Sample testing method, device and computer readable storage medium for non-diagnostic purposes

Abstract

The application discloses a sample detection method, device and computer readable storage medium for non-diagnostic purposes. The method comprises: determining a target particle set of lymphocytes in a to-be-detected sample; determining a corresponding curve graph according to a characteristic parameter of each particle in the target particle set; wherein the curve graph is used for representing the relationship between the particle quantity and the characteristic parameter, and the characteristic parameter is the particle volume and / or the particle internal complexity; in response to the existence of two wave crests in the curve graph, determining the wave trough position of the curve graph; determining that the particle with the characteristic parameter smaller than the characteristic parameter corresponding to the wave trough position in the target particle set is a nucleated red blood cell. Through the above manner, the application can improve the efficiency and accuracy of nucleated red blood cell identification and reduce the missed detection.

Description

Technical Field

[0001] This application relates to the field of sample testing, and in particular to methods, apparatus and computer-readable storage media for sample testing for non-diagnostic purposes. Background Technology

[0002] Nucleated red blood cells are immature red blood cells that have not yet lost their nuclei. There are four types: primitive erythrocytes, early erythroblasts, intermediate erythroblasts, and late erythroblasts. Nucleated red blood cells are not found in the peripheral blood of normal adults, therefore, blood tests need to detect the presence of nucleated red blood cells. Summary of the Invention

[0003] This application provides a sample detection method, apparatus, and computer-readable storage medium, which solves the problem of low accuracy in identifying nucleated red blood cells in the prior art.

[0004] To address the aforementioned technical problems, the first aspect of this application provides a sample detection method, comprising: determining a target particle set of lymphocytes in a sample to be tested; determining a corresponding curve based on the characteristic parameters of each particle in the target particle set; wherein the curve represents the relationship between the number of particles and the characteristic parameters, the characteristic parameters being particle volume and / or particle internal complexity; determining the trough position of the curve in response to the presence of two peaks in the curve; and determining that particles in the target particle set whose characteristic parameters are less than the characteristic parameters corresponding to the trough position are nucleated red blood cells.

[0005] The step of determining the location of the trough in the curve includes: determining a dividing line parallel to the horizontal axis in the curve based on the peak; and determining the trough of the curve above the dividing line.

[0006] The step of determining a dividing line parallel to the horizontal axis in the curve graph based on the wave peak includes: determining the dividing line within a preset ratio range of the vertical axis corresponding to the highest wave peak.

[0007] The preset ratio range is 5% to 15%.

[0008] The method further includes: in response to the absence of the trough, defining a first region in the particle distribution scatter plot based on the morphological characteristics of other particles in the sample to be tested in the region where they are located in the particle distribution scatter plot; wherein the other particles include at least one of neutrophils, monocytes, lymphocytes and eosinophils; identifying the particles in the first region to determine the nucleated red blood cells in the first region.

[0009] The step of defining a first region in the particle distribution scatter plot based on the morphological characteristics of other particles in the sample to be tested in the region where they are located in the particle distribution scatter plot includes: determining a second region where the neutrophils are located in the particle distribution scatter plot; and determining the first region based on a set ratio of the position parameters of the second region.

[0010] The method further includes: counting the nucleated red blood cells to determine the number of nucleated red blood cells; and determining whether to activate an alarm procedure based on the number of nucleated red blood cells and / or the total number of particles in the sample to be tested.

[0011] The step of determining the corresponding curve based on the characteristics of the target particle set includes: forming a particle distribution scatter plot based on the characteristics of the target particle set; and projecting the scatter points in the particle distribution scatter plot onto the dimension of the characteristic parameter to obtain the curve.

[0012] To address the aforementioned technical problems, a second aspect of this application provides a sample detection device, comprising: a preliminary screening module for identifying a set of target particles of the lymphocyte category in a sample to be tested; a data processing module for determining a corresponding curve based on the characteristic parameters of each particle in the target particle set; wherein the curve represents the relationship between the number of particles and the characteristic parameters, the characteristic parameters being particle volume and / or particle internal complexity; and, in response to the presence of two peaks in the curve, determining the trough position of the curve; and a classification module for identifying particles in the target particle set whose characteristic parameters are less than the characteristic parameters corresponding to the trough position as nucleated red blood cells.

[0013] To address the aforementioned technical problems, a third aspect of this application provides a sample detection device, comprising a processor and a memory coupled to each other; the memory stores a computer program, and the processor executes the computer program to implement the sample detection method provided in the first aspect above.

[0014] To address the aforementioned technical problems, a fourth aspect of this application provides a computer-readable storage medium storing program data, which, when executed by a processor, implements the sample detection method provided in the first aspect.

[0015] The beneficial effects of this application are as follows: Unlike existing technologies, this application first identifies the target particle set of lymphocytes in the sample to be tested. Based on the characteristic parameters of each particle in the target particle set, a corresponding curve is determined. This curve represents the relationship between the number of particles and the characteristic parameters, which are particle volume and / or internal complexity. Then, in response to the presence of two peaks in the curve, the trough positions are determined. Finally, particles in the target particle set whose characteristic parameters are less than the characteristic parameters corresponding to the trough positions are identified as nucleated red blood cells. This application uses particles initially identified as lymphocytes as the target particle set, narrowing the identification range of nucleated red blood cells and improving their identification efficiency. Furthermore, based on the differences in characteristic parameters between nucleated red blood cells and other particles in the target particle set, the trough positions are determined using the curve. Finally, particles in the target particle set whose corresponding characteristic parameters are less than the values ​​corresponding to the troughs are identified as nucleated red blood cells, improving the efficiency and accuracy of nucleated red blood cell identification and reducing false negatives. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of an embodiment of the sample detection method for non-diagnostic purposes of this application;

[0018] Figure 2 This is a flowchart illustrating an embodiment of step S12 of this application;

[0019] Figure 3 This is the curve of the application. Figure 1 Schematic diagram of the embodiment;

[0020] Figure 4 This is a flowchart illustrating an embodiment of step S13 of this application;

[0021] Figure 5 This is a flowchart illustrating another embodiment of the sample detection method of this application;

[0022] Figure 6 This is a flowchart illustrating an embodiment of step S21 of this application;

[0023] Figure 7 This is a scatter plot of the particle distribution of the sample to be tested in this application. Figure 1 Schematic diagram of the embodiment;

[0024] Figure 8This is a flowchart illustrating another embodiment of the sample detection method of this application;

[0025] Figure 9 This is a scatter plot of particle distribution under normal conditions for the sample to be tested in this application;

[0026] Figure 10 This is a scatter plot of particle distribution under abnormal conditions in the sample to be tested in this application;

[0027] Figure 11 This is a schematic block diagram of the structure of an embodiment of the sample detection device of this application;

[0028] Figure 12 This is a schematic block diagram of another embodiment of the sample detection device of this application;

[0029] Figure 13 This is a schematic block diagram of an embodiment of a computer-readable storage medium of this application. Detailed Implementation

[0030] 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.

[0031] The terms "first" and "second" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0032] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0033] In the process of detecting nucleated red blood cells, a blood analyzer can be used to analyze the sample, identify nucleated red blood cells, and divide the nucleated red blood cell region into regions with other cell types. In some abnormal cases, the distribution of nucleated red blood cells is similar to that of other cell types, resulting in low identification accuracy and difficulty in accurately identifying the nucleated red blood cell region. At the same time, the poor identification effect leads to a low alarm rate, false negative results, and easy misdiagnosis. Secondly, the presence of nucleated red blood cells can cause inaccurate white blood cell (WBC) count results and also affect the classification results.

[0034] This application provides a sample detection method aimed at improving the accuracy of nucleated red blood cell identification, thereby enhancing the reliability of the identification results. Please refer to the embodiments provided below for details.

[0035] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating an embodiment of the sample testing method for non-diagnostic purposes according to this application. It should be noted that if substantially the same result is obtained, this embodiment is not necessarily identical. Figure 1 The illustrated process sequence is limited. This embodiment includes the following steps:

[0036] Step S11: Determine the set of target particles of the lymphocyte category in the sample to be tested.

[0037] This step initially identifies the target particle set belonging to lymphocytes from a large number of particles in the sample to be tested. In addition to lymphocytes, the target particle set may also include nucleated red blood cells or a small number of other types of particles.

[0038] Understandably, researchers have found through long-term studies that lymphocytes and nucleated red blood cells have similar distributions and unclear boundaries. Therefore, this step first preliminarily determines the lymphocyte set based on the detection signal, and then distinguishes lymphocytes and nucleated red blood cells in the lymphocyte set through subsequent steps, narrowing the scope of nucleated red blood cells and improving the accuracy of nucleated red blood cell identification.

[0039] The sample to be tested is a blood sample. After the sample is collected, it is processed by a sample detection device for particle identification. For example, the sample detection device is a blood analysis device, which can classify and count the sample according to impedance methods, light scattering methods, etc. This application uses light scattering method as an example to explain the steps of the sample detection method.

[0040] Light scattering method involves reacting a diluted sample with reagents and delivering it to a fluidic unit. The fluidic unit then transports the reacted sample to a flow chamber in the detection unit. This flow chamber provides an optical detection area where the sheath flow principle is used to encapsulate the particle flow in the sample, allowing each particle to pass through the detection channel individually. The light source in the detection unit, typically a laser, provides an illumination beam that shines onto the detection area of ​​the flow chamber. When cells flow through this area, the illumination beam scatters onto the cell particles, collecting light signals from multiple directions (including low-angle (LAS) and medium-angle (MAS) scattered light signals) and converting them into electrical pulses. The pulse magnitudes are then used to create a two-dimensional scatter plot. The LAS scattering reflects cell size, while the MAS scattering reflects internal cell complexity. Each measured cell is plotted on the two-dimensional scatter plot based on the pulse intensity in both angular directions.

[0041] Step S12: Determine the corresponding curve based on the characteristic parameters of each particle in the target particle set.

[0042] The graph is used to represent the relationship between the number of particles and their characteristic parameters, namely particle volume and / or particle internal complexity. Specifically, the graph can count the number of particles with the same volume or internal complexity dimension. The horizontal axis represents particle volume or internal complexity, and the vertical axis represents the number of particles with the corresponding volume or internal complexity. Alternatively, the graph can use the calculated results of particle volume and internal complexity as the horizontal axis and the number of particles as the vertical axis. The counted number of particles includes information about both characteristic parameters. For example, it can calculate the average signal value for each particle in terms of volume and internal complexity, and count the number of particles in the target particle set with the same average signal value for both volume and internal complexity. This average value is then used as the horizontal axis of the graph, and the number of particles is used as the vertical axis.

[0043] Please see Figure 2 , Figure 2 This is a schematic flowchart of an embodiment of step S12 of this application. It should be noted that if substantially the same result is achieved, this embodiment does not necessarily follow that approach. Figure 2 The illustrated process sequence is limited. This embodiment includes the following steps:

[0044] Step S121: Based on the characteristic parameters of each particle in the target particle set, form a particle distribution scatter plot.

[0045] In this embodiment, particle volume and internal complexity are used as the two axes of the particle distribution scatter plot. The information of each particle in the target particle set is projected onto a two-dimensional plane according to the two dimensions of volume and internal complexity to obtain the particle distribution scatter plot.

[0046] In a particle distribution scatter plot, different types of particles are clustered in various regions of the scatter plot due to differences in volume and internal complexity. Particles with smaller differences in volume and internal complexity are located close to each other in the scatter plot and are difficult to distinguish.

[0047] Step S122: Project the scatter points in the particle distribution scatter plot onto the dimension of the feature parameters to obtain a curve.

[0048] In one embodiment, the characteristic parameter is the particle volume. The scatter points in the particle distribution scatter plot are projected onto the volume dimension to obtain the number of particles in each volume, and a curve is formed based on this.

[0049] In another embodiment, the feature parameter is the particle internal complexity. Each scatter point in the particle distribution scatter plot is projected onto the internal complexity dimension to obtain the number of particles under each internal complexity, and a curve is formed based on this.

[0050] In another embodiment, the characteristic parameters are calculated based on both particle volume and particle internal complexity. For example, the average particle volume and internal complexity can be used as the horizontal axis, and the number of particles satisfying that average can be used as the vertical axis to form a curve. This is only for illustrative purposes; the horizontal axis can also be obtained using other calculation methods, such as the sum of 30% of the particle volume and 70% of the internal complexity, or the sum of 40% of the particle volume and 60% of the internal complexity, etc. Further details are omitted here. Those skilled in the art can adapt and adjust these methods to obtain other calculation methods, and this embodiment is not the only limitation.

[0051] Step S13: In response to the presence of two peaks in the curve, determine the location of the trough in the curve.

[0052] Please see Figure 3 , Figure 3 This is the curve of the application. Figure 1 A schematic diagram of the embodiment. Peak detection is performed on the curve. If the curve has two peaks, P1 and P2, it indicates that nucleated red blood cells may be present in the target particle set.

[0053] In one embodiment, the peak of the curve is detected by: performing extreme value detection on the curve to determine extreme points; determining the extreme point type for each extreme point, and determining the peak corresponding to the extreme point when the extreme point is a maximum value point.

[0054] In another embodiment, the peak of the curve can also be detected by the following method: perform forward or backward difference on the curve, and the point where the adjacent data in the difference result is one positive and one negative is the extreme point; determine the size relationship between the data and the two adjacent data at the extreme point, and the data point that is larger than the two adjacent data is determined as the peak.

[0055] Prior to step S13, the curve is filtered to remove noise, thus making the particle counting results more accurate. The filtering method can be one of mean filtering, median filtering, or Gaussian filtering.

[0056] Please see Figure 4 , Figure 4 This is a schematic flowchart illustrating an embodiment of step S13 of this application. It should be noted that if substantially the same result is achieved, this embodiment does not necessarily follow that approach. Figure 4 The illustrated process sequence is limited. This embodiment includes the following steps:

[0057] Step S131: Based on the peak, determine the dividing line parallel to the horizontal axis in the curve graph.

[0058] Within a predetermined scale range corresponding to the highest peak on the vertical axis, a dividing line is determined. This dividing line is parallel to the horizontal axis. Specifically, when there are two peaks in the curve, the one with the larger peak value is determined as the highest peak of the curve. For example, Figure 3 In the curve graph shown, there are two peaks P1 and P2. P2 is the highest peak. A region F is defined within the preset scale range of the vertical axis of P2, and a dividing line L is defined within region F.

[0059] In one embodiment, to reduce the probability of misjudgment, the preset ratio range is set to 5% to 15% based on the statistical analysis of the curve shape characteristics, and verified by experimental results. This range greatly improves the accuracy of the judgment.

[0060] Step S132: Above the dividing line, determine the trough of the curve.

[0061] This step identifies the trough above the dividing line L on the curve graph.

[0062] Similarly, in one embodiment, the troughs of the curve are detected by: performing extreme value detection on the curve to determine extreme points; determining the extreme point type for each extreme point, and determining the corresponding trough when the extreme point is a minimum point.

[0063] In another embodiment, the troughs of the curve can also be detected by the following method: perform forward or backward difference on the curve, and the point where adjacent data are one positive and one negative in the difference result is the extreme point; determine the size relationship between the extreme point and the two adjacent data, and the data point smaller than the two adjacent data is determined as the trough.

[0064] Unlike existing technologies, researchers have conducted long-term studies to explore the curve characteristics when nucleated red blood cells are present. They have finally determined the boundary line within a preset proportion of the highest peak area and identified the trough position in the curve above the boundary line. The trough position is identified faster and more accurately, thus improving the accuracy of nucleated red blood cell identification.

[0065] Step S14: Determine that in the target particle set, particles whose particle characteristic parameters are less than the characteristic parameters corresponding to the trough position are nucleated red blood cells.

[0066] like Figure 3 As shown, the horizontal axis value corresponding to the trough is 'a'. If the characteristic parameter corresponding to the horizontal axis of the curve is particle volume, then particles with a volume less than 'a' are identified as nucleated red blood cells; if the characteristic parameter corresponding to the horizontal axis of the curve is particle internal complexity, then particles with an internal complexity less than 'a' are identified as nucleated red blood cells; if the horizontal axis of the curve is determined based on the average particle volume and internal complexity, then particles with an average volume and internal complexity less than 'a' are identified as nucleated red blood cells.

[0067] After initially determining the target particle set of lymphocytes in the sample to be tested in the above embodiments, a curve is generated based on the relationship between the particle volume and / or the internal complexity of the particles and the number of particles in the target particle set. Nucleated red blood cells in the target particle set are determined based on the peaks and troughs of the curve, thereby improving the accuracy of nucleated red blood cell identification.

[0068] Please see Figure 5 , Figure 5 This is a schematic flowchart of another embodiment of the sample detection method of this application. It should be noted that if substantially the same result is obtained, this embodiment is not necessarily identical. Figure 5 The illustrated process sequence is limited. This embodiment includes the following steps:

[0069] Step S21: In response to the absence of a trough, the first region is delineated in the particle distribution scatter plot based on the morphological characteristics of other particles in the sample to be tested in the region where they are located.

[0070] Other particles include at least one of neutrophils, monocytes, lymphocytes, and eosinophils.

[0071] Specifically, the sample to be tested must include at least one of the following: neutrophils, monocytes, lymphocytes, and eosinophils. Each type of particle differs in its volume and internal complexity; therefore, in the particle distribution scatter plot, neutrophils, monocytes, lymphocytes, and eosinophils have their specific distribution areas, characterized by morphological features such as the coordinates and positional features of their distribution areas in the scatter plot.

[0072] Please see Figure 6 , Figure 6 This is a schematic flowchart illustrating an embodiment of step S21 of this application. It should be noted that if substantially the same result is achieved, this embodiment does not necessarily follow that approach. Figure 6 The illustrated process sequence is limited. This embodiment includes the following steps:

[0073] Step S211: Determine the second region where neutrophils are located in the particle distribution scatter plot.

[0074] This step can determine the second region where neutrophils are located in the particle distribution scatter plot based on the volume or internal complexity characteristics of neutrophils.

[0075] For example, please see Figure 7 , Figure 7 This is a scatter plot of the particle distribution of the sample to be tested in this application. Figure 1 A schematic diagram of the embodiment. Multiple regions can be defined therein, and particles of different categories fall into each region. For example, the second region where neutrophils are located is region A. Region A can be determined directly in the particle distribution scatter plot according to a preset position, or the position of neutrophils in the particle distribution scatter plot can be determined according to the particle clustering method, and then region A can be defined.

[0076] Step S212: Determine the first region according to the set ratio of the position parameters of the second region.

[0077] The location parameters of the second region may include the coordinates of the geometric center of the second region, the coordinates of the centroid, and the equation of the line containing the edge of the second region.

[0078] Taking the coordinates of the geometric center of the second region as an example, the first region can be determined according to the preset ratios of the horizontal and vertical coordinates of the geometric center. Specifically, the coordinates of the geometric center of the second region are represented as (m, n). Then, the equations of the first and second straight lines can be determined according to the first and second ratios of the horizontal coordinate m, respectively. The lines corresponding to the first and second straight line equations are both perpendicular to the horizontal axis of the scatter plot. The equations of the third and fourth straight lines can be determined according to the third and fourth ratios of the vertical coordinate n, respectively. The lines corresponding to the third and fourth straight line equations are both perpendicular to the vertical axis of the scatter plot. The region enclosed by the four straight lines is the first region. The equations of the first, second, and third straight lines can be expressed as: x1 = k1*m, x2 = k2*m, y1 = k3*n, and y2 = k4*n, respectively, where k1, k2, k3, and k4 represent the first, second, third, and fourth ratios, respectively.

[0079] Similarly, after knowing the centroid coordinates of the second region and the equation of the line containing the edge of the second region, the equation of the line containing the edge of the first region can be determined in the same way as described above, and thus the first region can be determined. This will not be elaborated further here.

[0080] like Figure 7 As shown, region B is the first region that has been determined. The first region determined by the above method is a square region. It can be understood that in order to determine the first region more accurately, more linear equations can be determined, or the linear equations can be determined in other forms. Those skilled in the art can make adjustments according to actual needs, and are not limited to the above methods.

[0081] Step S22: Identify the particles in the first region and determine the nucleated red blood cells in the first region.

[0082] Since there may be particles other than nucleated red blood cells in the first region, this step can reclassify the particles in the first region to identify the nucleated red blood cells.

[0083] For example, particles in the first region can be identified using steps S11 to S14 or other clustering algorithms to determine nucleated red blood cells.

[0084] Please see Figure 8 , Figure 8 This is a schematic flowchart of another embodiment of the sample detection method of this application. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily reflect that result. Figure 8 The illustrated process sequence is limited. This embodiment includes the following steps:

[0085] Step S31: Count the nucleated red blood cells to determine the number of nucleated red blood cells.

[0086] This step counts the particles identified as nucleated red blood cells to determine the number of nucleated red blood cells in the sample to be tested.

[0087] Step S32: Determine whether to activate the alarm procedure based on the number of nucleated red blood cells and / or the total number of particles in the sample to be tested.

[0088] In one embodiment, an alarm procedure is activated when the number of nucleated red blood cells exceeds a preset threshold.

[0089] In another embodiment, the number of nucleated red blood cells and the total number of particles in the sample to be tested can be determined first, and the proportion of nucleated red blood cells in the total number of particles can be determined. When the proportion exceeds a preset ratio, an alarm procedure is initiated.

[0090] In other embodiments, the alarm procedure may be activated only when the number of nucleated red blood cells exceeds a preset threshold and the proportion of nucleated red blood cells in the total number of particles exceeds a preset ratio.

[0091] Once the alarm program is activated, it can send alarm messages through flashing indicator lights, voice broadcasts, or display the warning content on the screen in the control center.

[0092] Please see Figure 9 and Figure 10 , Figure 9 This is a scatter plot of the particle distribution of the sample to be tested under normal conditions in this application. Figure 10 This is a scatter plot of particle distribution under abnormal conditions of the sample to be tested in this application. The low-angle scattered light signal is used as the vertical axis, and the medium-angle scattered light signal is used as the horizontal axis. Through sample detection in the above embodiments, when the number of nucleated red blood cells is low or even absent, the particle distribution scatter plot of the sample to be tested shows no abnormality; when the number of nucleated red blood cells is high, reaching the alarm condition, the particle distribution scatter plot of the sample to be tested shows an abnormality, and region C can be roughly represented as the area where nucleated red blood cells are located.

[0093] Unlike existing technologies, this application initially identifies the target particle set of lymphocytes in the sample to be tested. Based on the particle characteristic parameters of the target particle set, a curve is determined. The location of the trough is determined based on the curve's characteristics. Nucleated red blood cells are then identified based on their characteristic parameters being less than the corresponding value of the trough. The trough location is determined within a preset proportion range above the highest peak. When no trough is detected, a first region is defined based on the location parameters of other particle types. Nucleated red blood cells are then identified within this first region, improving the efficiency and accuracy of nucleated red blood cell identification and reducing false positives. Without increasing the false positive rate, this application improves the alarm rate for nucleated red blood cells using impedance and laser scattering methods. The alarm rate for severe nucleated red blood cell samples is significantly improved. Overall, the identification of nucleated red blood cells reaches the same level as similar instruments or even exceeds that of comparable instruments.

[0094] Please see Figure 11 , Figure 11 This is a schematic block diagram of an embodiment of the sample detection device of this application. The sample detection device 100 includes a preliminary screening module 101, a data processing module 102, and a classification module 103.

[0095] The initial screening module 101 is used to determine the target particle set of lymphocytes in the sample to be tested; the data processing module 102 is used to determine the corresponding curve based on the characteristic parameters of each particle in the target particle set; wherein the curve is used to represent the relationship between the number of particles and the characteristic parameters, which are particle volume and / or particle internal complexity; in response to the existence of two peaks in the curve, the position of the trough of the curve is determined; the classification module 103 is used to determine that particles in the target particle set whose particle characteristic parameters are less than the characteristic parameters corresponding to the trough position are nucleated red blood cells.

[0096] The data processing module 102 is also used to determine the dividing line parallel to the horizontal axis in the curve graph based on the peak; and to determine the trough of the curve graph above the dividing line.

[0097] The data processing module 102 is also used to determine the dividing line within a preset ratio range of the vertical coordinate corresponding to the highest peak.

[0098] The data processing module 102 is also used to delineate a first region in the particle distribution scatter plot based on the morphological characteristics of other particles in the sample to be tested in the region where they are located in the particle distribution scatter plot when there is no trough; wherein, other particles include at least one of neutrophils, monocytes, lymphocytes and eosinophils; and to identify the particles in the first region to determine the nucleated red blood cells in the first region.

[0099] The data processing module 102 is also used to determine the second region where neutrophils are located in the particle distribution scatter plot; and to determine the first region according to the set ratio of the position parameters of the second region.

[0100] The data processing module 102 is also used to count nucleated red blood cells to determine the number of nucleated red blood cells; and to determine whether to activate the alarm procedure based on the number of nucleated red blood cells and / or the total number of particles in the sample to be tested.

[0101] The data processing module 102 is also used to form a particle distribution scatter plot based on the characteristics of the target particle set; and to project the scatter points in the particle distribution scatter plot onto the dimension of the feature parameters to obtain a curve.

[0102] For details on the specific methods of each step of the processing, please refer to the description of each step in the above embodiment of the sample detection method of this application, which will not be repeated here.

[0103] Please see Figure 12 , Figure 12 This is a schematic block diagram of another embodiment of the sample detection device of this application. The sample detection device 200 includes a processor 210 and a memory 220 coupled to each other. The memory 220 stores a computer program, and the processor 210 is used to execute the computer program to implement the sample detection method described in the above embodiments.

[0104] For a description of each step of the processing, please refer to the description of each step in the above embodiment of the sample detection method of this application, and it will not be repeated here.

[0105] The memory 220 can be used to store program data and modules. The processor 210 executes various functional applications and data processing by running the program data and modules stored in the memory 220. The memory 220 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as particle classification function, scatter projection function, curve graph statistics function, etc.), etc.; the data storage area may store data created based on the use of the sample detection device 200 (such as particle signals, particle distribution scatter plots, curve graph data, second region position parameters, etc.). In addition, the memory 220 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 220 may also include a memory controller to provide the processor 210 with access to the memory 220.

[0106] In the various embodiments of this application, the disclosed methods and apparatus can be implemented in other ways. For example, the embodiments of the sample detection apparatus 200 described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium.

[0110] See Figure 13 , Figure 13 This is a schematic block diagram of a computer-readable storage medium according to an embodiment of the present application. The computer-readable storage medium 300 stores program data 310, which, when executed, implements the steps of the sample detection method embodiments described above.

[0111] For a description of each step of the processing, please refer to the description of each step in the above embodiment of the sample detection method of this application, and it will not be repeated here.

[0112] The computer-readable storage medium 300 can be any medium capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0113] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A sample detection method for non-diagnostic purposes, characterized in that, The method includes: Identify the set of target particles of the lymphocyte category in the sample to be tested; wherein the set of target particles includes at least lymphocytes and nucleated red blood cells; Based on the characteristic parameters of each particle in the target particle set, a corresponding curve is determined; wherein, the curve is used to represent the relationship between the number of particles and the characteristic parameters, the characteristic parameters being particle volume and / or particle internal complexity; In response to the presence of two peaks in the curve, determining the location of the trough in the curve includes: within a preset percentage range of the vertical axis corresponding to the highest peak, determining a boundary line parallel to the horizontal axis in the curve; and determining the trough of the curve above the boundary line; the preset percentage range is 5% to 15%. In the target particle set, particles whose particle characteristic parameters are less than the characteristic parameters corresponding to the trough position are identified as nucleated red blood cells.

2. The method according to claim 1, characterized in that, The method further includes: In response to the absence of the trough, a first region is delineated in the particle distribution scatter plot based on the morphological characteristics of the regions where other particles in the sample to be tested are located; wherein, the other particles include at least one of neutrophils, monocytes, lymphocytes and eosinophils; The particles in the first region are identified to determine the nucleated red blood cells in the first region.

3. The method according to claim 2, characterized in that, The step of defining a first region in the particle distribution scatter plot based on the morphological characteristics of other particles in the sample to be tested in the region where they are located includes: Determine the second region where the neutrophils are located in the particle distribution scatter plot; The first region is determined based on the set ratio of the location parameters of the second region.

4. The method according to claim 1 or 2, characterized in that, The method further includes: The number of nucleated red blood cells is determined by counting the nucleated red blood cells. Whether to initiate an alarm procedure is determined based on the number of nucleated red blood cells and / or the total number of particles in the sample to be tested.

5. The method according to claim 1, characterized in that, The step of determining the corresponding curve based on the characteristics of the target particle set includes: Based on the characteristics of the target particle set, a particle distribution scatter plot is generated; The scatter points in the particle distribution scatter plot are projected onto the dimension of the feature parameter to obtain the curve.

6. A sample detection device, characterized in that, The device includes: The initial screening module is used to determine the set of target particles of the lymphocyte category in the sample to be tested; wherein the set of target particles includes at least lymphocytes and nucleated red blood cells; The data processing module is used to determine a corresponding curve based on the characteristic parameters of each particle in the target particle set; wherein the curve represents the relationship between the number of particles and the characteristic parameters, which are particle volume and / or particle internal complexity; in response to the existence of two peaks in the curve, the module determines the location of the trough of the curve, including: determining a boundary line parallel to the horizontal axis in the curve within a preset proportion range of the vertical axis corresponding to the highest peak; and determining the trough of the curve above the boundary line; the preset proportion range is 5%~15%; The classification module is used to determine that particles in the target particle set whose particle feature parameters are less than the feature parameters corresponding to the trough position are nucleated red blood cells.

7. A sample detection device, characterized in that, The sample detection device includes a processor and a memory coupled to each other; the memory stores a computer program, and the processor executes the computer program to implement the steps of the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program data that, when executed by a processor, implements the steps of the method as described in any one of claims 1-5.

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