Particle analyzer, particle analysis method, and computer program

The present invention relates to a particle analyzer, a particle analysis method and a computer program, and more particularly to an analyzer, an analysis method and a computer program for classifying and counting particles contained in a sample based on the characteristic parameters of the particles.

Conventionally, particles contained in samples such as blood and urine have been classified and counted. For example, in particle analysis using a flow cytometer, a particle-containing liquid wrapped in a sheath liquid is irradiated with light, characteristic parameters are detected from each particle, and a scattergram is created using the characteristic parameters. To. Then, the particles appearing in the scattergram are classified into a plurality of particle groups, and the particles of each particle group are counted to classify and count the particles (for example, Patent Document 1). Further, the particle analyzer described in Patent Document 1 determines a fractionation abnormality of particles appearing in a scattergram when measuring a blood sample containing nucleated red blood cells, and displays the determination result on a display unit. Prevents erroneous analysis.

By the way, in Patent Document 1, in a blood sample containing nucleated red blood cells, nucleated red blood cells appear in the distribution region of lymphocytes on the scattergram that classifies leukocytes into four and the region below the distribution region, and the neutrophils. It is stated that it does not appear in the distribution area.

<patcit num="1"><text>Japanese Unexamined Patent Publication No. 2003-106984</text></patcit>

<p> However, in blood samples of patients with special diseases, nucleated red blood cells may appear in the distribution region of neutrophils or erythrocyte ghosts (erythrocyte particles after hemolysis) on the scattergram. Therefore, the particle analyzer described in Patent Document 1 accurately corrects particle fractionation abnormalities when measuring a special blood sample in which nucleated red blood cells appear in the neutrophil distribution region or the erythrocyte ghost distribution region. Cannot be determined.</p><p> The present invention has been made in view of the above circumstances, and provides a particle analyzer, a particle analysis method, and a computer program capable of classifying and counting particles in a sample with higher accuracy.</p>

<p> The particle analyzer according to the first aspect of the present invention is a feature information acquisition means for measuring a measurement sample obtained from a sample containing particles and acquiring a plurality of feature information of the particles contained in the sample, and the feature information acquisition means. With the particle number acquisition means for acquiring the number of particles of a specific type of particles contained in the sample and the number of each particle of a plurality of types of particles different from the specific type based on the plurality of feature information acquired by Select the target particle for which the number of particles is to be corrected from the plurality of types of particles whose number of particles has been acquired by the number of particles acquisition means based on the plurality of feature information acquired by the feature information acquisition means. It is characterized by comprising means and a correction means for correcting the number of particles of the target particle selected by the selection means by using the number of particles of the specific type of particles acquired by the particle number acquisition means. To do.</p><p> The particle analyzer according to the second aspect of the present invention is a feature information acquisition means for measuring a measurement sample obtained from a sample containing particles and acquiring a plurality of feature information of the particles contained in the sample, and the feature information acquisition means. A particle number acquisition means for acquiring the number of particles of a specific type of particles and the number of each particle of a plurality of types of particles different from the specific type based on the plurality of feature information acquired by the above, and the number of particles. A determination means for determining the degree of correction for correcting each particle number of the plurality of types of particles acquired by the acquisition means, a particle number of the specific type of particles acquired by the particle number acquisition means, and the said. It is characterized by comprising a correction means for correcting each particle number of the plurality of types of particles acquired by the particle number acquisition means based on the degree of correction determined by the determination means.</p><p> The particle analysis method according to the third aspect of the present invention includes (a) a step of measuring a measurement sample obtained from a sample containing particles and acquiring a plurality of characteristic information of the particles contained in the sample, and (b) a step ( Based on the plurality of feature information acquired in a), the number of particles of a specific type of particles contained in the sample and the number of each particle of a plurality of types of particles different from the specific type are obtained. (c) Based on the plurality of feature information acquired in step (a), a step of selecting a target particle whose number of particles is to be corrected from among the plurality of types of particles whose number of particles has been acquired in step (b). And (d) a step of correcting the number of particles of the target particle selected in step (c) by using the number of particles of the specific type of particles acquired in step (b). And.</p><p> The computer program according to the fourth aspect of the present invention measures a measurement sample obtained from a sample containing particles, and uses the measuring device as a computer connected to a measuring device that acquires a plurality of characteristic information of the particles contained in the sample. With the particle number acquisition means for acquiring the number of particles of a specific type of particles contained in the sample and the number of each particle of a plurality of types of particles different from the specific type based on the plurality of feature information acquired by Select the target particle for which the number of particles is to be corrected from the plurality of types of particles whose number of particles has been acquired by the number of particles acquisition means based on the plurality of feature information acquired by the feature information acquisition means. It is characterized in that it functions as a correction means for correcting the number of particles of the target particle selected by the selection means by using the means and the number of particles of the specific type of particles acquired by the particle number acquisition means. ..</p>

<p> According to the present invention, it is possible to provide a particle analyzer, a particle analysis method, and a computer program capable of classifying and counting particles in a sample with higher accuracy.</p>

Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.

FIG. 1 shows the blood analyzer 1. This analyzer 1 is configured as a multi-item automatic blood cell analyzer for performing a blood test, measures a blood sample contained in a sample container (collecting blood vessel), and analyzes the measurement result.

The analyzer 1 includes a measuring unit 2 having a function of measuring blood as a sample, and a data processing unit 3 that processes the measurement result output from the measuring unit 2 and obtains the analysis result. There is.

Although the measuring unit 2 and the data processing unit 3 are configured as separate devices in FIG. 1, they may be configured as an integrated device.

FIG. 2 shows a block diagram of the measuring unit 2 of the analyzer 1. As shown in FIG. 2, the measurement unit 2 includes a blood cell detection unit 4, an analog processing unit 5 for the output of the detection unit 4, a microcomputer unit 6, a display / operation unit 7, and a device mechanism unit 8 for blood measurement. I have.

The detection unit 4 includes a white blood cell detection unit that detects white blood cells. The leukocyte detection unit is also used to detect nucleated red blood cells. In addition to the white blood cell detection unit, the detection unit 4 also includes an RBC / PLT detection unit that measures the number of red blood cells and platelets, an HGB detection unit that measures the amount of hemoglobin in blood, and an IMI detection unit that detects juvenile spheres. ing.

The leukocyte detection unit is configured as an optical detection unit, and specifically, is configured as a detection unit by a flow cytometry method.

Here, cytometry is the measurement of the physical and chemical properties of cells and other biological particles, and flow cytometry is the passage of these particles through a narrow stream. It refers to the method of making measurements.

FIG. 3 shows the optical system of the leukocyte detection unit. In the figure, the beam emitted from the laser diode 401 irradiates blood cells passing through the sheath flow cell 403 via the collimating lens 402.

In this leukocyte detection unit, the intensity of forward scattered light emitted from blood cells in the sheath flow cell irradiated with light, the intensity of laterally scattered light, and the intensity of lateral fluorescence are detected as characteristic parameters of blood cells.

Here, light scattering is a phenomenon that occurs when particles such as blood cells exist as obstacles in the traveling direction of light and the light changes the traveling direction. By detecting this scattered light, it is possible to obtain characteristic information of the particles regarding the size and components of the particles. The forward scattered light is scattered light emitted from particles in a direction substantially the same as the traveling direction of the irradiated light. From the forward scattered light, characteristic information regarding the size of particles (blood cells) can be obtained. Further, the laterally scattered light is scattered light emitted from the particles in a direction substantially perpendicular to the traveling direction of the irradiated light. From the laterally scattered light, characteristic information about the inside of the particle can be obtained. When blood cell particles are irradiated with laser light, the intensity of laterally scattered light depends on the internal complexity of the cell (nucleus shape, size, density and amount of granules). Therefore, by utilizing this characteristic of the lateral scattered light intensity, the number of blood cells can be measured after classifying (discriminating) the blood cells. In the present embodiment, the configuration in which the forward scattered light and the side scattered light are used as the scattered light has been described, but the present invention is not limited to this, and the scattered light signal showing the characteristics of the particles necessary for the analysis is used. If it can be obtained, scattered light at any angle with respect to the optical axis of the light transmitted from the light source through the sheath flow cell may be used.

Further, when a fluorescent substance such as a stained blood cell is irradiated with light, light having a wavelength longer than the wavelength of the irradiated light is emitted. The fluorescence intensity becomes stronger if it is well stained, and by measuring this fluorescence intensity, characteristic information regarding the degree of staining of blood cells can be obtained. Therefore, leukocyte classification and other measurements can be made based on the difference in (lateral) fluorescence intensity.

As shown in FIG. 3, the forward scattered light emitted from blood cells (white blood cells and nucleated red blood cells) passing through the sheath flow cell 403 is passed through a condenser lens 404 and a pinhole portion 405 to a photodiode (forward scattered light receiving portion). Received by 406.

The laterally scattered light is received by the photomultiplier (side scattered light receiving unit) 411 via the condenser lens 407, the dichroic mirror 408, the optical filter 409, and the pinhole unit 410.

The lateral fluorescence is received by the photomultiplier (lateral fluorescence receiving unit) 412 via the condenser lens 407 and the dichroic mirror 408.

The received light signals output from the light receiving units 406,411,412 are subjected to analog processing such as amplification and waveform processing by the analog processing unit 5 composed of amplifiers 51, 52, 53, etc., and are given to the microcomputer unit 6.

The microcomputer unit 6 includes an A / D conversion unit 61 that converts a light receiving signal given from the analog processing unit 5 into a digital signal. The output of the A / D conversion unit 61 is given to the calculation unit 62 of the microcomputer unit 6, and the calculation unit 62 performs an operation to perform a predetermined process on the received signal.

Further, the microcomputer unit 6 creates a distribution map including a control processor and a memory for operating the control processor, and a memory for operating the distribution map creating processor and the distribution map creating processor. It has a part 64.

The control unit 63 controls the device mechanism unit 8 including a sampler (not shown) that automatically supplies a blood collection tube, a fluid system for preparing and measuring a sample, and other controls.

The distribution map creation unit 64 creates a two-dimensional scattergram (unclassified) based on the output of the detection unit 4. The distribution map creation unit 64 is connected to the data processing unit 3 via the external interface unit 65, and can transmit the measurement result of the created scattergram or the like to the data processing unit 3.

Further, the microprocessor unit 6 includes an interface unit 66 interposed between the display / operation unit 7 and an interface unit 67 interposed between the device mechanism unit 8.

Further, the calculation unit 62, the control unit 63, and the interface units 66 and 67 are connected via the bus 68, and the control unit 63 and the distribution map creation unit 64 are connected via the bus 69.

The measurement unit 2 of the analyzer 1 of the present embodiment has a first measurement (NRBC measurement) as a measurement of nucleated erythrocytes and a second measurement (DIFF measurement) as a measurement of leukocytes for the same blood sample. A third measurement (WBC / BASO measurement) can be performed as a measurement of leukocytes. The first to third measurements are started by the user pressing a start button (not shown) provided on the device mechanism unit 8. Here, leukocytes are roughly classified into lymphocytes, monocytes, and granulocytes. Furthermore, granulocytes are divided into neutrophils, basophils, and eosinophils depending on the stainability of the granules.

First, the first measurement (NRBC measurement) will be described.

FIG. 4 is a diagram showing the flow of NRBC measurement, DIFF measurement, and WBC / BASO measurement by the analyzer 1. At the time of NRBC measurement, a first measurement sample is prepared by mixing a blood sample with a first reagent for the first measurement (NRBC measurement), and the first measurement sample is measured by a leukocyte detection unit.

In FIG. 4, the blood sample in the blood collection tube 11 is sucked into the sampling valve 12 from a suction pipette (not shown). The aspirated blood sample is distributed to three aliquots by the sampling valve 12. The first aliquot is diluted with a predetermined amount of hemolytic agent (Stomatolyzer NR hemolytic agent, manufactured by Sysmex Corporation) as the first reagent in the sampling valve 12, and is carried to the reaction chamber 13 as a diluted sample. A predetermined amount of a staining solution (Stomatolyzer NR staining solution, manufactured by Sysmex Corporation) as another first reagent is further supplied to the reaction chamber 13, and the diluted sample is further diluted. In this state, the diluted sample is reacted in the reaction chamber 13 for a predetermined time to obtain a first measurement sample in which the erythrocytes in the blood sample are hemolyzed and the leukocytes and nucleated red blood cells are stained.

The first measurement sample is sent to the leukocyte detection unit together with the sheath liquid (Cellpack (II), manufactured by Sysmex Corporation) by a quantitative syringe (not shown), and is measured by the flow cytometry method in the leukocyte detection unit.

In the case of the first measurement, the distribution map creation unit 64 uses the forward scattered light and the lateral fluorescence signals among the received signals output from the leukocyte detection unit as characteristic parameters, and the two-dimensional scattergram (particle distribution) shown in FIG. Figure) is generated as the first measurement result.

This scattergram (hereinafter referred to as NRBC scattergram) is drawn with the lateral fluorescence intensity on the X-axis and the forward scattered light intensity on the Y-axis, and when the blood sample contains nucleated red blood cells. "Red blood cell ghost particle population", "nucleated red blood cell particle population", and "white blood cell particle population" appear in. These particle populations are recognized by processing the NRBC scattergram by the data processor 3. The data processing unit 3 also performs the first classification, classification of nucleated red blood cells (NRBC classification) and counting of nucleated red blood cells.

Next, the second measurement (DIFF measurement) will be described with reference to FIG.

At the time of DIFF measurement, a second measurement sample is prepared by mixing a blood sample with a second reagent for the second measurement (DIFF measurement), and the second measurement sample is measured by a leukocyte detection unit.

In FIG. 4, of the above three aliquots, the second aliquot is diluted with a predetermined amount of hemolytic agent (Stomatolyzer 4DL, manufactured by Sysmex Corporation) as a second reagent in the sampling valve 12, and the diluted sample. Is transported to the reaction chamber 14. A predetermined amount of a staining solution (Stomato Riser 4DS, manufactured by Sysmex Corporation) as another second reagent is further supplied to the reaction chamber 14, and the diluted sample is further diluted. In this state, the diluted sample is reacted in the reaction chamber 14 for a predetermined time to obtain a second measurement sample in which the red blood cells in the blood sample are hemolyzed and the leukocytes are stained.

The second measurement sample is sent to the leukocyte detection unit together with the sheath liquid (Cellpack (II), manufactured by Sysmex Corporation) by a quantitative syringe (not shown), and is measured by the flow cytometry method in the leukocyte detection unit.

In the case of the second measurement, the distribution map creation unit 64 uses the laterally scattered light and the lateral fluorescence signals among the received signals output from the leukocyte detection unit as characteristic parameters, and the two-dimensional scattergram (particles) shown in FIG. Distribution map) is generated as the second measurement result.

This scattergram (hereinafter referred to as DIFF scattergram) is drawn with the lateral scattered light intensity on the X-axis and the lateral fluorescence intensity on the Y-axis, and is usually drawn as "red blood cell ghost particle population" or "lymph". "Spherical particle population", "monocyte particle population", "neutrophil + basophil particle population" and "eosinophil particle population" appear. These particle populations are recognized by processing the DIFF scattergram by the data processing unit 3. In addition, the data processing unit 3 also performs the second classification, classification of white blood cells (DIFF classification), and counting of each classified white blood cell.

The hemolytic agent (Stomatolyzer 4DL, manufactured by Sysmex Corporation) as the second reagent has a relatively low hemolytic ability. This is because the hemolytic agent hemolyzes and contracts not only red blood cells but also white blood cells, and if the white blood cells are too hemolyzed, it becomes difficult to classify the white blood cells. Hemolyzes the white blood cells and suppresses the contraction of white blood cells.

Next, the third measurement (WBC / BASO measurement) will be described with reference to FIG. At the time of WBC / BASO measurement, a third measurement sample is prepared by mixing a blood sample with a third reagent for the third measurement (WBC / BASO measurement), and the third measurement sample is measured by a white blood cell detection unit.

In FIG. 4, of the above three aliquots, the third aliquot is diluted with a predetermined amount of hemolytic agent (Stomatolyzer FB (II), manufactured by Sysmex Corporation) as a third reagent in the sampling valve 12. , It is transported to the reaction chamber 15 as a diluted sample. In this state, the diluted sample is reacted in the reaction chamber 15 for a predetermined time to obtain a third measurement sample in which the red blood cells in the blood sample are hemolyzed.

The third measurement sample is sent to the leukocyte detection unit together with the sheath liquid (Cellpack (II), manufactured by Sysmex Corporation) by a quantitative syringe (not shown), and is measured by the flow cytometry method in the leukocyte detection unit.

In the case of the third measurement, the distribution map creation unit 64 uses the signals of the laterally scattered light and the forward scattered light among the received signals output from the white blood cell detection unit as characteristic parameters, and the two-dimensional scattergram (particles) shown in FIG. (Distribution map) is created as the third measurement result.

This scattergram (hereinafter referred to as WBC / BASO scattergram) is drawn with the lateral scattered light intensity on the X-axis and the forward scattered light intensity on the Y-axis. "Basophil particle population" and "particle population of other white blood cells (lymphocytes, monospheres, neutrophils, eosinophils)" appear. These particle populations are recognized by processing the WBC / BASO scattergram by the data processor 3. In addition, the data processing unit 3 also performs the classification of white blood cells (WBC / BASO classification), which is the third classification, and the counting of each classified white blood cell.

The hemolytic agent as the third reagent (Stomatolyzer FB (II) 4DL, manufactured by Sysmex Corporation) has a relatively higher hemolytic ability than the hemolytic agent as the second reagent (Stomatolyzer 4DL). .. In the third measurement (WBC / BASO), white blood cells are not classified in detail, so even if the white blood cells are contracted to some extent by the hemolytic agent, the white blood cells are surely counted by the hemolytic agent, and the white blood cells are counted. The white blood cell count can be detected by suppressing the influence of changes in white blood cells over time after blood collection.

The microcomputer unit 6 of the measurement unit 2 sends the first, second, and third measurement results to the data processing unit 3. As shown in FIG. 8, the data processing unit 3 is composed of a computer mainly composed of a main body 301, a display 302, and an input device 303. The main body 301 is mainly composed of a CPU 301a, a ROM 301b, a RAM 301c, a hard disk 301d, a reading device 301e, an input / output interface 301f, and an image output interface 301h. The device 301e, the input / output interface 301f, and the image output interface 301h are connected by the bus 301i so that data communication is possible.

The CPU301a can execute the computer program stored in the ROM301b and the computer program loaded in the RAM301c. Then, when the CPU 301a executes the application program 305a as described later, the computer functions as the data processing unit 3.

The ROM301b is composed of a mask ROM, a PROM, an EPROM, an EEPROM, etc., and records a computer program executed on the CPU301a, data used for the program, and the like.

RAM301c is composed of SRAM, DRAM, or the like. RAM301c is used to read computer programs recorded on ROM301b and hard disk 301d. It is also used as a work area for the CPU 301a when executing these computer programs.

On the hard disk 301d, various computer programs such as an operating system and an application program to be executed by the CPU 301a and data used for executing the computer programs are installed. The application program 305a, which will be described later, is also installed on this hard disk 301d.

The reading device 301e is composed of a flexible disk drive, a CD-ROM drive, a DVD-ROM drive, or the like, and can read a computer program or data recorded on the portable recording medium 305. Further, the portable recording medium 305 stores an application program 305a for realizing a predetermined function in the computer, and the computer as the data processing unit 3 reads the application program 305a from the portable recording medium 305. The application program 305a can be installed on the hard disk 301d.

The application program 305a is provided not only by the portable recording medium 305, but also by an external device that is communicably connected to the data processing unit 3 by a telecommunication line (whether wired or wireless). It can also be provided through a communication line. For example, the application program 305a may be stored in the hard disk of a server computer on the Internet, and the data processing unit 3 may access the server computer to download the computer program and install it on the hard disk 301d. It is possible.

Further, the hard disk 301d is installed with an operating system that provides a graphical user interface environment such as Windows (registered trademark) manufactured and sold by Microsoft Corporation in the United States. In the following description, it is assumed that the application program 305a according to the present embodiment operates on the operating system.

The input / output interface 301f is composed of, for example, a serial interface such as USB, IEEE1394, RS-232C, a parallel interface such as SCSI, IDE, IEEE1284, and an analog interface composed of a D / A converter, an A / D converter, and the like. ing. An input device 303 including a keyboard and a mouse is connected to the input / output interface 301f, and a user can input data to the data processing unit 3 by using the input device 303.

The image output interface 301h is connected to a display 302 composed of an LCD, a CRT, or the like, and outputs a video signal corresponding to the image data given by the CPU 301a to the display 302. The display 302 displays an image (screen) according to the input video signal.

Next, the flow of processing by the measuring unit 2 and the data processing unit 3 will be described with reference to FIG. FIG. 9 is a flowchart showing an operation flow of the measurement unit 2 and the data processing unit 3 according to the present embodiment. First, when the power of the measuring unit 2 and the data processing unit 3 is turned on by the user's operation, each mechanism unit of the measuring unit 2 is initialized, and the computer program and the like stored in the data processing unit 3 are initialized. (Steps S2-1 and S3-1).

Subsequently, in the measuring unit 2, after the sample number is input and the operating conditions are set by the user's operation, the measuring unit 2 determines whether or not the start button (not shown) is pressed (step S2-). 2).

When it is determined that the start button has been pressed, the measuring unit 2 prepares the first to third measurement samples (step S2-3). The preparation of the first to third measurement samples in step S2-4 is performed in parallel.

Subsequently, the first measurement (NRBC measurement) (step S2-4), the second measurement (4DIFF measurement) (step S2-5), and the third measurement (WBC / BASO) are performed by one leukocyte detection unit of the measurement unit 2. Measurement) (step S2-6) is performed in sequence. In addition, although the configuration in which the first to third measurements are sequentially performed by one leukocyte detection unit is described here, the present invention is not limited to this, and the detection unit that performs the first measurement and the detection that performs the second measurement are described. A unit and a detection unit for performing the third measurement may be provided, respectively, and the first to third measurements may be performed in parallel.

Next, the measurement unit 2 transmits the measurement results (first to third measurement results) acquired in the first to third measurements to the data processing unit 3 (step S2-7). The data processing unit 3 determines whether or not the first to third measurement results have been received from the measurement unit 2 (step S3-2), and if it determines that the measurement results have been received, executes the classification / counting process described below. ..

[Classification / counting processing]

The CPU 301a of the data processing unit 3 executes the classification / counting process of the particles contained in the measurement sample based on the first to third measurement results (step S3-3). Specifically, as shown in FIG. 10, the CPU301a executes the first classification / counting process for classifying / counting nucleated red blood cells with respect to the NRBC measurement (first measurement) result (step S31). In addition, CPU301a executes the second and third classification / counting processes for classifying / counting leukocytes with respect to the DIFF measurement (second measurement) result and the WBC / BASO measurement (third measurement) result (step). S32, S33). Hereinafter, the first classification / counting process will be described.

(First classification / counting process) In the first classification / counting process, the CPU 301a of the data processing unit 3 first executes the first classification process (step S31A). In the first classification process, nucleated red blood cell particle populations (clusters), leukocyte clusters, and erythrocyte ghost clusters are classified on the NRBC scattergram. In the first classification process of the present embodiment, the degree of attribution of each particle to each cluster can be obtained from the distance between each particle plotted on the scattergram and the position of the center of gravity of each cluster. Then, each particle is assigned to each cluster according to the degree of these attributions. This particle classification method is described in detail in Japanese Patent Application Laid-Open No. 5-149863. This results in a boundary on the NRBC scattergram to distinguish nucleated red blood cell clusters from other regions, a border to distinguish white blood cell clusters from other regions, and erythrocytes, as shown in FIG. Boundaries are created to distinguish ghost clusters from other areas. Then, the particles existing within these boundaries are recognized as particles belonging to the cluster.

Next, the CPU 301a of the data processing unit 3 executes the first counting process (step S31B). In the first counting process, the particles of the cluster of nucleated red blood cells classified in the first classification process are counted.

(Second classification / counting process) In the second classification / counting process, the CPU 301a of the data processing unit 3 first executes the second classification process (step S32A). In the second classification process, lymphocyte clusters, monocyte clusters, neutrophil + basophil clusters, eosinophil clusters, and erythrocyte ghost clusters are classified on the DIFF scattergram. Ru. Since the algorithm of the second classification process in step S32B is the same as the first classification process in step S31A described above, the description thereof will be omitted. As a result, as shown in FIG. 6, the boundary for distinguishing lymphocyte clusters from other regions, the boundary for distinguishing monocyte clusters from other regions, and neutrophils on the DIFF scattergram. Boundaries to distinguish clusters of spheres + basophils from other regions, boundaries to distinguish clusters of eosinophils from other regions, and clusters of erythrocyte ghosts from other regions Boundary is generated. Then, the particles existing within these boundaries are recognized as particles belonging to the cluster.

Next, the CPU 301a of the data processing unit 3 executes the second counting process (step S32B). In the second counting process, particles of four clusters of white blood cells and particles of clusters of erythrocyte ghosts classified in the second classification process are counted. As a result, the number of blood cells of lymphocytes, monocytes, neutrophils + basophils and eosinophils and the number of erythrocyte ghost particles are obtained.

(Third classification / counting process) In the third classification / counting process, the CPU 301a of the data processing unit 3 first executes the third classification process (step S33A). In the third classification process, basophil clusters, non-basophil leukocyte clusters, and erythrocyte ghost clusters are classified on the WBC / BASO scattergram. Since the algorithm of the third classification process in step S33A is the same as the first classification process in step S31A described above, the description thereof will be omitted. As a result, as shown in FIG. 7, on the WBC / BASO scattergram, the boundary for distinguishing the cluster of basophils from other regions, and the cluster of leukocytes other than basophils and other regions are distinguished. Boundaries are created to distinguish between clusters of erythrocyte ghosts and other regions. Then, the particles existing within these boundaries are recognized as particles belonging to the cluster.

Next, the CPU 301a of the data processing unit 3 executes the third counting process (step S33B). In the third counting process, particles of two clusters of white blood cells classified in the third classification process are counted. As a result, the blood cell counts of basophils and other white blood cells are obtained. Further, in the present embodiment, the total number of particles contained in the blood sample is calculated by calculating the sum of the number of particles of the clusters of basophils and the clusters of leukocytes other than basophils classified on the WBC / BASO scattergram. Obtain the white blood cell count.

(5 classifications of white blood cells) CPU301a of the data processing unit 3 classifies and counts white blood cells into 4 by the 2nd classification and counting process, and 2 classifications and counts by the 3rd classification and counting process. Based on the above, the white blood cells contained in the blood sample are classified into 5 (step S34). Specifically, CPU301a calculates the "number of basophil blood cells" obtained by the third classification / counting process from the "number of neutrophil + basophil blood cells" obtained by the second classification / counting process. Subtract to obtain the blood cell count of neutrophils and the blood cell count of basophils, respectively. As a result, white blood cells are classified into 5 categories (lymphocytes, monocytes, neutrophils, basophils, and eosinophils), and the blood cell count of each category is obtained.

[Determination process]

Next, the CPU 301a of the data processing unit 3 determines whether or not the measured blood sample contains nucleated red blood cells based on the counting result of the first classification / counting process (step S3-4).

In the analyzer 1 according to the present embodiment, when a blood sample containing nucleated red blood cells is measured, nucleated red blood cells appear in the distribution region of leukocytes other than basophils on the WBC / BASO scattergram. In this embodiment, the total number of white blood cells is the sum of the number of particles of the clusters of basophils and the clusters of white blood cells other than basophils classified on the WBC / BASO scattergram, so that the nucleated red blood cells are blood samples. If it is included in, the total white blood cell count obtained by the third classification / counting process includes an error due to nucleated red blood cells.

Therefore, when the CPU301a determines in this step that the blood sample contains nucleated red blood cells, it is necessary to correct the total white blood cell count obtained by the third classification / counting process.

In addition, when a blood sample containing nucleated red blood cells is measured, an error due to nucleated red blood cells may be included in the results of the five classifications of leukocytes obtained as described above. For example, as shown in FIG. 11, in the case of a blood sample in which nucleated red blood cells appear in the lower left of the distribution region of lymphocytes on the DIFF scattergram, as a result of the above-mentioned second classification process, nucleated red blood cells Is classified as lymphocyte cluster C1. In addition, as shown in FIG. 12, in the case of a blood sample in which nucleated red blood cells appear in the lower left of the distribution region of neutrophils on the DIFF scattergram, the nucleated red blood cells are cluster C2 of neutrophils. Classified as. Also, although not shown, nucleated red blood cells may be classified as clusters of erythrocyte ghosts.

Therefore, if CPU301a determines in this step that the blood sample contains nucleated red blood cells, it corrects the number of lymphocytes or neutrophils obtained by the 5 classifications of white blood cells (see Fig. 10). It may be necessary to do so.

Therefore, when the CPU 301a of the data processing unit 3 determines in this step that the blood sample contains nucleated red blood cells, it executes the selection process in step S3-5 described later. If the CPU 301a of the data processing unit 3 determines in this step that the blood sample does not contain nucleated red blood cells, the classification results obtained by the five classifications of leukocytes and the third classification / counting process are used. The acquired total white blood cell count is output (displayed) on the display 302 (step S3-7). In this case, the classification results and the total white blood cell count obtained by the five white blood cell classifications are displayed without correction.

[Selection process]

FIG. 13 shows a procedure of selection processing by the CPU 301a of the data processing unit 3. In this selection process, CPU301a selects the cluster to be corrected by subtracting the number of nucleated red blood cells (NRBC correction) from the cluster of lymphocytes and the cluster of neutrophils on the DIFF scattergram (step S3-). Five). Hereinafter, this selection process will be described in detail.

In this embodiment, as shown in FIG. 14, a specific region (hatched portion) on the DIFF scattergram is preset as a nucleated red blood cell (NRBC) detection area. This NRBC detection area is an area where many nucleated red blood cells appear on the DIFF scattergram when a blood sample containing nucleated red blood cells is measured. Therefore, it can be considered that the cluster of particles appearing in this NRBC detection area contains nucleated red blood cells. Therefore, in the selection process of the present embodiment, in the CPU301a, first, particles of any of lymphocyte clusters, neutrophil clusters, and erythrocyte ghost clusters appeared most frequently in the NRBC detection area. Is determined (step S51). Hereinafter, the cluster in which the most particles appear in the NRBC detection area is referred to as a correction candidate cluster.

In step S51, if the correction candidate cluster is a lymphocyte cluster, CPU301a selects the lymphocyte cluster as the target cluster for NRBC correction (step S52) and returns the process.

On the other hand, in step S51, when the correction candidate cluster is a neutrophil cluster, the CPU301a determines that the particle number NAREA of the neutrophil cluster in the NRBC detection area exceeds a predetermined number (T1) and It is determined whether the nucleated red blood cell count NR obtained in the first classification / counting process exceeds the predetermined number (T2), that is, whether both the following equations (1) and (2) are satisfied. (Step S53). NAREA T1 (1) NR T2 (2)

When the CPU301a determines that both the above equations (1) and (2) are satisfied, it selects the cluster of neutrophils as the target cluster for NRBC correction (step S54) and returns the process. When the CPU301a determines that at least one of the above equations (1) and (2) does not hold, it selects a cluster of lymphocytes as the cluster to be corrected for NRBC (step S52) and returns the process.

Further, in step S51, when the correction candidate cluster is a red blood cell ghost cluster, the CPU301a has the number of particles GAREA of the red blood cell ghost cluster in the NRBC detection area exceeding a predetermined number (T3), and the first It is determined whether the nucleated red blood cell count NR obtained by the classification / counting process exceeds the predetermined number (T2), that is, whether both the following equations (3) and (4) are satisfied (step). S55). GAREA T3 (3) NR T2 (4)

When the CPU301a determines that both the above equations (3) and (4) are satisfied, it does not select any cluster as the cluster to be corrected by NRBC and returns the process. When the CPU301a determines that at least one of the above equations (3) and (4) does not hold, it selects a cluster of lymphocytes as the cluster to be corrected for NRBC (step S52) and returns the process.

[Correction processing]

Next, the CPU 301a of the data processing unit 3 corrects the total white blood cell count acquired by the third classification / counting process and the particle number of the cluster selected as the correction target by the above selection process (NRBC correction) (step S3-). 6). FIG. 15 is a flowchart showing the procedure of the correction process. The correction process will be described in detail with reference to FIG. First, CPU301a subtracts the nucleated red blood cell count NR obtained by the first classification / counting process (NRBC classification / counting process) from the total white blood cell count WALL obtained by the third classification / counting process (step S61). Next, CPU301a determines which cluster is selected as the cluster to be corrected, or whether any cluster is selected as the cluster to be corrected (step S62). In step S62, when the cluster to be corrected is a cluster of lymphocytes, CPU301a is obtained from the number of lymphocytes (total number of particles contained in the cluster of lymphocytes) LALL obtained by 5 classifications of white blood cells (see FIG. 10). , The nucleated red blood cell count NR obtained by the NRBC classification / counting process is subtracted (step S63), and the process is returned. On the other hand, in step S62, when the cluster to be corrected is a cluster of neutrophils, CPU301a is obtained by NRBC classification / counting processing from the neutrophil count NALL obtained by 5 classifications of leukocytes. Subtract the nuclear red blood cell count NR (step S64) and return the process. Further, in step S62, if there is no cluster to be corrected, the CPU 301a returns the process as it is.

[Output processing]

The CPU 301a of the data processing unit 3 outputs (displays) the analysis result obtained as described above to the display 302 (step S3-7). Specifically, in this output process, as shown in FIG. 16, the total leukocyte (WBC) count result, the five types of leukocyte (NEUT, LYMPH, MONO, EO, BASO) count results, and the nucleated red blood cells The (NRBC) count result, WBC / BASO scattergram, DIFF scattergram, and NRBC scattergram are displayed. For example, when the particle number and total white blood cell count of lymphocyte clusters are corrected by the above correction process, the display positions of LYMPH (lymphocyte count) and WBC (total white blood cell count) are displayed as shown in FIG. , &amp; Mark is added to indicate that the correction has been performed. This allows the user to know that the particle number of lymphocyte clusters and the total white blood cell count have been corrected based on the nucleated red blood cell count. When the particle number and the total white blood cell count of the neutrophil cluster are corrected by the above correction process, the &amp; mark is added to the display position of NEUT (neutrophil) and WBC. This allows the user to know that the particle number and total white blood cell count of the neutrophil cluster have been corrected based on the nucleated red blood cell count. If the cluster to be corrected is not selected by the above selection process and only the total white blood cell count is corrected based on the nucleated red blood cell count, the &amp; mark is added only to the WBC display position.

As described above, according to the blood analyzer 1 of the present embodiment, a specific type of particles (nucleated red blood cells) is a plurality of types of particles (neutrophils, lymphocytes) different from the specific type of particles. ), Even if it is included in any of the clusters, the cluster containing this specific type of particles is selected as the correction target, and the number of particles in this correction target cluster is corrected. Can be classified and counted.

In the present embodiment, NRBC correction is performed by subtracting the number of nucleated red blood cells from the number of particles in the cluster in which the most particles appear in the NRBC detection area, but the embodiment of the present invention is not limited to this. Depending on the blood sample, nucleated red blood cells may be contained in multiple clusters on the DIFF scattergram. For example, as shown in the scattergram shown in FIG. 17, cluster C1 classified as lymphocyte contains a part of nucleated red blood cells, and cluster C2 classified as neutrophil contains another part of nucleated red blood cells. It may be included. In such a case, the degree of correction of each cluster C1 and C2 is obtained, and each cluster C1 and C2 are adjusted according to the degree of correction. NRBC correction may be performed on the number of particles of C2. FIG. 18 is a flowchart showing the procedure of the correction processing of the modified example. In this modified example, in the selection process of step S4-5 shown in FIG. 9, CPU301a determines the degree of correction of lymphocyte clusters and the degree of correction of neutrophil clusters, respectively. Specifically, in the CPU301a, among the number of particles appearing in the NRBC detection area as described above, the ratio Lratio of the number of particles contained in the lymphocyte cluster and the ratio Nratio of the number of particles contained in the neutrophil cluster. And are calculated. Then, in the correction process S4-6, the CPU301a first subtracts the nucleated red blood cell count NR acquired in the NRBC classification / counting process from the total white blood cell count WALL acquired in the third classification / counting process (step S611). Next, CPU301a calculates the lymphocyte number correction value Lcom by multiplying the nucleated erythrocyte number NR by the lymphocyte particle number ratio Lratio, and multiplies the nucleated erythrocyte number NR by the neutrophil particle number ratio Nratio. Calculate the neutrophil number correction value Ncom (step S612). Next, CPU301a subtracts the lymphocyte count correction value Lcom from the lymphocyte count LALL obtained by the 5 classifications of white blood cells (see Fig. 10), and neutrophils from the neutrophil count NALL obtained by the 5 classifications of white blood cells. Subtract the lymphocyte correction value Ncom (step S613) and return the process. By the above processing, even when nucleated red blood cells are divided into a plurality of clusters on the DIFF scattergram and contained, the number of particles in each cluster is corrected according to the degree of correction determined for each cluster. be able to.

Further, in the present embodiment, the scattergram is created by the distribution map creation unit 64 of the measurement unit 2, but the scattergram may be created by the CPU 301a of the data processing unit 3.

Further, in the present embodiment, the data processing unit 3 performs particle classification / counting processing, determination processing of the presence / absence of nucleated red blood cells, selection processing of the cluster to be corrected, and correction of the number of particles of the selected cluster to be corrected. Although the processes are being executed, these processes may be executed by the measuring unit 2, and the obtained analysis result may be received by the data processing unit 3 and output to the display 302.

Further, in the present embodiment, a scattergram is created using the characteristic parameters of blood cells detected by the leukocyte detection unit, and nucleated red blood cells are classified / counted and leukocytes are classified / counted based on the created scattergram. However, the nucleated red blood cell classification / counting process and the white blood cell classification / counting process may be performed without creating a scattergram.

Further, in the present embodiment, when nucleated red blood cells are contained in the cluster of erythrocytes, the correction of subtracting the number of nucleated red blood cells from the number of particles in the cluster of erythrocytes is not performed. Corrections may be made to subtract the number of nucleated red blood cells from the number of particles in the ghost cluster.

Further, in the present embodiment, when a blood sample containing nucleated erythrocytes is measured, a cluster containing nucleated erythrocytes is selected from clusters of lymphocytes or neutrophils on the scattergram, and the selected clusters are selected. Although correction is performed by subtracting the number of nucleated red blood cells from the number of particles, the present invention is not limited to this, and for example, a blood sample containing neutrophils that affect the classification / counting result of white blood cells is used. When measured, a cluster containing neutrophils is selected from multiple clusters of white blood cells on the scattergram, and the number of neutrophils is subtracted from the number of particles in the selected cluster. The present invention can be applied to the measurement of a sample in which it is desired to select a particle to be corrected from a plurality of particles and correct the number of particles of the particle by using the number of particles of another type of particle. it can.

Further, in the present embodiment, the fluorescence intensity emitted from the blood cell is detected and used as one of the characteristic parameters of the blood cell. However, the intensity of the absorbed light is detected instead of the fluorescence intensity, and this is used as the characteristic parameter of the blood cell. You may use it.

Further, in the present embodiment, the measurement sample is irradiated with light to acquire the characteristic parameters of blood cells, but the present invention is not limited to this, and for example, direct current is applied to the micropores through which the sample flows. A signal based on the change in electrical resistance generated when an electric current is passed and the blood cell passes through the micropore may be acquired as a characteristic parameter of the blood cell. Further, a high-frequency current may be passed through the micropores, and a signal based on a change in the dielectric constant of the blood cells generated when the blood cells pass through the micropores may be acquired as a characteristic parameter of the blood cells.

<figref num="1">It is a perspective view which shows the blood analyzer.</figref><figref num="2">It is a functional block diagram of a measuring part.</figref><figref num="3">It is a block diagram of the detection part.</figref><figref num="4">It is a figure which shows the flow of NRBC measurement, DIFF measurement and WBC / BASO measurement.</figref><figref num="5">NRBC scattergram.</figref><figref num="6">It is a 4DIFF scattergram.</figref><figref num="7">WBC / BASO scattergram.</figref><figref num="8">It is a functional block diagram of a data processing part.</figref><figref num="9">It is a flowchart of processing by a measuring part and a data processing part.</figref><figref num="10">It is a flowchart which shows the details of the processing procedure of the classification / counting process.</figref><figref num="11">It is explanatory drawing which shows that nucleated red blood cells were classified as lymphocytes on 4DIFF scattergram.</figref><figref num="12">It is explanatory drawing which shows that nucleated red blood cells were classified as neutrophils on a 4DIFF scattergram.</figref><figref num="13">It is a flowchart of selection processing in a data processing unit.</figref><figref num="14">It is explanatory drawing for selecting the particle group which corrects the number of particles on a scattergram.</figref><figref num="15">It is a flowchart of correction processing in a data processing unit.</figref><figref num="16">This is a display screen that displays the classification result.</figref><figref num="17">It is explanatory drawing which shows that some nucleated red blood cells were classified as lymphocytes and the other part of nucleated red blood cells was classified as neutrophils on a 4DIFF scattergram.</figref><figref num="18">It is a flowchart which shows the correction process of the modification.</figref>

Code description

1 Blood analyzer 2 Measuring unit 3 Data processing unit 301a CPU 301b ROM 301c RAM 301d Hard disk 301e Reading device 301f Input / output interface 301h Image output interface 301i Bus 302 Display 4 Detection unit 6 Microcomputer unit 63 Control unit 64 Distribution map creation unit