WO2020133257A1 - 处理测量对象的检测值的方法、血细胞分析仪及存储介质 - Google Patents

处理测量对象的检测值的方法、血细胞分析仪及存储介质 Download PDF

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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
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Prior art keywords
measurement object
type information
temperature
volume distribution
temperature correction
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PCT/CN2018/125003
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English (en)
French (fr)
Inventor
叶波
郑文波
王官振
陈鹏震
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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Priority to CN201880097223.0A priority Critical patent/CN112654857B/zh
Priority to PCT/CN2018/125003 priority patent/WO2020133257A1/zh
Priority to CN202410626489.7A priority patent/CN118518636A/zh
Publication of WO2020133257A1 publication Critical patent/WO2020133257A1/zh
Anticipated expiration legal-status Critical
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    • 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.

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Abstract

一种修正测量对象的参数的方法、血细胞分析仪（400）及存储介质，修正测量对象的参数的方法用于血细胞分析仪，修正测量对象的参数的方法包括：获取测量对象的类型信息（101）；获取测量对象的检测值（102）；根据测量对象的类型信息对测量对象的检测值进行处理（103）。

Description

处理测量对象的检测值的方法、血细胞分析仪及存储介质 技术领域
本申请实施例涉及血液分析领域，尤其涉及一种处理测量对象的检测值的方法、血细胞分析仪及存储介质。
背景技术
通常，血细胞分析仪的测量对象除了新鲜血液，还有质控物和校准物。其中，质控物为临床医学检验中用含量已知的处于与实际标本相同基质的特性明确的物质，质控物作为一种用于常规工作中核查分析过程或仪器准确性的物质，对实验室的质量控制起着非常重要的作用。校准物为保证血细胞分析仪的检测结果准确可靠,具有可溯源性，《血细胞分析的校准指南》要求必须使用制造商推荐使用的校准物或者校准实验室提供的定值新鲜血。然而，质控物和校准物虽然是新鲜血液的模拟物，但是在粒子性质方面仍然有所差别。
此外，医学检验结果的可靠性直接影响医疗质量，其中，温度是影响检验结果的一个重要的因素。例如，血细胞分析仪的正常工作温度一般为18-25摄氏度的范围之内，但是受实际条件所限，血细胞分析仪可能并不在正常温度范围内工作，从而对检验结果产生影响。为了解决温度对体积测量的不利影响，目前采用温控系统。但温控系统一方面会增加成本，另一方面也会增加额外的时间去保持稀释液恒温，降低测量速度。
发明内容
为解决上述技术问题，本申请实施例期望提供一种处理测量对象的检测值的方法、血细胞分析仪及存储介质，其能够简单地提高血液分析仪对不同类型的测试样本的检测结果的准确性，尤其是能够简单地降低温度对 血细胞分析仪的检测结果的影响，从而能够避免血液分析仪的质控或者校准流程的准确性受损甚至失效。
本申请的第一方面涉及一种修正测量对象的参数的方法，所述方法包括：
获取测量对象的类型信息；
获取所述测量对象的检测值；
根据所述测量对象的类型信息对所述测量对象的检测值进行处理。
在本申请的一些实施例中，所述测量对象的类型信息包括血液样本、质控物或校准物。
在本申请的一些实施例中，所述获取测量对象的类型信息的步骤可以包括：
获取所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；
根据所述至少两种光信号形成测量对象散点图；
将所述测量对象散点图和预设散点图进行比较，以确定所述测量对象的类型信息。
备选地，在本申请的一些实施例中，所述获取测量对象的类型信息的步骤也可以包括：
根据预先设定的测量模式获取测量对象的类型信息。
在本申请的一些实施例中，所述获取所述测量对象的检测值的步骤可以包括：
获取所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；
并且相应地
所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤包括：
根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理，以分类和计数所述测量对象中的粒子，和/或根据所述测量对象的类型信息对所述至少两种光信号、尤其是荧光信号进行增益放大处理。
在本申请的方法的一些实施例中，在所述根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理的步骤之前，所述方法还可以包括：
根据所述测量对象的类型信息从预存的分类算法数据库中或者通过RFID或互联网获取所述分类算法。
进一步地，在本申请的方法的一些实施例中，所述获取所述测量对象的检测值的步骤还可以包括：
获取悬浮在稀释液中的测量对象的体积分布信息以及所述稀释液的温度；
并且相应地，
所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤包括：
根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述测量对象的体积分布信息进行修正。
在本申请的方法的一些实施例中，在所述根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述测量对象的体积分布信息进行修正的步骤之前，所述方法还可以包括：
根据所述测量对象的类型信息从预存的温度修正算法数据库中或者通过RFID或互联网获取所述温度修正算法。
在本申请的一些实施例中，所述温度修正算法可以为温度修正曲线；其中，所述温度修正曲线可以为所述测量对象的体积分布信息相对于温度的拟合曲线、尤其是分段拟合曲线，例如可以为一次拟合曲线或二次拟合曲线、尤其是分段线性函数。
在本申请的方法的一些实施例中，与血液样本对应的温度修正算法为所述血液样本的体积分布信息与温度的负相关函数，与质控物或校准物所对应的温度修正算法为所述质控物或校准物的体积分布信息与温度的正相关函数。
在本申请的方法的一些实施例中，所述方法还可以包括：
获取所述稀释液的批次，并且根据所述测量对象的类型信息和所述稀释液的批次确定所述温度修正算法。
在本申请的方法的一些实施例中，所述体积分布信息可以包括平均细胞体积和细胞分布宽度标准差。
在本申请的方法的一些实施例中，所述方法还可以包括：
根据修正后的所述体积分布信息，计算所述测量对象的与所述体积分布信息相关的其他参数信息。
在本申请的方法的一些实施例中，所述方法还可以包括：
输出修正前的所述体积分布信息、修正后的所述体积分布信息以及所述其他参数信息；
可选地还输出所述温度修正算法。
优选地，所述方法还可以包括对修正后的所述体积分布信息和所述其他参数信息进行标记。
本申请的第二方面涉及一种血细胞分析仪，其中，所述血细胞分析仪包括：
采样装置，设置用于吸取测量对象；
预处理装置，设置用于对所述测量对象进行预处理，以便获得预处理后的测量对象；
检测装置，设置用于对所述预处理后的测量对象进行检测；
处理器，设置用于执行下列步骤：
获取所述测量对象的类型信息；
获取所述测量对象的检测值；
根据所述测量对象的类型信息对所述测量对象的检测值进行处理。
在本申请的血细胞分析仪的一些实施例中，所述测量对象的类型信息可以包括血液样本、质控物或校准物。
在本申请的血细胞分析仪的一些实施例中，所述处理器设置用于在执行获取所述测量对象的类型信息的步骤时可以执行下列步骤：
获取所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；
根据所述至少两种光信号形成测量对象散点图；
将所述测量对象散点图和预设散点图进行比较，以确定所述测量对象的类型信息；
或者，
所述处理器设置用于在执行获取所述测量对象的类型信息的步骤时还可以根据预先设定的测量模式获取测量对象的类型信息。
在本申请的血细胞分析仪的一些实施例中，所述处理器可以设置用于：在执行所述获取所述测量对象的检测值的步骤时获取所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；并且在执行所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤时，根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理，以分类和计数所述测量对象中的粒子，和/或根据所述测量对象的类型信息对所述至少两种光信号、尤其是荧光信号进行增益放大处理。
在本申请的血细胞分析仪的一些实施例中，所述处理器还可以设置用于在根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理之前，根据所述测量对象的类型信息从预存的分类算法数据库中或者通过RFID或互联网获取所述分类算法。
在本申请的血细胞分析仪的一些实施例中，所述血细胞分析仪还可以包括传感装置，设置用于检测所述测量对象所处的稀释液的温度；以及
所述处理器可以设置用于：在执行获取所述测量对象的测量值的步骤时获取悬浮在所述稀释液中的测量对象的体积分布信息以及所述稀释液的温度；并且在执行所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤时，根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正。
在本申请的血细胞分析仪的一些实施例中，所述处理器可以设置用于在执行所述根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正的步骤之前，根据所述测量对象的类型信息从预存的温度修正算法数据库中或者通过RFID或互联网获取所述温度修正算法。
在本申请的血细胞分析仪的一些实施例中，所述温度修正算法可以为温度修正曲线，所述温度修正曲线为所述测量对象的体积分布信息相对于温度的拟合曲线、尤其是分段拟合曲线，例如可以为一次拟合曲线或二次拟合曲线、尤其是分段线性函数。
在本申请的血细胞分析仪的一些实施例中，与血液样本对应的温度修正算法为所述血液样本的体积分布信息与温度的负相关函数，与质控物或校准物所对应的温度修正算法为所述质控物或校准物的体积分布信息与温度的正相关函数。
在本申请的血细胞分析仪的一些实施例中，所述处理器还可以设置用于根据修正后的所述体积分布信息，计算所述测量对象的与所述体积分布信息相关的其他参数信息。
在本申请的一些实施例中，所述处理器还可以设置用于输出修正前的所述体积分布信息、修正后的所述体积分布信息以及所述其他参数信息并且可选地输出所述温度修正曲线；以及对修正后的所述体积分布信息和所 述其他参数信息进行标记。
本申请的第三方面涉及一种计算机可读存储介质，其上存储有程序，所述程序被处理器执行时实现上述本申请第一方面的处理测量对象的参数的方法的各个步骤。
本申请的方法及血液细胞分析仪通过采用与测量对象的类型相关的处理算法对其检测值进行处理，能够简单地提高血液分析仪对不同类型的测试样本的检测结果的准确性，尤其是能够简单地降低温度对血细胞分析仪的检测结果的影响，从而能够避免血液分析仪的质控或者校准流程的准确性受损甚至失效。
附图说明
图1为本申请实施例提出的一种处理测量对象的检测值的方法的示意流程图一；
图2为本申请实施例提出的一种处理测量对象的检测值的方法的示意流程图二；
图3A至3C为不同测量对象的散点图与参考散点图的比较图；
图4A为质控物的荧光-前向散射光散点图；
图4B为血液样本的荧光-前向散射光散点图；
图4C为对质控物的荧光信号进行放大之后质控物的荧光-前向散射光散点图；
图5A为质控物的划分出粒子团的荧光-前向散射光散点图；
图5B为血液样本的划分出粒子团的荧光-前向散射光散点图；
图6A为质控物中的红细胞MCV随温度变化的相对偏差的示意图；
图6B为质控物中的红细胞RDW-SD随温度变化的相对偏差的示意图；
图7A为校准物中的红细胞MCV随温度变化的相对偏差的示意图；
图7B为校准物中的红细胞RDW-SD随温度变化的相对偏差的示意图；
图8A为血液样本中的红细胞MCV随温度变化的相对偏差的示意图；
图8B为血液样本中的红细胞RDW-SD随温度变化的相对偏差的示意图；
图9A为质控物中的红细胞MCV随温度变化的绝对偏差以及相应的本申请实施例提出的一次拟合温度修正曲线的示意图；
图9B为质控物中的红细胞MCV的相对偏差在利用图9A所示的一次拟合曲线进行温度修正前后的示意图；
图9C为质控物中的红细胞RDW-SD随温度变化的绝对偏差以及相应的本申请实施例提出的一次拟合温度修正曲线的示意图；
图9D为质控物中的红细胞RDW-SD的相对偏差在利用图9C所示的一次拟合曲线进行温度修正前后的示意图；
图10A为校准物中的红细胞MCV随温度变化的绝对偏差以及相应的本申请实施例提出的一次拟合温度修正曲线的示意图；
图10B校准物中的红细胞MCV的相对偏差在利用图10A所示的一次拟合曲线进行温度修正前后的示意图；
图10C为校准物中的红细胞RDW-SD随温度变化的绝对偏差以及相应的本申请实施例提出的一次拟合温度修正曲线的示意图；
图10D校准物中的红细胞RDW-SD的相对偏差在利用图10C所示的一次拟合曲线进行温度修正前后的示意图；
图11A为血液样本中的红细胞MCV随温度变化的绝对偏差以及相应的本申请实施例提出的一次拟合温度修正曲线的示意图；
图11B血液样本中的红细胞MCV的相对偏差在利用图11A所示的一次拟合曲线进行温度修正前后的示意图；
图11C为血液样本中的红细胞RDW-SD随温度变化的绝对偏差以及相应的本申请实施例提出的一次拟合温度修正曲线的示意图；
图11D为血液样本中的红细胞RDW-SD的相对偏差在利用图11C所示 的一次拟合曲线进行温度修正前后的示意图；
图12A为质控物中的红细胞MCV随温度变化的绝对偏差以及相应的本申请实施例提出的二次拟合温度修正曲线的示意图；
图12B为质控物中的红细胞MCV的相对偏差在利用图12A所示的二次拟合曲线进行温度修正前后的示意图；
图12C为质控物中的红细胞RDW-SD随温度变化的绝对偏差以及相应的本申请实施例提出的二次拟合温度修正曲线的示意图；
图12D为质控物中的红细胞RDW-SD的相对偏差在利用图12C所示的二次拟合曲线进行温度修正前后的示意图；
图13A为校准物中的红细胞MCV随温度变化的绝对偏差以及相应的本申请实施例提出的二次拟合温度修正曲线的示意图；
图13B为校准物中的红细胞MCV的相对偏差在利用图13A所示的二次拟合曲线进行温度修正前后的示意图；
图13C为校准物中的红细胞RDW-SD随温度变化的绝对偏差以及相应的本申请实施例提出的二次拟合温度修正曲线的示意图；
图13D为校准物中的红细胞RDW-SD的相对偏差在利用图13C所示的二次拟合曲线进行温度修正前后的示意图；
图14A为血液样本中的红细胞MCV随温度变化的绝对偏差以及相应的本申请实施例提出的二次拟合温度修正曲线的示意图；
图14B为血液样本中的红细胞MCV的相对偏差在利用图14A所示的二次拟合曲线进行温度修正前后的示意图；
图14C为血液样本中的红细胞RDW-SD随温度变化的绝对偏差以及相应的本申请实施例提出的二次拟合温度修正曲线的示意图；
图14D为血液样本中的红细胞RDW-SD的相对偏差在利用图14D所示的二次拟合曲线进行温度修正前后的示意图；
图15为本申请实施例提出的一种处理测量对象的检测值的方法的示意 流程图三；
图16为本申请实施例提出的一种处理测量对象的检测值的方法的示意流程图四。
图17为本申请实施例提出的一种血细胞分析仪的结构示意图。
具体实施方式
为了能够更加详尽地了解本申请实施例的特点与技术内容，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述。可以理解的是，此处所描述的具体实施例仅仅用于解释相关申请，而非对该申请的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与有关申请相关的部分。
本申请的一个实施例提供了一种处理测量对象的检测值的方法，尤其是应用于血细胞分析仪中。图1为按照本申请的方法的示意流程图一。如图1所示，所述方法包括以下步骤：
步骤101、获取测量对象的类型信息。
需要说明的是，在本申请的实施例中，上述测量对象可以为血液样本、质控物或校准物。虽然质控物或校准物是血液样本的模拟物，但是其在粒子方面的性质并不是完全相同的，例如不同温度对血液样本、质控物以及校准物的细胞体积测量的影响程度是不一样的，因此在对测量对象的检测值进行分析时需要先获取该测量对象的类型信息。
如图2所示的按照本申请的方法的示意流程图二，在本申请的实施例中，所述获取测量对象的类型信息的步骤101可以包括以下步骤：
101a、获取经过溶血剂处理和荧光染料处理的测量对象的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号。
101b、根据至少两种光信号形成测量对象散点图。
101c、将测量对象散点图和预设散点图进行比较，以确定测量对象的 类型信息。
具体地，所述预设散点图可以包括质控物散点图、校准物散点图和血液散点图中的至少两种。例如当所述预设散点图包括质控物散点图和校准物散点图时，如果测量对象散点图与质控物或校准物散点图相似，则判断该测量对象为质控物或校准物，否则判断该测量对象为血液样本。在此参考3A至3C，图3A至3C为不同测量对象的散点图与参考散点图的比较图，其中，图3A为测量对象为质控物时的散点图与预设散点图、即参考散点图的比较图；图3B为测量对象为校准物时的散点图与预设散点图、即参考散点图的比较图；图3C为测量对象为血液样本时的散点图与预设散点图、即参考散点图的比较图。其中，测量对象散点图与预定散点图的相似程度可以是散点图之差/相关系数/互信息/散点图的边缘包络信息。由于血样的散点图形态多种多样，而质控物和校准物相对稳定，因此所述预设散点图优选包括质控物散点图和校准物散点图。
此外，在备选的实施例中，也可以通过设定不同的测量模式来获得测量对象的类型信息。所述测量模式可以是血液测量模式或质控物\校准物测量模式。例如，在本申请的实施例中，在对测量对象进行分析检测之前，先设定测量模式，由此直接确定测量对象的类型信息。
步骤102、获取测量对象的检测值。
具体地，在本申请的实施中，上述检测值可以包括经过溶血剂处理和荧光染料处理的测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号。在该情况下，在获取所述至少两种光信号之前，首先对所述测量对象中的粒子进行预处理、例如溶血处理和荧光染色处理。具体地，将所述测量对象与具有荧光染料和溶血剂的试剂按一定比例进行混合，形成处理后的测量对象。例如，采用溶血剂溶解测量对象中的红细胞，使之不干扰白细胞等粒子的计数，同时荧光染料与白细胞的核酸结合以标记细胞，由于各类细胞结合荧光染料的能力有差异，会产生 不同的荧光信息。另外，由于各类细胞的大小不同会产生不同的前向散射光信息，由于细胞内形态或复杂程度不同会产生不同的侧向散射光信息。
此外，上述检测值还可以包括测量对象中的粒子的体积分布信息、尤其是红细胞粒子的体积分布信息以及所述测量对象所在的稀释液的温度。例如，采用电阻抗法对稀释液中的测量对象进行检测，从而获得悬浮在稀释液中的测量对象的红细胞粒子的体积分布信息。
所述体积分布信息可以包括平均细胞体积和细胞分布宽度标准差。具体地，体积分布信息可以为平均红细胞体积(Mean Corpuscular Volume，MCV)和红细胞分布宽度标准差(Red Blood Cell Distribution Width Standard Deviation，RDW-SD)。
进一步地，在本申请的实施例中，由于不同温度下根据库尔特原理测量获得细胞存在明显差异，因此稀释液温度这一因素会对检测结果的准确性产生较大的影响，因此，血细胞分析仪在对悬浮在稀释液中的测量对象进行分析检测时，还需要检测稀释液的温度，以根据稀释液温度确定如何对所述测量对象中的粒子的体积分布参数进行处理。
步骤103、根据测量对象的类型信息对测量对象的检测值进行处理。
在本申请的实施例中，如果上述检测值包括经过溶血剂处理和荧光染料处理的所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号，那么步骤103可以包括：根据与测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理，以分类和计数测量对象中的粒子，和/或根据测量对象的类型信息对所述至少两种光信号、尤其是荧光信号进行增益放大处理。
一方面如图4A和图4B所示，图4A为质控物的荧光-前向散射光散点图，图4B为血液样本的荧光-前向散射光散点图，由于质控物或校准物中的粒子经过了试剂固定，所以吸收荧光物质能力偏弱，造成质控物或校准物在荧光方向上的信号比血液样本小，从而在荧光-前向散射光信号组成的 散点图上粒子团的位置存在差异。通过适当放大质控物或校准物在荧光方向的信号，能够使得质控物或校准物中的粒子团在荧光-前向散射光散点图中的位置与血液样本基本类似，从而更好地对不同类型的测量对象中的粒子进行分类，如图4C所示，图4C为对质控物的荧光信号进行放大80％之后质控物的荧光-前向散射光散点图。
另一方面如图5A和图5B所示，图5A为质控物的划分出粒子团的荧光-前向散射光散点图，图5B为血液样本的划分出粒子团的荧光-前向散射光散点图，质控物中的嗜碱性粒子(BASO粒子)由于在制备质控物时的工艺处理而与血液样本中的BASO粒子不同，从而造成质控物中的BASO粒子在荧光-前向散射光散点图中的位置与血液样本中的BASO粒子不同。因此，需要采用与测量对象的类型信息相关的分类算法对测量对象中的粒子的光信号进行处理，以准确地分类和计数测量对象中的粒子。也就是说，针对不同类型的测量对象采用不同的分类算法。
进一步地，在本申请的实施例中，在根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理之前的步骤之前，可以先获取与所述测量对象的类型信息相关的分类算法。具体地，上述血细胞分析仪可以根据所述测量对象的类型信息从预存的分类算法数据库中或者通过射频识别(Radio Frequency Identification，RFID)或互联网获取所述分类算法。
在本申请的实施例中，如果上述检测值还包括悬浮在稀释液中的测量对象的粒子、例如红细胞粒子的体积分布信息和所述稀释液的温度，那么步骤103进一步可以包括：根据稀释液的温度，利用与测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正。
目前，医用粒子分析仪、如血细胞分析仪对血细胞、例如红细胞的测量采用微孔阻抗原理的占多数，其基本依据是库尔特原理。具体的检测过程包括以下步骤：悬浮在电解液中的颗粒随电解液，即稀释液，在通过检 测微孔时时，检测微孔处的等效电阻会发生变化，在微孔两侧恒流源的作用下，所述检测微孔两侧的电压发生了变化，通过电路系统采集此电压变化，形成电压脉冲波形。脉冲波形的高度反映了细胞的体积大小，表征了粒子的体积信息，因此仪器会据此提供所检测粒子的体积分布情况，即提供所测粒子的体积分布直方图。对细胞体积直方图进行分析，即可进行细胞分类与计数等。该测量方法也称为电阻抗法。
申请人通过大量研究和临床实验发现，不同温度下根据库尔特原理测量获得细胞存在明显差异。并且，温度对新鲜血和质控物或校准物细胞体积测量的影响程度是不一样的。一方面，这会影响正常血液样本测量的准确性，另一方面，也会导致质控或者校准流程的准确性受损甚至失效，例如，温度升高导致质控物检测体积降低，超出质控范围，但实际仪器并无问题，实际为假阳。
在本申请的实施例中，上述血细胞分析仪在获得稀释液中的测量对象的粒子的体积分布信息、稀释液的温度以及测量对象的类型信息之后，便可以根据上述稀释液的温度，利用与上述测量对象的类型信息相关的温度修正算法对上述测量对象的粒子的体积分布信息进行修正。具体地，在获取测量对象的类型信息之后，可以获取与该测量对象的类型信息对应的温度修正算法。
在该实施例中，通过根据稀释液的温度，利用与测量对象的类型信息相关的温度修正算法对测量对象的体积分布信息进行修正，能够以简单的方式消除温度对不同测量对象的检测结果的影响，从而能够提高血液样本的检测结果的准确性以及避免质控或者校准流程的准确性受损甚至失效。
需要说明的是，在本申请的实施例中，与上述测量对象的类型信息对应的温度修正算法优选可以为与上述测量对象的类型信息相对应的温度修正曲线。
具体地，上述血细胞分析仪在根据所述稀释液的温度，利用与所述测 量对象的类型信息相关的温度修正算法对所述测量对象的体积分布信息进行修正时，可以先从所述温度修正算法中确定与所述稀释液温度对应的温度修正参数，然后根据所述温度修正参数对所述体积分布信息进行修正。
需要说明的是，由于温度对不同类型的测量对象的检测结果的影响是不同的，因此应采用与测量对象的类型信息相关的温度修正曲线来对测量对象的检测结果进行修正。
进一步地，在本申请的实施例中，当测量对象的类型为质控物/校准物时，其对应的温度修正算法、尤其是温度修正曲线与温度变化呈正相关；而当测量对象的类型为血液样本时，其对应的温度修正算法、尤其是温度修正曲线与温度变化呈负相关。也就是说，与血液样本对应的温度修正算法、尤其是温度修正曲线为血液样本的体积分布信息与温度的负相关函数；与质控物/校准物对应的温度修正算法、尤其是温度修正曲线为质控物/校准物的体积分布信息与温度的正相关函数。
具体参见图6至图8。其中，图6A为质控物中的红细胞MCV随温度变化的相对偏差的示意图，图6B为质控物中的红细胞RDW-SD随温度变化的相对偏差示意图，由此可见，质控物中的红细胞MCV和RDW-SD均随温度增加而减小，因此，与质控物对应的温度修正曲线应与温度变化呈正相关。图7A为校准物中的红细胞MCV随温度变化的相对偏差的示意图，图7B为校准物中的红细胞RDW-SD随温度变化的相对偏差的示意图，同样地，校准物中的红细胞MCV和RDW-SD均随温度增加而减小，因此，与校准物对应的温度修正曲线也应与温度变化呈正相关。图8A为血液样本中的红细胞MCV随温度变化的相对偏差的示意图，图8B为血液样本中的红细胞RDW-SD随温度变化的相对偏差示意图，血液样本中的红细胞MCV和RDW-SD均随温度增加而增大，因此，与血液样本对应的温度修正曲线则应与温度变化呈正负相关。
在此应该说明的是，本申请中的“偏差”是以22℃的温度为参考标准。 本领域技术人员能够根据实际情况采用不同的参考标准以得到相应的偏差值。
此外需要说明的是，在本申请的实施例中，对于不同批次的稀释液，对应的温度修正算法、尤其是温度修正曲线也是不相同的，因此血细胞分析仪在获取温度修正算法时，可以先获取稀释液对应的标识信息；其中，该标识信息用于确定稀释液的批次；然后根据所述测量对象的类型信息和所述稀释液的批次确定所述温度修正算法。
进一步地，在本申请的实施例中，上述血细胞分析仪在根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述测量对象的体积分布信息进行修正之前，可以先获取上述温度修正算法。具体地，上述从血细胞分析仪可以根据所述测量对象的类型信息从预存的温度修正算法数据库中或者通过RFID或互联网获取所述温度修正算法。
需要说明的是，在本申请的实施例中，所述温度修正曲线优选可以为所述测量对象的体积分布信息相对于温度的拟合曲线、尤其是分段拟合曲线，例如为一次拟合曲线或二次拟合曲线，尤其是分段线性函数。
进一步地，在本申请的实施例中，上述血细胞分析仪在进行温度修正时，测量对象的类型、体积分布信息的类型以及温度修正曲线是一一对应的，即对于一种测量对象和该测量对象的一种体积分布信息，都存在一个相对应的温度修正曲线。
现在参考图9至图11说明采用一次拟合曲线、即直线对测量对象的粒子、尤其是红细胞粒子的体积分布信息进行修正的实施方式。图9A为质控物中的红细胞MCV随温度变化的绝对偏差以及相应的一次拟合温度修正曲线的示意图，其中该一次拟合温度修正曲线为y＝0.2785t-6.4055，在进行温度修正之后，在进行温度修正前后的质控物MCV的相对偏差如图9B所示；图9C为质控物中的红细胞RDW-SD随温度变化的绝对偏差以及相应的一次拟合温度修正曲线的示意图，其中该一次拟合温度修正曲线为 y＝0.0811t-1.8653，图9D为质控物中的红细胞RDW-SD的相对偏差在利用图9C所示的一次拟合曲线进行温度修正前后的示意图。同理，图10A为校准物中的红细胞MCV随温度变化的绝对偏差以及相应的一次拟合温度修正曲线的示意图，其中该一次拟合温度修正曲线为y＝0.2761t-6.35，图10B则为校准物中的红细胞MCV的相对偏差在利用图10A所示的一次拟合曲线进行温度修正前后的示意图；图10C为校准物中的红细胞RDW-SD随温度变化的绝对偏差以及相应的一次拟合温度修正曲线的示意图，其中该一次拟合温度修正曲线为y＝0.1032t-2.3736，图10D则为校准物中的红细胞RDW-SD的相对偏差在利用图10C所示的一次拟合曲线进行温度修正前后的示意图。最后，图11A为血液样本中的红细胞MCV随温度变化的绝对偏差以及相应的一次拟合温度修正曲线的示意图，其中该一次拟合温度修正曲线为y＝-0.3648t+8.0256，图11B则为血液样本中的红细胞MCV的相对偏差在利用图11A所示的一次拟合曲线进行温度修正前后的示意图；图11C为血液样本中的红细胞RDW-SD随温度变化的绝对偏差以及相应的一次拟合温度修正曲线的示意图，其中该一次拟合温度修正曲线为y＝-0.2117t+4.6574，图11D则为血液样本中的红细胞RDW-SD的相对偏差在利用图11C所示的一次拟合曲线进行温度修正前后的示意图。
接着参考图12至图14说明采用二次拟合曲线对测量对象的粒子、尤其是红细胞粒子的体积分布信息进行修正的实施方式。图12A为质控物中的红细胞MCV随温度变化的绝对偏差以及相应的二次拟合温度修正曲线的示意图，其中该二次拟合温度修正曲线为y＝-0.0048*t 2+0.5006*t-9.0381，图12B则为质控物中的红细胞MCV的相对偏差在利用图12A所示的二次拟合曲线进行温度修正前后的示意图；图12C为质控物中的红细胞RDW-SD随温度变化的绝对偏差以及相应的二次拟合温度修正曲线的示意图，其中该二次拟合温度修正曲线为y＝-0.0018*t 2+0.163*t-2.7708，而图12D为质控物中的红细胞RDW-SD的相对偏差在利用图12C所示的二次拟合曲 线进行温度修正前后的示意图。同理，图13A为校准物中的红细胞MCV随温度变化的绝对偏差以及相应的二次拟合温度修正曲线的示意图，其中该二次拟合温度修正曲线为y＝-0.0078*t 2+0.6373*t-10.518，而图13B为校准物中的红细胞MCV的相对偏差在利用图13A所示的二次拟合曲线进行温度修正前后的示意图；图13C为校准物中的红细胞RDW-SD随温度变化的绝对偏差以及相应的二次拟合温度修正曲线的示意图，其中该二次拟合温度修正曲线为y＝-0.0019*t 2+0.1896*t-3.3836，而图13D为校准物中的红细胞RDW-SD的相对偏差在利用二次拟合曲线进行温度修正前后的示意图。最后，图14A为血液样本中的红细胞MCV随温度变化的绝对偏差以及相应的二次拟合温度修正曲线的示意图，其中该二次拟合温度修正曲线为y＝0.0004*t 2-0.3821*t+8.3162，而图14B为血液样本中的红细胞MCV的相对偏差在利用图14A所示的二次拟合曲线进行温度修正前后的示意图；图14C为血液样本中的红细胞RDW-SD随温度变化的绝对偏差以及相应的二次拟合温度修正曲线的示意图，其中该二次拟合温度修正曲线为y＝0.0016*t 2-0.2838*t+5.5721，而图14D为血液样本中的红细胞RDW-SD的相对偏差在利用图14C所示的二次拟合曲线进行温度修正前后的示意图。
由此可见，上述血细胞分析仪在根据温度修正曲线进行温度修正之后，对于质控物、校准物以及血液样本的粒子的体积分布信息而言，都能够相应地获得更准确的测量结果。
图15为本申请实施例提出的处理测量对象的检测值的方法的示意流程图三。在图15所示的实施例中，所述方法包括以下步骤：
步骤201、获取测量对象的类型信息；
步骤202、获取悬浮在稀释液中的测量对象的粒子的体积分布参数以及稀释液的温度；
步骤203、判断所述稀释液的温度是否落入预设温度范围内；
当稀释液温度落入预设温度范围内时，实施步骤203b，即不进行温度 修正；
当稀释液温度不在预设温度范围内时，实施步骤203a，即根据稀释液温度，利用与测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正。
在本申请的该实施例中，由于在稀释液温度合适的实验条件下不需要对检测结果进行温度修正，因此稀释液的温度还可以用于判断是否开启温度修正功能。具体地，血细胞分析仪在获取稀释液的温度之后可以判断稀释液的温度是否落入预设温度范围，如果是，那么关闭温度修正功能，否则进行温度修正。其中，上述预设温度范围用于表征上述血细胞分析仪的正常工作温度范围，例如，上述预设温度范围可以为18-25摄氏度。
此外，图16为本申请实施例提出的处理测量对象的检测值的方法的示意流程图四。该实施例中，步骤301至303分别与图15所示的实施例的步骤201至203相同，不同之处在于：所述方法在步骤303之后还包括：步骤304、根据修正后的所述体积分布信息，计算所述测量对象的与所述体积分布信息相关的其他参数信息；以及步骤305、输出修正前的所述体积分布信息、修正后的所述体积分布信息以及其他参数信息，优选地，还可以输出所述温度修正算法。
因此，在本申请的该实施例中，上述血细胞分析仪在根据稀释液温度和温度修正曲线，对测量对象的体积分布参数进行温度修正之后，同时显示修正前的体积分布参数和修正后的体积分布参数以及可选地显示与所述体积分布信息相关的其他参数信息和温度修正曲线，以帮助医护人员更加直观的进行对比和分析。
具体地，上述血细胞分析仪例如可以根据修正后的红细胞MCV和RDW-SD求得红细胞压积(Hematocrit，HCT)、平均红细胞血红蛋白含量(Mean Corpuscular Hemoglobin，MCH)以及平均红细胞血红蛋白浓度(Mean Corpuscular Hemoglobin Concentration，MCHC)等相关参数。
需要说明的是，在本申请的实施例中，上述血细胞分析仪在根据修正后的所述体积分布信息，计算所述测量对象的与所述体积分布信息相关的其他参数信息之后，可以将修正前的所述体积分布信息、所述温度修正算法、修正后的所述体积分布信息以及所述其他参数信息进行输出，同时，还可以对修正后的所述体积分布信息和所述其他参数信息进行标记。
如图17所示，本申请还提供一种血细胞分析仪400，该血液细胞分析仪400包括：采样装置(未示出)，设置用于吸取测试对象；预处理装置410，设置用于对所述测量对象进行预处理，以便获得预处理后的测量对象，所述预处理例如可以包括对所述测量对象中的粒子进行溶血处理和荧光染色处理；检测装置420，设置对所述预处理后的测量对象进行检测；以及用于执行上述方法的处理器430。
所述检测装置420可以但不仅限于包括光源421和具有孔口的鞘流流动室422等，血液样本中的粒子可在鞘流流动室422内流动，并逐个经过孔口。光源421所发出的光可照射到孔口中的粒子并对应产生散射光信号和/或荧光信号。所述光学检测装置420还可以包括作为检测区域的流动室422、设置在光轴上的前向散射光收集部423、设置在光轴侧边的侧向散射光收集装置部424和荧光收集部425。经预处理装置410处理后的测量对象的粒子依次经管道440进入流动室422(检测区)，其中，在同一个通道中的前向散射光收集部423、侧向散射光收集装置部424和荧光收集部425依次检测并收集每一细胞的前向散射光信息、侧向散射光信息和荧光信息，并传送给处理器430。
所述处理器430可以在主机中实现并且执行如下方法步骤：
获取所述测量对象的类型信息；
获取所述测量对象的检测值；
根据所述测量对象的类型信息对所述测量对象的检测值进行处理。
进一步地，在本申请的实施例中，所述测量对象的类型信息包括血液 样本、质控物或校准物。
进一步地，在本申请的实施例中，所述处理器530具体设置用于在执行获取所述测量对象的类型信息的步骤时执行下列步骤：
获取经过溶血剂处理和荧光染料处理的所述测量对象的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；
根据所述至少两种光信号形成测量对象散点图；
将所述测量对象散点图和预设散点图进行比较，以确定所述测量对象的类型信息。
在本申请的一种备选的实施例中，所述处理器530可以具体设置用于在执行获取所述测量对象的类型信息的步骤时根据预先设定的测量模式获取测量对象的类型信息。
进一步地，在本申请的实施例中，所述处理器设置用于：在执行所述获取所述测量对象的检测值的步骤时获取经过溶血剂处理和荧光染料处理的所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；并且在执行所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤时，根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理，以分类和计数所述测量对象中的粒子，和/或根据所述测量对象的类型信息对所述至少两种光信号、尤其是荧光信号进行增益放大处理。
进一步地，在本申请的实施例中，所述处理器430还设置用于在根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理之前，根据所述测量对象的类型信息从预存的分类算法数据库中或者通过RFID或互联网获取所述分类算法。
进一步地，在本申请的实施例中，所述血细胞分析仪还包括传感装置，设置用于检测所述测量对象所处的稀释液的温度。此时，所述处理器设置用于：在执行获取所述测量对象的测量值的步骤时获取悬浮在所述稀释液 中的测量对象的体积分布信息以及所述稀释液的温度；并且在执行所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤时，根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正。
进一步地，在本申请的实施例中，所述处理器430设置用于在执行所述根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正的步骤之前，根据所述测量对象的类型信息从预存的温度修正算法数据库中或者通过RFID或互联网获取所述温度修正算法。
进一步地，在本申请的实施例中，所述处理器430还设置用于在根据所述稀释液温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正之前，判断所述稀释液温度是否落入预设温度范围内，当所述稀释液温度落入所述预设温度范围内时，不进行所述修正。
进一步地，在本申请的实施例中，所述温度修正算法可以为温度修正曲线，优选地所述温度修正曲线可以为所述测量对象的体积分布信息相对于温度的拟合曲线，例如为一次拟合曲线或二次拟合曲线。进一步地，所述温度修正曲线也可以为所述测量对象的体积分布信息相对于温度的分段拟合曲线，例如分段线性函数。
进一步地，在本申请的实施例中，与血液样本对应的修正算法可以为所述血液样本的体积分布信息与温度的负相关函数；与质控物或校准物所对应的修正算法可以为所述质控物或校准物的体积分布信息与温度的正相关函数。
进一步地，在本申请的实施例中，所述体积分布信息可以包括平均细胞体积和细胞分布宽度标准差。
进一步地，在本申请的实施例中，所述处理器430还可以设置用于根据修正后的所述体积分布信息，计算所述测量对象的与所述体积分布信息 相关的其他参数信息；以及输出所述修正前的体积分布信息、修正后的体积分布信息以及所述体积分布信息相关的其他参数信息，可选地输出所述修正曲线。
进一步地，在本申请的实施例中，所述血细胞分析仪400包括显示装置(未示出)，设置用于显示由处理器输出的修正前的所述体积分布信息、修正后的所述体积分布信息以及所述体积分布信息相关的其他参数信息，可选地显示所述修正算法。
进一步地，在本申请的实施例中，其中，所述血细胞分析仪400包括开关装置(未示出)，设置用于开启或关闭所述修正。该开关装置例如可以是血细胞分析仪400上的实体按钮或者可以是在显示装置上的虚拟按钮。
在本申请的实施例中，上述处理器430可以为特定用途集成电路(Application Specific Integrated Circuit，ASIC)、数字信号处理器(Digital Signal Processor，DSP)、数字信号处理装置(Digital Signal Processing Device，DSPD)、可编程逻辑装置(ProgRAMmable Logic Device，PLD)、现场可编程门阵列(Field ProgRAMmable Gate Array，FPGA)、中央处理器(Central Processing Unit，CPU)、控制器、微控制器、微处理器中的至少一种。可以理解地，对于不同的设备，用于实现上述处理器功能的电子器件还可以为其它，本申请实施例不作具体限定。
另外，在本实施例中的各功能模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。
集成的单元如果以软件功能模块的形式实现并非作为独立的产品进行销售或使用时，可以存储在一个计算机可读取存储介质中，基于这样的理解，本实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产 品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或processor(处理器)执行本实施例方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
此外，本申请实施例还提供一种计算机可读存储介质，其上存储有程序，该程序被处理器执行时实现如上所述的处理测量对象的检测值的方法。
具体来讲，本实施例中的一种处理测量对象的检测值的方法对应的程序指令可以被存储在光盘，硬盘，U盘等存储介质上，当存储介质中的与一种处理测量对象的检测值的方法对应的程序指令被一电子设备读取或被执行时，执行如下步骤：
获取测量对象的类型信息；
获取所述测量对象的检测值；
根据所述测量对象的类型信息对所述测量对象的检测值进行处理。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用硬件实施例、软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器和光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的实现流程示意图和/或方框图来描述的。应理解可由计算机程序指令实现流程示意图和/或方框图中的每一流程和/或方框、以及实现流程示意图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在实现流程示意图一个流程或多个流程和/或方框图 一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在实现流程示意图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在实现流程示意图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上提及的特征，只要在本发明的范围内是有意义的，均可以任意相互组合。针对本发明的方法所说明的优点和特征以相应的方式适用于本发明的血液细胞分析仪。
以上所述仅为本发明的一些实施方式，并非因此限制本发明的专利范围，凡是在本发明的发明构思下，利用本发明说明书及附图内容所作的等效结构变换，或直接/间接运用在其他相关的技术领域均包括在本发明的专利保护范围内。
工业实用性
本申请实施例提供一种处理测量对象的检测值的方法、血细胞分析仪及存储介质，血细胞分析仪获取测量对象的类型信息；获取测量对象的检测值；根据测量对象的类型信息对测量对象的检测值进行处理。可见，在本申请的实施例中，血细胞分析仪可以针对不同类型的测量对象采用不同的处理算法对其检测值进行处理。例如，血细胞分析仪可以针对类型的测量对象采用不同的分类算法对该测量对象中的粒子进行分类。此外，血细胞分析仪在对稀释液中的测量对象进行检测分析时，可以同时检测获得包 括测量对象的粒子的体积分布信息和稀释液温度的检测值以及测量对象的类型信息，从而可以根据稀释液温度，利用与测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正，即可以对于校准物、质控物和血液样本分别采用的不同的温度修正算法进行修正处理，以获得修正后的体积分布信息及其相关的其他参数信息，从而可以消除温度对测量对象的体积分布信息检测的影响，能够提高检测结果的准确性，同时可以避免质控或者校准流程的准确性受损甚至失效。

Claims (25)

  1. 一种处理测量对象的检测值的方法，所述方法包括：
    获取测量对象的类型信息；
    获取所述测量对象的检测值；
    根据所述测量对象的类型信息对所述测量对象的检测值进行处理。
  2. 根据权利要求1所述的方法，其中，所述测量对象的类型信息包括血液样本、质控物或校准物。
  3. 根据权利要求1或2所述的方法，其中，所述获取测量对象的类型信息的步骤包括：
    获取所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；
    根据所述至少两种光信号形成测量对象散点图；
    将所述测量对象散点图和预设散点图进行比较，以确定所述测量对象的类型信息。
    或者，
    所述获取测量对象的类型信息的步骤包括：
    根据预先设定的测量模式获取所述测量对象的类型信息。
  4. 根据权利要求1至3中任一项所述的方法，其中，所述获取所述测量对象的检测值的步骤包括：
    获取所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；
    并且相应地，
    所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤包括：
    根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信 号进行处理，以分类和计数所述测量对象中的粒子，和/或根据所述测量对象的类型信息对所述至少两种光信号、尤其是荧光信号进行增益放大处理。
  5. 根据权利要求4所述的方法，其中，在所述根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理的步骤之前，所述方法还包括：
    根据所述测量对象的类型信息从预存的分类算法数据库中或者通过RFID或互联网获取所述分类算法。
  6. 根据权利要求1至5中任一项所述的方法，其中，所述获取所述测量对象的检测值的步骤包括：
    获取悬浮在稀释液中的测量对象的粒子的体积分布信息以及所述稀释液的温度；
    并且相应地，
    所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤包括：
    根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正。
  7. 根据权利要求6所述的方法，其中，在所述根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正的步骤之前，所述方法还包括：
    根据所述测量对象的类型信息从预存的温度修正算法数据库中或者通过RFID或互联网获取所述温度修正算法。
  8. 根据权利要求6或7所述的方法，其中，所述温度修正算法为温度修正曲线，所述温度修正曲线为所述测量对象的体积分布信息相对于温度的拟合曲线、尤其是分段拟合曲线，例如为一次拟合曲线或二次拟合曲线、尤其是分段线性函数。
  9. 根据权利要求6至8中任一项所述的方法，其中，与血液样本对应 的温度修正算法为所述血液样本的体积分布信息与温度的负相关函数，与质控物或校准物所对应的温度修正算法为所述质控物或校准物的体积分布信息与温度的正相关函数。
  10. 根据权利要求7所述的方法，其中，所述方法还包括：
    获取所述稀释液的批次，并且根据所述测量对象的类型信息和所述稀释液的批次从预存的温度修正算法数据库中或者通过RFID或互联网获得所述温度修正算法。
  11. 根据权利要求6至10中任一项所述的方法，其中，所述体积分布信息包括平均细胞体积和细胞分布宽度标准差。
  12. 根据权利要求6至11中任一项所述的方法，其中，所述方法还包括：
    根据修正后的所述体积分布信息，计算所述测量对象的与所述体积分布信息相关的其他参数信息。
  13. 根据权利要求12所述的方法，其中，所述方法还包括：
    输出修正前的所述体积分布信息、修正后的所述体积分布信息以及所述其他参数信息；
    优选地，还对修正后的所述体积分布信息和所述其他参数信息进行标记。
  14. 一种血细胞分析仪，其中，所述血细胞分析仪包括：
    采样装置，设置用于吸取测量对象；
    预处理装置，设置用于对所述测量对象进行预处理，以便获得预处理后的测量对象；
    检测装置，设置用于对所述预处理后的测量对象进行检测；
    处理器，设置用于执行下列步骤：
    获取所述测量对象的类型信息；
    获取所述测量对象的检测值；
    根据所述测量对象的类型信息对所述测量对象的检测值进行处理。
  15. 根据权利要求14所述的血细胞分析仪，其中，所述测量对象的类型信息包括血液样本、质控物或校准物。
  16. 根据权利要求14或15所述的血细胞分析仪，其中，
    所述处理器设置用于在执行获取所述测量对象的类型信息的步骤时执行下列步骤：
    获取所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；
    根据所述至少两种光信号形成测量对象散点图；
    将所述测量对象散点图和预设散点图进行比较，以确定所述测量对象的类型信息；
    或者，
    所述处理器设置用于在执行获取所述测量对象的类型信息的步骤时根据预先设定的测量模式获取测量对象的类型信息。
  17. 根据权利要求14至16中任一项所述的血细胞分析仪，其中，
    所述处理器设置用于：在执行所述获取所述测量对象的检测值的步骤时获取所述测量对象中的粒子的前向散射光信号、侧向散射光信号和荧光信号中的至少两种光信号；并且在执行所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤时，根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理，以分类和计数所述测量对象中的粒子，和/或根据所述测量对象的类型信息对所述至少两种光信号、尤其是荧光信号进行增益放大处理。
  18. 根据权利要求17所述的血细胞分析仪，其中，
    所述处理器还设置用于在根据与所述测量对象的类型信息相关的分类算法对所述至少两种光信号进行处理之前，根据所述测量对象的类型信息从预存的分类算法数据库中或者通过RFID或互联网获取所述分类算法。
  19. 根据权利要求14至18中任一项所述的血细胞分析仪，其中，所述血细胞分析仪还包括传感装置，设置用于检测所述测量对象所处的稀释液的温度；以及
    所述处理器设置用于：在执行获取所述测量对象的测量值的步骤时获取悬浮在所述稀释液中的测量对象的体积分布信息以及所述稀释液的温度；并且在执行所述根据所述测量对象的类型信息对所述测量对象的检测值进行处理的步骤时，根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正。
  20. 根据权利要求19所述的血细胞分析仪，其中，
    所述处理器设置用于在执行所述根据所述稀释液的温度，利用与所述测量对象的类型信息相关的温度修正算法对所述体积分布信息进行修正的步骤之前，根据所述测量对象的类型信息从预存的温度修正算法数据库中或者通过RFID或互联网获取所述温度修正算法。
  21. 根据权利要求19或20所述的血细胞分析仪，其中，所述温度修正算法为温度修正曲线，所述温度修正曲线为所述测量对象的体积分布信息相对于温度的拟合曲线、尤其是分段拟合曲线，例如为一次拟合曲线或二次拟合曲线、尤其是分段线性函数。
  22. 根据权利要求19至21中任一项所述的血细胞分析仪，其中，与血液样本对应的温度修正算法为所述血液样本的体积分布信息与温度的负相关函数，与质控物或校准物所对应的温度修正算法为所述质控物或校准物的体积分布信息与温度的正相关函数。
  23. 根据权利要求19至22中任一项所述的血细胞分析仪，其中，
    所述处理器还设置用于根据修正后的所述体积分布信息，计算所述测量对象的与所述体积分布信息相关的其他参数信息。
  24. 根据权利要求19至23中任一项所述的血细胞分析仪，其中，
    所述处理器还设置用于输出修正前的所述体积分布信息、修正后的所述 体积分布信息以及所述其他参数信息；以及对修正后的所述体积分布信息和所述其他参数信息进行标记。
  25. 一种计算机可读存储介质，其上存储有程序，所述程序被处理器执行时实现如权利要求1-13中任一项所述的处理测量对象的参数的方法的各个步骤。
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