Sample analysis device and sample analysis method

Abstract

The present invention relates to a specimen analyzer and a specimen analysis method, which make it possible to screen CML patients in the early stage of onset, which are difficult to implement in a blood cell counting test. A specimen analyzer includes: a measurement unit that acquires optical information of cells by irradiating a plurality of diffracted lights generated by light incident on a diffractive optical element to the cells contained in a specimen; and an analysis unit that analyzes the optical information obtained by the measurement unit by means of an artificial intelligence algorithm, thereby acquiring information on leukemia cells contained in the specimen.

Description

TECHNICAL FIELD

[0001] The present application relates to a specimen analysis device and a specimen analysis method. BACKGROUND

[0002] Chronic myeloid leukemia (CML) is a leukemia caused by abnormality of pluripotent hematopoietic stem cells, and is characterized by Philadelphia chromosome formed by t(9;22)(q34;q11). In CML, BCR::ABL1 tyrosine kinase (TK) encoded by BCR::ABL1 fusion gene on Philadelphia chromosome is always activated and participates in proliferation of leukemia cells, and the development goes through three phases. The three phases are chronic phase (3 to 5 years) in which symptoms are slightly conscious, blast crisis (3 to 6 months) in which blast cells increase and resemble acute leukemia, and accelerated phase (3 to 9 months) in which granulocyte differentiation abnormalities develop, and eventually lead to death.

[0003] A CML diagnosis method is disclosed in Non-Patent Literature 1, which is based on the phenomenon that leukocytosis, especially characteristic neutrophil or basophilic granulocyte increase, is caused by blood cell count examination.

[0004] [Related Art Literature]

[0005] [Non-Patent Literature]

[0006] Non-Patent Literature 1: Jabbour E, Kantarjian H, Chronic myeloid leukemia: updates in diagnosis, therapy, and monitoring in 2020, Am J Hematol. 2020 Jun; 95(6): 691-709. doi: 10.1002 / ajh.25792. Epub 2020 Apr 10. PMID: 32239758 SUMMARY

[0007] [Problems to be Solved by the Invention]

[0008] CML is considered to be a disease whose prognosis has been significantly improved by the appearance of tyrosine kinase inhibitors (TKIs), and the treatment is further improved in terms of effectiveness by early detection. Although an increase in white blood cells in the peripheral blood or an increase in basophilic granulocytes is seen in CML, in actual clinical practice, CML is suspected to be CML based on abnormalities in white blood cell count or basophilic granulocyte count until diagnosis, and therefore it is difficult to screen patients in the early stage of the disease before the number of blood cells becomes abnormal in the conventional blood cell count examination disclosed in Non-Patent Literature 1.

[0009] In view of this problem, an object of the present application is to enable screening of CML patients in the early stage of onset, which is difficult to achieve in a blood cell count examination.

[0010] [Technical means for solving the problem]

[0011] According to a first embodiment of the present application, a specimen analysis device includes: a measurement section that acquires optical information of cells by irradiating a plurality of diffracted lights generated by causing light to be incident to a diffractive optical element to cells contained in a specimen; and an analysis section that analyzes the optical information acquired by the measurement section by an artificial intelligence algorithm, thereby acquiring information of leukemia cells contained in the specimen.

[0012] According to a second embodiment of the present application, a specimen analysis method is to acquire optical information of cells by irradiating a plurality of diffracted lights generated by causing light to be incident to a diffractive optical element to cells contained in a specimen, and analyze the acquired optical information by an artificial intelligence algorithm, thereby acquiring information of leukemia cells contained in the specimen.

[0013] According to a third embodiment of the present application, a method is to acquire optical information of cells by irradiating a plurality of diffracted lights generated by causing light to be incident to a diffractive optical element to cells contained in a specimen, the specimen includes a first specimen collected from a patient with chronic myelogenous leukemia before treatment starts and a second specimen collected from a healthy person, a classification model that classifies leukemia cells is generated based on the optical information acquired from the first specimen and the second specimen, an index related to classification performance of leukemia cells and normal cells by the generated classification model is acquired, and information related to the efficacy of a therapeutic drug for chronic myelogenous leukemia on the patient is output based on the index.

[0014] [Effects of the invention]

[0015] According to the present application, for example, analysis by an artificial intelligence algorithm is performed based on optical information of cells contained in a specimen, thereby enabling acquisition of information of leukemia cells contained in the specimen. Leukemia cells are cancerized cells that are visible in the blood of CML patients, and are genetically positive for Philadelphia chromosome (BCR::ABL gene). Leukemia cells cannot be distinguished from conventional white blood cells in terms of morphology, and cannot be detected by a blood cell count examination or a blood test performed as a screening examination. Therefore, in the past, a patient in which a characteristic manifestation of CML such as leukocytosis or basophilic granulocytosis was found in a blood cell count examination was diagnosed with CML by performing a genetic examination. Leukocytosis or basophilic granulocytosis is caused as a result of abnormal proliferation of leukemia cells in the bone marrow, and therefore CML has often progressed to a certain stage by the time these symptoms occur. According to the present application, information of leukemia cells present in the blood of a CML patient can be acquired, and therefore early detection of CML can be achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a front view schematically showing the structure of the specimen analysis device of the first embodiment.

[0017] Figure 2 is a block diagram showing an example of the functional structure of the GCM measurement unit of the first embodiment.

[0018] Figure 3 is a block diagram showing an example of the functional structure of the sample preparation section of the GCM measurement unit of the first embodiment.

[0019] Figure 4 is a view schematically showing the structure of the optical measurement section of the GCM measurement unit of the first embodiment.

[0020] Figure 5 is a view schematically showing the flow cell and the diffracted illumination light of the GCM measurement unit of the first embodiment.

[0021] Figure 6 is a view schematically showing the distribution pattern of the diffracted light contained in the diffracted illumination light of the first embodiment.

[0022] Figure 7 is a block diagram showing an example of the functional structure of the control unit of the first embodiment.

[0023] Figure 8 is a view showing the AI algorithm before and after training of the first embodiment.

[0024] Figure 9 is a view schematically showing the structure of the cell analysis result screen of the first embodiment.

[0025] Figure 10 is a flowchart showing an example of the measurement-related control processing performed by the control unit of the first embodiment.

[0026] Figure 11 is a view showing the inspection information related to the specimens of the verification experiment of the first embodiment.

[0027] Figure 12 is a view showing the inspection information related to the specimens of the verification experiment of the first embodiment.

[0028] Figure 13 is a view showing the results of the verification experiment of the first embodiment.

[0029] Figure 14 is a view showing the results of the verification experiment of the first embodiment.

[0030] Figure 15is a graph showing results of a verification experiment of the first embodiment.

[0031] Figure 16 is a front view schematically showing a structure of the specimen analysis device of the second embodiment.

[0032] Figure 17 is a block diagram showing an example of a functional structure of the FCM measurement unit of the second embodiment.

[0033] Figure 18 is a block diagram showing an example of a functional structure of the sample conditioning section of the FCM measurement unit of the second embodiment.

[0034] Figure 19 is a view schematically showing a structure of the optical measurement section of the FCM measurement unit of the second embodiment.

[0035] Figure 20 is a view schematically showing a flow cell and straight-ahead illumination light of the FCM measurement unit of the second embodiment.

[0036] Figure 21 is a flowchart showing an example of a control process related to measurement performed by the control unit of the second embodiment.

[0037] Figure 22 is a flowchart showing an example of the FCM measurement process of the second embodiment.

[0038] Figure 23 is a view schematically showing a structure of a cell analysis result screen of the second embodiment.

[0039] Figure 24 is a view schematically showing a structure of a cell analysis result screen when the GCM measurement process is not performed of the second embodiment.

[0040] Figure 25 is a view schematically showing a structure of a cell analysis result screen when the GCM measurement process is performed of the second embodiment.

[0041] Figure 26 is a front view schematically showing a structure of the specimen analysis device of the third embodiment.

[0042] Figure 27 is a block diagram showing an example of a functional structure of the comprehensive measurement unit of the third embodiment.

[0043] Figure 28 is a block diagram showing an example of a functional structure of the sample conditioning section of the comprehensive measurement unit of the third embodiment.

[0044] Figure 29is a view schematically showing a structure of an optical measurement section of the comprehensive measurement unit of the third embodiment.

[0045] Figure 30 is a flowchart showing an example of a control process related to measurement performed by the control unit of the third embodiment.

[0046] Figure 31 is a flowchart showing an example of a control process related to measurement performed by the control unit of the third embodiment.

[0047] Figure 32 is a block diagram showing an example of a functional structure of a sample modulation section of the comprehensive measurement unit of the third embodiment.

[0048] Figure 33 is a graph showing a comparison of statistics of values of a PCR method in CML patient samples in a treatment course of the fourth embodiment.

[0049] Figure 34 is a graph showing an example of a result of a verification experiment of the fourth embodiment.

[0050] Figure 35 is a flowchart showing an example of a control process related to measurement performed by the control unit of the fourth embodiment.

[0051] BRIEF DESCRIPTION OF DRAWINGS

[0052] 1 (1A, 1B, 1C): sample analysis device

[0053] 10: FCM measurement unit

[0054] 20: GCM measurement unit

[0055] 30: control unit

[0056] 40: conveyance unit

[0057] 50: sample rack DETAILED DESCRIPTION

[0058] <First Embodiment>

[0059] The first embodiment is a basic embodiment related to acquisition of information of leukemia cells contained in a sample by irradiating a plurality of diffracted lights generated by causing light to be incident to a diffractive optical element to cells contained in the sample to acquire optical information of the cells, and analyzing the acquired optical information by an artificial intelligence algorithm.

[0060] In the present embodiment, as an example, information of leukemia cells contained in a sample is acquired based on a technique of Ghost Cytometry.

[0061] Figure 1 is a front view schematically showing a structure of a specimen analysis device 1A as an example of the specimen analysis device of the present embodiment.

[0062] The specimen analysis device 1A includes, for example, a ghost flow cytometry measurement unit (hereinafter referred to as "GCM measurement unit") 20, a control unit 30, and a conveyance unit 40.

[0063] The specimen analysis device 1A is, for example, a device that automatically analyzes specimens.

[0064] The specimen can be blood collected from a subject, and a specimen container 51 that houses the specimen can be conveyed while being held at a specimen rack 50.

[0065] An examiner who is an operator of the specimen analysis device 1A, for example, sets the specimen container 51 that houses the specimen at the specimen rack 50 and places the specimen rack 50 at a right end region of the conveyance unit 40.

[0066] The conveyance unit 40 conveys the specimen rack 50 and positions it in front of the GCM measurement unit 20.

[0067] The GCM measurement unit 20 takes out the specimen container 51 from the specimen rack 50 and transfers it into the GCM measurement unit 20, and measures the specimen in the specimen container 51.

[0068] When the measurement of the specimen in the specimen container 51 ends, the GCM measurement unit 20 returns the specimen container 51 to the original position of the specimen rack 50.

[0069] When the required measurement ends for all the specimen containers 51 on one specimen rack 50, the conveyance unit 40 conveys the specimen rack 50 to a left end region of the conveyance unit 40. The examiner takes out the specimen rack 50 conveyed to the left end region.

[0070] The GCM measurement unit 20 is configured to be able to measure the specimen conveyed on the conveyance unit 40. The conveyance unit 40 is configured to be able to automatically supply the specimen rack 50 that houses the specimen to the GCM measurement unit 20. By automatically supplying the specimen to the GCM measurement unit 20 by the conveyance unit 40, the examiner's working hours required to transfer the specimen toward the GCM measurement unit 20 can be reduced.

[0071] The control unit 30 controls, for example, the GCM measurement unit 20 and the conveyance unit 40.

[0072] Further, the control unit 30 analyzes measurement information obtained by the GCM measurement unit 20.

[0073] Figure 2is a block diagram showing an example of a functional configuration of the GCM measurement unit 20.

[0074] The GCM measurement unit 20 includes, for example, a measurement control section 21, a storage section 22, a communication section 23, a reading section 24, a sample preparation section 25, and a measurement section 26.

[0075] The measurement control section 21 includes, for example, a Field Programmable Gate Array (FPGA) or a Central Processing Unit (CPU).

[0076] The storage section 22 includes, for example, a Hard Disk Drive (HDD), a Solid State Drive (SSD), a Random Access Memory (RAM), or a Read Only Memory (ROM).

[0077] The measurement control section 21 performs various processes based on a program stored in the storage section 22, for example, and controls each section of the GCM measurement unit 20.

[0078] The communication section 23 includes, for example, a connection terminal based on the Universal Serial Bus (USB) standard, and communicates with the control unit 30.

[0079] The reading section 24 includes, for example, a bar code reader, and reads a bar code from a bar code label attached to the specimen container 51 to acquire a specimen identification (ID).

[0080] The sample preparation section 25 aspirates a specimen from the specimen container 51, for example, and mixes a reagent with the aspirated specimen to prepare a measurement sample.

[0081] The measurement section 26 includes, for example, an optical measurement section 200, and the optical measurement section 200 includes, for example, a fluid adjustment section 200a.

[0082] The fluid adjustment section 200a includes, for example, a container that accommodates sheath liquid, a syringe that transfers a measurement sample, and an air pressure source (pump) that transfers sheath liquid, and is configured. The fluid adjustment section 200a supplies sheath liquid to a flow cell 201 of the optical measurement section 200 together with a measurement sample prepared in the sample preparation section 25 (see Figure 4 ), and adjusts a flow rate of the measurement sample that flows through the flow cell 201 per unit time. The optical measurement section 200 measures a measurement sample supplied to the flow cell 201.

[0083] The optical measurement unit 200 includes an amplifier or an analog / digital (A / D) conversion unit, performs signal processing on the detection signal acquired by the measurement, and outputs the measurement information after the signal processing to the measurement control unit 21.

[0084] The measurement information is, for example, a Ghost Motion Imaging (GMI) waveform signal based on Ghost Cell Morphometry (GCM). It can also be referred to as Ghost Motion Imaging waveform information (GMI waveform information), and can be an example of optical information of cells acquired by irradiating a plurality of diffracted lights generated by causing light to be incident on a diffractive optical element to cells contained in a subject. Hereinafter, it will be simply referred to as "waveform signal" and will be described.

[0085] The measurement control unit 21 stores the waveform signal output from the measurement unit 26 in the storage unit 22. When the measurement of one subject ends, the measurement control unit 21 transmits the waveform signal stored in the storage unit 22 to the control unit 30 in association with the subject ID read by the reading unit 24.

[0086] Figure 3 is a block diagram showing an example of a functional configuration of a sample modulation unit 25 for modulating a measurement sample.

[0087] The sample modulation unit 25 includes, for example, a stirring unit 25a, a suction pipe 25b, and a reaction chamber C30.

[0088] The stirring unit 25a is configured to be able to grip the subject container 51 and swing the gripped subject container 51 to stir the subject in the subject container 51, for example.

[0089] The suction pipe 25b is, for example, a nozzle with a sharp tip at the lower end and is configured to be able to penetrate the cap portion of the subject container 51 including an elastic material. The suction pipe 25b aspirates the subject from the stirred subject container 51 and dispenses the aspirated subject to the reaction chamber C30.

[0090] In the reaction chamber C30, the subject, a hemolytic agent (an example of a reagent) for lysing red blood cells, and a staining solution (a staining agent: an example of a reagent) containing a fluorescent dye for staining a prescribed portion of a cell are mixed to modulate a measurement sample.

[0091] The hemolytic agent mixed in the reaction chamber C30 is, for example, a WDF hemolytic agent.

[0092] The staining solution mixed in the reaction chamber C30 is, for example, a WDF staining solution.

[0093] The measurement sample modulated in the reaction chamber C30 is measured by the optical measurement unit 200.

[0094] The optical measurement unit 200 acquires a detection signal corresponding to blood cells in the measurement sample, and acquires a waveform signal by signal processing of the acquired detection signal.

[0095] The control section 31 and the arithmetic section 32 of the control unit 30 analyze the waveform signal obtained by measurement of the measurement sample, perform classification of whether it is a CML cell or the like, and acquire the number of each blood cell.

[0096] At this time, the waveform signal includes, for example, time-series data of forward scattered light corresponding to each cell, indicating a change in intensity of forward scattered light received by the light receiving section 225 during a period in which each cell in the measurement sample flowing through the flow cell 201 (refer to FIG. 1) passes through the irradiation range R of the illumination light; Figure 4 、 Figure 5 time-series data of side scattered light corresponding to each cell, indicating a change in intensity of side scattered light received by the light receiving section 233 during a period in which each cell in the measurement sample flowing through the flow cell 201 passes through the irradiation range R of the illumination light, and time-series data of fluorescence corresponding to each cell, indicating a change in intensity of fluorescence received by the light receiving section 243 during a period in which each cell in the measurement sample flowing through the flow cell 201 passes through the irradiation range R of the illumination light.

[0097] As described later, the control section 31 and the arithmetic section 32 of the control unit 30 analyze, for example, the time-series data of forward scattered light and side scattered light by inputting them to the AI algorithm 62 after training.

[0098] In addition, the waveform signal is information reflecting the size, shape, internal structure, or nucleic acid amount of each cell, obtained by irradiating each cell in the measurement sample with light distributed with a plurality of diffracted light generated by the diffracted optical element 215 on which light is incident, and is not limited to the time-series data.

[0099] Furthermore, the hemolytic agent and the staining solution mixed in the reaction chamber C30 are not limited to the reagents.

[0100] Furthermore, the mixing of the staining solution in the reaction chamber C30 can also be omitted. That is, the reagent can not contain a staining agent, and in the reaction chamber C30, the measurement sample can be prepared by mixing the test object with the reagent (hemolytic agent).

[0101] Furthermore, a dilution solution can be mixed instead of the staining solution. That is, in the reaction chamber C30, the measurement sample can be prepared by mixing the test object with the reagent (hemolytic agent, dilution solution).

[0102] In these cases, information on leukemia cells contained in the test object can be acquired without identification (without the need for fluorescent identification), and the fluorescent condensing optical system 205 and the light receiving section 243 described later can be omitted. Figure 4 In these cases, information on leukemia cells contained in the test object can be acquired without identification (without the need for fluorescent identification), and the fluorescent condensing optical system 205 and the light receiving section 243 described later can be omitted.

[0103] Figure 4 Fig. 1 is a diagram schematically showing a structure of an optical measurement unit 200. Figure 4 In the drawing, for convenience, an X-axis, a Y-axis, and a Z-axis orthogonal to each other are attached. The Z-axis direction is a flow direction of a measurement sample in a flow cell 201.

[0104] The optical measurement unit 200 includes, for example, the flow cell 201, a light source 211, an irradiation optical system 202, a front light condensing optical system 203, a side light condensing optical system 204, a fluorescent light condensing optical system 205, a light receiving unit 225, a light receiving unit 233, and a light receiving unit 243.

[0105] The irradiation optical system 202 includes, for example, a collimator lens 212, a cylindrical lens 213, a cylindrical lens 214, a diffractive optical element (DOE) 215, and a condenser lens 216. The irradiation optical system 202 irradiates light from the light source 211 to a flow path 201a of the flow cell 201.

[0106] Hereinafter, light emitted from the light source 211 and irradiated to the flow path 201a will be referred to as "diffracted illumination light". The diffracted illumination light is light in which a plurality of diffracted lights are distributed, which is generated by the diffractive optical element 215. More specifically, the diffracted illumination light is light having a structured illumination pattern (structured light illumination).

[0107] The front light condensing optical system 203 includes, for example, a condenser lens 221, a beam blocker 222, a condenser lens 223, and an optical filter 224. The front light condensing optical system 203 condenses front scattered light generated from a blood cell to the light receiving unit 225, and blocks diffracted illumination light that has passed through the flow cell 201 without being irradiated to the blood cell.

[0108] The side light condensing optical system 204 includes, for example, a condenser lens 231 and an optical filter 232. The side light condensing optical system 204 condenses side scattered light generated from a blood cell to the light receiving unit 233.

[0109] The fluorescent light condensing optical system 205 includes, for example, a condenser lens 241 and an optical filter 242. The fluorescent light condensing optical system 205 condenses fluorescent light generated from a blood cell to the light receiving unit 243.

[0110] The light source 211 is, for example, a semiconductor laser light source. The light source 211 emits light of a prescribed wavelength λ20 in the X-axis direction. The wavelength λ20 is, for example, 405 nm. The fast axis direction and the slow axis direction of the light source 211 are parallel to the Y-axis direction and the Z-axis direction, respectively. The collimator lens 212 converts light emitted from the light source 211 into parallel light.

[0111] The cylindrical lens 213 is a concave cylindrical lens, and the cylindrical lens 214 is a convex cylindrical lens. The cylindrical lens 213 enlarges the width in the Z-axis direction without changing the width in the Y-axis direction of the light emitted from the light source 211, and sets the shape to be substantially a circle to be incident on the cylindrical lens 214. The cylindrical lens 214 converts the light emitted from the light source 211 into parallel light.

[0112] The collimator lens 212 and the cylindrical lenses 213, 214 are configured so that the light emitted from the light source 211 and transmitted through the collimator lens 212 and the cylindrical lenses 213, 214 has a substantially circular shape when viewed in the X-axis direction. Thus, the light incident on the diffractive optical element 215 has a substantially circular shape.

[0113] In addition, the structure of the light source 211, the collimator lens 212, and the cylindrical lenses 213, 214 can be other than the structure in which the light incident on the diffractive optical element 215 has a substantially circular shape. For example, the light source 211, the collimator lens 212, and the cylindrical lenses 213, 214 can each be rotated by 90 degrees with respect to the X-axis direction. In this case, the fast axis direction and the slow axis direction of the light source 211 are parallel to the Z-axis direction and the Y-axis direction, respectively. Also, as the light source 211, a light source that emits light having a substantially circular shape can be used, and the collimator lens 212 and the cylindrical lenses 213, 214 can be omitted.

[0114] In the diffractive optical element 215, a diffractive pattern including a complex concave-convex shape such as a groove or an inclination, which gives a diffractive action to the incident light, is formed. The diffractive optical element 215 can be manufactured, for example, based on the description of U.S. Patent No. 9477018. U.S. Patent No. 9477018 is incorporated by reference in the present specification. The diffractive optical element 215 diffracts the light in the X-axis direction incident from the side of the cylindrical lens 214 with respect to the X-axis direction, and generates a plurality of diffracted lights having different traveling directions. The plurality of diffracted lights are lights obtained by splitting the incident light. The number of diffractions of the plurality of diffracted lights is different from each other. The condenser lens 216 condenses the plurality of diffracted lights generated from the diffractive optical element 215 to the flow cell 201. The plurality of diffracted lights having different traveling directions generated by the diffractive optical element 215 are condensed to the flow cell 201 to form a diffracted illumination light.

[0115] In Figure 3 The measurement sample modulated in the reaction chamber C30 flows through the flow cell 201. The diffracted illumination light is irradiated to the cells in the measurement sample flowing through the flow cell 201, and the forward scattered light, the side scattered light, and the fluorescence are generated from the portions of the cells irradiated with each of the diffracted lights in the diffracted illumination light. The forward scattered light is generated in the X-axis direction, and the side scattered light and the fluorescence are generated in a direction intersecting the X-axis direction, for example, the Y-axis direction.

[0116] The condenser lens 221 condenses the forward scattered light generated from the cell and the diffracted illumination light transmitted through the flow cell 201 without being irradiated to the cell.

[0117] The beam stopper 222 passes the forward scattered light generated from the cell and blocks the diffracted illumination light transmitted through the flow cell 201.

[0118] The condenser lens 223 condenses the forward scattered light passed through the beam stopper 222 to the light receiving section 225.

[0119] The optical filter 224 is configured to transmit only light of the wavelength λ20.

[0120] The light receiving section 225 receives the forward scattered light transmitted through the optical filter 224 and outputs a detection signal corresponding to the light receiving intensity. The light receiving section 225 is, for example, a photomultiplier tube (PMT).

[0121] The condenser lens 231 condenses the side scattered light generated from the cell to the light receiving section 233.

[0122] The optical filter 232 is configured to transmit only light of the wavelength λ20.

[0123] The light receiving section 233 receives the side scattered light transmitted through the optical filter 232 and outputs a detection signal corresponding to the light receiving intensity. The light receiving section 233 is, for example, a photomultiplier tube (PMT).

[0124] The condenser lens 241 condenses the fluorescence generated from the cell to the light receiving section 243.

[0125] The optical filter 242 is configured to transmit only light of the wavelength λ21.

[0126] The light receiving section 243 receives the fluorescence transmitted through the optical filter 242 and outputs a detection signal corresponding to the light receiving intensity. The light receiving section 243 is, for example, a photomultiplier tube (PMT).

[0127] In addition, the light receiving section 225, the light receiving section 233, and the light receiving section 243 are not limited to the photomultiplier tube (PMT) and may, for example, be a photo diode (PD).

[0128] Figure 5 is a diagram schematically showing the flow cell 201 and the diffracted illumination light. Figure 5 The same X axis, Y axis, and Z axis are attached. Figure 4

[0129] ​Inside the flow cell 201, a flow path 201a through which a measurement sample flows is formed in parallel with the Z-axis. By causing a sheath solution to flow through the flow path 201a together with the measurement sample, cells contained in the measurement sample are wrapped in the sheath solution and pass through the center region CE of the flow path 201a. The diffracted illumination light condensed by the condenser lens 216 is irradiated to a prescribed irradiation range R in the center region CE of the flow path 201a. The flow rate of the measurement sample per unit time is adjusted so that only one cell is positioned in the irradiation range R at a time, in other words, so that two or more cells do not pass through the irradiation range R at the same time.

[0130] In Figure 5 the lower segment, an image obtained by irradiating the diffracted illumination light generated by the diffractive optical element 215 to the dark chamber and taking a picture with the video camera is illustrated. In the image of the diffracted illumination light in Figure 5 , the black portions indicate ranges in which light is not contained, and the white points indicate ranges in which light is contained. The white points of the image of the diffracted illumination light indicate the diffracted light generated by the diffractive optical element 215. Figure 5 In the example illustrated in FIG. 17, the diffracted light includes the 0th-order diffracted light, the +1st-order to +300th-order diffracted light, and the -1st-order to -300th-order diffracted light, and is displayed as a total of 601 white points in the image of the diffracted illumination light. In the diffractive optical element 215, a diffractive pattern (a step or a groove) or the like is formed, and the diffracted light is distributed as indicated in the image of the diffracted illumination light.

[0131] Figure 6 is a diagram schematically indicating a distribution pattern of the diffracted light contained in the diffracted illumination light.

[0132] Figure 6 indicates an image in which the irradiation range R (refer to Figure 5 ) is divided into a grid shape by a plurality of squares having sides of the same length as the diameter of the points of the diffracted light. The black squares indicate regions in which the points of the diffracted light are contained. The white squares indicate regions in which the points of the diffracted light are not contained. Figure 6 In the example illustrated in FIG. 18, the cell passing through the irradiation range R is indicated by a dotted circle. Figure 6 The diameter of the points of the diffracted light contained in the image of the diffracted illumination light illustrated in FIG. 18 is about 1 pm, and thus the size of each square at this time is 1 pm x 1 pm. The size of the cell is about 10 pm.

[0133] In a case where each region of the grid shape is set to one pixel, the size of the diffracted illumination light in the irradiation range R and the length in the Y-axis direction or the Z-axis direction can be expressed by the number of pixels. Figure 6 In the example illustrated in FIG. 19, the length of the diffracted illumination light in the flow direction (the Z-axis direction) of the measurement sample is pxl (pixels), the length of the short side direction (the Y-axis direction) of the diffracted illumination light is px2 (pixels), and the size of the diffracted illumination light is pxl x px2 (pixels).

[0134] The diffractive optical element 215 is designed so that a plurality of diffracted lights constituting the diffracted illumination light are distributed in a prescribed pattern. The prescribed pattern is set to a random pattern here. In addition, the so-called pattern can be either completely free from repetition of a specific pattern or have periodicity of repetition of a specific pattern. However, it is preferable that at least one diffracted light is arranged in a region of 1 pixel in length in the Y-axis direction and extending in the Z-axis direction so that the entire site of the cell is exposed to the diffracted illumination light at least once.

[0135] When the measurement sample flows through the flow path 201a of the flow cell 201 at the time of measurement, the cells in the measurement sample move in the Z-axis direction within the irradiation range R of the diffracted illumination light. At this time, the flow rate per unit time is adjusted to be substantially fixed by the fluid adjusting section 200a (refer to Figure 2 ). When the diffracted light contained in the diffracted illumination light is irradiated to the cells flowing in the Z-axis direction, the forward scattering light and the side scattering light are generated from the portion of the cells irradiated with the diffracted light. Also, when the diffracted light is irradiated to the cells dyed with the fluorescent pigment, the fluorescence is generated from the fluorescent pigment irradiated with the diffracted light. The light receiving section 225 (refer to Figure 4 ) receives the forward scattering light generated by the plurality of diffracted lights irradiated to the cells, the light receiving section 233 (refer to Figure 4 ) receives the side scattering light generated by the plurality of diffracted lights irradiated to the site of the cells, and the light receiving section 243 (refer to Figure 4 ) receives the fluorescence generated by the plurality of diffracted lights irradiated to the prescribed site of the dyed cells.

[0136] In correspondence with the flow of the cells in the Z-axis direction, the number of the diffracted lights irradiated to the cells is known, or the site of the cells irradiated by each diffracted light changes, whereby the intensities of the forward scattering light, the side scattering light, and the fluorescence generated from the cells change with time. Therefore, the detection signals of each of the light receiving sections 225, 233, and 243 also change in time series. As described later, the operation section 32 (refer to Figure 7 ) classifies the cells based on the waveform signals acquired from these detection signals by the AI algorithm 62.

[0137] Figure 7 is a block diagram showing an example of a functional structure of the control unit 30.

[0138] The control unit 30 includes, for example, a control section 31, an operation section 32, a storage section 33, a display section 34, an input section 35, and a communication section 36.

[0139] The control section 31 includes, for example, a CPU.

[0140] The arithmetic unit 32 includes, for example, a graphics processing unit (GPU) or a neural network processing unit (NPU).

[0141] The storage unit 33 includes, for example, an HDD, an SSD, a RAM, or a ROM.

[0142] The control unit 31 executes a program stored in the storage unit 33 to control each unit of the control unit 30 and performs analysis of cells based on measurement information acquired by the GCM measurement unit 20.

[0143] The control unit 31 performs analysis of the waveform signal acquired by the optical measurement unit 200 of the GCM measurement unit 20 by means of the AI algorithm 62 to acquire a GCM analysis result.

[0144] The AI algorithm 62 at this time includes, for example, a statistical machine learning model such as a support vector machine (SVM) or a neural network model such as a multi-layer perceptron (MLP).

[0145] Alternatively, the control unit 31 and the arithmetic unit 32 can be integrated into a control arithmetic unit having the functions of the control unit 31 and the arithmetic unit 32.

[0146] The display unit 34 includes, for example, a liquid crystal display.

[0147] The input unit 35 includes, for example, a pointing device including a keyboard, a mouse, and a touch panel. Alternatively, the liquid crystal display of the display unit 34 and the touch panel of the input unit 35 can be integrated.

[0148] The communication unit 36 includes, for example, a connection terminal based on a USB specification to communicate with the GCM measurement unit 20 and the conveyance unit 40.

[0149] Figure 8 is a schematic view of the AI algorithm 61 before training and the AI algorithm 62 after training.

[0150] When the AI algorithm 61 is trained, a specimen in which a large proportion of white blood cells in peripheral blood is considered to be replaced with CML cells by gene examination can be used for training of the AI algorithm. For example, a specimen in which the proportion of white blood cells positive for the BCR::ABL fusion gene is equal to or more than a predetermined value can be used. For example, a specimen in which the Major BCR::ABL1 mRNA (%) is 80% or more, more preferably 90% or more, by PCR examination can be used. As such a specimen, peripheral blood collected from a CML-positive patient who has not been treated with TKI (hereinafter referred to as a "CML-positive untreated specimen") can be preferably used.

[0151] The control section 31 causes the conveyance unit 40 to convey the specimen rack 50 in which the specimen container 51 in which the CML-positive untreated specimen is housed is housed to the GCM measurement unit 20, for example. Also, the control section 31 acquires a waveform signal for training indicating CML positivity (hereinafter, expressed as a "CML(+) waveform signal" for convenience) from the GCM measurement unit 20, for example.

[0152] Also, the control section 31 causes the conveyance unit 40 to convey the specimen rack 50 in which the specimen container 51 in which blood collected from a healthy person is housed is housed to the GCM measurement unit 20, for example. The control section 31 acquires a waveform signal for training indicating CML negativity (hereinafter, expressed as a "CML(-) waveform signal" for convenience) from the GCM measurement unit 20, for example. In the specimen in which blood is collected from a healthy person, normal white blood cells (not containing CML cells) are considered to be contained.

[0153] For example, the "CML(+) waveform signal" or the "CML(-) waveform signal" can also be set as a series of waveform signal sets (training data sets) including a plurality of cells.

[0154] As described in the above paragraph, Figure 8 The waveform signal for training ("CML(+) waveform signal" and "CML(-) waveform signal") used to train the AI algorithm 61 before training is information obtained by measurement of specific cells (normal white blood cells and CML cells) by the GCM measurement unit 20, for example.

[0155] In addition, for example, only the waveform signal corresponding to white blood cells (for example, all white blood cells, granulocytes, lymphocytes, monocytes) in the "CML(+) waveform signal" and the "CML(-) waveform signal" of whole blood can be used as the "CML(+) waveform signal" or the "CML(-) waveform signal".

[0156] At this time, for example, by using the waveform signal (GMI waveform signal), it is possible to determine whether the determination target cell is a white blood cell, and which white blood cell it is.

[0157] The type of white blood cells can be set, for example, based on white blood cell division, as granulocytes (neutrophils, eosinophils, basophils), lymphocytes, monocytes.

[0158] As the determination method at this time, for example, the method disclosed in "High-content cell phenotyping for CRISPR pool screening using ghost cell technology" Tsubouchi A, An Y, Kawamura Y, [...], Ota S, Cell Rep Methods, 2024-03-25 (https: / / doi.org / 10.1016 / j.crmeth.2024.100737) can be applied.

[0159] The AI algorithm 61, for example, includes a neural network including a plurality of intermediate layers. The neural network at this time can also be set as a convolutional neural network (CNN) having a convolutional layer. The AI algorithm 61 has an input layer, an output layer, and an intermediate layer. A data group of a waveform signal obtained by sampling a simulated detection signal obtained from one cell at a prescribed sampling period ("CML(+) waveform signal" or "CML(-) waveform signal") is input to the input layer, and a label value corresponding to the type of cell (normal white blood cell or CML cell) is input to the output layer, whereby the AI algorithm 61 is trained. By repeatedly performing such training in advance, a trained AI algorithm 62 is generated.

[0160] As shown in the lower segment of Figure 8 The trained AI algorithm 62 also has an input layer, an output layer, and an intermediate layer, for example.

[0161] The waveform signal obtained based on the test body is input to the input layer. Thereby, classification information related to the type of cell (whether it is a CML cell) corresponding to the waveform signal is output from the output layer.

[0162] For example, in the case where the output layer is one node, the label value corresponding to the CML cell can be set to "1", and the label value corresponding to the normal white blood cell can be set to "0".

[0163] Furthermore, for example, in the case where the output layer is two nodes, learning can be performed so that when the "CML(+) waveform signal" is input to the input layer, "1" is output at the node corresponding to the CML cell, and "0" is output at the node corresponding to the normal white blood cell. Furthermore, learning can be performed so that when the "CML(-) waveform signal" is input to the input layer, "0" is output at the node corresponding to the CML cell, and "1" is output at the node corresponding to the normal white blood cell.

[0164] The classification information can include a probability that the object cell is a CML cell. For example, in a case where the output layer is one node, the classification information is an output value of the output layer, and takes a value range of "0 to 1". Also, for example, in a case where the output layer is two nodes, the classification information is an output value of the node corresponding to the CML cell, and takes a value range of "0 to 1". The closer the classification information is to "1", for example, the higher the probability that the object cell is a CML cell.

[0165] Further, the control section 31 performs threshold determination, for example, based on the classification information, and determines whether or not the object cell is a CML cell in the waveform signal corresponding to a certain cell in the test subject's specimen.

[0166] The training of such an AI algorithm 61 and the classification using the AI algorithm 62 are performed, for example, by inputting a data group of the waveform signal obtained by the one or more light-receiving sections for each cell to the input layer as input data. Specifically, in a case where n data groups are obtained from the detection signal obtained with respect to each cell using the waveform signal obtained from any one of the light-receiving sections 225, 233, 243, the number of data of the detection signal input to the AI algorithm 61, the AI algorithm 62 corresponding to one cell is n, and the number of nodes of the input layer is also n. Also, for example, in a case where a data group of three detection signals obtained from each of the three light-receiving sections 225, 233, 243 is input to the input layer as input data, 3n data groups are obtained from the three detection signals, and the number of nodes of the input layer is also 3n. Figure 4 ) Specifically, in a case where n data groups are obtained from the detection signal obtained with respect to each cell using the waveform signal obtained from any one of the light-receiving sections 225, 233, 243, the number of data of the detection signal input to the AI algorithm 61, the AI algorithm 62 corresponding to one cell is n, and the number of nodes of the input layer is also n. Also, for example, in a case where a data group of three detection signals obtained from each of the three light-receiving sections 225, 233, 243 is input to the input layer as input data, 3n data groups are obtained from the three detection signals, and the number of nodes of the input layer is also 3n.

[0167] In addition, in the present embodiment, the cell classification by the AI algorithm 62 is performed by the arithmetic section 32, but the cell classification by the AI algorithm 62 can also be performed by the control section 31. However, the arithmetic section 32 including a GPU or the like can perform the cell classification by the AI algorithm 62 more quickly. Also, the cell classification by the AI algorithm 62 can also be performed by the aforementioned control arithmetic section.

[0168] Also, the AI algorithm 62 is not limited to a neural network model. For example, it can also be provided as a machine learning model (classification model) such as an SVM. At this time, in the learning stage, the "CML(+) waveform signal" and the "CML(-) waveform signal" can also be used as explanatory variables, and the target variable can be defined in such a manner that the two can be classified, whereby learning is performed.

[0169] The control unit 30 repeatedly determines whether or not the object cell is a CML cell in the waveform signal corresponding to one cell in the test subject's specimen and accumulates the determination results, whereby the proportion (for example, "%") of the plurality of blood cells contained in the specimen that are CML cells is calculated.

[0170] The waveform signal (GMI waveform signal) can also be a waveform signal corresponding to each cell in whole blood. That is, it is possible to determine whether a cell is a CML cell based on all cells, regardless of whether it is a white blood cell, and the number of CML cells in all cells or the proportion of CML cells to the total number of cells can also be calculated.

[0171] Furthermore, the waveform signal can also be the waveform signal corresponding to the cell identified as a white blood cell. That is, it is also possible to determine whether a cell is a CML cell based solely on the cell identified as a white blood cell, and it is also possible to calculate the number of CML cells among all white blood cells or the proportion of CML cells to the total number of white blood cells.

[0172] Furthermore, the waveform signal can also be a waveform signal corresponding to a specific type of white blood cell (granulocytes, lymphocytes, monocytes). That is, it is also possible to determine whether a cell is a CML cell based solely on the cell type identified as such, and the number of CML cells in the specific cell type or the ratio of the number of CML cells to the number of cells in the specific cell type can also be calculated.

[0173] Figure 9 This is a schematic representation of the analysis result output processing described later. Figure 10 The diagram shows the structure of the cell analysis result screen 600 that is displayed and output to the display unit 34 in step S150. The cell analysis result screen 600 can also be referred to as the output screen for CML cell analysis result information based on classification information.

[0174] The cell analysis results screen 600 includes, for example, a count value display area 610 and a CML cell content display area 620.

[0175] In this example, the count display area 610 is configured to display, for example, the number of white blood cells analyzed in the sample (total white blood cell count), the number of cells identified as CML cells out of the total white blood cell count (CML cell count), and the number of cells identified as not CML cells (normal cells) out of the total white blood cell count (normal cell count).

[0176] In the CML cell content display area 620, for example, it is configured to display the proportion of CML cells contained in the specimen (CML cell content) in a unit such as "%" based on the number of CML cells and the total number of white blood cells.

[0177] Alternatively, in the CML cell content display area 620, if the CML cell content exceeds a specified percentage (e.g., "20%)" or is above the specified percentage, marker information 621 may be displayed indicating that for subjects who have collected samples, a gene test with higher sensitivity compared to this method, such as Major BCR::ABL1 mRNA quantitative PCR or FISH, is recommended.

[0178] Next, the measurement processing of the specimen analysis device 1A will be described with reference to Figure 10

[0179] Figure 10 is a flowchart showing an example of a flow of the measurement-related control processing performed by the control unit 30.

[0180] First, in step S110, the control section 31 of the control unit 30 controls the GCM measurement unit 20 to cause the sample modulation section 25 (see Figure 2 , Figure 3 ) to perform sample modulation. Thus, a measurement sample is modulated in the reaction chamber C30.

[0181] In step S120, the control section 31 controls the GCM measurement unit 20 to perform measurement using the optical measurement section 200. Thus, the optical measurement section 200 irradiates cells in the specimen contained in the measurement sample flowing through the flow cell 201 with diffracted illumination light, and acquires a waveform signal as shown in Figure 5 . Also, the control section 31 acquires the waveform signal acquired by the measurement section 26 from the GCM measurement unit 20.

[0182] In step S130, the control section 31 and the arithmetic section 32 input the waveform signal acquired in step S120 to the trained AI algorithm 62 to perform analysis.

[0183] In step S140, the control section 31 and the arithmetic section 32 generate CML cell analysis information through the analysis in step S130.

[0184] The CML cell analysis information can include a count value and / or a CML cell content rate of blood cells / CML cells / non-CML cells obtained by analyzing the waveform signal based on the measurement by the optical measurement section 200. The CML cell analysis information is, for example, information based on classification information, and can also be referred to as GML analysis information.

[0185] For example, when the examiner inputs a display instruction via the input section 35, in step S150, the control section 31 displays (an example of output) the cell analysis result screen 300 including the CML cell analysis information generated in step S140 on the display section 34.

[0186] In addition, the "output" of information including analysis results and the like can include, in addition to display (display output) of information in the self device, for example, output (internal output) of information to other functional sections in the self device, or output (external output) or transmission (external transmission) of information to devices (external devices) other than the self device, and the like. It can also include sound output (output including voice). ​

[0187] In step S160, for example, the control unit 31 determines whether to end the process based on the input operation of the inspection technician via the input unit 35. If it is determined that the process should continue (S160: No), the control unit 31, for example, controls the GCM measurement unit 20 to remove other specimen containers 51 from the specimen holder 50 and transfer them into the GCM measurement unit 20, preparing for the measurement of the specimen in the new specimen container 51. Then, for example, the process is returned to step S110.

[0188] If the process is determined to end (S160: Yes), the control unit 31, for example, controls the sample container 51 to return to its original position from the GCM measurement unit 20 to the sample holder 50, and ends the process.

[0189] <Verification Experiment>

[0190] To confirm the effectiveness of this method, CML patients treated with TKIs in standard CML treatment were assumed to be in the early stages of CML. A validation experiment was conducted to determine whether samples from TKI-treated CML patients could be distinguished from those from healthy individuals. In the validation experiment, as per [reference needed]... Figure 29 As will be described later, an optical measurement unit 400 is used, which is capable of acquiring flow cytometry information from a single cell in addition to GMI waveform information.

[0191] Figure 11 This comparison presents blood test values ​​from samples taken from CML patients before TKI treatment (n=6) and from CML patients undergoing TKI treatment (n=11). The bottom section shows the values ​​obtained using PCR-based gene analysis. It is evident that in CML patients before TKI treatment, the proportion of CML cells, calculated as BCR::ABL1 mRNA (IS%), was over 90%, meaning almost all white blood cells in their blood were CML cells. Conversely, in CML patients undergoing TKI treatment, the median proportion of CML cells in white blood cells was approximately 50%. However, the median white blood cell count (WBC), used as a diagnostic indicator of CML, was 49.9 (normal range: 36.0 × 10² / μL–88.0 × 10² / μL) and the median basophil percentage (BASO%) was 1.0 (normal range: 0%–1.0%), both within the normal range.

[0192] Figure 12 It means in Figure 11The chart indicates whether each sample is flagged as abnormal (Flag positive) or not flagged as abnormal (Flag negative) when blood cell counts are performed in an automated blood cell measuring device. The histogram for "Untreated" corresponds to a collection of samples from CML patients (n=6) before TKI treatment, and the histogram for "Treated" corresponds to a collection of samples from CML patients (n=11) during TKI treatment. It can be seen in this chart that in CML patients during TKI treatment, almost no abnormal flags are indicated that indicate a characteristic manifestation of suspected CML, namely an increase in WBC (white blood cells) or an increase in Baso (basophils). Figure 11 Figure 11 The histogram for "Untreated" corresponds to a collection of samples from CML patients (n=6) before TKI treatment, and the histogram for "Treated" corresponds to a collection of samples from CML patients (n=11) during TKI treatment. It can be seen in this chart that in CML patients during TKI treatment, almost no abnormal flags are indicated that indicate a characteristic manifestation of suspected CML, namely an increase in WBC (white blood cells) or an increase in Baso (basophils).

[0193] According to the results of Figure 11 and Figure 12 it is difficult to suspect CML even in samples of a condition in which CML cells account for more than 50% of the white blood cells in the blood, relying on blood cell counts in an automated blood cell measuring device. Therefore, in this verification experiment, CML patients during TKI treatment are assumed to be CML patients in the early stages of the disease that are difficult to screen relying on abnormal flags in blood cell counts in an automated blood cell measuring device, and it is verified whether or not they can be discriminated with the aid of the present method.

[0194] Figure 13 This is a verification example when the AI algorithm 61 is trained (learned) using the waveforms of all white blood cells of CML positive patients who have not been treated with TKI as "CML(+) waveforms".

[0195] <Learning phase>

[0196] On the left side of the chart, a scattergram of samples of CML positive patients who have not been treated with TKI is shown. As described above, in this verification experiment, an optical system that can acquire flow cytometry signals in addition to the waveforms of cells is used. In the scattergram, the horizontal axis indicates the peak value (peak value of intensity) of the forward scattered light obtained as a flow cytometry signal, and the vertical axis indicates the peak value (peak value of intensity) of the side scattered light obtained as a flow cytometry signal. In the scattergram, the cells within the dotted line are determined to be white blood cells. The AI algorithm 61 is trained using the waveforms of the determined white blood cells as "CML(+) waveforms". The AI algorithm 61 is trained using the waveforms of white blood cells contained in samples of healthy people as "CML(-) waveforms". In this way, the trained AI algorithm 62 is obtained.

[0197] <Inference phase>

[0198] ​The waveform signals and flow cytometry signals of the cells were obtained for the samples of healthy persons (n = 5) and the samples of CML patients assumed to be in the early stage of onset, i.e., the samples of CML patients in TKI treatment (n = 11). The white blood cells were determined based on the peak value of the forward scatter light and the peak value of the side scatter light. The waveform signals of the determined white blood cells were input to the trained AI algorithm 62, and the CML cell content rate was calculated.

[0199] On the right side of the scatter plot, the CML cell content rates of the samples of healthy persons (expressed as "HC" in the figure) and the samples of the assumed CML patients in the early stage of onset (expressed as "CML" in the figure) are shown. According to this graph, a clear difference in the CML cell content rate in the white blood cells determined in HC and CML was produced, which was a result strongly suggesting that it can contribute to the early diagnosis of CML patients in the early stage of onset.

[0200] On the right side thereof, the ROC curve at the time of training of the AI algorithm 61 is shown. According to this ROC curve, it is known that the AUC value is "0.98", and the learned AI algorithm 62 is a model effective as a classification model.

[0201] On the right side of the figure, the results of correlation analysis of the CML cell content rate in the white blood cells determined in this method and the Philadelphia chromosome BCR::ABL1 gene content rate detected by PCR are shown. According to this graph, a positive correlation was produced between this method and the PCR result, and the effectiveness of this method was known.

[0202] Figure 14 is a verification example at the time of training (learning) of the AI algorithm 61 using the waveform signals of granulocytes in the white blood cells of CML-positive patients who have not been treated with TKI as "CML(+) waveform signals". As in the verification example of Figure 13 In order to match the total of the cells set as the analysis target in the learning phase and the inference phase, the waveform signals of granulocytes were input to the AI algorithm 62 in the inference phase. Also, the waveform signals of granulocytes contained in the samples of healthy persons were used as "CML(-) waveform signals". The viewing method of each graph was the same as that of Figure 13 Also.

[0203] According to these results, it was known that if the waveform signals of granulocytes are used to learn the AI algorithm 61, the CML cell content rate in the white blood cells determined in HC and CML does not overlap but produces a clear difference. Also, it was known that the AUC value is "1", and the learned AI algorithm 62 is a model very effective as a classification model. That is, by training the AI algorithm 61 by converging to granulocytes, a result strongly suggesting that it can contribute to the early diagnosis of CML patients in the early stage of onset was produced.

[0204] Figure 15 Validation Example at the time of training (learning) of the AI algorithm 61 using the waveform signal of lymphocytes in white blood cells of CML-positive patients who were not treated with TKI as "CML(+) waveform signal". As with the validation example of the AI algorithm 61 of Figure 13 As with the validation example of the AI algorithm 61 of Figure 13 .

[0205] In the case where the training of the AI algorithm 61 was performed with convergence to lymphocytes, it became a result that suggested the possibility that, although the AUC value decreased to "0.92", considering the characteristic of lymphocytes that is tolerable to long-term preservation, even a sample from which the time elapsed after blood collection was taken into account was useful for early diagnosis of CML.

[0206] According to the results of the validation experiment shown in Figures 13-15 indicate that, according to the present method, it is possible to recognize CML cells in blood, and thus it is possible to accurately discriminate CML patients in the early stage of onset that are difficult to screen in a blood cell count examination. As a reason why it is possible to recognize CML cells by GCM, it is considered that the reason is that GCM can investigate morphological characteristics specific to CML cells in more detail compared to flow cytometry signals used for a blood cell count examination. The present inventors and others have clarified that a BCR::ABL fusion gene characteristic of CML cells causes changes in the morphology of mitochondria of cells, causing excessive division of mitochondria. It is considered that, according to GCM, by comprehensively capturing fine morphological changes and the like of mitochondria in cells that are difficult to capture in conventional flow cytometry, it is possible to distinguish normal cells from CML cells, leading to the results shown above.

[0207] In the verification experiment, in order to determine a specific kind of cells (e.g., leukocytes, granulocytes, lymphocytes), an optical system capable of acquiring flow cytometry signals in addition to the GMI waveform signals was used, but the structure for acquiring the flow cytometry signals is not essential, and the present method can be implemented even with only the GMI waveform signals. For example, an AI algorithm (kind determination AI) for determining whether or not a cell is a specific kind of cell (e.g., a leukocyte) based on the waveform signals can be used in addition to the AI algorithm trained by the CML waveform signals. At this time, for example, in the learning phase, the waveform signals of the cells determined as leukocytes by the kind determination AI in the cells of the TKI-untreated CML-positive patient are input as CML(+) waveform signals, whereby the AI algorithm 61 can be trained. In the inference phase, the waveform signals of the cells determined as leukocytes by the kind determination AI in the cells of the patient sample are input to the trained AI algorithm 62, whereby the determination result can be obtained. Alternatively, learning and inference can be performed without performing the gating shown in the scatter diagram, using the waveform signals of all the particles in the sample for which the waveform signals were acquired. As is also known from the scatter diagram according to the present embodiment, almost all the particles in the blood lysed by the hemolytic agent are leukocytes, and therefore even without gating, it is possible to analyze substantially only the leukocytes. Figures 13-15 Figure 13 As is also known from the scatter diagram according to the present embodiment, almost all the particles in the blood lysed by the hemolytic agent are leukocytes, and therefore even without gating, it is possible to analyze substantially only the leukocytes.

[0208] <Effects of the First Embodiment>

[0209] According to the present embodiment, in the sample analysis device (e.g., the sample analysis device 1A), the measurement unit (e.g., the GCM measurement unit 20) acquires optical information (e.g., GMI waveform signals (waveform information)) by irradiating a plurality of diffracted lights generated by causing light to be incident on a diffractive optical element (e.g., the diffractive optical element 215) to cells contained in a sample (e.g., blood). Further, the analysis unit (e.g., the control unit 30) analyzes the optical information acquired by the measurement unit by an artificial intelligence algorithm (e.g., the trained AI algorithm 62), thereby acquiring information on leukemic cells contained in the sample (e.g., whether or not it is a leukemic cell, the proportion of leukemic cells, the number of leukemic cells).

[0210] Thus, by analyzing the optical information acquired by the measurement unit by the artificial intelligence algorithm, it is possible to simply and appropriately acquire information on leukemic cells contained in the sample. That is, it is possible to accurately find CML at an early stage of the onset of CML using a simple blood test, and thus it is possible to contribute to early diagnosis of CML.

[0211] Further, according to the present embodiment, the analysis unit acquires the proportion of leukemic cells in leukocytes (e.g., CML cell content rate) as information on leukemic cells.

[0212] ​Thus, by analyzing the optical information obtained by the measurement section by an artificial intelligence algorithm, the proportion of leukemia cells contained in the specimen can be simply and appropriately obtained.

[0213] Further, according to the present embodiment, the analysis section obtains the number of leukemia cells (e.g., CML cell number) as information of leukemia cells.

[0214] Thus, by analyzing the optical information obtained by the measurement section by an artificial intelligence algorithm, the number of leukemia cells contained in the specimen can be simply and appropriately obtained.

[0215] Further, according to the present embodiment, the specimen analysis device includes a sample preparation section (e.g., sample preparation section 25) that mixes a specimen with a reagent to prepare a measurement sample. Further, the sample preparation section prepares a measurement sample in which red blood cells contained in the specimen are lysed by using a hemolytic agent (e.g., WDF hemolytic agent) as the reagent.

[0216] By using a hemolytic agent as the reagent, a measurement sample in which red blood cells contained in the specimen are lysed can be appropriately prepared.

[0217] Further, the reagent can also be free of a staining agent at this time.

[0218] Thus, a specimen and a reagent free of a staining agent can be mixed to prepare a measurement sample. Further, since the reagent is free of a staining agent, information of leukemia cells contained in the specimen can be obtained without labeling (without the need for fluorescent labeling), and an optical system for condensing fluorescence or a light receiving section for fluorescence can be omitted.

[0219] In addition, the reagent can also include a diluent instead of a staining agent.

[0220] Further, in the present embodiment, the artificial intelligence algorithm (e.g., AI algorithm 62) has been trained, for example, using optical information of white blood cells contained in a specimen collected from a patient with chronic myelogenous leukemia (e.g., waveform signals of all white blood cells of a TKI-untreated CML-positive patient) as teaching data.

[0221] The artificial intelligence algorithm has been trained using optical information of white blood cells contained in a specimen collected from a patient with chronic myelogenous leukemia as teaching data, whereby information of leukemia cells contained in the specimen can be appropriately obtained.

[0222] Further, according to the present embodiment, the artificial intelligence algorithm (e.g., AI algorithm 62) has been trained, for example, using optical information of granulocytes contained in a specimen collected from a patient with chronic myelogenous leukemia (e.g., waveform signals of granulocytes of a TKI-untreated CML-positive patient) as teaching data.

[0223] An artificial intelligence algorithm is trained with optical information of granulocytes contained in a specimen collected from a patient with chronic myelogenous leukemia as teaching data, whereby information of leukemia cells contained in the specimen can be appropriately acquired.

[0224] <Second Embodiment>

[0225] In the first embodiment, the specimen is measured based on the ghost flow cytometry performed by the GCM measuring unit 20, and CML cells are discriminated.

[0226] The second embodiment is an embodiment related to the following content, that is, a specimen is preparatorily measured using flow cytometry having higher productivity than ghost flow cytometry, and ghost flow cytometry is applied to a screened specimen (a specimen suspected of being CML), whereby CML cells are discriminated.

[0227] The content described in the second embodiment is equally applicable to each of the other embodiments or each of the other modifications.

[0228] Figure 16 is a front view schematically showing the structure of a specimen analysis device 1B as an example of a specimen analysis device in the second embodiment.

[0229] The specimen analysis device 1B includes, for example, a flow cytometry measuring unit (hereinafter referred to as "FCM measuring unit") 10, a GCM measuring unit 20, a control unit 30, and a conveyance unit 40.

[0230] An examining technician who is an operator of the specimen analysis device 1B sets a specimen container 51 in which a specimen is housed in a specimen rack 50, and places the specimen rack 50 at a right end region of the conveyance unit 40. The conveyance unit 40 conveys the specimen rack 50 and appropriately positions it in front of the FCM measuring unit 10 and the GCM measuring unit 20.

[0231] The FCM measuring unit 10 takes out the specimen container 51 from the specimen rack 50 and transfers it into the FCM measuring unit 10, and measures the specimen in the specimen container 51. When the measurement of the specimen in the specimen container 51 is completed, the FCM measuring unit 10 returns the specimen container 51 to the original position of the specimen rack 50.

[0232] Similarly, the GCM measuring unit 20 takes out the specimen container 51 from the specimen rack 50 and transfers it into the GCM measuring unit 20, and measures the specimen in the specimen container 51. When the measurement of the specimen in the specimen container 51 is completed, the GCM measuring unit 20 returns the specimen container 51 to the original position of the specimen rack 50.

[0233] When the required measurement ends for all the specimen containers 51 on the specimen rack 50, the conveyance unit 40 conveys the specimen rack 50 to the left end region of the conveyance unit 40. The examiner takes out the specimen rack 50 conveyed to the left end region.

[0234] The FCM measurement unit 10 and the GCM measurement unit 20 are configured to be able to perform measurement on the specimen conveyed on the conveyance unit 40.

[0235] The conveyance unit 40 is configured to be able to automatically supply the specimen rack 50 in which the specimen is housed to the FCM measurement unit 10 and the GCM measurement unit 20.

[0236] Since the specimen can be automatically supplied to the FCM measurement unit 10 and the GCM measurement unit 20 via the conveyance unit 40, the man-hour required for the examiner to transfer the specimen between the FCM measurement unit 10 and the GCM measurement unit 20 can be reduced.

[0237] The control unit 30, for example, controls the FCM measurement unit 10, the GCM measurement unit 20, and the conveyance unit 40. Also, the control unit 30, for example, analyzes the measurement information obtained by the FCM measurement unit 10 and the GCM measurement unit 20.

[0238] Figure 17 is a block diagram showing an example of the functional structure of the FCM measurement unit 10.

[0239] The FCM measurement unit 10, for example, includes a measurement control section 11, a storage section 12, a communication section 13, a reading section 14, a sample preparation section 15, and a measurement section 16.

[0240] The measurement control section 11, for example, includes an FPGA or a CPU.

[0241] The storage section 12, for example, includes an HDD, an SSD, a RAM, and a ROM.

[0242] The measurement control section 11 performs various processes based on a program stored in the storage section 12, and controls each section of the FCM measurement unit 10.

[0243] The communication section 13, for example, includes a connection terminal based on the USB standard, and communicates with the control unit 30.

[0244] The reading section 14, for example, includes a bar code reader. The reading section 14 reads a bar code from a bar code label attached to the specimen container 51, and acquires a specimen ID.

[0245] The sample preparation section 15 aspirates a specimen from the specimen container 51, and mixes a reagent with the aspirated specimen to prepare a measurement sample.

[0246] The measurement section 16 includes, for example, an electrical measurement section 16a, a hemoglobin (HGB) measurement section 16b, and an optical measurement section 100.

[0247] The electrical measurement section 16a performs measurement of cells (blood cells) in a specimen by, for example, a sheath flow DC detection method.

[0248] The HGB measurement section 16b performs measurement of hemoglobin in cells (blood cells) in a specimen by, for example, an SLS-hemoglobin method.

[0249] The optical measurement section 100 performs measurement of cells (blood cells) in a specimen by, for example, a flow cytometry method.

[0250] The electrical measurement section 16a and the HGB measurement section 16b include, for example, an amplifier or an A / D conversion section, perform signal processing on a detection signal acquired by measurement, and output the measurement information after signal processing to the measurement control section 11.

[0251] The optical measurement section 100 includes, for example, an amplifier or an A / D conversion section, performs signal processing on a detection signal acquired by measurement, and outputs the measurement data after signal processing to the measurement control section 11.

[0252] The measurement control section 11 causes the measurement data output from the measurement section 16 to be stored in the storage section 12. When measurement of one specimen ends, the measurement control section 11 transmits the measurement data stored in the storage section 12 to the control unit 30 in association with the specimen ID read by the reading section 14.

[0253] Figure 18 is a block diagram showing an example of a functional structure of a specimen modulation section 15 for modulating a measurement specimen.

[0254] The specimen modulation section 15 includes, for example, a stirring section 15a, a suction pipe 15b, and reaction chambers C11, C12, C21 to C24.

[0255] The stirring section 15a is configured to be able to grip a specimen container 51 and swing the gripped specimen container 51 to stir a specimen in the specimen container 51.

[0256] The suction pipe 15b is, for example, a nozzle with a sharp tip at a lower end and is configured to be able to penetrate a lid portion of the specimen container 51 including an elastic material. The suction pipe 15b suctions a specimen from the stirred specimen container 51 and appropriately dispenses the suctioned specimen to the reaction chambers C11, C12, C21 to C24.

[0257] In the reaction chamber C11, the specimen is mixed with an RBC / PLT diluent to prepare an RBC / PLT measurement sample. The RBC / PLT diluent is, for example, CELLPACK (registered trademark) DCL. The RBC / PLT measurement sample prepared in the reaction chamber C11 is measured by the electrical measurement section 16a. The electrical measurement section 16a acquires detection signals corresponding to blood cells in the RBC / PLT measurement sample, and acquires measurement information by signal processing of the acquired detection signals. The control section 31 of the control unit 30 analyzes the measurement information obtained by measurement of the RBC / PLT measurement sample, and acquires the number of red blood cells and the number of platelets, and the like. Figure 7 ) analyzes the measurement information obtained by measurement of the RBC / PLT measurement sample, and acquires the number of red blood cells and the number of platelets, and the like.

[0258] In the reaction chamber C12, the specimen, an HGB lysing agent, and an HGB diluent are mixed to prepare an HGB measurement sample. The HGB lysing agent is, for example, SULFOLYSER (registered trademark), and the HGB diluent is, for example, CELLPACK (registered trademark) DCL. The HGB measurement sample prepared in the reaction chamber C12 is measured by the HGB measurement section 16b. The HGB measurement section 16b acquires detection signals corresponding to the hemoglobin concentration, and acquires measurement information by signal processing of the acquired detection signals. The control section 31 of the control unit 30 analyzes the measurement information obtained by measurement of the HGB measurement sample, and acquires the hemoglobin concentration, and the like.

[0259] In the reaction chamber C21, the specimen, a WDF lysing agent, and a WDF staining solution are mixed to prepare a WDF measurement sample. The WDF lysing agent is, for example, LYSERCELL (registered trademark) WDF II, and the WDF staining solution is, for example, FLUOROCELL (registered trademark) WDF. The WDF measurement sample prepared in the reaction chamber C21 is measured by the optical measurement section 100. The optical measurement section 100 acquires detection signals corresponding to blood cells in the WDF measurement sample, and acquires measurement data by signal processing of the acquired detection signals. The control section 31 of the control unit 30 analyzes the measurement data obtained by measurement of the WDF measurement sample and the measurement data obtained by measurement of the WNR measurement sample described later, performs classification of neutrophils, normal lymphocytes, monocytes, eosinophils, basophils, blast cells, abnormal lymphocytes, atypical lymphocytes, promyelocytes, and nucleated red blood cells, and acquires the number of each blood cell.

[0260] At this time, the measurement data includes time series data of side scatter light corresponding to each cell, and indicates the flow rate of each cell flowing through the flow cell 101 (refer to FIG. 1) and the size of each cell. Figure 19the time-series data of the fluorescence corresponding to each cell, which indicates a change in intensity of the fluorescence received by the light-receiving portion 143 during the period in which each cell in the WNR measurement sample flowing through the flow cell 101 passes through the beam spot BS. The control portion 31 of the control unit 30 acquires the peak values of the side scatter and the fluorescence corresponding to each cell from the time-series data of the side scatter and the time-series data of the fluorescence, and generates a scatter plot.

[0261] In the reaction chamber C22, the specimen, a WNR lysing agent, and a WNR staining solution are mixed to prepare a WNR measurement sample. The WNR lysing agent is, for example, LYSERCELL (registered trademark) WNR, and the WNR staining solution is, for example, FLUOROCELL (registered trademark) WNR. The WNR measurement sample prepared in the reaction chamber C22 is measured by the optical measurement unit 100. The optical measurement unit 100 acquires detection signals corresponding to blood cells in the WNR measurement sample, and acquires measurement data by signal processing of the acquired detection signals. The control portion 31 of the control unit 30 analyzes the measurement data obtained by the measurement of the WNR measurement sample, classifies leukocytes, nucleated red blood cells, and the like, and acquires the number of each blood cell.

[0262] At this time, the measurement data includes: the time-series data of the forward scatter corresponding to each cell, which indicates a change in intensity of the forward scatter received by the light-receiving portion 124 during the period in which each cell in the WNR measurement sample flowing through the flow cell 101 (see FIG. 1) passes through the beam spot BS; and the time-series data of the fluorescence corresponding to each cell, which indicates a change in intensity of the fluorescence received by the light-receiving portion 143 during the period in which each cell in the WNR measurement sample flowing through the flow cell 101 passes through the beam spot BS. The control portion 31 of the control unit 30 acquires the peak values of the forward scatter and the fluorescence corresponding to each cell from the time-series data of the forward scatter and the time-series data of the fluorescence, and generates a scatter plot. Figure 19

[0263] In the reaction chamber C23, the specimen, a RET diluent, and a RET staining solution are mixed to prepare a RET measurement sample. The RET diluent is, for example, CELLPACK (registered trademark) DFL, and the RET staining solution is, for example, FLUOROCELL (registered trademark) RET. The RET measurement sample prepared in the reaction chamber C23 is measured by the optical measurement unit 100. The optical measurement unit 100 acquires detection signals corresponding to blood cells in the RET measurement sample, and acquires measurement data by signal processing of the acquired detection signals. The control portion 31 of the control unit 30 analyzes the measurement data obtained by the measurement of the RET measurement sample, classifies reticulocytes, and acquires the number of each blood cell.​

[0264] At this time, the measurement data includes: time-series data of the forward scattered light corresponding to each cell, indicating a change in intensity of the forward scattered light received by the light receiver 124 during a period in which each cell in the RET measurement sample flowing through the flow cell 101 (refer to Figure 19 ) passes through the light beam spot BS; and time-series data of the fluorescence corresponding to each cell, indicating a change in intensity of the fluorescence received by the light receiver 143 during a period in which each cell in the RET measurement sample flowing through the flow cell 101 passes through the light beam spot BS. The control section 31 of the control unit 30 acquires the peak values of the forward scattered light and the fluorescence corresponding to each cell from the time-series data of the forward scattered light and the time-series data of the fluorescence, and generates a scatter plot.

[0265] In the reaction chamber C24, the specimen, the PLT-F diluent, and the PLT-F staining solution are mixed to prepare a PLT-F measurement sample. The PLT-F diluent is, for example, CELLPACK (registered trademark) DFL, and the PLT-F staining solution is, for example, FLUOROCELL (registered trademark) PLT. The PLT-F measurement sample prepared in the reaction chamber C24 is measured by the optical measurement section 100. The optical measurement section 100 acquires detection signals corresponding to blood cells in the PLT-F measurement sample, and acquires measurement data by signal processing of the acquired detection signals. The control section 31 of the control unit 30 analyzes the measurement data obtained by measurement of the PLT-F measurement sample, classifies platelets and the like, and acquires the number of each blood cell.

[0266] At this time, the measurement data includes: time-series data of the forward scattered light corresponding to each cell, indicating a change in intensity of the forward scattered light received by the light receiver 124 during a period in which each cell in the PLT-F measurement sample flowing through the flow cell 101 (refer to Figure 19 ) passes through the light beam spot BS; and time-series data of the fluorescence corresponding to each cell, indicating a change in intensity of the fluorescence received by the light receiver 143 during a period in which each cell in the PLT-F measurement sample flowing through the flow cell 101 passes through the light beam spot BS. The control section 31 of the control unit 30 acquires the peak values of the forward scattered light and the fluorescence corresponding to each cell from the time-series data of the forward scattered light and the time-series data of the fluorescence, and generates a scatter plot.

[0267] In addition, the measurement data is information reflecting the size, shape, internal structure, or nucleic acid amount of each cell, obtained by irradiating each cell in the measurement sample with at least one light having a single light beam spot, and is not limited to the peak values.

[0268] Moreover, the diluent, hemolytic agent, and staining solution mixed in the reaction chamber C11, the reaction chamber C12, and the reaction chambers C21 to C24 are not limited to the reagents.

[0269] Figure 19 Fig. 1 is a diagram schematically showing a structure of the optical measurement section 100. Figure 19 In Fig. 1, for convenience, an X axis, a Y axis, and a Z axis orthogonal to each other are attached. The Z axis direction is a flow direction of a measurement sample in the flow cell 101.

[0270] The optical measurement section 100 includes the flow cell 101, a light source 111, a collimator lens 112, a cylindrical lens 113, a condenser lens 114, a condenser lens 121, a condenser lens 131, a beam stopper 122, an optical filter 123, an optical filter 132, an optical filter 142, a light receiving section 124, a light receiving section 133, a light receiving section 143, and a dichroic mirror 141.

[0271] The light source 111 is, for example, a semiconductor laser light source. The light source 111 emits light of a prescribed wavelength λ10 in the X axis direction. The wavelength λ10 is, for example, 488 nm or 642 nm.

[0272] The collimator lens 112 converts the light emitted from the light source 111 into parallel light.

[0273] The cylindrical lens 113 converges the light from the light source 111 toward the Y axis direction.

[0274] The condenser lens 114 converges the light from the light source 111 toward the Y axis direction and the Z axis direction, and sets the position of the flow cell 101 to a flat shape and condenses the light to the flow path 101a of the flow cell 101.

[0275] Figure 20 Fig. 2 is a side view schematically showing a structure of the flow cell 101.

[0276] The light from the light source 111 is set to a single beam spot BS of a flat shape having a small width in the Z axis direction by the actions of the cylindrical lens 113 and the condenser lens 114, and is irradiated to the irradiation position of the flow path 101a of the flow cell 101. Hereinafter, the light emitted from the light source 111 and irradiated to the irradiation position of the flow path 101a will be referred to as "straight-ahead illumination light". When the straight-ahead illumination light is irradiated to a cell flowing through the flow path 101a, forward scattering light, side scattering light, and fluorescence are generated from the portion of the cell on which the light is irradiated. It is assumed here that when the straight-ahead illumination light of the wavelength λ10 is irradiated to a fluorescent dye that stains the cell, light of the wavelength λ11 is generated from the fluorescent dye.

[0277] In addition, as described above, the flow rate is adjusted in the flow rate adjustment section 200a of the flow cell 201 so that the flow rate per unit time is substantially fixed, and therefore the productivity of the measurement performed by the GCM measurement unit 20 is sometimes lower than the productivity of the measurement performed by the FCM measurement unit 10. Also, the control section 31 can control the operation of the GCM measurement unit 20 in correspondence with the flow rate adjustment so that the number of cells measured by the GCM measurement unit 20 is less than the number of cells measured by the FCM measurement unit 10.

[0278] Returning to Figure 19 The condenser lens 121 condenses the front-scattered light of the wavelength λ10 generated from the cells to the light-receiving section 124. The beam blocker 122 blocks the light of the wavelength λ10 that is not irradiated to the cells but transmitted through the flow cell 101, and transmits the front-scattered light of the wavelength λ10 generated from the cells. The optical filter 123 is configured to transmit only the light of the wavelength λ10. The light-receiving section 124 receives the front-scattered light of the wavelength λ10 transmitted through the optical filter 123, and outputs a detection signal corresponding to the light-receiving intensity. The light-receiving section 124 is, for example, a photodiode (PD).

[0279] The condenser lens 131 condenses the side-scattered light of the wavelength λ10 generated from the cells to the light-receiving section 133, and condenses the fluorescence of the wavelength λ11 generated from the cells to the light-receiving section 143. The dichroic mirror 141 transmits the light of the wavelength λ10 and reflects the light of the wavelength λ11. The optical filter 132 is configured to transmit only the light of the wavelength λ10 from the dichroic mirror 141. The light-receiving section 133 receives the side-scattered light of the wavelength λ10 transmitted through the optical filter 132, and outputs a detection signal corresponding to the light-receiving intensity. The light-receiving section 133 is, for example, a photodiode (PD).

[0280] The optical filter 142 is configured to transmit only the light of the wavelength λ11 from the dichroic mirror 141. The light-receiving section 143 receives the fluorescence of the wavelength λ11 transmitted through the optical filter 142, and outputs a detection signal corresponding to the light-receiving intensity. The light-receiving section 143 is, for example, a photomultiplier tube (PMT), an Avalanche Photodiode (APD), or a photodiode (PD).

[0281] The control section 31 performs analysis of the cells based on the measurement information acquired by the FCM measurement unit 10 and the GCM measurement unit 20, for example.

[0282] The control section 31 acquires an FCM analysis result by analyzing the measurement data acquired by the optical measurement section 100 of the FCM measurement unit 10 and the measurement information acquired by the electrical measurement section 16a and the HGB measurement section 16b of the FCM measurement unit 10. For example, the control section 31 generates a scatter plot for each specimen based on the measurement data, and classifies the cells based on the generated scatter plot.

[0283] For example, the control section 31 generates a scatter plot based on the measurement data obtained by measuring the WDF measurement sample, and groups a plurality of cell groups corresponding to the plots on the generated scatter plot. In the grouping of the cell groups, for example, the control section 31 calculates the center of gravity of the plots in a prescribed region on the scatter plot, performs cluster analysis of the cell groups based on the distances from each plot to the center of gravity, to classify each cell. An example of the scatter plot will be described later.

[0284] By the grouping of the cell groups, for example, a region corresponding to normal lymphocytes, a region corresponding to monocytes, a region corresponding to neutrophils and basophils, and a region corresponding to eosinophils are set. Also, in the case where the measurement sample contains blast cells, abnormal lymphocytes, atypical lymphocytes, myeloblasts, and nucleated red blood cells, regions corresponding to these blood cells are also set. The control section 31 counts the plots in the regions set in the scatter plot to obtain the number of blood cells of each classification.

[0285] In addition, the control section 31 can perform the grouping of the cell groups without necessarily actually generating the scatter plot, for example, by processing the data corresponding to the scatter plot.

[0286] Next, the measurement processing of the specimen analysis device IB will be described with reference to Figure 21 and Figure 22 .

[0287] Figure 21 is a flowchart showing an example of the flow of the control processing related to measurement performed by the control unit 30.

[0288] In step S11, the control section 31 of the control unit 30 controls the FCM measurement unit 10 to perform the FCM measurement processing. Thereby, the control section 31 analyzes the measurement data and the measurement data obtained in the FCM measurement unit 10, and obtains the FCM analysis result.

[0289] The FCM analysis result can include, for example, a count value of the cells in the specimen (the number per unit volume, etc.), an abnormal cell marker indicating the presence of abnormal cells, and a scatter plot or a histogram.

[0290] The count value of the FCM analysis result can be set, for example, to the count values of red blood cells, white blood cells, neutrophils, normal lymphocytes, monocytes, eosinophils, basophils, platelets, abnormal cells, and the like.

[0291] The abnormal cell marker of the FCM analysis result can include, for example, a marker indicating that the number of abnormal cells such as blast cells, abnormal lymphocytes, atypical lymphocytes, myeloblasts, and nucleated red blood cells in the specimen as the object is equal to or more than a predetermined threshold per unit volume, and a marker indicating that the classification state of white blood cells is abnormal. The abnormal cell can refer to a cell that is not present or present only in a small number in the peripheral blood of a healthy person in the case where the specimen is blood. Further, the case where the number of abnormal cells per unit volume is equal to or more than the predetermined threshold can be considered as the presence of abnormal cells.

[0292] If the condition that the number of abnormal cells such as blast cells, abnormal lymphocytes, atypical lymphocytes, myeloblasts, and nucleated red blood cells in the specimen is equal to or more than a predetermined threshold per unit volume is not satisfied, the abnormal cell marker is not included in the FCM analysis result.

[0293] The FCM measurement processing will be described with reference to the flowchart of FIG. 10. Figure 22

[0294] In step S12, the control section 31 determines whether the FCM analysis result acquired in step S11 includes an abnormal cell marker.

[0295] If it is determined that the FCM analysis result includes an abnormal cell marker (step S12: Yes), in step S13, the control section 31 controls the GCM measurement unit 20 to perform GCM measurement processing. Thus, the control section 31 analyzes the waveform signal acquired in the GCM measurement unit 20, and acquires a GCM analysis result.

[0296] The GCM measurement processing can be performed, for example, in accordance with steps S110 to S140 of FIG. 9. Figure 10

[0297] Subsequently, in step S14, the control section 31 generates an analysis result of cells in the specimen as the object (cell analysis result) based on the FCM analysis result acquired in step S11 and the GCM analysis result acquired in step S13.

[0298] On the other hand, if it is determined that the FCM analysis result does not include an abnormal cell marker (step S12: No), in step S15, the control section 31 generates an analysis result of cells in the specimen as the object (cell analysis result) based on the FCM analysis result acquired in step S11.

[0299] That is, in step S12, the control section 31 selectively determines whether to generate a cell analysis result based on the FCM analysis result or to generate a cell analysis result based on the FCM analysis result and the GCM analysis result.

[0300] ​​For example, when the technician inputs a display instruction via the input unit 35, in step S16, the control unit 31 displays the cell analysis result screen 300, which includes the cell analysis results generated in step S14 or S15, on the display unit 34. For further information on the cell analysis result screen 300, please refer to [reference needed]. Figures 23-30 Let me explain.

[0301] As mentioned above, in Figure 21 In the example, GCM assays are performed when the FCM analysis results contain abnormal cell markers; therefore, the assay frequency of GCM assay unit 20 is lower than that of FCM assay unit 10. That is, control unit 31 can control each assay unit to ensure that the assay frequency of GCM assay unit 20 is lower than that of FCM assay unit 10. (See reference...) Figure 5 As explained, in the GCM measurement unit 20, the flow rate of the sample is adjusted so that only one cell is positioned within the irradiation range R of the diffraction illumination light at a time. Therefore, the productivity of measurements performed by the GCM measurement unit 20 is sometimes lower than that performed by the FCM measurement unit 10. Consequently, if the measurement frequency of the GCM measurement unit 20 is the same as that of the FCM measurement unit 10, it can sometimes lead to a decrease in the overall productivity of the examination chamber. By making the measurement frequency of the GCM measurement unit 20 lower than that of the FCM measurement unit 10, the advantages of the GCM measurement unit 20 can be effectively utilized while suppressing a decrease in the overall productivity of the examination chamber.

[0302] Figure 22 This is a flowchart illustrating an example of the FCM measurement process.

[0303] In step S101, the control unit 31 of the control unit 30 controls the FCM measurement unit 10 to perform the sample modulation unit 15 of the FCM measurement unit 10 (see reference). Figure 17 , Figure 18 The sample is prepared in reaction chambers C11, C12, and C21 to C24.

[0304] For convenience, this description assumes that the test sample is prepared in all reaction chambers C11, C12, and C21 to C24. However, in practice, the required test sample is prepared according to the test items specified for the specimen. In the subsequent steps S102 and S103, the test sample is measured using the electrical measuring unit 16a, HGB measuring unit 16b, and optical measuring unit 100 corresponding to the prepared test sample.

[0305] In step S102, the control unit 31 controls the FCM measurement unit 10 to perform measurements using the electrical measurement unit 16a and the HGB measurement unit 16b, and obtains measurement information based on these measurements from the FCM measurement unit 10.

[0306] In step S103, the control unit 31 controls the FCM measurement unit 10 to perform measurement using the optical measurement unit 100. Thus, the optical measurement unit 100... Figure 20 As shown, cells in the sample contained in the test specimen flowing through the flow cell 101 are irradiated with direct illumination light having a single beam point BS, and measurement data is acquired. Furthermore, the control unit 31 acquires the measurement data acquired by the measurement unit 16 from the FCM measurement unit 10.

[0307] In step S104, the control unit 31 analyzes the measurement information obtained in step S102 and the measurement data obtained in step S103.

[0308] In step S105, the control unit 31 generates FCM analysis information through the analysis in step S104. The FCM analysis information includes: blood cell counts obtained by analyzing measurement information based on measurements from the electrical measurement unit 16a and the HGB measurement unit 16b, and blood cell counts and abnormal cell markers obtained by analyzing measurement data based on measurements from the optical measurement unit 100.

[0309] Next, refer to Figures 23-25 To explain Figure 21 The cell analysis results screen 300 shown in step S16 is an example.

[0310] Figure 23 This is a schematic diagram illustrating the structure of screen 300, representing the results of cell analysis.

[0311] The cell analysis results screen 300 includes, for example, a count value display area 310 and an abnormal cell marker display area 320.

[0312] The count value display area 310 includes, for example, display areas 311 to 314 corresponding to the analysis modes of CBC, DIFF, RET and PLT-F respectively.

[0313] In display area 311, count values ​​corresponding to CBC mode are displayed, such as white blood cell count, red blood cell count, hemoglobin level, hematocrit value, mean corpuscular volume, etc.

[0314] In the display area 312, count values ​​corresponding to the DIFF mode are displayed, such as the number of neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

[0315] In the display area 313, as a count value corresponding to the RET mode, for example, a reticulocyte ratio, a reticulocyte count, a reticulocyte immature index, a reticulocyte hemoglobin amount, or the like is displayed.

[0316] In the display area 314, as a count value corresponding to the PLT-F mode, an immature platelet ratio or the like is displayed.

[0317] In addition, in the cell analysis result screen 300, a scatter diagram or a histogram included in the FCM analysis result can also be displayed.

[0318] The abnormal cell marker display area 320 includes display areas 321 to 323 that respectively display abnormal cell markers related to white blood cells, abnormal cell markers related to red blood cells, and abnormal cell markers related to platelets.

[0319] A label 321a is attached to the display area 321, and a number 1 or 2 is displayed in the label 321a. The label 321a in which "1" is displayed indicates that the abnormal cell marker related to white blood cells displayed in the display area 321 is based on the FCM analysis result, and the label 321a in which "2" is displayed indicates that the abnormal cell marker related to white blood cells displayed in the display area 321 is based on the GCM analysis result.

[0320] In the case where the step S15 of executing Figure 21 is performed, since the GCM measurement processing is not performed, the GCM analysis result is not acquired, and the cell analysis result is generated based on the FCM analysis result based on the FCM measurement processing. Therefore, as shown in Figure 24 , in order to indicate that the cell analysis result based on only the FCM analysis result is displayed on the cell analysis result screen 300, the abnormal cell marker based on the FCM analysis result is displayed in the display area 321 that displays the abnormal cell marker related to white blood cells, and "1" is displayed in the label 321a. In addition, in the example shown in Figure 23 , there is no abnormal cell marker based on the FCM analysis result, and therefore no abnormal cell marker is displayed in the display areas 321 to 323.

[0321] In the case where the step S14 of executing Figure 21 is performed, the GCM measurement processing has been performed, and the cell analysis result is generated based on the FCM analysis result based on the FCM measurement processing and the GCM analysis result. At this time, as shown in Figure 25In the example shown, in the cell analysis result screen 300, in the count value display area 310 and the display area 322, the display area 323, the count value and the abnormal cell marker based on the FCM analysis result are displayed, and in the display area 321 in which the abnormal cell marker related to white blood cells is displayed, display based on the GCM analysis result is performed instead of the abnormal cell marker based on the FCM analysis result. In order to indicate that the GCM analysis result is displayed in the display area 321, "2" is displayed in the label 321a. In addition, in the display area 321, "CML?" indicating that there is a suspicion of CML and the CML cell content rate "(20%)" are displayed based on the GCM analysis result. That is, in this example, at least a part of the result based on the FCM analysis result is supplemented based on the GCM analysis result. For example, the display of "CML?" can be performed in a case where the CML cell content rate exceeds a prescribed rate (for example, "20%") or is a prescribed rate or more. Also, the CML cell content rate can not be displayed, and only the CML cell content rate can be displayed. Figure 25 In the example shown, in the display area 321, "CML?" indicating that there is a suspicion of CML and the CML cell content rate "(20%)" are displayed based on the GCM analysis result. That is, in this example, at least a part of the result based on the FCM analysis result is supplemented based on the GCM analysis result. For example, the display of "CML?" can be performed in a case where the CML cell content rate exceeds a prescribed rate (for example, "20%") or is a prescribed rate or more. Also, the CML cell content rate can not be displayed, and only the CML cell content rate can be displayed.

[0322] In addition, in a case where the GCM measurement processing is performed and the suspicion of CML is low (for example, the CML cell content rate is less than a prescribed rate or is a prescribed rate or less) in the GCM analysis result, "CML?" of the comparative example can not be displayed in the display area 321, and "2" can be displayed in the label 321a.

[0323] Thus, in a case where the GCM measurement processing is performed regardless of the presence or absence and the kind of the abnormal marker of the FCM analysis result, display based on the GCM analysis result is performed.

[0324] Thus, the examiner can accurately grasp the presence of abnormal cells in the subject specimen, and thus can accurately determine whether or not the preparation of the smear specimen is necessary in the latter stage of the specimen analysis device IB. Even in a case where the preparation of the smear specimen is performed, the confirmation of the smear specimen can be smoothly performed based on the accurate abnormal cell marker based on the GCM analysis result.

[0325] Effects of the Second Embodiment

[0326] In the present embodiment, the specimen analysis device (e.g., the specimen analysis device 1B) further includes a first measuring unit (e.g., the FCM measuring unit 10) that acquires a measurement result related to information of blood cells contained in a specimen. The aforementioned measuring unit is a second measuring unit (e.g., the GCM measuring unit 20) that is different from the first measuring unit, and the second measuring unit acquires optical information of cells contained in the specimen by performing measurement of the specimen when a measurement result of the specimen by the first measuring unit satisfies a predetermined condition (e.g., the number of abnormal cells such as blast cells, abnormal lymphocytes, atypical lymphocytes, myeloblasts, and nucleated red blood cells in the specimen per unit volume is each equal to or greater than a predetermined threshold value).

[0327] Accordingly, the second measuring unit acquires optical information of cells contained in the specimen by performing measurement of the specimen when a measurement result of the specimen by the first measuring unit satisfies a condition of an increase in a specific type of leukocyte or appearance of a blast cell.

[0328] Also at this time, the predetermined condition can include an increase in a specific type of leukocyte or appearance of a blast cell.

[0329] Accordingly, the second measuring unit acquires optical information of cells contained in the specimen by performing measurement of the specimen when a measurement result of the specimen by the first measuring unit satisfies a condition of an increase in a specific type of leukocyte or appearance of a blast cell.

[0330] In a case where an increase in a specific type of leukocyte or appearance of a blast cell is found, it is possible that the subject has CML. Therefore, the second measuring unit can acquire optical information of cells contained in the specimen by performing measurement of the specimen with an increase in basophils or appearance of a blast cell as the predetermined condition.

[0331] Also at this time, the specific type of leukocyte can be basophils.

[0332] Accordingly, the second measuring unit acquires optical information of cells contained in the specimen by performing measurement of the specimen when a measurement result of the specimen by the first measuring unit satisfies a condition of an increase in a specific type of leukocyte or appearance of a blast cell.

[0333] In a case where an increase in basophils is found, it is possible that the subject has CML. Therefore, the second measuring unit can acquire optical information of cells contained in the specimen by performing measurement of the specimen with an increase in basophils as the predetermined condition.

[0334] In addition, the predetermined condition can include appearance of abnormal cells such as abnormal lymphocytes, atypical lymphocytes, myeloblasts, and nucleated red blood cells in addition to a blast cell.

[0335] <Second embodiment variant>

[0336] In the described embodiment, in the case where the GCM measurement processing is performed, the FCM analysis result is displayed as the cell analysis result in addition to the GCM analysis result, but it is not limited thereto, and the FCM analysis result can not necessarily be displayed.

[0337] For example, in step S14 of the described embodiment, the control section 31 of the control unit 30 can generate the cell analysis result based on only the GCM analysis result. The structure of the cell analysis result screen 300 in the case where the GCM measurement processing is performed can also be the same as the cell analysis result screen 600 of the first embodiment. Figure 21 Figure 9

[0338] In this variant, according to the GCM analysis result, the examiner can reduce the number of times of production and confirmation of smearing specimens and the like due to the reduction in the number of specimens showing false positives, can confirm the smearing specimens and the like with reference to the more accurate cell analysis result, and thus can omit the production and confirmation of smearing specimens. As a result, the burden on the examiner can be reduced.

[0339] <Third embodiment>

[0340] In the second embodiment, the FCM measurement processing and the GCM measurement processing are performed by the FCM measurement unit 10 and the GCM measurement unit 20, respectively.

[0341] In contrast, the third embodiment is an embodiment related to the content of causing the FCM measurement processing and the GCM measurement processing to be performed by a flow cytometry / phantom flow cytometry comprehensive measurement unit (hereinafter referred to as a "comprehensive measurement unit").

[0342] The content described in the third embodiment can be similarly applied to the other embodiments or the other variants.

[0343] Figure 26 is a front view schematically showing the structure of a specimen analysis device 1C as an example of a specimen analysis device of the third embodiment.

[0344] The specimen analysis device 1C includes, in place of the FCM measurement unit 10 and the GCM measurement unit 20 of the second embodiment shown in Figure 16

[0345] Figure 27 is a block diagram showing an example of the functional structure of the comprehensive measurement unit 70.

[0346] The comprehensive measurement unit 70 includes, in place of the GCM measurement unit 20 of the first embodiment shown in Figure 2 Figure 17 ​​​​The electrical measuring unit 16a and HGB measuring unit 16b shown are replaced by a sample modulation unit 27 and an optical measuring unit 400, respectively. The fluid adjustment unit 400a within the optical measuring unit 400 adjusts the flow rate of the measured sample per unit time in the flow cell 201 of the optical measuring unit 400, and... Figure 2 The fluid conditioning unit 200a is similarly constructed. Regarding the sample conditioning unit 27, see additional reference. Figure 28 This will be explained in more detail. Regarding the optical system of the optical measuring unit 400, please refer to the following: Figure 29 Let me explain.

[0347] The electrical measuring unit 16a and the HGB measuring unit 16b perform signal processing on the detection signals obtained through the measurement, and output the signal-processed measurement information to the measuring control unit 21.

[0348] The optical measuring unit 400 processes the detection signal acquired through the measurement and outputs the measurement data to the measuring control unit 21.

[0349] Measurement control unit 21 stores the measurement data output from measurement unit 26 into storage unit 22. When the measurement of a sample is completed, measurement control unit 21 sends the measurement data stored in storage unit 22, along with the sample ID read by reading unit 24, to control unit 30.

[0350] Figure 28 This is a block diagram illustrating an example of the functional structure of the sample modulation section 27.

[0351] Sample modulation section 27 and Figure 3 Compared to the sample modulation unit 25 of the first embodiment shown, it includes Figure 18 The reaction chambers C11, C12, C21 to C24 are shown. Reaction chambers C11 and C12 are connected to the electrical measuring unit 16a and the HGB measuring unit 16b, respectively, while reaction chambers C21 to C24 and C30 are connected to the optical measuring unit 400.

[0352] The suction tube 25b draws the sample from the sample container 51, which has been stirred by the stirring unit 25a, and appropriately dispenses the drawn sample into reaction chambers C11, C12, C21 to C24, and C30.

[0353] The test samples prepared in reaction chambers C21 to C24 and reaction chamber C30 flow through flow cell 201 respectively, and are measured by optical measuring unit 400.

[0354] The optical measuring unit 400 measures the test sample modulated in reaction chambers C21 to C24 to acquire a detection signal, and performs signal processing on the acquired detection signal to acquire measurement data.

[0355] Furthermore, the optical measuring unit 400 measures the measuring sample modulated in the reaction chamber C30 to obtain a detection signal, and performs signal processing on the obtained detection signal to obtain a waveform signal.

[0356] Figure 29 This is a schematic diagram showing the structure of the optical measuring unit 400.

[0357] Optical measuring unit 400 and Figure 4 Compared to the optical measuring unit 200, including Figure 19 The optical measuring unit 100 shown includes a light source 111, a collimating lens 112, a cylindrical lens 113, a beam blocker 122, an optical filter 123, an optical filter 132, an optical filter 142, and light receiving units 124, 133, and 143. It also includes a dichroic mirror 115, 125, 134, 144, and a condenser lens 126.

[0358] Dichroic mirror 115 reflects light of wavelength λ10 from light source 111 and transmits light of wavelength λ20 from light source 211. Through dichroic mirror 115, the optical axis of the light from light source 111 is aligned with the central axis of the light from diffractive optical element 215. Condensing lens 216 focuses the light from light sources 111 and 211 into the flow path 201a of flow cell 201. Condensing lens 216 is configured to suppress chromatic aberration in light of wavelengths λ10 and λ20. The beam point BS of the direct illumination light from light source 111 (see reference). Figure 20 ) was located in Figure 5 The center position of the illumination range R shown. The diffracted illumination light from light source 211 and... Figure 5 Similarly, irradiate the irradiation area R.

[0359] The collimating lens 112, cylindrical lens 113, dichroic mirror 115, and condenser lens 216 constitute an illumination optical system 206 that illuminates the cells passing through the flow cell 201 with light from the light source 111 as direct illumination light.

[0360] As with the second embodiment, when the straight-ahead illumination light of wavelength λ10 is irradiated to the cells flowing through the flow cell 201, the front-scattered light of wavelength λ10, the side-scattered light of wavelength λ10, and the fluorescence of wavelength λ11 are generated from the portion of the cells irradiated with the light. When the diffracted illumination light of wavelength λ20 is irradiated to the cells flowing through the flow cell 201, the front-scattered light of wavelength λ20, the side-scattered light of wavelength λ20, and the fluorescence of wavelength λ21 are generated from the portion of the cells irradiated with the light.

[0361] The dichroic mirror 125 reflects the straight-ahead illumination light and the front-scattered light based on the straight-ahead illumination light, and transmits the diffracted illumination light and the front-scattered light based on the diffracted illumination light. The straight-ahead illumination light and the diffracted illumination light that have passed through the flow cell 201 are blocked by the beam stopper 122 and the beam stopper 222, respectively. The condenser lens 126 condenses the front-scattered light based on the diffracted illumination light that has passed through the beam stopper 122 to the light-receiving portion 124. The dichroic mirror 134 reflects the side-scattered light based on the straight-ahead illumination light, and transmits the side-scattered light based on the diffracted illumination light. The dichroic mirror 144 reflects the fluorescence based on the straight-ahead illumination light, and transmits the fluorescence based on the diffracted illumination light. The light-receiving portion 124, the light-receiving portion 133, the light-receiving portion 143, the light-receiving portion 225, the light-receiving portion 233, and the light-receiving portion 243 receive the corresponding light to output detection signals.

[0362] Figure 30 is a flowchart showing an example of a flow of control processing performed by the control unit 30 in relation to measurement.

[0363] Figure 30 The control processing of Figure 21 In comparison with the second embodiment shown in FIG. 17, the GCM measurement processing of step S13 is performed between step S11 and step S12. That is, in the second embodiment, the GCM measurement processing is performed regardless of the FCM analysis result. In the FCM measurement processing of the second embodiment, the same processing as Figure 22 is performed in the comprehensive measurement unit 70, and in the GCM measurement processing, the same processing as Figure 10 steps S110 to S140 of the first embodiment is performed in the comprehensive measurement unit 70, for example.

[0364] <Effects of the Third Embodiment>

[0365] According to the present embodiment, in a specimen analysis device (for example, the specimen analysis device 1C), a measurement section (for example, the measurement section 26, the optical measurement section 400) acquires information that specifies at least white blood cells among cells contained in a specimen. Also, an analysis section (for example, the control unit 30) acquires the number (for example, the CML cell count) or the proportion (for example, the CML cell content rate) of leukemia cells among the white blood cells, which is determined based on the information acquired by the measurement section, based on the optical information acquired by the measurement section.

[0366] Thus, the number or proportion of leukemic cells in leukocytes determined based on information on at least leukocytes among cells contained in the specimen can be acquired based on the optical information.

[0367] Moreover, according to the present embodiment, the measurement unit acquires information on at least granulocytes among cells contained in the specimen, and the analysis unit acquires the number or proportion of leukemic cells in granulocytes determined based on the information acquired by the measurement unit based on the optical information acquired by the measurement unit.

[0368] Thus, the number or proportion of leukemic cells in granulocytes determined based on information on at least granulocytes among cells contained in the specimen can be acquired based on the optical information.

[0369] At this time, the information on at least granulocytes among cells contained in the specimen can include the intensity of scattered light obtained by irradiating a plurality of diffracted lights or a single irradiation light to the cells.

[0370] Thus, the number or proportion of leukemic cells in granulocytes determined based on information on at least granulocytes among cells contained in the specimen and including the intensity of scattered light obtained by irradiating a plurality of diffracted lights or a single irradiation light to the cells can be acquired based on the optical information.

[0371] <First Modification of the Third Embodiment>

[0372] In the third embodiment, as shown in FIG. 12, either one of step S14 and step S15 is executed in step S12 based on the first analysis result. However, the present embodiment is not limited thereto, and the determination in step S12 can be omitted. Figure 30

[0373] Figure 31 FIG. 13 is a flowchart showing an example of a flow of a control process performed by the control unit 30 in relation to measurement in the present modification.

[0374] In the control process of the present modification, as compared with the flowchart shown in FIG. 12, step S41 and step S42 are added instead of step S12, step S14, and step S16. Figure 30

[0375] ​​In step S41, the control section 31 of the control unit 30 generates a cell analysis result based on the GCM analysis result. The cell analysis result at this time contains a count value (e.g., the number of all leukocytes or the number of CML cells) contained in the GCM analysis result or a CML cell content rate. In step S42, the control section 31 outputs (e.g., displays) the cell analysis result generated in step S41 to the cell analysis result screen 300 and outputs (e.g., displays) the FCM analysis result acquired in the FCM measurement processing of step S11 as reference information. At this time, the FCM analysis result can be displayed in addition to the cell analysis result screen 300 when a button provided on the cell analysis result screen 300 is operated, or the FCM analysis result can be displayed together with a label indicating reference information in the cell analysis result screen 300, for example.

[0376] In Figure 31 In steps S41 and S42 of the third embodiment, the cell analysis result is generated based on the GCM analysis result and the FCM analysis result is displayed as reference information, but the cell analysis result can be generated based on the FCM analysis result and the GCM analysis result can be displayed as reference information. For example, the control unit 30 can selectively determine which of the FCM analysis result and the GCM analysis result to generate the cell analysis result based on. For example, the control unit 30 can selectively determine which of the FCM analysis result and the GCM analysis result to generate the cell analysis result based on a setting or an operation performed by an examiner.

[0377] <Second Modification Example of Third Embodiment>

[0378] In the third embodiment, the measurement sample used in the GCM measurement processing is prepared in the reaction chamber C30, but is not limited thereto, and the RBC / PLT measurement sample prepared in the reaction chamber C11 can be used for the GCM measurement processing.

[0379] Figure 32 is a block diagram showing the functional structure of the sample preparation section 27 of the present modification example.

[0380] The sample preparation section 27 of the present modification example is compared with the third embodiment shown in Figure 28 The reaction chamber C30 is omitted and the reaction chamber C11 is connected to the optical measurement section 400 in the present modification example. In the present modification example, in the second measurement sample, the RBC / PLT measurement sample prepared in the reaction chamber C11 flows through the flow cell 201, and a diffracted illumination light is irradiated to the measurement sample to acquire a waveform signal. In addition, at this time, fluorescence based on the diffracted illumination light is not acquired, and thus the GCM analysis result is acquired by analysis of the waveform signal based on the forward scattering light and the side scattering light based on the diffracted illumination light.

[0381] According to the present modification, since the reaction chamber C30 can be omitted, the structure of the specimen analysis device 1C can be simplified.

[0382] In addition, in the present modification, in the GCM measurement processing, the RBC / PLT measurement sample is caused to flow through the flow cell 201 to acquire the waveform signal, but the present application is not limited thereto, and the WDF measurement sample can be caused to flow through the flow cell 201 to acquire the waveform signal.

[0383] At this time, the GCM measurement processing can also be performed simultaneously with the FCM measurement processing. That is, information for determining white blood cells and the waveform signal can also be acquired simultaneously when the WDF measurement sample modulated in the reaction chamber C21 flows through the flow cell 201. However, at this time, in order to appropriately acquire the waveform signal, the flow rate per unit time of the WDF measurement sample flowing through the flow cell 201 must be smaller than when information for determining white blood cells is acquired only from the WDF measurement sample by the control of the fluid adjusting section 400a. However, since information for determining white blood cells and the waveform signal can be acquired simultaneously, the productivity of specimen analysis can also be shortened.

[0384] <Fourth Embodiment>

[0385] As a standard treatment for CML, for example, due to the appearance of TKI as one of the aforementioned molecular targeted drugs, the prognosis of CML has been significantly improved. However, most patients must take TKI for a long period of time, and various adverse events have been reported in conjunction with long-term administration. As a treatment goal for CML, it is important to aim for treatment-free remission (TFR) that maintains remission even after TKI is discontinued, but in reality, patients who can achieve TFR are about 50%.

[0386] BCR::ABL1, which is a pathogenic gene of CML, is measured by a quantitative PCR method, but it has been reported that the initial sharp decrease in BCR::ABL1 after the start of TKI is correlated with the likelihood of achieving TFR (Shanmuganathan N et al., Early BCR-ABL1 kinetics as a prognostic effect for treatment-free remission in chronic myeloid leukemia, Blood. 2021, DOI: 10.1182 / blood.2020005514).

[0387] That is, in CML treatment, predicting TKI treatment responsiveness can be considered by the attending physician to be information useful in judging the selection or discontinuation of TKI.

[0388] The present embodiment is an embodiment related to predicting (speculating) the treatment responsiveness of CML based on the discrimination result of the GCM of the white blood cells of the CML patient at the time of initial diagnosis.

[0389] The content described in the fourth embodiment is equally applicable to each of the other embodiments or each of the other modifications.

[0390] Figure 33 BCR::ABL1 mRNA (IS%) at the third month of treatment in 10 CML patients who received treatment with a second-generation TKI, i.e., patients who were low in the efficacy of the therapeutic drug (hereinafter referred to as "late responders") and patients who were high in the efficacy of the therapeutic drug (hereinafter referred to as "early responders") were statistically compared. The left chart corresponds to "late responders" (n = 4 (4 cases)), and the right chart corresponds to "early responders" (n = 6 (6 cases)). Moreover, the vertical axis is (IS%).

[0391] From this chart, it is known that there is a significant difference in the value of BCR::ABL1 mRNA (IS%) at the third month of treatment between late responders and early responders. As described above, there are reports indicating that an initial sharp decrease in BCR::ABL1 after the start of TKI is correlated with the likelihood of achieving TFR, and thus it is important to assume early responders as patients who are likely to achieve TFR and late responders as patients who are difficult to achieve TFR, to establish respective treatment plans.

[0392] Late responders and early responders can be distinguished by pursuing time-series data using the PCR method, but the value of BCR::ABL1 mRNA (IS%) at the time of initial diagnosis is around 100% in 10 CML patients, and thus it is not possible to distinguish between them at this time point. Therefore, the inventors and others have researched a method that can distinguish between late responders and early responders even at the time of initial diagnosis. In this method, a classification model that classifies cells contained in a specimen collected from a patient with chronic myeloid leukemia before the start of treatment (hereinafter referred to as a "target patient") as CML cells and classifies cells contained in a specimen collected from a healthy person as normal cells is generated (constructed), and the target patient is determined to be a late responder (a group with low treatment efficacy) or an early responder (a group with high treatment efficacy) by using an index related to the classification performance of the classification model for CML cells and normal cells. As the index, as long as it is an index indicating the classification performance of the classification model, an F1 score or an AUC can be used. The higher the classification performance, the more significant the morphological difference between CML cells and normal cells contained in the specimen of the target patient.

[0393] In the above paragraph, it is indicated that in the method described in the fourth embodiment, the classification model is generated (constructed) by using a plurality of specimens collected from a plurality of patients with chronic myeloid leukemia before the start of treatment (hereinafter referred to as "training specimens") and a plurality of specimens collected from a plurality of healthy persons (hereinafter referred to as "normal specimens"). Figure 34 In the above paragraph, it is indicated that in the method described in the fourth embodiment, the classification model is generated (constructed) by using a plurality of specimens collected from a plurality of patients with chronic myeloid leukemia before the start of treatment (hereinafter referred to as "training specimens") and a plurality of specimens collected from a plurality of healthy persons (hereinafter referred to as "normal specimens"). Figure 33A graph comparing the F1 score when the AI algorithm trained using specimens of CML patients classifies CML cells and normal cells is shown for the late responders and the early responders. The left graph corresponds to "late responders" (n = 4), and the right graph corresponds to "early responders" (n = 6). Also, the vertical axis is the F1 score, which is the discrimination result when comparing CML patient specimens and healthy person specimens using phantom flow cytometry. That is, it can be said that the vertical axis indicates how much morphological difference there is between the cells of the CML patient specimens and the cells of the healthy person specimens.

[0394] According to this graph, it is known that the F1 score is significantly higher for the late responders than for the early responders.

[0395] In Figure 34 In the lower graph, an ROC curve when classifying late responders and early responders based on the F1 score is shown.

[0396] As a result of this ROC analysis, it is known that when the cutoff value is set to "85.5%", discrimination can be made at a sensitivity of "75%" and a specificity of "100%".

[0397] That is, by focusing on the F1 score using the discrimination result of the peripheral blood leukocytes of the CML patient at the time of initial diagnosis and the peripheral blood leukocytes of the healthy person, it is possible to discriminate the late responders and the early responders according to the CML cell content rate at the time of initial diagnosis.

[0398] Figure 35 is a flowchart showing an example of a flow of control processing related to measurement performed by the control unit 30.

[0399] In addition, in the present embodiment, the control unit 30 in the specimen analysis device 1 described in the various embodiments described above can perform the following processing.

[0400] In step S13, the control section 31 controls the GCM measurement unit 20 to perform GCM measurement processing on the specimen of the target patient. Thereby, the control section 31 acquires waveform signals from CML cells of the target patient in the GCM measurement unit 20.

[0401] In step S51, the control section 31 trains the AI algorithm 61 using, as teaching data, the waveform signals of a part of the cells (for example, 75% of all cells) among the waveform signals of a plurality of cells contained in the specimen of the target patient, that is, as "CML (+) signals". Furthermore, the control section 31 trains the AI algorithm 61 using, as teaching data, the waveform signals of a part of the cells (for example, 75% of all cells) among the waveform signals of a plurality of cells contained in the specimen of one or more healthy persons, that is, as "CML (-) signals". Thereby, the control section 31 acquires the trained AI algorithm 62.

[0402] The control section 31 inputs, to the trained AI algorithm 62, a waveform signal (for example, a waveform signal of the remaining 25% of cells) of the plurality of waveform signals obtained from the test subject's test body, which is not used as teaching data, and causes the AI algorithm 62 to classify the waveform signal as positive / negative for CML cells. At this time, the AI algorithm 62 classifies as TP (True Positive) when positive and as FN (False Negative) when negative. Similarly, the control section 31 inputs, to the trained AI algorithm 62, a waveform signal of the plurality of waveform signals obtained from the healthy person's test body, which is not used as teaching data, and causes the AI algorithm 62 to classify the waveform signal as positive / negative for CML cells. At this time, the AI algorithm 62 classifies as FP (False Positive) when positive and as TN (True Negative) when negative.

[0403] Subsequently, in step S52, the control section 31 calculates an index (for example, an F1 score) indicating the classification performance of the classification model, based on the classification results of the AI algorithm 62, that is, TP, FN, FP, and TN.

[0404] In step S53, the control section 31 predicts the TKI treatment responsiveness of the target patient (whether the target patient is an early responder or a slow responder), based on the calculated index (for example, by comparing the F1 score with a threshold (cut-off value)).

[0405] Next, in step S54, the control section 31 outputs (for example, displays) a cell analysis result screen including the predicted TKI treatment responsiveness to the display section 34. In the TKI treatment responsiveness, for example, the F1 score can also be included.

[0406] <Effects of the Fourth Embodiment>

[0407] According to the method of the present embodiment, the plurality of diffracted lights generated by the light incident to the diffractive optical element are caused to irradiate cells contained in a test body to acquire optical information of the cells, the test body including a first test body (for example, a test body collected from a patient with chronic myeloid leukemia before the start of treatment (a target patient)) collected from a patient with chronic myeloid leukemia before the start of treatment and a second test body collected from a healthy person, a classification model that classifies leukemia cells is generated based on the optical information obtained from the first test body and the second test body, an index related to the classification performance of leukemia cells and normal cells by the generated classification model is acquired, and information related to the efficacy of a therapeutic drug for chronic myeloid leukemia on a patient is output based on the index.

[0408] By using optical information obtained from a first sample collected from a patient with chronic myelogenous leukemia before the start of treatment and a second sample collected from a healthy person, a classification model for classifying leukemia cells can be simply generated. Furthermore, by acquiring an index related to the classification performance of the generated classification model for leukemia cells and normal cells, and outputting information related to the effectiveness of a therapeutic drug for chronic myelogenous leukemia on the patient with chronic myelogenous leukemia before the start of treatment based on the acquired index, the attending physician or the like can be enabled to confirm.

[0409] Furthermore, at this time, the therapeutic drug for chronic myelogenous leukemia can also be a tyrosine kinase inhibitor (TKI).

[0410] <Variant of the fourth embodiment>

[0411] As the therapeutic drug for chronic myelogenous leukemia, a tyrosine kinase inhibitor, which is one of molecular targeted drugs, is exemplified, but a molecular targeted drug other than a tyrosine kinase inhibitor can also be provided. Furthermore, while a molecular targeted therapy is currently used as a standard treatment, a treatment other than a molecular targeted therapy can also be applied, and a therapeutic drug selected in this treatment can also be provided.

[0412] <Other embodiments>

[0413] In the described embodiment, one straight illumination light having one single beam spot BS is irradiated to the cells flowing through the flow cell, but a plurality of straight illumination lights having single beam spots can also be irradiated to the cells flowing through the flow cell. That is, in the optical measurement unit 100 shown in Figure 19 In the optical measurement unit 100 shown in Figure 29 In the optical measurement unit 400 shown in

[0414] In the described embodiment, the diffractive optical element 215 can also have a condensing action. At this time, for example, it can be that the diffractive pattern formed in the diffractive optical element 215 itself has a condensing action, or a diffractive pattern that generates diffracted light can be formed on the incident surface of the diffractive optical element 215, and a pattern having a lens action or a Fresnel lens can be formed on the exit surface of the diffractive optical element 215. Furthermore, in the case where the diffractive optical element 215 has a condensing action, the condensing lens 216 can also be omitted.

[0415] In the described embodiment, the diffractive optical element 215 is a transmissive diffractive optical element, but it can also be a reflective diffractive optical element.

[0416] In the embodiment described above, the arithmetic unit 32 of the control unit 30 classifies the cells by the AI algorithm 62 based on the detection signals of the light receiving portions 225, 233, and 243, but the classification of the cells is not limited to this and can be performed by comparing the patterns of the detection signals of the light receiving portions 225, 233, and 243 with the patterns stored in advance in the storage unit 33.

[0417] In the embodiment described above, the count values or abnormal cell markers related to the CML cells are acquired in the GCM measurement processing, but count values or abnormal cell markers related to cells other than the CML cells, such as neutrophils, normal lymphocytes, monocytes, eosinophils, basophils, blast cells, abnormal lymphocytes, atypical lymphocytes, promyelocytes, and nucleated red blood cells, can be acquired.

[0418] In the embodiment described above, the specimen is blood, but the specimen is not limited to this and can be a body fluid other than blood.

[0419] In the embodiment described above, the GCM measurement processing is performed when the FCM analysis result satisfies the prescribed condition shown in step S12, but the GCM measurement processing can be performed regardless of the FCM analysis result.

[0420] In the embodiment described above, the control unit 30 can configure the specimen container 51 measured by the FCM measurement unit 10 to the specimen intake position (specimen supply position) of the GCM measurement unit 20 when the FCM analysis result satisfies the prescribed condition shown in step S12, and control the conveyance unit 40 so that the specimen container 51 passes through the GCM measurement unit 20 when the FCM analysis result does not satisfy the prescribed condition.

[0421] The embodiments of the present application can be appropriately changed within the scope of the technical idea shown in the claims.

Claims

1. A sample analysis device, comprising: The measuring unit uses multiple diffracted beams generated by incident light onto a diffractive optical element to irradiate the cells contained in the sample and obtain the optical information of the cells; as well as The analysis unit analyzes the optical information obtained by the measurement unit using artificial intelligence algorithms, thereby obtaining information about the leukemia cells contained in the specimen.

2. The sample analysis device according to claim 1, wherein... The analysis unit obtains the proportion of leukemia cells among white blood cells as information about leukemia cells.

3. The sample analysis device according to claim 1, wherein... The analysis unit obtains the number of leukemia cells as information about leukemia cells.

4. The sample analysis device according to claim 1, further comprising: The sample preparation unit mixes the sample and reagents to prepare the sample for testing. The sample preparation unit uses a hemolysin as a reagent to prepare a test sample containing dissolved red blood cells from the sample.

5. The sample analysis device according to claim 4, wherein... The reagent does not contain dye.

6. The sample analysis device according to claim 1, wherein... The measuring unit is configured to acquire information to determine at least white blood cells among the cells contained in the sample. The analysis unit uses the optical information to obtain the number or proportion of leukemia cells in the white blood cells determined by the information.

7. The sample analysis device according to claim 1, wherein... The measuring unit is configured to acquire information to determine at least granulocytes among the cells contained in the sample. The analysis unit uses the optical information to obtain the number or proportion of leukemia cells in granulocytes, which is determined based on the information.

8. The sample analysis device according to claim 6 or 7, wherein The information includes the intensity of the scattered light obtained when a single illumination light is shone onto a cell.

9. The sample analysis device according to claim 1, wherein... The artificial intelligence algorithm was trained using optical information of white blood cells contained in specimens collected from patients with chronic myeloid leukemia as teaching data.

10. The sample analysis device according to claim 1, wherein... The artificial intelligence algorithm was trained using optical information of granulocytes contained in specimens collected from patients with chronic myeloid leukemia as teaching data.

11. The sample analysis device according to claim 1, further comprising: The first testing unit acquires test results related to information about the blood cells contained in the sample. The measuring unit is a second measuring unit, different from the first measuring unit. When the measurement results of the first measurement unit on the specimen meet the specified conditions, the second measurement unit performs the measurement of the specimen to obtain the optical information of the cells contained in the specimen.

12. The sample analysis device according to claim 11, wherein... The specified conditions include an increase in a specific type of white blood cell or the appearance of buds.

13. The sample analysis device according to claim 12, wherein... The specific type of white blood cell mentioned is a basophil.

14. A method for analyzing a sample, wherein multiple diffracted beams generated by incident light onto a diffractive optical element are irradiated onto cells contained in the sample to obtain optical information of the cells. The obtained optical information is analyzed using artificial intelligence algorithms to obtain information about leukemia cells contained in the specimen.

15. A method for analyzing a sample, wherein multiple diffracted beams generated by incident light onto a diffractive optical element are irradiated onto cells contained in a sample to obtain optical information of the cells, said sample comprising a first sample collected from a patient with chronic myeloid leukemia prior to the initiation of treatment and a second sample collected from a healthy individual. Based on the optical information obtained from the first and second samples, a classification model for classifying leukemia cells is generated. Obtain metrics related to the classification performance of the generated classification model for leukemia cells and normal cells. Based on the aforementioned indicators, information related to the effectiveness of treatments for chronic myeloid leukemia in the patients is output.

16. The sample analysis method according to claim 15, wherein... The therapeutic drug is a tyrosine kinase inhibitor.

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