, and / or output an alarm message if the particle characteristic information meets a preset condition, and the alarm message indicates that the blood sample to be tested is from a patient with infectious mononucleosis.

[0015] A second aspect of the present application provides another blood cell analyzer, including:

[0016] A sampling device for sucking a blood sample to be tested;

[0017] A sample preparation device for mixing at least a part of the blood sample to be tested, a hemolytic agent and a staining agent to prepare a measurement sample;

[0018] An optical detection device, including a flow cell, a light source and a photodetector, wherein the flow cell is used for the measurement sample to pass through, the light source is used to irradiate the measurement sample passing through the flow cell with light, and the photodetector is used to detect the optical information generated after the measurement sample is irradiated by light when passing through the flow cell;

[0019] A processor configured to:

[0020] Generate a scatter plot based on the scattered light intensity signal and the fluorescence intensity signal in the optical information;

[0021] Determine a characteristic region from the scatter plot and obtain particle characteristic information of the particles falling into the characteristic region;

[0022] Judge whether to output an alarm message according to the particle characteristic information, where the alarm message indicates that the blood sample to be tested is from a patient suffering from infectious mononucleosis. Among them, when it is judged to output the alarm message, there is no valley point in the fluorescence intensity distribution histogram of the particles falling into the characteristic region and the width of the fluorescence intensity distribution histogram is greater than a preset threshold.

[0023] The third aspect of the present application provides another blood cell analyzer, including:

[0024] A sampling device for sucking a blood sample to be tested;

[0025] A sample preparation device for mixing at least a part of the blood sample to be tested, a hemolytic agent and a staining agent to prepare a measurement sample;

[0026] An optical detection device includes a flow cell, a light source and a photodetector. The flow cell is used for the measurement sample to pass through. The light source is used to irradiate the measurement sample passing through the flow cell with light, and the photodetector is used to detect the optical information generated after the measurement sample is irradiated by light when passing through the flow cell;

[0027] A processor configured to:

[0028] Obtain a particle distribution map of the particles in the measurement sample at least based on the fluorescence intensity signal in the optical information;

[0029] Compare the particle distribution map with a preset reference map;

[0030] If the result of the comparison meets the preset conditions, output an alarm message, where the alarm message indicates that the blood sample to be tested is from a patient suffering from infectious mononucleosis.

[0031] The fourth aspect of the present application further provides a blood cell analyzer, including:

[0032] A sampling device for sucking a blood sample to be tested;

[0033] A sample preparation device for mixing at least a part of the blood sample to be tested, a hemolytic agent and a staining agent to prepare a measurement sample;

[0034] An optical detection device includes a flow cell, a light source and a photodetector. The flow cell is used for the measurement sample to pass through. The light source is used to irradiate the measurement sample passing through the flow cell with light, and the photodetector is used to detect the optical information generated after the measurement sample is irradiated by light when passing through the flow cell;

[0035] A processor, configured to:

[0036] Input the feature information extracted from the optical information into a trained neural network model to obtain the output of the neural network model;

[0037] Output an alarm message according to the output of the neural network model, where the alarm message indicates that the blood sample to be tested is from a patient with infectious mononucleosis.

[0038] In the blood cell analyzer provided in various aspects of the present application, without additional reagent costs and labor costs, the characteristic parameters for determining whether the blood sample to be tested is from a patient with infectious mononucleosis and / or the alarm message indicating that the blood sample to be tested is from a patient with infectious mononucleosis can be quickly and accurately output through the optical information obtained by the optical detection channel, thereby improving the accuracy of clinical diagnosis of patients with infectious mononucleosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic structural diagram of a blood cell analyzer according to some embodiments of the present application.

[0040] Figure 2 It is a schematic structural diagram of an optical detection device according to some embodiments of the present application.

[0041] Figure 3 It shows a three-dimensional scatter plot of non-IM patient samples and IM patient samples according to some embodiments of the present application.

[0042] Figure 4 It shows a two-dimensional scatter plot of non-IM patient samples and IM patient samples according to some embodiments of the present application.

[0043] Figure 5 It shows a characteristic region in the SS-FL scatter plot of the blood sample to be tested according to some embodiments of the present application.

[0044] Figure 6 It shows a scatter plot of different patient samples and the corresponding fluorescence intensity distribution histogram according to some embodiments of the present application.

[0045] Figure 7 It shows a fluorescence intensity distribution histogram of non-IM patient samples according to some embodiments of the present application.

[0046] Figure 8 It shows the SS-FL scatter plots of different patient samples according to some embodiments of the present application.

[0047] Figure 9Shows the SS-FL scatter plot and the white blood cell classification results according to some embodiments of the present application.

[0048] Figure 10 Shows the SS-FL scatter plot and the SS-FS scatter plot of different patient samples according to some embodiments of the present application.

[0049] Figure 11 Shows the fluorescence intensity distribution histogram of lymphocyte clusters according to some embodiments of the present application.

[0050] Figure 12 Shows the particle distribution diagram and the preset reference diagram according to some embodiments of the present application.

[0051] Figure 13 Shows the schematic diagram of the solution for obtaining the output of the neural network model according to some embodiments of the present application. Detailed implementation manners

[0052] The embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0053] 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.

[0054] Those skilled in the art of the present technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used here have the same meaning as the general understanding of those of ordinary skill in the field to which the present application belongs. For the convenience of subsequent description, some terms involved in the following are briefly described here:

[0055] 1) Scatter plot: A two-dimensional or three-dimensional graph generated by a blood cell analyzer, on which the two-dimensional or three-dimensional characteristic information of multiple particles is distributed. The X-axis, Y-axis, and Z-axis of the scatter plot all represent a characteristic of each particle. For example, in a scatter plot, the X-axis represents the forward scatter light intensity, the Y-axis represents the fluorescence intensity, and the Z-axis represents the side scatter light intensity.

[0056] 2) Cell population: A particle cluster formed by multiple particles with the same characteristics distributed in a certain area of the scatter plot, such as the white blood cell population, and the neutrophil population, lymphocyte population, monocyte population, eosinophil population, or basophil population in white blood cells, etc.

[0057] 3) Ghost: Fragment particles obtained by dissolving red blood cells and platelets in blood with a hemolytic reagent.

[0058] The embodiment of the present application proposes a simple, effective, economical and affordable method for differentiating infectious mononucleosis, which can be used for screening of infectious mononucleosis and prompting clinicians to perform peripheral blood smear microscopy or specific laboratory tests.

[0059] For patients with infectious mononucleosis, due to the stimulation of EBV on B lymphocytes and T lymphocytes, there are differences in blood picture compared with normal blood and other types of infections. A blood cell analyzer is used to obtain the overall quantity change and morphological distribution of blood cells, and can reflect information such as the volume, nucleic acid content, and intracellular complexity of blood cells. Through these information of blood cells in the blood, it can be used to differentiate infectious mononucleosis or obtain parameters characterizing the patient's infection status.

[0060] At present, blood cell analyzers can test samples such as human blood and the blood of other mammals (such as dogs, cats, horses, etc.), avian blood, and fish blood. Generally, a blood cell analyzer counts and classifies white blood cells through the DIFF channel (white blood cell classification channel), for example, classifying white blood cells into five types of white blood cells: lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), eosinophils (Eos), and basophils (Eos).

[0061] The hematology analyzer used in this application classifies and counts particles in a blood sample through flow cytometry that combines laser scattering and fluorescence staining methods. Here, the principle of the hematology analyzer for detecting a blood sample can be, for example: First, a blood sample is aspirated and treated with a hemolytic agent and a fluorescent dye. Among them, red blood cells are destroyed and dissolved by the hemolytic agent, while white blood cells are not dissolved, but the fluorescent dye can enter the cell nucleus of white blood cells with the help of the hemolytic agent and bind to the nucleic acid substances in the cell nucleus; then the particles in the sample pass through the detection hole irradiated by the laser beam one by one. When the laser beam irradiates the particles, the characteristics of the particles themselves (such as volume, staining degree, size and content of cell contents, nuclear density, etc.) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to the particle characteristics. After these scattered lights are received by the signal detector, relevant information on the particle structure and composition can be obtained. Among them, forward scatter light (FS) reflects the number and volume of particles, side scatter light (SS) reflects the complexity of the internal structure of cells (such as intracellular granules or cell nuclei), and fluorescence (FL) reflects the content of nucleic acid substances in cells. Using this optical information, the particles in the sample can be classified and counted.

[0062] Figure 1 It is a schematic structural diagram of the hematology analyzer according to some embodiments of this application. The hematology analyzer 100 includes a sampling device 110, a sample preparation device 120, an optical detection device 130, and a processor 140. The hematology analyzer 100 also has a liquid path system (not shown) for connecting the sampling device 110, the sample preparation device 120, and the optical detection device 130 to facilitate liquid transportation between these devices.

[0063] The sampling device 110 is used to aspirate the blood sample to be tested from the subject.

[0064] The sample preparation device 120 is used to mix at least a part of the blood sample to be tested, a hemolytic agent, and a staining agent to prepare a measurement sample.

[0065] In the embodiments of this application, the hemolytic agent is used to dissolve red blood cells in the blood and lyse red blood cells into fragments, but can keep the morphology of white blood cells basically unchanged. The staining agent can be, for example, a fluorescent dye.

[0066] The optical detection device 130 includes a flow cell, a light source, and a light detector. The flow cell is used for the measurement sample to pass through, the light source is used to irradiate the measurement sample passing through the flow cell with light, and the light detector is used to detect the optical information generated after the measurement sample is irradiated by light when passing through the flow cell.

[0067] In the first embodiment, the processor 140 is configured to:

[0068] Generate a scatter plot based on the scattered light intensity signal and the fluorescence intensity signal in the optical information;

[0069] Determine a characteristic region from the scatter plot and obtain particle characteristic information of the particles falling into the characteristic region;

[0070] Obtain and output characteristic parameters according to the particle characteristic information, the characteristic parameters are used to determine whether the blood sample to be tested is from a patient with infectious mononucleosis, and / or output an alarm message if the particle characteristic information meets a preset condition, the alarm message indicates that the blood sample to be tested is from a patient with infectious mononucleosis.

[0071] In this way, without additional reagent costs and labor costs, characteristic parameters for determining whether the blood sample to be tested is from a patient with infectious mononucleosis and / or an alarm message indicating that the blood sample to be tested is from a patient with infectious mononucleosis can be quickly and accurately output through the optical information obtained by the optical detection channel, which helps to improve the doctor's vigilance and perception ability for patients with infectious mononucleosis, and thus helps to make a more accurate diagnosis clinically for patients with infectious mononucleosis. For patients, only venous blood needs to be drawn for a blood routine test to perform early diagnosis and treatment for infectious mononucleosis, which helps to improve the prognosis of patients.

[0072] In some embodiments, the characteristic parameters can also be used to determine the severity of IM patients.

[0073] The blood cell analyzer 100 will be further described below in conjunction with some embodiments.

[0074] In some embodiments, the sampling device 110 has a sampling needle (not shown) for sucking the blood sample to be tested. In addition, the sampling device 110 may further include a driving device, which is used to drive the sampling needle to quantitatively suck the blood sample to be tested through the nozzle of the sampling needle. The sampling device 110 can transport the sucked blood sample to the sample preparation device 120.

[0075] In some embodiments, the sample preparation device 120 may include at least one reaction cell and a reagent supply device (not shown in the figure). The at least one reaction cell is used to receive the blood sample to be tested sucked by the sampling device 110, and the reagent supply device supplies processing reagents (including hemolytic agents, staining agents, etc.) to the at least one reaction cell, so that the blood sample to be tested sucked by the sampling device 110 is mixed with the processing reagents provided by the reagent supply device in the reaction cell to prepare a measurement sample.

[0076] In some embodiments, the flow cell in the optical detection device 130 refers to a chamber adapted to detect the focused fluid flow of light scattering signals and fluorescence signals. When a particle, such as a blood cell, passes through the detection hole of the flow cell, the particle scatters the incident light beam from the light source directed to the detection hole in all directions. Light detectors can be arranged at one or more different angles relative to the incident light beam to detect the light scattered by the particle, thereby obtaining a light scattering signal. Since different particles have different light scattering characteristics, the light scattering signal can be used to distinguish different particle populations.

[0077] Specifically, the light scattering signal detected near the incident light beam is usually referred to as the forward light scattering signal or the small angle light scattering signal. In some embodiments, the forward light scattering signal can be detected at an angle from about 1° to about 10° with respect to the incident light beam. In some other embodiments, the forward light scattering signal can be detected at an angle from about 2° to about 6° with respect to the incident light beam. The light scattering signal detected in the direction approximately 90° to the incident light beam is usually referred to as the side light scattering signal. In some embodiments, the side light scattering signal can be detected at an angle from about 65° to about 115° with respect to the incident light beam. Generally, the fluorescence signal emitted from the blood cells stained with a fluorescent dye is also usually detected in the direction approximately 90° to the incident light beam.

[0078] In some embodiments, the light detectors in the optical detection device 130 can include a forward light scattering detector for detecting the forward scattered light signal, a side light scattering detector for detecting the side scattered light signal, and a fluorescence detector for detecting the fluorescence signal. For example, the optical information can include measuring multiple signals such as the forward scattered light signal, the side scattered light signal, and the fluorescence signal of the particles in the sample.

[0079] In a specific example of the optical detection device 130, as Figure 2 shown, the optical detection device 130 has a light source 101, a beam shaping component 102, a flow cell 103, and a forward light scattering detector 104 arranged in a straight line in sequence. On one side of the flow cell 103, a dichroic mirror 106 is arranged at an angle of 45° to the straight line. A part of the side light emitted by the particles in the flow cell 103 passes through the dichroic mirror 106 and is captured by the fluorescence detector 105 arranged at an angle of 45° to the dichroic mirror 106 behind the dichroic mirror 106; another part of the side light is reflected by the dichroic mirror 106 and is captured by the side light scattering detector 107 arranged at an angle of 45° to the dichroic mirror 106 in front of the dichroic mirror 106.

[0080] In some embodiments, the processor 140 is configured to process and calculate data to obtain the required results. For example, it can generate a two-dimensional scatter plot or a three-dimensional scatter plot based on the collected original optical information, and perform particle analysis on the scatter plot according to the gating method. The processor 140 can also perform visualization processing on the intermediate processing result or the final processing result, and then display it through the display device 150. In the embodiments of the present application, the processor 140 is configured to implement the method steps described in further detail later.

[0081] In some embodiments, the processor 140 can perform visualization processing on the intermediate processing result or the final processing result, and then display it through the display device 150. For example, the display device 150 may include a user interface, and the processor 140 can output the obtained characteristic parameters or the alarm information to be output on the user interface of the display device 150.

[0082] In some embodiments, the processor 140 includes, but is not limited to, a Central Processing Unit (CPU), a Micro Controller Unit (MCU), a Field-Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), etc., which are devices for interpreting computer instructions and processing data in computer software. For example, the processor is configured to execute each computer application program in the computer-readable storage medium, so that the blood cell analyzer 100 executes the corresponding detection process and analyzes the optical information detected by the optical detection device 130 in real time.

[0083] In addition, the blood cell analyzer 100 may further include a first housing 160 and a second housing 170. The optical detection device 130 and the processor 140 are disposed inside the second housing 170. The sample preparation device 120 is disposed, for example, inside the first housing 160, and the display device 150 is disposed, for example, on the outer surface of the first housing 160 and is used to display the detection results of the blood cell analyzer 100.

[0084] Next, some embodiments will be combined to further illustrate the steps to be executed by the processor 140.

[0085] In some embodiments, the scattered light intensity signal may include one or more of a forward scattered light intensity signal FS and a side scattered light intensity signal SS. That is, here, the processor 140 can generate the scatter plot based on one or more of the forward scattered light intensity signal FS and the side scattered light intensity signal SS in the optical information and the fluorescence intensity signal FL.

[0086] Preferably, the scattered light intensity signal includes at least the side scattered light intensity signal SS. That is, the processor 140 may generate the scattergram based on the side scattered light intensity signal SS and the fluorescence intensity signal FL in the optical information.

[0087] In some embodiments, the scattergram generated by the processor 140 based on the side scattered light intensity signal SS and the fluorescence intensity signal FL in the optical information may be a two-dimensional or three-dimensional scattergram.

[0088] For example, Figure 3 and Figure 4 As shown, in Figure 3 In the figure, scatter plot (a) is a three-dimensional scatter plot generated based on the forward scattered light intensity signal FS, the side scattered light intensity signal SS, and the fluorescence intensity signal FL in the optical information when the measurement sample is from a non-IM patient sample (i.e., a sample from a patient who does not suffer from infectious mononucleosis); scatter plot (b) is a three-dimensional scatter plot generated based on the forward scattered light intensity signal FS, the side scattered light intensity signal SS, and the fluorescence intensity signal FL in the optical information when the measurement sample is from an IM patient sample (i.e., a sample from a patient who suffers from infectious mononucleosis); Figure 4 In the figure, scatter plot (a) is a two-dimensional scatter plot generated based on the side scattered light intensity signal SS and the fluorescence intensity signal FL in the optical information when the measurement sample comes from a non-IM patient sample, and scatter plot (b) is a two-dimensional scatter plot generated based on the side scattered light intensity signal SS and the fluorescence intensity signal FL in the optical information when the measurement sample comes from an IM patient sample.

[0089] In some embodiments, the characteristic region determined by the processor 140 from the generated scatter plot may be a region in the scatter plot where the side scattered light intensity signal SS is lower than a first threshold and the fluorescence intensity signal FL is higher than a second threshold, such as Figure 5 As shown in the “Mark Area” marked in the figure.

[0090] In some embodiments, the preset condition may include that the fluorescence intensity distribution histogram of the particles falling within the characteristic region has no valleys and the width of the fluorescence intensity distribution histogram is greater than a first preset threshold. In other words, if the fluorescence intensity distribution histogram of the particles falling within the characteristic region has no valleys and the width of the fluorescence intensity distribution histogram is greater than the first preset threshold, the processor 140 will output an alarm indicating that the blood sample to be tested is from a patient suffering from infectious mononucleosis.

[0091] Figure 6A scatter plot of different patient samples and the corresponding fluorescence intensity distribution histogram are shown. Among them, scatter plot (a) is the SS-FL two-dimensional scatter plot of the IM patient sample (i.e., the sample of a patient with infectious mononucleosis); scatter plot (b) is the SS-FL two-dimensional scatter plot of the non-IM patient sample (i.e., the sample of a patient without infectious mononucleosis); the solid line in the fluorescence intensity distribution histogram (c) shows the particle distribution of the number of cell particles varying with the fluorescence intensity signal FL in the IM patient sample, and the dashed line shows the particle distribution of the number of cell particles varying with the fluorescence intensity signal FL in the non-IM patient sample.

[0092] As Figure 6 shown in the fluorescence intensity distribution histogram (c), when the test sample is from a non-IM patient sample, there is a valley point in the fluorescence intensity distribution histogram of the characteristic region "Mark Area", while when the test sample is from an IM patient sample, there is no valley point in the fluorescence intensity distribution histogram of the characteristic region "Mark Area". Specifically, Figure 7 it shows the distribution of the number of cell particles varying with the fluorescence intensity signal FL when the test sample is from a non-IM patient sample. In a specific example, the valley point can be searched from the left boundary to the right boundary of the fluorescence intensity distribution histogram of the characteristic region "Mark Area".

[0093] In a specific example, the connectivity of the particle clusters falling into the characteristic region can be defined to characterize whether there is a valley point in the fluorescence intensity distribution histogram of the particles falling into the characteristic region. This connectivity can be used as the above-mentioned particle characteristic information.

[0094] In the embodiment of the present application, the connectivity of the particle clusters falling into the characteristic region can be understood as the adhesion degree of the first particle cluster and the second particle cluster adjacent to each other in the characteristic region. If the first particle cluster and the second particle cluster adjacent to each other in the characteristic region are adhered (if there is only one particle cluster in the characteristic region, it means that the first particle cluster and the second particle cluster are completely adhered), then this means that there is no valley point in the fluorescence intensity distribution histogram of the particles falling into the characteristic region. And if the first particle cluster and the second particle cluster are not adhered, then this means that there is a valley point in the fluorescence intensity distribution histogram of the particles falling into the characteristic region.

[0095] In a specific example, the connectivity Area_Connect can be calculated by the following method: As " Figure 7As shown, starting from the left boundary to the right boundary of the fluorescence intensity distribution histogram of the feature region "Mark Area", it is searched to determine whether there is a valley point in the fluorescence intensity distribution histogram. In the case where there is a valley point, the position of the valley point is taken as the demarcation line, and the number of cell particles Cell_Valley at the valley point, and the maximum number of cell particles Cell_Peak in the region from the demarcation line (i.e., the valley point) to the right boundary are determined. Then the connectivity of the particle cluster in this feature region and the particle cluster in other regions is In particular, if there is no valley point, the connectivity Area_Connect of the particle cluster in this feature region and the particle cluster in other regions is 1.

[0096] In some embodiments, the preset conditions may include that the fluorescence intensity distribution histogram of the particles falling into the feature region has no valley point and includes at least one of the following conditions:

[0097] The total number of particles falling into the feature region is greater than a second preset threshold;

[0098] The ratio of the total number of particles falling into the feature region to the total number of particles in the scatter plot is greater than a third preset threshold; and

[0099] The area of the feature region is greater than a fourth preset threshold.

[0100] That is to say, in the case where the fluorescence intensity distribution histogram of the particles falling into the feature region has no valley point, if one or more of the above three conditions are also satisfied, the processor 140 will output an alarm message indicating that the blood sample to be tested is from a patient with infectious mononucleosis.

[0101] It can be understood here that the above three conditions can all be used for the width of the fluorescence intensity distribution histogram of the particles falling into the feature region to be greater than a first preset threshold.

[0102] In some embodiments, by counting the particles in the feature region, the total number of particles Cell_Num falling into the feature region can be obtained, and by counting all the particles in the scatter plot, the total number of particles Cell_Num_All in the scatter plot can be obtained. Then the ratio of the total number of particles falling into the feature region to the total number of particles in the scatter plot

[0103] In some embodiments, the area Cell_Area of the feature region can be determined based on the count sum of the points with pixel values greater than 0 in the feature region. For example, the count sum of the points with pixel values greater than 0 in the feature region can be determined as the area of the feature region.

[0104] Figure 8The SS-FL scatter plots of samples from 3 different patients are shown. Among them, scatter plot (a) is the two-dimensional SS-FL scatter plot of the sample of an IM patient; scatter plot (b) is the two-dimensional SS-FL scatter plot of the sample of a patient with viral pneumonia; scatter plot (c) is the two-dimensional SS-FL scatter plot of the sample of an ordinary patient.

[0105] Table 1 shows the situation of Figure 8 outputting alarm information for the three samples shown.

[0106] Table 1

[0107]

[0108]

[0109] It is assumed that alarm information is output only when the particle characteristic information simultaneously satisfies (1) the total number of particles falling into the characteristic region ≥ 500, (2) the ratio of the total number of particles falling into the characteristic region to the total number of particles in the scatter plot ≥ 3%, (3) the area of the characteristic region ≥ 250, and (4) the connectivity ≥ 0.7.

[0110] As shown in Table 1, since the connectivity of the IM patient sample is 1 (greater than 0.7), that is, there is no valley point in the fluorescence intensity distribution histogram of this sample, and, the total number of particles falling into the characteristic region in this sample is 1083 (greater than 500), the ratio of the total number of particles falling into the characteristic region to the total number of particles in the scatter plot of this sample is 7.7% (greater than 3%), and the area of the characteristic region is 541 (greater than 250), so alarm information can be output for this sample to prompt the doctor that this sample is from a patient with infectious mononucleosis.

[0111] In some embodiments, the preset condition may include that the similarity between the particle distribution in the characteristic region and the particle distribution in the reference region (which can also be referred to as a template or a preset template) is greater than the fifth preset threshold.

[0112] In some embodiments, the processor 140 may be further configured to determine the similarity between the particle distribution in the characteristic region and the particle distribution in the reference region through a template matching algorithm.

[0113] In a specific example, by calculating the correlation coefficient Cross-Correlation between the scatter plot Image1 of the measured sample and the template scatter plot Image2, the similarity between the particle distribution in the feature region and the particle distribution in the reference region can be determined. For example, the calculated correlation coefficient Cross-Correlation can be used as the similarity. For example, the correlation coefficient Cross-Correlation can be calculated using the formula shown below:

[0114]

[0115] It can be understood that the larger the correlation coefficient Cross-Correlation, the more similar the images are, that is, the greater the similarity between the particle distribution in the feature region and the particle distribution in the reference region.

[0116] In some embodiments, the processor 140 can be further configured to match the image features extracted from the feature region with the image features extracted from the reference region to determine the similarity between the particle distribution in the feature region and the particle distribution in the reference region. For example, the extracted image features can include one or more parameters such as the effective area of the image, the average pixel of the image, the center of gravity of the pixel distribution of the image, and the boundary position of the image. For example, the correlation coefficient between the image features extracted from the feature region and the image features extracted from the reference region can be calculated. If the correlation coefficient between the two is larger, the similarity between the particle distribution in the feature region and the particle distribution in the reference region is greater.

[0117] In some embodiments, the processor 140 can be further configured to determine the similarity between the particle distribution in the feature region and the particle distribution in the reference region according to the mean square error between the feature region and the reference region. For example, the mean square error MSE between the feature region and the reference region can be obtained by calculating the average of the sum of the squares of the pixel differences between the scatter plot Image1 of the measured sample and the template scatter plot Image2. For example, the mean square error MSE between the feature region and the reference region can be calculated according to the following formula:

[0118]

[0119] If the mean square error MSE between the feature region and the reference region is smaller, the similarity between the particle distribution in the feature region and the particle distribution in the reference region is greater.

[0120] In some embodiments, the processor 140 may be further configured to compare the fluorescence intensity distribution histogram of the particles falling within the feature region with the fluorescence intensity distribution histogram of the particles falling within the reference region to determine the similarity between the particle distribution in the feature region and the particle distribution in the reference region. For example, the shape of the fluorescence intensity distribution histogram of the particles falling within the feature region may be compared with the shape of the fluorescence intensity distribution histogram of the particles falling within the reference region to determine the similarity between the particle distribution in the feature region and the particle distribution in the reference region. For another example, the respective histogram feature parameters may be extracted from the fluorescence intensity distribution histogram of the particles falling within the feature region and the fluorescence intensity distribution histogram of the particles falling within the reference region, and the histogram feature parameters of these two fluorescence intensity distribution histograms thus extracted may be compared to determine the similarity between the particle distribution in the feature region and the particle distribution in the reference region. Among them, the extracted histogram feature parameters may include one or more parameters such as the width, height, and skewness of the histogram.

[0121] Table 2 shows the similarity of the feature regions of the samples of patients with infectious mononucleosis and the samples of patients without infectious mononucleosis determined based on the template matching algorithm.

[0122] Table 2

[0123] Similarity Whether to output alarm information IM patient sample 0.95 Yes Non-IM patient sample 0.73 No

[0124] As shown in Table 2, assuming that the fifth preset threshold is 0.9, the similarity of the sample of a patient with infectious mononucleosis (i.e., an IM patient) is 0.95 (greater than this fifth preset threshold), while the similarity of the sample of a patient without infectious mononucleosis is 0.73 (less than this fifth preset threshold). Then, an alarm message will be output for the sample of the patient with infectious mononucleosis, and no alarm message will be output for the sample of the patient without infectious mononucleosis.

[0125] In some embodiments, the processor 140 may be further configured to input the particle feature information into a trained neural network model to obtain the output of the neural network model, and determine whether the particle feature information meets the preset conditions according to the output of the neural network model. For example, after inputting the particle feature information into the trained neural network model, if the output of the neural network model is 0, it indicates that the particle feature information does not meet the preset conditions; if the output of the neural network model is 1, it indicates that the particle feature information meets the preset conditions.

[0126] In some embodiments, the particle feature information may include at least one of the following parameters: the total number of particles Cell_Num falling into the feature region, the ratio Cell_Ratio of the total number of particles falling into the feature region to the total number of particles in the scatter plot, the connectivity Area_Connect of the particle clusters falling into the feature region, and the area Cell_Area of the feature region. That is, by using at least one of the parameters of the total number of particles Cell_Num falling into the feature region, the ratio Cell_Ratio of the total number of particles falling into the feature region to the total number of particles in the scatter plot, the connectivity Area_Connect of the particle clusters falling into the feature region, and the area Cell_Area of the feature region, the characteristic parameters for determining whether the blood sample to be tested is from a patient with infectious mononucleosis can be obtained.

[0127] For the specific descriptions of the total number of particles Cell_Num falling into the feature region, the ratio Cell_Ratio, the connectivity Area_Connect, and the area Cell_Area of the feature region, reference can be made to the descriptions in the foregoing related embodiments, which will not be elaborated herein. In some embodiments, the processor 140 may be further configured to classify the white blood cells in the blood sample to be tested based on the optical information, so as to classify the white blood cells into four types: lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), and eosinophils (Eos). For example, the processor 140 may compare the optical signal of the particle cluster in the blood sample to be tested with the optical signals of normal monocyte clusters, lymphocyte clusters (including atypical lymphocytes and abnormal lymphocytes), neutrophil clusters, and eosinophil clusters based on the distribution characteristics of the fluorescence intensity signal FL, the side scatter light intensity signal SS, and the forward scatter light intensity signal FS in the optical information, and then the classification information of the cells can be obtained.

[0128] For example, please refer to Figure 9 , scatter plot (a) is a two-dimensional scatter plot generated based on the side scatter light intensity signal SS and the fluorescence intensity signal FL in the optical information, and the classification result obtained by classifying white blood cells based on the distribution characteristics of the side scatter light intensity signal SS and the fluorescence intensity signal FL in scatter plot (a) is shown in scatter plot (b).

[0129] In some embodiments, the particles in the characteristic region at least include lymphocyte clusters, and the particle characteristic information of the particles in the characteristic region includes at least one of the following parameters of the lymphocyte clusters: the peak-to-valley ratio Fl_Peak_Valley_Ratio of the fluorescence intensity distribution histogram of the lymphocyte clusters, the fluorescence intensity distribution width Fl_Width, the fluorescence intensity distribution centroid Fl_P, the forward scatter light intensity distribution width Fs_Width, and the forward scatter light intensity distribution centroid Fs_P.

[0130] That is, at least one of the peak-to-valley ratio Fl_Peak_Valley_Ratio of the fluorescence intensity distribution histogram of the lymphocyte clusters, the fluorescence intensity distribution width Fl_Width, the fluorescence intensity distribution centroid Fl_P, the forward scatter light intensity distribution width Fs_Width, and the forward scatter light intensity distribution centroid Fs_P in the characteristic region can be used to obtain characteristic parameters for determining whether the blood sample to be tested is from a patient with infectious mononucleosis.

[0131] As Figure 10 shown, scatter plots (a), (b), and (c) respectively show the SS-FL scatter plots of samples from patients with infectious mononucleosis (i.e., IM patients), samples from patients with other viral infections, and samples from normal patients, and the fluorescence intensity distribution width Fl_Width and the fluorescence intensity distribution centroid Fl_P of the particle clusters are schematically shown in scatter plots (a), (b), and (c).

[0132] Please continue to refer to Figure 10 , scatter plots (d), (e), and (f) respectively show the SS-FS scatter plots of samples from patients with infectious mononucleosis (i.e., IM patients), samples from patients with other viral infections, and samples from normal patients, and the forward scatter light intensity distribution width Fs_Width and the forward scatter light intensity distribution centroid Fs_P of the particle clusters are schematically shown in scatter plots (d), (e), and (f).

[0133] Figure 11 shows the fluorescence intensity distribution histogram of the lymphocyte clusters.

[0134] In some embodiments, as Figure 11As shown, the main peak point Left_Peak_Hei in the fluorescence direction can be determined first, and then whether there is a valley point is searched from the main peak point in the direction of increasing fluorescence intensity. In the case where there is a valley point Valley_Hei, continue to search for the maximum ordinate value point Right_Peak_Hei from the valley point in the direction of increasing fluorescence intensity, then the peak-to-valley ratio Fl_Peak_Valley_Ratio of the lymphocyte cluster in the fluorescence direction can be calculated according to the following formula:

[0135]

[0136] Specifically, in the case where no valley point is found when searching from the main peak point in the direction of increasing fluorescence intensity, the peak-to-valley ratio Fl_Peak_Valley_Ratio of the lymphocyte cluster in the fluorescence direction is 1.

[0137] For example, Table 3 shows the parameter values of the peak-to-valley ratio Fl_Peak_Valley_Ratio, fluorescence intensity distribution width Fl_Width, and fluorescence intensity distribution centroid Fl_P of the lymphocyte cluster obtained from the SS-FS scatter plots of 3 samples based on Figure 10 shown.

[0138] Table 3

[0139] Fl_Width Fl_P Fl_Peak_Valley_Ratio IM patient sample 1472 0.36 1 Samples of patients with other viral infections 1792 0.55 1.4 Normal sample 960 0.3 1

[0140] In some embodiments, the processor 140 may be further configured to obtain the blood routine parameters of the measured sample, and obtain the characteristic parameters according to the particle characteristic information and the blood routine parameters. Here, the blood routine parameters include at least one of white blood cell parameters, red blood cell parameters, and platelet parameters.

[0141] Preferably, the blood routine parameters include red blood cell parameters and / or platelet parameters. For example, the red blood cell parameters may include one or more of hemoglobin concentration HGB, red blood cell count RBC, and nucleated red blood cell count NRBC, and the platelet parameters may include platelet count PLT.

[0142] As some implementation manners, when obtaining and outputting the characteristic parameters according to the particle characteristic information, the processor 140 may linearly or non-linearly combine multiple parameters selected from the particle characteristic information and optionally the blood routine parameters to obtain and output the characteristic parameters.

[0143] That is, the characteristic parameter is IM_Judge_Para = f(x1, x2, x3......, x n ), where, x1 to x nIt may include the above particle characteristic information or the above blood routine parameters.

[0144] For example, x1 to x n It may be selected from the following parameter groups: the total number of particles Cell_Num falling into the feature region, the ratio Cell_Ratio of the total number of particles falling into the feature region to the total number of particles in the scatter plot, the connectivity Area_Connect of the particle clusters falling into the feature region, the area Cell_Area of the feature region, the peak-to-valley ratio Fl_Peak_Valley_Ratio of the fluorescence intensity distribution histogram of the lymphocyte cluster, the fluorescence intensity distribution width Fl_Width, the fluorescence intensity distribution centroid Fl_P, the forward scatter light intensity distribution width Fs_Width and the forward scatter light intensity distribution centroid Fs_P, hemoglobin concentration HGB, red blood cell count RBC, nucleated red blood cell count NRBC, and platelet count PLT.

[0145] For example, the peak-to-valley ratio Fl_Peak_Valley_Ratio, the fluorescence intensity distribution width Fl_Width, and the fluorescence intensity distribution centroid Fl_P of the fluorescence intensity distribution histogram of the lymphocyte cluster may be linearly combined to obtain and output the feature parameter IM_Judge_Para, where IM_Judge_Para = k1×Fl_Width + k2×Fl_P + k3×Fl_Peak_Valley_Ratio + k4, where k1 to k3 are the weight coefficients corresponding to each parameter, and k4 is a constant.

[0146] In some embodiments, the processor 140 may be further configured to output an alarm message if the feature parameter meets a preset condition. Here, the alarm message indicates that the blood sample to be tested is from a patient with infectious mononucleosis.

[0147] That is to say, after obtaining the feature parameter according to the particle characteristic information, it can be determined whether to output an alarm message indicating that the blood sample to be tested is from a patient with infectious mononucleosis according to whether the feature parameter meets a preset condition. For example, if the parameter value of the feature parameter is greater than the sixth preset threshold, an alarm message indicating that the blood sample to be tested is from a patient with infectious mononucleosis is output.

[0148] For example, Table 4 shows the parameter values of the feature parameters obtained by linearly combining the peak-to-valley ratio Fl_Peak_Valley_Ratio, the fluorescence intensity distribution width Fl_Width, and the fluorescence intensity distribution centroid Fl_P of the lymphocyte cluster in the fluorescence direction according to three different weight settings and the corresponding judgment thresholds (i.e., the sixth preset threshold).

[0149] Table 4

[0150]

[0151]

[0152] Alternatively, in the second embodiment, the processor 140 may be configured to:

[0153] generate a scatter plot based on the scattered light intensity signal and the fluorescence intensity signal in the optical information;

[0154] determine a characteristic region from the scatter plot and obtain particle characteristic information of the particles falling into the characteristic region; and

[0155] judge whether to output an alarm message according to the particle characteristic information, where the alarm message indicates that the blood sample to be tested is from a patient suffering from infectious mononucleosis. Among them, when it is judged to output the alarm message, there is no valley point in the fluorescence intensity distribution histogram of the particles falling into the characteristic region and the width of the fluorescence intensity distribution histogram is greater than a preset threshold.

[0156] In some embodiments, the scattered light intensity signal in the optical information may include one or more of the forward scattered light intensity signal FS and the side scattered light intensity signal SS.

[0157] Preferably, the scattered light intensity signal at least includes the side scattered light intensity signal SS.

[0158] Alternatively, in the third embodiment, the processor 140 may be configured to:

[0159] at least obtain a particle distribution map of the particles in the test sample based on the fluorescence intensity signal in the optical information;

[0160] compare the particle distribution map with a preset reference map; and

[0161] if the result of the comparison meets a preset condition, output an alarm message, where the alarm message indicates that the blood sample to be tested is from a patient suffering from infectious mononucleosis.

[0162] In some embodiments, when the processor 140 at least obtains a particle distribution map of the particles in the test sample based on the fluorescence intensity signal in the optical information:

[0163] the processor 140 generates a scatter plot of the test sample as the particle distribution map based on at least the scattered light intensity signal and the fluorescence intensity signal FL in the optical information; or

[0164] The processor 140 generates a fluorescence intensity distribution histogram of the measurement sample as the particle distribution map based on the fluorescence intensity signal FL in the optical information.

[0165] That is, the particle distribution map can be a scatter plot or a fluorescence intensity distribution histogram of the measurement sample.

[0166] In some embodiments, the scattered light intensity signal in the optical information may include one or more of a forward scattered light intensity signal FS and a side scattered light intensity signal SS.

[0167] Preferably, the scattered light intensity signal at least includes the side scattered light intensity signal SS.

[0168] For example, when the particle distribution map is an SS-FL scatter plot, the preset reference map can be an SS-FL scatter plot; when the particle distribution map is a fluorescence intensity distribution histogram, the preset reference map can be a fluorescence intensity distribution histogram. As Figure 12 shown, Figure 12 shows the SS-FL scatter plot (a), the preset reference map (b), and the SS-FL scatter plot (c) of non-IM patients of IM patients when the particle distribution map is an SS-FL scatter plot. Comparing the scatter plot (a) or (c) with the preset reference map (b), if the result of the comparison meets the preset conditions, an alarm message is output.

[0169] In some embodiments, when the processor 140 compares the particle distribution map with the preset reference map, it can calculate the similarity between the particle distribution map and the preset reference map as the result of the comparison.

[0170] In some embodiments, the processor 140 can be further configured to determine the similarity between the particle distribution map and the preset reference map in one or more of the following three ways:

[0171] Determine the similarity through a template matching algorithm;

[0172] Match the image features extracted from the particle distribution map with the image features extracted from the preset reference map to determine the similarity; and

[0173] Determine the similarity according to the mean square error between the particle distribution map and the preset reference map.

[0174] Alternatively, in the fourth embodiment, the processor 140 can be configured to:

[0175] Input the feature information extracted from the optical information into a trained neural network model to obtain the output of the neural network model; and

[0176] Output an alarm message according to the output of the neural network model, where the alarm message characterizes that the blood sample to be tested is from a patient with infectious mononucleosis.

[0177] In some embodiments, the feature information may include one or more of the scattered light pulse signal and the fluorescence pulse signal in the optical information, the scatter plot obtained based on the scattered light intensity signal and the fluorescence intensity signal in the optical information, and the image features extracted from the scatter plot.

[0178] That is, one or more of the scattered light pulse signal and the fluorescence pulse signal in the optical information, the scatter plot obtained based on the scattered light intensity signal and the fluorescence intensity signal in the optical information, and the image features extracted from the scatter plot can be input into the trained neural network model to obtain the output of the neural network model.

[0179] In some embodiments, the scattered light pulse signal includes one or more of the forward scattered light pulse signal and the lateral scattered light pulse signal. Preferably, the scattered light pulse signal at least includes the lateral scattered light pulse signal.

[0180] In some embodiments, the scattered light intensity signal includes one or more of the forward scattered light intensity signal and the lateral scattered light intensity signal. Preferably, the scattered light intensity signal at least includes the lateral scattered light intensity signal.

[0181] As Figure 13 shown, Figure 13 It shows the manner of inputting the scatter plot into the trained neural network model to obtain the output of the neural network model when the feature information is the scatter plot obtained based on the lateral scattered light intensity signal SS and the fluorescence intensity signal FL in the optical information. For example, the neural network model can output the probability that the blood sample to be tested is from a patient with infectious mononucleosis, and an alarm message can be output when the probability is greater than the seventh preset threshold to indicate that the blood sample to be tested is from a patient with infectious mononucleosis.

[0182] In some embodiments, the neural network model may be one of a convolutional neural network model (Convolutional Neural Networks, CNN), a recurrent neural network model (Recurrent Neural Network, RNN), and a deep neural network model (such as a ResNet model).

[0183] Next, some specific embodiments are used to further illustrate the present application and its advantages.

[0184] The true positive rate %, false positive rate %, true negative rate %, and false negative rate % in the embodiments of this application are calculated by the following formulas:

[0185] True positive rate % = TP / (TP + FN) × 100%;

[0186] True negative rate % = TN / (FP + TN) × 100%;

[0187] False positive rate % = 1 - true negative rate %

[0188] False negative rate % = 1 - true positive rate %;

[0189] Among them, TP is the number of true positive individuals, FP is the number of false positive individuals, TN is the number of true negative individuals, and FN is the number of false negative individuals.

[0190] The blood samples from 605 patients were detected using the Mindray MR 75 hematology analyzer in the manner proposed in the embodiments of this application to identify infectious mononucleosis. Among them, the number of samples of patients with infectious mononucleosis, that is, the number of positive samples, was 184 cases, and the number of samples of patients without infectious mononucleosis, that is, the number of negative samples, was 421 cases.

[0191] The sample inclusion criteria were: patients with or suspected of having the typical "triad" of infectious mononucleosis.

[0192] The sample exclusion criteria were: patients with malignant blood diseases, patients with concurrent other viral or bacterial infections.

[0193] Differential diagnosis scheme 1: If the fluorescence intensity distribution histogram of the particles falling into the characteristic region has no valley point and the width of the fluorescence intensity distribution histogram is greater than the first preset threshold, an alarm message is output.

[0194] Differential diagnosis scheme 2: If the comparison result between the particle distribution diagram of the particles in the measured sample and the preset reference diagram meets the preset conditions, an alarm message is output.

[0195] Differential diagnosis scheme 3: The characteristic information extracted from the optical information is input into the trained neural network model, and an alarm message is output according to the output of the neural network model.

[0196] Differential diagnosis scheme 4: If the characteristic parameters of the lymphocyte clusters in the characteristic region meet the preset conditions, an alarm message is output.

[0197] Table 5 shows the differential diagnosis effects corresponding to the 4 schemes proposed in this application.

[0198] Table 5

[0199]

[0200]

[0201] As can be seen from the results shown in Table 5, each technical solution proposed in this application can accurately output the alarm information that the blood sample to be tested comes from a patient with infectious mononucleosis, which helps the clinical effective identification of patients with infectious mononucleosis.

[0202] The features or combinations of features mentioned in the specification, drawings and claims above can be used in any combination with each other or used alone as long as they are meaningful within the scope of this application and do not conflict with each other.

[0203] The above are only the preferred embodiments of this application, and do not limit the patent scope of this application. Any equivalent transformation made by using the content of the specification and drawings of this application under the inventive concept of this application, or directly / indirectly applied in other related technical fields are included in the patent protection scope of this application.

Claims

1. A blood cell analyzer comprising: A sample suction device, used for sucking a blood sample to be tested; a sample preparation device for mixing at least a portion of the blood sample to be tested, a hemolytic agent, and a staining agent to prepare a test sample; An optical detection device comprising a flow chamber, a light source, and a light detector, wherein the flow chamber is configured to allow the measurement sample to pass through, the light source is configured to illuminate the measurement sample passing through the flow chamber with light, and the light detector is configured to detect optical information generated by the measurement sample being illuminated by the light while passing through the flow chamber; The processor is configured to: generating a scatter plot based on the scattered light intensity signal and the fluorescence intensity signal in the optical information; determining a characteristic region from the scatter plot and acquiring particle characteristic information of particles falling into the characteristic region; Acquire and output characteristic parameters based on the particle characteristic information, wherein the characteristic parameters are used to determine whether the blood sample to be tested is from a patient suffering from infectious mononucleosis, and / or output an alarm message if the particle characteristic information meets a preset condition, wherein the alarm message indicates that the blood sample to be tested is from a patient suffering from infectious mononucleosis.

2. The blood cell analyzer according to claim 1, characterized in that The preset conditions include that a fluorescence intensity distribution histogram of particles falling into the characteristic area has no valley points and includes at least one of the following conditions: a width of the fluorescence intensity distribution histogram is greater than a first preset threshold, a total number of particles falling into the characteristic area is greater than a second preset threshold, a ratio of the total number of particles falling into the characteristic area to the total number of particles in the scatter plot is greater than a third preset threshold, and an area of the characteristic area is greater than a fourth preset threshold.

3. The blood cell analyzer according to claim 1, characterized in that The preset condition includes that the similarity between the particle distribution of the characteristic region and the particle distribution of the reference region is greater than a fifth preset threshold.

4. The blood cell analyzer according to claim 3, characterized in that The processor is further configured to: Determine the similarity by a template matching algorithm; or matching the image features extracted from the feature region with the image features extracted from the reference region to determine the similarity; or Determine the similarity based on a mean square error between the feature region and the reference region; or The fluorescence intensity distribution histogram of the particles falling into the characteristic region is compared with the fluorescence intensity distribution histogram of the particles falling into the reference region to determine the similarity.

5. The blood cell analyzer according to claim 1, characterized in that The processor is further configured to: Inputting the particle characteristic information into a trained neural network model to obtain an output of the neural network model; and Determine whether the particle characteristic information meets a preset condition based on the output of the neural network model.

6. The blood cell analyzer according to claim 1, characterized in that The particle feature information includes at least one of the following parameters: the total number of particles falling into the feature area, the ratio of the total number of particles falling into the feature area to the total number of particles in the scatter plot, the connectivity of the particle clusters falling into the feature area, and the area of the feature area.

7. The blood cell analyzer according to claim 1 or 6, characterized in that: The particles in the characteristic region include lymphocyte clusters, The particle characteristic information includes at least one of the following parameters of the lymphocyte cluster: peak-to-peak-to-valley ratio of the fluorescence intensity distribution histogram of the lymphocyte cluster, fluorescence intensity distribution width, fluorescence intensity distribution center of gravity, forward scattered light intensity distribution width and forward scattered light intensity distribution center of gravity.

8. The blood cell analyzer according to claim 6 or 7, characterized in that: The processor is further configured to: Acquiring a blood routine parameter of the measurement sample, wherein the blood routine parameter includes at least one of a white blood cell parameter, a red blood cell parameter, and a platelet parameter. Preferably, the blood routine parameter includes a red blood cell parameter and / or a platelet parameter; The characteristic parameters are obtained according to the particle characteristic information and the blood routine parameters.

9. The blood cell analyzer according to claim 8, characterized in that: The processor is further configured to: If the characteristic parameter meets the preset condition, an alarm message is output, wherein the alarm message indicates that the blood sample to be tested comes from a patient suffering from infectious mononucleosis.

10. The blood cell analyzer according to any one of claims 1 to 9, characterized in that: The scattered light intensity signal includes one or more of a forward scattered light intensity signal and a side scattered light intensity signal; Preferably, the scattered light intensity signal at least includes the side scattered light intensity signal.

11. A blood cell analyzer comprising: A sample suction device, used for sucking a blood sample to be tested; a sample preparation device for mixing at least a portion of the blood sample to be tested, a hemolytic agent, and a staining agent to prepare a test sample; An optical detection device comprising a flow chamber, a light source, and a light detector, wherein the flow chamber is configured to allow the measurement sample to pass through, the light source is configured to illuminate the measurement sample passing through the flow chamber with light, and the light detector is configured to detect optical information generated by the measurement sample being illuminated by the light while passing through the flow chamber; The processor is configured to: generating a scatter plot based on the scattered light intensity signal and the fluorescence intensity signal in the optical information; determining a characteristic region from the scatter plot and acquiring particle characteristic information of particles falling into the characteristic region; A determination is made based on the particle characteristic information whether to output an alarm message, wherein the alarm message indicates that the blood sample to be tested is from a patient suffering from infectious mononucleosis. When the alarm message is determined to be output, a fluorescence intensity distribution histogram of particles falling within the characteristic region has no valley points and a width of the fluorescence intensity distribution histogram is greater than a preset threshold.

12. A blood cell analyzer comprising: A sample suction device, used for sucking a blood sample to be tested; a sample preparation device for mixing at least a portion of the blood sample to be tested, a hemolytic agent, and a staining agent to prepare a test sample; An optical detection device comprising a flow chamber, a light source, and a light detector, wherein the flow chamber is configured to allow the measurement sample to pass through, the light source is configured to illuminate the measurement sample passing through the flow chamber with light, and the light detector is configured to detect optical information generated by the measurement sample being illuminated by the light while passing through the flow chamber; The processor is configured to: obtaining a particle distribution map of particles in the measurement sample based on at least a fluorescence intensity signal in the optical information; comparing the particle distribution map with a preset reference map; If the comparison result meets a preset condition, an alarm message is output, which indicates that the blood sample to be tested comes from a patient suffering from infectious mononucleosis.

13. The blood cell analyzer according to claim 12, characterized in that: The processor obtains a particle distribution map of particles in the measurement sample based on at least the fluorescence intensity signal in the optical information, comprising: The processor generates a scattergram of the measurement sample as the particle distribution map based on at least the scattered light intensity signal and the fluorescence intensity signal in the optical information; or The processor generates a fluorescence intensity distribution histogram of the measurement sample as the particle distribution map based on a fluorescence intensity signal in the optical information.

14. The blood cell analyzer according to claim 12 or 13, characterized in that: The processor compares the particle distribution map with a preset reference map, including: The processor calculates a similarity between the particle distribution map and the preset reference map as a result of the comparison.

15. The blood cell analyzer according to claim 14, characterized in that The processor is further configured to determine the similarity as follows: Determining the similarity by a template matching algorithm; and / or matching the image features extracted from the particle distribution map with the image features extracted from the preset reference map to determine the similarity; and / or The similarity is determined according to a mean square error between the particle distribution map and the preset reference map.

16. A blood cell analyzer comprising: A sample suction device, used for sucking a blood sample to be tested; a sample preparation device for mixing at least a portion of the blood sample to be tested, a hemolytic agent, and a staining agent to prepare a test sample; An optical detection device comprising a flow chamber, a light source, and a light detector, wherein the flow chamber is configured to allow the measurement sample to pass through, the light source is configured to illuminate the measurement sample passing through the flow chamber with light, and the light detector is configured to detect optical information generated by the measurement sample being illuminated by the light while passing through the flow chamber; The processor is configured to: Inputting feature information extracted from the optical information into a trained neural network model to obtain an output of the neural network model; According to the output of the neural network model, an alarm message is output, wherein the alarm message indicates that the blood sample to be tested comes from a patient suffering from infectious mononucleosis.

17. The blood cell analyzer according to claim 16, characterized in that: The characteristic information includes: The scattered light pulse signal and the fluorescent pulse signal in the optical information; and / or A scatter plot obtained based on the scattered light intensity signal and the fluorescence intensity signal in the optical information; and / or Image features are extracted from the scatter plot.

Images