WO2019206313A1 - 测定血小板浓度的方法及*** - Google Patents

测定血小板浓度的方法及*** Download PDF

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WO2019206313A1
WO2019206313A1 PCT/CN2019/084687 CN2019084687W WO2019206313A1 WO 2019206313 A1 WO2019206313 A1 WO 2019206313A1 CN 2019084687 W CN2019084687 W CN 2019084687W WO 2019206313 A1 WO2019206313 A1 WO 2019206313A1
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platelet
suspension
histogram
blood sample
distribution
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PCT/CN2019/084687
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English (en)
French (fr)
Inventor
祁欢
叶波
郑文波
胡长松
郁琦
李朝阳
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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 EP19792132.3A priority Critical patent/EP3789751B1/en
Priority to CN201980011879.0A priority patent/CN111801568B/zh
Publication of WO2019206313A1 publication Critical patent/WO2019206313A1/zh
Priority to US17/075,603 priority patent/US11841358B2/en
Priority to US18/536,047 priority patent/US20240230621A9/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/49—Blood
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06—Investigating concentration of particle suspensions
    • G01N15/0656—Investigating concentration of particle suspensions using electric, e.g. electrostatic methods or magnetic methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/1031—Investigating individual particles by measuring electrical or magnetic effects
    • G01N15/12—Investigating individual particles by measuring electrical or magnetic effects by observing changes in resistance or impedance across apertures when traversed by individual particles, e.g. by using the Coulter principle
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1456—Optical investigation techniques, e.g. flow cytometry without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
    • G01N15/1459—Optical investigation techniques, e.g. flow cytometry without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/48707—Physical analysis of biological material of liquid biological material by electrical means
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06—Investigating concentration of particle suspensions
    • G01N15/075—Investigating concentration of particle suspensions by optical means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • G01N2015/016—White blood cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • G01N2015/018—Platelets
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N2015/1006—Investigating individual particles for cytology
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N2015/1019—Associating Coulter-counter and optical flow cytometer [OFC]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1486—Counting the particles

Definitions

  • the present disclosure relates to methods and systems for determining platelet concentration in a blood sample.
  • the present disclosure relates to determining platelet concentration by combining impedance measurement data of a diluted blood sample and optical measurement data of a post-hemolytic blood sample.
  • platelets In order to determine the patient's course of treatment, it is often necessary to obtain accurate platelet counts in clinical practice. For example, if the platelet count is less than 20*10 9 per liter, platelets may need to be imported, otherwise the patient may experience potentially life-threatening bleeding.
  • impedance measurement systems provide relatively accurate results in measuring platelet counts in most cases, they still have some limitations. For example, impedance measurement methods cannot distinguish between platelets and interfering particles, such as microcytes and schistocytes (also known as red blood cell debris), resulting in a false increase in platelet count. On the other hand, large platelets and giant platelets may be classified as red blood cells beyond a predetermined threshold for platelet count in impedance measurement methods, which may result in a false decrease in platelet count.
  • a mathematical curve fit is typically performed on the platelet volume distribution between 2 and 20 femtoliters (fL) in the histogram to extend the dynamic range to 70 fL.
  • the above methods do not achieve accurate platelet counts. For example, when the distribution of platelets does not follow a lognormal distribution, or if the high segment of the platelet distribution curve does not decrease, the average platelet volume will exceed the normal range, and the fit may no longer apply. In these cases, only platelets falling within the range of 2 to 20 fL are usually reported by the impedance measurement system and the detection abnormality is indicated.
  • optical measurement channels for platelets have been added to some high-end blood analyzers. While optical measurements reduce the effects of such interference on platelet measurements, additional optical detection channels for platelet detection significantly increase the complexity of blood analysis instruments and increase the cost of instrument manufacturing and maintenance services.
  • the present disclosure relates to a method of determining platelet concentration in a blood sample.
  • the method comprises: mixing a first sample of the blood sample with a diluent to form a first suspension; mixing a second sample of the blood sample with a hemolytic agent and a fluorescent dye to dissolve red blood cells and staining white blood cells, forming a second suspension; measuring a DC impedance signal of the first suspension flowing through the orifice; measuring a light scattering signal and a fluorescence signal of the second suspension flowing through the optical flow chamber; and analyzing the DC impedance signal of the first suspension Obtaining a first platelet distribution; analyzing the light scattering signal of the second suspension and the fluorescent signal to distinguish between platelets and white blood cells and obtaining a second platelet distribution; and determining the blood based on the first platelet distribution and the second platelet distribution The platelet concentration of the sample.
  • the method further comprises distinguishing the white blood cells in the blood sample into a subset of white blood cells based on the light scattering signal of the second suspension and the fluorescent signal, including distinguishing monocytes, lymphocytes, neutrophils, and Eosinophils.
  • the present disclosure relates to a blood analysis system for determining platelet concentration in a blood sample.
  • the blood analysis system includes: a first module including a first mixing chamber and a DC impedance detector, the first mixing chamber for mixing the first sample of the blood sample with the diluent to form a first a suspension, the DC impedance detector is mounted in a small aperture of the flow path, the flow path being in communication with the first mixing chamber, the DC impedance detector for detecting a DC impedance signal of the first suspension through the aperture.
  • the second module comprises a second mixing chamber, a light source and at least one optical detector for mixing the second sample of the blood sample with the hemolytic agent and the fluorescent dye, dissolving the red blood cells and dyeing the white blood cells to form a second suspension for aligning a light beam with a detection aperture of an optical flow chamber in communication with the second mixing chamber, the at least one optical detector being mounted to the optical flow chamber for detecting passage through the optical flow a light scattering signal and a fluorescent signal of the second suspension of the chamber detection aperture; and a data processing module operatively associated with the DC impedance detector of the first module and the at least one optical detector of the second module Connecting, the data processing module comprising a processor and a non-transitory computer readable storage medium programmed with a computer application, when the computer application is executed by the processor, causing the processor to base the DC based on the first suspension
  • the impedance signal generates a first platelet distribution
  • the light scattering signal and the fluorescent signal are used to distinguish platelets from white blood cells based on
  • the data processing module further distinguishes white blood cells in the blood sample into subpopulations of white blood cells based on the light scattering signal of the second suspension and the fluorescent signal, including distinguishing monocytes, lymphocytes, and neutrophils And eosinophils.
  • the present disclosure relates to a method of determining platelet concentration in a blood sample.
  • the method comprises: mixing a first sample of the blood sample with a diluent to form a first suspension; mixing a second sample of the blood sample with a hemolytic agent to dissolve red blood cells to form a second suspension; measuring a first DC current signal flowing through the orifice; measuring a forward light scattering signal of the second suspension flowing through the optical flow chamber and a lateral light scattering signal or a medium angle light scattering signal; analyzing the first suspension The DC impedance signal to obtain a first platelet distribution; analyzing the forward light scatter signal of the second suspension and the lateral light scatter signal or the medium angle light scatter signal to distinguish between platelets and white blood cells and obtain a second platelet distribution And determining a platelet concentration of the blood sample based on the first platelet distribution and the second platelet distribution.
  • the method further comprises dividing the white blood cells in the blood sample into subpopulations of white blood cells based on the forward light scattering signal of the second suspension and the lateral light scattering signal or the medium angle light scattering signal, including distinguishing Monocytes, lymphocytes, neutrophils, and eosinophils.
  • the present disclosure relates to a blood analysis system for determining platelet concentration in a blood sample.
  • the blood analysis system includes: a first module including a first mixing chamber and a DC impedance detector, the first mixing chamber for mixing the first sample of the blood sample with the diluent to form a first a suspension, the DC impedance detector is mounted in a small aperture of the flow path, the flow path being in communication with the first mixing chamber, the DC impedance detector for detecting a DC impedance signal of the first suspension through the aperture.
  • the second module comprises a second mixing chamber, a light source and at least one optical detector for mixing the second sample of the blood sample with the hemolytic agent and the fluorescent dye, dissolving the red blood cells and dyeing the white blood cells to form a second suspension for aligning a light beam with a detection aperture of an optical flow chamber in communication with the second mixing chamber, the at least one optical detector being mounted to the optical flow chamber for detecting passage through the optical flow chamber a forward light scatter signal and a lateral light scatter signal or a medium angle light scatter signal of the second suspension of the detection hole; and a data processing module, the DC resistance detector and the second module of the first module
  • the at least one optical detector is operatively coupled, the data processing module comprising a processor and a non-transitory computer readable storage medium programmed with a computer application, when the computer application is executed by the processor The processor generates a first platelet distribution based on the DC impedance signal of the first suspension, the forward light scattering signal based on the second suspension, and the The lateral light scatter signal
  • the data processing module further distinguishes white blood cells in the blood sample into subpopulations of white blood cells based on the light scattering signal of the second suspension and the fluorescent signal, including distinguishing monocytes, lymphocytes, and neutrophils And eosinophils.
  • Figure 1 shows the platelet DC impedance histogram H Plt-D of the first suspension from a blood sample.
  • Figure 2 shows a fluorescence-forward scattered light (SFL-FSC) scatter plot of the second suspension from the blood sample of Figure 1.
  • 3A-3C illustrate a process for obtaining a derived platelet volume histogram H Plt-L from a second suspension of a blood sample in an embodiment of the present disclosure.
  • 3A is a SFL-FSC scatter plot of the second suspension
  • FIG. 3B is an enlarged view of the platelet region of the SFL-FSC scatter plot of FIG. 3A
  • FIG. 3C is a derivative platelet volume of the second suspension.
  • Figure H Plt-L illustrates a process for obtaining a derived platelet volume histogram H Plt-L from a second suspension of a blood sample in an embodiment of the present disclosure.
  • 3A is a SFL-FSC scatter plot of the second suspension
  • FIG. 3B is an enlarged view of the platelet region of the SFL-FSC scatter plot of FIG. 3A
  • FIG. 3C is a derivative platelet volume of the second suspension.
  • Figure H Plt-L illustrates a process for obtaining a derived platelet volume histogram H Plt
  • Figure 4 shows a superposition of the platelet DC histogram H Plt-D of a first suspension of a blood sample containing red blood cell fragments and the derived platelet volume histogram H Plt-L of the second suspension.
  • FIG. 5 shows a process for determining platelet concentration based on the generation of a fusion platelet histogram H Plt-LD from a platelet DC histogram H Plt-D and a derived platelet volume histogram H Plt-L in the embodiment shown in FIG. 4 of the present disclosure.
  • FIGS. 6A and 6B illustrate a platelet DC histogram H Plt-D and a derived platelet histogram H Plt-L of the blood sample during determination of platelet concentration in a blood sample in an embodiment.
  • Figure 7 shows a platelet DC histogram H Plt-D of a first suspension of a blood sample containing large platelets.
  • Figure 8 shows a designated region of the platelet region of the fluorescence-forward light scattering (SFL-FSC) scatter plot of the second suspension of the blood sample employed in Figure 7.
  • SFL-FSC fluorescence-forward light scattering
  • FIGS 9A-9C illustrate a process embodiment of the present disclosure separated by a threshold value T d is derived to determine platelet concentration in the blood sample embodiment.
  • Figures 10A-10C further illustrate the process of partitioning the threshold value T d derived by determining the concentration of platelets in the blood sample.
  • FIG. 11A and 11B respectively show forward light scattering-lateral light scattering (FSC-SSC) dispersion of a second blood suspension of a normal blood sample and an abnormal blood sample containing large platelets in still another embodiment of the present disclosure. Dot map.
  • FSC-SSC forward light scattering-lateral light scattering
  • Figure 12 shows a three-dimensional scatter plot of SFL, SSC and FSC for illustrating a second suspension of blood samples that distinguish leukocyte subpopulations in an embodiment of the present disclosure.
  • Figure 13 shows a three-dimensional scatter plot of FSC-SSC-SFL for illustrating a second suspension of blood samples that distinguish immature cells in an embodiment of the present disclosure.
  • Figure 14 shows an FSC-SSC scatter plot for illustrating a second suspension of a blood sample that distinguishes leukocyte subsets in an embodiment of the present disclosure.
  • 15 is a simplified block diagram of a blood analysis system of the present disclosure.
  • Example 16 shows the correlation between the platelet concentration of these blood samples obtained by the conventional DC impedance detecting method described in Example 1 and the platelet concentration of these blood samples obtained by the flow cytometry reference method.
  • Example 17 shows the correlation between the platelet concentration of the blood sample obtained by the method of the embodiment of the present disclosure described in Example 1 and the platelet concentration of the blood sample obtained by the flow cytometry reference method.
  • Example 18 shows the correlation between the platelet concentration of the blood sample obtained by the method of the still further embodiment of the present disclosure described in Example 2 and the platelet concentration of the blood sample obtained by the flow cytometry reference method.
  • Example 19 shows the correlation between the platelet concentration of the blood sample obtained by the method of another embodiment of the present disclosure described in Example 3 and the platelet concentration of the blood sample obtained by the flow cytometry reference method.
  • Example 20 shows the correlation between the platelet concentration of the blood sample obtained by the method of another embodiment of the present disclosure described in Example 4 and the platelet concentration of the blood sample obtained by the flow cytometry reference method.
  • Example 21 shows the correlation between the platelet concentration of the blood sample obtained by the method of the further embodiment of the present disclosure described in Example 5 and the platelet concentration of the blood sample obtained by the flow cytometry reference method.
  • FIG. 22 illustrates a process for determining platelet concentration based on a platelet DC histogram H Plt-D and a derived platelet volume histogram H Plt-L to generate a fusion platelet histogram H Plt-LD in an embodiment of the present disclosure.
  • FIG. 23 is a diagram showing a platelet distribution region corresponding to an SFL-SSC scattergram obtained by a second suspension in an embodiment of the present disclosure.
  • Figure 24 is a diagram showing the number of occurrences of a designated region PG in a platelet region of a FSC-SSC scattergram obtained by a second suspension in an embodiment of the present disclosure.
  • FIGS 25A-25C illustrate a process further separated by a threshold value derived T d determined platelet concentration in the blood sample.
  • Figure 26 is a view showing an FSC-SFL scattergram for explaining a second suspension of a blood sample for identifying nucleated red blood cells in Example 6 of the present disclosure.
  • the present disclosure relates to methods and blood analysis systems for determining platelet concentrations in a blood sample. Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. The various embodiments may be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein.
  • the present disclosure provides a method of determining platelet concentration by combining impedance measurement data of a diluted blood sample and optical measurement data of a post-hemolytic blood sample.
  • the platelet concentration can be considered as the platelet count described in hematology and is reported as the number of platelets per liter of blood.
  • the method comprises: mixing a first sample of a blood sample with a diluent to form a first suspension; mixing a second sample of the blood sample with a hemolytic agent and a fluorescent dye to lyse Red blood cells and staining white blood cells to form a second suspension; measuring a direct current (DC) impedance signal when the first suspension passes through an aperture; measuring light scattering and fluorescence signals of the second suspension through the optical flow chamber Analyzing a DC impedance signal of the first suspension to obtain a first platelet distribution; analyzing a light scattering and fluorescence signal of the second suspension to distinguish between platelets and white blood cells to obtain a second platelet distribution; based on the first platelet distribution and the The second platelet distribution determines the platelet concentration of the blood sample.
  • DC direct current
  • the first suspension is a diluted blood sample.
  • Blood dilutions are commonly used in blood analyzers to dilute blood samples to measure red blood cells and platelets.
  • 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 sample of the blood sample can be diluted with a commercial blood dilution to form a first suspension, for example, using M-68DS diluent, M-53D, manufactured by Shenzhen Mindray Biomedical Electronics Co., Ltd. (Shenzhen, China). Diluent, etc.
  • the DC impedance signal of the first suspension can be measured by a flow path equipped with a DC impedance detector and a non-focused flow aperture or a focused flow aperture.
  • the electrical signal can be measured based on the impedance change.
  • the pulse shape, height and width of the 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 impedance measurement can reflect the size distribution of these particles.
  • a frequency histogram of platelets and red blood cells in the diluted blood sample can be generated in accordance with the methods disclosed herein.
  • the first platelet distribution D1 is a platelet DC histogram H Plt-D from the first suspension, indicating the size distribution of the platelets 10a in the first suspension.
  • the volume Vol p of the platelets 10a is expressed in terms of flying rise (fL).
  • fL flying rise
  • the second suspension is a hemolyzed blood sample.
  • the red blood cells in the blood sample may be dissolved by the hemolytic agent, and the hemolytic agent may be any one or a combination of cationic, nonionic, anionic, amphiphilic surfactants.
  • the hemolytic agent used in the present disclosure to dissolve red blood cells in the second sample may be any known lysing reagent for blood cell classification of blood analyzers.
  • the lysing reagent for leukocyte classification of the blood analyzer is typically an aqueous solution containing one or more hemolytic agents, which may include cationic, nonionic, anionic, amphiphilic surfactants, or combinations thereof.
  • the lytic reagent may include one or more lysing agents for lysing red blood cells and a fluorescent dye for staining nucleated blood cells, thereby nucleating blood cells such as white blood cells by measuring light scattering and fluorescence.
  • a dissolution reagent formulation as described 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.
  • White blood cells are classified into subpopulations by dissolving red blood cells and using fluorescence and light scattering measurements.
  • 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 dissolution reagent that does not contain a fluorescent dye.
  • the staining solution may be added to the blood sample before, after or simultaneously with the hemolytic agent to stain the nucleated blood cells.
  • the light scattering signal and the fluorescent signal of the second suspension can be measured by one or more optical detectors disposed in the optical flow chamber.
  • an optical flow cell refers to a focused-flow flow cell suitable for detecting a light-scattering signal and a fluorescent signal, such as optical flow used in existing flow cytometers and blood analyzers. room.
  • a particle such as a blood cell
  • the incident beam from the source directed to the detection aperture is scattered by the particle in all directions.
  • the scattered light or light scattering signal can be detected by the photodetector at various angles relative to the incident beam. Since different blood cell populations have different light scattering properties, light scattering signals can be used to distinguish different cell populations.
  • the light scatter signal detected near the incident beam is commonly referred to as a forward light scatter signal or a small angle light scatter signal.
  • the forward light scatter signal can be measured from an angle of from about 1° to about 10° with the incident beam.
  • the forward light scatter signal can be detected from an angle of from about 2[deg.] to about 6[deg.] with the incident beam.
  • the light scattering signal detected in the direction of about 90° with the incident beam is generally referred to as a lateral light scattering signal, and the fluorescent signal from the blood cells stained by the fluorescent dye is also typically about 90° from the incident beam. Detect in the direction.
  • the lateral light scatter signal is measured from an angle of from about 65[deg.] to about 115[deg.] with the incident beam.
  • Forward light scatter, lateral light scatter signals, and fluorescent signals from the second suspension can be measured using one or more optical detectors.
  • optical detectors For the purposes of this disclosure, a variety of known designs of optical inspection hardware can be used.
  • the platelet region P (the platelet region herein refers to may contain platelets)
  • SFL scatter plot of fluorescence
  • FSC forward light scattering
  • the platelet region and the leukocyte region can also be distinguished from the scatter plot of the fluorescence and lateral light scattering (SSC) of the second suspension.
  • FIG. 3A through 5 further illustrate a method of determining platelet concentration in a blood sample in some embodiments provided by the present invention.
  • the platelet region P is distinguished from the white blood cell region W.
  • the two-dimensional distribution of platelets 10b in the enlarged platelet region P is shown in the SFL vs. FSC scattergram shown in Fig. 3B.
  • the two-dimensional distribution of the platelets 10b is a form of the second platelet distribution D2 obtained from the platelet light scattering and fluorescence signals of the second suspension.
  • the second platelet distribution D2 shown in Figure 3B can be further converted to a derived platelet volume histogram H Plt-L by utilizing the light scattering signal of platelet 10b in platelet region P.
  • the derived platelet volume histogram H Plt-L is another form of the second platelet distribution, as shown in Figure 3C, D2', which is a one-dimensional distribution of platelets in the second suspension.
  • the derived platelet volume of platelets in the second suspension can be calculated as a function of the light scattering signal of platelet 10b in platelet region P.
  • the derived platelet volume Vol p2 of each platelet of platelet region P can be calculated using equation (1):
  • FSC is a forward light scattering signal of a separate event of the platelet region
  • ⁇ is a constant
  • the derived platelet volume of each platelet of the platelet region P can also be calculated using equation (2):
  • FSC is a forward light scatter signal of a single event of the platelet region, and ⁇ and ⁇ are constant.
  • the derived platelet volume of each platelet of the platelet region P can also be calculated by using the forward light scattering and the lateral light scattering signals of the second suspension according to the Mie scattering theory. Moreover, there is a size dependence between the platelet volume in the platelet DC histogram and the corresponding light scattering signal obtained from the second suspension, when using equation (1) or equation (2) or according to the Mie scattering theory When calculated, the platelet-derived platelet volume of the second suspension is correlated with the platelet volume in the platelet DC histogram. Therefore, the platelet size range in the derived platelet volume histogram H Plt-L shown in FIG. 3C is the same as the platelet size range in the platelet DC histogram shown in FIG. 1.
  • the derived platelet volume and the derived platelet volume histogram obtained by using equation (1) or equation (2) or according to the Mie scattering theory may be referred to as a derived platelet volume histogram H Plt-L .
  • FIG. 4 schematically overlaps the two histograms obtained by the above method.
  • a platelet DC histogram H Plt-D of a first suspension from a blood sample is superimposed on a derived platelet volume histogram H Plt-L from a second suspension of the blood sample, wherein the derived platelet
  • the volume histogram H Plt-L is generated using the derived platelet volume Vol p2 obtained by equation (1).
  • the blood sample used in the embodiment shown in Figure 4 is an abnormal blood sample containing red blood cell debris determined by a manual reference method.
  • the two histograms are due to the interference of red blood cell debris leading to platelet DC histogram (H Plt-D ) elevation.
  • H Plt-D platelet DC histogram
  • the figures basically overlap each other. It will be appreciated that red blood cells in the second suspension, including small red blood cells and red blood cell fragments, etc., are dissolved. Therefore, in the derived platelet volume histogram H Plt-L obtained from the second suspension, the high segment of the platelet population distribution only reflects the information of platelet 10b, and is not affected by interfering substances such as red blood cells and red blood cell fragments. .
  • the derived platelet volume histogram H Plt-L obtained from the second suspension reflects the distribution of platelets 10b including large platelets, and does not resemble the first suspension.
  • the platelet DC histogram obtained by the fluid may overlap with platelets and red blood cells. Similarly, this feature also applies to blood samples containing giant platelets.
  • the fused platelet histogram H Plt-LD incorporates information from platelet detection of the first suspension and the second suspension.
  • the fused platelet histogram H Plt-LD is generated using equation (3):
  • H Plt-LD is the event in the fusion platelet histogram (i);
  • H Plt-L is the event in the derived platelet volume histogram of the second suspension (i);
  • H Plt- D is the event (i) in the platelet DC histogram of the first suspension; and
  • k i1 and k i2 are coefficients.
  • k i1 and k i2 in equation (3) may be constant.
  • k i1 and k i2 are set according to the following criteria:
  • Figure 5 further illustrates the process of detecting the abnormal blood sample of Figure 4 using the above method and the criteria for generating a fused platelet histogram H Plt-LD .
  • the platelet size range is the same as the platelet DC histogram H Plt- D shown in Fig. 1 and the derived platelet histogram H Plt-L shown in Fig. 3C.
  • the elevation of the curve of the platelet population occurring in the embodiment of FIG. 4 due to the interference of red blood cell debris in the blood sample has been corrected in the fusion platelet histogram H Plt-LD .
  • the platelet concentration in the blood sample can then be determined based on the area under the curve in the fused platelet histogram H Plt-LD .
  • k i1 and k i2 in equation (3) may be variables determined based on the platelet peak -to- peak ratio R v/p (valley/peak ratio) of the platelet DC histogram H Plt-D of the first suspension.
  • k i1 and k i2 may be determined based on equations (4) and (5):
  • the fusion platelet histogram H Plt-LD can be used as a third platelet distribution, which utilizes a first platelet distribution from the first suspension and a second platelet distribution from the second suspension. That is, the derived platelet volume histogram is obtained.
  • the platelet concentration can be obtained by the third platelet distribution.
  • the fusion platelet histogram H Plt-LDa may be a platelet DC histogram H Plt-D from the first suspension and a derived platelet volume histogram H Plt-L from the second suspension Determined according to the criteria set by equation (6) and equation (7):
  • H Plt-LDa is the event in the fusion platelet histogram (i); H Plt-L (i) is the event in the derived platelet volume histogram of the second suspension (i); H Plt- D (i) is the event (i) in the platelet DC histogram of the first suspension; min means that event i takes the smallest of the two histograms.
  • the demarcation point 15fL is an empirical value whose value can vary with the instrument and/or reagent used in the method.
  • the platelet concentration of the blood sample can be determined based on the area under the curve in the fusion platelet histogram HPlt-LDa.
  • the derived platelet volume histogram HPlt-Lb can also be obtained by fitting the light scattering signal curve of the platelet region 10b of the platelet region P in the SFL-FSC scattergram of the second suspension, thereby replacing the equation ( 1) or the method of equation (2) or Mie scattering theory.
  • the derived platelet volume histogram HPlt-Lb the derived platelet volume Volp2b of the individual event can be expressed by equation (8):
  • FSC is a forward light scattering signal of a single event of the platelet region in the SFL-FSC scattergram
  • ⁇ and ⁇ are fitting parameters of the fitting curve.
  • the fusion platelet histogram H Plt-LDb may be the derived platelet volume histogram H Plt-Lb obtained by the equation (8) and the platelet DC histogram H Plt-D obtained from the first suspension. Generated by the criteria set by equations (9) and (10):
  • H Plt-LDb (i) is the event in the fusion platelet histogram (i);
  • H Plt-Lb (i) is the event in the derived platelet volume histogram of the second suspension obtained by equation (8) (i);
  • H Plt-D (i) is the event (i) in the platelet DC histogram of the first suspension.
  • the demarcation point 12fL is an empirical value whose value can vary with the instrument and/or reagent used in the method.
  • the platelet concentration of the blood sample can be determined based on the area under the curve in the fused platelet histogram H Plt-LDb .
  • the fusion platelet histogram H Plt-LDb can also be the derived platelet volume histogram H Plt-Lb obtained by the equation (8) and the platelet DC histogram H Plt-D obtained from the first suspension. It is generated according to any of the methods related to equation (3) described above.
  • the fused platelet histogram H Plt-LDa may also be a platelet DC histogram H Plt-D from the first suspension and a derived platelet volume histogram H Plt-L from the second suspension according to equation (9). And generated by the criteria set by equation (10).
  • the platelet concentration in the blood sample can be determined using a platelet DC histogram HPlt-D obtained from the first suspension and a derived platelet volume histogram obtained from the second suspension according to the following procedure, wherein
  • the derived platelet volume histogram may be a derived platelet volume histogram HPlt-L obtained by the method of equation (1), equation (2), Mie scattering theory or a derived platelet volume histogram HPlt-Lb obtained by equation (8).
  • the curve in the DC histogram H Plt-D of the platelet obtained from the first suspension is calculated to have a line below the line indicating the platelet volume Vol p of 15 fL and Line-S.
  • the curve in the histogram of the derived platelet volume H Plt-L obtained from the second suspension is calculated to have an area on the right side of the line indicating that the derived platelet volume Vol p2 is 15 fL, which is the area Designated as Area-2.
  • the Area-2 is associated with platelets having a volume greater than 15 fL in the histogram. Then, the absolute difference ⁇ between Area-1 and Area-2 is compared with a predetermined area threshold A T .
  • the preset area threshold A T is an empirical value. According to a fusion criterion, the platelet concentration of the blood sample is calculated using equations (11) and (12); when ⁇ >A T , equation (11) is used; when ⁇ ⁇ A T , equation (12) is used:
  • V HD (1, 2, ... n) is the value of the platelet DC histogram H Plt-D corresponding to the position of the platelet volume of 1fL, 2fL, ...., nfL, respectively, or height
  • V HL (1, 2, ... n) are the values of the position of the derived platelet volume histogram H Plt-L corresponding to the position of the derived platelet volume of 1fL, 2fL, ...., n fL, respectively, or height
  • C plt is Platelet concentration.
  • the demarcation point 15fL for counting Area-1 and Area-2 is an empirical value, and the value of the demarcation point may vary depending on the instrument and/or reagent used in the method. It can be understood that the platelet concentration C plt obtained by using equations (11) and (12) should be generated by using the fusion criterion to select platelet DC histogram H Plt-D and derived platelet volume histogram H Plt-L in the above embodiment. The platelet concentration obtained by fusing the platelet histogram H Plt-LDc was similar.
  • the fused platelet histogram H Plt-LDc is generated using the platelet DC histogram H Plt-D ; when ⁇ > A T , the fused platelet histogram H Plt-LDc is based on H
  • the corresponding fraction of the derived platelet volume histogram H Plt-L was generated for the fraction of platelet volume greater than 15 fL generated by Plt-D and H Plt-L .
  • the platelet concentration can be calculated based on the area under the curve in the fused platelet histogram.
  • the fused platelet histogram in each of the above embodiments is a graphical form of platelet volume distribution and is a common form of presenting a probability distribution of continuous variables.
  • the platelet volume distribution may also be presented in digital form with a table or list having equivalent or similar resolution to the volume histogram, or in any other suitable manner known in the art.
  • the above-described fused platelet histogram can be used to refer to a fusion platelet distribution without being limited by its graphical representation.
  • the above-described derived platelet volume histogram can also be used to refer to the derived platelet volume distribution without being limited by its graphical representation.
  • the platelet DC histogram obtained from the first suspension can also be referred to as a DC platelet volume distribution without being limited by its graphical representation.
  • the fused platelet distribution is obtained by using the first platelet distribution obtained from the first suspension and the second platelet distribution obtained from the second suspension.
  • the third platelet distribution A platelet concentration can be obtained based on the third platelet distribution.
  • the platelet region P can be distinguished from the nucleated red blood cells in the SFL-FSC scattergram obtained from the second suspension.
  • determining the platelet concentration in the blood sample using the first platelet distribution obtained from the first suspension and the second platelet distribution obtained from the second suspension may be as described below with reference to Figures 7-10C method.
  • the method comprises determining a platelet trough-to-peak ratio R v/p in a platelet DC histogram of the first suspension, and comparing the resulting platelet trough-to-peak ratio to a predetermined ratio threshold R T .
  • the platelet peak-to-peak ratio R v/p is determined by dividing the number of platelets corresponding to the line-S by the number of platelets corresponding to the peak at the line-P, in other words, the curve.
  • the height at Line-S is divided by the height of the peak at Line-P.
  • the predetermined ratio threshold R T can be obtained with a large number of normal blood samples.
  • the predetermined ratio threshold R T can be the maximum of the platelet trough-to-peak ratio of a normal blood sample.
  • the method further comprises: determining the number N of events in a designated region P G in the platelet region P in the SFL-FSC scatter plot of the second suspension.
  • the population of the platelet region P and the designated region PG in Fig. 8 can be more clearly seen in the enlarged view shown in Fig. 9A.
  • the number N of events in the designated region P G in the platelet region P is associated with large platelets. For normal blood samples, the number of events that occur in this designated area P G is very limited.
  • the elevation of the number of events N in the designated region P G indicates the potential interference of the DC impedance measurement of the platelet result of the first suspension due to the overlap of the large platelets and the red blood cells, and the degree of elevation can further reflect the degree of potential interference described above.
  • the elevation of the number of events N in the designated area P G can be evaluated in accordance with a predetermined number of event thresholds G T .
  • the predetermined number of event thresholds G T can be obtained from a large number of normal blood samples that reflect the maximum number of events in the designated region PG in the normal blood sample. In the analysis of the blood sample, if the detected N value exceeds the G T value, it indicates that the number of events in the designated region P G is abnormally elevated.
  • the method After determining the platelet peak-to-peak ratio R v/p of a blood sample and the number of events N in the designated region PG , the method further determines the trough between the platelets and the red blood cells in the platelet DC histogram obtained from the first suspension. derived partition threshold T d, such that these parameters will be separated from red blood platelets region.
  • the derived separation threshold Td can be determined according to equation (13):
  • T ap is an apparent separation threshold, which is a threshold value of platelets and red blood cells in the platelet DC histogram H Plt-D separating the first suspension in the prior art, according to the bottom of the trough between the two populations The end position and the known size range of the platelets are determined;
  • F of is the offset, which is the platelet peak -to- peak ratio R v/p in the platelet DC histogram H Plt-D of the first suspension and the SFL of the above second suspension a function of the number of events N in the designated region P G in the platelet region P in the FSC scatter plot.
  • F of may be determined according to an offset criterion using equation (14) or equation (15):
  • R v/p is the platelet peak -to- peak ratio in the platelet DC histogram H Plt-D of the first suspension
  • N is the designated region P G in the platelet region P in the SFL-FSC scattergram of the second suspension
  • b 1 , b 2 are constants greater than 0
  • c is a constant.
  • R v/p and N are the same as in the equation (14); b 11 and b 21 are constants greater than 0; and c 1 is a constant.
  • the offset criterion may be specified such that if R v / p is greater than R T and N is less than G T , the derived separation threshold T d in equation (13) is determined using equation (14); if R v / p is greater than R T and N are also greater than G T , and the derived separation threshold T d in equation (13) is determined using equation (15). Further, according to the offset criterion, if R v / p does not exceed R T , Equation (14) or Equation (15) is not used, that is, F of Equation (13) is 0.
  • the partition platelet-derived DC histogram H Plt-D threshold value T d are used to distinguish the two first suspension A population of cells, ie, used to separate platelets from red blood cells. Is derived based on the histogram of the area under the partition threshold platelet population curve T d may determine the determined concentration of platelets in the blood sample.
  • Figures 9A-9C and Figures 10A-10C illustrate the process of determining platelet concentrations of abnormal blood samples using the methods described above, respectively.
  • Figures 9A-9C illustrate the process of determining platelet concentration in an abnormal blood sample containing large platelets.
  • FIG. 9A in the SFL-FSC scatter plot of the second suspension from the blood sample, a larger number of events N appear in the designated region PG , the N exceeding a predetermined event number threshold G T .
  • the platelet DC histogram H Plt-D of the first suspension shown in Fig. 9B the platelet peak-to-peak ratio R v/p also exceeds a predetermined ratio threshold R T .
  • equation (15) is used to determine the offset Fof .
  • the derived separation threshold T d obtained by the equation (13) is relative to the apparent separation threshold T ap
  • the degree of offset is determined by the F of the equation (15).
  • the resulting platelet concentration was detected by flow cytometry as a reference method to be 87*10 9 /L, while the existing apparent separation threshold T ap shown in Figure 9C was employed.
  • the platelet concentration reported by the impedance detection method is 63*10 9 /L, which is much lower than the results obtained by the flow cytometry reference method.
  • the platelet concentration obtained by using the derivation separation threshold T d obtained by the equation (13) and the above-described offset criterion was 84*10 9 /L.
  • the method can evaluate the presence of large platelets in the SFL-FSC scattergram of the second suspension, and can also compensate for the influence of large platelets on the detection results of the platelet DC histogram H Plt-D , so the method can correct the present There is an impedance method to detect the often occurring errors in the platelet concentration of blood samples containing large platelets.
  • FIG. 10A-10C further illustrate the process of determining the platelet concentration of an abnormal blood sample containing red blood cell debris.
  • the platelet peak -to- peak ratio R v/p in the platelet DC histogram H Plt-D from the first suspension exceeds a predetermined ratio threshold R T ; however,
  • the number N of events of the designated area P G in the SFL-FSC scatter plot of the second suspension is normal and does not exceed the predetermined event number threshold G T .
  • equation (14) is used to determine the offset Fof . As shown in FIG.
  • the derived separation threshold T d obtained by the equation (13) is relative to the apparent separation threshold T ap
  • the left offset the degree of which is offset by the F of the equation (14).
  • the platelet concentration obtained by the flow cytometry reference method is 46*10 9 /L, and the platelet reported by the existing impedance detection method using the apparent separation threshold T ap shown in FIG. 10C.
  • the concentration was 66*10 9 /L, which was higher than the result obtained by the flow cytometry reference method by 40%.
  • the platelet concentration obtained by using the derivation separation threshold T d obtained by the equation (13) and the above-described offset criterion was 42*10 9 /L.
  • the present method can correct the error that is often present in the platelet concentration of blood samples containing red blood cell fragments by the existing impedance method.
  • the derived separation threshold can also be determined based on equation (16):
  • T d ' T ap +g*(NG T )+h*(R v/p -R T )+s Equation (16)
  • N is the number of events in the designated region PG of the platelet region P in the SFL-FSC scattergram of the second suspension
  • G T is a predetermined threshold number of events
  • R v/p is the platelet DC of the first suspension The plateau valley-to-peak ratio in the histogram H Plt-D
  • R T is a predetermined ratio threshold
  • g, h, and s are constants, where when v v/p ⁇ R T , the values of g, h, and s are all 0. .
  • the derived separation threshold Td ' is calculated as a function of N and Rv/p , which are derived from the analysis of light scattering and fluorescence signals of the second suspension, respectively. And analysis of the DC impedance signal of the first suspension, as described above.
  • the platelet and red blood cells in the platelet DC histogram H Plt-D of the first suspension can be distinguished by the derivation separation threshold T d ' obtained by the equation (16) in the same manner as shown in Figs. 9C and 10C. Thereafter, the platelet concentration of the blood sample can be determined based on the area under the curve of the platelet population determined by the derived separation threshold Td ' in the histogram.
  • the platelet distribution obtained by distinguishing the platelets from the red blood cells by using the platelet DC histogram obtained by deriving the separation threshold from the first suspension is another a third form of platelet distribution based on a first platelet distribution from the first suspension and a second platelet distribution from the second suspension (ie, two of the platelets in the platelet region of the scatter plot above) Dimensional distribution) obtained.
  • the platelet concentration can be obtained based on the third platelet distribution.
  • the curve of the platelet population differentiated in the platelet DC histogram various forms of platelet analysis data can be obtained.
  • the resulting platelet analysis data includes, but is not limited to, platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PCT), and the like.
  • the method may further comprise the step of separating the white blood cells into their subpopulations using light scattering and fluorescent signals of the second suspension.
  • the major subpopulations of white blood cells include lymphocytes, monocytes, neutrophils, eosinophils, and basophils.
  • Figure 12 shows a three-dimensional scatter plot of SFL, SSC, and FSC that separates white blood cells into four subpopulations based on the fluorescence signal, lateral light scatter signal, and forward light scatter signal of a second suspension of a blood sample: lymph Cells, monocytes, neutrophils and eosinophils.
  • basophils in leukocytes can be distinguished from other leukocyte subpopulations based on light scattering signals and fluorescent signals of the second suspension.
  • the method can further comprise the step of counting the number of white blood cells in the second suspension 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 of the second suspension, and can distinguish white blood cells into four subgroups: Lymphocytes, monocytes, neutrophils, and eosinophils.
  • FIG 15 is a simplified block diagram of a blood analysis system 100 suitable for use in performing the methods of the present disclosure.
  • the blood analysis system 100 includes a first module 200, a second module 300, and a data processing module 400.
  • the first module 200 includes a first mixing chamber 210 and a DC impedance detector 230.
  • the first mixing chamber 210 is for mixing a portion of the blood sample with the diluent to form a first suspension.
  • the DC impedance detector 230 has an electrode provided in the small hole 220 of the flow path, and the flow path communicates with the first mixing chamber 210.
  • the DC impedance detector 230 is configured to detect a DC impedance signal of the first suspension through the aperture 220.
  • the second module 300 includes a second mixing chamber 310, a light source 330, and at least one optical detector 340.
  • the second mixing chamber 310 is for mixing a sample of the blood sample with a hemolytic agent and a fluorescent dye to form a second suspension.
  • the light source 330 is configured to align the light beam it emits with the detection aperture of the optical flow chamber 320 in communication with the second mixing chamber 310.
  • the optical flow chamber 320 is equipped with the at least one optical detector 340 for detecting light scattering signals and fluorescent signals of the second suspension passing through the detection holes of the optical flow chamber 320.
  • the blood analysis system 100 can include a red semiconductor laser having an emission wavelength of 640 nm as a light source and three optical detectors capable of detecting forward light scattering, lateral light scattering, and fluorescent signals, respectively.
  • the forward light scatter signal can be detected at an angle of from about 1° to about 10° from the incident beam or from about 2° to about 6°
  • the lateral light scatter signal and the fluorescent signal It can be detected in the direction of the incident beam by about 90°.
  • the forward light scatter signal can be detected from an angle of from about 1° to about 10° from the incident beam, the lateral light scatter signal being from about 65° to about 115° from the incident beam. The angle is detected.
  • the blood analysis system 100 can also include one or more optical detectors for detecting light scatter signals at other angles.
  • the first mixing chamber 210 and the second mixing chamber 310 are used to prepare respective suspensions, respectively.
  • the blood analysis system can have a common mixing chamber, for example, preparing the first and second suspensions in a sequential manner.
  • the common mixing chamber can be cleaned by dilution between two different suspensions.
  • the data processing module 400 is operatively coupled to the DC impedance detector 230 of the first module 200 and the optical detector 340 of the second module 300, respectively.
  • the data processing module 400 includes at least one processor 410 and a storage system 420 that can store the underlying programs and data structures for implementing the functions of the various aspects of the methods disclosed herein.
  • the storage system 420 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 is programmed by a computer application to perform the functions of the methods disclosed herein, and the corresponding program is executed by one or more processors 410.
  • the processor executes the computer application stored in the non-transitory computer readable storage medium described above, the processor determines a platelet concentration of a blood sample according to the methods disclosed herein, and performs white blood cell classification and counting, or identifies Nuclear red blood cells, immature cells or primitive cells.
  • the data processing module 400 is configured to perform various aspects of the methods described herein.
  • the DC impedance signal detected by the first module and the light scattering and fluorescence signals detected by the second module can be processed in real time, respectively.
  • these signals may be processed using a Field-Programmable Gate Array (FPGA), digital signal processing (DSP), or CPU.
  • FPGA Field-Programmable Gate Array
  • DSP digital signal processing
  • the processed DC impedance signal, light scatter, and fluorescence signal are then automatically analyzed by a programmed computer application to obtain first and second platelet distributions, and the platelet concentration of the blood sample is determined according to the methods described herein.
  • the signal of the second module can also be used to classify and count white blood cells, or to identify nucleated red blood cells, immature cells or primitive cells.
  • the blood analysis system 100 further includes a user interface 500 that includes a user interface input device and an output device.
  • the results obtained by the methods described herein can be displayed on a user interface output device, such as a computer screen.
  • the user interface output device can display graphical results, in addition to the platelet concentration and white blood cell classification results obtained by the methods described herein, such as the fusion platelet histogram H Plt-LD (or H Plt-LDa , H as shown in FIG. 5).
  • Plt-LDb a platelet DC histogram H Plt-D having a derivation separation threshold T d as shown in FIG. 9C or FIG. 10C, or a 2D or 3D scattergram showing leukocyte subpopulation classification.
  • the displayed content may further comprise information obtained from intermediate steps of the methods described herein, for example, platelet DC histogram H Plt-D of the first suspension, derived platelet volume histogram H Plt-L (or H Plt- Lb ), an overlay of H Plt-D and H Plt-L as shown in FIG. 4, or an overlay of H Plt-D and H Plt-LD , an SFL-FSC scatter plot, as shown in FIGS. 3A and 3B A partial enlarged view of the platelet region P shown, a designated region P G of the SFL-FSC scattergram shown in FIGS. 9A and 10A, and the like.
  • the displayed platelet information can be presented in a number of different ways, such as displayed along with the results of analysis of other types of cells in the blood sample being tested, displayed on a designated platelet screen, displayed in a hierarchical manner for the user to select based on interest Specific displays, and other alternatives.
  • the above method for determining platelet concentration in a blood sample can be achieved by multi-angle light scattering detection of the second suspension of the blood sample without the need for fluorescence detection.
  • a lateral light scattering signal or a medium angle light scattering signal from the second suspension can be used to obtain a second platelet distribution, thereby being able to replace the determination of the platelet concentration of the blood sample in the method described above.
  • Fluorescent signal wherein, the angle light scattering signal is detected at an angle between forward light scattering and lateral light scattering.
  • the forward light scatter signal can be detected from an angle of from about 1° to about 10° from the incident beam, the lateral light scatter signal being from about 65° to about 115° from the incident beam. The angle is detected. In another exemplary embodiment, the forward light scatter signal can be detected from an angle of about 2[deg.] to about 6[deg.] with the incident light beam, and the light scatter signal at the low medium angle can be from about 8[deg.] to the incident light beam. Detection is performed at an angle of about 24°, the latter being referred to as low-middle-angle light scattering. In addition, it is also possible to detect a light scattering signal at a high school angle from an angle of about 25° to about 65° with the incident light beam, which is called a high-intensity angle light scattering signal.
  • FIGS. 11A and 11B show forward light scattering-lateral light scattering (FSC-SSC) scatter plots of a second suspension obtained from a normal blood sample and an abnormal blood sample containing large platelets, respectively.
  • the platelet region P' is distinguished from the white blood cell region W' in the FSC-SSC scattergram.
  • the second platelet distribution may be a two-dimensional distribution of platelets located in the platelet region P' as shown in FIGS. 11A and 11B, It may be a one-dimensional distribution of the derived platelet volume histogram as shown in Figure 3C.
  • the second platelet distribution obtained from the light scattering signal of the second suspension and the first platelet distribution obtained from the first suspension can be used to determine the platelet concentration of the blood sample, the specific method of which is as described above.
  • the platelet concentration of the blood sample can be determined by referring to the methods described in the previous equations (1) to (3).
  • the forward light scattering signal of the platelet in the platelet region P' in the FSC-SSC scattergram of the second suspension can be obtained based on Equation (1) or Equation (2) to obtain a derived platelet volume histogram H.
  • Plt-L' can also obtain the derived platelet volume histogram H based on the Mie scattering theory using the forward light scattering signal and the lateral light scattering signal of the platelets located in the platelet region P' in the FSC-SSC scattergram of the second suspension.
  • Plt-L' the specific method of the two is as described above, and will not be described here.
  • the fusion platelet histogram H Plt-LD' is obtained using the platelet DC histogram H Plt-D of the first suspension and the derived platelet volume histogram H Plt-L' of the second suspension.
  • the platelet concentration can then be determined from the area under the curve in the fused platelet histogram H Plt-LD' .
  • the platelet concentration of a blood sample can also be determined by referring to the method described above for the fusion platelet histogram H Plt-LDa .
  • the platelet DC histogram H Plt-D of the first suspension and the FSC-SSC scatter plot of the second suspension may be located in the platelet region P' Platelet Light Scattering Signal Derived Platelet Volume Histogram H Plt-L' Generates Fusion Platelet Histogram H Plt-LDa' .
  • the equation (6) is substituted derivatives as platelet volume histograms H Plt-L event (i) platelet volume histograms derived events (i) 'is H Plt-L.
  • the platelet concentration can then be determined from the area under the curve in the fused platelet histogram H Plt-LDa' .
  • the platelet concentration of the blood sample can also be determined by referring to the method described above for the fusion platelet histogram H Plt-LDb .
  • the platelet DC histogram H Plt- D of the first suspension and the second suspension obtained by the equation (8) can be utilized based on the criteria set by equations (9) and (10).
  • the derived platelet volume histogram H Plt-Lb' of the forward light scatter signal of the platelets in the platelet region P' in the FSC-SSC scatter plot generates a fusion platelet histogram H Plt-LDb' .
  • the platelet concentration can then be determined from the area under the curve in the fused platelet histogram H Plt-LDb' .
  • the platelet concentration of the blood sample can also be determined by referring to the methods relating to equations (11) and (12) described above.
  • the platelet concentration can be calculated using Equations (11) and (12) in accordance with the fusion criterion described above.
  • each element in the derived platelet volume histogram H Plt-L defined in equation (11) is a derivative of the light scattering signal of the platelet located in the platelet region P' in the FSC-SSC scattergram.
  • the absolute difference ⁇ ' in the fusion criterion is Area-1 of the platelet DC histogram H Plt-D and the derived platelet volume histogram H Plt-L'
  • the absolute difference between Area-2' is further compared to a preset area threshold A T '.
  • the fused platelet histogram H Plt-LDa' or H Plt-LDb' is a third platelet distribution
  • the third platelet distribution is used
  • the first platelet distribution obtained from the first suspension and the second platelet distribution obtained from the second suspension i.e., the derived platelet volume histogram
  • a platelet concentration can be obtained based on the third platelet distribution.
  • the platelet concentration can also be determined by referring to the methods related to equations (13)-(15).
  • FIG. 10B illustrates a method for determining a platelet concentration in the case where there is no fluorescent signal, and the blood sample has a larger number of events in the designated region P G in the platelet region P′ of the FSC-SSC scattergram obtained from the second suspension.
  • the above information about the second platelet distribution can be correlated with the first suspension from the first suspension using equations (13)-(15) and offset criteria as described above in a similar manner.
  • the platelet trough peak in the platelet DC histogram H Plt-D is used together with R v/p to determine the offset F of and the derived separation threshold T d .
  • N in the equations (14) and (15) is the number of events existing in the designated region PG ' in the platelet region P' of the FSC-SSC scattergram acquired from the second suspension.
  • the resulting derivatization separation threshold Td was used to distinguish platelets from red blood cells in the platelet DC histogram H Plt-D of the first suspension, thereby obtaining platelet concentrations as shown in Figs. 9C and 10C.
  • the predetermined event number threshold G T ' can be obtained by a large number of normal blood samples, which reflect the FSC-SSC scatter plot of the second suspension of the normal blood sample in the plateau region P' located in the designated area. The maximum number of events in P G '.
  • the derivation separation threshold Td can also be determined by equation (16), where N is the designated region P G in the platelet region P' of the FSC-SSC scattergram acquired from the second suspension.
  • the number of events of 'G T is the predetermined number of events threshold for the designated area P G '.
  • the blood analysis system for performing the method of the above embodiments comprises a second module comprising one or more optical detectors for detecting a second suspension through the detection aperture of the optical flow chamber Forward light scatter signal and lateral light scatter signal.
  • the optical detector of the second module may also be arranged to detect a forward light scatter signal and a medium angle light scatter signal of the second suspension passing through the detection aperture of the optical flow chamber.
  • the blood analysis system shown in Figure 15 can include two optical detectors, one for detecting the forward light scattering signal of the second suspension and the other for detecting the lateral direction of the second suspension. Light scattering signal or medium angle light scattering signal.
  • the data processing module is configured to separately analyze the DC impedance signal of the first suspension from the first module and the light scattering signal of the second suspension from the second module, and implement the implementation Various aspects of the way.
  • the processor executes a computer application stored in the non-transitory computer readable storage medium, the processor determines the platelet concentration of the blood sample and performs according to the method described in the present embodiment. White blood cell classification and counting.
  • the process of preparing the second suspension does not require the addition of a fluorescent dye.
  • the lysing reagent includes one or more solvating agents for dissolving red blood cells in the second sample, but does not contain a fluorescent dye.
  • a variety of existing lysis reagents for blood cell sorting of blood analyzers can be used to prepare the second suspension. For example, a dissolution reagent formulation as described in U.S. Patent No. 7,413,905, the entire disclosure of which is incorporated herein by reference.
  • the lysing reagent may comprise one or more surfactants as a hemolytic agent to dissolve red blood cells and partially damage the cell membrane of leukocytes, one having the ability to bind to cationic components present in leukocytes, as described in US Pat. No. 7,413,905.
  • the anionic group of organic compounds is used to cause morphological differences between subpopulations of leukocytes, as well as buffer solutions for adjusting the pH of the reagent to between 2-8.
  • the hemolytic agent can include one or more cationic surfactants, one or more anionic surfactants, one or more amphoteric surfactants, one or more cationic surfactants, and one or more A combination of an amphoteric surfactant or a combination of one or more anionic surfactants with one or more amphoteric surfactants.
  • the system provided by the present embodiment has the advantages of simple structure and low cost compared to the existing blood analyzer.
  • the present embodiment can be implemented on a conventional blood analyzer equipped with a forward light scattering and side light scattering detection function or a blood analyzer equipped with a forward light scattering and a medium angle light scattering detection function.
  • the method provided by the present embodiment greatly reduces reagent costs due to the elimination of the need for fluorescent dyes in the assay.
  • the mode of the present embodiment is also applicable to a blood analyzer for detecting fluorescence, which can simultaneously detect forward scattered light, side scattered light, and fluorescent signal.
  • the method of the present embodiment may further comprise the step of classifying white blood cells into subpopulations based on light scattering signals of the second suspension.
  • the FSC-SSC scatter plot in Figure 14 shows an example of the differentiation of white blood cells into lymphocytes, monocytes, neutrophils and eosinophils based on the light scattering signal of the second suspension.
  • basophils may be further distinguished from other leukocyte subpopulations based on the light scattering signal of the second suspension.
  • the method of the present embodiment may further comprise the step of counting the number of white blood cells in the second suspension and reporting the white blood cell count in the blood sample.
  • Examples 1-7 further illustrate the above method of determining platelet concentration in a blood sample.
  • the platelet concentration of 25 blood samples including 5 normal blood samples and 20 abnormalities was detected using a blood analyzer capable of detecting forward light scattering, lateral light scattering, and fluorescent signals.
  • Blood samples which include red blood cell debris, small red blood cells, or large platelets.
  • the platelet concentration of the same blood sample was measured using a flow cytometer as a reference method, and the platelet concentration of these blood samples was measured as a control by a conventional DC impedance detection method.
  • Figure 16 shows the correlation between the platelet concentration of these blood samples obtained by the conventional DC impedance detecting method and the platelet concentration of these blood samples obtained by the flow cytometry reference method.
  • the correlation between the platelet concentration of these blood samples obtained by the conventional DC impedance detection method and the results obtained by the reference method is poor. This is because most blood samples are abnormal blood samples that include known red blood cell debris, small red blood cells, or large platelets that interfere with conventional platelet DC impedance measurements.
  • the correlation coefficient R 2 in the linear regression analysis of these 25 blood samples was 0.8343.
  • the conventional DC impedance detection method detects blood platelets containing red blood cell fragments or small red blood cells and the platelet concentration is significantly higher than that obtained by the reference method.
  • the conventional DC impedance detection method detects blood platelets containing large platelets and the platelet concentration is significantly lower than that. Refer to the results obtained by the method.
  • the platelet concentration of the 25 blood samples obtained by the fusion platelet histogram H Plt-LD generated by the equation (3) in the method of the present disclosure is closely related to the results obtained by the flow cytometry reference method, and is related thereto.
  • the coefficient R 2 is 0.9940. It can thus be explained that the use of the fusion platelet histogram H Plt-LD described in the present disclosure can effectively correct the error of the conventional DC impedance method for platelet detection results of abnormal blood samples in the presence of red blood cell debris, small red blood cells or large platelets.
  • FIG. 18 shows the 25 obtained by the fusion platelet histogram H Plt-LDa generated by the criteria set by Equations (6) and (7) in the method of the present disclosure.
  • the platelet concentration of the blood sample is closely related to the results obtained by the flow cytometry reference method, and the correlation coefficient R 2 is 0.9739.
  • the curve-fitting process set by equation (8) in the method of the present disclosure is used to obtain the derived platelet volume histogram H Plt-Lb using the light scattering signal of the platelet region P to generate a fusion platelet histogram H Plt -LDb .
  • FIG. 19 shows that the platelet concentrations of the 25 blood samples obtained by fusing the platelet histogram H Plt-LDb are closely related to the results obtained by the flow cytometry reference method, and the correlation coefficient R 2 is 0.9681.
  • FIG. 20 shows the results of the platelet concentration and flow cytometry reference method of the 25 blood samples obtained by the equations (11) and (12) based on the fusion criterion by the method of the present disclosure. Closely related, the correlation coefficient R 2 is 0.9797. All three examples demonstrate that the method is capable of accurately detecting the platelet concentration of a blood sample and effectively correcting the error of the conventional DC impedance method for platelet detection results of abnormal blood samples having red blood cell debris, small red blood cells or large platelets.
  • Example 5 further illustrates a method of detecting platelet concentration of a blood sample using equations (13)-(15) and the offset criterion by the method of the present disclosure.
  • the same 25 blood samples as in Example 1 were also used in the test.
  • Figure 21 shows that the platelet concentrations of the 25 blood samples obtained by equations (13)-(15) are closely related to the results obtained by the flow cytometry reference method, and the correlation coefficient R 2 is 0.9937. This indicates that the method can accurately detect the platelet concentration of the blood sample and effectively correct the error of the conventional DC impedance method for the platelet detection result of the abnormal blood sample having red blood cell debris, small red blood cells or large platelets.
  • Example 6 illustrates that the method of the present disclosure is applied to the step of performing nucleated red blood cell sorting after hemolysis of red blood cells.
  • Examples 6 and 7 illustrate a fused platelet histogram H Plt-ND generated by equation (3) by the method of the present disclosure.
  • Figure 22 further illustrates the process of generating the fused platelet histogram H Plt-ND in Example 6, which is similar to the generation of H Plt-LD in Figure 5, and will not be described again.
  • the platelet region can also be distinguished by using the fluorescence-side scattered dot map (SFL-SSC) as shown in FIG. 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 A derived platelet volume histogram HPlt-ND was obtained based on the forward scattered light signal of each cell.
  • SFL-SSC fluorescence-side scattered dot map
  • the platelet concentration of the sample can also be determined by the method of Equation 13-15.
  • Figure 24 shows that the number of events occurring in the designated region PG in the platelet region of the pre-scattered-side scattered dot map (FSC-SSC) acquired by the second suspension was normal.
  • the above information about the second platelet distribution can be correlated with the first suspension from the first suspension using equations (13)-(15) and offset criteria as described above in a similar manner. Platelet peaks in the platelet DC histogram HPlt-D are used together with Rv/p to determine the offset Fof and the derived separation threshold Td.
  • N in the equations (14) and (15) is the number of events existing in the designated region PG in the platelet region of the FSC-SSC scattergram acquired from the second suspension.
  • the resulting derivatization separation threshold Td was used to distinguish platelets from red blood cells in the platelet DC histogram HPlt-D of the first suspension, thereby obtaining platelet concentration in the manner shown below.
  • the designated area PG can be identified by the fluorescence-side scattered light (SFL-SSC) scattergram to determine whether the number of events is normal.
  • the methods of the present disclosure provide for accurate detection of platelet concentrations in blood samples, particularly where conventional DC impedance methods detect platelet interference, such as abnormal blood samples containing red blood cell debris, small red blood cells, or large platelets. Therefore, the method of the present disclosure solves the problems existing in the existing platelet impedance detecting method, and satisfies the long-standing need for accurate determination of platelet concentration in the field of in vitro diagnostic analysis.
  • some of the existing high-end blood analyzers mentioned in the previous section perform separate optical detection of platelets in addition to conventional impedance detection to distinguish interfering substances and eliminate the effects of interference on platelet detection results. However, this greatly increases the complexity and manufacturing cost of the instrument.
  • the method of the present disclosure can be implemented on various commercial blood analyzers with full blood cell technology (CBC) and white blood cell sorting detection functions without increasing instrument costs.
  • CBC full blood cell technology
  • the methods of the present disclosure can be broadly applied to existing instruments in the field of in vitro diagnostics to improve the accuracy of platelet detection.
  • the BC-6800 Blood Analyzer includes a CBC module and a classification module.
  • the CBC module includes a mixing chamber and a DC impedance detector configured to mix a portion of the blood sample with a diluent to form a first suspension, the DC impedance detector being configured to measure flow through A DC impedance signal of the first suspension of the orifice of the flow path.
  • the classification module includes another mixing chamber, an infrared semiconductor laser, and a plurality of optical detectors configured to mix another sample of the blood sample with a hemolytic agent and a fluorescent dye to form a second suspension
  • the infrared semiconductor laser as a light source has an emission wavelength of 640 nm and aligns the emitted light beam with the detection hole of the optical flow chamber
  • the plurality of optical detectors are capable of detecting the secondary and incident light beams of the second suspension passing through the detection hole of the optical flow chamber
  • a forward light scatter signal at an angle of from about 1[deg.] to about 10[deg.], a lateral light scatter signal from an angle of from about 65[deg.] to about 115[deg.] to the incident light beam, and a fluorescent signal.
  • CBC module 4 ⁇ L of anticoagulated whole blood sample was mixed with 1.5 mL of M-68DS dilution (product of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China) to form a first suspension.
  • M-68DS dilution product of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China
  • this classification module 20 ⁇ L of the same whole blood sample was dissolved with red blood cells and stained with nucleic acid material with 1 mL of M-68LD Lyse and 20 ⁇ LM-68FD dye (both products of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China). Blood cells form a second suspension.
  • the M-68LD Lyse is an aqueous solution containing a cationic surfactant, a nonionic surfactant, and an anionic compound for dissolving red blood cells in a blood sample.
  • the M-68FD dye is an aqueous solution containing a cationic cyanine compound for staining blood cells having a nucleic acid substance in a blood sample.
  • the collected data of the DC impedance signal detection of the first suspension is analyzed to generate a platelet DC histogram H Plt-D as shown in FIG. 1 .
  • the collected data of the forward light scattering signal and the fluorescent signal of the second suspension were analyzed to distinguish the platelet region P from the white blood cell region W in the SFL-FSC scattergram as shown in FIGS. 3A-3B.
  • the derived platelet volume is calculated using equation (1) based on the forward light scatter signal of the platelets in the platelet region P.
  • a fusion platelet histogram H Plt-LD is generated according to the above equation (3).
  • the platelet concentration of the blood sample is determined based on the area under the curve in the fused platelet histogram H Plt-LD .
  • Figure 16 shows the correlation between the platelet concentration of these blood samples obtained by the conventional DC impedance detecting method and the results obtained by the flow cytometry reference method. As shown, the correlation between the platelet concentration of these blood samples obtained by the conventional DC impedance detection method and the results obtained by the reference method is poor.
  • the conventional DC impedance detection method detects abnormal platelets containing red blood cell fragments or small red blood cells, and the platelet concentration is significantly higher than that obtained by the reference method.
  • the conventional DC impedance detection method detects blood platelets containing large platelets and the platelet concentration is significantly lower than the reference. The results obtained by the method.
  • the correlation coefficient R 2 in the linear regression analysis of these 25 blood samples was 0.8343.
  • Figure 17 shows the correlation between the platelet concentration of these blood samples obtained by the method related to the equation (3) in the present disclosure and the results obtained using the flow cytometry reference method.
  • the platelet concentration of the blood sample obtained by the fusion platelet histogram generated by equation (3) in the method of the present disclosure is closely related to the results obtained by the flow cytometry reference method, and the correlation coefficient R 2 is 0.9940.
  • the error caused by the conventional impedance method detection due to the red blood cell debris, small red blood cells or large platelets present in the abnormal blood sample can be effectively corrected by the fusion platelet histogram H Plt-LD in the present embodiment.
  • Example 1 the collected data of the DC impedance signal detection of the first suspension was analyzed to generate a platelet DC histogram H Plt-D .
  • the collected data of the forward light scattering signal and the fluorescent signal of the second suspension were analyzed to distinguish the platelet region P from the white blood cell region W in the SFL-FSC scattergram.
  • the derived platelet volume histogram H Plt-L and the derived platelet volume were obtained by equation (1).
  • the fused platelet histogram H Plt- LDa generated by the criteria set by equations (6) and (7) in the method of the present disclosure.
  • the platelet concentration of the blood sample is determined based on the area under the curve in the fused platelet histogram H Plt-LDa .
  • Fig. 18 shows the correlation between the platelet concentration of these blood samples obtained in the present example and the results obtained by the flow cytometry reference method in Example 1. As shown, the platelet concentration of the blood sample obtained using the method described in this example is closely related to the results obtained by the flow cytometry reference method, and the correlation coefficient R 2 is 0.9739.
  • the collected data of the DC impedance signal detection of the first suspension is analyzed to generate a platelet DC histogram H Plt-D .
  • the collected data of the forward light scattering signal and the fluorescent signal of the second suspension were analyzed to distinguish the platelet region P from the white blood cell region W in the SFL-FSC scattergram.
  • the curve fitting process set by equation (8) generates a derived platelet volume histogram H Plt-Lb using the light scattering signal of the platelet region P.
  • the fused platelet histogram H Plt-LDb is generated according to the criteria set by equations (9) and (10).
  • the platelet concentration of the blood sample is determined based on the area under the curve in the fused platelet histogram H Plt-LDb .
  • Fig. 19 shows the correlation between the platelet concentration of these blood samples obtained in the present example and the results obtained by the flow cytometry reference method in Example 1. As shown, the platelet concentration of the blood sample obtained using the method described in this example is closely related to the results obtained by the flow cytometry reference method, and the correlation coefficient R 2 is 0.9681.
  • the collected data of the DC impedance signal detection of the first suspension is analyzed to generate a platelet DC histogram H Plt-D .
  • the collected data of the forward light scattering signal and the fluorescent signal of the second suspension were analyzed to distinguish the platelet region P from the white blood cell region W in the SFL-FSC scattergram.
  • the derived platelet volume histogram H Plt-L and the derived platelet volume were obtained by equation (1).
  • the platelet concentration of the blood sample is calculated based on the fusion criterion by equations (11) and (12) in the method of the present disclosure.
  • Fig. 20 shows the correlation between the platelet concentration of these blood samples obtained in the present example and the results obtained by the flow cytometry reference method in Example 1. As shown, the platelet concentration of the blood sample obtained using the method described in this example is closely related to the results obtained by the flow cytometry reference method, and the correlation coefficient R 2 is 0.9797.
  • the collected data of the DC impedance signal of the first suspension is analyzed to generate a platelet DC histogram H Plt-D as shown in FIGS. 9B and 10B, and the Platelet peak -to- peak ratio R v/p of platelet DC histogram H Plt-D .
  • the collected data of the forward light scattering signal and the fluorescent signal of the second suspension are analyzed to distinguish the platelet region P from the white blood cell region W in the SFL-FSC scattergram, and further determine the designated region P G The number of events N. Then, in accordance with equations (13) - (15) and offset threshold criterion determines separated derived T d.
  • the resulting derivatives are separated by a threshold value T d for the platelet histogram H Plt-D DC distinguish platelets, red blood cells as shown in FIG. 9C, and 1OC, and is determined based on the DC platelet histogram partition threshold of the derivative T d
  • the area under the curve of the platelet population is calculated as the platelet concentration of the blood sample.
  • Fig. 21 shows the correlation between the platelet concentration of these blood samples obtained in the present example and the results obtained by the flow cytometry reference method in Example 1.
  • the platelet concentration of the blood sample obtained using the method described in this example is closely related to the results obtained by the flow cytometry reference method, and the correlation coefficient R 2 is 0.9937.
  • the method of the present embodiment can effectively correct the substantial error of the conventional DC impedance method for platelet detection results of abnormal blood samples having red blood cell debris, small red blood cells or large platelets.
  • a commercial blood analyzer BC-6800 (Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China) was used to measure a whole blood sample containing nucleated red blood cells, and the data obtained from the first and second suspensions were subjected to the aforementioned implementation. Ways to analyze the method.
  • the BC-6800 Blood Analyzer includes a CBC module and a classification module.
  • the CBC module includes a mixing chamber and a DC impedance detector configured to mix a portion of the blood sample with a diluent to form a first suspension, the DC impedance detector being configured to measure flow through A DC impedance signal of the first suspension of the orifice of the flow path.
  • the classification module is a nucleated red blood cell classification module comprising another mixing chamber, an infrared semiconductor laser and a plurality of optical detectors, the mixing chamber being arranged to mix another sample of the blood sample with the hemolytic agent and the fluorescent dye to Forming a second suspension, the infrared semiconductor laser as a light source having an emission wavelength of 640 nm and aligning the emitted light beam with the detection aperture of the optical flow chamber, the plurality of optical detectors being capable of detecting the second through the detection aperture of the optical flow chamber
  • the forward light scatter signal of the suspension from an angle of from about 1° to about 10° to the incident beam, a lateral light scatter signal from an angle of from about 65° to about 115° with the incident beam, and a fluorescent signal.
  • CBC module 4 ⁇ L of anticoagulated whole blood sample was mixed with 1.5 mL of M-68DS dilution (product of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China) to form a first suspension.
  • M-68DS dilution product of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China
  • this classification module 20 ⁇ L of the same whole blood sample was dissolved with red blood cells and stained with nucleic acid material with 1 mL of M-68LN Lyse and 20 ⁇ LM-68FN dyes (all products of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China). Blood cells form a second suspension.
  • the M-68LN Lyse is an aqueous solution containing a cationic surfactant and an anionic compound for dissolving red blood cells in a blood sample.
  • the M-68FN dye is an aqueous solution containing a cationic cyanine compound for staining blood cells having a nucleic acid substance in a blood sample.
  • the collected data of the DC impedance signal detection of the first suspension is analyzed to generate a platelet DC histogram H Plt-D as shown in FIG. 1 .
  • the collected data of the forward light scattering signal and the fluorescent signal of the second suspension were analyzed to distinguish the platelet region P from the white blood cell region W in the SSC-FSC scattergram shown in FIG.
  • the derived platelet volume is calculated using equation (1) based on the forward light scatter signal of the platelets in the platelet region P.
  • a derived platelet volume histogram H Plt- N as shown in Fig. 22 can be obtained.
  • a fusion platelet histogram H Plt-ND is generated according to the above equation (3).
  • the platelet concentration of the blood sample is determined based on the area under the curve in the fused platelet histogram H Plt-ND .
  • the light scattering and fluorescence signals of the second suspension of the sample can identify nucleated red blood cells and white blood cells, and perform nucleated red blood cell and white blood cell counting.
  • the sample of red blood cell debris was confirmed by artificial microscopy.
  • the reference value was obtained by flow cytometry.
  • the detection result was 86 ⁇ 109/L, and the platelet count obtained by impedance method was 110 ⁇ 109/L.
  • the platelet count was 91 x 109/L, which was closer to the reference value.
  • a commercial blood analyzer BC-6800 (Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China) was used to measure a whole blood sample containing nucleated red blood cells, and the data obtained from the first and second suspensions were subjected to the aforementioned implementation. Ways to analyze the method.
  • the BC-6800 Blood Analyzer includes a CBC module and a classification module.
  • the CBC module includes a mixing chamber and a DC impedance detector configured to mix a portion of the blood sample with a diluent to form a first suspension, the DC impedance detector being configured to measure flow through A DC impedance signal of the first suspension of the orifice of the flow path.
  • the classification module is a nucleated red blood cell classification module comprising another mixing chamber, an infrared semiconductor laser and a plurality of optical detectors, the mixing chamber being arranged to mix another sample of the blood sample with the hemolytic agent and the fluorescent dye to Forming a second suspension, the infrared semiconductor laser as a light source having an emission wavelength of 640 nm and aligning the emitted light beam with the detection aperture of the optical flow chamber, the plurality of optical detectors being capable of detecting the second through the detection aperture of the optical flow chamber
  • the forward light scatter signal of the suspension from an angle of from about 1° to about 10° to the incident beam, a lateral light scatter signal from an angle of from about 65° to about 115° with the incident beam, and a fluorescent signal.
  • CBC module 4 ⁇ L of anticoagulated whole blood sample was mixed with 1.5 mL of M-68DS dilution (product of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China) to form a first suspension.
  • M-68DS dilution product of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China
  • this classification module 20 ⁇ L of the same whole blood sample was dissolved with red blood cells and stained with nucleic acid material with 1 mL of M-68LN Lyse and 20 ⁇ LM-68FN dyes (all products of Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China). Blood cells form a second suspension.
  • the M-68LN Lyse is an aqueous solution containing a cationic surfactant and an anionic compound for dissolving red blood cells in a blood sample.
  • the M-68FN dye is an aqueous solution containing a cationic cyanine compound for staining blood cells having a nucleic acid substance in a blood sample.
  • the collected data of the DC impedance signal of the first suspension is analyzed to generate a platelet DC histogram HPlt-D, as shown in FIG. 25B, and the platelet peak-to-peak ratio Rv of the platelet DC histogram HPlt-D is determined. /p.
  • the collected data of the forward light scattering signal and the fluorescent signal of the second suspension are analyzed to distinguish the platelet region P from the white blood cell region W in the FSC-SSC scattergram, and further determine the event in the designated region PG Number N. Then, the derived separation threshold Td is determined according to equations (13)-(15) and the offset criterion.
  • the resulting derivatization separation threshold Td was used to distinguish between platelets and red blood cells in the platelet DC histogram HPlt-D.
  • the number of events N in the PG is normal, and the derived separation threshold Td is shifted to the left relative to the apparent separation threshold Tap, as shown in FIG. 25C, based on the derived separation threshold Td in the platelet DC histogram.
  • the area under the curve of the determined platelet population is calculated as the platelet concentration of the blood sample.
  • the sample of red blood cell debris was confirmed by artificial microscopy.
  • the reference value was obtained by flow cytometry.
  • the detection result was 86 ⁇ 109/L, and the platelet count obtained by impedance method was 110 ⁇ 109/L.
  • the platelet count was 95 x 109/L, which is closer to the reference value.

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Abstract

一种分析血液样本中血小板浓度的方法、血液分析***(100)及存储介质，测定血液样本中血小板浓度的方法包括：将血液样本的第一份与稀释液混合，形成第一悬浮液；将血液样本的第二份与溶血剂和荧光染料混合以溶解红细胞和染色白细胞，形成第二悬浮液；测量第一悬浮液流过小孔的直流阻抗信号；测量第二悬浮液流过光学流动室(20)的光散射信号和荧光信号；分析第一悬浮液的该直流阻抗信号以获取第一血小板分布；分析第二悬浮液的光散射信号和荧光信号以区分血小板和白细胞并获取第二血小板分布；基于第一血小板分布和第二血小板分布确定血液样本的血小板分析数据，如血小板浓度。

Description

测定血小板浓度的方法及*** 技术领域
本公开涉及用于测定血液样本中血小板浓度的方法及***。具体地，本公开涉及通过组合稀释后血液样本的阻抗测量数据和溶血后血液样本的光学测量数据确定血小板浓度。
背景技术
为了确定患者的治疗进程，在临床实践中通常需要获取准确的血小板计数。例如，如果血小板计数低于每升20*10 9，则可能需要输入血小板，否则患者可能会发生潜在危及生命的出血。
大多数现有的血液分析仪通过阻抗测量法对血小板进行计数。通过测量稀释后血液样本的阻抗，可以获得细胞的体积信息，进而可以根据细胞的体积分类血小板和红细胞。虽然，在大多数情况下阻抗测量***在测量血小板计数中提供了相对准确的结果，它仍存在一定的局限性。例如，阻抗测量方法不能区分血小板与干扰粒子，例如小红细胞(microcytes)和裂红细胞(schistocytes，也称红细胞碎片)，导致血小板计数假性增高。另一方面，大血小板和巨血小板可能会超出阻抗测量方法中用于血小板计数的预定阈值而被分类为红细胞，这会导致血小板计数假性降低。
在阻抗测量中，通常是通过对直方图中2至20飞升(femtoliters,fL)之间的血小板体积分布进行数学曲线拟合以便将动态范围扩展到70fL。然而，在某些情况下上述方法无法获得准确的血小板计数。例如，当血小板的分布不服从对数正态分布时，或者血小板分布曲线的高段没有下降时，平均血小板体积会超出正常范围，该拟合方式可能不再适用。在这些情况下，通常只有落在2至20fL范围内的血小板被阻抗测量***报告，并标示该检测异常。
为克服阻抗测量方法的缺点，一些高端的血液分析仪中增加了对于血小板的光学测量通道。虽然光学测量降低了上述干扰对血小板测量的影响，但是用于血小板检测的附加光学检测通道显著地增加了血液分析仪器的复杂性，并且提高了仪器制造和维护服务的成本。
因此，需要一种简单、成本较低的且可靠的检测方法及仪器***，用于在存在干扰物质的情况下准确地确定血液样本中血小板浓度。
发明内容
一方面，本公开涉及确定血液样本中血小板浓度的方法。该方法包括：将该血液样本的第一份试样与稀释液混合，形成第一悬浮液；将该血液样本的第二份试样与溶血剂和荧光染料混合以溶解红细胞和染色白细胞，形成第二悬浮液；测量该第一悬浮液流过小孔的直流阻抗信号；测量该第二悬浮液流过光学流动室的光散射信号和荧光信号；分析该第一悬浮液的该直流阻抗信号以获取第一血小板分布；分析该第二悬 浮液的该光散射信号和该荧光信号以区分血小板和白细胞并获取第二血小板分布；以及基于该第一血小板分布和该第二血小板分布确定该血液样本的血小板浓度。进一步地，该方法还包括基于该第二悬浮液的该光散射信号和该荧光信号将该血液样本中的白细胞区分为白细胞的亚群，包括区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞。
另一方面，本公开涉及用于确定血液样本中血小板浓度的血液分析***。该血液分析***包括：第一模块，该第一模块包括第一混合室和直流阻抗检测器，该第一混合室用于将该血液样本的第一份试样与稀释液混合以形成第一悬浮液，该直流阻抗检测器被装配于流通路径的小孔，该流通路径与该第一混合室相连通，该直流阻抗检测器用于检测该第一悬浮液通过该小孔的直流阻抗信号。第二模块，包括第二混合室、光源及至少一光学检测器，该第二混合室用于将该血液样本的第二份试样与溶血剂及荧光染料混合、溶解红细胞并染色白细胞以形成第二悬浮液，该光源用于将光束对准与该第二混合室相连通的光学流动室的检测孔，该至少一光学检测器被装配于该光学流动室，用于检测通过该光学流动室检测孔的该第二悬浮液的光散射信号和荧光信号；以及数据处理模块，与该第一模块的该直流阻抗检测器和该第二模块中的该至少一光学检测器分别可操作地连接，该数据处理模块包括处理器和编程有计算机应用程序的非暂时性计算机可读存储介质，当该计算机应用程序被该处理器执行时，使该处理器基于该第一悬浮液的该直流阻抗信号生成第一血小板分布，基于该第二悬浮液的该光散射信号和该荧光信号区分血小板与白细胞、生成第二血小板分布，基于该第一血小板分布和该第二血小板分布确定该血液样本的血小板浓度。进一步地，该数据处理模块还基于该第二悬浮液的该光散射信号和该荧光信号将该血液样本中的白细胞区分为白细胞的亚群，包括区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞。
另一方面，本公开涉及确定血液样本中血小板浓度的方法。该方法包括：将该血液样本的第一份试样与稀释液混合，形成第一悬浮液；将该血液样本的第二份试样与溶血剂混合以溶解红细胞，形成第二悬浮液；测量该第一悬浮液流过小孔的直流阻抗信号；测量该第二悬浮液流过光学流动室的前向光散射信号与侧向光散射信号或中角度光散射信号；分析该第一悬浮液的该直流阻抗信号以获取第一血小板分布；分析该第二悬浮液的该前向光散射信号与该侧向光散射信号或该中角度光散射信号以区分血小板和白细胞并获取第二血小板分布；以及基于该第一血小板分布和该第二血小板分布确定该血液样本的血小板浓度。进一步地，该方法还包括基于该第二悬浮液的该前向光散射信号与该侧向光散射信号或该中角度光散射信号将该血液样本中的白细胞区分为白细胞的亚群，包括区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞。
又一方面，本公开涉及用于确定血液样本中血小板浓度的血液分析***。该血液分析***包括：第一模块，该第一模块包括第一混合室和直流阻抗检测器，该第一混合室用于将该血液样本的第一份试样与稀释液混合以形成第一悬浮液，该直流阻抗检测器被装配于流通路径的小孔，该流通路径与该第一混合室相连通，该直流阻抗检测器用于检测该第一悬浮液通过该小孔的直流阻抗信号。第二模块，包括第二混合室、光源及至少一光学检测器，该第二混合室用于将该血液样本的第二份试样与溶血剂及荧光染料混合、溶解红细胞并染色白细胞以形成第二悬浮液，该光源用于将光束对准与该第二混合室相连通的光学流动室的检测孔，该至少一光学检测器被装配于该光学流动室用于检测通过该光学流动室的该检测孔的该第二悬浮液的前向光散射信号和侧向光散射信号或中角度光散射信号；以及数据处理模块，与该第一模块的该直流阻 抗检测器和该第二模块中的该至少一光学检测器分别可操作地连接，该数据处理模块包括处理器和编程有计算机应用程序的非暂时性计算机可读存储介质，当该计算机应用程序被该处理器执行时，使该处理器基于该第一悬浮液的该直流阻抗信号生成第一血小板分布，基于该第二悬浮液的该前向光散射信号和该侧向光散射信号或该中角度光散射信号区分血小板与白细胞、生成第二血小板分布，基于该第一血小板分布和该第二血小板分布确定该血液样本的血小板浓度。进一步地，该数据处理模块还基于该第二悬浮液的该光散射信号和该荧光信号将该血液样本中的白细胞区分为白细胞的亚群，包括区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞。
下面将结合附图及示例性的实施方式对本公开进行描述，本公开的优点将体现地更加明显。
附图说明
图1示出了来自血液样本的第一悬浮液的血小板DC阻抗直方图H Plt-D。
图2示出了来自图1中的血液样本的第二悬浮液的荧光-前向散射光(SFL-FSC)散点图。
图3A-3C示出了本公开的一实施方式中通过血液样本的第二悬浮液获取衍生血小板体积直方图H Plt-L的过程。其中，图3A为该第二悬浮液的SFL-FSC散点图，图3B为图3A的SFL-FSC散点图中血小板区域的放大视图，图3C为该第二悬浮液的衍生血小板体积直方图H Plt-L。
图4示出了一含有红细胞碎片的血液样本的第一悬浮液的血小板DC直方图H Plt-D与第二悬浮液的衍生血小板体积直方图H Plt-L的叠加图。
图5示出了本公开图4所示的实施方式中基于由血小板DC直方图H Plt-D和衍生血小板体积直方图H Plt-L生成融合血小板直方图H Plt-LD确定血小板浓度的过程。
图6A和图6B示出了在一实施方式中确定一血液样本中血小板浓度过程中的该血液样本的血小板DC直方图H Plt-D和衍生血小板直方图H Plt-L。
图7示出了一含有大血小板的血液样本的第一悬浮液的血小板DC直方图H Plt-D。
图8示出了与图7所采用的血液样本的第二悬浮液的荧光-前向光散射(SFL-FSC)散点图的血小板区域中一指定区域。
图9A-9C示出了本公开一实施方式中用衍生分隔阈值T d确定血液样本中血小板浓度的过程。
图10A-10C进一步示出了用衍生分隔阈值T d确定血液样本中血小板浓度的过程。
图11A和图11B分别示出了本公开的又一实施方式中一正常血液样本和一含有大血小板的异常血液样本第二悬浮液的前向光散射-侧向光散射(FSC-SSC)散点图。
图12示出了本公开一实施方式中用于说明区分白细胞亚群的血液样本第二悬浮液的SFL、SSC和FSC三维散点图。
图13示出了本公开一实施方式中用于说明区分未成熟细胞的血液样本第二悬浮液的FSC-SSC-SFL三维散点图。
图14示出了本公开一实施方式中用于说明区分白细胞亚群的血液样本第二悬浮液的FSC-SSC散点图。
图15是本公开的血液分析***的简化框图。
图16示出了实施例1中所述的通过常规DC阻抗检测方法所获取的这些血液样本的血小板浓度与通过流式细胞仪参考方法获取的这些血液样本的血小板浓度的相关性。
图17示出了通过实施例1中所述的本公开的一实施方式的方法所获取的血液样本的血小板浓度与通过流式细胞仪参考方法获取的血液样本的血小板浓度的相关性。
图18示出了通过实施例2中所述的本公开的又一实施方式的方法所获取的血液样本的血小板浓度与通过流式细胞仪参考方法获取的血液样本的血小板浓度的相关性。
图19示出了通过实施例3中所述的本公开的另一实施方式的方法所获取的血液样本的血小板浓度与通过流式细胞仪参考方法获取的血液样本的血小板浓度的相关性。
图20示出了通过实施例4中所述的本公开的另一实施方式的方法所获取的血液样本的血小板浓度与通过流式细胞仪参考方法获取的血液样本的血小板浓度的相关性。
图21示出了通过实施例5中所述的本公开的又一实施方式的方法所获取的血液样本的血小板浓度与通过流式细胞仪参考方法获取的血液样本的血小板浓度的相关性。
图22示出了本公开一实施方式中基于由血小板DC直方图H Plt-D和衍生血小板体积直方图H Plt-L生成融合血小板直方图H Plt-LD确定血小板浓度的过程。
图23示出了本公开一实施方式中第二悬浮液获取的SFL-SSC散点图对应的血小板分布区域图。
图24示出了本公开一实施方式中第二悬浮液获取的FSC-SSC散点图的血小板区域中指定区域P G出现事件数示意图。
图25A-25C进一步示出了用衍生分隔阈值T d确定血液样本中血小板浓度的过程。
图26示出了本公开实施例6中用于说明识别有核红细胞的血液样本第二悬浮液的FSC-SFL散点图。
需要指出的是，在附图中相同的数字或符号表示相同的部件。
具体实施方式
本公开涉及用于确定血液样本中血小板浓度的方法及血液分析***。下面将参考附图对本公开的实施方式进行更全面的描述。各实施方式可以以多种不同的形式实施，并不应被解释为仅限于本文中所阐述的实施例。
在一些实施方式中，本公开提供了通过组合稀释后血液样本的阻抗测量数据和溶血后血液样本的光学测量数据以确定血小板浓度的方法。在此，血小板浓度可以被认为是血液学中所述的血小板计数，并被报告为每升血液中的血小板数量。
在一实施方式中，该方法包括：将一血液样本的第一份试样与稀释液混合以形成第一悬浮液；将该血液样本的第二份试样与溶血剂及荧光染料混合以裂解红细胞并染色白细胞从而形成第二悬浮液；测量该第 一悬浮液通过小孔(aperture)时直流(direct current,DC)阻抗信号；测量该第二悬浮液通过光学流动室的光散射和荧光信号；分析该第一悬浮液的DC阻抗信号，获取第一血小板分布；分析该第二悬浮液的光散射和荧光信号以区分血小板和白细胞，获取第二血小板分布；基于该第一血小板分布和该第二血小板分布确定该血液样本的血小板浓度。
该第一悬浮液为被稀释的血液样本。血液稀释液通常被应用于血液分析仪，用于稀释血液样本以测量红细胞和血小板。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。可以采用商业血液稀释液稀释血液样本的第一份试样以形成第一悬浮液，例如，采用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。
该第一悬浮液的DC阻抗信号可以通过装配有DC阻抗检测器和非聚焦流动孔或聚焦流动孔的流动路径进行测量。当悬浮在导电溶液中的粒子或血液细胞通过小孔时，可以基于阻抗变化测量电信号。该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。通过配置有DC阻抗测量设备的血液分析仪对血细胞进行技术的检测方法是已知的，在美国专利U.S.2,656,508及U.S.3,810,011中均有所描述，其全部公开内容通过引证结合于此。
依照本文所公开的方法，在分析来自第一悬浮液的DC阻抗信号中，可以生成该稀释后的血液样本中血小板和红细胞的频率直方图。如图1所示，第一血小板分布D1为来自第一悬浮液的血小板DC直方图H Plt-D，表示该第一悬浮液中血小板10a的尺寸分布。在该直方图中，血小板10a的体积Vol p以飞升(fL)表示。从图1中可以看出，在直方图中红细胞20的一部分与血小板10a紧密相邻。
该第二悬浮液为溶血后的血液样本。血液样本中的红细胞可以被溶血剂溶解，溶血剂可以是阳离子、非离子、阴离子、两亲性表面活性剂中的任意一种或几种的组合。本公开中用于溶解第二份试样中红细胞的溶血剂可以是任意一种用于血液分析仪白细胞分类的已知的溶解试剂。用于血液分析仪白细胞分类的溶解试剂通常是含有一种或多种溶血剂的水溶液，其中可以包括阳离子、非离子、阴离子、两亲性表面活性剂、或其组合。在一些实施方式中，所述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，从而通过测量光散射和荧光将例如白细胞的有核血细胞与其他类型的细胞进行分类。例如，可以采用美国专利U.S.8,367,358所描述的溶解试剂配方，其全部公开内容通过引证结合于此。在美国专利U.S.8,367,358中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和使用荧光和光散射测量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。此外，在一些实施方式中，荧光染料可以包含在独立的染色溶液中，其可以与不含有荧光染料的溶解试剂一起使用。所述染色溶液可以在溶血剂之前、之后或同时加入血液样本以染色有核血细胞。
该第二悬浮液的光散射信号和荧光信号可以通过设置在光学流动室的一个或多个光学检测器进行测量。在本文中，光学流动室指适于检测光散射信号和荧光信号的聚焦液流的流通池(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。当粒子，例如一血细胞，通过光学流动室的检 测孔(orifice)时，来自光源的被导向该检测孔的入射光束被该粒子向各方向散射。可以在相对于该入射光束的各角度通过光检测器检测被散射的光或光散射信号。由于不同的血细胞群体具有不同的光散射特性，因此光散射信号可以用于区分不同的细胞群体。在入射光束附近所检测的光散射信号通常被称为前向光散射信号或小角度光散射信号。在一些实施方式中，前向光散射信号可以从与入射光束约1°至约10°的角度上进行测量。在其他一些实施方式中，前向光散射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°的方向所检测的光散射信号通常被称为侧向光散射信号，且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°的方向上检测。在一些实施方式中，该侧向光散射信号是从与入射光束呈约65°至约115°的角度测量。
可以使用一个或多个光学检测器测量来自该第二悬浮液的前向光散射、侧向光散射信号和荧光信号。基于本公开的目的，可以使用多种已知的光学检测硬件的设计。
在此描述基于所获取的来自该第二悬浮液的光散射信号和荧光信号区分第二悬浮液中的血小板与白细胞的方法。如图2所示，在由血液样本的第二悬浮液所得到的荧光(SFL)与前向光散射(FSC)的散点图中，血小板区域P(本文中的血小板区域是指可能含有血小板的区域，不排除其他粒子一定程度与血小板粒子群重叠)与白细胞区域W可以明显地被区分，其中血小板区域对应于第二悬浮液中的血小板10b在散点图中的位置，白细胞区域对应于第二悬浮液中的白细胞30在散点图中的位置。可选地，也可以从第二悬浮液的荧光与侧向光散射(SSC)的散点图中区分血小板区域与白细胞区域。
图3A至图5进一步示出了本发明所提供的一些实施方式中确定血液样本中血小板浓度的方法。如图3A所示，将血小板区域P从白细胞区域W区分出来。如图3B所示的SFL vs.FSC散点图中，显示了放大的血小板区域P中血小板10b的二维分布。该血小板10b的二维分布是从该第二悬浮液的血小板光散射和荧光信号所获取的第二血小板分布D2的一种形式。
在下文中所描述的一些实施方式中，通过利用血小板区域P中血小板10b的光散射信号，可以将图3B中所示的第二血小板分布D2进一步转换为衍生血小板体积直方图H Plt-L。该衍生血小板体积直方图H Plt-L是所述第二血小板分布的另一形式，如图3C中示出的D2’，其是该第二悬浮液中血小板的一维分布。
该第二悬浮液中血小板的衍生血小板体积可以通过血小板区域P中血小板10b的光散射信号的函数计算。在一实施例中，血小板区域P的每一血小板的衍生血小板体积Vol p2可以使用方程式(1)计算：
Vol p2＝α*FSC        方程式(1)
其中，FSC为该血小板区域的一单独事件(individual event)的前向光散射信号，α为一常数。
可选地，该血小板区域P的每一血小板的衍生血小板体积也可以采用方程式(2)计算：
Vol p2a＝β*exp(γ*FSC)        方程式(2)
其中，FSC为该血小板区域的一单独事件的前向光散射信号，β和γ为常数。
进一步地，也可以依据Mie散射理论利用该第二悬浮液的前向光散射和侧向光散射信号计算该血小板区域P的每一血小板的衍生血小板体积。而且，基于血小板DC直方图中的血小板体积与相应的从第二悬浮液获取的光散射信号之间具有尺寸相关性，当采用方程式(1)或方程式(2)或依据Mie散射理论的方法进行 计算时，该第二悬浮液的血小板的衍生血小板体积与血小板DC直方图中的血小板体积是相关的。因此，在图3C所示的衍生血小板体积直方图H Plt-L中的血小板尺寸范围与图1所示的血小板DC直方图中的血小板尺寸范围相同。在本文中，采用方程式(1)或方程式(2)或依据Mie散射理论所得到的衍生血小板体积和衍生血小板体积直方图均可以被称为衍生血小板体积直方图H Plt-L。
图4示意性地将上述方法所获得的两种直方图进行重叠。如图4所示，将来自血液样本的第一悬浮液的血小板DC直方图H Plt-D叠加于来自该血液样本的第二悬浮液的衍生血小板体积直方图H Plt-L，其中该衍生血小板体积直方图H Plt-L是采用方程式(1)得到的衍生血小板体积Vol p2所生成的。图4所示的实施例中所采用的该血液样本为通过手工参考方法所确定的含有红细胞碎片的异常血样样本。如图4所示，除了在血小板群体的高段(high end)，即约20fL及其以上的区域，因为红细胞碎片的干扰导致血小板DC直方图(H Plt-D)抬升之外，两个直方图基本上相互重叠。可以理解地，在该第二悬浮液中的红细胞，包括小红细胞和红细胞碎片等，被溶解。因此，在从该第二悬浮液获取的衍生血小板体积直方图H Plt-L中，血小板群体分布的高段仅反映血小板10b的信息，而不受到红细胞如小红细胞和红细胞碎片等干扰物质的影响。此外，对于含有大血小板(large platelets)的血液样本，从该第二悬浮液获取的衍生血小板体积直方图H Plt-L反映包括大血小板的血小板10b的分布，并不会像从该第一悬浮液获取的血小板DC直方图可能发生血小板与红细胞的重叠。相似地，这一特点也适用于含有巨大血小板(giant platelets)的血液样本。
在一些实施方式中，在获取衍生血小板体积直方图H Plt-L之后，该方法生成一融合血小板直方图H Plt- LD，该融合血小板直方图H Plt-LD是第一悬浮液的血小板DC直方图H Plt-D与第二悬浮液的衍生血小板体积直方图H Plt-L的函数：H Plt-LD＝f(H Plt-L,H Plt-D)。该融合血小板直方图H Plt-LD并入了来自第一悬浮液和第二悬浮液的血小板检测的信息。
在一示范性实施方式中，该融合血小板直方图H Plt-LD是采用方程式(3)生成的：
H Plt-LD(i)＝k i1*H Plt-L(i)+k i2*H Plt-D(i)   (i＝1,2,…,n)      方程式(3)
其中，H Plt-LD(i)是该融合血小板直方图中的事件(i)；H Plt-L(i)是第二悬浮液的衍生血小板体积直方图中的事件(i)；H Plt-D(i)是第一悬浮液的血小板DC直方图中的事件(i)；及k i1和k i2是系数。
在一些实施方式中，方程式(3)中的k i1和k i2可以是常数。例如，在一示范性实施例中，k i1和k i2依照下述判据设定：
当Vol p(i)>20fL时,k i1＝1,k i2＝0；及
当Vol p(i)≤20fL时,k i1＝0,k i2＝1。
图5进一步示意了采用上述方法及生成融合血小板直方图H Plt-LD的判据检测图4中的异常血样样本的过程。在图5所示的融合血小板直方图H Plt-LD中，血小板的尺寸范围与图1所示的血小板DC直方图H Plt- D及图3C所示的衍生血小板直方图H Plt-L相同。如图5所示，发生在图4实施例中由于血液样本中红细胞碎片的干扰而产生的血小板群体的曲线在高段的升高在该融合血小板直方图H Plt-LD中已被校正。然后，基于融合血小板直方图H Plt-LD中曲线下方的面积可以确定该血液样本中的血小板浓度。
可选地，方程式(3)中的k i1和k i2可以是变量，基于第一悬浮液的血小板DC直方图H Plt-D的血小板谷 峰比R v/p(valley/peak ratio)确定。在一替代实施方式中，k i1和k i2可以基于方程式(4)和方程式(5)确定：
k i1＝1-k i2         方程式(4)
k i2＝K _Coef*R v/p+e      方程式(5)
其中，K _Coef是小于零的常数；e为大于零的常数；如果依照方程式(4)所得的k i1<0，则设定k i1＝0，如果依照方程式(5)所得的k i2<0，则设定k i2＝0，然后分别代入方程式(3)得到融合血小板直方图H Plt-LD。
可以理解的是，该融合血小板直方图H Plt-LD可以作为第三血小板分布，该第三血小板分布是利用来自第一悬浮液的第一血小板分布和来自第二悬浮液的第二血小板分布，即衍生血小板体积直方图所得到的。通过该第三血小板分布可以得到血小板浓度。
在又一示范性实施方式中，融合血小板直方图H Plt-LDa可以是用来自第一悬浮液的血小板DC直方图H Plt-D和来自第二悬浮液的衍生血小板体积直方图H Plt-L依照由方程式(6)和方程式(7)所设定的判据确定的：
当Vol p(i)>15fL时,H Plt-LDa(i)＝min(H Plt-L(i),H Plt-D(i))  (i＝1,2,…,n)
                                                                 方程式(6)
当Vol p(i)≤15fL时,H Plt-LDa(i)＝H Plt-D(i)   (i＝1,2,…,n)
                                                                 方程式(7)
其中，H Plt-LDa(i)是该融合血小板直方图中的事件(i)；H Plt-L(i)是第二悬浮液的衍生血小板体积直方图中的事件(i)；H Plt-D(i)是第一悬浮液的血小板DC直方图中的事件(i)；min意味事件i取两个直方图中的最小数。
在方程式(6)和方程式(7)所设定的判据中，分界点15fL为经验值，该分界点的值可以随该方法中所使用的仪器和/或试剂变化。与上述实施方式相同，基于该融合血小板直方图HPlt-LDa中曲线下方的面积可以确定血液样本的血小板浓度。
在又一实施方式中，也可以通过利用第二悬浮液的SFL-FSC散点图中血小板区域P的血小板10b的光散射信号曲线拟合得到衍生血小板体积直方图HPlt-Lb，从而替代方程式(1)或方程式(2)或Mie散射理论的方法。在该衍生血小板体积直方图HPlt-Lb中，单独事件的衍生血小板体积Volp2b可以通过方程式(8)表达：
Vol p2b＝[1/(FSC*σ(2π) 1/2)]exp(-(ln FSC-μ) 2/2σ 2)       方程式(8)
其中，FSC为该SFL-FSC散点图中血小板区域一单独事件的前向光散射信号，μ和σ是该拟合曲线的拟合参数。
在该实施方式中，融合血小板直方图H Plt-LDb可以是用方程式(8)所得到的衍生血小板体积直方图H Plt-Lb和从第一悬浮液得到的血小板DC直方图H Plt-D依照由方程式(9)和方程式(10)所设定的判据生成的：
当Vol p(i)>12fL,H Plt-LDb(i)＝H Plt-Lb(i)(i＝1,2,…,n)      方程式(9)
当Vol p(i)≤12fL,H Plt-LDb(i)＝H Plt-D(i)(i＝1,2,…,n)    方程式(10)
其中，H Plt-LDb(i)是该融合血小板直方图中的事件(i)；H Plt-Lb(i)是用方程式(8)得到的第二悬浮液的衍生血小板体积直方图中的事件(i)；及H Plt-D(i)是第一悬浮液的血小板DC直方图中的事件(i)。
在方程式(9)和方程式(10)所设定的判据中，分界点12fL为经验值，该分界点的值可以随该方法中所使用的仪器和/或试剂变化。与上述实施方式相同，基于该融合血小板直方图H Plt-LDb中曲线下方的面积可以确定血液样本的血小板浓度。
可以理解的是，融合血小板直方图H Plt-LDb也可以是用方程式(8)所得到的衍生血小板体积直方图H Plt-Lb和从第一悬浮液所得到的血小板DC直方图H Plt-D依照前文中所述的与方程式(3)相关的任意一种方法生成的。相似地，融合血小板直方图H Plt-LDa也可以是用来自第一悬浮液的血小板DC直方图H Plt-D和来自第二悬浮液的衍生血小板体积直方图H Plt-L依照由方程式(9)和方程式(10)所设定的判据生成的。
在又一实施方式中，可以根据下述过程用从第一悬浮液得到的血小板DC直方图 HPlt-D和从第二悬浮液得到的衍生血小板体积直方图确定血液样本中的血小板浓度，其中该衍生血小板体积直方图可以是由方程式(1)、方程式(2)、Mie散射理论的方法所得的衍生血小板体积直方图 HPlt-L或由方程式(8)所得的衍生血小板体积直方图 HPlt-Lb。
如图6A所示，在该实施方式中，计算从第一悬浮液得到的血小板DC直方图H Plt-D中的曲线下方具有一位于表示血小板体积Vol p为15fL的线与Line-S的线之间的面积，其中Line-S表示该直方图中血小板和红细胞之间的曲线波谷，该面积被指定为Area-1。进一步地，如图6B所示，计算从第二悬浮液得到的衍生血小板体积直方图H Plt-L中的曲线下方具有一位于表示衍生血小板体积Vol p2为15fL的线以右的面积，该面积被指定为Area-2。该Area-2与直方图中体积大于15fL的血小板相关。然后，将Area-1与Area-2之间的绝对差值δ与一预设的面积阈值A T进行比较。该预设的面积阈值A T为经验值。根据一融合判据，使用方程式(11)和方程式(12)计算血液样本的血小板浓度；当δ>A T时，使用方程式(11)；当δ≤A T时，使用方程式(12)：
C plt＝V HD(1)+V HD(2)+...+V HD(15)+V HL(16)+V HL(17)+...+V HL(n)
                                                                 方程式(11)
C plt＝V HD(1)+V HD(2)+...+V HD(15)+V HD(16)+V HD(17)+...+V HD(n)
                                                                 方程式(12)
其中，V HD(1,2,…n)分别为血小板DC直方图H Plt-D上对应于血小板体积为1fL、2fL、.....、nfL的位置的值，或者说高度；V HL(1,2,…n)分别为衍生血小板体积直方图H Plt-L上对应于衍生血小板体积为1fL、2fL、.....、n fL的位置的值，或者说高度；C plt为血小板浓度。
在本实施方式中，用于计数Area-1和Area-2的分界点15fL为经验值，该分界点的值可以随该方法中所使用的仪器和/或试剂变化。可以理解地，使用方程式(11)和方程式(12)得到的血小板浓度C plt应该与上述实施方式中采用融合判据选取血小板DC直方图H Plt-D和衍生血小板体积直方图H Plt-L生成融合血小板直方图H Plt-LDc所得到的血小板浓度相似。换言之，当δ≤A T时，该融合血小板直方图H Plt-LDc是用血小板DC直方图H Plt-D生成的；当δ>A T时，该融合血小板直方图H Plt-LDc是基于H Plt-D和H Plt-L生成的，对于血小板体 积大于15fL的部分采用衍生血小板体积直方图H Plt-L的相应部分。然后，与上述实施方式相同，基于该融合血小板直方图中曲线下方的面积可以计算血小板浓度。
上述各实施方式中的融合血小板直方图是血小板体积分布的图形形式，是呈现连续变量概率分布的常见形式。可选地，该血小板体积分布也可以采用与该体积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。因此，为了本公开的目的，上述融合血小板直方图可以被用于指代融合血小板分布，而不受其图形呈现形式的局限。相似地，上述衍生血小板体积直方图也可以被用于指代衍生血小板体积分布，而不受其图形呈现形式的局限。此外，从第一悬浮液获取的血小板DC直方图也可以被指代为DC血小板体积分布，而不受其图形呈现形式的局限。
进一步地，与上文中融合血小板直方图H Plt-LD所述内容相同，该融合血小板分布是利用从第一悬浮液获取的第一血小板分布和从第二悬浮液获取的第二血小板分布所得的第三血小板分布。基于该第三血小板分布可以得到血小板浓度。
进一步地，在本公开所描述的方法中，当异常血样样本中含有有核红细胞时，从第二悬浮液获取的该SFL-FSC散点图中可以将血小板区域P与有核红细胞区分开。
在其他实施方式中，利用从第一悬浮液获取的第一血小板分布和从第二悬浮液获取的第二血小板分布确定血液样本中的血小板浓度可以使用下文中参考图7至图10C所描述的方法。
在一实施方式中，该方法包括：确定第一悬浮液的血小板DC直方图中的血小板谷峰比R v/p，将所得该血小板谷峰比与一预定的比值阈值R T进行比较。如图7所示，该血小板谷峰比R v/p是通过将Line-S处所对应的血小板数量除以位于Line-P所示的波峰处所对应的血小板数量所确定的，换言之，是将曲线在Line-S处的高度除以位于Line-P处的波峰的高度。如上文中所述，Line-S位于图7的两个群体之间的边界区域B，其示出该直方图中血小板与红细胞之间的波谷的底部。可以用大量的正常血液样本得到所述预定的比值阈值R T。例如，该预定的比值阈值R T可以是正常血液样本的血小板谷峰比的最大值。
进一步地，如图8所示，该方法还包括：确定第二悬浮液的SFL-FSC散点图中血小板区域P中一指定区域P G中的事件数N。图8中位于该血小板区域P和该指定区域P G的群体可以更清晰地在图9A所示的放大图中看出。我们发现该血小板区域P内的该指定区域P G中的事件数N与大血小板相关。对于正常血液样本，在出现在该指定区域P G中的事件数非常有限。因此，该指定区域P G中的事件数N的抬高表示由于大血小板与红细胞的重叠对DC阻抗测量第一悬浮液的血小板结果的潜在干扰，其抬高程度可以进一步反映上述潜在干扰的程度。可以依照一预定的事件数阈值G T评估该指定区域P G中的事件数N的抬高。该预定的事件数阈值G T可以通过大量的正常血液样本得到，其反映正常血液样本中该指定区域P G中的事件数的最大值。在血液样本的分析中，如果检测到的N值超过G T值，表明该指定区域P G中的事件数存在不正常的抬高。
当一血液样本的血小板谷峰比R v/p和指定区域P G中的事件数N确定之后，该方法进一步确定从第一悬浮液获取的血小板DC直方图中血小板和红细胞之间的波谷的衍生分隔阈值T d，从而用这些参数将血小板同红细胞区分开。
在一实施方式中，该衍生分隔阈值T d可以依照方程式(13)确定：
T d＝T ap+F of             方程式(13)
其中，T ap为表观(apparent)分隔阈值，为现有技术中分隔第一悬浮液的血小板DC直方图H Plt-D中的血小板与红细胞的阈值，根据这两个群体之间波谷的底端位置和血小板已知的尺寸范围确定；F of为偏移量，是第一悬浮液的血小板DC直方图H Plt-D中的血小板谷峰比R v/p和上述第二悬浮液的SFL-FSC散点图中血小板区域P中指定区域P G中的事件数N的函数。
在一示范性实施方式中，F of可以依照一偏移量判据用方程式(14)或方程式(15)确定：
F of＝b 1*R v/p–b 2*N+c            方程式(14)
其中，R v/p为第一悬浮液的血小板DC直方图H Plt-D中的血小板谷峰比；N为第二悬浮液的SFL-FSC散点图中血小板区域P中指定区域P G中的事件数；b 1、b 2为大于0的常量；c为一常量。
F of＝b 11*R v/p+b 21*N+c 1           方程式(15)
其中，R v/p与N的含义与方程式(14)中相同；b 11、b 21为大于0的常量；c 1为一常量。
该偏移量判据可以规定为，如果R v/p大于R T而N小于G T，使用方程式(14)确定方程式(13)中的该衍生分隔阈值T d；如果R v/p大于R T且N也大于G T，使用方程式(15)确定方程式(13)中的该衍生分隔阈值T d。此外，根据该偏移量判据，如果R v/p没有超过R T，不使用方程式(14)或方程式(15)，也即是说方程式(13)中的F of为0。
通过方程式(13)-(15)及该偏移量判据得到该衍生分隔阈值T d之后，该衍生分隔阈值T d被用于区分第一悬浮液的血小板DC直方图H Plt-D中两个细胞群体，即，用于分隔血小板与红细胞。基于该直方图中该衍生分隔阈值T d所确定的血小板群体的曲线下方的面积可以确定该血液样本的血小板浓度。
图9A-9C及图10A-10C分别示出了用上述方法确定异常血液样本的血小板浓度的过程。图9A-9C示出了确定一含有大血小板的异常血液样本中的血小板浓度的过程。如图9A所示，在来自该血液样本的第二悬浮液的SFL-FSC散点图中，该指定区域P G中出现较大数量的事件数N，该N超过预定的事件数阈值G T。另一方面，在图9B所示的第一悬浮液的血小板DC直方图H Plt-D中，其血小板谷峰比R v/p也超过了预定的比值阈值R T。因此，根据上述偏移量判据，方程式(15)被用于确定偏移量F of。如图9C所示，在该血液样本的第一悬浮液的血小板DC直方图H Plt-D中，由方程式(13)所得到的该衍生分隔阈值T d相对于该表观分隔阈值T ap向右偏移，其偏移的程度有方程式(15)所得的F of决定。
在图9A-9C所示的实施例中，通过流式细胞仪作为参考方法检测所得的血小板浓度为87*10 9/L，而采用图9C中所示的表观分隔阈值T ap的现有阻抗检测方法所报告的血小板浓度为63*10 9/L，后者远远低于流式细胞仪参考方法所得的结果。采用由方程式(13)所得的衍生分隔阈值T d及上文所述的偏移量判据得到的血小板浓度为84*10 9/L。由此说明，本方法能够评估第二悬浮液的SFL-FSC散点图中大血小板的存在，亦能够补偿大血小板对血小板DC直方图H Plt-D检测结果的影响，因此本方法能够修正现有阻抗方法检测含有大血小板的血液样本的血小板浓度中经常现的误差。
图10A-10C进一步示出了确定一含有红细胞碎片的异常血液样本的血小板浓度的过程。如图10B所示，在该血液样本的检测中，来自第一悬浮液的血小板DC直方图H Plt-D中的血小板谷峰比R v/p超过了预定的比值阈值R T；然而，如图10A所示，第二悬浮液的SFL-FSC散点图中该指定区域P G的事件数N是正常的，并没有超过预定的事件数阈值G T。根据上述偏移量判据，方程式(14)被用于确定偏移量F of。如图10C所示，在该血液样本的第一悬浮液的血小板DC直方图H Plt-D中，由方程式(13)所得到的该衍生分隔阈值T d相对于该表观分隔阈值T ap向左偏移，其偏移的程度由方程式(14)所得的F of决定。在该实施例中，基于流式细胞仪参考方法检测所得的血小板浓度为46*10 9/L，而采用图10C中所示的表观分隔阈值T ap的现有阻抗检测方法所报告的血小板浓度为66*10 9/L，高于流式细胞仪参考方法所得结果40％。采用由方程式(13)所得的衍生分隔阈值T d及上文所述的偏移量判据得到的血小板浓度为42*10 9/L。由此说明，本方法能够修正现有阻抗方法检测含有红细胞碎片的血液样本的血小板浓度中经常现的误差。
进一步地，在某些实施方式中，该衍生分隔阈值也可以基于方程式(16)确定：
T d’＝T ap+g*(N-G T)+h*(R v/p-R T)+s         方程式(16)
其中，N为第二悬浮液的SFL-FSC散点图中血小板区域P中指定区域P G中的事件数；G T为预定的事件数阈值；R v/p为第一悬浮液的血小板DC直方图H Plt-D中的血小板谷峰比；R T为预定的比值阈值；g、h及s为常量，其中当R v/p≤R T时，g、h及s的值均为0。
当使用方程式(16)确定血液样本的血小板浓度时，该衍生分隔阈值T d’通过一N和R v/p的函数计算，其分别来自于对第二悬浮液的光散射和荧光信号的分析和对第一悬浮液的DC阻抗信号的分析，如上文所述。通过与图9C和图10C所示的相同方式，采用方程式(16)所得的衍生分隔阈值T d’可以区分第一悬浮液的血小板DC直方图H Plt-D中的血小板和红细胞。之后，基于该直方图中该衍生分隔阈值T d’所确定的血小板群体的曲线下方的面积可以确定该血液样本的血小板浓度。
可以理解的是，在上述与方程式(13)-(16)相关的实施方式中，用衍生分隔阈值从第一悬浮液所得的血小板DC直方图将血小板与红细胞区分后所得的血小板分布是另一种形式的第三血小板分布，该第三血小板分布是基于来自第一悬浮液的第一血小板分布和来自第二悬浮液的第二血小板分布(即，上述散点图中血小板区域的血小板的二维分布)获取的。根据该第三血小板分布可得到血小板浓度。
可以理解的是，在上文所描述的任一实施方式中，根据所获取的第三血小板分布，如融合血小板直方图H Plt-LDa、H Plt-LDb、H Plt-LDc、或利用衍生分隔阈值T d或T d’在血小板DC直方图中所区分的血小板群体的曲线，可以得到多种形式的血小板分析数据。所得的血小板分析数据包括但不仅限于血小板计数(PLT)、平均血小板体积(MPV)、血小板分布宽度(PDW)、血小板压积(PCT)等。
进一步地，在某些实施方式中本方法可以进一步包括利用第二悬浮液的光散射和荧光信号将白细胞区分为其亚群的步骤。白细胞的主要亚群包括淋巴细胞、单核细胞、中性粒细胞、嗜酸性粒细胞和嗜碱性粒细胞。图12示出一SFL、SSC和FSC的三维散点图，基于一血液样本的第二悬浮液的荧光信号、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，可以基于该第二悬浮液的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第二悬浮液中白细胞的数量，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13所示，当血液样本中存在未成熟细胞时，本方法基于该第二悬浮液的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。
图15简要示出了适用于执行本公开所述方法的血液分析***100的方框图。该血液分析***100包括第一模块200、第二模块300和数据处理模块400。该第一模块200包括第一混合室210和DC阻抗检测器230。该第一混合室210用于将血液样本的份试样与稀释液混合以形成第一悬浮液。该DC阻抗检测器230具有设置于流通路径的小孔220的电极，该流通路径与该第一混合室210相连通。该DC阻抗检测器230用于检测该第一悬浮液通过该小孔220的DC阻抗信号。
该第二模块300包括第二混合室310、光源330及至少一光学检测器340。该第二混合室310用于将血液样本的份试样与溶血剂及荧光染料混合以形成第二悬浮液。该光源330用于将其发射的光束对准与该第二混合室310相连通的光学流动室320的检测孔。该光学流动室320装配有该至少一光学检测器340，用于检测通过该光学流动室320的检测孔的第二悬浮液的光散射信号和荧光信号。在一示范性实施方式中，该血液分析***100可以包括一发射波长为640nm的红色半导体激光器作为光源和分别能够检测前向光散射、侧向光散射和荧光信号的3个光学检测器。如上文中所述的，该前向光散射信号可以从与入射光束约1°至约10°的角度上或者约2°至约6°的角度上进行检测，该侧向光散射信号和荧光信号可以与入射光束呈约90°的方向进行检测。在一示范性实施方式中，该前向光散射信号可以从与入射光束约1°至约10°的角度上进行检测，该侧向光散射信号可以从与入射光束约65°至约115°的角度上进行检测。该血液分析***100还可以包括用于检测其它角度的光散射信号的一个或多个光学检测器。
在图15所示的实施方式中，该第一混合室210和该第二混合室310分别用于制备相应的悬浮液。在一替代实施方式中，该血液分析***可以具有一共用混合室，例如，按顺序方式制备第一和第二悬浮液。该共用混合室可以在制备两种不同的悬浮液之间通过稀释液进行清洗。
该数据处理模块400分别与第一模块200的DC阻抗检测器230和第二模块300中的光学检测器340可操作地连接。如图15所示，该数据处理模块400包括至少一处理器410和一存储***420，该存储***420可以存储用于实现本文所公开的方法的各个方面的功能的基础程序和数据结构。该存储***420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被计算机应用程序编程以实现本文所公开的方法的功能，并由一个或多个处理器410执行相应程序。当所述处理器执行上述存储于该非暂时性计算机可读存储介质的计算机应用时，所述处理器根据本文所公开的方法确定血液样本的血小板浓度，以及进行白细胞分类和计数，或识别有核红细胞、未成熟细胞或原始细胞。
该数据处理模块400被设置用于执行本文所描述的方法的各个方面。第一模块所检测的DC阻抗信号 和第二模块所检测的光散射和荧光信号可以分别被实时地处理。在示范性实施方式中，可以用现场可编程门阵列(Field-Programmable Gate Array，FPGA)、数字信号处理(DSP)或CPU处理这些信号。然后，处理后的DC阻抗信号、光散射和荧光信号被编程的计算机应用自动分析以获取第一和第二血小板分布，并根据本文所描述的方法确定血液样本的血小板浓度。进一步地，该第二模块的信号还可以被用于分类和计数白细胞，或识别有核红细胞、未成熟细胞或原始细胞。
该血液分析***100进一步包括用户界面500，该用户界面500包括用户界面输入设备和输出设备。本文所述方法得到的结果可以被显示于用户界面输出设备，如计算机屏幕。该用户界面输出设备除了可以显示本文所述方法得到的血小板浓度和白细胞分类结果，还可以显示图形结果，例如如图5所示的融合血小板直方图H Plt-LD(或H Plt-LDa、H Plt-LDb)、如图9C或图10C所示的具有衍生分隔阈值T d的血小板DC直方图H Plt-D、或呈现白细胞亚群分类的2D或3D散点图。所显示的内容还可以进一步包括本文所述方法的中间步骤所得到的信息，例如，第一悬浮液的血小板DC直方图H Plt-D、衍生血小板体积直方图H Plt-L(或H Plt- Lb)、如图4所示的H Plt-D和H Plt-L的叠加图、或H Plt-D和H Plt-LD的叠加图、SFL-FSC散点图、如图3A和图3B所示的血小板区域P的局部放大图、如图9A和图10A所示的SFL-FSC散点图的指定区域P G等。所显示的血小板信息可以以多种不同的方式呈现，例如与被测血液样本中其他类型的细胞的分析结果一起被显示、在指定的血小板屏幕显示、以分层的方式显示以便用户根据兴趣选择特定显示、及其他可替代方案。
在又一实施方式中，上述用于确定血液样本中血小板浓度的方法可以通过对该血液样本的第二悬浮液的多角度光散射检测实现，而不需进行荧光检测。具体地，发明人发现来自第二悬浮液的侧向光散射信号或中角度光散射信号可以用于获取第二血小板分布，从而可以替换掉上文所述方法中确定血液样本的血小板浓度中的荧光信号。其中，在前向光散射和侧向光散射之间的一角度检测中角度光散射信号。在一示范性实施方式中，该前向光散射信号可以从与入射光束约1°至约10°的角度上进行检测，该侧向光散射信号可以从与入射光束约65°至约115°的角度上进行检测。在另一示范性实施方式中，该前向光散射信号可以从与入射光束约2°至约6°的角度上进行检测，位于低中角度的光散射信号可以从与入射光束约8°至约24°的角度上进行检测，后者被称为低中角度光散射。此外，也可以从与入射光束约25°至约65°的角度上进行检测位于高中角度的光散射信号，被称为高中角度光散射信号。
下面将参照图11A和图11B进一步描述本实施方式。图11A和图11B分别示出了获取自一正常血液样本及一含有大血小板的异常血液样本的第二悬浮液的前向光散射-侧向光散射(FSC-SSC)散点图。如图所示，在该FSC-SSC散点图中血小板区域P’与白细胞区域W’被区分。类似于上文中所述的利用荧光和光散射检测的方法，在本实施方式中，第二血小板分布可以是如图11A和图11B所示的位于血小板区域P’的血小板的二维分布形式，也可以是如图3C所示的衍生血小板体积直方图的一维分布形式。从第二悬浮液的光散射信号所获取的第二血小板分布与从第一悬浮液所获取的第一血小板分布可以用于确定血液样本的血小板浓度，其具体方法如上文所述。
更具体地，血液样本的血小板浓度可以参照前文有关方程式(1)-(3)所述的方法确定。在本实施方式中，可以利用第二悬浮液的FSC-SSC散点图中位于血小板区域P’的血小板的前向光散射信号基于方程式(1)或方程式(2)得到衍生血小板体积直方图H Plt-L’，也可以利用第二悬浮液的FSC-SSC散点图中位于血小板区域 P’的血小板的前向光散射信号和侧向光散射信号基于Mie散射理论得到衍生血小板体积直方图H Plt-L’，二者的具体方法如上文所述，在此不再赘叙。然后，基于方程式(3)，利用第一悬浮液的血小板DC直方图H Plt-D和第二悬浮液的衍生血小板体积直方图H Plt-L’得到融合血小板直方图H Plt-LD’。在本实施方式中，方程式(3)中所代入的衍生血小板体积直方图H Plt-L中的事件(i)为衍生血小板体积直方图H Plt-L’中的事件(i)。之后根据该融合血小板直方图H Plt-LD’中曲线下方的面积可以确定血小板浓度。
相似地，血液样本的血小板浓度也可以参照前文所述的有关融合血小板直方图H Plt-LDa的方法确定。在本实施方式中，可以基于方程式(6)和方程式(7)，利用第一悬浮液的血小板DC直方图H Plt-D和第二悬浮液的FSC-SSC散点图中位于血小板区域P’的血小板的光散射信号的衍生血小板体积直方图H Plt-L’生成融合血小板直方图H Plt-LDa‘。在本实施方式中，方程式(6)中所代入的衍生血小板体积直方图H Plt-L中的事件(i)为衍生血小板体积直方图H Plt-L’中的事件(i)。之后根据该融合血小板直方图H Plt-LDa’中曲线下方的面积可以确定血小板浓度。
进一步地，血液样本的血小板浓度也可以参照前文所述的有关融合血小板直方图H Plt-LDb的方法确定。在本实施方式中，可以基于方程式(9)和方程式(10)所设定的判据，利用第一悬浮液的血小板DC直方图H Plt- D和用方程式(8)所得的第二悬浮液的FSC-SSC散点图中位于血小板区域P’的血小板的前向光散射信号的衍生血小板体积直方图H Plt-Lb’生成融合血小板直方图H Plt-LDb‘。在本实施方式中，方程式(9)中所代入的衍生血小板体积直方图H Plt-L中的事件(i)为衍生血小板体积直方图H Plt-L’中的事件(i)。之后根据该融合血小板直方图H Plt-LDb’中曲线下方的面积可以确定血小板浓度。
此外，血液样本的血小板浓度也可以参照前文所述的有关方程式(11)和方程式(12)的方法确定。在本实施方式中，可以依照前文所述的融合判据用方程式(11)和方程式(12)计算血小板浓度。在本实施方式中，方程式(11)中所定义的衍生血小板体积直方图H Plt-L中的各元素为所述FSC-SSC散点图中位于血小板区域P’的血小板的光散射信号的衍生血小板体积直方图H Plt-L’中的对应要素，所述融合判据中的绝对差值δ’为血小板DC直方图H Plt-D的Area-1与衍生血小板体积直方图H Plt-L’的Area-2’之间的绝对差值，并被进一步与一预设的面积阈值A T’进行比较。
如前文所述，在利用多角度光散射测量第二悬浮液的实施方式中，该融合血小板直方图H Plt-LDa’或H Plt-LDb’为第三血小板分布，该第三血小板分布是用从第一悬浮液获取的第一血小板分布和从第二悬浮液获取的第二血小板分布(即衍生血小板体积直方图)得到的。基于该第三血小板分布可以得到血小板浓度。
进一步地，该血小板浓度也可以参照方程式(13)-(15)相关的方法确定。以图10B来说明没有荧光信号情况下的血小板浓度确定方法，该血液样本，从第二悬浮液获取的FSC-SSC散点图的血小板区域P’中的指定区域P G出现的事件数较大。在该实施方式中，以相似的方式利用如前文中所述的方程式(13)-(15)及偏移量判据，可以将有关该第二血小板分布的上述信息与来自第一悬浮液的血小板DC直方图H Plt-D中的血小板谷峰比R v/p一起用于确定偏移量F of和衍生分隔阈值T d。在这种情况下，方程式(14)和方程式(15)中的N为从第二悬浮液获取的FSC-SSC散点图的血小板区域P’中指定区域P G’中存在的事件数。所得的该衍生分隔阈值T d被用于区分第一悬浮液的血小板DC直方图H Plt-D中的血小板与红细胞，从而如图9C和图10C所示的方式得到血小板浓度。在本实施方式中，该预定的事件数阈值G T’可以通过大量的正常血液样本 得到，其反映正常血液样本的第二悬浮液的FSC-SSC散点图的血小板区域P’中位于指定区域P G’中的事件数的最大值。
进一步地，在本实施方式中，该衍生分隔阈值T d也可以用方程式(16)确定，其中N为从第二悬浮液获取的FSC-SSC散点图的血小板区域P’中指定区域P G’的事件数，G T为该指定区域P G’的预定的事件数阈值。
相应于该实施方式，用于执行上述实施方式所述方法的血液分析***包括第二模块，该第二模块包括一个或多个光学检测器用于检测通过光学流动室的检测孔的第二悬浮液的前向光散射信号和侧向光散射信号。可选地，该第二模块的光学检测器也可以被设置为用于检测通过光学流动室的检测孔的第二悬浮液的前向光散射信号和中角度光散射信号。在这种情况下，图15中所示的血液分析***可以包括两个光学检测器，一个用于检测第二悬浮液的前向光散射信号，另一个用于检测第二悬浮液的侧向光散射信号或中角度光散射信号。
此外，在本实施方式中，该数据处理模块被设置用于分别分析来自第一模块的第一悬浮液的DC阻抗信号和来自第二模块的第二悬浮液的光散射信号，并实施本实施方式的各个方面。与图15所描述的方式相同地，当处理器执行存储于该非暂时性计算机可读存储介质的计算机应用时，所述处理器根据本实施方式所述的方法确定血液样本的血小板浓度并执行白细胞分类和计数。
可以理解地，在本实施方式中，制备第二悬浮液的过程不需要加入荧光染料。在本实施方式中，所述溶解试剂中包括用于溶解第二份试样中的红细胞的一种或多种溶解剂，但不含有荧光染料。进一步地，多种现有的用于血液分析仪白细胞分类的溶解试剂可以用于制备所述第二悬浮液。例如，可以采用美国专利U.S.7,413,905所描述的溶解试剂配方，其全部公开内容通过引证结合于此。具体地，如美国专利U.S.7,413,905所述，该溶解试剂可以包括一种或多种表面活性剂作为溶血剂溶解红细胞并部分地损伤白细胞的细胞膜、一种具有能够与白细胞中所存在的阳离子成分结合的阴离子基团的有机化合物用于使白细胞的亚群之间出现形态差异、以及用于将试剂的pH值调节至2-8之间的缓冲溶液。该溶血剂可以包括一种或多种阳离子表面活性剂、一种或多种阴离子表面活性剂、一种或多种两性表面活性剂、一种或多种阳离子表面活性剂与一种或多种两性表面活性剂的组合、或者一种或多种阴离子表面活性剂与一种或多种两性表面活性剂的组合。
由于不需要检测荧光，本实施方式所提供的***相较于现有血液分析仪具有结构简单和成本低的优点。本实施方式可以在现有的配置有前向光散射和侧向光散射检测功能的血液分析仪或配置有前向光散射和中角度光散射检测功能的血液分析仪上实施。此外，由于检测中不需要使用荧光染料，本实施方式所提供的方法大大降低了试剂成本。本领域技术人员能够理解，本实施例的方式，也适用于检测荧光的血液分析仪中，该分析仪能同时检测前向散射光、侧向散射光和荧光信号即可。
进一步地，本实施方式所述的方法还可以包括基于第二悬浮液的光散射信号将白细胞分类为其亚群的步骤。图14中的FSC-SSC散点图示出了基于第二悬浮液的光散射信号将白细胞区分为淋巴细胞、单核细胞、中性粒细胞和嗜酸性粒细胞的实施例。在其他实施方式中，还可以基于第二悬浮液的光散射信号 进一步将嗜碱性粒细胞与其他白细胞亚群区分开。此外，本实施方式所述的方法还可以进一步包括计数第二悬浮液中白细胞的数量，报告血液样本中白细胞计数的步骤。
实施例1-7进一步说明上述确定血液样本中血小板浓度的方法。在实施例1中，采用一能够检测前向光散射、侧向光散射和荧光信号的血液分析仪检测25个血液样本的血小板浓度，这25个血液样本包括5个正常血液样本和20个异常血液样本，这些异常血液样本中包括红细胞碎片、小红细胞或大血小板。用流式细胞仪作为参考方法检测相同的血液样本的血小板浓度，并用常规DC阻抗检测方法检测这些血液样本的血小板浓度作为对照。
图16示出了通过常规DC阻抗检测方法所获取的这些血液样本的血小板浓度与通过流式细胞仪参考方法获取的这些血液样本的血小板浓度的相关性。如图所示，通过常规DC阻抗检测方法所获取的这些血液样本的血小板浓度与参考方法所得的结果之间相关性较差。这是因为大部分血液样本都是包括已知的会干扰常规血小板DC阻抗检测的红细胞碎片、小红细胞或大血小板的异常血液样本。这25个血液样本的线性回归分析中的相关系数R 2为0.8343。我们发现常规DC阻抗检测方法检测含有红细胞碎片或小红细胞的血液样本所得的血小板浓度明显高于参考方法所得的结果，而常规DC阻抗检测方法检测含有大血小板的血液样本所得的血小板浓度明显低于参考方法所得的结果。
图17示出了通过本公开的方法中方程式(3)生成的融合血小板直方图H Plt-LD所得的这25个血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9940。由此可以说明，使用本公开所描述的融合血小板直方图H Plt-LD可以有效地校正常规DC阻抗方法对存在红细胞碎片、小红细胞或大血小板的异常血液样本的血小板检测结果的误差。
相似地，在实施例2中，图18示出了通过本公开的方法中方程式(6)和方程式(7)所设定的判据生成的融合血小板直方图H Plt-LDa所得的这25个血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9739。在实施例3中，采用本公开的方法中方程式(8)所设定的曲线拟合过程用血小板区域P的光散射信号获取衍生血小板体积直方图H Plt-Lb以生成融合血小板直方图H Plt-LDb。图19示出了通过融合血小板直方图H Plt-LDb所得的这25个血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9681。在实施例4中，图20示出了通过本公开的方法基于融合判据用方程式(11)和方程式(12)所得的这25个血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9797。这三个实施例均说明其方法能够准确地检测血液样本的血小板浓度，并有效地校正常规DC阻抗方法对存在红细胞碎片、小红细胞或大血小板的异常血液样本的血小板检测结果的误差。
实施例5进一步说明通过本公开的方法用方程式(13)-(15)和所述偏移量判据检测血液样本的血小板浓度的方法。在检测中也采用了与实施例1相同的25个血液样本。图21示出了通过方程式(13)-(15)所得的这25个血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9937。这说明该方法能够准确地检测血液样本的血小板浓度，并有效地校正常规DC阻抗方法对存在红细胞碎片、小红细胞或大血小板的异常血液样本的血小板检测结果的误差。
实施例1-5的检测血小板的方法被应用于对红细胞溶血后进行白细胞分类的步骤中。实施例6说 明了本公开的方法被应用于对红细胞溶血后进行有核红细胞分类的步骤中。实施例6和7说明了通过本公开的方法用方程式(3)生成的融合血小板直方图H Plt-ND。图22进一步示意了实施例6中，生成融合血小板直方图H Plt-ND的过程，与图5生成H Plt-LD类似，具体不再赘述。
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23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HPlt-ND。具体过程如前所述，不再赘述。
类似的，在实施例7中，也可以用方程式13-15相关方法确定样本的血小板浓度。图24显示第二悬浮液获取的前散-侧散散点图(FSC-SSC)的血小板区域中的指定区域PG出现的事件数正常。在该实施方式中，以相似的方式利用如前文中所述的方程式(13)-(15)及偏移量判据，可以将有关该第二血小板分布的上述信息与来自第一悬浮液的血小板DC直方图HPlt-D中的血小板谷峰比Rv/p一起用于确定偏移量Fof和衍生分隔阈值Td。在这种情况下，方程式(14)和方程式(15)中的N为从第二悬浮液获取的FSC-SSC散点图的血小板区域中指定区域PG中存在的事件数。所得的该衍生分隔阈值Td被用于区分第一悬浮液的血小板DC直方图HPlt-D中的血小板与红细胞，从而如下所示的方式得到血小板浓度。同样的，也可以利用荧光-侧向散射光(SFL-SSC)散点图识别指定区域PG，判断事件数是否正常。
上述实施例说明本公开的方法提供对血液样本的血小板浓度准确的检测，尤其对常规DC阻抗方法检测血小板存在干扰的情况非常有效，如含有红细胞碎片、小红细胞或大血小板的异常血液样本。因此，本公开的方法解决了现有血小板阻抗检测方法中存在的问题，满足了体外诊断分析领域长久以来对准确测定血小板浓度的需求。此外，如前文中提到的一些现有的高端血液分析仪在常规阻抗检测之外还对血小板进行单独的光学检测，以区分干扰物质并消除干扰对血小板检测结果的影响。然而，这大大增加了仪器的复杂性和制造成本。相较于现有技术，本公开的方法可以在具有全血细胞技术(CBC)和白细胞分类检测功能的各种商业血液分析仪上实施，而不会增加仪器成本。因此，特别有利地，本公开的方法可以广泛地应用于体外诊断领域现有仪器上以提高血小板检测的准确度。
下面的实施例仅用于说明，并不能被理解为对本公开保护范围的限定。可以理解的是，根据本公开的精神进行各种变形或修改都包含在本发明的保护范围之内。
实施例1
使用商业血液分析仪BC-6800(深圳迈瑞生物医疗电子股份有限公司，中国深圳)测量全血样本，并且对从第一和第二悬浮液获取的数据采用前述一实施方式的方法进行分析。
BC-6800血液分析仪包括CBC模块和分类模块。该CBC模块包括一混合室和DC阻抗检测器，该混合室被设置用于将血液样本的份试样与稀释液混合以形成第一悬浮液，该DC阻抗检测器被设置用于测量流过流通路径的小孔的该第一悬浮液的DC阻抗信号。该分类模块包括另一混合室、红外半导体激光器和多个光学检测器，该混合室被设置用于将血液样本的另一份试样与溶血剂及荧光染料混合以形成第二悬浮液，该红外半导体激光器作为光源具有640nm的发射波长并将其发射的光束对准光学流动室的检测 孔，多个光学检测器能够检测通过该光学流动室的检测孔的第二悬浮液的从与入射光束约1°至约10°的角度上的前向光散射信号、从与入射光束约65°至约115°的角度上的侧向光散射信号及荧光信号。
在该CBC模块中，4μL抗凝全血样本被与1.5mL M-68DS稀释液(为深圳迈瑞生物医疗电子股份有限公司的产品，中国深圳)混合，形成第一悬浮液。在该分类模块中，20μL相同的全血样本被与1mL M-68LD Lyse和20μLM-68FD染料(均为深圳迈瑞生物医疗电子股份有限公司的产品，中国深圳)以溶解红细胞并染色有核酸物质的血细胞，形成第二悬浮液。该M-68LD Lyse为含有阳离子表面活性剂、非离子表面活性剂和阴离子化合物的水溶液，用于溶解血液样本中的红细胞的。该M-68FD染料为含有阳离子花菁化合物的水溶液，用于染色血液样本中的有核酸物质的血细胞。
对该第一悬浮液的DC阻抗信号检测所收集到的数据进行分析，生成如图1所示的血小板DC直方图H Plt-D。对该第二悬浮液的前向光散射信号和荧光信号检测所收集到的数据进行分析以在如图3A-3B所示的SFL-FSC散点图中区分血小板区域P与白细胞区域W。在该实施例中，基于该血小板区域P中的血小板的前向光散射信号用方程式(1)计算衍生血小板体积。然后，依照上述方程式(3)生成融合血小板直方图H Plt-LD。在该实施例中，方程式(3)中的k i1和k i2是常数，其中，当Vol p(i)>20fL时,k i1＝1,k i2＝0；当Vol p(i)≤20fL时,k i1＝0,k i2＝1。基于该融合血小板直方图H Plt-LD中曲线下方的面积确定该血液样本的血小板浓度。
25个全血血液样本被分析，包括5个正常血液样本、10个含有红细胞碎片或小红细胞的异常血液样本和10个含有大血小板的异常血液样本。通过在显微镜下手工检查血液涂片证实这些异常血液样本中存在的红细胞碎片、小红细胞或大血小板。在BriCyte E6流式细胞仪上(为深圳迈瑞生物医疗电子股份有限公司的产品，中国深圳)将这些血液样本采用RBC/血小板比例方法(一种国际血液学标准化委员会(ICSH)和国际实验室血液学学会(ISLH)所推荐的参考方法)，进行分析。此外，由BC-6800血液分析仪使用常规DC阻抗检测方法报告的这些血液样本的血小板浓度被用于比较对照。
图16示出了通过常规DC阻抗检测方法所获取的这些血液样本的血小板浓度与通过流式细胞仪参考方法所得的结果的相关性。如图所示，通过常规DC阻抗检测方法所获取的这些血液样本的血小板浓度与参考方法所得的结果之间相关性较差。常规DC阻抗检测方法检测含有红细胞碎片或小红细胞的异常血液样本所得的血小板浓度明显高于参考方法所得的结果，而常规DC阻抗检测方法检测含有大血小板的血液样本所得的血小板浓度明显低于参考方法所得的结果。这25个血液样本的线性回归分析中的相关系数R 2为0.8343。
图17示出了通过本公开中与方程式(3)相关的方法所得的这些血液样本的血小板浓度与使用流式细胞仪参考方法所得的结果的相关性。如图所示，通过本公开的方法中方程式(3)生成的融合血小板直方图所得的血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9940。由于异常血液样本中所存在的红细胞碎片、小红细胞或大血小板而给常规阻抗方法检测带来的误差，可以通过本实施例中的融合血小板直方图H Plt-LD进行有效地校正。
实施例2
在BC-6800血液分析仪对从第一和第二悬浮液的检测中所收集的数据，如实施例1中所述，被进一步分析以使用融合血小板直方图HPlt-LDa的方法确定各血液样本的血小板浓度。
更具体地，与实施例1相同，对该第一悬浮液的DC阻抗信号检测所收集到的数据进行分析生成血小板DC直方图H Plt-D。对该第二悬浮液的前向光散射信号和荧光信号检测所收集到的数据进行分析以在SFL-FSC散点图中区分血小板区域P与白细胞区域W。用方程式(1)得到衍生血小板体积直方图H Plt-L与衍生血小板体积。然后，通过本公开的方法中方程式(6)和方程式(7)所设定的判据生成的融合血小板直方图H Plt- LDa。基于该融合血小板直方图H Plt-LDa中曲线下方的面积确定该血液样本的血小板浓度。
在本实施例中所分析的血液样本与实施例1中所使用的25个血液样本相同。图18示出了本实施例中所得的这些血液样本的血小板浓度与实施例1中使用流式细胞仪参考方法所得的结果的相关性。如图所示，使用本实施例中所描述的方法得到的血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9739。
实施例3
在BC-6800血液分析仪对从第一和第二悬浮液的检测中所收集的数据，如实施例1中所述，被进一步分析以使用融合血小板直方图H Plt-LDb的方法确定血液样本的血小板浓度。
更具体地，对该第一悬浮液的DC阻抗信号检测所收集到的数据进行分析生成血小板DC直方图H Plt-D。对该第二悬浮液的前向光散射信号和荧光信号检测所收集到的数据进行分析以在SFL-FSC散点图中区分血小板区域P与白细胞区域W。在本实施例中，用方程式(8)所设定的曲线拟合过程用血小板区域P的光散射信号生成衍生血小板体积直方图H Plt-Lb。然后，依照方程方程式(9)和方程式(10)所设定的判据生成融合血小板直方图H Plt-LDb。基于该融合血小板直方图H Plt-LDb中曲线下方的面积确定该血液样本的血小板浓度。
在本实施例中所分析的血液样本与实施例1中所使用的25个血液样本相同。图19示出了本实施例中所得的这些血液样本的血小板浓度与实施例1中使用流式细胞仪参考方法所得的结果的相关性。如图所示，使用本实施例中所描述的方法得到的血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9681。
实施例4
在BC-6800血液分析仪对从第一和第二悬浮液的检测中所收集的数据，如实施例1中所述，被进一步分析以使用方程式(11)和方程式(12)及所述融合判据确定血液样本的血小板浓度。
更具体地，对该第一悬浮液的DC阻抗信号检测所收集到的数据进行分析生成血小板DC直方图H Plt-D。对该第二悬浮液的前向光散射信号和荧光信号检测所收集到的数据进行分析以在SFL-FSC散点图中区分血小板区域P与白细胞区域W。用方程式(1)得到衍生血小板体积直方图H Plt-L与衍生血小板体积。然后，通过本公开的方法中方程式(11)和方程式(12)基于所述融合判据计算血液样本的血小板浓度。
在本实施例中所分析的血液样本与实施例1中所使用的25个血液样本相同。图20示出了本实施例中所得的这些血液样本的血小板浓度与实施例1中使用流式细胞仪参考方法所得的结果的相关性。如图所示，使用本实施例中所描述的方法得到的血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切 相关，其相关系数R 2为0.9797。
实施例5
在BC-6800血液分析仪对从第一和第二悬浮液的检测中所收集的数据，如实施例1中所述，被用上述一实施方式中所述方法进一步分析。
更具体地，与实施例1相同，对该第一悬浮液的DC阻抗信号检测所收集到的数据进行分析生成血小板DC直方图H Plt-D，如图9B和10B中所示，并确定该血小板DC直方图H Plt-D的血小板谷峰比R v/p。对该第二悬浮液的前向光散射信号和荧光信号检测所收集到的数据进行分析以在SFL-FSC散点图中区分血小板区域P与白细胞区域W，并进一步确定指定区域P G中的事件数N。然后，依照方程式(13)-(15)及偏移量判据确定衍生分隔阈值T d。所得的衍生分隔阈值T d被用于在血小板DC直方图H Plt-D区分血小板与红细胞，如图9C和10C中所示，并基于该血小板DC直方图中该衍生分隔阈值T d所确定的血小板群体的曲线下方的面积计算该血液样本的血小板浓度。
在本实施例中所分析的血液样本与实施例1中所使用的25个血液样本相同。图21示出了本实施例中所得的这些血液样本的血小板浓度与实施例1中使用流式细胞仪参考方法所得的结果的相关性。如图所示，使用本实施例中所描述的方法得到的血液样本的血小板浓度与流式细胞仪参考方法所得的结果密切相关，其相关系数R 2为0.9937。与上述其他实施例相同，本实施例的方法能够有效地校正常规DC阻抗方法对存在红细胞碎片、小红细胞或大血小板的异常血液样本的血小板检测结果的实质性误差。
实施例6
使用商业血液分析仪BC-6800(深圳迈瑞生物医疗电子股份有限公司，中国深圳)测量一份含有有核红细胞的全血样本，并且对从第一和第二悬浮液获取的数据采用前述一实施方式的方法进行分析。
BC-6800血液分析仪包括CBC模块和分类模块。该CBC模块包括一混合室和DC阻抗检测器，该混合室被设置用于将血液样本的份试样与稀释液混合以形成第一悬浮液，该DC阻抗检测器被设置用于测量流过流通路径的小孔的该第一悬浮液的DC阻抗信号。该分类模块为有核红细胞分类模块，包括另一混合室、红外半导体激光器和多个光学检测器，该混合室被设置用于将血液样本的另一份试样与溶血剂及荧光染料混合以形成第二悬浮液，该红外半导体激光器作为光源具有640nm的发射波长并将其发射的光束对准光学流动室的检测孔，多个光学检测器能够检测通过该光学流动室的检测孔的第二悬浮液的从与入射光束约1°至约10°的角度上的前向光散射信号、从与入射光束约65°至约115°的角度上的侧向光散射信号及荧光信号。
在该CBC模块中，4μL抗凝全血样本被与1.5mL M-68DS稀释液(为深圳迈瑞生物医疗电子股份有限公司的产品，中国深圳)混合，形成第一悬浮液。在该分类模块中，20μL相同的全血样本被与1mL M-68LN Lyse和20μLM-68FN染料(均为深圳迈瑞生物医疗电子股份有限公司的产品，中国深圳)以溶解红细胞并染色有核酸物质的血细胞，形成第二悬浮液。该M-68LN Lyse为含有阳离子表面活性剂和阴离子化合物的水溶液，用于溶解血液样本中的红细胞的。该M-68FN染料为含有阳离子花菁化合物的水溶液，用于染色血液样本中的有核酸物质的血细胞。
对该第一悬浮液的DC阻抗信号检测所收集到的数据进行分析，生成如图1所示的血小板DC直方图H Plt-D。对该第二悬浮液的前向光散射信号和荧光信号检测所收集到的数据进行分析以在如图22所示的SSC-FSC散点图中区分血小板区域P与白细胞区域W。在该实施例中，基于该血小板区域P中的血小板的前向光散射信号用方程式(1)计算衍生血小板体积。可以得到如图22所示的衍生血小板体积直方图H Plt- N。然后，依照上述方程式(3)生成融合血小板直方图H Plt-ND。在该实施例中，方程式(3)中的k i1和k i2是常数，其中，当Vol p(i)>20fL时,k i1＝1,k i2＝0；当Vol p(i)≤20fL时,k i1＝0,k i2＝1。基于该融合血小板直方图H Plt-ND中曲线下方的面积确定该血液样本的血小板浓度。
如图26该份样本的第二悬浮液的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。
经人工镜检确认为红细胞碎片样本，以流式细胞分析仪检测结果得到参考值，其检测结果为86×109/L，阻抗法获得的血小板计数结果为110×109/L，本文技术获得的血小板计数结果为91×109/L，与参考值更接近。
实施例7
使用商业血液分析仪BC-6800(深圳迈瑞生物医疗电子股份有限公司，中国深圳)测量一份含有有核红细胞的全血样本，并且对从第一和第二悬浮液获取的数据采用前述一实施方式的方法进行分析。
BC-6800血液分析仪包括CBC模块和分类模块。该CBC模块包括一混合室和DC阻抗检测器，该混合室被设置用于将血液样本的份试样与稀释液混合以形成第一悬浮液，该DC阻抗检测器被设置用于测量流过流通路径的小孔的该第一悬浮液的DC阻抗信号。该分类模块为有核红细胞分类模块，包括另一混合室、红外半导体激光器和多个光学检测器，该混合室被设置用于将血液样本的另一份试样与溶血剂及荧光染料混合以形成第二悬浮液，该红外半导体激光器作为光源具有640nm的发射波长并将其发射的光束对准光学流动室的检测孔，多个光学检测器能够检测通过该光学流动室的检测孔的第二悬浮液的从与入射光束约1°至约10°的角度上的前向光散射信号、从与入射光束约65°至约115°的角度上的侧向光散射信号及荧光信号。
在该CBC模块中，4μL抗凝全血样本被与1.5mL M-68DS稀释液(为深圳迈瑞生物医疗电子股份有限公司的产品，中国深圳)混合，形成第一悬浮液。在该分类模块中，20μL相同的全血样本被与1mL M-68LN Lyse和20μLM-68FN染料(均为深圳迈瑞生物医疗电子股份有限公司的产品，中国深圳)以溶解红细胞并染色有核酸物质的血细胞，形成第二悬浮液。该M-68LN Lyse为含有阳离子表面活性剂和阴离子化合物的水溶液，用于溶解血液样本中的红细胞的。该M-68FN染料为含有阳离子花菁化合物的水溶液，用于染色血液样本中的有核酸物质的血细胞。
对该第一悬浮液的DC阻抗信号检测所收集到的数据进行分析生成血小板DC直方图HPlt-D，如图25B中所示，并确定该血小板DC直方图HPlt-D的血小板谷峰比Rv/p。对该第二悬浮液的前向光散射信号和荧光信号检测所收集到的数据进行分析以在FSC-SSC散点图中区分血小板区域P与白细胞区域W，并进一步确定指定区域PG中的事件数N。然后，依照方程式(13)-(15)及偏移量判据确定衍生分隔阈值Td。 所得的衍生分隔阈值Td被用于在血小板DC直方图HPlt-D区分血小板与红细胞。本实施中，PG中的事件数N正常，该衍生分隔阈值Td相对于该表观分隔阈值Tap向左偏移，如图25C中所示，基于该血小板DC直方图中该衍生分隔阈值Td所确定的血小板群体的曲线下方的面积计算该血液样本的血小板浓度。
经人工镜检确认为红细胞碎片样本，以流式细胞分析仪检测结果得到参考值，其检测结果为86×109/L，阻抗法获得的血小板计数结果为110×109/L，本文技术获得的血小板计数结果为95×109/L，与参考值更接近。
上文中结合附图对本公开进行详细的描述，但这些内容仅是示范性的实施方式，并不应被解释为对本公开保护范围的限制。显而易见地，在本公开保护范围内所做出的任何修改和变化仍属于本公开的说明书所披露和权利要求所限定的范围，具有同等的法律效应。

Claims (69)

  1. 一种测定血液样本中血小板浓度的方法，包括：
    将所述血液样本的第一份与稀释液混合，形成第一悬浮液；
    将所述血液样本的第二份与溶血剂和荧光染料混合以溶解红细胞和染色白细胞，形成第二悬浮液；
    测量所述第一悬浮液流过小孔的直流阻抗信号；
    测量所述第二悬浮液流过光学流动室的光散射信号和荧光信号；
    分析所述第一悬浮液的所述直流阻抗信号以获取第一血小板分布；
    分析所述第二悬浮液的所述光散射信号和所述荧光信号以区别血小板与白细胞和/或有核红细胞并获取第二血小板分布；以及
    基于所述第一血小板分布和所述第二血小板分布确定所述血液样本的血小板浓度。
  2. 如权利要求1所述的方法，其特征在于，所述区别血小板与白细胞和/或有核红细胞包括在由所述第二悬浮液所得的光散射和荧光散点图中区分血小板区域与白细胞区域和/或有核红细胞区域。
  3. 如权利要求2所述的方法，其特征在于，所述第一血小板分布为由所述第一悬浮液所得的血小板直流阻抗直方图。
  4. 如权利要求3所述的方法，其特征在于，所述第二血小板分布为利用所述血小板区域的血小板的光散射信号生成的衍生血小板体积直方图。
  5. 如权利要求4所述的方法，其特征在于，所述确定所述血液样本的血小板浓度的步骤包括用由所述第一悬浮液所得的所述血小板直流阻抗直方图和由所述第二悬浮液所得的所述衍生血小板体积直方图生成融合血小板直方图，基于所述融合血小板直方图获取血小板浓度。
  6. 如权利要求5所述的方法，其特征在于，进一步包括向用户显示所述血小板浓度、由所述第一悬浮液所得的血小板直流阻抗直方图、由所述第二悬浮液所得的所述衍生血小板体积直方图、所述融合血小板直方图、由所述第一悬浮液所得的血小板直流阻抗直方图与由所述第二悬浮液所得的所述衍生血小板体积直方图的叠加图、由所述第一悬浮液所得的血小板直流阻抗直方图与所述融合血小板直方图的叠加图、由所述第二悬浮液所得的所述光散射和荧光散点图的所述血小板区域、或其组合。
  7. 如权利要求3所述的方法，其特征在于，所述第二血小板分布是由所述第二悬浮液所得的前向光散射和荧光散点图中一血小板区域中血小板的二维分布。
  8. 如权利要求7所述的方法，其特征在于，所述确定所述血液样本的血小板浓度的步骤包括：
    确定所述第一悬浮液的所述血小板直流阻抗直方图的血小板谷峰比；
    确定由所述第二悬浮液所得的所述前向光散射和荧光散点图的所述血小板区域中一指定区域的事件数；
    基于所述血小板谷峰比和所述指定区域的事件数确定所述血小板直流阻抗直方图中血小板与红细胞之间波谷的衍生分隔阈值；以及
    利用所述衍生分隔阈值区分所述血小板直流阻抗直方图中的血小板与红细胞，获取所述血液样本的血小板浓度。
  9. 如权利要求8所述的方法，其特征在于，进一步包括向用户显示所述血小板浓度、由所述第二悬浮液所得的所述前向光散射和荧光散点图中所述血小板区域、所述血小板区域中的所述指定区域、所述指定区域的事件数、由所述第一悬浮液所得的所述血小板直流阻抗直方图中所述血小板谷峰比、具有所述衍生分隔阈值的由所述第一悬浮液所得的血小板直流阻抗直方图、或其组合。
  10. 如权利要求1所述的方法，其特征在于，所述确定所述血液样本的血小板浓度的步骤包括：基于所述第一血小板分布和所述第二血小板分布生成第三血小板分布，基于所述第三血小板分布获取血小板浓度。
  11. 如权利要求1所述的方法，其特征在于，进一步包括基于所述第二悬浮液的所述光散射信号和所述荧光信号将所述血液样本中的白细胞区分为白细胞的亚群，优选的，所述将所述血液样本中的白细胞区分为白细胞的亚群包括：
    区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞；或
    区分嗜碱性粒细胞。
  12. 如权利要求1所述的方法，其特征在于，
    进一步包括对所述第二悬浮液进行所述血液样本的白细胞计数；或
    识别所述第二悬浮液中的有核红细胞或未成熟白细胞。
  13. 一种用于测定血液样本中血小板浓度的血液分析***，包括：
    第一模块，所述第一模块包括第一混合室和直流阻抗检测器，所述第一混合室用于将所述血液样本的第一份与稀释液混合以形成第一悬浮液，所述直流阻抗检测器被装配于流通路径的小孔，所述流通路径与所述第一混合室相连通，所述直流阻抗检测器用于检测所述第一悬浮液通过所述小孔的直流阻抗信号；
    第二模块，包括第二混合室、光源及至少一光学检测器，所述第二混合室用于将所述血液样本的第二份与溶血剂及荧光染料混合、溶解红细胞并染色白细胞以形成第二悬浮液，所述光源用于将光束对准与所述第二混合室相连通的光学流动室的检测孔，所述至少一光学检测器被装配于所述光学流动室用于检测通过所述光学流动室的所述检测孔的所述第二悬浮液的光散射信号和荧光信号；以及
    数据处理模块，与所述第一模块的所述直流阻抗检测器和所述第二模块中的所述至少一光学检测器分别可操作地连接，所述数据处理模块包括处理器和编程有计算机应用程序的非暂时性计算机可读存储介质，当所述计算机应用程序被所述处理器执行时，使所述处理器基于所述第一悬浮液的所述直流阻抗信号生成第一血小板分布，基于所述第二悬浮液的所述光散射信号和所述荧光信号区分血小板与白细胞和/或有核红细胞、生成第二血小板分布，基于所述第一血小板分布和所述第二血小板分布确定所述血液样本的血小板浓度。
  14. 如权利要求13所述的血液分析***，其特征在于，所述第一血小板分布为由所述第一悬浮液所得的血小板直流阻抗直方图，所述第二血小板分布为基于所述第二悬浮液所得的光散射和荧光散点图中一血 小板区域的血小板的光散射信号生成的衍生血小板体积直方图。
  15. 如权利要求14所述的血液分析***，其特征在于，当所述数据处理模块的所述计算机应用程序被所述处理器执行时，使所述处理器用由所述第一悬浮液所得的血小板直流阻抗直方图和由所述第二悬浮液所得的所述衍生血小板体积直方图生成融合血小板直方图，基于所述融合血小板直方图获取血小板浓度。
  16. 如权利要求15所述的血液分析***，其特征在于，进一步包括用户界面，所述用户界面可操作地与所述数据处理模块相连并用于显示所述血小板浓度、由所述第一悬浮液所得的血小板直流阻抗直方图、由所述第二悬浮液所得的所述衍生血小板体积直方图、所述融合血小板直方图、由所述第一悬浮液所得的血小板直流阻抗直方图与由所述第二悬浮液所得的所述衍生血小板体积直方图的叠加图、由所述第一悬浮液所得的血小板直流阻抗直方图与所述融合血小板直方图的叠加图、由所述第二悬浮液所得的所述光散射和荧光散点图的所述血小板区域、或其组合。
  17. 如权利要求13所述的血液分析***，其特征在于，所述第一血小板分布为由所述第一悬浮液所得的血小板直流阻抗直方图，所述第二血小板分布是由所述第二悬浮液所得的前向光散射和荧光散点图中一血小板区域中血小板的二维分布。
  18. 如权利要求17所述的血液分析***，其特征在于，当所述数据处理模块的所述计算机应用程序被所述处理器执行时，使所述处理器
    确定所述第一悬浮液的所述血小板直流阻抗直方图的血小板谷峰比；
    确定由所述第二悬浮液所得的所述前向光散射和荧光散点图的所述血小板区域中一指定区域的事件数；
    基于所述血小板谷峰比和所述指定区域的事件数确定所述血小板直流阻抗直方图中血小板与红细胞之间波谷的衍生分隔阈值；以及
    利用所述衍生分隔阈值区分所述血小板直流阻抗直方图中的血小板与红细胞，获取所述血液样本的血小板浓度。
  19. 如权利要求13所述的血液分析***，其特征在于，所述确定所述血液样本的血小板浓度的步骤包括：基于所述第一血小板分布和所述第二血小板分布生成第三血小板分布，基于所述第三血小板分布获取血小板浓度。
  20. 如权利要求13所述的血液分析***，其特征在于，当所述数据处理模块的所述计算机应用程序被所述处理器执行时，使所述处理器基于所述第二悬浮液的所述光散射信号和所述荧光信号将所述血液样本中的白细胞区分为白细胞的亚群，优选的，所述将所述血液样本中的白细胞区分为白细胞的亚群包括：
    区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞；或
    区分嗜碱性粒细胞。
  21. 如权利要求13所述的血液分析***，其特征在于，
    进一步包括对所述第二悬浮液进行所述血液样本的白细胞计数；或
    识别所述第二悬浮液中的有核红细胞或未成熟白细胞。
  22. 一种测定血液样本中血小板浓度的方法，包括：
    将所述血液样本的第一份与稀释液混合，形成第一悬浮液；
    将所述血液样本的第二份与溶血剂混合以溶解红细胞，形成第二悬浮液；
    测量所述第一悬浮液流过小孔的直流阻抗信号；
    测量所述第二悬浮液流过光学流动室的前向光散射信号与侧向光散射信号或中角度光散射信号；
    分析所述第一悬浮液的所述直流阻抗信号以获取第一血小板分布；
    分析所述第二悬浮液的所述前向光散射信号与所述侧向光散射信号或所述中角度光散射信号以区分血小板和白细胞并获取第二血小板分布；以及
    基于所述第一血小板分布和所述第二血小板分布确定所述血液样本的血小板浓度。
  23. 如权利要求22所述的方法，其特征在于，所述区分血小板和白细胞包括在由所述第二悬浮液所得的前向光散射信号和侧向光散射信号散点图或前向光散射信号和中角度光散射信号散点图中区分血小板区域和白细胞区域。
  24. 如权利要求23所述的方法，其特征在于，所述第一血小板分布为由所述第一悬浮液所得的血小板直流阻抗直方图。
  25. 如权利要求24所述的方法，其特征在于，所述第二血小板分布为利用所述血小板区域的血小板的光散射信号生成的衍生血小板体积直方图。
  26. 如权利要求25所述的方法，其特征在于，所述确定所述血液样本的血小板浓度的步骤包括用由所述第一悬浮液所得的所述血小板直流阻抗直方图和由所述第二悬浮液所得的所述衍生血小板体积直方图生成融合血小板直方图，基于所述融合血小板直方图获取血小板浓度。
  27. 如权利要求24所述的方法，其特征在于，所述第二血小板分布是由所述第二悬浮液所得的所述前向光散射信号和侧向光散射信号散点图或所述前向光散射信号和中角度光散射信号散点图中一血小板区域中血小板的二维分布。
  28. 如权利要求27所述的方法，其特征在于，所述确定所述血液样本的血小板浓度的步骤包括：
    确定所述第一悬浮液的所述血小板直流阻抗直方图的血小板谷峰比；
    确定由所述第二悬浮液所得的所述前向光散射信号和侧向光散射信号散点图或所述前向光散射信号和中角度光散射信号散点图的所述血小板区域中一指定区域的事件数；
    基于所述血小板谷峰比和所述指定区域的事件数确定所述血小板直流阻抗直方图中血小板与红细胞之间波谷的衍生分隔阈值；以及
    利用所述衍生分隔阈值区分所述血小板直流阻抗直方图中的血小板与红细胞，获取所述血液样本的血小板浓度。
  29. 如权利要求22所述的方法，其特征在于，所述确定所述血液样本的血小板浓度的步骤包括：基于 所述第一血小板分布和所述第二血小板分布生成第三血小板分布，基于所述第三血小板分布获取血小板浓度。
  30. 如权利要求22所述的方法，其特征在于，进一步包括基于所述第二悬浮液的所述光散射信号将所述血液样本中的白细胞区分为白细胞的亚群。
  31. 如权利要求30所述的方法，其特征在于，所述将所述血液样本中的白细胞区分为白细胞的亚群包括：
    区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞；或
    区分嗜碱性粒细胞；或
    对所述第二悬浮液进行所述血液样本的白细胞计数。
  32. 一种用于测定血液样本中血小板浓度的血液分析***，包括：
    第一模块，所述第一模块包括第一混合室和直流阻抗检测器，所述第一混合室用于将所述血液样本的第一份与稀释液混合以形成第一悬浮液，所述直流阻抗检测器被装配于流通路径的小孔，所述流通路径与所述第一混合室相连通，所述直流阻抗检测器用于检测所述第一悬浮液通过所述小孔的直流阻抗信号；
    第二模块，包括第二混合室、光源及至少一光学检测器，所述第二混合室用于将所述血液样本的第二份与溶血剂混合、溶解红细胞以形成第二悬浮液，所述光源用于将光束对准与所述第二混合室相连通的光学流动室的检测孔，所述至少一光学检测器被装配于所述光学流动室用于检测通过所述光学流动室的所述检测孔的所述第二悬浮液的前向光散射信号和侧向光散射信号或中角度光散射信号；以及
    数据处理模块，与所述第一模块的所述直流阻抗检测器和所述第二模块中的所述至少一光学检测器分别可操作地连接，所述数据处理模块包括处理器和编程有计算机应用程序的非暂时性计算机可读存储介质，当所述计算机应用程序被所述处理器执行时，使所述处理器基于所述第一悬浮液的所述直流阻抗信号生成第一血小板分布，基于所述第二悬浮液的所述光散射信号和所述侧向光散射信号或所述中角度光散射信号区分血小板与白细胞、生成第二血小板分布，基于所述第一血小板分布和所述第二血小板分布确定所述血液样本的血小板浓度。
  33. 如权利要求32所述的血液分析***，其特征在于，所述第一血小板分布为由所述第一悬浮液所得的血小板直流阻抗直方图。
  34. 如权利要求33所述的血液分析***，其特征在于，所述第二血小板分布为基于所述第二悬浮液所得的前向光散射信号和侧向光散射信号散点图或前向光散射信号和中角度光散射信号散点图中一血小板区域的血小板的光散射信号生成的衍生血小板体积直方图。
  35. 如权利要求34所述的血液分析***，其特征在于，当所述数据处理模块的所述计算机应用程序被所述处理器执行时，使所述处理器用由所述第一悬浮液所得的血小板直流阻抗直方图和由所述第二悬浮液所得的所述衍生血小板体积直方图生成融合血小板直方图，基于所述融合血小板直方图获取血小板浓度。
  36. 如权利要求33所述的血液分析***，其特征在于，所述第二血小板分布是由所述第二悬浮液所得 的前向光散射信号和侧向光散射信号散点图或前向光散射信号和中角度光散射信号散点图中一血小板区域中血小板的二维分布。
  37. 如权利要求36所述的血液分析***，其特征在于，当所述数据处理模块的所述计算机应用程序被所述处理器执行时，使所述处理器
    确定所述第一悬浮液的所述血小板直流阻抗直方图的血小板谷峰比；
    确定由所述第二悬浮液所得的所述前向光散射信号和侧向光散射信号散点图或所述前向光散射信号和中角度光散射信号散点图的所述血小板区域中一指定区域的事件数；
    基于所述血小板谷峰比和所述指定区域的事件数确定所述血小板直流阻抗直方图中血小板与红细胞之间波谷的衍生分隔阈值；以及
    利用所述衍生分隔阈值区分所述血小板直流阻抗直方图中的血小板与红细胞，获取所述血液样本的血小板浓度。
  38. 如权利要求32所述的血液分析***，其特征在于，所述确定所述血液样本的血小板浓度的步骤包括：基于所述第一血小板分布和所述第二血小板分布生成第三血小板分布，基于所述第三血小板分布获取血小板浓度。
  39. 如权利要求32所述的血液分析***，其特征在于，当所述数据处理模块的所述计算机应用程序被所述处理器执行时，使所述处理器基于所述第二悬浮液的所述光散射信号将所述血液样本中的白细胞区分为白细胞的亚群。
  40. 如权利要求32所述的血液分析***，其特征在于，当所述数据处理模块的所述计算机应用程序被所述处理器执行时，使所述处理器基于所述第二悬浮液的所述光散射信号将所述血液样本中的白细胞区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞或区分嗜碱性粒细胞；或者
    对所述第二悬浮液进行所述血液样本的白细胞计数。
  41. 一种分析血液样本的方法，包括：
    获取所述血液样本的第一悬浮液的直流阻抗信号；
    获取所述血液样本的第二悬浮液的至少两种光学信号，所述至少两种光学信号包括前向光散射信号和用于提供细胞内容物信息的第一光学信号；
    基于所述直流阻抗信号获取第一血小板分布；
    基于所述至少两种光学信号获取第二血小板分布；及
    基于所述第一血小板分布和所述第二血小板分布获取第三血小板分布；
    所述第一悬浮液由所述血液样本的第一份与稀释液混合后形成，所述第二悬浮液由所述血液样本的第二份与一处理试剂混合后形成，所述处理试剂包括溶血剂以溶解所述血液样本的第二份中的红细胞。
  42. 如权利要求41所述的分析血液样本的方法，其特征在于，进一步包括：输出所述第一血小板分布、 第二血小板分布和第三血小板分布中的至少一种。
  43. 如权利要求41所述的分析血液样本的方法，其特征在于，所述获取第二血小板分布的步骤包括：
    基于所述至少两种光学信号得到所述第二悬浮液的散点图；及
    在所述散点图中将血小板与白细胞和/或有核红细胞区分开，得到血小板区域。
  44. 如权利要求43所述的分析血液样本的方法，其特征在于，所述获取第二血小板分布的步骤还包括：基于所述血小板区域的散射光学信号获取所述血液样本的衍生血小板体积直方图。
  45. 如权利要求44所述的分析血液样本的方法，其特征在于，所述第一血小板分布包括所述血液样本的血小板直流阻抗直方图；所述获取第三血小板分布的步骤包括：基于所述血小板直流阻抗直方图和所述衍生血小板体积直方图，根据一预设条件获取融合血小板直方图。
  46. 如权利要求45所述的分析血液样本的方法，其特征在于，所述获取第三血小板分布的步骤还包括：基于所述融合血小板直方图获取所述血液样本的血小板分析数据，所述血小板分析数据选自血小板计数、平均血小板体积及血小板体积分布宽度中的一种或几种。
  47. 如权利要求46所述的分析血液样本的方法，其特征在于，进一步包括：显示所述血小板区域、所述衍生血小板体积直方图、所述血小板直流阻抗直方图和所述衍生血小板体积直方图的叠加图、所述融合血小板直方图、所述血小板直流阻抗直方图和所述融合血小板直方图的叠加图、所述血小板分析数据中的至少一种。
  48. 如权利要求43所述的分析血液样本的方法，其特征在于，所述获取第二血小板分布的步骤还包括：获取所述血小板区域中一指定区域的事件数。
  49. 如权利要求48所述的分析血液样本的方法，其特征在于，所述第一血小板分布包括所述血液样本的血小板直流阻抗直方图；所述获取第三血小板分布的步骤包括：
    确定所述血小板直流阻抗直方图的血小板谷峰比；及
    基于所述血小板谷峰比和所述指定区域的事件数确一衍生分隔阈值，在所述血小板直流阻抗直方图中利用所述衍生分隔阈值区分血小板与红细胞，获取所述血液样本的血小板分析数据，所述血小板分析数据选自血小板计数、平均血小板体积及血小板分布宽度中的一种或几种。
  50. 如权利要求49所述的分析血液样本的方法，其特征在于，进一步包括：显示所述血小板区域、所述指定区域、所述血小板谷峰比、包括所述衍生分隔阈值的所述血小板直流阻抗直方图、所述血小板分析数据中的至少一种。
  51. 如权利要求41-50中任一项所述的分析血液样本的方法，其特征在于，所述第一光学信号选自侧向光散射信号和中角度光散射信号中的至少一种。
  52. 如权利要求41-50任一项所述的分析血液样本的方法，其特征在于，所述处理试剂还包括用于染色白细胞的荧光染料，所述第一光学信号选自荧光信号、侧向光散射信号和中角度光散射信号中的至少一种。
  53. 如权利要求51或52所述的分析血液样本的方法，其特征在于，进一步包括：基于所述第二悬浮液 的所述至少两种光学信号将所述血液样本中的白细胞区分为白细胞的亚群。
  54. 如权利要求51或52所述的分析血液样本的方法，其特征在于，基于所述第二悬浮液的所述至少两种光学信号将所述血液样本中的白细胞区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞或区分嗜碱性粒细胞；或者
    对所述第二悬浮液进行所述血液样本的白细胞计数；或者
    识别所述第二悬浮液中的有核红细胞或未成熟白细胞。
  55. 一种非易失性计算机可读存储介质，其上存储有计算机应用程序，其特征在于：所述计算机应用程序被处理器执行时实现如权利要求41-54中任一项所述的分析血液样本的方法的步骤。
  56. 一种血液分析***，包括：
    第一模块，所述第一模块包括第一混合室和直流阻抗检测器，所述第一混合室用于将所述血液样本的第一份与稀释液混合以形成第一悬浮液，所述直流阻抗检测器被装配于流通路径的小孔，所述流通路径与所述第一混合室相连通，所述直流阻抗检测器用于检测所述第一悬浮液通过所述小孔的直流阻抗信号；
    第二模块，所述第二模块包括第二混合室、光源及至少一光学检测器，所述第二混合室用于将所述血液样本的第二份与一处理试剂混合以形成第二悬浮液，所述处理试剂包括溶血剂以溶解所述血液样本的第二份中的红细胞，所述光源用于将光束对准与所述第二混合室相连通的光学流动室的检测孔，所述至少一光学检测器被装配于所述光学流动室用于检测通过所述光学流动室的所述检测孔的所述第二悬浮液的至少两种光学信号，所述至少两种光学信号包括前向光散射信号和用于提供细胞内容物信息的第一光学信号；以及
    数据处理模块，与所述第一模块的所述直流阻抗检测器和所述第二模块中的所述至少一光学检测器分别可操作地连接，所述数据处理模块包括处理器和编程有计算机应用程序的非暂时性计算机可读存储介质，当所述计算机应用程序被所述处理器执行时，所述数据处理模块基于所述直流阻抗信号获取第一血小板分布，基于所述至少两种光学信号获取第二血小板分布，基于所述第一血小板分布和所述第二血小板分布获取所述血液样本的第三血小板分布。
  57. 如权利要求56所述的血液分析***，其特征在于，进一步包括用户界面，所述用户界面可操作地与所述数据处理模块相连并用于输出所述第一血小板分布、第二血小板分布和第三血小板分布中的至少一种。
  58. 如权利要求56所述的血液分析***，其特征在于，所述数据处理模块获取第二血小板分布的步骤包括：
    基于所述至少两种光学信号得到所述第二悬浮液的散点图；及
    在所述散点图中将血小板与白细胞区分开，得到血小板区域。
  59. 如权利要求58所述的血液分析***，其特征在于，所述数据处理模块获取第二血小板分布的步骤还包括：基于所述血小板区域的所述至少两种光学信号获取所述血液样本的衍生血小板体积直方图。
  60. 如权利要求59所述的血液分析***，其特征在于，所述第一血小板分布包括所述血液样本的血小板直流阻抗直方图；所述数据处理模块获取第三血小板分布的步骤包括：基于所述血小板直流阻抗直方图和所述衍生血小板体积直方图，根据一预设条件获取融合血小板直方图。
  61. 如权利要求60所述的血液分析***，其特征在于，所述数据处理模块获取第三血小板分布的步骤还包括：基于所述融合血小板直方图获取所述血液样本的血小板分析数据，所述血小板分析数据选自血小板计数、平均血小板体积及血小板体积分布宽度中的一种或几种。
  62. 如权利要求61所述的血液分析***，其特征在于，进一步包括用户界面，所述用户界面可操作地与所述数据处理模块相连并用于显示所述血小板区域、所述衍生血小板体积直方图、所述血小板直流阻抗直方图和所述衍生血小板体积直方图的叠加图、所述融合血小板直方图、所述血小板直流阻抗直方图和所述融合血小板直方图的叠加图、所述血小板分析数据中的至少一种。
  63. 如权利要求58所述的血液分析***，其特征在于，所述获取第二血小板分布的步骤还包括：获取所述血小板区域中一指定区域的事件数。
  64. 如权利要求63所述的血液分析***，其特征在于，所述第一血小板分布包括所述血液样本的血小板直流阻抗直方图；所述获取第三血小板分布的步骤包括：
    确定所述血小板直流阻抗直方图的血小板谷峰比；及
    基于所述血小板谷峰比和所述指定区域的事件数确一衍生分隔阈值，在所述血小板直流阻抗直方图中利用所述衍生分隔阈值区分血小板与红细胞，获取所述血液样本的血小板分析数据，所述血小板分析数据选自血小板计数、平均血小板体积及血小板体积分布宽度中的一种或几种。
  65. 如权利要求64所述的血液分析***，其特征在于，进一步包括用户界面，所述用户界面可操作地与所述数据处理模块相连并用于显示所述血小板区域、所述指定区域、所述血小板谷峰比、包括所述衍生分隔阈值的所述血小板直流阻抗直方图、所述血小板分析数据中的至少一种。
  66. 如权利要求56-65中任一项所述的血液分析***，其特征在于，所述第一光学信号选自侧向光散射信号和中角度光散射信号中的至少一种。
  67. 如权利要求56-65任一项所述的血液分析***，其特征在于，所述处理试剂还包括用于染色白细胞的荧光染料，所述第一光学信号选自荧光信号、侧向光散射信号和中角度光散射信号中的至少一种。
  68. 如权利要求67所述的血液分析***，其特征在于，进一步包括：基于所述第二悬浮液的所述至少两种光学信号将所述血液样本中的白细胞区分为白细胞的亚群，优选的，所述将所述血液样本中的白细胞区分为白细胞的亚群的步骤包括：
    区分单核细胞、淋巴细胞、中性粒细胞和嗜酸性粒细胞；或区分嗜碱性粒细胞。
  69. 如权利要求68所述的血液分析***，其特征在于，
    进一步包括对所述第二悬浮液进行所述血液样本的白细胞计数；或识别所述第二悬浮液中的有核红细胞或未成熟白细胞。
PCT/CN2019/084687 2018-04-28 2019-04-26 测定血小板浓度的方法及*** WO2019206313A1 (zh)

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