WO2020133257A1 - Procédé de traitement de valeur de détection d'un objet en cours de mesure, analyseur d'hémocytes et support d'informations - Google Patents

Procédé de traitement de valeur de détection d'un objet en cours de mesure, analyseur d'hémocytes et support d'informations Download PDF

Info

Publication number
WO2020133257A1
WO2020133257A1 PCT/CN2018/125003 CN2018125003W WO2020133257A1 WO 2020133257 A1 WO2020133257 A1 WO 2020133257A1 CN 2018125003 W CN2018125003 W CN 2018125003W WO 2020133257 A1 WO2020133257 A1 WO 2020133257A1
Authority
WO
WIPO (PCT)
Prior art keywords
measurement object
type information
temperature
volume distribution
temperature correction
Prior art date
Application number
PCT/CN2018/125003
Other languages
English (en)
Chinese (zh)
Inventor
叶波
郑文波
王官振
陈鹏震
Original Assignee
深圳迈瑞生物医疗电子股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳迈瑞生物医疗电子股份有限公司 filed Critical 深圳迈瑞生物医疗电子股份有限公司
Priority to CN201880097223.0A priority Critical patent/CN112654857B/zh
Priority to PCT/CN2018/125003 priority patent/WO2020133257A1/fr
Publication of WO2020133257A1 publication Critical patent/WO2020133257A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G01N21/49—Scattering, i.e. diffuse reflection within a body or fluid
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N35/00—Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor

Definitions

  • the embodiments of the present application relate to the field of blood analysis, and in particular, to a method for processing a detection value of a measurement object, a blood cell analyzer, and a storage medium.
  • the measurement object of the hematology analyzer is not only fresh blood, but also quality control and calibrators.
  • the quality control substance is a substance with a clear content in the same matrix as the actual specimen used in clinical medical testing.
  • the quality control substance is used as a substance for checking the analysis process or instrument accuracy in routine work.
  • the quality control of the laboratory plays a very important role.
  • Calibrator In order to ensure that the test results of the hematology analyzer are accurate, reliable, and traceable, the "Calibration Guide for Hematology Analysis" requires the use of calibrators recommended by the manufacturer or fresh blood provided by the calibration laboratory. However, although the quality control and calibrators are simulants of fresh blood, there are still differences in particle properties.
  • the reliability of medical test results directly affects the quality of medical care, of which temperature is an important factor affecting test results.
  • the normal operating temperature of the blood cell analyzer is generally within the range of 18-25 degrees Celsius, but due to the actual conditions, the blood cell analyzer may not work within the normal temperature range, thereby affecting the test results.
  • a temperature control system is currently used. However, on the one hand, the temperature control system will increase the cost, and on the other hand, it will add extra time to maintain the constant temperature of the diluent and reduce the measurement speed.
  • the embodiments of the present application are expected to provide a method for processing the detection value of the measurement object, a hematology analyzer, and a storage medium, which can simply improve the accuracy of the detection results of different types of test samples by the blood analyzer In particular, it can simply reduce the influence of temperature on the detection result of the blood cell analyzer, so as to avoid the quality control of the blood analyzer or the accuracy of the calibration process from being impaired or even invalid.
  • the first aspect of the present application relates to a method of correcting a parameter of a measurement object, the method including:
  • the detection value of the measurement object is processed according to the type information of the measurement object.
  • the type information of the measurement object includes a blood sample, a quality control substance, or a calibrator.
  • the step of acquiring the type information of the measurement object may include:
  • the step of acquiring the type information of the measurement object may also include:
  • the step of acquiring the detection value of the measurement object may include:
  • the step of processing the detection value of the measurement object according to the type information of the measurement object includes:
  • At least two optical signals are subjected to gain amplification processing.
  • the method before the step of processing the at least two optical signals according to the classification algorithm related to the type information of the measurement object, the method may further include:
  • the classification algorithm is obtained from a pre-stored classification algorithm database or through RFID or the Internet according to the type information of the measurement object.
  • the step of acquiring the detection value of the measurement object may further include:
  • the step of processing the detection value of the measurement object according to the type information of the measurement object includes:
  • a temperature correction algorithm related to the type information of the measurement object is used to correct the volume distribution information of the measurement object.
  • the method may further include:
  • the temperature correction algorithm may be a temperature correction curve; wherein, the temperature correction curve may be a fitting curve of the volume distribution information of the measurement object with respect to temperature, especially a piecewise simulation
  • the combined curve may be, for example, a first-fit curve or a second-fit curve, especially a piecewise linear function.
  • the temperature correction algorithm corresponding to the blood sample is a negative correlation function between the volume distribution information of the blood sample and the temperature
  • the temperature correction algorithm corresponding to the quality control substance or calibrator is The positive correlation function between the volume distribution information of the quality control or calibrator and the temperature.
  • the method may further include:
  • the volume distribution information may include an average cell volume and a standard deviation of the cell distribution width.
  • the method may further include:
  • the method may further include:
  • the temperature correction algorithm is also output.
  • the method may further include marking the corrected volume distribution information and the other parameter information.
  • the second aspect of the present application relates to a blood cell analyzer, wherein the blood cell analyzer includes:
  • a pre-processing device configured to pre-process the measurement object to obtain the pre-processed measurement object
  • a detection device configured to detect the pre-processed measurement object
  • the detection value of the measurement object is processed according to the type information of the measurement object.
  • the type information of the measurement object may include a blood sample, a quality control substance, or a calibrator.
  • the processor is configured to perform the following steps when performing the step of acquiring the type information of the measurement object:
  • the processor is configured to, when performing the step of acquiring the type information of the measurement object, also obtain the type information of the measurement object according to a preset measurement mode.
  • the processor may be configured to acquire forward scattered light of particles in the measurement object when performing the step of acquiring the detection value of the measurement object At least two optical signals among a signal, a side scattered light signal, and a fluorescent signal; and when performing the step of processing the detection value of the measurement object according to the type information of the measurement object, according to the measurement
  • the classification algorithm related to the type information of the object processes the at least two optical signals to classify and count the particles in the measurement object, and/or the at least two optical signals according to the type information of the measurement object
  • the fluorescence signal is gain-amplified.
  • the processor may be further configured to process the at least two optical signals according to the classification algorithm before processing the at least two optical signals according to the classification information related to the type information of the measurement object.
  • the type information of the measurement object is obtained from a pre-stored classification algorithm database or through RFID or the Internet.
  • the blood cell analyzer may further include a sensing device configured to detect the temperature of the diluent where the measurement object is located;
  • the processor may be configured to: acquire the volume distribution information of the measurement object suspended in the dilution liquid and the temperature of the dilution liquid when performing the step of acquiring the measurement value of the measurement object;
  • the temperature distribution algorithm related to the type information of the measurement object is used to adjust the volume distribution information Make corrections.
  • the processor may be configured to use the temperature correction algorithm related to the type information of the measurement object according to the temperature of the dilution Before the volume distribution information is corrected, the temperature correction algorithm is obtained from a pre-stored temperature correction algorithm database or through RFID or the Internet according to the type information of the measurement object.
  • the temperature correction algorithm may be a temperature correction curve
  • the temperature correction curve is a fitting curve of the volume distribution information of the measurement object relative to the temperature, especially a segment
  • the fitting curve may be, for example, a first-fitting curve or a second-fitting curve, especially a piecewise linear function.
  • the temperature correction algorithm corresponding to the blood sample is a negative correlation function between the volume distribution information of the blood sample and the temperature
  • the temperature correction algorithm corresponding to the quality control substance or calibrator It is a positive correlation function between the volume distribution information of the quality control substance or the calibration substance and the temperature.
  • the processor may be further configured to calculate other parameter information of the measurement object related to the volume distribution information according to the corrected volume distribution information.
  • the processor may be further configured to output the volume distribution information before correction, the volume distribution information after correction, and the other parameter information and optionally output the temperature A correction curve; and marking the corrected volume distribution information and the other parameter information.
  • a third aspect of the present application relates to a computer-readable storage medium on which a program is stored, which when executed by a processor implements the steps of the above-described method of processing a parameter of a measurement object of the first aspect of the present application.
  • the method and blood cell analyzer of the present application process the detection value by using a processing algorithm related to the type of measurement object, which can simply improve the accuracy of the test results of the blood analyzer on different types of test samples, especially Simply reduce the influence of temperature on the detection results of the hematology analyzer, so that the quality control of the hematology analyzer or the accuracy of the calibration process can be prevented from being impaired or even invalid.
  • FIG. 1 is a schematic flowchart 1 of a method for processing a detection value of a measurement object according to an embodiment of the present application
  • FIG. 2 is a schematic flowchart 2 of a method for processing a detection value of a measurement object proposed by an embodiment of the present application
  • 3A to 3C are comparison charts of scatter plots of different measurement objects and reference scatter plots
  • FIG. 4A is a fluorescence-forward scattered light scatter diagram of a quality control substance
  • 4B is a fluorescence-forward scattered light scattergram of a blood sample
  • 4C is a scatter diagram of the fluorescence-forward scattered light of the quality control substance after the fluorescence signal of the quality control substance is amplified;
  • FIG. 5A is a fluorescence-forward scattered light scatter diagram of the divided particle group of the quality control substance
  • FIG. 5B is a fluorescence-forward scattered light scatter diagram of a blood sample divided into particle clusters
  • 6A is a schematic diagram of the relative deviation of the red blood cell MCV in the quality control with temperature change
  • 6B is a schematic diagram of the relative deviation of red blood cell RDW-SD in temperature control with temperature change
  • 7A is a schematic diagram of the relative deviation of the red blood cell MCV in the calibration with temperature change
  • 7B is a schematic diagram of the relative deviation of the red blood cell RDW-SD in the calibration with temperature change
  • 8A is a schematic diagram of the relative deviation of red blood cell MCV in blood samples with temperature changes
  • 8B is a schematic diagram of the relative deviation of red blood cell RDW-SD in blood samples with temperature changes
  • 9A is a schematic diagram of the absolute deviation of the red blood cell MCV with the temperature change in the quality control and the corresponding one-time fitting temperature correction curve proposed by the embodiment of the present application;
  • FIG. 9B is a schematic diagram of the relative deviation of the red blood cell MCV in the quality control before and after temperature correction using the first-fit curve shown in FIG. 9A;
  • 9C is a schematic diagram of the absolute deviation of the red blood cell RDW-SD in the quality control with temperature and the corresponding one-time temperature correction curve proposed by the embodiment of the present application;
  • FIG. 9D is a schematic diagram of the relative deviation of the red blood cell RDW-SD in the quality control before and after temperature correction using the first-fit curve shown in FIG. 9C;
  • 10A is a schematic diagram of the absolute deviation of the red blood cell MCV with temperature change in the calibration and the corresponding one-time fitting temperature correction curve proposed by the embodiment of the present application;
  • FIG. 10B is a schematic diagram of the relative deviation of the red blood cell MCV in the calibration before and after temperature correction using the first-fit curve shown in FIG. 10A;
  • 10C is a schematic diagram of the absolute deviation of the red blood cell RDW-SD in the calibration with temperature change and the corresponding one-time temperature correction curve proposed by the embodiment of the present application;
  • FIG. 10D is a schematic diagram of the relative deviation of red blood cell RDW-SD in the calibration before and after temperature correction using the first-fit curve shown in FIG. 10C;
  • 11A is a schematic diagram of the absolute deviation of the red blood cell MCV with temperature change in the blood sample and the corresponding one-time temperature correction curve proposed by the embodiment of the present application;
  • FIG. 11B is a schematic diagram of the relative deviation of the red blood cell MCV in the blood sample before and after temperature correction using the first-fit curve shown in FIG. 11A;
  • 11C is a schematic diagram of the absolute deviation of erythrocyte RDW-SD with temperature change in a blood sample and the corresponding one-time temperature correction curve proposed by the embodiment of the present application;
  • FIG. 11D is a schematic diagram of the relative deviation of the red blood cell RDW-SD in the blood sample before and after temperature correction using the one-time fitting curve shown in FIG. 11C;
  • 12A is a schematic diagram of the absolute deviation of the red blood cell MCV with temperature change in the quality control and the corresponding second-fit temperature correction curve proposed by the embodiment of the present application;
  • FIG. 12B is a schematic diagram of the relative deviation of the red blood cell MCV in the quality control before and after temperature correction using the quadratic fitting curve shown in FIG. 12A;
  • 12C is a schematic diagram of the absolute deviation of erythrocyte RDW-SD with temperature change in the quality control and the corresponding second-fit temperature correction curve proposed by the embodiment of the present application;
  • FIG. 12D is a schematic diagram of the relative deviation of red blood cell RDW-SD in the quality control before and after temperature correction using the quadratic fitting curve shown in FIG. 12C;
  • 13A is a schematic diagram of the absolute deviation of the red blood cell MCV with temperature change in the calibration and the corresponding second-fit temperature correction curve proposed by the embodiment of the present application;
  • FIG. 13B is a schematic diagram of the relative deviation of the red blood cell MCV in the calibration before and after temperature correction using the quadratic fitting curve shown in FIG. 13A;
  • 13C is a schematic diagram of the absolute deviation of the red blood cell RDW-SD in the calibration with temperature change and the corresponding second-fit temperature correction curve proposed by the embodiment of the present application;
  • FIG. 13D is a schematic diagram of the relative deviation of the red blood cell RDW-SD in the calibration before and after temperature correction using the quadratic fitting curve shown in FIG. 13C;
  • 14A is a schematic diagram of the absolute deviation of the red blood cell MCV with temperature change in the blood sample and the corresponding second-fit temperature correction curve proposed by the embodiment of the present application;
  • 14B is a schematic diagram of the relative deviation of the red blood cell MCV in the blood sample before and after temperature correction using the quadratic fitting curve shown in FIG. 14A;
  • 14C is a schematic diagram of the absolute deviation of red blood cell RDW-SD with temperature change in a blood sample and the corresponding second-fit temperature correction curve proposed in the embodiment of the present application;
  • 14D is a schematic diagram of the relative deviation of the red blood cell RDW-SD in the blood sample before and after temperature correction using the quadratic fitting curve shown in FIG. 14D;
  • 15 is a schematic flowchart 3 of a method for processing a detection value of a measurement object according to an embodiment of the present application
  • FIG. 16 is a schematic flowchart 4 of a method for processing a detection value of a measurement object according to an embodiment of the present application.
  • FIG. 17 is a schematic structural diagram of a blood cell analyzer according to an embodiment of the present application.
  • FIG. 1 is a schematic flowchart 1 of the method according to the present application. As shown in Figure 1, the method includes the following steps:
  • Step 101 Acquire the type information of the measurement object.
  • the measurement object may be a blood sample, a quality control substance, or a calibrator.
  • the control or calibrator is a simulant of a blood sample, its particle properties are not exactly the same. For example, different temperatures have different effects on the cell volume measurement of blood samples, control and calibrators. , So when analyzing the detection value of the measurement object, you need to obtain the type information of the measurement object first.
  • the step 101 of acquiring type information of the measurement object may include the following steps:
  • the preset scatter plot may include at least two of a quality control scatter plot, a calibrator scatter plot, and a blood scatter plot.
  • the preset scatterplot includes a quality control scatterplot and a calibration scatterplot
  • the measurement scatterplot is similar to the quality control or calibrator scatterplot
  • the measurement target is determined to be quality control Objects or calibrators, otherwise the measurement object is determined to be a blood sample.
  • FIGS. 3A to 3C are comparison diagrams of scatter plots of different measurement objects and reference scatter diagrams, wherein FIG. 3A is a scatter diagram and a preset scatter diagram when the measurement object is a quality control substance , That is, the comparison chart of the reference scattergram;
  • FIG. 3A is a scatter diagram and a preset scatter diagram when the measurement object is a quality control substance , That is, the comparison chart of the reference scattergram;
  • FIG. 3A is a scatter diagram and a preset scatter diagram when the measurement object is a quality control substance , That is, the comparison chart of
  • FIG. 3B is a comparison chart of the scattergram when the measurement object is a calibrator and a preset scattergram, that is, the reference scattergram;
  • FIG. 3C is the scatter diagram when the measurement object is a blood sample
  • the degree of similarity between the scatter diagram of the measurement object and the predetermined scatter diagram may be the difference/correlation coefficient/mutual information/edge envelope information of the scatter diagram of the scatter diagram. Since the scatter diagram of the blood sample has various shapes, and the quality control substance and the calibration substance are relatively stable, the preset scatter plot preferably includes the quality control substance scatter plot and the calibration substance scatter plot.
  • the type information of the measurement object can also be obtained by setting different measurement modes.
  • the measurement mode may be a blood measurement mode or a quality control/calibration measurement mode.
  • the measurement mode is set first, thereby directly determining the type information of the measurement object.
  • Step 102 Obtain the detection value of the measurement object.
  • the above detection value may include at least two kinds of light of the forward scattered light signal, the side scattered light signal, and the fluorescent signal of the particles in the measurement object treated with the hemolytic agent and the fluorescent dye signal.
  • the particles in the measurement object are first subjected to pretreatment, such as hemolysis treatment and fluorescent staining treatment.
  • the measurement object is mixed with a reagent having a fluorescent dye and a hemolytic agent at a certain ratio to form a processed measurement object.
  • a hemolytic agent is used to dissolve the red blood cells in the measurement object so that it does not interfere with the counting of white blood cells and other particles.
  • the fluorescent dye is combined with the nucleic acid of the white blood cells to label the cells. Due to the different ability of various types of cells to bind fluorescent dyes, different Fluorescence information. In addition, different forward scattered light information will be generated due to the different size of various types of cells, and different side scattered light information due to the different morphology or complexity of the cells.
  • the above detection value may further include volume distribution information of the particles in the measurement object, especially the volume distribution information of the red blood cell particles and the temperature of the diluent where the measurement object is located.
  • the electrical impedance method is used to detect the measurement object in the diluent, thereby obtaining volume distribution information of the red blood cell particles of the measurement object suspended in the diluent.
  • the volume distribution information may include the average cell volume and the standard deviation of the cell distribution width.
  • the volume distribution information may be mean red blood cell volume (Mean Corpuscular Volume, MCV) and red blood cell distribution width standard deviation (Red Blood Cell Distribution Distribution Width Standard, Deviation, RDW-SD).
  • the analyzer When analyzing and measuring the measurement object suspended in the diluent, the analyzer also needs to detect the temperature of the diluent to determine how to process the volume distribution parameter of the particles in the measurement object according to the temperature of the diluent.
  • Step 103 Process the detection value of the measurement object according to the type information of the measurement object.
  • step 103 may include: processing the at least two optical signals according to a classification algorithm related to the type information of the measurement object to classify and count particles in the measurement object, and/or based on the type information of the measurement object.
  • the at least two optical signals, especially fluorescent signals, are subjected to gain amplification processing.
  • FIG. 4A is a fluorescence-forward scattered light scatter diagram of the quality control
  • FIG. 4B is a fluorescence-forward scattered light scatter diagram of the blood sample. Due to the quality control or calibration The particles in the substance are fixed by the reagent, so the ability to absorb the fluorescent substance is weak, resulting in the signal of the quality control substance or the calibrator in the direction of the fluorescence being smaller than that of the blood sample, thus on the scatter diagram composed of the fluorescence-forward scattered light signal There is a difference in the location of the particle cluster.
  • FIG. 4C is a fluorescence-forward scattered light scatter diagram of the quality control substance after the fluorescence signal of the quality control substance is amplified by 80%.
  • FIG. 5A is the fluorescence-forward scattering light scatter diagram of the divided particles of the quality control substance
  • FIG. 5B is the fluorescence-forward scattering of the divided particles of the blood sample.
  • Light scatter diagram the basophilic particles (BASO particles) in the quality control are different from the BASO particles in the blood sample due to the process when preparing the quality control, which causes the BASO particles in the quality control to fluoresce-
  • the position of the forward scattered light scattergram is different from the BASO particles in the blood sample. Therefore, it is necessary to use a classification algorithm related to the type information of the measurement object to process the optical signal of the particles in the measurement object to accurately classify and count the particles in the measurement object. In other words, different classification algorithms are used for different types of measurement objects.
  • the type of the measurement object may be obtained first Information-related classification algorithm.
  • the above blood cell analyzer may obtain the classification algorithm from a pre-stored classification algorithm database or through radio frequency identification (Radio Frequency Identification, RFID) or the Internet according to the type information of the measurement object.
  • step 103 may further include: according to the diluent Temperature, the volume distribution information is corrected using a temperature correction algorithm related to the type information of the measurement object.
  • medical particle analyzers such as blood cell analyzers, for measuring blood cells, such as red blood cells
  • the specific detection process includes the following steps: When the particles suspended in the electrolyte pass the electrolyte, that is, the diluent, the equivalent resistance at the detection micropore will change when passing through the detection micropore, and a constant current source on both sides of the micropore Under the action of, the voltage on both sides of the detection microhole has changed, and this voltage change is collected by the circuit system to form a voltage pulse waveform.
  • the height of the pulse waveform reflects the volume of the cell and characterizes the volume information of the particles. Therefore, the instrument will provide the volume distribution of the detected particles accordingly, that is, provide the histogram of the volume distribution of the detected particles. By analyzing the cell volume histogram, you can sort and count cells. This measurement method is also called electrical impedance method.
  • the applicant has found through a lot of research and clinical experiments that there are obvious differences in cells obtained by measuring according to the Coulter principle at different temperatures. Moreover, the temperature has different effects on the cell volume measurement of fresh blood and quality control or calibrator. On the one hand, this will affect the accuracy of the normal blood sample measurement, on the other hand, it will also lead to impaired or even inaccurate quality control or calibration procedures. For example, an increase in temperature leads to a decrease in the volume of the quality control test, which exceeds the quality control Range, but there is no problem with the actual instrument, which is actually a false positive.
  • the hematology analyzer can use the The temperature correction algorithm related to the type information of the measurement object corrects the volume distribution information of the particles of the measurement object. Specifically, after acquiring the type information of the measurement object, a temperature correction algorithm corresponding to the type information of the measurement object may be acquired.
  • the temperature correction algorithm related to the type information of the measurement object to correct the volume distribution information of the measurement object according to the temperature of the diluent, it is possible to eliminate the detection results of different measurement objects by the temperature in a simple manner This can improve the accuracy of blood sample test results and prevent the quality control or calibration process from being impaired or even invalid.
  • the temperature correction algorithm corresponding to the type information of the measurement object may preferably be a temperature correction curve corresponding to the type information of the measurement object.
  • the blood cell analyzer when it corrects the volume distribution information of the measurement object using a temperature correction algorithm related to the type information of the measurement object according to the temperature of the diluent, it may first correct from the temperature The algorithm determines a temperature correction parameter corresponding to the temperature of the diluent, and then corrects the volume distribution information according to the temperature correction parameter.
  • a temperature correction curve related to the type information of the measurement objects should be used to correct the detection results of the measurement objects.
  • the corresponding temperature correction algorithm when the type of measurement object is quality control substance/calibrator, the corresponding temperature correction algorithm, especially the temperature correction curve is positively correlated with the temperature change; and when the type of measurement object is For blood samples, the corresponding temperature correction algorithm, especially the temperature correction curve, is inversely related to the temperature change.
  • the temperature correction algorithm corresponding to the blood sample, especially the temperature correction curve is a negative correlation function of the volume distribution information of the blood sample and the temperature; the temperature correction algorithm corresponding to the quality control substance/calibrator, especially the temperature correction curve It is the positive correlation function of the volume distribution information of the quality control substance/calibrator and temperature.
  • FIG. 6A is a schematic diagram of the relative deviation of the red blood cell MCV in the quality control with temperature
  • FIG. 6B is a schematic diagram of the relative deviation of the red blood cell RDW-SD in the quality control with temperature.
  • Both red blood cell MCV and RDW-SD decrease with increasing temperature. Therefore, the temperature correction curve corresponding to the quality control substance should be positively correlated with the temperature change.
  • 7A is a schematic diagram of the relative deviation of the red blood cell MCV in the calibrator with temperature
  • FIG. 7B is a schematic diagram of the relative deviation of the red blood cell RDW-SD in the calibrator with temperature.
  • the red blood cell MCV and RDW- in the calibrator SD decreases with increasing temperature.
  • Fig. 8A is a schematic diagram of the relative deviation of red blood cell MCV in blood sample with temperature change
  • Fig. 8B is a schematic diagram of the relative deviation of red blood cell RDW-SD in blood sample with temperature, and red blood cell MCV and RDW-SD in blood sample are related to temperature The increase increases, so the temperature correction curve corresponding to the blood sample should be positively and negatively related to the temperature change.
  • the "deviation" in this application refers to a temperature of 22°C as a reference standard. Those skilled in the art can use different reference standards according to the actual situation to obtain corresponding deviation values.
  • the corresponding temperature correction algorithm especially the temperature correction curve is also different. Therefore, when the blood cell analyzer obtains the temperature correction algorithm, it can First obtain the identification information corresponding to the diluent; wherein the identification information is used to determine the batch of the diluent; then the temperature correction algorithm is determined according to the type information of the measurement object and the batch of the diluent.
  • the blood cell analyzer corrects the volume distribution information of the measurement object by using a temperature correction algorithm related to the type information of the measurement object according to the temperature of the diluent .
  • the above secondary blood cell analyzer may obtain the temperature correction algorithm from a pre-stored temperature correction algorithm database or through RFID or the Internet according to the type information of the measurement object.
  • the temperature correction curve may preferably be a fitting curve of the volume distribution information of the measurement object with respect to temperature, especially a piecewise fitting curve, such as a one-time fitting Curve or quadratic fitting curve, especially piecewise linear function.
  • the type of measurement object, the type of volume distribution information and the temperature correction curve are in one-to-one correspondence, that is, for a measurement object and the measurement There is a corresponding temperature correction curve for the volume distribution information of the object.
  • FIGS. 9 to 11 an embodiment in which the volume distribution information of the particles to be measured, especially the red blood cell particles, is corrected using a first-fit curve, that is, a straight line will be described.
  • FIG. 9D is a schematic diagram of the relative deviation of red blood cell RDW-SD in the quality control before and after temperature correction using the first-fit curve shown in FIG. 9C
  • FIG. 10A is a schematic diagram of the absolute deviation of the red blood cell MCV with temperature change in the calibration and the corresponding first-
  • FIG. 10B is The relative deviation of the red blood cell MCV in the calibrator before and after temperature correction using the first-fit curve shown in FIG. 10A;
  • FIG. 10D is the relative deviation of the red blood cell RDW-SD in the calibration. Schematic diagram before and after correction.
  • FIG. 11B is The relative deviation of the red blood cell MCV in the blood sample before and after temperature correction using the one-time fitting curve shown in FIG. 11A;
  • FIG. 11D is the relative deviation of the red blood cell RDW-SD in the blood sample using the first-fit curve shown in FIG. 11C Schematic diagram before and after temperature correction.
  • FIG. 12B is a schematic diagram of the relative deviation of the red blood cell MCV in the quality control before and after temperature correction using the quadratic fitting curve shown in FIG. 12A;
  • FIG. 12B is a schematic diagram of the relative deviation of the red blood cell MCV in the quality control before and after temperature correction using the quadratic fitting curve shown in FIG. 12A;
  • FIG. 12A is a schematic diagram of the absolute deviation of the red blood cell MCV with temperature change in the
  • FIG. 12C is the red blood cell RDW-SD in the quality control with temperature
  • FIG. 12D is the Relative deviation of RDW-SD of red blood cells before and after temperature correction using the quadratic fitting curve shown in FIG. 12C.
  • FIG. 13B is a schematic diagram of the relative deviation of the red blood cell MCV in the calibrator before and after temperature correction using the quadratic fitting curve shown in FIG. 13A;
  • FIG. 13C is the red blood cell RDW-SD in the calibrator with temperature
  • FIG. 13D is the red blood cells in the calibration Schematic diagram of the relative deviation of RDW-SD before and after temperature correction using the quadratic fitting curve.
  • FIG. 14C is the variation of the red blood cell RDW-SD in the blood sample with temperature
  • FIG. 14D is the red blood cell RDW-SD in the blood sample
  • the relative deviation of is before and after the temperature correction using the quadratic fitting curve shown in FIG. 14C.
  • the blood cell analyzer can obtain more accurate measurement results for the volume distribution information of the particles of the quality control substance, the calibrator, and the blood sample.
  • FIG. 15 is a schematic flowchart 3 of a method for processing a detection value of a measurement object according to an embodiment of the present application. In the embodiment shown in FIG. 15, the method includes the following steps:
  • Step 201 Obtain the type information of the measurement object
  • Step 202 Obtain the volume distribution parameter of the particles of the measurement object suspended in the diluent and the temperature of the diluent;
  • Step 203 Determine whether the temperature of the diluent falls within a preset temperature range
  • step 203b When the temperature of the diluent falls within the preset temperature range, step 203b is implemented, that is, no temperature correction is performed;
  • step 203a is implemented, that is, the volume distribution information is corrected using a temperature correction algorithm related to the type information of the measurement object according to the temperature of the diluent.
  • the temperature of the diluent can also be used to determine whether to enable the temperature correction function. Specifically, after acquiring the temperature of the diluent, the blood cell analyzer can determine whether the temperature of the diluent falls within the preset temperature range, and if so, turn off the temperature correction function, otherwise perform temperature correction.
  • the above-mentioned preset temperature range is used to characterize the normal working temperature range of the above-mentioned blood cell analyzer, for example, the above-mentioned preset temperature range may be 18-25 degrees Celsius.
  • FIG. 16 is a schematic flowchart 4 of a method for processing a detection value of a measurement object proposed in an embodiment of the present application.
  • steps 301 to 303 are the same as steps 201 to 203 of the embodiment shown in FIG. 15 respectively, except that the method further includes step 303 after step 303: step 304, based on the revised volume Distribution information, calculating other parameter information of the measurement object related to the volume distribution information; and step 305, outputting the volume distribution information before correction, the volume distribution information after correction and other parameter information, preferably , You can also output the temperature correction algorithm.
  • the blood cell analyzer performs temperature correction on the volume distribution parameter of the measurement object according to the temperature of the diluent and the temperature correction curve
  • the volume distribution parameter before correction and the volume after correction are simultaneously displayed.
  • the above hematology analyzer can obtain, for example, hematocrit (HCT), average red blood cell hemoglobin content (Mean Corpuscular Hemoglobin (MCH), and average red blood cell hemoglobin concentration (Mean Corpuscular Hemoglobin) based on the corrected red blood cell MCV and RDW-SD Concentration, MCHC) and other related parameters.
  • HCT hematocrit
  • MCH average red blood cell hemoglobin content
  • MHC average red blood cell hemoglobin concentration
  • MCHC average red blood cell hemoglobin concentration
  • the blood cell analyzer may correct the The volume distribution information before, the temperature correction algorithm, the corrected volume distribution information and the other parameter information are output, and at the same time, the corrected volume distribution information and the other parameter information can also be Tag.
  • the present application also provides a blood cell analyzer 400.
  • the blood cell analyzer 400 includes: a sampling device (not shown) configured to absorb a test object; a pretreatment device 410 configured to be used for The measurement object is pre-processed to obtain the pre-measured measurement object, and the pre-treatment may include, for example, performing hemolysis treatment and fluorescent staining treatment on the particles in the measurement object; the detection device 420 is configured to perform the pre-treatment To detect the measured object; and a processor 430 for performing the above method.
  • the detection device 420 may include, but is not limited to, a light source 421 and a sheath flow chamber 422 having an orifice. Particles in the blood sample may flow in the sheath flow chamber 422 and pass through the orifice one by one. The light emitted by the light source 421 can illuminate the particles in the orifice and correspondingly generate scattered light signals and/or fluorescent signals.
  • the optical detection device 420 may further include a flow chamber 422 as a detection area, a forward scattered light collection part 423 provided on the optical axis, a side scattered light collection device part 424 provided on the side of the optical axis, and a fluorescence collection part 425.
  • the particles of the measurement object processed by the pretreatment device 410 sequentially enter the flow chamber 422 (detection area) through the pipe 440, wherein the forward scattered light collecting part 423, the side scattered light collecting device part 424 and the The fluorescence collection section 425 sequentially detects and collects forward scattered light information, side scattered light information, and fluorescence information of each cell, and transmits it to the processor 430.
  • the processor 430 may be implemented in the host and execute the following method steps:
  • the detection value of the measurement object is processed according to the type information of the measurement object.
  • the type information of the measurement object includes a blood sample, a quality control substance or a calibrator.
  • the processor 530 is specifically configured to perform the following steps when performing the step of acquiring the type information of the measurement object:
  • the processor 530 may be specifically configured to acquire the type information of the measurement object according to a preset measurement mode when performing the step of acquiring the type information of the measurement object.
  • the processor is configured to: when performing the step of acquiring the detection value of the measurement object, obtain the measurement object processed by the hemolytic agent and the fluorescent dye At least two light signals of the forward scattered light signal, the side scattered light signal and the fluorescent signal of the particle; and when performing the step of processing the detection value of the measurement object according to the type information of the measurement object , Processing the at least two optical signals according to a classification algorithm related to the type information of the measurement object to classify and count particles in the measurement object, and/or according to the type information of the measurement object
  • the at least two optical signals, especially the fluorescent signal are subjected to gain amplification processing.
  • the processor 430 is further configured to: according to the measurement before processing the at least two optical signals according to a classification algorithm related to the type information of the measurement object
  • the type information of the object is obtained from a pre-stored classification algorithm database or through RFID or the Internet.
  • the blood cell analyzer further includes a sensing device configured to detect the temperature of the diluent where the measurement object is located.
  • the processor is configured to acquire volume distribution information of the measurement object suspended in the dilution liquid and the temperature of the dilution liquid when performing the step of acquiring the measurement value of the measurement object;
  • a temperature correction algorithm related to the type information of the measurement object is used to adjust the volume Correct the distribution information.
  • the processor 430 is configured to execute the temperature distribution algorithm based on the temperature of the diluent, using a temperature correction algorithm related to the type information of the measurement object to the volume distribution Before the information correction step, the temperature correction algorithm is acquired from a pre-stored temperature correction algorithm database or through RFID or the Internet according to the type information of the measurement object.
  • the processor 430 is further configured to correct the volume distribution information using a temperature correction algorithm related to the type information of the measurement object according to the temperature of the diluent Before, it is determined whether the temperature of the diluent falls within a preset temperature range, and when the temperature of the diluent falls within the preset temperature range, the correction is not performed.
  • the temperature correction algorithm may be a temperature correction curve.
  • the temperature correction curve may be a fitting curve of the volume distribution information of the measurement object relative to the temperature, for example, a first time Fitting curve or quadratic fitting curve.
  • the temperature correction curve may also be a piecewise fitting curve of the volume distribution information of the measurement object with respect to temperature, such as a piecewise linear function.
  • the correction algorithm corresponding to the blood sample may be a negative correlation function between the volume distribution information of the blood sample and the temperature; the correction algorithm corresponding to the quality control substance or the calibration substance may be The positive correlation function between the volume distribution information of the quality control or calibrator and the temperature.
  • the volume distribution information may include an average cell volume and a standard deviation of the cell distribution width.
  • the processor 430 may be further configured to calculate other parameter information of the measurement object related to the volume distribution information according to the corrected volume distribution information; and Output the volume distribution information before correction, the volume distribution information after correction, and other parameter information related to the volume distribution information, and optionally output the correction curve.
  • the hematology analyzer 400 includes a display device (not shown) configured to display the volume distribution information before correction output by the processor and the volume after correction
  • the distribution information and other parameter information related to the volume distribution information optionally display the correction algorithm.
  • the blood cell analyzer 400 includes a switch device (not shown), which is configured to turn on or turn off the correction.
  • the switching device may be, for example, a physical button on the blood cell analyzer 400 or may be a virtual button on the display device.
  • the processor 430 may be an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a digital signal processor (Digital Signal Processor, DSP), a digital signal processing device (Digital Signal Processing Device, DSPD ), programmable logic device (ProgRAMmable Logic Device, PLD), field programmable gate array (Field ProgRAMmable Gate Array, FPGA), central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor At least one. Understandably, for different devices, there may be other electronic devices for implementing the above-mentioned processor functions, and the embodiments of the present application are not specifically limited.
  • each functional module in this embodiment may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above integrated unit may be implemented in the form of hardware or software function modules.
  • the integrated unit is implemented in the form of a software function module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the technical solution of this embodiment is essentially or right
  • the part of the existing technology or all or part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium and includes several instructions to make a computer device (which can be an individual) A computer, a server, or a network device, etc.) or a processor (processor) executes all or part of the steps of the method of this embodiment.
  • the foregoing storage media include various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (Read Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk, or an optical disk.
  • program codes such as a USB flash drive, a mobile hard disk, a read-only memory (Read Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk, or an optical disk.
  • an embodiment of the present application further provides a computer-readable storage medium on which a program is stored, and when the program is executed by the processor, the method for processing the detection value of the measurement object as described above is implemented.
  • the program instructions corresponding to the method for processing the detection value of the measurement object in this embodiment may be stored on a storage medium such as an optical disk, a hard disk, or a USB flash drive.
  • a storage medium such as an optical disk, a hard disk, or a USB flash drive.
  • the detection value of the measurement object is processed according to the type information of the measurement object.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer usable program code.
  • a computer usable storage media including but not limited to disk storage and optical storage, etc.
  • These computer program instructions may also be stored in a computer readable memory that can guide a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, the instructions
  • the device implements the functions specified in the implementation flow diagram one flow or multiple flows and/or the block diagram one block or multiple blocks.
  • Embodiments of the present application provide a method for processing a detection value of a measurement object, a blood cell analyzer, and a storage medium.
  • the blood cell analyzer obtains the type information of the measurement object; obtains the detection value of the measurement object; according to the type information of the measurement object The detected value is processed.
  • the blood cell analyzer may use different processing algorithms to process the detection value for different types of measurement objects.
  • the blood cell analyzer may use different classification algorithms for the type of measurement object to classify the particles in the measurement object.
  • the hematology analyzer detects and analyzes the measurement object in the diluent, it can simultaneously detect and obtain the volume distribution information of the particles including the measurement object and the detection value of the dilution temperature and the type information of the measurement object, so that Temperature, the temperature distribution algorithm related to the type information of the measurement object is used to correct the volume distribution information, that is, different temperature correction algorithms used for the calibrator, quality control substance and blood sample can be corrected to obtain the correction
  • the volume distribution information and other related parameter information it can eliminate the influence of temperature on the measurement of the volume distribution information of the measurement object, can improve the accuracy of the detection result, and can avoid the quality control or the accuracy of the calibration process from being damaged or even Failure.

Landscapes

  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

L'invention concerne un procédé de correction de paramètres d'un objet en cours de mesure, un analyseur d'hémocytes et un support d'informations. Le procédé de correction de paramètres d'un objet en cours de mesure consiste à : acquérir des informations de type de l'objet en cours de mesure (101) ; acquérir une valeur de détection de l'objet en cours de mesure (102) ; et traiter la valeur de détection de l'objet en cours de mesure conformément aux informations de type de l'objet en cours de mesure (103).
PCT/CN2018/125003 2018-12-28 2018-12-28 Procédé de traitement de valeur de détection d'un objet en cours de mesure, analyseur d'hémocytes et support d'informations WO2020133257A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN201880097223.0A CN112654857B (zh) 2018-12-28 2018-12-28 处理测量对象的检测值的方法、血细胞分析仪及存储介质
PCT/CN2018/125003 WO2020133257A1 (fr) 2018-12-28 2018-12-28 Procédé de traitement de valeur de détection d'un objet en cours de mesure, analyseur d'hémocytes et support d'informations

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2018/125003 WO2020133257A1 (fr) 2018-12-28 2018-12-28 Procédé de traitement de valeur de détection d'un objet en cours de mesure, analyseur d'hémocytes et support d'informations

Publications (1)

Publication Number Publication Date
WO2020133257A1 true WO2020133257A1 (fr) 2020-07-02

Family

ID=71125783

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/125003 WO2020133257A1 (fr) 2018-12-28 2018-12-28 Procédé de traitement de valeur de détection d'un objet en cours de mesure, analyseur d'hémocytes et support d'informations

Country Status (2)

Country Link
CN (1) CN112654857B (fr)
WO (1) WO2020133257A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114152760B (zh) * 2021-11-29 2024-02-13 湖北省妇幼保健院 β-HCG浓度检测系统和方法

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5017497A (en) * 1986-04-21 1991-05-21 Sequoia-Turner Corporation Particle discriminator and method
US5555196A (en) * 1991-11-27 1996-09-10 Toa Medical Electronics Co., Ltd. Method of counting particles using degree of membership in clustering data points into subgroups
US20020149763A1 (en) * 2001-04-11 2002-10-17 Mamoru Tsukada Analytical method and device using disc cytometry
CN1643368A (zh) * 2002-02-06 2005-07-20 特里达因伊斯科公司 分析液体的方法及其设备
CN101545861A (zh) * 2008-03-28 2009-09-30 希森美康株式会社 试样分析仪及试样分析方法
CN101625324A (zh) * 2008-07-08 2010-01-13 希森美康株式会社 细菌分析仪、细菌分析方法及其控制程序
CN101988894A (zh) * 2009-07-31 2011-03-23 恩德莱斯和豪瑟尔测量及调节技术分析仪表两合公司 用于确定液体样品的参数的方法
CN103890564A (zh) * 2011-10-17 2014-06-25 埃佩多夫股份公司 用于定量光学测量的方法和实验室设备
CN104395730A (zh) * 2012-06-25 2015-03-04 株式会社日立高新技术 自动分析装置以及试样测量方法
CN104471381A (zh) * 2012-07-20 2015-03-25 株式会社日立高新技术 自动分析装置以及自动分析方法
CN105388099A (zh) * 2014-08-25 2016-03-09 希森美康株式会社 尿分析装置及尿或体液中异型细胞的分析方法
CN105987881A (zh) * 2015-03-05 2016-10-05 天津先阳科技发展有限公司 光谱数据干扰抑制方法、建模方法、预测方法和处理装置

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4926812B2 (ja) * 2007-02-01 2012-05-09 シスメックス株式会社 血球分析装置および体液分析方法
JP5511149B2 (ja) * 2008-03-28 2014-06-04 シスメックス株式会社 生体試料分析装置及び生体試料分析方法

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5017497A (en) * 1986-04-21 1991-05-21 Sequoia-Turner Corporation Particle discriminator and method
US5555196A (en) * 1991-11-27 1996-09-10 Toa Medical Electronics Co., Ltd. Method of counting particles using degree of membership in clustering data points into subgroups
US20020149763A1 (en) * 2001-04-11 2002-10-17 Mamoru Tsukada Analytical method and device using disc cytometry
CN1643368A (zh) * 2002-02-06 2005-07-20 特里达因伊斯科公司 分析液体的方法及其设备
CN101545861A (zh) * 2008-03-28 2009-09-30 希森美康株式会社 试样分析仪及试样分析方法
CN101625324A (zh) * 2008-07-08 2010-01-13 希森美康株式会社 细菌分析仪、细菌分析方法及其控制程序
CN101988894A (zh) * 2009-07-31 2011-03-23 恩德莱斯和豪瑟尔测量及调节技术分析仪表两合公司 用于确定液体样品的参数的方法
CN103890564A (zh) * 2011-10-17 2014-06-25 埃佩多夫股份公司 用于定量光学测量的方法和实验室设备
CN104395730A (zh) * 2012-06-25 2015-03-04 株式会社日立高新技术 自动分析装置以及试样测量方法
CN104471381A (zh) * 2012-07-20 2015-03-25 株式会社日立高新技术 自动分析装置以及自动分析方法
CN105388099A (zh) * 2014-08-25 2016-03-09 希森美康株式会社 尿分析装置及尿或体液中异型细胞的分析方法
CN105987881A (zh) * 2015-03-05 2016-10-05 天津先阳科技发展有限公司 光谱数据干扰抑制方法、建模方法、预测方法和处理装置

Also Published As

Publication number Publication date
CN112654857B (zh) 2024-06-07
CN112654857A (zh) 2021-04-13

Similar Documents

Publication Publication Date Title
JP5557842B2 (ja) 粒子分析における同時発生の検出および処理
US10274414B2 (en) Particle processing systems and methods for normalization/calibration of same
CN111656161B (zh) 样本分析仪异常的报警方法、系统及存储介质
JP2012505406A5 (fr)
US11501852B2 (en) Blood cell analysis method and blood cell analyzer
EP0178910A2 (fr) Méthodes pour la surveillance de la coagulation du sang
US11125687B2 (en) Method and device for identifying fragmented red blood cells, blood cell analyzer and analysis method
BR112013025329B1 (pt) método não-fluorescente para enumerar células de granulócitos prematuras (ecgs) compreendendo promielócitos, mielócitos e metamielócitos em uma amostra de sangue
CN111684263A (zh) 血液分析系统、血液分析仪、血液分析方法及存储介质
CN112673088A (zh) 一种检测白细胞的方法、血液细胞分析仪及存储介质
Wang et al. Analytical comparison between two hematological analyzer systems: Mindray BC‐5180 vs Sysmex XN‐1000
WO2020133257A1 (fr) Procédé de traitement de valeur de détection d'un objet en cours de mesure, analyseur d'hémocytes et support d'informations
WO2021077327A1 (fr) Procédé pour l'analyse de globules rouges dans un échantillon de sang et système d'analyse de sang
CN110887818A (zh) 一种血液样本的分析方法和血液细胞分析仪及存储介质
US11402370B2 (en) Alarming method for platelet aggregation sample, blood cell analyzer and storage medium
CN113567327A (zh) 用于检验血液的方法、设备和系统
US20230314296A1 (en) Sample testing method and sample analyzer
WO2020042027A1 (fr) Méthode d'essai d'échantillon de sang, dispositif d'essai d'échantillon de sang et support d'informations
Roleff et al. Clinical evaluation of the CA530‐VET hematology analyzer for use in veterinary practice
Kausar et al. Frequency of Causes of Spurious Platelets Count on Routine Complete Blood Count by an Automated Hematology Cell Analyser
Ruan et al. The Shanghai biobanking DNA quality control program
CN108957018A (zh) 一种自动生化分析仪
US20210041345A1 (en) Detecting and reporting subpopulations of neutrophils
CN115372207A (zh) 样本检测方法、装置及计算机可读存储介质
US20220334099A1 (en) Blood analysis apparatus, blood analysis method, and storage medium

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18944524

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 19/11/2021)

122 Ep: pct application non-entry in european phase

Ref document number: 18944524

Country of ref document: EP

Kind code of ref document: A1