Blood cell analyzer and red blood cell detection method
By using impedance analysis to obtain red blood cell volume distribution information and performing automatic correction in a blood cell analyzer, the problems of high detection cost and low efficiency caused by red blood cell agglutination are solved, and rapid and accurate red blood cell counting is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN MINDRAY BIO MEDICAL ELECTRONICS CO LTD
- Filing Date
- 2024-12-27
- Publication Date
- 2026-06-30
AI Technical Summary
Existing blood cell analyzers require manual processing and retesting when they detect red blood cell agglutination, resulting in high testing costs and low efficiency.
The original red blood cell volume distribution information is obtained by impedance method. The processor then obtains red blood cell agglutination information based on this information and makes corrections to directly obtain an accurate red blood cell count.
This technology enables the rapid acquisition of accurate red blood cell counts without increasing testing costs, thus improving testing efficiency.
Smart Images
Figure CN122306662A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of blood analysis, and in particular to blood cell analyzers and red blood cell detection methods. Background Technology
[0002] Currently, medical particle analyzers (such as blood cell analyzers) are widely used in the field of medical testing. They typically employ the micropore impedance principle to measure red blood cells. When red blood cells suspended in an electrolyte solution pass through a detection micropore, they change the previously constant resistance inside and outside the micropore. This change is sensed by a sensor inside the micropore and processed by a circuit to generate an electrical pulse. The size of the pulse can be used to measure the cell volume, and the number of pulses can be used to determine the cell count.
[0003] Under normal circumstances, red blood cells are evenly distributed in the blood. However, when there are defects in the blood sample, such as abnormal levels of cold agglutinins, or when the blood collection process is not standardized, the evenly suspended red blood cells may clump together, causing red blood cell agglutination. Red blood cell agglutination can interfere with the measurement results of blood cell analyzers, for example, leading to: falsely low red blood cell count (RBC) results; falsely high mean corpuscular volume (MCV); falsely low hematocrit (HCT); and falsely high red blood cell distribution variation coefficient (RDW-CV), MCH (mean corpuscular hemoglobin content), and MCHC (mean corpuscular hemoglobin concentration).
[0004] In related technologies, an alarm is only triggered when red blood cell agglutination is detected, indicating that the test result is unreliable. However, this method still requires manual processing of the sample before retesting to obtain an accurate red blood cell count, resulting in high testing costs and low testing efficiency. Summary of the Invention
[0005] Therefore, the objective of this application is to provide a blood cell analyzer and a corresponding red blood cell detection method, which can quickly obtain accurate red blood cell count results without increasing detection costs.
[0006] To achieve the aforementioned objectives of this application, the first aspect of this application proposes a blood cell analyzer, comprising:
[0007] A sampling device is used to collect blood samples for testing.
[0008] A sample preparation apparatus for mixing a portion of the blood sample to be tested with a diluent to prepare a test sample;
[0009] An impedance detection device includes a counting cell and a detection component. The counting cell is used to allow the test sample to pass through, and the detection component is used to acquire the electronic signal of each particle in the test sample as it passes through the counting cell.
[0010] The processor is configured as follows:
[0011] The original red blood cell volume distribution information is obtained based on the electronic signals of each particle in the test sample.
[0012] Red blood cell agglutination information is obtained based on the original red blood cell volume distribution information, and
[0013] The red blood cell count of the blood sample to be tested is obtained based on the original red blood cell volume distribution information and the red blood cell agglutination information.
[0014] A second aspect of this application provides a method for detecting red blood cells, comprising:
[0015] Collect the blood sample to be tested;
[0016] A portion of the blood sample to be tested is mixed with a diluent to prepare the test sample;
[0017] The test sample is subjected to impedance measurement to obtain the electronic signal of each particle in the test sample;
[0018] The original red blood cell volume distribution information is obtained based on the electronic signals of each particle in the test sample.
[0019] Red blood cell agglutination information is obtained based on the original red blood cell volume distribution information; and
[0020] The red blood cell count of the blood sample to be tested is obtained based on the original red blood cell volume distribution information and the red blood cell agglutination information.
[0021] In the technical solutions provided in this application, the accurate measurement of red blood cell agglutination information can be achieved by using only the original red blood cell volume distribution information obtained by impedance method. Thus, the accurate red blood cell count value can be obtained based on the obtained red blood cell agglutination information and the original red blood cell volume distribution information, without the need for manual intervention and retesting, thereby improving detection efficiency. Attached Figure Description
[0022] Figure 1 This diagram illustrates a process flow chart of a related technology for obtaining accurate red blood cell count results.
[0023] Figure 2 The diagram shows structural schematics of some embodiments of the blood cell analyzer according to this application.
[0024] Figure 3 The diagram shows structural schematics of some embodiments of the impedance detection device according to this application.
[0025] Figure 4The diagram shows a histogram of red blood cell volume distribution in a normal sample according to some embodiments of this application.
[0026] Figure 5 The diagram shows a histogram of red blood cell volume distribution in RBC agglutinated samples according to some embodiments of this application.
[0027] Figure 6 The diagram shows a histogram of red blood cell volume distribution in RBC agglutinated samples according to other embodiments of this application.
[0028] Figure 7 The diagram shows a histogram of red blood cell volume distribution in RBC agglutinated samples after deducting red blood cell agglutination information, according to some embodiments of this application.
[0029] Figure 8 The diagram shows the correlation curves between the reference values of red blood cell counts obtained from the detection of multiple RBC agglutination samples according to some embodiments of this application and the red blood cell counts before correction.
[0030] Figure 9 The diagram shows the relative deviations between the reference values for red blood cell counts obtained from testing multiple RBC agglutination samples according to some embodiments of this application and the red blood cell counts before and after correction.
[0031] Figure 10 The diagram shows the correlation curves between the MCV reference value obtained from detecting multiple RBC agglutinated samples according to some embodiments of this application and the MCV before correction.
[0032] Figure 11 The diagram shows the relative deviations between the MCV reference values obtained from testing multiple RBC agglutinated samples according to some embodiments of this application and the MCV before and after correction.
[0033] Figure 12 The diagram shows the correlation curves between the reference red blood cell count and the corrected red blood cell count obtained by detecting multiple RBC agglutination samples according to some embodiments of this application.
[0034] Figure 13 The diagram shows the correlation curves between the MCV reference value and the corrected MCV obtained by detecting multiple RBC agglutinated samples according to some embodiments of this application.
[0035] Figure 14 A schematic flowchart of a red blood cell detection method according to some embodiments of this application is shown. Detailed Implementation
[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0037] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific order of objects. It can be understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted.
[0038] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0039] As mentioned earlier, in related technologies, the instrument will output an agglutination alarm when it detects red blood cell agglutination, but manual reprocessing of the sample is still required before retesting to obtain accurate test results.
[0040] Figure 1 This diagram illustrates a process flow chart of a related technology for obtaining accurate red blood cell count results.
[0041] like Figure 1 As shown, after the instrument issues an alarm for red blood cell agglutination, the RBC agglutinated sample must be manually incubated at 37°C for at least 30 minutes before being tested. The result of the retest is used to determine if the red blood cell agglutination has been corrected. If the abnormality caused by red blood cell agglutination has been corrected, a blood smear microscopic examination is performed to determine if any other abnormalities exist. If no other abnormalities are found in the retest, a test report is output, noting that it is the corrected result. If the abnormality caused by red blood cell agglutination has not been corrected (e.g., in cases of severe agglutination), the RBC agglutinated sample must be manually incubated and diluted, or replaced with an equal volume of plasma and then incubated before being tested. The result of the retest is then used to determine if the red blood cell agglutination has been corrected. This process is repeated until the red blood cell agglutination is corrected and no other abnormalities are found in the retest, at which point a correct test report is output.
[0042] It is evident that this method involves complex manual processing, resulting in low detection efficiency and high detection costs.
[0043] Therefore, a solution is needed to intelligently correct the test results of RBC agglutinated samples, so as to output correct test reports without human intervention when testing RBC agglutinated samples.
[0044] Figure 2 A schematic diagram of one embodiment of the blood cell analyzer according to this application is shown. Figure 2 As shown, the blood cell analyzer 100 includes at least a sample aspiration device 110, a sample preparation device 120, an impedance detection device 130, and a processor 140. The blood cell analyzer 100 also has a fluid system (not shown) for connecting the sample aspiration device 110, the sample preparation device 120, the impedance detection device 130, and the processor 140 to facilitate fluid transfer between these devices. The sample aspiration device 110 is used to aspirate the blood sample to be tested.
[0045] In some embodiments, the sampling device 110 has a sampling needle (not shown) for drawing up a blood sample to be tested. Furthermore, the sampling device 110 may also include, for example, a driving device for driving the sampling needle to quantitatively draw up the blood sample through the tip of the sampling needle. The sampling device 110 can deliver the collected blood sample to the sample preparation device 120.
[0046] The sample preparation device 120 is used to mix a portion of the blood sample to be tested with a diluent to prepare the test sample.
[0047] In some embodiments, the diluent may include, for example, tris(hydroxymethyl)aminomethane buffer. By adjusting the amount of tris(hydroxymethyl)aminomethane buffer, the osmotic pressure of the diluent is adjusted to approximately 200 mOsm / kg (the osmotic pressure of mammalian blood cells is generally around 260–320). When blood cells are mixed with the diluent, red blood cells and white blood cells absorb water in the hypotonic solution, resulting in an increase in volume, while the volume of platelets remains relatively unchanged.
[0048] In some embodiments, the sample preparation apparatus 120 may include at least one reaction cell and a reagent supply device (not shown). The at least one reaction cell is used to receive a blood sample to be tested drawn by the sampling device 110, and the reagent supply device provides a processing reagent (e.g., a diluent) to the at least one reaction cell, thereby mixing the blood sample to be tested drawn by the sampling device 110 with the processing reagent provided by the reagent supply device in the at least one reaction cell to prepare a test sample.
[0049] Impedance detection device 130 includes a counting cell and a detection component. The counting cell is used to allow the test sample to pass through. The detection component is used to acquire the electronic signal, such as a voltage pulse, as each particle in the test sample passes through the counting cell.
[0050] In one embodiment of the impedance detection device 130, the impedance detection device 130 may be configured as a sheath current impedance detection device. For example... Figure 3As shown, the sheath current impedance detection device 130 includes a counting cell 1301 with an aperture 1302 having an electrode 1303. The sheath current impedance detection device 130 detects the DC impedance generated when a particle in the test sample passes through the aperture 1302 and outputs an electronic signal reflecting information about the particle passing through the aperture.
[0051] Specifically, after aspirating the blood sample to be tested, the sampling device 110 is driven by its drive device and moves to the reaction cell of the sample preparation device 120, injecting a portion of the aspirated blood sample into the reaction cell. The delivery line 1306 delivers the test sample, after being treated with diluent in the reaction cell, to the counting cell 1301. The sheath flow impedance detection device 130 may also be equipped with a sheath fluid chamber (not shown) to provide sheath fluid to the counting cell 1301. In the counting cell 1301, the test sample, enveloped by the sheath fluid, flows through the orifice 1302, causing the test sample flow to become a thin stream, allowing particles contained in the test sample to pass through the orifice 1302 one by one. The electrode 1303 is electrically connected to a DC power supply 1304, which provides DC current between the pair of electrodes 1303. During the DC current supply by the DC power supply 1304, the impedance between the pair of electrodes 1303 can be detected, and the electronic signal representing the impedance change (also known as a resistance signal) is amplified by the amplifier 1305 and then sent to the processor 140. Since the magnitude of the electronic signal corresponds to the volume (size) of the particle, the processor 140 can process the electronic signal to obtain the original red blood cell volume distribution information of the blood sample to be tested.
[0052] Here, the raw red blood cell volume distribution information can include the volume size of the red blood cells to be tested in the blood sample and the number of red blood cells of that volume size. For example, the electronic signal is presented in the form of voltage pulses; the number of red blood cells to be tested can be determined based on the number of voltage pulses, and the volume size of the corresponding red blood cells can be determined based on the magnitude of the voltage pulses.
[0053] In some embodiments, such as Figure 2 As shown, the blood cell analyzer 100 may further include a display device 150, a first housing 160, and a second housing 170. The display device 150 is configured to display information related to red blood cell count, such as red blood cell count values. A detection device 130 and a processor 140 are disposed, for example, inside the second housing 170. A sample preparation device 120 is disposed, for example, inside the first housing 160, and the display device 150 is disposed, for example, on the outer surface of the first housing 160 and is used to display the detection results of the blood cell analyzer 100.
[0054] In some embodiments, the processor 140 is used to process and perform operations on data to obtain the desired results, such as generating a volume distribution map based on the collected electron signals and performing particle analysis based on the volume distribution map.
[0055] In some embodiments, the processor 140 may visualize intermediate or final processing results and then display them via the display device 150. For example, the display device 150 may include a user interface, and the processor 140 may output red blood cell count values and display them on the user interface of the display device 150.
[0056] In some embodiments, the processor 140 includes, but is not limited to, devices such as a central processing unit (CPU), a microcontroller unit (MCU), a field-programmable gate array (FPGA), and a digital signal processor (DSP) for interpreting computer instructions and processing data in computer software. For example, the processor 140 is used to execute various computer applications in a computer-readable storage medium, thereby enabling the blood cell analyzer 100 to perform corresponding detection procedures and analyze the electronic signals detected by the impedance detection device 130 in real time.
[0057] This application proposes a technical solution to correct the original red blood cell volume distribution information or the original red blood cell count value obtained based on the original red blood cell volume distribution information based on the red blood cell agglutination information.
[0058] Therefore, according to the embodiments of this application, the processor 140 can be configured as follows:
[0059] The original red blood cell volume distribution information is obtained based on the electronic signals of each particle in the test sample.
[0060] Red blood cell agglutination information is obtained based on the original red blood cell volume distribution information, and
[0061] The red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information and red blood cell agglutination information (hereinafter also referred to as the corrected red blood cell count value).
[0062] In the aforementioned blood cell analyzer 100, the raw red blood cell volume distribution information obtained by impedance method enables accurate measurement of red blood cell agglutination information. Therefore, an accurate red blood cell count can be obtained based on the acquired red blood cell agglutination information and the raw red blood cell volume distribution information. In this method, the entire detection process requires no manual intervention, achieving low-cost, efficient, and accurate impedance-based red blood cell counts.
[0063] The following examples further illustrate how this application obtains accurate red blood cell counts.
[0064] The inventors of this application discovered through research that when measuring red blood cells using the electrical impedance method, due to particle concentration, multiple red blood cells may pass through the detector simultaneously. The simultaneous passage of multiple red blood cells can be termed erythrocyte polyploidy; for example, two red blood cells passing through the detector simultaneously can be termed erythrocyte diploidy, and three red blood cells passing through the detector simultaneously can be termed erythrocyte triploidy, etc. Furthermore, the electronic signal characteristics of agglutinated red blood cells are similar to those of erythrocyte polyploidy. For example, particles from two agglutinated red blood cells are difficult to distinguish from diploid red blood cells, and particles from three agglutinated red blood cells are difficult to distinguish from triploid red blood cells, etc.
[0065] Figure 4 This shows a histogram of raw red blood cell volume distribution (e.g., a raw red blood cell volume distribution histogram in the volume range of 50 fL to 320 fL) for blood samples without red blood cell agglutination, where the horizontal axis represents the volume size of the red blood cells being tested, and the vertical axis represents the number of red blood cells of that volume size. Figure 4 As shown, for blood samples without red blood cell agglutination, red blood cell polyploidy occurs in volumes greater than 100 fL.
[0066] Figure 5 This shows a histogram of the original red blood cell volume distribution (e.g., a histogram of the original red blood cell volume distribution in the volume range of 50 fL to 320 fL) as information on the original red blood cell volume distribution of a blood sample with red blood cell agglutination. The horizontal axis represents the volume size of the red blood cells being tested, and the vertical axis represents the number of red blood cells of that volume size. Figure 5 As shown, for blood samples with red blood cell agglutination, compared to Figure 4 The histogram showing the original erythrocyte volume distribution indicates a higher number of particles in the volume range greater than 100 fL. This means that agglutinated erythrocytes are mixed together with polyploid erythrocytes, making them difficult to distinguish.
[0067] In view of this, in some embodiments, the volume distribution information of erythrocyte polyploidy is estimated based on the original erythrocyte volume distribution information; however, erythrocyte agglutination information is obtained based on both the original erythrocyte volume distribution information and the erythrocyte polyploidy volume distribution information to obtain the erythrocyte count value. That is, the processor 140 obtaining erythrocyte agglutination information based on the original erythrocyte volume distribution information may include:
[0068] Based on the original erythrocyte volume distribution information, polyploid volume distribution information of erythrocyte polyploidy is obtained, and
[0069] Red blood cell agglutination information is obtained based on the original red blood cell volume distribution information and the polyploid volume distribution information.
[0070] Here, the types of erythrocyte polyploidy can include a variety of types, such as erythrocyte diploid, erythrocyte triploid, and erythrocyte tetraploid.
[0071] In some embodiments, erythrocyte polyploidy includes at least erythrocyte diploid and erythrocyte triploid. Preferably, erythrocyte polyploidy includes at least erythrocyte diploid, erythrocyte triploid, and erythrocyte tetraploid.
[0072] In particular, erythrocyte polyploids include at least diploid, triploid, tetraploid, and pentaploid erythrocytes.
[0073] In other embodiments, erythrocyte polyploids may include polyploids within a specific volume range, such as erythrocyte polyploids in the range of 100 fL to 320 fL.
[0074] In some embodiments, the processor 140 may obtain polyploid volume distribution information of erythrocyte polyploids based on the original erythrocyte volume distribution information, which may include:
[0075] Calculate the total number of polyploid red blood cells, i.e., the sum of the number of different types of polyploids, based on the original red blood cell volume distribution information;
[0076] Calculate the volume distribution shape of polyploid red blood cells based on the original red blood cell volume distribution information;
[0077] The polyploid volume distribution information is calculated based on the total number and volume distribution shape of polyploid red blood cells.
[0078] Preferably, the polyploid volume distribution information may include the number of polyploids within a set volume range (e.g., a volume range of 100 fL to 320 fL). The set volume range may be a volume range in which the volume of the red blood cells to be tested is greater than or equal to a first preset value and less than or equal to a second preset value, wherein the first preset value is less than the second preset value.
[0079] In some embodiments, if red blood cell polyploids include multiple types of polyploids, the defined volume range may include multiple non-overlapping volume ranges corresponding one-to-one with these multiple types of polyploids, for example, diploids correspond to one volume range and triploids correspond to another volume range.
[0080] There are many methods for estimating the number of polyploids based on the original red blood cell volume distribution information. The following description uses the estimation of the number of diploids as an example. The number of other types of polyploids can be obtained in a similar way.
[0081] In some embodiments, the total number of polyploid red blood cells can be calculated using the effective sensing area volume. For example, the number of m-ploid cells is Nm = {[-ln(1-αn)]} m+1 / (αm!)}e ln(1-αn)Where n is the number of particles measured by the instrument, and α is the effective sensing area volume, that is, the size of the aperture volume of the small hole covered by the pulse generated by the small hole sensing area when the particle passes through the small hole sensing area.
[0082] In other embodiments, the empirical formula N2 = N*e can also be used. -λ*N The number of diploids is calculated, where N² is the number of diploids, N is the number of detected particles, and λ is an algorithm parameter derived from empirical values. λ differs for other polyploids that are not diploids.
[0083] In other embodiments, the formula N2 = f(N) can also be used to calculate the number of diploids, where N2 is the number of diploids, N is the number of detected particles, and f(N) is an m-th degree polynomial. Taking a cubic polynomial as an example, N2 = f(N) = a3*N3 + a2*N3 + a1*N + a0, where a3, a2, a1, and a0 are algorithm parameters. Those skilled in the art will know that the algorithm parameters in the above empirical formula may differ for different instruments.
[0084] Here, the volume distribution shape of erythrocyte polyploids can characterize the correspondence between the size and distribution probability of erythrocyte polyploids. For example, the probability corresponding to the size of the erythrocyte to be tested can be calculated based on the original erythrocyte volume distribution information, and then the volume distribution shape of erythrocyte polyploids can be calculated according to the following formula:
[0085]
[0086] Where p(x) represents the probability that the size of the red blood cell being tested is x, p n (z) represents the probability that the volume of the n-fold body is z, where n is greater than or equal to 2. When n = 2, p n-1 (zx) is equivalent to p(zx).
[0087] In some embodiments, the processor 140 calculates polyploid volume distribution information based on the total number and volume distribution shape of erythrocyte polyploids, which may include:
[0088] The correspondence between the size of a polyploid red blood cell and the number of polyploid red blood cells of that size is determined based on the total number and volume distribution shape of the polyploid red blood cells, and the polyploid volume distribution information is calculated based on this correspondence.
[0089] For example, based on the correspondence between the size of a polyploid red blood cell and the number of polyploid red blood cells of that size, the number of polyploid red blood cells with a size within a set volume range can be calculated to obtain polyploid volume distribution information.
[0090] It is understandable that different red blood cell polyploids correspond to different set volume ranges; for example, diploid, triploid, and tetraploid red blood cells all have different set volume ranges.
[0091] The set volume range can be a fixed volume range that has been set in advance based on empirical values.
[0092] In some embodiments, the processor 140 obtains erythrocyte agglutination information based on the original erythrocyte volume distribution information and the polyploid volume distribution information, which may include:
[0093] The polyploid volume distribution information is subtracted from the original erythrocyte volume distribution information to obtain the erythrocyte agglutination volume distribution information, and the number of agglutinated erythrocytes is obtained based on the erythrocyte agglutination volume distribution information.
[0094] For example, the original red blood cell volume distribution information may include the total number of red blood cells to be tested within a preset volume range, and the polyploid volume distribution information may include the number of polyploids within that preset volume range. Subtracting the number of polyploids from the total number of red blood cells yields the difference, which is the number of agglutinated red blood cells (also known as the red blood cell agglutination amount).
[0095] In some embodiments, based on Figure 6 The original red blood cell volume distribution histogram shown shows that the volume distribution information of N (=2, 3, 4, ...) red blood cell agglutinations, RBC_Agg_Hist_N(Ni), can be calculated as follows:
[0096] RBC_Agg_Hist_N(Ni)=RBC_Hist(Ni)-RBC_N_Mutiple_Hist(Ni) where RBC_Hist(Ni) represents the original red blood cell volume distribution information, RBC_N_Mutiple_Hist(Ni) represents the polyploid volume distribution information, and Ni represents the volume range (also known as the volume distribution region) of N red blood cell agglutinations.
[0097] For example, such as Figure 6 As shown, the volume range for 2 red blood cell agglutinations is the region between LineLeft_2 and LineRight_2; the set volume range for 3 red blood cell agglutinations is the region between LineRight_2 and LineLeft_3; and the set volume range for 4 red blood cell agglutinations is the region between LineLeft_3 and LineRight_3. It can be understood here that... Figure 5In the original red blood cell volume distribution histogram shown, there are both diploid red blood cells and agglutination clusters of two red blood cells in the region between LineLeft_2 and LineRight_2; there are both triploid red blood cells and agglutination clusters of three red blood cells in the region between LineRight_2 and LineLeft_3; and there are both tetraploid red blood cells and agglutination clusters of four red blood cells in the region between LineLeft_3 and LineRight_3.
[0098] One approach is to subtract the estimated polyploid volume distribution histogram RBC_N_Mutiple_Hist(Ni) from the original erythrocyte volume distribution histogram RBC_Hist(Ni) to obtain the erythrocyte agglutination volume distribution histogram RBC_Agg_Hist_N(Ni). The total number of N erythrocyte agglutinations can be determined based on the total area of the estimated erythrocyte agglutination volume distribution histogram RBC_Agg_Hist_N(Ni).
[0099] For example, Figure 6 The volume distribution information of polyploid cells (i.e., the dashed line portion) is subtracted from the original erythrocyte volume distribution information to obtain the volume distribution information of erythrocyte agglutination. Then, based on the volume distribution information of erythrocyte agglutination, the number of erythrocytes agglutinated within the corresponding set volume range can be calculated (i.e., the number of agglutinated erythrocytes).
[0100] In some alternative embodiments, the processor 140 may obtain erythrocyte agglutination information based on the original erythrocyte volume distribution information, which may include:
[0101] The original red blood cell volume distribution information is input into the machine learning model to obtain the output of the machine learning model as red blood cell agglutination information.
[0102] For example, raw red blood cell volume distribution information (e.g., red blood cell volume distribution histogram) can be input into a pre-trained machine learning model (e.g., neural network model) to obtain the output of the machine learning model as red blood cell aggregation information, without having to calculate polyploid volume distribution information.
[0103] In some embodiments, the processor 140 obtains the red blood cell count value of the blood sample to be tested based on the original red blood cell volume distribution information and red blood cell agglutination information, which may include:
[0104] The raw red blood cell count of the blood sample to be tested is obtained based on the raw red blood cell volume distribution information (hereinafter also referred to as the uncorrected red blood cell count).
[0105] The red blood cell count of the blood sample to be tested is obtained based on the original red blood cell count and red blood cell agglutination information.
[0106] Furthermore, the processor 140 can also be configured to output the raw red blood cell count (i.e., the red blood cell count before correction) and the red blood cell count of the blood sample to be tested (i.e., the corrected red blood cell count). For example, the processor 140 can output and display the raw red blood cell count and the red blood cell count of the blood sample to be tested on the user interface of the display device 150.
[0107] In a specific example, the processor 140 obtains the red blood cell count value of the blood sample to be tested based on the original red blood cell count value and red blood cell agglutination information, which may include:
[0108] The original red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information;
[0109] The number of agglutinated red blood cells is obtained based on the volume distribution information of red blood cell agglutination; and
[0110] The sum of the original red blood cell count and the number of agglutinated red blood cells is determined as the red blood cell count of the blood sample to be tested.
[0111] As one implementation method, the red blood cell count of the blood sample to be tested is calculated according to the following modified model:
[0112] RBC2 = RBC1 + M2 * (number of RBC aggregates) + M3 * (number of RBC aggregates) + ... + M k *N number of RBC agglutinations, where RBC2 is the corrected red blood cell count (also known as the true RBC value), RBC1 is the original red blood cell count before correction (also known as the RBC measurement value), M2...M k All are constants, and represent the weighting coefficients corresponding to the number of agglutinated red blood cells of the Nth (=2, 3, 4, …)th erythrocyte.
[0113] In a specific example, RBC2 = RBC1 + 2*2 RBC aggregates + 3*3 RBC aggregates + ... + N*N RBC aggregates.
[0114] In this way, by correcting the original red blood cell count based on the volume distribution information of the agglutinated red blood cells, an accurate red blood cell count can be obtained from the blood sample to be tested.
[0115] Furthermore, the processor 140 can also be configured to output a red blood cell agglutination prompt when the number of agglutinated red blood cells exceeds a preset threshold, and output the original red blood cell count and the red blood cell count of the blood sample to be tested. For example, the processor 140 can display the red blood cell agglutination prompt, the original red blood cell count, and the red blood cell count of the blood sample to be tested on the user interface of the display device 150.
[0116] If the number of agglutinated red blood cells exceeds a preset threshold, it indicates that red blood cell agglutination has occurred in the blood sample being tested, and the processor 140 will issue a red blood cell agglutination alarm, such as outputting a red blood cell agglutination prompt. If the number of agglutinated red blood cells is less than or equal to the preset threshold, it indicates that red blood cell agglutination has not occurred in the blood sample being tested, and the processor 140 does not need to issue a red blood cell agglutination alarm.
[0117] The preset threshold can be adjusted according to the type of erythrocyte polyploidy. For example, if the erythrocyte polyploidy is diploid, the preset threshold can be 0.03 × 10⁻⁶. 12 / L to 1.00×10 12 / L, for example, can be 0.035×10 12 / L. In other embodiments, the preset threshold may also be determined based on the number of agglutinated red blood cells in the blood sample to be tested, where red blood cell agglutination is known to occur.
[0118] In other words, the processor 140 can determine that red blood cell agglutination has occurred in the blood sample if the number of agglutinated red blood cells determined from the blood sample to be tested is greater than a preset threshold. It can then output a red blood cell agglutination prompt, as well as the original red blood cell count value before correction and the corrected red blood cell count value, for clinical reference, which helps to further improve the accuracy of clinical diagnosis.
[0119] In some embodiments, the processor 140 may also be configured to:
[0120] The volume distribution information of the erythrocyte agglutination is subtracted from the original erythrocyte volume distribution information to obtain the volume distribution information after removing the interference of erythrocyte agglutination; and
[0121] Based on the volume distribution information after removing interference from red blood cell agglutination, at least one of the following is obtained from the blood sample to be tested: mean red blood cell volume (MCV), red blood cell distribution width standard deviation (RDW-SD), red blood cell distribution width coefficient of variation (RDW-CV), hematocrit (HCT), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC).
[0122] Figure 5 The original red blood cell volume distribution histogram of RBC agglutination samples without deducting red blood cell agglutination information is shown. Figure 7The histogram of red blood cell volume distribution for RBC agglutination samples after removing information on red blood cell agglutination is shown. Among them, Figure 7 The red blood cell volume distribution histogram shown is in Figure 5 The original red blood cell volume distribution histogram is obtained by subtracting the red blood cell aggregation volume distribution information from the original red blood cell volume distribution histogram shown.
[0123] based on Figure 7 The red blood cell volume distribution histogram shown can be used to calculate at least one of the following: mean red blood cell volume (MCV), red blood cell distribution width standard deviation (RDW-SD), red blood cell distribution width coefficient of variation (RDW-CV), hematocrit (HCT), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC) of the blood sample being tested.
[0124] For example, calculation Figure 7 The mean value of the red blood cell volume distribution histogram shown is the corrected MCV result. Calculation Figure 7 The standard deviation (SD) and coefficient of variation (CV) of the red blood cell volume distribution histogram shown are the corrected RDW-SD and RDW-CV. The corrected hematocrit (HCT) is calculated by multiplying the corrected RBC count by the corrected MCV. The corrected MCH is the hemoglobin concentration (HGB) divided by the corrected RBC count. The corrected MCHC is the hemoglobin concentration (HGB) divided by the corrected HCT result.
[0125] In this way, it is possible to correct the results of parameters such as MCV, RDW-SD, RDW-CV, HCT, MCH and MCHC of RBC agglutinated samples at zero cost using only the original red blood cell volume distribution information obtained by electrical impedance method.
[0126] The embodiments of this application are verified through specific examples below.
[0127] 177 RBC agglutination samples with different degrees of agglutination were collected and analyzed using a BC-6800Plus hematology analyzer manufactured by Mindray Bio-Medical Electronics Co., Ltd. The raw red blood cell count (i.e., the uncorrected red blood cell count) and raw mean corpuscular volume (MCV) (i.e., the uncorrected MCV) of each sample were measured. Each sample was incubated at 37°C for at least 30 minutes and then re-analyzed. The measurements from this re-analysis were used as reference values for the red blood cell count and mean corpuscular volume. The correlation and relative deviation between the reference values and the raw red blood cell count (i.e., the uncorrected red blood cell count) were then determined. Figure 8 and Figure 9 As shown, the correlation and relative deviation between the MCV reference value and the original MCV are obtained, as follows: Figure 10 and Figure 11 As shown. Among them, Figure 8 The correlation curve between the reference red blood cell count and the original red blood cell count (i.e., the red blood cell count before correction) is shown. Figure 9 This shows the relative deviation between the reference red blood cell count and the original red blood cell count (i.e., the red blood cell count before correction). Figure 10 The correlation curve between the MCV reference value and the original MCV (i.e., the MCV before correction) is shown. Figure 11 The relative deviation between the MCV reference value and the original MCV (i.e., the MCV before correction) is shown.
[0128] from Figure 8 and Figure 9 It can be seen that, for these 177 RBC agglutination samples, the correlation between the uncorrected red blood cell count and the red blood cell count reference value was 0.626, and the relative deviation between the uncorrected red blood cell count and the red blood cell count reference value was relatively large.
[0129] from Figure 10 and Figure 11 It can be seen that for these 177 RBC agglutination samples, the correlation between the uncorrected MCV and the MCV reference value was 0.356, and the relative deviation between the uncorrected MCV and the MCV reference value was relatively large.
[0130] The original red blood cell count and original mean erythrocyte volume (MCV) of each RBC agglutination sample were corrected using the method described in this application to obtain the corrected red blood cell count and corrected MCV for each RBC agglutination sample. This allows for the determination of the correlation and relative deviation between the red blood cell count reference value and the corrected red blood cell count value. Figure 9 and Figure 12 As shown, the correlation and relative deviation between the MCV reference value and the corrected MCV are obtained, as follows: Figure 11 and Figure 13 As shown. Among them, Figure 9 It also shows the relative deviation between the reference red blood cell count and the corrected red blood cell count. Figure 12 The correlation curve between the reference red blood cell count and the corrected red blood cell count is shown. Figure 11 The relative deviations between the MCV reference value and the corrected MCV are also shown. Figure 13 The correlation curve between the MCV reference value and the corrected MCV is shown.
[0131] from Figure 9 and Figure 12It can be seen that by using the original red blood cell volume distribution information to obtain red blood cell agglutination information, and then using the red blood cell agglutination information to correct the original red blood cell count value, the correlation between the corrected red blood cell count value and the red blood cell count reference value increased from 0.626 to 0.947, and the corrected red blood cell count value was basically consistent with the red blood cell count reference value (i.e., the relative deviation was small).
[0132] from Figure 11 and Figure 13 It can be seen that by using the original red blood cell volume distribution information to obtain red blood cell agglutination information, and then using the red blood cell agglutination information to correct the original red blood cell volume distribution information, the correlation between the corrected MCV and the MCV reference value increased from 0.356 to 0.996, and the corrected MCV was basically consistent with the MCV reference value (i.e., the relative deviation was small).
[0133] Therefore, for RBC agglutination samples exhibiting erythrocyte agglutination, the embodiments of this application can obtain relatively accurate erythrocyte counts and mean erythrocyte volume (MCV).
[0134] This application also provides a method for red blood cell detection, which can obtain accurate red blood cell counts for red blood cell agglutination samples without manual intervention or retesting, thereby improving the efficiency of red blood cell detection. This red blood cell detection method can be implemented, in particular, on a blood cell analyzer 100 according to any of the above embodiments.
[0135] like Figure 14 As shown, the red blood cell detection method 1400 includes:
[0136] Step 1410: Collect the blood sample to be tested;
[0137] Step 1420: Mix a portion of the blood sample to be tested with the diluent to prepare the test sample;
[0138] Step 1430: Perform impedance measurement on the test sample to obtain the electronic signal of each particle in the test sample.
[0139] Step 1440: Obtain the original red blood cell volume distribution information based on the electronic signals of each particle in the test sample;
[0140] Step 1450: Obtain red blood cell agglutination information based on the original red blood cell volume distribution information;
[0141] Step 1460: Obtain the red blood cell count value of the blood sample to be tested based on the original red blood cell volume distribution information and red blood cell agglutination information.
[0142] In some embodiments, step 1450 may include:
[0143] Based on the original erythrocyte volume distribution information, polyploid volume distribution information of erythrocyte polyploidy is obtained, and
[0144] Red blood cell agglutination information is obtained based on the original red blood cell volume distribution information and the polyploid volume distribution information.
[0145] In some embodiments, obtaining erythrocyte agglutination information based on the original erythrocyte volume distribution information and the polyploid volume distribution information may include: subtracting the polyploid volume distribution information from the original erythrocyte volume distribution information to obtain the erythrocyte agglutination volume distribution information, and obtaining the number of agglutinated erythrocytes based on the erythrocyte agglutination volume distribution information.
[0146] Here, step 1460 may include: obtaining the original red blood cell count value of the blood sample to be tested based on the original red blood cell volume distribution information, and determining the sum of the original red blood cell count value and the number of agglutinated red blood cells as the red blood cell count value of the blood sample to be tested.
[0147] In some embodiments, method 1400 may further include:
[0148] The volume distribution information of erythrocyte agglutination is subtracted from the original erythrocyte volume distribution information to obtain the volume distribution information after removing the interference of erythrocyte agglutination; and
[0149] Based on the volume distribution information after removing interference from erythrocyte agglutination, obtain at least one of the following: MCV, RDW-SD, RDW-CV, HCT, MCH, and MCHC of the blood sample to be tested.
[0150] In some embodiments, erythrocyte polyploids include at least erythrocyte diploids and erythrocyte triploids; preferably, erythrocyte polyploids include at least erythrocyte diploids, erythrocyte triploids, and erythrocyte tetraploids; in particular, erythrocyte polyploids include at least erythrocyte diploids, erythrocyte triploids, erythrocyte tetraploids, and erythrocyte pentaploids.
[0151] In some embodiments, step 1450 may include:
[0152] The original red blood cell volume distribution information is input into the machine learning model to obtain the output of the machine learning model as red blood cell agglutination information.
[0153] In some embodiments, step 1460 may include:
[0154] The original red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information;
[0155] The red blood cell count of the blood sample to be tested is obtained based on the original red blood cell count and red blood cell agglutination information; and
[0156] Output the raw red blood cell count and the red blood cell count of the blood sample to be tested.
[0157] In some embodiments, step 1460 may include:
[0158] The original red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information;
[0159] The number of agglutinated red blood cells is obtained based on the volume distribution information of red blood cell agglutination.
[0160] The sum of the original red blood cell count and the number of agglutinated red blood cells is determined as the red blood cell count of the blood sample to be tested; and
[0161] When the number of agglutinated red blood cells exceeds a preset threshold, a red blood cell agglutination prompt is output, along with the original red blood cell count and the red blood cell count of the blood sample to be tested.
[0162] For further embodiments of the red blood cell detection method 1400 provided in this application, please refer to the above description of the blood cell analyzer 100 and its embodiments.
[0163] All features or combinations of features mentioned above in the specification, drawings, and claims may be used in any combination or individually, provided they are meaningful within the scope of this application and do not contradict each other. The advantages and features described in the blood cell analyzer provided in this application shall be applied accordingly to the red blood cell detection method provided in this application, and vice versa.
[0164] The above description is merely a preferred embodiment of this application and does not limit the scope of patent protection of this application. All equivalent modifications made based on the content of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.
Claims
1. A blood cell analyzer, characterized in that, include: A sampling device is used to collect blood samples for testing. A sample preparation apparatus for mixing a portion of the blood sample to be tested with a diluent to prepare a test sample; An impedance detection device includes a counting cell and a detection component. The counting cell is used to allow the test sample to pass through, and the detection component is used to acquire the electronic signal of each particle in the test sample as it passes through the counting cell. The processor is configured as follows: The original red blood cell volume distribution information is obtained based on the electronic signals of each particle in the test sample. Red blood cell agglutination information is obtained based on the original red blood cell volume distribution information, and The red blood cell count of the blood sample to be tested is obtained based on the original red blood cell volume distribution information and the red blood cell agglutination information.
2. The blood cell analyzer according to claim 1, characterized in that, The processor obtains red blood cell agglutination information based on the original red blood cell volume distribution information, including: Based on the original erythrocyte volume distribution information, polyploid volume distribution information of erythrocyte polyploids is obtained, and The red blood cell agglutination information is obtained based on the original red blood cell volume distribution information and the polyploid volume distribution information.
3. The blood cell analyzer according to claim 2, characterized in that, The processor obtains erythrocyte agglutination information based on the original erythrocyte volume distribution information and the polyploid volume distribution information, including: subtracting the polyploid volume distribution information from the original erythrocyte volume distribution information to obtain erythrocyte agglutination volume distribution information, and obtaining the number of agglutinated erythrocytes based on the erythrocyte agglutination volume distribution information. The processor obtains the red blood cell count of the blood sample to be tested based on the original red blood cell volume distribution information and the red blood cell agglutination information, including: obtaining the original red blood cell count of the blood sample to be tested based on the original red blood cell volume distribution information, and determining the sum of the original red blood cell count and the number of agglutinated red blood cells as the red blood cell count of the blood sample to be tested.
4. The blood cell analyzer according to claim 3, characterized in that, The processor is also configured to: The volume distribution information of the erythrocyte agglutination is subtracted from the original erythrocyte volume distribution information to obtain the volume distribution information after removing the interference of erythrocyte agglutination; and Based on the volume distribution information after removing interference from erythrocyte agglutination, at least one of the following values of the blood sample to be tested is obtained: MCV, RDW-SD, RDW-CV, HCT, MCH, and MCHC.
5. The blood cell analyzer according to any one of claims 2 to 4, characterized in that, The erythrocyte polyploids include at least erythrocyte diploids and erythrocyte triploids; Preferably, the erythrocyte polyploids include at least erythrocyte diploids, erythrocyte triploids, and erythrocyte tetraploids; In particular, the erythrocyte polyploids include at least erythrocyte diploids, erythrocyte triploids, erythrocyte tetraploids, and erythrocyte pentaploids.
6. The blood cell analyzer according to claim 1, characterized in that, The processor obtains red blood cell agglutination information based on the original red blood cell volume distribution information, including: The original red blood cell volume distribution information is input into a machine learning model to obtain the output of the machine learning model as the red blood cell agglutination information.
7. The blood cell analyzer according to any one of claims 1 to 6, characterized in that, The processor obtains the red blood cell count value of the blood sample to be tested based on the original red blood cell volume distribution information and the red blood cell agglutination information, including: The original red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information; The red blood cell count of the blood sample to be tested is obtained based on the original red blood cell count and the red blood cell agglutination information; and Output the original red blood cell count and the red blood cell count of the blood sample to be tested.
8. The blood cell analyzer according to any one of claims 1 to 6, characterized in that, The processor obtains the red blood cell count value of the blood sample to be tested based on the original red blood cell volume distribution information and the red blood cell agglutination information, including: The original red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information; The number of agglutinated red blood cells is obtained based on the volume distribution information of the agglutinated red blood cells; The sum of the original red blood cell count and the number of agglutinated red blood cells is determined as the red blood cell count of the blood sample to be tested; and When the number of agglutinated red blood cells exceeds a preset threshold, a red blood cell agglutination prompt is output, along with the original red blood cell count and the red blood cell count of the blood sample to be tested.
9. A method for detecting red blood cells, characterized in that, include: Collect the blood sample to be tested; A portion of the blood sample to be tested is mixed with a diluent to prepare the test sample; The test sample is subjected to impedance measurement to obtain the electronic signal of each particle in the test sample; The original red blood cell volume distribution information is obtained based on the electronic signals of each particle in the test sample. Red blood cell agglutination information is obtained based on the original red blood cell volume distribution information; as well as The red blood cell count of the blood sample to be tested is obtained based on the original red blood cell volume distribution information and the red blood cell agglutination information.
10. The method according to claim 9, characterized in that, Based on the original red blood cell volume distribution information, red blood cell agglutination information is obtained, including: Based on the original erythrocyte volume distribution information, polyploid volume distribution information of erythrocyte polyploids is obtained, and The red blood cell agglutination information is obtained based on the original red blood cell volume distribution information and the polyploid volume distribution information.
11. The method according to claim 10, characterized in that, Obtaining erythrocyte agglutination information based on the original erythrocyte volume distribution information and the polyploid volume distribution information includes: subtracting the polyploid volume distribution information from the original erythrocyte volume distribution information to obtain the erythrocyte agglutination volume distribution information, and obtaining the number of agglutinated erythrocytes based on the erythrocyte agglutination volume distribution information. Obtaining the red blood cell count of the blood sample to be tested based on the original red blood cell volume distribution information and the red blood cell agglutination information includes: obtaining the original red blood cell count of the blood sample to be tested based on the original red blood cell volume distribution information, and determining the sum of the original red blood cell count and the number of agglutinated red blood cells as the red blood cell count of the blood sample to be tested.
12. The method according to claim 11, characterized in that, Also includes: The volume distribution information of the erythrocyte agglutination is subtracted from the original erythrocyte volume distribution information to obtain the volume distribution information after removing the interference of erythrocyte agglutination. as well as Based on the volume distribution information after removing interference from erythrocyte agglutination, at least one of the following values of the blood sample to be tested is obtained: MCV, RDW-SD, RDW-CV, HCT, MCH, and MCHC.
13. The method according to any one of claims 10 to 12, characterized in that, The erythrocyte polyploids include at least erythrocyte diploids and erythrocyte triploids; Preferably, the erythrocyte polyploids include at least erythrocyte diploids, erythrocyte triploids, and erythrocyte tetraploids; In particular, the erythrocyte polyploids include at least erythrocyte diploids, erythrocyte triploids, erythrocyte tetraploids, and erythrocyte pentaploids.
14. The method according to claim 1, characterized in that, Based on the original red blood cell volume distribution information, red blood cell agglutination information is obtained, including: The original red blood cell volume distribution information is input into a machine learning model to obtain the output of the machine learning model as the red blood cell agglutination information.
15. The method according to any one of claims 9 to 14, characterized in that, The red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information and the red blood cell agglutination information, including: The original red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information; The red blood cell count of the blood sample to be tested is obtained based on the original red blood cell count and the red blood cell agglutination information; and Output the original red blood cell count and the red blood cell count of the blood sample to be tested.
16. The method according to any one of claims 9 to 14, characterized in that, The red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information and the red blood cell agglutination information, including: The original red blood cell count value of the blood sample to be tested is obtained based on the original red blood cell volume distribution information; The number of agglutinated red blood cells is obtained based on the volume distribution information of the agglutinated red blood cells; The sum of the original red blood cell count and the number of agglutinated red blood cells is determined as the red blood cell count of the blood sample to be tested; and When the number of agglutinated red blood cells exceeds a preset threshold, a red blood cell agglutination prompt is output, along with the original red blood cell count and the red blood cell count of the blood sample to be tested.