Sample analyzer and polymer particle identification method thereof

Abstract

The invention discloses a sample analyzer and a polymer particle identification method thereof. The sample analyzer includes an optical detection unit and a control unit. The optical detection unit is used for performing optical detection on a sample and comprises at least one optical detection channel; the control unit is connected with the optical detection unit, and the control unit is used for performing optical detection on particles in the sample through an optical detection channel of the optical detection unit to obtain optical detection pulses; acquiring a pulse parameter of the optical detection pulse; based on the pulse parameter of the optical detection pulse, obtaining pulse parameter comprehensive processing data; generating a histogram based on the pulse parameter comprehensive processing data; and obtaining a multimerization region of the particles based on the histogram. The sample analyzer provided by the invention has relatively high recognition accuracy on the polymer particles, and the sample detection accuracy of the sample analyzer can be improved.

Description

Technical Field

[0001] This application relates to the technical field of medical devices, and particularly to a sample analyzer and a method for identifying multi-polymer particles thereof. Background Art

[0002] When a sample analyzer performs immunoassay on a sample by flow cytometry fluorescence luminescence method, it can distinguish the signal regions of joint detection items based on parameters such as classification fluorescence and magnetic bead particle size. After identifying the signal regions of each item and obtaining the quantitative fluorescence information of each magnetic bead in the item, the concentration result of the corresponding item can be calculated.

[0003] However, during the actual detection process of the sample analyzer, aggregation of two or more particles in the sample may occur. When multi-polymer particles appear in the sample, it may misclassify the particles into a cluster number that does not belong to them during particle clustering, resulting in clustering errors and affecting the accuracy of the sample detection result. Summary of the Invention

[0004] This application provides a sample analyzer and a method for identifying multi-polymer particles thereof, so as to solve the technical problem in the prior art that when multi-polymer particles appear in the sample, it may misclassify the particles into a cluster number that does not belong to them during particle clustering, resulting in clustering errors and affecting the accuracy of the sample detection result.

[0005] To solve the above technical problem, one technical solution adopted by this application is: providing a sample analyzer, which includes an optical detection unit and a control unit. Among them, the optical detection unit is used to perform optical detection on the sample, and the optical detection unit includes at least one optical detection channel; the control unit is connected to the optical detection unit, and the control unit is used to: perform optical detection on the particles in the sample through the optical detection channel of the optical detection unit to obtain an optical detection pulse; obtain the pulse parameters of the optical detection pulse; obtain pulse parameter comprehensive processing data based on the pulse parameters of the optical detection pulse; generate a histogram based on the pulse parameter comprehensive processing data; obtain the multi-polymer region of the particles based on the histogram.

[0006] Further, the optical detection unit at least includes a forward scatter channel and a side scatter channel, and the control unit is further used to: perform optical detection on the particles in the sample through the forward scatter channel and the side scatter channel of the optical detection unit to obtain an optical detection pulse, where the optical detection pulse includes a forward detection pulse and a side detection pulse, and the pulse parameters of the optical detection pulse include the pulse parameters of the forward detection pulse and the pulse parameters of the side detection pulse.

[0007] Further, the pulse parameters of the forward detection pulse include the pulse height of the forward detection pulse, and the pulse parameters of the lateral detection pulse include the pulse height of the lateral detection pulse. The control unit is further configured to: perform data processing on the pulse height of the forward detection pulse and the pulse height of the lateral detection pulse through formula (1) to obtain the pulse parameter comprehensive processing data.

[0008] CH1 = (FSC1 × W1 + SSC1 × W2) / 2 (1)

[0009] Wherein, CH1 is the pulse parameter comprehensive processing data, FSC1 is the pulse height of the forward detection pulse, SSC1 is the pulse height of the lateral detection pulse, and W1 and W2 are weight values.

[0010] Further, the pulse parameters of the forward detection pulse include the pulse area of the forward detection pulse, and the pulse parameters of the lateral detection pulse include the pulse area of the lateral detection pulse. The control unit is further configured to: perform data processing on the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse through formula (2) to obtain the pulse parameter comprehensive processing data.

[0011] CH2 = (FSC2 × W3 + SSC2 × W4) / 2 (2)

[0012] Wherein, CH2 is the pulse parameter comprehensive processing data, FSC2 is the pulse height of the forward detection pulse, SSC2 is the pulse height of the lateral detection pulse, and W3 and W4 are weight values.

[0013] Further, the optical detection unit further includes: a classification fluorescence channel. The control unit is further configured to: perform optical detection on the particles in the sample through the classification fluorescence channel of the optical detection unit, and remove the polymer particles corresponding to the classification fluorescence channel based on the polymer region of the particles; obtain the classification information of the particles based on the detection data of the classification fluorescence channel after removal.

[0014] Further, the control unit is further configured to: find the trough position between the two largest peaks in the histogram to obtain the boundary position between the polymer region of the particles and the normal particle region, and obtain the polymer region of the particles based on the boundary position.

[0015] Further, the pulse parameters of the forward detection pulse further include the pulse area of the forward detection pulse, and the pulse parameters of the lateral detection pulse further include the pulse area of the lateral detection pulse. The control unit is further configured to: remove the aggregation region of the particles, and when it is confirmed that the aggregation region of the particles corresponding to the sample has not been completely cleared, obtain the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse; based on the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse, comprehensively process the data with the new pulse parameters; generate a new histogram based on the comprehensive processing of the data with the new pulse parameters; obtain the aggregation region of the new particles based on the new histogram, and remove the aggregation region of the new particles.

[0016] Further, the optical detection unit further includes a forward scattering channel, a lateral scattering channel, a classification fluorescence channel, and a quantitative fluorescence channel. The sample analyzer further includes a particle cluster recognition module. The optical detection unit is configured to perform optical detection on the particles in the sample through the forward scattering channel, the lateral scattering channel, the classification fluorescence channel, and the quantitative fluorescence channel to obtain optical detection pulse data; the particle cluster recognition module is configured to identify the pulses of each corresponding particle from the optical detection pulse data to obtain particle pulses; obtain the median of a preset number of particle pulses with the smallest pulse width in the forward scattering channel; confirm whether there are aggregated particles based on the pulse width and the median of the particle pulses in the forward scattering channel; when it is confirmed that there are aggregated particles, remove the aggregated particles corresponding to at least one of the forward scattering channel, the lateral scattering channel, the classification fluorescence channel, and the quantitative fluorescence channel.

[0017] Further, the sample analyzer further includes: a baseline removal module, a pulse recognition module, and a multi-peak wave recognition module. The baseline removal module is configured to perform data processing on the forward raw detection data, the lateral raw detection data, the classification fluorescence raw detection data, and the quantitative fluorescence raw detection data obtained by the optical detection unit to respectively obtain forward baseline-removed data, lateral baseline-removed data, classification fluorescence baseline-removed data, and quantitative fluorescence baseline-removed data; the pulse recognition module is configured to perform pulse recognition on the forward baseline-removed data, the lateral baseline-removed data, the classification fluorescence baseline-removed data, and the quantitative fluorescence baseline-removed data to respectively obtain forward baseline-removed pulses, lateral baseline-removed pulses, classification fluorescence baseline-removed pulses, and quantitative fluorescence baseline-removed pulses, and obtain the pulse width and pulse height of the forward baseline-removed pulses, the pulse width and pulse height of the lateral baseline-removed pulses, the pulse width and pulse height of the classification fluorescence baseline-removed pulses, and the pulse width and pulse height of the quantitative fluorescence baseline-removed pulses to obtain optical detection pulse data; the multi-peak wave recognition module is configured to perform multi-peak wave recognition on the forward baseline-removed pulses whose pulse width meets a preset range; when it is confirmed that the forward baseline-removed pulse is a multi-peak wave, it is confirmed that the particle corresponding to the multi-peak wave is an aggregated particle.

[0018] To solve the above technical problems, another technical solution adopted in this application is as follows: Provide a method for identifying multi-polymer particles of a sample analyzer. Based on the sample analyzer in any of the above embodiments, the identification method includes: optically detecting particles in the sample through the optical detection channels of the optical detection unit to obtain optical detection pulses; acquiring the pulse parameters of the optical detection pulses; obtaining pulse parameter comprehensive processing data based on the pulse parameters of the optical detection pulses; generating a histogram based on the pulse parameter comprehensive processing data; and obtaining the multi-polymer region of the particles based on the histogram.

[0019] To solve the above technical problems, another technical solution adopted in this application is as follows: Provide a sample analyzer, which includes: an optical detection unit and a particle cluster identification module. Among them, the optical detection unit includes a forward scatter channel, a side scatter channel, a classification fluorescence channel, and a quantitative fluorescence channel. The optical detection unit is used to optically detect particles in the sample through the forward scatter channel, the side scatter channel, the classification fluorescence channel, and the quantitative fluorescence channel to obtain optical detection pulse data; the particle cluster identification module is used to identify the pulses of each corresponding particle from the optical detection pulse data to obtain particle pulses; acquire the median of a preset number of particle pulses with the smallest pulse width in the forward scatter channel; confirm whether there are multi-polymer particles based on the pulse width and median of the particle pulses in the forward scatter channel. When it is confirmed that there are multi-polymer particles, remove the multi-polymer particles corresponding to at least one of the forward scatter channel, the side scatter channel, the classification fluorescence channel, and the quantitative fluorescence channel.

[0020] The beneficial effects of this application are as follows: Different from the prior art, the sample analyzer of this application includes: an optical detection unit and a control unit. The optical detection unit is used to optically detect the sample, and the optical detection unit includes at least one optical detection channel; the control unit is used to: optically detect particles in the sample through the optical detection channels of the optical detection unit to obtain optical detection pulses; acquire the pulse parameters of the optical detection pulses; obtain pulse parameter comprehensive processing data based on the pulse parameters of the optical detection pulses; generate a histogram based on the pulse parameter comprehensive processing data; and obtain the multi-polymer region of the particles based on the histogram. The sample analyzer of this application identifies multi-polymer particles through the pulse parameters of optical detection pulses. The identification method is simple, and the accuracy of multi-polymer particle identification is relatively high, which can improve the accuracy of sample detection by the sample analyzer. Description of the Drawings

[0021] The drawings here are incorporated into the specification and form a part of this specification. These drawings show embodiments that conform to this application and are used together with the specification to illustrate the technical solutions of this application.

[0022] Figure 1It is a schematic diagram of the framework of an embodiment of a sample analyzer provided by this application;

[0023] Figure 2 It is a schematic flowchart of an embodiment of a method for identifying polymer particles of a sample analyzer provided by this application;

[0024] Figure 3 It is Figure 2 A schematic diagram of particle distribution of an embodiment in the dimension of pulse height of the forward detection pulse - pulse height of the lateral detection pulse obtained in step S12;

[0025] Figure 4 It is Figure 2 A schematic diagram of particle distribution of an embodiment in the dimension of pulse area of the forward detection pulse - pulse area of the lateral detection pulse obtained in step S12;

[0026] Figure 5 It is based on Figure 3 A schematic structural diagram of an embodiment of a histogram generated from the pulse parameter comprehensive processing data obtained from the two - dimensional data;

[0027] Figure 6 It is a schematic diagram of the framework of another embodiment of a sample analyzer provided by this application;

[0028] Figure 7 It is a schematic flowchart of another embodiment of a method for identifying polymer particles of a sample analyzer provided by this application. Detailed implementation manners

[0029] To make the above - mentioned objects, features, and advantages of this application more obvious and understandable, the following combines the accompanying drawings to make a detailed description of the specific implementation manners of this application. It can be understood that the specific embodiments described herein are only used to explain this application, rather than limiting this application. Additionally, it should be noted that for the convenience of description, only parts related to this application rather than all structures are shown in the accompanying drawings. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0030] The terms "first", "second", etc. in this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non - exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0031] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase occurring in various places in the specification is not necessarily referring to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of 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.

[0032] The present application first provides a sample analyzer, which can identify multi-polymer particles in a sample, with a simple identification method and relatively high identification accuracy, and can improve the accuracy of sample detection by the sample analyzer.

[0033] Please refer to Figure 1 as shown Figure 1 FIG. 10 is a schematic framework diagram of an embodiment of a sample analyzer provided by the present application. Specifically, the sample analyzer 10 includes an optical detection unit 11 and a control unit 12, wherein the control unit 12 is connected to the optical detection unit 11.

[0034] Among them, the optical detection unit 11 can be used to perform optical detection on the particles in the sample flowing through the flow cell. The sample can be a blood sample, a sweat sample, a saliva sample, a urine sample, or the like. The optical detection unit 11 can be used to perform immunoassay on the sample.

[0035] In the present application, the optical detection unit 11 includes at least one optical detection channel. For example, the optical detection unit 11 can include a forward scatter channel and a side scatter channel. Further, the optical detection unit can also include a classification fluorescence channel and a quantitative fluorescence channel. The scattering angles of the scattered light beams of the samples corresponding to each channel are different. The structures of the respective channels in the optical detection unit 11 are within the scope that can be understood by those skilled in the art and will not be elaborated herein.

[0036] The optical detection unit 11 can be used to perform optical detection on the particles in the sample through the forward scatter channel, the side scatter channel, the classification fluorescence channel, and the quantitative fluorescence channel to obtain forward raw detection data, side raw detection data, classification fluorescence raw detection data, and quantitative fluorescence raw detection data. The control unit 12 can be used to generate forward detection pulses, side detection pulses, classification fluorescence detection pulses, and quantitative fluorescence detection pulses based on the above raw detection data.

[0037] Specifically, the optical detection unit 11 may include an optical transmitter and an optical receiver. The optical transmitter is used to emit an illumination beam, which irradiates the sample flowing through the flow cell. Multiple optical receivers receive the scattered light signals of the particles in the sample at different scattering angles and convert them into electrical signals. The electrical signals pass through an analog-to-digital conversion chip, which can convert the analog signals into digital signals and input them into the processor for processing to obtain the detection pulses of each channel.

[0038] In this application, the control unit 12 is connected to the optical detection unit 11 and is used to obtain the original detection data from the optical detection unit 11 and process the obtained detection data.

[0039] Based on the sample analyzer 10 of any of the above embodiments, this application also provides a method for identifying multi-polymer particles of a sample analyzer. Please refer to Figure 2 as shown in Figure 2 FIG. is a schematic flowchart of an embodiment of a method for identifying multi-polymer particles of a sample analyzer provided by this application. Specifically, the identification method includes:

[0040] S11: Optically detect the particles in the sample through the optical detection channels of the optical detection unit to obtain optical detection pulses.

[0041] The control unit 12 optically detects the particles in the sample through at least one optical detection channel of the optical detection unit 11 to obtain optical detection pulses.

[0042] For example, the control unit 12 optically detects the particles in the sample through the forward scattering channel and the side scattering channel. Based on the signals of the scattered light in the above channels, forward original detection data and side original detection data can be obtained. The control unit 12 can obtain forward detection pulses and side detection pulses based on the above forward original detection data and side original detection data. In other embodiments, the control unit 12 can also optically detect the particles in the sample through the classification fluorescence channel and / or the quantitative fluorescence channel to obtain optical detection pulses.

[0043] S12: Obtain the pulse parameters of the optical detection pulses.

[0044] After the control unit 12 obtains the optical detection pulses, it then obtains the pulse parameters of the optical detection pulses. The pulse parameters of the optical detection pulses may include the height and / or the area of the pulse.

[0045] For example, the control unit 12 can obtain the forward detection pulse and side detection pulse parameters. Among them, the pulse parameters of the forward detection pulse may include the pulse height and / or the pulse area of the forward detection pulse. The pulse parameters of the side detection pulse may include the pulse height and / or the pulse area of the side detection pulse. Optionally, as Figure 3As shown, after the control unit 12 obtains the forward detection pulse and the lateral detection pulse, it identifies the pulse height of the forward detection pulse and the pulse height of the lateral detection pulse to obtain the pulse height of the forward detection pulse - the pulse height dimension of the lateral detection pulse. Among them, the pulse height refers to the peak value of the pulse.

[0046] Optionally, as Figure 4 shown, after the control unit 12 obtains the forward detection pulse and the lateral detection pulse, it can also identify the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse to obtain the pulse area of the forward detection pulse - the pulse area dimension of the lateral detection pulse.

[0047] Optionally, after the control unit 12 obtains the forward detection pulse and the lateral detection pulse, it can also simultaneously identify the pulse height and pulse area of the forward detection pulse and the pulse area of the lateral detection pulse.

[0048] S13: Obtain the comprehensive processing data of the pulse parameters based on the pulse parameters of the optical detection pulse.

[0049] After the control unit 12 obtains the pulse parameters of the optical detection pulse, it comprehensively processes the obtained pulse parameters to obtain the data of the comprehensive processing of the pulse parameters, so as to obtain the comprehensive distribution characteristics of the particles.

[0050] For example, after the control unit 12 obtains the pulse parameters of the forward detection pulse and the pulse parameters of the lateral detection pulse, it can process the above pulse parameters. For example, it processes the pulse parameters of the forward detection pulse and the lateral detection pulse according to a preset formula to obtain the comprehensive processing data of the pulse parameters, so as to obtain the comprehensive distribution characteristics of the particles, which is beneficial to the subsequent identification of multi-polymer particles.

[0051] Optionally, the control unit 12 can obtain the pulse height of the forward detection pulse and the pulse height of the lateral detection pulse, and comprehensively process the pulse height data through a preset formula to obtain the comprehensive processing data of the pulse height as the comprehensive processing data of the pulse parameters.

[0052] For example, the control unit 12 can comprehensively process the pulse height of the forward detection pulse and the pulse height of the lateral detection pulse based on formula (1).

[0053] CH1 = (FSC1 × W1 + SSC1 × W2) / 2 (1)

[0054] In the above formula (1), CH1 is the comprehensive processing data of pulse parameters, FSC1 is the pulse height of the forward detection pulse, SSC1 is the pulse height of the lateral detection pulse, and W1 and W2 are weight values. Among them, W1 + W2 = 1, and W1 and W2 can be obtained based on historical data and empirical statistics. Specifically, the two values of W1 and W2 can be determined according to the slope of the diagonal line formed by the polymerized region. This diagonal line refers to the line connecting the center of the polymerized region determined by manual visual observation and the center of the closest cluster of particle regions to the polymerized region. Specifically, the ratio of the projection length of this diagonal line on the coordinate axis corresponding to the forward scattering channel and the projection length of this diagonal line on the coordinate axis corresponding to the lateral scattering channel is used as the ratio of W1 and W2. Then, based on W1 + W2 = 1, the values of W1 and W2 can be calculated respectively.

[0055] By comprehensively processing the pulse height of the forward detection pulse and the pulse height of the lateral detection pulse, the comprehensive information of the pulse height in these two dimensions can be obtained, so as to identify the polymerized particles according to the comprehensive information of the pulse height. The calculation method is simple and the accuracy is relatively high.

[0056] Optionally, the control unit 12 can also obtain the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse, and perform comprehensive processing through the corresponding formula to obtain the comprehensive processing data of the pulse area as the comprehensive processing data of the pulse parameters, so as to identify the polymerized particles according to the comprehensive processing data of the pulse parameters.

[0057] Specifically, the control unit 12 can process the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse based on formula (2).

[0058] CH2 = (FSC2 × W3 + SSC2 × W4) / 2 (2)

[0059] In the above formula, CH2 is the comprehensive processing data of pulse parameters, FSC2 is the pulse height of the forward detection pulse, SSC2 is the pulse height of the lateral detection pulse, and W3 and W4 are weight values. Among them, W3 + W4 = 1, and W3 and W4 can be obtained from historical data and empirical statistics. The calculation method of W3 and W4 can refer to the calculation method of the aforementioned W1 and W2, which will not be elaborated here.

[0060] By comprehensively processing the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse, the comprehensive information of the pulse area in these two dimensions can be obtained, so as to identify the polymerized particles according to the comprehensive information of the pulse area. The calculation method is simple and the reliability is relatively high.

[0061] S14: Generate a histogram based on the comprehensive processing data of pulse parameters.

[0062] After obtaining the comprehensive processing data of the pulse parameters, the control unit 12 can, for example, Figure 5 As shown, make a histogram based on the comprehensive processing data of the pulse parameters. Specifically, make a histogram on the change curve graph of the comprehensive processing data of the pulse parameters and the number of particles, and then perform smoothing processing.

[0063] S15: Obtain the aggregation region of the particles based on the histogram.

[0064] After obtaining the histogram generated from the comprehensive processing data of the pulse parameters, the aggregation region of the particles can be found according to this histogram. Specifically, as Figure 5 shown, the boundary position between the aggregation region of the particles and the normal particle region can be found on the histogram, so as to divide the aggregation region of the particles and the normal particle region based on this boundary position.

[0065] Furthermore, as Figure 5 shown, since the pulse height / pulse area of the aggregated particles is generally large, the valley position between the two largest peaks can be found on the curve graph based on the made histogram, and thus the boundary position between the aggregation region of the particles and the normal particle region can be obtained. The right side of the boundary position is divided into the aggregation region of the particles, and the left side of the boundary line is divided into the normal particle region.

[0066] In the above embodiments, by processing the pulse parameters of the optical detection pulses, for example, comprehensively processing the pulse height / pulse area of the forward detection pulses and the lateral detection pulses to obtain the comprehensive processing data of the pulse parameters, and then making a histogram based on this comprehensive processing data of the pulse parameters, finding the boundary position of the histogram to distinguish the aggregation region of the particles. The recognition method of this aggregation region is simple and has high accuracy, which can improve the accuracy of the sample analyzer for sample detection.

[0067] After obtaining the aggregation region of the particles, remove the particles corresponding to this aggregation region. Specifically, the aggregated particles corresponding to each channel can be removed, and then subsequent calculations can be performed. For example, after identifying the aggregation region through the forward scattering channel and the lateral scattering channel, synchronously remove the aggregated particles corresponding to the classification fluorescence channel based on this aggregation region; then obtain the classification information of the particles based on the detection data of the classification fluorescence channel after removal. In this way, the influence of the aggregated particles on the sample detection can be reduced, and the accuracy of the sample detection can be improved.

[0068] Specifically, the sample analyzer can be used to perform immunoassay detection data; the optical detection unit 11 can be used to optically detect the particles of the sample flowing through the flow cell; each particle is conjugated with at least one substance to be detected, magnetic beads, and a quantitative fluorescent substance, and the magnetic beads are coated with at least one classification fluorescent substance corresponding to the intensity level of the substance to be detected. The substance to be detected can be various antigens or antibodies in a blood sample, such as antigen A / B / C / D / etc., or other substances that need to be classified and counted. Each substance to be detected corresponds to a classification detection item. The sample analyzer can distinguish classification fluorescent substances of different intensity levels through the detection data of the classification fluorescent channel to classify different substances to be detected, and combine the detection data of the quantitative fluorescent channel to output the quantitative information corresponding to each classification detection item respectively, realizing the joint detection of multiple classification detection items.

[0069] Compared with blood cell detection or chemiluminescent immunoassay, for the multi-item joint detection of immunoassay using flow cytometry, due to the large number of detection items, the reagent needs to contain magnetic beads corresponding to different classification detection items respectively, and the concentration of the sample flowing through the flow cell prepared is often relatively high, thus easily resulting in the aggregation of two particles or multiple particles. When classifying using the detection data of the classification fluorescent channel, the aggregated particles will be classified into other categories, resulting in inaccurate classification results.

[0070] The technical solution provided by the embodiments of the present application processes data comprehensively according to the pulse parameters obtained from the forward detection pulse and the lateral detection pulse to generate a histogram, and obtains a poly-aggregation region based on the histogram, so as to remove the corresponding poly-aggregated particles from the detection data obtained from the classification fluorescent channel according to the poly-aggregation region. According to the detection data of the classification fluorescent channel after removal, the classification information of the particles can be accurately obtained, which can improve the accuracy of the multi-item joint detection of immunoassay.

[0071] Further, when identifying the poly-aggregation region, multiple identifications can be performed by combining the pulse height and pulse area of the forward detection pulse and the lateral detection pulse to more accurately identify the poly-aggregation region and further improve the accuracy of sample detection. In some embodiments, the sample analyzer can process the pulse parameters of the optical detection pulses of one or more channels among the forward scatter channel, the lateral scatter channel, and the quantitative fluorescent channel to obtain pulse parameter comprehensive processing data, generate a histogram based on the pulse parameter comprehensive processing data, and obtain the poly-aggregation region of the particles based on the histogram.

[0072] For example, the control unit 12 can first obtain the pulse height of the forward detection pulse and the pulse height of the lateral detection pulse, and then comprehensively process the pulse height of the forward detection pulse and the pulse height of the lateral detection pulse based on the above formula (1) to obtain the comprehensive processing data of the pulse parameters. Then, a histogram is made based on the comprehensive processing data of the pulse parameters and smoothed. Then, the boundary position between the poly-aggregation region of the particles and the normal particle region is found in the histogram, and then the poly-aggregation region of the particles is found based on this boundary position, and the particles in the poly-aggregation region of the particles are removed. After removing the poly-aggregation region of the particles through the pulse height, it is then determined whether the poly-aggregation region of the particles is completely removed. Specifically, it can be determined whether the poly-aggregation particle region is completely removed by whether the number of identified particle clusters is equal to the number of joint inspection items.

[0073] When the control unit 12 determines that the number of identified particle clusters is not equal to the number of joint inspections, it can be considered that the poly-aggregation region of the particles has not been completely cleared. Therefore, the poly-aggregation region can be further identified based on the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse. Specifically, the control unit 12 obtains the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse again, and then comprehensively processes the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse according to the above formula (2) to obtain new comprehensive processing data of the pulse parameters. Then, a new histogram is generated based on the new comprehensive processing data of the pulse parameters and corresponding smoothing is performed. The boundary position between the poly-aggregation region of the particles and the normal particle region is found on the generated new histogram, and the new poly-aggregation region of the particles is found based on this boundary position, and then the found new poly-aggregation region of the particles is removed. Since the method of identifying poly-aggregation particles through the pulse height may cause some poly-aggregation particles to be missed, therefore, identifying poly-aggregation particles through the pulse area can further improve the accuracy of identifying poly-aggregation particles, thereby improving the accuracy of the sample analyzer for sample detection.

[0074] It can be understood that when identifying poly-aggregation particles, the poly-aggregation region of the particles can also be first identified through the pulse area of the forward detection pulse and the pulse area of the lateral detection pulse, and the particles corresponding to the identified poly-aggregation region are removed. When it is confirmed that the poly-aggregation region has not been completely cleared, the poly-aggregation region of the particles is identified again through the pulse height of the forward detection pulse and the pulse height of the lateral detection pulse. By this means, the accuracy of identifying the poly-aggregation region can also be improved, and the accuracy of sample detection can be improved.

[0075] In the above embodiments, the height and / or area of the pulses of the particles obtained through the forward scattering channel and the lateral scattering channel are used to identify the particle aggregation region. When one of the identification methods fails, the other method can be used for supplementary identification, so as to improve the accuracy of identifying the particle aggregation region and provide the accuracy of sample detection.

[0076] This application also provides a sample analyzer. Please refer to Figure 6 as shown in Figure 6 which is a schematic framework diagram of another embodiment of a sample analyzer provided by this application. Specifically, the sample analyzer 20 may include an optical detection unit 21, a baseline removal module 22, a pulse identification module 23, and a particle cluster identification module 24. Further, the sample analyzer 20 may also include a multi-peak wave identification module 25.

[0077] Specifically, the optical detection unit 21 includes a forward scattering channel, a lateral scattering channel, a classification fluorescence channel, and a quantitative fluorescence channel, and the angles of the scattered light of the above channels are different. The optical detection unit 21 performs optical detection on the particles in the sample through the above optical detection channels to obtain optical detection pulse data. Specifically, the optical detection unit 21 obtains the forward original detection data of the particles in the sample through the forward scattering channel, the optical detection unit 21 obtains the lateral original detection data of the particles in the sample through the lateral scattering channel, the optical detection unit 21 obtains the classification fluorescence original detection data of the particles in the sample through the classification fluorescence channel, and the optical detection unit 21 obtains the quantitative fluorescence original detection data of the particles in the sample through the quantitative fluorescence channel. For the introduction of the optical detection unit 21, reference can be made to the introduction of the above embodiments, which will not be elaborated here.

[0078] The baseline removal module 22 can be used to process the original detection data of the above four channels to obtain the baseline-removed data corresponding to each channel. For example, the baseline removal module 22 is used to process the forward original detection data, the lateral original detection data, the classification fluorescence original detection data, and the quantitative fluorescence original detection data to obtain the forward baseline-removed data, the lateral baseline-removed data, the classification fluorescence baseline-removed data, and the quantitative fluorescence baseline-removed data.

[0079] Specifically, the method for obtaining the baseline-removed data of each channel is as follows: In each channel, sampling is performed at a preset frequency, and an original detection data sequence is obtained. For example, a forward original detection data sequence is obtained through the forward scattering channel. In each channel, each sampling time point corresponds to an original detection data. The baseline-removing module 22 calculates the mean value of the original detection data corresponding to a predetermined number of sampling time points before and after each sampling time point as the moving average value, and then takes the minimum value between the moving average value corresponding to this sampling time point and the original detection data at this sampling time point as the baseline-removed value corresponding to this sampling time point, thereby obtaining the sampling baseline. By calculating the moving average values corresponding to each sampling time point, noise can be better removed, and the accuracy of particle recognition can be improved.

[0080] The pulse recognition module 23 is used to perform pulse recognition on the baseline-removed data corresponding to the above four channels to identify the baseline-removed pulses of the four channels. Specifically, the pulse recognition module 23 is used to recognize the forward baseline-removed data to obtain forward baseline-removed pulses, recognize the lateral baseline-removed data to obtain lateral baseline-removed pulses, recognize the classified fluorescence baseline-removed data to obtain classified fluorescence baseline-removed pulses, and recognize the quantitative fluorescence baseline-removed data to obtain quantitative fluorescence baseline-removed pulses. After the pulse recognition module 23 recognizes a pulse, it further obtains data such as the pulse width and pulse height of the pulse as pulse data. Specifically, after the pulse recognition module 23 recognizes the pulse of each channel, it obtains the pulse width and pulse height of the forward baseline-removed pulse, the pulse width and pulse height of the lateral baseline-removed pulse, the pulse width and pulse height of the classified fluorescence baseline-removed pulse, and the pulse width and pulse height of the quantitative fluorescence baseline-removed pulse to obtain the optical detection pulse data.

[0081] The multi-peak wave recognition module 25 can be used to perform multi-peak wave recognition on the forward baseline-removed pulses whose pulse width meets the preset range, that is, to recognize the baseline-removed data within a certain pulse width range of the forward scattering channel to identify whether the forward baseline-removed pulse corresponding to the forward scattering channel is a multi-peak wave. A multi-peak wave refers to a pulse waveform that presents multiple peaks within a certain pulse width range. When the forward baseline-removed pulse is a multi-peak wave, it can be considered that the particle corresponding to this multi-peak wave is a multi-particle. The multi-particle can be marked or the recognized multi-particle can be removed.

[0082] Optionally, after obtaining the above optical detection pulse data, the optical detection pulse data can be input into the particle cluster recognition module 24. The particle cluster recognition module 24 is used to identify the pulses corresponding to each of the corresponding particles from the pulse data to obtain particle pulses. Then, in the forward scattering channel, find the median of a preset number of particle pulses with the smallest pulse width. For example, in the forward scattering channel, obtain the median corresponding to partial pulses with the smallest pulse width in the particle pulses (such as 10% or 20% of the total). Then, based on the pulse width of the particle pulses in the forward scattering channel and the above median, determine whether there are aggregated particles. For example, in the forward scattering channel, if the pulse width of the forward detection pulse is larger than the median by a preset ratio, it is confirmed that the particle corresponding to the forward detection pulse is an aggregated particle. In this way, by using the median of partial pulses with a smaller pulse width for the identification of aggregated particles, the influence of some small pulses can be avoided, thereby improving the accuracy of aggregated particle identification.

[0083] For example, if the pulse width of the forward detection pulse of a certain particle is larger than the median by a percentage K, it is considered that the particle corresponding to the pulse is an aggregated particle, and then the particle pulse is removed. The range of K can be 10% - 18%. For example, K can be 10%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, etc. In this way, aggregated particles can be identified more accurately. After identifying the aggregated particles, remove the aggregated particles corresponding to the forward scattering channel, lateral scattering channel, classification fluorescence channel, and quantitative fluorescence channel, and then perform subsequent calculations to improve the accuracy of sample detection.

[0084] Further, if the sample includes particles of multiple particle sizes, it is necessary to identify aggregated particles for particles of multiple particle sizes. The particle cluster recognition module 24 can be used to first group the particle pulses; then, in each group, the above method is used to identify aggregated particles. Specifically, within each group, obtain the median of partial particle pulses with the smallest pulse width in the forward scattering channel; based on the pulse width of the particle pulses in the forward scattering channel within each group and the median, confirm whether there are aggregated particles. That is, find the median of the pulse widths of the smaller partial pulses within each group, and remove the pulse data corresponding to the particles whose pulse widths in each group are greater than a certain percentage of the pulse width median.

[0085] Based on the sample analyzer 20 of any of the above embodiments, the present application also provides a method for identifying aggregated particles of a sample analyzer, as Figure 7 shown. The method for identifying aggregated particles includes:

[0086] S71: Optically detect the particles in the sample through the forward scattering channel, lateral scattering channel, classification fluorescence channel, and quantitative fluorescence channel of the optical detection unit to obtain optical detection pulse data.

[0087] S72: Identify the pulses corresponding to each of the particles from the optical detection pulse data through the particle cluster identification module to obtain particle pulses; obtain the median of a preset number of particle pulses with the smallest pulse width in the forward scattering channel; confirm whether there are polymeric particles based on the pulse width and median of the particle pulses in the forward scattering channel. When it is confirmed that there are polymeric particles, remove the polymeric particles corresponding to at least one of the forward scattering channel, the side scattering channel, the classification fluorescence channel, and the quantitative fluorescence channel.

[0088] In the polymeric particle identification method provided in the above embodiment, the polymeric particles are identified by the pulse width of the forward scattering channel and whether the forward scattering detection pulse is a multi-peak wave. The identification method is simple and has high accuracy.

[0089] It can be understood that Figure 1 the polymeric particle identification method in the sample analyzer 10 shown and Figure 6 the polymeric particle identification method in the sample analyzer 20 shown in can be combined and used. For example, first identify the polymeric particles in the sample through Figure 6 the polymeric particle identification method in the sample analyzer 20 shown, and then identify the polymeric particles in the sample through Figure 1 the polymeric particle identification method in the sample analyzer 10 shown, so as to improve the accuracy of polymeric particle identification. That is, first identify the polymeric particles again through the pulse width, and then identify the polymeric particles through the pulse height / pulse area. It can also first identify the polymeric particles through the pulse height / pulse area, and then identify the polymeric particles through the above pulse width. In this way, the polymeric particles can be identified multiple times through different methods, improving the accuracy of polymeric particle identification, and thus improving the reliability of the sample analyzer for sample detection. For the specific steps of the polymeric particle identification method, please refer to the introduction of the above embodiment and will not be elaborated here.

[0090] The above are only the embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A sample analyzer, characterized in that, The sample analyzer includes: An optical detection unit for optically detecting a sample, the optical detection unit including at least one optical detection channel; A control unit connected to the optical detection unit, the control unit being configured to: Optically detect particles in the sample through the optical detection channels of the optical detection unit to obtain optical detection pulses; Obtain pulse parameters of the optical detection pulses; Based on the pulse parameters of the optical detection pulses, obtain pulse parameter comprehensive processing data; Generate a histogram based on the pulse parameter comprehensive processing data; Obtain a poly-aggregation region of the particles based on the histogram.

2. The sample analyzer according to claim 1, wherein The optical detection unit at least includes a forward scatter channel and a side scatter channel; The control unit is further configured to: Optically detect particles in the sample through the forward scatter channel and the side scatter channel of the optical detection unit to obtain the optical detection pulses, wherein the optical detection pulses include forward detection pulses and side detection pulses; The pulse parameters of the forward detection pulses include the pulse height of the forward detection pulses, and the pulse parameters of the side detection pulses include the pulse height of the side detection pulses; The control unit is further configured to: Process the pulse height of the forward detection pulses and the pulse height of the side detection pulses through formula (1) to obtain the pulse parameter comprehensive processing data, CH1 = (FSC1 × W1 + SSC1 × W2) / 2 (1) Wherein, the CH1 is the pulse parameter comprehensive processing data, the FSC1 is the pulse height of the forward detection pulses, the SSC1 is the pulse height of the side detection pulses, and the W1 and the W2 are weight values.

3. The sample analyzer according to claim 1, characterized in that, The optical detection unit at least includes a forward scatter channel and a side scatter channel; The control unit is further configured to: Optically detect particles in the sample through the forward scatter channel and the side scatter channel of the optical detection unit to obtain the optical detection pulses, wherein the optical detection pulses include forward detection pulses and side detection pulses; The pulse parameters of the forward detection pulses include the pulse area of the forward detection pulses, and the pulse parameters of the side detection pulses include the pulse area of the side detection pulses, The control unit is further configured to: Process the pulse area of the forward detection pulses and the pulse area of the side detection pulses through formula (2) to obtain the pulse parameter comprehensive processing data, CH2 = (FSC2 × W3 + SSC2 × W4) / 2 (2) Wherein, the CH2 is the pulse parameter comprehensive processing data, the FSC2 is the pulse height of the forward detection pulses, the SSC2 is the pulse height of the side detection pulses, and the W3 and the W4 are weight values.

4. The sample analyzer according to any one of claims 1 to 3, characterized in that, The optical detection unit further includes: a classification fluorescence channel, and the control unit is further configured to: Optically detect particles in the sample through the classification fluorescence channel of the optical detection unit, Remove the poly-aggregated particles corresponding to the classification fluorescence channel based on the poly-aggregation region of the particles; Obtain the classification information of the particles based on the detection data of the classified fluorescence channels after removal.

5. The sample analyzer according to claim 1, characterized in that, The control unit is further configured to: find the trough position between the two largest peaks in the histogram to obtain the boundary position between the polyregion and the normal particle region of the particles, and obtain the polyregion of the particles based on the boundary position.

6. The sample analyzer according to claim 2, wherein The pulse parameters of the forward detection pulse further include the pulse area of the forward detection pulse, and the pulse parameters of the side detection pulse further include the pulse area of the side detection pulse. The control unit is further configured to: Remove the polyregion of the particles, and when it is confirmed that the polyregion of the particles corresponding to the sample is not completely cleared, obtain the pulse area of the forward detection pulse and the pulse area of the side detection pulse. Based on the pulse area of the forward detection pulse and the pulse area of the side detection pulse, perform comprehensive processing on the data to obtain new pulse parameter comprehensive processing data. Generate a new histogram based on the new pulse parameter comprehensive processing data. Obtain the polyregion of the new particles based on the new histogram, and remove the polyregion of the new particles.

7. The sample analyzer according to claim 1, wherein The optical detection unit further includes a forward scattering channel, a side scattering channel, a classified fluorescence channel, and a quantitative fluorescence channel, and the sample analyzer further includes a particle cluster recognition module. The optical detection unit is configured to perform optical detection on the particles in the sample through the forward scattering channel, the side scattering channel, the classified fluorescence channel, and the quantitative fluorescence channel to obtain optical detection pulse data. The particle cluster recognition module is configured to identify the pulses of each corresponding particle from the optical detection pulse data to obtain particle pulses. Obtain the median of the preset number of particle pulses with the smallest pulse width in the forward scattering channel. Based on the pulse width of the particle pulses and the median in the forward scattering channel, confirm whether there are poly-particles. When it is confirmed that there are poly-particles, remove the poly-particles corresponding to at least one of the forward scattering channel, the side scattering channel, the classified fluorescence channel, and the quantitative fluorescence channel.

8. The sample analyzer according to claim 7, characterized in that, The sample analyzer further includes: a baseline removal module, a pulse recognition module, and a multi-peak wave recognition module. The baseline removal module is configured to perform data processing on the forward original detection data, side original detection data, classified fluorescence original detection data, and quantitative fluorescence original detection data obtained by the optical detection unit to respectively obtain forward baseline-removed data, side baseline-removed data, classified fluorescence baseline-removed data, and quantitative fluorescence baseline-removed data. The pulse recognition module is configured to perform pulse recognition on the forward baseline-removed data, the side baseline-removed data, the classified fluorescence baseline-removed data, and the quantitative fluorescence baseline-removed data to respectively obtain forward baseline-removed pulses, side baseline-removed pulses, classified fluorescence baseline-removed pulses, and quantitative fluorescence baseline-removed pulses, and obtain the pulse width and pulse height of the forward baseline-removed pulses, the pulse width and pulse height of the side baseline-removed pulses, the pulse width and pulse height of the classified fluorescence baseline-removed pulses, and the pulse width and pulse height of the quantitative fluorescence baseline-removed pulses to obtain the optical detection pulse data. The multi-peak wave recognition module is used to perform multi-peak wave recognition on the forward baseline-removed pulses whose pulse widths meet the preset range; when it is confirmed that the forward baseline-removed pulse is a multi-peak wave, it is confirmed that the particles corresponding to the multi-peak wave are multi-polymer particles.

9. A method for identifying multi-polymer particles of a sample analyzer, based on the sample analyzer according to any one of claims 1-10, characterized in that, The recognition method includes: Optically detecting the particles in the sample through the optical detection channels of the optical detection unit to obtain optical detection pulses; Obtaining the pulse parameters of the optical detection pulses; Based on the pulse parameters of the optical detection pulses, obtaining pulse parameter comprehensive processing data; Generating a histogram based on the pulse parameter comprehensive processing data; Obtaining the multi-polymer region of the particles based on the histogram.

10. A sample analyzer, characterized in that, The sample analyzer includes: An optical detection unit, the optical detection unit includes a forward scatter channel, a side scatter channel, a classification fluorescence channel, and a quantitative fluorescence channel, and the optical detection unit is used to optically detect the particles in the sample through the forward scatter channel, the side scatter channel, the classification fluorescence channel, and the quantitative fluorescence channel to obtain optical detection pulse data; A particle cluster recognition module, which is used to identify the pulses corresponding to each of the particles from the optical detection pulse data to obtain particle pulses; obtaining the median of the preset number of particle pulses with the smallest pulse width in the forward scatter channel; based on the pulse width of the particle pulses in the forward scatter channel and the median, confirming whether there are multi-polymer particles, and when it is confirmed that there are multi-polymer particles, removing the multi-polymer particles corresponding to at least one of the forward scatter channel, the side scatter channel, the classification fluorescence channel, and the quantitative fluorescence channel.

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