CN117825243A - Blood cell analyzer, blood analysis method and use of infection marker parameters - Google Patents
Blood cell analyzer, blood analysis method and use of infection marker parameters Download PDFInfo
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Abstract
The present invention relates to a blood cell analyzer, a blood analysis method and the use of infection marker parameters in assessing the infection status of a subject. The sampling device collects a blood sample to be tested of a subject; the sample preparation device prepares a first measurement sample from a blood sample to be measured; the optical detection device detects first optical information of a first measurement sample; and the processor is configured to: generating a first scatter diagram based on at least two kinds of light intensity information in the first optical information, obtaining particle information of a first cell population including toxic granulocytes based on first cell population characteristic region information in the first scatter diagram, obtaining particle information of naive granulocytes, and obtaining particle information of toxic granulocytes based on the particle information of the first cell population and the particle information of the naive granulocytes. Information of toxic granulosa cells in the blood sample can thereby be obtained using the blood cell analyzer for subsequent assessment of the infection status of the subject.
Description
Technical Field
The present invention relates to the field of in vitro diagnostics, in particular to a blood cell analyzer, a blood analysis method and the use of infection marker parameters in assessing the infection status of a subject.
Background
The blood cell analyzer is an instrument capable of analyzing and detecting cells in a blood sample, and can obtain counting and classifying information of cells such as White Blood Cells (WBC), red Blood Cells (RBC), platelets (PLT), nucleated Red Blood Cells (NRBC), reticulocytes (RET) and the like; under certain conditions, the blood cell analyzer may further obtain five-class information of White Blood Cells (WBCs), including Lymphocytes (LYM), monocytes (MON), neutrophils (NEU), eosinophils (EOS), BASO. In addition, in special or abnormal blood samples, there may be abnormal cells, and detection of these abnormal cells is often significant in indicating a special condition of some diseases or organisms.
For example, infectious diseases are clinically common diseases in which Sepsis (sepis) belongs to a serious infectious disease. The incidence rate of sepsis is high, more than 1800 ten thousand severe sepsis cases are present every year worldwide, and the illness state of sepsis is dangerous, the illness death rate is high, and about 14,000 people die of the complications every day worldwide. According to foreign epidemiological surveys, the death rate of sepsis has exceeded myocardial infarction, and is the main cause of death of non-heart patients in intensive care units. In recent years, despite advances in anti-infective therapy and organ function support techniques, the mortality rate of sepsis is still as high as 30% to 70%. The sepsis treatment cost is high, the medical resource consumption is high, the life quality of human beings is seriously influenced, and the human health is greatly threatened.
Studies have shown that, in the case of severe infections or extensive burns in the body, some coarse, unequal-sized, unevenly distributed purple-black or dark-purple-brown particles, called toxic particles (Toxic Granulation, TG), appear in the cytoplasm of Neutrophils (NEU) ("clinical laboratory foundation", 5 th edition, liu Chengyu, luo Chunli, people health publishers). The cause of toxic granule cell production may be related to the blocked production process of specific granules or the denaturation of granules, resulting in fusion of 2-3 azurophil granules.
Therefore, analytical detection of toxic particles (Toxic Granulation, TG) in blood samples is critical for clinical indication of the infection status of patients.
Disclosure of Invention
Therefore, in order to solve the above-mentioned technical problems at least partially, it is an object of the present application to provide a blood cell analyzer, a blood analysis method and a use of an infection marker parameter in assessing an infection status of a subject, which can obtain information of poisoning particles from an original signal of a blood routine detection process, and further obtain an infection marker parameter with high diagnostic efficacy, so as to provide accurate and effective prompt information for a user based on the infection marker parameter to prompt the infection status of the subject.
To achieve the above-described object of the present application, a first aspect of the present application provides a blood cell analyzer including:
the sampling device is used for collecting a blood sample to be tested of a subject;
sample preparation means for preparing a first assay sample; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent;
an optical detection device including a flow cell for passing the first measurement sample, a light source for irradiating the first measurement sample passing through the flow cell with light, and a light detector for detecting first optical information generated after the first measurement sample is irradiated with light while passing through the flow cell; and
a processor configured to:
generating a first scatter plot based on at least two light intensity information in the first optical information,
obtaining particle information of a first cell population of the subject based on first cell population characteristic region information in the first scattergram, the first cell population comprising toxic granulosa cells;
Obtaining particle information of naive granulocytes of the subject,
obtaining particle information of toxic granulocytic cells of the subject based on the particle information of the first population of cells and the particle information of the naive granulocytes; and is also provided with
Based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject.
To achieve the above task of the present application, the second aspect of the present application further provides a method for evaluating an infection status of a subject, the method comprising:
collecting a blood sample to be tested of a subject;
preparing a first measurement sample; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent;
passing the particles in the first measurement sample one by one through the optical detection area irradiated by the light to obtain first optical information generated by the particles in the first measurement sample after the particles are irradiated by the light; and
generating a first scatter plot based on at least two light intensity information in the first optical information,
obtaining particle information of a first cell population of the subject based on first cell population characteristic region information in the first scattergram, the first cell population comprising toxic granulosa cells;
Obtaining particle information of naive granulocytes of the subject;
obtaining particle information of toxic granulocytic cells of the subject based on the particle information of the first population of cells and the particle information of the naive granulocytes; and is also provided with
Based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject.
To achieve the above task of the present application, a third aspect of the present application further provides a use of an infection marker parameter in assessing an infection status of a subject, wherein the infection marker parameter is obtained by:
acquiring optical information obtained by detecting a first measurement sample by flow cytometry; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent; and
generating a scatter plot based on at least two light intensity information of the optical information,
obtaining particle information of a first cell population of the subject based on first cell population characteristic region information in the scatter plot, the first cell population comprising toxic granulosa cells;
Obtaining particle information of naive granulocytes of the subject;
obtaining particle information of toxic granulocytic cells of said subject based on the particle information of said first population of cells and the particle information of said naive granulocytes,
based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject.
Drawings
The invention will be more clearly elucidated in connection with the examples and the accompanying drawings. The above-described and other advantages will become apparent to those skilled in the art from the detailed description of embodiments of the invention. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. The same or similar reference numbers will be used throughout the drawings to refer to the same parts. In the drawings:
fig. 1 is a schematic diagram of a blood cell analyzer according to some embodiments of the present application.
Fig. 2 is a schematic structural diagram of an optical detection device according to some embodiments of the present application.
FIG. 3 is a FL-SSC two-dimensional scattergram of an assay sample according to some embodiments of the present application.
FIG. 4 is a two-dimensional scatter plot of SSC-FSC of an assay sample according to some embodiments of the present application.
FIG. 5 is a FL-SSC two-dimensional scattergram of an assay sample according to some embodiments of the present application.
Fig. 6 is a schematic diagram of clinical toxic granulosa cell classification.
FIG. 7 is a FL-FS two-dimensional scatter plot of an assay sample according to some embodiments of the present application.
FIG. 8 is an SS-FS two-dimensional scatter plot of an assay sample according to some embodiments of the present application.
FIG. 9 is a FL-SS-FS three-dimensional scatter plot of an assay sample according to some embodiments of the present application.
Fig. 10 illustrates particle information for determining a population of white blood cells in a sample according to some embodiments of the present application.
FIG. 11 is a FL-FS two-dimensional scatter plot of an assay sample according to some embodiments of the present application.
FIG. 12 is an SS-FS two-dimensional scatter plot of an assay sample according to some embodiments of the present application.
FIG. 13 is a FL-SS-FS three-dimensional scatter plot of an assay sample according to some embodiments of the present application.
Fig. 14 is a schematic flow chart for determining patient progression according to some embodiments of the present application.
Fig. 15 is a schematic flow chart of a blood analysis method according to some embodiments of the present application.
Fig. 16 is an ROC curve in an early-stage sepsis prediction scenario according to some embodiments of the present application.
Fig. 17 is an ROC curve in a sepsis diagnostic context according to some embodiments of the present application.
Fig. 18 is an ROC curve in the context of a prognostic analysis of sepsis according to some embodiments of the present application.
Fig. 19 is a ROC curve in a scene of identification of common and severe infections according to some embodiments of the present application.
Fig. 20 is a ROC curve in the context of identification of viral and bacterial infections according to some embodiments of the present application.
Fig. 21 is a ROC curve in the context of the identification of infectious and non-infectious inflammation according to some embodiments of the present application.
FIG. 22 is a graph of the change in the values of an infection marker parameter used to monitor the progression of a severe infectious condition, according to some embodiments of the present application.
Fig. 23 is an ROC curve in the context of evaluation of the effect of treatment of sepsis according to some embodiments of the present application.
Detailed Description
Embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which it is shown, however, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, the term "first\second\third" related to the embodiment of the present invention is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise.
For convenience of the following description, some terms referred to hereinafter are first briefly described as follows.
1) Scatter plot: the two-dimensional or three-dimensional image is generated by the blood cell analyzer, and two-dimensional or three-dimensional characteristic information of a plurality of particles is distributed on the two-dimensional or three-dimensional image, wherein an X coordinate axis, a Y coordinate axis and a Z coordinate axis of the scatter diagram represent a characteristic of each particle, for example, in the scatter diagram, the X coordinate axis represents forward scattered light intensity, the Y coordinate axis represents fluorescence intensity, and the Z coordinate axis represents side scattered light intensity.
2) Particle mass/cell population: a population of particles, e.g., a population of leukocytes (including all types of leukocytes), and a subpopulation of leukocytes, e.g., a neutrophil population, a lymphocyte population, a monocyte population, an eosinophil population, a basophil population, etc., formed from a plurality of particles having the same cellular characteristics, distributed in a region of the scatter plot.
3) Blood shadow: is a particle of fragments obtained by dissolving red blood cells and platelets in blood with a hemolyzing agent.
4) ROC curve: the working characteristic curve of the test subject is a curve drawn by taking the true positive rate as an ordinate and the false positive rate as an abscissa according to a series of different classification modes (demarcation threshold values), and ROC_AUC represents the area enclosed by the ROC curve and the horizontal coordinate axis.
5) Toxic granulosa cells: refers to neutrophils that appear in the cytoplasm as coarse, non-uniform sized, unevenly distributed purple-black or dark purple-brown particles (i.e., toxic particles (Toxic Granulation, TG)).
6) Particle concentration: refers to the ratio information of the number of particles of a certain type to the number of total leukocyte particles in the blood sample. For example, the particle concentration of toxic granulosa cells is the ratio of the number of particles of toxic granulosa cells to the total number of leukocytes (also known as the toxic proportion, TG%).
7) Particle ratio: refers to the ratio information of the number of particles of a certain type to the number of particles of neutrophils, or to the ratio information of the particle concentration of particles of a certain type to the particle concentration of neutrophils. For example, the particle ratio of toxic granulocytes is the ratio of the particle number of toxic granulocytes to the particle number of neutrophils, or the ratio of the particle concentration of toxic granulocytes to the particle concentration of neutrophils (also known as the toxicity index, TGI%).
Currently, blood cell analyzers typically count and sort leukocytes through DIFF channels and/or WNB channels. Among them, the blood cell analyzer classifies leukocytes into four types of leukocytes, i.e., lymphocytes (Lym), monocytes (Mon), neutrophils (Neu) and eosinophils (Eos) through the DIFF channel. The blood cell analyzer recognizes nucleated red blood cells through the WNB channel, and can obtain nucleated red blood cell count, white blood cell count and basophil count at the same time. Combining DIFF channels with WNB channels can yield five classes of white blood cells, including lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), eosinophils (Eos), basophils (Baso) and the like.
Blood cell analyzers as used herein classify and count particles in blood samples by flow cytometry in combination with laser light scattering and/or fluorescent staining. Here, the principle of the blood cell analyzer for detecting a blood sample may be, for example: firstly sucking a blood sample, and treating the blood sample with a hemolytic agent and/or a fluorescent dye, wherein red blood cells are destroyed and dissolved by the hemolytic agent, but white blood cells are not dissolved, but the fluorescent dye can enter cell nuclei of the white blood cells with the help of the hemolytic agent and be combined with nucleic acid substances in the cell nuclei; the particles in the sample then pass through the detection holes irradiated by the laser beam one by one, and when the laser beam irradiates the particles, the characteristics of the particles (such as volume, dyeing degree, cell content size and content, cell nucleus density and the like) can block or change the direction of the laser beam, so that scattered light with various angles corresponding to the characteristics of the scattered light can be generated, and the scattered light can obtain relevant information on the structure and composition of the particles after being received by the signal detector. Wherein Forward scatter (FS/FSC) reflects the number and volume of particles, side scatter (SS/SSC) reflects the complexity of the internal cell structures (e.g., intracellular particles or nuclei), and Fluorescence (FL/SFL) reflects the content of nucleic acid material in the cell. The light information can be used to classify and count particles in the sample.
FIG. 1 is a schematic diagram of a blood cell analyzer according to some embodiments of the present application. The blood cell analyzer 100 includes a sampling device 110, a sample preparation device 120, an optical detection device 130, and a processor 140. The blood cell analyzer 100 also has a fluid path system, not shown, for communicating the sample application device 110, the sample preparation device 120, and the optical detection device 130 for fluid transfer between these devices.
The sampling device 110 is used for sucking a blood sample to be tested of a subject.
In some embodiments, sampling device 110 has a sampling needle (not shown) for aspirating a blood sample to be tested. Furthermore, the sampling device 110 may comprise, for example, a drive device for driving the sampling needle to quantitatively draw the blood sample to be measured through the mouth of the sampling needle. Sampling device 110 may deliver the aspirated blood sample to sample preparation device 120.
The sample preparation device 120 is at least for preparing a measurement sample containing at least a portion of a blood sample to be measured, a hemolysis agent and/or a staining agent for cell sorting.
In some embodiments, sample preparation device 120 is for preparing a first assay sample comprising at least a portion of the blood sample to be tested, a first hemolysis agent, and a first staining agent, or for preparing a first assay sample comprising at least a portion of the blood sample to be tested and a first hemolysis agent, or for preparing a first assay sample comprising at least a portion of the blood sample to be tested, a first hemolysis agent, a first staining agent, and a second staining agent.
In some embodiments, the sample preparation device 120 is further configured to prepare a second assay sample comprising at least a portion of the blood sample to be tested, a second hemolysis agent, and a second staining agent.
In the examples herein, the hemolytic agent is used to lyse erythrocytes in blood, lyse the erythrocytes into fragments, but can keep the morphology of leukocytes substantially unchanged.
In some embodiments, the hemolysis agent may be any one or a combination of several of cationic surfactants, nonionic surfactants, anionic surfactants, amphiphilic surfactants. In other embodiments, the hemolytic agent may include at least one of an alkyl glycoside, a triterpenoid saponin, a steroid saponin.
In some embodiments, the first hemolytic agent is different from the second hemolytic agent, and in particular the first hemolytic agent lyses erythrocytes to a lesser extent than the second hemolytic agent lyses erythrocytes.
In embodiments of the present application, the stain is a fluorescent dye for achieving cell classification, for example, the first stain may be a fluorescent dye capable of achieving differentiation of at least white blood cells and nucleated red blood cells in a blood sample, and the second stain is a fluorescent dye capable of achieving classification of white blood cells in a blood sample into at least three subpopulations of white blood cells (monocytes, lymphocytes and neutrophils).
In some embodiments, the stain may comprise a membrane-specific dye or a mitochondrial-specific dye, for more details, reference may be made to PCT patent application WO2019/206300A1 filed by applicant at 2019, month 4, 26, the entire disclosure of which is incorporated herein by reference.
In other embodiments, the colorant may comprise a cationic cyanine compound, for more details, reference to chinese patent application CN101750274a filed by applicant at 2019, 9, 28, the entire disclosure of which is incorporated herein by reference.
In some embodiments, the sample preparation device 120 may include at least one reaction cell and a reagent supply device (not shown). The at least one reaction cell is for receiving a blood sample to be measured sucked by the sampling device 110, and the reagent supply device supplies a processing reagent (including a hemolyzing agent, a staining agent, etc.) to the at least one reaction cell, so that the blood sample to be measured sucked by the sampling device 110 and the processing reagent supplied by the reagent supply device are mixed in the reaction cell to prepare a measurement sample.
For example, the at least one reaction cell may include a first reaction cell and a second reaction cell, and the reagent supply device may include a first reagent supply part and a second reagent supply part. The sampling device 110 is used for partially distributing the sucked blood sample to be tested to the first reaction tank and the second reaction tank respectively. The first reagent supply section is configured to supply a first hemolyzing agent and a first staining agent for identifying nucleated red blood cells to the first reaction cell, so that a portion of the blood sample to be measured allocated to the first reaction cell is mixed and reacted with the first hemolyzing agent and the first staining agent to prepare a first measurement sample. The second reagent supply section is configured to supply a second hemolyzing agent and a second staining agent for leukocyte classification to the second reaction cell, so that a part of the blood sample to be measured allocated to the second reaction cell is mixed and reacted with the second hemolyzing agent and the second staining agent to prepare a second measurement sample.
The optical detection device 130 includes a flow cell for passing the measurement sample, a light source for irradiating the measurement sample passing through the flow cell, respectively, with light, and a photodetector for detecting optical information generated after the measurement sample is irradiated with light while passing through the flow cell.
For example, the first measurement sample and the second measurement sample pass through the flow cell, respectively, the first measurement sample and the second measurement sample passing through the flow cell are irradiated by the light source, and the photodetector is used for detecting first optical information and second optical information generated after the first measurement sample and the second measurement sample are irradiated by the light while passing through the flow cell, respectively.
It will be understood herein that the first detection channel (also referred to as the WNB channel) for identifying nucleated red blood cells refers to the detection of a first measurement sample prepared by the sample preparation device 120 by the optical detection device 130, and the second detection channel (also referred to as the DIFF channel) for classifying white blood cells refers to the detection of a second measurement sample prepared by the sample preparation device 120 by the optical detection device 130.
In this context, a flow cell refers to a chamber adapted to detect focused liquid flow of light scattering signals and fluorescent signals. When a particle, such as a blood cell, passes through the detection aperture of the flow cell, the particle scatters the incident light beam from the light source directed toward the detection aperture in various directions. The light detector may be arranged at one or more different angles relative to the incident light beam to detect light scattered by the particles to obtain a light scattering signal. Since different particles have different light scattering properties, the light scattering signal can be used to distinguish between different populations of particles. In particular, the light scattering signal detected in the vicinity of the incident light beam is generally referred to as a forward light scattering signal or a small angle light scattering signal. In some embodiments, the forward light scatter signal may be detected from an angle of about 1 ° to about 10 ° from the incident light beam. In other embodiments, the forward light scatter signal may be detected from an angle of about 2 ° to about 6 ° from the incident light beam. The light scattering signal detected in a direction at about 90 ° to the incident light beam is generally referred to as a side light scattering signal. In some embodiments, the side scatter signal may be detected from an angle of about 65 ° to about 115 ° from the incident light beam. Typically, fluorescent signals from blood cells stained with a fluorescent dye are also typically detected in a direction that is about 90 ° from the incident light beam.
In some embodiments, the light detector may include a forward scatter light detector for detecting a forward scatter light signal (or forward scatter light intensity), a side scatter light detector for detecting a side scatter light signal (or side scatter light intensity), and/or a fluorescence light detector for detecting a fluorescence signal (or fluorescence intensity). Accordingly, the optical information may include forward scattered light signals, side scattered light signals, and/or fluorescent signals of particles in the assay sample.
Fig. 2 shows a specific example of the optical detection device 130. The optical detection device 130 has a light source 101, a beam shaping assembly 102, a flow cell 103 and a forward scatter detector 104 arranged in that order in a straight line. On one side of the flow chamber 103, a dichroic mirror 106 is arranged at an angle of 45 ° to the straight line. A part of the lateral light emitted by the particles in the flow cell 103 is transmitted through the dichroic mirror 106, and is captured by the fluorescence detector 105 arranged behind the dichroic mirror 106 at an angle of 45 ° to the dichroic mirror 106; another portion of the side light is reflected by the dichroic mirror 106 and captured by a side scatter detector 107 arranged in front of the dichroic mirror 106 at an angle of 45 ° to the dichroic mirror 106.
The processor 140 is configured to process and calculate the data to obtain a desired result, for example, a two-dimensional scattergram or a three-dimensional scattergram may be generated from various collected optical signals, and particle analysis may be performed on the scattergram according to a gating (gating) method. The processor 140 may also perform a visualization process on the intermediate operation result or the final operation result, and then display the result on the display device 150. In the present embodiment, the processor 140 is configured to implement the method steps described in further detail below.
In an embodiment of the application, the processor includes, but is not limited to, a central processing unit (Central Processing Unit, CPU), a micro control unit (Micro Controller Unit, MCU), a Field programmable gate array (Field-Programmable Gate Array, FPGA), a Digital Signal Processor (DSP), etc., for interpreting computer instructions and processing data in computer software. For example, the processor is configured to execute computer applications in the computer readable storage medium, thereby causing the blood cell analyzer 100 to perform a corresponding detection procedure and analyze the optical information or optical signals detected by the optical detection device 130 in real time.
In addition, blood cell analyzer 100 may also include a first housing 160 and a second housing 170. The display device 150 may be, for example, a user interface. The optical detection device 130 and the processor 140 are disposed inside the second housing 170. The sample preparation device 120 is disposed inside the first housing 160, for example, and the display device 150 is disposed on an outer surface of the first housing 160 and is used to display a detection result of the blood cell analyzer, for example.
As mentioned in the background art, analysis and detection of toxic particles (Toxic Granulation, TG) in a blood sample are critical for clinical indication of the infection status of a patient, therefore, by intensively studying the original signal characteristics of blood routine detection of a large number of blood samples of an infected patient, the inventor finds that particle information of toxic particle cells can be obtained on a blood cell analyzer by analyzing specific cell group characteristic region information, and further, infection marker parameters with higher diagnostic efficacy can be obtained based on the particle information of toxic particle cells, so that accurate and effective prompt information can be provided for a user based on the infection marker parameters to indicate the infection status of a subject.
Accordingly, embodiments of the present application first provide a blood cell analyzer, comprising:
a sampling device 110 for collecting a blood sample to be tested of a subject;
sample preparation means 120 for preparing a first assay sample; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent;
an optical detection device 130 including a flow cell for passing the first measurement sample, a light source for irradiating the first measurement sample passing through the flow cell with light, and a light detector for detecting first optical information generated after the first measurement sample is irradiated with light while passing through the flow cell; and
A processor 140 configured to:
generating a first scatter plot based on at least two light intensity information in the first optical information,
obtaining particle information for a first cell population of the subject based on first cell population characteristic region information in the first scattergram, the first cell population comprising toxic granulosa cells,
obtaining particle information of naive granulocytes of the subject,
based on the particle information of the first cell population and the particle information of the naive granulocytes, particle information of toxic granulocytes of the subject is obtained.
Further, the processor 140 may be configured to: based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject. The infection flag parameter may be used to indicate an infection status of the subject.
Further, the processor 140 may be configured to output the acquired particle information and/or infection flag parameters of the toxic granulosa cells to a display device for display. The display device may be the display device 150 of the blood cell analyzer 100 or may be another display device communicatively connected to the processor 140. For example, the processor 140 may output the prompt information to a display device on the user (doctor) side through the hospital information management system.
In a specific embodiment, as shown in fig. 3, the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolysis agent, and a first staining agent, and the at least two light intensity information includes first side scattered light intensity information and first fluorescence intensity information from the first staining agent. Based on the first side scatter light intensity information and the first fluorescence intensity information from the first stain, a first cell population characteristic region in the assay sample can be partitioned, the first cell population comprising a population of toxic granulocytes and a population of naive granulocytes. The inventors found through analysis that the side scatter light intensity SSC of the toxic granulocyte population is greater than the side scatter light intensity SSC of the neutrophil population or the lymphocyte population LYM, the fluorescence intensity FL of the toxic granulocyte population is greater than the fluorescence intensity SFL of the lymphocyte population LYM, but the side scatter light information SSC and the fluorescence information FL of the toxic granulocyte population substantially overlap with the side scatter light information SSC and the fluorescence information SFL of the naive granulocyte population (i.e., both toxic granulocyte and naive granulocyte fall into the characteristic region of the first cell population), so that the particle information of the naive granulocyte can interfere with the acquisition of the particle information of the toxic granulocyte, and further acquisition of the particle information of the naive granulocyte and elimination of interference thereof in the particle information of the first cell population are required, thereby obtaining the particle information of the toxic granulocyte. Fig. 3 is a two-dimensional scattergram generated based on the side scattered light intensity SSC and the fluorescence intensity SFL in the optical information.
In another specific embodiment, as shown in fig. 4, the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolysis agent, and the at least two light intensity information includes first side scattered light intensity information and first forward scattered light intensity information. Based on the first side scattered light intensity information and the first forward scattered light intensity information, a first cell population characteristic region in the assay sample can be partitioned, the first cell population comprising a population of toxic granulosa cells and a population of naive granulosa cells; further acquiring particle information of the naive granulocytes and subtracting the interference thereof in the particle information of the first cell population, thereby obtaining particle information of the toxic granulocytes. Fig. 4 is a two-dimensional scattergram generated based on the side scattered light intensity SSC and the forward scattered light intensity FSC in the optical information.
Further, in a specific embodiment, as shown in fig. 5-6, the particle information of the toxic granulocytes obtained based on the particle information of the first cell population and the particle information of the naive granulocytes includes classification information of the toxic granulocytes, and the classification information of the toxic granulocytes includes: particle information of slightly toxic granulosa cells (LTG), particle information of moderately toxic granulosa cells (MTG), particle information of severely toxic granulosa cells (HTG), and particle information of extremely toxic granulosa cells (BTG). Here, the processor 140 is further configured to: and obtaining classification information of the toxic granular cells based on the first side scattered light intensity information.
The individual toxic granule cells are classified clinically according to the size, shade and quantity of toxic granule in proportion to cytosol, and the higher the classification level, the higher the infection degree (see reference Cai Yan, etc., volume 29, 19 of journal of practical medicine 2013). The classification information of the toxic granulosa cells comprises:
non-toxic granulosa cells (class 0): toxic particles (TG) were not seen in the cytoplasm;
particle information of slightly toxic granulosa cells (class 1+): toxic particles (TG) present in the cytoplasm are finer and less numerous (0-25%);
particle information of moderately toxic granulosa cells (class rating 2+): the toxic particles (TG) in the cytoplasm are finer and more in quantity (25-50%);
particle information of severely toxic granulosa cells (class 3+): toxic particles (TG) present in the cytoplasm are present in coarse particles (50-75%);
particle information of extremely toxic granulosa cells (class 4+): the cytoplasm is filled with coarse toxic particles (TG) (75-100%).
Fig. 5 is a three-dimensional scatter diagram generated based on the side scatter intensity SSC, the forward scatter intensity FSC, and the fluorescence intensity SFL in the optical information, and fig. 6 is a schematic diagram of clinical toxic granulocytic classification.
For example, the blood cell analyzer may compare the side scatter light intensity information in the particle information of the toxic granulosa cells to a threshold from weak to strong, thereby classifying the toxic granulosa cells. For example, particle information having a side scatter light intensity information smaller than a first threshold value is reported as non-toxic particle cells (0), particle information having a side scatter light intensity information larger than a first threshold value smaller than a second threshold value is reported as slightly toxic particle cells (1+), particle information having a side scatter light intensity information larger than a second threshold value smaller than a third threshold value is reported as moderately toxic particle cells (2+), particle information having a side scatter light intensity information larger than a third threshold value smaller than a fourth threshold value is reported as severely toxic particle cells (3+), and particle information having a side scatter light intensity information larger than a fourth threshold value is reported as extremely toxic particle cells (4+).
In some embodiments, the processor 140 may be configured to obtain particle information for naive granulocytes of the subject based on user input. That is, the particle information of the immature granulocytes of the subject is input into the blood cell analysis by the user.
In other embodiments, the sample preparation device 120 is further configured to prepare a second assay sample comprising at least a portion of the blood sample to be tested, a second hemolysis agent, and a second staining agent; the flow cell 130 is further configured to pass the second measurement sample, the light source is further configured to irradiate the second measurement sample passing through the flow cell with light, and the photodetector is further configured to detect second optical information generated after the second measurement sample is irradiated with light while passing through the flow cell; the processor 140 generates a second scatter plot based on at least two light intensity information in the second optical information, the at least two light intensity information in the second optical information including second side scatter light intensity information and second fluorescence intensity information from a second stain, the processor 140 being further configured to obtain particle information of naive granulocytes of the subject based on the naive granulocyte characterization region in the second scatter plot. For example, the blood cell analyzer detects a sample to be tested of a subject using the above-described WNB channel and DIFF channel, that is, acquires particle information of a first cell group of the subject from the WNB channel for identifying nucleated red blood cells, acquires particle information of naive granulocytes of the subject from the DIFF channel for classifying white blood cells, and further acquires particle information of toxic granulocytes of the subject based on the particle information of the first cell group and the particle information of the naive granulocytes. Thus, the particle information of the toxic particle cells of the subject can be obtained by utilizing the existing detection channel of the blood cell analyzer.
In yet other embodiments, the processor 140 may also be configured to obtain particle information of naive granulocytes of the subject from another blood cell analyzer.
In some embodiments, the particle information of the first population of cells comprises a particle concentration of the first population of cells, the particle information of the naive granulocytes comprises a particle concentration of the naive granulocytes, and the particle information of the toxic granulocytes comprises a particle concentration of the toxic granulocytes. The processor 140 obtains the particle concentration of the toxic granulosa cells based on the particle concentration of the first cell population and the particle concentration of the naive granulosa cells, wherein the particle concentration of the toxic granulosa cells is the difference between the particle concentration of the first cell population and the particle concentration of the naive granulosa cells, as shown in the following formula:
particle concentration of toxic granulosa cells (TG%) =particle concentration of first cell population-particle concentration of naive granulosa cells (img#).
In some embodiments, the processor 140 may be further configured to: particle information of neutrophils of the subject is acquired, and particle information of toxic granulocytes of the subject is obtained based on the particle information of the first cell population, the particle information of naive granulocytes, and the particle information of neutrophils.
In some specific examples, the processor 140 further obtains particle information of neutrophils of the subject, wherein the particle information of neutrophils includes a particle concentration of neutrophils. The particle information of the first cell population comprises a particle concentration of the first cell population and a particle proportion of the first cell population, wherein the particle proportion of the first cell population is a ratio of the particle concentration of the first cell population to the particle concentration of the neutrophil. The particle information of the immature granulocytes comprises the particle concentration of the immature granulocytes and the particle proportion of the immature granulocytes, wherein the particle proportion of the immature granulocytes is the ratio of the particle concentration of the immature granulocytes to the particle concentration of the neutrophils. The particle information of the toxic granular cells comprises the particle proportion of the toxic granular cells. Here, the processor 140 further obtains the particle ratio of the toxic granular cells based on the particle ratio of the first cell population and the particle ratio of the naive granular cells, wherein the particle ratio of the toxic granular cells is a difference between the particle ratio of the first cell population and the particle ratio of the naive granular cells, as shown in the following formula:
Particle ratio of first cell population = particle concentration of toxic granulosa cells/particle concentration of neutrophils x 100%
Particle ratio of naive granulocytes = particle concentration of naive granulocytes/particle concentration of neutrophils x 100%
Particle ratio of toxic granulosa cells (TGI%) = particle ratio of first cell population-particle ratio of naive granulosa cells (IMG%).
In other specific examples, the processor 140 further obtains particle information of neutrophils of the subject, wherein the particle information of neutrophils includes a particle concentration of neutrophils. The particle information of the first cell population includes a particle concentration of the first cell population, the particle information of the naive granulocytes includes a particle concentration of the naive granulocytes, and the particle information of the toxic granulocytes includes a particle proportion of the toxic granulocytes. Here, the processor 140 further obtains the particle ratio of the toxic granular cells based on the particle concentration of the first cell population, the particle concentration of the naive granular cells, and the particle concentration of the neutrophils, wherein the particle ratio of the toxic granular cells is a ratio of a difference between the particle concentration of the first cell population and the particle concentration of the naive granular cells to the particle concentration of the neutrophils, as shown in the following formula:
Particle ratio of toxic granulocytes (TGI%) = (particle concentration of first cell population-particle concentration of naive granulocytes (img#))/particle concentration of neutrophils.
In some embodiments, the processor 140 may be configured to obtain particle information of neutrophils of the subject based on user input. That is, the particle information of neutrophils in the subject is input into the blood cell analysis by the user.
In other embodiments, the sample preparation device 120 is further configured to prepare a second assay sample comprising at least a portion of the blood sample to be tested, a second hemolysis agent, and a second staining agent; the flow cell 130 is further configured to pass the second measurement sample, the light source is further configured to irradiate the second measurement sample passing through the flow cell with light, and the photodetector is further configured to detect second optical information generated after the second measurement sample is irradiated with light while passing through the flow cell 130; the processor 140 generates a second scatter plot based on at least two light intensity information in the second optical information, the at least two light intensity information in the second optical information including second side scatter light intensity information and second fluorescence intensity information from a second stain, the processor 140 further obtaining particle information of neutrophils of the subject based on a neutrophil characteristic region in the second scatter plot. For example, the blood cell analyzer detects a sample to be tested of a subject using the above-described WNB channel and DIFF channel, that is, acquires particle information of a first cell group of the subject from the WNB channel for identifying nucleated red blood cells, acquires particle information of naive granulocytes of the subject and particle information of neutrophils of the subject from the DIFF channel for classifying white blood cells, and further acquires particle information of toxic granulocytes of the subject based on the particle information of the first cell group, the particle information of the naive granulocytes, and the particle information of the neutrophils. Thus, the particle information of the toxic particle cells of the subject can be obtained by utilizing the existing detection channel of the blood cell analyzer.
In yet other embodiments, the processor 140 may also be configured to obtain particle information of neutrophils of the subject from another blood cell analyzer.
Further, in some embodiments, the processor 140 obtains particle information of nucleated red blood cells of the subject based on the first optical information, and/or obtains particle information of lymphocytes, particle information of monocytes, particle information of eosinophils, and particle information of neutrophils of the subject based on the second optical information. Illustratively, the processor 140 obtains nucleated red blood cell characteristic region information in a scatter plot formed based on the first forward scattered light intensity information in the first optical information and the first fluorescent light intensity information from the first colorant, and/or the processor 140 obtains particle information of nucleated red blood cells, particle information of monocytes, particle information of eosinophils, and particle information of neutrophils of the subject based on the second side scattered light intensity information in the second optical information and the second fluorescent light intensity information from the second colorant.
In another specific embodiment, the sample preparation device 120 is configured to prepare a first assay sample comprising at least a portion of the blood sample to be tested, a first hemolysis agent, a first staining agent, and a second staining agent; the flow cell 130 is further configured to pass the first measurement sample, the light source is further configured to irradiate the first measurement sample passing through the flow cell 130 with light, and the light detector is further configured to detect first optical information generated after the first measurement sample is irradiated with light while passing through the flow cell 130; wherein the first optical information includes first side scatter light intensity information, first fluorescence intensity information from a first stain, and second fluorescence intensity information from a second stain. The processor 140 obtains particle information of a first population of cells of the subject based on a first scatter plot formed from first side scatter light intensity information and first fluorescence intensity information from a first stain, obtains particle information of naive granulocytes of the subject based on a second scatter plot formed from first side scatter light intensity information and second fluorescence intensity information from a second stain (a naive granulocyte signature region in the second scatter plot), and obtains particle information of toxic granulocytes based on the particle information of the first population of cells and the particle information of the naive granulocytes; or the processor 140 obtains the particle information of the immature granulocytes and the particle information of the neutrophils of the subject based on a second scatter plot (a immature granulocyte characteristic region and a neutrophil characteristic region in the second scatter plot) formed by the first side scatter light intensity information and the second fluorescence intensity information from the second staining agent, and obtains the particle information of the toxic granulocytes based on the particle information of the first cell population, the particle information of the immature granulocytes and the particle information of the neutrophils.
In another specific embodiment, the sample preparation device 120 is configured to prepare a first assay sample comprising at least a portion of the blood sample to be tested, a first hemolysis agent, and a second staining agent; the flow cell 130 is further configured to pass the first measurement sample, the light source is further configured to irradiate the first measurement sample passing through the flow cell 130 with light, and the light detector is further configured to detect first optical information generated after the first measurement sample is irradiated with light while passing through the flow cell 130; wherein the first optical information includes first side-scattered light intensity information, the first forward-scattered light intensity information, and second fluorescence intensity information from the second stain. The processor 140 obtains particle information of a first population of cells of the subject based on a first scatter plot formed from the first side scatter light intensity information and the first forward scatter light intensity information, obtains particle information of naive granulocytes of the subject based on a second scatter plot (a naive granulocyte feature region in the second scatter plot) formed from the first side scatter light intensity information and the second fluorescence intensity information from the second stain, and obtains particle information of toxic granulocytes based on the particle information of the first population of cells and the particle information of the naive granulocytes; or the processor 140 obtains the particle information of the immature granulocytes and the particle information of the neutrophils of the subject based on a second scatter plot (a immature granulocyte characteristic region and a neutrophil characteristic region in the second scatter plot) formed by the first side scatter light intensity information and the second fluorescence intensity information from the second staining agent, and obtains the particle information of the toxic granulocytes based on the particle information of the first cell population, the particle information of the immature granulocytes and the particle information of the neutrophils.
In some embodiments, the processor 140 is further configured to give/output a count result of the toxic granulosa cells based on the acquired particle information of the toxic granulosa cells, or to indicate the presence of toxic granulosa cells in the sample and to output the count result of toxic granulosa cells. The processor 140 may also be configured to output the obtained particle information of the toxic granulosa cells and/or the results to a display device for display. The display device may be the display device 150 of the blood cell analyzer 100 or may be another display device communicatively connected to the processor 140. For example, the processor 140 may output the prompt information to a display device on the user (doctor) side through the hospital information management system.
Next, some calculation methods of the infection flag parameters proposed in the present application are described, but the present application is not limited thereto.
In some embodiments, after the processor 140 obtains the particle information of the toxic granulosa cells, an infection marker parameter for assessing the infection status of the subject is further obtained based on the particle information of the toxic granulosa cells, the infection marker parameter being used to indicate the infection status of the subject.
In some embodiments, the infection marker parameter may consist of only particle information of toxic granulosa cells. In some examples, processor 140 calculates the infection marker parameter based only on particle information of the toxic granulosa cells. For example, the infection marker parameter may be the particle concentration (TG%) of the toxic granulosa cells, or the particle ratio (TGI%) of the toxic granulosa cells.
In other embodiments, the infection marker parameter may also be calculated from a combination of particle information of the toxic particle cells and particle information of at least one target particle population different from the toxic particle cells. To this end, the processor 140 may be further configured to:
particle information of at least one target particle group of the first measurement sample, which is different from the first cell group, is obtained based on the first optical information, e.g. a first scatter plot, and the infection marker parameter is calculated, in particular by a linear function, based on the particle information of the toxic particle cells and the particle information of the at least one target particle group.
Preferably, the processor 140 may be configured to combine the particle information of the toxic particle cells and the particle information of the at least one target particle population into an infection marker parameter by a linear function, i.e. calculate the infection marker parameter by the following formula:
Y＝A*X1+B*X2+C
Wherein Y represents an infection flag parameter, X1 represents particle information of the toxic granular cells, X2 represents particle information of the at least one target particle group, and A, B, C is a constant.
It will be appreciated that the infection marker parameter is not limited to being composed of the particle information of the toxic particle cells and the particle information of the target particle group with which the one difference is combined, but may be composed of the particle information of the toxic particle cells and the particle information of the target particle group with which the two or more differences are combined.
Of course, in other embodiments, the particle information of the toxic particle cells and the particle information of the target particle group different from the at least one particle may be combined into the infection flag parameter by a nonlinear function, which is not specifically limited in this application.
In some embodiments, the white blood cell population Wbc (including all types of white blood cells) in the measurement sample can be identified based on the forward scattered light intensity information FS, the side scattered light intensity information SS, and the fluorescence intensity information FL in the optical information from the first measurement sample, while the neutrophil population Neu and the lymphocyte population Lym in the white blood cells in the first measurement sample can be identified, as shown in fig. 7 to 9. Fig. 7 is a two-dimensional scattergram generated based on the forward scattered light signal FS and the fluorescent signal FL in the optical information, fig. 8 is a two-dimensional scattergram generated based on the forward scattered light signal FS and the side scattered light signal SS in the optical information, and fig. 9 is a three-dimensional scattergram generated based on the forward scattered light signal FS, the side scattered light signal SS, and the fluorescent signal FL in the optical information. In addition, the processor 140 is further configured to identify nucleated red blood cells in the first assay sample based on the optical information to obtain a nucleated red blood cell count.
Accordingly, in some embodiments, the at least one population of target particles in the first assay sample may comprise at least one population of cells in the first assay sample selected from the group consisting of a white blood cell population Wbc, a neutrophil population Neu, and a lymphocyte population Lym. For example, the at least one target particle population comprises a lymphocyte population Lym and a leukocyte population Wbc in the first assay sample, or comprises a neutrophil population Neu and a leukocyte population Wbc in the first assay sample, or comprises a lymphocyte population Lym and a neutrophil population Neu in the first assay sample. That is, the at least one target particle population may include one or more of lymphocyte population Lym, neutrophil population Neu, and leukocyte population Wbc in the first assay sample.
Preferably, the at least one population of target particles comprises a population of leukocytes Wbc and/or a population of neutrophils Neu. The inventors found that the combined use of particle information of the white blood cell population Wbc and/or the neutrophil population Neu in the first assay sample is advantageous for efficient assessment of the infection status during the study of blood routine detection of a large number of subject samples.
In some specific embodiments, the particle information of the at least one target particle population in the first assay sample is selected from one or more of the following particle information:
A center of gravity (n_wbc_fs_p), a center of gravity (n_wbc_ss_p), a center of gravity (n_wbc_fl_p), a width (n_wbc_fs_w), a width (n_wbc_ss_w), a width (n_wbc_fl_w), a coefficient of variation (n_wbc_fs_cv), a coefficient of variation (n_wbc_ss_cv), a coefficient of variation (n_wbc_fl_cv), a mean value, and a mean value of pulse width of forward scattered light;
the area of a distribution region of the white blood cell population in the first measurement sample in a two-dimensional scattergram generated from two light intensities based on the forward scattered light intensity, the side scattered light intensity, and the side fluorescent light intensity, and the volume of a distribution region of the white blood cell population in a three-dimensional scattergram generated from the forward scattered light intensity, the side scattered light intensity, and the fluorescent light intensity, for example: the Area of the distribution region of the white blood cell group in the two-dimensional scatter diagram generated by the side scattered light intensity and the forward scattered light intensity (n_wbc_ssfs_area), the Area of the distribution region of the white blood cell group in the two-dimensional scatter diagram generated by the side fluorescent light intensity and the forward scattered light intensity (n_wbc_flfs_area), and the Area of the distribution region of the white blood cell group in the two-dimensional scatter diagram generated by the side fluorescent light intensity and the side scattered light intensity (n_wbc_flss_area);
A center of gravity of forward scattered light intensity distribution (n_neu_fs_p), a center of gravity of side scattered light intensity distribution (n_neu_ss_p), a center of gravity of side scattered light intensity distribution (n_neu_fl_p), a width of forward scattered light intensity distribution (n_neu_fs_w), a width of side scattered light intensity distribution (n_neu_ss_w), a width of side scattered light intensity distribution (n_neu_fl_w), a coefficient of variation of forward scattered light intensity distribution (n_neu_fs_cv), a coefficient of variation of side scattered light intensity distribution (n_neu_ss_cv), a coefficient of variation of side scattered light intensity distribution (n_neu_fl_cv), a mean value of forward scattered light pulse width, a mean value of side scattered light pulse width, and a mean value of side scattered light pulse width of side fluorescence in the first measurement sample;
the area of the distribution region of the neutrophil group in the two-dimensional scattergram generated based on the two light intensities of the forward scattered light intensity, the side scattered light intensity, and the side fluorescent light intensity, and the volume of the distribution region of the neutrophil group in the three-dimensional scattergram generated from the forward scattered light intensity, the side scattered light intensity, and the fluorescent light intensity in the first measurement sample are, for example: the Area of the distribution region of the neutrophil group in the two-dimensional scattergram generated by the side scattered light intensity and the forward scattered light intensity (n_neu_ssfs_area), the Area of the distribution region of the neutrophil group in the two-dimensional scattergram generated by the side fluorescent light intensity and the forward scattered light intensity (n_neu_flfs_area), and the Area of the distribution region of the neutrophil group in the two-dimensional scattergram generated by the side fluorescent light intensity and the side scattered light intensity (n_neu_flss_area);
A center of gravity of forward scattered light intensity distribution (n_lym_fs_p), a center of gravity of side scattered light intensity distribution (n_lym_ss_p), a center of gravity of side scattered light intensity distribution (n_lym_fl_p), a width of forward scattered light intensity distribution (n_lym_fs_w), a width of side scattered light intensity distribution (n_lym_ss_w), a width of side scattered light intensity distribution (n_lym_fl_w), a coefficient of variation of forward scattered light intensity distribution (n_lym_fs_cv), a coefficient of variation of side scattered light intensity distribution (n_lym_ss_cv), a mean value of forward scattered light pulse width, a mean value of side scattered light pulse width, and a mean value of side scattered light pulse width in the first measurement sample; and
the area of the distribution region of the lymphocyte group in the two-dimensional scattergram generated based on the two light intensities of the forward scattered light intensity, the side scattered light intensity, and the side fluorescent light intensity, and the volume of the distribution region of the lymphocyte group in the three-dimensional scattergram generated from the forward scattered light intensity, the side scattered light intensity, and the fluorescent light intensity in the first measurement sample, for example: the Area of the distribution region of the lymphocyte group in the two-dimensional scattergram generated from the side scattered light intensity and the forward scattered light intensity (n_lym_ssfs_area), the Area of the distribution region of the lymphocyte group in the two-dimensional scattergram generated from the side fluorescent light intensity and the forward scattered light intensity (n_lym_flfs_area), and the Area of the distribution region of the lymphocyte group in the two-dimensional scattergram generated from the side fluorescent light intensity and the side scattered light intensity (n_lym_flss_area).
The meaning of the distribution width, the distribution center of gravity, the coefficient of variation, and the area or volume of the distribution region will be described with reference to fig. 10, wherein fig. 10 shows particle information of the white blood cell population in the measurement sample according to some embodiments of the present application.
As shown in fig. 10, W (n_wbc_fs_w) represents the forward scattered light intensity distribution width of the white blood cell population in the measurement sample, where n_wbc_fs_w is equal to the difference between the upper forward scattered light intensity distribution limit (UP) of the white blood cell population and the lower forward scattered light intensity distribution limit (DOWN) of the white blood cell population. N_wbc_fs_p represents the center of gravity of the forward scattered light intensity distribution of the white blood cell population in the measurement sample, i.e., the average position of white blood cells in the FS direction (in the figure+), where n_wbc_fs_p is calculated by the following formula:
wherein FS (i) is the forward scattered light intensity of the ith white blood cell. N_WBC_FS_CV represents the forward scattered light intensity distribution variation coefficient of the white blood cell population in the measurement sample, where N_WBC_FS_CV is equal to N_WBC_FS_W divided by N_WBC_FS_P.
In fig. 10, area (n_wbc_flfs_area) represents the Area of a distribution region of the white blood cell population in the measurement sample in a scatter diagram generated from forward scattered light intensity and fluorescence intensity. As shown in fig. 10, C represents a contour distribution curve of a leukocyte population, and for example, the total number of positions located in the contour distribution curve C can be expressed as the area of the leukocyte population.
It will be appreciated herein that the definition of the particle information of other target particle populations may be referred to in a corresponding manner with respect to the embodiment shown in fig. 10.
In other embodiments, the sample preparation device is further configured to prepare a second assay sample comprising a portion of the blood sample to be tested, a second hemolysis agent, and a second staining agent; the flow cell is further configured to pass the second measurement sample, the light source is further configured to illuminate the second measurement sample passing through the flow cell with light, and the light detector is further configured to detect second optical information generated after the second measurement sample is illuminated while passing through the flow cell. Accordingly, the processor 140 may be further configured to:
generating a second scatter plot based on at least two light intensity information in the second optical information,
calculating particle information of at least one target particle group different from the first cell group in the second measurement sample based on the second scattergram,
the infection marker parameter is calculated, in particular by a linear function, based on the particle information of the toxic particle cells and the particle information of the at least one target particle population.
In some embodiments, as shown in fig. 11 to 13, the white blood cells in the second assay sample may be classified into a monocyte population Mon, a neutrophil population Neu, a lymphocyte population Lym, and an eosinophil population Eos based on the forward scattered light intensity FS, the side scattered light intensity SS, and the fluorescence intensity FL in the second optical information of the second assay sample. Here, fig. 11 is a two-dimensional scattergram generated based on the side scattered light information SS and the fluorescence information FL in the second optical information, fig. 12 is a two-dimensional scattergram generated based on the forward scattered light information FS and the side scattered light information SS in the second optical information, and fig. 13 is a three-dimensional scattergram generated based on the forward scattered light information FS, the side scattered light information SS, and the fluorescence information FL in the second optical information.
Alternatively or additionally, in some embodiments, the at least one first target particle population of the second assay sample may comprise at least one cell population of the monocyte population Mon, the neutrophil population Neu, and the lymphocyte population Lym in the second assay sample, i.e., the particle information of the at least one target particle population may comprise one or more of the particle information of the monocyte population Mon, the neutrophil population Neu, and the lymphocyte population Lym in the second assay sample.
Preferably, the at least one first target particle population may comprise at least one cell population of the monocyte population Mon and the neutrophil population Neu in the second assay sample, i.e. the particle information of the at least one target particle population may comprise one or more, such as one or two or more combinations, of the particle information of the monocyte population Mon and the neutrophil population Neu in the second assay sample.
In some specific embodiments, the particle information of the target particle population of at least one particle information different from the poisoning particle is selected from one or more of the following particle information:
a forward scattered light intensity distribution width D_MON_FS_W, a forward scattered light intensity distribution center of gravity D_MON_FS_P, a forward scattered light intensity distribution variation coefficient D_MON_FS_CV, a side scattered light intensity distribution width D_MON_SS_W, a side scattered light intensity distribution center of gravity D_MON_SS_P, a side scattered light intensity distribution variation coefficient D_MON_SS_CV, a fluorescence intensity distribution width D_MON_FL_W, a fluorescence intensity distribution center of gravity D_MON_FL_CV, and a fluorescence intensity distribution variation coefficient D_MON_FL_CV of the mononuclear cell population in the second measurement sample;
the Area d_mon_flfs_area of the distribution region of the mononuclear cell group in the two-dimensional scattergram generated by two light intensities of the forward scattered light intensity, the side scattered light intensity, and the fluorescence intensity (the Area of the distribution region of the mononuclear cell group in the two-dimensional scattergram generated by the forward scattered light intensity and the fluorescence intensity), the d_mon_flss_area (the Area of the distribution region of the mononuclear cell group in the two-dimensional scattergram generated by the side scattered light intensity and the fluorescence intensity), the d_mon_ssfs_area (the Area of the distribution region of the mononuclear cell group in the two-dimensional scattergram generated by the forward scattered light intensity and the side scattered light intensity), and the volume of the distribution region of the mononuclear cell group in the three-dimensional scattergram generated by the forward scattered light intensity, the side scattered light intensity, and the fluorescence intensity in the second measurement sample;
A forward scattered light intensity distribution width d_neu_fs_w, a forward scattered light intensity distribution center of gravity d_neu_fs_p, a forward scattered light intensity distribution variation coefficient d_neu_fs_cv, a side scattered light intensity distribution width d_neu_ss_w, a side scattered light intensity distribution center of gravity d_neu_ss_p, a side scattered light intensity distribution variation coefficient d_neu_ss_cv, a fluorescent intensity distribution width d_neu_fl_w, a fluorescent intensity distribution center of gravity d_neu_fl_p, and a fluorescent intensity distribution variation coefficient d_neu_fl_cv of the neutrophil population in the second measurement sample;
the Area d_neu_flfs_area of the distribution region of the neutrophil group in the two-dimensional scattergram generated by the two light intensities of the forward scattered light intensity, the side scattered light intensity, and the fluorescence intensity (the Area of the distribution region of the neutrophil group in the two-dimensional scattergram generated by the forward scattered light intensity and the fluorescence intensity), the Area d_neu_flss_area of the distribution region of the neutrophil group in the two-dimensional scattergram generated by the side scattered light intensity and the fluorescence intensity), the Area d_neu_ssfs_area of the distribution region of the neutrophil group in the two-dimensional scattergram generated by the forward scattered light intensity and the side scattered light intensity, and the volume of the distribution region of the neutrophil group in the three-dimensional scattergram generated by the forward scattered light intensity, the side scattered light intensity, and the fluorescence intensity in the second measurement sample.
In some embodiments, the processor 140 may be further configured to: and outputting prompt information indicating that the infection mark parameter is abnormal when the value of the infection mark parameter is out of a preset range. For example, when the value of the infection flag parameter abnormally increases, an arrow pointing upward may be output to indicate an abnormal increase.
Optionally, the processor 140 may be further configured to output the preset range.
In some embodiments, the processor 140 may be further configured to: outputting a hint information indicative of an infection status of the subject based on the infection flag parameter. For example, the processor 140 may be configured to output the prompt to a display device for display.
In some embodiments, the processor 140 may be further configured to: when the particle information of the toxic particle cells or the particle information of the target particle group satisfies a preset condition, for example, when the total number of particles of the target particle group is smaller than a preset threshold value, and/or when the target particle group overlaps with other particle groups, the value of the infection flag parameter is not output, or the value of the infection flag parameter is output and at the same time a hint information indicating that the value of the infection flag parameter is unreliable is output.
As can be appreciated, when the total number of particles of the toxic granulosa cells or of the target population is less than a preset threshold, the amount of information of particle characterization is limited, and the calculation result of the infection marker parameter may not be reliable; when the toxic particle cells or the target particle group overlap with other particle groups, the acquired particle information of the toxic particle cells or the target particle group may be disturbed, and the calculation result of the infection flag parameter may be unreliable.
In other embodiments, the processor 140 may be further configured to: when the subject suffers from a blood disease or abnormal cells (e.g., primitive cells, abnormal lymphocytes, etc.), particularly primitive cells, are present in the blood sample to be tested, for example, when it is judged that abnormal cells, particularly primitive cells, are present in the blood sample to be tested based on the optical information, the value of the infection flag parameter is not outputted, or the value of the infection flag parameter is outputted and at the same time, a hint information indicating that the value of the infection flag parameter is unreliable is outputted.
It will be appreciated that a blood image abnormality in a subject suffering from a hematological disorder may result in unreliable calculations based on the infection marker parameters.
Next, some application scenarios of the infection flag parameter proposed in the present application are described, but the present application is not limited thereto.
In some embodiments, the infection marker parameter may be used to predict sepsis, early diagnosis of sepsis, condition monitoring of infection, prognosis evaluation of sepsis, treatment efficacy evaluation of sepsis, identification of common and severe infections, identification of viral and bacterial infections, or identification of infectious and non-infectious inflammation in a subject. For example, the processor 140 may be further configured to predict sepsis, early diagnosis of sepsis, condition monitoring of infection, prognosis evaluation of sepsis, treatment effect evaluation of sepsis, identification of common and severe infections, identification of viral and bacterial infections, or identification of infectious and non-infectious inflammation based on the infection marker parameters.
Sepsis is a severe infectious disease with a high incidence of mortality, with 7% increase in mortality per 1 hour of treatment delay. Therefore, early warning of sepsis is particularly important, early recognition and early warning of sepsis can increase precious diagnosis and treatment time for patients, and survival rate is greatly improved.
To this end, in an application scenario of early prediction of sepsis, the processor 140 may be configured to output a prompt message indicating that the subject is likely to progress to sepsis within a certain period of time after the blood sample to be measured is collected, when the infection flag parameter satisfies the first preset condition.
In some embodiments, the certain period of time is no greater than 48 hours, i.e., embodiments of the present application are capable of predicting whether a subject is likely to progress to sepsis up to two days in advance. For example, the certain period of time is between 24 hours and 48 hours, i.e., embodiments of the present application are capable of predicting whether a subject is likely to progress to sepsis one to two days in advance. Preferably, the certain period of time is not more than 24 hours. The first predetermined condition may be, for example, that the value of the infection flag parameter is greater than a predetermined threshold value. The preset threshold may be determined based on specific particle information and combinations thereof and the blood cell analyzer.
The clinical symptoms of the early stage of sepsis are similar to those of common/severe infections, and patients with sepsis are easy to misdiagnose as common/severe infectious diseases, and delay treatment time. Therefore, differential diagnosis of sepsis is particularly important.
To this end, in the context of application of sepsis diagnosis, i.e. the infection marker parameter is used for sepsis identification, in some embodiments the processor 140 may be configured to output a prompt indicating that the subject has sepsis when the infection marker parameter fulfils the second preset condition. The second predefined condition may also be that the value of the infection flag parameter is greater than a predefined threshold value. The preset threshold may be determined based on the specific combination of particle information and the blood cell analyzer.
According to the severity of infection and the functional state of organs, bacterial infection patients can be classified into common infection and severe infection, and the clinical treatment means and nursing measures of the two infections are different, so that the identification of the common infection and the severe infection can assist doctors in identifying life-threatening patients, and medical resources can be allocated more reasonably.
To this end, in an application scenario of the identification of a common infection and a severe infection, i.e. the infection flag parameter is used to determine whether the subject suffers from a common infection or a severe infection, the processor 140 may be configured to output a prompt indicating that the subject suffers from a severe infection when the infection flag parameter satisfies a third preset condition in some embodiments. The third predefined condition may also be that the value of the infection flag parameter is greater than a predefined threshold value. The preset threshold may be determined based on the specific combination of particle information and the blood cell analyzer.
It is understood that in the present application, severe infection refers to life-threatening infection, which is a systemic side effect caused by the multiplication of pathogenic microorganisms in the human body, causing a dysfunction of one organ or the whole body. Clinically, it is mainly manifested by the phenomena of general organ dysfunction and failure of general organ functions. Severe infections may include, but are not limited to, severe pneumonia, severe abdominal infections, urinary tract infections, skin infections, and infections of the central nervous system.
In the context of infection condition monitoring, a subject is an infected patient (i.e., a patient with infectious inflammation), particularly a patient with a severe infection or with sepsis, e.g., a subject from an intensive care unit. Sepsis is a serious infectious disease with a high incidence and high mortality. The patient suffering from sepsis has great fluctuation of illness state, needs daily monitoring, prevents the illness state of the patient from aggravating but does not treat in time. Therefore, it is important that clinical symptoms are combined with laboratory examination results to judge the disease progression and treatment effect of sepsis patients.
To this end, the processor 140 may be configured to monitor the progression of an infectious condition of the subject according to the infection flag parameter.
In some embodiments, the processor 140 may be further configured to monitor the progression of an infectious condition of the subject by:
obtaining values of the infection marker parameter obtained by detecting blood samples from the subject at different points in time a plurality of consecutive tests, in particular at least three consecutive tests; and is also provided with
Judging whether the condition of the subject is improved or not according to the change trend of the value of the infection mark parameter obtained by the continuous multiple detection.
In a specific example, the processor 140 may be further configured to: outputting a prompt message indicating improvement of the subject's condition when the value of the infection flag parameter obtained by the continuous multiple detection gradually decreases in trend; and outputting a prompt message indicating that the subject is suffering from an exacerbation of the disease when the value of the infection flag parameter obtained by the continuous multiple detection gradually increases in trend.
For example, values of an infection marker parameter are obtained for a plurality of consecutive days, e.g., 7 days, after the diagnosis of sepsis, and when these values of the infection marker parameter show a decreasing trend, the patient is considered to be in an improved condition, thus giving an indication of improvement in the condition.
In other embodiments, the processor 140 may be further configured to prompt the subject for a disease progression by:
obtaining a current value of the infection marker parameter obtained by a current detection of a current blood sample from a subject, and obtaining a previous value of the infection marker parameter obtained by a previous detection of a previous blood sample from the subject; and is also provided with
Monitoring the progression of the condition of the subject based on a comparison of the prior value of the infection marker parameter to a first threshold value and a comparison of the prior value of the infection marker parameter to the current value of the infection marker parameter.
Fig. 14 is a schematic flow chart for determining patient progression according to some embodiments of the present application.
As shown in fig. 14, the processor 140 may be further configured to, when the prior value of the infection flag parameter is equal to or greater than the first threshold:
outputting a prompt indicating that the subject is suffering from an exacerbation if the current value of the infection flag parameter (i.e., the current result in fig. 14) is greater than the previous value of the infection flag parameter (i.e., the previous result in fig. 14) and the difference is greater than a second threshold;
outputting a prompt message indicating that the condition of the subject is improved and the infection degree is reduced if the current value of the infection flag parameter is smaller than the previous value of the infection flag parameter and the difference value of the current value of the infection flag parameter is larger than the second threshold value and the current value of the infection flag parameter is smaller than the first threshold value;
If the current value of the infection mark parameter is smaller than the previous value of the infection mark parameter and the difference value of the current value of the infection mark parameter is larger than the second threshold value, but the current value of the infection mark parameter is larger than or equal to the first threshold value, outputting prompt information indicating that the illness state of the subject is improved but the infection is still heavier or not outputting any prompt information; and
if the difference between the current value of the infection flag parameter and the previous value of the infection flag parameter is not greater than the second threshold, outputting a prompt indicating that the subject is not significantly improved and the infection is still heavy or not outputting any prompt.
Further, as shown in fig. 14, the processor 140 may be configured to: when the prior value of the infection flag parameter is less than the first threshold:
outputting a prompt message indicating that the subject's condition is improved and the degree of infection is reduced if the current value of the infection flag parameter is less than the previous value of the infection flag parameter and the difference between the current value and the previous value is greater than the second threshold value;
outputting a prompt message indicating that the subject is ill and has a heavier infection if the current value of the infection flag parameter is greater than the previous value of the infection flag parameter and the difference value of the two is greater than the second threshold value and the current value of the infection flag parameter is greater than the first threshold value;
Outputting a prompt message indicating that the subject's condition fluctuates or infection is likely to be aggravated or not outputting a prompt message if the current value of the infection flag parameter is greater than the previous value of the infection flag parameter and the difference value of the two is greater than the second threshold value, but the current value of the infection flag parameter is less than the first threshold value; and
if the difference between the current value of the infection flag parameter and the previous value of the infection flag parameter is not greater than the second threshold, outputting a prompt indicating that the infection of the subject is not aggravated or not outputting a prompt.
In the embodiment shown in fig. 14, when the infection flag parameter is used to monitor the progress of a disease in a critically infected patient, the first threshold may be a preset threshold for determining whether the subject is suffering from a critically infected patient. And when the infection marker parameter is used to monitor the progression of the condition in a patient with sepsis, the first threshold may be a preset threshold for determining whether the subject is suffering from sepsis.
In the context of application of a sepsis prognosis assay, a subject is a sepsis patient who has received a treatment, and the infection marker parameters are used to determine whether the subject's sepsis prognosis is good. In this regard, in some embodiments, the processor 140 may be further configured to determine whether the sepsis prognosis of the subject is good based on the infection marker parameters. For example, when the infection flag parameter satisfies a fourth preset condition, a hint information is output indicating that the subject has a good prognosis of sepsis.
Infectious diseases can be classified into different infection types, such as bacterial infection, viral infection, and fungal infection, with bacterial infection and viral infection being the most common. Although the clinical symptoms of both infections are about the same, the treatment methods are quite different, so that defining the type of infection is helpful in choosing the correct treatment method. To this end, the infection marker parameter is used for identification of bacterial infection and viral infection, and in some embodiments, the processor 140 may be further configured to determine whether the type of infection of the subject is a viral infection or a bacterial infection based on the infection marker parameter.
In addition, inflammation is classified into infectious inflammation caused by infection with pathogenic microorganisms and non-infectious inflammation caused by physical factors, chemical factors or tissue necrosis. The clinical symptoms of both inflammations are almost the same, and the symptoms such as redness and fever appear, but the treatment modes of the two inflammations are not completely the same, so that it is clear what factor the inflammatory response of the patient is caused by is helpful for symptomatic treatment.
To this end, the infection marker parameter is used for the identification of non-infectious inflammation and infectious inflammation, and in some embodiments, the processor 140 may be further configured to determine whether the subject is suffering from infectious inflammation or non-infectious inflammation based on the infection marker parameter. For example, when the infection flag parameter satisfies a fifth preset condition, a prompt message indicating that the subject suffers from infectious inflammation is output.
Furthermore, the subject may be a sepsis patient who has received treatment. Accordingly, in some embodiments, the processor 140 may be further configured to determine whether the subject's sepsis prognosis is good based on the infection marker parameters.
In some embodiments, the subject may be an infected patient receiving antibiotic treatment, and the processor 140 may be further configured to determine the therapeutic effect of sepsis in the subject based on the infection marker parameters, accordingly.
The embodiment of the application also provides a sample analysis method, as shown in fig. 15. The method 200 comprises the steps of:
s210, collecting a blood sample to be tested of a subject;
s220, preparing a first measurement sample; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent; that is, in step S220, at least a portion of the blood sample to be measured, a first hemolyzing agent, and a first staining agent are mixed, or at least a portion of the blood sample to be measured and a first hemolyzing agent are mixed, to prepare a first measurement sample.
S230, enabling particles in the first measurement sample to pass through an optical detection area irradiated by light one by one, so as to obtain first optical information generated by the particles in the first measurement sample after the particles are irradiated by light;
s240, generating a first scatter diagram based on at least two light intensity information in the first optical information;
s250, obtaining particle information of a first cell population of the subject based on the first cell population characteristic region information in the first scatter plot, the first cell population including toxic granular cells;
s260, acquiring particle information of naive granulocytes of the subject; and
s270, obtaining particle information of toxic granulosa cells of the subject based on the particle information of the first cell population and the particle information of the naive granulosa cells.
In some embodiments, the at least two light intensity information in the first optical information may include first side-scattered light intensity information and first fluorescence intensity information, or the at least two light intensity information in the first optical information may include first side-scattered light intensity information and first forward-scattered light intensity information.
In some embodiments, the particle information of the toxic granulosa cells may include classification information of the toxic granulosa cells, the classification information of the toxic granulosa cells including: particle information of lightly toxic particle cells, particle information of moderately toxic particle cells, particle information of severely toxic particle cells, and particle information of extremely toxic particle cells. Accordingly, the blood analysis method may further include: and obtaining classification information of the toxic granular cells based on the first side scattered light intensity information.
In some embodiments, step S260, that is, the acquiring particle information of the naive granulocytes of the subject, may include: particle information of the naive granulocytes is acquired based on user input.
In other alternative embodiments, step S260, that is, the acquiring particle information of the naive granulocytes of the subject, may include:
preparing a second measurement sample comprising at least a portion of the blood sample to be measured, a second hemolysis agent, and a second staining agent, i.e., mixing at least a portion of the blood sample to be measured, the second hemolysis agent, and the second staining agent to prepare a second measurement sample;
passing the particles in the second measurement sample individually through the optical detection zone illuminated by the light to obtain second optical information generated by the particles in the second measurement sample after the particles are illuminated by the light;
generating a second scatter plot based on at least two light intensity information in the second optical information, the at least two light intensity information in the second optical information including second side scatter light intensity information and second fluorescence intensity information; and
particle information of the naive granulocytes of the subject is obtained based on the naive granulocyte characterization region in the second scattergram.
In some embodiments, the particle information of the first cell population may include a particle concentration of the first cell population, the particle information of the naive granulocytes includes a particle concentration of the naive granulocytes, and the particle information of the toxic granulocytes includes a particle concentration of the toxic granulocytes. Accordingly, step S270, that is, the obtaining of the particle information of the toxic granulosa cells of the subject based on the particle information of the first cell population and the particle information of the naive granulocytes, may include: obtaining a particle concentration of toxic granulosa cells based on the particle concentration of the first population of cells and the particle concentration of naive granulosa cells, wherein the particle concentration of toxic granulosa cells is a difference between the particle concentration of the first population of cells and the particle concentration of naive granulosa cells.
In some embodiments, the blood analysis method may further comprise: particle information of neutrophils of the subject is obtained, wherein the particle information of neutrophils includes a particle concentration of neutrophils. The particle information of the first cell population comprises the particle concentration of the first cell population and the particle proportion of the first cell population, wherein the particle proportion of the first cell population is the ratio of the particle concentration of the first cell population to the particle concentration of the neutrophil; the particle information of the immature granulocytes comprises the particle concentration of the immature granulocytes and the particle proportion of the immature granulocytes, wherein the particle proportion of the immature granulocytes is the ratio of the particle concentration of the immature granulocytes to the particle concentration of the neutrophils; the particle information of the toxic granular cells comprises the particle proportion of the toxic granular cells. Accordingly, step S270, that is, obtaining particle information of toxic granulosa cells of the subject based on the particle information of the first cell population and the particle information of the naive granulocytes, includes:
Obtaining the particle ratio of the toxic granulosa cells based on the particle ratio of the first cell population and the particle ratio of the naive granulosa cells, wherein the particle ratio of the toxic granulosa cells is the difference between the particle ratio of the first cell population and the particle ratio of the naive granulosa cells.
In other embodiments, the blood analysis method may further comprise: particle information of neutrophils of the subject is obtained, wherein the particle information of neutrophils includes a particle concentration of neutrophils. The particle information of the first cell population includes a particle concentration of the first cell population, the particle information of the naive granulocytes includes a particle concentration of the naive granulocytes, and the particle information of the toxic granulocytes includes a particle proportion of the toxic granulocytes. Accordingly, step S270, that is, obtaining the particle information of the toxic granulosa cells based on the particle information of the first cell population and the particle information of the naive granulosa cells, may include:
obtaining a particle ratio of the toxic granulocytes based on the particle concentration of the first population of cells, the particle concentration of the naive granulocytes, and the particle concentration of the neutrophils, wherein the particle ratio of the toxic granulocytes is a ratio of a difference between the particle concentration of the first population of cells and the particle concentration of the naive granulocytes to the particle concentration of the neutrophils.
In some embodiments, the obtaining particle information of neutrophils of the subject may include: particle information of the neutrophils is acquired based on user input.
In other embodiments, the obtaining particle information of neutrophils of the subject may include:
preparing a second measurement sample comprising at least a portion of the blood sample to be measured, a second hemolysis agent, and a second staining agent, i.e., mixing at least a portion of the blood sample to be measured, the second hemolysis agent, and the second staining agent to prepare a second measurement sample;
passing the particles in the second measurement sample individually through the optical detection zone illuminated by the light to obtain second optical information generated by the particles in the second measurement sample after the particles are illuminated by the light;
generating a second scatter plot based on at least two light intensity information in the second optical information, the at least two light intensity information in the second optical information including second side scatter light intensity information and second fluorescence intensity information; and
particle information of neutrophils of the subject is acquired based on the neutrophil characteristic region in the second scattergram.
In some embodiments, the first assay sample further comprises a second stain, i.e., at step S220, at least a portion of the blood sample to be tested, a first hemolysis agent, a first stain, and a second stain are mixed, or at least a portion of the blood sample to be tested, a first hemolysis agent, and a second stain are mixed, to prepare a first assay sample. At this time, the first optical information further includes second fluorescence intensity information from a second stain. Accordingly, step S260, i.e., acquiring particle information of naive granulocytes of the subject, may include: generating a second scattergram based on at least the first side scatter light intensity information and the second fluorescence intensity information from a second stain in the first optical information, and acquiring particle information of the naive granulocytes of the subject based on the naive granulocyte characterization region in the second scattergram.
In some embodiments, the first assay sample further comprises a second stain, i.e., at step S220, at least a portion of the blood sample to be tested, a first hemolysis agent, a first stain, and a second stain are mixed, or at least a portion of the blood sample to be tested, a first hemolysis agent, and a second stain are mixed, to prepare a first assay sample. At this time, the first optical information further includes second fluorescence intensity information from a second stain. Accordingly, obtaining particle information of naive granulocytes of the subject and obtaining particle information of neutrophils of the subject may include: generating a second scattergram based on at least the first side scatter light intensity information and the second fluorescence intensity information from the second stain in the first optical information, obtaining particle information of the naive granulocytes of the subject based on the naive granulocyte feature region in the second scattergram, and obtaining particle information of the neutrophils of the subject based on the neutrophil feature region in the second scattergram.
In some embodiments, the blood analysis method may further comprise: based on the obtained particle information of the toxic granulosa cells, a count of toxic granulosa cells is given and/or the presence of toxic granulosa cells is indicated.
In some embodiments, the blood analysis method may further comprise: particle information of nucleated red blood cells of the subject is obtained based on the first optical information.
In some embodiments, the blood analysis method may further comprise: based on the leukocyte characteristic zone information in the second scattergram, particle information of lymphocytes, particle information of monocytes, particle information of eosinophils, and particle information of neutrophils of the subject are obtained.
In some embodiments, the method 200 further comprises step S280: based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject.
In some embodiments, the method 200 further comprises: the infection marker parameter is used to perform early prediction of sepsis, diagnosis of sepsis, condition monitoring of infection, prognosis analysis of sepsis, evaluation of sepsis treatment effect, identification of common infection and severe infection, identification of viral infection and bacterial infection, or identification of infectious inflammation and non-infectious inflammation on the subject.
The method 200 presented in the embodiments of the present application is implemented, inter alia, by the blood cell analyzer 100 described above presented in the embodiments of the present application.
Further embodiments and advantages of the method 200 according to the embodiments of the present application may refer to the above description of the blood cell analyzer 100 according to the embodiments of the present application, especially the description of the method steps performed by the processor 140, which is not repeated here.
The present embodiments also provide for the use of an infection marker parameter in assessing the infection status of a subject, wherein the infection marker parameter is obtained by:
acquiring optical information obtained by detecting a first measurement sample by flow cytometry; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent; and
generating a scatter plot based on at least two light intensity information of the optical information,
obtaining particle information of a first cell population of the subject based on first cell population characteristic region information in the scatter plot, the first cell population comprising toxic granulosa cells;
Obtaining particle information of naive granulocytes of the subject;
obtaining particle information of toxic granulocytic cells of said subject based on the particle information of said first population of cells and the particle information of said naive granulocytes,
based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject.
Further embodiments and advantages of the use of the infection marker parameters set forth in embodiments of the present application in assessing the infection status of a subject are described above with reference to the description of the blood cell analyzer 100 set forth in embodiments of the present application, and in particular, the description of the method steps performed by the processor 140, and are not repeated herein.
The present application and its advantages are further illustrated by the following detailed examples.
The true positive rate%, false positive rate%, true negative rate% and false negative rate% of the examples of the present application are calculated by the following formulas:
true positive%tp/(tp+fn) ×100%;
true negative%TN/(FP+TN) ×100%;
pseudo-positive rate% = 1-true negative rate;
pseudo-yin rate% = 1-true positive rate;
wherein TP is true positive number, FP is false positive number, TN is true negative number, and FN is false negative number.
Example 1 early sepsis prediction
362 blood samples were tested for early sepsis prediction using a BC-6800Plus blood cell analyzer manufactured by Shenzhen Michael medical electronics Co., ltd, according to the method proposed in the examples of the present application. Of these, 95 blood samples were positive samples clinically diagnosed with sepsis after 1 day, and 267 blood samples were negative samples (did not develop sepsis). Table 1 shows the use of TGI% as an infection marker parameter and its corresponding diagnostic efficacy. Table 2 shows the diagnostic efficacy of using a combination of two particle information as an infection marker parameter. Table 3 shows other parameters for early prediction of sepsis and their diagnostic efficacy. Fig. 16 shows ROC curves corresponding to the infection marker parameters in table 1.
D_Neu_SFL_P；TGI％＝0.0153794*D_Neu_SFL_P+0.16182362*TGI％-9.53708371
N_WBC_SFL_P；TGI％＝0.00463776*N_WBC_SFL_P+0.19661694*TGI％-10.21609621
N_WBC_SFL_W；TGI％＝0.00707363*N_WBC_SFL_W+0.16695818*TGI％-15.55177662
TABLE 1 efficacy of TGI% for early prediction of sepsis risk
| Poisoning particles | ROC_AUC | Judgment threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| TGI％ | 0.867 | >3.2 | 18.8％ | 73.7％ | 81.2％ | 26.3％ |
Table 2 efficacy of combination parameters for early prediction of sepsis risk
| Parameter combination | ROC_AUC | Diagnostic threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| N_WBC_SFL_P；TGI％ | 0.900 | >-1.7276 | 12％ | 81.1％ | 88％ | 18.9％ |
| N_WBC_SFL_W；TGI％ | 0.903 | >-1.7079 | 13.2％ | 80％ | 86.8％ | 20％ |
| D_Neu_SFL_P；TGI％ | 0.873 | >-2.0072 | 22.6％ | 83.2％ | 77.4％ | 16.8％ |
Table 3 other parameters for early prediction of sepsis and diagnostic efficacy thereof
| Parameter combination | ROC_AUC | Judgment threshold | FP | TP | TN | FN |
| TGI％ | 0.8673 | >3.25 | 18.8 | 73.7 | 81.2 | 26.3 |
| N_WBC_SFL_W；TGI％ | 0.9024 | >-1.7079 | 13.2 | 80 | 86.8 | 20 |
| N_WBC_SFL_P；TGI％ | 0.8996 | >-1.7276 | 12 | 81.1 | 88 | 18.9 |
| D_Neu_SFL_P；TGI％ | 0.8721 | >-2.0072 | 22.6 | 83.2 | 77.4 | 16.8 |
| D_Neu_SFL_W；TGI％ | 0.8717 | >-2.0794 | 24.8 | 85.3 | 75.2 | 14.7 |
| D_Mon_SSC_W；TGI％ | 0.8689 | >-1.9144 | 17.3 | 77.9 | 82.7 | 22.1 |
| D_Neu_FSC_W；TGI％ | 0.8636 | >-1.9064 | 19.2 | 75.8 | 80.8 | 24.2 |
| D_Mon_SFL_W；TGI％ | 0.8617 | >-1.5703 | 11.3 | 72.6 | 88.7 | 27.4 |
| D_Mon_FSC_P；TGI％ | 0.8595 | >-1.7622 | 16.5 | 72.6 | 83.5 | 27.4 |
| D_Mon_SFL_P；TGI％ | 0.859 | >-1.877 | 18 | 76.8 | 82 | 23.2 |
| D_Mon_SSC_P；TGI％ | 0.8575 | >-1.6558 | 12 | 72.6 | 88 | 27.4 |
| N_WBC_SSC_W；TGI％ | 0.8566 | >-1.9573 | 21.8 | 77.9 | 78.2 | 22.1 |
| D_Neu_SSFL_Area；TGI％ | 0.853 | >-1.7317 | 20.7 | 76.8 | 79.3 | 23.2 |
| N_WBC_FSC_P；TGI％ | 0.851 | >-2.0083 | 22.9 | 82.1 | 77.1 | 17.9 |
| D_Neu_FSC_P；TGI％ | 0.8499 | >-2.0052 | 23.7 | 76.8 | 76.3 | 23.2 |
| D_Neu_SSC_W；TGI％ | 0.8497 | >-1.9235 | 21.8 | 75.8 | 78.2 | 24.2 |
| D_Neu_SSC_P；TGI％ | 0.8486 | >-1.8618 | 18.4 | 73.7 | 81.6 | 26.3 |
| N_WBC_FSC_W；TGI％ | 0.8476 | >-1.6648 | 15.4 | 76.8 | 84.6 | 23.2 |
| N_WBC_SSC_P；TGI％ | 0.8453 | >-1.8694 | 19.9 | 74.7 | 80.1 | 25.3 |
| N_WBC_SSFS_Area；TGI％ | 0.844 | >-2.012 | 23.3 | 76.8 | 76.7 | 23.2 |
| D_Mon_FSC_W；TGI％ | 0.8382 | >-1.6757 | 14.7 | 71.6 | 85.3 | 28.4 |
| N_WBC_FLSS_Area；TGI％ | 0.8368 | >-1.8469 | 18.8 | 75.8 | 81.2 | 24.2 |
| N_WBC_FLFS_Area；TGI％ | 0.8329 | >-1.8871 | 19.9 | 77.9 | 80.1 | 22.1 |
From tables 1-3, it can be seen that the infection marker parameters presented in this application can be used to predict sepsis risk more effectively one day in advance.
Example 2 sepsis diagnosis
251 blood samples were tested for early sepsis prediction using a BC-6800Plus blood cell analyzer manufactured by shenzhen micui medical electronics inc. Of these, 80 blood samples were positive samples for clinical diagnosis of sepsis, and 171 blood samples were negative samples (non-sepsis). Table 4 shows the use of TGI% as an infection marker parameter and its corresponding diagnostic efficacy. Table 5 shows the diagnostic efficacy of using a combination of two particle information as an infection marker parameter. Table 6 shows other parameters for sepsis diagnosis and their diagnostic efficacy. Fig. 17 shows ROC curves corresponding to the infection marker parameters in table 4.
N_WBC_SFL_P；TGI％＝0.00339019*N_WBC_SFL_P+0.1540621*TGI％-9.01342704
N_WBC_SFL_W；TGI％＝0.0049259*N_WBC_SFL_W+0.14932328*TGI％-12.78281673
D_Mon_SSC_W；TGI％＝0.06241769*D_Mon_SSC_W+0.13136088*TGI％-8.76193273
TABLE 4 efficacy of TGI% for sepsis diagnosis
| Poisoning particles | ROC_AUC | Judgment threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| TGI％ | 0.886 | 6.1 | 9.9％ | 77.5％ | 90.1％ | 22.5％ |
Table 5 efficacy of combination parameters for sepsis diagnosis
| Parameter combination | ROC_AUC | Diagnostic threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| N_WBC_SFL_P；TGI％ | 0.937 | >-1.9254 | 7.6％ | 83.8％ | 92.4％ | 16.2％ |
| N_WBC_SFL_W；TGI％ | 0.937 | >-1.9123 | 10.5％ | 85％ | 89.5％ | 15％ |
| D_Mon_SSC_W；TGI％ | 0.941 | >-2.1533 | 10％ | 84.6％ | 90％ | 15.4％ |
Table 6 other parameters for sepsis diagnosis and diagnostic efficacy thereof
From tables 4-6, it can be seen that the infection marker parameters set forth herein can be used to more effectively determine whether a subject has sepsis.
EXAMPLE 3 prognosis analysis of sepsis
229 blood samples were tested for prognosis of sepsis using a BC-6800Plus blood cell analyzer manufactured by Shenzhen Michael medical electronics Co., ltd, according to the method proposed in the examples of the present application. Of these, 57 were positive samples with 28-day death, and 172 were negative samples with 28-day survival. Table 7 shows the use of TGI% as an infection marker parameter and its corresponding diagnostic efficacy. Table 8 shows the diagnostic efficacy of using a combination of two particle information as an infection marker parameter. Table 9 shows other parameters for sepsis prognosis analysis and their diagnostic efficacy. Fig. 18 shows ROC curves corresponding to the infection marker parameters in table 7.
D_Mon_SSC_W；TGI％＝0.06499438*D_Mon_SSC_W+0.13424088*TGI％-8.86906839
N_WBC_SFL_P；TGI％＝0.00365067*N_WBC_SFL_P+0.16610204*TGI％-9.67395009
N_WBC_SFL_W；TGI％＝0.00627788*N_WBC_SFL_W+0.15614322*TGI％-15.97915142
TABLE 7 efficacy of TGI% for sepsis prognosis analysis
| Poisoning particles | ROC_AUC | Judgment threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| TGI％ | 0.839 | >4.65 | 26.7％ | 78.9％ | 73.3％ | 21.1％ |
Table 8 efficacy of combination parameters for sepsis prognosis analysis
| Parameter combination | ROC_AUC | Diagnostic threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| D_Mon_SSC_W；TGI％ | 0.878 | >-2.0449 | 22.1％ | 82.5％ | 77.9％ | 17.5％ |
| N_WBC_SFL_P；TGI％ | 0.886 | >-1.71％ | 16.9％ | 78.9％ | 83.1％ | 21.1％ |
| N_WBC_SFL_W；TGI％ | 0.914 | >-1.4321 | 10.5％ | 78.9％ | 89.5％ | 21.1％ |
Table 9 other parameters for sepsis prognosis analysis and diagnostic efficacy thereof
From tables 7-9, it can be seen that the infection marker parameters set forth herein can be used to more effectively perform prognostic assays in sepsis patients.
Example 4 identification of common and Critical infections
1287 blood samples were tested for severe infection identification using a BC-6800Plus blood cell analyzer manufactured by Shenzhen Michael biomedical electronics Co., ltd, according to the method proposed in the examples of the present application. Of these, 553 cases of severe infection samples are positive samples, and 734 cases of non-severe infection samples are negative samples. Table 10 shows the use of TGI% as an infection marker parameter and its corresponding diagnostic efficacy. Table 11 shows the diagnostic efficacy of using a combination of two particle information as an infection marker parameter. Table 12 shows other parameters for common and severe infection identification and their diagnostic efficacy. Fig. 19 shows ROC curves corresponding to the infection marker parameters in table 10.
N_WBC_SFL_P；TGI％＝0.00527249*N_WBC_SFL_P+0.10059071*TGI％-9.35595739
D_Mon_SSC_W；TGI％＝0.09377719*D_Mon_SSC_W+0.07114422*TGI％-9.02942534
N_WBC_SFL_W；TGI％＝0.00872715*N_WBC_SFL_W+0.08107372*TGI％-16.88392555
TABLE 10 efficacy of TGI% for identification of common and severe infections
| Poisoning particles | ROC_AUC | Judgment threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| TGI％ | 0.874 | >3.0 | 6.7％ | 69.5％ | 93.3％ | 30.5％ |
Table 11 efficacy of combination parameters for identification of common and severe infections
| Parameter combination | ROC_AUC | Diagnostic threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| N_WBC_SFL_P；TGI％ | 0.934 | >-0.713 | 10.9％ | 86.2％ | 89.1％ | 13.8％ |
| D_Mon_SSC_W；TGI％ | 0.944 | >-1.1338 | 12.2％ | 88.8％ | 87.8％ | 11.2％ |
| N_WBC_SFL_W；TGI％ | 0.950 | >-0.9615 | 8.1％ | 86.9％ | 91.9％ | 13.1％ |
Table 12 other parameters for common and severe infection identification and diagnostic efficacy thereof
| Parameter combination | ROC_AUC | Judgment threshold | FP | TP | TN | FN |
| N_WBC_SFL_W；TGI％； | 0.9532 | >-0.9615 | 8.1 | 86.9 | 91.9 | 13.1 |
| D_Mon_SSC_W；TGI％； | 0.9471 | >-1.1338 | 12.2 | 88.8 | 87.8 | 11.2 |
| N_WBC_SFL_P；TGI％； | 0.9374 | >-0.713 | 10.9 | 86.2 | 89.1 | 13.8 |
| D_Neu_SSFL_Area；TGI％； | 0.9261 | >-1.1049 | 16.3 | 83.7 | 83.7 | 16.3 |
| D_Neu_FSFL_Area；TGI％； | 0.9255 | >-1.2395 | 12.6 | 83.7 | 87.4 | 16.3 |
| N_WBC_FLSS_Area；TGI％； | 0.9245 | >-1.0275 | 12.4 | 85.1 | 87.6 | 14.9 |
| N_WBC_SSC_W；TGI％； | 0.9237 | >-1.1216 | 9.2 | 84.2 | 90.8 | 15.8 |
| N_WBC_FLFS_Area；TGI％； | 0.9185 | >-1.049 | 12.7 | 85.1 | 87.3 | 14.9 |
| D_Neu_SFL_W；TGI％； | 0.9164 | >-0.92 | 15.3 | 83.3 | 84.7 | 16.7 |
| N_WBC_FSC_W；TGI％； | 0.9081 | >-1.0627 | 12 | 82.4 | 88 | 17.6 |
| D_Eos_SFL_W；TGI％； | 0.9062 | >-1.2381 | 17.6 | 81.8 | 82.4 | 18.2 |
| D_Mon_SFL_W；TGI％； | 0.906 | >-0.8034 | 12 | 82.4 | 88 | 17.6 |
| D_Wbc_FSC_W；TGI％； | 0.9045 | >-0.4936 | 21 | 83.3 | 79 | 16.7 |
| N_SFL_PULWID_MEAN；TGI％； | 0.9025 | >-0.8962 | 16.3 | 83.3 | 83.7 | 16.7 |
| N_WBC_FSC_P；TGI％； | 0.9023 | >-0.9846 | 21.4 | 85.8 | 78.6 | 14.2 |
| N_WBC_SSC_P；TGI％； | 0.9021 | >-0.9174 | 16.4 | 80.9 | 83.6 | 19.1 |
| D_Neu_SFL_P；TGI％； | 0.8978 | >-0.7752 | 15.3 | 79.5 | 84.7 | 20.5 |
| D_Eos_SFL_P；TGI％； | 0.8967 | >-1.0586 | 15.6 | 78.9 | 84.4 | 21.1 |
| N_FSC_PULWID_MEAN；TGI％； | 0.8944 | >-0.6968 | 12.4 | 78.2 | 87.6 | 21.8 |
| D_Lym_SSC_W；TGI％； | 0.8941 | >-0.9468 | 16.9 | 82.2 | 83.1 | 17.8 |
| D_Wbc_SSC_P；TGI％； | 0.8885 | >-0.5478 | 12.7 | 76.2 | 87.3 | 23.8 |
| D_Lym_SFL_CV；TGI％； | 0.8878 | >-0.9145 | 13 | 80.4 | 87 | 19.6 |
| D_Mon_SSC_P；TGI％； | 0.8867 | >-0.8328 | 14.2 | 78.4 | 85.8 | 21.6 |
| D_Lym_SSC_P；TGI％； | 0.8858 | >-0.8353 | 14.2 | 78.4 | 85.8 | 21.6 |
| D_Wbc_SSC_W；TGI％； | 0.885 | >-0.7053 | 22.4 | 82.4 | 77.6 | 17.6 |
| D_Wbc_SFL_W；TGI％； | 0.8818 | >-0.6807 | 22.5 | 83.3 | 77.5 | 16.7 |
| N_WBC_SSC_CV；TGI％； | 0.8802 | >-1.0565 | 15.2 | 81.3 | 84.8 | 18.7 |
| N_WBC_SFL_CV；TGI％； | 0.88 | >-0.6635 | 13.3 | 74.2 | 86.7 | 25.8 |
As can be seen from tables 10-12, the infection marker parameters set forth in the present application can be used to more effectively determine common and severe infections.
EXAMPLE 5 identification of viral infection and bacterial infection
A sample of 491 cases of blood was tested for infection type determination using a BC-6800Plus blood cell analyzer manufactured by Shenzhen Michael biomedical electronics Co., ltd, according to the method proposed in the examples of the present application. Among them, 237 cases are bacterial infection samples, namely positive samples, 254 cases are viral infection samples, namely negative samples. Table 13 shows the use of TGI% as an infection marker parameter and its corresponding diagnostic efficacy. Table 14 shows the diagnostic efficacy of using a combination of two particle information as an infection marker parameter. Table 15 shows other parameters for the identification of viral and bacterial infections and their diagnostic efficacy. Fig. 20 shows ROC curves corresponding to the infection marker parameters in table 13.
N_WBC_SFL_P；TGI％＝0.00530888*N_WBC_SFL_P+0.06912886*TGI％-8.57823481
N_WBC_FSC_P；TGI％＝0.02504578*N_WBC_FSC_P+0.0472231*TGI％-33.64886867
D_Neu_SSFL_Area；TGI％＝0.00667728*D_Neu_SSFL_Area+0.02129529*TGI％-5.61688896
TABLE 13 efficacy of TGI% for identification of viral and bacterial infections
| Poisoning particles | ROC_AUC | Judgment threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| TGI％ | 0.809 | >1.25 | 27.6％ | 77.5％ | 72.4％ | 22.5％ |
Table 14 efficacy of combination parameters for identification of viral and bacterial infections
| Parameter combination | ROC_AUC | Diagnostic threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| N_WBC_SFL_P；TGI％ | 0.893 | >-0.4943 | 15.4％ | 81.4％ | 84.6％ | 18.6％ |
| N_WBC_FSC_P；TGI％ | 0.895 | >-0.5432 | 17.7％ | 83.5％ | 82.3％ | 16.5％ |
| D_Neu_SSFL_Area；TGI％ | 0.916 | >-0.6917 | 15％ | 82.6％ | 85％ | 17.4％ |
Table 15 other parameters for identification of viral and bacterial infections and diagnostic efficacy thereof
From tables 13-15, it can be seen that the infection marker parameters set forth herein can be used to more effectively determine the type of infection in a subject.
Example 6 identification of infectious and non-infectious inflammation
295 blood samples were tested for infectious inflammation identification using a BC-6800Plus blood cell analyzer manufactured by Shenzhen Michael biomedical electronics Co., ltd, according to the method proposed in the examples of the present application. Of these, 152 cases are positive samples, which are infectious inflammation samples, and 143 cases are non-infectious inflammation samples, which are negative samples. Table 16 shows the use of TGI% as an infection marker parameter and its corresponding diagnostic efficacy. Table 17 shows the diagnostic efficacy of using a combination of two particle information as an infection marker parameter. Table 18 shows other parameters for the identification of infectious and non-infectious inflammation and their diagnostic efficacy. Fig. 21 shows ROC curves corresponding to the infection marker parameters in table 16.
D_Mon_SSC_W；TGI％＝0.35554369*D_Mon_SSC_W+0.04377673*TGI％-28.23892327
N_WBC_SFL_P；TGI％＝0.01813696*N_WBC_SFL_P+0.19079458*TGI％-25.09194014
N_WBC_SFL_P；TGI％＝0.05002133*N_WBC_SFL_W+0.20166681*TGI％-81.3491252
TABLE 16 efficacy of TGI% for identification of infectious and non-infectious inflammation
| Poisoning particles | ROC_AUC | Judgment threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| TGI％ | 0.886 | >0.85 | 15.4％ | 81.6％ | 84.6％ | 18.4％ |
Table 17 efficacy of combination parameters for identification of infectious and non-infectious inflammation
Table 18 other parameters are used for identification of infectious and non-infectious inflammation and diagnostic efficacy thereof
| Parameter combination | ROC_AUC | Judgment threshold | FP | TP | TN | FN |
| N_WBC_SFL_W；TGI； | 0.9971 | >-3.5723 | 1.4 | 99.3 | 98.6 | 0.7 |
| N_WBC_SFL_P；TGI； | 0.9926 | >-0.6633 | 3.5 | 96.7 | 96.5 | 3.3 |
| D_Mon_SSC_W；TGI； | 0.9782 | >-0.8349 | 6.3 | 93.4 | 93.7 | 6.6 |
| D_Neu_SSFL_Area；TGI； | 0.9497 | >-0.9487 | 10.5 | 88.8 | 89.5 | 11.2 |
| N_WBC_SSC_P；TGI； | 0.9483 | >-0.7187 | 13.3 | 92.8 | 86.7 | 7.2 |
| N_WBC_FLSS_Area；TGI； | 0.9442 | >-0.6816 | 7.7 | 89.5 | 92.3 | 10.5 |
| N_WBC_FSC_P；TGI； | 0.9424 | >-0.3206 | 10.5 | 86.2 | 89.5 | 13.8 |
| D_Neu_FSFL_Area；TGI； | 0.9397 | >-0.7413 | 13.3 | 90.1 | 86.7 | 9.9 |
| N_WBC_FLFS_Area；TGI； | 0.9381 | >-0.3625 | 7.7 | 86.8 | 92.3 | 13.2 |
| N_WBC_SSC_W；TGI； | 0.9372 | >-0.746 | 12.6 | 86.8 | 87.4 | 13.2 |
| D_Wbc_FSC_W；TGI； | 0.9364 | >0.0051 | 11.2 | 88.2 | 88.8 | 11.8 |
| D_Wbc_SSC_P；TGI； | 0.9315 | >-0.4201 | 14.7 | 86.8 | 85.3 | 13.2 |
| N_FSC_PULWID_MEAN；TGI； | 0.9302 | >-0.3282 | 13.3 | 86.2 | 86.7 | 13.8 |
| D_Neu_SFL_W；TGI； | 0.924 | >-0.4076 | 9.1 | 84.2 | 90.9 | 15.8 |
| N_SFL_PULWID_MEAN；TGI； | 0.9214 | >-0.3468 | 12.6 | 84.9 | 87.4 | 15.1 |
| D_Eos_SFL_P；TGI； | 0.916 | >-0.603 | 15.8 | 85.9 | 84.2 | 14.1 |
| D_Mon_SFL_W；TGI； | 0.9117 | >-0.3166 | 14.7 | 82.9 | 85.3 | 17.1 |
| D_Eos_SFL_W；TGI； | 0.9111 | >-0.7259 | 16.8 | 85.5 | 83.2 | 14.5 |
| D_FSC_PULWID_MEAN；TGI； | 0.9088 | >-0.1353 | 10.5 | 77.6 | 89.5 | 22.4 |
| D_Wbc_FSC_P；TGI； | 0.9056 | >-0.2272 | 15.4 | 84.2 | 84.6 | 15.8 |
| D_Mon_SSC_P；TGI； | 0.9041 | >-0.1968 | 12.6 | 81.6 | 87.4 | 18.4 |
| D_Lym_FSFL_Area；TGI； | 0.9024 | >-1.2783 | 18.2 | 90.1 | 81.8 | 9.9 |
| N_WBC_FSC_W；TGI； | 0.9016 | >-0.4806 | 12.6 | 80.9 | 87.4 | 19.1 |
| D_Lym_SSC_W；TGI； | 0.8981 | >-0.3613 | 11.2 | 80.3 | 88.8 | 19.7 |
| D_Neu_SFL_P；TGI； | 0.8955 | >-0.1689 | 11.9 | 78.9 | 88.1 | 21.1 |
| D_SFL_PULWID_MEAN；TGI； | 0.8935 | >-0.3738 | 16.1 | 82.2 | 83.9 | 17.8 |
| D_Lym_SSC_P；TGI； | 0.8935 | >-0.3108 | 11.9 | 77 | 88.1 | 23 |
| D_Wbc_SSC_W；TGI； | 0.8928 | >-0.3184 | 14.7 | 86.2 | 85.3 | 13.8 |
| D_Wbc_SFL_W；TGI； | 0.8924 | >-0.0794 | 11.2 | 84.2 | 88.8 | 15.8 |
| D_Eos_FSC_W；TGI； | 0.8901 | >-0.864 | 19 | 88.7 | 81 | 11.3 |
| N_WBC_SFL_CV；TGI； | 0.8898 | >-0.2293 | 14.7 | 80.3 | 85.3 | 19.7 |
| D_Neu_FSC_W；TGI； | 0.889 | >-0.402 | 14 | 80.9 | 86 | 19.1 |
From tables 16-18, it can be seen that the infection marker parameters set forth herein can be used to more effectively identify infectious and non-infectious inflammation.
EXAMPLE 7 disease Condition monitoring of infection
A BC-6800Plus blood cell analyzer manufactured by Shenzhen Michael biomedical electronics Co., ltd was used to continuously test blood samples of 51 sepsis patients according to the method proposed in the examples of the present application to monitor the progression of severe infections. 51 critically infected patients were grouped according to their condition at day 7 post diagnosis. Patients had improved infection and stable disease status in the improved group (positive samples n=24) on day 7 after diagnosis. If the degree of infection is not significantly improved, the patient is still in the severe stage of infection or the patient dies, inclusion of a plus-recombination (negative sample n=27). Fig. 22 shows the use of TGI% as an infection marker parameter and its corresponding trend graph.
EXAMPLE 8 evaluation of the Effect of sepsis treatment
A BC-6800Plus blood cell analyzer manufactured by Shenzhen Michael medical electronics Co., ltd was used to examine 48 blood samples subjected to sepsis treatment for 3 days according to the method proposed in the examples of the present application, to analyze the therapeutic effects of sepsis. Of these, 29 positive samples were not treated for 3 days, and 19 negative samples were not treated for 3 days. Table 19 shows the use of TGI% as an infection marker parameter and its corresponding diagnostic efficacy. Fig. 23 shows ROC curves corresponding to the infection marker parameters in table 19.
Table 19 efficacy of TGI% for evaluation of sepsis treatment efficacy
| Poisoning particles | ROC_AUC | Judgment threshold | Rate of false positive | True yang rate | True yin rate | Rate of false negative |
| TGI％ | 0.907 | 0 | 10.3％ | 78.9％ | 89.7％ | 21.1％ |
The features or combinations of features mentioned above in the description, in the drawings and in the claims may be used in any combination with one another or individually, as long as they are significant and do not contradict one another within the scope of the invention.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent modifications made by the present invention and the accompanying drawings, or direct/indirect application in other related technical fields are included in the scope of the present invention.
Claims (44)
1. A blood cell analyzer, comprising:
the sampling device is used for collecting a blood sample to be tested of a subject;
sample preparation means for preparing a first assay sample; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent;
an optical detection device including a flow cell for passing the first measurement sample, a light source for irradiating the first measurement sample passing through the flow cell with light, and a light detector for detecting first optical information generated after the first measurement sample is irradiated with light while passing through the flow cell; and
a processor configured to:
generating a first scatter plot based on at least two light intensity information in the first optical information,
obtaining particle information for a first cell population of the subject based on first cell population characteristic region information in the first scattergram, the first cell population comprising toxic granulosa cells,
obtaining particle information of naive granulocytes of the subject,
Based on the particle information of the first cell population and the particle information of the naive granulocytes, particle information of toxic granulocytes of the subject is obtained.
2. The blood cell analyzer of claim 1, wherein the at least two light intensity information in the first optical information comprises first side scatter light intensity information and first fluorescence intensity information from a first stain, or wherein the at least two light intensity information in the first optical information comprises first side scatter light intensity information and first forward scatter light intensity information.
3. The blood cell analyzer of claim 2, wherein,
the particle information of the toxic granular cells comprises classification information of the toxic granular cells, and the classification information of the toxic granular cells comprises: particle information of slightly toxic particle cells, particle information of moderately toxic particle cells, particle information of severely toxic particle cells, and particle information of extremely toxic particle cells;
the processor is further configured to:
and obtaining classification information of the toxic granular cells based on the first side scattered light intensity information.
4. A blood cell analyzer according to any one of claims 1-3, wherein the processor obtains particle information of naive granulocytes of the subject, comprising:
Particle information of naive granulocytes of the subject is obtained based on user input.
5. A blood cell analyzer according to any one of claim 1 to 3, wherein,
the sample preparation device is further for preparing a second assay sample comprising at least a portion of the blood sample to be tested, a second hemolysis agent and a second staining agent;
the flow cell is further configured to pass the second measurement sample, the light source is further configured to irradiate the second measurement sample passing through the flow cell with light, and the photodetector is further configured to detect second optical information generated after the second measurement sample is irradiated with light while passing through the flow cell; and
the processor obtains particle information for naive granulocytes of the subject, comprising:
generating a second scatter plot based on at least two light intensity information in the second optical information, the at least two light intensity information in the second optical information including second side scatter light intensity information and second fluorescence intensity information from a second stain,
particle information of the naive granulocytes of the subject is obtained based on the naive granulocyte characterization region in the second scattergram.
6. A blood cell analyzer according to any one of claims 1-3, wherein the particle information of the first cell population comprises a particle concentration of the first cell population, the particle information of the naive granulocytes comprises a particle concentration of the naive granulocytes, and the particle information of the toxic granulocytes comprises a particle concentration of the toxic granulocytes; and
the processor obtains particle information of toxic granulosa cells of the subject based on the particle information of the first population of cells and the particle information of the naive granulosa cells, comprising:
obtaining a particle concentration of the toxic granulosa cells based on the particle concentration of the first population of cells and the particle concentration of the naive granulosa cells, wherein the particle concentration of the toxic granulosa cells is the difference between the particle concentration of the first population of cells and the particle concentration of the naive granulosa cells.
7. A blood cell analyzer according to any one of claim 1 to 3, wherein,
the processor is further configured to obtain particle information of neutrophils of the subject, wherein the particle information of neutrophils includes a particle concentration of neutrophils;
the particle information of the first cell population comprises the particle concentration of the first cell population and the particle proportion of the first cell population, wherein the particle proportion of the first cell population is the ratio of the particle concentration of the first cell population to the particle concentration of the neutrophil;
The particle information of the immature granulocytes comprises the particle concentration of the immature granulocytes and the particle proportion of the immature granulocytes, wherein the particle proportion of the immature granulocytes is the ratio of the particle concentration of the immature granulocytes to the particle concentration of the neutrophils;
the particle information of the toxic granular cells comprises the particle proportion of the toxic granular cells;
the processor obtains particle information of toxic granulosa cells of the subject based on the particle information of the first population of cells and the particle information of the naive granulosa cells, comprising:
obtaining the particle ratio of the toxic granulosa cells based on the particle ratio of the first cell population and the particle ratio of the naive granulosa cells, wherein the particle ratio of the toxic granulosa cells is the difference between the particle ratio of the first cell population and the particle ratio of the naive granulosa cells.
8. A blood cell analyzer according to any one of claim 1 to 3, wherein,
the processor is further configured to obtain particle information of neutrophils of the subject, wherein the particle information of neutrophils includes a particle concentration of neutrophils;
the particle information of the first cell population comprises the particle concentration of the first cell population, the particle information of the naive granulocytes comprises the particle concentration of the naive granulocytes, and the particle information of the toxic granulocytes comprises the particle proportion of the toxic granulocytes;
The processor obtains particle information of toxic granulosa cells based on the particle information of the first population of cells and the particle information of the naive granulosa cells, comprising:
obtaining a particle ratio of the toxic granulocytes based on the particle concentration of the first population of cells, the particle concentration of the naive granulocytes, and the particle concentration of the neutrophils, wherein the particle ratio of the toxic granulocytes is a ratio of a difference between the particle concentration of the first population of cells and the particle concentration of the naive granulocytes to the particle concentration of the neutrophils.
9. The blood cell analyzer of any one of claims 7-8, wherein the processor obtains particle information of neutrophils of the subject, comprising:
particle information of the neutrophils is acquired based on user input.
10. A blood cell analyzer according to any one of claims 7 to 8, wherein,
the sample preparation device is further for preparing a second assay sample comprising at least a portion of the blood sample to be tested, a second hemolysis agent and a second staining agent;
the flow cell is further configured to pass the second measurement sample, the light source is further configured to irradiate the second measurement sample passing through the flow cell with light, and the photodetector is further configured to detect second optical information generated after the second measurement sample is irradiated with light while passing through the flow cell; and
The processor obtains particle information of neutrophils of the subject, comprising:
generating a second scatter plot based on at least two light intensity information in the second optical information, the at least two light intensity information in the second optical information including second side scatter light intensity information and second fluorescence intensity information from a second stain,
particle information of neutrophils of the subject is acquired based on the neutrophil characteristic region in the second scattergram.
11. A blood cell analyzer according to any one of claim 2 to 3, wherein,
the first measurement sample further contains a second stain;
the first optical information further includes second fluorescence intensity information from a second stain;
the processor obtains particle information for naive granulocytes of the subject, comprising:
generating a second scatter plot based on at least the first side scatter light intensity information and the second fluorescence intensity information from a second stain in the first optical information, and
particle information of the naive granulocytes of the subject is obtained based on the naive granulocyte characterization region in the second scattergram.
12. A blood cell analyzer according to any one of claims 7 to 8, wherein,
The first measurement sample further contains a second stain;
the first optical information further includes second fluorescence intensity information from a second stain;
the processor obtains particle information of naive granulocytes of the subject and particle information of neutrophils of the subject, comprising:
generating a second scatter plot based on at least the first side scatter light intensity information and the second fluorescence intensity information from a second stain in the first optical information,
acquiring particle information of naive granulocytes of the subject based on the naive granulocyte characterization region in the second scattergram, and
particle information of neutrophils of the subject is acquired based on the neutrophil characteristic region in the second scattergram.
13. A blood cell analyzer according to any one of claims 1-3, wherein the processor is further configured to:
based on the obtained particle information of the toxic granulosa cells, a count of toxic granulosa cells is given and/or the presence of toxic granulosa cells is indicated.
14. A blood cell analyzer according to any one of claims 1-3, wherein the processor is further configured to:
Particle information of nucleated red blood cells of the subject is obtained based on the first optical information.
15. A blood cell analyzer according to any one of claims 1-3, wherein the processor is further configured to:
based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject.
16. The blood cell analyzer of claim 15, wherein the processor is further configured to:
outputting a hint information indicative of an infection status of the subject based on the infection flag parameter.
17. The blood cell analyzer of claim 15, wherein the processor obtains an infection marker parameter for assessing the infection status of the subject based on particle information of the toxic granulosa cells, comprising:
obtaining particle information of at least one target particle group different from the first cell group in the first measurement sample based on the first optical information,
the infection marker parameter is calculated, in particular by a linear function, based on the particle information of the toxic particle cells and the particle information of the at least one target particle population.
18. The blood cell analyzer of claim 17, wherein the at least one population of target particles is selected from one or more of a population of leukocytes, a population of neutrophils, a population of lymphocytes;
preferably, the at least one population of target particles comprises a population of leukocytes and/or neutrophils.
19. The blood cell analyzer of claim 17 or 18, wherein the particle information of the at least one target particle population is selected from one or more of the following particle information:
the center of gravity of forward scattering light intensity distribution, center of gravity of side scattering light intensity distribution, width of forward scattering light intensity distribution, width of side scattering light intensity distribution, variation coefficient of forward scattering light intensity distribution, variation coefficient of side scattering light intensity distribution, average value of forward scattering light pulse width, average value of side scattering light pulse width, and average value of side fluorescence pulse width of the white blood cell population in the first measurement sample;
an area or volume of a distribution region of the white blood cell population in the first measurement sample in a scatter plot generated based on at least two of forward scattered light intensity, side scattered light intensity, and side fluorescent light intensity;
The center of gravity of forward scattering light intensity distribution, center of gravity of side scattering light intensity distribution, width of forward scattering light intensity distribution, width of side scattering light intensity distribution, variation coefficient of forward scattering light intensity distribution, variation coefficient of side scattering light intensity distribution, average value of forward scattering light pulse width, average value of side scattering light pulse width and average value of side fluorescence pulse width of the neutrophil population in the first measurement sample;
an area or volume of a distribution region of the neutrophil population in the first measurement sample in a scatter plot generated based on at least two of forward scattered light intensity, side scattered light intensity, and side fluorescent light intensity;
the center of gravity of forward scattered light intensity distribution, center of gravity of side scattered light intensity distribution, width of forward scattered light intensity distribution, width of side scattered light intensity distribution, variation coefficient of forward scattered light intensity distribution, variation coefficient of side scattered light intensity distribution, average value of forward scattered light pulse width, average value of side scattered light pulse width of the first measurement sample; and
An area or volume of a distribution region of the lymphocyte population in the first measurement sample in a scatter plot generated based on at least two of forward scattered light intensity, side scattered light intensity, and side fluorescent light intensity.
20. The blood cell analyzer according to any one of claims 15 to 16, wherein,
the sample preparation device is further for preparing a second assay sample comprising a portion of the blood sample to be tested, a second hemolysis agent and a second staining agent;
the flow cell is further configured to pass the second measurement sample, the light source is further configured to irradiate the second measurement sample passing through the flow cell with light, and the photodetector is further configured to detect second optical information generated after the second measurement sample is irradiated with light while passing through the flow cell; and
the processor is further configured to:
generating a second scatter plot based on at least two light intensity information in the second optical information,
calculating particle information of at least one target particle group different from the first cell group in the second measurement sample based on the second scattergram,
the infection marker parameter is calculated, in particular by a linear function, based on the particle information of the toxic particle cells and the particle information of the at least one target particle population.
21. The blood cell analyzer of claim 20, wherein the at least one population of particles of interest is selected from one or more of a monocyte population, a neutrophil population, a lymphocyte population;
preferably the at least one population of target particles is selected from a monocyte population and/or a neutrophil population.
22. The blood cell analyzer of any one of claims 20 or 21, wherein the particle information of the at least one target particle population is selected from one or more of the following particle information:
the second measurement sample comprises a single cell population of the second measurement sample, which has a forward scattered light intensity distribution width, a forward scattered light intensity distribution centroid, a forward scattered light intensity distribution coefficient of variation, a side scattered light intensity distribution width, a side scattered light intensity distribution centroid, a side scattered light intensity distribution coefficient of variation, a fluorescence intensity distribution width, a fluorescence intensity distribution centroid, a fluorescence intensity distribution coefficient of variation, and
an area or volume of a distribution region of the mononuclear cell population in the second measurement sample in a scatter plot generated from at least two of forward scattered light intensity, side scattered light intensity, and fluorescence intensity; and
the neutrophil population in the second measurement sample has a forward scattered light intensity distribution width, a forward scattered light intensity distribution center of gravity, a forward scattered light intensity distribution variation coefficient, a side scattered light intensity distribution width, a side scattered light intensity distribution center of gravity, a side scattered light intensity distribution variation coefficient, a fluorescence intensity distribution width, a fluorescence intensity distribution center of gravity, a fluorescence intensity distribution variation coefficient, and
The area or volume of the distribution region of the neutrophil population in the second measurement sample in the scatter plot generated by at least two of forward scattered light intensity, side scattered light intensity and fluorescence intensity.
23. The blood cell analyzer of any one of claims 5, 10, 11, 12, or 20, wherein the processor is further configured to:
based on the leukocyte characteristic zone information in the second scattergram, particle information of lymphocytes, particle information of monocytes, particle information of eosinophils, and particle information of neutrophils of the subject are obtained.
24. The blood cell analyzer of any one of claims 15-23, wherein the processor is further configured to:
prediction of sepsis, early diagnosis of sepsis, monitoring of the condition of the infection, prognosis evaluation of sepsis, evaluation of the therapeutic effect of sepsis, identification of common and severe infections, identification of viral and bacterial infections, or identification of infectious and non-infectious inflammation is performed based on the infection marker parameters.
25. The blood cell analyzer of any one of claims 17-24, wherein the processor is further configured to:
When the particle information of the toxic particle cells or the particle information of the target particle group satisfies a preset condition, for example, when the total number of particles of the target particle group is smaller than a preset threshold value, and/or when the target particle group overlaps with other particle groups, the value of the infection flag parameter is not output, or the value of the infection flag parameter is output and at the same time a hint information indicating that the value of the infection flag parameter is unreliable is output.
26. The blood cell analyzer of any one of claims 15-24, wherein the processor is further configured to:
when the subject suffers from a blood disease or an abnormal cell, particularly an original cell, is present in the blood sample to be tested, for example, when it is judged that an abnormal cell, particularly an original cell, is present in the blood sample to be tested based on the optical information, the value of the infection flag parameter is not output, or the value of the infection flag parameter is output and at the same time, a hint information indicating that the value of the infection flag parameter is unreliable is output.
27. A blood analysis method comprising:
collecting a blood sample to be tested of a subject;
preparing a first measurement sample; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent;
Passing the particles in the first measurement sample one by one through the optical detection area irradiated by the light to obtain first optical information generated by the particles in the first measurement sample after the particles are irradiated by the light;
generating a first scatter plot based on at least two light intensity information in the first optical information;
obtaining particle information of a first cell population of the subject based on first cell population characteristic region information in the first scattergram, the first cell population comprising toxic granulosa cells;
obtaining particle information of naive granulocytes of the subject; and
based on the particle information of the first cell population and the particle information of the naive granulocytes, particle information of toxic granulocytes of the subject is obtained.
28. The method of claim 27, wherein the at least two light intensity information in the first optical information comprises first side-scattered light intensity information and first fluorescence intensity information, or wherein the at least two light intensity information in the first optical information comprises first side-scattered light intensity information and first forward-scattered light intensity information.
29. The method of claim 28, wherein the blood analysis is performed,
The particle information of the toxic granular cells comprises classification information of the toxic granular cells, and the classification information of the toxic granular cells comprises: particle information of slightly toxic particle cells, particle information of moderately toxic particle cells, particle information of severely toxic particle cells, and particle information of extremely toxic particle cells;
the blood analysis method further comprises: and obtaining classification information of the toxic granular cells based on the first side scattered light intensity information.
30. The method of any one of claims 27-29, wherein the obtaining particle information of naive granulocytes of the subject comprises:
particle information of the naive granulocytes is acquired based on user input.
31. The method of any one of claims 27-29, wherein the obtaining particle information of naive granulocytes of the subject comprises:
preparing a second measurement sample comprising at least a portion of the blood sample to be measured, a second hemolysis agent, and a second staining agent;
passing the particles in the second measurement sample individually through the optical detection zone illuminated by the light to obtain second optical information generated by the particles in the second measurement sample after the particles are illuminated by the light;
Generating a second scatter plot based on at least two light intensity information in the second optical information, the at least two light intensity information in the second optical information including second side scatter light intensity information and second fluorescence intensity information; and
particle information of the naive granulocytes of the subject is obtained based on the naive granulocyte characterization region in the second scattergram.
32. The method of claim 27 to 29, wherein,
the particle information of the first cell population includes a particle concentration of the first cell population, the particle information of the naive granulocytes includes a particle concentration of the naive granulocytes, and the particle information of the toxic granulocytes includes a particle concentration of the toxic granulocytes; and
the obtaining particle information of toxic granulosa cells of the subject based on the particle information of the first cell population and the particle information of naive granulosa cells, comprises:
obtaining a particle concentration of toxic granulosa cells based on the particle concentration of the first population of cells and the particle concentration of naive granulosa cells, wherein the particle concentration of toxic granulosa cells is a difference between the particle concentration of the first population of cells and the particle concentration of naive granulosa cells.
33. A method of blood analysis according to any one of claims 27 to 29 comprising:
obtaining particle information of neutrophils of the subject, wherein the particle information of neutrophils comprises a particle concentration of neutrophils;
the particle information of the first cell population comprises the particle concentration of the first cell population and the particle proportion of the first cell population, wherein the particle proportion of the first cell population is the ratio of the particle concentration of the first cell population to the particle concentration of the neutrophil;
the particle information of the immature granulocytes comprises the particle concentration of the immature granulocytes and the particle proportion of the immature granulocytes, wherein the particle proportion of the immature granulocytes is the ratio of the particle concentration of the immature granulocytes to the particle concentration of the neutrophils;
the particle information of the toxic granular cells comprises the particle proportion of the toxic granular cells;
obtaining particle information of toxic granulosa cells of the subject based on the particle information of the first population of cells and the particle information of naive granulosa cells, comprising:
obtaining the particle ratio of the toxic granulosa cells based on the particle ratio of the first cell population and the particle ratio of the naive granulosa cells, wherein the particle ratio of the toxic granulosa cells is the difference between the particle ratio of the first cell population and the particle ratio of the naive granulosa cells.
34. A method of blood analysis according to any one of claims 27 to 29 comprising:
obtaining particle information of neutrophils of the subject, wherein the particle information of neutrophils comprises a particle concentration of neutrophils;
the particle information of the first cell population comprises the particle concentration of the first cell population, the particle information of the naive granulocytes comprises the particle concentration of the naive granulocytes, and the particle information of the toxic granulocytes comprises the particle proportion of the toxic granulocytes;
obtaining particle information of toxic granulosa cells based on the particle information of the first population of cells and the particle information of naive granulosa cells, comprising:
obtaining a particle ratio of the toxic granulocytes based on the particle concentration of the first population of cells, the particle concentration of the naive granulocytes, and the particle concentration of the neutrophils, wherein the particle ratio of the toxic granulocytes is a ratio of a difference between the particle concentration of the first population of cells and the particle concentration of the naive granulocytes to the particle concentration of the neutrophils.
35. The method of any one of claims 33-34, wherein the obtaining particle information of neutrophils in the subject comprises:
Particle information of the neutrophils is acquired based on user input.
36. The method of any one of claims 33-34, wherein the obtaining particle information of neutrophils in the subject comprises:
preparing a second measurement sample comprising at least a portion of the blood sample to be measured, a second hemolysis agent, and a second staining agent;
passing the particles in the second measurement sample individually through the optical detection zone illuminated by the light to obtain second optical information generated by the particles in the second measurement sample after the particles are illuminated by the light;
generating a second scatter plot based on at least two light intensity information in the second optical information, the at least two light intensity information in the second optical information including second side scatter light intensity information and second fluorescence intensity information; and
particle information of neutrophils of the subject is acquired based on the neutrophil characteristic region in the second scattergram.
37. The method of claim 27 to 29, wherein,
the first measurement sample further contains a second stain;
the first optical information further includes second fluorescence intensity information from a second stain;
Obtaining particle information of naive granulocytes of the subject, comprising:
generating a second scatter plot based on at least the first side scatter light intensity information and the second fluorescence intensity information from a second stain in the first optical information, and
particle information of the naive granulocytes of the subject is obtained based on the naive granulocyte characterization region in the second scattergram.
38. The method of claim 33 to 34, wherein,
the first measurement sample further contains a second stain;
the first optical information further includes second fluorescence intensity information from a second stain;
acquiring particle information of naive granulocytes of the subject and acquiring particle information of neutrophils of the subject, comprising:
generating a second scatter plot based on at least the first side scatter light intensity information and the second fluorescence intensity information from a second stain in the first optical information,
acquiring particle information of naive granulocytes of the subject based on the naive granulocyte characterization region in the second scattergram, and
particle information of neutrophils of the subject is acquired based on the neutrophil characteristic region in the second scattergram.
39. The blood analysis method according to claims 27-29, wherein the blood analysis method further comprises:
based on the obtained particle information of the toxic granulosa cells, a count of toxic granulosa cells is given and/or the presence of toxic granulosa cells is indicated.
40. The blood analysis method according to claims 27-29, wherein the blood analysis method further comprises:
particle information of nucleated red blood cells of the subject is obtained based on the first optical information.
41. The blood analysis method according to any one of claims 31, 36 to 38, wherein the blood analysis method further comprises:
based on the leukocyte characteristic zone information in the second scattergram, particle information of lymphocytes, particle information of monocytes, particle information of eosinophils, and particle information of neutrophils of the subject are obtained.
42. The blood analysis method according to claims 27-29, wherein the blood analysis method further comprises:
based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject.
43. The method of claim 42, further comprising:
The infection marker parameter is used to perform early prediction of sepsis, diagnosis of sepsis, condition monitoring of infection, prognosis analysis of sepsis, evaluation of sepsis treatment effect, identification of common infection and severe infection, identification of viral infection and bacterial infection, or identification of infectious inflammation and non-infectious inflammation on the subject.
44. Use of an infection marker parameter in assessing the infection status of a subject, wherein the infection marker parameter is obtained by:
acquiring optical information obtained by detecting a first measurement sample by flow cytometry; the first measurement sample contains at least a portion of the blood sample to be measured, a first hemolytic agent, and a first staining agent, or the first measurement sample contains at least a portion of the blood sample to be measured and a first hemolytic agent;
generating a scatter plot based on at least two light intensity information of the optical information,
obtaining particle information of a first cell population of the subject based on first cell population characteristic region information in the scatter plot, the first cell population comprising toxic granulosa cells;
obtaining particle information of naive granulocytes of the subject;
Obtaining particle information of toxic granulocytic cells of the subject based on the particle information of the first population of cells and the particle information of the naive granulocytes; and
based on the particle information of the toxic granulosa cells, an infection marker parameter is obtained for assessing the infection status of the subject.
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