Blood cell analyzers, methods, and use of infection signature parameters

Abstract

Embodiments relate to a blood cell analyzer, a method and use of an infection marker parameter. The blood cell analyzer comprises a sampling device for aspirating a blood sample of a subject to be tested, a sample preparation device for preparing an assay sample, an optical detection device for detecting the assay sample to obtain optical information, and a processor. The processor is configured to obtain a first white blood cell parameter of a first target particle group in a first assay sample from first optical information of the first assay sample, obtain a second white blood cell parameter of a second target particle group in a second assay sample from second optical information of the second assay sample, the first and / or second white blood cell parameter comprising a cell characteristic parameter, obtain an infection marker parameter for assessing an infection status of the subject based on the first white blood cell parameter and the second white blood cell parameter, and output the infection marker parameter. Thereby, an accurate and effective infection marker parameter can be quickly provided to a user to effectively assist the user in assessing the infection status of the subject.

Description

Technical Field

[0001] This application relates to the field of in vitro diagnostics, and in particular to blood cell analyzers, methods for assessing the infection status of subjects, and the use of infection marker parameters in assessing the infection status of subjects. Background Technology

[0002] Infectious diseases are common in clinical practice, among which sepsis is a serious infectious disease. Sepsis has a high incidence rate, with over 18 million severe cases worldwide each year. It is a dangerous condition with a high mortality rate; approximately 14,000 people die daily globally from its complications. Epidemiological surveys abroad show that the mortality rate of sepsis has surpassed that of myocardial infarction, becoming the leading cause of death among non-cardiac patients in intensive care units. Despite advancements in anti-infective treatments and organ function support techniques in recent years, the mortality rate of sepsis remains as high as 30%–70%. Sepsis treatment is costly and consumes significant medical resources, severely impacting quality of life and posing a substantial threat to human health.

[0003] Therefore, clinicians need to promptly diagnose whether patients are infected and identify the pathogen in order to develop effective treatment plans. Consequently, how to rapidly screen and diagnose infectious diseases at an early stage has become an urgent problem that clinical laboratories need to solve.

[0004] For the rapid differential diagnosis of infectious diseases, the existing solutions in the industry and their shortcomings are as follows:

[0005] 1. Microbial culture: Microbial culture is considered the most reliable gold standard, as it can directly culture and detect bacteria in clinical specimens such as body fluids or blood, thereby determining the type and drug resistance of bacteria and directly guiding clinical medication. However, this microbial culture method has a long reporting cycle, is prone to specimen contamination, and has a high false negative rate, which cannot well meet the clinical requirements for rapid and accurate results.

[0006] 2. Detection of inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT), and serum amyloid A (SAA): Inflammatory markers such as CRP, PCT, and SAA have good sensitivity and are widely used in the auxiliary diagnosis of infectious diseases. However, these inflammatory markers have relatively weak specificity and require additional testing fees, increasing the financial burden on patients. Furthermore, CRP and PCT are affected by specific diseases and cannot accurately reflect the patient's infection status. For example, CRP is produced in the liver; infected patients with liver damage may have normal CRP levels, leading to false negatives.

[0007] 3. Serum antigen and antibody testing: Serum antigen and antibody testing can identify specific virus types, but its effectiveness is limited in situations where the type of pathogen is unclear. In addition, the testing is expensive and requires additional examination fees, increasing the financial burden on patients.

[0008] 4. Complete blood count (CBC): CBC can indicate the occurrence of infection and differentiate the type of infection to some extent. However, the CBC values ​​such as WBC and Neu% currently used in clinical practice are affected by many factors, such as other non-infectious inflammatory responses and normal physiological fluctuations of the body. Therefore, they cannot accurately and promptly reflect the patient's condition and have poor diagnostic and treatment value in infectious diseases. Summary of the Invention

[0009] In order to at least partially solve the above-mentioned technical problems, the object of this application is to provide a blood cell analyzer, a method for assessing the infection status of a subject, and the use of infection marker parameters in assessing the infection status of a subject. It can obtain infection marker parameters with high diagnostic efficacy from the raw signals of the blood routine test process, thereby providing users with accurate and effective prompts based on the infection marker parameters to indicate the infection status of the subject.

[0010] To achieve the above-mentioned objectives of this application, a first aspect of this application provides a blood cell analyzer, which includes:

[0011] A sampling device is used to collect blood samples from the subject for testing.

[0012] A sample preparation apparatus for preparing a first assay sample containing a portion of the blood sample to be tested, a first hemolysin and a first staining agent for white blood cell classification, and for preparing a second assay sample containing another portion of the blood sample to be tested, a second hemolysin and a second staining agent for identifying nucleated red blood cells;

[0013] An optical detection device includes a flow chamber, a light source, and a photodetector. The flow chamber allows a first test sample and a second test sample to pass through, respectively. The light source illuminates the first and second test samples as they pass through the flow chamber. The photodetector detects first and second optical information generated by the illumination of the first and second test samples as they pass through the flow chamber.

[0014] The processor is configured as follows:

[0015] Calculate at least one first white blood cell parameter of at least one first target particle cluster in the first measurement sample from the first optical information.

[0016] At least one second leukocyte parameter of at least one second target particle cluster in the second measurement sample is calculated from the second optical information, wherein at least one of the first leukocyte parameter and the second leukocyte parameter includes cellular characteristic parameters.

[0017] Infection marker parameters for assessing the infection status of the subject are calculated based on the at least one first white blood cell parameter and the at least one second white blood cell parameter.

[0018] Output the infection marker parameters.

[0019] To achieve the above-mentioned objectives of this application, a second aspect of this application also provides a method for assessing the infection status of a subject, the method comprising:

[0020] Collect the blood sample to be tested from the subject;

[0021] Prepare a first assay sample containing a portion of the blood sample to be tested, a first hemolysin, and a first staining agent for white blood cell classification; and prepare a second assay sample containing another portion of the blood sample to be tested, a second hemolysin, and a second staining agent for identifying nucleated red blood cells.

[0022] The particles in the first test sample are passed one by one through the optical detection area of ​​the flow chamber that is irradiated by light, so as to obtain the first optical information generated by the particles in the first test sample after being irradiated by light.

[0023] The particles in the second test sample are passed one by one through the optical detection area that is irradiated by light to obtain the second optical information generated by the particles in the second test sample after being irradiated by light.

[0024] At least one first leukocyte parameter of at least one first target particle cluster in the first test sample is calculated from the first optical information and at least one second leukocyte parameter of at least one second target particle cluster in the second test sample is calculated from the second optical information, wherein at least one of the first leukocyte parameter and the second leukocyte parameter includes a cell characteristic parameter.

[0025] Infection marker parameters are calculated based on the at least one first leukocyte parameter and the at least one second leukocyte parameter; and

[0026] Output the infection marker parameters.

[0027] To achieve the above-mentioned objectives of this application, a third aspect of this application also provides the use of infection marker parameters in assessing the infection status of a subject, wherein the infection marker parameters are obtained by the following method:

[0028] Calculate at least one first white blood cell parameter of at least one first target particle cluster obtained by flow cytometry detection of a first assay sample containing a portion of a test blood sample from a subject, a first hemolysin, and a first staining agent for white blood cell classification;

[0029] Calculate at least one second leukocyte parameter of at least one second target particle cluster obtained by flow cytometry detection of a second assay sample containing another portion of the blood sample to be tested, a second hemolytic agent, and a second staining agent for identifying nucleated red blood cells, wherein at least one of the first and second leukocyte parameters includes a cell characteristic parameter; and

[0030] Infection marker parameters are calculated based on at least one first leukocyte parameter and at least one second leukocyte parameter.

[0031] In the technical solutions provided in this application, a first white blood cell parameter obtained from a first detection channel for white blood cell classification and a second white blood cell parameter obtained from a second detection channel for identifying nucleated red blood cells are combined to form an infection marker parameter, wherein at least one of the first and second white blood cell parameters includes a cell characteristic parameter. This enables rapid, accurate, and efficient assistance to physicians in predicting or diagnosing infectious diseases. In particular, this infection marker parameter can effectively provide information indicating the infection status of a subject. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the structure of a blood cell analyzer according to some embodiments of this application.

[0033] Figure 2 This is a schematic diagram of the structure of an optical detection device according to some embodiments of this application.

[0034] Figure 3 This is a two-dimensional SS-FL scatter plot of a first test sample according to some embodiments of this application.

[0035] Figure 4 This is a two-dimensional SS-FS scatter plot of a first test sample according to some embodiments of this application.

[0036] Figure 5 This is a three-dimensional scatter plot of the SS-FS-FL of a first test sample according to some embodiments of this application.

[0037] Figure 6 This is a two-dimensional FL-FS scatter plot of a second test sample according to some embodiments of this application.

[0038] Figure 7 This is a two-dimensional SS-FS scatter plot of a second test sample according to some embodiments of this application.

[0039] Figure 8 This is a three-dimensional scatter plot of the SS-FS-FL of a second test sample according to some embodiments of this application.

[0040] Figure 9 Cellular characteristic parameters of neutrophil clusters in a first assay sample according to some embodiments of this application are shown.

[0041] Figure 10 Cellular characteristic parameters of white blood cell clusters in a second assay sample according to some embodiments of this application are shown.

[0042] Figure 11 This is a schematic flowchart illustrating the determination of a patient's condition progression based on some embodiments of this application.

[0043] Figure 12 This is a scatter plot showing the presence of anomalies in the first test specimen according to some embodiments of this application.

[0044] Figure 13 This is a scatter plot showing the presence of anomalies in a second test sample according to some embodiments of this application.

[0045] Figure 14 Scatter plots are shown before and after logarithmic processing according to some embodiments of this application.

[0046] Figure 15 This is a schematic flowchart illustrating a method for assessing the infection status of a subject according to some embodiments of this application.

[0047] Figure 16 ROC curves for early sepsis prediction scenarios according to some embodiments of this application.

[0048] Figure 17 ROC curves for identifying severe infections according to some embodiments of this application.

[0049] Figure 18 ROC curves in a sepsis diagnosis scenario according to some embodiments of this application.

[0050] Figure 19 This is a graph showing the numerical changes of infection marker parameters used to monitor the progression of severe infections according to some embodiments of this application.

[0051] Figure 20 This is a graph showing the numerical changes of infection marker parameters used to monitor the progression of sepsis according to some embodiments of this application.

[0052] Figure 21A - Figure 21DThe results of using the combination of two parameters, “N_WBC_FL_W” and “D_Neu_FL_W”, as infection marker parameters to detect the efficacy of sepsis treatment are presented intuitively. Figure 21A The combined measurements of this two-parameter analysis are shown for each patient in the effective and ineffective groups before and 5 days after antibiotic treatment. Figure 21B Box-and-whisker diagrams of patients in the effective and ineffective groups are shown. Figure 21C The comparison shows the mean of this two-parameter combination in the effective group before and 5 days after antibiotic treatment, as well as the comparison of the mean of this two-parameter combination in the ineffective group before and 5 days after antibiotic treatment. Figure 21D The ROC curves for detecting the efficacy of this two-parameter combination in treating sepsis are shown.

[0053] Figure 22A - Figure 22D The results of using the combination of two parameters, “N_WBC_FL_W” and “D_Neu_FL_CV”, as infection marker parameters to detect the efficacy of sepsis treatment are presented intuitively. Figure 22A The combined measurements of this two-parameter analysis are shown for each patient in the effective and ineffective groups before and 5 days after antibiotic treatment. Figure 22B Box-and-whisker diagrams of patients in the effective and ineffective groups are shown. Figure 22C The comparison shows the mean of this two-parameter combination in the effective group before and 5 days after antibiotic treatment, as well as the comparison of the mean of this two-parameter combination in the ineffective group before and 5 days after antibiotic treatment. Figure 22D The ROC curves for detecting the efficacy of this two-parameter combination in treating sepsis are shown.

[0054] Figure 23 This is an algorithmic calculation step for the area parameter D_NEU_FLSS_Area of ​​the neutrophil population according to some embodiments of this application.

[0055] Figure 24 The ROC curve is shown in Embodiment 10 of this application in the context of sepsis diagnosis. Detailed Implementation

[0056] The technical solutions of the embodiments of this 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 this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0057] To facilitate subsequent explanations, a brief explanation of some terms used in the following text is provided below.

[0058] 1) Scatter plot: A 2D or 3D graph generated by a blood cell analyzer, displaying 2D or 3D feature information of multiple particles. The X, Y, and Z axes of the scatter plot each represent a characteristic of each particle. For example, in a scatter plot, the X axis represents forward scattered light intensity, the Y axis represents fluorescence intensity, and the Z axis represents lateral scattered light intensity. As used in this disclosure, the term "scatter plot" refers not only to the distribution of at least two sets of data as data points in a Cartesian coordinate system, but also to data arrays, i.e., it is not limited by its graphical representation.

[0059] 2) Particle clusters / cell clusters: A group of particles with the same cellular characteristics distributed in a certain area of ​​a scatter plot, such as white blood cell (including all types of white blood cells) clusters, as well as white blood cell subgroups, such as neutrophil clusters, lymphocyte clusters, monocyte clusters, eosinophil clusters, or basophil clusters.

[0060] 3) Blood shadow: This is a fragment particle obtained by dissolving red blood cells and platelets in blood with a hemolytic agent.

[0061] 4) ROC Curve: Receiver Operating Characteristic (ROC) curve is a curve plotted using a series of different binary classification methods (cutoff thresholds), with the true positive rate on the ordinate and the false positive rate on the abscissa. ROC_AUC represents the area enclosed by the ROC curve and the horizontal axis. The principle behind creating 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 the curve with sensitivity as the ordinate and 1-specificity as the abscissa. Since the ROC curve is composed of multiple critical values ​​representing their respective sensitivity and specificity, it can be used to select the optimal diagnostic threshold for a particular diagnostic method. The closer the ROC curve is to the upper left corner, the higher the sensitivity and the lower the false positive rate, indicating better diagnostic performance. It is known that the point on the ROC curve closest to the upper left corner has the highest sum of sensitivity and specificity; this point or its neighboring points are often used as diagnostic reference values ​​(also called diagnostic thresholds, judgment thresholds, preset conditions, or preset ranges).

[0062] Currently, blood cell analyzers generally use the DIFF channel and / or WNB channel to count and classify white blood cells. Specifically, the DIFF channel performs a four-part differential classification of white blood cells, categorizing them into lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), and eosinophils (Eos). The WNB channel identifies nucleated red blood cells, simultaneously providing nucleated red blood cell counts, white blood cell counts, and basophil counts. Combining the DIFF and WNB channels yields a five-part differential classification, including lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), eosinophils (Eos), and basophils (Baso).

[0063] The blood cell analyzer used in this application classifies and counts particles in blood samples using flow cytometry, which combines laser scattering and fluorescence staining methods. The principle of the blood cell analyzer in detecting blood samples can be illustrated as follows: First, a blood sample is drawn and treated with a hemolysin and a fluorescent dye. Red blood cells are destroyed and dissolved by the hemolysin, while white blood cells are not dissolved. However, the fluorescent dye can enter the nucleus of white blood cells with the help of the hemolysin and bind to nucleic acid substances in the nucleus. Next, each particle in the sample passes through a detection aperture illuminated by a laser beam. When the laser beam illuminates the particles, the characteristics of the particles themselves (such as volume, staining degree, size and content of cell contents, and nuclear density) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to their characteristics. This scattered light is received by a signal detector to obtain information about the particle structure and composition. 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 particles or the cell nucleus), and fluorescence (FL) reflects the content of nucleic acid substances in the cells. This optical information can be used to classify and count particles in a sample.

[0064] Figure 1 This is a schematic diagram of the structure of a blood cell analyzer according to some embodiments of this application. The blood cell analyzer 100 includes a sample aspiration device 110, a sample preparation device 120, an optical detection device 130, and a processor 140. The blood cell analyzer 100 also has a liquid path system (not shown) for connecting the sample aspiration device 110, the sample preparation device 120, and the optical detection device 130 to facilitate liquid transfer between these devices.

[0065] The sampling device 110 is used to collect the blood sample to be tested from the subject.

[0066] In some embodiments, the sampling device 110 has a sampling needle (not shown) for drawing up a blood sample to be tested. Furthermore, the sampling device 110 may also include, for example, a driving device for driving the sampling needle to quantitatively draw up the blood sample through the tip of the sampling needle. The sampling device 110 can deliver the drawn blood sample to the sample preparation device 120.

[0067] The sample preparation apparatus 120 is used to prepare a first test sample containing a portion of the blood sample to be tested, a first hemolysin, and a first staining agent for white blood cell classification, and to prepare a second test sample containing another portion of the blood sample to be tested, a second hemolysin, and a second staining agent for identifying nucleated red blood cells.

[0068] In the embodiments of this application, the hemolytic agent is used to dissolve red blood cells in the blood, breaking them into fragments, but maintaining the morphology of white blood cells essentially unchanged.

[0069] In some embodiments, the hemolytic agent may be any one or a combination of several of cationic surfactants, nonionic surfactants, anionic surfactants, and amphiphilic surfactants. In other embodiments, the hemolytic agent may include at least one of alkyl glycosides, triterpenoid saponins, and steroidal saponins. For example, the hemolytic agent may be selected from octylquinoline bromide, octylisoquinoline bromide, decylquinoline bromide, decylisoquinoline bromide, dodecylquinoline bromide, dodecylisoquinoline bromide, tetradecylquinoline bromide, tetradecylisoquinoline bromide, octyltrimethylammonium chloride, octyltrimethylammonium bromide, decyltrimethylammonium chloride, decyltrimethylammonium bromide, dodecyltrimethylammonium chloride, dodecyltrimethylammonium bromide, tetradecyltrimethylammonium chloride, tetradecyltrimethylammonium bromide; dodecyl alcohol polyoxyethylene (23) ether, hexadecyl alcohol polyoxyethylene (25) ether, hexadecyl alcohol polyoxyethylene (30) ether, etc.

[0070] In some embodiments, the first hemolytic agent is different from the second hemolytic agent, in particular the first hemolytic agent has a greater lysis effect on red blood cells than the second hemolytic agent.

[0071] In this embodiment, the first staining agent is a fluorescent dye used for white blood cell classification, such as a fluorescent dye capable of classifying white blood cells in a blood sample into at least three white blood cell subsets (monocytes, lymphocytes, and neutrophils). The second staining agent is different from the first staining agent and is a fluorescent dye capable of identifying nucleated red blood cells in a blood sample (capable of distinguishing nucleated red blood cells from white blood cells).

[0072] In some embodiments, the first staining agent may include a membrane-specific dye or a mitochondrial-specific dye, for further details of which can be found in the applicant’s PCT patent application WO2019 / 206300A1 filed on April 26, 2019, the entire disclosure of which is incorporated herein by reference.

[0073] In other embodiments, the first dye may include a cationic cyanine compound, for more details of which can be found in Chinese patent application CN101750274A filed by the applicant on September 28, 2019, the entire disclosure of which is incorporated herein by reference.

[0074] Currently available commercially available reagents for the four-part differential leukocyte assay can also be used as the first hemolytic agent and the first staining agent in this application, such as M-60LD and M-6FD; commercially available reagents for identifying nucleated red blood cells can also be used as the second hemolytic agent and the second staining agent in this application, such as M-6LN and M-6FN.

[0075] In some embodiments, the sample preparation apparatus 120 may include at least one reaction cell and a reagent supply device (not shown). The at least one reaction cell is used to receive the blood sample to be tested drawn by the sampling device 110, and the reagent supply device provides processing reagents (including hemolysin, first staining agent, second staining agent, etc.) to the at least one reaction cell, thereby mixing the blood sample to be tested drawn by the sampling device 110 with the processing reagents provided by the reagent supply device in the reaction cell to prepare a test sample (including a first test sample and a second test sample).

[0076] 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 used to partially distribute the aspirated blood sample to be tested into the first reaction cell and the second reaction cell, respectively. The first reagent supply unit is used to provide a first hemolysin and a first staining agent to the first reaction cell, thereby mixing and reacting the portion of the blood sample to be tested allocated to the first reaction cell with the first hemolysin and the first staining agent to prepare a first test sample. The second reagent supply unit is used to provide a second hemolysin and a second staining agent to the second reaction cell, thereby mixing and reacting the portion of the blood sample to be tested allocated to the second reaction cell with the second hemolysin and the second staining agent to prepare a second test sample.

[0077] The optical detection device 130 includes a flow chamber, a light source, and a photodetector. The flow chamber is used for the first test sample and the second test sample to pass through, respectively. The light source is used to illuminate the first test sample and the second test sample that have passed through the flow chamber. The photodetector is used to detect the first optical information and the second optical information generated by the first test sample and the second test sample after being illuminated by light when passing through the flow chamber.

[0078] As can be understood here, the first detection channel for white blood cell classification (also known as the DIFF channel) refers to the detection of the first test sample prepared by the sample preparation device 120 by the optical detection device 130, while the second detection channel for identifying nucleated red blood cells (also known as the WNB channel) refers to the detection of the second test sample prepared by the sample preparation device 120 by the optical detection device 130.

[0079] In this document, a flow chamber refers to a chamber with a focused liquid flow suitable for detecting light scattering and fluorescence signals. When a particle, such as a blood cell, passes through the detection aperture of the flow chamber, the particle scatters an incident light beam from a light source, directed through the aperture, in various directions. Photodetectors can be positioned at one or more different angles relative to the incident light beam to detect the light scattered by the particle, thereby obtaining a light scattering signal. Since different particles have different light scattering characteristics, the light scattering signal can be used to distinguish different groups of particles. Specifically, the light scattering signal detected near the incident light beam is generally referred to as a forward light scattering signal or a small-angle light scattering signal. In some embodiments, the forward light scattering signal can be detected from an angle of about 1° to about 10° relative to the incident light beam. In other embodiments, the forward light scattering signal can be detected from an angle of about 2° to about 6° relative to the incident light beam. The light scattering signal detected at about 90° relative to the incident light beam is generally referred to as a side light scattering signal. In some embodiments, the side light scattering signal can be detected from an angle of about 65° to about 115° relative to the incident light beam. Typically, the fluorescent signal emitted by blood cells stained with fluorescent dye is also detected in a direction at approximately 90° to the incident light beam.

[0080] In some embodiments, the photodetector may include a forward-scattering light detector for detecting forward-scattered light signals, a side-scattering light detector for detecting side-scattered light signals, and a fluorescence detector for detecting fluorescence signals. Accordingly, the first optical information may include the forward-scattered light signals, side-scattered light signals, and fluorescence signals of particles in the first measurement sample, and the second optical information may include the forward-scattered light signals, side-scattered light signals, and fluorescence signals of particles in the second measurement sample.

[0081] Figure 2A specific example of an optical detection device 130 is shown. This optical detection device 130 has a light source 101, a beam shaping assembly 102, a flow chamber 103, and a forward scattering detector 104 arranged sequentially in a straight line. A dichroic mirror 106 is arranged at a 45° angle to the line on one side of the flow chamber 103. A portion of the side light emitted by particles in the flow chamber 103 passes through the dichroic mirror 106 and is captured by a fluorescence detector 105 arranged at a 45° angle behind the dichroic mirror 106; the other portion of the side light is reflected by the dichroic mirror 106 and captured by a side scattering detector 107 arranged at a 45° angle in front of the dichroic mirror 106.

[0082] The processor 140 is used to process and perform calculations on data to obtain the required results. For example, it can generate two-dimensional or three-dimensional scatter plots based on various collected optical signals, and perform particle analysis on the scatter plots using a gating method. The processor 140 can also visualize intermediate or final calculation results and then display them through the display device 150. In this embodiment, the processor 140 is configured to implement the method steps, which will be described in further detail below.

[0083] In the embodiments of the application, the processor includes, but is not limited to, devices used to interpret computer instructions and process data in computer software, such as a central processing unit (CPU), a microcontroller unit (MCU), a field-programmable gate array (FPGA), and a digital signal processor (DSP). For example, the processor is used to execute various computer applications in a computer-readable storage medium, thereby enabling the blood cell analyzer 100 to perform corresponding detection procedures and analyze the optical information or optical signals detected by the optical detection device 130 in real time.

[0084] Furthermore, the blood cell analyzer 100 may also include a first housing 160 and a second housing 170. The display device 150 may be, for example, a user interface. An optical detection device 130 and a processor 140 are disposed inside the second housing 170. A sample preparation device 120 is disposed, for example, inside the first housing 160, and the display device 150 is disposed, for example, on the outer surface of the first housing 160 and is used to display the detection results of the blood cell analyzer.

[0085] As mentioned in the background section, routine blood tests using blood cell analyzers can indicate the occurrence of infection and differentiate the type of infection. However, the WBC and Neu% values ​​currently used in clinical practice are affected by various factors and cannot accurately and promptly reflect the patient's condition. Moreover, existing technologies have poor sensitivity and specificity in the diagnosis and treatment of bacterial infections and sepsis.

[0086] Against this backdrop, the inventors, through in-depth research into the raw signal characteristics of routine blood tests on a large number of blood samples from infected patients, unexpectedly discovered infection marker parameters that can be obtained by combining leukocyte parameters, especially cell characteristic parameters, from the DIFF channel and leukocyte parameters, especially cell characteristic parameters, from the WNB channel to efficiently assess the infection status of subjects. Here, this application proposes a solution for obtaining infection marker parameters by combining leukocyte parameters from the DIFF channel and the WNB channel for effective infection status assessment. While not wanting to be bound by theory, the inventors found through in-depth research that neutrophils and monocytes in patient samples are valuable in reflecting the degree of infection, and combining the characteristics of these two particle groups may better reflect the degree of infection. Secondly, the leukocyte classification channel of the DIFF channel is more precise in distinguishing leukocytes and is generally considered easier to find characteristics. However, the reagents used in the WNB channel and the DIFF channel are different, resulting in different degrees of cell treatment and different staining preferences for nucleic acids with fluorescent dyes. In the DIFF channel, the dye binds more to the cell nucleus, while in the WNB channel, the dye binds more to the cytoplasm. This may lead to different cellular characteristic signals. Using the two channels in combination may have a synergistic effect. Based on these research findings, the inventors, through extensive clinical validation, proposed a method to obtain infection marker parameters by combining leukocyte parameters from the DIFF channel and the WNB channel for effective assessment of infection symptoms.

[0087] Therefore, processor 140 is configured as follows:

[0088] At least one first white blood cell parameter of at least one first target particle cluster in the first measurement sample is obtained from the first optical information;

[0089] At least one second leukocyte parameter of at least one second target particle cluster in the second measurement sample is obtained from the second optical information, wherein at least one of the first leukocyte parameter and the second leukocyte parameter includes a cell characteristic parameter;

[0090] Infection marker parameters for assessing the infection status of the subject are calculated based on the at least one first leukocyte parameter and the at least one second leukocyte parameter; and

[0091] Output the infection marker parameters.

[0092] Preferably, both the first and second leukocyte parameters include cellular characteristic parameters; that is, the first leukocyte parameter includes cellular characteristic parameters of the first target particle cluster, and the second leukocyte parameter includes cellular characteristic parameters of the second target particle cluster. This provides infection marker parameters with further enhanced diagnostic efficacy.

[0093] It should be understood here that the cellular characteristic parameters of particle clusters or cell clusters do not include cell count or classification parameters of the cell cluster, but rather include characteristic parameters that reflect the cellular characteristics of the cells in the cell cluster, such as volume, internal granularity, and internal nucleic acid content.

[0094] Of course, in other embodiments, it is also possible that the first leukocyte parameter includes cellular characteristic parameters of the first target particle cluster, while the second leukocyte parameter includes classification or counting parameters of the second target particle cluster. Alternatively, the first leukocyte parameter may include classification or counting parameters of the first target particle cluster, while the second leukocyte parameter may include cellular characteristic parameters of the second target particle cluster.

[0095] Preferably, the processor 140 can be further configured to combine the at least one first leukocyte parameter and the at least one second leukocyte parameter into an infection marker parameter using a linear function, i.e., to calculate the infection marker parameter using the following formula:

[0096] Y = A*X1 + B*X2 + C

[0097] Where Y represents the infection marker parameter, X1 represents the first white blood cell parameter, X2 represents the second white blood cell parameter, and A, B, and C are constants. The functional relationship between features can be obtained, for example, through linear discriminant analysis (LDA), which is an induction of Fisher's linear discrimination method. This method uses statistical, pattern recognition, and machine learning methods to find a linear combination of features for two types of events (e.g., sepsis or lack thereof, bacterial or viral infection, infectious or non-infectious inflammation, effective or ineffective sepsis treatment). This linear combination reduces multidimensional data to one-dimensional data, thereby characterizing or distinguishing the two types of events. The coefficients of this linear combination ensure maximum discriminative power between the two types of events. The resulting linear combination can be used for subsequent event classification.

[0098] Of course, in other embodiments, the at least one first leukocyte parameter and the at least one second leukocyte parameter can also be combined into infection marker parameters by nonlinear functions, and this application does not specifically limit this.

[0099] Those skilled in the art will understand that in other embodiments, instead of calculating these two leukocyte parameters using a function, the first and second leukocyte parameters can be used together and compared with their respective thresholds to obtain infection marker parameters. For example, diagnostic thresholds for the two parameters can be set separately: threshold 1 and threshold 2, and then the diagnostic efficacy of "parameter 1 ≥ threshold 1 or parameter 2 ≥ threshold 2" and the diagnostic efficacy of "parameter 1 ≥ threshold 1 and parameter 2 ≥ threshold 2" can be analyzed.

[0100] In other embodiments, the infection marker parameter can be calculated from white blood cell parameters and other blood cell parameters; that is, the infection marker parameter can be calculated from at least one white blood cell parameter and at least one other blood cell parameter. The other blood cell parameter can be the classification or counting parameters of platelets (PLT), nucleated red blood cells (NRBC), or reticulocytes (RET), or it can be the concentration of hemoglobin.

[0101] Furthermore, in some embodiments, based on the first optical information, the white blood cells in the first test sample can be classified into at least mononuclear cell clusters, neutrophil clusters, and lymphocyte clusters, and in particular, into mononuclear cell clusters, neutrophil clusters, lymphocyte clusters, and eosinophil clusters.

[0102] In a specific example, such as Figures 3 to 5 As shown, based on the forward scattered light signal (or forward scattered light intensity) FS, the side scattered light signal (or side scattered light intensity) SS, and the fluorescence signal (or fluorescence intensity) FL in the first optical information, white blood cells in the first test sample can be classified into mononuclear cell clusters (Mon), neutrophil clusters (Neu), lymphocyte clusters (Lym), and eosinophil clusters (Eos). Among these, Figure 3 This is a two-dimensional scatter plot generated based on the side-scattered light signal SS and the fluorescence signal FL from the first optical information. Figure 4 This is a two-dimensional scatter plot generated based on the forward scattered light signal FS and the side scattered light signal SS from the first optical information. Figure 5 A three-dimensional scatter plot is generated based on the forward scattered light signal FS, the side scattered light signal SS, and the fluorescence signal FL in the first optical information.

[0103] Accordingly, in some embodiments, the at least one first target particle cluster may include at least one cell cluster from the mononuclear cell cluster Mon, the neutrophil cluster Neu, and the lymphocyte cluster Lym in the first assay sample. That is, the at least one first leukocyte parameter may include one or more of the cellular characteristic parameters of the mononuclear cell cluster Mon, the neutrophil cluster Neu, and the lymphocyte cluster Lym in the first assay sample. Preferably, the at least one first target particle cluster may include at least one cell cluster from the mononuclear cell cluster Mon and the neutrophil cluster Neu in the first assay sample. That is, the at least one first leukocyte parameter may include one or more, for example, one, two, or more than two, cellular characteristic parameters of the mononuclear cell cluster Mon and the neutrophil cluster Neu in the first assay sample.

[0104] In other embodiments, the at least one first white blood cell parameter may also include classification or counting parameters for the mononuclear cell cluster Mon, neutrophil cluster Neu, and lymphocyte cluster Lym in the first assay sample.

[0105] Alternatively or additionally, in some embodiments, the white blood cell clusters (including all types of white blood cells) in the second assay sample can be identified based on the second optical information, and the neutrophil clusters (Neu) and lymphocyte clusters (Lym) within the white blood cells in the second assay sample can also be identified, such as... Figures 6 to 8 As shown. Among them, Figure 6 This is a two-dimensional scatter plot generated based on the forward scattered light signal FS and the fluorescence signal FL from the second optical information. Figure 7 This is a two-dimensional scatter plot generated based on the forward scattered light signal FS and the side scattered light signal SS from the second optical information. Figure 8 A three-dimensional scatter plot is generated based on the forward scattered light signal FS, the side scattered light signal SS, and the fluorescence signal FL in the second optical information.

[0106] Accordingly, in some embodiments, the at least one second target particle cluster may include at least one cell cluster from the lymphocyte cluster (Lym), neutrophil cluster (Neu), and leukocyte cluster (Wbc) in the first assay sample. That is, the at least one second leukocyte parameter includes one or more parameters from the cellular characteristic parameters of the lymphocyte cluster (Lym), neutrophil cluster (Neu), and leukocyte cluster (Wbc) in the second assay sample. Preferably, the at least one second target particle cluster may include at least one cell cluster from the neutrophil cluster (Neu) and leukocyte cluster (Wbc) in the first assay sample. That is, the at least one second leukocyte parameter may include one or more parameters from the cellular characteristic parameters of the neutrophil cluster (Neu) and leukocyte cluster (Wbc) in the second assay sample.

[0107] In other embodiments, the at least one second white blood cell parameter may also include a classification parameter or a counting parameter of the neutrophil cluster Neu in the second assay sample, or a counting parameter of the white blood cell cluster Wbc.

[0108] In some preferred embodiments, the at least one first white blood cell parameter may include one or more of the cellular characteristic parameters of the monocyte cluster (Mon) and neutrophil cluster (Neu) in the first assay sample; and the at least one second white blood cell parameter may include one or more of the cellular characteristic parameters of the neutrophil cluster (Neu) and white blood cell cluster (Wbc) in the second assay sample. The inventors discovered in their study of the raw signals from a large number of subject samples during routine blood tests that combining the cellular characteristic parameters of the monocyte cluster (Mon) and / or neutrophil cluster (Neu) from the DIFF channel with the cellular characteristic parameters of the neutrophil cluster (Neu) and / or white blood cell cluster (Wbc) from the WNB channel provides more diagnostically valid infection marker parameters.

[0109] More preferably, the at least one first white blood cell parameter may include one or more of the cell characteristic parameters of the mononuclear cell cluster Mon in the first assay sample; and the at least one second white blood cell parameter may include one or more of the cell characteristic parameters of the white blood cell cluster Wbc in the second assay sample.

[0110] In some embodiments, the at least one first leukocyte parameter may include one or more of the following parameters: the width of the forward-scattered light intensity distribution of the first target particle cluster, the centroid of the forward-scattered light intensity distribution, the coefficient of variation of the forward-scattered light intensity distribution, the width of the side-scattered light intensity distribution, the centroid of the side-scattered light intensity distribution, the coefficient of variation of the side-scattered 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 first target particle cluster in a two-dimensional scatter plot generated by two of the light intensities of forward-scattered light intensity, side-scattered light intensity, and fluorescence intensity, and the volume of the distribution region of the first target particle cluster in a three-dimensional scatter plot generated by forward-scattered light intensity, side-scattered light intensity, and fluorescence intensity, for example... Figure 8 The volume of space occupied by white blood cell clusters.

[0111] In some specific examples, the at least one first leukocyte parameter may include one or more, for example, one or two, of the following parameters: the forward scattered light intensity distribution width D_MON_FS_W, the forward scattered light intensity distribution centroid D_MON_FS_P, the forward scattered light intensity distribution coefficient of variation D_MON_FS_CV, the side scattered light intensity distribution width D_MON_SS_W, the side scattered light intensity distribution centroid D_MON_SS_P, the side scattered light intensity distribution coefficient of variation D_MON_SS_CV, the fluorescence intensity distribution width D_MON_FL_W, the fluorescence intensity distribution centroid D_MON_FL_P, and the fluorescence intensity distribution coefficient of variation D_MON_FL_CV. And the area of ​​the distribution region of the mononuclear cell cluster in a two-dimensional scatter plot generated by two of the light intensities of forward scattering, side scattering, and fluorescence intensity, D_MON_FLFS_Area (area of ​​the distribution region of the mononuclear cell cluster in a two-dimensional scatter plot generated by forward scattering and fluorescence intensity), D_MON_FLSS_Area (area of ​​the distribution region of the mononuclear cell cluster in a two-dimensional scatter plot generated by side scattering and fluorescence intensity), D_MON_SSFS_Area (area of ​​the distribution region of the mononuclear cell cluster in a two-dimensional scatter plot generated by forward scattering and side scattering intensity), and the volume of the distribution region of the mononuclear cell cluster in a three-dimensional scatter plot generated by forward scattering, side scattering, and fluorescence intensity;The first determination sample contains the following parameters: forward scattered light intensity distribution width D_NEU_FS_W, forward scattered light intensity distribution centroid D_NEU_FS_P, forward scattered light intensity distribution coefficient of variation D_NEU_FS_CV, lateral scattered light intensity distribution width D_NEU_SS_W, lateral scattered light intensity distribution centroid D_NEU_SS_P, lateral scattered light intensity distribution coefficient of variation D_NEU_SS_CV, fluorescence intensity distribution width D_NEU_FL_W, fluorescence intensity distribution centroid D_NEU_FL_P, fluorescence intensity distribution coefficient of variation D_NEU_FL_CV, and the neutrophil clusters in the sample measured by the forward scattered light intensity and lateral scattered light intensity. The areas of the distribution regions in a two-dimensional scatter plot generated by two light intensities (forward scatter and fluorescence intensity) are represented by D_NEU_FLFS_Area (the area of ​​the neutrophil clusters in the two-dimensional scatter plot generated by forward scatter and fluorescence intensity), D_NEU_FLSS_Area (the area of ​​the neutrophil clusters in the two-dimensional scatter plot generated by side scatter and fluorescence intensity), D_NEU_SSFS_Area (the area of ​​the neutrophil clusters in the two-dimensional scatter plot generated by forward scatter and side scatter intensity), and the volume of the distribution region of the neutrophil clusters in a three-dimensional scatter plot generated by forward scatter, side scatter, and fluorescence intensity. The distribution of the forward scattered light intensity in the lymphocyte cluster in the first test sample includes: the width of the forward scattered light intensity distribution (D_LYM_FS_W), the centroid of the forward scattered light intensity distribution (D_LYM_FS_P), the coefficient of variation of the forward scattered light intensity distribution (D_LYM_FS_CV), the width of the lateral scattered light intensity distribution (D_LYM_SS_W), the centroid of the lateral scattered light intensity distribution (D_LYM_SS_P), the coefficient of variation of the lateral scattered light intensity distribution (D_LYM_SS_CV), the width of the fluorescence intensity distribution (D_LYM_FL_W), the centroid of the fluorescence intensity distribution (D_LYM_FL_P), the coefficient of variation of the fluorescence intensity distribution (D_LYM_FL_CV), and the distribution of the lymphocyte cluster in the first test sample. The area of ​​the distribution region in a two-dimensional scatter plot generated by two light intensities (forward scatter and fluorescence) is represented by D_LYM_FLFS_Area (the area of ​​the lymphocyte cluster distribution region in a two-dimensional scatter plot generated by forward scatter and fluorescence), D_LYM_FLSS_Area (the area of ​​the lymphocyte cluster distribution region in a two-dimensional scatter plot generated by side scatter and fluorescence), and D_LYM_SSFS_Area (the area of ​​the lymphocyte cluster distribution region in a two-dimensional scatter plot generated by forward scatter and side scatter). The volume of the distribution region of the lymphocyte cluster in a three-dimensional scatter plot generated by forward scatter, side scatter, and fluorescence intensities is also represented by D_LYM_FLFS_Area (the area of ​​the lymphocyte cluster distribution region in a two-dimensional scatter plot generated by forward scatter and side scatter).

[0112] Preferably, the at least one first leukocyte parameter may include one or more, for example, one or two, of the following parameters: the width of the forward scattered light intensity distribution D_MON_FS_W, the centroid of the forward scattered light intensity distribution D_MON_FS_P, the coefficient of variation of the forward scattered light intensity distribution D_MON_FS_CV, the width of the side scattered light intensity distribution D_MON_SS_W, the centroid of the side scattered light intensity distribution D_MON_SS_P, the coefficient of variation of the side scattered light intensity distribution D_MON_SS_CV, the width of the fluorescence intensity distribution D_MON_FL_W, the centroid of the fluorescence intensity distribution D_MON_FL_P, the coefficient of variation of the fluorescence intensity distribution D_MON_FL_CV, and the area of ​​the distribution region of the monocyte cluster in a two-dimensional scatter plot generated by two of the light intensities of forward scattered light intensity, side scattered light intensity, and fluorescence intensity D_MON_FLFS_Area, D_MON_FLSS_Area, D_MON_SSFS_Area, and the area of ​​the monocyte cluster in a two-dimensional scatter plot generated by two of the light intensities of forward scattered light intensity, side scattered light intensity, and fluorescence intensity D_MON_FLFS_Area, D_MON_FLSS_Area, D_MON_SSFS_Area, and the area of ​​the monocyte cluster in a two-dimensional scatter plot generated by two of the light intensities of forward scattered light intensity, side scattered light intensity, and fluorescence intensity D_MON_FLFS_Area. The volume of the distribution region in the three-dimensional scatter plot generated by the degree; and the forward scattered light intensity distribution width D_NEU_FS_W, forward scattered light intensity distribution centroid D_NEU_FS_P, forward scattered light intensity distribution coefficient of variation D_NEU_FS_CV, lateral scattered light intensity distribution width D_NEU_SS_W, lateral scattered light intensity distribution centroid D_NEU_SS_P, lateral scattered light intensity distribution coefficient of variation D_NEU_SS_CV, and fluorescence intensity distribution width D_NEU_FL in the first measured sample. =W, the centroid of fluorescence intensity distribution D_NEU_FL_P, the coefficient of variation of fluorescence intensity distribution D_NEU_FL_CV, and the area of ​​the distribution region of the neutrophil cluster in a two-dimensional scatter plot generated by two of the light intensities of forward scattered light intensity, side scattered light intensity, and fluorescence intensity D_NEU_FLFS_Area, D_NEU_FLSS_Area, D_NEU_SSFS_Area, and the volume of the distribution region of the neutrophil cluster in a three-dimensional scatter plot generated by forward scattered light intensity, side scattered light intensity, and fluorescence intensity.

[0113] In other embodiments, the at least one first white blood cell parameter may also include the classification parameter Mon% or the count parameter Mon# of the mononuclear cell cluster Mon in the first assay sample, or the classification parameter Neu% or the count parameter Neu# of the neutrophil cluster Neu, or the classification parameter Lym% or the count parameter Mon# of the lymphocyte cluster Lym.

[0114] Here, with the help of Figure 9 Explain the meaning of distribution width, distribution centroid, coefficient of variation, and the area or volume of the distribution region. Figure 9 Cellular characteristic parameters of neutrophil clusters in a first assay sample according to some embodiments of this application are shown.

[0115] like Figure 9 As shown, D_NEU_FL_W represents the fluorescence intensity distribution width of the neutrophil clusters in the first test sample, where D_NEU_FL_W is equal to the difference between the upper limit S1 and the lower limit S2 of the fluorescence intensity distribution of the neutrophil clusters. D_NEU_FL_P represents the centroid of the fluorescence intensity distribution of the neutrophil clusters in the first test sample, i.e., the average position of neutrophils in the FL direction, where D_NEU_FL_P is calculated by the following formula:

[0116]

[0117] Wherein, FL(i) is the fluorescence intensity of the i-th neutrophil. D_NEU_FL_CV represents the coefficient of variation of fluorescence intensity distribution of the neutrophil cluster in the first assay sample, where D_NEU_FL_CV is equal to D_NEU_FL_W divided by D_NEU_FL_P.

[0118] Furthermore, D_NEU_FLSS_Area represents the area of ​​the neutrophil clusters in the first measured sample in the scatter plot generated by the side-scattered light intensity and fluorescence intensity. For example... Figure 9 As shown, C1 represents the contour distribution curve of the neutrophil cluster. For example, the total number of positions located within the contour distribution curve C1 can be recorded as the area of ​​the neutrophil cluster. Those skilled in the art will understand that the contour distribution curve of the particle population can be easily obtained using the classification algorithms of conventional blood analyzers or image processing techniques.

[0119] It is understood that the definitions of other primary white blood cell parameters can be referenced accordingly. Figure 9 The example shown.

[0120] Alternatively or additionally, in some embodiments, the at least one second leukocyte parameter may include one or more of the following parameters: the width of the forward-scattered light intensity distribution of the second target particle cluster, the centroid of the forward-scattered light intensity distribution, the coefficient of variation of the forward-scattered light intensity distribution, the width of the side-scattered light intensity distribution, the centroid of the side-scattered light intensity distribution, the coefficient of variation of the side-scattered 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 a two-dimensional scatter plot generated by two of the light intensities of forward-scattered light intensity, side-scattered light intensity, and fluorescence intensity, and the volume of the distribution region of the second target particle cluster in a three-dimensional scatter plot generated by forward-scattered light intensity, side-scattered light intensity, and fluorescence intensity.

[0121] In some specific examples, the at least one second leukocyte parameter may include one or more, for example, one or two, of the following parameters: the forward scattered light intensity distribution width N_NEU_FS_W, the forward scattered light intensity distribution centroid N_NEU_FS_P, the forward scattered light intensity distribution coefficient of variation N_NEU_FS_CV, the side scattered light intensity distribution width N_NEU_SS_W, the side scattered light intensity distribution centroid N_NEU_SS_P, the side scattered 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, and the fluorescence intensity distribution coefficient of variation N_NEU_FL_CV. The area of ​​the neutrophil cluster distribution region in a two-dimensional scatter plot generated by two of the following light intensities: forward scattering intensity, side scattering intensity, and fluorescence intensity: N_NEU_FLFS_Area (area of ​​the neutrophil cluster distribution region in a two-dimensional scatter plot generated by forward scattering intensity and fluorescence intensity), N_NEU_FLSS_Area (area of ​​the neutrophil cluster distribution region in a two-dimensional scatter plot generated by side scattering intensity and fluorescence intensity), N_NEU_SSFS_Area (area of ​​the neutrophil cluster distribution region in a two-dimensional scatter plot generated by forward scattering intensity and side scattering intensity), and the volume of the neutrophil cluster distribution region in a three-dimensional scatter plot generated by forward scattering intensity, side scattering intensity, and fluorescence intensity;And, the forward scattered light intensity distribution width N_WBC_FS_W, forward scattered light intensity distribution centroid N_WBC_FS_P, forward scattered light intensity distribution coefficient of variation N_WBC_FS_CV, lateral scattered light intensity distribution width N_WBC_SS_W, lateral scattered light intensity distribution centroid N_WBC_SS_P, lateral scattered 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, and the white blood cell clusters in the second measured sample, based on the forward scattered light intensity and lateral scattered light intensity distribution. The area of ​​the distribution region in a two-dimensional scatter plot generated by two light intensities (forward scatter and fluorescence) is N_WBC_FLFS_Area (the area of ​​the distribution region of leukocyte clusters in a two-dimensional scatter plot generated by forward scatter and fluorescence), N_WBC_FLSS_Area (the area of ​​the distribution region of leukocyte clusters in a two-dimensional scatter plot generated by side scatter and fluorescence), N_WBC_SSFS_Area (the area of ​​the distribution region of leukocyte clusters in a two-dimensional scatter plot generated by forward scatter and side scatter), and the volume of the distribution region of leukocyte clusters in a three-dimensional scatter plot generated by forward scatter, side scatter, and fluorescence.

[0122] In other embodiments, the at least one second white blood cell parameter may also include the count parameter WBC# of white blood cell clusters in the second assay sample.

[0123] and Figure 9 Similarly, Figure 10 Cellular characteristic parameters of white blood cell clusters in a second assay sample according to some embodiments of this application are shown.

[0124] like Figure 10 As shown, N_WBC_FS_W represents the width of the forward scattered light intensity distribution of the leukocyte clusters in the second test sample, where N_WBC_FS_W is equal to the difference between the upper limit and the lower limit of the forward scattered light intensity distribution of the leukocyte clusters. N_WBC_FS_P represents the centroid of the forward scattered light intensity distribution of the leukocyte clusters in the second test sample, i.e., the average position of the leukocytes in the FS direction, where N_WBC_FS_P is calculated by the following formula:

[0125]

[0126] Where FS(i) is the forward scattered light intensity of the i-th white blood cell. N_WBC_FS_CV represents the coefficient of variation of the forward scattered light intensity distribution of the white blood cell cluster in the second test sample, where N_WBC_FS_CV is equal to N_WBC_FS_W divided by N_WBC_FS_P.

[0127] In addition, N_WBC_FLFS_Area represents the area of ​​the distribution region of the leukocyte clusters in the second assay sample in the scatter plot generated by the forward scattered light intensity and fluorescence intensity.

[0128] In some embodiments, such as Figure 10 As shown, C2 represents the contour distribution curve of the white blood cell cluster. For example, the total number of positions located within the contour distribution curve C2 can be recorded as the area of ​​the white blood cell cluster. Those skilled in the art will understand that the contour distribution curve of the particle population can be easily obtained using the classification algorithms of conventional blood analyzers or image processing techniques.

[0129] In other embodiments, D_NEU_FLSS_Area can also be implemented through the following algorithm steps ( Figure 23 ):

[0130] Randomly select a particle P1 from the neutrophil (NEU) particle cluster, and find the particle P2 that is farthest from P1;

[0131] Construct vector V1(P1-P2), and with P1 as the starting point of the vector, find another particle P3 in the neutrophil (NEU) particle cluster, and construct vector V2(P1-P3) such that vector V2(P1-P3) and vector V1(P1-P2) form the maximum angle.

[0132] Then, taking P1 as the starting point of the vector, find another particle P4 in the neutrophil (NEU) particle cluster, and construct vector V3(P1-P4) such that vector V3(P1-P4) and vector V1(P1-P2) form the maximum angle.

[0133] By analogy, the outermost group of particles P1, P2, P3, P4, ... Pn of the neutrophil (NEU) particle cluster were obtained respectively;

[0134] Use an ellipse to fit the particle points P1, P2, P3, P4, ... Pn, and obtain the major axis a and minor axis b of the ellipse.

[0135] The D_NEU_FLSS_Area is the product of the major axis a and the minor axis b.

[0136] Similarly, the volume parameters of the distribution area of ​​the neutrophil population in the three-dimensional scatter plot generated by forward scattering light intensity, side scattering light intensity, and fluorescence intensity can also be obtained by the corresponding calculation method.

[0137] It is understood that the definitions of other second white blood cell parameters can be referenced accordingly. Figure 10 and Figure 23 The example shown.

[0138] Those skilled in the art will understand that the overall distribution characteristics of a particle scat plot can be utilized, such as the width of the forward scattered light intensity distribution of the entire white blood cell cluster, or the characteristics of particle distribution in a certain region of a particle scat plot, such as the distribution area of ​​the part with higher density in the middle of the neutrophil cluster, or the region that differs from the neutrophil or lymphocyte particle cluster in the scatter plot of a normal person.

[0139] In some embodiments, the processor 140 may be further configured to output a prompt message indicating an abnormality in the infection flag parameter when the value of the infection flag parameter is outside a preset range. For example, when the value of the infection flag parameter is abnormally high, an upward-pointing arrow may be output to indicate the abnormal increase.

[0140] Alternatively, the processor 140 may also be configured to output the preset range.

[0141] In some embodiments, the processor 140 may be further configured to output a prompt message indicating the infection status of the subject based on the infection marker parameters. For example, the processor 140 may be configured to output the prompt message to a display device for display. The display device may be the display device 150 of the blood cell analyzer 100, or another display device communicatively connected to the processor 140. For example, the processor 140 may output the prompt message to the display device on the user (doctor) side via a hospital information management system.

[0142] The following describes some application scenarios of the infection marker parameters proposed in this application, but this application is not limited thereto.

[0143] In some embodiments, the infection marker parameters can be used for early prediction of sepsis in subjects, diagnosis of sepsis, differentiation between common and severe infections, monitoring of infection status, prognostic analysis of sepsis, differentiation between bacterial and viral infections or between non-infectious and infectious inflammation, and evaluation of the treatment efficacy of sepsis. For example, processor 140 can be further configured to perform early prediction of sepsis in subjects based on infection marker parameters, diagnosis of sepsis, differentiation between common and severe infections, monitoring of infection status, prognostic analysis of sepsis, differentiation between bacterial and viral infections or between non-infectious and infectious inflammation, and evaluation of the treatment efficacy of sepsis.

[0144] Sepsis is a serious infectious disease with a high incidence and mortality rate; for every hour of treatment delay, the mortality rate increases by 7%. Therefore, early warning of sepsis is particularly important. Early identification and early warning of sepsis can increase valuable treatment time for patients and greatly improve their survival rate.

[0145] Therefore, in the application scenario of early sepsis prediction, 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 collection of the blood sample to be tested, when the infection marker parameters meet the first preset condition.

[0146] In some embodiments, the time period is no more than 48 hours, meaning that the embodiments of this application can predict whether a subject is likely to progress to sepsis up to two days in advance. For example, the time period is between 24 and 48 hours, meaning that the embodiments of this application can predict whether a subject is likely to progress to sepsis one to two days in advance. Preferably, the time period is no more than 24 hours.

[0147] 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 based on a specific combination of parameters and the blood cell analyzer.

[0148] Here, infection marker parameters can be calculated using the combinations of parameters listed in Table 1 for early prediction of sepsis.

[0149] Table 1. Parameter combinations used for early prediction of sepsis.

[0150]

[0151]

[0152]

[0153] Here, a combination of D_Mon_SS_W and N_WBC_FL_W can be preferred to calculate the infection marker parameters for early prediction of sepsis.

[0154] The clinical symptoms of early sepsis are similar to those of ordinary / severe infectious diseases, making sepsis patients easily misdiagnosed as having ordinary / severe infectious diseases, thus delaying treatment. Therefore, differential diagnosis of sepsis is particularly important.

[0155] Therefore, in the application scenario of sepsis diagnosis, the processor 140 can be configured to output a prompt indicating that the subject has sepsis when the infection marker parameter meets a 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 based on the specific parameter combination and the blood cell analyzer.

[0156] Here, infection marker parameters can be calculated using the combinations of parameters listed in Table 2 for the diagnosis of sepsis.

[0157] Table 2. Parameter combinations used for sepsis diagnosis

[0158]

[0159]

[0160]

[0161]

[0162] Here, a combination of D_Mon_SS_W and N_WBC_FL_W can be preferred to calculate the infection marker parameters used for the diagnosis of sepsis.

[0163] Based on the severity of their infection and organ function, patients with bacterial infections can be divided into ordinary infections and severe infections. The clinical treatments and nursing care measures for the two types of infections are different. Therefore, differentiating between ordinary and severe infections can help doctors identify patients in life-threatening situations and allocate medical resources more rationally.

[0164] Therefore, in applications that differentiate between common and severe infections, the processor 140 can be configured to output a message indicating that the subject has a severe infection when the infection marker parameter meets a third preset condition. Here, the third 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 based on the specific parameter combination and the blood cell analyzer.

[0165] Here, infection marker parameters can be calculated using the parameter combinations listed in Table 3 to differentiate between common and severe infections. In Table 3, D_EOS_FS_W is the width of the forward scattered light intensity distribution of the eosinophil clusters in the first test sample, D_EOS_FS_P is the centroid of its forward scattered light intensity distribution, D_EOS_SS_W is the width of its side scattered light intensity distribution, D_EOS_SS_P is the centroid of its side scattered light intensity distribution, D_EOS_FL_W is the width of its fluorescence intensity distribution, and D_EOS_FL_P is the centroid of its fluorescence intensity distribution.

[0166] Table 3. Parameter combinations used for differentiating between common and severe infections.

[0167]

[0168]

[0169]

[0170]

[0171] Here, a combination of D_Mon_SS_W and N_WBC_FL_W can be preferred to calculate the infection marker parameters used to differentiate between common and severe infections.

[0172] In infection monitoring applications, subjects are infected patients (i.e., patients with infectious inflammation), especially those with severe infections or sepsis. For example, subjects may be patients with severe infections or sepsis from the intensive care unit. Sepsis is a serious infectious disease with a high incidence and mortality rate. Sepsis patients experience significant fluctuations in their condition, requiring daily monitoring to prevent deterioration without timely intervention. Therefore, combining clinical symptoms with laboratory test results is crucial for assessing the progression of sepsis and the effectiveness of treatment.

[0173] Therefore, the processor 140 can be configured to monitor the progression of infection in the subject based on infection marker parameters.

[0174] In some embodiments, the processor 140 may be further configured to monitor the progression of the subject's infection in the following manner:

[0175] The values ​​of the infection marker parameters are obtained through multiple tests, particularly at least three tests, of blood samples from the subject at different time points; and

[0176] The improvement of the subject's condition is determined by the changing trend of the values ​​of the infection marker parameters obtained through the multiple tests.

[0177] In a specific example, the processor 140 can be further configured to: output a prompt indicating that the subject's condition is improving when the value of the infection marker parameter obtained through the multiple tests gradually decreases; and output a prompt indicating that the subject's condition is worsening when the value of the infection marker parameter obtained through the multiple tests gradually increases. The multiple tests can be performed continuously every day, or at regular intervals.

[0178] For example, by obtaining the infection marker values ​​of a patient over several consecutive days, such as 7 days, after the patient is diagnosed with sepsis, and considering that the patient's condition is improving when these infection marker values ​​show a decreasing trend, a suggestion of improvement is given.

[0179] In other embodiments, the processor 140 may further be configured to alert the subject to the progression of their condition in the following manner:

[0180] Obtain the current value of the infection marker parameter obtained from the current test of the subject's current blood sample, and obtain the prior value of the infection marker parameter obtained from the previous test of the subject's previous blood sample, such as the prior value obtained from a routine blood test the previous day; and

[0181] The disease progression of the subject is monitored by comparing the prior value of the infection marker parameter with a first threshold and by comparing the prior value of the infection marker parameter with the current value of the infection marker parameter.

[0182] In a specific example, such as Figure 11 As shown, processor 140 can be further configured to: when the prior value of the infection flag parameter is greater than or equal to a first threshold:

[0183] If the current value of the infection flag parameter (i.e. Figure 11 The current result is greater than the prior value of the infection marker parameter (i.e., Figure 11 If the difference between the two results (the previous result in the previous test) is greater than the second threshold, then a prompt message indicating that the subject's condition has worsened will be output.

[0184] If the current value of the infection marker parameter is less than the previous value of the infection marker parameter and the difference between the two is greater than the second threshold, and the current value of the infection marker parameter is less than the first threshold, then output a prompt message indicating that the subject's condition has improved and the degree of infection has decreased.

[0185] If the current value of the infection marker parameter is less than its prior value and the difference between the two is greater than the second threshold, but the current value of the infection marker parameter is greater than or equal to the first threshold, then output a message indicating that the subject's condition has improved but the infection is still severe, or output no message.

[0186] If the difference between the current value of the infection marker parameter and the previous value of the infection marker parameter is not greater than the second threshold, then output a prompt message indicating that the subject's condition has not improved significantly and the infection is still severe, or output no prompt message.

[0187] Furthermore, such as Figure 11 As shown, processor 140 can be configured to: when the prior value of the infection flag parameter is less than a first threshold:

[0188] If the current value of the infection marker parameter is less than the prior value of the infection marker parameter and the difference between the two is greater than the second threshold, then output a prompt message indicating that the subject's condition has improved and the degree of infection has decreased.

[0189] If the current value of the infection marker parameter is greater than the previous value of the infection marker parameter and the difference between the two is greater than the second threshold, and the current value of the infection marker parameter is greater than the first threshold, then output a prompt message indicating that the subject's condition has worsened and the infection is more severe.

[0190] If the current value of the infection marker parameter is greater than its prior value and the difference between the two is greater than a second threshold, but the current value of the infection marker parameter is less than a first threshold, then output a message indicating fluctuations in the subject's condition or a possible worsening of the infection, or do not output a message.

[0191] If the difference between the current value of the infection marker parameter and the previous value of the infection marker parameter is not greater than the second threshold, then output a prompt message indicating that the subject's infection has not worsened, or do not output a prompt message.

[0192] exist Figure 11 In the illustrated embodiment, when the infection marker parameters are used to monitor the progression of a severely infected patient's condition, the first threshold can be a preset threshold used to determine whether the subject has a severely infected condition. Similarly, when the infection marker parameters are used to monitor the progression of a sepsis patient's condition, the first threshold can be a preset threshold used to determine whether the subject has sepsis.

[0193] Here, for example, a combination of D_Mon_SS_W and N_WBC_FL_W is preferred to calculate the infection marker parameters for monitoring the infection status.

[0194] In the application scenario of sepsis prognostic analysis, the subjects are sepsis patients who have received treatment. The processor 140 can be further configured to determine whether the subject's sepsis prognosis is good based on infection marker parameters. For example, when the value of the infection marker parameter is greater than a preset threshold, the subject's sepsis prognosis is considered good. This preset threshold can be determined based on specific parameter combinations and a blood cell analyzer.

[0195] Here, for example, a combination of D_Mon_SS_W and N_WBC_FL_W is preferably used to calculate infection marker parameters used to determine whether the prognosis of sepsis in a subject is good.

[0196] Infectious diseases can be classified into different types, such as bacterial infections, viral infections, and fungal infections, with bacterial and viral infections being the most common. Although the clinical symptoms of the two types of infections are largely the same, the treatment methods are completely different. Therefore, it is necessary to identify the type of infection in order to select the correct treatment method. To this end, the processor 140 can be further configured to determine whether the subject's infection type is a viral or bacterial infection based on the infection marker parameters.

[0197] Here, for example, infection marker parameters can be calculated using the combinations of parameters listed in Table 4 for the identification of bacterial and viral infections.

[0198] Table 4. Parameter combinations used for the identification of bacterial and viral infections.

[0199]

[0200]

[0201]

[0202] Here, a combination of D_Mon_SS_W and N_WBC_FL_W can be preferred to calculate the infection marker parameters for differentiating between bacterial and viral infections.

[0203] Furthermore, inflammation is divided into infectious inflammation caused by pathogenic microorganisms and non-infectious inflammation caused by physical factors, chemical factors, or tissue necrosis. The clinical symptoms of both types of inflammation are largely the same, including redness and fever. However, the treatment methods for the two types of inflammation are not entirely the same. Therefore, clinicians need to determine the underlying cause of the patient's inflammatory response in order to provide appropriate treatment.

[0204] Therefore, the processor 140 can be further configured to determine whether a subject has infectious or non-infectious inflammation based on infection marker parameters. For example, when the value of the infection marker parameter is greater than a preset threshold, the subject is determined to have infectious inflammation. This preset threshold can be determined based on specific parameter combinations and the blood cell analyzer.

[0205] Here, for example, infection marker parameters can be calculated using the combinations of parameters listed in Table 5, which can be used to differentiate between infectious and non-infectious inflammation.

[0206] Table 5. Parameter combinations used for differentiating between infectious and non-infectious inflammation.

[0207] First white blood cell parameter Second white blood cell parameter First white blood cell parameter Second white blood cell parameter First white blood cell parameter Second white blood cell parameter D_Mon_SS_W N_WBC_FL_W D_Mon_SS_P N_WBC_FL_W D_Mon_FS_P N_WBC_FL_W D_Neu_FL_W N_WBC_FL_W D_Mon_SS_W N_WBC_SS_CV D_Neu_FLFS_Area N_WBC_FL_W D_Mon_SS_W N_WBC_SS_W D_Lym_FLSS_Area N_WBC_FL_W D_Mon_FL_P N_WBC_FL_W D_Mon_FS_W N_WBC_FL_W D_Neu_SS_P N_WBC_FL_W D_Mon_SS_W N_WBC_FL_P D_Neu_FL_CV N_WBC_FL_W D_Neu_SS_CV N_WBC_FL_W D_Lym_FLFS_Area N_WBC_FL_W D_Neu_FLSS_Area N_WBC_FL_W D_Mon_SS_W N_WBC_FS_W D_Neu_FS_CV N_WBC_FL_W D_Neu_SS_W N_WBC_FL_W D_Neu_FL_P N_WBC_FL_W D_Neu_FS_W N_WBC_FL_W D_Mon_FL_W N_WBC_FL_W D_Mon_SS_W N_WBC_FS_CV D_Neu_FS_P N_WBC_FL_W

[0208] Here, a combination of D_Mon_SS_W and N_WBC_FL_W can be preferred to calculate the infection marker parameters used to differentiate between infectious and non-infectious inflammation.

[0209] After a doctor's consultation and physical examination, one or more preliminary diagnoses are typically made. These are then used for differential diagnosis or definitive diagnosis through laboratory tests, imaging examinations, and other methods. Therefore, it can be said that doctors order laboratory tests with a specific purpose in mind. In other words, doctors know exactly where the parameters will be applied when they order tests. For example, a patient with fever in a general outpatient clinic, without symptoms of organ damage, might be initially diagnosed with a common infection, rather than a severe infection or sepsis. However, the specific medication prescribed depends on whether it is a viral or bacterial infection, so a complete blood count (CBC) is ordered. The results will focus on whether the parameters exceed the threshold for "bacterial infection vs. viral infection," not the threshold for "sepsis diagnosis." Therefore, the infection marker parameters output in this application are intended as a clinical reference for doctors, not for diagnostic purposes.

[0210] The following describes some embodiments for further ensuring the reliability of diagnosis or indications based on infection marker parameters, but it should be understood that the embodiments of this application are not limited thereto.

[0211] To avoid interference with the reliability of diagnosis or indication from the first and second white blood cell parameters used to calculate infection marker parameters, in some embodiments, the processor 140 may be further configured to either not output the value of the infection marker parameter (i.e., mask the value of the infection marker parameter) or output the value of the infection marker parameter and simultaneously output an indication message indicating that the value of the infection marker parameter is unreliable when the preset characteristic parameters of the first target particle cluster and / or the second target particle cluster meet a fourth preset condition.

[0212] When the processor 140 is further configured to output a prompt message indicating the infection status of the subject based on the infection marker parameters, if the preset characteristic parameters of the first target particle cluster and / or the second target particle cluster meet the fourth preset condition, the processor 140 will not output the prompt message indicating the infection status of the subject, or will output the prompt message indicating the infection status of the subject and output additional information that the prompt message is unreliable.

[0213] In some specific examples, the processor 140 may be configured to either not output the value of the infection flag parameter when the total number of particles in the first target particle cluster and / or the second target particle cluster is less than a preset threshold, or to output the value of the infection flag parameter and simultaneously output a prompt message indicating that the value of the infection flag parameter is unreliable.

[0214] In other words, when the total number of particles in the target particle cluster is less than a preset threshold, meaning the target particle cluster has relatively few particles and the amount of information represented by the particles is limited, the calculated results of the infection marker parameters may be unreliable. For example, ... Figure 12 As shown in (a), the total number of particles in the leukocyte clump in the first assay sample is too low, which may cause the infection marker parameters calculated from the first leukocyte parameter of that leukocyte clump to be unreliable. For example, as... Figure 13 As shown in (a), the total number of particles in the leukocyte cluster in the second assay sample is too low, which may cause the infection marker parameters calculated from the second leukocyte parameter of the leukocyte cluster to be unreliable.

[0215] Here, for example, the first optical information can be used to determine whether the preset characteristic parameters of the first target particle cluster are abnormal, such as whether the total number of particles in the first target particle cluster is lower than a preset threshold. Similarly, the second optical information can be used to determine whether the preset characteristic parameters of the second target particle cluster are abnormal, such as whether the total number of particles in the second target particle cluster is lower than a preset threshold.

[0216] In other examples, processor 140 may be configured to either not output the value of the infection flag parameter when the first target particle cluster and / or the second target particle cluster overlaps with other particle clusters, or output the value of the infection flag parameter and simultaneously output a prompt message indicating that the value of the infection flag parameter is unreliable.

[0217] For example, such as Figure 12 As shown in (b), the overlap of mononuclear cell clusters and lymphocyte clusters in the first assay sample may lead to unreliable calculations of infection marker parameters based on the first white blood cell parameter from either the mononuclear cell cluster or the lymphocyte cluster. For example, as... Figure 13 As shown in (b), the overlap of neutrophil clusters with other particles in the second assay sample may lead to unreliable infection marker parameters calculated from the second leukocyte parameter of the neutrophil cluster. Here, for example, the overlap between the first target particle cluster and other particle clusters can be determined using first optical information. Similarly, the overlap between the second target particle cluster and other particle clusters can be determined using second optical information.

[0218] Similarly, when the processor 140 is further configured to output a prompt indicating the infection status of the subject based on the infection flag parameters, if the total number of particles in the first target particle cluster and / or the second target particle cluster is less than a preset threshold, and / or if the first target particle cluster and / or the second target particle cluster overlaps with other particle clusters, the processor 140 does not output the prompt indicating the infection status of the subject, or outputs the prompt indicating the infection status of the subject and outputs additional information that the prompt is unreliable.

[0219] Furthermore, the subject's disease condition and abnormal cells in the subject's blood may also affect the diagnostic or suggestive power of the infection marker parameters. Therefore, the processor 140 can be further configured to determine the reliability of the infection marker parameters based on whether the subject has a specific disease and / or based on the presence of a preset type of abnormal cells (e.g., primitive cells, abnormal lymphocytes, immature granulocytes, etc.) in the blood sample to be tested.

[0220] In some specific examples, processor 140 may be configured to either not output the value of the infection marker parameter when the subject has a blood disorder or when abnormal cells, particularly primitive cells, are present in the blood sample being tested, or to output the value of the infection marker parameter along with a message indicating that the value of the infection marker parameter is unreliable. Understandably, the abnormal blood counts of a subject with a blood disorder render any diagnosis or indication based on the infection marker parameter unreliable.

[0221] For example, the processor 140 can determine whether a subject has a blood disease based on the subject's identity information.

[0222] For example, processor 140 can be configured to determine whether abnormal cells, especially primitive cells, are present in the blood sample to be tested based on first optical information and / or second optical information.

[0223] In some embodiments, the processor 140 may also be configured to perform data processing on the first and second white blood cell parameters, such as noise (impurity particle) removal, before calculating the infection marker parameters. Figure 12 (c), 13(c) shown) or logarithmic processing (as shown in) Figure 14 (as shown), in order to calculate infection marker parameters more accurately, for example, to avoid signal changes caused by different instruments and reagents.

[0224] The following examples illustrate how the processor 140 configures priorities for each infection flag parameter group.

[0225] In some embodiments, the processor 140 may be further configured to prioritize each group of infection marker parameters based on at least one of infection diagnostic efficacy, parameter stability, and parameter limitations.

[0226] Preferably, the processor 140 can be further configured to prioritize each group of infection marker parameters based at least on the diagnostic efficacy of the infection. For example, the processor 140 can prioritize each group of infection marker parameters based solely on the diagnostic efficacy of the infection; as another example, the processor 140 can prioritize each group of infection marker parameters based on both diagnostic efficacy and parameter stability; yet another example, the processor 140 can prioritize each group of infection marker parameters based on diagnostic efficacy, parameter stability, and parameter limitations.

[0227] In some embodiments, the infection marker parameter set of this application can be used to assess multiple infection states, such as early prediction of sepsis, diagnosis of sepsis, differentiation between common and severe infections, monitoring of infection status, prognostic analysis of sepsis, differentiation between bacterial and viral infections, evaluation of treatment efficacy for sepsis, or differentiation between non-infectious and infectious inflammation based on the infection marker parameters. Accordingly, taking the differentiation between common and severe infections as an example, the diagnostic efficacy includes diagnostic efficacy for differentiating between common and severe infections. For example, when the infection marker parameter set of this application is only set for assessing a certain infection state, such as only for differentiating severe infections, a priority can be configured for each infection marker parameter set according to its diagnostic efficacy for assessing that infection state, such as differentiating severe infections.

[0228] As one implementation, the processor 140 can be further configured to prioritize each infection marker parameter group according to the area ROC_AUC enclosed by the ROC curve and the horizontal axis for each infection marker parameter group, wherein the larger the ROC_AUC, the higher the priority of the corresponding infection marker parameter group. The ROC curve is a receiver operating characteristic curve plotted with the true positive rate on the ordinate and the false positive rate on the abscissa, and the ROC_AUC of each infection marker parameter group reflects the diagnostic efficacy of that infection marker parameter group.

[0229] In some embodiments, the parameter stability includes at least one of numerical repeatability, aging stability, temperature stability, and inter-instrument consistency. Numerical repeatability refers to the consistency of the values ​​of the infection marker parameter set used when the same blood sample is tested multiple times within a short period of time using the same instrument under the same environment. Aging stability refers to the stability of the values ​​of the infection marker parameter set used when the same blood sample is tested at different time points using the same instrument under the same environment. Temperature stability refers to the stability of the values ​​of the infection marker parameter set used when the same blood sample is tested under different temperature environments using the same instrument. Inter-instrument consistency refers to the consistency of the values ​​of the infection marker parameter set used when the same blood sample is tested using different instruments under the same environment.

[0230] In some examples, if the same blood sample is repeatedly tested multiple times in a short period of time using the same instrument under the same environment, the higher the consistency of the values ​​of the infection marker parameter group used, that is, the higher the numerical repeatability, the higher the priority of the infection marker parameter group.

[0231] Alternatively or additionally, if the same blood sample is tested using the same instrument at different time points under the same environment, the higher the stability of the values ​​of the infection marker parameter group used (i.e., the smaller the fluctuation of the values), that is, the higher the aging stability, the higher the priority of the infection marker parameter group.

[0232] Alternatively or additionally, if the same instrument is used to test the same blood sample under different temperature conditions, the higher the stability of the values ​​of the infection marker parameter group used (i.e., the smaller the fluctuation of the values), that is, the higher the temperature stability, the higher the priority of the infection marker parameter group.

[0233] Alternatively or additionally, when testing the same blood sample on different instruments in the same environment, the higher the consistency of the values ​​of the infection marker parameter group used, i.e., the higher the inter-instrument consistency, the higher the priority of the infection marker parameter group.

[0234] In some embodiments, the parameter limitation refers to the range of subjects to which the infection marker parameter is applicable. In some examples, a larger range of subjects to which a group of infection marker parameters is applicable indicates a smaller parameter limitation of that group, and correspondingly, a higher priority for that group of infection marker parameters.

[0235] In some embodiments, the priorities of the plurality of infection marker parameter groups acquired by the processor 140 are preset, for example, based on at least one of infection diagnostic efficacy, parameter stability, and parameter limitations. Here, the processor 140 can configure a priority for each infection marker parameter group according to this preset setting. For example, the priorities of the plurality of infection marker parameter groups can be pre-stored in memory, and the processor 140 can retrieve the priorities of the plurality of infection marker parameter groups from memory.

[0236] Next, the method by which the processor 140 calculates the confidence level of the infection flag parameter set will be further explained with reference to the following embodiments.

[0237] The inventors of this application discovered through research that blood samples from subjects may contain abnormal classification results and / or abnormal cells, leading to unreliable infection marker parameter sets. Therefore, the blood analyzer provided in this application can calculate the reliability of multiple acquired infection marker parameter sets, so as to select more reliable infection marker parameter sets from multiple infection marker parameter sets according to the priority and reliability of each infection marker parameter set.

[0238] In some embodiments, the processor 140 may be configured to calculate the confidence level of each infection flag parameter group in the following manner:

[0239] The confidence level of the infection marker parameter set is calculated based on the classification results of at least one target particle cluster used to obtain the infection marker parameter set and / or based on abnormal cells in the blood sample to be tested.

[0240] In some embodiments, the classification result may include at least one of the following: the count value of the target particle cluster, the percentage of the count value of the target particle cluster compared to another particle cluster, and the degree of overlap (also known as the degree of adhesion) between the target particle cluster and its neighboring particle clusters. For example, the degree of overlap between the target particle cluster and its neighboring particle clusters can be determined by the distance between the centroid of the target particle cluster and the centroid of its neighboring particle clusters. For example, if the total number of particles in the target particle cluster, i.e., the count value, is less than a preset threshold, meaning that the target particle cluster has few particles and the amount of information represented by the particles is limited, then the infection marker parameter set obtained through the relevant parameters of the target particle cluster may be unreliable, and therefore the reliability of the infection marker parameter set is low.

[0241] Next, the method by which the processor 140 filters infection marker parameter groups will be further explained with reference to some embodiments.

[0242] In this embodiment of the application, the processor 140 can be configured to calculate the confidence level of all infection marker parameter groups in the plurality of infection marker parameter groups at once, and then select at least one infection marker parameter group from them according to the priority and confidence level of all infection marker parameter groups and output its parameter value.

[0243] In other embodiments, processor 140 may be configured to perform the following steps to filter the infection flag parameter group and output its parameter values:

[0244] The processor calculates multiple first leukocyte parameters of at least one first target particle cluster in the first test sample from the first optical information, and calculates multiple second leukocyte parameters of at least one second target particle cluster in the second test sample from the second optical information.

[0245] Based on the plurality of first leukocyte parameters and the plurality of second leukocyte parameters, a plurality of infection marker parameter sets are obtained for assessing the infection status of the subject;

[0246] Configure a priority for each of the plurality of infection flag parameter groups;

[0247] Calculate the confidence level of each of the plurality of infection marker parameter groups. Select at least one infection marker parameter group from the plurality of infection marker parameter groups according to their priority and confidence level to obtain the infection marker parameter. Alternatively, calculate the confidence level of the plurality of infection marker parameter groups sequentially according to their priority and determine whether the confidence level reaches the corresponding confidence level threshold. When the confidence level of the current infection marker parameter group reaches the corresponding confidence level threshold, obtain the infection marker parameter based on the infection marker parameter group and stop the calculation and determination.

[0248] In some embodiments, the processor 140 may be further configured to output an alarm when the parameter value of the selected infection flag parameter group is greater than the infection positive threshold.

[0249] For example, normalization can be performed on each group of infection marker parameters to ensure that the infection positivity thresholds of each infection marker parameter are consistent.

[0250] In other embodiments, the processor 140 may also be configured to: calculate the confidence level of each of the plurality of infection flag parameter groups, and determine whether the confidence level of each infection flag parameter group reaches a corresponding confidence level threshold.

[0251] The infection marker parameter groups whose confidence level reaches the corresponding confidence threshold among the multiple infection marker parameter groups are selected as candidate infection marker parameter groups.

[0252] Based on the priority of the candidate infection marker parameter groups, at least one candidate infection marker parameter group is selected from the candidate infection marker parameter groups, preferably the infection marker parameter group with the highest priority, for obtaining the infection marker parameters.

[0253] In some embodiments, the processor may be further configured to: calculate, from the first optical information, a plurality of first leukocyte parameters of at least one first target particle cluster in the first test sample, and calculate, from the second optical information, a plurality of second leukocyte parameters of at least one second target particle cluster in the second test sample.

[0254] Based on the plurality of first leukocyte parameters and the plurality of second leukocyte parameters, a plurality of infection marker parameter sets are obtained for assessing the infection status of the subject.

[0255] Calculate the confidence level of each of the plurality of infection marker parameter groups, and select at least one infection marker parameter group from the plurality of infection marker parameter groups based on the confidence level of the plurality of infection marker parameter groups to obtain the infection marker parameters.

[0256] In some embodiments, the processor may be further configured to:

[0257] For each infection marker parameter group, the confidence level of the infection marker parameter group is calculated based on the classification results of at least one target particle cluster used to obtain the infection marker parameter group and / or based on abnormal cells in the blood sample to be tested.

[0258] The classification results may include, for example, at least one of the following: the count value of the target particle cluster, the percentage of the count value of the target particle cluster compared to another particle cluster, and the degree of overlap between the target particle cluster and its neighboring particle clusters.

[0259] Furthermore, the processor is further configured to:

[0260] An alarm will be triggered when the value of the selected infection marker parameter group is greater than the infection positivity threshold.

[0261] In other embodiments, the processor 140 may also be configured to: determine, based on the first optical information and the second optical information, whether the blood sample to be tested has any abnormality that affects the assessment of infection status;

[0262] When it is determined that the blood sample to be tested has an abnormality that affects the assessment of infection status, at least one first white blood cell parameter of at least one first target particle cluster matching the abnormality is obtained from the first optical information, and at least one second white blood cell parameter of at least one second target particle cluster matching the abnormality is obtained from the second optical information.

[0263] The infection marker parameters are obtained based on at least one first leukocyte parameter and at least one second leukocyte parameter.

[0264] In one example, if it is determined that there is an abnormal classification result in the blood sample to be tested that affects the assessment of infection status, such as the overlap of monocyte clusters and neutrophil clusters in the blood sample to be tested, multiple parameters of other cell clusters (e.g., lymphocyte clusters) other than monocyte clusters and neutrophil clusters can be obtained from optical information, and infection marker parameters used to assess the infection status of the subject can be obtained from the multiple parameters of other cell clusters.

[0265] In another example, if it is determined that there are abnormal cells, such as primitive cells, in the blood sample to be tested that affect the assessment of infection status, multiple parameters of other cell clusters besides those affected by primitive cells can be obtained from optical information, and infection marker parameters for assessing the subject's infection status can be obtained from the multiple parameters of other cell clusters.

[0266] Next, the method of controlling the retesting of the processor 140 will be further explained with reference to some embodiments.

[0267] In some embodiments, the processor may be further configured to, before calculating at least one first white blood cell parameter of at least one first target particle cluster in the first test sample from the first optical information, and calculating at least one second white blood cell parameter of at least one second target particle cluster in the second test sample from the second optical information, obtain white blood cell counts of the first test sample and the second test sample based on the first optical information and the second optical information, and output a retest command to retest the blood sample of the subject when the white blood cell count is less than a preset threshold, wherein the sample measurement quantity measured based on the retest command is greater than the sample measurement quantity used to acquire the optical information; and

[0268] The processor is further configured to calculate at least another first leukocyte parameter of at least another first target particle cluster in the first test sample from the first optical information measured based on the retest instruction, and to calculate at least another second leukocyte parameter of at least another second target particle cluster in the second test sample from the second optical information, and to obtain infection marker parameters for assessing the infection status of the subject based on the at least another first leukocyte parameter and the at least another second leukocyte parameter.

[0269] This application also provides another blood analyzer, including a sample aspiration device, a sample preparation device, an optical detection device, and a processor:

[0270] A sampling device is used to collect blood samples from the subject for testing.

[0271] A sample preparation apparatus for preparing a first assay sample containing a portion of the blood sample to be tested, a first hemolysin and a first staining agent for white blood cell classification, and for preparing a second assay sample containing another portion of the blood sample to be tested, a second hemolysin and a second staining agent for identifying nucleated red blood cells;

[0272] An optical detection device includes a flow chamber, a light source, and a photodetector. The flow chamber allows a first test sample and a second test sample to pass through, respectively. The light source illuminates the first and second test samples as they pass through the flow chamber. The photodetector detects first and second optical information generated by the illumination of the first and second test samples as they pass through the flow chamber.

[0273] The processor is configured as follows:

[0274] Receive mode setting command,

[0275] When the mode setting command indicates that the blood routine test mode is selected, the measuring device is controlled to perform optical measurements on the first and second test samples of the first measurement quantity, so as to obtain the first optical information of the first test sample and the second optical information of the second test sample, respectively, and to obtain and output the blood routine parameters based on the first and second optical information.

[0276] When the mode setting instruction indicates that the sepsis detection mode is selected, the measuring device is controlled to perform optical measurements on the first and second test samples with a second measurement amount greater than the first measurement amount, so as to obtain the first optical information of the first test sample and the second optical information of the second test sample respectively. From the first optical information, at least one first white blood cell parameter of at least one first target particle cluster in the first test sample is calculated, and from the second optical information, at least one second white blood cell parameter of at least one second target particle cluster in the second test sample is calculated. Based on the at least one first white blood cell parameter and the at least one second white blood cell parameter, infection marker parameters for assessing the infection status of the subject are obtained, and the infection marker parameters are output.

[0277] This application also proposes a method for assessing the infection status of a subject. For example... Figure 15 As shown, the method 200 includes the following steps:

[0278] S210, Collect the blood sample to be tested from the subject;

[0279] S220, prepare a first test sample containing a portion of the blood sample to be tested, a first hemolytic agent and a first staining agent for white blood cell classification, and prepare a second test sample containing another portion of the blood sample to be tested, a second hemolytic agent and a second staining agent for identifying nucleated red blood cells.

[0280] S230, the particles in the first test sample are passed one by one through the optical detection area irradiated by light to obtain the first optical information generated by the particles in the first test sample after being irradiated by light.

[0281] S240, the particles in the second test sample are passed one by one through the optical detection area irradiated by light to obtain the second optical information generated by the particles in the second test sample after being irradiated by light.

[0282] S250, obtaining at least one first leukocyte parameter of at least one first target particle cluster in the first test sample from the first optical information and obtaining at least one second leukocyte parameter of at least one second target particle cluster in the second test sample from the second optical information, wherein at least one of the first leukocyte parameter and the second leukocyte parameter includes a cell characteristic parameter.

[0283] S260, calculate infection marker parameters based on the at least one first leukocyte parameter and the at least one second leukocyte parameter; and

[0284] S270, assess the infection status of the subject based on the infection marker parameters.

[0285] The method 200 proposed in this application embodiment is implemented, in particular, by the blood cell analyzer 100 described in the above-described embodiment of this application.

[0286] Further, the at least one first white blood cell parameter may include one or more of the cell characteristic parameters of the mononuclear cell clusters, neutrophil clusters and lymphocyte clusters in the first test sample; and / or the at least one second white blood cell parameter may include one or more of the cell characteristic parameters of the mononuclear cell clusters, neutrophil clusters and white blood cell clusters in the second test sample.

[0287] Preferably, the at least one first white blood cell parameter may include one or more of the cell characteristic parameters of the mononuclear cell clusters and neutrophil clusters in the first assay sample, and the at least one second white blood cell parameter may include one or more of the cell characteristic parameters of the mononuclear cell clusters, neutrophil clusters and white blood cell clusters in the second assay sample.

[0288] In some embodiments, the at least one first leukocyte parameter may include one or more of the following parameters: the width of the forward-scattered light intensity distribution of the first target particle cluster, the centroid of the forward-scattered light intensity distribution, the coefficient of variation of the forward-scattered light intensity distribution, the width of the lateral-scattered light intensity distribution, the centroid of the lateral-scattered light intensity distribution, the coefficient of variation of the lateral-scattered 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 first target particle cluster in a two-dimensional scatter plot generated by two of the light intensities of forward-scattered light intensity, lateral-scattered light intensity, and fluorescence intensity, and the volume of the distribution region of the first target particle cluster in a three-dimensional scatter plot generated by forward-scattered light intensity, lateral-scattered light intensity, and fluorescence intensity; and / or

[0289] The at least one second leukocyte parameter may include one or more of the following parameters: the forward scattered light intensity distribution width, the centroid of the forward scattered light intensity distribution, the coefficient of variation of the forward scattered light intensity distribution, the side scattered light intensity distribution width, the centroid of the side scattered light intensity distribution, the coefficient of variation of the side scattered light intensity distribution, the fluorescence intensity distribution width, 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 a two-dimensional scatter plot generated by two of the light intensities of forward scattered light intensity, side scattered light intensity, and fluorescence intensity, and the volume of the distribution region of the second target particle cluster in a three-dimensional scatter plot generated by forward scattered light intensity, side scattered light intensity, and fluorescence intensity.

[0290] In some embodiments, the method may further include: performing early sepsis prediction, sepsis diagnosis, differentiation between common and severe infections, infection monitoring, sepsis prognosis analysis, differentiation between bacterial and viral infections, or differentiation between non-infectious and infectious inflammation on the subject based on the infection marker parameters.

[0291] In some embodiments, the method may further include: outputting a prompt message indicating the infection status of the subject.

[0292] In some embodiments, step S270 may include: when the infection marker parameter meets a first preset condition, outputting a prompt message indicating that the subject may develop sepsis within a certain period of time after the blood sample to be tested is collected; preferably, the certain period of time is no more than 48 hours, and especially no more than 24 hours.

[0293] In some embodiments, step S270 may include: when the infection marker parameter meets a second preset condition, outputting a prompt message indicating that the subject has sepsis.

[0294] In some embodiments, step S270 may include: when the infection marker parameter meets a third preset condition, outputting a prompt message indicating that the subject has a severe infection.

[0295] In some embodiments, the subject is an infected patient, particularly one with a severe infection or sepsis. Accordingly, step S270 may include monitoring the progression of the subject's infection based on the infection marker parameters.

[0296] In some specific examples, monitoring the progression of the subject's infection based on the infection marker parameters includes:

[0297] The values ​​of the infection marker parameters are obtained through multiple tests, especially at least three tests, of blood samples from the subject at different time points;

[0298] The improvement of the subject's condition is determined by the changing trend of the values ​​of the infection marker parameters obtained through the multiple tests. Preferably, when the values ​​of the infection marker parameters obtained through the multiple tests gradually decrease, a prompt message indicating that the subject's condition is improving is output.

[0299] In other examples, monitoring the progression of the infection in the subject based on the infection marker parameters includes:

[0300] Obtain the current value of the infection marker parameter obtained from the current test of the current blood sample from the subject, and obtain the previous value of the infection marker parameter obtained from the previous test of the previous blood sample from the subject; and

[0301] The disease progression of the subject is monitored by comparing the prior value of the infection marker parameter with a first threshold and by comparing the prior value of the infection marker parameter with the current value of the infection marker parameter.

[0302] Furthermore, the subject can be a sepsis patient who has received treatment. Accordingly, step S270 may include: determining whether the subject's sepsis prognosis is good based on the infection marker parameters.

[0303] In some embodiments, step S270 may include: determining whether the subject's infection type is a viral infection or a bacterial infection based on the infection marker parameters.

[0304] In some embodiments, step S270 may include: determining whether the subject has infectious or non-infectious inflammation based on the infection marker parameters.

[0305] In some embodiments, the method may further include: when the preset characteristic parameters of the first target particle cluster and / or the second target particle cluster meet a fourth preset condition, such as when the total number of particles in the first target particle cluster and / or the second target particle cluster is less than a preset threshold, and / or when the first target particle cluster and / or the second target particle cluster overlaps with other particle clusters, not outputting the value of the infection flag parameter, or outputting the value of the infection flag parameter and simultaneously outputting a prompt message indicating that the value of the infection flag parameter is unreliable.

[0306] Alternatively or additionally, the method may further include: when the subject has a blood disease or when abnormal cells, especially primitive cells, are present in the blood sample to be tested, for example when it is determined based on the first optical information and / or the second optical information that abnormal cells, especially primitive cells, are present in the blood sample to be tested, not outputting the value of the infection marker parameter, or outputting the value of the infection marker parameter and simultaneously outputting a prompt message indicating that the value of the infection marker parameter is unreliable.

[0307] Further embodiments and advantages of the method 200 proposed in this application can be found in the above description of the blood cell analyzer 100 proposed in this application, especially the description of the method steps implemented by the processor 140, and will not be repeated here.

[0308] This application also proposes the use of infection marker parameters in assessing the infection status of subjects, wherein the infection marker parameters are obtained by the following method:

[0309] Calculate at least one first white blood cell parameter of at least one first target particle cluster obtained by flow cytometry detection of a first assay sample containing a portion of a test blood sample from a subject, a first hemolysin, and a first staining agent for white blood cell classification;

[0310] Calculate at least one second leukocyte parameter of at least one second target particle cluster obtained by flow cytometry detection of a second assay sample containing another portion of the blood sample to be tested, a second hemolytic agent, and a second staining agent for identifying nucleated red blood cells, wherein at least one of the first and second leukocyte parameters includes a cell characteristic parameter; and

[0311] Infection marker parameters are calculated based on at least one first leukocyte parameter and at least one second leukocyte parameter.

[0312] Further embodiments and advantages of the use of the infection marker parameters proposed in this application in assessing the infection status of subjects can be found in the above description of the blood cell analyzer 100 proposed in this application, and in particular the description of the method steps implemented by the processor 140, which will not be repeated here.

[0313] The present application and its advantages will be further illustrated by some specific embodiments below.

[0314] The true positive rate (%), false positive rate (%), true negative rate (%), and false negative rate (%) of this application embodiment are calculated using the following formula:

[0315] True positive rate % = TP / (TP+FN) × 100%;

[0316] True negative rate % = TN / (FP+TN) × 100%;

[0317] False positive rate % = 1 - True negative rate %;

[0318] False negative rate % = 1 - True positive rate %;

[0319] Wherein, TP represents the number of true positive individuals, FP represents the number of false positive individuals, TN represents the number of true negative individuals, and FN represents the number of false negative individuals.

[0320] Example 1: Early prediction of sepsis

[0321] Using the BC-6800Plus hematology analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., and Mindray's matching hemolytic agents M-60LD and M-6LN, and staining agents M-6FD and M-6FN, routine blood tests were performed on 152 blood samples. Scatter plots of the WNB and DIFF channels were obtained, and early sepsis prediction was performed according to the method proposed in the embodiments of this application. The next day, 87 of these samples were clinically diagnosed as sepsis-positive, and 65 blood samples were negative (did not develop sepsis).

[0322] Inclusion criteria for these 152 cases: Adult ICU patients with present or suspected acute infection. Exclusion criteria: Pregnant women, patients undergoing chemotherapy-induced myelosuppression, patients receiving immunosuppressant therapy, and patients with hematologic disorders.

[0323] The donors of the sepsis samples must meet the following criteria: have a suspected or confirmed site of infection, a positive laboratory culture result, and organ failure; have a suspected or confirmed acute infection and a SOFA score ≥ 2, where a suspected infection is defined as having any one of the following ① to ③ and ④ no definitive result; or have any one of the following ① to ③ and ⑤.

[0324] ① Acute (within 72 hours) fever or hypothermia;

[0325] ② The total white blood cell count may be increased or decreased;

[0326] ③ Increased CRP and IL-6 levels

[0327] ④ Elevated PCT, SAA, and HBP levels;

[0328] ⑤ There are suspected sites of infection.

[0329] The SOFA scoring criteria are shown in Table A below:

[0330] Table A: SOFA Scoring Calculation Method

[0331]

[0332] Note: 1 mmHg = 0.133 kPa.

[0333] Table 6 shows the infection marker parameters used and their corresponding diagnostic efficacy. Figure 16 The ROC curves corresponding to the infection marker parameters in Table 6 are shown. In Table 6:

[0334] Combined parameter 1 = 0.028849 * D_Mon_SS_W + 0.002448 * N_WBC_SS_W - 5.72185;

[0335] The combined parameter 2 = 0.02523 * D_Mon_SS_W + 0.002796 * N_WBC_FL_W - 7.43236.

[0336] Table 6. Efficacy of different infection marker parameters in early prediction of sepsis risk.

[0337]

[0338] In addition, Table 7-1 shows the efficacy of using other infection marker parameters to predict the risk of sepsis in the early stage in this embodiment. The infection marker parameters are calculated by the function Y = A*X1 + B*X2 + C based on the first and second white blood cell parameters in Table 7-1. Here, Y represents the infection marker parameter, X1 represents the first white blood cell parameter, X2 represents the second white blood cell parameter, and A, B, and C are constants.

[0339] Table 7-1 Efficacy of Other Infection Marker Parameters in Early Prediction of Sepsis Risk

[0340]

[0341]

[0342] Table 7-2 shows the efficacy of using existing PCT (procalcitonin) and single-DIFF channel parameters for early prediction of sepsis risk.

[0343] Infection marker parameters ROC_AUC Determine the threshold False positive rate True Yang Rate True negative rate False negative rate PCT (procalcitonin) 0.634 >2 14.0％ 39.7％ 86.0％ 60.3％ D_Neu_SS_W 0.613 >253 47.7％ 67.8％ 52.3％ 32.2％ D_Neu_FL_W 0.633 >205 47.7％ 72.4％ 52.3％ 27.6％ D_Neu_FS_W 0.543 >559 32.3％ 48.3％ 67.7％ 51.7％

[0344] A comparison of Table 7-2 with Tables 6 and 7-1 shows that the combination of WNB channel parameters and DIFF channel parameters exhibits superior diagnostic performance in sepsis prediction compared to PCT or the DIFF channel alone. In the table, D_Neu_SS_W refers to the distribution width of the lateral scattered light intensity of neutrophil clusters in the DIFF channel scatter plot; D_Neu_FL_W refers to the distribution width of the fluorescence intensity of neutrophil clusters in the DIFF channel scatter plot; and D_Neu_FS_W refers to the distribution width of the forward scattered light intensity of neutrophil clusters in the DIFF channel scatter plot.

[0345] Table 7-3 illustrates the statistical and testing methods used in this embodiment, taking two parameters as an example.

[0346]

[0347] As shown in Table 7-3, the Welch test analysis revealed a statistically significant difference between the two groups for this parameter (p < 0.0001).

[0348] As shown in Tables 6 and 7-1, 7-2, and 7-3, the infection marker parameters proposed in this application can be used to predict the risk of sepsis one day in advance relatively effectively.

[0349] Example 2 Differentiation between common infection and severe infection

[0350] Using a BC-6800Plus blood cell analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., and following similar steps to those in Example 1 of this application, routine blood tests were performed on 1528 blood samples. Based on scatter plots, the aforementioned method was used to identify severe infections. Among these, 756 samples were classified as severe infections (positive samples), and 792 samples were classified as non-severe infections (negative samples).

[0351] In this study, 1548 donors were included based on the following inclusion criteria: adult ICU patients with or suspected of having acute infection. Exclusion criteria included: pregnant women, patients undergoing chemotherapy-induced myelosuppression, patients receiving immunosuppressant therapy, and patients with hematologic disorders.

[0352] The donors of the severely infected samples must meet one or more of the following criteria: suspected or clearly identified infection site, positive laboratory culture results, and organ dysfunction:

[0353] ① Evidence of systemic, widespread, or body cavity disseminated infection exists.

[0354] ② There is a life-threatening infection in a special site.

[0355] ③ The infection causes at least one abnormality in organ function indicators.

[0356] The other samples were from non-severe infections.

[0357] Table 8 shows the infection marker parameters used and their corresponding diagnostic efficacy. Figure 17 The ROC curves corresponding to the infection marker parameters in Table 8 are shown. In Table 8:

[0358] Combined parameter 1 = 0.006064 * N_WBC_FL_W + 0.054716 * D_Mon_SS_W - 16.1568;

[0359] Combined parameter 2 = 0.006662 * N_WBC_FL_W + 0.000248 * D_Mon_FS_W - 14.6388;

[0360] The combined parameter 3 = 0.006651*N_NEU_FL_W + 0.014098*D_NEU_FL_P - 15.8676.

[0361] Table 8. Efficacy of different infection marker parameters in diagnosing severe infections.

[0362] Infection marker parameters ROC_AUC Determine the threshold False positive rate True Yang Rate True negative rate False negative rate Combined parameter 1 0.9023 >-0.3964 17.8％ 83.2％ 82.2％ 16.8％ Combination parameter 2 0.8784 >-0.3668 20.1％ 80.8％ 79.9％ 19.2％ Combined parameters 3 0.8575 >-0.1588 19.2％ 74.5％ 80.8 25.5％

[0363] A true positive means that the results obtained in this embodiment are consistent with the patient's clinical condition, both indicating a severe infection; a false positive means that the results obtained in this embodiment indicate a severe infection, but the patient's actual condition is a common infection; a true negative means that the results obtained in this embodiment are consistent with the patient's clinical condition, both indicating a common infection; a false negative means that the results obtained in this embodiment indicate a common infection, but the patient's actual condition is a severe infection.

[0364] In addition, Tables 9-1 to 9-4 show the efficacy of using other infection marker parameters to diagnose severe infections in this embodiment. The infection marker parameters are calculated based on the first and second white blood cell parameters in Tables 9-1 to 9-4 using the function Y = A*X1 + B*X2 + C, where Y represents the infection marker parameter, X1 represents the first white blood cell parameter, X2 represents the second white blood cell parameter, and A, B, and C are constants.

[0365] Table 9-1 shows the efficacy of combined parameters including N_WBC_FL_W in diagnosing severe infections.

[0366]

[0367] Table 9-2 shows the efficacy of combined parameters including D_Mon_SS_W in diagnosing severe infections.

[0368]

[0369]

[0370] Table 9-3 shows the efficacy of combined parameters including N_WBC_FL_P in diagnosing severe infections.

[0371]

[0372] Table 9-4, Efficacy of Other Combined Parameters in Diagnosing Severe Infections

[0373]

[0374]

[0375]

[0376] Table 9-5 shows the efficacy of using existing PCT (procalcitonin) and single-DIFF channel parameters to differentiate between common and severe infections.

[0377] Infection marker parameters ROC_AUC Determine the threshold False positive rate True Yang Rate True negative rate False negative rate PCT 0.806 >0.46 31.8％ 80.5％ 68.2％ 19.5％ D_Neu_SSC_W 0.664 >259.324 39.3％ 633.3％ 60.7％ 36.7％ D_Neu_SFL_W 0.758 >220.767 13.6％ 54.3％ 86.4％ 45.7％ D_Neu_FSC_W 0.542 >572.274 34.3％ 41.9％ 65.7％ 58.1％

[0378] Existing technology has been reported (Crouser E, Parrillo J, Seymour C et al. Improved Early Detection of Sepsis in the ED With a Novel Monocyte Distribution Width Biomarker. CHEST. 2017; 152(3):518-526). Using the scatter plot of blood routine tests in the DIFF channel of a BCI blood analyzer, the distribution width of neutrophils was used to differentiate between common and severe infections. The ROC_AUC was 0.79, the judgment threshold was >20.5, the false positive rate was 27%, the true positive rate was 77.0%, the true negative rate was 73%, and the false negative rate was 23%. Based on the reported data, this is similar in efficacy to Mindray's DIFF channel for differentiating between common and severe infections.

[0379] As can be seen from the comparison between Table 9-5 and Tables 8, 9-1, 9-2, 9-3, and 9-4, the combination of WNB channel parameters and DIFF channel parameters has similar or even better diagnostic performance to PCT in sepsis prediction. It may replace PCT markers and provide clues to distinguish between common and severe infections using routine blood test data at no cost. In addition, it also has better diagnostic performance than DIFF channel parameters.

[0380] Table 9-6 illustrates the statistical and testing methods used in this embodiment, taking three parameters as an example.

[0381]

[0382]

[0383] As shown in Table 9-6, the Welch test analysis revealed a statistically significant difference between the two groups for this parameter (p < 0.0001).

[0384] As shown in Tables 8 and 9-1 to 9-6, the infection marker parameters proposed in this application can be used to determine whether a subject has a severe infection in a relatively effective manner.

[0385] Example 3: Diagnosis of Sepsis

[0386] Using a BC-6800 Plus blood cell analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., and following similar steps to those in Example 1 of this application, routine blood tests were performed on 1748 blood samples. Sepsis was diagnosed based on a scatter plot using the aforementioned method. Of these, 506 were sepsis samples (positive samples) and 1242 were non-sepsis samples (negative samples).

[0387] Inclusion criteria for these 1748 cases: adult ICU patients with or suspected of having acute infection. Exclusion criteria: pregnant women, patients receiving chemotherapy-induced myelosuppression, patients receiving immunosuppressant therapy, and patients with hematologic disorders.

[0388] Table 10 shows the infection marker parameters used and their corresponding diagnostic efficacy. Figure 18 The ROC curves corresponding to the infection marker parameters in Table 10 are shown. In Table 10:

[0389] Combined parameter 1 = 0.006048 * N_WBC_FL_W + 0.068161 * D_Mon_SS_W - 18.54084598;

[0390] Combined parameter 2 = 0.006514 * N_WBC_FL_W + 0.00675 * D_NEU_SS_P - 15.78556712.

[0391] Table 10. Efficacy of different infection marker parameters in diagnosing sepsis.

[0392] Infection marker parameters ROC_AUC Determine the threshold False positive rate True Yang Rate True negative rate False negative rate Combined parameter 1 0.91 >17.7079 13.1％ 82.6％ 86.9％ 17.4％ Combination parameter 2 0.8804 >14.7255 20.3％ 82.3％ 79.7％ 17.7％

[0393] In addition, Table 11-1 shows the efficacy of using other infection marker parameters to diagnose sepsis in this embodiment. The infection marker parameters are calculated based on the first and second white blood cell parameters in Table 11-1 using the function Y = A*X1 + B*X2 + C, where Y represents the infection marker parameter, X1 represents the first white blood cell parameter, X2 represents the second white blood cell parameter, and A, B, and C are constants.

[0394] Table 11-1 Efficacy of Other Infection Marker Parameters in Diagnosing Sepsis

[0395]

[0396]

[0397]

[0398]

[0399]

[0400]

[0401]

[0402] Table 11-2 shows the efficacy of using existing PCT (procalcitonin) and single-DIFF channel parameters for diagnosing sepsis.

[0403] Infection marker parameters ROC_AUC Determine the threshold False positive rate True Yang Rate True negative rate False negative rate PCT 0.787 0.64 37.3％ 81.0％ 62.7％ 19.0％ D_Neu_SS_W 0.687 252.764 45.4％ 74.1％ 54.6％ 25.9％ D_Neu_FL_W 0.791 213.465 22.8％ 68.0％ 77.2％ 32.0％ D_Neu_FS_W 0.545 586.385 22.6％ 32.2％ 77.4％ 67.8％

[0404] As can be seen from the comparison between Table 11-2 and Tables 10 and 11-1, the combination of WNB channel parameters and DIFF channel parameters has similar or even better diagnostic performance to PCT in the diagnosis of sepsis. It may replace PCT markers and provide sepsis indications at no cost using blood routine test data. In addition, the diagnostic efficacy of the dual-channel combination is also better than that of the single DIFF channel parameters.

[0405] Table 11-3 illustrates the statistical and testing methods used in this embodiment, taking three parameters as an example.

[0406]

[0407] As shown in Table 11-3, the Welch test analysis revealed a statistically significant difference between the two groups for this parameter (p < 0.0001).

[0408] As shown in Tables 10, 11-1, 11-2, and 11-3, the infection marker parameters proposed in this application can be used to determine whether a subject has sepsis relatively effectively.

[0409] Example 4: Monitoring of Severe Infections

[0410] Using a BC-6800Plus hematology analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., continuous routine blood tests were performed on blood samples from 50 patients with severe infections, following the steps in Example 1 of this application. The progression of severe infection was monitored using the aforementioned method based on scatter plots. The 50 patients were grouped according to their condition on day 7 after diagnosis. Patients whose infection severity improved and whose condition stabilized on day 7 were included in the improvement group (positive samples N=26). Patients whose infection severity did not improve significantly, remained in the severe infection stage, or died were included in the additive group (negative samples N=24). Figure 19 The graph shows the dynamic trend changes monitored using a linear combination of parameters D_Mon_SS_W and N_WBC_FL_W, where the number of days after the diagnosis of severe infection is plotted on the horizontal axis and the average value of the infection marker parameter in the two groups of patients is plotted on the vertical axis.

[0411] Depend on Figure 19 It is evident that the infection marker parameters proposed in this application can be used to effectively monitor the development of severe infection in subjects.

[0412] Example 5: Monitoring of Sepsis

[0413] Using a BC-6800Plus hematology analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., continuous routine blood tests were performed on blood samples from 76 patients with sepsis, following the steps in Example 1 of this application. The progression of sepsis was monitored using the aforementioned method based on a scatter plot. The 76 patients were grouped according to their condition on day 7 after sepsis diagnosis. Patients whose infection severity improved and whose condition stabilized on day 7 after diagnosis were included in the improvement group (positive samples N=55). Patients whose infection severity did not improve significantly, who remained in a severe infection stage, or who died were included in the worsening group (negative samples N=21). A dynamic trend graph was constructed with the number of days after sepsis diagnosis as the horizontal axis and the median values ​​of the infection marker parameters of the two groups as the vertical axis, as shown below. Figure 20 As shown in the figure. In this embodiment, the infection marker parameters are calculated by a linear combination of D_Mon_SS_W and N_WBC_FL_W.

[0414] Depend on Figure 20 It is evident that the infection marker parameters proposed in this application can be used to effectively monitor the sepsis development status of subjects.

[0415] Example 6: Prognostic Analysis of Sepsis

[0416] Using a BC-6800Plus blood cell analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., blood routine tests were performed on 270 blood samples according to the steps in Example 1 of this application. Based on a scatter plot, the aforementioned method was used for sepsis prognosis analysis. Among them, 68 positive samples died within 28 days, and 202 negative samples survived within 28 days. Table 12 shows the infection marker parameters used and their corresponding diagnostic efficacy. The infection marker parameters were calculated using the function Y = A*X1 + B*X2 + C based on the first and second white blood cell parameters in Table 12, where Y represents the infection marker parameter, X1 represents the first white blood cell parameter, X2 represents the second white blood cell parameter, and A, B, and C are constants.

[0417] Table 12. Efficacy of different infection marker parameters in diagnosing sepsis and determining prognosis.

[0418]

[0419]

[0420]

[0421] As shown in Table 12, the infection marker parameters proposed in this application can be used to effectively determine whether the prognosis of sepsis is good.

[0422] Example 7: Infection Type Determination

[0423] Using the BC-6800Plus blood cell analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., blood routine tests were performed on 491 blood samples according to the steps in Example 1 of this application. The infection type was determined based on the scatter plot using the aforementioned method. Among them, 237 samples were bacterial infections and 254 samples were viral infections.

[0424] Inclusion criteria for these cases: Adult ICU patients with present or suspected acute infection. Exclusion criteria: Pregnant women, patients undergoing chemotherapy for myelosuppression, patients receiving immunosuppressant therapy, and patients with hematologic disorders.

[0425] The bacterial infection sample must have a suspected or clear site of infection and a positive laboratory bacterial culture result, i.e., simultaneously meet ①-③.

[0426] ① Evidence of bacterial infection: (Meeting any one of 1-4 below is sufficient)

[0427] 1. There is a clearly defined site of infection.

[0428] 2. Elevated inflammatory markers (WBC, CRP, and PCT, etc.)

[0429] 3. Positive microbial culture

[0430] 4. Imaging results suggest infection.

[0431] ②The SOFA score changed by less than 2 points from baseline

[0432] ③ Changes in clinically recognized organ failure index scores < 2 points

[0433] The virus-infected sample must have a suspected or clearly defined site of infection and test positive for viral antigens or antibodies. For example, meeting any one of the following criteria is sufficient:

[0434] ① Positive antibody test for influenza A or influenza B virus

[0435] ② EB virus antibody test positive

[0436] ③ Positive for cytomegalovirus antibody test.

[0437] Table 13-1 shows the infection marker parameters used and their corresponding diagnostic efficacy. The infection marker parameters are calculated using the function Y = A*X1 + B*X2 + C based on the first and second white blood cell parameters in Table 13-1. Here, Y represents the infection marker parameter, X1 represents the first white blood cell parameter, X2 represents the second white blood cell parameter, and A, B, and C are constants.

[0438] Table 13-1 Efficacy of Different Infection Marker Parameters in Determining Infection Type

[0439]

[0440]

[0441]

[0442]

[0443]

[0444] Table 13-2 shows the efficacy of using existing technology's PCT (procalcitonin) and single-DIFF channel parameters to differentiate between bacterial and viral infections.

[0445] Infection marker parameters ROC_AUC Determine the threshold False positive rate True Yang Rate True negative rate False negative rate PCT 0.851 0.554 7.9％ 67.3％ 92.1％ 32.7％ D_Neu_SS_W 0.733 259.275 24.4％ 60.2％ 75.6％ 39.8％ D_Neu_FL_W 0.836 206.183 20.1％ 75.0％ 79.9％ 25.0％ D_Neu_FS_W 0.601 611.240 34.6％ 56.4％ 65.4％ 43.6％

[0446] A comparison of Tables 13-2 and 13-1 shows that the combination of WNB channel parameters and DIFF channel parameters has diagnostic efficacy comparable to, or even better than, PCT in differentiating between bacterial and viral infections; it is more effective than the DIFF channel parameters alone. The infection marker parameters proposed in this application can be used to more effectively determine the type of infection in a subject.

[0447] Example 8: Differentiation between infectious and non-infectious inflammation

[0448] Using the BC-6800Plus blood cell analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., blood routine tests were performed on 515 blood samples according to the steps in Example 1 of this application. Infectious inflammation was identified based on scatter plots using the aforementioned method. Among them, 399 samples were infectious inflammation samples (i.e., positive samples), and 116 samples were non-infectious inflammation samples (i.e., negative samples).

[0449] Inclusion criteria for these cases: Adult ICU patients with or suspected of having acute inflammation. Exclusion criteria: Pregnant women, patients undergoing chemotherapy for myelosuppression, patients receiving immunosuppressant therapy, and patients with hematologic disorders.

[0450] The infectious inflammatory sample must have evidence of bacterial and / or viral infection and must contain inflammation (meeting any one of the following is sufficient).

[0451] 1. Local inflammatory manifestations or systemic inflammatory response manifestations

[0452] 2. Tissue damage: Damage caused by physical or chemical factors such as high temperature, low temperature, radioactive substances, and ultraviolet radiation.

[0453] 3. Mechanical damage: Damage caused by chemical substances such as strong acids and alkalis.

[0454] 4. Tissue necrosis: Tissue necrosis and damage caused by ischemia or hypoxia, etc.

[0455] 5. Allergic reactions: Abnormal immune response in the body, such as autoimmune diseases.

[0456] The non-infectious inflammatory samples: inflammatory responses caused by physical, chemical, or other factors, simultaneously satisfying ① and ②:

[0457] ① No evidence of bacterial infection

[0458] ② Inflammation is present (any one of the following is sufficient)

[0459] 1. Local inflammatory manifestations or systemic inflammatory response manifestations

[0460] 2. Tissue damage: Damage caused by physical or chemical factors such as high temperature, low temperature, radioactive substances, and ultraviolet radiation.

[0461] 3. Mechanical damage: Damage caused by chemical substances such as strong acids and alkalis.

[0462] 4. Tissue necrosis: Tissue necrosis and damage caused by ischemia or hypoxia, etc.

[0463] 5. Allergic reactions: Abnormal immune response in the body, such as autoimmune diseases.

[0464] Table 14-1 shows the infection marker parameters used and their corresponding diagnostic efficacy. The infection marker parameters are calculated using the function Y = A*X1 + B*X2 + C based on the first and second white blood cell parameters in Table 14-1. Here, Y represents the infection marker parameter, X1 represents the first white blood cell parameter, X2 represents the second white blood cell parameter, and A, B, and C are constants.

[0465] Table 14-1 The diagnostic efficacy of different infection marker parameters for infectious inflammation.

[0466]

[0467]

[0468] Table 14-2 shows the efficacy of using existing PCT (procalcitonin) and single-DIFF channel parameters to differentiate between infectious and non-infectious inflammation.

[0469] Infection marker parameters ROC_AUC Determine the threshold False positive rate True Yang Rate True negative rate False negative rate PCT 0.855 0.44 32.1％ 89.6％ 67.9％ 10.4％ D_Neu_SSC_W 0.744 290.101 7.8％ 45.7％ 92.2％ 54.3％ D_Neu_SFL_W 0.836 220.534 14.7％ 67.3％ 85.3％ 32.7％ D_Neu_FSC_W 0.557 563.910 37.9％ 51.3％ 62.1％ 48.7％

[0470] A comparison of Table 14-2 and Table 14-1 shows that the combination of WNB channel parameters and DIFF channel parameters has better diagnostic efficacy in differentiating between bacterial and viral infections than PCT or DIFF channel parameters alone. The infection marker parameters proposed in this application can be used to more effectively determine infectious inflammation.

[0471] Example 9: Evaluation of treatment efficacy for sepsis

[0472] Using a BC-6800Plus hematology analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., complete blood count (CBC) tests were performed on blood samples from 28 patients receiving sepsis treatment, following the steps in Example 1. The efficacy of sepsis treatment was evaluated using the aforementioned method based on scatter plots. Specifically, 28 patients diagnosed with sepsis received antibiotic treatment. After 5 days, CBC tests were performed on their blood samples. The combined parameters of the WNB and DIFF channels obtained using the aforementioned method were used to divide patients into an effective group and an ineffective group based on the 5-day treatment effect. The effective group was defined as those with significant clinical symptom improvement; otherwise, they were classified as ineffective. Eleven patients were in the ineffective group, and 17 patients were in the effective group.

[0473] Table 15 shows the combination of the DIFF+WNB dual-channel parameters "N_WBC_FL_W" and "D_Neu_FL_W" used in this embodiment as infection marker parameters to determine the efficacy of treatment for sepsis. The physical meaning of this dual-parameter combination is to combine the distribution width of nucleic acid content inside WBC particles in the first detection channel and the distribution width of nucleic acid content inside neutrophils in the second detection channel.

[0474] This two-parameter combination is achieved through the function

[0475] The infection marker parameter is obtained by calculating Y = 0.00623272 × N_WBC_FL_W + 0.01806527 × D_Neu_FL_W - 16.84312131, where Y represents the infection marker parameter.

[0476] Table 15

[0477]

[0478] Figure 21A - Figure 21D The results of using the combination of two parameters, “N_WBC_FL_W” and “D_Neu_FL_W”, as infection marker parameters to detect the efficacy of sepsis treatment are presented intuitively.

[0479] Table 16 shows the combination of the DIFF+WNB dual-channel parameters "N_WBC_FL_W" and "D_Neu_FL_CV" used in this embodiment as infection marker parameters to determine the efficacy of treatment for sepsis. The physical meaning of this dual-parameter combination is to combine the distribution width of nucleic acid content inside WBC particles in the first detection channel and the dispersion of nucleic acid content inside neutrophils in the second detection channel.

[0480] This two-parameter combination is achieved through the function

[0481] The infection marker parameters are obtained by calculating Y = 0.00688519 × N_WBC_FL_W + 11.27099282 × D_Neu_FL_CV - 19.2998686, where Y represents the infection marker parameters.

[0482] Table 16

[0483]

[0484] Figure 22A - Figure 22D The results of using the combination of two parameters, “N_WBC_FL_W” and “D_Neu_FL_CV”, as infection marker parameters to detect the efficacy of sepsis treatment are presented intuitively.

[0485] Example 10: Count values ​​combined with parameters for sepsis diagnosis

[0486] Using a BC-6800Plus blood cell analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., following similar steps to those in Example 3 of this application, routine blood tests were performed on 1748 blood samples. Sepsis was diagnosed based on a scatter plot using the aforementioned method. Of these, 506 were sepsis samples (positive samples) and 1242 were non-sepsis samples (negative samples).

[0487] Inclusion criteria for these 1748 cases: adult ICU patients with or suspected of having acute infection. Exclusion criteria: pregnant women, patients receiving chemotherapy-induced myelosuppression, patients receiving immunosuppressant therapy, and patients with hematologic disorders.

[0488] Table 17 shows the infection marker parameters used and their corresponding diagnostic efficacy. Figure 24 The ROC curves corresponding to the infection marker parameters in Table 17 are shown. In Table 17:

[0489] Combined parameter 1 = -0.61535116*Mon# + 0.00766353*N_WBC_FL_W - 15.04738706;

[0490] Combined parameter 2 = -0.03077968*HGB + 0.08933918*N_WBC_FL_W - 5.72270269;

[0491] Combined parameter 3 = -0.00395999*PLT + 0.00606333*N_WBC_FL_W - 11.55000862.

[0492] Table 17. Efficacy of different infection marker parameters in diagnosing sepsis.

[0493]

[0494]

[0495] Comparing Table 11-2 with Table 17, the combined parameters of monocyte count, hemoglobin level, or platelet count from a complete blood count, along with parameters from the WNB channel, demonstrate superior diagnostic performance in sepsis diagnosis compared to PCT or the DIFF channel alone. This indicates that the white blood cell count and platelet count from a complete blood count, as well as the hemoglobin concentration of red blood cells, can be used as the primary white blood cell parameter. Combined with the secondary white blood cell parameter, these parameters can be used to calculate infection characteristic parameters for sepsis diagnosis.

[0496] Table 18 illustrates the statistical and testing methods used in this embodiment, taking three parameters as an example.

[0497]

[0498] As shown in Table 18, the Welch test analysis revealed a statistically significant difference between the two groups for this parameter (p < 0.0001).

[0499] All features or combinations of features mentioned above in the specification, drawings, and claims may be used in any combination or individually, provided they are meaningful within the scope of this application and do not contradict each other. The advantages and features described in the blood cell analyzer provided in the embodiments of this application are applicable in a corresponding manner to the use of the blood cell analysis method and infection marker parameters provided in the embodiments of this application, and vice versa.

[0500] The above description is merely a preferred embodiment of this application and does not limit the patent scope of this application. All equivalent modifications made based on the inventive concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A blood cell analyzer, comprising: A sampling device is used to collect blood samples from the subject for testing. A sample preparation apparatus for preparing a first assay sample containing a portion of the blood sample to be tested, a first hemolysin and a first staining agent for white blood cell classification, and for preparing a second assay sample containing another portion of the blood sample to be tested, a second hemolysin and a second staining agent for identifying nucleated red blood cells; An optical detection device includes a flow chamber, a light source, and a photodetector. The flow chamber is used for the first test sample and the second test sample to pass through, respectively. The light source is used to illuminate the first test sample and the second test sample that have passed through the flow chamber. The photodetector is used to detect the first optical information and the second optical information generated by the first test sample and the second test sample after being illuminated by light when passing through the flow chamber. as well as The processor is configured as follows: Calculate at least one first white blood cell parameter of at least one first target particle cluster in the first measurement sample from the first optical information. At least one second leukocyte parameter of at least one second target particle cluster in the second measurement sample is calculated from the second optical information, wherein at least one of the first leukocyte parameter and the second leukocyte parameter includes cellular characteristic parameters. Infection marker parameters for assessing the infection status of the subject are calculated based on the at least one first white blood cell parameter and the at least one second white blood cell parameter. These infection marker parameters are used to perform at least one of the following functions for assessing the subject: early prediction of sepsis, diagnosis of sepsis, differentiation between common and severe bacterial infections, monitoring of infection status, prognostic analysis of sepsis, differentiation between bacterial and viral infections, differentiation between non-infectious and infectious inflammation, and evaluation of treatment efficacy for sepsis. Output the infection marker parameters.

2. The blood cell analyzer according to claim 1, characterized in that, The at least one first white blood cell parameter includes one or more of the cellular characteristic parameters of mononuclear cell clusters, neutrophil clusters, and lymphocyte clusters in the first assay sample; and / or The at least one second white blood cell parameter includes one or more of the cellular characteristic parameters of lymphocyte clusters, neutrophil clusters, and white blood cell clusters in the second assay sample.

3. The blood cell analyzer according to claim 2, characterized in that, The at least one first white blood cell parameter includes one or more of the cell characteristic parameters of the mononuclear cell clusters and neutrophil clusters in the first assay sample, and the at least one second white blood cell parameter includes one or more of the cell characteristic parameters of the neutrophil clusters and white blood cell clusters in the second assay sample.

4. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The at least one first leukocyte parameter includes one or more of the following parameters: the forward-scattered light intensity distribution width, the centroid of the forward-scattered light intensity distribution, the coefficient of variation of the forward-scattered light intensity distribution, the lateral-scattered light intensity distribution width, the centroid of the lateral-scattered light intensity distribution, the coefficient of variation of the lateral-scattered light intensity distribution, the fluorescence intensity distribution width, 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 first target particle cluster in a two-dimensional scatter plot generated by two of the light intensities of forward-scattered light intensity, lateral-scattered light intensity, and fluorescence intensity, and the volume of the distribution region of the first target particle cluster in a three-dimensional scatter plot generated by forward-scattered light intensity, lateral-scattered light intensity, and fluorescence intensity; and / or The at least one second leukocyte parameter includes one or more of the following parameters: the forward scattered light intensity distribution width, the centroid of the forward scattered light intensity distribution, the coefficient of variation of the forward scattered light intensity distribution, the side scattered light intensity distribution width, the centroid of the side scattered light intensity distribution, the coefficient of variation of the side scattered light intensity distribution, the fluorescence intensity distribution width, 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 a two-dimensional scatter plot generated by two of the light intensities of forward scattered light intensity, side scattered light intensity, and fluorescence intensity, and the volume of the distribution region of the second target particle cluster in a three-dimensional scatter plot generated by forward scattered light intensity, side scattered light intensity, and fluorescence intensity.

5. The blood cell analyzer according to claim 4, characterized in that, The at least one first leukocyte parameter is selected from one or more of the following parameters: the forward scatter intensity distribution width, the centroid of the forward scatter intensity distribution, the coefficient of variation of the forward scatter intensity distribution, the lateral scatter intensity distribution width, the centroid of the lateral scatter intensity distribution, the coefficient of variation of the lateral scatter intensity distribution, the fluorescence intensity distribution width, 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 mononuclear cell clusters in the first test sample in a two-dimensional scatter plot generated by two of the forward scatter intensity, lateral scatter intensity, and fluorescence intensity, and the volume of the distribution region of the mononuclear cell clusters in the first test sample in a three-dimensional scatter plot generated by the forward scatter intensity, lateral scatter intensity, and fluorescence intensity; and / or The at least one second leukocyte parameter is selected from one or more of the following parameters: the width of the forward scattered light intensity distribution, the centroid of the forward scattered light intensity distribution, the coefficient of variation of the forward scattered light intensity distribution, the width of the side scattered light intensity distribution, the centroid of the side scattered light intensity distribution, the coefficient of variation of the side scattered 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 leukocyte clusters in the second test sample in a two-dimensional scatter plot generated by two of the light intensities of forward scattered light intensity, side scattered light intensity, and fluorescence intensity, and the volume of the distribution region of the leukocyte clusters in the second test sample in a three-dimensional scatter plot generated by forward scattered light intensity, side scattered light intensity, and fluorescence intensity.

6. The blood cell analyzer according to claim 5, characterized in that, The at least one first leukocyte parameter is selected from the lateral scattered light intensity distribution width of the mononuclear cell cluster in the first test sample, and the at least one second leukocyte parameter is selected from the fluorescence intensity distribution width of the leukocyte cluster in the second test sample. The calculation of infection marker parameters for assessing the infection status of the subject based on the at least one first leukocyte parameter and the at least one second leukocyte parameter includes: The infection marker parameters used to assess the infection status of the subject are calculated based on the lateral scattered light intensity distribution width of the mononuclear cell clusters in the first test sample and the fluorescence intensity distribution width of the leukocyte clusters in the second test sample.

7. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The processor is further configured to: When the value of the infection flag parameter is outside the preset range, a prompt message indicating that the infection flag parameter is abnormal is output.

8. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The processor is further configured to output a prompt message indicating the infection status of the subject based on the infection marker parameters.

9. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The infection marker parameters are used for early prediction of sepsis in the subjects.

10. The blood cell analyzer according to claim 9, characterized in that, The processor is further configured to output a prompt message indicating that the subject may develop sepsis within a certain period of time after the blood sample to be tested is collected, when the infection marker parameter meets a first preset condition.

11. The blood cell analyzer according to claim 10, characterized in that, The specified time period shall not exceed 48 hours.

12. The blood cell analyzer according to claim 11, characterized in that, The specified time period shall not exceed 24 hours.

13. The blood cell analyzer according to claim 9, characterized in that, The at least one first leukocyte parameter is selected from the lateral scattered light intensity distribution width of the mononuclear cell cluster in the first test sample, and the at least one second leukocyte parameter is selected from the fluorescence intensity distribution width or the lateral scattered light intensity distribution width of the leukocyte cluster in the second test sample. The calculation of infection marker parameters for assessing the infection status of the subject based on the at least one first leukocyte parameter and the at least one second leukocyte parameter includes: The infection marker parameters used to assess the infection status of the subject are calculated based on the lateral scattered light intensity distribution width of the mononuclear cell clusters in the first test sample and the fluorescence intensity distribution width of the leukocyte clusters in the second test sample. The infection marker parameters used to assess the infection status of the subject are calculated based on the lateral scattered light intensity distribution width of the mononuclear cell clusters in the first test sample and the lateral scattered light intensity distribution width of the leukocyte clusters in the second test sample.

14. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The infection marker parameters are used to diagnose sepsis in the subject.

15. The blood cell analyzer according to claim 14, characterized in that, The processor is further configured to output a prompt message indicating that the subject has sepsis when the infection marker parameter meets a second preset condition.

16. The blood cell analyzer according to claim 14, characterized in that, The at least one first leukocyte parameter is selected from the width of the lateral scattered light intensity distribution of the mononuclear cell cluster in the first test sample or the centroid of the lateral scattered light intensity distribution of the neutrophil cluster in the first test sample, and the at least one second leukocyte parameter is selected from the width of the fluorescence intensity distribution of the leukocyte cluster in the second test sample. The calculation of infection marker parameters for assessing the infection status of the subject based on the at least one first leukocyte parameter and the at least one second leukocyte parameter includes: The infection marker parameters used to assess the infection status of the subject are calculated based on the lateral scattered light intensity distribution width of the mononuclear cell clusters in the first test sample and the fluorescence intensity distribution width of the leukocyte clusters in the second test sample. The infection marker parameters used to assess the infection status of the subject are calculated based on the centroid of the lateral scattered light intensity distribution of the neutrophil clusters in the first test sample and the fluorescence intensity distribution width of the leukocyte clusters in the second test sample.

17. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The infection marker parameters are used to differentiate between common and severe infections in the subjects.

18. The blood cell analyzer according to claim 17, characterized in that, The processor is further configured to output a prompt message indicating that the subject has a severe infection when the infection marker parameter meets a third preset condition.

19. The blood cell analyzer according to claim 17, characterized in that, The at least one first leukocyte parameter is selected from the lateral scattered light intensity distribution width or the forward scattered light intensity distribution width of the mononuclear cell cluster in the first test sample, and the at least one second leukocyte parameter is selected from the fluorescence intensity distribution width of the leukocyte cluster in the second test sample. The calculation of infection marker parameters for assessing the infection status of the subject based on the at least one first leukocyte parameter and the at least one second leukocyte parameter includes: The infection marker parameters used to assess the infection status of the subject are calculated based on the lateral scattered light intensity distribution width of the mononuclear cell clusters in the first test sample and the fluorescence intensity distribution width of the leukocyte clusters in the second test sample. The infection marker parameters used to assess the infection status of the subject are calculated based on the forward scattered light intensity distribution width of the mononuclear cell clusters in the first test sample and the fluorescence intensity distribution width of the leukocyte clusters in the second test sample.

20. The blood cell analyzer according to any one of claims 1 to 3, wherein, The subject is an infected patient, and the infection marker parameters are used to monitor the subject's infection condition.

21. The blood cell analyzer according to claim 20, wherein, The subjects were patients with severe infections or sepsis.

22. The blood cell analyzer according to claim 20, characterized in that, The processor is further configured to monitor the progression of the subject's infection based on the infection marker parameters.

23. The blood cell analyzer according to claim 22, characterized in that, The processor is further configured to: The values ​​of the infection marker parameters were obtained by repeatedly testing blood samples from the subject at different time points; and The improvement of the subject's condition is determined by the changing trend of the values ​​of the infection marker parameters obtained through the multiple tests.

24. The blood cell analyzer according to claim 23, characterized in that, The multiple detections include at least three detections; and / or Judging whether the subject's condition has improved based on the changing trend of the values ​​of the infection marker parameters obtained through the multiple tests includes: when the values ​​of the infection marker parameters obtained through the multiple tests gradually decrease in a trend, outputting a prompt message indicating that the subject's condition has improved.

25. The blood cell analyzer according to claim 20, characterized in that, The at least one first leukocyte parameter is selected from the lateral scattered light intensity distribution width of the mononuclear cell cluster in the first test sample, and the at least one second leukocyte parameter is selected from the fluorescence intensity distribution width of the leukocyte cluster in the second test sample. The calculation of infection marker parameters for assessing the infection status of the subject based on the at least one first leukocyte parameter and the at least one second leukocyte parameter includes: The infection marker parameters used to assess the infection status of the subject are calculated based on the lateral scattered light intensity distribution width of the mononuclear cell clusters in the first test sample and the fluorescence intensity distribution width of the leukocyte clusters in the second test sample.

26. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The subjects were sepsis patients who had received treatment, and the infection marker parameters were used to perform sepsis prognostic analysis on the subjects.

27. The blood cell analyzer according to claim 26, characterized in that, The processor is further configured to determine whether the subject's sepsis prognosis is good based on the infection marker parameters.

28. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The infection marker parameters are used to differentiate between bacterial and viral infections in the subjects.

29. The blood cell analyzer according to claim 28, characterized in that, The processor is further configured to determine, based on the infection marker parameters, whether the subject's infection type is viral or bacterial.

30. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The infection marker parameters are used to differentiate between infectious and non-infectious inflammation in the subjects.

31. The blood cell analyzer according to claim 30, characterized in that, The processor is further configured to determine whether the subject has infectious or non-infectious inflammation based on the infection marker parameters.

32. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The subjects were sepsis patients receiving drug treatment, and the infection marker parameters were used to assess the efficacy of sepsis treatment in the subjects.

33. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The processor is further configured to, before calculating at least one first white blood cell parameter of at least one first target particle cluster in the first test sample from the first optical information, and calculating at least one second white blood cell parameter of at least one second target particle cluster in the second test sample from the second optical information, obtain white blood cell counts of the first test sample and the second test sample based on the first optical information and the second optical information, and output a retest command to retest the blood sample of the subject when the white blood cell count is less than a preset threshold, wherein the sample measurement quantity measured based on the retest command is greater than the sample measurement quantity used to acquire the optical information; and The processor is further configured to calculate at least another first leukocyte parameter of at least another first target particle cluster in the first test sample from the first optical information measured based on the retest instruction, and to calculate at least another second leukocyte parameter of at least another second target particle cluster in the second test sample from the second optical information, and to obtain infection marker parameters for assessing the infection status of the subject based on the at least another first leukocyte parameter and the at least another second leukocyte parameter.

34. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The processor is further configured to: When the preset characteristic parameters of the first target particle cluster and / or the second target particle cluster meet the fourth preset condition, the value of the infection flag parameter is not output, or the value of the infection flag parameter is output and at the same time a prompt message indicating that the value of the infection flag parameter is unreliable is output.

35. The blood cell analyzer according to claim 34, characterized in that, The processor is further configured to: When the total number of particles in the first target particle cluster and / or the second target particle cluster is less than a preset threshold, and / or when the first target particle cluster and / or the second target particle cluster overlaps with other particle clusters, the value of the infection flag parameter is not output, or the value of the infection flag parameter is output along with a prompt message indicating that the value of the infection flag parameter is unreliable.

36. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The processor is further configured to: When the subject has a blood disease or there are abnormal cells in the blood sample to be tested, the value of the infection marker parameter is not output, or the value of the infection marker parameter is output along with a prompt message indicating that the value of the infection marker parameter is unreliable.

37. The blood cell analyzer according to claim 36, characterized in that, The abnormal cells are primitive cells; and / or, based on the first optical information and / or the second optical information, it is determined that there are abnormal cells in the blood sample to be tested.

38. The blood cell analyzer according to any one of claims 1 to 3, wherein, The processor calculates at least one first leukocyte parameter of at least one first target particle cluster in the first test sample from the first optical information, and calculates at least one second leukocyte parameter of at least one second target particle cluster in the second test sample from the second optical information, wherein at least one of the first and second leukocyte parameters includes cellular characteristic parameters. Based on the at least one first leukocyte parameter and the at least one second leukocyte parameter, it calculates infection marker parameters for assessing the infection status of the subject, including: the processor Calculate multiple first leukocyte parameters of at least one first target particle cluster in the first test sample from the first optical information, and calculate multiple second leukocyte parameters of at least one second target particle cluster in the second test sample from the second optical information; Based on the plurality of first leukocyte parameters and the plurality of second leukocyte parameters, a plurality of infection marker parameter sets are obtained for assessing the infection status of the subject; Configure a priority for each of the plurality of infection flag parameter groups; Calculate the confidence level of each of the plurality of infection marker parameter groups. Select at least one infection marker parameter group from the plurality of infection marker parameter groups according to their priority and confidence level to obtain the infection marker parameter. Alternatively, calculate the confidence level of the plurality of infection marker parameter groups sequentially according to their priority and determine whether the confidence level reaches the corresponding confidence level threshold. When the confidence level of the current infection marker parameter group reaches the corresponding confidence level threshold, obtain the infection marker parameter based on the infection marker parameter group and stop the calculation and determination.

39. The blood cell analyzer according to claim 38, characterized in that, The processor is further configured to: Calculate the confidence level of each infection marker parameter group in the plurality of infection marker parameter groups, and determine whether the confidence level of each infection marker parameter group reaches the corresponding confidence level threshold; The infection marker parameter groups whose confidence level reaches the corresponding confidence threshold among the multiple infection marker parameter groups are selected as candidate infection marker parameter groups. At least one candidate infection marker parameter group is selected from the candidate infection marker parameter groups according to their priority, for obtaining the infection marker parameters.

40. The blood cell analyzer according to claim 39, characterized in that, Based on the priority of the candidate infection marker parameter groups, at least one candidate infection marker parameter group is selected from the candidate infection marker parameter groups to obtain the infection marker parameters, including: Based on the priority of the candidate infection marker parameter groups, the infection marker parameter group with the highest priority is selected from the candidate infection marker parameter groups to obtain the infection marker parameters.

41. The blood cell analyzer according to any one of claims 1 to 3, wherein, The processor calculates at least one first leukocyte parameter of at least one first target particle cluster in the first test sample from the first optical information, and calculates at least one second leukocyte parameter of at least one second target particle cluster in the second test sample from the second optical information, wherein at least one of the first and second leukocyte parameters includes cellular characteristic parameters. Based on the at least one first leukocyte parameter and the at least one second leukocyte parameter, it calculates infection marker parameters for assessing the infection status of the subject, including: the processor Multiple first leukocyte parameters of at least one first target particle cluster in the first test sample are calculated from the first optical information, and multiple second leukocyte parameters of at least one second target particle cluster in the second test sample are calculated from the second optical information. Based on the plurality of first leukocyte parameters and the plurality of second leukocyte parameters, a plurality of infection marker parameter sets are obtained for assessing the infection status of the subject. Calculate the confidence level of each of the plurality of infection marker parameter groups, and select at least one infection marker parameter group from the plurality of infection marker parameter groups based on the confidence level of the plurality of infection marker parameter groups to obtain the infection marker parameters.

42. The blood cell analyzer according to any one of claims 1 to 3, wherein, The processor calculates at least one first leukocyte parameter of at least one first target particle cluster in the first test sample from the first optical information, and calculates at least one second leukocyte parameter of at least one second target particle cluster in the second test sample from the second optical information, wherein at least one of the first and second leukocyte parameters includes cellular characteristic parameters. Based on the at least one first leukocyte parameter and the at least one second leukocyte parameter, it calculates infection marker parameters for assessing the infection status of the subject, including: the processor Based on the first optical information and the second optical information, determine whether the blood sample to be tested has any abnormalities that affect the assessment of infection status; When it is determined that the blood sample to be tested has an abnormality that affects the assessment of infection status, at least one first white blood cell parameter of at least one first target particle cluster matching the abnormality is obtained from the first optical information, and at least one second white blood cell parameter of at least one second target particle cluster matching the abnormality is obtained from the second optical information. The infection marker parameters are obtained based on at least one first leukocyte parameter and at least one second leukocyte parameter.

43. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The processor is further configured to combine the at least one first leukocyte parameter and the at least one second leukocyte parameter into the infection marker parameter using a linear function.

44. The blood cell analyzer according to any one of claims 1 to 3, characterized in that, The processor is further configured to select the at least one first leukocyte parameter and the at least one second leukocyte parameter and obtain the infection marker parameter accordingly, such that the diagnostic power of the infection marker parameter is greater than 0.

5.

45. The blood cell analyzer according to claim 44, characterized in that, The processor is further configured to select the at least one first leukocyte parameter and the at least one second leukocyte parameter and thereby obtain the infection marker parameter, such that the diagnostic efficacy of the infection marker parameter is 0.

6.

46. ​​The blood cell analyzer according to claim 45, characterized in that, The processor is further configured to select the at least one first leukocyte parameter and the at least one second leukocyte parameter and obtain the infection marker parameter accordingly, such that the diagnostic power of the infection marker parameter is greater than 0.

8.

47. A blood cell analyzer, comprising: A sampling device is used to collect blood samples from the subject for testing. A sample preparation apparatus for preparing a first assay sample containing a portion of the blood sample to be tested, a first hemolysin and a first staining agent for white blood cell classification, and for preparing a second assay sample containing another portion of the blood sample to be tested, a second hemolysin and a second staining agent for identifying nucleated red blood cells; An optical detection device includes a flow chamber, a light source, and a photodetector. The flow chamber is used for the first test sample and the second test sample to pass through, respectively. The light source is used to illuminate the first test sample and the second test sample that have passed through the flow chamber. The photodetector is used to detect the first optical information and the second optical information generated by the first test sample and the second test sample after being illuminated by light when passing through the flow chamber. as well as The processor is configured as follows: Receive mode setting command, When the mode setting command indicates that the blood routine test mode is selected, the measuring device is controlled to perform optical measurements on the first and second test samples of the first measurement quantity, so as to obtain the first optical information of the first test sample and the second optical information of the second test sample, respectively, and to obtain and output the blood routine parameters based on the first and second optical information. When the mode setting instruction indicates that the sepsis detection mode is selected, the measuring device is controlled to perform optical measurements on the first and second test samples with a second measurement amount greater than the first measurement amount, so as to obtain the first optical information of the first test sample and the second optical information of the second test sample, respectively. From the first optical information, at least one first white blood cell parameter of at least one first target particle cluster in the first test sample is calculated, and from the second optical information, at least one second white blood cell parameter of at least one second target particle cluster in the second test sample is calculated. Based on the at least one first white blood cell parameter and the at least one second white blood cell parameter, infection marker parameters for assessing the infection status of the subject are obtained, and the infection marker parameters are output. The infection marker parameters are used to perform at least one of the following for the subject: early prediction of sepsis, diagnosis of sepsis, differentiation between common bacterial infection and severe bacterial infection, monitoring of infection condition, sepsis prognosis analysis, differentiation between bacterial infection and viral infection, differentiation between non-infectious inflammation and infectious inflammation, and evaluation of the treatment efficacy of sepsis.

48. The use of infection marker parameters in at least one of the following: early prediction of sepsis in subjects, diagnosis of sepsis, differentiation between common bacterial infections and severe bacterial infections, monitoring of infection status, prognostic analysis of sepsis, differentiation between bacterial and viral infections, differentiation between non-infectious and infectious inflammation, and evaluation of treatment efficacy for sepsis. The infection marker parameters are obtained using the following method: Calculate at least one first white blood cell parameter of at least one first target particle cluster obtained by flow cytometry detection of a first assay sample containing a portion of a test blood sample from a subject, a first hemolysin, and a first staining agent for white blood cell classification; Calculate at least one second white blood cell parameter of at least one second target particle cluster obtained by flow cytometry detection of another portion of the blood sample to be tested, a second hemolysin, and a second staining agent for identifying nucleated red blood cells, wherein at least one of the first white blood cell parameter and the second white blood cell parameter includes a cell characteristic parameter. and Infection marker parameters are calculated based on at least one first leukocyte parameter and at least one second leukocyte parameter.

Images