Lipid detection device, lipid detection method, electronic apparatus, and storage medium

Abstract

The invention discloses a lipid detection device, a lipid detection method, electronic equipment and a storage medium, the lipid detection device comprises: a detection module for detecting particles in a sample to obtain fluorescence detection data, front astigmatism detection data and lateral astigmatism detection data of the particles; the processing module is used for determining particles in a target area in all the particles based on at least one of the fluorescence detection data, the front astigmatism detection data and the lateral astigmatism detection data; and performing lipid detection on the sample based on the front astigmatism detection data and the lateral astigmatism detection data of the particles in the target area. Based on the mode, the accuracy of lipid detection can be improved.

Description

Technical Field

[0001] This application relates to the field of detection technology, and in particular to a lipid detection device, a lipid detection method, an electronic device, and a storage medium. Background Art

[0002] In the prior art, if there are lipids in a sample, it is likely to have a relatively serious impact on the results of hemoglobin detection, white blood cell detection, and other types of sample detections, thus resulting in incorrect sample detection results.

[0003] However, due to the reason of its own algorithm, the existing lipid detection device cannot form a relatively accurate judgment on the lipids in the sample, that is, the existing lipid detection device has a low detection accuracy for whether there are lipids in the sample. Summary of the Invention

[0004] The main technical problem to be solved by this application is how to improve the accuracy of lipid detection.

[0005] To solve the above technical problem, the first technical solution adopted by this application is: a lipid detection device, including: a detection module for detecting particles in a sample to obtain fluorescence detection data, forward scatter detection data, and side scatter detection data of the particles; a processing module for: determining particles in a target region among all particles based on at least one of the fluorescence detection data, forward scatter detection data, and side scatter detection data; and performing lipid detection on the sample based on the forward scatter detection data and side scatter detection data of the particles in the target region.

[0006] Among them, the anterior astigmatism detection data includes the anterior astigmatism intensity of the particles in the corresponding target area, and the lateral astigmatism detection data includes the lateral astigmatism intensity of the particles in the corresponding target area; based on the anterior astigmatism detection data and the lateral astigmatism detection data of the particles in the target area, lipid detection is performed on the sample, including: constructing a scatter plot of the scattered light of the particles in the target area based on the anterior astigmatism detection data and the lateral astigmatism detection data of the particles in all target areas; the first-dimensional coordinate axis of the scattered light scatter plot is the anterior astigmatism intensity, and the second-dimensional coordinate axis is the lateral astigmatism intensity; evenly dividing all areas of the scattered light scatter plot into multiple areas, and constructing a three-dimensional statistical chart of the areas; in the three-dimensional statistical chart, the first-dimensional coordinate axis is the average anterior astigmatism intensity of the particles in the target area in the corresponding area, the second-dimensional coordinate axis is the average lateral astigmatism intensity of the particles in the target area in the corresponding area, and the third-dimensional coordinate axis is the number of particles in the target area in the corresponding area; determining the area where the number of particles in the target area in the three-dimensional statistical chart is greater than a preset number threshold as the target area; determining the correlation coefficient between the anterior astigmatism and the lateral astigmatism of the particles in the target area based on the average anterior astigmatism intensity and the average lateral astigmatism intensity of all target areas; in response to the correlation coefficient being greater than a preset correlation coefficient threshold, it is determined that the sample contains lipids.

[0007] Among them, determining the correlation coefficient between the anterior astigmatism and the lateral astigmatism of the particles in the target area based on the average anterior astigmatism intensity and the average lateral astigmatism intensity of all target areas includes: obtaining the first anterior astigmatism average value based on the average value of the average anterior astigmatism intensity of all target areas; obtaining the first lateral astigmatism average value based on the average value of the average lateral astigmatism intensity of all target areas; performing covariance calculation based on the average anterior astigmatism intensity of all target areas, the average lateral astigmatism intensity of all target areas, the first anterior astigmatism average value, and the first lateral astigmatism average value to obtain the first covariance between the anterior astigmatism and the lateral astigmatism of the particles in the target area; obtaining the first anterior astigmatism standard deviation based on the standard deviation of the average anterior astigmatism intensity of all target areas; obtaining the first lateral astigmatism standard deviation based on the standard deviation of the average lateral astigmatism intensity of all target areas; performing correlation coefficient calculation based on the first covariance, the first anterior astigmatism standard deviation, and the first lateral astigmatism standard deviation to obtain the correlation coefficient between the anterior astigmatism and the lateral astigmatism of the particles in the target area.

[0008] Among them, before constructing a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and the lateral scattered light detection data of the particles in all target areas, lipid detection is performed on the sample based on the forward scattered light detection data and the lateral scattered light detection data of the particles in the target area, and it further includes: obtaining the concentration of the particles in the target area; the concentration of the particles in the target area is a value obtained by dividing the number of particles in the target area by the number of all particles; constructing a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and the lateral scattered light detection data of the particles in all target areas, including: in response to the concentration of the particles in the target area being greater than the first preset concentration threshold, constructing a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and the lateral scattered light detection data of the particles in all target areas.

[0009] Among them, after obtaining the concentration of the particles in the target area, constructing a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and the lateral scattered light detection data of the particles in all target areas, including: in response to the concentration of the particles in the target area being greater than the preset concentration threshold, constructing a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and the lateral scattered light detection data of the particles in all target areas; lipid detection is performed on the sample based on the forward scattered light detection data and the lateral scattered light detection data of the particles in the target area, and it further includes: in response to the concentration of the particles in the target area not being greater than the preset concentration threshold, determining that the correlation coefficient between the forward scattered light and the lateral scattered light of the particles in the target area is a value not greater than the preset correlation coefficient threshold.

[0010] Among them, detecting the particles in the sample to obtain the fluorescence detection data, forward scattered light detection data, and lateral scattered light detection data of the particles, including: adding the sample into the reaction cell, adding the lysing agent into the reaction cell, adding the fluorescent reagent into the reaction cell, and stirring the mixture in the reaction cell to obtain the sample; inputting the sample into the flow cell to emit a detection beam to the particles in the sample, exciting to generate a fluorescent beam, and scattering to generate a forward scattered beam and a lateral scattered beam; generating the fluorescence detection data based on the fluorescent beam, generating the forward scattered light detection data based on the forward scattered beam, and generating the lateral scattered light detection data based on the lateral scattered beam.

[0011] Among them, the target area is the area where the ghost particles are located, and the particles in the target area are ghost particles; and / or, determining the particles in the target area among all the particles based on at least one of the fluorescence detection data, forward scattered light detection data, and lateral scattered light detection data, including: determining the ghost particles among all the particles based on the fluorescence detection data.

[0012] Among them, the fluorescence detection data includes the fluorescence signal intensity and fluorescence intensity of the corresponding particles; based on the fluorescence detection data, determining the ghost particles among all the particles includes: constructing a fluorescence scatter plot of the particles based on the fluorescence signals and fluorescence intensities of all the particles; wherein, the first-dimensional coordinate axis of the fluorescence scatter plot is the fluorescence intensity, and the second-dimensional coordinate axis is the fluorescence signal intensity; determining the ghost particles among all the particles based on the fluorescence scatter plot.

[0013] To solve the above technical problems, the second technical solution adopted by this application is: a lipid detection method, including: detecting the particles in the sample to obtain the fluorescence detection data, forward scatter detection data, and side scatter detection data of the particles; based on the fluorescence detection data, determining the particles in the target region among all the particles; performing lipid detection on the sample based on the forward scatter detection data and side scatter detection data of the particles in the target region.

[0014] To solve the above technical problems, the third technical solution adopted by this application is: an electronic device, including: a memory and a processor; the memory is used to store program instructions, and the processor is used to execute the program instructions to implement the above method.

[0015] To solve the above technical problems, the fourth technical solution adopted by this application is: a computer-readable storage medium, the computer-readable storage medium stores program instructions, and when the program instructions are executed by the processor, the above method is implemented.

[0016] The beneficial effect of this application lies in: different from the prior art, in the technical solution of this application, the detection module detects each particle in the sample to obtain the corresponding fluorescence detection data, forward scatter detection data, and side scatter detection data of the particle, and the processing module can classify the particles in the sample based on at least one of the fluorescence detection data, forward scatter detection data, and side scatter detection data, so as to determine the particles in the target region among all the particles, and then can process and analyze all the particles in the target region and their corresponding forward scatter detection data and side scatter detection data to realize lipid detection on the sample. Based on the above method, according to the principle of Mie scattering, when there is lipid in the sample, when the particle sizes of the lipid-formed particles are different, the intensities of the forward scatter light and side scatter light generated by their scattering after being illuminated have different characteristics. Therefore, by processing and analyzing the forward scatter detection data and side scatter detection data corresponding to the particles in the target region, it is possible to accurately determine whether there is lipid in the particles in the target region, improving the accuracy of lipid detection. Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0018] Figure 1 is one of the schematic structural diagrams of an embodiment of the lipid detection device of the present application;

[0019] Figure 2 is the second of the schematic structural diagrams of an embodiment of the lipid detection device of the present application;

[0020] Figure 3 is a schematic diagram of an embodiment of the scattered light scatter plot of the present application;

[0021] Figure 4 is a schematic diagram of an embodiment of the three-dimensional statistical chart of the present application;

[0022] Figure 5 is a schematic flowchart of an embodiment of the lipid detection method of the present application;

[0023] Figure 6 is a schematic structural diagram of an embodiment of the electronic device of the present application;

[0024] Figure 7 is a schematic structural diagram of an embodiment of the computer-readable storage medium of the present application. Detailed Embodiments

[0025] The following will further describe the present application in detail in conjunction with the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are only used to illustrate the present application, but do not limit the scope of the present application. Similarly, the following embodiments are only some embodiments of the present application rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0026] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in conjunction with the embodiments can be included in at least one embodiment of the present application. The phrase appears at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the present application, it should be noted that, unless otherwise clearly specified and defined, the terms "installation", "setting", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be determined according to specific circumstances.

[0028] The present application first proposes a lipid detection device. Refer to Figure 1 , Figure 1 which is one of the schematic structural diagrams of an embodiment of the lipid detection device of the present application. As shown in Figure 1 , the lipid detection device 10 includes a detection module 11 and a processing module 12.

[0029] The detection module 11 is used to detect the particles in the sample to obtain the fluorescence detection data, forward scatter detection data, and side scatter detection data of the particles.

[0030] Among them, the detection module 11 can specifically be a flow cytometry device or other detection devices that can detect the particles in the sample to obtain the corresponding fluorescence detection data, forward scatter detection data, and side scatter detection data, which is not limited herein.

[0031] The fluorescence detection data can specifically be the detection data corresponding to the fluorescence beam generated by irradiating the particles with a laser, and the forward scatter detection data and side scatter detection data can be the detection data corresponding to the scattered light at different angles generated by irradiating the particles with a laser.

[0032] The processing module 12 is used for:

[0033] Based on at least one of the fluorescence detection data, forward scatter detection data, and side scatter detection data, determine the particles in the target region among all the particles.

[0034] Based on the forward scatter detection data and side scatter detection data of the particles in the target region, perform lipid detection on the sample.

[0035] Among them, through the fluorescence detection data of all the particles, combined with the characteristics of at least one of the fluorescence detection data, forward scatter detection data, and side scatter detection data of the particles in the target region, five-category classification of the sample can be realized, lymphocytes, monocytes, eosinophils, basophils, and neutrophils among all the particles can be determined, and the remaining particles in the target region among all the particles can be determined, thereby realizing the screening of the particles in the target region from all the particles.

[0036] The target region can specifically be a region where lipids are present or may be present. For example, the target region can be the region where ghost particles are located, or the region where neutrophils are located, or the region where eosinophils are located. Specifically, it can also be other regions where lipids may exist, which is not limited here. Among them, the particles in the target region can specifically be ghost particles, or other particle clusters containing lipid particles, which is not limited here. The main component of ghost particles is usually the cell membrane structure that remains in its original shape and size after the erythrocytes are treated with hypotonic solution and the plasma membrane ruptures.

[0037] According to the principle of Mie scattering, when light is incident on particles, the light will pass through the particles to form forward scattered light and side scattered light. When the size relationship between the size of the particles and the wavelength of the light incident on the particles is different, the light intensity of the forward scattered light, the light intensity of the side scattered light, and the light intensity ratio between the forward scattered light and the side scattered light will all be different. For example, when the particles are relatively small, the light intensity of the forward scattered light and the light intensity of the side scattered light formed are both small, while when the particles are relatively large, the light intensity of the forward scattered light and the light intensity of the side scattered light formed are both large. In addition, for particles in some particle size ranges, the light intensity of the forward scattered light is greater than the light intensity of the side scattered light, and for particles in some particle size ranges, the light intensity of the forward scattered light is less than the light intensity of the side scattered light. The above are only examples, and the actual situation needs to be determined in combination with the wavelength of the light incident on the particles and the particles of the actual sample, which is not limited here.

[0038] Based on the above principle, by processing and analyzing the forward scattered light detection data and side scattered light detection data of the particles in the target region, it can be determined whether there are particles in the particles in the target region that conform to the scattered light characteristics of lipid particles, so as to realize the lipid detection of the sample.

[0039] Specifically, referring to Figure 2 , Figure 2 is the second structural schematic diagram of an embodiment of the lipid detection device of the present application. As shown in Figure 2 , the detection module 11 includes a semiconductor laser, a sheath flow device, an incubation pool, and an optical detection device group.

[0040] A sample (such as a blood sample) can be added to the incubation pool, and then a hemolytic agent and a fluorescent dye are added, stirred and mixed, and the mixed sample solution is input into the sheath flow device. The semiconductor laser emits laser light to the particles flowing through the sheath flow device one by one, exciting the generation of fluorescent light beams, forward scattered light beams, and side scattered light beams. Corresponding photoelectric sensors can be respectively set in the optical detection device group to receive the fluorescent light beam, the forward scattered light beam, and the side scattered light beam, so as to respectively generate the fluorescence signal, forward scattered light signal, and side scattered light signal corresponding to the particles.

[0041] The processing module 12 includes a driving device, a digital signal gain group, a controller, and a memory.

[0042] The fluorescence signal, forward scattered light signal, and side scattered light signal can be gain-processed by a digital signal booster group and sent to a controller. The controller processes and analyzes them to obtain fluorescence detection data, forward scattered light detection data, and side scattered light detection data, and stores the obtained data in a memory.

[0043] The controller can also control a driving device to perform relevant control or other control on a sheath flow device for flow detection, which is not limited here.

[0044] Different from the prior art, in the technical solution of this application, the detection module detects each particle in a sample to obtain fluorescence detection data, forward scattered light detection data, and side scattered light detection data corresponding to the particle. The processing module can classify the particles in the sample based on at least one of the fluorescence detection data, forward scattered light detection data, and side scattered light detection data, so as to determine the particles in the target area among all the particles. Furthermore, the particles in all the target areas and their corresponding forward scattered light detection data and side scattered light detection data can be processed and analyzed to realize lipid detection in the sample. Based on the above method, according to the principle of Mie scattering, when there is lipid in the sample and the particle size of the lipid-formed particles is different, the light intensity of the forward scattered light and the light intensity of the side scattered light generated after being irradiated by light have different characteristics. Therefore, by processing and analyzing the forward scattered light detection data and side scattered light detection data corresponding to the particles in the target area, it can be accurately determined whether there is lipid in the particles in the target area, improving the accuracy of lipid detection.

[0045] In one embodiment, the target area is the area where the ghost particles are located, and the particles in the target area are ghost particles.

[0046] And / or

[0047] Determining the particles in the target area among all the particles based on at least one of the fluorescence detection data, forward scattered light detection data, and side scattered light detection data includes: determining the ghost particles among all the particles based on the fluorescence detection data.

[0048] Specifically, there may be a situation where ghost particles and lipid particles coexist in the area where the ghost particles are located. Therefore, lipid detection can be realized by detecting the ghost particles in the area where the ghost particles are located.

[0049] When the particles in the target area are ghost particles, the ghost particles can be identified through the fluorescence detection data of each particle, and then corresponding lipid detection can be performed based on the ghost particles.

[0050] Optionally, the fluorescence detection data includes the fluorescence signal intensity and fluorescence light intensity of the corresponding particle.

[0051] Based on the fluorescence detection data, determine the ghost particles among all the particles, including:

[0052] Based on the fluorescence signals and fluorescence intensities of all the particles, construct a fluorescence scatter plot of the particles. Among them, the first-dimensional coordinate axis of the fluorescence scatter plot is the fluorescence intensity, and the second-dimensional coordinate axis is the fluorescence signal intensity.

[0053] Based on the fluorescence scatter plot, determine the ghost particles among all the particles.

[0054] Specifically, in the fluorescence scatter plot, the particles with signal intensities within the preset signal intensity range of the ghost particles and fluorescence intensities within the preset fluorescence intensity range of the ghost particles can be determined as ghost particles. For example, the particles with signal intensities in the lowest range can be determined as ghost particles, and the ghost particles can also be determined according to other appropriate criteria, which are not limited here.

[0055] Based on the above method, the accuracy of the ghost particles determined from all the particles can be improved, and further the accuracy of lipid detection can be improved.

[0056] In one embodiment, the forward scattered light detection data includes the forward scattered light intensity of the particles in the corresponding target area, and the side scattered light detection data includes the side scattered light intensity of the particles in the corresponding target area.

[0057] Based on the forward scattered light detection data and side scattered light detection data of the particles in the target area, perform lipid detection on the sample, including:

[0058] Based on the forward scattered light detection data and side scattered light detection data of all the particles in the target area, construct a scattered light scatter plot of the particles in the target area. Among them, the first-dimensional coordinate axis of the scattered light scatter plot is the forward scattered light intensity, and the second-dimensional coordinate axis is the side scattered light intensity.

[0059] Evenly divide all the areas of the scattered light scatter plot into multiple areas, and construct a three-dimensional statistical chart of the areas. Among them, in the three-dimensional statistical chart, the first-dimensional coordinate axis is the average forward scattered light intensity of the particles in the target area within the corresponding area, the second-dimensional coordinate axis is the average side scattered light intensity of the particles in the target area within the corresponding area, and the third-dimensional coordinate axis is the number of the particles in the target area within the corresponding area.

[0060] Determine the areas in the three-dimensional statistical chart where the number of the particles in the target area is greater than the preset number threshold as the target areas.

[0061] Based on the average forward scattered light intensity and average side scattered light intensity of all the target areas, determine the correlation coefficient between the forward scattered light and side scattered light of the particles in the target area.

[0062] In response to the correlation coefficient being greater than the preset correlation coefficient threshold, it is determined that the sample contains lipids.

[0063] Specifically, after determining the particles in the target region among all the particles, after obtaining the forward astigmatism detection data and the side astigmatism detection data respectively corresponding to the particles in each target region, a scatter plot of scattered light with the first dimension axis being the forward astigmatism intensity and the second dimension axis being the side astigmatism intensity can be constructed, and corresponding points can be determined on the graph based on the forward astigmatism intensity and the side astigmatism intensity corresponding to the particles in each target region, so as to form as Figure 3 shown in the scatter plot of scattered light, and the points corresponding to the forward astigmatism intensity and the side astigmatism intensity of the particles in each target region on the graph.

[0064] The entire area of the scatter plot of scattered light is evenly divided into multiple regions. For example, the scatter plot of scattered light can be a 4096*4096 scatter plot, and the 4096*4096 scatter plot can be evenly divided into 128*128 grid regions, and the number of particles in the target region existing in each grid region is counted.

[0065] The average value of the forward astigmatism intensity of all the particles in the target region within the grid region can be used as its average forward astigmatism intensity to obtain the first dimension coordinate in the three-dimensional statistical graph corresponding to the grid region, and the average value of the side astigmatism intensity of all the particles in the target region within the grid region can be used as its average side astigmatism intensity to obtain the second dimension coordinate in the three-dimensional statistical graph corresponding to the grid region, and the number of all the particles in the target region within the grid region can be used as the third dimension coordinate in the three-dimensional statistical graph corresponding to the grid region, and the three-dimensional coordinates of each grid region in the three-dimensional statistical graph are determined.

[0066] Based on the three-dimensional coordinates, the position of each grid region on the three-dimensional statistical graph is determined.

[0067] The grid regions in the three-dimensional statistical graph with the third dimension coordinate greater than the preset quantity threshold can be determined as the target region.

[0068] Based on the average forward astigmatism intensity and the average side astigmatism intensity of all the target regions, the correlation coefficient between the forward astigmatism and the side astigmatism of the particles in the target region is determined. Subsequently, it can be determined whether there are lipid particles in the particles in the target region according to the correlation coefficient, and then the lipid detection of the sample is realized.

[0069] The preset correlation coefficient threshold can specifically be 0.55, 0.75, 0.95 or other values, which can be determined according to actual needs and are not limited here. When the correlation coefficient is greater than the preset correlation coefficient threshold, it can be determined that the sample contains lipids and corresponding alarm or prompt information is output.

[0070] Based on the above method, data processing can be performed only on the relevant parameters of the scattered light corresponding to the area where the particles in the target area of the scattered light scatter plot and the three-dimensional statistical chart are relatively dense, so as to obtain the lipid detection result, reduce the possibility of the accuracy of lipid detection being affected by a small number of abnormal conditions, and improve the accuracy of lipid detection.

[0071] Optionally, before determining the area where the number of particles in the target area of the three-dimensional statistical chart is greater than the preset number threshold as the target area, based on the forward scattered light detection data and the side scattered light detection data of the particles in the target area, performing lipid detection on the sample may further include:

[0072] Performing erosion or other image processing on the three-dimensional statistical chart to remove stray points in the three-dimensional statistical chart, such as Figure 4 In the three-dimensional statistical chart shown, the upper figure is obtained after erosion processing to get the lower figure. It can be seen that the stray points in the three-dimensional statistical chart can be effectively removed through erosion processing.

[0073] Based on the above method, the possibility of the accuracy of lipid detection being affected by a small number of abnormal conditions can be further reduced, and the accuracy of lipid detection can be improved.

[0074] Optionally, based on the average forward scattered light intensity and the average side scattered light intensity of all target areas, determining the correlation coefficient between the forward scattered light and the side scattered light of the particles in the target area includes:

[0075] Based on the average value of the average forward scattered light intensity of all target areas, obtaining the first forward scatter average value.

[0076] Based on the average value of the average side scattered light intensity of all target areas, obtaining the first side scatter average value.

[0077] Based on the average forward scattered light intensity of all target areas, the average side scattered light intensity of all target areas, the first forward scatter average value and the first side scatter average value, performing covariance calculation to obtain the first covariance between the forward scattered light and the side scattered light of the particles in the target area.

[0078] Based on the standard deviation of the average forward scattered light intensity of all target areas, obtaining the first forward scatter standard deviation.

[0079] Based on the standard deviation of the average side scattered light intensity of all target areas, obtaining the first side scatter standard deviation.

[0080] Based on the first covariance, the first forward scatter standard deviation and the first side scatter standard deviation, performing correlation coefficient calculation to obtain the correlation coefficient between the forward scattered light and the side scattered light of the particles in the target area.

[0081] Specifically, the average forward scattered light intensity of a single target area can be denoted as pfsc, and the average side scattered light intensity of a single target area can be denoted as pssc.

[0082] The average value of pfsc for all target regions is the first front scatter average value, denoted as mean_a.

[0083] The average value of pssc for all target regions is the first side scatter average value, denoted as mean_b.

[0084] The formula for covariance calculation is as follows:

[0085] cov1(pfsc, pssc) = {Σ[(pfsc(i) - mean_a) * (pfsc(i) - mean_b)]} / (n - 1)

[0086] Where pfsc(i) is the pfsc of the i-th target region, pssc(i) is the pssc of the i-th target region, n is the number of target regions, and cov1(pfsc, pssc) is the first covariance.

[0087] The standard deviation of pfsc for all target regions is the first front scatter standard deviation, denoted as std_a.

[0088] The standard deviation of pssc for all target regions is the first side scatter standard deviation, denoted as std_b.

[0089] The formula for correlation coefficient calculation is as follows:

[0090] core = cov1(pfsc, pssc) / (std_a * std_b)

[0091] Where core is the correlation coefficient between the front scatter and side scatter of the particles in the target region.

[0092] It should be noted that the above formula for correlation coefficient calculation is the Pearson correlation coefficient calculation formula. Other types of correlation coefficient calculation algorithms can also be used to calculate the correlation coefficient between the front scatter and side scatter of the particles in the target region, such as: Spearman rank correlation coefficient, Spearman rank correlation coefficient, chi-square test, point-biserial correlation coefficient, Cramer's V coefficient, etc., which are not limited here.

[0093] Based on the above method, for the target regions with more particles in the target regions obtained after screening, the correlation coefficient between the front scatter and side scatter of the particles in the target region can be calculated, and a higher-accuracy correlation coefficient between the front scatter and side scatter of the particles in the target region can be obtained. Furthermore, the accuracy of the lipid detection result based on the correlation coefficient between the front scatter and side scatter of the particles in the target region and the preset correlation coefficient threshold is higher, improving the reliability of lipid detection.

[0094] Optionally, before constructing a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and side scattered light detection data of the particles in all target areas, lipid detection is performed on the sample based on the forward scattered light detection data and side scattered light detection data of the particles in the target area, and it further includes:

[0095] Obtain the concentration of the particles in the target area. Wherein, the concentration of the particles in the target area is the value obtained by dividing the number of particles in the target area by the number of all particles.

[0096] Constructing a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and side scattered light detection data of the particles in all target areas includes:

[0097] In response to the concentration of the particles in the target area being greater than the first preset concentration threshold, construct a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and side scattered light detection data of the particles in all target areas.

[0098] Specifically, based on the received fluorescence detection data, forward scattered light detection data, and side scattered light detection data, the total number of all particles detected in this sample detection can be determined, and based on the fluorescence detection data, the particles in the target area among all these particles and their number can be determined. By dividing the number of particles in the target area by this total number, the concentration of the particles in the target area can be obtained.

[0099] Based on the above method, the above-mentioned scatter plot construction and subsequent steps can be carried out only when the concentration of the particles in the target area is greater than the first preset concentration threshold, that is, when the concentration of the particles in the target area is relatively high. When the concentration of the particles in the target area is relatively high, the correlation coefficient can be calculated by a relatively accurate correlation coefficient calculation method, reducing the possibility of low accuracy of lipid detection results caused by too high a concentration of the particles in the target area, and improving the reliability of lipid detection.

[0100] Further, after obtaining the concentration of the particles in the target area, constructing a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and side scattered light detection data of the particles in all target areas includes:

[0101] In response to the concentration of the particles in the target area being greater than the preset concentration threshold, construct a scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and side scattered light detection data of the particles in all target areas.

[0102] Lipid detection is performed on the sample based on the forward scattered light detection data and side scattered light detection data of the particles in the target area, and it further includes:

[0103] In response to the concentration of the particles in the target area being not greater than a preset concentration threshold, determine that the correlation coefficient between the forward scattered light and the side scattered light of the particles in the target area is a value not greater than a preset correlation coefficient threshold.

[0104] Specifically, when the concentration of the particles in the target area is greater than the preset concentration threshold, the steps of constructing the scatter plot of the scattered light of the particles in the target area based on the forward scattered light detection data and the side scattered light detection data of all the particles in the target area described in the foregoing embodiments and subsequent steps can be executed.

[0105] When the concentration of the particles in the target area is not greater than the preset concentration threshold, it can be determined that the correlation coefficient between the forward scattered light and the side scattered light of the particles in the target area is a value not greater than the preset correlation coefficient threshold, so as to execute the step of determining that the sample does not contain lipids in response to the correlation coefficient not being greater than the preset correlation coefficient threshold, and determine that the sample does not contain lipids.

[0106] Based on the forward scattered light detection data and the side scattered light detection data of the particles in the target area, lipid detection of the sample may further include: in response to the correlation coefficient not being greater than the preset correlation coefficient threshold, determining that the sample does not contain lipids.

[0107] Based on the above method, the steps of constructing the scatter plot of the scattered light and subsequent steps can be performed when the concentration of the particles in the target area itself is relatively high to achieve lipid detection, and when the concentration of the particles in the target area itself is relatively low, it can be directly determined that there is no lipid in the sample, reducing the amount of calculation and improving the efficiency of lipid detection.

[0108] In one embodiment, detecting the particles in the sample to obtain the fluorescence detection data, forward scattered light detection data, and side scattered light detection data of the particles includes:

[0109] Adding the sample into the reaction cell, adding a hemolytic agent into the reaction cell, adding a fluorescent reagent into the reaction cell, and stirring the mixture in the reaction cell to obtain the sample.

[0110] Inputting the sample into the flow cell to emit a detection beam to the particles in the sample, exciting to generate a fluorescent beam, and scattering to generate a forward scattered beam and a side scattered beam.

[0111] Generating fluorescence detection data based on the fluorescent beam, generating forward scattered light detection data based on the forward scattered beam, and generating side scattered light detection data based on the side scattered beam.

[0112] Specifically, as Figure 2 shown, the detection module 11 includes a semiconductor laser, a sheath flow device, an incubation pool, and an optical detection device group.

[0113] A sample (such as a blood sample) can be added to an incubation pool, followed by adding a hemolytic agent and a fluorescent dye, stirring and mixing them, and inputting the obtained sample liquid into a sheath flow device. A semiconductor laser emits laser light to the particles flowing through the sheath flow device one by one, exciting the generation of fluorescent light beams, forward scattered light beams, and side scattered light beams. Corresponding photoelectric sensors can be respectively set in the optical detection device group to receive the fluorescent light beam, forward scattered light beam, and side scattered light beam, so as to respectively generate the fluorescent signal, forward scattered light signal, and side scattered light signal corresponding to the particles.

[0114] The processing module 12 includes a driving device, a digital signal gain group, a controller, and a memory.

[0115] The fluorescent signal, forward scattered light signal, and side scattered light signal can be subjected to gain processing by the digital signal gain group and sent to the controller. The controller obtains fluorescent detection data, forward scattered light detection data, and side scattered light detection data through processing and analysis, and stores the obtained data in the memory.

[0116] The controller can also control the driving device to perform relevant control or other control for flow cytometry detection on the sheath flow device, which is not limited here.

[0117] This application also proposes a lipid detection method. Refer to Figure 5 , Figure 5 which is a schematic flowchart of an embodiment of the lipid detection method of this application. As Figure 5 shown, the lipid detection method includes:

[0118] Step S11: Detect the particles in the sample to obtain the fluorescent detection data, forward scattered light detection data, and side scattered light detection data of the particles.

[0119] Step S12: Based on at least one of the fluorescent detection data, forward scattered light detection data, and side scattered light detection data, determine the particles in the target region among all the particles.

[0120] Step S13: Based on the forward scattered light detection data and side scattered light detection data of the particles in the target region, perform lipid detection on the sample.

[0121] Specifically, the lipid detection method may further include the steps performed by the processing module 12 described in any of the foregoing embodiments, which will not be elaborated here.

[0122] Different from the prior art, in the technical solution of this application, the detection module detects each particle in the sample to obtain the fluorescence detection data, forward scatter detection data, and side scatter detection data corresponding to the particle. The processing module can classify the particles in the sample based on at least one of the fluorescence detection data, forward scatter detection data, and side scatter detection data, so as to determine the particles in the target region among all the particles. Furthermore, the particles in all the target regions and their corresponding forward scatter detection data and side scatter detection data can be processed and analyzed to realize lipid detection of the sample. Based on the above method, according to the principle of Mie scattering, when there is lipid in the sample and the particle sizes of the lipid-formed particles are different, the intensities of the forward scatter light and side scatter light generated by scattering after being illuminated have different characteristics. Therefore, by processing and analyzing the forward scatter detection data and side scatter detection data corresponding to the particles in the target region, it is possible to accurately determine whether there is lipid in the particles in the target region, improving the accuracy of lipid detection.

[0123] This application also proposes an electronic device. Refer to Figure 6 , Figure 6 which is a schematic structural diagram of an embodiment of the electronic device of this application. As Figure 6 shown, the electronic device 20 includes a processor 21, a memory 22, and a bus 23.

[0124] The processor 21 and the memory 22 are respectively connected to the bus 23. The memory 22 stores program instructions, and the processor 21 is configured to execute the program instructions to implement the lipid detection method in the above embodiment.

[0125] In this embodiment, the processor 21 can also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 21 may be an integrated circuit chip with signal processing capabilities. The processor 21 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor 21 can also be any conventional processor, etc.

[0126] Different from the prior art, in the technical solution of the present application, the detection module detects each particle in the sample to obtain the fluorescence detection data, forward scatter detection data, and side scatter detection data corresponding to the particle. The processing module can classify the particles in the sample based on at least one of the fluorescence detection data, forward scatter detection data, and side scatter detection data, so as to determine the particles in the target area among all the particles. Furthermore, the particles in all the target areas and their corresponding forward scatter detection data and side scatter detection data can be processed and analyzed to realize lipid detection of the sample. Based on the above method, according to the principle of Mie scattering, when there is lipid in the sample, when the particle sizes of the lipid-formed particles are different, the light intensities of the forward scatter and side scatter generated after being irradiated by light have different characteristics. Therefore, by processing and analyzing the forward scatter detection data and side scatter detection data corresponding to the particles in the target area, it is possible to accurately determine whether there is lipid in the particles in the target area, improving the accuracy of lipid detection.

[0127] The present application also proposes a computer-readable storage medium. Refer to Figure 7 , Figure 7 is a schematic structural diagram of an embodiment of the computer-readable storage medium of the present application. As Figure 7 shown, the computer-readable storage medium 30 stores program instructions 31 thereon. When the program instructions 31 are executed by a processor (not shown in the figure), the lipid detection method in the above embodiment is implemented.

[0128] The computer-readable storage medium 30 in this embodiment may be, but is not limited to, a U disk, an SD card, a PD optical drive, a mobile hard disk, a large-capacity floppy drive, a flash memory, a multimedia memory card, a server, a storage unit in an FPGA or an ASIC, etc.

[0129] Different from the prior art, in the technical solution of the present application, the detection module detects each particle in the sample to obtain the fluorescence detection data, forward scatter detection data, and side scatter detection data corresponding to the particle. The processing module can classify the particles in the sample based on at least one of the fluorescence detection data, forward scatter detection data, and side scatter detection data, so as to determine the particles in the target area among all the particles. Furthermore, the particles in all the target areas and their corresponding forward scatter detection data and side scatter detection data can be processed and analyzed to realize lipid detection of the sample. Based on the above method, according to the principle of Mie scattering, when there is lipid in the sample, when the particle sizes of the lipid-formed particles are different, the light intensities of the forward scatter and side scatter generated after being irradiated by light have different characteristics. Therefore, by processing and analyzing the forward scatter detection data and side scatter detection data corresponding to the particles in the target area, it is possible to accurately determine whether there is lipid in the particles in the target area, improving the accuracy of lipid detection.

[0130] In the description of the present application, the descriptions with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0131] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0132] Any process or method description shown in a flowchart or described in other ways herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered as a definitional sequence of executable instructions for implementing logical functions, and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device (which can be a personal computer, a server, a network device, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions). For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0134] The above description is only for the implementation modes of this application, and does not limit the patent scope of this application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of this application, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of this application by the same token.

Claims

1. A lipid detection device, characterized in that, it includes: a detection module for detecting particles in a sample to obtain fluorescence detection data, forward scatter detection data, and side scatter detection data of the particles; a processing module for: determining particles in a target region among all the particles based on at least one of the fluorescence detection data, forward scatter detection data, and side scatter detection data; performing lipid detection on the sample based on the forward scatter detection data and the side scatter detection data of the particles in the target region.

2. The lipid detection device according to claim 1, characterized in that, the forward scatter detection data includes the forward scatter light intensity of the particles in the corresponding target region, and the side scatter detection data includes the side scatter light intensity of the particles in the corresponding target region; the performing lipid detection on the sample based on the forward scatter detection data and the side scatter detection data of the particles in the target region includes: constructing a scatter plot of the scattered light of the particles in the target region based on the forward scatter detection data and the side scatter detection data of all the particles in the target region; wherein, the first-dimensional coordinate axis of the scatter plot of the scattered light is the forward scatter light intensity, and the second-dimensional coordinate axis is the side scatter light intensity; evenly dividing all regions of the scatter plot of the scattered light into multiple regions, and constructing a three-dimensional statistical chart of the regions; wherein, in the three-dimensional statistical chart, the first-dimensional coordinate axis is the average forward scatter light intensity of the particles in the target region in the corresponding region, the second-dimensional coordinate axis is the average side scatter light intensity of the particles in the target region in the corresponding region, and the third-dimensional coordinate axis is the number of the particles in the target region in the corresponding region; determining a region where the number of the particles in the target region in the three-dimensional statistical chart is greater than a preset number threshold as the target region; determining a correlation coefficient between the forward scatter and the side scatter of the particles in the target region based on the average forward scatter light intensity and the average side scatter light intensity of all the target regions; responding to the correlation coefficient being greater than a preset correlation coefficient threshold, and then determining that the sample contains lipids.

3. The lipid detection device according to claim 2, characterized in that, the determining a correlation coefficient between the forward scatter and the side scatter of the particles in the target region based on the average forward scatter light intensity and the average side scatter light intensity of all the target regions includes: obtaining a first forward scatter average value based on the average value of the average forward scatter light intensity of all the target regions; obtaining a first side scatter average value based on the average value of the average side scatter light intensity of all the target regions; performing covariance calculation based on the average forward scatter light intensity of all the target regions, the average side scatter light intensity of all the target regions, the first forward scatter average value, and the first side scatter average value to obtain a first covariance between the forward scatter and the side scatter of the particles in the target region; obtaining a first forward scatter standard deviation based on the standard deviation of the average forward scatter light intensity of all the target regions; Based on the standard deviation of the average side-scattered light intensity of all the target regions, a first side-scattering standard deviation is obtained; Based on the first covariance, the first forward-scattering standard deviation, and the first side-scattering standard deviation, a correlation coefficient calculation is performed to obtain the correlation coefficient between the forward-scattered light and the side-scattered light of the particles in the target region.

4. The lipid detection device according to claim 2 or 3, wherein, before constructing the scatter plot of the scattered light of the particles in the target region based on the forward-scattered light detection data and the side-scattered light detection data of the particles in all the target regions, the lipid detection of the sample based on the forward-scattered light detection data and the side-scattered light detection data of the particles in the target region further includes: obtaining the concentration of the particles in the target region; wherein, the concentration of the particles in the target region is a value obtained by dividing the number of the particles in the target region by the number of all the particles; The constructing of the scatter plot of the scattered light of the particles in the target region based on the forward-scattered light detection data and the side-scattered light detection data of the particles in all the target regions includes: in response to the concentration of the particles in the target region being greater than a first preset concentration threshold, constructing the scatter plot of the scattered light of the particles in the target region based on the forward-scattered light detection data and the side-scattered light detection data of the particles in all the target regions.

5. The lipid detection device according to claim 4, wherein, after obtaining the concentration of the particles in the target region, the constructing of the scatter plot of the scattered light of the particles in the target region based on the forward-scattered light detection data and the side-scattered light detection data of the particles in all the target regions includes: in response to the concentration of the particles in the target region being greater than a preset concentration threshold, constructing the scatter plot of the scattered light of the particles in the target region based on the forward-scattered light detection data and the side-scattered light detection data of the particles in all the target regions; The lipid detection of the sample based on the forward-scattered light detection data and the side-scattered light detection data of the particles in the target region further includes: in response to the concentration of the particles in the target region not being greater than the preset concentration threshold, determining that the correlation coefficient between the forward-scattered light and the side-scattered light of the particles in the target region is a value not greater than a preset correlation coefficient threshold.

6. The lipid detection device according to any one of claims 1 to 3, wherein, the detecting of the particles in the sample to obtain the fluorescence detection data, the forward-scattered light detection data, and the side-scattered light detection data of the particles includes: adding the sample into a reaction cell, adding a hemolytic agent into the reaction cell, adding a fluorescent reagent into the reaction cell, and stirring the mixture in the reaction cell to obtain the sample; inputting the sample into a flow cell to emit a detection beam to the particles in the sample, exciting to generate a fluorescent beam, and scattering to generate a forward-scattered beam and a side-scattered beam; generating the fluorescence detection data based on the fluorescent beam, generating the forward-scattered light detection data based on the forward-scattered beam, and generating the side-scattered light detection data based on the side-scattered beam.

7. The lipid detection device according to any one of claims 1 to 3, characterized in that, the target area is the area where the ghost particles are located, and the particles in the target area are the ghost particles; and / or, determining the particles in the target area among all the particles based on at least one of the fluorescence detection data, forward scattered light detection data, and side scattered light detection data, includes: determining the ghost particles among all the particles based on the fluorescence detection data.

8. The lipid detection device according to claim 7, characterized in that, the fluorescence detection data includes the fluorescence signal intensity and fluorescence intensity of the corresponding particles; determining the ghost particles among all the particles based on the fluorescence detection data, includes: constructing a fluorescence scatter plot of the particles based on the fluorescence signals and fluorescence intensities of all the particles; wherein, the first-dimensional coordinate axis of the fluorescence scatter plot is the fluorescence intensity, and the second-dimensional coordinate axis is the fluorescence signal intensity; determining the ghost particles among all the particles based on the fluorescence scatter plot.

9. A lipid detection method, characterized in that, includes: detecting the particles in the sample to obtain the fluorescence detection data, forward scattered light detection data, and side scattered light detection data of the particles; determining the particles in the target area among all the particles based on at least one of the fluorescence detection data, forward scattered light detection data, and side scattered light detection data; performing lipid detection on the sample based on the forward scattered light detection data and the side scattered light detection data of the particles in the target area.

10. An electronic device, characterized in that, includes: a memory and a processor; the memory is used to store program instructions, and the processor is used to execute the program instructions to implement the method according to claim 9.

11. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores program instructions, and when the program instructions are executed by a processor, the method according to claim 9 is implemented.

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