Method and device for determining mechanical fatigue characteristic parameters of red blood cells, and terminal

Abstract

A method and device for determining a mechanical fatigue characteristic parameter of red blood cells, and a terminal, the method comprising: using a first experimental cycle number N, circulating a plurality of red blood cells in a sample to be sucked into and expelled from a microtube of the same diameter, and in each cycle, photographing each red blood cell flowing in the microtube to determine a plurality of flowing red blood cell images; determining a deformation parameter of each red blood cell in each cycle according to the plurality of flowing red blood cell images, and determining a physical characteristic parameter of each red blood cell according to the deformation parameter of each red blood cell; performing function fitting processing on the physical characteristic parameters of the plurality of red blood cells in the sample in each cycle to determine a mechanical fatigue characteristic parameter of the red blood cells in the sample; wherein N is a positive integer. The above scheme can efficiently and accurately obtain the mechanical fatigue characteristic parameter of the red blood cells in the fatigue stimulation process of multiple cycles.

Description

Technical Field

[0001] This invention relates to the field of research on the mechanical fatigue characteristics of cells, and in particular to a method, apparatus, and terminal for determining the mechanical fatigue characteristic parameters of red blood cells. Background Technology

[0002] Normal human red blood cells have a lifespan of 110-120 days. During their lifespan, they undergo multiple cycles of loading and relaxation in the large blood vessels and capillaries. In particular, red blood cells experience significant mechanical stimulation when passing through the reticuloendothelial slits, a unique structure of the spleen. This cyclic loading and unloading of red blood cells is a common form of mechanical fatigue.

[0003] Taking the mechanical stimulation of the splenic slit as an example, this stimulation promotes the detachment of reticulocyte organelles. Simultaneously, the membrane shear modulus of erythrocytes decreases during this process, and cell stability continuously increases, transforming them into mature biconcave disc-shaped erythrocytes within a few days. Furthermore, the mechanical stimulation of the spleen plays an important physiological role in the aging process of erythrocytes: prolonged blood circulation causes the accumulation of erythrocyte damage, such as membrane damage caused by oxidative stress. Erythrocytes maintain their morphology and prolong circulation time by generating cell vesicles to clear damaged structures and regulating cell surface area. Some researchers have found that the spleen promotes the production and shedding of erythrocyte vesicles, leading to a decrease in erythrocyte surface area and hemoglobin loss. Recent numerical simulation studies suggest that weakened connections between the cytoskeleton and cell membrane promote the shedding of microvesicles during splenic flow, which may explain the significantly larger erythrocytes in splenectomized patients compared to those in non-splenectomized patients. Furthermore, in hematological disorders, patients with hereditary spherocytosis experience weakened binding between the erythrocyte membrane skeleton and lipid membrane due to chromosomal inheritance. The circulatory loading of the spleen leads to a significantly increased rate of erythrocyte membrane loss, accelerating the formation and clearance of spherocytes and resulting in hemolytic anemia. Therefore, studying the fatigue characteristics of erythrocytes under repeated cycles of mechanical fatigue stimulation is of significant value.

[0004] In existing technologies, Garcia-Herreros utilizes parallel multi-channel (less than 1 micrometer wide) through which a population of erythrocytes repeatedly squeezes through a spleen-like slit over a prolonged period to measure the physical and biological properties of the cell population at different cycles. The average value of the mechanical fatigue-related parameters of the cell population is then used as the mechanical fatigue-related parameter value for a single erythrocyte. Therefore, the overall accuracy of the measured parameters may not be high enough to accurately detect subtle changes in erythrocytes. Furthermore, this experiment is not conducted in real time; after cyclic loading of the erythrocyte population, the loaded erythrocytes need to be collected for parameter measurement experiments. Therefore, the overall operation is cumbersome and time-consuming (often requiring 2-3 days). Sakuma used a microchannel (rectangular cross-section) with a width of 3 micrometers and a height of 4 micrometers to conduct single-cell reciprocating flow experiments on red blood cells, studying the effects of mechanical fatigue on the deformability (simple parameter: aspect ratio) and mechanical stability (elastic recovery ability) of red blood cells. He found that the deformability and mechanical stability of red blood cells decreased significantly with the increase of fatigue cycles. However, in this experiment, the deformability of red blood cells is a comprehensive indicator coupled with physical properties such as cell surface area, volume, membrane shear modulus, and cell viscosity, and cannot be used to quantitatively study the changes in the physical properties of red blood cells during mechanical fatigue. Qiang used dielectrophoresis to perform single-cell cyclic loading and unloading on erythrocytes adhered to microchannels, studying the changes in the mechanical properties of erythrocytes. He found that mechanical fatigue significantly increased the shear modulus and viscoelasticity of the erythrocyte membrane. ATP depletion and hypoxia accelerated the changes in cell mechanical properties. However, in dielectrophoresis, erythrocytes are often not in a flowing state, but are fixed in a specific position and subjected to reciprocating stretching. In this case, the magnitude, direction, and position of the stretching force on the erythrocytes may differ from the shearing force of fluids and the compression of the reticuloendothelium experienced by erythrocytes in the human body. The accuracy of the obtained erythrocyte fatigue characteristics may be insufficient, and the difference between the erythrocyte fatigue characteristics obtained and those in the real environment in the human body is difficult to assess. Summary of the Invention

[0005] One of the objectives of this invention is to provide a method for determining the mechanical fatigue characteristic parameters of red blood cells, which allows red blood cells to undergo reciprocating cyclic loading and unloading similar to that under body mechanical stimulation, thereby efficiently and accurately obtaining the mechanical fatigue characteristic parameters of red blood cells during multiple cycles of fatigue stimulation.

[0006] To achieve the above objectives, embodiments of the present invention provide a method for determining the mechanical fatigue characteristic parameters of red blood cells, comprising the following steps: using a first experimental cycle N, multiple red blood cells in a sample are cyclically drawn in and expelled from a microtube of the same diameter; during each cycle, images of each red blood cell flowing in the microtube are captured to determine multiple frames of flowing red blood cell images; based on the multiple frames of flowing red blood cell images, the deformation parameters of each red blood cell during each cycle are determined, and based on the deformation parameters of each red blood cell, the physical characteristic parameters of each red blood cell are determined; the physical characteristic parameters of the multiple red blood cells in the sample during each cycle are subjected to function fitting processing to determine the mechanical fatigue characteristic parameters of the red blood cells in the sample; wherein, N is a positive integer.

[0007] Optionally, the physical property parameters include surface area, and the mechanical fatigue property parameters include surface area decay index and limiting residual surface area; the mechanical fatigue property parameters of the multiple red blood cells in the sample are determined by performing function fitting processing on the physical property parameters of the multiple red blood cells in the sample during each cycle, including: differentiating the surface area of ​​the multiple red blood cells in the sample during each cycle to obtain the first rate of change of the surface area of ​​each red blood cell with each cycle; performing a first linear function fitting processing on the first rate of change of the surface area of ​​each red blood cell with each cycle and the surface area of ​​each red blood cell after the nth cycle to determine the surface area decay index and limiting residual surface area of ​​the red blood cells in the sample; where n is a positive integer and 1≤n≤N.

[0008] Optionally, the formula for fitting the first linear function is:

[0009] A' n =-k(A n -A ∞ );

[0010] Where n indicates the nth cycle in the first experimental cycle number N, A n A' is used to indicate the surface area of ​​the red blood cells after the nth cycle. n For A n The derivative of A' is used to indicate the first rate of change of the surface area of ​​the red blood cells with each cycle, where k is the derivative of A'. n Follow A n The absolute value of the slope under the linear change assumption, used to indicate the surface area decay index of the red blood cells, A. ∞ It is A' n and A n The x-intercept of the fitted first linear function is used to indicate the limiting residual surface area of ​​the red blood cells.

[0011] Optionally, after determining the surface area decay index and the limiting residual surface area of ​​the red blood cells in the sample, the method further includes: inputting the surface area decay index and the limiting residual surface area of ​​the red blood cells into a mechanical fatigue surface area change model to determine the health status coupling parameters of the red blood cells; wherein the health status coupling parameters are used to indicate the coupling relationship between all surface areas that the red blood cells can lose during their life cycle from birth to death and the current age index of the red blood cells.

[0012] Optionally, before inputting the surface area decay index and the limiting residual surface area of ​​the red blood cells into the mechanical fatigue surface area change model to determine the health status coupling parameters of the red blood cells, the method further includes: using a second experimental cycle M, cyclically drawing in and expelling multiple experimental red blood cells from the microtube, and taking pictures of the experimental red blood cells flowing in the microtube during each cycle to determine multiple frames of flowing red blood cell experimental images; determining the deformation parameters of the experimental red blood cells at each cycle based on the multiple frames of flowing red blood cell experimental images, and determining the surface area of ​​the experimental red blood cells based on the deformation parameters of the experimental red blood cells; performing exponential function fitting processing based on the surface area of ​​the experimental red blood cells at each cycle to obtain the mechanical fatigue surface area change model; wherein, the mechanical fatigue surface area change model is: A m =ae -k(m+b) +A ∞ ;

[0013] Where m indicates the m-th cycle in the second experimental cycle number M, A m A is used to indicate the surface area of ​​the experimental red blood cells at the m-th cycle. ∞ The value of m is used to indicate the limit of residual surface area of ​​the experimental red blood cells; k is used to indicate the surface area decay index of the experimental red blood cells; a is used to indicate the total surface area that the experimental red blood cells can lose during their life cycle from birth to death; b is used to indicate the current age of the experimental red blood cells; M and m are both positive integers, and 1≤m≤M.

[0014] Optionally, the physical property parameters further include membrane shear modulus, and the mechanical fatigue property parameters further include the correlation coefficient between the second rate of change of membrane shear modulus with each cycle and surface area, as well as the membrane shear modulus correlation constant; performing function fitting processing on the physical property parameters of multiple red blood cells in the sample during each cycle to determine the mechanical fatigue property parameters of the red blood cells in the sample further includes: differentiating the membrane shear modulus of multiple red blood cells in the sample during each cycle to obtain the second rate of change of membrane shear modulus of each red blood cell with each cycle; performing second linear function fitting on the second rate of change of membrane shear modulus of each red blood cell with each cycle and the surface area of ​​each red blood cell after the nth cycle to determine the correlation coefficient between the second rate of change of membrane shear modulus of red blood cells in the sample with each cycle and surface area, as well as the membrane shear modulus correlation constant; where n is a positive integer, and 1≤n≤N.

[0015] Optionally, the formula for fitting the second linear function is: E' Sn =gA n +C;

[0016] Where n indicates the nth cycle in the first experimental cycle number N, A n E' is used to indicate the surface area of ​​the red blood cells after the nth cycle. Sn It is E Sn The derivative of E' is used to indicate the second rate of change of the membrane shear modulus of the red blood cells with each cycle, where g is E' Sn Follow A n The slope under the linear assumption, used to indicate the correlation coefficient between the second rate of change of the membrane shear modulus of the red blood cells with each cycle and the surface area, C is E' Sn With A n The y-intercept of the fitted second linear function is used to indicate the membrane shear modulus-related constant of the red blood cells, where n is a positive integer and 1 ≤ n ≤ N.

[0017] Optionally, the method further includes: measuring the surface area decay index k and the limiting residual surface area A of the red blood cells. ∞ The parameters include: health status coupling parameter lna-kb; correlation coefficient g between the second rate of change of the erythrocyte membrane shear modulus with each cycle and the surface area; correlation constant C of the erythrocyte membrane shear modulus; and erythrocyte membrane shear modulus E at the first cycle. S0 Input the mechanical fatigue membrane shear modulus change model to determine the membrane shear modulus of the red blood cell when it has undergone the theoretical number of cycles Q.

[0018] The mechanical fatigue membrane shear modulus change model is as follows:

[0019]

[0020] Where q indicates the q-th iteration in the theoretical iteration count Q, E Sq The mechanical fatigue membrane shear modulus used to indicate the red blood cell undergoing the qth cycle, where q and Q are both positive integers, 1≤q≤Q, and Q>N.

[0021] Optionally, the diameter of the microtube is greater than or equal to 2.8 micrometers and less than or equal to 4.0 micrometers.

[0022] Optionally, the multi-frame flowing red blood cell images are captured from the stable flow region of the microtube, and the deformation parameter is the cell contour. Determining the deformation parameter of each red blood cell in each cycle based on the multi-frame flowing red blood cell images includes: for each cycle of the flowing red blood cell images, selecting an image containing red blood cells as a control image and selecting an image without red blood cells as a background image; using differential motion analysis to perform differential processing on the control image and the background image to determine a differential image; and determining the cell contour of each red blood cell in each cycle based on the differential image.

[0023] Optionally, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the deformation parameter is the cell outline, and the physical property parameters include surface area. Determining the physical property parameters of each red blood cell based on its deformation parameters includes: for each red blood cell, dividing its cell outline into multiple frustums; for each frustum, determining its left diameter, right diameter, and height; determining the surface area of ​​each frustum based on its left diameter, right diameter, and height; and using an integral calculation method to obtain the sum of the surface areas of the multiple frustums as the surface area of ​​the red blood cell.

[0024] Optionally, the surface area of ​​the red blood cells can be determined using the following formula:

[0025]

[0026] Where A represents the surface area of ​​a red blood cell, A j D refers to the surface area of ​​the j-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'.

[0027] Optionally, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the deformation parameter is the cell contour, and the physical property parameters include the membrane shear modulus; determining the physical property parameters of each red blood cell based on the deformation parameters of each red blood cell includes: for each red blood cell, determining the stretching length of the red blood cell in the microtube based on the cell contour of the red blood cell, and determining the flow velocity of the red blood cell in the stable flow region of the microtube; using a machine learning algorithm, determining the membrane shear modulus of the red blood cell based at least on the stretching length and the flow velocity of the red blood cell.

[0028] 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.

[0029] Optionally, the machine learning algorithm is a neural network algorithm, satisfying 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 in the stable flow region of the microtube, 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.

[0030] This invention also provides a device for determining the mechanical fatigue characteristic parameters of red blood cells, comprising: a flowing red blood cell image acquisition module, used to cyclically draw in and expel multiple red blood cells from a sample through a microtube of the same diameter using a first experimental cycle number N, and to capture images of each red blood cell flowing in the microtube during each cycle to determine multiple frames of flowing red blood cell images; a red blood cell physical characteristic parameter determination module, used to determine the deformation parameters of each red blood cell during each cycle based on the multiple frames of flowing red blood cell images, and to determine the physical characteristic parameters of each red blood cell based on the deformation parameters of each red blood cell; and a mechanical fatigue characteristic parameter determination module, used to perform function fitting processing on the physical characteristic parameters of the multiple red blood cells in the sample during each cycle to determine the mechanical fatigue characteristic parameters of the red blood cells in the sample; wherein, N is a positive integer.

[0031] This invention also provides a storage medium, which is a computer-readable storage medium storing computer instructions thereon. When the computer instructions are executed, they perform the steps of the method for determining the mechanical fatigue characteristic parameters of red blood cells described above.

[0032] This invention also provides a terminal, including a memory and a processor. The memory stores computer instructions that can be executed on the processor. When the processor executes the computer instructions, it performs the steps of the method for determining the mechanical fatigue characteristic parameters of red blood cells described above.

[0033] Compared with the prior art, the technical solution of the embodiments of the present invention has the following beneficial effects:

[0034] In this embodiment of the invention, a first experimental cycle N is used to cyclically draw in and expel multiple red blood cells from a microtube of the same diameter in a sample. During each cycle, images of each red blood cell flowing in the microtube are captured to determine multiple frames of flowing red blood cell images. Based on the multiple frames of flowing red blood cell images, the deformation parameters of each red blood cell during each cycle are determined, and based on the deformation parameters of each red blood cell, the physical property parameters of each red blood cell are determined. The physical property parameters of the multiple red blood cells in the sample during each cycle are subjected to function fitting to determine the mechanical fatigue property parameters of the red blood cells in the sample; where N is a positive integer. Compared to existing experiments that involve cyclically squeezing cell populations through microchannels, which are time-consuming and lack real-time accuracy; or single-cell reciprocating flow experiments using rectangular microchannels to study the effect of mechanical fatigue on erythrocyte deformability, erythrocyte deformability is a comprehensive indicator coupling cell geometric and physical properties, and cannot be used to quantitatively study the changes in erythrocyte physical properties during mechanical fatigue; while dielectrophoresis applies electromagnetic forces to cells, causing repeated stretching, which differs from the fluid shear forces and endothelial cell compression forces experienced by erythrocytes in vivo, potentially leading to insufficient accuracy in obtaining erythrocyte fatigue characteristic parameters, and making it difficult to assess the differences between these parameters and the actual erythrocyte fatigue characteristics in the human body; the embodiments of this invention, by employing microfluidic technology, can... While maintaining the standard for red blood cell count, this method improves the measurement speed of red blood cell physical and mechanical fatigue parameters. Furthermore, by adjusting the pressure direction and magnitude within the microtube, it controls the repeated inhalation and exhalation of red blood cells, simulating the reciprocating loading-unloading environment of red blood cells in a human biomechanical environment, thus enhancing the reliability of the measured parameters. In addition, based on dynamic images of flowing red blood cells, multiple independent intrinsic physical parameters of red blood cells can be accurately calculated in each cycle, improving overall measurement efficiency. This method also helps solve the parameter coupling problem in existing technologies studying the influence of mechanical fatigue on red blood cell deformability parameters, improving the accuracy and reliability of the measured physical and mechanical fatigue parameters.

[0035] Furthermore, before inputting the surface area decay index and the ultimate residual surface area of ​​the red blood cells into the mechanical fatigue surface area change model to determine the coupling parameters of the red blood cells' health status, the method further includes: using a second experimental cycle M, cyclically drawing in and expelling multiple experimental red blood cells from the microtube; during each cycle, photographing the experimental red blood cells flowing in the microtube to determine multiple frames of flowing red blood cell experimental images; determining the deformation parameters of the experimental red blood cells at each cycle based on the multiple frames of flowing red blood cell experimental images, and determining the surface area of ​​the experimental red blood cells based on the deformation parameters; performing exponential function fitting processing based on the surface area of ​​the experimental red blood cells at each cycle to obtain the mechanical fatigue surface area change model; wherein, the mechanical fatigue surface area change model is: A m =ae -k(m+b) +A ∞ In this embodiment of the invention, the above-described technical solution simulates the human body's mechanical stimulation environment by subjecting red blood cells to a large number of reciprocating cyclic loading and unloading cycles. This improves the reliability and accuracy of the fitted mechanical fatigue surface area change model and accurately characterizes the change pattern of red blood cell surface area in the actual human body environment with each mechanical fatigue stimulus.

[0036] Furthermore, the surface area decay index k and the limiting residual surface area A of the red blood cells are further... ∞ The parameters include: health status coupling parameter lna-kb; correlation coefficient g between the second rate of change of the erythrocyte membrane shear modulus with each cycle and the surface area; correlation constant C of the erythrocyte membrane shear modulus; and erythrocyte membrane shear modulus E at the first cycle. S0 Input a mechanical fatigue membrane shear modulus change model to determine the membrane shear modulus of the red blood cell after the theoretical number of cycles Q; wherein, the mechanical fatigue membrane shear modulus change model is: In practical implementation, in addition to the limited lifespan of red blood cells themselves, the experimental conditions, experimental time, and other subjective and objective factors may also limit the membrane shear modulus of red blood cells after a limited number of cycles in the actual experiment. In this embodiment of the invention, after determining the various mechanical fatigue characteristic parameters of red blood cells, the various known mechanical fatigue characteristic parameters of red blood cells can be input into the mechanical fatigue membrane shear modulus change model obtained by a limited number of cycles in the pre-experiment. In this way, the membrane shear modulus of the red blood cells after the theoretical number of cycles can be determined. The theoretical number of cycles can be much greater than the number of cycles in the experimental process in which the mechanical fatigue membrane shear modulus change model is obtained.

[0037] Furthermore, the diameter of the microtube is greater than or equal to 2.8 micrometers and less than or equal to 4.0 micrometers. Unlike the microtubes or microchannels used in the prior art, the microtubes used in the embodiments of the present invention have a targeted diameter range, suitable for measuring the physical and mechanical fatigue properties of erythrocytes with excellent deformability. This provides a suitable narrow channel for erythrocyte flow, preventing blockage and generating a reasonable range of mechanical stimulation. Furthermore, the microtubes exhibit axial symmetry, thereby significantly reducing the error in measuring the surface area of ​​erythrocytes and improving the accuracy of obtaining the membrane shear modulus of erythrocytes during subsequent machine learning.

[0038] Furthermore, by employing differential motion analysis, the cell contours of each red blood cell within the microtube are determined based on the multi-frame images of flowing red blood cells. This effectively removes some noise unrelated to the cell contours of flowing red blood cells and eliminates static background regions irrelevant to the detection of flowing red blood cells, thereby obtaining the most complete and clear possible contours of the red blood cells within the microtube, thus improving the accuracy of subsequent calculations of the physical property parameters of the red blood cells based on their contours.

[0039] 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 (dynamic images) with a machine learning algorithm to determine the membrane shear modulus of red blood cells, can effectively improve the measurement accuracy. Attached Figure Description

[0040] Figure 1 This is a flowchart of a method for determining the mechanical fatigue characteristic parameters of red blood cells according to an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of the structure of an experimental system for measuring the mechanical fatigue characteristics of red blood cells according to an embodiment of the present invention;

[0042] 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;

[0043] Figure 4 This is a schematic diagram of the cell outline of flowing red blood cells extracted according to the differential motion analysis method in an embodiment of the present invention;

[0044] Figure 5 yes Figure 1 A flowchart of the first specific implementation method of step S12;

[0045] Figure 6 This is a planar schematic diagram illustrating the calculation of the surface area of ​​red blood cells based on their cell outlines in an embodiment of the present invention.

[0046] Figure 7 yes Figure 1 A flowchart of the second specific implementation method of step S12;

[0047] Figure 8 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;

[0048] Figure 9 yes Figure 1 A flowchart of the first specific implementation of step S13;

[0049] Figure 10 This is a schematic diagram of fitting the first linear function to determine the surface area decay index and the limiting residual surface area of ​​red blood cells in an embodiment of the present invention;

[0050] Figure 11 This is a schematic diagram of fitting the exponential function to determine the mechanical fatigue surface area change model in an embodiment of the present invention;

[0051] Figure 12 yes Figure 1 A partial flowchart of the second specific implementation of step S13;

[0052] Figure 13 This is a schematic diagram of fitting the second linear function to determine the correlation coefficient and the membrane shear modulus correlation constant in an embodiment of the present invention;

[0053] Figure 14 This is a schematic diagram of the curve showing the change of the membrane shear modulus of red blood cells with each cycle in an embodiment of the present invention.

[0054] Figure 15 This is a schematic diagram of a device for determining the mechanical fatigue characteristic parameters of cells according to an embodiment of the present invention.

[0055] Explanation of reference numerals in the attached figures:

[0056] 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; Electric three-way valve-223; Image analysis and data processing module-23. Detailed Implementation

[0057] As mentioned earlier, studying the fatigue characteristics of erythrocytes during repeated cycles of mechanical fatigue stimulation is of great value.

[0058] In existing technologies, Garcia-Herreros utilizes parallel multi-channel (less than 1 micrometer wide) through which a population of erythrocytes repeatedly squeezes through a spleen-like slit over a prolonged period to measure the physical and biological properties of the cell population at different cycle counts. However, this method is time-consuming (often requiring 2-3 days), is not a single-cell experiment, has low experimental precision, and cannot accurately determine the fatigue characteristics of erythrocytes. Sakuma used a microchannel (rectangular cross-section) with a width of 3 micrometers and a height of 4 micrometers to conduct single-cell reciprocating flow experiments on erythrocytes, studying the effect of mechanical fatigue on the deformability and mechanical stability of erythrocytes. They found that the deformability and mechanical stability of erythrocytes decreased significantly with increasing fatigue cycles. However, in this experiment, the deformability of erythrocytes is a comprehensive indicator coupled with physical properties such as cell surface area, volume, membrane shear modulus, and cell viscosity, and cannot be used to quantitatively study the changes in the physical properties of erythrocytes during mechanical fatigue. Qiang used dielectrophoresis to perform single-cell cyclic loading and unloading on erythrocytes adhered to microchannels. He found that mechanical fatigue significantly increased the shear modulus and viscoelasticity of the erythrocyte membrane. However, in dielectrophoresis, erythrocytes are often not in a dynamic flow state, but are fixed in a specific position and subjected to reciprocating stretching. In this case, the magnitude, direction, and position of the stretching force on the erythrocytes may differ from the shearing force of fluids and the compression of reticuloendothelial cells on erythrocytes in the human body. The accuracy of the obtained erythrocyte fatigue characteristics may be insufficient, and the difference between the erythrocyte fatigue characteristics and those in the real environment in the human body is difficult to assess.

[0059] In this embodiment of the invention, a first experimental cycle N is used to cyclically draw in and expel multiple red blood cells from a microtube of the same diameter in a sample. During each cycle, images of each red blood cell flowing in the microtube are captured to determine multiple frames of flowing red blood cell images. Based on the multiple frames of flowing red blood cell images, the deformation parameters of each red blood cell during each cycle are determined, and based on the deformation parameters of each red blood cell, the physical property parameters of each red blood cell are determined. The physical property parameters of the multiple red blood cells in the sample during each cycle are subjected to function fitting to determine the mechanical fatigue property parameters of the red blood cells in the sample; where N is a positive integer. Compared to existing experiments that involve cyclically squeezing cell populations through microchannels, which are time-consuming and lack real-time accuracy; or single-cell reciprocating flow experiments using rectangular microchannels to study the effect of mechanical fatigue on erythrocyte deformability, erythrocyte deformability is a comprehensive indicator coupling cell geometric and physical properties, and cannot be used to quantitatively study the changes in erythrocyte physical properties under mechanical fatigue; while dielectrophoresis applies electromagnetic forces to cells, causing repeated stretching, which differs from the fluid shear forces and endothelial cell compression forces experienced by erythrocytes in vivo, potentially leading to insufficient accuracy in obtaining erythrocyte fatigue characteristic parameters, and making it difficult to discern the differences between these parameters and the actual erythrocyte fatigue characteristics in the human body. For evaluation; In this embodiment of the invention, by using microfluidic technology to adjust the pressure direction and magnitude within the microtube, red blood cells are repeatedly drawn into and expelled from the microtube, thereby simulating the reciprocating loading-unloading of red blood cells in a human biomechanical stimulation environment as closely as possible, which can improve the reliability of experimentally measured parameters; In addition, based on dynamic images of red blood cells in flow, multiple independent intrinsic physical property parameters of red blood cells can be accurately calculated in each cycle, which can improve the overall measurement efficiency and help solve the parameter coupling problem in the prior art for studying the influence of mechanical fatigue on red blood cell deformability parameters, thereby improving the accuracy and reliability of the measured physical property parameters and mechanical fatigue property parameters.

[0060] 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.

[0061] Reference Figure 1 , Figure 1 This is a flowchart of a method for determining the mechanical fatigue characteristic parameters of red blood cells according to an embodiment of the present invention. The method may include steps S11 to S13:

[0062] Step S11: Using the first experimental cycle N, multiple red blood cells in the sample are cyclically drawn in and expelled from a microtube of the same diameter. During each cycle, each red blood cell flowing in the microtube is photographed to determine multiple frames of flowing red blood cell images.

[0063] Step S12: Based on the multi-frame flowing red blood cell images, determine the deformation parameters of each red blood cell in each cycle, and based on the deformation parameters of each red blood cell, determine the physical property parameters of each red blood cell;

[0064] Step S13: Perform function fitting on the physical property parameters of multiple red blood cells in the sample during each cycle to determine the mechanical fatigue property parameters of the red blood cells in the sample.

[0065] Where N is a positive integer.

[0066] In the specific implementation of step S11, the microtube can be a glass microtube with a straight tip. By adding a cell solution into the microtube and using a pressure system to adjust the direction and magnitude of the pressure inside the microtube, red blood cells are driven to circulate repeatedly from one end of the microtube into the microtube and out of the other end, realizing the cyclic loading and unloading of red blood cells. During the process of entering the microtube from its opening and flowing within it, the red blood cells are deformed by the squeezing force of the microtube, thus simulating the real mechanical fatigue stimulation environment experienced by red blood cells in the human body, such as fluid shearing and reticuloendothelial compression.

[0067] The multiple red blood cells in the sample may be multiple red blood cells from the same human body.

[0068] The imaging of cells flowing in the microtubes can be performed using a combination of a magnifying microscope and a high-speed camera, and the number of image frames and exposure time captured by the camera can be controlled by software.

[0069] In practical implementation, to improve the accuracy and reliability of the determined mechanical fatigue characteristic parameters of red blood cells, the value of the first experimental cycle number N should be increased as much as possible, taking into account factors such as experimental conditions, experimental efficiency, experimental precision, and the maximum number of fatigue stimuli that red blood cells can withstand. In some non-limiting embodiments, the first cycle number N can be as low as about 200 times and as high as about 1000 times (here referring to the number of cycles per red blood cell).

[0070] Reference Figure 2 , Figure 2 This is a schematic diagram of an experimental system for measuring the mechanical fatigue characteristics of red blood cells according to an embodiment of the present invention. The experimental system for measuring the mechanical fatigue characteristics of red blood cells may include an image acquisition module 21, a pressure control module 22, and an image analysis and data processing module 23.

[0071] 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.

[0072] 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 between the mass (grams) of bovine serum albumin and 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, such as a high-speed camera, used to capture multiple frames of images of flowing red blood cells, and the high-speed camera is 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 drawing a cylindrical borosilicate capillary into a glass microchannel with a straight tip using a needle puller, and then using a micro-fusing instrument to cut the glass microchannel into a flat opening at about 3.0-3.2 micrometers with an inner wall angle within 0.05°, and the microtube 217 is filled with 1% BSA-PBS solution.

[0073] The multi-frame images of flowing red blood cells can also be captured from the stable flow region of the microtube 217. In this case, the multi-frame images of flowing red blood cells can reflect the stable flow state after the red blood cells have completely entered the microtube 217. As a non-limiting embodiment, a microtube segment 70 to 120 micrometers away 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.

[0074] It should be noted that the diameter of microtubule 217 is related to the cell type or cell diameter and the flow rate of the cell within the microtubule 217. Understandably, the larger the cell diameter, the larger the diameter of the microtubule 217; conversely, the smaller the cell diameter and the higher the flow rate of the cell within the microtubule 217, the smaller the diameter of the microtubule 217.

[0075] Furthermore, the diameter of the microtube is greater than or equal to 2.8 micrometers and less than or equal to 4.0 micrometers, which is targeted and specific. It is suitable for measuring the mechanical properties of red blood cells with excellent deformability. It can provide a suitable narrow channel for red blood cell flow, so that red blood cells do not become blocked in the microtube, and can generate a reasonable range of mechanical stimulation. It also exhibits an axially symmetric state, thereby significantly reducing the error in measuring the area and volume of red blood cells and improving the accuracy of obtaining the membrane shear modulus of red blood cells during machine learning.

[0076] The pressure control module 22 includes three connecting pipes 221, two water tanks 222, and an electric three-way valve 223. The two water tanks 222 are at different horizontal heights. There is a negative pressure -Δρ between the left water tank 222 and the microtube 217, and a positive pressure Δρ between the right water tank 222 and the microtube 217. The electric three-way valve is connected to the microtube 217 and the two water tanks 222 through the connecting pipes 221. It can switch the pressure at regular intervals to adjust the pressure direction and magnitude in the microtube. This drives the red blood cells to be drawn into the microtube from the port area at one end of the microtube and flow through the stable flow area before being discharged from the port at the other end of the microtube, thus realizing the cyclic loading and unloading of red blood cells.

[0077] In the image analysis and data processing module 23, the white, compressed and deformed small circles and ellipses represent the morphology of red blood cells in the orifice region of microtube 217 and the stable flow region of microtube 217, respectively. Lp represents the length of the red blood cell entering the microtube in the orifice region of microtube 217, and L represents the length of the red blood cell in the stable flow region of microtube 217. By performing image analysis and data processing on multiple frames of flowing red blood cell images captured from the stable flow region of microtube 217, the surface area and membrane shear modulus of the red blood cells can be determined.

[0078] In this embodiment of the invention, compared to existing experiments that involve cyclically squeezing cell populations through microchannels, which are time-consuming, lack real-time performance, and are inaccurate; or using rectangular microchannels for single-cell reciprocating flow experiments to study the effect of mechanical fatigue on erythrocyte deformability, erythrocyte deformability is a comprehensive indicator coupling cell geometric and physical properties, and cannot be used to quantitatively study the changes in erythrocyte physical properties during mechanical fatigue; while dielectrophoresis applies electromagnetic forces to cells, causing repeated stretching, which differs from the fluid shear forces and endothelial cell compression forces experienced by erythrocytes in vivo, potentially resulting in insufficient accuracy of the obtained erythrocyte fatigue characteristic parameters, and the differences between these parameters and the actual erythrocyte fatigue characteristics in the human body are difficult to assess; this embodiment of the invention employs microfluidic technology This technique can improve the measurement speed of red blood cell physical and mechanical fatigue parameters while maintaining the standard for red blood cell count. Furthermore, by adjusting the direction and magnitude of pressure within the microtube, it controls the repeated inhalation and exhalation of red blood cells, simulating the reciprocating loading-unloading environment of red blood cells in the human body's mechanical stimulation environment, thus improving the reliability of the measured parameters. In addition, based on dynamic images of flowing red blood cells, multiple independent intrinsic physical parameters of red blood cells can be accurately calculated in each cycle, improving overall measurement efficiency. This technique also helps solve the parameter coupling problem in existing technologies studying the influence of mechanical fatigue on red blood cell deformability parameters, improving the accuracy and reliability of the measured physical and mechanical fatigue parameters.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] The steady-flow region is a study area used to determine the surface area and membrane shear modulus of erythrocytes, within which individual erythrocytes flow at a stable velocity within the microtube. For each flowing erythrocyte, by capturing multiple frames of images of the flowing erythrocytes within the steady-flow region using a camera, the cell outline of the erythrocyte can be determined, and the surface area of ​​the erythrocyte can then be calculated. Furthermore, based on the cell outline, the stretching length and flow velocity of the erythrocyte within the steady-flow region can also be determined (e.g., calculated based on the positional changes of the cell outline at different times t), thereby determining the membrane shear modulus of the erythrocyte.

[0083] Continue to refer to Figure 1 In the specific implementation of step S12, the deformation parameters of each red blood cell in each cycle are determined based on the multi-frame flowing red blood cell images, and the physical characteristic parameters of each red blood cell are determined based on the deformation parameters of each red blood cell.

[0084] Furthermore, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, and the deformation parameter is the cell contour. Determining the deformation parameters of each red blood cell in each cycle based on the multi-frame images of flowing red blood cells includes: for each cycle of images of flowing red blood cells, selecting an image containing red blood cells as a control image and selecting an image without red blood cells as a background image; using differential motion analysis to perform differential processing on the control image and the background image to determine a differential image; and determining the cell contour of each red blood cell in each cycle based on the differential image.

[0085] It is understood that, in specific implementations, each captured image of flowing red blood cells may contain one or more red blood cells, or it may not contain red blood cells. For one or more red blood cells flowing through the stable flow region of the microtube in each cycle, multiple frames of images are captured continuously (at least one frame of which does not contain red blood cells). The image containing red blood cells is selected as the control image (an image containing clear red blood cell outlines can be selected), and the image not containing red blood cells is selected as the background image. Then, differential analysis is performed on the differential image and the control image to determine the cell outline of each red blood cell in each cycle.

[0086] Reference Figure 4 , Figure 4 This is a schematic diagram of the cell outline of flowing red blood cells extracted according to the differential motion analysis method in an embodiment of the present invention.

[0087] The two images on the left, from top to bottom, are an image of a microtube containing a flowing red blood cell (control image) and an image of a microtube without a flowing red blood cell (background image). The image on the right is an image containing a complete and clear outline of a flowing red blood cell, determined by differential processing of the control image and the background image.

[0088] In this embodiment of the invention, differential motion analysis is used to determine the cell contour of each red blood cell within the microtube based on the multi-frame images of flowing red blood cells. This effectively removes some noise unrelated to the cell contour of flowing red blood cells and eliminates static background areas irrelevant to the detection of flowing red blood cells. Furthermore, the background image update mechanism can adapt to changes in background and lighting to a certain extent, thereby obtaining the most complete and clear contour curve of the red blood cells within the microtube, thus improving the accuracy of subsequent calculations of the physical property parameters of the red blood cells based on their cell contours.

[0089] Reference Figure 5 , Figure 5 yes Figure 1 A flowchart of the first specific implementation of step S12.

[0090] Specifically, the multi-frame flow cell images are captured from the stable flow region of the microtube, the deformation parameter is the cell outline, and the physical property parameters include surface area; determining the physical property parameters of each red blood cell based on the deformation parameters of each red blood cell may include steps S51 to S54, and each step is described in detail below.

[0091] In step S51, for each red blood cell, the cell outline of the red blood cell is divided into multiple frustums.

[0092] In some non-limiting embodiments, the cell outline of the red blood cells can be divided equally or arbitrarily according to a preset total number of frustums; the cell outline of the red blood cells can also be divided according to a preset average value; other methods can also be used for division. The embodiments of the present invention do not limit the specific division method.

[0093] Understandably, the more frustums formed, the more accurate the result will be in subsequent steps calculating the surface area of ​​red blood cells. However, the number of frustums should not be excessive, otherwise it will increase computational overhead and reduce efficiency.

[0094] In step S52, for each frustum, the left diameter, right diameter, and height of the frustum are determined respectively.

[0095] 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.

[0096] In step S53, the surface area of ​​each frustum is determined based on its left diameter, right diameter, and height.

[0097] In step S54, an integral calculation method is used to obtain the sum of the surface areas of the multiple frustums as the surface area of ​​the red blood cell.

[0098] Furthermore, the surface area of ​​the red blood cells can be determined using the following formula:

[0099]

[0100] The volume of the cell is determined using the following formula:

[0101]

[0102] Where A represents the surface area of ​​a red blood cell, A j D refers to the surface area of ​​the j-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'.

[0103] It should be noted that the above steps for calculating the surface area of ​​red blood cells are based on the premise that red blood cells have an axisymmetric morphology in the stable flow region of microtubules.

[0104] Reference Figure 6 , Figure 6 This is a planar schematic diagram illustrating the calculation of the surface area of ​​red blood cells based on the cell outline of red blood cells in an embodiment of the present invention.

[0105] 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 of ​​each frustum can be determined. By integrating the surface areas of each frustum, the surface area of ​​the red blood cell can be calculated.

[0106] Reference Figure 7 , Figure 7 yes Figure 1 A flowchart of the second specific implementation of step S12.

[0107] Specifically, the multi-frame images of flowing red blood cells are captured from the stable flow region of the microtube, the deformation parameter is the cell contour, and the physical property parameters include the membrane shear modulus; determining the physical property parameters of each red blood cell based on the deformation parameters of each red blood cell may include steps S71 to S72, which will be explained below.

[0108] In step S71, for each red blood cell, the stretching length of the red blood cell within the microtube is determined based on the cell profile of the red blood cell, and the flow velocity of the red blood cell in the stable flow region of the microtube is determined.

[0109] In specific implementations, since the red blood cells are compressed and deformed within the microtubules, the cell outline of the red blood cells can 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 cell outline (ellipsoid), or an average diameter determined based on multiple diameters of the ellipsoid, or the stretched length of the red blood cells can be determined in other ways. This embodiment of the invention does not impose any limitations on these methods.

[0110] In practical implementation, the flow velocity of 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 cell outline of the red blood cells in the images are determined. The quotient between the difference between any two positions and the difference between the corresponding two capture times is 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 movement time); alternatively, the average of the calculated quotients can be used as the flow velocity of the red blood cells to more accurately determine the flow velocity of the red blood cells in the stable flow region of the microtube.

[0111] In step S72, 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.

[0112] 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.

[0113] 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 1% BSA-PBS 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.

[0114] Furthermore, the machine learning algorithm can be a neural network algorithm, and the neural network algorithm can employ a neural network model.

[0115] Reference Figure 8 , Figure 8 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.

[0116] 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 rate u0 when there are no red blood cells in the microtube; the hidden layer uses an activation function; the output layer is the membrane shear modulus Es of the red blood cells.

[0117] The neural network algorithm can be a three-layer error backpropagation neural network model; the activation function can be a sigmoid linear activation function.

[0118] 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.

[0119] In a non-limiting embodiment, the total number of red blood cell samples used to train the three-layer error backpropagation neural network model 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, wherein the ratio of training set:validation set:test set = 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 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.

[0120] In this embodiment of the invention, the membrane shear modulus of red blood cells can be determined by combining the analysis of multiple frames of flowing red blood cell images (dynamic images) with machine learning algorithms, which can effectively improve the accuracy of the measurement.

[0121] Reference Figure 9 , Figure 9 yes Figure 1 A flowchart of the first specific implementation of step S13.

[0122] Specifically, the physical property parameters include surface area, and the mechanical fatigue property parameters include surface area decay index and limiting residual surface area. Performing function fitting processing on the physical property parameters of multiple red blood cells in the sample during each cycle to determine the mechanical fatigue property parameters of the red blood cells in the sample may include steps S91 to S92, which are described below.

[0123] In step S91, the surface area of ​​multiple red blood cells in the sample is differentiated in each cycle to obtain the first rate of change of the surface area of ​​each red blood cell with each cycle.

[0124] In step S92, the surface area of ​​each red blood cell with each cycle is subjected to a first linear function fitting process and the surface area of ​​each red blood cell after the nth cycle is determined to determine the surface area decay index and the limiting residual surface area of ​​the red blood cells in the sample.

[0125] Where n is a positive integer, and 1≤n≤N.

[0126] Furthermore, the formula for fitting the first linear function is:

[0127] A' n =-k(A n -A ∞ );

[0128] Where n indicates the nth cycle in the first experimental cycle number N, A n A' is used to indicate the surface area of ​​the red blood cells after the nth cycle. n For A n The derivative of A' is used to indicate the first rate of change of the surface area of ​​the red blood cells with each cycle, where k is the derivative of A'. n Follow A n The absolute value of the slope under the linear change assumption, used to indicate the surface area decay index of the red blood cells, A. ∞ It is A' n and A n The x-intercept of the fitted first linear function is used to indicate the limiting residual surface area of ​​the red blood cells.

[0129] Reference Figure 10 , Figure 10 This is a schematic diagram of fitting the first linear function to determine the surface area decay index and the limiting residual surface area of ​​red blood cells in an embodiment of the present invention.

[0130] Where the horizontal axis represents the surface area A of each red blood cell after the nth cycle. n (um 2 The vertical axis represents the rate of change A' of the surface area of ​​each red blood cell with each cycle.n The absolute value of the slope of the fitted linear function is the surface area decay index k of the red blood cells in the sample, and the intercept of the fitted linear function on the x-axis is the limiting residual surface area A of the red blood cells. ∞ .

[0131] In practical implementation, when performing the first linear function fitting, in order to minimize the error in the surface area decay index and the limiting residual surface area of ​​the red blood cells obtained from the fitting, the value of n can be selected from 1 to N for a reasonable number of iterations. In some non-limiting embodiments, if N is 200 iterations, then n can be 100 iterations, and the number of red blood cells selected during fitting can be around 20.

[0132] It should be noted that, in practice, for the same subject, the surface area decay index k and the finite residual surface area A of all red blood cells in their body are different. ∞ They often have many different values ​​and cannot be uniquely determined, but the surface area decay index k and the limiting residual surface area A of all red blood cells in the same subject ∞ As an individual, physiological factors conform to a concentrated distribution type similar to a normal distribution. Therefore, when performing the first linear function fitting in this embodiment of the invention, it is assumed that the surface area decay index k and the limiting residual surface area A of all red blood cells of the same subject are... ∞ They are all the same. Under this assumption, the value of k can be based on A' n and A n The linear relationship between them was obtained using the least squares method, so that the measured A' of each red blood cell was accurate. n The value and the corresponding linear fit value k(A) obtained by fitting n -A ∞ Minimize the sum of errors between ), where the sum of errors is:

[0133]

[0134] Where min{} indicates the function for finding the minimum value, all indicates the total number of red blood cells selected during fitting, x indicates the x-th red blood cell selected during fitting, and A x,n A is used to indicate the surface area of ​​the x-th cell during the n-th cycle. x,∞ A' is used to indicate the limiting residual surface area of ​​the x-th cell. x,n This is used to indicate the first rate of change of the surface area of ​​the x-th cell with each cycle. After the above data processing, we obtain the surface area decay index k and the limiting residual surface area A of the red blood cells in the sample. ∞ .

[0135] Furthermore, after determining the surface area decay index and the ultimate residual surface area of ​​the red blood cells in the sample, the method further includes: inputting the surface area decay index and the ultimate residual surface area of ​​the red blood cells into a mechanical fatigue surface area change model to determine the health status coupling parameters of the red blood cells; wherein, the health status coupling parameters are used to indicate the coupling relationship between the total surface area that the red blood cells can lose during their life cycle from birth to death and the current age index of the red blood cells.

[0136] Furthermore, before inputting the surface area decay index and the ultimate residual surface area of ​​the red blood cells into the mechanical fatigue surface area change model to determine the coupling parameters of the red blood cells' health status, the method further includes: using a second experimental cycle M, cyclically drawing in and expelling multiple experimental red blood cells from the microtube; during each cycle, photographing the experimental red blood cells flowing in the microtube to determine multiple frames of flowing red blood cell experimental images; determining the deformation parameters of the experimental red blood cells at each cycle based on the multiple frames of flowing red blood cell experimental images, and determining the surface area of ​​the experimental red blood cells based on the deformation parameters; performing exponential function fitting processing based on the surface area of ​​the experimental red blood cells at each cycle to obtain the mechanical fatigue surface area change model; wherein, the mechanical fatigue surface area change model is: A m =ae -k(m+b) +A ∞ ;

[0137] Where m indicates the m-th cycle in the second experimental cycle number M, A m A is used to indicate the surface area of ​​the experimental red blood cells at the m-th cycle. ∞ The value of m is used to indicate the limit of residual surface area of ​​the experimental red blood cells; k is used to indicate the surface area decay index of the experimental red blood cells; a is used to indicate the total surface area that the experimental red blood cells can lose during their life cycle from birth to death; b is used to indicate the current age of the experimental red blood cells; M and m are both positive integers, and 1≤m≤M.

[0138] The multiple experimental red blood cells may originate from the same individual.

[0139] In practice, the second experimental cycle number M can be the same as or different from the first experimental cycle number N. To maximize the accuracy and reliability of the mechanical fatigue surface area change model obtained by exponential function fitting, the value of the second experimental cycle number M should be increased as much as possible (it can be greater than the value of N), taking into account factors such as experimental conditions, experimental efficiency, experimental precision, and the maximum number of fatigue stimuli that the experimental red blood cells can withstand.

[0140] In practice, for a subject's red blood cells, if the aforementioned method is used to determine the subject's red blood cell surface area decay index and limiting residual surface area, these two known parameters are input into the mechanical fatigue surface area change model A. m =ae -k(m+b) +A ∞ This allows us to determine the coupling parameter lna-kb of the subject's red blood cell health status (which characterizes the coupling relationship between the total surface area that the subject's red blood cells can lose during their life cycle from birth to death and the current age of the red blood cells).

[0141] Reference Figure 11 , Figure 11 This is a schematic diagram of fitting the exponential function to determine the mechanical fatigue surface area change model in an embodiment of the present invention.

[0142] Where the horizontal axis represents the number of cycles m, and the vertical axis represents the surface area A of the red blood cells. m (um 2 The three curves in the figure represent the changes in surface area of ​​experimental red blood cells from three different individuals with each cycle. As can be seen from the figure, the surface area changes (mechanical fatigue characteristics) of the three experimental red blood cells from different individuals with each mechanical stimulus are not consistent, and the health status coupling parameter lna-kb is also different.

[0143] In this embodiment of the invention, the above-mentioned technical solution simulates the human body's mechanical stimulation environment by subjecting red blood cells to a large number of reciprocating loading-unloading cycles. This can improve the reliability and accuracy of the fitted mechanical fatigue surface area change model and accurately characterize the change pattern of red blood cell surface area in the actual human body environment with each mechanical fatigue stimulus.

[0144] Reference Figure 12 , Figure 12 yes Figure 1 A partial flowchart of the second specific implementation of step S13.

[0145] Specifically, the physical property parameters include surface area and membrane shear modulus. The mechanical fatigue property parameters include surface area decay index and ultimate residual surface area, as well as the correlation coefficient between the second rate of change of membrane shear modulus with each cycle and surface area, and the membrane shear modulus correlation constant. Function fitting is performed on the physical property parameters of multiple red blood cells in the sample during each cycle to determine the mechanical fatigue property parameters of the red blood cells in the sample, which may include... Figure 9The steps S91 to S92 shown may also include steps S121 to S122. The execution order of steps S91 to S92 and steps S121 to S122 may not be sequential (they are parallel). The following describes each step.

[0146] In step S121, the membrane shear modulus of multiple red blood cells in the sample is differentiated in each cycle to obtain the second rate of change of the membrane shear modulus of each red blood cell with each cycle.

[0147] In step S122, a second linear function is fitted to the second rate of change of the membrane shear modulus of each red blood cell with each cycle and the surface area of ​​each red blood cell after the nth cycle, so as to determine the correlation coefficient between the second rate of change of the membrane shear modulus of the red blood cells in the sample with each cycle and the surface area, as well as the membrane shear modulus correlation constant.

[0148] Furthermore, the formula for fitting the second linear function is: E' Sn =gA n +C;

[0149] Where n indicates the nth cycle in the first experimental cycle number N, A n E' is used to indicate the surface area of ​​the red blood cells after the nth cycle. Sn It is E Sn The derivative of E' is used to indicate the second rate of change of the membrane shear modulus of the red blood cells with each cycle, where g is E' Sn Follow A n The slope under the linear assumption, used to indicate the correlation coefficient between the second rate of change of the membrane shear modulus of the red blood cells with each cycle and the surface area, C is E' Sn With A n The y-intercept of the fitted second linear function is used to indicate the membrane shear modulus-related constant of the red blood cells, where n is a positive integer and 1 ≤ n ≤ N.

[0150] Reference Figure 13 , Figure 13 This is a schematic diagram of fitting the second linear function to determine the correlation coefficient and the membrane shear modulus correlation constant in an embodiment of the present invention.

[0151] Where the horizontal axis represents the surface area A of each red blood cell after the nth cycle. n (um 2 The vertical axis represents the second rate of change E' of the membrane shear modulus of each red blood cell with each cycle. SnThe slope of the fitted linear function is the correlation coefficient g between the second rate of change of the membrane shear modulus of the red blood cells in the sample with each cycle and the surface area. The intercept of the fitted second linear function on the y-axis is the correlation constant C of the membrane shear modulus of the red blood cells in the sample.

[0152] In practice, when performing the second linear function fitting, the value of n and the number of red blood cells selected can be determined by referring to the relevant methods mentioned above for performing the first linear function fitting, and will not be repeated here.

[0153] Furthermore, the method also includes: determining the surface area decay index k and the limiting residual surface area A of the red blood cells. ∞ The parameters include: health status coupling parameter lna-kb; correlation coefficient g between the second rate of change of the erythrocyte membrane shear modulus with each cycle and the surface area; correlation constant C of the erythrocyte membrane shear modulus; and erythrocyte membrane shear modulus E at the first cycle. S0 Input the mechanical fatigue membrane shear modulus change model to determine the membrane shear modulus of the red blood cell when it has undergone the theoretical number of cycles Q.

[0154] The mechanical fatigue membrane shear modulus change model is as follows:

[0155]

[0156] Where q indicates the q-th iteration in the theoretical iteration count Q, E Sq The mechanical fatigue membrane shear modulus used to indicate the red blood cell undergoing the qth cycle, where q and Q are both positive integers, 1≤q≤Q, and Q>N.

[0157] In specific implementation, the mechanical fatigue film shear modulus change model The mechanical fatigue surface area change model A can be used. m =ae -k(m+b) +A ∞ Substitute E' Sn =gA n The result is obtained by adding C and then integrating (where, during the calculation, the loop count parameter q = m = n in each formula).

[0158] Reference Figure 14 , Figure 14 This is a schematic diagram of the curve showing the change of the membrane shear modulus of red blood cells with each cycle in an embodiment of the present invention.

[0159] Where the horizontal axis represents the number of cycles q, and the vertical axis represents the membrane shear modulus E of the red blood cell. SqThe three curves in the figure represent the changes in membrane shear modulus of three experimental red blood cells from different individuals with each cycle. As can be seen from the figure, the changes in membrane shear modulus (mechanical fatigue characteristics) of the three experimental red blood cells from different individuals with each mechanical stimulus are not consistent.

[0160] In practical implementation, besides the limited lifespan of red blood cells themselves, there may also be limitations due to subjective and objective factors such as experimental conditions and experimental time. In actual experiments, only the membrane shear modulus of red blood cells after undergoing a limited number of cycles can be obtained. However, in this embodiment of the invention, after determining the various mechanical fatigue characteristic parameters of red blood cells, the various known mechanical fatigue characteristic parameters of red blood cells can be input into the mechanical fatigue membrane shear modulus change model obtained by a limited number of cycles in a pre-experiment. This allows the determination of the membrane shear modulus of the red blood cells after the theoretical number of cycles, which can be much greater than the number of cycles in the experimental process used to obtain the mechanical fatigue membrane shear modulus change model.

[0161] Reference Figure 15 , Figure 15 This is a schematic diagram of a device for determining the mechanical fatigue characteristic parameters of cells according to an embodiment of the present invention. The device for determining the mechanical fatigue characteristic parameters of red blood cells may include:

[0162] The flowing red blood cell image acquisition module 151 is used to cyclically draw in and expel multiple red blood cells from a microtube of the same diameter in a sample using a first experimental cycle number N. During each cycle, the module takes pictures of each red blood cell flowing in the microtube to determine multiple frames of flowing red blood cell images.

[0163] The red blood cell physical property parameter determination module 152 is used to determine the deformation parameters of each red blood cell in each cycle based on the multi-frame flowing red blood cell images, and to determine the physical property parameters of each red blood cell based on the deformation parameters of each red blood cell.

[0164] The mechanical fatigue characteristic parameter determination module 153 is used to perform function fitting processing on the physical characteristic parameters of multiple red blood cells in the sample during each cycle, so as to determine the mechanical fatigue characteristic parameters of the red blood cells in the sample.

[0165] Where N is a positive integer.

[0166] For the principle, specific implementation, and beneficial effects of the device for determining the mechanical fatigue characteristic parameters of red blood cells, please refer to the previous text. Figures 1 to 14 The description of the method for determining the mechanical fatigue characteristic parameters of red blood cells shown is not repeated here.

[0167] This invention also provides a storage medium storing computer instructions, which, when executed, perform the steps of the method for determining the mechanical fatigue 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.

[0168] Specifically, in this embodiment of the invention, the processor can be a central processing unit (CPU), but it can also 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.

[0169] 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).

[0170] This invention also provides a terminal, including a memory and a processor. The memory stores computer instructions that can be executed on the processor. When the processor executes the computer instructions, it performs the steps of the method for determining the mechanical fatigue 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.

[0171] 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.

[0172] In the embodiments of this application, "multiple" refers to two or more.

[0173] 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.

[0174] 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.

[0175] 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 mechanical fatigue characteristic parameters of red blood cells, characterized in that, include: Using the first experimental cycle number N, multiple red blood cells in the sample are cyclically drawn in and expelled from a microtube of the same diameter. During each cycle, each red blood cell flowing in the microtube is photographed to determine multiple frames of flowing red blood cell images. Based on the multi-frame flowing red blood cell images, the deformation parameters of each red blood cell in each cycle are determined, and the physical property parameters of each red blood cell are determined based on the deformation parameters of each red blood cell. The physical property parameters of multiple red blood cells in the sample during each cycle are subjected to function fitting to determine the mechanical fatigue property parameters of the red blood cells in the sample. Where N is a positive integer.

2. The method according to claim 1, characterized in that, The physical property parameters include surface area, and the mechanical fatigue property parameters include surface area decay index and ultimate residual surface area. The physical property parameters of multiple red blood cells in the sample during each cycle were subjected to function fitting to determine the mechanical fatigue property parameters of the red blood cells in the sample, including: The surface area of ​​multiple red blood cells in the sample during each cycle is differentiated to obtain the first rate of change of the surface area of ​​each red blood cell with each cycle. The surface area of ​​each red blood cell changes with each cycle at the first rate of change and the surface area of ​​each red blood cell after the nth cycle are fitted by a first linear function to determine the surface area decay index and the limiting residual surface area of ​​the red blood cells in the sample. Where n is a positive integer, and 1≤n≤N.

3. The method according to claim 2, characterized in that, The formula for fitting the first linear function is: ; in, Used to indicate the first experimental cycle number N. The next loop Used to indicate that the red blood cells have undergone the first Surface area in the next cycle for The derivative of is used to indicate the first rate of change of the surface area of ​​the red blood cells with each cycle. yes Follow The absolute value of the slope under the linear change assumption is used to indicate the surface area decay index of the red blood cells. yes and The x-intercept of the fitted first linear function is used to indicate the limiting residual surface area of ​​the red blood cells.

4. The method according to claim 2, characterized in that, After determining the surface area decay index and the limiting residual surface area of ​​the red blood cells in the sample, the method further includes: The surface area decay index and the ultimate residual surface area of ​​the red blood cells are input into the mechanical fatigue surface area change model to determine the health status coupling parameters of the red blood cells. The health status coupling parameter is used to indicate the coupling relationship between the total surface area that the red blood cell can lose during its life cycle from birth to death and the current age index of the red blood cell.

5. The method according to claim 4, characterized in that, Before inputting the surface area decay index and the ultimate residual surface area of ​​the red blood cells into a mechanical fatigue surface area change model to determine the health status coupling parameters of the red blood cells, the method further includes: Using a second experimental cycle number M, multiple experimental red blood cells are cyclically drawn into and expelled from the microtube. During each cycle, the experimental red blood cells flowing in the microtube are photographed to determine multiple frames of experimental images of flowing red blood cells. Based on the multi-frame images of flowing red blood cells, the deformation parameters of the experimental red blood cells in each cycle are determined, and the surface area of ​​the experimental red blood cells is determined based on the deformation parameters of the experimental red blood cells. Based on the surface area of ​​the experimental red blood cells in each cycle, an exponential function fitting process was performed to obtain the mechanical fatigue surface area change model. The mechanical fatigue surface area change model is as follows: ; Where m indicates the m-th cycle in the second experimental cycle number M. Used to indicate the surface area of ​​the experimental red blood cells at the m-th cycle. Used to indicate the limiting residual surface area of ​​the experimental red blood cells; Used to indicate the surface area decay index of the experimental red blood cells; Used to indicate the total surface area that the experimental red blood cells can lose during their life cycle from birth to death; The indicators used to indicate the current age of the experimental red blood cells are M and m, both of which are positive integers, and 1 ≤ m ≤ M.

6. The method according to claim 4, characterized in that, The physical property parameters also include the membrane shear modulus, and the mechanical fatigue property parameters also include the correlation coefficient between the second rate of change of the membrane shear modulus with each cycle and the surface area, as well as the membrane shear modulus correlation constant. The process of performing function fitting on the physical property parameters of multiple red blood cells in the sample during each cycle to determine the mechanical fatigue property parameters of the red blood cells in the sample further includes: The membrane shear modulus of multiple red blood cells in the sample at each cycle is differentiated to obtain the second rate of change of the membrane shear modulus of each red blood cell with each cycle. The second rate of change of the membrane shear modulus of each red blood cell with each cycle and the surface area of ​​each red blood cell after the nth cycle are fitted with a second linear function to determine the correlation coefficient between the second rate of change of the membrane shear modulus of the red blood cells in the sample with each cycle and the surface area, as well as the membrane shear modulus correlation constant. Where n is a positive integer, and 1≤n≤N.

7. The method according to claim 6, characterized in that, The formula for fitting the second linear function is: ; in, Used to indicate the first experimental cycle number N. The next loop. Used to indicate that the red blood cells have undergone the first Surface area in the next cycle yes The derivative of is used to indicate the second rate of change of the membrane shear modulus of the red blood cells with respect to each cycle. yes Follow The slope under the linear assumption is used to indicate the correlation coefficient between the second rate of change of the membrane shear modulus of the red blood cells with each cycle and the surface area. yes and The y-intercept of the fitted second linear function is used to indicate the membrane shear modulus-related constant of the red blood cells, where n is a positive integer and 1 ≤ n ≤ N.

8. The method according to claim 6, characterized in that, The method further includes: The surface area attenuation index of the red blood cells Limiting residual surface area Health status coupling parameters The correlation coefficient g between the second rate of change of the membrane shear modulus of the red blood cells with each cycle and the surface area, and the correlation constant of the membrane shear modulus of the red blood cells. and the membrane shear modulus during the first circulation of the red blood cells Input the mechanical fatigue membrane shear modulus change model to determine the membrane shear modulus of the red blood cell when it has undergone the theoretical number of cycles Q. The mechanical fatigue membrane shear modulus change model is as follows: ； in, The number used to indicate the theoretical cycle number Q is the first The next loop Used to indicate that the red blood cells have undergone the first The mechanical fatigue membrane shear modulus at the next cycle, where q and Q are both positive integers, and 1≤q≤Q, and Q>N; Used to indicate the limiting residual surface area of ​​experimental red blood cells; Used to indicate the surface area decay index of the experimental red blood cells; Used to indicate the total surface area that the experimental red blood cells can lose during their life cycle from birth to death; This is used to indicate the current age of the experimental red blood cells.

9. The method according to claim 1, characterized in that, The diameter of the microtube is greater than or equal to 2.8 micrometers and less than or equal to 4.0 micrometers.

10. 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 deformation parameter is the cell contour. Based on the multi-frame images of flowing red blood cells, the deformation parameters of each red blood cell in each cycle are determined as follows: For the images of flowing red blood cells captured during each cycle, images containing red blood cells are selected as control images, and images not containing red blood cells are selected as background images; The differential motion analysis method is used to perform differential processing on the control image and the background image to determine the differential image; The cell outline of each red blood cell in each cycle is determined based on the difference image.

11. 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 deformation parameter is the cell outline, and the physical property parameters include surface area. Based on the deformation parameters of each red blood cell, the physical properties of each red blood cell are determined, including: For each red blood cell, the cell 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 of ​​each frustum based on its left diameter, right diameter, and height. The surface area of ​​the red blood cells is obtained by using an integral calculation method to sum the surface areas of the multiple frustums.

12. The method according to claim 11, characterized in that, The surface area 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 The diameter on the left side of the frustum. The diameter of the right side of the frustum. The height of the frustum.

13. 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 deformation parameter is the cell contour, and the physical property parameters include the membrane shear modulus. Based on the deformation parameters of each red blood cell, the physical properties of each red blood cell are determined, including: For each red blood cell, the stretching length of the red blood cell within the microtube is determined based on the cell profile of the red blood cell, and the flow velocity of the red blood cell in the stable flow region of the microtube is also 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.

14. The method according to claim 13, 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.

15. The method according to claim 14, 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 stretching length of the red blood cells, the flow velocity of the red blood cells in the stable flow region of the microtube, the diameter of the microtube, and the liquid flow velocity 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.

16. A device for determining the mechanical fatigue characteristic parameters of red blood cells, characterized in that, include: The flowing red blood cell image acquisition module is used to cyclically draw in and expel multiple red blood cells from a microtube of the same diameter in a sample using a first experimental cycle number N. During each cycle, the module takes pictures of each red blood cell flowing in the microtube to determine multiple frames of flowing red blood cell images. The red blood cell physical property parameter determination module is used to determine the deformation parameters of each red blood cell in each cycle based on the multi-frame flowing red blood cell images, and to determine the physical property parameters of each red blood cell based on the deformation parameters of each red blood cell. The mechanical fatigue characteristic parameter determination module is used to perform function fitting processing on the physical characteristic parameters of multiple red blood cells in the sample during each cycle, so as to determine the mechanical fatigue characteristic parameters of the red blood cells in the sample. Where N is a positive integer.

17. 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 mechanical fatigue characteristic parameters of red blood cells according to any one of claims 1 to 15.

18. 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 mechanical fatigue characteristic parameters of red blood cells according to any one of claims 1 to 15.

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