Method and device for determining physical characteristic parameters of red blood cells, storage medium, terminal

Abstract

A method and device for determining a physical characteristic parameter of red blood cells, a storage medium and a terminal, the method comprising: taking pictures of red blood cells flowing in a micro tube to determine a plurality of red blood cell images; determining a morphological parameter of the red blood cells according to the plurality of red blood cell images, the morphological parameter being selected from a length value of the red blood cells and a contour of the red blood cells; and determining a physical characteristic parameter of the red blood cells according to the morphological parameter of the red blood cells, wherein a diameter of the micro tube ranges from greater than or equal to 2.0 microns to less than or equal to 5.0 microns. The present application can obtain a plurality of intrinsic physical characteristic parameters of red blood cells in one experiment, and effectively improve the measurement speed and accuracy of the physical characteristic parameters of red blood cells while considering the number of red blood cells.

Description

Technical Field

[0001] This invention relates to the field of cell physical property parameter measurement technology, and in particular to a method, apparatus, storage medium, and terminal for determining the physical property parameters of red blood cells. Background Technology

[0002] The physical properties of cells, along with their biochemical phenotypes, are enriching the fundamental theories of cell mechanics and gradually becoming an emerging topic in the development of personalized precision medicine. Red blood cells are among the most important cells in the human body, and their physical properties, including cell area, volume, and membrane shear modulus, are closely related to erythrocyte-mediated flow resistance and substance transport in cardiovascular, metabolic, and neurological diseases. Compared to other cells, the superior deformability of erythrocytes and the massive data sampling requirements make the development of precise, high-throughput single-cell erythrocyte measurements both significant and challenging.

[0003] Existing classical methods for measuring the physical properties of single cells include micropipette method, atomic force microscopy, and optical tweezers method. These methods can measure the inherent mechanical properties of a single red blood cell, but the measurement speed is only 10-100 cells per hour, while there are 3-5 million blood cells per milliliter of human blood. Therefore, these methods are difficult to apply in both biological and clinical medicine while maintaining timeliness and statistical standards. In recent years, many researchers have developed high-throughput methods to characterize the deformability of nucleated cells through morphological parameters after cell deformation, such as deformability cytometry (DC) and suspended microchannel resonator (SMR). However, these high-throughput methods do not reflect the inherent mechanical properties of cells, and the obtained cell deformation parameters are related to cell size. Furthermore, cell deformation parameters obtained by treating cells as viscoelastic granules are coupled with the combined effects of cell membrane, nuclear elasticity, and cytoplasmic viscosity, and the weights of these factors cannot be clearly distinguished.

[0004] To eliminate the interaction between cell size and deformability in flow-based high-throughput methods, more complex methods have begun to utilize the fluid-cell membrane coupling properties in narrow channels to invert the cell's elastic modulus. For example, real-time deformability cytometry (RT-DC) is used to measure the Young's modulus of cells. This method treats the cell as an elastically homogeneous solid sphere, neglecting the fluid-structure interaction effects in narrow channels. Furthermore, the measurement error increases with increasing throughput, making it unsuitable for measuring the mechanical properties of erythrocytes, which possess excellent deformability. Building on this, Fregin et al. further developed a dynamic (dRT-DC) high-throughput method, which separates and reconstructs the cell deformation parameters and deformation characteristic time corresponding to the cell's elastic deformation and viscous dynamic response to obtain cell viscosity parameters. However, the dynamic high-throughput method, based on elastic or viscoelastic spherical particle mechanics models, is not applicable to non-spherical, heterogeneous cells (such as erythrocytes) with large redundant areas, and its measurement accuracy requires further validation.

[0005] Therefore, there is an urgent need for a method to measure the physical properties of red blood cells that can obtain multiple intrinsic physical property parameters of red blood cells in a single experiment, while taking into account the standard of red blood cell count and effectively improving the measurement speed and accuracy. Summary of the Invention

[0006] One of the objectives of this invention is to provide a method, device, storage medium, and terminal for determining the physical characteristic parameters of red blood cells, which can obtain multiple intrinsic physical characteristic parameters of red blood cells in a single experiment, while taking into account the standard of red blood cell count and effectively improving measurement speed and accuracy.

[0007] To achieve the above objectives, embodiments of the present invention provide a method for determining the physical characteristic parameters of red blood cells, comprising the following steps: photographing red blood cells flowing in a microtube to determine multiple frames of flowing red blood cell images; determining the morphological parameters of the red blood cells based on the multiple frames of flowing red blood cell images, wherein the morphological parameters are selected from the length value and the outline of the red blood cells; and determining the physical characteristic parameters of the red blood cells based on the morphological parameters; wherein the diameter of the microtube is greater than or equal to 2.0 micrometers and less than or equal to 5.0 micrometers.

[0008] Optionally, the multi-frame images of flowing red blood cells are captured from the orifice region of the microtube, the morphological parameters are selected from the length values ​​of the red blood cells, and the physical property parameters of the red blood cells are the viscoelastic parameters of the red blood cells; determining the physical property parameters of the red blood cells based on the morphological parameters includes: determining the creep function of the red blood cells in the orifice region of the microtube based on the length values ​​of the red blood cells in the multi-frame images of flowing red blood cells; and determining the viscoelastic parameters of the red blood cells based on the creep function.

[0009] Optionally, determining the creep function of the red blood cell in the microtube orifice region based on the length value of the red blood cell in the multi-frame flowing red blood cell images includes: determining the length value of the red blood cell at the time of each frame of flowing red blood cell images;

[0010] The creep value of the red blood cells at each time point in each frame of the moving red blood cell image is determined using the following formula:

[0011] ε=2Lp / D;

[0012] Based on the creep values ​​at various time points, a creep function of the red blood cells over time is fitted.

[0013] The viscoelastic parameters of the erythrocytes are determined using the following formula based on the creep function:

[0014]

[0015] Where ε(t) is used to indicate the creep function, t refers to time, Lp refers to the length of the red blood cell in the microtube at different times, D refers to the diameter of the microtube, β refers to the viscoelastic parameter of the red blood cell, τ0 is the standardized time parameter, and a is a constant.

[0016] Optionally, the multi-frame flowing red blood cell images are captured from the stable flow region of the microtube, and the morphological parameters are selected from the contour of the red blood cells. Determining the morphological parameters of the red blood cells based on the multi-frame flowing red blood cell images includes: selecting one frame containing the red blood cells as the experimental image and selecting one frame not containing the red blood cells as the background image; performing differential motion analysis on the experimental image and the background image to determine a differential image; and determining the contour of the red blood cells based on the differential image.

[0017] Optionally, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the morphological parameters are selected from the outline of the red blood cells, and the physical characteristic parameters of the red blood cells are the surface area and volume of the red blood cells. Determining the physical characteristic parameters of the red blood cells based on their morphological parameters includes: dividing the outline of the red blood cells into multiple frustums; for each frustum, determining its left diameter, right diameter, and height; determining the frustum surface area and volume of each frustum based on its left diameter, right diameter, and height; and using an integral calculation method, using the sum of the frustum surface areas of the multiple frustums as the surface area of ​​the red blood cell, and the sum of the frustum volumes of the multiple frustums as the volume of the red blood cell.

[0018] Optionally, the surface area of ​​the red blood cells can be determined using the following formula:

[0019]

[0020] The volume of the red blood cells is determined using the following formula:

[0021]

[0022] Where A represents the surface area of ​​a red blood cell, A i V refers to the surface area of ​​the i-th frustum, and V refers to the volume of the red blood cell. i D refers to the volume of the i-th frustum. left The diameter of the left side of the frustum, D right The diameter of the right side of the frustum is 'h', and the height of the frustum is 'h'.

[0023] Optionally, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the morphological parameters are selected from the contour of the red blood cells, and the physical property parameters of the red blood cells are the membrane shear modulus of the red blood cells; determining the physical property parameters of the red blood cells based on the morphological parameters includes: determining the elongation length of the red blood cells in the microtube based on the contour of the red blood cells, and determining the flow velocity of the red blood cells in the stable flow region of the microtube; and using a machine learning algorithm to determine the membrane shear modulus of the red blood cells based at least on the elongation length and the flow velocity of the red blood cells.

[0024] Optionally, using a machine learning algorithm to determine the membrane shear modulus of the red blood cells based at least on the elongation length and flow velocity of the red blood cells includes: inputting the elongation length of the red blood cells, the flow velocity of the red blood cells, the diameter of the microtube, and the liquid flow velocity when there are no red blood cells in the microtube into the machine learning algorithm; and using the output of the machine learning algorithm as the membrane shear modulus of the red blood cells.

[0025] Optionally, the machine learning algorithm is a neural network algorithm that satisfies one or more of the following: the input layer parameters of the neural network are the stretching length of the red blood cell, the flow velocity of the red blood cell, the diameter of the microtube, and the liquid flow velocity when there are no red blood cells in the microtube; the hidden layer of the neural network uses an activation function; and the output layer parameter of the neural network is the membrane shear modulus of the red blood cell.

[0026] Optionally, the method further includes: predicting the glycated hemoglobin ratio of the red blood cells based on the physical characteristic parameters of the red blood cells; or, predicting the serum bilirubin and serum albumin levels of the subject based on the physical characteristic parameters of the red blood cells; or, characterizing the morphology and mechanical properties distribution of the red blood cells based on the physical characteristic parameters of the red blood cells.

[0027] Optionally, the physical property parameters of the red blood cells include the viscoelastic parameters and the membrane shear modulus of the red blood cells; predicting the glycated hemoglobin ratio of the red blood cells based on the physical property parameters of the red blood cells includes: constructing a first parameter product value using the viscoelastic parameters and the membrane shear modulus of the red blood cells; and predicting the glycated hemoglobin ratio of the red blood cells based on the comparison result of the first parameter product value and a first preset threshold.

[0028] Optionally, the physical properties of the red blood cells include the surface area and volume of the red blood cells, the membrane shear modulus of the red blood cells, and the viscoelastic parameters of the red blood cells. Predicting the serum bilirubin and serum albumin levels of the subject based on these physical properties includes: constructing a second parameter product value and a third parameter product value based on the surface area and volume of the red blood cells, the membrane shear modulus of the red blood cells, and the viscoelastic parameters of the red blood cells, respectively; predicting the subject's serum bilirubin level based on a comparison between the second parameter product value and a second preset threshold; and predicting the subject's serum albumin level based on a comparison between the third parameter product value and a third preset threshold.

[0029] Optionally, the physical property parameters of the red blood cells include the surface area and volume of the red blood cells, the viscoelastic parameters of the red blood cells, and the membrane shear modulus of the red blood cells; characterizing the morphology and mechanical property distribution of the red blood cells based on the physical property parameters of the red blood cells includes: constructing a probability density function of the morphology and mechanical property distribution of the red blood cells based on the surface area and volume of the red blood cells, the viscoelastic parameters of the red blood cells, and the membrane shear modulus of the red blood cells, thereby characterizing the morphology and mechanical property distribution of the red blood cells.

[0030] This invention also provides a device for determining the physical characteristic parameters of red blood cells, comprising:

[0031] A flowing red blood cell image determination module is used to capture images of red blood cells flowing in microtubes to determine multiple frames of flowing red blood cell images; a red blood cell morphology parameter determination module is used to determine the morphology parameters of the red blood cells based on the multiple frames of flowing red blood cell images, wherein the morphology parameters are selected from the length value and the outline of the red blood cells; and a red blood cell physical characteristic parameter determination module is used to determine the physical characteristic parameters of the red blood cells based on the morphology parameters of the red blood cells.

[0032] This invention also provides a storage medium, which is a computer-readable storage medium storing a computer program thereon. When the computer program is run by a processor, it executes the steps of the above-described method for determining the physical characteristic parameters of red blood cells.

[0033] This invention also provides a terminal, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the steps of the above-described method for determining the physical characteristic parameters of red blood cells.

[0034] Compared with the prior art, the technical solution of the embodiments of the present invention has the following beneficial effects:

[0035] In this embodiment of the invention, red blood cells flowing in a microtube are photographed to determine multiple frames of flowing red blood cell images; then, based on the multiple frames of flowing red blood cell images, the morphological parameters of the red blood cells are determined; and then, based on the morphological parameters of the red blood cells, the physical characteristic parameters of the red blood cells are determined; wherein, the diameter of the microtube is greater than or equal to 2.0 micrometers and less than or equal to 5.0 micrometers. Compared to existing technologies such as traditional micropipette methods, atomic force microscopy, and optical tweezers, which have limited measurement speeds, and high-throughput measurement methods such as deformable flow cytometry, microchannel cantilever resonators, and real-time deformable flow cytometry, which have insufficient accuracy or cannot measure multiple cell characteristic parameters at once, making them unsuitable for measuring the physical characteristics of cells with excellent deformability (such as erythrocytes), the microtube diameter range used in the embodiments of this invention is optimized for high-throughput experimental microtubes. This is suitable for measuring the mechanical properties of erythrocytes with excellent deformability, providing a suitable narrow channel for erythrocyte flow, minimizing clogging, and allowing for reasonable deformation and axial symmetry. This significantly reduces the error in measuring erythrocyte area and volume, and improves the accuracy of obtaining the membrane shear modulus of erythrocytes during machine learning. Furthermore, by employing microfluidic technology, this invention can improve the measurement speed of red blood cell physical property parameters while maintaining the standard for red blood cell count. Compared with existing high-throughput technologies, this invention also uses dimensionless mechanical analysis and machine learning methods to decouple the influence of cell size and cell elastic modulus on the deformability of red blood cells, enabling the measurement of the intrinsic physical properties of red blood cells and improving measurement accuracy. Moreover, the embodiments of this invention overcome the limitation that a single measurement experiment can only measure a single physical property parameter of the cell, achieving the effect of measuring multiple intrinsic physical property parameters of red blood cells in the same experiment.

[0036] Furthermore, by employing differential motion analysis, the contour of the red blood cells within the microtubes is determined based on the multi-frame images of flowing red blood cells. This effectively removes some noise unrelated to the red blood cell contour and eliminates static background regions irrelevant to the detection of flowing red blood cells, thereby obtaining the most complete and clear possible contour of the red blood cells within the microtubes. This improves the accuracy of subsequent calculations of the physical property parameters of the red blood cells based on their contours.

[0037] Furthermore, the determination of the membrane shear modulus of red blood cells using a machine learning algorithm, at least based on the elongation length and flow velocity of the red blood cells, includes: inputting the elongation length of the red blood cells, the flow velocity of the red blood cells, the diameter of the microtube, and the liquid flow velocity when there are no red blood cells in the microtube into the machine learning algorithm; and using the output of the machine learning algorithm as the membrane shear modulus of the red blood cells. This embodiment of the invention, by combining the analysis of multiple frames of flowing red blood cell images with a machine learning algorithm to determine the membrane shear modulus of red blood cells, can effectively improve the measurement accuracy.

[0038] Furthermore, in this embodiment of the invention, a parameter product value is constructed based on the determined viscoelastic parameters and membrane shear modulus of the red blood cells; and the glycated hemoglobin ratio of the red blood cells is predicted based on the comparison result of the parameter product value and a preset threshold, thereby accurately distinguishing between normal and abnormal glycated hemoglobin ratios of red blood cells. Therefore, this embodiment of the invention can be applied to the prediction of diabetes staging and severity, improving the timeliness, sensitivity and accuracy of diabetes prediction.

[0039] Furthermore, based on the surface area of ​​the red blood cells, the membrane shear modulus of the red blood cells, and the viscoelastic parameters of the red blood cells, a parameter product value is constructed; and based on the comparison result of the parameter product value with a preset threshold, the serum bilirubin content and serum albumin content of the subject are predicted, thereby accurately distinguishing between normal and abnormal serum bilirubin and serum albumin content in the subject. Therefore, the embodiments of the present invention can improve the timeliness, sensitivity, and accuracy of predicting related diseases expressed by serum bilirubin and serum albumin.

[0040] Furthermore, based on the surface area and volume of the red blood cells, their viscoelastic parameters, and their membrane shear modulus, a probability density function for the morphological and mechanical property distribution of the red blood cells is constructed, thereby characterizing the morphological and mechanical property distribution of the red blood cells. In this embodiment of the invention, by comparing the differences between the morphological and mechanical property distribution characteristics of red blood cells derived from a subject and those derived from red blood cells from a healthy person (reference sample), blood diseases (such as sickle cell disease and spherocytosis) in the subject can be effectively monitored. Attached Figure Description

[0041] Figure 1 This is a flowchart of a method for determining the physical characteristic parameters of red blood cells according to an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of an experimental apparatus for measuring the physical properties of red blood cells according to an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram illustrating the state of red blood cells entering microtubules and flowing stably in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the contour extraction of red blood cells based on the differential motion analysis method in an embodiment of the present invention;

[0045] Figure 5 yes Figure 1 A flowchart of the first specific implementation of step S13;

[0046] Figure 6 yes Figure 1 A flowchart of the second specific implementation method of step S13;

[0047] Figure 7 This is a planar schematic diagram illustrating the calculation of the surface area and volume of red blood cells based on their outlines in an embodiment of the present invention.

[0048] Figure 8 yes Figure 1 A flowchart of the third specific implementation method of step S13;

[0049] Figure 9 This is a schematic diagram of the neural network model used in calculating the membrane shear modulus of red blood cells in an embodiment of the present invention;

[0050] Figure 10 It is a two-dimensional correlation graph of the glycated hemoglobin ratio of the subject's red blood cells and the product of the membrane shear modulus and viscoelastic parameters of the red blood cells;

[0051] Figure 11 This is a schematic diagram of a device for determining the physical characteristic parameters of red blood cells according to an embodiment of the present invention;

[0052] Explanation of reference numerals in the attached figures:

[0053] Image acquisition module-21; Light-emitting device-211; Red blood cell solution container-212; Red blood cell solution-213; Microscope-214; Magnifying lens-215; Image sensor-216; Microtube-217; Pressure control module-22; Connecting pipeline-221; Water tank-222; Image analysis and data processing module-23. Detailed Implementation

[0054] As mentioned earlier, developing precise, rapid, and high-throughput measurement technologies for the physical properties of red blood cells is of great significance and presents both challenges.

[0055] In existing technologies, traditional techniques for measuring the physical properties of cells often employ micropipette methods, atomic force microscopy, and optical tweezers. These methods can measure the inherent mechanical properties of individual red blood cells, but the measurement speed is limited to only 10-100 cells per hour, while each milliliter of human blood contains 3-5 million blood cells. In recent years, many researchers have developed high-throughput methods to characterize the deformability of nucleated cells through morphological parameters after cell deformation. Examples include deformable flow cytometry, which indirectly evaluates cell deformability by measuring the instantaneous major-minor axis ratio of cells after compression in countercurrent flow; and microchannel cantilever resonators, which measure cell flexibility by measuring the time it takes for cells to pass through a narrow slit of similar size to their characteristic dimensions. To eliminate the interaction between cell size and deformability in flow-based high-throughput methods, more complex methods are beginning to utilize the fluid-cell membrane coupling properties in narrow channels to invert the cell's elastic modulus. Examples include real-time deformable flow cytometry and dynamic real-time deformable flow cytometry.

[0056] The inventors of this invention discovered through research that the micropipette method, atomic force microscopy, and optical tweezers method are difficult to balance in terms of timeliness and statistical standards in biology and clinical medicine; high-throughput methods such as deformable flow cytometry and microchannel cantilever resonators cannot reflect the inherent mechanical properties of cells, and the obtained cell deformation parameters are related to cell size. In addition, the cell deformation parameters obtained by treating cells as viscoelastic particles are coupled with the combined effects of cell membrane, nuclear elasticity, and cytoplasmic viscosity, and the weights of these factors cannot be distinguished individually; real-time deformable flow cytometry measurement technology treats cells as elastic, uniform, solid spheres, ignoring the fluid-structure interaction effects in narrow channels, and its measurement error increases with increasing throughput, making it unsuitable for measuring the mechanical properties of erythrocytes with excellent deformability; dynamic real-time deformable flow cytometry developed on this basis is not suitable for non-spherical and heterogeneous cells with large redundant areas, and its measurement model lacks accuracy.

[0057] In this embodiment of the invention, red blood cells flowing in a microtube are photographed to determine multiple frames of flowing red blood cell images; then, based on the multiple frames of flowing red blood cell images, the morphological parameters of the red blood cells are determined; and then, based on the morphological parameters of the red blood cells, the physical characteristic parameters of the red blood cells are determined; wherein, the diameter of the microtube is greater than or equal to 2.0 micrometers and less than or equal to 5.0 micrometers. Compared to existing technologies such as traditional micropipette methods, atomic force microscopy, and optical tweezers, which have limited measurement speeds, and high-throughput measurement methods such as deformable flow cytometers, microchannel cantilever resonators, and real-time deformable flow cytometers, which have insufficient accuracy, cannot measure multiple red blood cell characteristic parameters in a single measurement, or are not suitable for measuring the physical characteristic parameters of red blood cells with excellent deformability, the microtubes used in the embodiments of this invention optimize the diameter range of high-throughput experimental microtubes. This provides a suitable narrow channel for red blood cell flow, minimizing blockage and allowing red blood cells to deform within a reasonable range and exhibit axial symmetry. This significantly reduces the error in measuring red blood cell area and volume, and improves the accuracy of obtaining the membrane shear modulus of red blood cells during machine learning. Furthermore, by employing microfluidic technology, this invention can improve the measurement speed of red blood cell physical property parameters while maintaining the standard for red blood cell count. Compared with existing high-throughput technologies, this invention uses dimensionless mechanical analysis and machine learning methods to decouple the influence of red blood cell size and elastic modulus on red blood cell deformability, enabling the measurement of the intrinsic physical properties of red blood cells and improving measurement accuracy. Moreover, the embodiments of this invention overcome the limitation that a single measurement experiment can only measure a single physical property parameter of the cell, achieving the effect of measuring multiple intrinsic physical property parameters of red blood cells in the same experiment.

[0058] To make the above-mentioned objectives, features and beneficial effects of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0059] Reference Figure 1 , Figure 1 This is a flowchart of a method for determining the physical characteristic parameters of red blood cells according to an embodiment of the present invention. The method may include steps S11 to S13:

[0060] Step S11: Take pictures of the red blood cells flowing in the microtubes to determine multiple frames of images of flowing red blood cells;

[0061] Step S12: Based on the multi-frame flowing red blood cell images, determine the morphological parameters of the red blood cells, wherein the morphological parameters are selected from the length value and the outline of the red blood cells;

[0062] Step S13: Determine the physical characteristic parameters of the red blood cells based on their morphological parameters.

[0063] The diameter of the microtube is greater than or equal to 2.0 micrometers and less than or equal to 5.0 micrometers.

[0064] In the specific implementation of step S11, the microtube can be a glass microtube with a straight tip. By adding a red blood cell solution into the microtube and using a constant hydrostatic pressure difference through a pressure system, the red blood cells are driven from the microtube opening into the microtube and flow within it. The imaging of the red blood cells flowing in the microtube can be performed using a combination of a magnifying microscope and a high-speed camera. Simultaneously, the number of image frames and exposure time captured by the camera can be controlled by software.

[0065] In specific implementations, the diameter of the microtube can be greater than or equal to 2.0 micrometers and less than or equal to 5.0 micrometers. As a preferred embodiment, a microtube with a diameter range of greater than or equal to 3.0 micrometers and less than or equal to 3.2 micrometers can be selected as the experimental microtube. The reason is that: with a diameter of less than 3.0 micrometers, the probability of red blood cells blocking the microtube increases sharply, while with a diameter of more than 3.2 micrometers, the red blood cell morphology is no longer strictly axially symmetric, the area and volume measurement error increases, and the larger the diameter, the weaker the influence of the red blood cell membrane shear modulus on the flow inside the microtube, and the lower the accuracy of machine learning.

[0066] Reference Figure 2 , Figure 2 This is a schematic diagram of an experimental device for measuring the physical characteristics of red blood cells according to an embodiment of the present invention. The experimental device for measuring the physical characteristics of red blood cells includes an image acquisition module 21, a pressure control module 22, and an image analysis and data processing module 23.

[0067] The image acquisition module 21 includes a light-emitting device 211, a red blood cell solution container 212, a red blood cell solution 213, a microscope 214, a magnifying lens 215, an image sensor 216, and a microtube 217.

[0068] The light-emitting device 211 provides the light source required for shooting and can be a light-emitting diode (LED); the red blood cell solution container 212 can be a 1.5mm thick hollow glass container containing many red blood cells; the red blood cell solution container 212 is filled with red blood cell solution 213, which can be a 1% bovine serum albumin-phosphate buffered solution. Saline (BSA-PBS), where 1% refers to the ratio of the mass (grams) of bovine serum albumin to the volume (ml) of phosphate buffered saline solution; microscope 214 can be a differential interference microscope, which makes it easier to identify the outline of red blood cells, and the objective lens of microscope 214 can be a 60x oil immersion objective lens; the teleconverter 215 can be a 2x teleconverter lens to further magnify the imaging area; image sensor 216 can be a conventional image acquisition device used to capture multiple frames of images of flowing red blood cells, such as a high-speed camera, and the high-speed camera can be controlled by software to capture video at 200 frames per second (fps) with an exposure time of 1ms; microtube 217 can be a microchannel made by pulling a cylindrical borosilicate capillary into a glass microchannel with a straight tip using a needle puller, and then using a micro-fusing instrument to break the glass microchannel into a flat opening at about 3.0-3.2 micrometers with an inner wall angle of less than 0.05°, and the microtube 217 is filled with 1% BSA-PBS solution.

[0069] The multi-frame flowing red blood cell images can be captured from the opening region of the microtube 217, in which case the multi-frame flowing red blood cell images can reflect the creep and deformation state of red blood cells during the process of being drawn into the microtube 217 from the opening region; the multi-frame flowing red blood cell images can also be captured from the stable flow region of the microtube 217, in which case the multi-frame flowing red blood cell images can reflect the stable flow state after the red blood cells have completely entered the microtube 217.

[0070] Specifically, a region 70 to 120 micrometers from the opening of the microtube 217 can be used as the stable flow region of the red blood cells to ensure that the red blood cells deform and the flow velocity reaches a stable state.

[0071] The pressure control module 22 includes connecting pipes 221 and water tank 222, which can drive red blood cells into microtubes 217 by maintaining a constant hydrostatic pressure difference ΔP = 847 Pa through the pressure system.

[0072] In the image analysis and data processing module 23, the small white circles represent red blood cells. The areas outlined by the dashed lines on the lower left and right sides represent the orifice region A and the stable flow region B of the microtube 217, respectively. In orifice region A, Lp represents the length of the red blood cell within the microtube 217; in stable flow region B, L represents the stretching length of the red blood cell within the microtube 217. By performing image analysis and data processing on multiple frames of flowing red blood cell images taken from orifice region A of the microtube 217, the viscoelastic parameters of the red blood cells can be determined; by performing image analysis and data processing on multiple frames of flowing red blood cell images taken from stable flow region B of the microtube 217, the surface area, volume, and membrane shear modulus of the red blood cells can be determined.

[0073] In this embodiment, the diameter range of the microtubes used is optimized for high-throughput experiments, making them suitable for measuring the mechanical properties of erythrocytes with excellent deformability. This provides a suitable narrow channel for erythrocyte flow, minimizing blockage and allowing for reasonable deformation within the microtube, resulting in an axially symmetric state. This significantly reduces errors in measuring erythrocyte area and volume, improving the accuracy of obtaining the membrane shear modulus of erythrocytes during machine learning. Furthermore, by employing microfluidic technology, this invention can improve the measurement speed of erythrocyte physical property parameters while maintaining erythrocyte quantity standards.

[0074] Reference Figure 3 , Figure 3 This is a schematic diagram illustrating the state of red blood cells entering microtubules and flowing stably in an embodiment of the present invention.

[0075] In this embodiment of the invention, the microtube is divided into three specific regions: the tube opening region, the acceleration region, and the stable flow region.

[0076] The orifice region is a study area used to determine the viscoelastic parameters of erythrocytes. Since erythrocytes undergo creep deformation during their absorption into the microtube from the orifice region, multiple frames of images captured by a camera in the orifice region can be used to determine the length of the erythrocyte absorbed into the microtube at different times (t). This allows for the fitting of a creep function for the erythrocyte, which in turn determines its viscoelastic parameters.

[0077] The acceleration zone is a region of accelerated flow after the red blood cells are completely drawn into the microtubules, where the red blood cells have not yet reached a stable flow state.

[0078] The steady-flow region is a study area used to determine the surface area, volume, and membrane shear modulus of erythrocytes, within which erythrocytes flow at a stable velocity within the microtube. By capturing multiple frames of images of the steady-flow region using a camera, the outline of the erythrocytes can be determined, allowing for the calculation of their surface area and volume. Furthermore, based on the erythrocyte outline, the stretching length and flow velocity of the erythrocytes within the steady-flow region can be determined (calculated based on the positional changes of the erythrocyte outline at different times t), thus enabling the determination of the erythrocyte's membrane shear modulus.

[0079] Continue to refer to Figure 1 In a specific implementation of step S12, the length value of the red blood cell can be used to indicate the length of the red blood cell drawn into the microtube in the orifice region of the microtube, and the outline of the red blood cell can be used to indicate the complete outline of the red blood cell in the stable flow region of the microtube.

[0080] Specifically, considering that the red blood cells may exhibit an uneven semi-ellipsoidal shape during the process of being aspirated into the microtube, in some non-limiting embodiments, the length value of the red blood cells (length of the aspirated microtube) can be the distance between the cross-section (circular surface) of the microtube opening and the farthest end of the red blood cell's outline in the microtube, or it can be the average value of the distance between the cross-section (circular surface) of the microtube opening and the red blood cell's outline at different positions in the microtube, or the length value of the red blood cells aspirated into the microtube can be determined by other reasonable calculation methods.

[0081] Furthermore, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, and the morphological parameters are selected from the contours of the red blood cells. Determining the morphological parameters of the red blood cells based on the multi-frame images includes: selecting one frame containing the red blood cells as the experimental image and selecting one frame not containing the red blood cells as the background image; using differential motion analysis to perform differential processing on the experimental image and the background image to determine a differential image; and determining the contours of the red blood cells based on the differential image.

[0082] Reference Figure 4 , Figure 4 This is a schematic diagram of the contour extraction of red blood cells based on the differential motion analysis method in an embodiment of the present invention.

[0083] The two images on the left, from top to bottom, are an image of flowing red blood cells in a microtube (experimental image) and an image of no flowing red blood cells in a microtube (background image). The image on the right is an image containing a complete and clear outline of red blood cells, determined by differential processing of the experimental image and the background image.

[0084] In this embodiment of the invention, differential motion analysis is used to determine the contour of the red blood cells within the microtube based on the multi-frame images of flowing red blood cells. This effectively removes some noise unrelated to the red blood cell contour and eliminates static background areas irrelevant to the detection of flowing red blood cells. Furthermore, a background image update mechanism is employed to adapt to changes in background and lighting conditions, thereby obtaining the most complete and clear possible contour curve of the red blood cells in the stable flow region of the microtube. This improves the accuracy of subsequent calculations of the physical property parameters of the red blood cells based on their contours.

[0085] Continue to refer to Figure 1 In the specific implementation of step S13, when the morphological parameters of the red blood cells are selected from the length value of the red blood cells, the viscoelastic parameters of the red blood cells can be determined based on the length value of the red blood cells; when the morphological parameters of the red blood cells are selected from the outline of the red blood cells, the surface area, volume and membrane shear modulus of the red blood cells can be determined based on the outline of the red blood cells.

[0086] Reference Figure 5 , Figure 5 yes Figure 1 A flowchart of the first specific implementation of step S13.

[0087] Specifically, the multi-frame images of flowing red blood cells are captured from the opening region of the microtube, the morphological parameters are selected from the length value of the red blood cells, and the physical property parameters of the red blood cells are the viscoelastic parameters of the red blood cells. Determining the physical property parameters of the red blood cells based on the morphological parameters may include steps S51 to S52, which will be explained below.

[0088] In step S51, the creep function of the red blood cell in the microtube orifice region is determined based on the length value of the red blood cell in the multi-frame flowing red blood cell image.

[0089] It is understood that, in the region of the microtube's opening, the red blood cells are gradually drawn into the microtube, and the length of the red blood cells drawn into the microtube in each frame of the flowing red blood cell image changes with the shooting time. Based on the length value of the red blood cells at the shooting time of each frame of the flowing red blood cell image (the length drawn into the microtube), the creep value of the red blood cells at each corresponding time can be determined.

[0090] Furthermore, the creep value of the red blood cells at each time point in each frame of the flowing red blood cell image can be determined using the following formula:

[0091] ε=2Lp / D;

[0092] Wherein, ε is used to indicate the creep value of red blood cells at a certain moment, Lp is used to indicate the length of red blood cells in the microtubule at the corresponding moment, and D is used to indicate the diameter of the microtubule.

[0093] Based on the creep values ​​at various times, the creep function of the red blood cells over time can be fitted.

[0094] In step S52, the viscoelastic parameters of the red blood cells are determined based on the creep function.

[0095] Furthermore, the viscoelastic parameters of the red blood cells can be determined using the following formula based on the creep function:

[0096]

[0097] Where ε(t) represents the creep function, t represents time, β represents the viscoelastic parameter of the red blood cell, τ0 represents the standardized time parameter, and a represents a constant.

[0098] Reference Figure 6 , Figure 6 yes Figure 1 A flowchart of the second specific implementation of step S13.

[0099] Specifically, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the morphological parameters are selected from the outline of the red blood cells, and the physical characteristic parameters of the red blood cells are the surface area and volume of the red blood cells. Determining the physical characteristic parameters of the red blood cells based on the morphological parameters may include steps S61 to S64, which will be explained below.

[0100] In step S61, the outline of the red blood cell is divided into multiple frustums.

[0101] In some non-limiting embodiments, the outline of the red blood cells can be divided equally or arbitrarily according to a preset total number of frustums; the outline of the red blood cells can also be divided according to a preset average value; other methods can also be used for division. This embodiment of the invention does not impose specific limitations on the method of dividing the outline of the red blood cells.

[0102] Understandably, the more frustums formed, the more accurate the results will be in subsequent steps calculating the surface area and volume of the red blood cells. However, the number of frustums should not be excessive, otherwise it will increase computational overhead and reduce efficiency. In practice, the specific number of frustums can be determined based on the required accuracy, computational overhead, and efficiency requirements.

[0103] In step S62, for each frustum, the left diameter, right diameter, and height of the frustum are determined respectively.

[0104] Wherein, the left diameter of the frustum can be the diameter of the left cross section (circular surface) of the frustum, the right diameter of the frustum can be the diameter of the right cross section (circular surface) of the frustum, and the height of the frustum can be the vertical distance between the left and right cross sections of the frustum.

[0105] In step S63, the surface area and volume of each frustum are determined based on the left diameter, right diameter, and height of each frustum.

[0106] In step S64, an integral calculation method is used to take the sum of the surface areas of the frustums of the plurality of frustums as the surface area of ​​the red blood cell, and the sum of the volumes of the frustums of the plurality of frustums as the volume of the red blood cell.

[0107] Furthermore, the surface area of ​​the red blood cells is determined using the following formula:

[0108]

[0109] The volume of the red blood cells is determined using the following formula:

[0110]

[0111] Where A represents the surface area of ​​a red blood cell, Ai represents the surface area of ​​the i-th frustum, V represents the volume of a red blood cell, Vi represents the volume of the i-th frustum, and D... left D is used to indicate the left diameter of a frustum. right The value of h represents the diameter of the right side of the frustum, and h represents the height of the frustum.

[0112] It should be noted that the above steps for calculating the surface area and volume of the red blood cells are based on the premise that the red blood cells are axially symmetrical.

[0113] Reference Figure 7 , Figure 7 This is a planar schematic diagram illustrating the calculation of the surface area and volume of red blood cells based on their outlines in an embodiment of the present invention.

[0114] The left side is a planar view of the complete outline of a red blood cell in the stable flow region of a microtube, divided into multiple frustums based on two mutually perpendicular axes of symmetry; the rightmost side is one of these frustums, defined by the left diameter D of each frustum. left Right side diameter D right Given the height h of the frustum, the surface area and volume of each frustum can be determined. By integrating the surface area and volume of each frustum, the surface area and volume of the red blood cell can be calculated.

[0115] Reference Figure 8 , Figure 8 yes Figure 1 A flowchart of the third specific implementation method of step S13.

[0116] Specifically, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the morphological parameters are selected from the outline of the red blood cells, and the physical property parameters of the red blood cells are the membrane shear modulus of the red blood cells. Determining the physical property parameters of the red blood cells based on the morphological parameters may include steps S81 to S82, which will be explained below.

[0117] In step S81, the elongation of the red blood cells within the microtube is determined based on the outline of the red blood cells, and the flow velocity of the red blood cells in the stable flow region of the microtube is determined.

[0118] It should be noted that the stretching length of the red blood cells within the microtube refers to the stretching length of the red blood cells in the stable flow region of the microtube, which is different from the length value of the red blood cells mentioned above (which refers to the length of the red blood cells drawn into the microtube at the opening region).

[0119] In specific implementations, since the red blood cells are compressed and deformed within the microtubules, the outline of the red blood cells will be a regular or irregular ellipsoidal shape. In some non-limiting embodiments, the stretched length of the red blood cells can be the longest diameter of the red blood cell outline (ellipsoid), an average diameter determined based on multiple internal diameters of the ellipsoid, or determined by other reasonable means.

[0120] In specific implementation, the flow velocity of the red blood cells in the stable flow region of the microtube can reflect the change in the position of the red blood cells in the microtube over time. The flow velocity of the red blood cells can be determined using conventional methods. For example, based on multiple frames of flowing red blood cell images captured from the stable flow region of the microtube, the capture time of each frame of flowing red blood cell images and the position of the same point on the outline of the red blood cells in the images can be determined. The quotient between the difference between any two positions and the difference between the corresponding two capture times can be calculated as the flow velocity of the red blood cells (i.e., the flow velocity of the red blood cells = the distance the red blood cells move in the microtube / the time of movement); alternatively, the average of the calculated quotients can be used as the flow velocity of the red blood cells to determine the flow velocity of the red blood cells more accurately.

[0121] In step S82, a machine learning algorithm is used to determine the membrane shear modulus of the red blood cells based at least on the stretching length and flow rate of the red blood cells.

[0122] Specifically, the elongation length of the red blood cells, the flow velocity of the red blood cells, the diameter of the microtube, and the liquid flow velocity when there are no red blood cells in the microtube can be input into the machine learning algorithm; the output of the machine learning algorithm can be used as the membrane shear modulus of the red blood cells.

[0123] In a specific implementation, the fluid flow rate in the microtube when there are no red blood cells can be determined as follows: a fluorescent particle solution is used as the solution for red blood cells, wherein the fluorescent particle solution can be a 10 μl / ml 0.1 μm fluorescent particle-BSA / PBS solution; the microtube is filled with the fluorescent particle solution, and the flow of the solution is recorded by photographing the same microtube under the same experimental pressure difference; assuming that the velocity of the fluorescent particles is the same as the flow rate of the solution, the maximum velocity of the fluorescent particles in the microtube is measured using image processing software (ImageJ) as the fluid flow rate in the microtube when there are no red blood cells.

[0124] Furthermore, the machine learning algorithm can be a neural network algorithm, and the neural network algorithm can employ a neural network model.

[0125] This invention, through combining the analysis of multiple frames of flowing red blood cell images with machine learning algorithms, determines the membrane shear modulus of red blood cells, which can effectively improve the measurement accuracy.

[0126] Reference Figure 9 , Figure 9 This is a schematic diagram of the neural network model used to calculate the membrane shear modulus of red blood cells in an embodiment of the present invention. The neural network model includes an input layer, a hidden layer, and an output layer.

[0127] The input layer parameters are the stretching length L of the red blood cells and the flow velocity u of the red blood cells. c The microtube diameter D and the liquid flow velocity u0 when there are no red blood cells in the microtube; the hidden layer uses an activation function; the output layer parameter is the membrane shear modulus Es of the red blood cells.

[0128] The neural network algorithm can be a three-layer error backpropagation neural network model; the activation function can be a sigmoid linear activation function.

[0129] In practice, two machine learning algorithms can be used to calculate the membrane shear modulus of the red blood cells: First, multiple linear regression analysis is used to analyze the linear relationship between the dimensionless red blood cell membrane shear modulus and other dimensionless parameters, thereby obtaining the calculation formula for the red blood cell membrane shear modulus; at the same time, a neural network algorithm is applied to predict the membrane shear modulus.

[0130] In a non-limiting embodiment, the total sample size can be 118. To alleviate the overfitting problem of the neural network, an early stopping strategy is adopted, dividing the entire sample into a training set, a validation set, and a test set, where the ratio of training set:validation set:test set is 70%:15%:15%. The training set can be used to calculate gradients, update connection weights and thresholds, and for the neural network to learn the features of red blood cell-related parameters. The validation set can be used to calculate the mean squared error. If the mean squared error of the training set decreases but the mean squared error of the validation set increases, training is stopped, and the connection weights and thresholds with the minimum mean squared error of the validation set are returned. The fitting training algorithm can use the Levenberg-Marquardt (LM) algorithm.

[0131] Furthermore, the method for determining the physical characteristic parameters of red blood cells also includes: predicting the glycated hemoglobin ratio of the red blood cells based on the physical characteristic parameters of the red blood cells; or, predicting the serum bilirubin content and serum albumin content of the subject based on the physical characteristic parameters of the red blood cells; or, characterizing the morphology and mechanical properties distribution of the red blood cells based on the physical characteristic parameters of the red blood cells.

[0132] Furthermore, the physical property parameters of the red blood cells include the viscoelastic parameters and the membrane shear modulus of the red blood cells; predicting the glycated hemoglobin ratio of the red blood cells based on the physical property parameters of the red blood cells includes: constructing a first parameter product value using the viscoelastic parameters and the membrane shear modulus of the red blood cells; and predicting the glycated hemoglobin ratio of the red blood cells based on the comparison result of the first parameter product value and a first preset threshold.

[0133] In specific implementation, the methods for determining the viscoelastic parameters and membrane shear modulus of erythrocytes refer to the specific steps for determining the viscoelastic parameters and membrane shear modulus of erythrocytes described above, and will not be repeated here.

[0134] The glycated hemoglobin ratio of red blood cells is used to indicate the ratio between the glycated hemoglobin content and the hemoglobin content of red blood cells.

[0135] Specifically, high-throughput methods were used to measure the intrinsic mechanical properties of erythrocytes in six subjects whose glycated hemoglobin (HbA1c) ratios ranged from 5.7% (normal) to 9.6% (diagnosed diabetes). Nonparametric tests of erythrocyte mechanical properties under different HbA1c conditions showed significant specificity and statistical significance for erythrocyte membrane elastic shear modulus, cell viscoelasticity parameters, cell area, and cell volume. However, the correlation between erythrocyte mechanical properties and HbA1c changes was inconsistent: as the HbA1c ratio increased, the cell membrane shear modulus increased significantly, cell viscoelasticity increased, while the average and ratio of cell volume and area did not change much. Correlation analysis showed that the HbA1c values ​​in the subjects' blood samples were significantly positively correlated with erythrocyte membrane shear modulus and also clearly positively correlated with erythrocyte viscoelasticity. This indicates that the level of HbA1c significantly affects hydrodynamic parameters such as erythrocyte membrane shear modulus and cell viscoelasticity. Further analysis revealed a significant tailing distribution characteristic in the mechanical property measurements of the six blood samples. Considering the tailing distribution characteristics of the data while avoiding the influence of data specificity, it was found that when the 90th percentile sample value of erythrocyte membrane shear modulus was selected, its correlation with the glycated hemoglobin ratio was as high as 0.943, and the correlation between the 75th percentile sample value of cell viscosity and the glycated hemoglobin ratio was significantly increased to 0.886. Furthermore, the glycated hemoglobin value showed a significant two-dimensional correlation with membrane shear modulus and cell viscoelasticity, reaching 1.00.

[0136] Reference Figure 10 , Figure 10 This is a two-dimensional correlation graph showing the relationship between the glycated hemoglobin ratio of the subject's red blood cells and the product of the red blood cell membrane shear modulus and viscoelastic parameters.

[0137] The horizontal axis represents the glycated hemoglobin ratio of red blood cells, and the vertical axis represents the product of the parameters constructed from the top 90 percentile sample values ​​of the membrane shear modulus of red blood cells and the top 75 percentile sample values ​​of the viscoelastic parameters of red blood cells.

[0138] In this embodiment of the invention, by establishing a two-dimensional flow cytometry distribution spectrum of erythrocyte mechanical properties, including erythrocyte membrane shear modulus and erythrocyte viscoelasticity, and using the top 90% percentile of erythrocyte membrane shear modulus and the top 75% percentile of erythrocyte viscoelasticity as characteristic distinguishing intervals, normal and abnormal glycated hemoglobin ratios can be clearly distinguished. Thus, based on the product of erythrocyte viscoelastic parameters and membrane shear modulus, healthy individuals and diabetic patients can be differentiated. Therefore, this embodiment of the invention can be applied to assist in the diagnosis of diabetic patients, predict the stage and severity of diabetes, and improve the timeliness, sensitivity, and accuracy of diabetes prediction or diagnosis.

[0139] Furthermore, the physical properties of the red blood cells include the surface area and volume of the red blood cells, the membrane shear modulus of the red blood cells, and the viscoelastic parameters of the red blood cells. Predicting the serum bilirubin and serum albumin levels of the subject based on these physical properties includes: constructing a second parameter product value and a third parameter product value using the surface area and volume of the red blood cells, the membrane shear modulus of the red blood cells, and the viscoelastic parameters of the red blood cells, respectively; predicting the subject's serum bilirubin level based on a comparison between the second parameter product value and a second preset threshold; and predicting the subject's serum albumin level based on a comparison between the third parameter product value and a third preset threshold.

[0140] In specific implementation, the specific method for constructing the product value of the second parameter and the product value of the third parameter can be combined with the needs of the actual application scenario, referring to the scheme described above that uses the product value of the first parameter constructed by the viscoelastic parameters and membrane shear modulus of the red blood cells to predict the glycated hemoglobin ratio of the red blood cells. This embodiment of the invention does not limit this.

[0141] Serum bilirubin is produced from hemoglobin released by the lysis of aging red blood cells in the body, and it includes indirect bilirubin and direct bilirubin. Serum albumin is the main protein component of serum total protein, and it plays an important role in maintaining blood colloid osmotic pressure, transporting metabolic substances in the body, and providing nutrition. In this embodiment of the invention, by using the product of the surface area of ​​the subject's red blood cells, the membrane shear modulus of the red blood cells, and the viscoelastic parameters of the red blood cells, the normal and abnormal levels of serum bilirubin and serum albumin in the subject can be accurately distinguished, thereby improving the timeliness, sensitivity, and accuracy of predicting diseases related to serum bilirubin and serum albumin.

[0142] Furthermore, the physical property parameters of the red blood cells include the surface area and volume of the red blood cells, the viscoelastic parameters of the red blood cells, and the membrane shear modulus of the red blood cells; based on the physical property parameters of the red blood cells, characterizing the morphology and mechanical property distribution of the red blood cells includes: constructing a probability density function of the morphology and mechanical property distribution of the red blood cells based on the surface area and volume of the red blood cells, the viscoelastic parameters of the red blood cells, and the membrane shear modulus of the red blood cells, thereby characterizing the morphology and mechanical property distribution of the red blood cells.

[0143] In this embodiment of the invention, by comparing the differences between the morphological and mechanical property distribution characteristics of red blood cells derived from the subject and those derived from red blood cells of healthy individuals (reference sample) (analyzing the difference between the probability density function of the morphological and mechanical property distribution of red blood cells of the subject and that of red blood cells of healthy individuals), the subject's hematological diseases (such as sickle cell disease and spherocytosis) can be effectively monitored.

[0144] Furthermore, in practice, by employing big data analysis methods and based on the probability density function of the constructed distribution of the morphological and mechanical properties of the red blood cells, it is also possible to predict the subject's age, blood pressure, and the results of the Mini-Mental State Examination.

[0145] Age and blood pressure are common quantitative indicators of human health. As coarse indicators of human health, they can be used for screening cardiovascular diseases. The Mini-Mental State Examination (MMSE) is one of the most influential standardized cognitive assessment tools. As a method for assessing cognitive impairment, it can be used for screening Alzheimer's disease. In this embodiment of the invention, by learning and correlating the probability density function of the morphological and mechanical properties distribution of red blood cells with the subject's age, blood pressure, and MMSE results, accurate predictions of the subject's age, blood pressure, and MMSE results can be achieved, thereby assessing the subject's cardiac function and Alzheimer's disease.

[0146] It should be noted that the methods for determining the surface area and volume of the red blood cells, the viscoelastic parameters of the red blood cells, and the membrane shear modulus of the red blood cells mentioned above are as follows: Figure 1 , Figure 5 , Figure 6 and Figure 8 The specific steps and descriptions for measuring the surface area and volume of red blood cells, viscoelastic parameters, and membrane shear modulus mentioned earlier will not be repeated here.

[0147] Reference Figure 11 , Figure 11 This is a schematic diagram of a device for determining the physical characteristic parameters of red blood cells according to an embodiment of the present invention. The device for determining the physical characteristic parameters of red blood cells may include:

[0148] The flowing red blood cell image determination module 111 is used to capture images of red blood cells flowing in microtubes to determine multiple frames of flowing red blood cell images;

[0149] Red blood cell morphology parameter determination module 112 is used to determine the morphology parameters of the red blood cells based on the multi-frame flowing red blood cell images, wherein the morphology parameters are selected from the length value and the outline of the red blood cells;

[0150] The red blood cell physical characteristic parameter determination module 113 is used to determine the physical characteristic parameters of the red blood cells based on the morphological parameters of the red blood cells.

[0151] For the principle, implementation, and beneficial effects of the device for determining the physical properties of red blood cells, please refer to the preceding text. Figures 1 to 10 The description of the method for determining the physical properties of red blood cells shown is not repeated here.

[0152] This invention also provides a storage medium storing a computer program, which, when executed, performs the steps of the method for determining the physical characteristic parameters of red blood cells described above. The computer-readable storage medium may include non-volatile or non-transitory memory, and may also include optical discs, hard disk drives, solid-state drives, etc.

[0153] Specifically, in this embodiment of the invention, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0154] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0155] This invention also provides a terminal, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the steps of the method for determining the physical characteristic parameters of red blood cells described above. The terminal may include, but is not limited to, mobile phones, computers, tablets, and other terminal devices, and may also be servers, cloud platforms, etc.

[0156] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article indicates that the preceding and following related objects have an "or" relationship.

[0157] In the embodiments of this application, "multiple" refers to two or more.

[0158] The descriptions of "first," "second," etc., appearing in the embodiments of this application are for illustrative purposes and to distinguish the objects being described. They have no order and do not indicate any special limitation on the number of devices in the embodiments of this application, nor do they constitute any limitation on the embodiments of this application.

[0159] It should be noted that the sequence number of each step in this embodiment does not represent a limitation on the execution order of each step.

[0160] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for determining the physical characteristic parameters of red blood cells, characterized in that, include: Images of red blood cells flowing in microtubules were captured to determine multiple frames of images of flowing red blood cells; Based on the multi-frame images of flowing red blood cells, the morphological parameters of the red blood cells are determined, wherein the morphological parameters are selected from the length value and the outline of the red blood cells; Based on the morphological parameters of the red blood cells, the physical property parameters of the red blood cells are determined; the multi-frame flowing red blood cell images are captured from the opening region of the microtube, the morphological parameters are selected from the length value of the red blood cells, and the physical property parameters of the red blood cells are the viscoelastic parameters of the red blood cells; Determining the physical properties of the red blood cells based on their morphological parameters includes: determining the creep function of the red blood cells in the microtubule orifice region based on the length value of the red blood cells in the multi-frame flowing red blood cell images; and determining the viscoelastic parameters of the red blood cells based on the creep function. Determining the creep function of the red blood cells in the microtubule orifice region based on the length values ​​of the red blood cells in the multi-frame flowing red blood cell images includes: determining the length value of the red blood cells at the time corresponding to each frame of the flowing red blood cell image; and determining the creep value of the red blood cells at the time corresponding to each frame of the flowing red blood cell image using the following formula: Based on the creep values ​​at various time points, a creep function of the red blood cells over time is fitted; the viscoelastic parameters of the red blood cells are determined using the following formula based on the creep function: ;in, Used to indicate the creep function It refers to a moment. This refers to the length of the red blood cell within the microtubule at different times. This refers to the diameter of the microtube. This refers to the viscoelastic parameters of the red blood cells. It is a standardized time parameter. It is a constant; The diameter of the microtube is greater than 3.0 micrometers and less than or equal to 3.2 micrometers.

2. The method according to claim 1, characterized in that, The multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, and the morphological parameters are selected from the outline of the red blood cells. Based on the multi-frame images of flowing red blood cells, the morphological parameters of the red blood cells are determined as follows: For the multi-frame flowing red blood cell images, one frame containing the red blood cells is selected as the experimental image, and one frame not containing the red blood cells is selected as the background image; The differential motion analysis method is used to perform differential processing on the experimental image and the background image to determine the differential image; The outline of the red blood cells is determined based on the difference image.

3. The method according to claim 1, characterized in that, The multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the morphological parameters are selected from the outline of the red blood cells, and the physical property parameters of the red blood cells are the surface area and volume of the red blood cells. Based on the morphological parameters of the red blood cells, the physical properties of the red blood cells are determined, including: The outline of the red blood cell is divided into multiple frustums; For each frustum, determine the left diameter, right diameter, and height of the frustum. Determine the surface area and volume of each frustum based on its left diameter, right diameter, and height. Using an integral calculation method, the sum of the surface areas of the multiple frustums is taken as the surface area of ​​the red blood cell, and the sum of the volumes of the multiple frustums is taken as the volume of the red blood cell.

4. The method according to claim 3, characterized in that, The surface area of ​​the red blood cells is determined using the following formula: The volume of the red blood cells is determined using the following formula: ； in, Used to represent the surface area of ​​red blood cells Refers to the first The surface area of ​​a frustum. Refers to the volume of red blood cells. Refers to the first The volume of a frustum. The diameter on the left side of the frustum. The diameter of the right side of the frustum. The height of the frustum.

5. The method according to claim 1, characterized in that, The multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the morphological parameters are selected from the outline of the red blood cells, and the physical property parameters of the red blood cells are the membrane shear modulus of the red blood cells. Based on the morphological parameters of the red blood cells, the physical properties of the red blood cells are determined, including: Based on the outline of the red blood cells, the stretching length of the red blood cells within the microtubes and the flow velocity of the red blood cells in the stable flow region of the microtubes are determined. Using a machine learning algorithm, the membrane shear modulus of the red blood cells is determined based at least on the stretching length and flow rate of the red blood cells.

6. The method according to claim 5, characterized in that, Determining the membrane shear modulus of a red blood cell using a machine learning algorithm, based at least on the elongation length and flow velocity of the red blood cell, includes: The elongation length of the red blood cells, the flow rate of the red blood cells, the diameter of the microtube, and the flow rate of the liquid when there are no red blood cells in the microtube are input into the machine learning algorithm; The result output by the machine learning algorithm is used as the membrane shear modulus of the red blood cell.

7. The method according to claim 6, characterized in that, The machine learning algorithm is a neural network algorithm, and satisfies one or more of the following: The input layer parameters of the neural network are the elongation length of the red blood cells, the flow velocity of the red blood cells, the diameter of the microtube, and the flow velocity of the liquid when there are no red blood cells in the microtube. The hidden layers of the neural network employ activation functions; The output layer parameter of the neural network is the membrane shear modulus of the red blood cell.

8. The method according to claim 1, characterized in that, The method further includes: Based on the physical properties of the red blood cells, the glycated hemoglobin ratio of the red blood cells is predicted; or, Based on the physical properties of the red blood cells, the serum bilirubin and serum albumin levels of the subjects are predicted. or, Based on the physical properties of the red blood cells, the morphology and mechanical properties of the red blood cells are characterized.

9. The method according to claim 8, characterized in that, The physical properties of the red blood cells include the viscoelastic parameters of the red blood cells and the membrane shear modulus of the red blood cells; Predicting the glycated hemoglobin ratio of red blood cells based on their physical properties includes: The first parameter product value is constructed using the viscoelastic parameters and membrane shear modulus of the red blood cells. The glycated hemoglobin ratio of the red blood cells is predicted based on the comparison between the product of the first parameter and the first preset threshold.

10. The method according to claim 8, characterized in that, The physical properties of the red blood cells include the surface area and volume of the red blood cells, the membrane shear modulus of the red blood cells, and the viscoelastic parameters of the red blood cells; Based on the physical properties of the red blood cells, the prediction of the subject's serum bilirubin and serum albumin levels includes: The product of the second parameter and the product of the third parameter are constructed using the surface area and volume of the red blood cells, the membrane shear modulus of the red blood cells, and the viscoelastic parameters of the red blood cells, respectively. Based on the comparison between the product of the second parameter and the second preset threshold, the serum bilirubin level of the subject is predicted, and based on the comparison between the product of the third parameter and the third preset threshold, the serum albumin level of the subject is predicted.

11. The method according to claim 8, characterized in that, The physical properties of the red blood cells include the surface area and volume of the red blood cells, the viscoelastic parameters of the red blood cells, and the membrane shear modulus of the red blood cells; Characterizing the morphology and mechanical properties of the red blood cells based on their physical parameters includes: Based on the surface area and volume of the red blood cells, the viscoelastic parameters of the red blood cells, and the membrane shear modulus of the red blood cells, a probability density function for the distribution of the morphology and mechanical properties of the red blood cells is constructed, thereby characterizing the distribution of the morphology and mechanical properties of the red blood cells.

12. A device for determining the physical characteristic parameters of red blood cells, characterized in that, include: The flowing red blood cell image determination module is used to capture images of red blood cells flowing in microtubes to determine multiple frames of flowing red blood cell images; The red blood cell morphology parameter determination module is used to determine the morphology parameters of the red blood cells based on the multi-frame flowing red blood cell images. The morphology parameters are selected from the length value and the outline of the red blood cells. The red blood cell physical property parameter determination module is used to determine the physical property parameters of the red blood cells based on the morphological parameters of the red blood cells; the multi-frame flowing red blood cell images are captured from the opening region of the microtube, the morphological parameters are selected from the length value of the red blood cells, and the physical property parameters of the red blood cells are the viscoelastic parameters of the red blood cells; Determining the physical properties of the red blood cells based on their morphological parameters includes: determining the creep function of the red blood cells in the microtubule orifice region based on their length values ​​in the multi-frame flowing red blood cell images; determining the viscoelastic parameters of the red blood cells based on the creep function; and determining the creep function of the red blood cells in the microtubule orifice region based on their length values ​​in the multi-frame flowing red blood cell images includes: determining the length value of the red blood cells at each time step of each frame of the flowing red blood cell images; and determining the creep value of the red blood cells at each time step of each frame of the flowing red blood cell images using the following formula: Based on the creep values ​​at various time points, a creep function of the red blood cells over time is fitted; the viscoelastic parameters of the red blood cells are determined using the following formula based on the creep function: ;in, Used to indicate the creep function It refers to a moment. This refers to the length of the red blood cell within the microtubule at different times. This refers to the diameter of the microtube. This refers to the viscoelastic parameters of the red blood cells. It is a standardized time parameter. It is a constant; The diameter of the microtube is greater than 3.0 micrometers and less than or equal to 3.2 micrometers.

13. A storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it performs the steps of the method for determining the physical characteristic parameters of red blood cells according to any one of claims 1 to 11.

14. A terminal comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, characterized in that, When the processor runs the computer program, it performs the steps of the method for determining the physical characteristic parameters of red blood cells according to any one of claims 1 to 11.

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