WO2019206312A1 - 样本分析仪异常的报警方法、***及存储介质 - Google Patents

样本分析仪异常的报警方法、***及存储介质 Download PDF

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WO2019206312A1
WO2019206312A1 PCT/CN2019/084686 CN2019084686W WO2019206312A1 WO 2019206312 A1 WO2019206312 A1 WO 2019206312A1 CN 2019084686 W CN2019084686 W CN 2019084686W WO 2019206312 A1 WO2019206312 A1 WO 2019206312A1
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Prior art keywords
platelet
scattered light
detection data
sample
light signal
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PCT/CN2019/084686
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English (en)
French (fr)
Inventor
祁欢
郑文波
叶波
李朝阳
叶燚
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深圳迈瑞生物医疗电子股份有限公司
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Priority to CN201980008301.XA priority Critical patent/CN111656161B/zh
Publication of WO2019206312A1 publication Critical patent/WO2019206312A1/zh
Priority to US17/078,705 priority patent/US11781983B2/en
Priority to US18/377,880 priority patent/US20240044793A1/en

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    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
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Definitions

  • the present disclosure relates to the field of in vitro detection, and in particular to a blood analyzer, a blood analysis system, an analysis method of a blood sample, and a storage medium thereof.
  • Blood analysis is widely used in medical research and detection to obtain information about blood cells such as red blood cells, white blood cells, and platelets.
  • Common automated blood analyzers typically analyze blood cells in blood samples based on the principle of electrical impedance (also known as the Coulter principle).
  • the principle of electrical impedance when the particles suspended in the electrolyte pass through the detection micropores, the equivalent resistance at the detection micropores changes.
  • the voltage on both sides of the detection micropore changes.
  • a voltage pulse waveform can be formed by collecting voltage changes on both sides of the detection micropore through a circuit system, wherein the height of the pulse waveform reflects the volume of the particle.
  • the analytical instrument can provide volume distribution information of the particles in the sample according to the acquired pulse waveform.
  • the blood analyzer can provide a histogram of the volume distribution of blood cells in the blood sample under test based on the principle of electrical impedance, and analyze the volume distribution histogram to obtain blood analysis data such as cell classification and counting.
  • the detection signal based on the principle of electrical impedance can only reflect the volume information of the particles passing through the detection micropores, and it is impossible to distinguish different particles having the same or similar volume.
  • blood cell analysis methods based on electrical impedance methods cannot distinguish large-sized platelets, schistocytes, and microcytes, and blood analyzers may mistake large-sized large platelets as red blood cells.
  • the platelet test results are falsely reduced; the blood analyzer may also misidentify the relatively small volume of red blood cells (such as red blood cell debris and small red blood cells) as platelets, resulting in a false increase in platelet test results.
  • insufficient cleaning of the detection channel between the detection of different blood sample samples may also affect the platelet test results.
  • red blood cell debris adhering to the impurity particles of the detection channel or the previously unmeasured sample may be mixed into the blood sample to be tested, resulting in a false increase in platelet detection results.
  • the platelets themselves may be easily activated to adhere to the detection channel, causing a false increase in platelet detection results.
  • An embodiment of the present disclosure provides an alarm method for an abnormality of a sample analyzer, the method comprising:
  • An evaluation result is obtained based on a difference between the first platelet detection data and the second platelet detection data
  • the alarm first platelet detection is abnormal and/or the sample analyzer's electrical impedance signal detecting step is abnormal.
  • the obtaining, by the at least two optical signals, the second platelet detection data of the blood sample comprises:
  • a second platelet detection data of the blood sample is obtained based on the platelet region.
  • the reason for outputting the abnormality of the first platelet detection is that the abnormality of the electrical impedance signal detecting step is abnormal and/or the first platelet detecting result is unreliable.
  • the lysing reagent includes a hemolytic agent for dissolving red blood cells and a fluorescent dye for staining blood cells, and the at least two optical signals include a forward scattered light signal and Fluorescent signal.
  • the lysing reagent includes a hemolytic agent for dissolving red blood cells
  • the at least two optical signals include a first scattered light signal and a second scattered light signal
  • the first scattered light signal is a forward scattered light signal
  • the second scattered light signal is at least one of a medium angle scattered light signal and a side scattered light signal.
  • the step of obtaining the second platelet detection data of the blood sample based on the platelet region includes:
  • Second platelet detection data for the blood sample is obtained based on the number of particles present in the platelet region.
  • the lysing reagent includes a hemolytic agent for dissolving red blood cells and a fluorescent dye for staining blood cells, and the at least two optical signals include a side scattered light signal and a fluorescent signal; obtaining second platelet detection data of the blood sample based on the number of particles present in the platelet region.
  • the platelet region includes a large platelet region, and the second platelet detection data of the blood sample is obtained by using the large platelet region.
  • the first platelet detection data is at least one characteristic parameter of the first platelet volume distribution data
  • the second platelet detection data is the second platelet volume distribution data. The at least one characteristic parameter.
  • the characteristic parameter is selected from one or more of a platelet count, a platelet volume histogram, an average platelet volume, and a platelet volume distribution width; or
  • the characteristic parameter is selected from one or more of a platelet count, a platelet volume histogram, an average platelet volume, and a platelet volume distribution width within a certain volume threshold range.
  • the two optical signals include a scattered light signal and a fluorescent signal
  • the method further distinguishes the white blood cells into white blood cell subgroups according to the scattered light signals and the fluorescent signals, or Leukocytes count or identify nucleated red blood cells or immature cells or basophils.
  • the two optical signals include a first scattered light signal and a second scattered light signal, and the first scattered light signal is a forward scattered light signal, and the second scattering
  • the optical signal is at least one of a medium angle scattered light signal and a side scattered light signal, and the method further distinguishes white blood cells into white blood cell subgroups or recognition according to the first scattered light signal and the second scattered light signal Basophils.
  • the step of determining whether the evaluation result meets a preset condition comprises:
  • the evaluation value is used to reflect a degree of difference between the first platelet detection data and the second platelet detection data
  • the method further includes the following steps:
  • the second platelet detection data is output.
  • the judgment result of the platelet detection evaluation value obtained by continuously selecting a plurality of blood samples is recorded and counted, and when the judgment results of the plurality of consecutive blood samples are all, the sample is alarmed.
  • the analyzer's electrical impedance signal detection step is abnormal.
  • An embodiment of the present disclosure provides a non-transitory computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the steps of the alarm method described in any of the above.
  • An embodiment of the present disclosure provides, in an aspect, a blood analysis system, the blood analysis system comprising:
  • a sample processing device comprising at least one mixing chamber for mixing a first portion of the blood sample with a diluent to obtain a first test sample for first platelet detection; mixing the second portion of the blood sample with a dissolution reagent a second sample to be tested for second platelet detection, wherein red blood cells are cleaved in the second sample to be tested;
  • a sample detecting device comprising an electrical impedance detecting component and an optical detecting component, the electrical impedance detecting component comprising a microhole and an electrical impedance detector, wherein the electrical impedance detector is configured to detect that the first test sample passes through the micropore
  • An electrical impedance component comprising an optical flow chamber, a light source and an optical detector, the optical flow chamber being in communication with the mixing chamber, the light source for aligning a light beam with the optical flow chamber, the optical a detector for detecting at least two optical signals of the second test sample passing through the optical flow chamber;
  • the data analysis module comprises a signal acquisition module, a classification counting module and an alarm module;
  • the signal acquisition module acquires the electrical impedance signal of the first sample to be tested, and the signal acquisition module acquires the at least two optical signals of the second sample to be tested;
  • the classification counting module obtains first platelet detection data of the blood sample based on the electrical impedance signal; the classification counting module generates a scattergram of the second tested sample based on the at least two optical signals, based on The at least two optical signals distinguish a white blood cell region and a platelet region in the scattergram, and obtain second platelet detection data of the blood sample based on the platelet region;
  • the alarm module obtains an evaluation result based on a difference between the first platelet detection data and the second platelet detection data; determines whether the evaluation result satisfies a preset condition; and when the determination result is YES, the alarm is first An abnormality occurs in the platelet detection and/or an abnormality occurs in the electrical impedance detecting member.
  • the classification and counting module includes:
  • a second platelet detection data of the blood sample is obtained based on the platelet region.
  • the alarm module includes: outputting the first platelet detection abnormality due to an abnormality of the electrical impedance detecting component and/or the first platelet detection result is unreliable .
  • the lysing reagent includes a hemolytic agent for dissolving red blood cells and a fluorescent dye for staining blood cells, and the at least two optical signals include a forward scattered light signal. And a fluorescent signal, the optical detecting component comprising at least one scattered light detector and at least one fluorescent detector.
  • the lysing reagent includes a hemolytic agent for dissolving red blood cells
  • the at least two optical signals include a first scattered light signal and a second scattered light signal.
  • the first scattered light signal is a forward scattered light signal
  • the second scattered light signal is at least one of a medium angle scattered light signal and a side scattered light signal
  • the optical detecting component includes at least two scattered light detection Device.
  • the classification counting module obtains a derived platelet volume histogram based on at least the forward scattered light signal of the particle group appearing in the platelet region;
  • the sorting and counting module obtains second platelet detection data of the blood sample based on the number of particles present in the platelet region.
  • the lysing reagent includes a hemolytic agent for dissolving red blood cells and a fluorescent dye for staining blood cells, and the at least two optical signals include a side scattered light signal. And a fluorescent signal; the classification module obtaining second platelet detection data of the blood sample based on the number of particles present in the platelet region.
  • the platelet region includes a large platelet region
  • the second platelet detection data includes second large platelet data
  • the blood sample is obtained by using the large platelet region.
  • the second platelet test data is obtained by using the large platelet region.
  • the first platelet detection data is at least one characteristic parameter of the first platelet volume distribution data
  • the second platelet detection data is the second platelet volume distribution data. The at least one characteristic parameter.
  • the characteristic parameter is selected from one or more of a platelet count, a platelet volume histogram, an average platelet volume, and a platelet volume distribution width; or
  • the characteristic parameter is selected from one or more of a platelet count, a platelet volume histogram, an average platelet volume, and a platelet volume distribution width within a certain volume threshold range.
  • the two optical signals include a scattered light signal and a fluorescent signal
  • the method further distinguishes the white blood cells into a white blood cell sub-group according to the scattered light signal and the fluorescent signal, or White blood cells are counted or identified as nucleated red blood cells or immature cells or basophils.
  • the two optical signals include a first scattered light signal and a second scattered light signal, and the first scattered light signal is a forward scattered light signal, and the second The scattered light signal is at least one of a medium angle scattered light signal and a side scattered light signal, and the method further distinguishes white blood cells into white blood cell subgroups according to the first scattered light signals and the second scattered light signals Identify basophils.
  • the alarm module includes:
  • the evaluation value is used to reflect a degree of difference between the first platelet detection data and the second platelet detection data
  • the user interface is further included:
  • the second platelet detection data is output.
  • the method, system and storage medium provided by the present disclosure can provide users with more abundant detection information, remind the user to review or recheck the abnormal platelet detection data, and improve the accuracy of platelet detection.
  • FIG. 1 is a functional block diagram of a blood analysis system provided by the present disclosure.
  • FIG. 2 is a functional block diagram of a sample detecting device of the blood analysis system shown in FIG. 1.
  • 3 is a flow chart of the steps of the alarm method provided by the present disclosure.
  • FIG. 4A is a scatter plot generated by an embodiment of a second exemplary embodiment of the present disclosure.
  • Fig. 4B is a partial enlarged view of the platelet region P in Fig. 4A.
  • Figure 4C is a derived volume histogram obtained from the platelet region P of Figure 4A.
  • Fig. 5A is a schematic diagram for detecting comparison of first and second platelet detection data under normal conditions.
  • Fig. 5B is a schematic diagram of comparison of the first and second platelet detection data in the case of detecting an abnormality.
  • FIG. 6A is a scatter diagram generated by an embodiment of a third exemplary embodiment of the present disclosure.
  • Fig. 6B is a derivative volume histogram obtained from the platelet region P' in Fig. 6A.
  • Figure 7A shows a forward scattered light-fluorescence scatter plot of a second sample tested fluorescently stained with Alexa Fluor 488 dye.
  • Figure 7B shows a forward scattered light-fluorescence scatter plot of a second sample tested using the Mitotracker Red dye for fluorescent staining.
  • Figure 7C shows a forward scattered light-fluorescence scatter plot of a second sample tested fluorescently stained with Rhodamine 123 dye.
  • Figure 7D shows a forward scattered light-fluorescence scatter plot of a second sample tested using the Mitotracker Deep Red dye for fluorescent staining.
  • FIG. 8A is a scatter diagram of a second sample to be measured obtained according to an embodiment of the fourth exemplary embodiment of the present disclosure.
  • FIG. 8B is a scatter diagram of a second sample to be measured obtained according to an embodiment of the fifth exemplary embodiment of the present disclosure.
  • FIG. 9 is a schematic diagram of distinguishing platelet regions in a scattergram in an embodiment of the fifth exemplary embodiment of the present disclosure.
  • FIG. 10 is an overall perspective view of a blood analysis system provided by the present disclosure.
  • 11 is a three-dimensional scattergram of SFL, SSC, and FSC for second platelet detection in an embodiment of the present disclosure.
  • FIG. 12 is a three-dimensional scattergram of FSC-SSC-SFL detected by a second platelet containing a blood sample of immature cells in an embodiment of the present disclosure.
  • Fig. 13 is a FSC-SFL scattergram of second platelet detection of a blood sample containing nucleated red blood cells according to an embodiment of the present disclosure.
  • Fig. 14 is a view showing a region of platelet distribution corresponding to the SFL-SSC scattergram obtained by the second platelet detection in the embodiment of the present disclosure.
  • Sample collection unit 10 Sample processing device 30 Mixing room 320, 320a, 320b Sample detection device 50 Electrical impedance detecting unit 51 Microporous 512 Electrical impedance detector 514 Optical detection unit 53 Optical flow chamber 532 light source 534 Optical detector 536 bus 60 Data analysis module 70 Storage System 710 processor 730 Signal acquisition module 750 Classification counting module 770 Alarm module 790 User Interface 90 First case 100 Second casing 200
  • a first aspect of the present disclosure relates to a method, system, and storage medium for utilizing an electrical impedance signal, a scattered light signal, and a fluorescent signal of a blood sample to alert a platelet detection anomaly and/or an impedance channel anomaly.
  • FIG. 1 is a schematic illustration of a blood analysis system.
  • the blood analysis system includes a sample collection component 10, a sample processing device 30, a sample detection device 50, a data analysis module 70, and a user interface 90.
  • the blood analysis system has a fluid path system (not shown) for communicating the sample collection component 10, the sample processing device 30, and the sample detection device 50 for liquid transport.
  • the sample collection component 10 is for providing a blood sample to the sample processing device 30.
  • the sample processing device 30 is for processing a blood sample to prepare a test sample, and supplies the test sample to the sample detecting device 50.
  • the sample processing device 30 can include one or more mixing chambers that prepare the blood sample to be tested as one or more test samples.
  • the sample detecting device 50 is configured to detect characteristics of particles in each test sample and acquire corresponding detection signals.
  • the data analysis module 70 and the sample collection component 10, the sample processing device 30, the sample detection device 50, and the user interface 90 can be electrically or directly coupled via bus 60 to transmit and exchange data or signals.
  • the sample processing device 30 includes at least one mixing chamber for mixing a first portion of the blood sample to be tested with a diluent to obtain a first test sample, and after washing, for The second portion of the blood sample to be tested is mixed with the dissolution reagent to obtain a second test sample.
  • the sample processing device 30 may further include a sample divider for dividing the blood sample to be tested into a plurality of parts. Each blood sample is delivered to the same or a different mixing chamber for processing for subsequent testing.
  • the sample processing device 30 includes a first mixing chamber 320a and a second mixing chamber 320b to prepare a first test sample and a second test sample, respectively.
  • the sample analysis device 30 can have only one mixing chamber, and prepare the first sample to be tested and the second sample to be tested.
  • the diluent used to prepare the first test sample is typically used to dilute a blood sample to detect red blood cells and platelets by an automated blood analyzer.
  • the diluent typically includes one or more salts, such as an alkali metal salt, and is adjusted to isotonic to maintain red blood cell volume.
  • the first portion of the blood sample can be diluted with a commercial dilution to form a first test sample.
  • the commercial dilutions include, but are not limited to, M-68DS dilutions, M-53D dilutions, etc., produced by Shenzhen Mindray Biomedical Electronics Co., Ltd. (Shenzhen, China).
  • the temperature conditions and/or agitation conditions for preparing the first test sample may be the same as or similar to those used by the existing automated blood analyzer for detecting red blood cells and platelets.
  • the dissolution reagent includes a hemolytic agent and a fluorescent dye.
  • the hemolytic agent may be any of the existing hemolytic reagents for automated blood analyzer white blood cell classification, which may be a cationic surfactant, a nonionic surfactant, an anionic surfactant, an amphiphilic surfactant. Any one or combination of several.
  • the fluorescent dye is used to stain blood cells.
  • the fluorescent dye can be a nucleic acid dye to classify nucleated blood cells, such as white blood cells or nucleated red blood cells, with other types of cells by measuring differences in scattered light and fluorescent signals.
  • the lysing reagent may be a lysing reagent formulation as disclosed in U.S. Patent No. 8,367,358, the entire disclosure of which is incorporated herein by reference.
  • the solvating reagent disclosed in U.S. Patent No. 8,367,358 includes a cationic cyanine compound (a fluorescent dye), a cationic surfactant, a nonionic surfactant, and an anionic compound which can be used for dissolution. Red blood cells and white blood cells are classified into subpopulations by the difference in fluorescence and scattered light intensity detected.
  • Other existing fluorescent dyes can also be used in the dissolution reagent.
  • the fluorescent dye can be included in a separate staining solution that can be used with a hemolytic agent that does not contain a fluorescent dye.
  • the staining solution may be added to the blood sample in the mixing chamber 320 before, after or simultaneously with the hemolytic agent to prepare a second test sample.
  • the temperature conditions and/or agitation conditions for preparing the second test sample may be the same as or similar to the sample preparation conditions used for the white blood cell classification by the existing automated blood analyzer.
  • the sample detecting device 50 of the blood analysis system includes an electrical impedance detecting section 51 and an optical detecting section 53.
  • FIG. 2 shows a functional block diagram of the sample detecting device 50.
  • the electrical impedance detecting unit 51 is configured to detect an electrical impedance signal of the first test sample.
  • the electrical impedance detecting component 51 includes a micro hole 512 and an electrical impedance detector 514, and the electrical impedance detector 514 is configured to detect an electrical impedance signal, such as a direct current, when the first test sample passes through the aperture. , DC) impedance signal.
  • an electrical impedance signal such as a direct current
  • the electrical impedance signal can be measured based on the impedance change.
  • the pulse shape, height and width of the electrical impedance signal are directly related to the size or volume of the particles and can be converted to the volume of the primary particles.
  • the frequency histogram obtained by the electrical impedance measurement can reflect the size distribution of these particles.
  • a method for automatically detecting blood cells by a blood analyzer provided with an electrical impedance component is described in U.S. Patent No. 2,656,508, the entire disclosure of which is incorporated herein by reference.
  • the optical detection component 53 includes a sheath flow system, an optical flow chamber 532, a light source 534, an optical detector 536, and corresponding detection circuitry.
  • the optical flow chamber 532 is in operative communication with the mixing chamber 320 such that the first test sample is delivered from the mixing chamber 320 to the optical flow chamber 532 by a sheath flow system.
  • the light source 534 is used to align the light beam with the optical flow chamber 532.
  • the optical detector 536 is configured to detect at least two optical signals of the first sample to be tested. In a first exemplary embodiment of the present disclosure, the at least two optical signals include a forward scattered light signal and a fluorescent signal.
  • the optical detector 536 of the optical detection component 53 is configured to detect forward scattered light signals and fluorescent signals of the first sample being tested through the optical flow chamber 532.
  • the at least two optical signals further comprise a side scattered light signal, the optical detector 536 being configured to detect a forward scattered light signal of the first sample being tested through the optical flow chamber 532, Side-scattering light signals and fluorescent signals.
  • an optical flow cell refers to a focused-flow flow cell suitable for detecting a focused flow of scattered light signals and fluorescent signals, such as optical flow used in existing flow cytometers and blood analyzers. room.
  • a particle such as a blood cell
  • the particle scatters the incident beam from the source directed toward the detection aperture in all directions.
  • Having a photodetector disposed at one or more different angles relative to the incident beam can detect light scattered by the particle to obtain a scattered light signal. Since different blood cell populations have different scattered light characteristics, the scattered light signal can be used to distinguish different cell populations.
  • the scattered light signal detected near the incident beam is generally referred to as a forward scattered light signal or a small angle scattered light signal.
  • the forward scattered light signal can be detected from an angle of from about 1[deg.] to about 10[deg.] with the incident beam. In other embodiments, the forward scattered light signal can be detected from an angle of from about 2 to about 6 with the incident beam.
  • the scattered light signal detected in a direction of about 90° to the incident beam is generally referred to as a side scattered light signal. In some embodiments, the side scattered light signal can be detected from an angle of from about 65[deg.] to about 115[deg.] with the incident beam.
  • fluorescent signals from blood cells stained with fluorescent dyes are also typically detected in a direction about 90° from the incident beam.
  • the data analysis module 70 includes a storage system 710 and a processor 730.
  • the storage system 710 can store the underlying programs and data structures for implementing the various functions of the methods disclosed herein.
  • the storage system 710 can include one or more memories and one or more non-transitory computer readable storage media.
  • the non-transitory computer readable storage medium may include a hard disk drive, a floppy disk, an optical disk, a secure digital memory card (SD card), a flash memory card, or the like.
  • the memory can include a primary random access memory (RAM) or dynamic RAM (DRAM) for storing program instructions and data, and a read only memory (ROM) for storing fixed instructions.
  • the non-transitory computer readable storage medium stores a computer application for implementing the methods disclosed herein.
  • the processor 730 includes means for interpreting computer instructions and processing data in computer software, including but not limited to a Central Processing Unit (CPU), a Micro Controller Unit (MCU), and the like.
  • the processor 730 is configured to execute each computer application in the non-transitory computer readable storage medium, so that the blood analysis system performs a corresponding detection process and analyzes and processes at least two optical signals detected by the sample detecting device 50 in real time.
  • the at least two optical signals may be processed by a Field-Programmable Gate Array (FPGA), a digital signal processing (DSP), or a CPU, and then automatically analyzed by a computer application to obtain Data on platelet and/or platelet subpopulations.
  • FPGA Field-Programmable Gate Array
  • DSP digital signal processing
  • the data analysis module 70 further includes a signal acquisition module 750 , a classification counting module 770 , and an alarm module 790 .
  • the signal acquisition module 750 is operatively coupled to the sample detection device 50.
  • the signal acquisition module 750 can respectively acquire the electrical impedance signal of the first sample and the forward scattered light signal and the fluorescent signal of the second sample.
  • the class counting module 770 is coupled to the signal acquisition module 750.
  • the classification counting module 770 obtains first platelet detection data of the blood sample based on the electrical impedance signal.
  • the classification counting module 770 generates a scattergram of the second test sample based on the at least two optical signals, distinguishes a white blood cell region and a platelet region in the scattergram, and then obtains the platelet region based on the scatterogram. Second platelet detection data for the blood sample.
  • the scatter plot of the present invention is not limited by the form of its graphic presentation, and may also exist in the form of a data array, such as a digital form of a table or a list having an equivalent or close resolution to a scatter plot or a histogram, Or presented in any other suitable manner known in the art.
  • the alarm module 790 is coupled to the sorting and counting module 770.
  • the alarm module 790 obtains an evaluation result based on a difference between the first platelet detection data and the second platelet detection data.
  • the alarm module 790 determines whether the evaluation result satisfies a preset condition. When the determination result is YES, the alarm module 790 alarms that the first platelet detection abnormality and/or the impedance detection process is abnormal.
  • the steps of the specific method performed by the sorting and counting module 770 and the alarming module 790 will be described in detail later.
  • User interface 90 is a medium for interaction and information exchange between the blood analysis system and the user.
  • the user interface 90 can present the blood analysis data obtained by the classification counting module 770 and/or the abnormal alarm signal obtained by the alarm module 790 to the user of the blood analysis system.
  • the user interface 90 can be a touch screen capable of recognizing a user's touch operation and presenting a detection result.
  • user interface 90 can include an input device and an output device.
  • the input device may be a data input medium such as a keyboard, a mouse, or a microphone electrically connected to the data analysis module 70.
  • the output device can be a display screen, a printer, a speaker, an indicator light, and the like.
  • the user interface can perform differential identification of the color, font or label on the blood sample in the detection report or the presented detection screen, and can also pass flash, sound, etc.
  • the method prompts the user that the first platelet detection of the blood sample is abnormal and/or that the sample analyzer's electrical impedance detection process is abnormal.
  • the method of the alarm provided by the second exemplary embodiment of the present disclosure will be described in detail below in conjunction with the respective functional modules of the blood analysis system of the first exemplary embodiment.
  • the alarm method can be used for an automated blood analyzer or for a blood analysis system equipped with a flow cytometer and an electrical impedance detector.
  • the alarm method can be implemented by a processor in the form of a computer application, which can be disposed in an automated blood analyzer or independently set in a computer capable of directly or indirectly acquiring blood cell detection signal data.
  • the abnormality alarm method of the sample analyzer includes the following steps:
  • Step S200 providing a blood sample.
  • Step S220 mixing the first portion of the blood sample with the diluent to obtain a first test sample for the first platelet detection.
  • Step S225 mixing the second portion of the blood sample with the dissolving reagent to obtain a second test sample for the second platelet detection.
  • the lysing reagent includes a hemolytic agent for dissolving red blood cells and a fluorescent dye for staining blood cells.
  • Step S230 Detecting an electrical impedance signal of the first test sample.
  • Step S235 Detecting at least two optical signals of the second sample to be tested.
  • the at least two optical signals comprise a forward scattered light signal and a fluorescent signal.
  • Step S250 Obtaining first platelet detection data of the blood sample based on the electrical impedance signal obtained in step S230.
  • Step S255 obtaining second platelet detection data of the blood sample based on the at least two optical signals obtained in step S235.
  • Step S270 The evaluation result is obtained based on the difference between the first platelet detection data and the second platelet detection data.
  • Step S280 determining whether the evaluation result satisfies a preset condition.
  • step S290 is performed, the alarm first platelet detection is abnormal and/or the electrical impedance signal detecting step is abnormal.
  • the judgment result is no, the flow ends.
  • the sample collection component 10 provides a blood sample for the blood analysis system or blood analyzer.
  • the sample processing device 30 prepares the first measured sample and the second measured sample, respectively.
  • the reagents and preparation conditions and the like used for preparing the first sample to be tested and the second sample to be tested have been described in detail above and will not be described herein.
  • the electrical impedance detecting component 51 of the sample detecting device 50 detects the electrical impedance signal of the first test sample; when the processor performs step S235, the optical detecting component 53 of the sample detecting device 50 detects the first The at least two optical signals of the two tested samples.
  • the data analysis module 70 obtains the first and second platelet detection data, respectively.
  • the alarm module 790 of the data analysis module 70 determines whether there is an abnormality in the platelet detection based on the first and second platelet detection data and alerts the abnormality. It can be understood that, in the step flow of the alarm abnormality, the steps S220, S230 and S250 for acquiring the first platelet detection data and the steps S225, S235 and S255 for acquiring the second platelet detection data can be performed in parallel in time, It can also be executed sequentially.
  • the electrical impedance volume histogram of platelets and red blood cells in the first sample to be tested can be generated based on the electrical impedance signal obtained in step S230.
  • the volume of blood cells is expressed in ascending (fL).
  • the platelet and red blood cell volume distribution curve can be distinguished in the volume histogram by one or more preset volume demarcation values, and then the characteristic parameters of the platelets in the blood sample can be obtained based on the volume distribution curve of the platelets.
  • the one or more preset volume demarcation values are empirical values or values that are dynamically obtainable based on empirical algorithms.
  • the volume range threshold for distinguishing platelets may be 2-30 fL.
  • Characteristic parameters of the platelet include, but are not limited to, platelet count PLT, mean platelet volume MPV, platelet volume distribution width PDW. It should be noted that the "first platelet detection data" described herein includes platelet volume distribution data and/or characteristic parameters of the reaction platelet volume distribution.
  • the platelet volume distribution data may be in digital form or in graphical form.
  • step S255 the present disclosure discloses a method of obtaining second platelet detection data based on at least two optical signals of a second sample to be tested.
  • red blood cells in the second sample to be tested are dissolved, and blood cells are fluorescently stained, and the at least two optical signals include a forward scattered light signal and a fluorescent signal.
  • step S255 may include the following steps.
  • Step S2551 Acquire the at least two optical signals of the second sample to be tested. Accordingly, for the blood analysis system in the first exemplary embodiment, the signal acquisition module 750 acquires the at least two optical signals of the second sample to be tested.
  • Step S2553 Generate a scattergram of the second test sample based on the at least two optical signals.
  • a scattergram of fluorescence-forward scattered light can be obtained. Accordingly, for the blood analysis system in the first exemplary embodiment, the sorting and counting module 770 generates a scattergram of the second sample to be tested.
  • the at least two optical signals acquired in step S235 include a forward scattered light signal, a side scattered light signal, and a fluorescent signal
  • the scattergram generated in step S2553 may also be a forward-side Scattered light scatter plot, fluorescence-side scattered light scatter plot, or fluorescence, forward-side scattered light three-dimensional scatter plot. It can be understood that when the at least two optical signals further include other optical signals (such as a medium-angle scattered light signal, a fluorescent signal), the scattergram may also be other forms of two-dimensional or three-dimensional scattergrams.
  • the horizontal and vertical coordinates of the scattergram can also adopt other parameters of the forward scattered light signal and the side scattered light signal that can reflect the characteristics of the first sample-like particle, and the horizontal and vertical coordinates of the scatter plot can also be used.
  • Non-linear axes such as logarithmic axes, are used to further highlight the difference in particle swarm distribution.
  • Step S2555 Differentiating the white blood cell region and the platelet region in the scattergram of the second test sample based on the at least two optical signals.
  • the sorting and counting module 770 distinguishes the white blood cell region and the platelet region in the scattergram of the second sample to be tested based on the at least two optical signals.
  • a white blood cell region W and a platelet region P can be distinguished based on the intensity difference between the forward scattered light signal and the fluorescent signal of the second sample to be tested.
  • the white blood cell region W includes a region in which white blood cells appear in the scattergram;
  • the platelet region P includes a region in which platelets appear in the scattergram.
  • a particle group having relatively small scattered light and fluorescence intensity mainly includes red blood cell fragments and platelets.
  • the lysing reagent may include one or more solvating agents for dissolving red blood cells and a fluorescent dye for staining nucleated blood cells, and the size of the platelets treated by the hemolytic agent is There are differences in cell contents from red blood cell fragments and white blood cells, and some or all of the platelets can be distinguished in the hemolyzed blood sample by optical methods such as measuring light scattering and fluorescent signals.
  • the platelet region P distinguished by the step S2555 may contain impurity particles such as red blood cell fragments.
  • the intensity of the forward scattered light signal of the platelet region P is substantially smaller than the intensity of the forward scattered light signal of the white blood cell region W
  • the intensity of the fluorescent signal of the platelet region P is substantially smaller than the white blood cell region W.
  • Fig. 4B is a partial enlarged view of Fig. 4A, which is obtained by enlarging the platelet region P in the scattergram shown in Fig. 4A.
  • Step S2557 obtaining second platelet detection data of the blood sample based on the platelet region P. Accordingly, for the blood analysis system in the first exemplary embodiment, the classification counting module 770 obtains the second platelet detection data of the blood sample based on the platelet region P.
  • step S2557 calculates second platelet detection data for the blood sample being tested based on the forward scattered light signal FSC of the population of particles characterized in the platelet 10b.
  • the volume Vol of each particle characterized in the platelet 10b can be calculated using equation (1):
  • FSC is the intensity of the forward scattered light signal of each particle (also referred to as "individual event", which is characterized in the platelet 10b, and a is a constant.
  • the volume Vol of each particle characterized in the platelet 10b can be calculated using equation (2):
  • FSC is the intensity of the forward scattered light signal for each individual event characterized in the platelet 10b, and ⁇ and ⁇ are constant.
  • the volume Vol of each particle characterized in the platelet 10b can be calculated using equation (3):
  • FSC is the intensity of the forward scattered light signal for each individual event characterized in the platelet 10b, and ⁇ and ⁇ are constant.
  • volume distribution data corresponding to the platelet 10b can be obtained based on the volume Vol of each particle in the particle group characterized by the platelet 10b and the corresponding number of particles. Further, a volume distribution curve can be obtained based on the volume distribution data of the platelet 10b, which is referred to herein as a derived volume histogram, as shown in FIG. 4C. Since the volume distribution data (or the derived volume histogram) of the platelet 10b contains platelet information in a hemolyzed blood sample, it is considered herein to be a form of second platelet detection data.
  • a larger volume of particles and smaller particles can be distinguished in the derived volume histogram using a preset derived volume separation threshold.
  • the derived volume separation threshold may be selected from a value between 10-20 fL, such as 10fL, 12fL, 15fL or 20fL. The inventors have repeatedly made assumptions and experiments that the larger particles are mainly the larger part of the platelets of the blood sample to be tested.
  • the electrical impedance method cannot distinguish between large-sized platelets, red blood cell fragments, and small red blood cells, by comparing the derived volume histogram obtained in the second exemplary embodiment with the portion of the electrical impedance volume histogram that is larger than the derivative volume separation threshold
  • the first platelet detection data abnormality obtained by the electrical impedance detecting method can be effectively alarmed.
  • the portion of the curve in which the particle volume is larger in the derived volume histogram separated by the derived volume separation threshold contains platelet information in the hemolyzed blood sample, it is also considered herein as a form of second platelet detection data.
  • characteristic parameters such as the area of the curve can be obtained, and the characteristic parameter is also a form of second platelet detection data.
  • the at least two optical signals acquired in step S235 include a forward scattered light signal, a side scattered light signal, and a fluorescent signal.
  • the Mie scattering theory can also be utilized based on the forward scattered light signal and the side scattered light signal of the platelet 10b (Zhang Wei, Lu Yuan, Du Shiming et al., Spherical particle Mie scattering characteristic analysis, Optical Technology, 2010 November: Volume 36, Number 6: 936-939.)
  • the volume of each particle in the platelet region is calculated, and then the volume distribution data of the particle group presented by the platelet 10b, that is, the second platelet detection data, is obtained.
  • a derived volume histogram can be obtained based on the volume distribution data of the platelets 10b.
  • a curve portion having a larger particle volume in the derived volume histogram can be obtained, and according to the curve portion, information of a relatively large volume of platelets in the blood sample to be tested can be obtained.
  • the second platelet detection data can also be obtained by equation (1), equation (2) or equation (3) based on the forward scattered light signal of the platelet 10b.
  • the second platelet detection data of the blood sample to be tested can be obtained by sequentially performing steps S2551-S2557 in step S255.
  • step S270 obtains an evaluation result based on a difference between the first platelet detection data and the second platelet detection data.
  • the alarm module 790 obtains an evaluation result based on a difference between the first platelet detection data and the second platelet detection data.
  • the step S270 may further include the following steps.
  • Step S2701 Acquire the first platelet detection data obtained in step S250 and the second platelet detection data obtained in step S255. It can be understood that, in an embodiment, step S2701 can selectively acquire first and second platelet detection data presentation forms that can be used for direct comparison, such as volume in the electrical impedance volume histogram and the derived volume histogram is greater than A segment of a curve that divides the threshold by a predetermined volume, or the integrated area of the curve segment to the volume of the horizontal axis of the coordinate axis.
  • the first second platelet detection data of other presentation forms may also be acquired by step S2701, and then acquired after step S2701 by step S2703.
  • the form of the first and second platelet detection data is matched, and the form of platelet detection data in which the comparison cannot be directly used for comparison is calculated and converted to make them comparable.
  • Step S2705 The evaluation result is obtained based on the first and second platelet detection data presentation forms that can be used for direct comparison.
  • the evaluation result may be a result obtained by comparing numerical values and/or graphic differences between the first platelet detection data and the second platelet detection data.
  • step S2705 may calculate the difference value (EV) of the difference between the first platelet detection data and the second platelet detection data by a mathematical formula, and then the evaluation value is A predetermined threshold is compared to obtain an evaluation result that the evaluation value is greater than, equal to, or less than the preset threshold.
  • the evaluation result obtained in step S2705 may be a predetermined qualitative description of the degree of difference between the first platelet detection data and the second platelet detection data curve, such as “substantially similar” or “The difference is big” and so on. It can be understood that the content of the evaluation result may include one or more analytical comparison results, such as a numerical evaluation result including a plurality of characteristic parameters reflecting platelet information in the blood sample.
  • the evaluation value may be the degree of difference between the second platelet detection data and the first platelet detection data, or may be the degree of difference between the first platelet detection data and the second platelet detection data. It should be noted that the manner in which the evaluation value is calculated is not limited to the manner disclosed herein. It can be understood that the preset threshold value described in step S2705 is set according to the setting manner of the evaluation value. Taking the platelet detection data as an area integral value within a volume range in the volume histogram, the evaluation value EV may be a difference, an absolute difference between the second platelet detection data PLT2 and the first platelet detection data PLT1. The value or quotient value can also be the reciprocal, multiple or exponent of its difference, absolute difference, or quotient.
  • EV a * (PLT2 / PLT1), where a is a predetermined coefficient.
  • EV a * (PLT1/PLT2), where a is a predetermined coefficient.
  • EV (PLT1 - PLT2) b , where b is a predetermined coefficient.
  • step S280 determines whether the evaluation result obtained in step S270 satisfies the preset condition.
  • step S290 is performed, the alarm first platelet detection is abnormal and/or the impedance signal detecting process is abnormal.
  • the judgment result is no, the flow ends.
  • the alarm module 790 determines whether the evaluation result satisfies a preset condition: when the determination result is YES, the alarm first platelet detection has an abnormality and/or an impedance signal. The detection process is abnormal; when the judgment result is no, the flow ends.
  • the alarm module 790 alerts the first platelet detection that an abnormality has occurred and/or that an abnormality in the impedance signal detection process may be transmitted to the user interface 90.
  • the difference between the first platelet detection data obtained by the electrical impedance method and the second platelet detection data obtained by the optical method of the present disclosure is small, that is, the method
  • the evaluation results obtained by the provided system and method include information that the difference between the first and second platelet detection data is relatively small, and when the predetermined condition is that the difference between the first and second platelet detection data is large, the determination of step S280 The result is no and the process ends.
  • Fig. 5A the difference between the first platelet detection data obtained by the electrical impedance method and the second platelet detection data obtained by the optical method of the present disclosure is small, that is, the method
  • the evaluation results obtained by the provided system and method include information that the difference between the first and second platelet detection data is relatively small, and when the predetermined condition is that the difference between the first and second platelet detection data is large, the determination of step S280 The result is no and the process ends.
  • Fig. 5A the difference between the first platelet detection data obtained by the electrical impedance method and the second platelet detection data
  • step S280 YES, and the alarm first platelet detection abnormality and/or the impedance channel signal detection process is abnormal.
  • the preset condition set in step S280 may be that the evaluation value should be greater than the preset threshold.
  • the preset condition set in step S280 may be that the difference between the first and second platelet detection data images is large. It can be understood that the preset condition may include a plurality of preset conditions, and when the plurality of preset conditions are all satisfied, the determination result of step S280 is YES.
  • a step of outputting other detection results and/or intermediate results may also be included.
  • the detection result includes, but is not limited to, the first platelet detection data obtained in step S250 and the second platelet detection data obtained in step S255.
  • the intermediate result includes, but is not limited to, the scattergram obtained in step S255, the platelet region in the scatter plot, the derived volume histogram, the portion of the curve where the particle volume is larger after being separated by the derived volume separation threshold, and the evaluation value obtained in step S270. Or evaluation results, etc.
  • the abnormalities described herein may be due to abnormalities in the blood analyzer.
  • the blood analyzer abnormality includes, but is not limited to, an abnormality of the electrical impedance detecting component and an abnormality of the optical detecting component.
  • the probability of abnormality of the optical detecting member is generally small, by comparing the first and second platelet detecting data, it can be used to indicate an abnormality of the electrical impedance detecting member.
  • the first and second platelet detection data of the plurality of samples can be continuously recorded, and if the data of the plurality of samples are inconsistent, the data of the electrical impedance detection component is abnormal, and the accuracy of the alarm is improved.
  • the second platelet detection can be performed using leukocyte detection or nucleated red blood cell detection by an existing analyzer. That is, the second test solution may be a white blood cell sort or count or a test sample for basophil count or nucleated red blood cell count. Since red blood cells are lysed in these test solutions, blood cells are stained with fluorescent dyes, and optical signals are also obtained for each cell particle in optical detection. The inventors have found through research that the scatter plot obtained by these tests also has a platelet region P, which can be used for the above method for alarming. At the same time, the results of white blood cell classification can be obtained. As shown in Fig.
  • white blood cells are divided into four subgroups based on fluorescence signals, lateral light scattering signals and forward light scattering signals: lymphocytes, monocytes, neutrophils and Eosinophils.
  • basophils in leukocytes can be distinguished from other leukocyte subpopulations based on light scattering signals and fluorescent signals.
  • the method can further comprise the step of counting the number of white blood cells and reporting the white blood cell count in the blood sample. It will be understood by those skilled in the art that the method may further comprise the step of identifying nucleated red blood cells, immature cells or primordial cells based on the light scattering signal and the fluorescent signal of the second suspension. For example, as shown in FIG.
  • the method when immature cells are present in a blood sample, the method can identify immature cells based on light scattering signals and fluorescent signals, and can distinguish white blood cells into four subpopulations: lymphocytes, monocytes. , neutrophils and eosinophils.
  • light scattering and fluorescence signals can recognize nucleated red blood cells and white blood cells, and perform nucleated red blood cells and white blood cell counts.
  • the platelet region can also be distinguished by using the fluorescence-side scattered dot map (SFL-SSC) as shown in Fig. 14. Therefore, when the sample passes through the nucleated red blood cell detecting portion and simultaneously acquires the fluorescent signal, the forward scattered light signal, and the side scattered light signal, the P region can be distinguished by the fluorescence-side scattered dot pattern (SFL-SSC), and then at least Based on the forward scattered light signal of each cell, a histogram of the derived platelet volume is obtained, and second platelet detection data is obtained.
  • SFL-SSC fluorescence-side scattered dot map
  • step S255a A method of alarm abnormality provided by the third exemplary embodiment of the present disclosure will be described below.
  • the third exemplary embodiment adopts a different method in step S255a to acquire second platelet detection data, the main analysis flow and other steps See Figure 3 and the content described above, and will not be described here.
  • step S255a obtains second platelet detection data based on at least two optical signals of the second sample to be tested, the at least two optical signals including the forward direction of the second test sample in which the red blood cells are dissolved Scattering light signals and fluorescent signals.
  • step S255a includes the following steps.
  • Step S2551a Acquire the at least two optical signals of the second sample to be tested.
  • Step S2553a Generate a scattergram of the second test sample based on the at least two optical signals.
  • Step S2555a distinguishing a white blood cell region and a platelet region in a scattergram of the second test sample based on the at least two optical signals.
  • the platelet region distinguished by step S2555a is a large platelet region P' which is a region in which the large platelets appear in the scattergram in the second sample to be tested.
  • the intensity of the forward scattered light signal of the large platelet region P' is substantially smaller than the intensity of the forward scattered light signal of the white blood cell region W, and is substantially larger than the lower left of the scatter plot. The intensity of the forward scattered light signal of the horn cell debris.
  • the intensity of the fluorescent signal of the large platelet region P' is substantially smaller than the fluorescence intensity of the white blood cell region W.
  • a platelet-derived volume histogram can also be obtained based on at least the large platelet region P' by the aforementioned method, as shown in Fig. 6B. It should be noted that Fig. 6B is a schematic view, and for the convenience of understanding, the left part of the curve is subjected to fitting processing.
  • Step S2557a obtaining second platelet detection data of the blood sample based on the large platelet region P'.
  • the second platelet detection data may be large platelet detection data, such as volume distribution data of large platelets, large platelet counts, or other characteristic parameters that may reflect a large platelet volume distribution.
  • step S2557a may obtain volume distribution data for large platelets based on the forward scattered light signal FSC of the population of particles characterized in the large platelet region P'.
  • the forward scattered light signal FSC can be converted into the volume of each particle in the large platelet region P' by the equation (1), the equation (2) or the equation (3), thereby obtaining volume distribution data of large platelets.
  • the at least two optical signals acquired in step S235a include a forward scattered light signal, a side scattered light signal, and a fluorescent signal
  • step S2557a may also be based on the large
  • the forward scattered light signal and the side scattered light signal of the particle group characterized in the platelet region P' are calculated by Mie scattering theory to obtain the volume of each particle in the large platelet region P', thereby obtaining volume distribution data of large platelets.
  • a large platelet derived volume histogram can be obtained based on the volume distribution data of the large platelets.
  • the count value of the large platelets can also be calculated based on the volume distribution data of the large platelets.
  • the volume threshold for defining large platelets may be set by a user, and the volume threshold may be any value between 10-20 fL, for example, the large platelets may be larger than 10 fL, 12 fL, 15 fL. Or 20fL of platelets. It will be understood by those skilled in the art that the extent of the large platelet region P' can vary correspondingly based on the set volume threshold of large platelets.
  • characteristic parameters reflecting the volume distribution of the large platelets such as a large platelet count value, a large platelet volume distribution width, and the like, can also be calculated.
  • step S2557a may also acquire the number of particles (or "event number") of the particle group characterized in the large platelet region P', and obtain a count value of large platelets based on the number of particles.
  • step S270a may acquire the first platelet detection data obtained in step S250 and the second platelet detection data obtained in step S255a, based on the first platelet detection data and the second platelet detection data. The difference between the two is evaluated.
  • the second platelet detection data for step S270a may be volume distribution data of the above-mentioned large platelets (such as a large platelet-derived volume histogram), a count value of large platelets, or other characteristic parameters reflecting the volume distribution of large platelets.
  • step S270a may include the step of pre-treating the first platelet detection data obtained in step S250, thereby matching the acquired forms of the first and second platelet detection data to obtain an evaluation result based on the difference therebetween.
  • the alarm method provided by the fourth exemplary embodiment of the present disclosure will be described below.
  • the fourth exemplary embodiment adopts different sample processing methods and data analysis methods in steps S225b and S255b to acquire second platelet detection data.
  • the second platelet detection data includes the second sample to be tested. Platelet information for each volume, including the platelet count value of the sample.
  • the content of the main analysis process and other steps can be referred to FIG. 3 and the content described in the second exemplary embodiment above, and will not be described here.
  • the lysing reagent for preparing the second test sample includes a hemolytic agent for dissolving red blood cells and a fluorescent dye for staining blood cells.
  • the optical difference between the platelets and the white blood cells and the red blood cell fragments in the second sample to be hemolyzed is made more remarkable by the selection of the hemolytic agent and/or the fluorescent dye, thereby realizing the platelet Distinguish and count.
  • step S225b specifically stains blood cells in the blood sample using a membrane dye or a mitochondrial dye, and lyses the red blood cells using the hemolytic agent as described in each of the exemplary embodiments described above to prepare a second sample to be tested, thereby
  • the at least two optical signals distinguish the platelets in the second sample to be tested.
  • the film dye may be an Alexa Fluor series dye, other commercially available dyes which are specifically film dyes, and a deformed structure in which these dyes are used as a matrix.
  • the mitochondrial dye may be Rhodamine 123, Mitotracker series dyes, other commercially available dyes specifically identified as membrane dyes, and deformed structures with these dyes as precursors.
  • Figure 7A shows a forward scattered light-fluorescence scatter plot of a second sample tested fluorescently stained with Alexa Fluor 488 dye.
  • Figure 7B shows a forward scattered light-fluorescence scatter plot of a second sample tested using the Mitotracker Red dye for fluorescent staining.
  • Figure 7C shows a forward scattered light-fluorescence scatter plot of a second sample tested fluorescently stained with Rhodamine 123 dye.
  • Figure 7D shows a forward scattered light-fluorescence scatter plot of a second sample tested using the Mitotracker Deep Red dye for fluorescent staining.
  • the coordinate axis of the scattergram generated in step S255b is a logarithmic coordinate axis.
  • the platelet region P" can be distinguished in the scatter plot by specifically staining blood cells in the blood sample using a membrane dye or mitochondrial dye.
  • the platelet region P" is the second test sample. The area in which the platelets appear in the scatter plot.
  • step S225b uses a lysing agent containing a glycoside compound as disclosed in Chinese Patent No. ZL200910109215.6 to dissolve red blood cells, but adjusts the amount of hemolytic agent, increases hemolysis strength, and dyes blood cells using nucleic acid dye to prepare second The sample is measured to distinguish platelets in the second sample to be tested by the at least two optical signals.
  • the entire disclosure of the Chinese invention patent ZL200910109215.6 is hereby incorporated by reference.
  • the dye may be selected from the membrane dyes or mitochondrial dyes described in the foregoing exemplary embodiments, or may be the fluorescent dyes mentioned in the aforementioned patents, or other fluorescent dyes suitable for staining of leukocytes or reticulocytes, such as the fluorescent dye SYTO9.
  • the hemolytic agent includes a glycoside compound, a nonionic surfactant, and an anionic organic compound.
  • the glycoside compound is selected from the group consisting of a saponin and an alkyl glycoside compound.
  • the glycoside compound has the formula R-(CH 2 ) n -CH 3 .
  • n is an integer between 5 and 17, preferably, n is an integer between 6 and 14; and
  • R is a monosaccharide, monosaccharide polymer or polysaccharide. More specifically, R may be selected from the group consisting of glucose, rhamnose, galactose, arabinose, xylose, maltose, mannose, ribose, lyxose, saccharide, and the like, and their deoxy sugars, and these substances. Polymer.
  • the nonionic surfactant has the general formula R 1 -R 2 -(CH 2 CH 2 O) n -H.
  • R 1 is an alkyl or alkenyl group of C8-23.
  • R 1 is selected from the group consisting of a straight-chain alkyl group of an octyl group, a ketone group, a lauryl group, a tetradecyl group, a hexadecyl group, and a stearyl group.
  • R 1 is selected from the group consisting of linear alkyl groups of lauryl, tetradecyl and hexadecyl groups.
  • R2 is selected from -O-, Or -COO-, n is an integer between 10-30.
  • the anionic organic compound is selected from the group consisting of an anionic organic compound having one or more hydroxyl or sulfonic acid groups or salts thereof.
  • FIG. 8A shows a forward scattered light-fluorescence scatter plot of a second sample to be measured obtained in an embodiment of the present embodiment.
  • the lysing reagent used in the step S225b includes the hemolytic agent and the nucleic acid dye as described above.
  • the components of the lysing reagent and their concentrations are as follows:
  • the pH of the dissolution reagent was 7.5. Twenty microliters of the blood sample was added to 1 mL of the above-dissolved solution, and after incubation at 45 ° C for 60 seconds, a forward scattered light signal and a 90-degree lateral fluorescence signal were collected at an excitation wavelength of 488 nm. Based on the forward scattered light signal and the fluorescent signal, a scattergram as shown in FIG. 8A can be obtained, and the platelet region P" can be further distinguished in the scattergram. The platelet region P" is in the second sample to be tested. The area in which the platelets appear in the scatter plot. It can be understood that, in order to further highlight the difference between different particle communities, in the present embodiment, the coordinate axis of the scattergram generated in step S255b is a logarithmic coordinate axis.
  • the lysing reagent used in the step S225 includes a hemolytic agent including a glycoside compound, a nonionic surfactant, and an anionic organic compound, and a fluorescent dye selected from a membrane dye or a mitochondrial dye.
  • step S255b may generate a scattergram as shown in FIGS. 7A-7D or 8A based on the at least two optical signals, including forward scattered light and fluorescent signals.
  • Step S255b distinguishes the white blood cell region and the platelet region P" in the scattergram of the second test sample based on the at least two optical signals, and then obtains second platelet detection data of the blood sample based on the platelet region P".
  • the volume distribution data of the platelets can be obtained based on the forward scattered light signal FSC of the particle population characterized in the platelet region P". Specifically, the equation (1), the equation (2) or the equation can be used ( 3) Converting the forward scattered light signal FSC into the volume of each particle in the platelet region P", thereby obtaining volume distribution data of the platelets.
  • the at least two optical signals acquired in step S235b include a forward scattered light signal, a side scattered light signal, and a fluorescent signal
  • step S255b may also be based on the particles characterized in the platelet region P"
  • the forward scattered light signal and the side scattered light signal of the group are calculated by Mie scattering theory to obtain the volume of each particle in the platelet region P", thereby obtaining volume distribution data of the platelets.
  • a platelet derived volume histogram can be obtained based on the volume distribution data of the platelets.
  • characteristic parameters reflecting the volume distribution of the platelets such as the count value of the platelets, the average platelet volume, the volume distribution width, and the like, can also be calculated.
  • the count value of the platelets can also be obtained by obtaining the number of particles of the particle group characterized in the platelet region P".
  • the second platelet detection data obtained in step S255b may be volume distribution data of platelets (such as a platelet-derived volume histogram), or may be characteristic parameters reflecting the volume distribution of platelets (such as platelet count value, average platelet volume). , volume distribution width, etc.).
  • the alarm abnormality includes a prompt indicating that the electrical impedance detecting component may be faulty during the sample testing, or a prompt that the detection result is unreliable due to the abnormal electrical impedance detecting component, or the electrical impedance detecting component and / or the optical detection component is faulty, or the prompt that the test result is not credible.
  • the steps described in the second, third or fourth exemplary embodiment may be directed to a related hardware implementation of a blood analyzer by a computer program.
  • the computer program can be stored in a computer readable storage medium and loaded into a blood analyzer having a corresponding hardware system.
  • the blood analyzer performs the analysis method of the blood sample disclosed in the second, third or fourth exemplary embodiment of the present disclosure when the processor runs the computer program.
  • a first aspect of the present disclosure also provides a blood analyzer comprising a processor and a non-transitory computer readable storage medium for performing storage in the non-transitory computer readable storage medium
  • the computer program implements the steps of the analysis method of the second, third or fourth exemplary embodiment.
  • a first aspect of the present disclosure also provides a non-transitory computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the second, third or fourth exemplary embodiment
  • the steps of the analytical method For specific steps, reference may be made to various embodiments and embodiments described above, and will not be described herein.
  • the analysis method of the second, third or fourth exemplary embodiment can thus be implemented in the form of a software functional unit and sold or used as a stand-alone product.
  • the product and method provided by the first aspect of the present disclosure can utilize the electrical impedance detection channel and the white blood cell classification detection channel (such as BC- produced by Shenzhen Mindray Biomedical Electronics Co., Ltd.) on the basis of the existing five-class blood analysis system.
  • the DIFF channel of the 6800 blood analyzer is used to obtain platelet detection data, and the abnormal detection results are alarmed by comparing the first and second platelet detection data obtained by the two detection channels.
  • the products and methods provided by the first aspect of the present disclosure do not require the use of independent detection channels, and can provide users with more abundant detection information in real time without increasing the cost of the blood analysis system, prompting the user to have abnormal platelet detection data. Perform a review or re-examination to improve the accuracy of platelet detection.
  • a second aspect of the present disclosure relates to a method, system, and storage medium for alerting a platelet detection anomaly and/or an impedance channel anomaly by an electrical impedance signal and a scattered light signal of a blood sample.
  • a second aspect of the present disclosure provides a product and method for achieving an abnormality in alarm platelet detection and/or abnormality in impedance channel detection without the use of a fluorescent dye.
  • a fluorescent dye can also be added to prepare a second test sample, and the presence or absence of the fluorescent dye does not affect the implementation of the corresponding embodiment.
  • a fifth exemplary embodiment of the present disclosure provides an alarm method. Referring again to the flow chart of the steps shown in Figure 3, the alarm method includes the following steps:
  • Step S200 providing a blood sample.
  • Step S220 mixing the first portion of the blood sample with the diluent to obtain a first test sample for the first platelet detection.
  • Step S225c mixing the second portion of the blood sample with the lysis reagent to obtain a second test sample for the second platelet detection.
  • the lysing reagent comprises a hemolytic agent for dissolving red blood cells.
  • Step S230 Detecting an electrical impedance signal of the first test sample.
  • Step S235c Detecting at least two optical signals of the second sample to be tested.
  • the at least two optical signals comprise a first scattered light signal and a second scattered light signal, the first scattered light signal being a forward scattered light signal, the second scattered light signal being a medium angle scattered light signal and a lateral direction At least one of the scattered light signals.
  • Step S250 Obtaining first platelet detection data of the blood sample based on the electrical impedance signal obtained in step S230.
  • Step S255c obtaining second platelet detection data of the blood sample based on at least two optical signals obtained in step S235.
  • Step S270c The evaluation result is obtained based on the difference between the first platelet detection data and the second platelet detection data.
  • Step S280 determining whether the evaluation result satisfies a preset condition.
  • step S290 is performed, the alarm first platelet detection is abnormal and/or the electrical impedance signal detecting process is abnormal.
  • the judgment result is no, the flow ends.
  • the second portion of the blood sample is mixed with the hemolytic agent to obtain a second test sample.
  • the hemolytic agent may be any of the existing hemolytic reagents for automated blood analyzer white blood cell classification, which may be a cationic surfactant, a nonionic surfactant, an anionic surfactant, an amphiphilic surfactant. Any one or combination of several.
  • the forward scattered light signal of the second sample to be tested, and at least one of the medium angle scattered light signal and the side scattered light signal may be acquired by one or more optical detectors.
  • the mid-angle scattered light signal can be detected by a photodetector at an angle between forward scattered light and side scattered light.
  • the medium-angle scattered light signal may be a low-middle-angle scattered light signal detected from an angle of about 8° to about 24° with the incident light beam, or may be from an angle of about 25° to about 65° with the incident light beam.
  • the detected high school angle scatters the light signal.
  • the forward scattered light signal can be detected from an angle of from about 1° to about 10° with respect to the incident beam, preferably the forward scattered light signal can be from about 2° to about 6° from the incident beam. The angle is detected.
  • the side scattered light signal can be detected at an angle of about 90 from the incident beam. Alternatively, the side scattered light signal can also be detected from an angle of about 65 to about 115 with the incident beam. .
  • step S255c may include the following steps:
  • Step S2551c Acquiring at least one of the at least two optical signals of the second measured sample, that is, the forward scattered light signal, and the medium angle scattered light signal and the side scattered light signal.
  • Step S2553c Generate a scattergram of the second test sample based on the at least two optical signals.
  • Step S2555c distinguishing the white blood cell region and the platelet region in the scattergram obtained in step S2553c based on the at least two optical signals.
  • Step S2557c obtaining second platelet detection data of the blood sample based on the platelet region obtained in step S2557c.
  • the platelet region P distinguished by step S2555c includes a region in which the platelets appear in the scattergram, which may include impurity particles such as red blood cell fragments. The area that appears in the scatter plot. Converting the forward scattered light signal of the particle group represented by the platelet region P into the volume of each particle in the platelet region P by the equation (1), the equation (2) or the equation (3) in step S2557c, thereby obtaining Volume distribution data for platelets.
  • the second scattered light signal is a side scattered light signal
  • step S2557c may also calculate the forward scattered light signal and the side scattered light signal of the particle group represented by the platelet region P by using Mie scattering theory.
  • the volume of each particle in the platelet region P thereby obtaining volume distribution data of the platelets.
  • the volume distribution data may be in digital form or in graphical form, such as a derived volume histogram.
  • a larger volume of particles and smaller particles can be distinguished in the derived volume histogram using a preset derived volume separation threshold.
  • the derived volume separation threshold may be selected from a value between 10-20 fL, such as 10fL, 12fL, 15fL or 20fL.
  • the portion of the curve in the derivatized volumetric histogram that has a larger particle volume contains platelet information in the hemolyzed blood sample and can be considered as a form of second platelet detection data.
  • characteristic parameters such as the area of the curve may be obtained, and the characteristic parameter may also be regarded as a form of second platelet detection data.
  • the platelet region differentiated by step S2555c is a large platelet region P', which is a large platelet in the second sample to be tested.
  • Figure 9 shows a forward-side scattered light scatter plot generated by an embodiment of this embodiment.
  • the scattered light signal of the particle group characterized in the large platelet region P' can be converted into the large platelet region P' by equation (1), equation (2), equation (3) or Mie scattering theory in step S2557c.
  • a large platelet derived volume histogram can be obtained based on the volume distribution data of the large platelets.
  • characteristic parameters reflecting the volume distribution of the large platelets such as a large platelet count value, a large platelet volume distribution width, and the like, can also be calculated.
  • the count value of the large platelets may also be obtained by acquiring the number of particles of the particle group characterized in the large platelet region P'.
  • the second platelet detection data may be the volume distribution data of the above large platelets (such as a large platelet derived volume histogram), the count value of large platelets or other characteristic parameters reflecting the volume distribution of large platelets. .
  • the platelet region differentiated by step S2555c is a platelet region P", which is the platelet in the second sample to be tested.
  • step S225c requires preparation of a second test sample using a hemolytic agent containing a glycoside compound as disclosed in Chinese Patent No. ZL200910109215.6, but does not use a nucleic acid dye to treat the sample. It has been found that the platelet region P" can also appear on the scatter plot of the two scattered light without the use of dyes, only by increasing the hemolysis intensity.
  • Figure 8B shows the forward-side scattering obtained in an embodiment of this embodiment. Light scatter plot.
  • volume distribution data of platelets can be obtained based on forward scattered light signals (or forward and side scatter light signals) of the particle population characterized in the platelet region P", and can also be based on The platelet volume distribution data further obtains a platelet-derived volume histogram and characteristic parameters reflecting the volume distribution of the platelets, such as platelet count values, mean platelet volume, volume distribution width, and the like.
  • the count value of the platelets can also be obtained by acquiring the number of particles of the particle group characterized in the platelet region P".
  • step S270c based on the first platelet detection data obtained in step S250 and the second platelet detection data obtained in step S255c, the difference between the two is analyzed to obtain an evaluation result.
  • step S280 it is determined whether the evaluation result obtained in step S270c satisfies the preset condition.
  • step S290 is performed, and an abnormality occurs in the alarm first platelet detection abnormality and/or the electrical impedance signal detection.
  • the judgment result is no, the flow ends.
  • the step of outputting other detection results and/or intermediate results may also be included.
  • the detection result includes, but is not limited to, the first platelet detection data obtained in step S250 and the second platelet detection data obtained in step S255c.
  • the intermediate result includes, but is not limited to, the scattergram obtained in step S255c, the platelet region in the scatter plot, the derived volume histogram, the portion of the curve where the particle volume is larger after being separated by the derived volume separation threshold, and the evaluation value obtained in step S270c. Or evaluation results, etc.
  • the probability of abnormality of the optical detecting component is generally small, in order to report the detection of the sample to be tested as soon as possible
  • the platelet count value obtained in the second platelet detection data can be output and reported to the user. That is, when the evaluation result is that the difference between the two is not large, the platelet count value obtained by the first platelet detection data is output, and when the evaluation result is that the difference between the two is large, the platelet count value obtained by the second platelet detection data is output.
  • the result can be labeled to prompt the user that the result has a platelet count value obtained by optical detection under hemolysis conditions, and the platelet count value obtained by the electrical impedance method has been distinguished.
  • the computer program can be stored in a computer readable storage medium and loaded into a blood analyzer having a corresponding hardware system.
  • the processor runs the computer program, the blood analyzer performs the analysis method of the blood sample disclosed in the fifth exemplary embodiment of the present disclosure.
  • a second aspect of the present disclosure also provides a blood analyzer comprising a processor and a non-transitory computer readable storage medium for performing storage in the non-transitory computer readable storage medium
  • the computer program implements the steps of the analysis method of the fifth exemplary embodiment.
  • a second aspect of the present disclosure also provides a non-transitory computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of the analysis method of the fifth exemplary embodiment.
  • a computer program that, when executed by a processor, implements the steps of the analysis method of the fifth exemplary embodiment.
  • the analysis method of the fifth exemplary embodiment can be implemented in the form of a software functional unit and sold or used as a stand-alone product.
  • the second aspect of the present disclosure also provides a blood analysis system.
  • the blood analysis system includes a sample collection component 10, a sample processing device 30, a sample detection device 50, a data analysis module 70, and a user interface 90.
  • the sample processing device 30 includes at least one mixing chamber for mixing a first portion of the blood sample with a diluent to obtain a first test sample, and mixing the second portion of the blood sample with a dissolution reagent to obtain a second test sample.
  • the lysing reagent comprises a hemolytic agent for dissolving red blood cells.
  • the sample detecting device 50 includes an electrical impedance detecting unit 51 and an optical detecting unit 53.
  • the electrical impedance detecting component is configured to detect an electrical impedance signal of the first tested sample.
  • the optical detecting component 53 is configured to detect at least two optical signals of the second test sample. Wherein the at least two optical signals comprise a first scattered light signal and a second scattered light signal, the first scattered light signal being a forward scattered light signal, the second scattered light signal being a medium angle scattered light signal and a lateral direction At least one of the scattered light signals.
  • the data analysis module 70 includes a signal acquisition module 750, a classification counting module 770, and an alarm module 790.
  • the signal acquisition module 750 acquires the electrical impedance signal of the first tested sample and the at least two optical signals of the second tested sample.
  • the classification counting module 770 obtains first platelet detection data of the blood sample based on the electrical impedance signal.
  • the classification counting module 770 generates a scattergram of the second test sample based on the at least two optical signals, and distinguishes a white blood cell region and a platelet region in the scattergram based on the at least two optical signals, and then based on the The platelet region obtains second platelet detection data for the blood sample.
  • the alarm module 790 obtains an evaluation result according to the difference between the first platelet detection data and the second platelet detection data, and then determines whether the evaluation result satisfies a preset condition. When the judgment result is YES, the alarm platelet detection is abnormal and/or the impedance channel is abnormal. When the judgment result is no, the flow ends.
  • the blood analysis system, the analysis method, the blood analyzer and the storage medium provided by the second aspect can realize abnormality of platelet detection and/or without using a fluorescent dye.
  • the impedance channel detects an abnormal alarm, which can provide users with more abundant detection information without increasing the cost of the blood analysis system and the reagent cost of the blood analysis process, and reminding the user to review or recheck the abnormal platelet detection data. To improve the accuracy of platelet detection, or to find out in time that the sample analyzer detection system is abnormal.
  • FIG. 10 is an overall perspective view of the blood analysis system provided by the present disclosure.
  • the blood analysis system includes a first cabinet 100, a second cabinet 200, a sample collection unit 10, a sample processing device 30, a sample detection device 50, a data analysis module 70, and a user interface 90.
  • the sample detecting device 50 and the data analyzing module 70 are disposed inside the second casing 200 and are respectively disposed on two sides of the second casing 200.
  • the sample processing device 30 is disposed inside the first cabinet 100, and the user interface 90 and the sample collection member 10 are on the outer surface of the first cabinet 100.

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Abstract

一种样本分析仪出现异常的报警方法、报警血小板检测异常和/或阻抗检测部件异常的***及存储介质，样本分析仪出现异常的报警方法包括：将血液样本的第一部分与稀释液混合得到第一被测试样(S220)；将血液样本的第二部分与溶解试剂混合得到第二被测试样(S225)；检测第一被测试样的电阻抗信号(S230)；检测第二被测试样的至少两种光学信号(S235)；基于电阻抗信号得到第一血小板检测数据(S250)；基于至少两种光学信号得到第二血小板检测数据(S255)；基于第一血小板检测数据和第二血小板检测数据之间的差异得到评估结果(S270)；判断评估结果是否满足预设条件(S280)以报警第一血小板检测异常和/或电阻抗信号检测出现异常(S290)。

Description

样本分析仪异常的报警方法、***及存储介质 技术领域
本公开涉及体外检测领域，具体地，涉及血液分析仪、血液分析***、血液样本的分析方法及其存储介质。
背景技术
血液分析被广泛地应用于医学研究与检测中，用于获取红细胞、白细胞、血小板等血细胞的相关信息。常见的自动化血液分析仪通常基于电阻抗原理(又称库尔特原理)对血液样本中的血细胞进行分析。根据电阻抗原理，当悬浮在电解液中的粒子随电解液通过检测微孔时，该检测微孔处的等效电阻会发生变化。在该检测微孔两侧恒流源的作用下，该检测微孔两侧的电压会发生变化。通过电路***采集该检测微孔两侧的电压变化，可以形成电压脉冲波形，其中，脉冲波形的高度反映了该粒子的体积大小。分析仪器根据所获取的脉冲波形可以提供样本中粒子的体积分布信息。对于血液样本，血液分析仪基于电阻抗原理可以提供被测血液样本中血细胞的体积分布直方图，并对体积分布直方图进行分析得到细胞分类与计数等血液分析数据。
然而，基于电阻抗原理的检测信号仅能反应通过检测微孔的粒子的体积信息，无法区分具有相同或相近体积的不同粒子。例如，基于电阻抗方法的血细胞分析方法无法区分体积相近的大血小板(large platelets)、红细胞碎片(schistocytes)和小红细胞(microcytes)，血液分析仪可能会将体积较大的大血小板误计为红细胞，造成血小板检测结果假性降低；血液分析仪也可能将体积相对较小的红细胞(如红细胞碎片和小红细胞)等误计为血小板，造成血小板检测结果假性增高。此外，在大量血液样本的自动化检测过程中，在不同血样样本的检测之间对检测通道的清洗不充分也可能会对血小板检测结果产生影响。例如，粘附在检测通道的杂质颗粒物或未被清洗干净的前次测量样本中红细胞碎片可能会混入被测血液样本，造成血小板检测结果假性增高。在某些情况下，血小板本身可能容易被活化而粘附在检测通道，造成血小板检测结果假性增高。
发明内容
鉴于以上内容，有必要提供新的样本分析仪的报警方法、***及存储介 质。
本公开实施例一方面提供一种样本分析仪出现异常的报警方法，所述方法包括：
提供血液样本；
将所述血液样本的第一部分与稀释液混合得到第一被测试样，用于第一血小板检测；
将所述血液样本的第二部分与溶解试剂混合得到第二被测试样，用于第二血小板检测，其中第二被测试样中红细胞被裂解；
检测所述第一被测试样的电阻抗信号；
检测所述第二被测试样的至少两种光学信号；
基于所述电阻抗信号得到所述血液样本的第一血小板检测数据；
基于所述至少两种光学信号得到所述血液样本的第二血小板检测数据；
基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果；
判断所述评估结果是否满足预设条件；以及
当判断结果为是时，报警第一血小板检测出现异常和/或样本分析仪的电阻抗信号检测步骤出现异常。
进一步的，在本公开实施例提供的上述报警方法中，所述基于所述至少两种光学信号得到所述血液样本的第二血小板检测数据包括：
基于所述至少两种光学信号生成所述第二被测试样的散点图；
基于所述至少两种光学信号在所述散点图中区分白细胞区域和血小板区域；
基于所述血小板区域得到所述血液样本的第二血小板检测数据。
进一步的，在本公开实施例提供的上述报警方法中，输出所述第一血小板检测异常的原因为电阻抗信号检测步骤出现异常和/或第一血小板检测结果不可信的提示信息。
进一步的，在本公开实施例提供的上述报警方法中，所述溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料，所述至少两种光学信号包括前向散射光信号和荧光信号。
进一步的，在本公开实施例提供的上述报警方法中，所述溶解试剂包括用于溶解红细胞的溶血剂，所述至少两种光学信号包括第一散射光信号和第二散射光信号，所述第一散射光信号为前向散射光信号，所述第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种。
进一步的，在本公开实施例提供的上述报警方法中，所述基于所述血小板区域得到所述血液样本的第二血小板检测数据的步骤包括：
至少基于所述血小板区域中出现的粒子群的所述前向散射光信号得到衍生血小板体积直方图；或者
基于所述血小板区域中出现的粒子数得到所述血液样本的第二血小板检测数据。
进一步的，在本公开实施例提供的上述报警方法中，所述溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料，所述至少两种光学信号包括侧向散射光信号和荧光信号；基于所述血小板区域中出现的粒子数得到所述血液样本的第二血小板检测数据。
进一步的，在本公开实施例提供的上述报警方法中，所述血小板区域包括大血小板区域，利用所述大血小板区域得到所述血液样本的第二血小板检测数据。
进一步的，在本公开实施例提供的上述报警方法中，所述第一血小板检测数据为第一血小板体积分布数据的至少一特征参数，所述第二血小板检测数据为第二血小板体积分布数据的所述至少一特征参数。
进一步的，在本公开实施例提供的上述报警方法中，所述特征参数选自血小板计数、血小板体积直方图、平均血小板体积及血小板体积分布宽度中的一种或几种；或者
所述特征参数选自某一体积阈值范围内的血小板计数、血小板体积直方图、平均血小板体积及血小板体积分布宽度中的一种或几种。
进一步的，本公开实施例提供的上述报警方法中，两种光信号包括散射光信号和荧光信号，所述方法还根据所述散射光信号和荧光信号，将白细胞区分为白细胞亚群，或对白细胞进行计数或识别有核红细胞或未成熟细胞或嗜碱性粒细胞。
进一步的，本公开实施例提供的上述报警方法中，两种光信号包括第一散射光信号和第二散射光信号，所述第一散射光信号为前向散射光信号，所述第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种，所述方法还根据所述第一散射光信号和所述第二散射光信号，将白细胞区分为白细胞亚群或识别嗜碱性粒细胞。
进一步的，在本公开实施例提供的上述报警方法中，所述判断所述评估结果是否满足预设条件的步骤包括：
比较所述第一血小板检测数据与所述第二血小板检测数据的图形差异 程度；
判断所述图形差异程度是否满足预设条件；或者
获取所述第一血小板检测数据与所述第二血小板检测数据的数值信息；
利用所述数值信息计算评估值，所述评估值用于反映所述第一血小板检测数据与所述第二血小板检测数据的差异程度；
判断所述评估值是否满足预设条件。
进一步的，在本公开实施例提供的上述报警方法中，还包括如下步骤：
若没有报警异常，则输出所述第一血小板检测数据；
若报警异常，则输出所述第二血小板检测数据。
进一步的，在本公开实施例提供的上述报警方法中，记录并统计连续多个血液样本得到的血小板检测评估值的判断结果，当连续多个血液样本的判断结果均为是，报警所述样本分析仪的电阻抗信号检测步骤出现异常。
本公开实施例一方面提供一种非易失性计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述任一项所述的报警方法的步骤。
本公开实施例一方面提供一种血液分析***，所述血液分析***包括：
样本处理装置，包括至少一混合室，用于将血液样本的第一部分与稀释液混合得到第一被测试样，用于第一血小板检测；将所述血液样本的第二部分与溶解试剂混合得到第二被测试样，用于第二血小板检测，其中第二被测试样中红细胞被裂解；
样本检测装置，包括电阻抗检测部件和光学检测部件，所述电阻抗检测部件包括微孔及电阻抗检测器，所述电阻抗检测器用于检测所述第一被测试样通过所述微孔的电阻抗信号，所述光学检测部件包括光学流动室、光源及光学检测器，所述光学流动室与所述混合室连通，所述光源用于将光束对准所述光学流动室，所述光学检测器用于检测通过所述光学流动室的所述第二被测试样的至少两种光学信号；
数据分析模块，包括信号获取模块、分类计数模块和报警模块；
所述信号获取模块获取所述第一被测试样的所述电阻抗信号，所述信号获取模块获取所述第二被测试样的所述至少两种光学信号；
所述分类计数模块基于所述电阻抗信号得到所述血液样本的第一血小板检测数据；所述分类计数模块基于所述至少两种光学信号生成所述第二被测试样的散点图，基于所述至少两种光学信号在所述散点图中区分白细胞区域和血小板区域，基于所述血小板区域得到所述血液样本的第二血小板检测 数据；
所述报警模块基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果；判断所述评估结果是否满足预设条件；以及当判断结果为是时，报警第一血小板检测出现异常和/或所述电阻抗检测部件出现异常。
进一步的，在本公开实施例提供的上述血液分析***中，所述分类计数模块包括：
基于所述至少两种光学信号生成所述第二被测试样的散点图；
基于所述至少两种光学信号在所述散点图中区分白细胞区域和血小板区域；
基于所述血小板区域得到所述血液样本的第二血小板检测数据。
进一步的，在本公开实施例提供的上述血液分析***中，所述报警模块包括输出第一血小板检测出现异常的原因是电阻抗检测部件出现异常和/或第一血小板检测结果不可信的提示信息。
进一步的，在本公开实施例提供的上述血液分析***中，所述溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料，所述至少两种光学信号包括前向散射光信号和荧光信号，所述光学检测部件包括至少一个散射光检测器和至少一个荧光检测器。
进一步的，在本公开实施例提供的上述血液分析***中，所述溶解试剂包括用于溶解红细胞的溶血剂，所述至少两种光学信号包括第一散射光信号和第二散射光信号，所述第一散射光信号为前向散射光信号，所述第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种，所述光学检测部件包括至少两个散射光检测器。
进一步的，在本公开实施例提供的上述血液分析***中，所述分类计数模块至少基于所述血小板区域中出现的粒子群的所述前向散射光信号得到衍生血小板体积直方图；或者
所述分类计数模块基于所述血小板区域中出现的粒子数得到所述血液样本的第二血小板检测数据。
进一步的，在本公开实施例提供的上述血液分析***中，所述溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料，所述至少两种光学信号包括侧向散射光信号和荧光信号；所述分类模块基于所述血小板区域中出现的粒子数得到所述血液样本的第二血小板检测数据。
进一步的，在本公开实施例提供的上述血液分析***中，所述血小板区 域包括大血小板区域，所述第二血小板检测数据包括第二大血小板数据，利用所述大血小板区域得到所述血液样本的第二血小板检测数据。
进一步的，在本公开实施例提供的上述血液分析***中，所述第一血小板检测数据为第一血小板体积分布数据的至少一特征参数，所述第二血小板检测数据为第二血小板体积分布数据的所述至少一特征参数。
进一步的，在本公开实施例提供的上述血液分析***中，所述特征参数选自血小板计数、血小板体积直方图、平均血小板体积及血小板体积分布宽度中的一种或几种；或者
所述特征参数选自某一体积阈值范围内的血小板计数、血小板体积直方图、平均血小板体积及血小板体积分布宽度中的一种或几种。
进一步的，本公开实施例提供的上述血液分析***中，两种光信号包括散射光信号和荧光信号，所述方法还根据所述散射光信号和荧光信号，将白细胞区分为白细胞亚群，或对白细胞进行计数或识别有核红细胞或未成熟细胞或嗜碱性粒细胞。
进一步的，本公开实施例提供的上述血液分析***中，两种光信号包括第一散射光信号和第二散射光信号，所述第一散射光信号为前向散射光信号，所述第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种，所述方法还根据所述第一散射光信号和所述第二散射光信号，将白细胞区分为白细胞亚群或识别嗜碱性粒细胞。
进一步的，在本公开实施例提供的上述血液分析***中，所述报警模块包括：
比较所述第一血小板检测数据与所述第二血小板检测数据的图形差异程度；
判断所述图形差异程度是否满足预设条件；或者
获取所述第一血小板检测数据与所述第二血小板检测数据的数值信息；
利用所述数值信息计算评估值，所述评估值用于反映所述第一血小板检测数据与所述第二血小板检测数据的差异程度；
判断所述评估值是否满足预设条件。
进一步的，在本公开实施例提供的上述血液分析***中，还包括用户界面：
若没有报警异常，则输出所述第一血小板检测数据；
若报警异常，则输出所述第二血小板检测数据。
相对于现有技术，本公开所提供的方法、***及存储介质可以为用户提 供更加丰富的检测信息，提醒用户对存在异常的血小板检测数据进行复查或复检，提高血小板检测的准确度。
附图说明
图1是本公开所提供的血液分析***的功能模块示意图。
图2是图1所示的血液分析***的样本检测装置的功能模块示意图。
图3是本公开所提供的报警方法的步骤流程图。
图4A是本公开第二示例性实施方式的一实施例所生成的散点图。图4B是图4A中血小板区域P的局部放大图。图4C是根据图4A中的血小板区域P所得的衍生体积直方图。
图5A是检测正常情况下的比较第一和第二血小板检测数据的示意图。图5B是检测异常情况下的比较第一和第二血小板检测数据的示意图。
图6A是本公开第三示例性实施方式的一实施例所生成的散点图。图6B是根据图6A中的血小板区域P’所得的衍生体积直方图。
图7A示出了使用Alexa Fluor 488染料荧光染色的第二被测样本的前向散射光-荧光散点图。图7B示出了使用Mitotracker Red染料荧光染色的第二被测样本的前向散射光-荧光散点图。图7C示出了使用罗丹明123染料荧光染色的第二被测样本的前向散射光-荧光散点图。图7D示出了使用Mitotracker Deep Red染料荧光染色的第二被测样本的前向散射光-荧光散点图。
图8A是本公开第四示例性实施方式的一实施例所得的第二被测样本的散点图。图8B是本公开第五示例性实施方式的一实施例所得的第二被测样本的散点图。
图9是本公开第五示例性实施方式的一实施例中在散点图中区分血小板区域的示意图。
图10是本公开所提供的血液分析***的一整体立体示意图。
图11是本公开一实施方式中第二血小板检测的SFL、SSC和FSC三维散点图。
图12是本公开一实施方式中含未成熟细胞血液样本的第二血小板检测的FSC-SSC-SFL三维散点图。
图13是本公开一实施方式中含有核红细胞的血液样本的第二血小板检测的FSC-SFL散点图。
图14是本公开一实施方式中第二血小板检测获取的SFL-SSC散点图对 应的血小板分布区域图。
主要元件符号说明
样本采集部件 10
样本处理装置 30
混合室 320,320a,320b
样本检测装置 50
电阻抗检测部件 51
微孔 512
电阻抗检测器 514
光学检测部件 53
光学流动室 532
光源 534
光学检测器 536
总线 60
数据分析模块 70
存储*** 710
处理器 730
信号获取模块 750
分类计数模块 770
报警模块 790
用户界面 90
第一机壳 100
第二机壳 200
如下具体实施方式将结合上述附图进一步说明本公开。
具体实施方式
下面将结合本公开的优选实施方式及实施例对本公开的技术方案进行描述。需要说明的是，当一个单元被描述为“连接”于另一个单元，它可以是直接连接到另一个单元或者可能同时存在居中单元。当一个单元被描述为 “设置于”另一个单元，它可以是直接设置在另一个单元上或者可能同时存在居中单元。除非另有定义，本文所使用的所有的技术和科学术语与属于本公开的技术领域的技术人员通常理解的含义相同。在本公开的说明书中所使用的元件或设备的名称只是为了描述具体的实施例的目的，不是旨在于限制本公开。
本公开的第一方面涉及利用血液样本的电阻抗信号、散射光信号及荧光信号报警血小板检测异常和/或阻抗通道异常的方法、***及存储介质。
图1为一血液分析***的示意图。该血液分析***包括样本采集部件10、样本处理装置30、样本检测装置50、数据分析模块70及用户界面90。该血液分析***具有一液路***(图中未示出)，用于连通该样本采集部件10、该样本处理装置30及该样本检测装置50以进行液体的传输。
该样本采集部件10用于将血液样本提供至样本处理装置30。该样本处理装置30用于将血液样本进行处理以制备被测试样，并将被测试样提供至样本检测装置50。该样本处理装置30可以包括一个或多个混合室，将待测血液样本制备为一份或多份被测试样。该样本检测装置50用于检测各被测试样中粒子的特性，获取相应的检测信号。该数据分析模块70与该样本采集部件10、样本处理装置30、样本检测装置50及用户界面90可通过总线60直接或间接地电性连接以传输及交换数据或信号。
在本公开的第一示例性实施方式中，该样本处理装置30包括至少一混合室，用于将待测血液样本的第一部分与稀释液混合得到第一被测试样，清洗后，用于并将待测血液样本的第二部分与溶解试剂混合得到第二被测试样。可选地，该样本处理装置30还可以包括一分样器，用于将待测血液样本分为多份。每一份血液样本被输送至相同或不同的混合室进行处理用于后续检测。可选地，该样本处理装置30中包括第一混合室320a和第二混合室320b以分别制备第一被测试样和第二被测试样。可选的，该样本分析装置30可以只有一个混合室，先后制备第一被测试样和第二被测试样。
具体地，用于制备第一被测试样的稀释液通常被用于稀释血液样本以通过自动化血液分析仪检测红细胞和血小板。所述稀释液通常包括一种或多种盐，例如碱金属盐，并被调节为等渗的(isotonic)以维持红细胞体积。在本公开的实施方式中，可以采用商品化的稀释液对血液样本的第一部分进行稀释以形成第一被测试样。所述商品化的稀释液包括但不仅限于由深圳迈瑞生物医疗电子股份有限公司(深圳，中国)生产的M-68DS稀释液、M-53D稀释液等。其中，用于制备第一被测试样的温度条件和/或搅拌条件可以与现有 自动化血液分析仪检测红细胞和血小板所采用的制样条件相同或相近。
具体地，在本公开的第一方面，所述溶解试剂包括溶血剂和荧光染料。所述溶血剂可以是任意一种现有的用于自动化血液分析仪白细胞分类的溶血试剂，其可以是阳离子表面活性剂、非离子表面活性剂、阴离子表面活性剂、两亲性表面活性剂中的任意一种或几种的组合。所述荧光染料用于染色血细胞。在本实施方式的一些实施例中，该荧光染料可以是一种核酸染料，从而通过测量散射光和荧光信号的差异将例如白细胞或有核红细胞的有核血细胞与其他类型的细胞进行分类。在本实施方式的一实施例中，所述溶解试剂可以采用美国专利U.S.8,367,358所公开的溶解试剂配方，其全部公开内容通过引证结合于此。美国专利U.S.8,367,358所披露的溶解试剂包括一种阳离子花菁化合物(一种荧光染料)、一种阳离子表面活性剂、一种非离子表面活性剂和一种阴离子化合物，该溶解试剂可以用于溶解红细胞并通过所检测的荧光和散射光强度差异将白细胞分类为其亚群。其他现有的荧光染料也可以被用在该溶解试剂中。例如，美国专利U.S.8,273,329中所描述的荧光染料，其全部公开内容通过引证结合于此，该试剂可以溶解红细胞并通过所检测的荧光和散射光强度差异识别有核红细胞。本领域技术人员可以理解，所述荧光染料可以被包含在独立的染色溶液中，所述染色溶液可以与不含有荧光染料的溶血剂一起使用。所述染色溶液可以在溶血剂之前、之后或同时加入至混合室320中的血液样本中以制备第二被测试样。其中，用于制备第二被测试样的温度条件和/或搅拌条件可以与现有自动化血液分析仪进行白细胞分类所采用的制样条件相同或相近。
在该第一示例性实施方式中，该血液分析***的样本检测装置50包括电阻抗检测部件51和光学检测部件53。图2示出了该样本检测装置50的功能模块示意图。
该电阻抗检测部件51用于检测该第一被测试样的电阻抗信号。该电阻抗检测部件51包括微孔512及电阻抗检测器514，该电阻抗检测器514用于检测该第一被测试样通过该微孔(aperture)时的电阻抗信号，例如直流(direct current,DC)阻抗信号。可以理解地，当悬浮在导电溶液中的粒子(或血细胞)通过微孔时，可以基于阻抗变化测量电阻抗信号。该电阻抗信号的脉冲形状、高度和宽度与粒子的尺寸或体积直接相关，并可以被转换为主要粒子的体积。当具有不同尺寸的两种或多种粒子被测量时，由电阻抗测量获得的频率直方图可以反映这些粒子的尺寸分布。现有技术中，美国专利U.S.2,656,508及U.S.3,810,011中均有所描述通过设置有电阻抗部件的血液分析 仪自动化检测血细胞的方法，其全部公开内容通过引证结合于此。
该光学检测部件53包括鞘流***、光学流动室532、光源534、光学检测器536及相应检测电路。该光学流动室532与该混合室320可操作地连通，从而使该第一被测试样从混合室320被鞘流***输送至该光学流动室532。该光源534用于将光束对准所述光学流动室532。该光学检测器536用于检测第一被测试样的至少两种光学信号。在本公开的第一示例性实施方式中，该至少两种光学信号包括前向散射光信号和荧光信号。在一实施例中，该光学检测部件53的光学检测器536被设置为适于检测通过光学流动室532的第一被测试样的前向散射光信号和荧光信号。在另一实施例中，该至少两种光学信号还包括侧向散射光信号，该光学检测器536被设置为适于检测通过光学流动室532的第一被测试样的前向散射光信号、侧向散射光信号和荧光信号。
在本文中，光学流动室指适于检测散射光信号和荧光信号的聚焦液流的流动室(focused-flow flow cell)，例如现有的流式细胞仪和血液分析仪中所使用的光学流动室。当一粒子，如一血细胞，通过光学流动室的检测孔(orifice)时，该粒子将来自光源的被导向该检测孔的入射光束向各方向散射。在相对于该入射光束的一个或多个不同角度设置光检测器可以检测被该粒子散射的光得到散射光信号。由于不同的血细胞群体具有不同的散射光特性，因此散射光信号可以用于区分不同的细胞群体。具体地，在入射光束附近所检测的散射光信号通常被称为前向散射光信号或小角度散射光信号。在一些实施例中，该前向散射光信号可以从与入射光束约1°至约10°的角度上进行检测。在其他一些实施例中，该前向散射光信号可以从与入射光束约2°至约6°的角度上进行检测。在与入射光束呈约90°的方向所检测的散射光信号通常被称为侧向散射光信号。在一些实施例中，该侧向散射光信号可以是从与入射光束呈约65°至约115°的角度上进行检测。通常地，来自被荧光染料染色的血细胞所发出的荧光信号一般也在与入射光束呈约90°的方向上进行检测。
该数据分析模块70包括存储***710和处理器730。该存储***710可以存储用于实现本文所公开的方法的各种功能的基础程序和数据结构。该存储***710可以包括一个或多个存储器和一个或多个非暂时性计算机可读存储介质。该非暂时性计算机可读存储介质可以包括硬盘驱动器、软盘、光盘、安全数字记忆卡(SD卡)、闪存卡或其类似物。该存储器可以包括用于存储程序指令和数据的主随机存取存储器(RAM)或动态RAM(DRAM)及用于存储固定指令的只读存储器(ROM)。该非暂时性计算机可读存储介质存储 有用于实现本公开所披露的方法的计算机应用程序。该处理器730包括含但不限于处理器(Central Processing Unit，CPU)、微控制单元(Micro Controller Unit，MCU)等用于解释计算机指令以及处理计算机软件中的数据的装置。该处理器730用于执行该非暂时性计算机可读存储介质中的各计算机应用程序，从而使血液分析***执行相应的检测流程并实时地分析处理该样本检测装置50所检测至少两种光学信号。在示范性实施例中，所述至少两种光学信号可以被现场可编程门阵列(Field-Programmable Gate Array，FPGA)、数字信号处理(DSP)或CPU处理，然后被计算机应用程序自动化分析以获取血小板和/或血小板亚群的相关数据。
如图1所示，在该第一示例性实施方式中，该数据分析模块70还包括信号获取模块750、分类计数模块770和报警模块790。该信号获取模块750与该样本检测装置50可操作地连接，该信号获取模块750可以分别获取该第一被测试样的电阻抗信号及该第二被测试样的前向散射光信号和荧光信号。该分类计数模块770与该信号获取模块750相连接。该分类计数模块770基于所述电阻抗信号得到该血液样本的第一血小板检测数据。该分类计数模块770基于所述至少两种光学信号生成该第二被测试样的散点图，在该散点图中区分白细胞区域和血小板区域，然后基于该散点图中的该血小板区域得到该血液样本的第二血小板检测数据。需要说明的是，本文的散点图不受其图形呈现形式的局限，还可以数据阵列的形式存在，比如与散点图或直方图具有等同或相近分辨率的表格或列表的数字形式呈现，或者采用任何本领域已知的其他适合的方式呈现。该报警模块790与该分类计数模块770相连接。该报警模块790基于该第一血小板检测数据和该第二血小板检测数据之间的差异得到评估结果。该报警模块790判断所述评估结果是否满足预设条件，当判断结果为是时，该报警模块790报警第一血小板检测异常和/或阻抗检测过程出现异常。关于该分类计数模块770和该报警模块790所执行的具体方法的步骤，将在后文中进行详述。
用户界面90为血液分析***和用户之间进行交互和信息交换的媒介。该用户界面90可以将该分类计数模块770所得到的血液分析数据和/或该报警模块790所得到的异常报警信号呈现给血液分析***的用户。在一实施例中，该用户界面90可以是一触控屏，能够识别用户的触控操作及呈现检测结果。在另一实施例中，用户界面90可以包括输入设备和输出设备。该输入设备可以是与该数据分析模块70电连接的键盘、鼠标、麦克风等数据输入介质。该输出设备可以是显示屏、打印机、扬声器、指示灯等。可以理解地， 当该报警模块790报警异常时，该用户界面可以通过在检测报告或所呈现的检测画面中对该血液样本进行颜色、字体或标签的差异化标示，也可以通过闪光、声音等方式提示用户该血液样本第一血小板检测出现异常和/或样本分析仪的电阻抗检测过程出现异常。
下面将结合该第一示例性实施方式所述的血液分析***的各功能模块详细描述本公开第二示例性实施方式所提供的报警的方法。该报警方法可以用于一自动化血液分析仪，也可以用于设置有流式细胞仪和电阻抗检测仪的血液分析***。该报警方法可以以计算机应用程序的形式被处理器执行实现，该计算机应用程序可以设置于自动化血液分析仪中，也可以独立设置于一能够直接或者间接地获取血细胞检测信号数据的计算机中。
请参看图3所示的步骤流程图。在该第二示例性实施方式中，该样本分析仪出现异常的报警方法包括以下步骤：
步骤S200：提供血液样本。
步骤S220：将该血液样本的第一部分与稀释液混合得到第一被测试样，用于第一血小板检测。
步骤S225：将该血液样本的第二部分与溶解试剂混合得到第二被测试样，用于第二血小板检测。其中，该溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料。
步骤S230：检测该第一被测试样的电阻抗信号。
步骤S235：检测该第二被测试样的至少两种光学信号。其中，所述至少两种光学信号包括前向散射光信号和荧光信号。
步骤S250：基于步骤S230所得的电阻抗信号得到该血液样本的第一血小板检测数据。
步骤S255：基于步骤S235所得的至少两种光学信号得到该血液样本的第二血小板检测数据。
步骤S270：基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果。
步骤S280：判断所述评估结果是否满足预设条件。当判断结果为是时，执行步骤S290，报警第一血小板检测出现异常和/或电阻抗信号检测步骤出现异常。当判断结果为否时，流程结束。
在一具体实施方式中，当处理器执行步骤S200时，样本采集部件10为血液分析***或血液分析仪提供血液样本。当处理器执行步骤S220和S225时，样本处理装置30分别制备该第一被测样本和该第二被测样本。关于制备 该第一被测样本和该第二被测样本所采用的试剂和制备条件等已在上文中详述，在此不再赘叙。当处理器执行步骤S230时，样本检测装置50的电阻抗检测部件51检测该第一被测试样的电阻抗信号；当处理器执行步骤S235时，样本检测装置50的光学检测部件53检测该第二被测试样的所述至少两种光学信号。当处理器执行步骤S250和S255时，数据分析模块70分别得到第一和第二血小板检测数据。处理器进一步执行步骤S270-S290时，数据分析模块70的报警模块790根据第一和第二血小板检测数据判断血小板检测是否存在异常并对异常进行报警。可以理解地，在报警异常的步骤流程中，用于获取第一血小板检测数据的步骤S220、S230及S250与用于获取第二血小板检测数据的步骤S225、S235及S255在时间上可以并行执行，也可以先后执行。
在步骤S250中，本领域技术人员可以理解，基于步骤S230所得的电阻抗信号可以生成该第一被测试样中血小板和红细胞的电阻抗体积直方图。通常，在该电阻抗体积直方图中，血细胞的体积以飞升(fL)表示。通过一个或多个预设体积分界值可以在该体积直方图中区分血小板与红细胞体积分布曲线，然后可以基于血小板的体积分布曲线的得到该血液样本中血小板的特征参数。该一个或多个预设体积分界值为经验值或基于经验算法可动态获取的值。在一实施例中，用于区分血小板的体积范围阈值可以是2-30fL。该血小板的特征参数包括但不仅限于血小板计数PLT、平均血小板体积MPV、血小板体积分布宽度PDW。需要指出的是，本文中所述的“第一血小板检测数据”包括血小板体积分布数据和/或反应血小板体积分布的特征参数。该血小板体积分布数据可以是数字形式的，也可以是图形形式的。
在步骤S255中，本公开披露了一种基于第二被测样本的至少两种光学信号得到第二血小板检测数据的方法。在本公开的第一方面，该第二被测样本中的红细胞被溶解、血细胞被荧光染色，该至少两种光学信号包括前向散射光信号和荧光信号。具体地，步骤S255可以包括以下步骤。
步骤S2551：获取该第二被测样本的该至少两种光学信号。相应地，对于第一示例性实施方式中的血液分析***，该信号获取模块750获取该第二被测样本的该至少两种光学信号。
步骤S2553：基于该至少两种光学信号生成该第二被测试样的散点图。在如图4A所示的实施例中，基于该第二被测试样的前向散射光信号和荧光信号强度，可以得到一荧光-前向散射光二维散点图。相应地，对于第一示例性实施方式中的血液分析***，该分类计数模块770生成该第二被测试样的 散点图。在一替代实施方式中，步骤S235所获取的至少两种光学信号包括前向散射光信号、侧向散射光信号和荧光信号，则在步骤S2553中生成的散点图也可以是前向-侧向散射光散点图、荧光-侧向散射光散点图、或者荧光、前向-侧向散射光三维散点图。可以理解地，当该至少两种光学信号还包括其他光学信号(如中角度散射光信号、荧光信号)时，该散点图也可以是其他形式的二维或三维散点图。可以理解地，该散点图的横纵坐标也可以采用其他能够反映第一被测试样粒子特性的前向散射光信号和侧向散射光信号的参数，该散点图的横纵坐标也可以采用非线性坐标轴，如对数坐标轴，以进一步凸显粒子群分布差异。
步骤S2555：基于所述至少两种光学信号在该第二被测试样的散点图中区分白细胞区域和血小板区域。相应地，对于第一示例性实施方式中的血液分析***，该分类计数模块770基于所述至少两种光学信号在该第二被测试样的散点图中区分白细胞区域和血小板区域。以图4A所示的实施例为例，在该散点图中，可以基于该第二被测试样的前向散射光信号和荧光信号的强度差异区分一白细胞区域W与一血小板区域P。其中，该白细胞区域W包括白细胞在该散点图中出现的区域；该血小板区域P包括血小板在该散点图中出现的区域。本领域技术人员可以理解，通过设门技术(gating technique)可以设定该白细胞区域W与该血小板区域P。
请参看图4A，现有技术一般认为在溶血后血样样本的光学散点图所表征的粒子群中，散射光和荧光强度相对较小的粒子群主要包括红细胞碎片和血小板。发明人经过反复多次的假设和实验发现，溶解试剂可以包括用于溶解红细胞的一种或多种溶解剂及用于染色有核血细胞的荧光染料，被溶血剂处理后的血小板在体积大小和细胞内容物上与红细胞碎片及白细胞均存在差异，可以通过光学方法(例如测量光散射和荧光信号)在溶血后的血液样本中区分一部分或全部的血小板。
在本第二示例性实施方式中，步骤S2555所区分的血小板区域P可以包含红细胞碎片等杂质粒子。如图4A所示，该血小板区域P的前向散射光信号的强度基本上小于该白细胞区域W的前向散射光信号的强度，该血小板区域P的荧光信号的强度基本上小于该白细胞区域W的荧光信号的强度。图4B为图4A的局部放大图，是对图4A所示的散点图中的血小板区域P进行放大得到的。
步骤S2557：基于该血小板区域P得到所述血液样本的第二血小板检测数据。相应地，对于第一示例性实施方式中的血液分析***，该分类计数模 块770基于该血小板区域P得到所述血液样本的第二血小板检测数据。
在一实施方式中，步骤S2557基于该血小板10b中所表征的粒子群的前向散射光信号FSC计算得到被测血液样本的第二血小板检测数据。
在一实施例中，该血小板10b中所表征的每一粒子的体积Vol可以使用方程式(1)计算：
Vol a＝α*FSC     方程式(1)
其中，FSC为该血小板10b中所表征的每一粒子(也可称为“每一单独事件”，individual event)的前向散射光信号强度，α为一常数。
在另一实施例中，该血小板10b中所表征的每一粒子的体积Vol可以使用方程式(2)计算：
Vol b＝β*exp(γ*FSC)       方程式(2)
其中，FSC为该血小板10b中所表征的每一单独事件的前向散射光信号强度，β和γ为常数。
在又一实施例中，该血小板10b中所表征的每一粒子的体积Vol可以使用方程式(3)计算：
Vol c＝[1/(FSC*σ(2π) 1/2)]exp(-(lnFSC-μ) 2/2σ 2)        方程式(3)
其中，FSC为该血小板10b中所表征的每一单独事件的前向散射光信号强度，μ和σ是常数。
在步骤S2557中，基于该血小板10b中所表征的粒子群中每一粒子的体积Vol及相应粒子数量可以得到该血小板10b所对应的体积分布数据。进一步地，基于该血小板10b的该体积分布数据可以得到体积分布曲线，在本文中将其称为衍生体积直方图，如图4C所示。由于该血小板10b的该体积分布数据(或该衍生体积直方图)含有溶血后血液样本中的血小板信息，在本文中将其认为是一种形式的第二血小板检测数据。
进一步地，采用预设的衍生体积分隔阈值可以在该衍生体积直方图中区分体积较大的粒子与体积较小的粒子。其中，该衍生体积分隔阈值可以选自10-20fL之间的数值，如10fL、12fL、15fL或20fL。发明人经过反复多次的假设和实验发现，其中体积较大粒子主要为被测血液样本的血小板中体积较大的部分。由于电阻抗方法无法区分体积相近的大血小板、红细胞碎片和小红细胞，通过对比该第二示例性实施方式中所得的该衍生体积直方图与电阻抗体积直方图中大于该衍生体积分隔阈值的部分，可以有效地报警基于电阻抗检测方法所得的第一血小板检测数据异常。由于被该衍生体积分隔阈值分隔后的衍生体积直方图中粒子体积较大的曲线部分含有溶血后血液样本 中的血小板信息，在本文中将其也认为是一种形式的第二血小板检测数据。可选地，基于衍生体积直方图中粒子体积较大的曲线部分还可以得到如该段曲线面积等特征参数，所述特征参数也是一种形式的第二血小板检测数据。
在一替代实施方式中，步骤S235所获取的至少两种光学信号包括前向散射光信号、侧向散射光信号和荧光信号。那么，在步骤S2557中，还可以基于该血小板10b的前向散射光信号和侧向散射光信号利用Mie散射理论(张伟,路远,杜石明等,球形粒子Mie散射特性分析,光学技术,2010年11月:第36卷第6期:936-939.)计算得到该血小板区域中每一粒子的体积，然后得到该血小板10b所呈现的粒子群的体积分布数据，即第二血小板检测数据。可选地，基于该血小板10b的该体积分布数据可以得到衍生体积直方图。可选地，基于该衍生体积直方图和一衍生体积分隔阈值可以得到该衍生体积直方图中粒子体积较大的曲线部分，根据该曲线部分可以获取被测血液样本中体积较大的血小板的信息。显而易见地，在该替代实施方式中，也可以基于该血小板10b的前向散射光信号通过方程式(1)、方程式(2)或方程式(3)得到第二血小板检测数据。
通过依次执行步骤S255中的步骤S2551-S2557，可以得到被测血液样本的第二血小板检测数据。在该第二示例性实施方式中，步骤S270基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果。相应地，对于第一示例性实施方式中的血液分析***，该报警模块790基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果。为得到所述评估结果，该步骤S270可以进一步包括以下步骤。
步骤S2701：获取步骤S250所得的第一血小板检测数据和步骤S255所得的第二血小板检测数据。可以理解地，在一实施方式中，步骤S2701可以有选择地获取可以用于直接比较的第一和第二血小板检测数据呈现形式，如所述电阻抗体积直方图和衍生体积直方图中体积大于某一预设体积分隔阈值的曲线片段，或者，该曲线片段对坐标轴横轴体积的积分面积。可以理解地，在另一实施方式中，也可以由步骤S2701获取其他呈现形式的第一第二血小板检测数据，例如半峰宽或半峰高等，然后在步骤S2701之后，由步骤S2703将所获取的第一和第二血小板检测数据的形式进行匹配，将其中无法直接用于比较的血小板检测数据的形式进行计算和转化使之可比较。
步骤S2705：基于可以用于直接比较的第一和第二血小板检测数据呈现形式，得到评估结果。所述评估结果可以是通过比较第一血小板检测数据与第二血小板检测数据的数值大小和/或图形差异所得的结果。对于数值形式的 血小板检测数据，步骤S2705可以将第一血小板检测数据与第二血小板检测数据通过一数学式计算得到反应二者差异程度的评估值(evaluation value,EV)，然后将该评估值与一预设阈值进行比较，得到该评估值大于、等于或者小于该预设阈值的评估结果。对于图像形式的血小板检测数据(如直方图)，步骤S2705所得到的评估结果可以是预设的对第一血小板检测数据与第二血小板检测数据曲线差异程度的定性描述，如“基本相似”或“差异较大”等。可以理解的是，所述评估结果的内容可以包括一项或多项分析比较结果，如包括多个反映血液样本中血小板信息的特征参数的数值评估结果。
需要指出的是，所述评估值可以是该第二血小板检测数据相对于该第一血小板检测数据的差异程度，也可以是该第一血小板检测数据相对于该第二血小板检测数据的差异程度。需要指出的是，计算该评估值的方式并不局限于本文中所披露的方式。可以理解地，步骤S2705中所述的预设阈值是根据所述评估值的设置方式设置的。以所述血小板检测数据为体积直方图中某一体积范围内的面积积分数值为例，该评估值EV可以是第二血小板检测数据PLT2与第一血小板检测数据PLT1之间的差值、绝对差值或商值，也可以是其差值、绝对差值、或商值的倒数、倍数或指数。在一实施例中，EV＝a*(PLT2/PLT1)，其中a为一预设的系数。在另一实施例中，EV＝a*(PLT1/PLT2)，其中a为一预设的系数。在又一实施例中，EV＝(PLT1-PLT2) b，其中b为一预设的系数。该评估值EV也可以是其他能够反映PLT1与PLT2的差异的值，例如，EV＝(PLT1-PLT2)/PLT1、EV＝(PLT1-PLT2)/PLT2等。
在该第二示例性实施方式中，步骤S280判断步骤S270所得的评估结果是否满足预设条件。当判断结果为是时，执行步骤S290，报警第一血小板检测出现异常和/或阻抗信号检测过程出现异常。当判断结果为否时，流程结束。相应地，对于第一示例性实施方式中的血液分析***，该报警模块790判断所述评估结果是否满足预设条件：当判断结果为是时，报警第一血小板检测出现异常和/或阻抗信号检测过程出现异常；当判断结果为否时，流程结束。该报警模块790报警第一血小板检测出现异常和/或阻抗信号检测过程出现异常的信息可以被输送至用户界面90。
可以理解地，在正常情况下(图5A)，电阻抗法得到的第一血小板检测数据与本公开所述的光学方法得到的该第二血小板检测数据之间的差异较小，即，本方法所提供的***和方法所得的评估结果包括第一与第二血小板检测数据差异相对较小的信息，当所述预设条件为第一与第二血小板检测数 据差异较大时，步骤S280的判断结果为否，流程结束。在异常情况下(图5B)，如含有小红细胞的异常血液样本或电阻抗检测通道内存在异常等，第一与第二血小板检测数据之间可能会存在较大差异，当所述预设条件为第一与第二血小板检测数据差异较大时，步骤S280的判断结果为是，报警第一血小板检测异常和/或阻抗通道信号检测过程出现异常。
具体地，当步骤S270得到评估结果为评估值与预设阈值之间的大小关系时，步骤S280中设置的预设条件可以是该评估值应大于该预设阈值。当步骤S270得到评估结果为第一与第二血小板检测数据图像之间的差异程度时，步骤S280中设置的预设条件可以是第一与第二血小板检测数据图像之间差异较大。可以理解的是，该预设条件可以包括多个预设条件，当该多个预设条件均被满足时，步骤S280的判断结果为是。
在该第二示例性实施方式中，可选地，还可以包括输出其他检测结果和/或中间结果的步骤。所述检测结果包括但不仅限于步骤S250所得的第一血小板检测数据、步骤S255所得的第二血小板检测数据。所述中间结果包括但不仅限于步骤S255所得的散点图、散点图中的血小板区域、衍生体积直方图、被衍生体积分隔阈值分隔后粒子体积较大的曲线部分、步骤S270所得的评估值或评估结果等。
需要指出的是，在本文中所描述的异常，可以是由于血液分析仪异常而导致的。所述血液分析仪异常包括但不限于：电阻抗检测部件异常、光学检测部件异常。本申请中，由于光学检测部件异常几率一般很小，通过比较第一和第二血小板检测数据，能够用来提示电阻抗检测部件的异常。进一步的，可以连续记录比较多个样本的第一和第二血小板检测数据，通过统计，如果连续多个样本两者数据不一致，再提示提示电阻抗检测部件出现异常，提高报警的准确性。
本文另一个实施例中，第二血小板检测可以利用现有分析仪的白细胞检测或者有核红细胞检测时进行。即，第二检测试液可以是白细胞分类或计数或嗜碱性粒细胞计数或有核红细胞计数用的试液。由于，这些试液中红细胞被裂解，血细胞被荧光染料染色，光学检测中，也会得到每个细胞粒子的光信号。发明人经过研究发现，这些检测得到的散点图上，也具有血小板区域P，可以用来前述的方法进行报警。同时，可以得到白细胞分类的结果，如图11示，基于荧光信号、侧向光散射信号和前向光散射信号将白细胞区分为四个亚群：淋巴细胞、单核细胞、中性粒细胞和嗜酸性粒细胞。进一步地，在其他实施方式中，可以基于光散射信号和荧光信号将白细胞中的嗜碱性粒细 胞与其他白细胞亚群进行区分。在其他实施例中，本方法可以进一步包括计数白细胞的数量，报告血液样本中白细胞计数的步骤。本领域技术人员可以理解，本方法还可以包括基于该第二悬浮液的光散射信号和荧光信号识别有核红细胞、未成熟细胞或原始细胞的步骤。例如，如图12所示，当血液样本中存在未成熟细胞时，本方法基于光散射信号和荧光信号可以识别未成熟细胞，并可以将白细胞区分为四个亚群：淋巴细胞、单核细胞、中性粒细胞和嗜酸性粒细胞。或者，例如图13所示，光散射和荧光信号可以识别出有核红细胞和白细胞，进行有核红细胞和白细胞计数。
研究发现，利用荧光-侧散散点图(SFL-SSC)上也可以区分出血小板区域如图14所示。因此，当样本经过有核红细胞检测部，同时获取荧光信号、前向散射光信号和侧向散射光信号，可以先通过荧光-侧散散点图(SFL-SSC)区分出P区，然后至少根据每个细胞的前向散射光信号，获得衍生血小板体积直方图，得到第二血小板检测数据。
下面将对本公开第三示例性实施方式所提供的报警异常的方法进行描述。相较于上文中所描述的该第二示例性实施方式的方法，该第三示例性实施方式在步骤S255a中采用了不同的方法获取第二血小板检测数据，其主要分析流程及其他各步骤内容的可参看图3及上文中所描述的内容，在此不再赘叙。
在该第三示例性实施方式中，步骤S255a基于第二被测样本的至少两种光学信号得到第二血小板检测数据，该至少两种光学信号包括红细胞被溶解的第二被测试样的前向散射光信号和荧光信号。具体地，步骤S255a包括以下步骤。
步骤S2551a：获取该第二被测样本的该至少两种光学信号。
步骤S2553a：基于该至少两种光学信号生成该第二被测试样的散点图。
步骤S2555a：基于所述至少两种光学信号在该第二被测试样的散点图中区分白细胞区域和血小板区域。在第三示例性实施方式中，步骤S2555a所区分的血小板区域为大血小板区域P’，该大血小板区域P’为第二被测试样中大血小板在该散点图中出现的区域。在图6A所示的实施例中，该大血小板区域P’的前向散射光信号的强度基本上小于该白细胞区域W的前向散射光信号的强度，且基本上大于位于该散点图左下角红细胞碎片的前向散射光信号的强度。该大血小板区域P’的荧光信号的强度基本上小于该白细胞区域W的荧光强度。至少基于该大血小板区域P’采用前述的方法也可以得到血小板衍生体积直方图，如图6B所示。需要说明的是，图6B为示意图，为方便理 解，曲线的左边部分，进行了拟合处理。
步骤S2557a：基于该大血小板区域P’得到所述血液样本的第二血小板检测数据。在该第三示例性实施方式中，所述第二血小板检测数据可以是大血小板检测数据，如大血小板的体积分布数据、大血小板计数或其他可以反映大血小板体积分布的特征参数。
在一实施方式中，步骤S2557a可以基于该大血小板区域P’中所表征的粒子群的前向散射光信号FSC得到大血小板的体积分布数据。具体地，可以通过方程式(1)、方程式(2)或方程式(3)将前向散射光信号FSC转换为该大血小板区域P’中每一粒子的体积，从而得到大血小板的体积分布数据。在该第三示例性实施方式的另一实施方式中，步骤S235a所获取的所述至少两种光学信号包括前向散射光信号、侧向散射光信号和荧光信号，步骤S2557a还可以基于该大血小板区域P’中所表征的粒子群的前向散射光信号和侧向散射光信号利用Mie散射理论计算得到该大血小板区域P’中每一粒子的体积，从而得到大血小板的体积分布数据。可选地，基于该大血小板的体积分布数据可以得到大血小板衍生体积直方图。
可选地，基于该大血小板的体积分布数据还可以计算得到大血小板的计数值。在本公开中，用于限定大血小板的体积阈值可以是由用户设置的，该体积阈值可以是在10-20fL之间的任意数值，例如，所述大血小板可以是体积大于10fL、12fL、15fL或20fL的血小板。本领域技术人员可以理解，所述大血小板区域P’的范围可以基于所设定的大血小板的体积阈值相应变化。可选地，基于该大血小板的体积分布数据还可以计算得到反映大血小板的体积分布的特征参数，如大血小板计数值、大血小板体积分布宽度等。
在一实施方式中，步骤S2557a也可以获取该大血小板区域P’中所表征的粒子群的粒子数(或称“事件数”，event number)，基于所述粒子数得到大血小板的计数值。
在该第三示例性实施方式中，步骤S270a可以获取步骤S250所得的第一血小板检测数据和步骤S255a所得的第二血小板检测数据，基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果。可以理解地，用于步骤S270a的第二血小板检测数据可以是上述大血小板的体积分布数据(如大血小板衍生体积直方图)、大血小板的计数值或其他反映大血小板的体积分布的特征参数。相应地，步骤S270a可以包括对步骤S250所得的第一血小板检测数据进行预处理的步骤，从而使所获取的第一和第二血小板检测数据的形式相匹配，以基于二者差异获取评估结果。
该第三示例性实施方式中的其他具体内容的可参看上文中第二示例性实施方式中所描述的内容，在此不再赘叙。
下面将对本公开第四示例性实施方式所提供的报警方法进行描述。相较于上文中所描述的该第二实施方式中提到的利用血小板区域得到第二血小板检测数据的方法、第三示例性实施方式中提到的利用大血小板区域得到第二血小板检测数据的方法，该第四示例性实施方式在步骤S225b和S255b中采用了不同的样本处理方法及数据分析方法以获取第二血小板检测数据，具体地，该第二血小板检测数据包括第二被测试样中各体积的血小板信息，包括得到样本的血小板计数值。其主要分析流程及其他各步骤内容的可参看图3及上文中第二示例性实施方式所描述的内容，在此不再赘叙。
在步骤S225b中，用于制备该第二被测试样的溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料。在该第四示例性实施方式中，通过对溶血剂和/或荧光染料的选择，使溶血的第二被测试样中的血小板与白细胞和红细胞碎片之间的光学差异更加显著，从而实现对血小板的区分和计数。
在一实施方式中，步骤S225b使用膜染料或线粒体染料对血液样本中的血细胞进行特异性染色，使用如前述各示例性实施方式所述的溶血剂溶解红细胞制备第二被测样本，从而通过所述至少两种光学信号区分第二被测样本中的血小板。该膜染料可以是Alexa Fluor系列染料、其他明确为膜染料的商品化染料、以及以这些染料为母体的变形结构。该线粒体染料可以是罗丹明123、Mitotracker系列染料、其他明确为膜染料的商品化染料，以及以这些染料为母体的变形结构。图7A示出了使用Alexa Fluor 488染料荧光染色的第二被测样本的前向散射光-荧光散点图。图7B示出了使用Mitotracker Red染料荧光染色的第二被测样本的前向散射光-荧光散点图。图7C示出了使用罗丹明123染料荧光染色的第二被测样本的前向散射光-荧光散点图。图7D示出了使用Mitotracker Deep Red染料荧光染色的第二被测样本的前向散射光-荧光散点图。可以理解地，为了进一步凸显不同粒子群落之间的差异，在本实施方式中，步骤S255b所生成散点图的坐标轴为对数坐标轴。由图7A-7D可以看出，通过使用膜染料或线粒体染料对血液样本中的血细胞进行特异性染色，可以在散点图中区分血小板区域P”。该血小板区域P”为第二被测试样中的血小板在该散点图中出现的区域。
在另一实施方式中，步骤S225b使用如中国发明专利ZL200910109215.6所公开的含有糖苷类化合物的溶血剂溶解红细胞、但调整溶血剂的用量，提 高溶血强度，使用核酸染料染色血细胞制备第二被测样本，从而通过所述至少两种光学信号区分第二被测样本中的血小板。中国发明专利ZL200910109215.6所公开的全部内容通过引证结合于此。染料可以选自前述个示例性实施方式所述的膜染料或线粒体染料，也可以是前述专利中提到的荧光染料，或者其他适合白细胞或网织红细胞染色的荧光染料，例如荧光染料SYTO9。
在本实施方式中，所述溶血剂包括糖苷类化合物、非离子表面活性剂和阴离子有机化合物。
所述糖苷类化合物选自皂苷和烷基糖苷类化合物。所述糖苷类化合物具有通式R-(CH 2) n-CH 3。其中，n为5-17之间的整数，优选地，n为6-14之间的整数；R为单糖、单糖的聚合物或多糖。更具体地，R可以选自葡萄糖、鼠李糖、半乳糖、***糖、木糖、麦芽糖、甘露糖、核糖、来苏糖、夫糖等常见的糖类及其去氧糖，以及这些物质的聚合物。
所述非离子表面活性剂具有通式R 1-R 2-(CH 2CH 2O) n-H。其中，R 1为C8-23的烷基或链烯基。优选地，R 1选自辛基、葵基、月桂基、十四烷基、十六烷基和硬脂基的直链烷基。特别优选地，R 1选自月桂基、十四烷基和十六烷基的直链烷基。其中，R2选自-O-、
Figure PCTCN2019084686-appb-000001
或-COO-，n为10-30之间的整数。
所述阴离子有机化合物选自带有一个或者多个羟基或磺酸基的酸或其盐的阴离子有机化合物。
图8A示出了本实施方式的一实施例所得到的第二被测样本的前向散射光-荧光散点图。其中，在步骤S225b中使用的溶解试剂包括如上所述的溶血剂及核酸染料。具体地，在图8A所示的实施例中，该溶解试剂的各成分及其浓度如下：
Figure PCTCN2019084686-appb-000002
该溶解试剂的pH为7.5。将20微升的血液样本加入到1mL上述溶解溶液中，在45℃条件下孵育60秒后，在激发波长为488nm下采集前向散射光信号和90度侧向荧光信号。基于该前向散射光信号和该荧光信号，可以得到如图8A所示的散点图，并可以进一步在散点图中区分血小板区域P”。该血 小板区域P”为第二被测试样中的血小板在该散点图中出现的区域。可以理解地，为了进一步凸显不同粒子群落之间的差异，在本实施方式中，步骤S255b所生成散点图的坐标轴为对数坐标轴。
在其他实施方式中，也可以将上述两种实施方式结合使用。也即是说，在步骤S225中所使用的溶解试剂包括溶血剂和荧光染料，该溶血剂包括糖苷类化合物、非离子表面活性剂和阴离子有机化合物，该荧光染料选自膜染料或线粒体染料。
请再次参看图3所示的本公开方法的步骤流程图。如上文所述，在该第四示例性实施方式中，步骤S255b可以基于该至少两种光学信号，包括前向散射光和荧光信号，生成如图7A-7D或图8A所示的散点图。步骤S255b基于所述至少两种光学信号在该第二被测试样的散点图中区分白细胞区域和血小板区域P”，然后基于该血小板区域P”得到所述血液样本的第二血小板检测数据。
在一实施方式中，可以基于该血小板区域P”中所表征的粒子群的前向散射光信号FSC得到血小板的体积分布数据。具体地，可以通过方程式(1)、方程式(2)或方程式(3)将前向散射光信号FSC转换为该血小板区域P”中每一粒子的体积，从而得到血小板的体积分布数据。在另一实施方式中，步骤S235b所获取的所述至少两种光学信号包括前向散射光信号、侧向散射光信号和荧光信号，步骤S255b还可以基于该血小板区域P”中所表征的粒子群的前向散射光信号和侧向散射光信号利用Mie散射理论计算得到该血小板区域P”中每一粒子的体积，从而得到血小板的体积分布数据。可选地，基于该血小板的体积分布数据可以得到血小板衍生体积直方图。可选地，基于该血小板的体积分布数据还可以计算得到反映血小板的体积分布的特征参数，如血小板的计数值、平均血小板体积、体积分布宽度等。在又一实施方式中，也可以通过获取该血小板区域P”中所表征的粒子群的粒子数得到血小板的计数值。
可以理解地，步骤S255b所得的第二血小板检测数据可以是血小板的体积分布数据(如血小板衍生体积直方图)，也可以是反映血小板的体积分布的特征参数(如血小板的计数值、平均血小板体积、体积分布宽度等)。
相似地，基于步骤S250和步骤S255b所得的第一和第二血小板检测数据，接续执行步骤S270b-S290b，可以在血液样本分析过程中报警异常。相关具体内容可参考前文，在此不再赘叙。本申请中，报警异常，包括给出此次样本检测中，电阻抗检测部件可能出现故障的提示，或因电阻抗检测部件 异常导致此次检测结果不可信的提示等，或者电阻抗检测部件和/或光学检测部件出现故障，或由此导致此次检测结果不可信的提示等。
本领域技术人员可以理解，该第二、第三或第四示例性实施方式中所述的全部或部分步骤可以通过计算机程序来指令一血液分析仪的相关硬件实现。该计算机程序可存储于一计算机可读存储介质并装入一具有相应硬件***的血液分析仪中。当处理器运行该计算机程序时，血液分析仪执行本公开第二、第三或第四示例性实施方式所披露的血液样本的分析方法。
本公开的第一方面还提供一种血液分析仪，该血液分析仪包括处理器和非易失性计算机可读存储介质，该处理器用于执行所述非易失性计算机可读存储介质中存储的计算机程序时实现所述第二、第三或第四示例性实施方式的分析方法的步骤。
本公开的第一方面还提供一种非易失性计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现所述第二、第三或第四示例性实施方式的分析方法的步骤。其具体步骤可参考上文所描述的各种实施方式及实施例，在此不再赘叙。从而使该第二、第三或第四示例性实施方式的分析方法可以以软件功能单元的形式实现并作为独立的产品销售或使用。
本公开第一方面所提供的产品及方法，可以在现有的五分类血液分析***的基础上，利用电阻抗检测通道和白细胞分类检测通道(如深圳迈瑞生物医疗电子股份有限公司生产的BC-6800血液分析仪的DIFF通道)分别获取血小板检测数据，通过对比两个检测通道所得的第一和第二血小板检测数据对异常检测结果进行报警。本公开第一方面所提供的产品及方法无需使用独立的检测通道，在不增加血液分析***成本的情况下，可以实时地为用户提供更加丰富的检测信息，提醒用户对存在异常的血小板检测数据进行复查或复检，提高血小板检测的准确度。
本公开的第二方面涉及通过血液样本的电阻抗信号及散射光信号报警血小板检测异常和/或阻抗通道异常的方法、***及存储介质。相较于本公开的第一方面，本公开的第二方面提供了一种不需要使用荧光染料即可实现报警血小板检测异常和/或阻抗通道检测出现异常的产品及方法。需要指出的是，在本公开的第二方面也可以加入荧光染料制备第二被测试样，荧光染料的有无并不会影响相应实施方式的实现。
本公开的第五示例性实施方式提供一种报警方法。请再次参看图3所示的步骤流程图，该报警方法包括以下步骤：
步骤S200：提供血液样本。
步骤S220：将该血液样本的第一部分与稀释液混合得到第一被测试样，用于第一血小板检测。
步骤S225c：将该血液样本的第二部分与溶解试剂混合得到第二被测试样，用于第二血小板检测。其中，该溶解试剂包括用于溶解红细胞的溶血剂。
步骤S230：检测该第一被测试样的电阻抗信号。
步骤S235c：检测该第二被测试样的至少两种光学信号。其中，所述至少两种光学信号包括第一散射光信号和第二散射光信号，该第一散射光信号为前向散射光信号，该第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种。
步骤S250：基于步骤S230所得的电阻抗信号得到该血液样本的第一血小板检测数据。
步骤S255c：基于步骤S235所得的至少两种光学信号得到该血液样本的第二血小板检测数据。
步骤S270c：基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果。
步骤S280：判断所述评估结果是否满足预设条件。当判断结果为是时，执行步骤S290，报警第一血小板检测出现异常和/或电阻抗信号检测过程出现异常。当判断结果为否时，流程结束。
本领域技术人员可以理解，上述全部或部分步骤可以通过计算机程序由如图1所示的血液分析***实现。
在步骤S225c中，将该血液样本的第二部分与溶血剂混合得到第二被测试样。所述溶血剂可以是任意一种现有的用于自动化血液分析仪白细胞分类的溶血试剂，其可以是阳离子表面活性剂、非离子表面活性剂、阴离子表面活性剂、两亲性表面活性剂中的任意一种或几种的组合。
在步骤S235c中，可以通过一个或多个光学检测器获取该第二被测试样的前向散射光信号，以及，中角度散射光信号和侧向散射光信号中的至少一种。所述中角度散射光信号可以由光检测器在前向散射光和侧向散射光之间的一角度检测得到。该中角度散射光信号可以是从与入射光束约8°至约24°的角度上进行检测的低中角度散射光信号，也可以是从与入射光束约25°至约65°的角度上进行检测的高中角度散射光信号。如前文所述，该前向散射光信号可以从与入射光束约1°至约10°的角度上进行检测，优选地，该前向散射光信号可以从与入射光束约2°至约6°的角度上进行检测。该侧向散射光信号可以从与入射光束呈约90°的角度上进行检测，可选地，该侧向散射光 信号也可以从与入射光束呈约65°至约115°的角度上进行检测。
与本公开第一方面所述的方法相似地，步骤S255c可以包括以下步骤：
步骤S2551c：获取该第二被测样本的所述至少两种光学信号，即前向散射光信号，以及，中角度散射光信号和侧向散射光信号中的至少一种。
步骤S2553c：基于所述至少两种光学信号生成该第二被测试样的散点图。
步骤S2555c：基于所述至少两种光学信号在步骤S2553c所得的散点图中区分白细胞区域和血小板区域。
步骤S2557c：基于步骤S2557c所得的血小板区域得到所述血液样本的第二血小板检测数据。
在一实施方式中，类似于上文所述的第二示例性实施方式，步骤S2555c所区分的血小板区域P包括血小板在该散点图中出现的区域，其可以包含红细胞碎片等杂质粒子在该散点图中出现的区域。在步骤S2557c中通过方程式(1)、方程式(2)或方程式(3)将血小板区域P中所表征的粒子群的前向散射光信号转换为该血小板区域P中每一粒子的体积，从而得到血小板的体积分布数据。当该第二散射光信号为侧向散射光信号时，步骤S2557c还可以基于该血小板区域P中所表征的粒子群的前向散射光信号和侧向散射光信号，利用Mie散射理论计算得到该血小板区域P中每一粒子的体积，从而得到血小板的体积分布数据。所述体积分布数据可以是数字形式，也可以是图形形式，如衍生体积直方图。
进一步地，采用预设的衍生体积分隔阈值可以在该衍生体积直方图中区分体积较大的粒子与体积较小的粒子。其中，该衍生体积分隔阈值可以选自10-20fL之间的数值，如10fL、12fL、15fL或20fL。分隔后的衍生体积直方图中粒子体积较大的曲线部分含有溶血后血液样本中的血小板信息，可以被认为是一种形式的第二血小板检测数据。可选地，基于衍生体积直方图中粒子体积较大的曲线部分还可以得到如该段曲线面积等特征参数，所述特征参数也可以被认为是一种形式的第二血小板检测数据。
在另一实施方式中，类似于上文所述的第三示例性实施方式，步骤S2555c所区分的血小板区域为大血小板区域P’，该大血小板区域P’为第二被测试样中大血小板在该散点图中出现的区域。图9示出了该实施方式的一实施例所生成的前向-侧向散射光散点图。在步骤S2557c中可以通过方程式(1)、方程式(2)、方程式(3)或Mie散射理论将大血小板区域P’中所表征的粒子群的散射光信号转换为该大血小板区域P’中每一粒子的体积，从而得到大血小板的体积分布数据。可选地，基于该大血小板的体积分布数据可以得到 大血小板衍生体积直方图。可选地，基于该大血小板的体积分布数据还可以计算得到反映大血小板的体积分布的特征参数，如大血小板计数值、大血小板体积分布宽度等。可选地，在步骤S2557c中，也可以通过获取该大血小板区域P’中所表征的粒子群的粒子数得到大血小板的计数值。可以理解地，在该实施方式中，第二血小板检测数据可以是上述大血小板的体积分布数据(如大血小板衍生体积直方图)、大血小板的计数值或其他反映大血小板的体积分布的特征参数。
在又一实施方式中，类似于上文所述的第四示例性实施方式，步骤S2555c所区分的血小板区域为血小板区域P”，该血小板区域P”为第二被测试样中血小板在该散点图中出现的区域。在该实施方式中，步骤S225c需要使用如中国发明专利ZL200910109215.6所公开的含有糖苷类化合物的溶血剂制备第二被测样本，但不使用核酸染料对样本进行处理。研究发现，不使用染料，仅增加溶血强度，也可以通过两种散射光的散点图上出现血小板区域P”。图8B示出了该实施方式的一实施例所得的前向-侧向散射光散点图。在步骤S2557c中，可以基于该血小板区域P”中所表征的粒子群的前向散射光信号(或前向与侧向散射光信号)得到血小板的体积分布数据，还可以基于该血小板体积分布数据进一步得到血小板衍生体积直方图和反映血小板的体积分布的特征参数，如血小板的计数值、平均血小板体积、体积分布宽度等。在步骤S2557c中，也可以通过获取该血小板区域P”中所表征的粒子群的粒子数得到血小板的计数值。
在该第五示例性实施方式中，在步骤S270c中，根据步骤S250所得的第一血小板检测数据和步骤S255c所得的第二血小板检测数据，分析二者之间的差异，得到评估结果。在步骤S280中，判断步骤S270c所得的评估结果是否满足预设条件。当判断结果为是时，执行步骤S290，报警第一血小板检测异常和/或电阻抗信号检测出现了异常。当判断结果为否时，流程结束。步骤S270c-290的具体内容可参考上文中第二、第三或第四示例性实施方式中所述的内容，在此不再赘叙。
在该第五示例性实施方式中，可选地，还可以包括输出其他检测结果和/或中间结果的步骤。所述检测结果包括但不仅限于步骤S250所得的第一血小板检测数据、步骤S255c所得的第二血小板检测数据。所述中间结果包括但不仅限于步骤S255c所得的散点图、散点图中的血小板区域、衍生体积直方图、被衍生体积分隔阈值分隔后粒子体积较大的曲线部分、步骤S270c所得的评估值或评估结果等。
进一步的，在以上示例性实施方式中，特别是第四和第五示例性实施方式中，由于能够得到血小板计数值，而光学检测部件异常的几率一般较小，为了尽快报告待测样本的检测结果，可以输出第二血小板检测数据中得到的血小板计数值，报告给用户。即，当评估结果为两者差别不大时，输出第一血小板检测数据得到的血小板计数值，当评估结果为两者差别大时，输出第二血小板检测数据得到的血小板计数值。优选的，可以标记结果来提示用户该结果有溶血条件下光学检测法得到的血小板计数值，已区分由电阻抗法得到的血小板计数值。
本领域技术人员可以理解，该第五示例性实施方式中所述的全部或部分步骤可以通过计算机程序来指令一血液分析仪的相关硬件实现。该计算机程序可存储于一计算机可读存储介质并装入一具有相应硬件***的血液分析仪中。当处理器运行该计算机程序时，血液分析仪执行本公开第五示例性实施方式所披露的血液样本的分析方法。
本公开的第二方面还提供一种血液分析仪，该血液分析仪包括处理器和非易失性计算机可读存储介质，该处理器用于执行所述非易失性计算机可读存储介质中存储的计算机程序时实现所述第五示例性实施方式的分析方法的步骤。
本公开的第二方面还提供一种非易失性计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现所述第五示例性实施方式的分析方法的步骤。其具体步骤可参考上文所描述的各种实施方式及实施例，在此不再赘叙。从而使该第五示例性实施方式的分析方法可以以软件功能单元的形式实现并作为独立的产品销售或使用。
相应于该第五示例性实施方式，本公开的第二方面还提供一种血液分析***。请再次参看图1，该血液分析***包括样本采集部件10、样本处理装置30、样本检测装置50、数据分析模块70及用户界面90。
该样本处理装置30包括至少一混合室，用于将血液样本的第一部分与稀释液混合得到第一被测试样，将该血液样本的第二部分与溶解试剂混合得到第二被测试样。其中，该溶解试剂包括用于溶解红细胞的溶血剂。
该样本检测装置50，包括电阻抗检测部件51和光学检测部件53。该电阻抗检测部件用于检测该第一被测试样的电阻抗信号。该光学检测部件53用于检测该第二被测试样的至少两种光学信号。其中，所述至少两种光学信号包括第一散射光信号和第二散射光信号，该第一散射光信号为前向散射光信号，该第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一 种。
该数据分析模块70包括信号获取模块750、分类计数模块770和报警模块790。该信号获取模块750获取该第一被测试样的电阻抗信号和该第二被测试样的所述至少两种光学信号。该分类计数模块770基于该电阻抗信号得到所述血液样本的第一血小板检测数据。该分类计数模块770基于所述至少两种光学信号生成该第二被测试样的散点图，再基于所述至少两种光学信号在该散点图中区分白细胞区域和血小板区域，然后基于该血小板区域得到所述血液样本的第二血小板检测数据。该报警模块790根据该第一血小板检测数据和该第二血小板检测数据之间的差异得到评估结果，然后判断所述评估结果是否满足预设条件。当判断结果为是时，报警血小板检测出现异常和/或阻抗通道出现异常。当判断结果为否时，流程结束。
该血液分析***的其他具体结构及功能模块的具体实施方式可参看上文中的相应内容，在此不再赘叙。
相较于本公开的第一方面所提供的产品及方法，该第二方面所提供的血液分析***、分析方法、血液分析仪及存储介质无需使用荧光染料即可实现对血小板检测异常和/或阻抗通道检测出现异常的报警，可以在不增加血液分析***成本及血液分析过程的试剂成本的情况下，为用户提供更加丰富的检测信息，提醒用户对存在异常的血小板检测数据进行复查或复检，提高血小板检测的准确度，或者及时发现样本分析仪检测***出现异常。
图10本公开所提供的血液分析***的一整体立体示意图。如图10所示，该血液分析***中包括第一机壳100、第二机壳200、样本采集部件10、样本处理装置30、样本检测装置50、数据分析模块70及用户界面90。本实施方式中，样本检测装置50与数据分析模块70设置在第二机壳200的内部，分别设置在第二机壳200两侧。样本处理装置30设置在第一机壳100的内部，用户界面90、样本采集部件10在第一机壳100的外表面。
上述实施例为本公开较佳的实施方式，但本公开的实施方式并不受上述实施例的限制，以上实施方式仅是用于解释权利要求书。任何熟悉本技术领域的技术人员在本公开披露的技术范围内，可轻易想到的变化或者替换，都包含在本公开的保护范围之内。

Claims (26)

  1. 一种样本分析仪出现异常的报警方法，其特征在于，所述方法包括：
    提供血液样本；
    将所述血液样本的第一部分与稀释液混合得到第一被测试样，用于第一血小板检测；
    将所述血液样本的第二部分与溶解试剂混合得到第二被测试样，用于第二血小板检测，其中第二被测试样中红细胞被裂解；
    检测所述第一被测试样的电阻抗信号；
    检测所述第二被测试样的至少两种光学信号；
    基于所述电阻抗信号得到所述血液样本的第一血小板检测数据；
    基于所述至少两种光学信号得到所述血液样本的第二血小板检测数据；
    基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果；
    判断所述评估结果是否满足预设条件；以及
    当判断结果为是时，报警所述第一血小板检测出现异常和/或所述样本分析仪的电阻抗信号检测步骤出现异常。
  2. 根据权利要求1所述的报警方法，其特征在于，所述基于所述至少两种光学信号得到所述血液样本的第二血小板检测数据包括：
    基于所述至少两种光学信号生成所述第二被测试样的散点图；
    基于所述至少两种光学信号在所述散点图中区分白细胞区域和血小板区域；
    基于所述血小板区域得到所述血液样本的第二血小板检测数据。
  3. 根据权利要求1所述的报警方法，其特征在于，输出所述第一血小板检测异常的原因为电阻抗信号检测步骤出现异常和/或第一血小板检测结果不可信的提示信息。
  4. 根据权利要求1所述的报警方法，其特征在于，所述溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料，所述至少两种光学信号包括前向散射光信号和荧光信号。
  5. 根据权利要求1所述的报警方法，其特征在于，所述溶解试剂包括用于溶解红细胞的溶血剂，所述至少两种光学信号包括第一散射光信号和第二散射光信号，所述第一散射光信号为前向散射光信号，所述第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种。
  6. 根据权利要求4或5所述的报警方法，其特征在于，所述基于所述血 小板区域得到所述血液样本的第二血小板检测数据的步骤包括：
    至少基于所述血小板区域中出现的粒子群的所述前向散射光信号得到衍生血小板体积直方图；或者
    基于所述血小板区域中出现的粒子数得到所述血液样本的第二血小板检测数据。
  7. 根据权利要求1所述的报警方法，其特征在于，所述溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料，所述至少两种光学信号包括侧向散射光信号和荧光信号；基于所述血小板区域中出现的粒子数得到所述血液样本的第二血小板检测数据。
  8. 根据权利要求2-7中任一项所述的报警方法，其特征在于，所述血小板区域包括大血小板区域，利用所述大血小板区域得到所述血液样本的第二血小板检测数据。
  9. 根据权利要求1-8中任一项所述的报警方法，其特征在于，所述第一血小板检测数据为第一血小板体积分布数据的至少一特征参数，所述第二血小板检测数据为第二血小板体积分布数据的所述至少一特征参数；
    优选的，所述特征参数选自血小板计数、血小板体积直方图、平均血小板体积及血小板体积分布宽度中的一种或几种；或者
    优选的，所述特征参数选自某一体积阈值范围内的血小板计数、血小板体积直方图、平均血小板体积及血小板体积分布宽度中的一种或几种。
  10. 根据权利要求1所述的报警方法，其特征在于，所述两种光信号包括散射光信号和荧光信号，所述方法还根据所述散射光信号和荧光信号，将白细胞区分为白细胞亚群，或对白细胞进行计数或识别有核红细胞或未成熟细胞或嗜碱性粒细胞；
    或者，所述两种光信号包括第一散射光信号和第二散射光信号，所述第一散射光信号为前向散射光信号，所述第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种，所述方法还根据所述第一散射光信号和所述第二散射光信号，将白细胞区分为白细胞亚群或识别嗜碱性粒细胞。
  11. 根据权利要求1所述的报警方法，其特征在于，所述判断所述评估结果是否满足预设条件的步骤包括：
    比较所述第一血小板检测数据与所述第二血小板检测数据的图形差异程度；
    判断所述图形差异程度是否满足预设条件；或者
    获取所述第一血小板检测数据与所述第二血小板检测数据的数值信息；
    利用所述数值信息计算评估值，所述评估值用于反映所述第一血小板检测数据与所述第二血小板检测数据的差异程度；
    判断所述评估值是否满足预设条件。
  12. 根据权利要求1中所述的报警方法，其特征在于，还包括如下步骤：
    若无报警异常，则输出所述第一血小板检测数据；
    若有报警异常，则输出所述第二血小板检测数据。
  13. 根据权利要求1所述的报警方法，其特征在于，记录并统计连续多个血液样本得到的血小板检测评估值的判断结果，当连续多个血液样本的判断结果均为是，报警电阻抗信号检测出现异常。
  14. 一种非易失性计算机可读存储介质，其上存储有计算机程序，其特征在于：所述计算机程序被处理器执行时实现如权利要求1-13任一项所述的报警方法的步骤。
  15. 一种血液分析***，其特征在于，所述血液分析***包括：
    样本处理装置，包括至少一混合室，用于将血液样本的第一部分与稀释液混合得到第一被测试样，用于第一血小板检测；将所述血液样本的第二部分与溶解试剂混合得到第二被测试样，用于第二血小板检测，其中第二被测试样中红细胞被裂解；
    样本检测装置，包括电阻抗检测部件和光学检测部件，所述电阻抗检测部件包括微孔及电阻抗检测器，所述电阻抗检测器用于检测所述第一被测试样通过所述微孔的电阻抗信号，所述光学检测部件包括光学流动室、光源及光学检测器，所述光学流动室与所述混合室连通，所述光源用于将光束对准所述光学流动室，所述光学检测器用于检测通过所述光学流动室的所述第二被测试样的至少两种光学信号；
    数据分析模块，包括信号获取模块、分类计数模块和报警模块；
    所述信号获取模块获取所述第一被测试样的所述电阻抗信号，所述信号获取模块获取所述第二被测试样的所述至少两种光学信号；
    所述分类计数模块基于所述电阻抗信号得到所述血液样本的第一血小板检测数据；所述分类计数模块基于所述至少两种光学信号生成所述第二被测试样的散点图，基于所述至少两种光学信号在所述散点图中区分白细胞区域和血小板区域，基于所述血小板区域得到所述血液样本的第二血小板检测数据；
    所述报警模块基于所述第一血小板检测数据和所述第二血小板检测数据之间的差异得到评估结果；判断所述评估结果是否满足预设条件；以及当 判断结果为是时，报警所述第一血小板检测出现异常和/或所述电阻抗检测部件出现异常。
  16. 根据权利要求15所述的血液分析***，其特征在于，所述分类计数模块包括：
    基于所述至少两种光学信号生成所述第二被测试样的散点图；
    基于所述至少两种光学信号在所述散点图中区分白细胞区域和血小板区域；
    基于所述血小板区域得到所述血液样本的第二血小板检测数据。
  17. 根据权利要求15所述的血液分析***，其特征在于，所述报警模块包括输出所述第一血小板检测异常的原因为电阻抗检测部件出现异常和/或第一血小板检测结果不可信的提示信息。
  18. 根据权利要求15所述的血液分析***，其特征在于，所述溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料，所述至少两种光学信号包括前向散射光信号和荧光信号，所述光学检测部件包括至少一个散射光检测器和至少一个荧光检测器。
  19. 根据权利要求15所述的血液分析***，其特征在于，所述溶解试剂包括用于溶解红细胞的溶血剂，所述至少两种光学信号包括第一散射光信号和第二散射光信号，所述第一散射光信号为前向散射光信号，所述第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种，所述光学检测部件包括至少两个散射光检测器。
  20. 根据权利要求18或19所述的血液分析***，其特征在于，所述分类计数模块至少基于所述血小板区域中出现的粒子群的所述前向散射光信号得到衍生血小板体积直方图；或者
    所述分类计数模块基于所述血小板区域中出现的粒子数得到所述血液样本的第二血小板检测数据。
  21. 根据权利要求15所述的血液分析***，其特征在于，所述溶解试剂包括用于溶解红细胞的溶血剂和用于染色血细胞的荧光染料，所述至少两种光学信号包括侧向散射光信号和荧光信号；所述分类模块基于所述血小板区域中出现的粒子数得到所述血液样本的第二血小板检测数据。
  22. 根据权利要求16-21所述的血液分析***，其特征在于，所述血小板区域包括大血小板区域，所述第二血小板检测数据包括第二大血小板数据，利用所述大血小板区域得到所述血液样本的第二血小板检测数据。
  23. 根据权利要求15-22所述的血液分析***，其特征在于，所述第一血 小板检测数据为第一血小板体积分布数据的至少一特征参数，所述第二血小板检测数据为第二血小板体积分布数据的所述至少一特征参数；
    优选的，所述特征参数选自血小板计数、血小板体积直方图、平均血小板体积及血小板体积分布宽度中的一种或几种；或者
    优选的，所述特征参数选自某一体积阈值范围内的血小板计数、血小板体积直方图、平均血小板体积及血小板体积分布宽度中的一种或几种。
  24. 根据权利要求15所述的血液分析***，其特征在于，所述两种光信号包括散射光信号和荧光信号，所述分类计数模块还根据所述散射光信号和荧光信号，将白细胞区分为白细胞亚群，或对白细胞进行计数或识别有核红细胞或未成熟细胞或嗜碱性粒细胞；
    或者所述两种光信号包括第一散射光信号和第二散射光信号，所述第一散射光信号为前向散射光信号，所述第二散射光信号为中等角度散射光信号和侧向散射光信号中的至少一种，所述分类计数模块还根据所述第一散射光信号和第二散射光信号，将白细胞区分为白细胞亚群或识别嗜碱性粒细胞。
  25. 根据权利要求15所述的血液分析***，其特征在于，所述报警模块包括：
    比较所述第一血小板检测数据与所述第二血小板检测数据的图形差异程度；
    判断所述图形差异程度是否满足预设条件；或者
    获取所述第一血小板检测数据与所述第二血小板检测数据的数值信息；
    利用所述数值信息计算评估值，所述评估值用于反映所述第一血小板检测数据与所述第二血小板检测数据的差异程度；
    判断所述评估值是否满足预设条件。
  26. 根据权利要求15所述的血液分析***，其特征在于，还包括用户界面：
    若无报警异常，则输出所述第一血小板检测数据；
    若有报警异常，则输出所述第二血小板检测数据。
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Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114281283B (zh) * 2020-09-27 2023-03-24 深圳市帝迈生物技术有限公司 散点图像的显示方法及样本分析设备、相关装置
CN112730203B (zh) * 2020-12-29 2023-06-16 深圳市科曼医疗设备有限公司 血球分析仪的光学***、光学增益校准方法和存储介质
CN114047109B (zh) * 2022-01-11 2022-06-21 深圳市帝迈生物技术有限公司 一种样本分析仪及其计数方法
CN114419620A (zh) * 2022-03-31 2022-04-29 深圳市帝迈生物技术有限公司 异常样本识别方法、装置、样本分析仪和存储介质
CN114441415B (zh) * 2022-04-06 2022-09-20 深圳市帝迈生物技术有限公司 一种微堵孔识别方法和样本分析仪
CN116952805A (zh) * 2022-04-14 2023-10-27 深圳迈瑞生物医疗电子股份有限公司 样本分析仪以及血小板计数方法
CN114550404B (zh) * 2022-04-22 2022-07-01 江苏河马自动化设备有限公司 一种消防用烟雾报警器

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1549925A (zh) * 2001-07-27 2004-11-24 有核红细胞的测量方法
US20070105230A1 (en) * 2005-11-09 2007-05-10 Beckman Coulter, Inc. Method for discriminating platelets from red blood cells
CN103472216A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种血细胞分析芯片、分析仪及分析方法
CN103471982A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种血细胞分析芯片、分析仪及分析方法
CN103472034A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种血细胞分析芯片、分析仪及分析方法
CN103471980A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种芯片式血细胞分析装置及方法
CN104458540A (zh) * 2014-12-04 2015-03-25 南昌百特生物高新技术股份有限公司 血液分析仪流动室
CN104541149A (zh) * 2012-07-05 2015-04-22 贝克曼考尔特公司 用于确定白血细胞计数的方法和装置

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5891734A (en) * 1994-08-01 1999-04-06 Abbott Laboratories Method for performing automated analysis
NZ506590A (en) * 1998-12-29 2002-10-25 Thomas Adam Shine A method of analysing a sample of free cells
JP4101994B2 (ja) * 1999-01-21 2008-06-18 シスメックス株式会社 粒子分析装置および自動粒子分析方法
US7198953B2 (en) * 2003-10-12 2007-04-03 Beckman Coulter, Inc. Method of using a reference control composition for measurement of nucleated red blood cells
US7208319B2 (en) * 2004-02-10 2007-04-24 Beckman Coulter, Inc. Method of measurement of nucleated red blood cells
JP5203889B2 (ja) * 2008-10-28 2013-06-05 シスメックス株式会社 検体分析装置及び検体分析方法
CN101750476B (zh) * 2008-12-08 2015-06-03 深圳迈瑞生物医疗电子股份有限公司 血液分析试剂及其使用方法
CN104458541A (zh) * 2013-09-12 2015-03-25 深圳迈瑞生物医疗电子股份有限公司 红细胞血红蛋白含量的分析方法、装置及血液细胞分析仪
WO2016106688A1 (zh) * 2014-12-31 2016-07-07 深圳迈瑞生物医疗电子股份有限公司 一种有核红细胞报警方法、装置及流式细胞分析仪
EP3258274B1 (en) * 2016-06-17 2019-11-06 Sysmex Corporation Method of controlling a blood analyzer for measuring platelets

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1549925A (zh) * 2001-07-27 2004-11-24 有核红细胞的测量方法
US20070105230A1 (en) * 2005-11-09 2007-05-10 Beckman Coulter, Inc. Method for discriminating platelets from red blood cells
CN104541149A (zh) * 2012-07-05 2015-04-22 贝克曼考尔特公司 用于确定白血细胞计数的方法和装置
CN103472216A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种血细胞分析芯片、分析仪及分析方法
CN103471982A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种血细胞分析芯片、分析仪及分析方法
CN103472034A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种血细胞分析芯片、分析仪及分析方法
CN103471980A (zh) * 2013-08-23 2013-12-25 深圳中科强华科技有限公司 一种芯片式血细胞分析装置及方法
CN104458540A (zh) * 2014-12-04 2015-03-25 南昌百特生物高新技术股份有限公司 血液分析仪流动室

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