Blood cell analyzer and method for detecting bilirubin and storage medium

Abstract

The invention relates to the field of in-vitro diagnosis, in particular to a blood cell analyzer and method for detecting bilirubin in a sample and a computer readable storage medium. Scattered light information and fluorescence information of leukocytes of to-be-detected sample liquid are obtained through a leukocyte measuring device; acquiring particle information of at least one target particle swarm from the scattered light information and the fluorescence information by using a data processing device, and inputting the acquired particle information of the at least one target particle swarm into a preset calculation model as an input parameter; the output of the calculation model is obtained to serve as bilirubin mark parameter information of the blood sample, information about whether bilirubin is abnormal or not and bilirubin concentration information, bilirubin information and leukocyte detection results of the blood sample are synchronously obtained in the blood routine detection process, extra detection reagents or methods are not needed, and the detection efficiency is improved. Clinical detection cost is not increased, and detection is convenient and accurate.

Description

Technical Field

[0001] The present application relates to the field of in vitro diagnosis, and particularly to a blood cell analyzer, a method, and a computer-readable storage medium for detecting bilirubin in a test sample. Background Art

[0002] Bilirubin is a product of red blood cell metabolism in the mononuclear-macrophage system of the liver, spleen, and bone marrow in the blood, and is one of the more important indicators in liver function tests. A high bilirubin level indicates biliary obstruction, hepatitis, cirrhosis, liver failure, hemolytic syndrome, etc.

[0003] Currently, the commonly used methods for clinically measuring bilirubin include the diazo reagent method, the chemical oxidation method, the bilirubin oxidase endpoint method, etc.

[0004] Currently, the commonly used methods for clinically measuring bilirubin include the diazo reagent method, the chemical oxidation method, the bilirubin oxidase endpoint method, etc. Diazo reagent method: Conjugated bilirubin in serum can directly react with the diazo reagent to form purple azobilirubin; under the same conditions, unconjugated bilirubin must have an accelerator to break the bilirubin hydrogen bond before it can react with the diazo reagent. For example, sodium benzoate is used as an accelerator, sodium acetate is used as a buffer to maintain the pH value of the reaction, and sodium azide destroys the remaining diazo reagent to terminate the azo reaction in the conjugated bilirubin determination tube. Finally, sodium tartrate is added, and the purple azobilirubin is converted to blue azobilirubin, shifting the maximum absorbance from 530 nm to 598 nm. The absorbance of the sample in the test tube is read, and the corresponding bilirubin content is determined through the corresponding standard curve.

[0005] Chemical oxidation method: Under the condition of pH = 3, direct bilirubin is oxidized by the oxidant sodium vanadate to biliverdin, and the specific absorbance of the yellow color of bilirubin decreases. By measuring the change in absorbance before and after the oxidation by vanadate, the content of direct bilirubin in the sample can be calculated.

[0006] Bilirubin oxidase endpoint method: Bilirubin is yellow and has a maximum absorption peak near 450 nm. Bilirubin oxidase (BOD) catalyzes the oxidation of bilirubin. As bilirubin is oxidized, the maximum absorption peak decreases, and the degree of decrease is related to the amount of bilirubin oxidized. Under the condition of pH = 8, both unconjugated bilirubin and conjugated bilirubin are oxidized. Therefore, detecting the decrease value of the absorbance at 450 nm can reflect the total bilirubin content.

[0007] These methods all require the use of special detection reagents and methods for bilirubin, which will increase the clinical detection cost. Therefore, it is necessary to develop a more convenient and accurate method for detecting bilirubin. Summary of the Invention

[0008] The purpose of the present application is to provide a convenient and accurate method for detecting bilirubin, so as to solve the technical problem that the current detection of bilirubin requires special detection reagents and methods for bilirubin, resulting in an increase in detection costs.

[0009] In a first aspect, the present application provides a blood cell analyzer for detecting bilirubin, comprising: A sampling device, at least for collecting a blood sample; A sample preparation device, at least for mixing at least a part of the blood sample with a processing reagent to prepare a sample solution to be tested; A white blood cell measurement device, for obtaining optical information of white blood cells in the sample solution to be tested, where the optical information includes scattered light information and fluorescence information; A data processing device, at least for obtaining particle information of at least one target particle group from the optical information of the white blood cells, and taking the obtained particle information of the at least one target particle group as input parameters and inputting them into a preset calculation model to obtain the output of the calculation model as bilirubin information of the blood sample, where the bilirubin information includes bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information.

[0010] Further, the at least one target particle group includes one or more of a neutrophil group, a lymphocyte group, and a monocyte group.

[0011] Further, the particle information of the target particle group includes one or more of the centroid of the fluorescence intensity distribution (FL_P), the centroid of the lateral scattered light intensity distribution (SS_P), the centroid of the forward scattered light intensity distribution (FS_P), the width of the fluorescence intensity distribution (FL_W), the width of the lateral scattered light intensity distribution (SS_W), the width of the forward scattered light intensity distribution (FS_W), the coefficient of variation of the fluorescence intensity distribution (FL_CV), the coefficient of variation of the lateral scattered light intensity distribution (SS_CV), and the coefficient of variation of the forward scattered light intensity distribution (FS_CV).

[0012] Further, the preset calculation model is a linear model. Specifically, it can be a linear model with a single input variable (simple linear regression model), or a linear model with multiple input variables (multiple linear regression model).

[0013] Further, the preset calculation model is a non - linear model. Specifically, it can be a polynomial regression model, a support vector machine model, a neural network model, etc.

[0014] Further, the data processing device obtains information on whether the bilirubin is abnormal based on the comparison result between the bilirubin marker parameter information and a preset bilirubin marker parameter threshold. The data processing device obtains corresponding bilirubin concentration information based on the bilirubin marker parameter information.

[0015] Further, the data processing device is further configured to calculate a white blood cell detection result of the blood sample according to the optical information of the white blood cells. Further, the white blood cell detection result includes at least one of the following: the number of white blood cells, the number of basophils, the percentage of basophils, the number of nucleated red blood cells, the percentage of nucleated red blood cells, the number of neutrophils, the percentage of neutrophils, the number of eosinophils, the percentage of eosinophils, the number of lymphocytes, the percentage of lymphocytes, the number of monocytes, the percentage of monocytes, the number of immature granulocytes, and the percentage of immature granulocytes.

[0016] Further, the blood cell analyzer further includes a result output device, and the result output device is at least configured to output bilirubin information and the white blood cell detection result, where the bilirubin information includes bilirubin marker parameter information, and / or information on whether the bilirubin is abnormal, and / or bilirubin concentration information.

[0017] Further, the sample preparation device has at least one reaction cell and a reagent supply unit. The at least one reaction cell is configured to receive at least a part of the blood sample collected by the sampling device, and the reagent supply unit supplies a processing reagent to the reaction cell, so that at least a part of the blood sample collected by the sampling device is mixed with the processing reagent supplied by the reagent supply unit in the reaction cell to prepare a sample solution to be measured.

[0018] Further, the white blood cell measuring device includes a light source, a flow cell, at least one scattered light detector, and a fluorescence detector. Particles of the sample solution to be measured can pass through the flow cell one by one. The light emitted by the light source irradiates the particles in the flow cell to generate optical information. The at least one scattered light detector is configured to collect at least one type of scattered light information, and the fluorescence detector is configured to collect fluorescence information, where the optical information includes the at least one type of scattered light information and the fluorescence information.

[0019] Further, the processing reagent at least includes a hemolytic agent, a fluorescent dye, and a diluent.

[0020] In a second aspect, the present application provides a bilirubin detection method, which is applied to a blood cell analyzer, and the method includes: Controlling a sampling device to obtain a blood sample to be measured; The control sample preparation device mixes at least a part of the blood sample to be tested with a processing reagent to prepare a sample solution to be tested; The control white blood cell measuring device acquires the optical information of white blood cells in the sample solution to be tested, and the optical information includes scattered light information and fluorescence information; The control data processing device acquires the particle information of at least one target particle group from the optical information of the white blood cells, and inputs the acquired particle information of the at least one target particle group as input parameters into a preset calculation model, and obtains the output of the calculation model as the bilirubin information of the blood sample, and the bilirubin information includes bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information.

[0021] In a third aspect, the present application provides a computer-readable storage medium, including a program that can be executed by a processor to implement the bilirubin detection method as described above in a blood cell analyzer.

[0022] In a fourth aspect, the present application provides a use of a blood cell analyzer for detecting bilirubin. The blood cell analyzer includes a white blood cell detection device. The blood cell analyzer is configured to acquire the optical information of white blood cells in the sample solution to be tested through the white blood cell detection device. The optical information includes scattered light information and fluorescence information. The blood cell analyzer outputs at least the bilirubin information and the white blood cell detection result of the blood sample based on the white blood cell optical information. The bilirubin information includes bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information. Beneficial effects

[0023] The blood cell analyzer for detecting bilirubin provided by the present application acquires the scattered light information and fluorescence information of white blood cells in the sample solution to be tested through the white blood cell measuring device, and then uses the data processing device to acquire the particle information of at least one target particle group from the scattered light information and fluorescence information, and inputs the acquired particle information of the at least one target particle group as input parameters into a preset calculation model, and obtains the output of the calculation model as the bilirubin marker parameter information, the information on whether bilirubin is abnormal, and the bilirubin concentration information of the blood sample, realizing the acquisition of the bilirubin information of the blood sample during the routine blood test, and then outputting the bilirubin information and the white blood cell detection result through the result output device of the blood cell analyzer. That is, the present application can give bilirubin information during the routine blood test, does not require the use of special detection reagents or methods, does not increase the clinical detection cost, is convenient and accurate for detection, and has important application value in clinical examinations. Description of the drawings

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 It is a DIFF scatter plot of a sample with normal bilirubin content; Figure 2 It is a DIFF scatter plot of a sample with high bilirubin content; Figure 3 It is a schematic diagram of an embodiment of the blood cell analyzer of the present application; Figure 4 It is a schematic diagram of an embodiment of the white blood cell measuring device of the present application; Figure 5 It is a schematic diagram for explaining the particle information of the target particle group of the present application Figure 1 ; Figure 6 It is a schematic diagram for explaining the particle information of the target particle group of the present application Figure 2 ; Figure 7 It is an ROC curve for calculating bilirubin marker parameters by inputting Neu_FL_P as an input parameter into the first calculation model; Figure 8 It is an ROC curve for calculating bilirubin marker parameters by inputting Mon_FL_P as an input parameter into the second calculation model.

[0026] Explanation of reference numerals: 10. Sampling device; 20. Sample preparation device; 30. White blood cell measuring device; 40. Data processing device; 31. Light source; 32. Front light assembly; 33. Flow cell; 34. Forward scatter light detector; 35. Dichroic mirror; 36. Side scatter light detector; 37. Fluorescence detector. Detailed implementation manners

[0027] [[ID=३९]]The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, rather than all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0028] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0029] It should also be understood that the terms used in this specification of the present invention are merely for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in this specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0030] It should be further understood that the term "and / or" used in this specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0031] Currently, the commonly used methods for measuring bilirubin in clinical practice include the diazo reagent method, the chemical oxidation method, the bilirubin oxidase endpoint method, etc. These methods all require the use of special detection reagents and methods for bilirubin, which will increase the clinical detection cost. Therefore, it is necessary to develop a more convenient and accurate method for detecting bilirubin.

[0032] This application provides a blood cell analyzer for detecting bilirubin, which can detect the bilirubin information of a blood sample while performing a complete blood count test (mainly white blood cell detection), achieving the detection of bilirubin without the need to use additional detection reagents or methods. It pre-screens bilirubin abnormal samples in the most common complete blood count test, can be widely promoted clinically, reduces the clinical detection cost, and has important application value. Of course, it should be understood that the blood cell analyzer for detecting bilirubin in this application can only perform the detection of bilirubin, and it is not necessary to perform a complete blood count test while detecting bilirubin.

[0033] The blood cell analyzer for detecting bilirubin in this application is a blood cell analyzer of the fluorescence platform. During the process of white blood cell detection, the reagents used include a lysing agent, a fluorescent dye and a diluent. Among them, the lysing agent refers to a reagent that can lyse red blood cells while keeping white blood cells in a basic cell shape. The fluorescent dye refers to a dye that can specifically bind to nucleic acid substances (such as DNA, RNA) and emit fluorescence of a specific wavelength under excitation. There is no particular limitation on the nucleic acid-specific dye used in the present invention. Commercial nucleic acid fluorescent dyes and nucleic acid-specific fluorescent dyes disclosed in some patent applications can be used in this application.

[0034] In this application, leukocyte detection in a blood cell analyzer with a fluorescence platform refers to the process where, after the blood cell analyzer aspirates a blood sample, the sample is first treated with a hemolytic agent and a fluorescent dye. The red blood cells are destroyed and dissolved by the hemolytic agent, while the white blood cells are not. The fluorescent dye can enter the nuclei of white blood cells with the help of the hemolytic agent and bind to the nucleic acid substances in the nuclei. Then, the particles in the treated sample pass through the laser detection hole one by one. When the laser beam passes through the particles, the characteristics of the particles themselves (such as volume, staining degree, size and content of cell contents, nuclear density, etc.) can block or change the direction of the laser beam, generating scattered light at various angles corresponding to their characteristics. After these scattered lights are received by the signal detector, relevant information about the particle structure and composition can be obtained. Among them, the forward scatter (FS) reflects the number and volume of the particles, the side scatter (SS) reflects the complexity of the internal structure of the particles (such as granules or nuclei inside the particles), and the fluorescence signal (FL) reflects the content of nucleic acid substances in the particles. These parameters can be used to classify and count the white blood cells in the sample.

[0035] The inventors of this application found that when a blood sample with normal white blood cells and high-concentration bilirubin is treated with reagents and then tested in the DIFF channel using a blood cell analyzer, when obtaining a scatter plot based on the fluorescence signal and scattered light, the exhibition intensity of each white blood cell in the fluorescence direction is weak, and the scattered light signal is also slightly affected. After research, it was confirmed that this phenomenon has a strong correlation with high-concentration bilirubin. As Figure 1-2 shown, Figure 1 is the DIFF scatter plot of a sample with normal bilirubin content, Figure 2 is the DIFF scatter plot of a sample with relatively high bilirubin content. By combining the two, it can be found that high-concentration bilirubin will affect the scattered light and fluorescence signal of white blood cells. By comparing the scattered light and fluorescence signal of the white blood cells in the sample to be tested with those of a blood sample with normal white blood cells and contrasting the signal difference degree between the two, the bilirubin information of the sample to be tested can be obtained.

[0036] In a sample containing a high concentration of bilirubin, particles or aggregates may form, increasing light scattering and absorption, affecting the scattered light signal while reducing the chance of the excitation light reaching the nucleic acid dye, and hindering the detection of the fluorescence signal, thereby reducing the fluorescence intensity of white blood cells; bilirubin may chemically react with the nucleic acid dye, changing the fluorescence characteristics of the dye, or binding to nucleic acids, affecting the binding efficiency of the dye to nucleic acids, resulting in a weakened fluorescence signal. That is to say, the fluorescence signal and scattered light signal of white blood cells in a blood cell analyzer based on a fluorescence platform contain the bilirubin information of the blood sample. There is a theoretical basis, practical advantage and application value in obtaining the bilirubin information of the blood sample by processing the fluorescence signal and scattered light signal of white blood cells, and it will not affect the normal detection of white blood cells.

[0037] Referring to Figure 1 , an embodiment of the present application provides a blood cell analysis for detecting bilirubin, including: a sampling device 10, a sample preparation device 20, a white blood cell measurement device 30, and a data processing device 40.

[0038] The sampling device 10 is used to collect a blood sample. The sample preparation device 20 is used to mix at least a part of the blood sample with a processing reagent to prepare a sample liquid to be measured. The sample preparation device 20 has a reaction cell and a reagent supply unit. The reaction cell is used to receive at least a part of the blood sample collected by the sampling device 10, and the reagent supply unit supplies the processing reagent to the reaction cell, so that at least a part of the blood sample collected by the sampling device 10 is mixed with the processing reagent provided by the reagent supply unit in the reaction cell to prepare a sample liquid to be measured.

[0039] The white blood cell measurement device 30 includes a light source 31, a front light assembly 32, a flow cell 33, a forward scattered light detector 34, a dichroic mirror 35, a side scattered light detector 36, and a fluorescence detector 37. The particles of the sample liquid to be measured can pass through the flow cell 33 one by one. The light emitted by the light source 31 irradiates the particles in the flow cell 33 to generate optical information. The forward scattered light detector 34 is used to collect forward scattered light information, the side scattered light detector 36 is used to collect side scattered light information, and the fluorescence detector 37 is used to collect fluorescence information.

[0040] The data processing device 40 is used to obtain the particle information of at least one target particle group from the optical information of white blood cells, and input the obtained particle information of at least one target particle group as input parameters into a preset calculation model to obtain the output of the calculation model as the bilirubin information of the blood sample. The bilirubin information includes bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information.

[0041] Among them, the target particle population includes one or more of neutrophil population, lymphocyte population, and monocyte population. The normal reference range for white blood cell differential count is as follows: neutrophils account for 45%-70%; lymphocytes account for 20%-40%; monocytes account for 3%-8%; eosinophils account for 0.5%-5%; basophils account for 0%-1%. Bilirubin in the blood sample mainly affects the scattered light and fluorescence signals of neutrophil population and lymphocyte population, and also has a certain impact on the scattered light and fluorescence signals of monocyte population, while having little impact on eosinophil and basophil populations (possibly because the content of these two types of particle populations is very small, the impact is not obvious, and it is not easy to be distinguished).

[0042] Therefore, in this embodiment, the selected target particle population is one or more of neutrophil population, lymphocyte population, and monocyte population. In some other alternative embodiments, other target particle populations can be selected by optimizing the detection process and the algorithm model for data processing.

[0043] Furthermore, the particle information of the target particle population selected in this embodiment includes one or more of the centroid of fluorescence intensity distribution (FL_P), the centroid of side scatter light intensity distribution (SS_P), the centroid of forward scatter light intensity distribution (FS_P), the width of fluorescence intensity distribution (FL_W), the width of side scatter light intensity distribution (SS_W), the width of forward scatter light intensity distribution (FS_W), the coefficient of variation of fluorescence intensity distribution (FL_CV), the coefficient of variation of side scatter light intensity distribution (SS_CV), and the coefficient of variation of forward scatter light intensity distribution (FS_CV).

[0044] Among them, as an example, the centroid of fluorescence intensity distribution (FL_P) is the average position of the target particles in the target particle population in the fluorescence direction; the centroid of side scatter light intensity distribution (SS_P) is the average position of the target particles in the target particle population in the side scatter light direction; the centroid of forward scatter light intensity distribution (FS_P) is the average position of the target particles in the target particle population in the forward scatter light direction. Specifically, as an example, as Figure 5 shown, the position indicated by the "+" therein is the average position of neutrophils in the fluorescence direction, that is, the centroid of fluorescence intensity distribution (FL_P) of the neutrophil population.

[0045] Among them, as an example, the width of fluorescence intensity distribution (FL_W) is equal to the difference between the upper limit (UP) and the lower limit (DOWN) of the fluorescence intensity distribution. As Figure 5As shown, the fluorescence intensity distribution width (FL_W) of the neutrophil population is equal to the difference between the upper limit (UP) of the fluorescence intensity distribution of the neutrophil population and the lower limit (DOWN) of the fluorescence intensity distribution of the neutrophil population. Correspondingly, the side scatter light intensity distribution width (SS_W) is equal to the difference between the upper limit of the side scatter light intensity distribution and the lower limit of the side scatter light intensity distribution; the forward scatter light intensity distribution width (FS_W) is equal to the difference between the upper limit of the forward scatter light intensity distribution and the lower limit of the forward scatter light intensity distribution.

[0046] The coefficient of variation of the fluorescence intensity distribution (FL_CV) is equal to the fluorescence intensity distribution width (FL_W) divided by the fluorescence intensity distribution centroid (FL_P); the coefficient of variation of the side scatter light intensity distribution (SS_CV) is equal to the side scatter light intensity distribution width (SS_W) divided by the side scatter light intensity distribution centroid (SS_P); the coefficient of variation of the forward scatter light intensity distribution (FS_CV) is equal to the forward scatter light intensity distribution width (FS_W) divided by the forward scatter light intensity distribution centroid (FS_P).

[0047] The particle information of the target particle swarm selected in this embodiment (as the input parameter for calculating the standard parameter information of bilirubin) specifically includes one or more of the following: the center of gravity of the fluorescence intensity distribution of the neutrophil population (Neu_FL_P), the center of gravity of the forward scatter light intensity distribution of the neutrophil population (Neu_FS_P), the center of gravity of the side scatter light intensity distribution of the neutrophil population (Neu_SS_P), the width of the fluorescence intensity distribution of the neutrophil population (Neu_FL_W), the width of the forward scatter light intensity distribution of the neutrophil population (Neu_FS_W), the width of the side scatter light intensity distribution of the neutrophil population (Neu_SS_W), the coefficient of variation of the fluorescence intensity distribution of the neutrophil population (Neu_FL_CV), the coefficient of variation of the forward scatter light intensity distribution of the neutrophil population (Neu_FS_CV), the coefficient of variation of the side scatter light intensity distribution of the neutrophil population (Neu_SS_CV), the center of gravity of the fluorescence intensity distribution of the lymphocyte population (Lym_FL_P), the center of gravity of the forward scatter light intensity distribution of the lymphocyte population (Lym_FS_P), the center of gravity of the side scatter light intensity distribution of the lymphocyte population (Lym_SS_P), the width of the fluorescence intensity distribution of the lymphocyte population (Lym_FL_W), the width of the forward scatter light intensity distribution of the lymphocyte population (Lym_FS_W), the width of the side scatter light intensity distribution of the lymphocyte population (Lym_SS_W), the coefficient of variation of the fluorescence intensity distribution of the lymphocyte population (Lym_FL_CV), the coefficient of variation of the forward scatter light intensity distribution of the lymphocyte population (Lym_FS_CV), the coefficient of variation of the side scatter light intensity distribution of the lymphocyte population (Lym_SS_CV), the center of gravity of the fluorescence intensity distribution of the monocyte population (Mon_FL_P), the center of gravity of the forward scatter light intensity distribution of the monocyte population (Mon_FS_P), the center of gravity of the side scatter light intensity distribution of the monocyte population (Mon_SS_P), the width of the fluorescence intensity distribution of the monocyte population (Mon_FL_W), the width of the forward scatter light intensity distribution of the monocyte population (Mon_FS_W), the width of the side scatter light intensity distribution of the monocyte population (Mon_SS_W), the coefficient of variation of the fluorescence intensity distribution of the monocyte population (Mon_FL_CV), the coefficient of variation of the forward scatter light intensity distribution of the monocyte population (Mon_FS_CV), and the coefficient of variation of the side scatter light intensity distribution of the monocyte population (Mon_SS_CV).

[0048] As an input parameter for calculating bilirubin standard parameter information, it can be the particle information of a single type of particle cluster or the particle information between multiple particle clusters. For example, it can be the distance between the centroids of two particle clusters in one-dimensional direction (i.e., one-dimensional centroid distribution distance) based on forward scatter light intensity, side scatter light intensity, and fluorescence intensity; the distance between the centroids of two particle clusters in two-dimensional direction (i.e., two-dimensional centroid distribution distance); the distance between the centroids of two particle clusters in three-dimensional direction (i.e., three-dimensional centroid distribution distance).

[0049] Taking the one-dimensional centroid distribution distance as an example, in the first alternative implementation, as an input parameter for calculating bilirubin standard parameter information, it can be the distance between the fluorescence intensity distribution centroids of neutrophil population and lymphocyte population (Neu_Lym_FL_Dis), the distance between the side scatter light intensity distribution centroids (Neu_Lym_SS_Dis), and the distance between the forward scatter light intensity distribution centroids (Neu_Lym_FS_Dis) in the DIFF channel; it can also be the distance between the fluorescence intensity distribution centroids of neutrophil population and monocyte population (Neu_Mon_FL_Dis), the distance between the side scatter light intensity distribution centroids (Neu_Mon_SS_Dis), and the distance between the forward scatter light intensity distribution centroids (Neu_Mon_FS_Dis) in the DIFF channel; it can further be the distance between the fluorescence intensity distribution centroids of lymphocyte population and monocyte population (Lym_Mon_FL_Dis), the distance between the side scatter light intensity distribution centroids (Lym_Mon_SS_Dis), and the distance between the forward scatter light intensity distribution centroids (Lym_Mon_FS_Dis) in the DIFF channel.

[0050] Taking the two-dimensional centroid distribution spacing as an example, in the second alternative implementation, the input parameters for calculating the bilirubin standard parameter information can be the spacing between the centroids of the fluorescence intensity distribution and the side scatter light intensity distribution of the neutrophil population and the lymphocyte population (Neu_Lym_FLSS_Dis), the spacing between the centroids of the side scatter light intensity distribution and the forward scatter light intensity distribution (Neu_Lym_SSFS_Dis), and the spacing between the centroids of the forward scatter light intensity distribution and the fluorescence intensity distribution (Neu_Lym_FSFL_Dis) in the DIFF channel; it can also be the spacing between the centroids of the fluorescence intensity distribution and the side scatter light intensity distribution of the neutrophil population and the monocyte population (Neu_Mon_FLSS_Dis), the spacing between the centroids of the side scatter light intensity distribution and the forward scatter light intensity distribution (Neu_Mon_SSFS_Dis), and the spacing between the centroids of the forward scatter light intensity distribution and the fluorescence intensity distribution (Neu_Mon_FSFL_Dis) in the DIFF channel; it can also be the spacing between the centroids of the fluorescence intensity distribution and the side scatter light intensity distribution of the lymphocyte population and the monocyte population (Lym_Mon_FLSS_Dis), the spacing between the centroids of the side scatter light intensity distribution and the forward scatter light intensity distribution (Lym_Mon_SSFS_Dis), and the spacing between the centroids of the forward scatter light intensity distribution and the fluorescence intensity distribution (Lym_Mon_FSFL_Dis).

[0051] Taking the three-dimensional centroid distribution spacing as an example, in the third alternative implementation, the input parameters for calculating the bilirubin standard parameter information can be the spacing between the centroids of the fluorescence intensity distribution, the side scatter light intensity distribution, and the forward scatter light intensity distribution of the neutrophil population and the lymphocyte population (Neu_Lym_FLSSFS_Dis) in the DIFF channel; it can also be the spacing between the centroids of the fluorescence intensity distribution, the side scatter light intensity distribution, and the forward scatter light intensity distribution of the neutrophil population and the monocyte population (Neu_Mon_FLSSFS_Dis) in the DIFF channel; it can also be the spacing between the centroids of the fluorescence intensity distribution, the side scatter light intensity distribution, and the forward scatter light intensity distribution of the lymphocyte population and the monocyte population (Lym_Mon_FLSSFS_Dis) in the DIFF channel.

[0052] As Figure 6 shown Figure 6The distances between the centroids of the fluorescence intensity distributions of the neutrophil population and the lymphocyte population in the DIFF channel (Neu_Lym_FL_Dis), the distances between the centroids of the side scatter light intensity distributions of the neutrophil population and the lymphocyte population in the DIFF channel (Neu_Lym_SS_Dis), and the distances between the centroids of the fluorescence intensity distributions and the side scatter light intensity distributions of the neutrophil population and the lymphocyte population in the DIFF channel (Neu_Lym_FLSS_Dis) are respectively indicated.

[0053] Neu_Lym_FL_Dis is calculated by the following formula:

[0054] where Neu_FL_P represents the centroid of the fluorescence intensity distribution of the neutrophil population, and Lym_FL_P represents the centroid of the fluorescence intensity distribution of the lymphocyte population.

[0055] Neu_Lym_FLSS_Dis is calculated by the following formula:

[0056] where Neu_FL_P represents the centroid of the fluorescence intensity distribution of the neutrophil population, Lym_FL_P represents the centroid of the fluorescence intensity distribution of the lymphocyte population, Neu_SS_P represents the centroid of the side scatter light intensity distribution of the neutrophil population, and Lym_SS_P represents the centroid of the side scatter light intensity distribution of the lymphocyte population.

[0057] In this embodiment, the preset calculation model is a linear model, which can be a linear model with a single input variable (i.e., a simple linear regression model) or a linear model with multiple input variables (i.e., a multiple linear regression model).

[0058] As an example, the linear model with a single input variable can be: Y1 = A1 * X1 + C1 where Y1 represents the bilirubin marker parameter, X1 represents the centroid of the fluorescence intensity distribution of the neutrophil population (Neu_FL_P), and A1, C1 are constants (A1, C1 can be obtained through model training).

[0059] As another example, the linear model with multiple input variables can be: Y2 = A2 * X1 + B2 * X2 + C2 where Y2 represents the bilirubin marker parameter, X1 represents the centroid of the fluorescence intensity distribution of the neutrophil population (Neu_FL_P), X2 represents the width of the forward scatter light intensity distribution of the lymphocyte population (Lym_FS_W), and A2, B2, C2 are constants (A2, B2, C2 can be obtained through model training).

[0060] In some other alternative embodiments, the preset calculation model is a non-linear model, specifically a machine learning model obtained by training with machine learning algorithms. Among them, the machine learning model includes, but is not limited to, traditional models such as Bayesian and support vector machines, and may also include machine learning models constructed based on ensemble learning algorithms such as random forest and XGBoost, and may also include machine learning models constructed based on machine learning algorithms such as clustering algorithms, dimensionality reduction algorithms, and similarity algorithms, and may also include deep learning models and neural network models. Among them, the neural network model is trained with the optical information and bilirubin information of white blood cells in multiple training blood samples. Further, the preset calculation model is a neural network model, and the neural network model is pre-trained and stored in the data processing device 40.

[0061] In a preferred embodiment of this embodiment, the data processing device 40 obtains information on whether the corresponding bilirubin is abnormal based on the comparison result between the bilirubin marker parameter information and the preset bilirubin marker parameter threshold; and obtains the corresponding bilirubin concentration information based on the bilirubin marker parameter information.

[0062] In another preferred embodiment of this embodiment, the data processing device 40 is further configured to calculate the white blood cell detection result of the blood sample according to the optical information of the white blood cells. Among them, the white blood cell detection result includes at least one of the following: the number of white blood cells, the number of basophils, the percentage of basophils, the number of nucleated red blood cells, the percentage of nucleated red blood cells, the number of neutrophils, the percentage of neutrophils, the number of eosinophils, the percentage of eosinophils, the number of lymphocytes, the percentage of lymphocytes, the number of monocytes, the percentage of monocytes, the number of immature granulocytes, and the percentage of immature granulocytes.

[0063] In a preferred embodiment, the blood cell analyzer further includes a result output device (not shown), and the result output device is at least configured to output bilirubin information and the white blood cell detection result, where the bilirubin information includes bilirubin marker parameter information, and / or information on whether the bilirubin is abnormal, and / or bilirubin concentration information.

[0064] In some alternative embodiments, the blood cell analyzer further includes a protein detection device (not shown) for detecting CRP (C-reactive protein), SAA (serum amyloid A), PCT (procalcitonin), etc. in the blood sample.

[0065] In some alternative embodiments, the blood cell analyzer further includes an HGB detection device (not shown) for detecting HGB (hemoglobin) in the blood sample.

[0066] In some alternative embodiments, the blood cell analyzer further includes a RET detection device (not shown) for detecting RET (reticulocytes) in a blood sample.

[0067] Embodiments of the present application also provide a method for detecting bilirubin, which is applied to a blood cell analyzer. The method includes: Controlling a sampling device to obtain a blood sample to be tested; Controlling a sample preparation device to mix at least a part of the blood sample to be tested with a processing reagent to prepare a sample solution to be tested; Controlling a white blood cell measurement device to obtain optical information of white blood cells in the sample solution to be tested, where the optical information includes scattered light information and fluorescence information; Controlling a data processing device to obtain particle information of at least one target particle group from the optical information of the white blood cells, and using the obtained particle information of the at least one target particle group as input parameters to input into a preset calculation model, and obtaining the output of the calculation model as bilirubin information of the blood sample, where the bilirubin information includes bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information.

[0068] Preferably, the target particle group includes one or more of a neutrophil group, a lymphocyte group, and a monocyte group. The particle information of the target particle group includes one or more of the centroid of the fluorescence intensity distribution (FL_P), the centroid of the lateral scattered light intensity distribution (SS_P), the centroid of the forward scattered light intensity distribution (FS_P), the width of the fluorescence intensity distribution (FL_W), the width of the lateral scattered light intensity distribution (SS_W), the width of the forward scattered light intensity distribution (FS_W), the coefficient of variation of the fluorescence intensity distribution (FL_CV), the coefficient of variation of the lateral scattered light intensity distribution (SS_CV), and the coefficient of variation of the forward scattered light intensity distribution (FS_CV).

[0069] Specifically, the particle information of the target particle swarm selected in this embodiment (as the input parameter for calculating the bilirubin standard parameter information) specifically includes: the centroid of the fluorescence intensity distribution of the neutrophil population (Neu_FL_P), the centroid of the forward scatter light intensity distribution of the neutrophil population (Neu_FS_P), the centroid of the side scatter light intensity distribution of the neutrophil population (Neu_SS_P), the width of the fluorescence intensity distribution of the neutrophil population (Neu_FL_W), the width of the forward scatter light intensity distribution of the neutrophil population (Neu_FS_W), the width of the side scatter light intensity distribution of the neutrophil population (Neu_SS_W), the coefficient of variation of the side fluorescence intensity distribution of the neutrophil population (Neu_FL_CV), the coefficient of variation of the forward scatter light intensity distribution of the neutrophil population (Neu_FS_CV), the coefficient of variation of the side scatter light intensity distribution of the neutrophil population (Neu_SS_CV), the centroid of the fluorescence intensity distribution of the lymphocyte population (Lym_FL_P), the centroid of the forward scatter light intensity distribution of the lymphocyte population (Lym_FS_P), the centroid of the side scatter light intensity distribution of the lymphocyte population (Lym_SS_P), the width of the fluorescence intensity distribution of the lymphocyte population (Lym_FL_W), the width of the forward scatter light intensity distribution of the lymphocyte population (Lym_FS_W), the width of the side scatter light intensity distribution of the lymphocyte population (Lym_SS_W), the coefficient of variation of the fluorescence intensity distribution of the lymphocyte population (Lym_FL_CV), the coefficient of variation of the forward scatter light intensity distribution of the lymphocyte population (Lym_FS_CV), the coefficient of variation of the side scatter light intensity distribution of the lymphocyte population (Lym_SS_CV), the centroid of the fluorescence intensity distribution of the monocyte population (Mon_FL_P), the centroid of the forward scatter light intensity distribution of the monocyte population (Mon_FS_P), the centroid of the side scatter light intensity distribution of the monocyte population (Mon_SS_P), the width of the fluorescence intensity distribution of the monocyte population (Mon_FL_W), the width of the forward scatter light intensity distribution of the monocyte population (Mon_FS_W), the width of the side scatter light intensity distribution of the monocyte population (Mon_SS_W), the coefficient of variation of the fluorescence intensity distribution of the monocyte population (Mon_FL_CV), the coefficient of variation of the forward scatter light intensity distribution of the monocyte population (Mon_FS_CV), the coefficient of variation of the side scatter light intensity distribution of the monocyte population (Mon_SS_CV), one or more of them.

[0070] As an input parameter for calculating bilirubin standard parameter information, it can be the particle information of a single particle group or the particle information between multiple particle groups. For example, it can be the distance between the centroids of two particle groups in one-dimensional direction (i.e., one-dimensional centroid distribution distance) based on the forward scattered light intensity, lateral scattered light intensity, and fluorescence intensity; the distance between the centroids of two particle groups in two-dimensional direction (i.e., two-dimensional centroid distribution distance) based on the forward scattered light intensity, lateral scattered light intensity, and fluorescence intensity; the distance between the centroids of two particle groups in three-dimensional direction (i.e., three-dimensional centroid distribution distance) based on the forward scattered light intensity, lateral scattered light intensity, and fluorescence intensity.

[0071] In this embodiment, the preset calculation model is a linear model, which can be a linear model with a single input variable (i.e., a simple linear regression model) or a linear model with multiple input variables (i.e., a multiple linear regression model).

[0072] In some other alternative embodiments, the preset calculation model is a non-linear model, specifically a machine learning model obtained by training with machine learning algorithms.

[0073] In a preferred embodiment of this embodiment, control the data processing device to obtain information on whether the corresponding bilirubin is abnormal based on the comparison result between the bilirubin marker parameter information and the preset bilirubin marker parameter threshold; and / or control the data processing device to obtain the corresponding bilirubin concentration information based on the bilirubin marker parameter information.

[0074] In a preferred embodiment of this embodiment, the bilirubin detection method further includes controlling the data processing device to calculate the white blood cell detection result of the blood sample according to the optical information of the white blood cells. Wherein, the white blood cell detection result includes at least one of the following: the number of white blood cells, the number of basophils, the percentage of basophils, the number of nucleated red blood cells, the percentage of nucleated red blood cells, the number of neutrophils, the percentage of neutrophils, the number of eosinophils, the percentage of eosinophils, the number of lymphocytes, the percentage of lymphocytes, the number of monocytes, the percentage of monocytes, the number of immature granulocytes, and the percentage of immature granulocytes.

[0075] In a preferred embodiment, the bilirubin detection method further includes controlling the result output device to output bilirubin information and the white blood cell detection result, wherein the bilirubin information includes bilirubin marker parameter information, and / or information on whether the bilirubin is abnormal, and / or bilirubin concentration information.

[0076] In some alternative embodiments, the bilirubin detection method further includes controlling a protein detection device to detect CRP (C-reactive protein), and / or SAA (serum amyloid A), and / or PCT (procalcitonin) in a blood sample.

[0077] In some alternative embodiments, the bilirubin detection method further includes controlling an HGB detection device to detect HGB (hemoglobin) in a blood sample.

[0078] In some alternative embodiments, the bilirubin detection method further includes controlling a RET detection device to detect RET (reticulocyte) in a blood sample.

[0079] An embodiment of the present application also provides a computer-readable storage medium, including a program that can be executed by a processor to implement the bilirubin detection method as described above in a blood cell analyzer.

[0080] An embodiment of the present application also provides a use of a blood cell analyzer for detecting bilirubin. The blood cell analyzer includes a white blood cell detection device. The blood cell analyzer is configured to obtain optical information of white blood cells in a sample liquid to be tested through the white blood cell detection device. The optical information includes scattered light information and fluorescence information. The blood cell analyzer outputs bilirubin information and white blood cell detection results of the blood sample at least based on the white blood cell optical information. The bilirubin information includes bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information.

[0081] Wherein, the white blood cell detection results include at least one of the following: the number of white blood cells, the number of basophils, the percentage of basophils, the number of nucleated red blood cells, the percentage of nucleated red blood cells, the number of neutrophils, the percentage of neutrophils, the number of eosinophils, the percentage of eosinophils, the number of lymphocytes, the percentage of lymphocytes, the number of monocytes, the percentage of monocytes, the number of immature granulocytes, and the percentage of immature granulocytes.

[0082] In a specific example, 127 blood samples were collected for routine blood tests and diazo method bilirubin tests. Among them, the routine blood tests were performed on a hematology analyzer with a fluorescence platform, and a three-dimensional scatter plot of FS_FL_SS in the DIFF channel (i.e., including fluorescence information, forward scatter light information, and side scatter light information) was obtained for each blood sample. After performing the diazo method bilirubin test, 72 of the 127 blood samples were positive samples (i.e., samples with high bilirubin), and the remaining 55 blood samples were negative samples (i.e., samples with normal bilirubin). Among them, the normal range of total bilirubin in adults is generally 3.4 - 17.1 μmol / L. In the positive samples (samples with high bilirubin), the bilirubin content is greater than 17.1 μmol / L; in the negative samples (samples with normal bilirubin), the bilirubin content is between 3.4 - 17.1 μmol / L.

[0083] Taking Neu_FL_P as the input parameter and inputting it into the first calculation model, it can be:

[0084]

[0085] Among them, "f1()" represents the formula for calculating bilirubin parameter information in the first calculation model; "f2()" represents the formula for calculating the information of whether bilirubin is abnormal in the first calculation model; Neu_FL_P and f1(Neu_FL_P) are the input parameters of these two formulas respectively. Correspondingly, in the calculation results of the two formulas, f1(Neu_FL_P) represents the bilirubin parameter information under the first calculation model, and f2(f1(Neu_FL_P)) represents the information of whether bilirubin is abnormal. When f2(f1(Neu_FL_P)) is 1, it indicates that bilirubin is abnormal (positive); when f2(f1(Neu_FL_P)) is 0, it indicates that bilirubin is normal (negative).

[0086] Through the calculation of the first calculation model, the information of whether the bilirubin in the 127 collected blood samples is abnormal was obtained, and the results are shown in Table 1. Figure 7 The ROC curve showing the calculation of the bilirubin marker parameter by taking Neu_FL_P corresponding to Table 1 as the input parameter and inputting it into the first calculation model is shown.

[0087] Taking Mon_FL_P as the input parameter and inputting it into the second calculation model, it can be:

[0088]

[0089] Among them, "f3()" represents the formula for calculating bilirubin parameter information in the second calculation model; "f4()" represents the formula for calculating whether bilirubin is abnormal in the second calculation model; Mon_FL_P and f3(Mon_FL_P) are the input parameters of these two formulas respectively. Correspondingly, in the calculation results of the two formulas, f3(Mon_FL_P) represents the bilirubin parameter information under the second calculation model, and f4(f3(Mon_FL_P)) represents the information on whether bilirubin is abnormal. When f4(f3(Mon_FL_P)) is 1, it indicates that bilirubin is abnormal (positive), and when f4(f3(Mon_FL_P)) is 0, it indicates that bilirubin is normal (negative).

[0090] Through the calculation of the second calculation model, the information on whether the bilirubin in the 127 collected blood samples is abnormal is obtained, and the results are shown in Table 2. Figure 8 The ROC curve showing the calculation of bilirubin marker parameters by taking Mon_FL_P in Table 2 as the input parameter and inputting it into the second calculation model is shown.

[0091] Taking Neu_FL_P and Mon_FL_P as input parameters and inputting them into the third calculation model, it can be:

[0092]

[0093] Among them, "f5()" represents the formula for calculating bilirubin parameter information in the third calculation model; "f6()" represents the formula for calculating whether bilirubin is abnormal in the third calculation model; Neu_FL_P and Mon_FL_P are the input parameters of "f5()", and f5(Neu_FL_P, Mon_FL_P) is the input parameter of "f6()". Correspondingly, in the calculation results of the two formulas, f5(Neu_FL_P, Mon_FL_P) represents the bilirubin parameter information under the third calculation model, and f6(f5(Neu_FL_P, Mon_FL_P)) represents the information on whether bilirubin is abnormal. When f6(f5(Neu_FL_P, Mon_FL_P)) is 1, it indicates that bilirubin is abnormal (positive), and when f6(f5(Neu_FL_P, Mon_FL_P)) is 0, it indicates that bilirubin is normal (negative).

[0094] Through the calculation of the third calculation model, the information on whether the bilirubin in the 127 collected blood samples is abnormal is obtained, and the results are shown in Table 3.

[0095] Taking Neu_FL_P and Neu_Mon_FL_Dis as input parameters and inputting them into the fourth calculation model, it can be:

[0096]

[0097] Among them, "f7()" represents the formula for calculating bilirubin parameter information in the fourth calculation model; "f8()" represents the formula for calculating whether bilirubin is abnormal in the fourth calculation model; Neu_FL_P and Neu_Mon_FL_Dis are the input parameters of "f7()", and f7(Neu_FL_P, Neu_Mon_FL_Dis) is the input parameter of "f8()". Accordingly, in the calculation results of the two formulas, f7(Neu_FL_P, Neu_Mon_FL_Dis) represents the bilirubin parameter information under the fourth calculation model, and f8(f7(Neu_FL_P, Neu_Mon_FL_Dis)) represents the information on whether bilirubin is abnormal. When f8(f7(Neu_FL_P, Neu_Mon_FL_Dis)) is 1, it indicates that bilirubin is abnormal (positive), and when f8(f7(Neu_FL_P, Neu_Mon_FL_Dis)) is 0, it indicates that bilirubin is normal (negative).

[0098] Through the calculation of the fourth calculation model, the information on whether the bilirubin in the 127 collected blood samples is abnormal is obtained, and the results are shown in Table 3.

[0099] Table 1: Input parameter ROC_AUC Judgment threshold False positive rate True positive rate False negative rate True negative rate Neu_FL_P 0.886 945.24 7.3% 77.8% 22.2% 92.7% Table 2: Parameter ROC_AUC Judgment threshold False positive rate True positive rate False negative rate True negative rate Mon_FL_P 0.862 1703.79 16.4% 86.1% 13.9% 83.6% Table 3: Parameter ROC_AUC False positive rate True positive rate False negative rate ​ ​ 0.87 23.7% 88.9% 11.1% 76.3% ​ 0.87 21.8% 86.1% 13.9% 78.2% From the result data in Tables 1 - 3, it can be found that the bilirubin detection method of the present application can well give the information on whether bilirubin is abnormal and has high accuracy.

[0100] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A blood cell analyzer for detecting bilirubin, characterized in that, Comprising: A sampling device, at least for collecting a blood sample; A sample preparation device, at least for mixing at least a part of the blood sample with a processing reagent to prepare a sample solution to be measured; A white blood cell measurement device, for obtaining optical information of white blood cells in the sample solution to be measured, the optical information including scattered light information and fluorescence information; A data processing device, at least for obtaining particle information of at least one target particle group from the optical information of the white blood cells, and using the obtained particle information of the at least one target particle group as input parameters to input into a preset calculation model, and obtaining bilirubin information of the blood sample according to the output of the calculation model, the bilirubin information including bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information.

2. The blood cell analyzer for detecting bilirubin according to claim 1, characterized in that, The at least one target particle group includes at least one of a neutrophil group, a lymphocyte group, and a monocyte group.

3. The blood cell analyzer for detecting bilirubin according to claim 2, wherein The particle information of the target particle group includes one or more of the centroid of the fluorescence intensity distribution (FL_P), the centroid of the lateral scattered light intensity distribution (SS_P), the centroid of the forward scattered light intensity distribution (FS_P), the width of the fluorescence intensity distribution (FL_W), the width of the lateral scattered light intensity distribution (SS_W), the width of the forward scattered light intensity distribution (FS_W), the coefficient of variation of the fluorescence intensity distribution (FL_CV), the coefficient of variation of the lateral scattered light intensity distribution (SS_CV), and the coefficient of variation of the forward scattered light intensity distribution (FS_CV).

4. The blood cell analyzer for detecting bilirubin according to claim 1, wherein, The preset calculation model is a linear model or a non-linear model.

5. A blood cell analyzer for detecting bilirubin according to any one of claims 1-4, characterized in that, The data processing device is further configured to calculate the white blood cell detection result of the blood sample according to the optical information of the white blood cells.

6. The blood cell analyzer for detecting bilirubin according to claim 5, characterized in that, The blood cell analyzer further includes a result output device, and the result output device is at least for outputting the bilirubin information and the white blood cell detection result.

7. A bilirubin detection method, characterized in that, The method is applied to a blood cell analyzer for detecting bilirubin according to any one of claims 1-6, and the method includes: Controlling the sampling device to obtain a blood sample to be measured; Controlling the sample preparation device to mix at least a part of the blood sample to be measured with a processing reagent to prepare a sample solution to be measured; Controlling the white blood cell measurement device to obtain optical information of white blood cells in the sample solution to be measured, the optical information including scattered light information and fluorescence information; Controlling the data processing device to obtain particle information of at least one target particle group from the optical information of the white blood cells, and using the obtained particle information of the at least one target particle group as input parameters to input into a preset calculation model, and obtaining the output of the calculation model as the bilirubin information of the blood sample, the bilirubin information including bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information.

8. A bilirubin detection method according to claim 7, characterized in that, It further includes controlling the result output device to output the bilirubin information and the white blood cell detection result.

9. A computer-readable storage medium, including a program, the program being capable of being executed by a processor to implement a bilirubin detection method according to claim 7 or 8 in a blood cell analyzer.

10. Use of a blood cell analyzer for detecting bilirubin, characterized in that, The blood cell analyzer includes a white blood cell detection device. The blood cell analyzer is configured to obtain optical information of white blood cells in a sample liquid to be measured through the white blood cell detection device. The optical information includes scattered light information and fluorescence information. The blood cell analyzer outputs bilirubin information and white blood cell detection results of the blood sample at least based on the white blood cell optical information. The bilirubin information includes bilirubin marker parameter information, and / or information on whether bilirubin is abnormal, and / or bilirubin concentration information.

Images