Device and method for automatically detecting platelet aggregation based on single PRP plasma

Abstract

The invention relates to an automatic plasma collection device which comprises a sample suction needle, a mechanical arm, a suction device and a control device, and an outer tube conductive end is formed at the sample suction end of an outer tube of the sample suction needle; the inner tube of the sample suction needle exposes the conductive material at the sample suction end to form an inner tube conductive end; the sample suction needle is also provided with a non-conductive plug which is connected with the outer tube and the inner tube at the sample suction end; the control device controls the mechanical arm, the suction device and the sample suction needle to automatically collect plasma based on electric signals formed by the outer tube conductive end and the inner tube conductive end. The invention further relates to an automatic platelet aggregation detection method based on the single PRP plasma, and the light transmittance A of the light waves of the multiple wavelengths of the PPP plasma is calculated based on the light transmittance A of the light waves of the multiple wavelengths of the PRP plasma by means of the trained prediction model, so that the maximum platelet aggregation rate is calculated. The device and the method realize the automation of platelet aggregation function detection, simplify the detection process, remarkably improve the detection efficiency and reduce the detection cost.

Description

Technical Field

[0001] The present invention relates to the field of medical detection, and in particular to an automatic platelet aggregation detection device and method based on single PRP plasma. Background Art

[0002] Platelet aggregation function testing is an important clinical testing method, which is of great significance for understanding the patient's blood coagulation state, guiding clinical treatment and evaluating disease risks. For example, platelet aggregation function testing can evaluate the specific functions of platelets in the process of hemostasis, coagulation and thrombosis, assist in the diagnosis of diseases such as thrombocytopenia, thrombotic diseases, platelet dysfunction, predict the risk of cardiovascular and cerebrovascular events, and evaluate surgical risks such as surgical bleeding and thrombosis.

[0003] Traditional platelet aggregation testing requires two different plasma samples, one platelet-rich plasma (PRP) sample is separated by low-speed centrifugation, such as centrifugation at 150-200g for 10 minutes, and the other platelet-poor plasma (PPP) sample is separated by high-speed centrifugation, such as centrifugation at 1500g-2000g for 15 minutes. After obtaining the PRP sample and the PPP sample, the platelet detection calculation model is used: The maximum platelet aggregation rate (MAR) is calculated to evaluate the aggregation function of platelets. This requires two plasma samples, which requires a large amount of plasma samples. On the other hand, different operating conditions are required to obtain PRP and PPP plasma samples respectively, which is complicated.

[0004] In addition, after centrifugation, although plasma and red blood cells will separate, especially for PRP plasma samples, the concentration distribution of platelets is a gradient distribution, and the plasma concentration in the upper layer is often lower than that in the lower layer. Since the platelet concentration of the sample plasma needs to meet the requirements of platelet-rich plasma during the platelet aggregation function test, the sampling needle is required to be as close to the interface between plasma and red blood cells as possible when extracting plasma samples, so as to extract plasma samples with higher concentrations. In addition, different plasma sources (such as from different people) may have different ratios of plasma and red blood cells, that is, the interface positions of different plasma sources are not consistent. Traditional liquid level detection methods can usually only detect the interface between air and liquid, or other interfaces with large differences in physical properties, while the properties of plasma and red blood cells are similar, and traditional methods are difficult to detect. Therefore, the industry currently relies on experienced operators to manually operate pipettes or pipettes to collect centrifuged plasma samples. Such manual collection makes the entire platelet aggregation function test inefficient, time-consuming and labor-intensive. Summary of the invention

[0005] In view of the above problems in the prior art, an object of the present invention is to provide an automatic platelet aggregation detection device that can automatically collect plasma and detect it based on only one PRP plasma sample.

[0006] In order to achieve the above-mentioned object, the present invention provides an automatic platelet aggregation detection device based on single PRP plasma, which comprises a centrifugal device, the automatic plasma collection device described in the present invention and a detection device based on single PRP plasma; wherein the centrifugal device is configured to obtain PRP plasma by low-speed centrifugation of the obtained plasma sample; the automatic plasma collection device is configured to automatically collect the PRP plasma of the required concentration and add it to the detection container of the detection device; the detection device comprises a detection container, a detection module, an estimation module and a calculation module, and the detection module is configured to detect the transmittance A (PRP) of multiple wavelengths of light waves of the PRP plasma located in the detection container P0), adding an inducing agent into the detection container to cause the PRP plasma to undergo aggregation reaction, continuously detecting the transmittance A(t) of the multiple wavelengths of light waves of the contents in the detection container at specified time intervals, and after a specified reaction time, detecting the transmittance A(PRP) of the multiple wavelengths of light waves of the contents in the container, the inference module being constructed to infer the transmittance A(PPP) of the multiple wavelengths of light waves of PPP plasma based on A(PRP0) and A(PRP) with the aid of a trained prediction model, and the calculation module being constructed to calculate the maximum platelet aggregation rate of the plasma sample based on A(PRP0), A(t), A(PRP) and A(PPP).

[0007] From the above, with the help of the automatic plasma collection device, the platelet aggregation automatic detection device of the present invention can realize the automatic collection of plasma, save labor costs, and improve detection efficiency. At the same time, with the help of the detection device based on a single PRP plasma, the invented platelet aggregation automatic detection device can obtain reliable detection results based on only one PRP plasma sample, reducing the amount of plasma samples used, saving time and labor.

[0008] Another object of the present invention is to propose a new automatic platelet aggregation detection method, which is based on only one PRP plasma sample without the need for a PPP plasma sample, thereby reducing the amount of plasma sample used and reducing operational complexity, saving time and effort.

[0009] In order to achieve the above-mentioned object, the present invention provides an automatic platelet aggregation detection method based on a single PRP plasma, comprising the following steps: obtaining a plasma sample and obtaining PRP plasma by low-speed centrifugation; automatically collecting the PRP plasma of the required concentration by means of the automatic plasma collection device of the present invention and adding it to a detection container; detecting the transmittance A(PRP0) of multiple wavelengths of light waves of the PRP plasma in the detection container; adding an inducing agent to the detection container to cause the PRP plasma to undergo an aggregation reaction, and continuously detecting the transmittance A(t) of the multiple wavelengths of light waves of the contents in the detection container at prescribed time intervals; after a prescribed reaction time, detecting the light transmittance A(PRP) of multiple wavelengths of light waves of the contents in the container; using a trained prediction model, inferring the transmittance A(PPP) of multiple wavelengths of light waves of PPP plasma based on A(PRP0) and A(PRP); and calculating the maximum platelet aggregation rate of the plasma sample based on A(PRP0), A(t), A(PRP) and A(PPP).

[0010] From the above, the transmittance A(PPP) of multiple wavelengths of PPP plasma can be calculated based on A(PRP0) and A(PRP) by using the prediction model, and the corresponding information of PPP plasma can be obtained from the transmittance of PRP plasma, which can avoid the use of PPP plasma. This reduces the amount of plasma sample used, while reducing the complexity of operation, saving time and effort. In addition, by using the automatic plasma collection device of the present invention, automatic collection of PRP plasma can be achieved, improving detection efficiency.

[0011] In some possible implementations of the present invention, the maximum platelet aggregation rate is calculated according to Formula I: Among them, MAR is the maximum platelet aggregation rate.

[0012] In some possible implementations of the platelet aggregation automatic detection method of the present invention, the prediction model is selected from iterative least squares method, system identification algorithm based on intelligent computing, and model learning algorithm based on machine learning. In some embodiments, the prediction model is selected from step response method, impulse response method, frequency response method, correlation analysis method, spectral analysis method, least squares method, maximum likelihood method, neural network, and system identification method of wavelet network based on fuzzy logic.

[0013] In some possible implementations of the method for automatically detecting platelet aggregation of the present invention, the multiple wavelengths include 425 nm, 575 nm, 660 nm and 800 nm.

[0014] In some possible implementations of the method for automatically detecting platelet aggregation of the present invention, the low-speed centrifugation treatment is centrifugation at a centrifugal force of 150 to 200 g for 5 to 15 minutes.

[0015] In some possible implementations of the method for automatically detecting platelet aggregation of the present invention, the inducing agent is selected from one or more of ADP adenosine diphosphate, AA arachidonic acid, COL collagen, and EPI epinephrine.

[0016] In some possible implementations of the platelet aggregation automatic detection method of the present invention, the prescribed time interval is 0.1-2 seconds. In some embodiments, the prescribed time interval is 1 second.

[0017] In some possible implementations of the platelet aggregation automatic detection method of the present invention, the prescribed reaction time is 300 seconds. In some embodiments, the prescribed reaction time is more than 300 seconds.

[0018] From the above, the transmittance of PPP plasma can be reliably obtained based on PRP plasma, thereby ensuring the detection accuracy.

[0019] Another object of the present invention is to provide a device that can separate red blood cells and plasma to achieve automatic plasma collection, which can be used in a platelet aggregation instrument, especially an automatic platelet aggregation detection device.

[0020] In order to achieve the above-mentioned purpose, the present invention provides an automatic plasma collection device, comprising: a sample suction needle, which has a base end, a sample suction end, an outer tube, an inner tube, a sample suction needle fixing seat located at the base end, an inner tube joint located at the base end connected to the inner tube, and a sample suction needle wire located at the base end and electrically connected to the outer tube and the inner tube respectively; a mechanical arm, which is connected to the sample suction needle fixing seat of the sample suction needle for displacing the sample suction needle; a suction device, which is connected to the inner tube joint of the sample suction needle for sucking the plasma to be detected through the sample suction needle; a control device, which is connected to the mechanical arm, the suction device and the sample suction needle in communication; wherein the suction device The outer tube of the sample needle is made of conductive material, and the outer tube of the sample suction needle is made of conductive material, forming an outer tube conductive end at the sample suction end; the inner tube of the sample suction needle passes through the interior of the outer tube and is longer than the outer tube at the sample suction end, is made of conductive material and is covered with insulating material inside, and the inner tube exposes the conductive material at the sample suction end to form an inner tube conductive end; the sample suction needle also has a non-conductive plug connecting the outer tube and the inner tube at the sample suction end; the control device controls the robotic arm, the suction device and the sample suction needle based on the electrical signal formed by the conductive end of the outer tube and the conductive end of the inner tube to automatically collect plasma.

[0021] From the above, the outer tube of the sample suction needle serves as a sensing electrode, and the inner tube of the sample suction needle serves as another sensing electrode. When the sample suction end of the sample suction needle is in a plasma environment, the two sensing electrodes can detect the resistance of the liquid. The inventors found that after centrifugation, a white film layer will form at the interface between red blood cells and plasma. The conductivity in plasma is close to that of physiological saline. When the electrode enters the white film layer, the conductivity decreases and the resistance increases. Therefore, the interface between red blood cells and plasma can be detected by detecting the resistance change in the plasma environment through the sensing electrode, thereby realizing automatic collection of plasma, eliminating the reliance on experienced operators, and improving detection efficiency.

[0022] In some possible implementations of the automatic plasma collection device of the present invention, the insulating material is an insulating hydrophobic coating. In some implementations, the insulating material is Teflon. In some implementations, the outer tube is covered with an insulating material, and a conductive material is exposed at the sample suction end to form an outer tube conductive end. In some implementations, the length of the outer tube conductive end in the longitudinal direction of the sample suction needle is 2-3 mm.

[0023] As a result, the sample is not easily adhered to the surface of the sample suction needle, thus avoiding contamination between samples.

[0024] In some possible implementations of the automatic plasma collection device of the present invention, the conductive material is selected from rust-proof alloys. In some implementations, the conductive material is stainless steel.

[0025] Therefore, the sample suction needle can be prevented from rusting with plasma and other reagents, thereby extending the service life of the sample suction needle. In addition, after the stainless steel surface is polished, it is not easy to hang water on the surface, avoiding contamination between samples.

[0026] In some possible implementations of the automatic plasma collection device of the present invention, the material of the non-conductive plug is POM plastic.

[0027] As a result, the inner tube and the outer tube of the sample suction needle can be reliably and electrically insulated, and water is not easily collected on the surface of the plug, thereby avoiding contamination between samples.

[0028] In some possible implementations of the automatic plasma collection device of the present invention, the inner tube is 2-3 mm longer than the outer tube at the sample aspiration end.

[0029] From the above, the boundary layer between red blood cells and plasma, i.e., the buffy coat layer, can be reliably and accurately sensed, thereby realizing reliable and accurate collection of the required plasma sample. During the research and development process, the inventors found that if the inner tube is too long and the distance between the two conductive ends is too far, the interference during the conductivity sensing will become greater, while if the inner tube is too short and the distance between the two conductive ends is too close, the sensing sensitivity will decrease, causing the sample suction needle to pierce the red blood cells.

[0030] The above contents of the present invention will be more clearly understood in the following description of multiple embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings of the present invention will be provided below. These drawings are only for embodying the present invention in a more intuitive form. They are exemplary and are not intended to limit the scope of the present invention.

[0032] Figure 1 A schematic structural block diagram of an embodiment of an automatic platelet aggregation detection device according to the present invention is shown;

[0033] Figure 2 A schematic flow chart of an embodiment of a method for automatically detecting platelet aggregation according to the present invention is shown.

[0034] Figure 3 This is a schematic diagram of the structure of the BP neural network.

[0035] Figure 4 A schematic structural diagram of an embodiment of an automatic plasma collection device according to the present invention is shown;

[0036] Figure 5 A schematic structural diagram of a sample aspirating needle according to an embodiment of an automatic plasma collection device of the present invention is shown;

[0037] Figure 6 Shows Figure 5 Schematic diagram of the enlarged structure of the sample aspiration end of the sample aspiration needle. DETAILED DESCRIPTION

[0038] To make the present invention easier to understand, the present invention is further described below in conjunction with specific examples. The experimental methods described in the present invention are conventional methods unless otherwise specified; the materials described are commercially available unless otherwise specified. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention. If there is any inconsistency, the meaning described in this specification or the meaning derived from the contents recorded in this specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0039] In order to accurately describe the technical contents of the present invention and to accurately understand the present invention, the following explanations or definitions are given for the words and terms used in this specification before describing the specific implementation methods.

[0040] The words "one embodiment" or "an embodiment" as used in this specification means that a particular feature, step, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment" or "in an embodiment" appearing throughout this specification do not necessarily all refer to the same embodiment, but may refer to the same embodiment. Furthermore, in one or more embodiments, the particular features, steps, or characteristics may be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure.

[0041] Figure 1 FIG. 2 shows a schematic structural block diagram of an embodiment of an automatic platelet aggregation detection device according to the present invention. Figure 1 As shown, the platelet aggregation automatic detection device of the present invention includes a centrifugal device, an automatic plasma collection device and a detection device. The detection device includes a detection container, a detection module, an estimation module and a calculation module. The detection module is configured to detect the transmittance A(PRP0) of multiple wavelengths of light waves of the PRP plasma located in the detection container; then an inducing agent is added to the detection container to cause the PRP plasma to undergo an aggregation reaction, and the transmittance A(t) of the multiple wavelengths of light waves of the contents in the detection container is continuously detected at a prescribed time interval, and after a prescribed reaction time, the light transmittance A(PRP) of the multiple wavelengths of light waves of the contents in the container is detected. The estimation module is configured to estimate the transmittance A(PPP) of multiple wavelengths of light waves of PPP plasma based on A(PRP0) and A(PRP) with the help of a trained prediction model, and the calculation module is configured to calculate the maximum platelet aggregation rate of the plasma sample based on A(PRP0), A(t), A(PRP) and A(PPP). The platelet aggregation automatic detection device of the present invention is configured to be able to implement the platelet aggregation automatic detection method of the present invention. Therefore, the introduction of the automatic platelet aggregation detection equipment, especially the detection device, will become clearer with the content of the automatic platelet aggregation detection method below.

[0042] Refer to the following Figure 2 The automatic platelet aggregation detection method of the present invention is exemplarily introduced.

[0043] like Figure 2 As shown, the automatic platelet aggregation detection method of the application includes steps S101-S107.

[0044] In S101, a plasma sample is obtained from a subject, and PRP plasma is obtained by low-speed centrifugation, for example, centrifugation at a centrifugal force of 150 to 200 g for 10 minutes. The concentration of platelets in the PRP plasma is gradually concentrated from top to bottom, and the platelets are distributed in a plasma layer (yellow clear liquid state), a white blood cell layer (buffy coat layer state), and a red blood cell layer from top to bottom.

[0045] In S102, the PRP plasma with the required platelet concentration is automatically collected at a suitable position of the PRP plasma by means of the automatic plasma collection device of the present invention, and added to a detection container.

[0046] In S103, the transmittance A (PRPO) of multiple wavelengths of light of the PRP plasma in the detection container is detected. The multiple wavelengths include 425 nm, 575 nm, 660 nm, and 800 nm.

[0047] In S104, an inducing agent is added to the detection container to cause an aggregation reaction of the PRP plasma, and the transmittance A(t) of the multiple wavelengths of light waves of the content in the detection container is continuously detected at a time interval of 1 second.

[0048] In S105, after a reaction time of 300 seconds, the light transmittance A (PRP) of the multiple wavelengths of light of the content in the container is detected.

[0049] In S106, with the aid of the trained prediction model, the transmittance A(PPP) of multiple wavelengths of light of PPP plasma is calculated based on A(PRP0) and A(PRP).

[0050] In S107, based on A(PRP0), A(t), A(PRP) and A(PPP), the maximum platelet aggregation rate MAR is calculated according to Formula I:

[0051] Here is a brief introduction to the training of the prediction model. First, PRP plasma and PPP plasma are obtained based on the same plasma sample. The transmittance of multiple wavelengths of light waves of PRP plasma before the aggregation reaction occurs is detected, PRP0 (575), PRP0 (425), PRP0 (660) and PRP0 (800), and then the PRP plasma is made to undergo an aggregation reaction. After a specified reaction time, the transmittance of multiple wavelengths of light waves PRP (575), PRP (425), PRP (660) and PRP (800) are detected. At the same time, the transmittance of multiple wavelengths of light waves of PPP plasma A (PPP) is detected. The eight groups of data PRP0 (575), PRP0 (425), PRP0 (660), PRP0 (800), PRP (575), PRP (425), PRP (660) and PRP (800) are used as the input of training data, and the group of data A (PPP) is used as the output of training data. A large amount of the above training data can be used to train the prediction model, so that after the prediction model is trained, A(PPP) can be inferred by only inputting A(PRP0) and A(PRP) data.

[0052] There are many prediction models suitable for such training. This application takes the BP neural network as an example to exemplify the prediction algorithm for the transmittance A(PPP) of multiple wavelengths of light waves of PPP plasma.

[0053] BP network is a multi-layer feedforward neural network consisting of an input layer, a hidden layer and an output layer. Figure 5 The topology of a typical three-layer BP network is shown, which includes an input layer, a hidden layer, and an output layer. The layers are fully interconnected, and there is no interconnection between the same layers. The hidden layer can have one or more layers. There are two types of signals circulating between the layers: one is the working signal, which is a signal that propagates forward after the input signal is applied until the actual output is generated at the output end. It is a function of the input and weight. The other is the error signal. The difference between the actual output of the network and the expected output is the error, which propagates backward from the output end layer by layer. The learning process of the BP network consists of a forward calculation process and an error back propagation process. In the forward calculation process, the input is calculated layer by layer from the input layer through the hidden layer and transmitted to the output layer. The state of each layer of neurons only affects the state of the next layer of neurons. If the output layer cannot obtain the expected output, it will enter the error back propagation process. The error signal returns along the original connection path, and the weights and thresholds of each layer of the network are adjusted successively until it reaches the input layer, and then the forward calculation is repeated. These two processes are repeated, and the weights and thresholds of each layer are continuously adjusted. When the network error is minimized or meets people's expected requirements, the learning process ends.

[0054] The following is the model construction of the BP neural network of this application.

[0055] Forward propagation: computing the output of the network

[0056] The input of the hidden layer neuron is the weighted sum of all inputs, that is,

[0057]

[0058] The output x of the hidden layer neuron ’ j Use S function to stimulate x j ,have to

[0059]

[0060] but

[0061]

[0062] The output of the output layer neurons is

[0063]

[0064] The lth output of the network and the corresponding ideal output The error is

[0065]

[0066] The error performance index function of the pth sample is

[0067]

[0068] Where N is the number of network output layers.

[0069] Back propagation: Use the gradient descent method to adjust the weights between layers.

[0070] The learning algorithm of weights is as follows: The connection weights w of the output layer and the hidden layer are jl The learning algorithm is

[0071]

[0072] The weight of the network at time k+1 is

[0073] w jl (k+1)=w jl (k)+Δw jl ,

[0074] The connection weights w between the hidden layer and the input layer ij The learning algorithm is

[0075]

[0076] in,

[0077] The weight of the network at time t+1 is

[0078] w ij (k+1)=w ij (k)+Δw ij .

[0079] If we consider the impact of the last weight on this weight change, we need to add a momentum factor α, and the weight at this time is

[0080] w jl (k+1)=w jl (k)+Δw jl +α((w jl (k)-w jl (k-1)) (5),

[0081] w ij (t+1)=w ij (t)+Δw ij +α((w ij (t)-w ij (t-1)) (6),

[0082] Where η is the learning rate, α is the momentum factor, η∈[0,1], α∈[0,1].

[0083] When the above training data is used, that is, eight groups of data PRP0(575), PRP0(425), PRP0(660), PRP0(800), PRP(575), PRP(425), PRP(660) and PRP(800) are used as the input of the training data, and the group of data A(PPP) is used as the output of the training data, the input nodes of the BP network are eight and the output node is one A(PPP). The following empirical formula is used to select the number of hidden layer neurons: Where n is the number of neurons in the input layer, m is the number of neurons in the output layer, and a is a constant between [1,10]. In this embodiment, 9 hidden layer neurons are selected; the number of iterations is set to 5000 times, the expected error is less than 0.2, the learning rate is 0.02, and the momentum factor is set to 0.1. First, according to the aforementioned forward propagation formula, the predicted output is calculated to obtain the output error. If the error is greater than 0.2, the back propagation algorithm is used to adjust the weights. After adjusting the weights, the predicted output is recalculated. If the error is less than 0.2, the training process ends and the model training is completed; if the error is greater than 0.2, the back propagation algorithm is continued to be used to adjust the weights. If the expected error is still not reached after the number of iterations is reached, the training parameters need to be adjusted and the training is repeated until a converged model can be obtained.

[0084] Figure 4 FIG. 2 is a schematic diagram showing a structure of an embodiment of an automatic plasma collection device according to the present invention. Figure 4 As shown, the automatic plasma collection device 100 includes a sample suction needle 1, an aspirating device 2, a mechanical arm 3 and a control device 4. The sample suction needle 1 is connected to the aspirating device 2 at the base end relative to the sample suction end, and the aspirating device 2 is further connected to the mechanical arm 3. The control device 4 is connected to the sample suction needle 1, the aspirating device 2 and the mechanical arm 3 for communication, and is configured to control the mechanical arm 3 to move the aspirating device 2 and the sample suction needle 1 up and down based on the electrical signal generated by the sample suction needle 1, and control the aspirating device 2 to aspirate plasma via the sample suction needle 1.

[0085] The illustrated automatic plasma collection device 100 also includes other components. For the sake of simplicity, this document only describes in detail the components related to the automatic collection of plasma and omits the description of other components.

[0086] Figure 5 The schematic diagram of the structure of the sample suction needle of an embodiment of the plasma automatic collection device according to the present invention is shown. The sample suction needle 1 comprises a base end 10, a sample suction end 20, an outer tube 11, an inner tube 12 (not shown in FIG. 1 ), and a base end 10, a sample suction end 20, an outer tube 11, and an inner tube 12 (not shown in FIG. 1 ). Figure 5 ), a sample suction needle fixing seat 13 located at the base end 10, an inner tube joint 14 located at the base end 10 connected to the inner tube 12, and a sample suction needle wire 15 located at the base end 10 and electrically connected to the outer tube 11 and the inner tube 12 respectively. In this embodiment, the outer tube 11 and the inner tube 12 are both made of conductive materials and are coated with insulating materials. The sample suction needle fixing seat 13 serves as a support to fix the outer tube 11, the inner tube 12, the inner tube joint 14 and the sample suction needle wire 15. The sample suction needle 1 can be connected to other components of the automatic plasma collection device 100, such as the suction device 2, via the sample suction needle fixing seat 13. The inner tube joint 14 is connected to the suction device 2. When extracting plasma, the plasma passes through the inner tube 12 and the inner tube joint 14 in turn to reach the suction device 2. The sample suction needle wire 15 is used to transmit the electrical signals generated by the outer tube 11 and the inner tube 12 to the control device.

[0087] Figure 6 The enlarged structural schematic diagram of the sample suction end 20 of the sample suction needle 1 is shown. The outer tube 11 has a portion 11A coated with an insulating material and an outer tube conductive end 11B formed by exposing a conductive material. The inner tube 12 has a portion 12A coated with an insulating material and an inner tube conductive end 12B formed by exposing a conductive material (not clearly shown in the figure). The inner tube 12 is 2-3 mm longer than the outer tube 11 at the sample suction end 20. The non-conductive plug 16 connects the outer tube 11 and the inner tube 12, more specifically connects the outer tube conductive end 11B and the inner tube conductive end 12B, on the one hand, to form insulation between the outer tube conductive end 11B and the inner tube conductive end 12B, and on the other hand, to prevent plasma or other liquids from flowing into the gap between the inner and outer tubes. The outer tube 11 and the inner tube 12 are respectively used as inner and outer electrode sensing electrodes, and an electrical signal is generated by sensing resistance changes through the outer tube conductive end 11B and the inner tube conductive end 12B.

[0088] Here is a brief introduction to the process of the sample suction needle 1 generating an electrical signal during detection. When both the inner and outer electrodes are in the air, the conductive ends of the two electrodes are not connected, the two electrodes are in an insulated state, and the resistance between the two electrodes is infinite. As the sample suction needle 1 moves downward, when the inner tube conductive end 12B of the sample suction needle 1 enters the plasma, and the outer tube conductive end 11B of the sample suction needle 1 also enters the plasma, the resistance between the two needles is the plasma resistance, and the resistance becomes smaller, so that the interface of the sample suction needle from air to plasma can be detected. As the sample suction needle 1 continues to move downward, when the inner and outer tube conductive ends are in the plasma at the same time, the resistance between the two ends hardly changes. The sample suction needle 1 continues to move downward, when the inner tube conductive end 12B enters the white film layer and the outer tube conductive end 11B is still in the plasma, the resistance becomes larger again, so that the interface between red blood cells and plasma, that is, the white film layer, can be detected.

[0089] The above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. For example, the illustrated automatic plasma collection device 100 exemplarily shows two groups of sample suction needles 1, suction devices 2 and mechanical arms 3 arranged side by side in a horizontal direction, but this is not restrictive. The number of groups and the arrangement between each group can be set according to actual needs. In addition, it is also possible that multiple sample suction needles are connected to a suction device and multiple suction devices are connected to a mechanical arm. The illustrated sample suction needle 1 exemplarily shows an outer tube 11 coated with an insulating material, but this is not restrictive. In some possible embodiments, the outer tube 11 can be made of a metal conductive material whose outer surface is processed and smooth, without the need to coat an insulating material. In the illustrated embodiment, four wavelengths of light of 425nm, 575nm, 660nm and 800nm ​​are used to detect the luminous flux, but this is not restrictive. Other wavelengths of light can be added according to actual needs, such as to further improve the detection accuracy and / or detection stability. In the illustrated embodiment, 1 second is used as the time interval and 300 seconds is used as the reaction time, which is also not restrictive. The curve of the change in transmittance during the aggregation process is not a curve with a very high fluctuation frequency. In clinical practice, a duration of 300 seconds is generally used to observe the change process of aggregation. Therefore, the time interval and reaction time can be appropriately adjusted according to actual needs.

[0090] also, Figure 1 The structural block diagram shown is basically divided according to the functions of each device and module, and is not intended to limit the actual structure of the platelet aggregation automatic detection device of the present invention. For example, it is understood that the control device in the automatic plasma collection device and the estimation module and calculation module in the detection device can be implemented by the same hardware and / or software entity.

[0091] Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present invention, all of which belong to the protection scope of the present invention.

Claims

1. An automatic plasma collection device, comprising: A sample suction needle, comprising a base end, a sample suction end, an outer tube, an inner tube, a sample suction needle fixing seat located at the base end, an inner tube joint located at the base end connected to the inner tube, and a sample suction needle wire located at the base end electrically connected to the outer tube and the inner tube respectively; A mechanical arm connected to the sample aspirating needle fixing seat of the sample aspirating needle for displacing the sample aspirating needle; A suction device, connected to the inner tube joint of the sample suction needle, for sucking the plasma to be tested through the sample suction needle; A control device, which is in communication with the mechanical arm, the suction device and the sample suction needle; in, The outer tube of the sample aspirating needle is made of a conductive material, and a conductive end of the outer tube is formed at the sample aspirating end; The inner tube of the sample aspirating needle passes through the inner part of the outer tube and is longer than the outer tube at the sample aspirating end, is made of a conductive material and is covered with an insulating material inside, and the conductive material of the inner tube is exposed at the sample aspirating end to form an inner tube conductive end; The sample aspirating needle also has a non-conductive plug connecting the outer tube and the inner tube at the sample aspirating end; The control device controls the robot arm, the suction device and the sample suction needle to automatically collect plasma based on the electrical signal formed by the conductive end of the outer tube and the conductive end of the inner tube.

2. The automatic plasma collection device according to claim 1, characterized in that: The inner tube is 2-3 mm longer than the outer tube at the sample suction end.

3. The automatic plasma collection device according to claim 1 or 2, characterized in that: The material of the non-conductive plug is POM plastic.

4. The automatic plasma collection device according to any one of claims 1 to 3, characterized in that: The insulating material is an insulating hydrophobic coating.

5. An automatic platelet aggregation detection device based on single PRP plasma, comprising a centrifugal device, an automatic plasma collection device according to any one of claims 1 to 4, and a detection device based on single PRP plasma; in, The centrifugal device is configured to obtain PRP plasma by low-speed centrifugation of the obtained plasma sample; The automatic plasma collection device is configured to automatically collect the PRP plasma of a desired concentration and add it to the detection container of the detection device; The detection device includes a detection container, a detection module, a calculation module and a calculation module. The detection module is configured to detect the transmittance A(PRP0) of multiple wavelengths of light waves of the PRP plasma in the detection container, add an inducing agent to the detection container to cause the PRP plasma to undergo an aggregation reaction, continuously detect the transmittance A(t) of the multiple wavelengths of light waves of the contents in the detection container at a prescribed time interval, and after a prescribed reaction time, detect the transmittance A(PRP) of the multiple wavelengths of light waves of the contents in the container, The calculation module is configured to calculate the transmittance A(PPP) of multiple wavelengths of PPP plasma based on A(PRP0) and A(PRP) by means of a trained prediction model. The calculation module is configured to calculate the maximum platelet aggregation rate of the plasma sample based on A(PRP0), A(t), A(PRP) and A(PPP).

6. The method for automatically detecting platelet aggregation based on single PRP plasma comprises the following steps: A plasma sample was obtained and processed by low-speed centrifugation to obtain PRP plasma; Automatically collect the PRP plasma of the required concentration by means of the automatic plasma collection device according to any one of claims 1 to 4, and add it to the detection container; Detecting the transmittance A(PRP0) of multiple wavelengths of light of the PRP plasma in the detection container; Adding an inducing agent into the detection container to cause the PRP plasma to undergo an aggregation reaction, and continuously detecting the transmittance A(t) of the multiple wavelengths of light waves of the contents in the detection container at prescribed time intervals; After a prescribed reaction time, detecting the light transmittance A (PRP) of multiple wavelengths of light waves of the content in the container; With the help of the trained prediction model, the transmittance of multiple wavelengths of light waves A(PPP) of PPP plasma is calculated based on A(PRP0) and A(PRP); The maximum platelet aggregation rate of the plasma sample was calculated based on A(PRP0), A(t), A(PRP) and A(PPP).

7. The method for automatic detection of platelet aggregation according to claim 6, characterized in that: The maximum platelet aggregation rate is calculated according to Formula I: Among them, MAR is the maximum platelet aggregation rate.

8. The automatic platelet aggregation detection method according to claim 6 or 5, characterized in that: The prediction model is selected from iterative least squares method, a system identification algorithm based on intelligent computing, and a model learning algorithm based on machine learning.

9. The automatic platelet aggregation detection method according to any one of claims 6 to 8, characterized in that: The plurality of wavelengths include 425 nm, 575 nm, 660 nm and 800 nm.

10. The automatic platelet aggregation detection method according to any one of claims 6 to 9, characterized in that: The specified time interval is 0.1-2 seconds.

Images