Method and device for passively detecting thrombus elasticity based on bubble stretching

Abstract

The present invention provides a method and device for passively detecting thrombus elasticity based on bubble stretching, which directly utilizes the self-induced bubble stretching deformation during blood coagulation to passively detect the blood coagulation function without using active stimulation methods such as probes, particle motion, and resonance. The optofluidic chip and detection device developed by the present invention have low cost and are easy to operate, and can ensure the accuracy, stability, and reliability of the results detected by this passive method.

Description

Technical Field

[0001] The present invention relates to the field of thromboelastography detection, and particularly to a method for passively detecting thromboelasticity based on bubble stretching, an optofluidic chip, and a detection device. Background Art

[0002] Thromboelastogram is of crucial significance for many clinical decisions, such as coagulopathy caused by trauma and rescue during massive blood transfusion. Thromboelastogram monitors the entire coagulation process from the start of coagulation, the formation of thrombus to fibrinolysis, and comprehensively detects and evaluates coagulation factors, fibrinogen, platelet aggregation, and fibrinolysis. Using thromboelastogram for perioperative coagulation management can detect coagulation abnormalities earlier, effectively predict intraoperative blood loss, save 20 - 50% of blood products, and improve the survival rate of patients during the operation.

[0003] Currently, the main device for thromboelastography monitoring is TEG5000, which has a high price, a high cost for single - test, and requires trained doctors to operate, making it difficult to meet the huge clinical timeliness requirements. The detection device for this clinical thromboelastogram mainly relies on an active - stimulation response system such as a probe, particle motion, resonance, etc. to monitor the blood coagulation process, and requires a high - precision input source - detection module, with a relatively large device size. In addition, using active - stimulation methods such as a probe, particle motion, resonance, etc. will affect the stability of the thrombus, and thrombus fragmentation and detachment often occur in actual clinical applications, interfering with the measurement. Summary of the Invention

[0004] Based on this, it is necessary to provide a method for passively detecting thromboelasticity based on bubble stretching, an optofluidic chip, and a detection device, which do not need to adopt active - stimulation methods such as a probe, particle motion, resonance, etc., and directly use the situation of self - induced bubble stretching deformation during the blood coagulation process to passively detect the blood coagulation ability, ensuring the stability and reliability of the detection results.

[0005] The present invention adopts the following technical solutions:

[0006] The present invention provides an optofluidic chip for passively detecting thromboelasticity based on bubble stretching, which has a microfluidic detection channel for filling a blood sample, and a bubble - column array is arranged in the microfluidic detection channel. Preferably, the optofluidic chip further includes an inlet and an outlet communicating with the microfluidic detection channel to facilitate filling the detection channel with the blood sample in a directed manner. The bubble - column array is preferably arranged on the side of the microfluidic detection channel. The microfluidic detection channel is provided with a steady - flow S - bend section.

[0007] In some embodiments, the bubble - column array includes 2 - 4 bubble columns, which are arranged side - by - side on the side of the microfluidic detection channel.

[0008] In some of these embodiments, the optofluidic chip is prepared by curing a polydimethylsiloxane prepolymer and a curing agent in a mold.

[0009] The present invention also provides a device for passively detecting thrombus elasticity based on bubble stretching, including: an optofluidic chip temperature control system for maintaining a constant temperature environment of the optofluidic chip; an optical imaging system for capturing images of the stretching deformation of bubbles autonomously generated during the blood coagulation process of the optofluidic chip; and an image edge data processing module for calculating the stretching deformation images of the bubbles and converting them into thrombus elasticity indexes.

[0010] In some of these embodiments, the optofluidic chip temperature control system includes a heating film and a temperature controller connected to the heating film, and the heating film is used to heat the optofluidic chip.

[0011] In some of these embodiments, the device for passively detecting thrombus elasticity based on bubble stretching further includes a power supply module and / or a display screen. The power supply module is used to supply power to the optofluidic chip temperature control system and / or the optical imaging system, and the display screen is used to display the stretching deformation images of the bubbles and the thrombus elasticity indexes.

[0012] In some of these embodiments, the device for passively detecting thrombus elasticity based on bubble stretching further includes a wireless remote terminal.

[0013] The present invention also provides a method for obtaining the stretching deformation of bubbles during the blood coagulation process, including the following steps: collecting a blood sample, preprocessing, filling the microfluidic detection channel of the optofluidic chip, and sealing the inlet and outlet with oil; capturing images of the stretching deformation of bubbles autonomously generated during the blood coagulation process; and collecting and calculating the stretching deformation data of the bubbles in the stretching deformation images of the bubbles.

[0014] In some of these embodiments, the ambient temperature during the blood coagulation process is 30 °C.

[0015] The present invention also provides a method for passively detecting thrombus elasticity based on bubble stretching, including the following steps: collecting a blood sample (containing sodium citrate), adding kaolin reagent, and then adding a calcium ion solution to obtain a preprocessed sample, filling the microfluidic detection channel of the optofluidic chip, sealing the outlet and inlet of the chip with oil (cover plate), capturing images of the stretching deformation of bubbles autonomously generated during the blood coagulation process, collecting and calculating the stretching deformation data (area) of the bubbles in the stretching deformation images of the bubbles, and converting them into thrombus elasticity index data.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] The present invention for the first time explores and discovers that the passive detection of blood coagulation can be directly carried out by utilizing the situation of self-induced bubble deformation during the blood coagulation process. The idea of this passive detection can be realized by relying on an optical microfluidic chip with a bubble column array and the processing and analysis of bubble stretching deformation data, which is convenient for low-cost, rapid, and stable detection of blood coagulation function, and belongs to a new technology for a disruptive change in blood coagulation function detection. Description of the Drawings

[0018] Figure 1 It is a schematic structural diagram of the optical microfluidic chip in Example 1.

[0019] Figure 2 It is a schematic diagram of the device composition for passively detecting thrombus elasticity based on bubble stretching in Example 2.

[0020] Figure 3 It is the test result of the influence of different temperatures on the bubble stretching process in Example 4.

[0021] Figure 4 It is the image processing process based on edge computing in Example 5, as well as the comparison of test results between the clinical TEG instrument and this system.

[0022] Figure 5 It is the comparative analysis of the R and K values measured by the clinical TEG instrument and this system in Example 5.

[0023] Figure 6 It is the comparative analysis of the angle values and thrombus strength measured by the clinical TEG instrument and this system in Example 5.

[0024] Figure 7 It is the comparative analysis of the LY30 values measured by the clinical TEG instrument and this system in Example 5.

[0025] Figure 8 It is the performance of the clinical TEG instrument and this system in diagnosing the comprehensive blood coagulation ability of 44 clinical patients in Example 6. Detailed Description of the Invention

[0026] The following specific examples are used to further elaborate the present invention in detail, so that those skilled in the art can understand the present invention more clearly.

[0027] The following examples are only used to illustrate the present invention, but not to limit the scope of the present invention. Based on the specific examples of the present invention, all other examples obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0028] In the embodiments of the present invention, unless otherwise specified, all raw material components are commercially available products well-known to those skilled in the art; in the embodiments of the present invention, unless specifically specified, the technical means used are conventional means well-known to those skilled in the art.

[0029] For the first time, the present invention explores and discovers that the passive detection of the blood coagulation process can be directly carried out by utilizing the self-induced stretching deformation of bubbles during the blood coagulation process. The idea of this passive detection can be realized by relying on an optical microfluidic chip with a bubble column array and the analysis of bubble stretching deformation data, which is convenient for low-cost, rapid, and stable detection of blood coagulation function, and belongs to a new technology for a subversive change in blood coagulation function detection.

[0030] Specific examples are illustrated as follows:

[0031] Example 1

[0032] Please refer to Figure 1 , this embodiment provides an optical microfluidic chip for passive detection of thrombus elasticity based on bubble stretching, including a microfluidic detection channel provided with a bubble column array, and an inlet and an outlet communicating with the microfluidic detection channel, which is convenient for filling the detection channel with a blood sample in a directional manner. Among them, the bubble column array is preferably arranged on the side of the microfluidic detection channel and consists of 3 bubble columns, which are used to observe the change in the area of bubbles during the blood coagulation process. The microfluidic detection channel is provided with a steady flow S-bend section.

[0033] The preparation method of the optical microfluidic chip for passive detection of thrombus elasticity based on bubble stretching in this embodiment includes the following steps: SU-8 photoresist (MicroChem, Westborough, MA, USA) is spin-coated on a silicon wafer (5 inches) to obtain a 60-micron-thick coating, and is exposed to ultraviolet light through a chromium-coated quartz mask, and baked and developed according to the manufacturer's specifications. A mixture of PDMS prepolymer and curing agent (Sylgard 184, Dow Corning Corporation, USA) is prepared at a weight ratio of 20:1, degassed thoroughly, poured on a mold, degassed again, and then placed in an oven and cured at 75°C for 1 hour. Subsequently, the PDMS is cleaned with 75% ethanol and dried using pressurized air.

[0034] The overall size of the optical microfluidic chip for passive detection of thrombus elasticity based on bubble stretching in this embodiment is: 30 mm (length) × 14 mm (width) × 3 mm (height). The size of a symmetric microfluidic channel is: 200 μm (width) × 100 μm (height). The size of the bubble column is: 300 μm (length) × 70 μm (width) × 100 μm (height). The bubble column array can include 3 bubble columns arranged in a row and is located on the side of the microfluidic channel. The specific chip structure size can be slightly adjusted.

[0035] Example 2

[0036] As Figure 2 shown, this embodiment provides a device for passively detecting thrombus elasticity based on bubble stretching (thromboelastogram passive analyzer), which includes a power supply module, an optical microfluidic chip temperature control system, an optical imaging system, an image edge data processing module, and a display screen.

[0037] In this embodiment, the power supply module includes a rechargeable lithium battery pack (ChenKe) with 12V and 8400 mAh, and a dedicated power supply module for Raspberry Pi (21700 power lithium battery, 5V, battery capacity: 9600 mAH), which can operate continuously for about 10 hours.

[0038] In this embodiment, the optical microfluidic chip temperature control system is a portable temperature control system, which includes a heating film and a temperature control system connected to the heating film. The heating film is used to heat the optical microfluidic chip to ensure a constant temperature environment for blood samples. The size of the heating film is 71 mm (length) × 60 mm (width), and the corresponding imaging area in the middle is 26 mm (length) × 25 mm (width).

[0039] For example, the heating film can be a polyimide heating film (12V, 3W, 48Ω, manufactured by Wuxi Tianbo Electric Appliance Co., Ltd.) to maintain the constant temperature of the optical microfluidic chip at 30°C to 38°C. The temperature controller can be a commercial temperature controller (XY-WT03-W remote temperature controller, accuracy: 0.1°C) to control the temperature of the heating film, which includes a circuit board, an NTC 10K temperature probe, and a WIFI module (Sinilink).

[0040] In this embodiment, the optical imaging system is an integrated imaging system, which can include a camera module (CMOS IMX214, RERVISION), an optical lens (KENWEIJIESI) module, a USB module, and an LED lamp module (C5050, BDQ). The pixel size is 1μm, the field of view (FOV) is 1.81 mm × 1.02 mm, and the working distance is 0.75 mm. The optical imaging system is used to capture the image of the bubble stretching deformation generated autonomously during the blood coagulation process of the optical microfluidic chip.

[0041] In this embodiment, the image edge data processing module is a Pi module with an optical reconstruction algorithm, which is used to calculate the bubble stretching deformation image and convert it into a thrombus elasticity index.

[0042] In this embodiment, the display screen is preferably a touch-operable display screen, such as a capacitive touch TFT screen (power consumption: 0.34 A × 5 V, resolution: 800 × 480), etc. The operation process can be realized by clicking on the icons on the screen. The touch screen display (Raspberry PI Foundation) is used to provide a real-time view of the bubble array stretching data during blood coagulation, and is convenient to operate.

[0043] In this embodiment, the device further includes a housing. At the same time, the interior of the device is divided into multiple working areas by baffles, such as an integrated optical imaging system, a power module, an edge computing module, a temperature control system module, an optofluidic chip module, and a heat dissipation module.

[0044] Compared with the clinical thromboelastogram detection device that relies on the active stimulation method, the overall cost (equipment cost and test cost) of the passive thrombus elasticity detection device based on the bubble stretching method in this embodiment is low, and it is easy to operate.

[0045] Embodiment 3

[0046] Using the device of Embodiment 2, this embodiment provides a method for exploring whether bubble stretching deformation can be autonomously generated during blood coagulation by using fluorescence intensity, including the following steps: In a dark environment at room temperature (26 °C), 100 μL of blood sample is incubated with 5 μL of platelet fluorescent dye (PE anti-human CD45 antibody, Biolegend, 0.2 mg / mL) and 0.13 μL of fibrinogen fluorescent dye (Alexa 488 human fibrinogen conjugate F-13191, Life Technologies, 2 mg / mL) for 15 minutes for staining. The fluorescence images of blood coagulation are captured by a confocal microscope (Nikon, A1R).

[0047] During blood coagulation, fluorescence characterization observation is carried out on the bubble stretching process on the chip. During blood coagulation, fibrin gradually forms and entraps blood cells to form a blood clot. The co-localization of platelets and fibrin will increase, indicating that the binding strength between platelets and fibrin increases, and the bubbles will be passively stretched during the blood coagulation process.

[0048] Further fluorescence analysis is carried out around the bubbles. The average platelet fluorescence intensity and the average platelet fluorescence area around the bubbles at 5 min, 15 min, 25 min, and 35 min are shown in the following table:

[0049]

[0050] The average fibrin fluorescence intensity around the bubbles at 5 min, 15 min, 25 min, and 35 min is shown in the following table:

[0051]

[0052] The above results show that the fluorescence area and fluorescence intensity of fibrin and platelets around the bubble continuously increase during the coagulation process, indicating that the formation and densification degree of the blood clot around the bubble gradually increase. The bubble will be stretched during this process. When the blood clot tends to be stable, the stretching change of the bubble is not obvious. This directly verifies that the bubble will be stretched passively due to the blood clotting process.

[0053] Example 4

[0054] Using the equipment of Example 2, this example explores the influence of different temperature environments on the stretching deformation of bubbles during blood sample coagulation, including the following steps:

[0055] S1, Collect a blood sample. Take out 20 μL of blood from the blood in a 1-mL sodium citrate anticoagulation tube and add it to the kaolin reagent bottle. After adding 0.59 μL of calcium ions and mixing evenly, inject it into the optical microfluidic chip, and seal the chip outlet and inlet with an oil seal cover.

[0056] S2, Detect the stretching deformation of the bubble during the blood coagulation process of the blood sample at temperatures of 26°C, 30°C, 33°C, and 36°C respectively.

[0057] As Figure 3 shown in (a), temperature has an impact on the process of bubble stretching. According to the ideal gas law equation, under a certain stretching stress generated by blood coagulation and contraction, the stretching area of the bubble increases. Before the bubble is stretched to R = D / 2 (R: stretching radius; D: bubble column width), the bubble curvature (K) continuously increases until the air pressure difference (the difference between atmospheric pressure and bubble pressure) is equal to the Young-Laplace pressure. The bubble will be very stable at this stage, and its maximum value is K = 2 / D.

[0058] The temperature range in this test example is 26 - 37°C, covering the temperature from room temperature coagulation test to clinical coagulation test. As Figure 3 shown in (b) of, the test results show that the stability of the bubble stretching process is relatively good at temperatures of 26°C and 30°C. Excessive temperature will cause the bubble to stretch beyond the critical point and grow unstably.

[0059] Taking everything into consideration, the optimal temperature control for the stretching deformation of bubbles during blood sample coagulation is preferably 30°C. This temperature condition can balance the stretching area and stability of the bubble.

[0060] Example 5

[0061] This embodiment provides a modeling method for obtaining clinical blood coagulation indicators based on the stretching characteristics of bubbles, including the following steps: Collect 273 clinical blood samples, including 166 healthy blood samples with CI values ranging from -3 to 3; 63 hypocoagulable blood samples with CI values < -3; and 44 hypercoagulable blood samples with CI values > 3. Use the clinical TEG5000 instrument and the device of Example 2 to perform blood coagulation detection tests on the same samples. Input the clinical indicators (R, K, Angle, MBSA) obtained from the stretching characteristics of the foam array into the vector table for model development. The trained neural network contains 6 convolutional layers and 3 fully connected layers, and the 3 fully connected layers contain 4, 16, and 8 vectors. The dropout method is used during the training process to avoid overfitting and improve the generalization performance. The training set accounts for 70%, and the test set accounts for 30%. Each time, 70% of the data is randomly selected as the training set, and the remaining 30% of the data is reserved as the validation set during the clinical sample training process. The training process undergoes twenty iterations of loops. Use a server with an Nvidia Tesla V100 GPU to run the training process of the entire training set.

[0062] The results are as Figures 4 to 7 shown. Among them, Figure 4 A shows the acquisition process of the user interface and the stretching characteristics of the bubble array. This integrated system can continuously acquire images of the stretching of the bubble array during coagulation (10 seconds per frame). Then, the acquisition of the stretching characteristics of the bubble array is achieved through regional image acquisition, optical reconstruction, object recognition, and calculation. Figure 4 B shows the changes in the stretching characteristics of optical bubbles during blood coagulation. The obtained stretching characteristics of the bubble array can be used for curve plotting and the diagnosis of clinical indicators (R, K, Angle, MA, LY30). Figure 4 C shows the patient thromboelastography analysis between the clinical TEG5000 instrument and this system.

[0063] In this work, blood samples from 273 clinical patients (166 healthy, CI values: -3 - 3; 63 hypocoagulable, CI values: < -3; and 44 hypercoagulable, CI values: > 3) are used to establish the relationship between the stretching characteristics of bubbles and clinical indicators (R, K, Angle, MA, LY30) and develop an intelligent diagnosis model.

[0064] The clinical R value indicator refers to the time required from blood coagulation activation to the start of fibrin formation. The R value reflects the level of coagulation factors in the patient's body. The normal R value (TEG5000 instrument) in clinical diagnosis is between 4 - 9 minutes.

[0065] As Figure 5As shown in A, the Passing-Bablok regression analysis of the R-value analysis between the two methods (n = 273) showed that the A intercept value was -0.4913, the confidence interval (CI) was from -0.6727 to -0.2588, the B slope value was 0.8696, and the CI was from 0.8235 to 0.9091. The Bland-Altman analysis (n = 273) compared the R-value tests of the TEG5000 instrument and this system, showing that the average deviation was 1.4267 minutes and the SD was 0.4674 minutes ( Figure 5 b). The limits of agreement (LOA) ranged from 0.5107 to 2.343 minutes. These statistical analyses indicate that there is a good correspondence between the R-value tests of this system and the clinical TEG5000 instrument.

[0066] Then we performed time interval segmentation to establish the association between the R-value range tested by this system and the clinical R-index diagnosis. As Figure 5 shown in C, this system achieved a 100% diagnostic accuracy for the clinical R-index (n = 273) because the normal interval range was 3 to 7 minutes.

[0067] The clinical K-value index refers to the time required from the R-time endpoint to the recording amplitude of 20 mm. In this system, the growth area of the stretched bubble was used to correspond to the probe amplitude change of the TEG5000 instrument. Since the clinical diagnosis accuracy was the best, the selected bubble growth area interval, from 0 to 1.0 (102 μm 2 ), corresponded to the recording amplitude change from 0 to 20 mm. A Passing-Bablok regression analysis ( Figure 5 D) was performed, and the results showed that the A intercept value was -0.5000, the confidence interval (CI) was from -0.5800 to -0.3938, the B slope value was 1.5000, and the CI was from 1.4375 to 1.5500. The Bland-Altman analysis (n = 273) compared the K-value tests of the TEG5000 instrument and this system, showing that the average deviation was -0.4718 minutes and the SD was 0.3864 minutes ( Figure 5 E). The limits of agreement (LOA) ranged from -1.229 to 0.2856 minutes. These statistical analyses indicate that the K-value test of this system corresponds well to the clinical K-value test of the TEG5000 instrument. The normal K-value interval range of this system was 1.1 - 3.9 min ( Figure 5 F, diagnostic accuracy 99.6%, n = 273).

[0068] The clinical angle value index refers to the angle value between the horizontal line and the tangent line (from the blood clot formation point to the maximum arc of the curve). In clinical diagnosis, the normal angle value (TEG5000 instrument) range is 53 - 72°. AsFigure 6 As shown in a-b, Passing-Bablok regression analysis and Bland-Altman analysis were then performed (n = 273), and the results showed good consistency between the TEG5000 instrument and this system in the measurement of the angle value. The normal angle range of this system is 53 - 71°( Figure 6 C, with a diagnostic accuracy rate of 98.9%, n = 273).

[0069] The MA value index in clinical practice refers to the maximum distance between curves, reflecting the strength of the blood clot. The normal range of the MA value (TEG5000 instrument) in clinical diagnosis is 50 - 70 mm.

[0070] In this system, the maximum bubble stretching area (MBSA) is used to correspond to the maximum amplitude difference of the TEG5000 instrument. Passing-Bablok regression analysis was performed( Figure 6 d), and the results showed that the A intercept value was -8.1171, the confidence interval (CI) was from -8.8234 to -7.5522, the B slope value was 0.2696, and the CI was from 0.2604 to 0.2810. The Bland-Altman analysis comparing the two methods (n = 273) showed that the average deviation was 52.70 minutes and the SD was 4.746( Figure 6 e). The limits of agreement (LOA) were between 43.40 and 62.00. These statistical analyses indicate a good corresponding relationship between the two methods. The normal MBSA value range of this system is 5.6 to 10.7×102μm 2 ( Figure 6 f, with a diagnostic accuracy rate of 99.6%, n = 273).

[0071] The clinical LY30 value index refers to the percentage of blood clot dissolution 30 minutes after measuring the MA value. The normal range of the LY30 value (TEG5000 instrument) in clinical diagnosis is 0 to 8%. Passing-Bablok regression analysis was performed( Figure 7 a), and the results showed that the A intercept value was 0.0000, the confidence interval (CI) was from 0.0000 to 0.0000, the B slope value was 1.3333, and the CI was from 1.2821 to 1.5000. The Bland-Altman analysis comparing the LY30 value measurements between the TEG5000 instrument and this system (n = 273) showed that the average deviation was -0.043 and the SD was 0.362( Figure 7 b). The limits of agreement (LOA) ranged from -0.753 to 0.667. These statistical analyses indicate good consistency in the LY30 test between the two systems. The normal LY30 value range of the device system of the present invention is 0 to 10.1% Figure 7c, the diagnostic accuracy rate was 99.6%, n = 273). The comprehensive coagulation index refers to the comprehensive evaluation of blood coagulation dynamics. Currently, clinical instruments mainly evaluate and quantify based on empirical algorithms.

[0072] The present invention ingeniously introduces artificial intelligence to achieve one-step intelligent comprehensive coagulation ability diagnosis. The indicators (R value, K value, angle, MBSA) obtained from the bubble stretching characteristics are imported into the cloud neural network for coagulation diagnosis. Blood samples of 273 clinical patients (166 healthy, CI value: -3; 63 with low coagulation, CI value: < -3; and 44 with hypercoagulable state, CI value: > 3) were set for the development of the neural network diagnostic model. The results showed that the developed model achieved an excellent diagnostic accuracy of 97.3% (n = 273).

[0073] These statistical analyses indicate that the diagnostic model developed by this system based on bubble stretching characteristics has an excellent accuracy of 99.6% for clinical indicators (R, K, Angle, MA, LY30), and the test accuracy of the artificial intelligence model for comprehensive coagulation diagnosis (healthy, low coagulation, hypercoagulation) reaches 97.3% (n = 273).

[0074] Example 6 Clinical Verification

[0075] In this example, the same clinical samples were used for blood coagulation detection with a clinical TEG5000 instrument and the device of Example 2.

[0076] Two senior laboratory physicians from Zhongnan Hospital were invited to jointly conduct third-party TEG instrument tests on 44 clinical blood samples. After logging in to the dedicated TEG analysis software, view the horizontal baseline and conduct the test; place the ordinary cup on the cup holder, add 20 μL of calcium chloride solution to the cup; add the sample type and sample description on the operation interface; add 1 mL of blood sample to the kaolin activation tube, and then add 340 μL of kaolin-activated blood sample to the cup; move the test rod to the "test" position and click "start" to start the test; after determining all the required parameters, click "stop".

[0077] Both the device system of Example 2 and the TEG5000 instrument used the same blood samples and standard preparation procedures (kaolin activation and Ca 2+ activation) to avoid result deviation caused by time sensitivity and accurately evaluate the performance of the system. Chemical activation is not important and is only used to precisely evaluate the clinical manifestations of the method, which can be easily extended to chemical-free activation based on natural coagulation in the future.

[0078] The results are as Figure 8As shown. The clustering analysis heat map (a) presents the differences in R value, K value, Angle, and MBSA based on this system among patients with hypercoagulable state, normal state, and hypocoagulable state. The three-dimensional T-SNE map is shown in Figure b. The results indicate that there are significant differences in the bubble stretching characteristics of different (hypercoagulable state, healthy state, hypocoagulable state) blood samples.

[0079] The detailed clinical index diagnoses (R, K, Angle, MA, LY30) between the two systems are shown in Figure 8c. The diagnostic accuracy of this system for the clinical indexes of 44 clinical patients reaches 99.1%.

[0080] The confusion matrix for the comparison between the TEG diagnosis and the developed neural network model is shown in Figure d. The diagnostic accuracy of this system for the intelligent comprehensive coagulation ability diagnosis (CI) of 44 clinical patients shows 100%. These clinical test results strongly verify the superior performance of this system.

[0081] Example 7 Stability Test for Different Users

[0082] In this example, three volunteers were selected to participate in this test. Among them, User 1 and User 2 are students who have received basic medical training, and User 3 is a well-trained senior experimental physician. They respectively performed thromboelastography tests on the same patient based on the clinical TEG5000 instrument and this system.

[0083] The results show that there are significant differences in the thromboelastogram tests of the three volunteers on the TEG5000 instrument, and the error rate of clinical index diagnosis is 20%. However, there are no significant differences on the device system of the present invention, and the diagnostic accuracy of clinical index diagnosis is 100%. These statistical data further verify the stability and simplicity of operation of this system.

[0084] It is necessary to point out here that the above examples are only for further elaboration and explanation of the technical solution of the present invention, and are not further limitations on the technical solution of the present invention. The method of the present invention is only a preferred implementation scheme and is not used to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An apparatus for passively detecting thrombus elasticity based on bubble stretching, characterized in that, Comprising: An optical flow control chip temperature control system for maintaining a constant temperature environment of the optical flow control chip. The optical flow control chip has a microfluidic detection channel for filling with a blood sample, an inlet and an outlet communicating with the microfluidic detection channel. The microfluidic detection channel is provided with a bubble column array and a steady flow S-bend section; An optical imaging system for capturing images of the stretching deformation of bubbles spontaneously generated during the blood coagulation process of the optical flow control chip; And An image edge data processing module for calculating the images of the stretching deformation of bubbles and converting them into thrombus elasticity indexes.

2. The device for passively detecting thrombus elasticity based on bubble stretching according to claim 1, wherein, The optical flow control chip is prepared by curing a polydimethylsiloxane prepolymer and a curing agent in a mold.

3. The device for passively detecting thrombus elasticity based on bubble stretching according to claim 1, wherein The bubble column array includes 2 to 4 bubble columns, which are arranged side by side on the side of the microfluidic detection channel.

4. The device for passively detecting thrombus elasticity based on bubble stretching according to claim 1, characterized in that, The optical flow control chip temperature control system includes a heating film and a temperature controller connected to the heating film. The heating film is used to heat the optical flow control chip.

5. The device for passively detecting thrombus elasticity based on bubble stretching according to claim 4, characterized in that, It further includes a power supply module and / or a display screen. The power supply module is used to supply power to the optical flow control chip temperature control system and / or the optical imaging system. The display screen is used to display images of the stretching deformation of bubbles and thrombus elasticity indexes.

6. The device for passively detecting thrombus elasticity based on bubble stretching according to any one of claims 1 to 5, wherein It further includes a wireless remote terminal.

7. A method for detecting thrombus elasticity based on bubble stretching deformation, characterized in that, Including the following steps: Collecting a blood sample and performing pretreatment; Based on the device for passively detecting thrombus elasticity based on bubble stretching according to any one of claims 1 to 6, filling the microfluidic detection channel of the optical flow control chip with the pretreated blood sample and sealing the inlet and the outlet; Capturing images of the stretching deformation of bubbles spontaneously generated during the blood coagulation process; Collecting and calculating the data of the stretching deformation of bubbles in the images of the stretching deformation and converting them into thrombus elasticity indexes.

8. The method for detecting thrombus elasticity based on bubble stretching deformation according to claim 7, wherein, The ambient temperature during the blood coagulation process is 30 °C.

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