Blood cell analyzer and method for assessing infection status of subject

Abstract

The invention relates to a blood cell analyzer and a method for evaluating the infection state of a subject. The blood cell analyzer comprises a sample suction device, a sample preparation device, an optical detection device and a processor, wherein the sample suction device is used for sucking a to-be-detected blood sample of a subject; the sample preparation device is used for mixing at least one part of the to-be-detected blood sample, a hemolytic agent and a coloring agent to prepare a determination sample; the optical detection device is used for detecting the determination sample to obtain optical information; the processor is configured to: input input information obtained based on the original optical information into a trained first nonlinear model to obtain an infection marker parameter; and outputting the infection mark parameter or outputting prompt information indicating the infection state of the subject based on the infection mark parameter. Therefore, the accurate infection mark parameters can be quickly obtained, and the user is effectively assisted in evaluating the infection state of the subject.

Description

Technical Field

[0001] The present application relates to the field of in vitro diagnostics, and particularly to a blood cell analyzer and a method for evaluating the infection status of a subject. Background Art

[0002] Infectious diseases are common clinical diseases, and sepsis belongs to severe infectious diseases. The incidence of sepsis is high, with more than 18 million severe sepsis cases globally every year. Moreover, the condition of sepsis is critical and the fatality rate is high. Approximately 14,000 people die from its complications globally every day. According to foreign epidemiological surveys, the fatality rate of sepsis has exceeded that of myocardial infarction and has become the main cause of death among non-heart disease patients in the intensive care unit. In recent years, despite the great progress made in anti-infection treatment and organ function support technologies, the fatality rate of sepsis is still as high as 30% - 70%. The treatment of sepsis is costly and consumes a large amount of medical resources, seriously affecting the quality of human life and posing a huge threat to human health. Clinicians need to promptly diagnose whether a patient is infected and identify the pathogen in order to develop an effective treatment plan. Therefore, how to quickly and early screen and diagnose infectious diseases has become an urgent problem to be solved in clinical laboratories.

[0003] For the rapid differential diagnosis of infectious diseases, the existing solutions in the industry include: microbial culture, inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT), and serum amyloid A (SAA), serum antigen-antibody detection, and blood routine detection.

[0004] Microbial culture is considered the most reliable gold standard. It can directly culture and detect bacteria in clinical specimens such as body fluids or blood, thereby interpreting their types and drug resistance, and can directly guide clinical medication. However, this method has a long reporting cycle, the specimens are easily contaminated, and the false negative rate is high, and it cannot well meet the requirements of the clinic for quickly and accurately obtaining results.

[0005] Since inflammatory factors such as CRP, PCT, and SAA have good sensitivity, they are widely used in the auxiliary diagnosis of infectious diseases. However, these detections have weak specificity for infectious diseases, and usually require the combined detection of these three items of CRP, PCT, and SAA, increasing the economic burden on patients. Moreover, CRP and PCT are interfered by specific diseases, and sometimes cannot correctly reflect the infection status of patients. For example, CRP is produced in the liver, and the CRP level of infected patients with liver damage is normal, resulting in false negative results in the diagnosis of infectious diseases.

[0006] Serum antigen-antibody detection can confirm specific virus types, but it has limited effectiveness in situations where the types of pathogens are unclear, and the detection cost is high, increasing the economic burden on patients.

[0007] Blood routine tests can, to a certain extent, indicate the occurrence of infection and help differentiate the types of infections. However, indicators such as white blood cells (WBC) and neutrophil percentage (Neu)% in blood routine test results are affected by many factors. For example, they are easily influenced by other non-infectious inflammatory reactions and normal physiological fluctuations in the body, and cannot accurately and promptly reflect the patient's condition, resulting in poor diagnostic value in infectious diseases. Summary of the Invention

[0008] Therefore, the task of this application is to provide a blood cell analyzer and a method for evaluating the infection status of a subject, which can quickly obtain accurate infection marker parameters, so that clinicians can make a more accurate judgment on the infection status of the subject based on these infection marker parameters.

[0009] To achieve the above task of this application, the first aspect of this application first proposes a blood cell analyzer, including:

[0010] A sampling device for sucking a blood sample to be tested from the subject;

[0011] A sample preparation device for mixing at least a part of the blood sample to be tested, a hemolytic agent, and a staining agent to prepare a measurement sample;

[0012] An optical detection device including a flow cell, a light source, and a light detector. The flow cell is used for the measurement sample to pass through, the light source is used to irradiate the measurement sample passing through the flow cell with light, and the light detector is used to detect the original optical information generated after the measurement sample is irradiated by light when passing through the flow cell;

[0013] A processor configured to:

[0014] Input the input information obtained based on the original optical information into a trained first non-linear model to obtain infection marker parameters; and

[0015] Output the infection marker parameters or output a prompt message indicating the infection status of the subject based on the infection marker parameters.

[0016] In the blood cell analyzer provided in the first aspect of this application, only by inputting the input information obtained based on the original optical information of the measurement sample into a trained first non-linear model, accurate infection marker parameters can be obtained, thus achieving the rapid and accurate acquisition of infection marker parameters and improving the accuracy of clinically evaluating the infection status of the subject.

[0017] The second aspect of the present application provides a method for evaluating the infection status of a subject, including:

[0018] Obtaining a blood sample to be tested of the subject;

[0019] Mixing at least a part of the blood sample to be tested, a hemolytic agent and a staining agent to prepare a measurement sample;

[0020] Making particles in the measurement sample pass through an optically detected area irradiated by light one by one to obtain original optical information generated by the particles in the measurement sample after being irradiated by light;

[0021] Inputting input information obtained based on the original optical information into a trained first non-linear model to obtain an infection marker parameter; and

[0022] Outputting the infection marker parameter or outputting a prompt message indicating the infection status of the subject based on the infection marker parameter.

[0023] The method for evaluating the infection status of a subject according to the second aspect of the present application is particularly applied to a blood cell analyzer according to the first aspect of the present application.

[0024] Other features and advantages of the method for evaluating the infection status of a subject according to the second aspect of the present application can be referred to the description of the blood cell analyzer according to the first aspect of the present application above. Description of the Drawings

[0025] Figure 1 It is a schematic structural diagram of a blood cell analyzer according to some embodiments of the present application.

[0026] Figure 2 It is a schematic structural diagram of an optical detection device according to some embodiments of the present application.

[0027] Figure 3 It is a two-dimensional scatter plot of FL-FS of a first measurement sample according to some embodiments of the present application.

[0028] Figure 4 It is a two-dimensional scatter plot of SS-FS of a first measurement sample according to some embodiments of the present application.

[0029] Figure 5 It is a three-dimensional scatter plot of FL-SS-FS of a first measurement sample according to some embodiments of the present application.

[0030] Figure 6 It is a two-dimensional scatter plot of SS-FL of a second measurement sample according to some embodiments of the present application.

[0031] Figure 7The SS-FS two-dimensional scatter plot of the second measurement sample according to some embodiments of the present application.

[0032] Figure 8 The SS-FS-FL three-dimensional scatter plot of the second measurement sample according to some embodiments of the present application.

[0033] Figure 9 The schematic diagram of the scheme for obtaining the infection marker parameter according to some embodiments of the present application.

[0034] Figure 10 and Figure 11 The scatter plot of the abnormal situation in the measurement sample according to some embodiments of the present application.

[0035] Figure 12 The scatter plot before and after taking the logarithm according to some embodiments of the present application.

[0036] Figure 13 The schematic diagram of the scheme for obtaining the infection marker parameter according to some other embodiments of the present application.

[0037] Figure 14 The schematic diagram of the scheme for obtaining the infection marker parameter according to some other embodiments of the present application.

[0038] Figure 15 The schematic flowchart of the method for prompting the infection status of the subject according to some embodiments of the present application. Detailed implementation manners

[0039] The embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0040] It should be noted that the terms "first / second / third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when permitted.

[0041] The term "scatter plot" involved in the embodiments of the present application is a two-dimensional or three-dimensional graph generated by a blood cell analyzer, on which the two-dimensional or three-dimensional characteristic information of multiple particles is distributed. The X-axis, Y-axis, and Z-axis of the scatter plot each represent a characteristic of each particle. For example, in an exemplary scatter plot, the X-axis represents the forward scatter light intensity, the Y-axis represents the fluorescence intensity, and the Z-axis represents the side scatter light intensity. The term "scatter plot" used in the present disclosure not only refers to a distribution graph of at least two sets of data in the form of data points in a rectangular coordinate system, but also includes a data array, that is, it is not limited by its graphical presentation form.

[0042] The term "particle cluster" or "cell cluster" involved in the embodiments of the present application is a particle population formed by multiple particles with the same cell characteristics, which is distributed in a certain area of the scatter plot. For example, a cluster of white blood cells (including all types of white blood cells), as well as subpopulations of white blood cells, such as a cluster of neutrophils, a cluster of lymphocytes, a cluster of monocytes, a cluster of eosinophils, or a cluster of basophils, etc.

[0043] The term "ROC curve (receiver operator characteristic curve)" involved in the embodiments of the present application is a receiver operating characteristic curve, which is a curve plotted with the true positive rate as the ordinate and the false positive rate as the abscissa according to a series of different binary classification methods (thresholds). ROC_AUC (area under the curve) represents the area enclosed by the ROC curve and the horizontal coordinate axis.

[0044] The principle of making the ROC curve is to set multiple different critical values for a continuous variable, calculate the corresponding sensitivity and specificity at each critical value, and then plot a curve with sensitivity as the ordinate and 1 - specificity as the abscissa.

[0045] Since the ROC curve is composed of multiple critical values representing their respective sensitivities and specificities, the best diagnostic threshold value of a certain diagnostic method can be selected with the help of the ROC curve. The closer the ROC curve is to the upper left corner, the higher the test sensitivity and the lower the misjudgment rate, and the better the performance of the diagnostic method. It can be seen that for the point on the ROC curve closest to the upper left corner, the sum of its sensitivity and specificity is the largest, and the value corresponding to this point or its adjacent point is often used as a diagnostic reference value (also called a diagnostic threshold or a judgment threshold or a preset condition or a preset range).

[0046] The hematology analyzer used in this application classifies and counts particles in a blood sample through flow cytometry that combines laser scattering and fluorescence staining techniques. Here, the principle of the hematology analyzer for detecting a blood sample can be, for example: First, a blood sample is aspirated and treated with a lysing agent and a fluorescent dye. Among them, red blood cells are destroyed and dissolved by the lysing agent, while white blood cells are not dissolved, but the fluorescent dye can enter the cell nucleus of white blood cells and bind to the nucleic acid substances in the cell nucleus with the help of the lysing agent; then the particles in the sample pass through the detection hole irradiated by the laser beam one by one. When the laser beam irradiates the particles, the characteristics of the particles themselves (such as volume, staining degree, size and content of cell contents, nuclear density, etc.) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to the particle characteristics. After these scattered lights are received by the signal detector, relevant information on the particle structure and composition can be obtained. Among them, forward scatter (FS) reflects the number and volume of particles, side scatter (SS) reflects the complexity of the internal structure of cells (such as intracellular granules or cell nuclei), and fluorescence (FL) reflects the content of nucleic acid substances in cells. Using this optical information, the particles in the sample can be classified and counted.

[0047] Figure 1 It is a schematic structural diagram of the hematology analyzer according to some embodiments of this application. The hematology analyzer 100 includes a sampling device 110, a sample preparation device 120, an optical detection device 130, and a processor 140. The hematology analyzer 100 also has a liquid path system (not shown) for connecting the sampling device 110, the sample preparation device 120, and the optical detection device 130 to facilitate liquid transportation between these devices.

[0048] The sampling device 110 is used to aspirate the blood sample to be tested from the subject.

[0049] The sample preparation device 120 is used to mix at least a part of the blood sample to be tested, a lysing agent, and a staining agent to prepare a measurement sample.

[0050] In the embodiments of this application, the lysing agent is used to dissolve red blood cells in the blood and lyse red blood cells into fragments, but can keep the morphology of white blood cells basically unchanged. The staining agent can be, for example, a fluorescent dye.

[0051] The optical detection device 130 includes a flow cell, a light source, and a light detector. The flow cell is used for the measurement sample to pass through, the light source is used to irradiate the measurement sample passing through the flow cell with light, and the light detector is used to detect the original optical information generated after the measurement sample is irradiated by light when passing through the flow cell.

[0052] The processor 140 is configured to:

[0053] Input the input information obtained based on the original optical information into the trained first non-linear model to obtain an infection marker parameter; and

[0054] Output the infection marker parameter or output a prompt message indicating the infection status of the subject based on the infection marker parameter.

[0055] In the above blood cell analyzer 100, only by inputting the input information obtained based on the original optical information of the measurement sample into the trained first non-linear model, the infection marker parameter can be obtained, thereby achieving rapid and accurate acquisition of the infection marker parameter and improving the accuracy of clinically evaluating the infection status of the subject.

[0056] The blood cell analyzer 100 will be further described below in conjunction with some embodiments.

[0057] In some embodiments, the sampling device 110 has a sampling needle (not shown) for aspirating a blood sample to be measured. In addition, the sampling device 110 may further include a driving device, which is used to drive the sampling needle to quantitatively aspirate the blood sample to be measured through the nozzle of the sampling needle. The sampling device 110 can transport the aspirated blood sample to the sample preparation device 120.

[0058] In some embodiments, the sample preparation device 120 may include at least one reaction cell and a reagent supply device (not shown in the figure). The at least one reaction cell is used to receive the blood sample to be measured aspirated by the sampling device 110, and the reagent supply device supplies a processing reagent (including a hemolytic agent, a staining agent, etc.) to the at least one reaction cell, so that the blood sample to be measured aspirated by the sampling device 110 is mixed with the processing reagent provided by the reagent supply device in the reaction cell to prepare a measurement sample.

[0059] In some embodiments, the sample preparation device 120 may further be used to mix a first part of the blood sample to be measured, a first hemolytic agent, and a first staining agent for identifying nucleated red blood cells to prepare a first measurement sample, and mix a second part of the blood sample to be measured, a second hemolytic agent, and a second staining agent for white blood cell classification to prepare a second measurement sample.

[0060] 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 unit and a second reagent supply unit. The sampling device 110 is configured to separately and partially distribute the aspirated blood sample to be tested into the first reaction cell and the second reaction cell. The first reagent supply unit is configured to supply a first hemolytic agent and a first stain for identifying nucleated red blood cells to the first reaction cell, so that the portion of the blood sample to be tested distributed to the first reaction cell is mixed and reacted with the first hemolytic agent and the first stain to prepare a first test sample. The second reagent supply unit is configured to supply a second hemolytic agent and a second stain for white blood cell classification to the second reaction cell, so that the portion of the blood sample to be tested distributed to the second reaction cell is mixed and reacted with the second hemolytic agent and the second stain to prepare a second test sample.

[0061] The first hemolytic agent may be different from the second hemolytic agent. For example, the degree of lysis of red blood cells by the first hemolytic agent is less than that by the second hemolytic agent. Here, the hemolytic agent (including the first hemolytic agent and the second hemolytic agent) is used to dissolve red blood cells in the blood, lysing the red blood cells into fragments, but capable of keeping the morphology of white blood cells basically unchanged. For example, the hemolytic agent may be any one or a combination of several of cationic surfactants, nonionic surfactants, anionic surfactants, and amphiphilic surfactants; for another example, the hemolytic agent may include at least one of alkyl glycosides, triterpenoid saponins, and steroidal saponins.

[0062] The first stain may be different from the second stain. For example, the first stain is a fluorescent dye for identifying nucleated red blood cells (i.e., for distinguishing nucleated red blood cells from white blood cells), and the second stain is a fluorescent dye for white blood cell classification (for example, a fluorescent dye for classifying white blood cells in the blood sample to be tested into at least three white blood cell subsets (monocytes, lymphocytes, and neutrophils)).

[0063] Currently, commercially available reagents for identifying nucleated red blood cells can be used as the first hemolytic agent and the first stain in the present application, such as M-6LN and M-6FN; commercially available reagents for four-classification of white blood cells can be used as the second hemolytic agent and the second stain in the present application, such as M-60LD and M-6FD.

[0064] In some embodiments, the flow cell in the optical detection device 130 refers to a chamber suitable for detecting the light scattering signal and the fluorescence signal of a focused liquid flow. When a particle, such as a blood cell, passes through the detection hole of the flow cell, the particle scatters the incident light beam from the light source that is directed to the detection hole in all directions. Light detectors can be arranged at one or more different angles relative to the incident light beam to detect the light scattered by the particle, thereby obtaining the light scattering signal. Since different particles have different light scattering characteristics, the light scattering signal can be used to distinguish different particle populations.

[0065] Specifically, the light scattering signal detected near the incident light beam is generally referred to as the forward light scattering signal or the small angle light scattering signal. In some embodiments, the forward light scattering signal can be detected at an angle of about 1° to about 10° with respect to the incident light beam. In some other embodiments, the forward light scattering signal can be detected at an angle of about 2° to about 6° with respect to the incident light beam. The light scattering signal detected in the direction approximately 90° with respect to the incident light beam is generally referred to as the side light scattering signal. In some embodiments, the side light scattering signal can be detected at an angle of about 65° to about 115° with respect to the incident light beam. Generally, the fluorescence signal emitted from the blood cells stained with the fluorescent dye is also generally detected in the direction approximately 90° with respect to the incident light beam.

[0066] In some embodiments, the light detectors in the optical detection device 130 may include a forward light scattering detector for detecting the forward scattered light signal, a side light scattering detector for detecting the side scattered light signal, and a fluorescence detector for detecting the fluorescence signal. For example, the original optical information may include the forward scattered light signal, the side scattered light signal, and the fluorescence signal of the particles in the measured sample.

[0067] In a specific example of the optical detection device 130, as Figure 2 shown, the optical detection device 130 has a light source 101, a beam shaping component 102, a flow cell 103, and a forward light scattering detector 104 arranged in sequence on a straight line. On one side of the flow cell 103, a dichroic mirror 106 is arranged at an angle of 45° with respect to the straight line. A part of the side light emitted by the particles in the flow cell 103 passes through the dichroic mirror 106 and is captured by the fluorescence detector 105 arranged at an angle of 45° with respect to the dichroic mirror 106 behind the dichroic mirror 106; another part of the side light is reflected by the dichroic mirror 106 and is captured by the side light scattering detector 107 arranged at an angle of 45° with respect to the dichroic mirror 106 in front of the dichroic mirror 106.

[0068] In some embodiments, the light detectors in the optical detection device 130 can be further used to detect the first original optical information generated after the first measured sample is irradiated by light when passing through the flow cell, and to detect the second original optical information generated after the second measured sample is irradiated by light when passing through the flow cell.

[0069] In some embodiments, the processor 140 is used to process and calculate data to obtain the required results. For example, a two-dimensional scatter plot or a three-dimensional scatter plot can be generated based on the collected original optical information, and particle analysis can be performed on the scatter plot according to the gating method. The processor 140 can also perform visualization processing on the intermediate processing result or the final processing result, and then display it through the display device 150. In the embodiments of the present application, the processor 140 is configured to implement the method steps described in further detail later.

[0070] In some embodiments, the processor 140 can perform visualization processing on the intermediate processing result or the final processing result, and then display it through the display device 150. For example, the display device 150 may include a user interface, and the processor 140 can output and display the obtained infection marker parameter or the prompt information indicating the infection status of the subject based on the infection marker parameter on the user interface of the display device 150.

[0071] In some embodiments, the processor 140 includes, but is not limited to, a Central Processing Unit (CPU), a Micro Controller Unit (MCU), a Field-Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), etc., which are devices for interpreting computer instructions and processing data in computer software. For example, the processor is used to execute each computer application program in the computer-readable storage medium, so that the blood cell analyzer 100 executes the corresponding detection process and analyzes the original optical information detected by the optical detection device 130 in real time.

[0072] In addition, the blood cell analyzer 100 may further include a first housing 160 and a second housing 170. 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 the outer surface of the first housing 160 and is used to display the detection results of the blood cell analyzer 100.

[0073] In the embodiments of the present application, the sample analyzer 100 can count and classify white blood cells through the DIFF channel and / or the WNB channel. Among them, the sample analyzer 100 performs four-classification of white blood cells through the DIFF channel, classifying white blood cells into four types: lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), and eosinophils (Eos). The sample analyzer 100 identifies nucleated red blood cells through the WNB channel and can obtain the nucleated red blood cell count, white blood cell count value, and basophil count at the same time. Combining the DIFF channel and the WNB channel can obtain the five-classification result of white blood cells, including five types of white blood cells: lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), eosinophils (Eos), and basophils (Baso).

[0074] Here, it can be understood that the WNB channel (also referred to as the first detection channel) for identifying nucleated red blood cells refers to the detection of the first measurement sample prepared by the sample preparation device 120 by the optical detection device 130 to obtain the first original optical information generated after the first measurement sample is irradiated by light when passing through the flow cell; and the DIFF channel (also referred to as the second detection channel) for white blood cell classification refers to the detection of the second measurement sample prepared by the sample preparation device 120 by the optical detection device 130 to obtain the second original optical information generated after the second measurement sample is irradiated by light when passing through the flow cell.

[0075] For example, in the first detection channel, parameters as shown in Table 1 can be obtained based on the first original optical information.

[0076] Table 1 First Detection Channel Parameters

[0077] Chinese Name Abbreviation White Blood Cell Count WBC Nucleated Red Blood Cell Count NRBC# Nucleated Red Blood Cell Percentage NRBC% Basophil Count Bas# Basophil Percentage Bas%

[0078] In the second detection channel, parameters as shown in Table 2 can be obtained based on the second original optical information.

[0079] Table 2 Second Detection Channel Parameters

[0080]

[0081]

[0082] Next, some embodiments are combined to further illustrate the steps that the processor 140 needs to execute to obtain the infection marker parameters.

[0083] In some embodiments, when the processor 140 is configured to input the input information obtained based on the original optical information into the trained first non-linear model to obtain the infection marker parameters: obtain the first input information based on the original optical information of the measured sample, and input the first input information into the first non-linear model to obtain the infection marker parameters. Here, the first input information includes at least one of the original optical information, the scatter plot obtained based on the original optical information, and the histogram obtained based on the original optical information.

[0084] In this way, the first non-linear model can output the infection marker parameters according to the scatter plot / histogram composed of the original optical information, or directly output the infection marker parameters according to the original optical signal, without constructing the scatter plot / histogram and calculating the corresponding characteristic parameters, so as to quickly and accurately obtain the infection marker parameters.

[0085] In some embodiments, the original optical information may include optical signals (such as at least one of the forward scatter light signal FS, the side scatter light signal, and the fluorescence signal) obtained from one or more channels of the WNB channel and the DIFF channel.

[0086] In some embodiments, the white blood cell clusters Wbc (including all types of white blood cells) in the first measured sample can be identified based on the forward scatter light signal (or forward scatter light intensity) FS, the side scatter light signal (or side scatter light intensity) SS, and the fluorescence signal (or fluorescence intensity) FL in the first original optical information obtained from the WNB channel. At the same time, the neutrophil clusters Neu and lymphocyte clusters Lym in the white blood cells in the first measured sample can be identified, as Figures 3 to 5 shown. Among them, Figure 3 is a two-dimensional scatter plot generated based on the forward scatter light signal FS and the fluorescence signal FL in the first original optical information, Figure 4 is a two-dimensional scatter plot generated based on the forward scatter light signal FS and the side scatter light signal SS in the first original optical information, Figure 5 is a three-dimensional scatter plot generated based on the forward scatter light signal FS, the side scatter light signal SS, and the fluorescence signal FL in the first original optical information.

[0087] In some embodiments, the white blood cells in the second measured sample can be classified into at least monocyte clusters, neutrophil clusters, and lymphocyte clusters based on the second original optical information obtained from the DIFF channel. In particular, they can be classified into monocyte clusters, neutrophil clusters, lymphocyte clusters, and eosinophil clusters.

[0088] In a specific example, such as Figures 6 to 8As shown, white blood cells in the second measurement sample can be classified into monocyte clusters Mon, neutrophil clusters Neu, lymphocyte clusters Lym, and eosinophil clusters Eos based on the forward scatter light signal (or forward scatter light intensity) FS, side scatter light signal (or side scatter light intensity) SS, and fluorescence signal (or fluorescence intensity) FL obtained from the DIFF channel. Among them, Figure 6 is a two-dimensional scatter plot generated based on the side scatter light signal SS and fluorescence signal FL in the second original optical information, Figure 7 is a two-dimensional scatter plot generated based on the forward scatter light signal FS and side scatter light signal SS in the second original optical information, Figure 8 is a three-dimensional scatter plot generated based on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL in the second original optical information.

[0089] In some embodiments, the scatter plots obtained based on the original optical information in the first input information may include two-dimensional or three-dimensional white blood cell scatter plots, and the histograms obtained based on the original optical information may include white blood cell distribution histograms (such as white blood cell volume distribution histograms).

[0090] In some embodiments, the scatter plots obtained based on the original optical information in the first input information may include at least one of the following scatter plots: two-dimensional or three-dimensional white blood cell scatter plots generated based on the first original optical information obtained from the WNB channel; and two-dimensional or three-dimensional white blood cell scatter plots generated based on the second original optical information obtained from the DIFF channel.

[0091] Furthermore, the scatter plots obtained based on the original optical information in the first input information may include at least one of the following scatter plots: a two-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS and side scatter light signal SS obtained from the WNB channel; a two-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS and fluorescence signal FL obtained from the WNB channel; a two-dimensional white blood cell scatter plot generated based on the side scatter light signal SS and fluorescence signal FL obtained from the WNB channel; a three-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the WNB channel; a two-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS and side scatter light signal SS obtained from the DIFF channel; a two-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS and fluorescence signal FL obtained from the DIFF channel; a two-dimensional white blood cell scatter plot generated based on the side scatter light signal SS and fluorescence signal FL obtained from the DIFF channel; a three-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the DIFF channel.

[0092] In some embodiments, the histogram obtained from the original optical information in the first input information may include at least one of the following distribution histograms: the distribution histogram of white blood cells on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the WNB channel; the distribution histogram of neutrophils on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the WNB channel; the distribution histogram of lymphocyte clusters on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the WNB channel; the distribution histogram of white blood cells on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the DIFF channel; the distribution histogram of neutrophils on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the DIFF channel; the distribution histogram of lymphocytes on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the DIFF channel; and the distribution histogram of monocytes on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL obtained from the DIFF channel.

[0093] In some embodiments, the first non-linear model may be a machine learning model or a neural network model.

[0094] For example, as Figure 9 shown, the scatter plot obtained from the WNB channel and the scatter plot obtained from the DIFF channel can be input into the first non-linear model (such as a non-linear classifier) to obtain an infection marker parameter.

[0095] In some embodiments, the processor 140 may be configured to, when inputting the input information obtained based on the original optical information into the trained first non-linear model to obtain an infection marker parameter: obtain second input information based on the original optical information of the measurement sample, and input the second input information into the first non-linear model to obtain an infection marker parameter. Here, the second input information includes at least one of the numerical information characterizing the internal nucleic acid content, internal granularity, and cell volume statistical distribution information of at least one target particle cluster in the measurement sample.

[0096] It should be understood here that the numerical information of the at least one target particle cluster may include the numerical values of one or more characteristic parameters reflecting the internal nucleic acid content, internal granularity, cell volume, and other cell characteristics in the at least one target particle cluster.

[0097] In some embodiments, the second input information includes at least one of numerical information characterizing the internal nucleic acid content, internal granularity, and cell volume statistical distribution information of at least one first target particle cluster in the first measurement sample. In some embodiments, the at least one first target particle cluster may be selected from one or more of white blood cell clusters Wbc, neutrophil clusters Neu, and lymphocyte clusters Lym in the measurement sample.

[0098] For example, the at least one first target particle cluster includes lymphocyte clusters Lym and white blood cell clusters Wbc in the measurement sample, or includes neutrophil clusters Neu and white blood cell clusters Wbc in the measurement sample, or includes lymphocyte clusters Lym and neutrophil clusters Neu in the measurement sample. That is to say, the numerical information of the at least one first target particle cluster may include the values of one or more characteristic parameters reflecting cell characteristics such as the volume, internal granularity, and internal nucleic acid content of the cells in lymphocyte clusters Lym, neutrophil clusters Neu, and white blood cell clusters Wbc in the first measurement sample.

[0099] In some alternative or additional embodiments, the second input information includes at least one of numerical information characterizing the internal nucleic acid content, internal granularity, and cell volume statistical distribution information of at least one second target particle cluster in the second measurement sample.

[0100] In some embodiments, the at least one second target particle cluster may include at least one cell cluster among monocyte clusters Mon, neutrophil clusters Neu, and lymphocyte clusters Lym in the second measurement sample. Preferably, the at least one second target particle cluster may include at least one cell cluster among monocyte clusters Mon and neutrophil clusters Neu in the second measurement sample.

[0101] That is to say, the numerical information of the at least one second target particle cluster may include the numerical values of one or more characteristic parameters reflecting the cell characteristics such as the volume, internal granularity, and internal nucleic acid content of the cells in the monocyte cluster Mon, neutrophil cluster Neu, and lymphocyte cluster Lym in the second measurement sample. In some embodiments, the characteristic parameters reflecting the cell characteristics such as the internal nucleic acid content, internal granularity, and cell volume in the at least one first target particle cluster may include one or more of the following characteristic parameters: the forward scatter light intensity distribution width, forward scatter light intensity distribution centroid, forward scatter light intensity distribution coefficient of variation, side scatter light intensity distribution width, side scatter light intensity distribution centroid, side scatter light intensity distribution coefficient of variation, fluorescence intensity distribution width, fluorescence intensity distribution centroid, fluorescence intensity distribution coefficient of variation of the at least one first target particle cluster (such as neutrophil cluster Neu and / or white blood cell cluster Wbc), and the area of the distribution region of the at least one first target particle cluster in the two-dimensional scatter plot generated by two of the forward scatter light intensity, side scatter light intensity, and fluorescence intensity, and the volume of the distribution region of the at least one first target particle cluster in the three-dimensional scatter plot generated by the forward scatter light intensity, side scatter light intensity, and fluorescence intensity, for example Figure 5 the volume of the space occupied by the white blood cell cluster.

[0102] In some specific examples, the characteristic parameters reflecting cell characteristics such as the internal nucleic acid content, internal granularity, and cell volume in the at least one first target particle cluster may include one or more of the following characteristic parameters: the forward scatter light intensity distribution width N_NEU_FS_W, the forward scatter light intensity distribution centroid N_NEU_FS_P, the forward scatter light intensity distribution coefficient of variation N_NEU_FS_CV, the side scatter light intensity distribution width N_NEU_SS_W, the side scatter light intensity distribution centroid N_NEU_SS_P, the side scatter light intensity distribution coefficient of variation N_NEU_SS_CV, the fluorescence intensity distribution width N_NEU_FL_W, the fluorescence intensity distribution centroid N_NEU_FL_P, the fluorescence intensity distribution coefficient of variation N_NEU_FL_CV of the neutrophil cluster in the first measurement sample, and the area N_NEU_FLFS_Area (the area of the distribution region of the neutrophil cluster in the two-dimensional scatter plot generated by the forward scatter light intensity and the fluorescence intensity), N_NEU_FLSS_Area (the area of the distribution region of the neutrophil cluster in the two-dimensional scatter plot generated by the side scatter light intensity and the fluorescence intensity), N_NEU_SSFS_Area (the area of the distribution region of the neutrophil cluster in the two-dimensional scatter plot generated by the forward scatter light intensity and the side scatter intensity), and the volume of the distribution region of the neutrophil cluster in the three-dimensional scatter plot generated by the forward scatter light intensity, the side scatter intensity, and the fluorescence intensity;The forward scatter light intensity distribution width N_LYM_FS_W, forward scatter light intensity distribution centroid N_LYM_FS_P, forward scatter light intensity distribution coefficient of variation N_LYM_FS_CV, side scatter light intensity distribution width N_LYM_SS_W, side scatter light intensity distribution centroid N_LYM_SS_P, side scatter light intensity distribution coefficient of variation N_LYM_SS_CV, fluorescence intensity distribution width N_LYM_FL_W, fluorescence intensity distribution centroid N_LYM_FL_P, fluorescence intensity distribution coefficient of variation N_LYM_FL_CV of the lymphocyte clusters in the first measurement sample, and the areas N_LYM_FLFS_Area (the area of the distribution region of the lymphocyte clusters in the two-dimensional scatter plot generated by the forward scatter light intensity and the fluorescence intensity), N_LYM_FLSS_Area (the area of the distribution region of the lymphocyte clusters in the two-dimensional scatter plot generated by the side scatter light intensity and the fluorescence intensity), N_LYM_SSFS_Area (the area of the distribution region of the lymphocyte clusters in the two-dimensional scatter plot generated by the forward scatter light intensity and the side scatter intensity), and the volume of the distribution region of the lymphocyte clusters in the three-dimensional scatter plot generated by the forward scatter light intensity, side scatter intensity, and fluorescence intensity; and, the forward scatter light intensity distribution width N_WBC_FS_W, forward scatter light intensity distribution centroid N_WBC_FS_P, forward scatter light intensity distribution coefficient of variation N_WBC_FS_CV, side scatter light intensity distribution width N_WBC_SS_W, side scatter light intensity distribution centroid N_WBC_SS_P, side scatter light intensity distribution coefficient of variation N_WBC_SS_CV, fluorescence intensity distribution width N_WBC_FL_W, fluorescence intensity distribution centroid N_WBC_FL_P, fluorescence intensity distribution coefficient of variation N_WBC_FL_CV of the white blood cell clusters in the first measurement sample, and the areas N_WBC_FLFS_Area (the area of the distribution region of the white blood cell clusters in the two-dimensional scatter plot generated by the forward scatter light intensity and the fluorescence intensity), N_WBC_FLSS_Area (the area of the distribution region of the white blood cell clusters in the two-dimensional scatter plot generated by the side scatter light intensity and the fluorescence intensity), N_WBC_SSFS_Area (the area of the distribution region of the white blood cell clusters in the two-dimensional scatter plot generated by the forward scatter light intensity and the side scatter intensity), and the volume of the distribution region of the white blood cell clusters in the three-dimensional scatter plot generated by the forward scatter light intensity, side scatter intensity, and fluorescence intensity.

[0103] In some alternative or additional embodiments, the characteristic parameters reflecting cell characteristics such as the internal nucleic acid content, internal granularity, and volume of cells in the at least one second target particle cluster may include one or more of the following characteristic parameters: the width of the forward scatter light intensity distribution, the centroid of the forward scatter light intensity distribution, the coefficient of variation of the forward scatter light intensity distribution, the width of the lateral scatter light intensity distribution, the centroid of the lateral scatter light intensity distribution, the coefficient of variation of the lateral scatter light intensity distribution, the width of the fluorescence intensity distribution, the centroid of the fluorescence intensity distribution, the coefficient of variation of the fluorescence intensity distribution, and the area of the distribution region of the second target particle cluster in the two-dimensional scatter plot generated by two of the forward scatter light intensity, lateral scatter light intensity, and fluorescence intensity, and the volume of the distribution region of the second target particle cluster in the three-dimensional scatter plot generated by the forward scatter light intensity, lateral scatter light intensity, and fluorescence intensity. For example Figure 8 The volume of the space occupied by the white blood cell cluster.

[0104] In some specific examples, the characteristic parameters reflecting cell characteristics such as the internal nucleic acid content, internal granularity, and cell volume in the at least one second target particle cluster may include one or more of the following characteristic parameters: the forward scatter light intensity distribution width D_MON_FS_W, the forward scatter light intensity distribution center of gravity D_MON_FS_P, the forward scatter light intensity distribution coefficient of variation D_MON_FS_CV, the side scatter light intensity distribution width D_MON_SS_W, the side scatter light intensity distribution center of gravity D_MON_SS_P, the side scatter light intensity distribution coefficient of variation D_MON_SS_CV, the fluorescence intensity distribution width D_MON_FL_W, the fluorescence intensity distribution center of gravity D_MON_FL_P, the fluorescence intensity distribution coefficient of variation D_MON_FL_CV of the monocyte cluster in the second measurement sample, and the area D_MON_FLFS_Area (the area of the distribution region of the monocyte cluster in the two-dimensional scatter plot generated by the forward scatter light intensity and the fluorescence intensity), D_MON_FLSS_Area (the area of the distribution region of the monocyte cluster in the two-dimensional scatter plot generated by the side scatter light intensity and the fluorescence intensity), D_MON_SSFS_Area (the area of the distribution region of the monocyte cluster in the two-dimensional scatter plot generated by the forward scatter light intensity and the side scatter intensity), and the volume of the distribution region of the monocyte cluster in the three-dimensional scatter plot generated by the forward scatter light intensity, the side scatter intensity, and the fluorescence intensity;The width D_NEU_FS_W of the forward scatter light intensity distribution, the center of gravity D_NEU_FS_P of the forward scatter light intensity distribution, the coefficient of variation D_NEU_FS_CV of the forward scatter light intensity distribution, the width D_NEU_SS_W of the side scatter light intensity distribution, the center of gravity D_NEU_SS_P of the side scatter light intensity distribution, the coefficient of variation D_NEU_SS_CV of the side scatter light intensity distribution, the width D_NEU_FL_W of the fluorescence intensity distribution, the center of gravity D_NEU_FL_P of the fluorescence intensity distribution, the coefficient of variation D_NEU_FL_CV of the fluorescence intensity distribution of neutrophil clusters in the second measurement sample, and the area D_NEU_FLFS_Area (the area of the distribution region of neutrophil clusters in the two-dimensional scatter plot generated by forward scatter light intensity and fluorescence intensity), D_NEU_FLSS_Area (the area of the distribution region of neutrophil clusters in the two-dimensional scatter plot generated by side scatter light intensity and fluorescence intensity), D_NEU_SSFS_Area (the area of the distribution region of neutrophil clusters in the two-dimensional scatter plot generated by forward scatter light intensity and side scatter intensity), and the volume of the distribution region of neutrophil clusters in the three-dimensional scatter plot generated by forward scatter light intensity, side scatter intensity, and fluorescence intensity; and, the width D_LYM_FS_W of the forward scatter light intensity distribution, the center of gravity D_LYM_FS_P of the forward scatter light intensity distribution, the coefficient of variation D_LYM_FS_CV of the forward scatter light intensity distribution, the width D_LYM_SS_W of the side scatter light intensity distribution, the center of gravity D_LYM_SS_P of the side scatter light intensity distribution, the coefficient of variation D_LYM_SS_CV of the side scatter light intensity distribution, the width D_LYM_FL_W of the fluorescence intensity distribution, the center of gravity D_LYM_FL_P of the fluorescence intensity distribution, the coefficient of variation D_LYM_FL_CV of the fluorescence intensity distribution of lymphocyte clusters in the second measurement sample, and the area D_LYM_FLFS_Area (the area of the distribution region of lymphocyte clusters in the two-dimensional scatter plot generated by forward scatter light intensity and fluorescence intensity), D_LYM_FLSS_Area (the area of the distribution region of lymphocyte clusters in the two-dimensional scatter plot generated by side scatter light intensity and fluorescence intensity), D_LYM_SSFS_Area (the area of the distribution region of lymphocyte clusters in the two-dimensional scatter plot generated by forward scatter light intensity and side scatter intensity), and the volume of the distribution region of lymphocyte clusters in the three-dimensional scatter plot generated by forward scatter light intensity, side scatter intensity, and fluorescence intensity.

[0105] In some embodiments, the processor 140 may be configured to: when the numerical information of the at least one target particle cluster is lower than a threshold value, preprocess the original optical information, and the preprocessing may include one or more of removing noise interference, performing logarithmic processing, and increasing the statistic of the at least one target particle cluster.

[0106] In some embodiments, abnormal conditions such as noise or a small number of particles may exist in the measurement sample. For example, the preprocessing may include removing noise interference (as Figure 10 shown). Also, for example, the preprocessing may include increasing the statistic of at least one target particle cluster to reduce the possibility of unreliable numerical information caused by a small total number of particles in the target particle cluster, so as to obtain more accurate infection marker parameters subsequently. That is, when the total number of particles in the target particle cluster is less than a preset threshold value, that is, the number of particles in the target particle cluster is small and the amount of information characterized by the particles is limited, the obtained infection marker parameters may be unreliable. For example, as Figure 11 shown, the total number of particles in the leukocyte cluster in the measurement sample is too low, which may lead to unreliable infection marker parameters obtained based on the numerical information of the leukocyte cluster.

[0107] Here, for example, it is possible to determine whether the total number of particles in the at least one target particle cluster is lower than a preset threshold value based on the original optical information.

[0108] In some embodiments, the preprocessing may further include performing logarithmic processing (as Figure 12 shown), so as to obtain more accurate infection marker parameters subsequently, for example, to avoid signal changes caused by different instruments and different reagents.

[0109] In some embodiments, when the input information obtained based on the original optical information is input into a trained first non-linear model to obtain infection marker parameters, the processor 140 may be configured to: input the first input information and the second input information obtained based on the original optical information into the first non-linear model to obtain infection marker parameters. Here, the specific description of the first input information and the second input information can be referred to the description in the above related embodiments, and will not be elaborated here.

[0110] That is, at least one of the original optical information, the scatter plot obtained based on the original optical information, the histogram obtained based on the original optical information, and the numerical information characterizing the internal nucleic acid content, internal granularity, and cell volume statistical distribution information of at least one target particle cluster in the measurement sample may be used jointly to obtain infection marker parameters.

[0111] For example, as Figure 13As shown, one or more scatter plots obtained through the WNB channel and the DIFF channel can be input into the first non-linear model together with at least one of the numerical information characterizing the internal nucleic acid content, internal granularity, and statistical distribution information of cell volume of at least one target particle cluster, so as to obtain an infection marker parameter.

[0112] In some embodiments, the processor 140 can be configured such that when the input information obtained based on the original optical information is input into the trained first non-linear model to obtain an infection marker parameter: the first input information is input into the trained second non-linear model to obtain third input information, and the third input information and the second input information are input into the first non-linear model to obtain the infection marker parameter. For example, the third input information can include image feature parameters.

[0113] For example, as Figure 14 shown, one or more scatter plots obtained through the WNB channel and the DIFF channel can be input into the trained second non-linear model to obtain image feature parameters of the one or more scatter plots, and the image feature parameters and at least one of the numerical information characterizing the internal nucleic acid content, internal granularity, and statistical distribution information of cell volume of at least one target particle cluster are input into the first non-linear model together to obtain an infection marker parameter.

[0114] It can be understood that the second non-linear model can be a machine learning model or a neural network model, such as a convolutional neural network model.

[0115] In some embodiments, the processor 140 can be configured such that when the input information obtained based on the original optical information is input into the trained first non-linear model to obtain an infection marker parameter: the input information obtained based on the first original optical information and the input information obtained based on the second original optical information are input into the first non-linear model to obtain an infection marker parameter. That is, the processor 140 can jointly use the input information obtained based on the first original optical information from the WNB channel and the input information obtained based on the second original optical information from the DIFF channel to obtain an infection marker parameter.

[0116] In some embodiments, the input information obtained based on the first original optical information includes at least one of the first original optical information, the first scatter plot obtained based on the first original optical information (such as the first white blood cell scatter plot), and the first histogram obtained based on the first original optical information.

[0117] In some embodiments, the input information obtained based on the second original optical information includes at least one of the second original optical information, a second scatter plot (such as a first white blood cell scatter plot) obtained based on the second original optical information, and a second histogram obtained based on the second original optical information.

[0118] For example, the processor 140 may combine a first scatter plot obtained based on the first original optical information and a second scatter plot obtained based on the second original optical information Figure 1 and input them into the first non-linear model to obtain an infection marker parameter.

[0119] Preferably, the input information obtained based on the first original optical information includes a first white blood cell scatter plot obtained based on the first original optical information, and the input information obtained based on the second original optical information includes a second white blood cell scatter plot obtained based on the second original optical information. That is, the processor 140 may combine the first white blood cell scatter plot and the second white blood cell scatter plot Figure 1 and input them into the first non-linear model to obtain an infection marker parameter.

[0120] In some embodiments, the first white blood cell scatter plot may include at least one of the following scatter plots: a two-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS and the side scatter light signal SS in the first original optical information; a two-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS and the fluorescence signal FL in the first original optical information; a two-dimensional white blood cell scatter plot generated based on the side scatter light signal SS and the fluorescence signal FL in the first original optical information; a three-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS, the side scatter light signal SS, and the fluorescence signal FL in the first original optical information.

[0121] In some embodiments, the second white blood cell scatter plot may include at least one of the following scatter plots: a two-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS and the side scatter light signal SS in the second original optical information; a two-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS and the fluorescence signal FL in the second original optical information; a two-dimensional white blood cell scatter plot generated based on the side scatter light signal SS and the fluorescence signal FL in the second original optical information; a three-dimensional white blood cell scatter plot generated based on the forward scatter light signal FS, the side scatter light signal SS, and the fluorescence signal FL in the second original optical information.

[0122] In some embodiments, the first histogram may include at least one of the following histograms: a distribution histogram of white blood cells on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL in the first original optical information; a distribution histogram of neutrophils on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL in the first original optical information; a distribution histogram of lymphocyte clusters on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL in the first original optical information.

[0123] In some embodiments, the second histogram may include at least one of the following histograms: a distribution histogram of white blood cells on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL in the second original optical information; a distribution histogram of neutrophils on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL in the second original optical information; a distribution histogram of lymphocyte clusters on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL in the second original optical information; and a distribution histogram of monocyte clusters on the forward scatter light signal FS, side scatter light signal SS, and fluorescence signal FL in the second original optical information.

[0124] In some embodiments, the processor 140 may be further configured to: when the value of the infection marker parameter is outside a preset range, output a prompt message indicating that the infection marker parameter is abnormal. For example, when the value of the infection marker parameter is abnormally increased, an upward-pointing arrow may be output to indicate the abnormal increase.

[0125] In some embodiments, the processor 140 may be further configured to: output the probability that the subject is in each of a plurality of infection states, where the plurality of infection states includes non-sepsis, early sepsis, and established sepsis. For example, the processor 140 may output that the probability that the subject is in a non-sepsis state is 0.7, the probability that the subject is in an early sepsis state is 0.2, and the probability that the subject is in an established sepsis state is 0.1.

[0126] In some embodiments, the disease condition of the subject and abnormal cells in the subject's blood (such as blast cells, abnormal lymphocytes, immature granulocytes) may also affect the diagnostic or indicative efficacy of the infection marker parameters. To this end, the processor 140 may be further configured to determine whether the infection marker parameters are reliable based on whether the subject has a specific disease and / or based on whether there are abnormal cells of a preset type (such as blast cells, abnormal lymphocytes, immature granulocytes) in the blood sample to be tested. That is, the processor 140 may be further configured to: when the subject has a blood disease, or when there are abnormal cells, especially blast cells, in the blood sample to be tested, not output the value of the infection marker parameter, or output a prompt message indicating that the value of the infection marker parameter is unreliable.

[0127] It can be understood that the abnormal blood picture of a subject with a blood disease results in unreliable infection marker parameters obtained.

[0128] The processor 140 can, for example, obtain whether the subject has a blood disease based on the identity information of the subject.

[0129] In some embodiments, the processor 140 may be configured to determine whether there are abnormal cells, especially blast cells, in the blood sample to be tested based on the original optical information.

[0130] Next, some application scenarios of the infection marker parameters proposed in this application are described, but this application is not limited thereto.

[0131] In some embodiments, the infection marker parameters can be applied to one or more of the following scenarios: early prediction of sepsis for the subject; diagnosis of sepsis for the subject; differentiation between common infection and severe infection for the subject; monitoring of the infection condition of the infected subject; prognosis analysis of sepsis for the subject who has received treatment; differentiation between bacterial infection and viral infection for the subject; differentiation between non-infectious inflammation and infectious inflammation for the subject; evaluation of the efficacy of sepsis treatment for the subject who is receiving medication treatment.

[0132] For example, the processor 140 may be further configured to perform early prediction of sepsis, diagnosis of sepsis, differentiation between common infection and severe infection, monitoring of the infection condition, prognosis analysis of sepsis, differentiation between bacterial infection and viral infection, or differentiation between non-infectious inflammation and infectious inflammation, and evaluation of the efficacy of sepsis treatment for the subject based on the infection marker parameters.

[0133] Sepsis is a severe infectious disease with a high incidence and a high fatality rate. For every hour of delayed treatment, the patient's mortality rate increases by 7%. Therefore, early warning of sepsis is particularly important. Early identification and warning of sepsis can provide valuable diagnosis and treatment time for patients and greatly improve the survival rate.

[0134] For this purpose, in the application scenario of early prediction of sepsis, the infection marker parameter can be used for early prediction of sepsis. That is, the processor 140 can be configured to output a prompt message indicating that the subject may progress to sepsis within a certain period of time after the blood sample to be tested is collected when the infection marker parameter meets the first preset condition.

[0135] In some embodiments, the certain period of time is not more than 48 hours. That is, the embodiments of the present application can predict at most two days in advance whether the subject may progress to sepsis. Further, the certain period of time is within 24 hours. That is, the embodiments of the present application can predict one day in advance whether the subject may progress to sepsis.

[0136] Here, the first preset condition may be, for example, that the value of the infection marker parameter is greater than a preset threshold. This preset threshold can be determined according to specific parameter combinations and the blood cell analyzer.

[0137] The clinical symptoms in the early stage of sepsis are similar to those of ordinary / severe infections. Sepsis patients are easily misdiagnosed as ordinary / severe infectious diseases, delaying the treatment opportunity. Therefore, the differential diagnosis of sepsis is particularly important.

[0138] For this purpose, in the application scenario of sepsis diagnosis, the infection marker parameter can be used for sepsis differentiation. That is, the processor 140 can be configured to output a prompt message indicating that the subject has sepsis when the infection marker parameter meets the second preset condition. Here, the second preset condition can also be that the value of the infection marker parameter is greater than a preset threshold. This preset threshold can be determined according to specific parameter combinations and the blood cell analyzer.

[0139] According to the severity of infection and the organ function status, patients with bacterial infections can be divided into ordinary infections and severe infections. The clinical treatment methods and nursing measures for the two types of infections are different. Therefore, the differentiation between ordinary infections and severe infections can assist doctors in identifying patients at risk of life and also allocate medical resources more reasonably.

[0140] To this end, in the application scenario of differentiating between common infections and severe infections, that is, when the infection marker parameter is used to determine whether the subject has a common infection or a severe infection, the processor 140 may be configured to output a prompt message indicating that the subject has a severe infection when the infection marker parameter meets the third preset condition. Here, the third preset condition may also be that the value of the infection marker parameter is greater than a preset threshold. This preset threshold can be determined according to specific parameter combinations and the hematology analyzer.

[0141] In the application scenario of monitoring the progression of an infection, the subject is an infected patient (i.e., a patient with an infectious inflammation), especially a patient with a severe infection or sepsis. For example, the subject is a patient with a severe infection or sepsis from the intensive care unit. Sepsis is a serious infectious disease with a high incidence and a high fatality rate. The condition of sepsis patients fluctuates greatly and requires daily monitoring to prevent the patient's condition from worsening without timely treatment. Therefore, it is very important to combine clinical symptoms with laboratory test results to judge the progression of the condition and the treatment effect of sepsis patients.

[0142] To this end, the processor 140 may be configured to monitor the development of the subject's infection condition based on the infection marker parameter.

[0143] In the application scenario of analyzing the prognosis of sepsis, the subject is a sepsis patient who has received treatment, and the infection marker parameter is used to determine whether the prognosis of the subject's sepsis is good. In this regard, the processor 140 may be further configured to determine whether the prognosis of the subject's sepsis is good based on the infection marker parameter. For example, when the infection marker parameter meets the fourth preset condition, a prompt message indicating that the prognosis of the subject's sepsis is good is output.

[0144] Infectious diseases can be divided into different infection types such as bacterial infections, viral infections, and fungal infections. Among them, bacterial infections and viral infections are the most common. Although the clinical symptoms of the two infections are roughly the same, the treatment methods are completely different. Therefore, clarifying the type of infection is helpful for choosing the correct treatment method. To this end, the infection marker parameter is used to differentiate between bacterial infections and viral infections, and the processor 140 may be further configured to determine whether the subject's infection type is a viral infection or a bacterial infection based on the infection marker parameter.

[0145] In addition, inflammation is divided into infectious inflammation caused by pathogenic microorganism infections and non-infectious inflammation caused by physical factors, chemical factors, or tissue necrosis. The clinical symptoms manifested by the two types of inflammation are roughly the same, and symptoms such as redness and fever will appear. However, the treatment methods for the two types of inflammation are not completely the same. Therefore, clarifying what factors cause the patient's inflammatory response is helpful for symptomatic treatment.

[0146] Therefore, the infection marker parameter is used to distinguish between non-infectious inflammation and infectious inflammation. The processor 140 can be further configured to determine whether the subject has infectious inflammation or non-infectious inflammation according to the infection marker parameter. For example, when the infection marker parameter meets the fifth preset condition, a prompt message indicating that the subject has infectious inflammation is output.

[0147] After the doctor inquires about the patient's condition and conducts a physical examination, there is usually one or several preliminary disease diagnoses. Then, differential diagnosis or disease confirmation is carried out through means such as laboratory tests and imaging examinations. Therefore, it can be said that the doctor orders laboratory tests with a purpose. In other words, when the doctor orders the tests, it is already clear in which scenario the parameters should be applied. For example: A patient with fever visits the general outpatient clinic without symptoms of organ damage. The doctor initially judges it to be a common infection rather than a severe infection or sepsis. However, to determine which specific drugs to prescribe, it is necessary to clarify whether it is a viral infection or a bacterial infection, so a blood routine test is ordered. When the results come out, attention will be paid to whether the parameter is greater than the threshold of "bacterial infection VS viral infection" rather than the threshold of "sepsis diagnosis". Therefore, the infection marker parameter output in this application is for the doctor's reference clinically, not for the purpose of diagnosis.

[0148] An embodiment of the present application also proposes a method for prompting the infection status of a subject. As Figure 15 shown, the method 200 includes the following steps:

[0149] S210, obtaining a blood sample to be tested of the subject;

[0150] S220, mixing at least a part of the blood sample to be tested, a hemolytic agent, and a staining agent to prepare a measurement sample;

[0151] S230, causing the particles in the measurement sample to pass through the optically detected area irradiated by light one by one to obtain the original optical information generated by the particles in the measurement sample after being irradiated by light;

[0152] S240, inputting the input information obtained based on the original optical information into the trained first non-linear model to obtain an infection marker parameter.

[0153] Optionally, the method 200 further includes:

[0154] S250, outputting the infection marker parameter or outputting a prompt message indicating the infection status of the subject based on the infection marker parameter.

[0155] The method 200 proposed by the embodiment of the present application is especially implemented by the above blood cell analyzer 100 proposed by the embodiment of the present application.

[0156] The method 200 proposed in the embodiments of the present application may further include other steps performed by the processor 140 in any of the above embodiments. For more embodiments and advantages of the method 200 proposed in the embodiments of the present application, reference may be made to the description of the blood cell analyzer 100 proposed in the embodiments of the present application above, especially the description of the method steps performed by the processor 140, which will not be elaborated here.

[0157] Next, some specific embodiments are used to further illustrate the present application and its advantages.

[0158] The true positive rate %, false positive rate %, true negative rate %, false negative rate %, and accuracy rate % in the embodiments of the present application are calculated by the following formulas:

[0159] True positive rate % = TP / (TP + FN) × 100%;

[0160] True negative rate % = TN / (FP + TN) × 100%;

[0161] False positive rate % = 1 - true negative rate %;

[0162] False negative rate % = 1 - true positive rate %;

[0163] Accuracy rate % = (TP + TN) / (TP + FP + TN + FN)

[0164] Wherein, TP is the number of true positive individuals, FP is the number of false positive individuals, TN is the number of true negative individuals, and FN is the number of false negative individuals.

[0165] Example 1 Use of scatter plots for sepsis differential diagnosis

[0166] Using the BC-6800Plus blood cell analyzer produced by Shenzhen Mindray Bio-Medical Electronics Co., Ltd. and the supporting lysing agents M-60LD, M-6LN and staining agents M-6FD, M-6FN of Mindray, routine blood tests were performed on 1748 blood samples to obtain scatter plots of the WNB channel and the DIFF channel, and sepsis differential diagnosis was performed according to the method proposed in the embodiments of the present application. Among them, there were 506 sepsis samples, that is, positive samples, and 1242 non-sepsis samples, that is, negative samples.

[0167] The inclusion criteria for these 1748 cases: adult ICU patients with existing or suspected acute infections. Exclusion criteria: pregnant women, chemotherapy-induced myelosuppression patients, immunosuppressive therapy patients, and patients with hematological diseases.

[0168] In this embodiment, only the scatter plots obtained through the WNB channel and / or the DIFF channel are input into the first non-linear model to obtain the infection marker parameters. Table 3 shows the sepsis differential diagnosis efficacy of the scatter plots used and the infection marker parameters obtained based on them. In Table 3:

[0169] Input 1 = SS_SL scatter plot of the DIFF channel (i.e., a scatter plot generated based on the side scatter light signal and the fluorescence signal obtained from the DIFF channel);

[0170] Input 2 = FL_FS scatter plot of the WNB channel (i.e., a scatter plot generated based on the forward scatter light signal and the fluorescence signal obtained from the DIFF channel);

[0171] Input 3 = SS_SL scatter plot of the DIFF channel + FL_FS scatter plot of the WNB channel.

[0172] Table 3 Efficacy of infection marker parameters obtained based on scatter plots for diagnosing sepsis

[0173] Input ROC_AUC Diagnostic Threshold False Positive Rate True Positive Rate True Negative Rate False Negative Rate Input 1 0.851 >0.5 25.7％ 81.4％ 74.3％ 18.6％ Input 2 0.835 >0.5 29.8％ 80.6％ 70.2％ 19.4％ Input 3 0.867 >0.5 24.2％ 81.6％ 75.8％ 18.4％

[0174] It can be seen that the infection marker parameters obtained only based on the scatter plot can be used to effectively distinguish sepsis. In addition, the diagnostic efficacy of the infection marker parameters obtained based on the scatter plot of the WNB channel and the scatter plot of the DIFF channel is also better than that of the infection marker parameters obtained based on the scatter plot of a single DIFF channel or the scatter plot of a single WNB channel.

[0175] Example 2 Using scatter plots and cell feature parameters in combination for differential diagnosis of sepsis

[0176] Using the BC-6800Plus hematology analyzer produced by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., the above 1748 blood samples were subjected to routine blood tests according to the steps similar to those in Example 1 of this application to obtain scatter plots of the WNB channel and the DIFF channel, and further obtain the corresponding infection marker parameters for differential diagnosis of sepsis.

[0177] In this example, the scatter plot of the WNB channel or the DIFF channel and at least one cell feature parameter were directly input into the first non-linear model to obtain the infection marker parameters. Table 4 shows the obtained infection marker parameters and their corresponding differential diagnosis efficacy for sepsis. In Table 4:

[0178] Input combination 1 = SS_SL scatter plot of the DIFF channel + value of N_WBC_FL_P + value of N_WBC_FS_W;

[0179] Input combination 2 = SS_SL scatter plot of the DIFF channel + value of N_WBC_FL_W + value of N_NEU_FL_W;

[0180] Input combination 3 = FL_FS scatter plot of the WNB channel + value of N_WBC_SS_P + value of N_WBC_FL_P.

[0181] Table 4 Potency of infection marker parameters obtained based on scatter plots and cell parameters for diagnosing sepsis

[0182] Input ROC_AUC Diagnostic Threshold False Positive Rate True Positive Rate True Negative Rate False Negative Rate Input Combination 1 0.862 >0.5 24.4％ 81.8％ 75.6％ 18.2％ Input Combination 2 0.855 >0.5 25.4％ 81.4％ 74.6％ 18.6％ Input Combination 3 0.85 >0.5 26.1％ 80.8％ 73.9％ 19.2％

[0183] In addition, Table 5 shows the infection marker parameters obtained based on other input combinations and their corresponding sepsis differential diagnosis potencies, where the first input information is a scatter plot and the second input information is a cell characteristic parameter.

[0184] Table 5 Potency of the remaining input combinations for diagnosing sepsis risk

[0185]

[0186]

[0187] Thus, it can be seen that the infection marker parameters obtained by combining a scatter plot and cell characteristic parameters can be used to effectively differentiate sepsis.

[0188] Example 3 Using two non - linear models for sepsis differential diagnosis

[0189] Using the BC - 6800Plus hematology analyzer produced by Shenzhen Mindray Bio - medical Electronics Co., Ltd., according to the steps similar to those in Example 1 of this application, routine blood tests were performed on the above 1748 blood samples to obtain scatter plots of the WNB channel and the DIFF channel, and further corresponding infection marker parameters were obtained for sepsis differentiation.

[0190] In this example, the scatter plot of the WNB channel or the DIFF channel was first input into the second non - linear model to obtain the third input information, and then the third input information and at least one cell characteristic parameter were input into the first non - linear model to obtain the infection marker parameters. Table 6 shows the obtained infection marker parameters and their corresponding sepsis differential diagnosis potencies. In Table 6: Input combination 1 = SS_SL scatter plot of the DIFF channel + value of N_WBC_SFL_P + value of N_WBC_FSC_W;

[0191] Input combination 2 = SS_SL scatter plot of the DIFF channel + value of N_WBC_SFL_W + value of N_NEU_SFL_W;

[0192] Input combination 3 = FL_FS scatter plot of the WNB channel + value of N_WBC_SSC_P + value of N_WBC_SFL_P.

[0193] Table 6 Potency of infection marker parameters obtained based on scatter plots and cell parameters for diagnosing sepsis

[0194] Input ROC_AUC Diagnostic Threshold False Positive Rate True Positive Rate True Negative Rate False Negative Rate Input Combination 1 0.861 >0.5 24.3％ 81.8％ 75.7％ 18.2％ Input Combination 2 0.857 >0.5 25.1％ 81.6％ 74.9％ 18.4％ Input Combination 3 0.848 >0.5 26.7％ 81.0％ 73.3％ 19.0％

[0195] It can be seen that the infection marker parameters obtained by using two non-linear models and using scatter plots and cell characteristic parameters can be used to effectively identify sepsis.

[0196] Example 4 Identification of common infection and severe infection using scatter plots

[0197] Using the BC-6800Plus hematology analyzer produced by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., and using Mindray's supporting lysing agents M-60LD, M-6LN and staining agents M-6FD, M-6FN, routine blood tests were performed on blood samples from 1548 donors to obtain scatter plots of the WNB channel and the DIFF channel, and the method proposed in the embodiments of the present application was used to identify common infection and severe infection. Among them, there were 756 severe infection samples, that is, positive samples, and 792 non-severe infection samples, that is, negative samples.

[0198] Inclusion criteria for the 1548 donors in this example: adult ICU patients with existing or suspected acute infections. Exclusion criteria: pregnant women, chemotherapy-induced myelosuppression patients, immunosuppressive therapy patients, and patients with hematological diseases.

[0199] In this example, only the scatter plots obtained through the WNB channel and / or the DIFF channel were input into the first non-linear model to obtain the infection marker parameters. Table 7 shows the scatter plots used and the diagnostic efficacy of the infection marker parameters obtained based on them for severe infection. In Table 7:

[0200] Input 1 = DIFF channel SS_SL scatter plot (i.e., the scatter plot generated based on the side scatter light signal and fluorescence signal obtained from the DIFF channel);

[0201] Input 2 = WNB channel FL_FS scatter plot (i.e., the scatter plot generated based on the forward scatter light signal and fluorescence signal obtained from the DIFF channel);

[0202] Input 3 = DIFF channel SS_SL scatter plot + WNB channel FL_FS scatter plot.

[0203] Table 7 Efficacy of infection marker parameters obtained based on scatter plots for identifying severe infection

[0204] Input ROC_AUC Accuracy True Positive Rate True Negative Rate Input 1 0.892 82.0％ 80.8％ 82.8％ Input 2 0.906 83.6％ 81.5％ 85.0％ Input 3 0.877 80.9％ 84.6％ 80.8％

[0205] It can be seen that the infection marker parameters obtained only based on scatter plots can be used to effectively identify severe infection.

[0206] Example 5 Identification of common infection and severe infection by combining scatter plots and cell characteristic parameters

[0207] Using the BC-6800Plus hematology analyzer produced by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., the above 1,548 blood samples were subjected to routine blood tests according to the steps similar to those in Example 1 of this application, and scatter plots of the WNB channel and the DIFF channel were obtained. The method proposed in Example 1 of this application was used to distinguish between common infections and severe infections.

[0208] In this embodiment, the scatter plot of the WNB channel or the DIFF channel and at least one cell feature parameter were directly input into the first non-linear model to obtain the infection marker parameter. Table 8 shows the obtained infection marker parameter and its corresponding diagnostic efficacy for severe infection.

[0209] Table 8 Efficacy of input combinations of scatter plots and cell feature parameters for differentiating severe infections

[0210]

[0211]

[0212]

[0213] It can be seen that the infection marker parameter obtained by combining the scatter plot and the cell feature parameter can be used to effectively distinguish severe infections.

[0214] The features or combinations of features mentioned in the above description, the drawings, and the claims, as long as they are meaningful within the scope of this application and do not conflict with each other, can be combined with each other arbitrarily or used alone. The advantages and features described for the hematology analyzer provided in this application are applicable to the sample analysis method provided in this application in a corresponding manner, and vice versa.

[0215] The above are only the preferred embodiments of this application, and do not limit the patent scope of this application. Any equivalent transformation made using the content of the specification and drawings of this application under the inventive concept of this application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of this application.

Claims

1. A blood cell analyzer, comprising: A sampling device for sucking a blood sample to be tested of a subject; A sample preparation device for mixing at least a part of the blood sample to be tested, a hemolytic agent and a staining agent to prepare a measurement sample; An optical detection device, including a flow cell, a light source and a light detector, wherein the flow cell is used for the measurement sample to pass through, the light source is used for irradiating the measurement sample passing through the flow cell with light, and the light detector is used for detecting the original optical information generated after the measurement sample is irradiated by light when passing through the flow cell; A processor configured to: Input the input information obtained based on the original optical information into a trained first non-linear model to obtain an infection marker parameter; And Output the infection marker parameter or output a prompt message indicating the infection status of the subject based on the infection marker parameter.

2. The blood cell analyzer according to claim 1, wherein The processor inputs the input information obtained based on the original optical information into a trained first non-linear model to obtain an infection marker parameter, including: the processor Obtains first input information based on the original optical information, where the first input information includes at least one of the original optical information, a scatter plot obtained based on the original optical information, and a histogram obtained based on the original optical information; and Inputs the first input information into the first non-linear model to obtain an infection marker parameter.

3. The blood cell analyzer according to claim 1, characterized in that, The processor inputs the input information obtained based on the original optical information into a trained first non-linear model to obtain an infection marker parameter, including: Obtains second input information based on the original optical information, where the second input information includes at least one of numerical information characterizing the internal nucleic acid content, internal granularity, and cell volume statistical distribution information of at least one target particle group in the measurement sample; and Inputs the second input information into the first non-linear model to obtain an infection marker parameter.

4. The blood cell analyzer according to claim 1, characterized in that, The processor inputs the input information obtained based on the original optical information into a trained first non-linear model to obtain an infection marker parameter, including: Obtains first input information based on the original optical information, where the first input information includes at least one of the original optical information, a scatter plot obtained based on the original optical information, and a histogram obtained based on the original optical information; Obtains second input information based on the original optical information, where the second input information includes at least one of numerical information characterizing the internal nucleic acid content, internal granularity, and cell volume statistical distribution information of at least one target particle group in the measurement sample; and Inputs the first input information and the second input information into the first non-linear model to obtain the infection marker parameter.

5. The blood cell analyzer according to claim 1, wherein, The processor inputs the input information obtained based on the original optical information into a trained first non-linear model to obtain an infection marker parameter, including: Obtain first input information based on the original optical information, where the first input information includes at least one of the original optical information, a scatter plot obtained based on the original optical information, and a histogram obtained based on the original optical information; Input the first input information into a trained second non - linear model to obtain third input information; Obtain second input information based on the original optical information, where the second input information includes at least one of numerical information characterizing the internal nucleic acid content, internal granularity, and cell volume statistical distribution information of at least one target particle group in the measured sample; and Input the third input information and the second input information into the first non - linear model to obtain the infection marker parameter.

6. The blood cell analyzer according to any one of claims 3-5, characterized in that, The at least one target particle group is selected from one or more of a leukocyte group, a neutrophil group, and a lymphocyte group.

7. The blood cell analyzer according to any one of claims 3-5, characterized in that, The processor is further configured to: When the numerical information of the at least one target particle group is lower than a threshold, pre - process the original optical information, where the pre - processing includes removing noise interference or increasing the statistic of the at least one target particle group.

8. The blood cell analyzer according to claim 1, wherein, The sample preparation device is further used to mix a first part of the blood sample to be tested, a first hemolytic agent, and a first staining agent for identifying nucleated red blood cells to prepare a first measured sample, and mix a second part of the blood sample to be tested, a second hemolytic agent, and a second staining agent for white blood cell classification to prepare a second measured sample; The light detector is further used to detect first original optical information generated after the first measured sample is irradiated by light when passing through the flow cell, and detect second original optical information generated after the second measured sample is irradiated by light when passing through the flow cell; The processor inputs the input information obtained based on the original optical information into a trained first non - linear model to obtain an infection marker parameter, including: Input the input information obtained based on the first original optical information and the input information obtained based on the second original optical information into the first non - linear model to obtain an infection marker parameter.

9. The blood cell analyzer according to claim 8, wherein, The input information obtained based on the first original optical information includes at least one of the first original optical information, a first scatter plot obtained based on the first original optical information, and a first histogram obtained based on the first original optical information; and / or The input information obtained based on the second original optical information includes at least one of the second original optical information, a second scatter plot obtained based on the second original optical information, and a second histogram obtained based on the second original optical information.

10. The blood cell analyzer according to claim 9, characterized in that, The input information obtained based on the first original optical information includes a first white blood cell scatter plot obtained based on the first original optical information, and the input information obtained based on the second original optical information includes a second white blood cell scatter plot obtained based on the second original optical information.

11. The blood cell analyzer according to any one of claims 1-10, characterized in that, The first non - linear model is a neural network model or a machine learning model.

12. The blood cell analyzer according to any one of claims 1-11, characterized in that, The processor is further configured to: Output the probability that the subject is in each of a plurality of infection states, the plurality of infection states including non-sepsis, early sepsis, and established sepsis.

13. The blood cell analyzer according to any one of claims 1-12, characterized in that, The processor is further configured to: When the subject has a blood disease, or when abnormal cells, especially blast cells, are present in the blood sample to be tested, not output the value of the infection marker parameter, or output a prompt message indicating that the value of the infection marker parameter is unreliable.

14. The blood cell analyzer according to any one of claims 1-15, characterized in that, The infection marker parameter is used for: Early prediction of sepsis in the subject; and / or Diagnosis of sepsis in the subject; and / or Differentiation between common infection and severe infection in the subject; and / or Monitoring of the infection condition of the infected subject; and / or Prognosis analysis of sepsis in the subject who has received treatment; and / or Differentiation between bacterial infection and viral infection in the subject; and / or Differentiation between non-infectious inflammation and infectious inflammation in the subject; and / or Evaluation of the efficacy of sepsis treatment in the subject who is receiving medication treatment.

15. A method for evaluating the infection state of a subject, comprising: Obtain a blood sample to be tested of the subject; Mix at least a part of the blood sample to be tested, a hemolytic agent, and a staining agent to prepare a measurement sample; Cause the particles in the measurement sample to pass through an optically detected area irradiated with light one by one to obtain the original optical information generated by the particles in the measurement sample after being irradiated with light; Input the input information obtained based on the original optical information into a trained first non-linear model to obtain an infection marker parameter, wherein the infection marker parameter is used to evaluate the infection state of the subject.

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