Sample analyzer and method for judging sample to be detected

Abstract

The invention discloses a sample analyzer and a judgment method of a to-be-detected sample, the sample analyzer comprises an optical detection assembly and a control assembly, the control assembly is connected with the optical detection assembly, the optical detection assembly is used for carrying out optical detection on the to-be-detected sample, and the control assembly is used for receiving detection data obtained by the optical detection assembly. A scatter diagram of the to-be-detected sample is obtained based on the detection data, data information of at least one particle group in the scatter diagram is obtained, the sample type of the to-be-detected sample is determined through the data information, and the data information comprises at least one of the particle number, particle group coordinates and coordinate distribution width of the particle group in the scatter diagram. By processing and analyzing the scatter diagram obtained after optical detection of the to-be-detected sample, the sample type of the to-be-detected sample can be determined, the practicability of the sample analyzer is improved, the operation efficiency of the sample analyzer is improved, and the use experience of a user on the sample analyzer is improved.

Description

Technical Field

[0001] The present application relates to the technical field of sample detection, and in particular to a sample analyzer and a method for determining a sample to be detected. Background Art

[0002] In the prior art, the performance of anemia samples on routine blood test instruments is basically presented in the form of reported parameter results and histograms. However, among various types of anemia, the reported parameter values ​​and histogram features are relatively similar, making it difficult to make a detailed distinction between anemia types.

[0003] The red blood cell nine-point chart can make a detailed distinction between the anemia types of the sample, but the sample needs to be specially processed to spheroidize the red blood cells for analysis and judgment, and an additional red blood cell optical detection channel is required, which makes the operation complicated and increases the cost of the instrument. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a sample analyzer, including an optical detection component and a control component, wherein the control component is connected to the optical detection component. The optical detection component is used to perform optical detection on the sample to be tested, and the control component is used to receive the detection data obtained by the optical detection component, obtain a scatter plot of the sample to be tested based on the detection data, and obtain data information of at least one particle cluster of red blood cell particles, low fluorescence intensity reticulocyte particles, and platelet particles in the scatter plot, and determine the sample type of the sample to be tested through the data information, and the sample type includes a normal sample, an α-thalassemia sample, a β-thalassemia sample, an iron deficiency anemia sample, and a hemorrhagic anemia sample; The data information includes at least one of the number of particles in the particle cluster in the scatter plot, the coordinates of the particle cluster and the coordinate distribution width.

[0005] Wherein, the optical detection component comprises a reticulum red detection channel, the reticulum red detection channel is used to perform optical detection on the sample to be tested to obtain a first optical signal of the sample to be tested, and the control component is used to obtain a first scatter plot of the sample to be tested based on the first optical signal; Among them, the control component is also used to obtain the center of gravity coordinates of the red blood cell particle cluster in the first scatter plot, and judge the first sample type of the sample to be tested based on the center of gravity coordinates of the red blood cell particle cluster, and the first sample type includes normal samples, iron deficiency or β-thalassemia samples, α-thalassemia samples, and hemorrhagic anemia samples.

[0006] Among them, the control component determines the first sample type of the sample to be tested based on the coordinates of the center of gravity of the red blood cell particle clusters in the first scatter plots corresponding to normal samples, iron deficiency or β-thalassemia samples, α-thalassemia samples, and hemorrhagic anemia samples becoming smaller in sequence.

[0007] Wherein, after determining the first sample type of the sample to be tested based on the coordinates of the center of gravity of the red blood cell particle group, the control component is further used for: Obtaining the number of particles and coordinate distribution width of the low-fluorescence-intensity reticulocyte particle clusters in the first scatter plot; Determining a second sample type of the sample to be tested based on the particle number and the coordinate distribution width, wherein the second sample type includes an iron deficiency anemia sample and a β-thalassemia sample; Among them, the number of particles and the coordinate distribution width of the low fluorescence intensity reticulocyte particle clusters in the first scatter plot corresponding to the iron deficiency anemia sample are smaller than the number of particles and the coordinate distribution width of the low fluorescence intensity reticulocyte particle clusters in the first scatter plot corresponding to the β-thalassemia sample.

[0008] Wherein, the optical detection component further includes a low-value platelet detection channel, the low-value platelet detection channel is used to perform optical detection on the sample to be tested to obtain a second optical signal of the sample to be tested, and the control component is used to obtain a second scattergram of the sample to be tested based on the second optical signal; Among them, the control component is also used to obtain the number of particles of platelet particle clusters and / or the number of particles of red blood cell particle clusters in the second scatter plot of the sample to be tested, and judge the sample type of the sample to be tested based on the number of particles of the platelet particle clusters and / or the number of particles of the red blood cell particle clusters.

[0009] Wherein, the control component is further used to determine the third sample type of the sample to be tested based on the particle number of the platelet particle group, and the third sample type includes a β-thalassemia sample, a hemorrhagic anemia sample, an α-thalassemia sample, and a normal or iron deficiency anemia sample; Among them, the number of particles of the platelet particle clusters in the second scatter plot corresponding to the β-thalassemia sample, the hemorrhagic anemia sample, the α-thalassemia sample, and the normal or iron deficiency anemia sample respectively decreases in sequence; and / or, The control component is also used to determine the fourth sample type of the sample to be tested based on the number of particles of the red blood cell particle group, and the fourth sample type includes a hemorrhagic anemia sample, an α-thalassemia sample, a β-thalassemia sample, and an iron deficiency anemia sample; Among them, the number of particles of the red blood cell particle clusters in the second scatter plots corresponding to the hemorrhagic anemia samples, α-thalassemia samples, β-thalassemia samples, and iron deficiency anemia samples respectively decreases in sequence.

[0010] Wherein, after determining the third sample type of the sample to be tested based on the number of particles of the platelet particle clusters, the control component is further used for: Obtaining the coordinate distribution width of the platelet particle clusters in the second scatter plot; Determining a fifth sample type of the sample to be tested based on the coordinate distribution width of the platelet particle cluster, wherein the fifth sample type includes a normal sample and an iron deficiency anemia sample; Among them, the coordinate distribution width of the platelet particle clusters in the second scatter plot corresponding to the normal sample is greater than the coordinate distribution width of the platelet particle clusters in the second scatter plot corresponding to the iron deficiency anemia sample.

[0011] Among them, the control component is also used to input the obtained scatter plot and data information of at least one particle cluster of red blood cell particles, low fluorescence intensity reticulocyte particles and platelet particles in the scatter plot into the data model, and output the sample type judgment result of the sample to be tested through the data model.

[0012] In order to solve the above technical problems, the present application also provides a method for determining a sample to be tested, which is applied to the sample analyzer as described above, comprising: Acquire detection data of the sample to be tested in the optical detection component, and obtain a scatter plot of the sample to be tested based on the detection data; Obtain data information of at least one of the red blood cell clusters, low fluorescence intensity reticulocyte clusters and platelet clusters in the scatter plot, and determine the sample type of the sample to be tested through the data information, wherein the sample types include normal samples, α-thalassemia samples, β-thalassemia samples, iron deficiency anemia samples and hemorrhagic anemia samples.

[0013] In order to solve the above technical problems, the present application also provides a method for determining a sample to be tested, which is applied to a sample analyzer, wherein the sample analyzer includes at least one of a reticulum detection channel and a low-value platelet detection channel; the method includes: Obtaining a detection mode of a sample to be tested; Detecting the sample to be tested based on the detection mode, and obtaining a scatter plot of at least one output of the reticulum detection channel and the low-value platelet detection channel; Acquire data information of at least one of the red blood cell clusters, low fluorescence intensity reticulocyte clusters and platelet clusters in the scatter plot; The sample type of the sample to be tested is determined by the data information, and the sample type includes a normal sample, an α-thalassemia sample, a β-thalassemia sample, an iron deficiency anemia sample, and a hemorrhagic anemia sample.

[0014] Beneficial effects of the present application: Different from the prior art, the sample analyzer of the present application includes an optical detection component and a control component, the control component and the optical detection component are connected, the optical detection component is used to perform optical detection on the sample to be tested, the control component is used to receive the detection data obtained by the optical detection component, obtain the scatter plot of the sample to be tested based on the detection data, and obtain data information of at least one particle cluster of red blood cell particles, low fluorescence intensity reticulocyte particles and platelet particles in the scatter plot, and determine the sample type of the sample to be tested through the data information, and the sample type includes normal samples, α-thalassemia samples, β-thalassemia samples, iron deficiency anemia samples, and hemorrhagic anemia samples; the data information includes at least one of the number of particles in the particle cluster in the scatter plot, the coordinates of the particle cluster and the coordinate distribution width. By processing and analyzing the scatter plot obtained after the optical detection of the sample to be tested, the sample type of the sample to be tested can be determined, the practicality and operation efficiency of the sample analyzer are improved, and the user experience of the sample analyzer is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 It is a structural schematic diagram of an embodiment of a sample analyzer of the present application; Figure 2 is a scatter diagram of an embodiment of a normal sample; Figure 3 is a scatter diagram of an embodiment of an iron deficiency anemia sample; Figure 4 is a scatter diagram of an embodiment of a β-thalassemia sample; Figure 5 is a scatter diagram of an embodiment of an α-thalassemia sample; Figure 6 is a scatter diagram of an embodiment of a hemorrhagic anemia sample; Figure 7 is a scatter diagram of another embodiment of a β-thalassemia sample; Figure 8 is a scatter diagram of another embodiment of a hemorrhagic anemia sample; Fig. 9 is a scatter diagram of another embodiment of an α-thalassemia sample; Fig.10 is a scatter diagram of another embodiment of a normal sample; Fig.11is a scatter diagram of another embodiment of an iron deficiency anemia sample; Fig.12 It is a flowchart of an embodiment of the judgment method of the present application.

[0016] Reference numerals: sample analyzer 1; optical detection component 11; control component 12; staining component 13; display component 14; red blood cell particle cluster 2; low fluorescence intensity reticulocyte particle cluster 3; platelet particle cluster 4. DETAILED DESCRIPTION

[0017] The scheme of the embodiment of the present application is described in detail below in conjunction with the drawings of the specification.

[0018] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0019] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] The term "and / or" in this application is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the objects associated before and after are in an "or" relationship. In addition, "many" in this article means two or more than two. In addition, the term "at least one" in this article means any combination of at least two of any one or more of a plurality of, for example, including at least one of A, B, and C, and can mean including any one or more elements selected from the set consisting of A, B, and C. In addition, the terms "first", "second", and "third" in this application are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features.

[0021] In the prior art, anemia samples are basically presented in the form of blood routine test report parameter results and histograms on blood routine instruments. However, the histogram has few observable dimensions for the type of anemia, and it is difficult to judge the type of anemia in the sample based on the histogram. In recent years, the technology of the erythroid nine-point diagram has made some progress, and the type of anemia in the sample can be judged based on the erythroid nine-point diagram. However, the technology of the erythroid nine-point diagram is not mature, the accuracy of the judgment result cannot be guaranteed, and additional detection channels are required, which places high requirements on the instrument.

[0022] In fact, the number of blood cells in the sample will vary due to the different types of anemia. For example, thalassemia (hereinafter referred to as α-thalassemia or β-thalassemia) is prone to hemolytic anemia due to the large number of atypical red blood cells, so there are more fragmented red blood cells. Because the disease is mostly chronic, compensatory erythroid hyperplasia will occur in the bone marrow, and increased target cells, atypical red blood cells and reticulocytes can be seen in the peripheral blood. Secondary chronic hemorrhagic anemia (hereinafter referred to as hemorrhagic anemia) is basically anemia secondary to tumors. Due to absorption disorders, poor diet and other factors, it causes severe iron deficiency and an increase in microcytes. The immune-mediated effect of tumors can easily cause hemolytic anemia, resulting in an increase in fragmented red blood cells.

[0023] In order to solve the problem that the histogram has few observable dimensions and cannot distinguish the types of anemia, this application proposes that a scatter plot of the sample to be tested can be obtained in optical detection, and the corresponding features on the scatter plot can be found through the size of the microerythrocytes and the severity of the broken red blood cells. The data analysis is performed through the parameters of the scatter plot, and the types of anemia are distinguished by the graphics and coordinates of the scatter plot. After the sample is tested using the optical detection channel based on the original instrument configuration, the anemia type of the sample can be analyzed in detail, without the need to add additional detection channels, without increasing the instrument cost, and the analysis is simple.

[0024] See also Figure 1 , Figure 1 The sample analyzer 1 provided in the embodiment of the present application comprises an optical detection component 11 and a control component 12 , and the control component 12 is connected to the optical detection component 11 .

[0025] Among them, the optical detection component 11 is used to perform optical detection on the sample to be tested, and the control component 12 is used to receive the detection data obtained by the optical detection component 11, obtain a scatter plot of the test sample based on the detection data, and obtain data information of at least one particle cluster of red blood cell particle clusters, low fluorescence intensity reticulocyte particle clusters and platelet particle clusters in the scatter plot, and determine the sample type of the sample to be tested through the data information. The sample type may include normal samples, α-thalassemia samples, β-thalassemia samples, iron deficiency anemia samples, and hemorrhagic anemia samples; wherein the data information may include the number of particles in the scatter plot, the coordinates of the particle clusters and the coordinate distribution width, etc.

[0026] Specifically, the optical detection component 11 may include a flow chamber, a light source and a photodetector, wherein the light source and the photodetector are arranged on opposite sides of the flow chamber, the sample to be tested flows through the flow chamber, the light beam generated by the light source irradiates the sample to be tested, and the photodetector receives the forward scattered light, side scattered light and side fluorescence emitted by cells in the sample to be tested after the light beam passes through the sample to be tested, and the photodetector can convert the received light signal into an electrical signal (that is, the detection data mentioned above) and transmit it to the control component 12, and the control component 12 obtains a scatter plot of the sample to be tested based on the received detection data, and obtains the number of particles, particle cluster coordinates, coordinate distribution width, etc. of each particle cluster on the scatter plot, and determines the sample type of the sample to be tested based on these parameters.

[0027] In one embodiment, preset intervals of each parameter may be set first, and these preset intervals may be transmitted to the control component 12. After receiving the preset intervals of the parameters, the control component 12 compares the acquired data information with the preset intervals, and then may determine the sample type of the sample to be tested. As mentioned above, different sample types have different corresponding blood cell characteristics, and therefore, the preset intervals may be set according to the blood cell parameter characteristics corresponding to different sample types and the user's experience.

[0028] In one embodiment, the sample analyzer 1 may further include a dyeing component 13, which is used to perform fluorescent dyeing on the sample to be tested and dye the blood cells in the sample to be tested, so that the subsequent control component 12 can distinguish the particle clusters in the obtained scatter plot, obtain the data information of each particle cluster in the scatter plot, and improve the detection data processing efficiency of the sample to be tested. The dyeing component 13 can be connected to the optical detection component 11, and then convey the dyed sample to be tested to the optical detection component 11, so that the optical detection component 11 performs optical detection on the dyed sample to be tested and obtains the detection data of the sample to be tested.

[0029] In another embodiment, the sample analyzer 1 may further include a display component 14. The display component 14 may include a display screen and is connected to the control component 12. After the control component 12 processes the detection data to obtain a scatter plot of the sample to be tested, the control component 12 may transmit the scatter plot to the display component 14 for display. The user may observe the scatter plot of the sample to be tested through the display component 14, thereby improving the interaction efficiency between the sample analyzer 1 and the user.

[0030] Among them, the scatter plots of the samples to be tested of different sample types can be compared experimentally to determine the reliability and accuracy of using the data information of the particle clusters in the scatter plots to judge the sample type of the sample to be tested. The coordinate values ​​of the scatter plot parameters of the four diseases (β-thalassemia, α-thalassemia, iron deficiency anemia and hemorrhagic anemia) are analyzed simultaneously, indicating that the independent variables (selected parameters) of the model are statistically significant for distinguishing the types of anemia.

[0031] Among them, in the scatter plot parameters, X represents the side scattered light intensity, Y represents the side fluorescence intensity, Z represents the forward scattered light intensity, WX represents the side scattered light distribution width, WY represents the side fluorescence distribution width, WZ represents the forward scattered light distribution width, RET represents the RET scatter plot, PLT-F represents the PLT-F scatter plot, for example, the parameter RET_RBC_WY represents the side scattered light distribution width of the red blood cell particle cluster in the RET scatter plot.

[0032] The scatter plot coordinates and related values ​​corresponding to α-thalassemia samples, β-thalassemia samples, iron deficiency anemia samples, and hemorrhagic anemia samples were used for multivariate logistic regression using SPSS to compare the scatter plot parameters that can be used to distinguish various types of anemia.

[0033] Model 1: The scatter plot parameters included in the comparison were: RET_RBC_X, RET_RBC_Y, RET_RBC_Z, RET_RBC_WX, RET_RBC_WY, RET_RBC_WZ, RET_X, RET_Y, PLTF_PLT_X, PLTF_PLT_Y, PLTF_PLT_Z, PLTF_PLT_WX, PLTF_PLT_WY, PLTF_PLT_WZ, RET_LFR%, RET_MFR%, RET_HFR%, PLTF_IPF%.

[0034] 1. Comparing the scatter plots corresponding to β-thalassemia samples and α-thalassemia samples, it can be found that on the scatter plots, the three parameters RET_RBC_WY, RET_X, and PLT-F_PLT_Y are clearly distinguished, and the model fitting significance levels are 0.002, 0.000, and 0.012, respectively, indicating that the independent variables of the model are statistically significant in distinguishing β-thalassemia samples from α-thalassemia samples.

[0035] 2. Comparing the scatter plots corresponding to the iron deficiency anemia samples and the α-thalassemia samples, it can be found that the RET_RBC_WY parameters are clearly distinguished on the scatter plots, and the model fitting significance level is 0.029, indicating that the independent variables of the model are statistically significant in distinguishing iron deficiency anemia samples from α-thalassemia samples.

[0036] 3. Comparing the scatter plots corresponding to hemorrhagic anemia samples and α-thalassemia samples, it can be found that on the scatter plot, the seven parameters RET_RBC_X, RET_Y, PLT-F_PLT_X, PLT-F_PLT_Y, PLT-F_PLT_Z, PLT-F_PLT_WX, and PLT-F_PLT_WY are obviously distinguished, and the model fitting significance levels are 0.011, 0.047, 0.014, 0.000, 0.027, 0.015, and 0.006, respectively, indicating that the independent variables of the model are statistically significant in distinguishing hemorrhagic anemia from α-thalassemia.

[0037] 4. Comparing the scatter plots corresponding to the hemorrhagic anemia samples and the β-thalassemia samples, it can be found that on the scatter plots, the four parameters RET_RBC_WY, PLT-F_PLT_X, PLT-F_PLT_WX, and PLT-F_PLT_WY have obvious statistical differences, and the model fitting significance levels are 0.012, 0.016, 0.007, and 0.022, respectively, indicating that the independent variables of the model are statistically significant in distinguishing between hemorrhagic anemia samples and β-thalassemia samples.

[0038] 5. Comparing the scatter plots corresponding to the iron deficiency anemia samples and the β-thalassemia samples, it can be found that on the scatter plots, the five parameters RET_RBC_Z, RET_RBC_WX, RET_RBC_WY, RET_RBC_WZ, and RET_X have significant statistical differences, and the model fitting significance levels are 0.035, 0.030, 0.000, 0.028, and 0.025, respectively, indicating that the independent variables of the model are statistically significant in distinguishing iron deficiency anemia samples from β-thalassemia samples.

[0039] 6. Comparing the scatter plots corresponding to hemorrhagic anemia samples and iron deficiency anemia samples, it can be found that on the scatter plot, the two parameters RET_RBC_WY and PLT-F_PLT_Y have obvious statistical differences, and the model fitting significance levels are 0.034 and 0.018, respectively, indicating that the independent variables of the model are statistically significant in distinguishing iron deficiency anemia samples from iron deficiency anemia samples.

[0040] Model 2: The scatter plot parameters included in the comparison include: RET_X, RET_Y, PLT-F_RBC_X, PLT-F_RBC_Y, PLT-F_RBC_Z, PLT-F_RBC_WX, PLT-F_RBC_WY, PLT-F_RBC_WZ, PLT-F_PLT_X, PLT-F_PLT_Y, PLT-F_PLT_Z, PLT-F_PLT_WX, PLT-F_PLT_WY, PLT-F_PLT_WZ, RET_LFR_Percentage2, RET_MFR_Percentage2, IPF_Number. The likelihood ratio test of model 2 showed that the parameters RET_X, PLT-F_RBC_X, PLT-F_RBC_Y, PLT-F_RBC_Z, PLT-F_RBC_WX, PLT-F_RBC_WY, PLT-F_RBC_WZ, PLT-F_PLT_X, PLT-F_PLT_Y, and PLT-F_PLT_WX were effective in distinguishing several types of anemia.

[0041] 1. Comparing the scatter plots corresponding to β-thalassemia samples and α-thalassemia samples, it can be found that on the scatter plot, the six parameters RET_X, PLT-F_RBC_X, PLT-F_RBC_Z, PLT-F_RBC_WX, PLT-F_RBC_WY, and PLT-F_RBC_WZ are obviously distinguished, and the model fitting significance levels are 0.000, 0.005, 0.009, 0.040, 0.001, and 0.002, respectively, indicating that the independent variables of the model are statistically significant in distinguishing β-thalassemia samples from α-thalassemia samples.

[0042] 2. Comparing the scatter plots corresponding to the iron deficiency anemia samples and the α-thalassemia samples, it can be found that on the scatter plots, the four parameters RET_X, PLT-F_RBC_X, PLT-F_RBC_Y, and PLT-F_PLT_X are clearly distinguished, and the model fitting significance levels are 0.027, 0.003, 0.018, and 0.044, indicating that the independent variables of the model are statistically significant in distinguishing iron deficiency anemia samples from α-thalassemia samples.

[0043] 3. Comparing the scatter plots corresponding to hemorrhagic anemia samples and α-thalassemia samples, it can be found that on the scatter plot, the model fitting significance levels of the five parameters RET_X, PLT-F_RBC_X, PLT-F_RBC_WY, PLT-F_PLT_Y, and PLT-F_PLT_WY were 0.019, 0.002, 0.004, 0.002, and 0.045, respectively, indicating that the independent variables of the model are statistically significant in distinguishing hemorrhagic anemia samples from α-thalassemia samples.

[0044] 4. Comparing the scatter plots corresponding to hemorrhagic anemia samples and β-thalassemia samples, it can be found that on the scatter plots, the three parameters PLT-F_RBC_WX, PLT-F_PLT_X, and PLT-F_PLT_WX are clearly distinguished, and the model fitting significance levels are 0.015, 0.020, and 0.029, respectively, indicating that the independent variables of the model are statistically significant in distinguishing hemorrhagic anemia samples from β-thalassemia samples.

[0045] 5. Comparing the scatter plots corresponding to the iron deficiency anemia samples and the β-thalassemia samples, it can be found that on the scatter plots, the six parameters RET_X, PLT-F_RBC_X, PLT-F_RBC_Z, PLT-F_RBC_WX, PLT-F_RBC_WY, and PLT-F_RBC_WZ are obviously distinguished, and the model fitting significance levels are 0.000, 0.005, 0.009, 0.040, 0.001, and 0.002, respectively, indicating that the independent variables of the model are statistically significant in distinguishing iron deficiency anemia samples from β-thalassemia samples.

[0046] 6. Comparing the scatter plots corresponding to the hemorrhagic anemia samples and the iron deficiency anemia samples, it can be found that on the scatter plots, the five parameters RET_X, PLT-F_RBC_X, PLT-F_RBC_WY, PLT-F_PLT_Y, and PLT-F_PLT_WY are obviously distinguished, and the model fitting significance levels are 0.019, 0.002, 0.004, 0.002, and 0.045, respectively, indicating that the independent variables of the model are statistically significant in distinguishing iron deficiency anemia samples from iron deficiency anemia samples.

[0047] Model 3: The proportions of RET particles of three fluorescence intensities were replaced with absolute numbers, and the scatter plot parameters included in the comparison included: RET_LFR, RET_MFR, RET_HFR, IPF_Number. It can be seen that the LFR (low fluorescence intensity reticulocytes) in the scatter plot corresponding to the β-thalassemia samples are significantly different from the LFR in the scatter plots corresponding to the α-thalassemia samples, hemorrhagic anemia samples, and iron deficiency anemia samples, respectively. The model fitting significance levels are 0.014, 0.005, and 0.024, respectively, indicating that the model independent variables are statistically significant in distinguishing β-thalassemia samples from α-thalassemia samples, hemorrhagic anemia samples, and iron deficiency anemia samples.

[0048] From the above analysis, it can be concluded that the method of determining the sample type of the sample to be tested by obtaining the data information of the scatter plot proposed in the embodiment of the present application is practical and reliable.

[0049] Optionally, the optical detection component 11 includes a reticulum detection channel, which is used to perform optical detection on the sample to be tested to obtain a first light signal of the sample to be tested, and the control component 12 is used to obtain a first scatter plot of the sample to be tested based on the first light signal. Further, the control component 12 is also used to obtain the centroid coordinates of the red blood cell particle cluster in the first scatter plot, and determine the first sample type of the sample to be tested based on the centroid coordinates of the red blood cell particle cluster, and the first sample type includes a normal sample, an iron deficiency or β-thalassemia sample, an α-thalassemia sample, and a hemorrhagic anemia sample.

[0050] Specifically, the reticulocyte detection channel can detect the reticulocytes in the sample to be tested. Then, in addition to displaying the status of red blood cells and platelets in the sample to be tested, the first scatter plot (RET scatter plot) can also display the status of reticulocytes in the sample to be tested. Reticulocytes are the immature stage of red blood cells and are an important indicator for reflecting the hematopoietic function of the bone marrow erythroid system and judging the efficacy of anemia and related diseases. There is still a small amount of RNA (Ribonucleic Acid) in the reticulocytes. Therefore, in the scatter plot obtained after staining the sample to be tested for optical detection, the red blood cell particle clusters and the reticulocyte particle clusters can be clearly distinguished in the scatter plot.

[0051] After obtaining the sample to be tested, the sample to be tested can be mixed with a diluent first, so that the red blood cells in the sample to be tested are treated with the diluent, and the cell morphology and dispersion of the red blood cells are optimized, which is convenient for subsequent detection. Then, the sample to be tested can be stained with a fluorescent dye, wherein the fluorescent dye in the fluorescent dye can stain the nucleic acid of reticulocytes and white blood cells. Reticulocytes will be strongly stained by the fluorescent dye because they contain more residual nucleic acid.

[0052] After the sample to be tested is processed, the diluted and stained sample to be tested can be introduced into the reticulocyte detection channel. The cells in the sample to be tested are arranged individually and irradiated by the flow chamber laser, which will generate forward scattered light (FSC), side scattered light (SSC) and side fluorescence (SFL). These light signals can reflect information such as the size, morphology, internal structure and nucleic acid content of the cells. Then, the optical detection component 11 collects the light signal, and the control component 12 processes the light signal to obtain the first scatter plot of the sample to be tested. The red blood cell particle clusters 2, reticulocyte particle clusters, etc. in the sample to be tested, as well as the data information of each particle cluster can be obtained through the first scatter plot, and the sample type of the sample to be tested can be judged.

[0053] See also Figure 2 , Figure 2 Schematic diagram of a scatter plot of an embodiment of a normal sample. Figure 2Shown is a RET scatter plot of a normal sample, which includes a red blood cell cluster 2, a reticulocyte cluster, and a platelet cluster 4.

[0054] Reticulocyte clusters can specifically include LFR (low fluorescence intensity reticulocyte clusters), MFR (medium fluorescence intensity reticulocyte clusters) and HFR (high fluorescence intensity reticulocyte clusters). In the process of reticulocytes gradually maturing into red blood cells, the RNA in the reticulocytes will gradually decrease, that is, the fluorescence emitted after being stained becomes less. Therefore, in the scatter plot, it can be reflected that the reticulocyte clusters from left to right are low fluorescence intensity reticulocyte cluster 3-medium fluorescence intensity reticulocyte cluster-high fluorescence intensity reticulocyte cluster.

[0055] For further information, please refer to Figure 3~Figure 6 , Figure 3 is a scatter diagram of an embodiment of an iron deficiency anemia sample, Figure 4 is a scatter diagram of an embodiment of a β-thalassemia sample, Figure 5 is a scatter diagram of an embodiment of an α-thalassemia sample, Figure 6 is a scatter diagram of an embodiment of a hemorrhagic anemia sample. Specifically, Figure 3 This is the RET scatter plot of iron deficiency anemia samples. Figure 4 is the RET scatter plot of β-thalassemia samples, Figure 5 is the RET scatter plot of α-thalassemia samples, Figure 6 is the RET scatter plot of hemorrhagic anemia samples.

[0056] As shown in the figure, when the sample to be tested is in a normal state (the sample type of the sample to be tested is a normal sample) and when the sample to be tested is in an abnormal state (the sample type of the sample to be tested is one of iron deficiency anemia samples, β-thalassemia samples, α-thalassemia samples, and hemorrhagic anemia samples), the aggregation center of gravity of the red blood cell particle cluster 2 in the scatter plot is quite different, wherein the vertical axis of the scatter plot is the forward scattered light information, which can reflect information such as the size of the particles. Since the red blood cells in the sample to be tested will be broken when the sample to be tested is in an abnormal state, that is, the volume of the red blood cells in the sample to be tested in the abnormal state will be smaller than the volume of the red blood cells in the sample to be tested in the normal state, it is reflected in the scatter plot that the aggregation center of gravity of the red blood cell particle cluster 2 in the scatter plot of the sample to be tested in the normal state is higher than the aggregation center of gravity of the red blood cell particle cluster 2 in the scatter plot of the sample to be tested in the abnormal state.

[0057] And further comparison Figure 3~Figure 6It can be obtained that the aggregation center of gravity of red blood cell cluster 2 in the scatter plots of different types of anemia is also different. Among them, the aggregation center of gravity of red blood cell cluster 2 in the scatter plots corresponding to iron deficiency anemia samples and β-thalassemia samples is higher than that of the other two, while the aggregation center of gravity of red blood cell cluster 2 in the scatter plot of α-thalassemia samples is higher than that of red blood cell cluster 2 in the scatter plot of hemorrhagic anemia samples. The aggregation center of gravity of red blood cell cluster 2 in the scatter plot of hemorrhagic anemia samples is the lowest. This is because hemorrhagic anemia is basically anemia secondary to tumors. The immune-mediated effect of tumors can easily cause hemolytic anemia, resulting in an increase in broken red blood cells. Therefore, if there are many broken red blood cells in the test sample and the red blood cells are small in size, the aggregation center of gravity is the lowest.

[0058] Based on the above, the control component 12 can determine the first sample type of the sample to be tested based on the coordinates of the center of gravity of the red blood cell particle cluster 2 in the first scatter plot corresponding to the normal sample, iron deficiency or β-thalassemia sample, α-thalassemia sample, and hemorrhagic anemia sample becoming smaller in sequence.

[0059] In one embodiment, as described above, a preset area can be set, and the rear control component 12 can determine the sample type of the sample to be tested based on the preset area where the center coordinates of the red blood cell particle cluster 2 in the scatter plot corresponding to the sample to be tested are located. For example, in response to the center of gravity coordinates of the red blood cell particle cluster 2 being greater than the first preset coordinates, the control component 12 determines that the first sample type of the sample to be tested is a normal sample; in response to the center of gravity coordinates of the red blood cell particle cluster 2 being less than the first preset coordinates and greater than the second preset coordinates, the first sample type of the sample to be tested is determined to be an iron deficiency or β-thalassemia sample; in response to the center of gravity coordinates of the red blood cell particle cluster 2 being less than the second preset coordinates and greater than the third preset coordinates, the first sample type of the sample to be tested is determined to be an α-thalassemia sample; in response to the center of gravity coordinates of the red blood cell particle cluster 2 being less than the third preset coordinates, the first sample type of the sample to be tested is determined to be a hemorrhagic anemia sample.

[0060] The first preset coordinate is greater than the second preset coordinate, and the second preset coordinate is greater than the third preset coordinate. The specific values ​​of the first preset coordinate, the second preset coordinate, and the third preset coordinate can be set by the user, and this application does not limit this.

[0061] Specifically, as described above, after the first light signal is obtained in the reticulum red detection channel, it can be converted into a first electrical signal and then transmitted to the control component 12, and then the control component 12 can obtain a scatter plot of the sample to be tested based on the first electrical signal, and the control component 12 can identify each particle cluster in the scatter plot to obtain data information of each particle cluster. In one embodiment, since the substances contained in each cell in the sample to be tested are different, the fluorescent colors emitted by each cell after being stained are also different, and the control component 12 can distinguish the corresponding different cell clusters based on the colors of each particle cluster in the scatter plot.

[0062] After the control component 12 obtains the data information of each particle cluster, it can specifically obtain the center of gravity coordinates of the red blood cell particle cluster 2 in the scatter plot, wherein the center of gravity coordinates of the red blood cell particle cluster 2 are also the coordinate values ​​of the aggregation center of gravity of the red blood cell particle cluster 2 mentioned above on the vertical axis of the scatter plot.

[0063] Then, the control component 12 can compare the coordinates of the center of gravity of the red blood cell particle cluster 2 in the scatter plot with the preset interval, wherein the preset interval here may include a first preset interval greater than the first preset coordinate, corresponding to a normal sample; a second preset interval less than the first preset coordinate but greater than the second preset coordinate, corresponding to an iron deficiency or β-thalassemia sample; a third preset interval less than the second preset coordinate but greater than the third preset coordinate, corresponding to an α-thalassemia sample; and a fourth preset interval less than the third preset coordinate, corresponding to a hemorrhagic anemia sample.

[0064] When the control component 12 responds that the center of gravity coordinates of the red blood cell particle cluster 2 in the scatter plot are greater than the first preset coordinates, that is, located in the first preset interval, the control component 12 can determine that the first sample type of the sample to be tested is a normal sample.

[0065] When the control component 12 responds that the center of gravity coordinate of the red blood cell particle cluster 2 is less than the first preset coordinate and greater than the second preset coordinate, that is, located in the second preset interval, the control component 12 can determine that the first sample type of the sample to be tested is an iron deficiency or β-thalassemia sample.

[0066] In response to the fact that the center of gravity coordinate of the red blood cell particle cluster 2 is less than the second preset coordinate and greater than the third preset coordinate, that is, located in the third preset interval, the control component 12 can determine that the first sample type of the sample to be tested is an α-thalassemia sample.

[0067] In response to the control component 12 that the coordinate of the center of gravity of the red blood cell particle group 2 is less than the third preset coordinate, that is, located in the fourth preset interval, the control component 12 can determine that the first sample type of the sample to be tested is a hemorrhagic anemia sample.

[0068] In other embodiments, the control component 12 may also use other methods to process the data information in the scatter plot to determine the sample type of the sample to be tested, and the present application does not impose any limitation on this.

[0069] It is understandable that since α-thalassemia and β-thalassemia are hereditary diseases and are genetic problems, after the control component 12 determines that the abnormal type of the sample to be tested is α-thalassemia or β-thalassemia, the patient can undergo genetic diagnosis again, and the results of the genetic diagnosis shall prevail.

[0070] Furthermore, after the control component 12 determines the first sample type of the sample to be tested based on the center of gravity coordinates of the red blood cell particle cluster 2, the control component 12 is also used to obtain the particle number and coordinate distribution width of the low fluorescence intensity reticulocyte particle cluster 3 in the first scatter plot to further distinguish the iron deficiency anemia samples from the β-thalassemia samples.

[0071] Among them, the horizontal coordinate in the scatter plot of the cells to be tested is lateral fluorescence, and the fluorescence intensity of the reticulocytes is determined by the maturity of the reticulocytes. The lower the maturity of the reticulocytes, the higher the fluorescence intensity; the higher the maturity of the reticulocytes, the lower the fluorescence intensity. In actual situations, immature reticulocytes can better reflect the compensatory ability of bone marrow hematopoiesis, especially when the bone marrow is stimulated or anemic. The more immature reticulocytes there are, the larger the coordinate distribution width of the low-fluorescence-intensity reticulocyte particle cluster 3. Therefore, the coordinate distribution width of the low-fluorescence-intensity reticulocyte particle cluster 3 can be used to judge the second sample type of the sample to be tested, and the second sample type of the sample to be tested includes iron deficiency anemia samples and β-thalassemia samples.

[0072] See also Figure 3 and Figure 4 It can be obviously obtained that the number of particles of low fluorescence intensity reticulocyte particle clusters 3 in the first scatter plot corresponding to the β-thalassemia sample is greater than the number of particles of low fluorescence intensity reticulocyte particle clusters 3 in the first scatter plot corresponding to the iron deficiency anemia sample; the coordinate distribution width of the low fluorescence intensity reticulocyte particle clusters 3 in the first scatter plot corresponding to the β-thalassemia sample is greater than the coordinate distribution width of the low fluorescence intensity reticulocyte particle clusters 3 in the first scatter plot corresponding to the iron deficiency anemia sample.

[0073] Then, in one embodiment, after determining the first sample type of the sample to be tested based on the centroid coordinates of the red blood cell particle cluster 2, in order to further determine that the second sample type of the sample to be tested is an iron deficiency anemia sample or a β-thalassemia sample, the control component 12 can obtain the particle number and coordinate distribution width of the low fluorescence intensity reticulocyte particle cluster 3 in the first scatter plot. In response to the particle number of the low fluorescence intensity reticulocyte particle cluster 3 being greater than the first preset particle number, and the coordinate distribution width of the low fluorescence intensity reticulocyte particle cluster 3 being greater than the first preset width, the second sample type of the sample to be tested is determined to be a β-thalassemia sample; in response to the particle number of the low fluorescence intensity reticulocyte particle cluster 3 being less than the first preset particle number, and the coordinate distribution width of the low fluorescence intensity reticulocyte particle cluster 3 being less than the first preset width, the second sample type of the sample to be tested is determined to be an iron deficiency anemia sample.

[0074] Among them, the specific values ​​of the first preset number of particles and the first preset width can be set by the user, and this application does not impose any restrictions on this.

[0075] In summary, after the reticulocyte detection channel performs optical detection on the sample to be tested, the control component 12 can obtain a first scatter plot of the sample to be tested based on the first light signal obtained by the reticulocyte detection channel, and obtain the center of gravity coordinates of the red blood cell particle cluster 2, the particle number of the low fluorescence intensity reticulocyte particle cluster 3 and the coordinate distribution width of the low fluorescence intensity reticulocyte particle cluster 3 in the first scatter plot, so as to determine the sample type of the sample to be tested, improve the practicality and operation efficiency of the sample analyzer 1, and enhance the user experience of the sample analyzer 1.

[0076] Optionally, the optical detection component 11 also includes a low-value platelet detection channel, which is used to perform optical detection on the sample to be tested to obtain a second light signal of the sample to be tested, and the control component 12 is used to obtain a second scatter plot of the sample to be tested based on the second light signal, wherein the second scatter plot can be a PLT-F (Low-value Platelet) scatter plot.

[0077] After obtaining the sample to be tested, the sample to be tested can be treated with a fluorescent staining solution, wherein the fluorescent dye in the fluorescent staining solution can specifically bind to the mitochondrial DNA (MtDNA) and the RNA (ribosomal RNA) of the microsome in the platelet. The sample to be tested after fluorescence treatment is then introduced into the low-value platelet detection channel, and the forward scattered light (FSC), side scattered light (SSC) and side fluorescence (SFL) are irradiated by laser. These light signals can reflect information such as the size, morphology, internal structure and nucleic acid content of the cell.

[0078] The optical detection component 11 collects these optical signals, and the control component 12 processes the optical signals to obtain a second scatter plot of the sample to be tested. The red blood cell particle clusters 2, platelet particle clusters 4, etc. in the sample to be tested, as well as the data information of each particle cluster can be obtained through the second scatter plot, and then the sample type of the sample to be tested can be judged.

[0079] Among them, the control component 12 is used to obtain the number of particles of platelet particle clusters 4 and / or the number of particles of red blood cell particle clusters 2 in the second scatter plot (PLT-F scatter plot) of the sample to be tested, and judge the sample type of the sample to be tested based on the number of particles of platelet particle clusters 4 and / or the number of particles of red blood cell particle clusters 2.

[0080] Please refer to Figure 7~Figure 11 , Figure 7 is a scatter diagram of another embodiment of a β-thalassemia sample; Figure 8 is a scatter diagram of another embodiment of a hemorrhagic anemia sample; Fig. 9 is a scatter diagram of another embodiment of an α-thalassemia sample; Fig.10 is a scatter diagram of another embodiment of a normal sample; Fig.11 is a scatter diagram of another embodiment of an iron deficiency anemia sample. Specifically, Figure 7 is the PLT-F scatter plot of β-thalassemia samples, Figure 8 This is the PLT-F scatter plot of hemorrhagic anemia samples. Fig. 9 is the PLT-F scatter plot of α-thalassemia samples, Fig.10 is the PLT-F scatter plot of normal samples, Fig.11 This is a PLT-F scatter plot of an iron deficiency anemia sample. In the PLT-F scatter plot, the horizontal axis is the side fluorescence information, and the vertical axis is the forward scattered light information.

[0081] After obtaining the particle number of the platelet particle cluster 4 in the second scatter plot, the control component 12 determines the third sample type of the sample to be tested based on the particle number of the platelet particle cluster 4, wherein the third sample type is β-thalassemia sample, hemorrhagic anemia sample, α-thalassemia sample, normal or iron deficiency anemia sample.

[0082] Specifically, as shown in the figure, the number of particles of the platelet particle cluster 4 in the second scatter plot corresponding to the β-thalassemia sample, the hemorrhagic anemia sample, the α-thalassemia sample, and the normal or iron-deficiency anemia sample respectively decreases successively.

[0083] In one embodiment, after the control component 12 obtains the particle number of the platelet particle cluster 4 in the second scatter plot, the control component 12 can determine that the sample type of the sample to be tested is a β-thalassemia sample in response to the particle number of the platelet particle cluster 4 being greater than the second preset particle number. In response to the particle number of the platelet particle cluster 4 being less than the second preset particle number and greater than the third preset particle number, the sample type of the sample to be tested is determined to be a hemorrhagic anemia sample. In response to the particle number of the platelet particle cluster 4 being less than the third preset particle number and greater than the fourth preset particle number, the sample type of the sample to be tested is determined to be an α-thalassemia sample. In response to the particle number of the platelet particle cluster 4 being less than the fourth preset particle number, the sample type of the sample to be tested is determined to be a normal or iron deficiency anemia sample.

[0084] Among them, the second preset number of particles is greater than the third preset number of particles, the third preset number of particles is greater than the fourth preset number of particles, and the specific values ​​of the second preset number of particles, the third preset number of particles and the fourth preset number of particles can be set by the user, and this application does not impose any restrictions on this.

[0085] It is understandable that the specific value of the first preset number of particles is independent of the specific values ​​of the second preset number of particles, the third preset number of particles, and the fourth preset number of particles, and the naming is only for clear description. The specific value of the first preset number of particles may be greater than or less than or equal to the specific values ​​of the second preset number of particles, the third preset number of particles, and the fourth preset number of particles, and the present application does not make specific restrictions on this.

[0086] Furthermore, after determining the third sample type of the sample to be tested based on the particle number of the platelet particle cluster 4, in order to distinguish whether the sample type of the sample to be tested is a normal sample or an iron deficiency anemia sample, the control component 12 also obtains the coordinate distribution width of the platelet particle cluster 4 in the second scatter plot to determine the fifth sample type of the sample to be tested, and the fifth sample type includes a normal sample and an iron deficiency anemia sample.

[0087] Among them, Fig.10 and Fig.11 As shown, the platelet particle clusters 4 in the second scatter plot corresponding to the normal sample are relatively "slender", while the platelet particle clusters 4 in the second scatter plot corresponding to the iron deficiency anemia sample are relatively "wide and short". The control component 12 distinguishes the normal sample from the iron deficiency anemia sample based on the coordinate distribution width of the platelet particle clusters 4 in the second scatter plot.

[0088] Specifically, the control component 12 can compare the platelet particle clusters 4 in the second scatter plot with the second preset width to determine whether the sample type of the sample to be tested is a normal sample or an iron deficiency anemia sample. In response to the coordinate distribution width of the platelet particle clusters 4 being greater than the second preset width, the sample type of the sample to be tested is determined to be a normal sample. In response to the coordinate distribution width of the platelet particle clusters 4 being less than the second preset width, the sample type of the sample to be tested is determined to be an iron deficiency anemia sample.

[0089] By acquiring the particle number and coordinate distribution width of the platelet particle cluster 4 in the second scatter plot by the control component 12, the sample type of the sample to be tested can be determined based on the particle number and coordinate distribution width of the platelet particle cluster 4, thereby improving the practicality and operating efficiency of the sample analyzer 1 and enhancing the user experience of the sample analyzer 1.

[0090] In one embodiment, if the sample analyzer 1 includes a reticulum erythrocyte detection channel, the reticulum erythrocyte detection channel in the sample analyzer 1 can perform optical detection on the sample to be tested to obtain a first optical signal, and then the control component 12 uses the first optical signal to obtain a first scatter plot of the sample to be tested, and obtains data information of the first scatter plot, determines the sample type of the sample to be tested, and improves the practicality and operation efficiency of the sample analyzer 1.

[0091] In another embodiment, if the sample analyzer 1 includes a low-value platelet detection channel, the low-value platelet detection channel in the sample analyzer 1 can perform optical detection on the sample to be tested to obtain a second light signal, and then the control component 12 uses the second light signal to obtain a second scatter plot of the sample to be tested, and obtains data information of the second scatter plot to determine the sample type of the sample to be tested, thereby improving the practicality and operating efficiency of the sample analyzer 1.

[0092] In other embodiments, if the sample analyzer 1 includes a reticulocyte detection channel and a low-value platelet detection channel, the reticulocyte detection channel and the low-value platelet detection channel in the sample analyzer 1 can both perform optical detection on the sample to be tested to obtain a first light signal and a second light signal, and then the control component 12 obtains a first scatter plot and a second scatter plot. Then, the control component can obtain a first confidence level of the sample type corresponding to the sample to be tested based on the first scatter plot, and obtain a second confidence level of the sample type corresponding to the sample to be tested based on the second scatter plot. The sample type of the sample to be tested is determined by combining the first confidence level of the sample type obtained from the first scatter plot and the second confidence level of the sample type obtained from the second scatter plot, thereby improving the accuracy and reliability of the judgment of the control component 12 and improving the practicality and operating efficiency of the sample analyzer 1.

[0093] The type of detection channel configured by the sample analyzer 1 can be selected to determine the sample type of the sample to be tested based on the original configuration of the sample analyzer 1 without the need to set up an additional optical detection channel, thereby improving the practicality of the sample analyzer 1, reducing the cost of the instrument, and enhancing the user experience of the sample analyzer 1.

[0094] Optionally, in some embodiments, after the control component 12 obtains the second scattergram, the control component 12 may also obtain the number of particles of the red blood cell particle cluster 2 in the second scattergram. Figure 7~Figure 11 As shown, the states of the red blood cell particle clusters 2 in the scatter diagrams of the samples to be tested in different states are different.

[0095] Among them, because the red blood cells of normal samples are of normal size, the second scatter plot corresponding to the normal samples cannot fully display the red blood cell cluster 2, and the red blood cell cluster 2 is like being truncated and the whole picture cannot be seen; in the second scatter plot corresponding to the iron deficiency anemia sample, the whole picture of the red blood cell cluster 2 can be seen, and there is another smaller red blood cell cluster 2 below the red blood cell cluster 2, and the interval between the two clusters is large; in the second scatter plot corresponding to the α-thalassemia sample, the whole picture of the red blood cell cluster 2 can be seen, and there is another longer red blood cell cluster 2 below the red blood cell cluster 2. There is a large interval between the two clusters, and the size of the two red blood cell particle clusters 2 is larger than the red blood cell particle cluster 2 in the second scatter plot corresponding to the iron deficiency anemia sample; in the second scatter plot corresponding to the β-thalassemia sample, the full picture of the red blood cell particle cluster 2 can be seen, and there are some scattered particles on the right side of the cluster. There is another cluster of red blood cell particle cluster 2 below the red blood cell particle cluster 2, and the interval between the two clusters is large. The two red blood cell particle clusters 2 are larger than the red blood cell particle cluster 2 in the second scatter plot corresponding to the iron deficiency anemia sample, but smaller than the red blood cell particle cluster 2 in the second scatter plot corresponding to the α-thalassemia sample; in the scatter plot corresponding to the hemorrhagic anemia sample, the full picture of the red blood cell particle cluster 2 can be seen, and there is another larger red blood cell particle cluster 2 below the red blood cell particle cluster 2. The two red blood cell particle clusters 2 are larger than the red blood cell particle cluster 2 in the second scatter plot corresponding to the α-thalassemia sample, and are the largest red blood cell particle clusters 2 among several types of anemia, and the interval between the two clusters is the smallest.

[0096] In combination with the above analysis, the present application further proposes that after the control component 12 obtains the second scatter plot, the control component 12 can also obtain the number of particles of the red blood cell particle cluster 2 in the second scatter plot to determine the third sample type of the sample to be tested.

[0097] Specifically, the control component 12 can determine that the sample type of the sample to be tested is a hemorrhagic anemia sample in response to the particle number of the red blood cell particle cluster 2 being greater than the fifth preset particle number; determine that the sample type of the sample to be tested is an α thalassemia sample in response to the particle number of the red blood cell particle cluster 2 being less than the fifth preset particle number and greater than the sixth preset particle number; determine that the sample type of the sample to be tested is a β thalassemia sample in response to the particle number of the red blood cell particle cluster 2 being less than the sixth preset particle number and greater than the seventh preset particle number; and determine that the sample type of the sample to be tested is an iron deficiency anemia sample in response to the particle number of the red blood cell particle cluster 2 being less than the seventh preset particle number.

[0098] Among them, the fifth preset number of particles is greater than the sixth preset number of particles, the sixth preset number of particles is greater than the seventh preset number of particles, and the specific values ​​of the fifth preset number of particles, the sixth preset number of particles and the seventh preset number of particles can be set by the user, and this application does not impose any restrictions on this.

[0099] It can be understood that the specific value of the first preset particle number, and the specific values ​​of the second preset particle number, the third preset particle number and the fourth preset particle number are independent of the specific values ​​of the fifth preset particle number, the sixth preset particle number and the seventh preset particle number.

[0100] Optionally, the control component 12 can also obtain the interval width between two groups of red blood cell particle clusters 2 in the second scatter plot. As described above, in the second scatter plot corresponding to the hemorrhagic anemia sample, the interval between the two groups of red blood cell particle clusters 2 is the smallest. Then, the control component 12 can determine that the sample type of the sample to be tested is a hemorrhagic anemia sample when it is determined that the interval width between the two groups of red blood cell particle clusters 2 in the second scatter plot is less than a preset width.

[0101] In one embodiment, the control component 12 may obtain the number of particles of the red blood cell particle cluster 2 in the second scattergram to determine the sample type of the sample to be tested, thereby improving the practicality and operation efficiency of the sample analyzer 1 .

[0102] In another embodiment, the control component 12 may also obtain the particle number and coordinate distribution width of the platelet particle clusters 4 in the second scattergram to determine the sample type of the sample to be tested, thereby improving the practicality and operating efficiency of the sample analyzer 1.

[0103] In other embodiments, the control component 12 may also obtain the particle number and coordinate distribution width of the platelet particle cluster 4 and the particle number of the red blood cell particle cluster 2 in the second scatter plot, obtain a first confidence level of the sample type of the sample to be tested based on the particle number and coordinate distribution width of the platelet particle cluster 4, and simultaneously obtain a second confidence level of the sample type of the sample to be tested based on the particle number of the red blood cell particle cluster 2, and then perform a combined judgment based on the first confidence level and the second confidence level to determine the sample type of the sample to be tested, thereby improving the accuracy and reliability of the control component 12 in judging the sample to be tested, and improving the practicality and operating efficiency of the sample analyzer 1.

[0104] In one embodiment, after the control component 12 obtains the scatter plot of the sample to be tested and the data information of at least one of the red blood cell clusters 2, the low fluorescence intensity reticulocyte clusters 3, and the platelet clusters 4 in the scatter plot, the control component 12 can input the obtained scatter plot and data into the trained data model, and output the sample type judgment result of the sample to be tested through the data model. The trained data model can include the model 1, model 2, or model 3 described above.

[0105] Among them, as described above, the data information can specifically be the coordinates of the center of gravity of the red blood cell particle cluster 2 in the first scatter plot, the number of particles and the coordinate distribution width of the low fluorescence intensity reticulocyte particle cluster 3; the number of particles and the coordinate distribution width of the platelet particle cluster 4 in the second scatter plot, and the number of particles of the red blood cell particle cluster 2. In other cases, the data information can also be the data information of other particle clusters, and this application does not limit this.

[0106] The data model may be built on the control component 12 or on an external server of the sample analyzer 1 , and this application does not impose any limitation on this.

[0107] By using the data model to determine the sample type of the sample to be tested, the processing and response rate of the control component 12 can be improved, and the operating efficiency of the sample analyzer 1 can be improved.

[0108] In summary, in the sample analyzer 1 of the present application, the optical detection component 11 performs optical detection on the sample to be tested, and the control component 12 obtains a scatter plot of the sample to be tested based on the detection data obtained by the optical detection component 11, and obtains data information of the scatter plot, and determines the sample type of the sample to be tested based on the data information, thereby improving the practicality and operating efficiency of the sample analyzer 1 and enhancing the user experience of the sample analyzer 1.

[0109] In order to solve the above technical problems, the present application also provides a method for determining a sample to be tested, which is applied to the sample analyzer 1 as described above, including: obtaining detection data of the sample to be tested in the optical detection component 11, and obtaining a scatter plot of the sample to be tested based on the detection data; obtaining data information of at least one particle cluster of red blood cell particles 2, low fluorescence intensity reticulocyte particles 3 and platelet particles 4 in the scatter plot, and determining the sample type of the sample to be tested based on the data information, and the sample types include normal samples, α-thalassemia samples, β-thalassemia samples, iron deficiency anemia samples, and hemorrhagic anemia samples.

[0110] Among them, the control component 12 can obtain the number of particles of the red blood cell particle cluster 2, the number of particles of the low fluorescence intensity reticulocyte particle cluster 3 and the coordinate distribution width, the number of particles of the platelet particle cluster 4 and the coordinate distribution width in the scatter plot to judge the sample type of the sample to be tested. The specific operation steps can be found in the previous description and will not be repeated here.

[0111] In order to solve the above technical problems, the present application also provides a method for determining a sample to be tested, which is applied to a sample analyzer. The sample analyzer may include at least one of a reticulocyte detection channel and a low-value platelet detection channel. Fig.12 , Fig.12 : is a flow chart of an embodiment of the judgment method of the present application. The judgment method provided in the embodiment of the present application specifically includes the following steps: S1: Obtain the detection mode of the sample to be tested.

[0112] Among them, when the sample analyzer only includes the reticulum detection channel, the detection mode of the sample to be tested is the reticulum detection channel detection; when the sample analyzer only includes the low-value platelet detection channel, the detection mode of the sample to be tested is the low-value platelet detection channel detection. When the sample analyzer includes the reticulum detection channel and the low-value platelet detection channel, the detection mode of the sample to be tested can be either the reticulum detection channel detection or the low-value platelet detection channel detection, or the reticulum detection channel detection and the low-value platelet detection channel detection.

[0113] S2: Detect the sample to be tested based on the detection mode to obtain a scatter plot of the sample to be tested.

[0114] Among them, when the detection mode of the sample to be tested is the reticulocyte detection channel detection, after the sample to be tested is detected, the control component can obtain the first scatter plot of the sample to be tested; when the detection mode of the sample to be tested is the low-value platelet detection channel detection, after the sample to be tested is detected, the control component can obtain the second scatter plot of the sample to be tested. When the detection mode of the sample to be tested is the reticulocyte detection channel detection and / or the low-value platelet detection channel detection, after the sample to be tested is detected, the control component can obtain the first scatter plot and / or the second scatter plot corresponding to the sample to be tested.

[0115] S3: Obtain data information in the scatter plot, and determine the sample type of the sample to be tested based on the data information.

[0116] The control component can obtain data information of at least one example of a red blood cell particle cluster, a low fluorescence intensity reticulocyte particle cluster, and a platelet particle cluster in the scatter plot. Specifically, the control component can obtain the coordinates of the center of gravity of the red blood cell particle cluster, the number of particles of the low fluorescence intensity reticulocyte particle cluster, and the coordinate distribution width based on the first scatter plot, or obtain the number of particles and the coordinate distribution width of the platelet particle cluster, the number of particles of the red blood cell particle cluster, etc. based on the second scatter plot. The control component can determine the sample type of the sample to be tested as one of a normal sample, an α-thalassemia sample, a β-thalassemia sample, an iron deficiency anemia sample, and a hemorrhagic anemia sample based on the acquired data information. The specific judgment process is as described above and will not be repeated here.

[0117] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A sample analyzer, characterized in that: It includes an optical detection component and a control component, wherein the control component is connected to the optical detection component. The optical detection component is used to perform optical detection on the sample to be tested, and the control component is used to receive the detection data obtained by the optical detection component, obtain a scatter plot of the sample to be tested based on the detection data, and obtain data information of at least one particle cluster of red blood cell particles, low fluorescence intensity reticulocyte particles, and platelet particles in the scatter plot, and determine the sample type of the sample to be tested through the data information, and the sample type includes a normal sample, an α-thalassemia sample, a β-thalassemia sample, an iron deficiency anemia sample, and a hemorrhagic anemia sample; The data information includes at least one of the number of particles in the particle cluster in the scatter plot, the coordinates of the particle cluster and the coordinate distribution width.

2. The sample analyzer according to claim 1, characterized in that: The optical detection component includes a reticulum reticularis detection channel, the reticulum reticularis detection channel is used to perform optical detection on the sample to be tested to obtain a first optical signal of the sample to be tested, and the control component is used to obtain a first scatter plot of the sample to be tested based on the first optical signal; Among them, the control component is also used to obtain the center of gravity coordinates of the red blood cell particle cluster in the first scatter plot, and judge the first sample type of the sample to be tested based on the center of gravity coordinates of the red blood cell particle cluster, and the first sample type includes normal samples, iron deficiency or β-thalassemia samples, α-thalassemia samples, and hemorrhagic anemia samples.

3. The sample analyzer according to claim 2, characterized in that: The control component determines the first sample type of the sample to be tested based on the coordinates of the center of gravity of the red blood cell particle clusters in the first scatter plots corresponding to the normal sample, the iron deficiency or β-thalassemia sample, the α-thalassemia sample, and the hemorrhagic anemia sample becoming smaller in sequence.

4. The sample analyzer according to claim 2, characterized in that: After determining the first sample type of the sample to be tested based on the coordinates of the center of gravity of the red blood cell particle group, the control component is further used to: Obtaining the number of particles and coordinate distribution width of the low-fluorescence-intensity reticulocyte particle clusters in the first scatter plot; Determining a second sample type of the sample to be tested based on the particle number and the coordinate distribution width, wherein the second sample type includes an iron deficiency anemia sample and a β-thalassemia sample; Among them, the number of particles and the coordinate distribution width of the low fluorescence intensity reticulocyte particle clusters in the first scatter plot corresponding to the iron deficiency anemia sample are smaller than the number of particles and the coordinate distribution width of the low fluorescence intensity reticulocyte particle clusters in the first scatter plot corresponding to the β-thalassemia sample.

5. The sample analyzer according to claim 1, characterized in that: The optical detection component further includes a low-value platelet detection channel, which is used to perform optical detection on the sample to be tested to obtain a second optical signal of the sample to be tested, and the control component is used to obtain a second scattergram of the sample to be tested based on the second optical signal; Among them, the control component is also used to obtain the number of particles of platelet particle clusters and / or the number of particles of red blood cell particle clusters in the second scatter plot of the sample to be tested, and judge the sample type of the sample to be tested based on the number of particles of the platelet particle clusters and / or the number of particles of the red blood cell particle clusters.

6. The sample analyzer according to claim 5, characterized in that: The control component is also used to determine the third sample type of the sample to be tested based on the number of particles of the platelet particle group, and the third sample type includes a β-thalassemia sample, a hemorrhagic anemia sample, an α-thalassemia sample, and a normal or iron deficiency anemia sample; Among them, the number of particles of the platelet particle clusters in the second scatter plot corresponding to the β-thalassemia sample, the hemorrhagic anemia sample, the α-thalassemia sample, and the normal or iron deficiency anemia sample respectively decreases in sequence; and / or, The control component is also used to determine the fourth sample type of the sample to be tested based on the number of particles of the red blood cell particle group, and the fourth sample type includes a hemorrhagic anemia sample, an α-thalassemia sample, a β-thalassemia sample, and an iron deficiency anemia sample; Among them, the number of particles of the red blood cell particle clusters in the second scatter plots corresponding to the hemorrhagic anemia samples, α-thalassemia samples, β-thalassemia samples, and iron deficiency anemia samples respectively decreases in sequence.

7. The sample analyzer according to claim 6, characterized in that: After determining the third sample type of the sample to be tested based on the number of particles of the platelet particle clusters, the control component is further used for: Obtaining the coordinate distribution width of the platelet particle clusters in the second scatter plot; Determining a fifth sample type of the sample to be tested based on the coordinate distribution width of the platelet particle cluster, wherein the fifth sample type includes a normal sample and an iron deficiency anemia sample; Among them, the coordinate distribution width of the platelet particle clusters in the second scatter plot corresponding to the normal sample is greater than the coordinate distribution width of the platelet particle clusters in the second scatter plot corresponding to the iron deficiency anemia sample.

8. The sample analyzer according to claim 1, characterized in that: The control component is also used to input the obtained scatter plot and data information of at least one particle cluster of red blood cell particles, low fluorescence intensity reticulocyte particles and platelet particles in the scatter plot into the data model, and output the sample type judgment result of the sample to be tested through the data model.

9. A method for determining a sample to be tested, characterized in that: A sample analyzer as claimed in any one of claims 1 to 8, comprising: Acquire detection data of the sample to be tested in the optical detection component, and obtain a scatter plot of the sample to be tested based on the detection data; Obtain data information of at least one of the red blood cell clusters, low fluorescence intensity reticulocyte clusters and platelet clusters in the scatter plot, and determine the sample type of the sample to be tested through the data information, wherein the sample types include normal samples, α-thalassemia samples, β-thalassemia samples, iron deficiency anemia samples and hemorrhagic anemia samples.

10. A method for determining a sample to be tested, characterized in that: Applied to a sample analyzer, the sample analyzer includes at least one of a reticulocyte detection channel and a low-value platelet detection channel; the method includes: Obtaining a detection mode of a sample to be tested; Detecting the sample to be tested based on the detection mode, and obtaining a scatter plot of at least one output of the reticulum detection channel and the low-value platelet detection channel; Acquire data information of at least one of the red blood cell clusters, low fluorescence intensity reticulocyte clusters and platelet clusters in the scatter plot; The sample type of the sample to be tested is determined by the data information, and the sample type includes a normal sample, an α-thalassemia sample, a β-thalassemia sample, an iron deficiency anemia sample, and a hemorrhagic anemia sample.

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