Blood analyzer

Abstract

The present disclosure provides a blood analyzer. The blood analyzer comprises: a sample suction device for sucking a blood sample to be detected; the sample preparation device comprises a reaction tank for mixing a blood sample to be detected with a first reagent comprising a first hemolytic agent and a first dye; an optical detector for detecting the first sample to obtain first scattered light information and first fluorescence information; and a processor configured to obtain at least one first parameter of at least one first target particle group in the first sample at least based on the first scattered light information and the first fluorescence information, and obtain information characterizing acute promyelocytic leukemia or abnormal promyelocytic information based on the at least one first parameter.

Description

Technical Field

[0001] The present disclosure relates to the field of in vitro diagnostics, and particularly to a hematology analyzer for leukemia-related subjects in a subject. Background Art

[0002] The treatment of malignant hematological diseases is difficult, but with the continuous progress of medicine, the cure rate and remission rate are both increasing. Malignant hematological diseases include various malignant diseases such as leukemia, lymphoma, multiple myeloma, etc., in which tumor cell lesions are found in tissues such as blood, lymphoid tissue, and bone marrow. Abnormal blood cells are often found in the blood or cerebrospinal fluid of patients with malignant hematological diseases. Among them, the more common leukemia is a type of malignant clonal disease of hematopoietic stem cells, which is divided into acute and chronic leukemia. Acute leukemia includes acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Acute myeloid leukemia AML includes eight types: granulocytic leukemia, undifferentiated type (M1), granulocytic leukemia, partially differentiated type (M2), promyelocytic type (M3), granulocyte-monocyte type (M4), monocytic type (M5), erythroleukemia (M6), megakaryocytic type (M7), acute myeloid microdifferentiated type (M0). Acute leukemia has a sudden onset and a rapid disease progression. Moreover, the treatment regimens for different types of leukemia are different. Especially for acute promyelocytic leukemia APL (i.e., AML-M3 leukemia), the average survival time of patients without treatment is less than 1 week. Delayed treatment can increase the mortality rate of patients within 30 days by 30%. Once APL can be diagnosed early, the cure rate can reach about 80%. It can be said that with a one-day difference in the time of medication, the prognosis of the patient is completely different. Therefore, the rapid diagnosis of leukemia is of great significance, and only by rapid diagnosis can the correct treatment plan be determined.

[0003] At present, the gold standard for the diagnosis of malignant hematological diseases is mostly based on the microscopic examination results after bone marrow aspiration sampling as the diagnostic criterion. This method is difficult to achieve rapid diagnosis.

[0004] Blood routine tests are widely used, with fast detection speed and low price. However, at present, blood routine tests can only alarm for abnormal blood cell parameters or the presence of abnormal cells in the blood, and there is still a large room for improvement in its accuracy. In addition, blood routine tests cannot alarm for the classification of leukemia. Summary of the Invention

[0005] The purpose of the present disclosure is to provide a hematology analyzer. The hematology analyzer utilizes a conventional detection channel, especially a white blood cell detection channel, and can more accurately alarm for malignant hematological diseases through the analysis of detection information, especially can identify and alarm for acute promyelocytic leukemia.

[0006] To this end, a first aspect of the present disclosure provides a hematology analyzer, including the following components.

[0007] A sampling device configured to aspirate at least a portion of a blood sample to be tested.

[0008] A specimen preparation device, comprising a reagent supply unit and a reaction unit, the reagent supply unit being configured to provide a first reagent into the reaction unit, the reaction unit being configured to react the first reagent with at least a portion of the blood sample to be tested to prepare a first specimen, wherein the first reagent comprises a first hemolytic agent and a first dye.

[0009] An optical detection device, comprising a light source, a flow cell, a scattered light detector and a fluorescence detector, the light source being configured to emit a light beam to irradiate a detection area of the flow cell, the flow cell being in communication with the reaction unit, whereby each particle in the first specimen in the reaction unit can pass through the detection area of the flow cell one by one, the scattered light detector being configured to detect first scattered light information generated after the particles passing through the detection area are irradiated by the light beam, and the fluorescence detector being configured to detect first fluorescence information generated after the particles passing through the flow cell are irradiated by the light beam.

[0010] A processor configured to: obtain at least one first parameter of at least one first target particle cluster in the first specimen based at least on the first scattered light information and the first fluorescence information, and obtain information characterizing acute promyelocytic leukemia or abnormal promyelocyte information based on the at least one first parameter.

[0011] A second aspect provides a blood analyzer comprising the following components.

[0012] A sampling device configured to aspirate at least a portion of a blood sample to be tested.

[0013] A specimen preparation device, comprising a reagent supply unit and a reaction unit, the reagent supply unit being configured to provide a first reagent into the reaction unit, the reaction unit being configured to react the first reagent with at least a portion of the blood sample to be tested to prepare a first specimen, wherein the first reagent comprises a first hemolytic agent and a first fluorescent dye.

[0014] An optical detection device, comprising a light source, a flow cell, a scattered light detector and a fluorescence detector, the light source being configured to emit a light beam to irradiate a detection area of the flow cell, the flow cell being in communication with the reaction unit, whereby each particle in the first specimen in the reaction unit can pass through the detection area of the flow cell one by one, the scattered light detector being configured to detect first scattered light information generated after the particles passing through the detection area are irradiated by the light beam, and the fluorescence detector being configured to detect first fluorescence information generated after the particles passing through the flow cell are irradiated by the light beam.

[0015] A processor, configured to: generate at least one scatter plot based at least on the first scattered light information and the first fluorescence information, and obtain information related to malignant blood diseases based on a comparison result between the at least one scatter plot and a preset scatter plot of a positive sample or based on a comparison result between at least a partial region of the at least one scatter plot and a corresponding region of the preset scatter plot of the positive sample.

[0016] In a third aspect, a blood analyzer is provided, which includes the following components.

[0017] A sampling device, configured to aspirate at least a part of a blood sample to be tested.

[0018] A specimen preparation device, including a reagent supply unit and a reaction unit, wherein the reagent supply unit is configured to provide a first reagent into the reaction unit, and the reaction unit is configured to react the first reagent with at least a part of the blood sample to be tested to prepare a first specimen, and the first reagent includes a first hemolytic agent and a first dye.

[0019] An optical detection device, including a light source, a flow cell, a scattered light detector and a fluorescence detector, the light source is used to emit a light beam to irradiate a detection area of the flow cell, the flow cell is communicated with the reaction unit, so that each particle in the first specimen in the reaction unit can pass through the detection area of the flow cell one by one, the scattered light detector is used to detect a first scattered light pulse signal generated after the particles passing through the detection area are irradiated by the light beam, and the fluorescence detector is used to detect a first fluorescence pulse signal generated after the particles passing through the flow cell are irradiated by the light beam.

[0020] A processor, configured to: at least input the first scattered light pulse signal and the first fluorescence pulse signal into a pre-trained intelligent model, and obtain information related to malignant blood diseases output by the intelligent model.

[0021] Alternatively, at least generate at least one first scatter plot from the first scattered light pulse signal and the first fluorescence pulse signal, obtain at least one first parameter of at least one first target particle cluster in the first specimen based on the first scatter plot, and input the at least one first parameter into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0022] In a fourth aspect, a blood analyzer is provided, which includes the following components.

[0023] A sampling device, configured to aspirate at least a part of a blood sample to be tested.

[0024] A sample preparation device includes a reagent supply unit and a reaction unit. The reagent supply unit is configured to provide a first reagent into the reaction unit. The reaction unit is configured to react the first reagent with at least a part of the blood sample to be tested to prepare a first sample. The first reagent includes a first hemolytic agent and a first dye.

[0025] An optical detection device includes a light source, a flow cell, a scattered light detector, and a fluorescence detector. The light source is used to emit a light beam to irradiate a detection area of the flow cell. The flow cell is in communication with the reaction unit, so that each particle in the first sample in the reaction unit can pass through the detection area of the flow cell one by one. The scattered light detector is used to detect first scattered light information generated after the particles passing through the detection area are irradiated by the light beam. The fluorescence detector is used to detect first fluorescence information generated after the particles passing through the flow cell are irradiated by the light beam.

[0026] A processor is configured to: generate at least one scatter plot based on at least the first scattered light information and the first fluorescence information, obtain at least one first parameter of at least one first target particle cluster in the first sample based on a specific area in the at least one scatter plot, and obtain information characterizing acute promyelocytic leukemia or abnormal promyelocyte information based on the at least one first parameter.

[0027] According to various embodiments of the present disclosure, the blood analyzer utilizes a conventional detection channel (especially a white blood cell detection channel, i.e., the DIFF channel). By analyzing the detection information of particles in the blood sample obtained by detection, it can more accurately obtain information about abnormal cells in the blood sample, thereby alarming for malignant blood diseases, especially acute promyelocytic leukemia. The blood analyzer has a wide clinical application, is simple, fast, and low-cost in detection, and utilizes a conventional detection channel without requiring additional reagents or changing the detection process, thus achieving rapid screening of malignant blood diseases and winning time for subsequent diagnosis and treatment. Description of the Drawings [[ID=1,3]]

[0028] Figure 1 It is a schematic structural diagram of a blood analyzer according to some embodiments.

[0029] Figure 2 It is a schematic structural diagram of an optical detection device according to some embodiments.

[0030] Figure 3 It is a side scatter light intensity - fluorescence intensity scatter plot of the DIFF channel obtained by using the blood analyzer to detect an APL positive sample in Example 1.

[0031] Figure 4For the scatter plot of side scatter light intensity - fluorescence intensity of the DIFF channel (Figure A), the scatter plot of fluorescence intensity - forward scatter light intensity of the WNB channel (Figure B), the red blood cell impedance - volume histogram (Figure C), and the platelet impedance - volume histogram (Figure D) obtained by detecting APL positive samples using a hematology analyzer in Example 2.

[0032] Figure 5 For the schematic diagram fitted by the decision tree method in Example 2.

[0033] Figure 6 For the standard template of positive samples of malignant hematological diseases obtained from the scatter plot detected for positive samples of malignant hematological diseases in Example 3.

[0034] Figure 7 For the standard template of APL positive samples obtained from the scatter plot detected for APL positive samples in Example 4.

[0035] Figure 8 For the schematic diagram of the deep learning artificial neural network model processing the first parameter and outputting the information representing malignant hematological diseases in Example 5.

[0036] Figure 9 For the schematic diagram of the deep learning artificial neural network model processing the first parameter and outputting the information representing acute promyelocytic leukemia in Example 6. Detailed implementation manners

[0037] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the specific embodiments and the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] It should be noted that the terms "first \ second \ third" involved in the embodiments of this 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.

[0039] Throughout the specification, unless otherwise specifically stated, the terms used herein should be understood as having the meanings commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meanings as generally understood by those skilled in the art to which the present invention pertains. In case of any contradiction, this specification shall prevail.

[0040] The "primitive cells" mentioned in this article refer to cells at the initial stage of the development of various blood cells. Primitive cells do not appear in the blood of healthy people, but are present in the blood of patients with certain diseases, especially blood diseases.

[0041] The "immature cells" mentioned in this article refer to blood cells that have not differentiated and matured, including primitive cells and blast cells.

[0042] The "information related to malignant blood diseases" mentioned in this article includes disease information related to malignant blood diseases and cell information related to malignant blood diseases. Disease information related to malignant blood diseases includes various malignant diseases such as leukemia, lymphoma, multiple myeloma, etc., where there are tumor cell lesions in tissues such as blood, lymphoid tissue, and bone marrow. Generally speaking, malignant blood diseases mainly include: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), lymphoid neoplasms, myeloid neoplasms, etc. Acute myeloid leukemia AML includes eight types: undifferentiated granulocytic leukemia (M1), partially differentiated granulocytic leukemia (M2), promyelocytic leukemia (M3), granulocytic-monocytic leukemia (M4), monocytic leukemia (M5), erythroleukemia (M6), megakaryocytic leukemia (M7), minimally differentiated acute myeloid leukemia (M0). Lymphoid neoplasms include lymphoma, chronic lymphocytic leukemia, etc. Myeloid neoplasms include myelodysplasia, chronic myeloid leukemia, etc.

[0043] Cell information related to malignant blood diseases includes primitive cells, abnormal promyelocytes, immature monocytes, immature lymphocytes, plasma cells, etc. When primitive cells are present in a sample, the sample may be an acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL) sample; when abnormal lymphocytes are present in the sample, the sample may be a chronic lymphocytic leukemia or lymphoma sample; when abnormal promyelocytes are present in the sample, the sample may be an acute promyelocytic leukemia sample; when immature monocytes are present in the sample, the sample may be an AML-M4 or AML-M5 sample; when plasma cells are present in the sample, the sample may be a plasma cell leukemia or multiple myeloma sample.

[0044] The term "scatter plot" used in this article is 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 each represent a characteristic of each particle. For example, in an exemplary 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. The term "scatter plot" not only refers to a distribution graph of at least two sets of data in the form of data points in a rectangular coordinate system, but also includes a data array, that is, it is not limited by its graphical presentation form.

[0045] The term "particle cluster" or "cell cluster" used in this article refers to a particle population formed by multiple particles with similar distribution of detection information or distributed in a certain area of the scatter plot and having the same cell characteristics. For example, a white blood cell cluster refers to a population including all types of white blood cells, and particle clusters of white blood cell subsets such as neutrophil clusters, lymphocyte clusters, monocyte clusters, etc.

[0046] The term "sensitivity" used in this article: the probability of detecting a positive result among people who are judged to have malignant blood diseases or a certain type of malignant blood disease (positive) by the gold standard, that is, true positive. The higher the sensitivity, the stronger the ability of the method to detect the disease.

[0047] The term "specificity" used in this article refers to the probability of detecting a negative result among people who are judged not to have malignant blood diseases or a certain type of malignant blood disease (negative) by the gold standard, that is, true negative. The higher the specificity, the lower the misdiagnosis rate of the method for detecting the disease.

[0048] The term "false positive rate" used in this article refers to the situation where the test result is positive but the subject does not have the disease, that is, the probability that the test result of the method is positive among people who are judged not to have malignant blood diseases or a certain type of malignant blood disease (negative) by the gold standard. That is, the misdiagnosis rate.

[0049] The term "false negative rate" used in this article refers to the situation where the test result is negative but the subject actually has the disease, that is, the probability that the test result of the method is negative among people who are judged to have malignant blood diseases or a certain type of malignant blood disease (positive) by the gold standard. That is, the missed diagnosis rate.

[0050] The term "ROC curve (receiver operator characteristic curve)" used in this article is a receiver operating characteristic curve, which is a curve plotted with the true positive rate as the ordinate and the false positive rate as the abscissa according to a series of different binary classification methods (cutoff thresholds). ROC_AUC (area under the curve) represents the area enclosed by the ROC curve and the horizontal coordinate axis.

[0051] The principle of constructing the ROC curve is to set multiple different critical values for continuous variables, calculate the corresponding sensitivity and specificity at each critical value, and then plot a curve with sensitivity as the ordinate and 1 - specificity as the abscissa.

[0052] Since the ROC curve is composed of multiple critical values representing their respective sensitivities and specificities, the optimal diagnostic cut-off value of a certain diagnostic method can be selected with the help of the ROC curve. The closer the ROC curve is to the upper left corner, the higher the test sensitivity and the lower the misjudgment rate, and the better the performance of the diagnostic method. It can be seen that for the point on the ROC curve closest to the upper left corner, the sum of its sensitivity and specificity is the largest, and the value corresponding to this point or its adjacent point is often used as the diagnostic reference value (also known as the diagnostic threshold or judgment threshold or preset condition or preset range).

[0053] The hematology analyzer used in traditional blood routine tests generally includes an optical detection system, which counts and classifies blood cells such as white blood cells through the DIFF channel and / or the WNB channel. Through the DIFF channel, the white blood cells can be identified and counted for four-way classification of white blood cells (lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), eosinophils (Eos)). Through the WNB channel, the white blood cell count and basophil (Baso) count can be obtained, and nucleated red blood cells (NRBC) can also be identified and counted.

[0054] The optical detection system of the hematology analyzer classifies and counts the particles in the blood sample through flow cytometry that combines laser scattering and fluorescence staining methods. The principle of the optical detection system of the hematology analyzer for detecting blood samples 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 nucleus of white blood cells with the help of the hemolytic agent and bind to the nucleic acid substances in the nucleus; then the particles in the sample pass through the detection hole irradiated by the laser beam in the flow cell one by one. When the laser beam irradiates the particles, the characteristics of the particles themselves (such as volume, staining degree, cell content size and content, nuclear density, etc.) can block or change the direction of the laser beam, thereby generating scattered light and fluorescence at various angles corresponding to their characteristics. After these scattered light and fluorescence are received by the signal detector, relevant information on the particle structure and composition can be obtained. Among them, the forward scatter light (FS) reflects the number and volume of the particles, the side scatter light (SS) reflects the complexity of the internal structure of the cells (such as intracellular granules or nuclei), and the fluorescence (FL) reflects the content of nucleic acid substances in the cells. Using these optical information, the particles in the sample can be classified and counted.

[0055] The hematology analyzer also has an electrical detection system, which can detect the impedance signals of the particles passing through the detector one by one by the resistance detector, and classify and count red blood cells (RBC) and platelets (PLT) in the diluted sample according to the differences in the impedance signals.

[0056] Through the detection by the hematology analyzer, relevant information of various types of cells in the peripheral blood of a subject can be obtained. For patients with malignant hematological diseases, abnormal cells that have not differentiated and matured, such as blasts (BLAST), often exist in their peripheral blood. Conventional hematology analyzers are not yet able to accurately identify and quantitatively count blasts, and can only alarm about the possible presence of abnormal cells based on abnormal information of some particle clusters. Moreover, the accuracy of the alarm cannot meet the requirements of screening, and there are often many false positives and false negatives. In addition, conventional hematology analyzers are difficult to identify specific abnormal cells, so they cannot obtain the classification information of malignant hematological diseases, and thus cannot give a more accurate alarm for the classification of hematological diseases.

[0057] Since the routine blood test performed by the hematology analyzer has the advantages of being fast, economical and widely used clinically, it is necessary to improve the hematology analyzer so that it can quickly and accurately screen for malignant hematological diseases through the hematology analyzer, and even identify the classification of hematological diseases, such as acute promyelocytic leukemia.

[0058] For this purpose, a first aspect of the present disclosure provides a hematology analyzer, including a sampling device, a detection device and a processor. The processor can identify promyelocytes through the processing of detection information, so as to alarm acute promyelocytic leukemia.

[0059] According to the first embodiment, in the hematology analyzer, the sampling device is configured to aspirate at least a part of the blood sample to be tested. The sample preparation device includes a reagent supply part and a reaction part. The reagent supply part is configured to provide a first reagent into the reaction part, and the reaction part is configured to make the first reagent react with at least a part of the blood sample to be tested to prepare a first sample, where the first reagent includes a first hemolytic agent and a first dye. The optical detection device includes a light source, a flow cell, a scattered light detector and a fluorescence detector. The light source is used to emit a light beam to irradiate the detection area of the flow cell. The flow cell is connected to the reaction part, so that each particle in the first sample in the reaction part can pass through the detection area of the flow cell one by one. The scattered light detector is used to detect the first scattered light information generated by the particles passing through the detection area after being irradiated by the light beam, and the fluorescence detector is used to detect the first fluorescence information generated by the particles passing through the flow cell after being irradiated by the light beam.

[0060] The processor is configured to obtain at least one first parameter of at least one first target particle cluster in the first sample based on at least the first scattered light information and the first fluorescence information; and obtain information characterizing acute promyelocytic leukemia or abnormal promyelocyte information based on the at least one first parameter.

[0061] The following will describe in detail the hematology analyzer of the first embodiment in conjunction with Figure 1 the hematology analyzer of the first embodiment.

[0062] Figure 1 FIG. is a schematic structural diagram of a hematology cell analyzer for some embodiments. The hematology cell analyzer 100 includes a sampling device 110, a specimen preparation device 120, an optical detection device 130, and a processor 140. The hematology cell analyzer 100 further has a liquid path system (not shown) for connecting the sampling device 110, the specimen preparation device 120, and the optical detection device 130 to enable liquid transfer between these devices.

[0063] The sampling device 110 is used to aspirate a blood sample to be tested from a subject. In some embodiments, the sampling device 110 has a sampling needle (not shown) for aspirating the blood sample to be tested. In addition, the sampling device 110 may, for example, further include a driving device for driving the sampling needle to quantitatively aspirate the blood sample to be tested through the nozzle of the sampling needle. The sampling device 110 can transfer the aspirated blood sample to the specimen preparation device 120. In some embodiments, the sampling device 110 can aspirate a part of the blood sample to be tested and, through multiple aspirations, prepare multiple specimens separately in the specimen preparation device 120. In other embodiments, the sampling device can also aspirate a certain amount of sample at one time, divide it into several sub-samples, and prepare multiple specimens separately in the specimen preparation device 120.

[0064] The specimen preparation device 120 includes a reagent supply part (not shown) and a reaction part (not shown). The sampling device 10 and the reagent supply part are respectively connected to the reaction part through pipelines. In some embodiments, the reaction part includes one or more reaction pools, and the sampling device 10 and the reagent supply part are respectively connected to the reaction pool. As needed, the reagent supply part can transfer a specific reagent to a specific reaction pool to mix with the blood sample to be tested provided by the sampling device to prepare a specimen for a predetermined detection purpose. Specimens for different detection items can be prepared successively in the same reaction pool or separately in different reaction pools.

[0065] The specimen preparation device 120 is used to prepare a first specimen including a part of the blood sample to be tested and a first reagent containing a first hemolytic agent and a first dye. Specifically, the reagent supply part is used to provide the first reagent to the reaction part, where the first reagent is mixed and reacts with at least part of the blood sample to be tested provided by the sampling device 110 to prepare the first specimen.

[0066] The hemolytic agent of the present disclosure is used to lyse red blood cells in blood, breaking the red blood cells into fragments, but capable of keeping the morphology of white blood cells basically unchanged. In some embodiments, the hemolytic agent can be any one or a combination of several of cationic surfactants, nonionic surfactants, anionic surfactants, and amphiphilic surfactants. For example, the hemolytic agent can be selected from quinoline octyl bromide, isoquinoline octyl bromide, quinoline decyl bromide, isoquinoline decyl bromide, quinoline dodecyl bromide, isoquinoline dodecyl bromide, quinoline tetradecyl bromide, isoquinoline tetradecyl bromide, octyl trimethyl ammonium chloride, octyl trimethyl ammonium bromide, decyl trimethyl ammonium chloride, decyl trimethyl ammonium bromide, dodecyl trimethyl ammonium chloride, dodecyl trimethyl ammonium bromide, tetradecyl trimethyl ammonium chloride, tetradecyl trimethyl ammonium bromide; polyoxyethylene (23) dodecyl ether, polyoxyethylene (25) cetyl ether, polyoxyethylene (30) cetyl ether, etc. In other embodiments, the hemolytic agent can include at least one of alkyl glycosides, triterpenoid saponins, and steroidal saponins.

[0067] The first hemolytic agent is those hemolytic agents commonly used in the DIFF channel, and the present disclosure has no particular limitation on this. The first hemolytic agent classifies white blood cells into at least three white blood cell subsets (lymphocytes, monocytes, neutrophils, and in some embodiments, can classify white blood cells into four subsets, that is, in addition to the aforementioned three subsets, eosinophils can also be recognized) by combining with the use of a stain containing the following first dye. The first hemolytic agent is different from the second hemolytic agent mentioned below. The hemolytic intensity of the second hemolytic agent on red blood cells is weaker than that of the first hemolytic agent. In other words, compared with the second hemolytic agent, the first hemolytic agent has a greater degree of lysis of red blood cells and can break red blood cells into smaller fragments.

[0068] The first dye is a fluorescent dye used to achieve white blood cell classification (the three-class or four-class classification as described above). The first dye is different from the second dye mentioned below. The second dye is a fluorescent dye capable of identifying nucleated red blood cells in a blood sample (capable of distinguishing nucleated red blood cells from white blood cells). In some embodiments, the second dye can also be used to identify basophils.

[0069] In some embodiments, the first dye can include a membrane-specific dye or a mitochondrial-specific dye, and more details thereof can be referred to the PCT patent application WO2019 / 206300A1 filed by the applicant on April 26, 2019, the entire disclosure of which is incorporated herein by reference.

[0070] In other embodiments, the first stain can include a cationic cyanine compound, and more details thereof can be referred to the Chinese patent application CN101750274A filed by the applicant on September 28, 2019, the entire disclosure of which is incorporated herein by reference.

[0071] The present disclosure places no particular restrictions on the first reagent, and commercially available reagents for the four-class classification of white blood cells can be used in the embodiments of the present disclosure, such as M-60LD and M-6FD.

[0072] The optical detection device 130 includes a flow cell, a light source, and an optical detector (both not shown). The optical detector includes a scattered light detector and a fluorescence detector. The flow cell is used for particles in the first sample to pass through one by one, and the light source is used to align a laser beam with the detection hole of the flow cell to irradiate the particles passing through the detection hole. The scattered light detector is used to detect the first scattered light information generated by the particles passing through the detection area after being irradiated by the light beam, and the fluorescence detector is used to detect the first fluorescence information generated by the particles passing through the flow cell after being irradiated by the light beam.

[0073] It can be understood here that the first detection channel (also referred to as the DIFF channel) for white blood cell classification refers to the detection of the first sample prepared by the sample preparation device 120 by the optical detection device 130.

[0074] When a particle (such as a white blood cell) passes through the detection hole of the flow cell, the particle scatters the incident light beam from the light source in all directions. Scattered light detectors can be set 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, to a certain extent, distinguish different particle populations. Specifically, the light scattering signal detected near the incident light direction 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 by a scattered light detector set at an angle of approximately 1° to approximately 10° with respect to the incident light direction. In some other embodiments, the forward light scattering signal can be detected at an angle of approximately 2° to approximately 6° with respect to the incident light beam. The light scattering signal detected by a scattered light detector arranged in the direction approximately 90° with respect to the incident light direction is usually referred to as the lateral light scattering signal. In some embodiments, the lateral light scattering signal can be detected at an angle of approximately 65° to approximately 115° with respect to the incident light direction. Generally, the fluorescence information emitted from blood cells stained with a fluorescent dye is usually detected by a fluorescence detector arranged at an angle of approximately 90° with respect to the incident light direction.

[0075] In the embodiments, the optical detection device 130 is used to obtain the first scattered light information and the first fluorescence information of each particle in the first sample. In some embodiments, the first scattered light information includes the first lateral scattered light information. In some other embodiments, the first scattered light information further includes the first forward scattered light information.

[0076] See further Figure 2, which shows a specific example of the optical detection device 130. The optical detection device 130 has a light source 101, a beam shaping component 102, a flow cell 103, and a forward scatter light detector 104 arranged in a straight line in the incident light direction in sequence. On one side of the flow cell 103, a dichroic mirror 106 is arranged at an angle of 45° with respect to the straight line. For the side light emitted by the particles in the flow cell 103, a part of it passes through the dichroic mirror 106 and is captured by a fluorescence detector 105 arranged at an angle of 45° with respect to the dichroic mirror 106 on the side of the dichroic mirror 106 away from the flow cell 103; another part of the side light is reflected by the dichroic mirror 106 and is captured by a side scatter light detector 107 arranged at an angle of 45° with respect to the dichroic mirror 106 on the side of the dichroic mirror 106 facing the flow cell 103.

[0077] The processor 140 is used to process and calculate data to obtain the required results. The processor 140 can also perform visualization processing on the intermediate calculation results or the final calculation results, and then display them through a display device 150 (such as a user interface). For example, the user interface is configured to output information characterizing acute promyelocytic leukemia or abnormal promyelocyte information (specifically, for example, output a prompt message about the risk of a subject having acute promyelocytic leukemia) and / or input instructions to the blood analyzer. In the embodiments of the present application, the processor 140 is configured to implement the steps described in detail below.

[0078] In some embodiments, the processor includes, but is not limited to, a central processing unit (CPU), a microcontroller 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 used to execute each computer application program in a computer-readable storage medium, so that the blood cell analyzer 100 executes the corresponding detection process and analyzes the optical information or optical signal detected by the optical detection device 130 in real time.

[0079] 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 arranged inside the second housing 170. The sample preparation device 120 is, for example, arranged inside the first housing 160, and the display device 150 is, for example, arranged on the outer surface of the first housing 160 and is used to display the detection results of the blood cell analyzer.

[0080] The applicant analyzed the test data of a large number of blood samples from patients with malignant blood diseases and found that the first parameters of the first target particle clusters, especially at least one particle cluster among monocyte clusters, neutrophil clusters, and lymphocyte clusters, such as the number of particles, distribution information, relative position relationship information, etc., obtained based on the first scattered light information and the first fluorescence information detected in the first sample, can identify promyelocytes present in the sample with high accuracy. Thus, the blood analyzer can be used to perform routine tests on the blood samples of the subjects to obtain a rapid alarm for possible acute promyelocytic leukemia.

[0081] In some embodiments, the processor 140 is configured to obtain at least one first parameter of at least one first target particle cluster in the first sample based at least on the first scattered light information and the first fluorescence information, and obtain information characterizing acute promyelocytic leukemia or abnormal promyelocyte information based on the at least one first parameter.

[0082] The at least one first parameter includes one or more of the parameters of monocyte clusters, neutrophil clusters, and lymphocyte clusters in the first sample.

[0083] According to one embodiment, the first scattered light information includes first lateral scattered light information, and the obtaining of at least one first parameter of at least one first target particle cluster in the first sample based at least on the first scattered light information and the first fluorescence information includes: obtaining at least one first parameter of at least one first target particle cluster in the first sample based on the first lateral scattered light information and the first fluorescence information.

[0084] In some embodiments, the first parameter includes: the number of particles of the first target particle cluster; the width of the first lateral scattered light intensity distribution, the center of gravity of the first lateral scattered light intensity distribution, the width of the first fluorescence intensity distribution, the center of gravity of the first fluorescence intensity distribution of the first target particle cluster; in the two-dimensional scatter plot generated by the first lateral scattered light intensity and the first fluorescence intensity, the area of the distribution region of the first target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the first target particle cluster, and the area ratio of the two distribution regions of the first target particle cluster.

[0085] The following uses specific example 1 to illustrate the steps executed by the processor of the blood analyzer in the above first embodiment.

[0086] In this example, 1592 samples collected from multiple hospitals were used as the research group samples. Among them, there were 92 positive samples of acute promyelocytic leukemia (APL) (double - confirmed as samples of APL patients before or during treatment through Lis (Laboratory Information Management System) data and microscopic examination data). The negative samples included 500 cases of other subtypes of AML (acute myeloid leukemia) other than APL (non - M3 type), 300 cases of ALL (acute lymphoblastic leukemia), 300 cases of anemic diseases, and 300 cases of non - blood diseases, totaling 1500 cases. A BC - 6800PLUS hematology analyzer produced by Shenzhen Mindray Bio - medical Electronics Co., Ltd. was used for the research.

[0087] See Figure 3 , which shows the side - scatter light intensity SS - fluorescence intensity FL scatter plot of three APL positive samples and a schematic diagram of obtaining the first parameter of the target particle cluster from the scatter plot. In this example, in the SS - FL perspective, the processor is configured to obtain the parameters of monocyte clusters (represented by the letter M), neutrophil clusters (represented by the letter N), and lymphocyte clusters (represented by the letter L).

[0088] Exemplarily, as Figure 3 shown in scatter plot A of Figure 3 , the area F1_NArea of the neutrophil cluster and the area F1_LArea of the lymphocyte cluster can be obtained. And further, the area ratio F1_NLP of the neutrophil cluster and the lymphocyte cluster can be obtained. As Figure 3 shown in scatter plot B of

[0089] where the area F2_MArea of the monocyte cluster and the area F1_NArea of the neutrophil cluster are obtained. Further see Figure 3 scatter plot C of

[0089] which illustrates the proportion F2_MuP of the number of particles in the area above and to the right of the neutrophil cluster in the monocyte cluster to the total number of particles in the monocyte cluster. Similarly, the proportion F3_MlL (not shown) of the number of particles in the area below and to the right of the lymphocyte cluster in the monocyte cluster to the total number of particles in the monocyte cluster can be further obtained.

[0089] In addition, in other embodiments, other parameters of the target particle cluster can also be obtained, such as the number of particles in the target particle cluster, the ratio of the number of particles in different target particle clusters; the fluorescence intensity distribution width, side - scatter light intensity distribution width, etc. of the target particle cluster.

[0090] The processor is configured to compare the obtained parameters with preset conditions to obtain information characterizing acute promyelocytic leukemia or abnormal promyelocyte information.

[0091] In this example, the parameters obtained above are respectively compared with the following three groups of preset conditions:

[0092] Condition 1: F1_NArea > 180 and F1_NLP > 1.5 and F2_MArea < 30;

[0093] Condition 2: F2_MuP > 15 and F3_MlL > 35 and F2_MArea > 30;

[0094] Condition 3: F2_MArea > 200 and F1_Larea < 150 and F1_NArea < 200.

[0095] If the said parameters meet at least one set of preset conditions, an alarm is given that the corresponding sample has a risk of acute promyelocytic leukemia; if none of the sets are met, the corresponding sample is output as APL negative.

[0096] Further, 1068 labeled samples collected from different hospitals are used as test group samples. Among them, there are 68 APL positive samples and 1000 negative samples diagnosed as non-APL (including other hematological disease samples or non-hematological disease samples). Each sample is detected by the same hematology analyzer according to the above method to obtain the side scatter light intensity SS - fluorescence intensity FL scatter plot of the DIFF channel, and further obtain the parameters of neutrophil clusters, monocyte clusters and lymphocyte clusters in each scatter plot, and compare them with the above three sets of preset conditions. The results of the test group samples are also confirmed by microscopy. The statistical results and microscopy results of the research group samples and the test group samples are shown in Table 1 below.

[0097] Table 1

[0098]

[0099] As can be seen from Table 1, this example obtains good detection results, and both the specificity and sensitivity of APL detection are relatively high.

[0100] In the above example, through the said hematology analyzer, the first side scatter light information and the first fluorescence information detected in the DIFF channel are first obtained, and from this, a side scatter light intensity SS - fluorescence intensity FL scatter plot is generated, and then the parameters of the first target particle cluster are further obtained. In other specific embodiments, the scatter plot may not be generated, and the parameters of the first target particle cluster can be directly obtained through the first scatter light information and the first fluorescence information.

[0101] In some embodiments, the first scatter light information further includes first forward scatter light information, and obtaining at least one first parameter of at least one first target particle cluster in the first sample based on at least the first scatter light information and the first fluorescence information includes: obtaining at least one first parameter of at least one first target particle cluster in the first sample based on the first side scatter light information, the first forward scatter light information and the first fluorescence information.

[0102] According to some embodiments, the at least one first parameter may include the following parameters: the number of particles in the first target particle cluster; the width of the first forward scatter light intensity distribution, the center of gravity of the first forward scatter light intensity distribution, the width of the first side scatter light intensity distribution, the center of gravity of the first side scatter light intensity distribution, the width of the first fluorescence intensity distribution, the center of gravity of the first fluorescence intensity distribution of the first target particle cluster; the area of the distribution region of the first target particle cluster in the two-dimensional scatter plot generated by the first side scatter light intensity and the first fluorescence intensity and in the two-dimensional scatter plot generated by the first forward scatter light intensity and the first side scatter light intensity or the first fluorescence intensity, the proportion of the number of particles in a specific region in one distribution region of the first target particle cluster, the areas of two distribution regions of the first target particle cluster; and in the three-dimensional scatter plot generated by the first forward scatter light intensity, the first side scatter light intensity and the first fluorescence intensity, the volume of the distribution region of the first target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the first target particle cluster, the volume ratio of two distribution regions of the first target particle cluster.

[0103] According to some embodiments, obtaining the information characterizing acute promyelocytic leukemia based on the at least one first parameter includes: comparing the at least one first parameter with a preset condition, and obtaining the information characterizing the acute promyelocytic leukemia according to the comparison result.

[0104] By further considering the forward scatter light information, the specificity and sensitivity of the detection can be further improved.

[0105] In the first embodiment, the optical signal obtained through the DIFF channel of the hematology analyzer 100 can be further used to form a scatter plot and classify particles, so as to obtain the white blood cell count and the count of the four types of white blood cells (i.e., monocytes, neutrophils, lymphocytes, and eosinophils).

[0106] According to the hematology analyzer of the first aspect, the present disclosure further provides a second embodiment. In the second embodiment, based on the characteristic parameters of the particle clusters obtained through the DIFF channel, the characteristic parameters of the particle clusters obtained through the WNB channel are further combined to jointly obtain the information of acute promyelocytic leukemia.

[0107] Specifically, based on the hematology analyzer 100 of the first embodiment, the sampling device 110 is further configured to aspirate at least another portion of the blood sample to be tested.

[0108] In the sample preparation device 120, the reagent supply unit is further configured to provide a second reagent into the reaction unit, and the reaction unit is further configured to react the second reagent with at least another part of the blood sample to prepare a second sample. The second reagent includes a second hemolytic agent and a second dye. As described above, different from the hemolytic agent used in the DIFF channel, the hemolytic intensity of the second hemolytic agent is weaker than that of the first hemolytic agent.

[0109] The present disclosure places no particular limitation on the second hemolytic agent, and any hemolytic agent conventionally used in the WNB channel can be used. In some embodiments, the second hemolytic agent may be any one or a combination of several of a cationic surfactant, a nonionic surfactant, anionic surfactant, and an amphiphilic surfactant. Specific examples include, for example, the hemolytic agent may be selected from quinoline octyl bromide, isoquinoline octyl bromide, quinoline decyl bromide, isoquinoline decyl bromide, quinoline dodecyl bromide, isoquinoline dodecyl bromide, quinoline tetradecyl bromide, isoquinoline tetradecyl bromide, octyltrimethylammonium chloride, octyltrimethylammonium bromide, decyltrimethylammonium chloride, decyltrimethylammonium bromide, dodecyltrimethylammonium chloride, dodecyltrimethylammonium bromide, tetradecyltrimethylammonium chloride, tetradecyltrimethylammonium bromide; polyoxyethylene (23) dodecyl ether, polyoxyethylene (25) cetyl ether, polyoxyethylene (30) cetyl ether, etc.

[0110] The present disclosure places no particular limitation on the second dye, and any hemolytic agent conventionally used in the WNB channel can be used. Commercially available reagents for identifying nucleated red blood cells can also be used for the second hemolytic agent and the second staining agent of the present application, such as M-6LN and M-6FN. Under hemolytic conditions, nucleated red blood cells and basophils in the blood sample can be identified from the detection signal by the second dye.

[0111] In the optical detection device 130, each particle in the second sample in the reaction unit passes through the detection area of the flow cell one by one. The scattered light detector is further configured to detect second scattered light information generated after the particles passing through the detection area are irradiated by the light beam, and the fluorescence detector is further configured to detect second fluorescence information generated after the particles passing through the flow cell are irradiated by the light beam.

[0112] It can be understood here that the second detection channel (also referred to as the WNB channel) for identifying nucleated red blood cells refers to the detection of the second sample prepared by the sample preparation device 120 by the optical detection device 130.

[0113] The processor 140 is further configured to obtain at least one second parameter of at least one second target particle cluster in the second sample at least based on the second scattered light information and the second fluorescence information, and obtain information characterizing acute promyelocytic leukemia or abnormal promyelocyte information based on the at least one first parameter and the at least one second parameter.

[0114] In some embodiments, at least one second parameter of at least one second target particle cluster includes one or more of the parameters of leukocyte clusters, basophil clusters, and nucleated erythrocyte clusters in the second sample.

[0115] According to some embodiments, the second scattered light information includes second forward scattered light information. The obtaining of at least one second parameter of at least one second target particle cluster in the second sample at least based on the second scattered light information and the second fluorescence information includes: obtaining at least one second parameter of at least one second target particle cluster in the second sample based on the second forward scattered light information and the second fluorescence information.

[0116] Similarly, in some embodiments, the at least one second parameter includes one or more of the following parameters: the number of particles of the second target particle cluster; the width of the second forward scattered light intensity distribution, the center of gravity of the second forward scattered light intensity distribution, the width of the second fluorescence intensity distribution, the center of gravity of the second fluorescence intensity distribution of the second target particle cluster; in the two-dimensional scatter plot generated by the second forward scattered light intensity and the second fluorescence intensity, the area of the distribution region of the second target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the second target particle cluster, and the area ratio of two distribution regions of the second target particle cluster.

[0117] According to some other embodiments, the second scattered light information further includes second side scattered light information. The obtaining of at least one second parameter of at least one second target particle cluster in the second sample at least based on the second scattered light information and the second fluorescence information includes: obtaining at least one second parameter of at least one second target particle cluster in the second sample based on the second forward scattered light information, the second side scattered light information, and the second fluorescence information.

[0118] In a specific embodiment, the at least one second parameter includes one or more of the following parameters: the number of particles in the second target particle cluster; the width of the second forward scattered light intensity distribution, the center of gravity of the second forward scattered light intensity distribution, the width of the second lateral scattered light intensity distribution, the center of gravity of the second lateral scattered light intensity distribution, the width of the second fluorescence intensity distribution, the center of gravity of the second fluorescence intensity distribution of the second target particle cluster; in the two-dimensional scatter plot generated by the second forward scattered light intensity and the second fluorescence intensity and the two-dimensional scatter plot generated by the second lateral scattered light intensity and the second forward scattered light intensity or the second fluorescence intensity, the area of the distribution region of the second target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the second target particle cluster, the area ratio of two distribution regions of the second target particle cluster; and in the three-dimensional scatter plot generated by the second forward scattered light intensity, the second lateral scattered light intensity and the second fluorescence intensity, the volume of the distribution region of the second target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the second target particle cluster, the volume ratio of two distribution regions of the second target particle cluster.

[0119] Based on the first parameters of the first target particle cluster detected in the DIFF channel, by further combining the second parameters of the second target particle cluster detected in the WNB channel, the specificity and sensitivity of APL alarm can be further improved.

[0120] According to the blood analyzer of the first aspect, the present disclosure further provides a third embodiment. In the third embodiment, based on the characteristic parameters of the particle cluster obtained in the DIFF channel, the characteristic parameters of the particle cluster obtained by further combining the impedance signal are used to jointly obtain the information of acute promyelocytic leukemia.

[0121] Specifically, based on the blood analyzer 100 of the first embodiment, the sampling device 110 is further configured to aspirate at least a further portion of the blood sample to be tested.

[0122] In the sample preparation device 120, the reagent supply unit is configured to provide a third reagent into the reaction unit, and make the third reagent react with at least a further portion of the blood sample to be tested to prepare a third sample. The third reagent is a diluent. The present disclosure has no particular limitation on the third reagent, and conventional diluents can be used.

[0123] The blood analyzer 100 further includes an electrical detection device (not shown). The electrical detection device includes a resistance detector, and the resistance detector is communicated with the reaction unit, so that each particle in the third sample passes through the resistance detector one by one to obtain the impedance signal of each particle in the third sample.

[0124] In a conventional peripheral blood test, cells can be classified and counted based on the impedance signals of each measured particle. The impedance method is based on the Coulter principle, and the acquired pulse signal corresponds to the cell volume. In the third sample treated with the third reagent, there are red blood cells with a medium volume and a large number, white blood cells with a large volume and a small number, and platelets with a small volume. Usually, the classification and counting of red blood cells and platelets can be obtained through a resistance detector.

[0125] In this embodiment, the processor is further configured to obtain at least one third parameter of at least one third target particle cluster in the third sample based on the impedance signal, and obtain information characterizing acute promyelocytic leukemia based on the at least one first parameter and the at least one third parameter.

[0126] In some embodiments, at least one third parameter of at least one third target particle cluster in the third sample includes one or more of the parameters of red blood cells and platelets in the third sample. In some specific embodiments, the third parameter includes: the number of red blood cell particles, the number of platelet particles, hematocrit, mean red blood cell volume, platelet distribution width, and platelet volume variation.

[0127] Based on the first parameters of the first target particle cluster obtained by detection in the DIFF channel, by further combining the third parameters of the third target particle cluster obtained by detection of the resistance detector, the specificity and sensitivity of APL alarm can be further improved.

[0128] According to the blood analyzer of the first aspect, the present disclosure further provides a fourth embodiment. In the fourth embodiment, based on the characteristic parameters of the particle clusters obtained in the DIFF channel and the WNB channel, the characteristic parameters of the particle clusters obtained by further combining the impedance signal are further combined.

[0129] Specifically, based on the blood analyzer 100 of the second embodiment, the sampling device 110 is further configured to aspirate at least a further part of the blood sample to be tested.

[0130] In the sample preparation device 120, the reagent supply unit is configured to provide the third reagent into the reaction unit. The third reagent is reacted with at least a further part of the blood sample to be tested to prepare a third sample. The third reagent and the third sample are the same as those described in the third embodiment.

[0131] The blood analyzer further includes an electrical detection device. The electrical detection device includes a resistance detector, and the resistance detector is connected to the reaction unit, so that each particle in the third sample can pass through the resistance detector one by one to obtain the impedance signal of each particle in the third sample.

[0132] The processor is further configured to obtain at least one third parameter of at least one third target particle cluster in the third sample based on the impedance signal, and obtain information characterizing acute promyelocytic leukemia based on the at least one first parameter, the at least one second parameter, and the at least one third parameter.

[0133] The third target particle cluster and the third parameter are the same as those described in the third embodiment.

[0134] In this fourth embodiment, by combining and analyzing the parameters detected by the DIFF channel, WNB channel, and impedance channel of the hematology analyzer, more accurate and sensitive alarms for APL can be achieved.

[0135] The following further illustrates the method steps executed by the hematology analyzer processor in the second to fourth embodiments through specific Example 2.

[0136] In Example 2, the research group samples and test group samples used are the same as those in Example 1.

[0137] See Figure 4 , which shows the side scatter light intensity SS - fluorescence intensity FL scatter plot (Figure A) of the DIFF channel, the fluorescence intensity FL - forward scatter light intensity FS scatter plot (Figure B) of the WNB channel, the red blood cell impedance - volume histogram (Figure C), and the platelet impedance - volume histogram (Figure D) obtained by detecting an APL - positive sample with the BC - 6800PLUS hematology analyzer produced by Shenzhen Mindray Bio - medical Electronics Co., Ltd.

[0138] Exemplarily, as shown in scatter plot A in Figure 4 , the first parameters of the first target particle cluster are obtained from this scatter plot, such as: the lateral scatter light intensity distribution width MON - SSC - W of the monocyte cluster, the lateral scatter light intensity distribution centroid NEU - SSC - P of the neutrophil cluster, the fluorescence intensity distribution width NEU - FL - W of the neutrophil cluster, etc. In addition, the particle number proportion of the first target particle cluster (such as the proportion Mon% of the monocyte cluster particle number relative to the white blood cell cluster particle number), the area and area ratio of the distribution region (such as the area F1_NArea of the neutrophil cluster), the proportion of the particle number in a specific region in a certain first target particle cluster region relative to the particle number of this particle cluster (such as the proportion F2_MuP of the particle number in the region above the neutrophil cluster in the monocyte cluster relative to the total particle number of the monocyte cluster) can also be obtained from this scatter plot.

[0139] While obtaining the above first parameter, the white blood cell count, the counts and percentages of white blood cell subsets (monocytes, neutrophils, eosinophils, and lymphocytes), etc. can be reported in this detection. In addition, this detection can also alarm information such as abnormal blasts and too high white blood cell count.

[0140] Further refer to Figure 4 Scatter plot B, from which the second parameter of the second target particle cluster is obtained, such as the width of the forward scatter light intensity distribution of the white blood cell cluster WBC-FSC-W, the center of gravity of the forward scatter light intensity distribution of the white blood cell cluster WBC-FSC-P, the width of the should light intensity distribution of the white blood cell cluster WBC-FL-W, etc. In addition, parameters of nucleated red blood cell clusters and basophil clusters can also be obtained from scatter plot B.

[0141] While obtaining the above second parameter, the white blood cell count, the classification and count of basophils and nucleated red blood cells can be reported in this detection. In addition, this detection can also alarm information such as abnormal nucleated red blood cells and too high basophil count.

[0142] Further refer to Figure 4 Impedance-volume histograms C and D, from which the third parameter of the third target particle cluster can be obtained, such as the number of red blood cell particles, the number of platelet particles, hematocrit, mean red blood cell volume, platelet distribution width, and platelet volume variation and other parameters.

[0143] In this Example 2, the second parameter of the second target particle cluster in the WNB channel and the third parameter of the third target particle cluster in the impedance channel obtained above can be combined with the first parameter of the first target particle cluster in the DIFF channel separately or simultaneously for analysis. Exemplarily, the parameters can be fitted to obtain a non-linear model. The fitting methods include but are not limited to decision tree, random forest, naive bayes, etc. Information characterizing acute promyelocytic leukemia is obtained according to the results of the non-linear model. Further refer to Figure 5 , in which an exemplary schematic diagram of fitting by the decision tree method is shown.

[0144] By the decision tree fitting method, the detection results of the samples in the above research group and test group, as well as the results verified by microscopy are shown in Table 2 below.

[0145] Table 2

[0146]

[0147] As can be seen from Table 2, this example obtained better results than Example 1, with a reduced false positive rate for APL detection and a further improved specificity.

[0148] The first aspect of the present disclosure also provides some other embodiments. The blood analyzer of the embodiment includes a sample suction device, a sample preparation device, and an optical detection device, all of which are the same as those in the above reference. Figure 1 The blood analyzer 100 is the same as the one described above. The processor is configured to: generate at least one scatter plot based on at least the first scattered light information and the first fluorescence information; obtain at least one first parameter of at least one first target particle cluster in the first sample based on a specific area in the at least one scatter plot; and obtain information characterizing acute promyelocytic leukemia or abnormal promyelocytic cell information based on the at least one first parameter.

[0149] The at least one first parameter is the same as that in the aforementioned first embodiment.

[0150] A second aspect of the present disclosure further provides a blood analyzer that generates a scatter plot by obtaining scattered light and fluorescence information from a DIFF channel, calculates the similarity between at least a portion of the scatter plot and a preset positive sample standard template, and obtains information characterizing a hematological malignancy based on the similarity.

[0151] The blood analyzer is combined with the first aspect Figure 1 The hematology analyzer 100 described in detail is similar, including a sample aspirator 110, a sample preparation device 120, an optical detection device 130, and a processor 140. It may also include a display device 150, a first housing 160, and a second housing 170. The optical detection device 130 and the processor 140 are disposed within the second housing 170. The sample preparation device 120 is disposed, for example, within the first housing 160, and the display device 150 is disposed, for example, on the exterior of the first housing 160 and is used to display the test results of the hematology analyzer. Identical components are not described again here.

[0152] According to the blood analyzer of the second aspect of the present disclosure, the processor 140 is configured to generate at least one scatter plot based on at least the first scattered light information and the first fluorescence information, and obtain information related to characterizing the malignant blood disease based on the comparison result of the at least one scatter plot with a preset positive sample standard scatter plot or based on the comparison result of at least a partial area of the at least one scatter plot with a corresponding area of the preset positive sample standard scatter plot.

[0153] According to some embodiments, information related to malignant blood diseases is obtained based on the comparison result of the at least one scatter plot with a preset standard scatter plot of positive samples or based on the comparison result of at least a part of the at least one scatter plot with the corresponding region of the preset standard scatter plot of positive samples, including: calculating the similarity between the at least one scatter plot and the preset standard scatter plot of positive samples or calculating the similarity between at least a part of the at least one scatter plot and the corresponding region of the preset standard template of positive samples, and obtaining information related to malignant blood diseases based on the similarity.

[0154] In some embodiments, the first scattered light information includes first side scattered light information, and the at least one scatter plot includes a two-dimensional scatter plot generated by the first side scattered light intensity and the first fluorescence intensity.

[0155] In some other embodiments, the first scattered light information further includes first forward scattered light information, and the at least one scatter plot further includes a two-dimensional scatter plot generated by the first forward scattered light intensity and the first fluorescence intensity and / or a two-dimensional scatter plot generated by the first side scattered light intensity and the first forward scattered light intensity.

[0156] In some embodiments, the similarity is calculated by at least one of the cosine similarity calculation method, the hash algorithm, the histogram calculation method, and the SSIM method.

[0157] In some embodiments, obtaining the information related to malignant blood diseases based on the similarity includes: comparing the calculated similarity with a preset condition, and obtaining information related to malignant blood diseases according to the comparison result, and particularly being able to obtain information related to acute promyelocytic leukemia or abnormal promyelocyte information.

[0158] The preset standard template of positive samples can be obtained by calculation from a scatter plot generated by detection information obtained by performing the same detection on a blood sample with a label using a blood analyzer.

[0159] The following further illustrates, through a specific example 3, the steps for the processor 140 of the blood analyzer in the third aspect to analyze the detection data to obtain information related to malignant leukemia.

[0160] A total of 1,693 samples were collected from several hospitals for this example. Among them, 852 were positive samples of malignant hematological diseases, including types such as AML, ALL, MDS, etc.; and 841 were negative samples of non-malignant hematological diseases, including anemic diseases, non-hematological diseases, and monitoring samples. The sample set was divided into a training set and a test set according to a ratio of 7:3. The BC-6800Plus hematology analyzer produced by Shenzhen Mindray Bio-Medical Electronics Co., Ltd. was used to detect each sample, and scatter plots of side scatter light intensity and fluorescence intensity in the DIFF channel were obtained. The scatter plots of positive samples in the training set were divided into five categories according to scatter plot characteristics, and after processing, they were used as positive sample standard templates, as Figure 6 shown, in which five obtained positive sample standard templates are shown. The cosine similarity method (representing a picture as a vector and calculating the cosine distance between vectors to characterize the similarity between two pictures) was used to calculate the similarity between the test samples and the five templates respectively, and the maximum value was taken as the calculated similarity result. If the similarity is greater than 0.85, it is a positive sample of malignant hematological disease, and on the contrary, it is a negative sample. The test set samples were used as verification set samples for testing.

[0161] The detection results of the training set samples and the verification set samples, as well as the results of the microscopic examination method, are shown in Table 3 below.

[0162] Table 3

[0163]

[0164] As can be seen from Table 3, the hematology analyzer of the second aspect and this example can relatively accurately alarm the risk of malignant hematological diseases in samples.

[0165] The following further illustrates the steps for the processor 140 of the hematology analyzer of the third aspect to analyze the detection data to obtain information characterizing acute promyelocytic leukemia through a specific example 4.

[0166] The same 1,592 research group samples as in Example 1 above were used as the training set, and 1,068 test case samples were used as the verification set. Among them, 92 positive samples in the training set were detected using the BC-6800Plus hematology analyzer produced by Shenzhen Mindray Bio-Medical Electronics Co., Ltd. to obtain scatter plots of side scatter light intensity and fluorescence intensity, which were divided into three categories according to scatter plot characteristics, and after processing, they were used as positive sample standard templates, as Figure 7 shown, in which three obtained positive sample standard templates are shown. The cosine similarity calculation method (representing a picture as a vector and calculating the cosine distance between vectors to characterize the similarity between two pictures) was used to calculate the similarity between the scatter plot from the test sample and the three templates, and the maximum value was taken. If the similarity is greater than 0.85, it is a positive sample of acute promyelocytic leukemia, and on the contrary, it is a negative sample.

[0167] The test results of the training set samples and the validation set samples as well as the microscopy results are shown in Table 4 below.

[0168] Table 4

[0169]

[0170] It can be seen from Table 4 that the blood analyzer according to the second aspect and this example can relatively accurately warn of the risk of APL in the sample.

[0171] The third aspect of the present disclosure further provides a blood analyzer. The blood analyzer obtains scattered light pulse signals and fluorescence pulse signals from a DIFF channel and uses these pulse signals as input into a pre-trained intelligent model to obtain information representative of hematological malignancies output by the intelligent model. Furthermore, the blood analyzer generates a scatter plot by obtaining scattered light pulse signals and fluorescence pulse signals from the DIFF channel and further obtains a first parameter of a first target particle cluster. This first parameter is then input into a pre-trained intelligent model to obtain information representative of hematological malignancies output by the intelligent model.

[0172] The blood analyzer is combined with the first aspect Figure 1 The hematology analyzer 100 described in detail is similar, including a sample aspirator 110, a sample preparation device 120, an optical detection device 130, and a processor 140. It may also include a display device 150, a first housing 160, and a second housing 170. The optical detection device 130 and the processor 140 are disposed within the second housing 170. The sample preparation device 120 is disposed, for example, within the first housing 160, and the display device 150 is disposed, for example, on the exterior of the first housing 160 and is used to display the test results of the hematology analyzer. Identical components are not described again here.

[0173] In addition, the first reagent used to process the sample to be tested to prepare the first test specimen is also the same as defined in the first aspect.

[0174] According to the first embodiment of the third aspect, the detection signal of the DIFF channel (unprocessed or processed) is input into a pre-trained deep learning intelligent model to obtain information related to malignant blood diseases output by the intelligent model.

[0175] Specifically, the scattered light detector of the optical detection device 130 is used to detect and obtain a first scattered light pulse signal of particles in the first sample, and the fluorescence detector is used to detect and obtain a first fluorescence pulse signal of particles in the first sample.

[0176] According to the first implementation, the processor 140 is configured to input at least the first scattered light pulse signal and the first fluorescence pulse signal into a pre-trained intelligent model, and obtain information related to malignant blood diseases output by the intelligent model. That is, the obtained optical pulse signals are input into a pre-trained deep learning intelligent model, and the intelligent model processes the optical pulse signals based on deep learning algorithms and then outputs information related to malignant blood diseases.

[0177] According to the second implementation, the processor 140 is configured to generate at least one first scatter plot from the first scattered light pulse signal and the first fluorescence pulse signal, obtain at least one first parameter of at least one first target particle cluster in the first sample based on the first scatter plot, and input the at least one first parameter into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model. The difference from the foregoing implementation is that the processor 140 processes the first scattered light pulse signal and the first fluorescence pulse signal, first generates at least one first scatter plot, then obtains at least one first parameter of at least one first target particle cluster in the first sample, and finally inputs the at least one first parameter into a pre-trained deep learning intelligent model, and the intelligent model processes the first parameter based on deep learning algorithms and then outputs the information related to malignant blood diseases.

[0178] It should be noted that the information related to malignant blood diseases is obtained by the deep learning intelligent model analyzing and processing the input data (optical pulse signal or first parameter) and output by the deep learning intelligent model. The analysis and processing at least include the analysis and processing of the information related to malignant blood diseases contained in the data by the deep learning intelligent model. That is to say, by making full use of the advantages of the deep learning intelligent model in data analysis and processing, the information related to malignant blood diseases can be directly obtained by the deep learning intelligent model without subsequent other analysis and processing, so the output of the result is more simple and direct.

[0179] The present disclosure places no particular limitation on the intelligent model, which may be a neural network model, an expert system, etc.

[0180] In some embodiments, the first scattered light pulse signal includes a first lateral scattered light pulse signal.

[0181] According to the first implementation, the processor 140 is configured to input the first lateral scattered light pulse signal and the first fluorescence pulse signal into a pre-trained intelligent model, and obtain the information related to the malignant blood diseases output by the intelligent model.

[0182] According to the second implementation manner, the processor 140 is configured to generate a first scatter plot from the first lateral scattered light pulse signal and the first fluorescence pulse signal, obtain at least one first parameter of at least one first target particle cluster in the first sample based on the first scatter plot, and input the at least one first parameter into a pre-trained intelligent model to obtain the information related to malignant blood diseases output by the intelligent model.

[0183] In the second implementation manner, at least one first parameter of the at least one first target particle cluster includes one or more of the parameters of monocyte clusters, neutrophil clusters, and lymphocyte clusters in the first sample.

[0184] According to a specific implementation manner, the at least one first parameter includes one or more of the following parameters: the number of particles of the first target particle cluster; the width of the first lateral scattered light intensity distribution, the center of gravity of the first lateral scattered light intensity distribution, the width of the first fluorescence intensity distribution, the center of gravity of the first fluorescence intensity distribution of the first target particle cluster; in the first scatter plot, the area of the distribution region of the first target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the first target particle cluster, and the area ratio of two distribution regions of the first target particle cluster.

[0185] In some embodiments, the first scattered light pulse signal further includes a first forward scattered light pulse signal. That is, on the basis of using the first lateral scattered light pulse signal and the first fluorescence pulse signal, the first forward scattered light pulse signal is further used. There are also two implementation manners for this embodiment.

[0186] According to the first implementation manner, the processor is configured to input the first lateral scattered light pulse signal, the first forward scattered light pulse signal, and the first fluorescence pulse signal into a pre-trained intelligent model to obtain the information related to malignant blood diseases output by the intelligent model.

[0187] According to the second implementation manner, a first scatter plot is generated from the first lateral scattered light pulse signal and the first fluorescence pulse signal, and at least another first scatter plot is generated from the first forward scattered light pulse signal and one of the first lateral scattered light pulse signal and the first fluorescence pulse signal. At least one first parameter of at least one first target particle cluster in the first sample is obtained based on the first scatter plot and the at least another scatter plot, and the at least one first parameter is input into a pre-trained intelligent model to obtain the information related to malignant blood diseases output by the intelligent model.

[0188] In the second implementation manner, at least one first parameter of at least one first target particle cluster includes one or more of the parameters of monocyte clusters, neutrophil clusters, and lymphocyte clusters in the first specimen.

[0189] According to a specific implementation manner, the at least one first parameter includes one or more of the following parameters: the number of particles in the first target particle cluster; the width of the first forward scatter light intensity distribution, the centroid of the first forward scatter light intensity distribution, the width of the first side scatter light intensity distribution, the centroid of the first side scatter light intensity distribution, the width of the first fluorescence intensity distribution, the centroid of the first fluorescence intensity distribution of the first target particle cluster; in the first scatter plot and the at least one other scatter plot, the area of the distribution region of the first target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the first target particle cluster, the area ratio of two distribution regions of the first target particle cluster; and in the three-dimensional scatter plot generated by the first forward scatter light intensity, the first side scatter light intensity, and the first fluorescence intensity, the volume of the distribution region of the first target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the first target particle cluster, the volume ratio of two distribution regions of the first target particle cluster. According to a preferred implementation manner, the information related to malignant blood diseases includes the information characterizing acute promyelocytic leukemia or abnormal promyelocyte information.

[0190] In some embodiments, the information related to malignant blood diseases includes the risk that the subject has a malignant blood disease.

[0191] According to the second embodiment of the third aspect, the detection signals (without processing or after processing) of the DIFF channel and the WNB channel are input into a pre-trained deep learning intelligent model, and the information related to malignant blood diseases output by the intelligent model is obtained.

[0192] Specifically, on the basis of the first embodiment of the third aspect, the sampling device 110 is further configured to aspirate at least another part of the blood sample to be tested.

[0193] In the specimen preparation device 120, the reagent supply unit is further configured to provide a second reagent into the reaction unit, so that the second reagent is mixed with at least another part of the blood sample therein to obtain a second specimen. The second reagent is the same as that defined in the first aspect and will not be elaborated here.

[0194] In the optical detection device 130, the flow chamber is connected to the reaction unit, and the scatter light detector is further used to detect the second scatter light pulse signals generated by each particle passing through the detection area after being irradiated by the light beam, and the fluorescence detector is further used to detect the second fluorescence pulse signals generated by the particles passing through the flow chamber after being irradiated by the light beam.

[0195] In the first implementation of this embodiment, the processor 140 is configured to input at least one of the first scattered light pulse signal and the first fluorescence pulse signal, and at least one of the second scattered light pulse signal and the second fluorescence pulse signal into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0196] Alternatively, in the second implementation of this embodiment, the processor 140 is further configured to generate at least one second scatter plot at least from the second scattered light pulse signal and the second fluorescence pulse signal, obtain at least one second parameter of at least one second target particle cluster in the second sample based on the second scatter plot, and input at least one first parameter and the at least one second parameter into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0197] In some embodiments, the second scattered light pulse signal includes a second lateral scattered light pulse signal. According to the first implementation, the processor is further configured to: input at least one of the first scattered light pulse signal and the first fluorescence pulse signal, and the second forward scattered light pulse signal and the second fluorescence pulse signal into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0198] According to the second implementation, the processor is further configured to: generate a second scatter plot from the second forward scattered light pulse signal and the second fluorescence pulse signal, obtain at least one second parameter of at least one second target particle cluster in the second sample based on the second scatter plot, input the at least one first parameter and the at least one second parameter into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0199] In the second implementation, at least one second parameter of at least one second target particle cluster includes one or more of the parameters of white blood cell clusters, basophil clusters, and nucleated red blood cell clusters in the second sample. The at least one second parameter includes one or more of the following parameters: the number of particles of the second target particle cluster; the width of the second forward scattered light intensity distribution, the center of gravity of the second forward scattered light intensity distribution, the width of the second fluorescence intensity distribution, the center of gravity of the second fluorescence intensity distribution of the second target particle cluster; in the second scatter plot generated from the second forward scattered light pulse signal and the second fluorescence pulse signal, the area of the distribution region of the second target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the second target particle cluster, and the area ratio of two distribution regions of the second target particle cluster.

[0200] In some embodiments, the second scattered light pulse signal further includes a second lateral scattered light pulse signal. That is, on the basis of using the second forward scattered light pulse signal and the second fluorescence pulse signal, the second lateral scattered light signal is further utilized.

[0201] According to the first implementation manner, the processor is further configured to input at least one of the first scattered light pulse signal and the first fluorescence pulse signal, and the second forward scattered light pulse signal, the second lateral scattered light pulse signal, and the second fluorescence pulse signal into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0202] According to the second implementation manner, the processor is further configured to generate a second scatter plot from the second forward scattered light pulse signal and the second fluorescence pulse signal, and generate at least one other second scatter plot from the second lateral scattered light pulse signal and one of the second forward scattered light pulse signal and the second fluorescence pulse signal. Based on the second scatter plot and the at least one other second scatter plot, at least one second parameter of at least one second target particle cluster in the second sample is obtained, and the at least one first parameter and the at least one second parameter are input into a pre-trained intelligent model, and the information related to the malignant blood diseases output by the intelligent model is obtained.

[0203] In the second implementation manner, the at least one second parameter includes one or more of the following parameters: the number of particles in the second target particle cluster; the width of the second forward scattered light intensity distribution, the center of gravity of the second forward scattered light intensity distribution, the width of the second lateral scattered light intensity distribution, the center of gravity of the second lateral scattered light intensity distribution, the width of the second fluorescence intensity distribution, the center of gravity of the second fluorescence intensity distribution of the second target particle cluster; in the second scatter plot and the at least one other second scatter plot, the area of the distribution region of the second target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the second target particle cluster, the area ratio of two distribution regions of the second target particle cluster; and in the second three-dimensional scatter plot generated by the second forward scattered light intensity, the second lateral scattered light intensity, and the second fluorescence intensity, the volume of the distribution region of the second target particle cluster, the proportion of the number of particles in a specific region in one distribution region of the second target particle cluster, the volume ratio of two distribution regions of the second target particle cluster.

[0204] According to the third embodiment of the third aspect, the detection signals (untreated or processed) of the DIFF channel and the impedance channel are input into a pre-trained deep learning intelligent model, and the information related to malignant blood diseases output by the intelligent model is obtained.

[0205] Specifically, based on the first embodiment of the third aspect, the sampling device 110 is further configured to aspirate at least a further portion of the blood sample to be tested.

[0206] In the sample preparation device 120, the reagent supply unit is configured to provide a third reagent into the reaction unit, and react the third reagent with at least a further portion of the blood sample to be tested to prepare a third sample. The third reagent is also a diluent.

[0207] The blood analyzer further includes an electrical detection device (not shown), the electrical detection device includes a resistance detector, and the resistance detector is in communication with the reaction unit, so that each particle in the third sample can pass through the resistance detector one by one to obtain an impedance pulse signal of each particle in the third sample.

[0208] In the first implementation manner of this embodiment, the processor 140 is configured to input at least one of the first scattered light pulse signal and the first fluorescence pulse signal and the impedance pulse signal into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0209] In the second implementation manner of this embodiment, the processor 140 is further configured to obtain at least one third parameter of at least one third target particle cluster in the third sample based on the impedance signal, and input the at least one first parameter and the at least one third parameter into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0210] According to the fourth embodiment of the third aspect, the detection signals (untreated or processed) of the DIFF channel, the WNB channel and the impedance channel are input into a pre-trained deep learning intelligent model, and the information related to malignant blood diseases output by the intelligent model is obtained.

[0211] Specifically, based on the second embodiment of the third aspect, the sampling device 110, the sample preparation device 120 and the electrical detection device are the same as those defined in the third embodiment.

[0212] In the first implementation manner of this embodiment, the processor 140 is configured to input at least one of the first scattered light pulse signal and the first fluorescence pulse signal, at least one of the second scattered light pulse signal and the second fluorescence pulse signal and the impedance pulse signal into a pre-trained intelligent model, and obtain the information related to malignant blood diseases output by the intelligent model.

[0213] In the second implementation manner of this embodiment, the processor 140 is further configured to obtain at least one third parameter of at least one third target particle cluster in the third sample based on the impedance pulse signal, and input the at least one first parameter, the at least one second parameter, and the at least one third parameter into a pre-trained intelligent model to obtain the information related to malignant blood diseases output by the intelligent model.

[0214] In the third and fourth embodiments, at least one third parameter of at least one third target particle cluster in the third sample includes one or more of the parameters of red blood cells and platelets in the third sample. According to a specific implementation manner, the third parameter includes: the number of red blood cell particles, the number of platelet particles, hematocrit, mean red blood cell volume, platelet distribution width, and platelet volume variation.

[0215] It should be noted that each embodiment of the third aspect of the present disclosure pre-uses training samples of processed or unprocessed detection data (the training samples include pulse signals of particles in the test sample or data samples of characteristic parameters of particle clusters in the scatter plot obtained by processing and the malignant blood disease diagnosis result labels of the data samples) to train a deep learning intelligent model, such as a neural network model. The trained deep learning intelligent model has the ability to analyze and judge whether the data is a malignant blood disease. In a specific embodiment, the data samples of the deep learning intelligent model need to correspond to the data types input when this specific embodiment is applied. In addition, the labels of the training samples determine the processing ability of the deep learning intelligent model.

[0216] The following further illustrates the specific steps executed by the processor of the hematology analyzer in the third aspect through a specific example 5 to obtain the information related to malignant blood diseases.

[0217] The training set samples and test set samples used in Example 5 are the same as those used in Example 3 above. Use the BC-6800Plus hematology analyzer produced by Shenzhen Mindray Bio-Medical Electronics Co., Ltd. to detect each sample, and obtain the lateral scatter light pulse signal and fluorescence pulse information of the DIFF channel, and further generate a scatter plot. Obtain the first parameter of each particle cluster from the scatter plot. Use the training data set of the training set samples to train an artificial neural network model. The training data set can include the above first parameter and the parameters obtained in the routine detection of the hematology analyzer, as well as the labels of the training set samples. Use ResNet18 in the convolutional neural network (CNN) model as the backbone network to extract features, and use the Sigmoid function to output a binary classification result, as Figure 8 shown. Use the test set samples as the validation set, and the results of the training set and validation set of the CNN model are shown in Table 5 below.

[0218] Table 5

[0219]

[0220] As can be seen from Table 5 above, by inputting the first parameter into the trained deep learning artificial neural network model, relatively accurate positive and negative reports of malignant blood diseases can be output. Since the deep learning intelligent model has the ability to continuously iterate and improve, with the increase of input sample data, the specificity and sensitivity of reporting malignant blood diseases will continuously improve.

[0221] The following further illustrates, through a specific example 6, the specific steps executed by the processor of the hematology analyzer in the third aspect to obtain information characterizing acute promyelocytic leukemia APL.

[0222] The training set samples and test set samples used in Example 6 are the same as those used in Example 4 above. The BC-6800Plus hematology analyzer produced by Shenzhen Mindray Bio-Medical Electronics Co., Ltd. is used to detect each sample, and the lateral scatter light pulse signal and fluorescence pulse information of the DIFF channel are obtained, and a scatter plot is further generated. The first parameter of each particle cluster is obtained from the scatter plot. The artificial neural network model is trained using the training data set of the training set samples (the first parameter training data set). The training data set can include the above first parameter and the parameters obtained in the routine detection of the hematology analyzer, as well as the labels of the training set samples. The ResNet18 in the convolutional neural network (CNN) model is used as the backbone network to extract features, and the Sigmoid function is used to output the binary classification result, as Figure 9 shown. The test set samples are used as the validation set, and the results of the training set and validation set of the CNN model are shown in Table 6 below.

[0223] Table 6

[0224]

[0225] As can be seen from Table 6 above, by inputting the first parameter into the trained deep learning artificial neural network model, relatively accurate positive and negative reports of APL can be output. Since the deep learning intelligent model has the ability to continuously iterate and improve, with the increase of input sample data, the specificity and sensitivity of reporting malignant blood diseases will continuously improve.

[0226] In addition, as understood by those skilled in the art, the principles herein can be embodied in a computer program product on a computer-readable storage medium that is preloaded with computer-readable program code. Any tangible, non-transitory computer-readable storage medium can be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-ROMs, DVDs, Blu-ray discs, etc.), flash memories, and / or the like. These computer program instructions can be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing devices to form a machine, such that the instructions executed on the computer or other programmable data processing devices can generate a device for implementing the specified functions. These computer program instructions can also be stored in a computer-readable memory, which can instruct the computer or other programmable data processing devices to operate in a specific manner, so that the instructions stored in the computer-readable memory can form a manufactured article, including an implementation device for implementing the specified functions. The computer program instructions can also be loaded onto a computer or other programmable data processing devices, thereby performing a series of operational steps on the computer or other programmable devices to generate a computer-implemented process, such that the instructions executed on the computer or other programmable devices can provide steps for implementing the specified functions.

[0227] The above are only some embodiments of the present disclosure and are not intended to limit the patent scope of the present application. Any equivalent structural transformation made under the inventive concept of the present disclosure by using the content of the specification and the drawings, or directly / indirectly applied in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. A blood analyzer comprising: a sample aspirating device configured to aspirate at least a portion of a blood sample to be tested; A sample preparation device, comprising a reagent supply portion and a reaction portion, wherein the reagent supply portion is configured to provide a first reagent to the reaction portion, and the reaction portion is configured to react the first reagent with at least a portion of the blood sample to be tested to prepare a first sample, wherein the first reagent comprises a first hemolytic agent and a first dye; An optical detection device comprising a light source, a flow chamber, a scattered light detector, and a fluorescence detector, wherein the light source is configured to emit a light beam to illuminate a detection region of the flow chamber, the flow chamber being in communication with the reaction portion such that individual particles of the first sample in the reaction portion can pass through the detection region of the flow chamber one by one, the scattered light detector being configured to detect first scattered light information generated by particles passing through the detection region after being illuminated by the light beam, and the fluorescence detector being configured to detect first fluorescence information generated by particles passing through the flow chamber after being illuminated by the light beam; The processor is configured to: obtaining at least one first parameter of at least one first target particle cluster in the first sample based at least on the first scattered light information and the first fluorescence information, Information characterizing acute promyelocytic leukemia or abnormal promyelocytic cell information is obtained based on the at least one first parameter.

2. The blood analyzer according to claim 1, wherein: The at least one first parameter includes one or more parameters of a monocyte population, a neutrophil population, and a lymphocyte population in the first sample.

3. The blood analyzer according to claim 1 or 2, wherein: The first scattered light information includes first side scattered light information, The obtaining of at least one first parameter of at least one first target particle cluster in the first sample based at least on the first scattered light information and the first fluorescence information includes: obtaining at least one first parameter of at least one first target particle cluster in the first sample based on the first side scattered light information and the first fluorescence information.

4. The blood analyzer according to claim 3, wherein: The at least one first parameter includes one or more of the following parameters: the number of particles in the first target particle cluster; the first side scattered light intensity distribution width, the first side scattered light intensity distribution center of gravity, the first fluorescence intensity distribution width, and the first fluorescence intensity distribution center of gravity of the first target particle cluster; in a two-dimensional scatter plot generated by the first side scattered light intensity and the first fluorescence intensity, the area of the distribution area of the first target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the first target particle cluster, and the area ratio of two distribution areas of the first target particle cluster.

5. The blood analyzer according to claim 3, wherein: The first scattered light information further includes first forward scattered light information, The obtaining of at least one first parameter of at least one first target particle cluster in the first sample based at least on the first scattered light information and the first fluorescence information includes: obtaining at least one first parameter of at least one first target particle cluster in the first sample based on the first side scattered light information, the first forward scattered light information and the first fluorescence information.

6. The blood analyzer according to claim 5, wherein: The at least one first parameter includes one or more of the following parameters: the number of particles of the first target particle cluster; the first forward scattered light intensity distribution width, the first forward scattered light intensity distribution center of gravity, the first side scattered light intensity distribution width, the first side scattered light intensity distribution center of gravity, the first fluorescence intensity distribution width, and the first fluorescence intensity distribution center of gravity of the first target particle cluster; the area of the distribution area of the first target particle cluster in a two-dimensional scatter plot generated by the first side scattered light intensity and the first fluorescence intensity and in a two-dimensional scatter plot generated by the first forward scattered light intensity and the first side scattered light intensity or the first fluorescence intensity, the proportion of the number of particles in a specific area in a distribution area of the first target particle cluster, and the area ratio of the two distribution areas of the first target particle cluster; and in a three-dimensional scatter plot generated by the first forward scattered light intensity, the first side scattered light intensity and the first fluorescence intensity, the volume of the distribution area of the first target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the first target particle cluster, and the volume ratio of the two distribution areas of the first target particle cluster.

7. The blood analyzer according to any one of claims 1 to 6, wherein: The obtaining of the information characterizing the acute promyelocytic leukemia or the abnormal promyelocytic cell information based on the at least one first parameter includes: comparing the at least one first parameter with a preset condition, and obtaining the information characterizing the acute promyelocytic leukemia according to the comparison result.

8. The blood analyzer according to claim 1, wherein: The sample aspirating device is further configured to aspirate at least another portion of the blood sample to be tested; The reagent supply unit is further configured to provide a second reagent to the reaction unit, and the reaction unit is further configured to react the second reagent with at least another portion of the blood sample to prepare a second test sample, wherein the second reagent includes a second hemolytic agent and a second dye, and the hemolytic strength of the second hemolytic agent is weaker than that of the first hemolytic agent; Each particle in the second sample in the reaction section passes through the detection area of the flow cell one by one, the scattered light detector is further used to detect second scattered light information generated by the particles passing through the detection area after being irradiated by the light beam, and the fluorescence detector is further used to detect second fluorescence information generated by the particles passing through the flow cell after being irradiated by the light beam; The processor is further configured to: obtaining at least one second parameter of at least one second target particle cluster in the second sample based at least on the second scattered light information and the second fluorescence information, Information characterizing acute promyelocytic leukemia or abnormal promyelocytic cell information is obtained based on the at least one first parameter and the at least one second parameter.

9. The blood analyzer according to claim 8, wherein: The at least one second parameter includes one or more parameters of a leukocyte mass, a basophil mass, and a nucleated red blood cell mass in the second sample.

10. The blood analyzer according to claim 8 or 9, wherein the second scattered light information includes second forward scattered light information, The obtaining at least one second parameter of at least one second target particle cluster in the second sample based at least on the second scattered light information and the second fluorescence information comprises: At least one second parameter of at least one second target particle cluster in the second sample is obtained based on the second forward scattered light information and the second fluorescence information.

11. The blood analyzer according to claim 10, wherein: The at least one second parameter includes one or more of the following parameters: the number of particles in the second target particle cluster; the second forward scattered light intensity distribution width, the second forward scattered light intensity distribution center of gravity, the second fluorescence intensity distribution width, and the second fluorescence intensity distribution center of gravity of the second target particle cluster; in a two-dimensional scatter plot generated by the second forward scattered light intensity and the second fluorescence intensity, the area of the distribution area of the second target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the second target particle cluster, and the area ratio of two distribution areas of the second target particle cluster.

12. The blood analyzer according to claim 10, wherein the second scattered light information further includes second side scattered light information, The obtaining at least one second parameter of at least one second target particle cluster in the second sample based at least on the second scattered light information and the second fluorescence information comprises: At least one second parameter of at least one second target particle cluster in the second sample is obtained based on the second forward scattered light information, the second side scattered light information, and the second fluorescence information.

13. A blood analyzer according to claim 12, wherein the at least one second parameter includes one or more of the following parameters: the number of particles in the second target particle cluster; the second forward scattered light intensity distribution width, the second forward scattered light intensity distribution center of gravity, the second side scattered light intensity distribution width, the second side scattered light intensity distribution center of gravity, the second fluorescence intensity distribution width, and the second fluorescence intensity distribution center of gravity of the second target particle cluster; in a two-dimensional scatter plot generated by the second forward scattered light intensity and the second fluorescence intensity and in a two-dimensional scatter plot generated by the second side scattered light intensity and the second forward scattered light intensity or the second fluorescence intensity, the area of the distribution area of the second target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the second target particle cluster, and the area ratio of the two distribution areas of the second target particle cluster; and in a three-dimensional scatter plot generated by the second forward scattered light intensity, the second side scattered light intensity and the second fluorescence intensity, the volume of the distribution area of the second target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the second target particle cluster, and the volume ratio of the two distribution areas of the second target particle cluster.

14. The blood analyzer according to claim 8, wherein: The sample aspirating device is further configured to aspirate at least another portion of the blood sample to be tested; In the sample preparation device, the reagent supply unit is configured to provide a third reagent to the reaction unit, and the reaction unit is further configured to react the third reagent with at least another portion of the blood sample to be tested to prepare a third sample, wherein the third reagent is a diluent; The blood analyzer further includes an electrical detection device, the electrical detection device including a resistance detector, the resistance detector being in communication with the reaction portion so that each particle in the third sample can pass through the resistance detector one by one to obtain an impedance signal of each particle in the third sample; The processor is further configured to: obtaining at least one third parameter of at least one third target particle cluster in the third sample based on the impedance signal; obtaining information characterizing acute promyelocytes or abnormal promyelocytes based on the at least one first parameter and the at least one third parameter, Alternatively, information characterizing acute promyelocytes or abnormal promyelocytes information is obtained based on the at least one first parameter, the at least one second parameter, and the at least one third parameter.

15. The blood analyzer according to claim 14, wherein: The at least one third parameter includes one or more parameters of red blood cells and platelets in the third sample, Preferably, the at least one third parameter comprises one or more of the following parameters: red blood cell particle number, platelet particle number, hematocrit, mean corpuscular volume, platelet distribution width and platelet volume coefficient of variation.

16. The blood analyzer according to claim 1, wherein: The processor is further configured to obtain information of eosinophils, monocytes, neutrophils, and lymphocytes based on the first scattered light information and the first fluorescence information.

17. The blood analyzer according to claim 8, wherein: The processor is further configured to obtain information of nucleated red blood cells and / or basophils based on the second scattered light information and the second fluorescence information.

18. The blood analyzer according to claim 14, wherein: The processor is further configured to obtain information of red blood cells and platelets based on the impedance signal.

19. The blood analyzer of claim 1, wherein the information characterizing acute promyelocytic leukemia comprises the subject's risk of having acute promyelocytic leukemia. 20 . The blood analyzer according to claim 1 , further comprising a user interface configured to output the information characterizing acute promyelocytic leukemia or abnormal promyelocytic cell information.

21. A blood analyzer comprising: a sample aspirating device configured to aspirate at least a portion of a blood sample to be tested; A sample preparation device, comprising a reagent supply portion and a reaction portion, wherein the reagent supply portion is configured to provide a first reagent to the reaction portion, and the reaction portion is configured to react the first reagent with at least a portion of the blood sample to be tested to prepare a first sample, wherein the first reagent comprises a first hemolytic agent and a first fluorescent dye; An optical detection device comprising a light source, a flow chamber, a scattered light detector, and a fluorescence detector, wherein the light source is configured to emit a light beam to illuminate a detection region of the flow chamber, the flow chamber being in communication with the reaction portion such that individual particles of the first sample in the reaction portion can pass through the detection region of the flow chamber one by one, the scattered light detector being configured to detect first scattered light information generated by particles passing through the detection region after being illuminated by the light beam, and the fluorescence detector being configured to detect first fluorescence information generated by particles passing through the flow chamber after being illuminated by the light beam; The processor is configured to: generating at least one scattergram based on at least the first scattered light information and the first fluorescence information, Based on the comparison result of the at least one scatter plot with a preset positive sample standard scatter plot or based on the comparison result of at least a partial area of the at least one scatter plot with a corresponding area of a preset positive sample standard scatter plot, information characterizing the malignant blood disease is obtained.

22. The blood analyzer according to claim 21, wherein: The obtaining of information related to the malignant blood disease based on the comparison result of the at least one scatter plot with a preset positive sample standard scatter plot or based on the comparison result of at least a partial area of the at least one scatter plot with a corresponding area of the preset positive sample standard scatter plot includes: calculating the similarity between the at least one scatter plot and the preset positive sample standard scatter plot or calculating the similarity between at least a partial area of the at least one scatter plot and a corresponding area of the preset positive sample standard scatter plot, and obtaining the information related to the malignant blood disease based on the similarity.

23. The blood analyzer according to claim 21 or 22, wherein: The first scattered light information includes first side scattered light information, and the at least one scattergram includes a two-dimensional scattergram generated by the first side scattered light intensity and the first fluorescence intensity; Preferably, the first scattered light information further includes first forward scattered light information, and the at least one scatter plot further includes a two-dimensional scatter plot generated by the first forward scattered light intensity and the first fluorescence intensity and / or a two-dimensional scatter plot generated by the first side scattered light intensity and the first forward scattered light intensity.

24. The blood analyzer according to claim 22, wherein: The similarity is calculated by at least one of a cosine similarity calculation method, a hash algorithm, a histogram calculation method, and an SSIM method.

25. The blood analyzer according to claim 22, wherein: The obtaining of the information related to the malignant blood disease based on the similarity includes: The similarity is compared with a preset condition, and the information characterizing the malignant blood disease is obtained according to the comparison result.

26. The blood analyzer according to claim 21, wherein The information characterizing the hematological malignancy includes the risk of the subject having the hematological malignancy. Preferably, the hematological malignancy includes acute promyelocytic leukemia. 27 . The blood analyzer according to claim 21 , further comprising a user interface configured to output the information representative of the hematological malignancy.

28. A blood analyzer comprising: a sample aspirating device configured to aspirate at least a portion of a blood sample to be tested; A sample preparation device, comprising a reagent supply portion and a reaction portion, wherein the reagent supply portion is configured to provide a first reagent to the reaction portion, and the reaction portion is configured to react the first reagent with at least a portion of the blood sample to be tested to prepare a first sample, wherein the first reagent comprises a first hemolytic agent and a first dye; An optical detection device comprising a light source, a flow cell, a scattered light detector, and a fluorescence detector, wherein the light source is configured to emit a light beam to illuminate a detection zone of the flow cell, the flow cell being in communication with the reaction portion so that individual particles in a first sample in the reaction portion can pass through the detection zone of the flow cell one by one, the scattered light detector being configured to detect first scattered light pulse signals generated by particles passing through the detection zone after being illuminated by the light beam, and the fluorescence detector being configured to detect first fluorescence pulse signals generated by particles passing through the flow cell after being illuminated by the light beam; The processor is configured to: Inputting at least the first scattered light pulse signal and the first fluorescent pulse signal into a pre-trained intelligent model to obtain information related to the malignant hematological disease output by the intelligent model; or, generating at least one first scattergram based on at least the first scattered light pulse signal and the first fluorescence pulse signal, obtaining at least one first parameter of at least one first target particle cluster in the first sample based on the first scattergram, The at least one first parameter is input into a pre-trained intelligent model to obtain the information related to the malignant blood disease output by the intelligent model.

29. The blood analyzer according to claim 28, wherein The first scattered light pulse signal includes a first side scattered light pulse signal, The processor is configured to: Inputting the first side scattered light pulse signal and the first fluorescence pulse signal into a pre-trained intelligent model to obtain information representing the hematological malignancy output by the intelligent model; or, generating a first scattergram based on the first side scattered light pulse signal and the first fluorescence pulse signal, obtaining at least one first parameter of at least one first target particle cluster in the first sample based on the first scattergram, The at least one first parameter is input into a pre-trained intelligent model to obtain the information related to the malignant blood disease output by the intelligent model.

30. The blood analyzer according to claim 29, wherein: The at least one first parameter of the at least one first target particle cluster comprises one or more parameters of the monocyte cluster, the neutrophil cluster, and the lymphocyte cluster in the first sample, Preferably, the at least one first parameter includes one or more of the following parameters: the number of particles in the first target particle cluster; the first side scattered light intensity distribution width, the first side scattered light intensity distribution center of gravity, the first fluorescence intensity distribution width, and the first fluorescence intensity distribution center of gravity of the first target particle cluster; in the first scatter plot, the area of the distribution area of the first target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the first target particle cluster, and the area ratio of two distribution areas of the first target particle cluster.

31. The blood analyzer according to claim 29, wherein: The first scattered light pulse signal further includes a first forward scattered light pulse signal, The processor is configured to: inputting the first side scattered light pulse signal, the first forward scattered light pulse signal, and the first fluorescence pulse signal into a pre-trained intelligent model to obtain the information related to the malignant hematological disease output by the intelligent model; or, generating a first scattergram based on the first side scattered light pulse signal and the first fluorescence pulse signal, and generating at least another first scattergram based on the first forward scattered light pulse signal and one of the first side scattered light pulse signal and the first fluorescence pulse signal, obtaining at least one first parameter of at least one first target particle cluster in the first sample based on the first scattergram and the at least one other scattergram, The at least one first parameter is input into a pre-trained intelligent model to obtain the information related to the malignant blood disease output by the intelligent model.

32. The blood analyzer according to claim 31, wherein The at least one first parameter of the at least one first target particle cluster comprises one or more parameters of the monocyte cluster, the neutrophil cluster, and the lymphocyte cluster in the first sample, Preferably, the at least one first parameter includes one or more of the following parameters: the number of particles of the first target particle cluster; the first forward scattered light intensity distribution width, the first forward scattered light intensity distribution center of gravity, the first side scattered light intensity distribution width, the first side scattered light intensity distribution center of gravity, the first fluorescence intensity distribution width, and the first fluorescence intensity distribution center of gravity of the first target particle cluster; the area of the distribution area of the first target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the first target particle cluster, and the area ratio of the two distribution areas of the first target particle cluster in the first scatter plot and the at least another scatter plot; and the volume of the distribution area of the first target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the first target particle cluster, and the volume ratio of the two distribution areas of the first target particle cluster in a three-dimensional scatter plot generated by the first forward scattered light intensity, the first side scattered light intensity and the first fluorescence intensity.

33. The blood analyzer according to any one of claims 28, wherein: The sample aspirating device is further configured to aspirate at least another portion of the blood sample to be tested; In the sample preparation device, the reagent supply unit is further configured to provide a second reagent to the reaction unit, and the reaction unit is configured to mix the second reagent with at least another portion of the blood sample to obtain a second sample, wherein the second reagent includes a second hemolytic agent and a second dye, and the hemolytic strength of the second hemolytic agent is weaker than that of the first hemolytic agent; In the optical detection device, each particle in the second sample in the reaction section can pass through the detection area of the flow cell one by one, the scattered light detector is further used to detect a second scattered light pulse signal generated by the particles passing through the detection area after being irradiated by the light beam, and the fluorescence detector is further used to detect a second fluorescence pulse signal generated by the particles passing through the flow cell after being irradiated by the light beam; The processor is further configured to: inputting at least one of the first scattered light pulse signal and the first fluorescence pulse signal, and at least one of the second scattered light pulse signal and the second fluorescence pulse signal, into a pre-trained intelligent model to obtain information related to the malignant hematological disease output by the intelligent model; or, generating at least one second scattergram based on at least the second scattered light pulse signal and the second fluorescence pulse signal, obtaining at least one second parameter of at least one second target particle cluster in the second sample based on the second scattergram, The at least one first parameter and the at least one second parameter are input into a pre-trained intelligent model to obtain the information related to the malignant blood disease output by the intelligent model.

34. The blood analyzer according to claim 33, wherein: The second scattered light pulse signal includes a second forward scattered light pulse signal, The processor is further configured to: inputting at least one of the first scattered light pulse signal and the first fluorescence pulse signal, as well as the second forward scattered light pulse signal and the second fluorescence pulse signal, into a pre-trained intelligent model to obtain information related to the malignant hematological disease output by the intelligent model; or, generating a second scattergram based on the second forward scattered light pulse signal and the second fluorescence pulse signal, obtaining at least one second parameter of at least one second target particle cluster in the second sample based on the second scattergram, The at least one first parameter and the at least one second parameter are input into a pre-trained intelligent model to obtain the information related to the malignant blood disease output by the intelligent model.

35. The blood analyzer according to claim 34, wherein: The at least one second parameter of the at least one second target particle cluster comprises one or more parameters of a second leukocyte cluster, a basophil cluster, and a nucleated red blood cell cluster in the second sample; The at least one second parameter includes one or more of the following parameters: the number of particles in the second target particle cluster; the second forward scattered light intensity distribution width, the second forward scattered light intensity distribution center of gravity, the second fluorescence intensity distribution width, and the second fluorescence intensity distribution center of gravity of the second target particle cluster; in the second scatter plot generated by the second forward scattered light pulse signal and the second fluorescence pulse signal, the area of the distribution area of the second target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the second target particle cluster, and the area ratio of the two distribution areas of the second target particle cluster.

36. The blood analyzer according to claim 34, wherein: The second scattered light pulse signal further includes a second side scattered light pulse signal, The processor is further configured to: inputting at least one of the first scattered light pulse signal and the first fluorescence pulse signal, as well as the second forward scattered light pulse signal, the second side scattered light pulse signal, and the second fluorescence pulse signal into a pre-trained intelligent model to obtain information related to the malignant hematological disease output by the intelligent model; or, generating a second scattergram based on the second forward scattered light pulse signal and the second fluorescence pulse signal, and generating at least another second scattergram based on the second side scattered light pulse signal and one of the second forward scattered light pulse signal and the second fluorescence pulse signal, obtaining at least one second parameter of at least one second target particle cluster in the second sample based on the second scattergram and at least another second scattergram, The at least one first parameter and the at least one second parameter are input into a pre-trained intelligent model to obtain information related to the malignant blood disease output by the intelligent model.

37. The blood analyzer according to claim 36, wherein: The at least one second parameter includes one or more of the following parameters: the number of particles of the second target particle cluster; the second forward scattered light intensity distribution width, the second forward scattered light intensity distribution center of gravity, the second side scattered light intensity distribution width, the second side scattered light intensity distribution center of gravity, the second fluorescence intensity distribution width, and the second fluorescence intensity distribution center of gravity of the second target particle cluster; in the second scatter plot and the at least another second scatter plot, the area of the distribution area of the second target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the second target particle cluster, and the area ratio of the two distribution areas of the second target particle cluster; and in the second three-dimensional scatter plot generated by the second forward scattered light intensity, the second side scattered light intensity and the second fluorescence intensity, the volume of the distribution area of the second target particle cluster, the proportion of the number of particles in a specific area in a distribution area of the second target particle cluster, and the volume ratio of the two distribution areas of the second target particle cluster.

38. The blood analyzer according to any one of claims 28, wherein: The sample aspirating device is further configured to aspirate at least another portion of the blood sample to be tested; In the sample preparation device, the reagent supply unit is configured to provide a third reagent to the reaction unit, and the reaction unit is configured to react the third reagent with at least another portion of the blood sample to be tested to prepare a third sample, wherein the third reagent is a diluent; The blood analyzer further includes an electrical detection device, the electrical detection device including a resistance detector, the resistance detector being in communication with the reaction portion so that each particle in the third sample can pass through the resistance detector one by one, thereby obtaining an impedance pulse signal of each particle in the third sample; The processor is further configured to: inputting at least one of the first scattered light pulse signal and the first fluorescence pulse signal and the impedance pulse signal into a pre-trained intelligent model to obtain the information related to the malignant hematological disease output by the intelligent model; or, obtaining at least one third parameter of at least one third target particle cluster in the third sample based on the impedance signal, The at least one first parameter and the at least one third parameter are input into a pre-trained intelligent model to obtain the information related to the malignant blood disease output by the intelligent model.

39. The blood analyzer according to claim 33, wherein: The sample aspirating device is further configured to aspirate at least another portion of the blood sample to be tested; In the sample preparation device, the reagent supply unit is configured to provide a third reagent to the reaction unit, and the reaction unit is configured to react the third reagent with at least another portion of the blood sample to be tested to prepare a third sample, wherein the third reagent is a diluent; The blood analyzer further includes an electrical detection device, the electrical detection device including a resistance detector, the resistance detector being in communication with the reaction portion, so that each particle in the third sample passes through the resistance detector one by one to obtain an impedance pulse signal of each particle in the third sample; The processor is further configured to: inputting at least one of the first scattered light pulse signal and the first fluorescence pulse signal, at least one of the second scattered light pulse signal and the second fluorescence pulse signal, and the impedance pulse signal into a pre-trained intelligent model to obtain information related to the malignant hematological disease output by the intelligent model; or, obtaining at least one third parameter of at least one third target particle cluster in the third sample based on the impedance pulse signal; The at least one first parameter, the at least one second parameter and the at least one third parameter are input into a pre-trained intelligent model to obtain the information related to the malignant blood disease output by the intelligent model.

40. The blood analyzer according to claim 38 or 39, wherein: The at least one third parameter of the at least one third target particle cluster in the third sample comprises one or more parameters of red blood cells and platelets in the third sample; Preferably, the at least one third parameter comprises one or more of the following parameters: red blood cell particle count, platelet particle count, hematocrit, mean corpuscular volume, platelet distribution width and platelet volume variation.

41. The blood analyzer according to claim 28, wherein The information characterizing hematological malignancies includes the risk of the subject having hematological malignancies; Preferably, the hematological malignancy comprises acute promyelocytic leukemia.

42. The blood analyzer according to claim 28, wherein The method further includes a user interaction interface configured to output the information related to the malignant blood disease.

43. A blood analyzer comprising: a sample aspirating device configured to aspirate at least a portion of a blood sample to be tested; A sample preparation device, comprising a reagent supply portion and a reaction portion, wherein the reagent supply portion is configured to provide a first reagent to the reaction portion, and the reaction portion is configured to react the first reagent with at least a portion of the blood sample to be tested to prepare a first sample, wherein the first reagent comprises a first hemolytic agent and a first dye; An optical detection device comprising a light source, a flow chamber, a scattered light detector, and a fluorescence detector, wherein the light source is configured to emit a light beam to illuminate a detection region of the flow chamber, the flow chamber being in communication with the reaction portion such that individual particles of the first sample in the reaction portion can pass through the detection region of the flow chamber one by one, the scattered light detector being configured to detect first scattered light information generated by particles passing through the detection region after being illuminated by the light beam, and the fluorescence detector being configured to detect first fluorescence information generated by particles passing through the flow chamber after being illuminated by the light beam; The processor is configured to: generating at least one scattergram based on at least the first scattered light information and the first fluorescence information, obtaining at least one first parameter of at least one first target particle cluster in the first sample based on a specific area in the at least one scattergram, Information characterizing acute promyelocytic leukemia or abnormal promyelocytic cell information is obtained based on the at least one first parameter.

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