Microfluid integrated multi-pathogen visual detection platform and system
By integrating smart materials, microelectromechanical systems and ultrasonic transducers in the microfluidic detection system, dynamically adjusting fluid channels and identifying pathogen species, the problem of insufficient sensitivity and specificity in detecting multiple pathogens is solved, and more efficient and accurate pathogen detection is achieved.
Patent Information
- Application Number
- CN202510357754.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-25
- Publication Date
- 2025-06-10
AI Technical Summary
Traditional microfluidic detection systems have problems with insufficient sensitivity and specificity when detecting multiple pathogens, and the fixed path design is difficult to adapt to the specific reaction requirements of different pathogens, which affects the accuracy and efficiency of the detection.
The microfluidic integrated multi-pathogen visual detection platform is adopted, including a microfluidic integration module, a fluid path adjustment module, acoustic wave manipulation module and a central control unit. Through the integration of intelligent materials, microelectromechanical systems and ultrasonic transducers, the geometry and flow rate of the fluid channel are dynamically adjusted, and the pathogen species are identified through acoustic frequency analysis.
The sensitivity and specificity of pathogen detection are improved, and the detection can be carried out under the optimal reaction conditions of different pathogens, which improves the accuracy and efficiency of detection, is highly adaptable, and reduces the cross-contamination of samples and the inconsistency of detection results.
Smart Images
Figure CN120121707A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of pathogen detection, and relates to a microfluidic integrated multiple pathogen visualization detection platform and system. Background Art
[0002] In the field of microfluidic detection, traditional systems usually rely on fixed fluid paths, which have problems with insufficient sensitivity and specificity when detecting multiple pathogens. The fixed path design limits the flexibility of fluid flow, making it difficult to achieve optimal detection conditions for different pathogens, thus affecting the accuracy and efficiency of detection. In addition, the fixed path cannot adapt to changes in different samples and detection requirements, resulting in limited system adaptability and detection performance when facing complex samples.
[0003] Current technologies still have some significant defects and drawbacks in pathogen detection. First, traditional microfluidic systems often require multiple independent channels or complex pretreatment steps when detecting multiple pathogens, which not only increases the complexity and cost of the system, but may also lead to sample cross-contamination and inconsistency in test results. Secondly, the fixed path design is difficult to adapt to the specific response requirements of different pathogens, resulting in deficiencies in detection sensitivity and specificity, especially in low-concentration or mixed samples, making it difficult to accurately identify and quantify pathogens. In addition, traditional systems usually rely on preset detection conditions, lack the ability to adjust and optimize in real time, and have difficulty coping with environmental changes or dynamic changes in sample characteristics. This inflexible design limits the scope of application and detection efficiency of the system. Summary of the invention
[0004] In view of the above problems existing in the prior art, the present invention provides a microfluidic integrated multiple pathogen visualization detection platform and system to solve the above technical problems.
[0005] In order to achieve the above purpose and other purposes, the technical solution adopted by the present invention is as follows: A first aspect of the present invention provides a microfluidic integrated multiple pathogen visualization detection system, comprising a microfluidic integration module, a fluid path adjustment module, an acoustic wave manipulation module, and a central control unit, wherein the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; Microfluidic integrated module: The microfluidic chip includes an adjustable fluid channel for accommodating and transmitting pathogen samples to be tested; the fluid channel integrates smart materials, micro-electromechanical systems and ultrasonic transducers at the same time, wherein the smart materials realize dynamic adjustment of the channel geometry through external electric field stimulation; Fluid path adjustment module: adjusts the fluid parameters of the pathogen sample to be tested so that the flow of the pathogen sample to be tested reaches the optimal reaction condition; Acoustic wave manipulation module: by changing the frequency of the acoustic wave through the ultrasonic transducer arranged in the fluid channel, the pathogen type of the pathogen sample to be tested can be identified; Central control unit: includes a high-performance processor and a storage module, which is used to coordinate the collaborative work of the microfluidic integration module, the fluid path adjustment module and the acoustic wave manipulation module, and feed back the pathogen type of the pathogen sample to be tested to the corresponding display terminal.
[0006] Exemplarily, the fluid parameters of the pathogen sample to be tested are adjusted, and the fluid parameters are divided into a flow rate in the fluid channel and a channel height of the fluid channel.
[0007] Exemplarily, adjusting the fluid parameters of the pathogen sample to be tested includes: Step 1: Obtaining the original size parameters of the fluid channel in the microfluidic chip, wherein the original size parameters include the original length L and the original height h; Obtain the curvature coefficient k of the fluid channel curve, and calculate the original length of the fluid channel in the microfluidic chip: , where a and b are the x-axis coordinates of the starting point and end point of the fluid channel, respectively. is the slope of the fluid channel curve at any point, that is, the rate of change of the fluid channel curve in the x-axis direction, , dx represents a small change in the fluid channel in the x-axis direction; Step 2: Calculate the change in fluid channel height △h based on the response formula △h=α×E, where α and E represent the electrostrictive coefficient of the smart material and the applied electric field value, respectively; Then, the original height h of the fluid channel in the microfluidic chip and the change △h of the fluid channel height are added to obtain the channel height of the fluid channel corresponding to the pathogen sample to be tested; Step 3: Import the channel height of the fluid channel corresponding to the pathogen sample to be tested into the flow rate analysis formula In the process of predicting the flow rate of the pathogen sample to be tested in the fluid channel, , μ is the dynamic viscosity of the fluid corresponding to the pathogen sample to be tested, △P is the pressure difference between the two ends of the fluid channel, and h' is the channel height of the fluid channel corresponding to the pathogen sample to be tested; Step 4: Feedback the channel height of the fluid channel corresponding to the pathogen sample to be tested and the flow rate of the pathogen sample to be tested in the fluid channel to the central control unit, and the central control unit adjusts the fluid parameters of the pathogen sample to be tested.
[0008] Exemplarily, the specific identification operation steps for identifying the pathogen type of the pathogen sample to be tested are: Step 1: Pre-build a resonant frequency database of different types of pathogens in a storage module in the central control unit; Step 2: Determine the appropriate sound wave frequency range according to the physical properties of the pathogen sample to be tested, and set the initial frequency and frequency step value; Step 3: Starting from the initial frequency, gradually increase the sound wave frequency and scan the entire sound wave frequency range according to the set frequency step value; Step 4: At each frequency, the interaction signal strength between each sound wave frequency and the pathogen sample to be tested is extracted by a high-sensitivity sensor; Step 5: Calculate the resonance frequency of the pathogen sample to be tested based on the interaction signal strength between each sound wave frequency and the pathogen sample to be tested; Step 6: Finally identify the pathogen type of the pathogen sample to be tested.
[0009] Exemplarily, the operation process of step 2 is as follows: Screening the maximum size and minimum size of the pathogen from the pathogen sample to be tested; wherein the physical properties of the pathogen sample to be tested are the maximum size and minimum size of the pathogen; At the same time, the flow rate υ of the pathogen sample to be tested in the fluid channel is extracted; Thus, the appropriate sound wave frequency range for the pathogen sample to be tested is determined ; are the lower and upper limits of the acoustic frequency of the pathogen sample to be tested; Respectively represent the minimum and maximum sizes of pathogens; The initial frequency of the pathogen sample to be tested is set to ; By analyzing the formula , calculate the frequency step value △f of the pathogen sample to be tested, where N is the preset number of scanning steps; The calculation formula for the preset number of scanning steps N in the above calculation formula is: Retrieve the minimum response time t1, single scan time t2 and total scan limit time T of the ultrasonic transducer; Based on the frequency resolution, calculate the preset number of scan steps at the frequency resolution , is the characteristic frequency interval of the pathogen sample to be tested; Based on the time limit, calculate the preset number of scan steps under the time limit ; Based on the signal response time, calculate the preset number of scan steps under the signal response time ; Screen N1, N2 and N3. The specific screening conditions are as follows: Condition 1: Compare N1, N2 and N3 with the number 1 respectively, and eliminate the calculated preset scanning steps that are less than the number 1 and are not positive integers; Condition 2: Import N1, N2, and N3 In the calculation formula, the frequency step values of the pathogen samples to be tested corresponding to N1, N2 and N3 are calculated respectively, and then compared with Compare the difference of the calculation formula and find the value greater than The preset number of scanning steps corresponding to the difference in the calculation formula is eliminated; The above two conditions must be met at the same time, thereby screening out the calculated preset scanning steps that meet the screening conditions; the calculated preset scanning steps that meet the screening conditions are compared with each other again, and the smallest calculated preset scanning step number is used as the preset scanning step number N.
[0010] Exemplarily, the operation process of step 4 is as follows: At each sound wave frequency, the specific steps for extracting the interaction signal strength through a high-sensitivity sensor are: The sensor captures the signals generated by the interaction of each sound wave frequency with the pathogens in the fluid in real time, including the intensity of the reflected signal, phase change, and directionality of the scattered signal; De-noising and enhancing the original signal using a signal processing algorithm to extract key characteristic parameters, wherein the key characteristic parameters are divided into resonance peak frequency, signal amplitude change, phase offset and distribution characteristics of scattering pattern; These features are compared with the standard data in the acoustic intensity database to identify the characteristic patterns related to the current frequency, and the signal response characteristics at each frequency are recorded, ultimately forming a complete set of frequency-feature mapping data.
[0011] Exemplarily, the operation process of step five is as follows: Step A1: Pair each sound wave frequency in sequence according to the numbering order, wherein each sound wave frequency pair contains three sound wave frequencies, thereby obtaining the signal strengths corresponding to the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in each sound wave frequency pair, and marking them in sequence as , r is the number of each sound wave frequency pair, and the value range of r is 1 to R; Step A2, selecting the sound wave frequency with the largest signal strength in each sound wave frequency pair from the signal strengths corresponding to the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in each sound wave frequency pair; If the sound wave frequency with the largest signal strength in each sound wave frequency pair is identified as the second sound wave frequency, then the signal strength of the third sound wave frequency is subtracted from the signal strength of the first sound wave frequency in each sound wave frequency pair to obtain the signal strength difference between the two boundary points in each sound wave frequency pair; Then through the analysis formula , the curvature of the change of the three sound wave frequencies in each sound wave frequency pair is obtained by analysis ; The resonant frequency of the pathogen sample to be tested is calculated from this , R is the total number of sound wave frequency pairs, is the signal strength difference between the two boundary points in the rth acoustic wave frequency pair; are the frequency Hertz values of the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in the rth sound wave frequency pair respectively; Step A3: If the sound wave frequency with the largest signal strength among the sound wave frequency pairs is identified as the first sound wave frequency, the first sound wave frequency, the second sound wave frequency and the third sound wave frequency corresponding to the sound wave frequency pairs are rearranged, and the first sound wave frequency is used as the position of the middle point of the second sound wave frequency in the above step A2, and the second sound wave frequency and the third sound wave frequency are used as new boundary points, and then the resonance frequency of the pathogen sample to be tested is calculated; Step A4: If the sound wave frequency with the largest signal intensity among the sound wave frequency pairs is identified as the third sound wave frequency, the first sound wave frequency, the second sound wave frequency and the third sound wave frequency corresponding to the sound wave frequency pairs are rearranged, and the third sound wave frequency is used as the position of the middle point of the second sound wave frequency in the above step A2, and the second sound wave frequency and the first sound wave frequency are used as new boundary points, and then the resonant frequency of the pathogen sample to be tested is calculated.
[0012] Exemplarily, the operation process of step six is as follows: Extracting standard values of resonance frequencies of different types of pathogens from the storage module; Subtract the resonant frequency of the pathogen sample to be tested from the standard value of the resonant frequency of different types of pathogens, perform an absolute value calculation on the difference result, and finally use the calculation result as the numerator of the fraction; The standard values of the resonance frequencies of different types of pathogens are used as the denominator of the fraction; Solve the fraction to obtain the matching degree of the pathogen sample to be tested corresponding to different types of pathogens; And from the matching degrees of the pathogen sample to be tested corresponding to different types of pathogens, the pathogen type with the smallest matching degree is screened out as the pathogen type of the pathogen sample to be tested.
[0013] A second aspect of the present invention provides a microfluidic integrated multiple pathogen visual detection platform, including a processor, a memory, and a communication bus; The memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, a microfluidic integrated multiple pathogen visualization detection system as described in the present invention is implemented.
[0014] As described above, the microfluidic integrated multiple pathogens visualization detection platform and system provided by the present invention have at least the following beneficial effects: The present invention provides a microfluidic integrated multiple pathogen visualization detection platform and system, which designs a microfluidic chip integrated with smart materials, microelectromechanical systems (MEMS) and ultrasonic transducers, which is used to adjust the fluid parameters of the pathogen sample to be tested and identify the pathogen type through sound wave frequency analysis. This design can first make full use of the response characteristics of smart materials (such as shape memory materials, piezoelectric materials or thermal response materials) to achieve dynamic regulation of fluid parameters in the microfluidic channel, thereby providing the best reaction conditions for pathogen detection. Through real-time adjustment of smart materials, the system can adapt to the detection needs of different pathogens, so that it can show specific acoustic characteristics in the most suitable environment, and improve the sensitivity and specificity of detection. Secondly, the introduction of microelectromechanical systems makes the entire chip highly integrated and programmable, which can achieve precise control of multiple physical and chemical parameters in the fluid channel, while significantly reducing the volume and energy consumption of the equipment. The layout of the ultrasonic transducer provides core technical support for pathogen identification. By changing the sound wave frequency and analyzing the interactive signals between the sound wave and the pathogen (such as reflection, scattering and resonance characteristics), the type and concentration of the pathogen can be quickly and non-invasively identified. This acoustic detection method not only has high sensitivity, but also avoids the consumption and contamination problems of chemical reagents in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0016] Figure 1 It is a schematic diagram of the connection of various modules of the system of the present invention. DETAILED DESCRIPTION
[0017] The above contents in combination with the implementation of the present invention are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.
[0018] Example 1 See also Figure 1As shown, a microfluidic integrated multiple pathogen visualization detection system includes: a microfluidic integration module, a fluid path adjustment module, an acoustic wave manipulation module and a central control unit, wherein the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; Microfluidic integrated module: The microfluidic chip includes an adjustable fluid channel for accommodating and transmitting pathogen samples to be tested; the fluid channel integrates smart materials, micro-electromechanical systems and ultrasonic transducers at the same time, wherein the smart materials realize dynamic adjustment of the channel geometry through external electric field stimulation; Smart materials include electroactive polymers and shape memory alloys, which can change shape under external stimulation. MEMS include high-precision sensors and actuators. Sensors monitor fluid parameters such as flow rate, pressure and temperature in real time. According to sensor feedback, actuators drive smart materials to adjust channel structures, thereby optimizing fluid paths, improving reaction conditions and signal amplification effects.
[0019] Fluid path adjustment module: adjusts the fluid path of the pathogen sample to be tested so that the flow of the pathogen sample to be tested reaches the optimal reaction condition; According to a preferred embodiment, the fluid parameters of the pathogen sample to be detected are adjusted, and the fluid parameters are divided into the flow rate in the fluid channel and the channel height of the fluid channel.
[0020] According to a preferred embodiment, adjusting the fluid parameters of the pathogen sample to be tested includes: Step 1: Obtaining the original size parameters of the fluid channel in the microfluidic chip, wherein the original size parameters include the original length L and the original height h; Obtain the curvature coefficient k of the fluid channel curve, and calculate the original length of the fluid channel in the microfluidic chip: , where a and b are the x-axis coordinates of the starting point and end point of the fluid channel, respectively. is the slope of the fluid channel curve at any point, that is, the rate of change of the fluid channel curve in the x-axis direction, , dx represents a small change in the fluid channel in the x-axis direction; Step 2: Calculate the change in fluid channel height △h based on the response formula △h=α×E, where α and E represent the electrostrictive coefficient of the smart material and the applied electric field value, respectively; Then, the original height h of the fluid channel in the microfluidic chip and the change △h of the fluid channel height are added to obtain the channel height of the fluid channel corresponding to the pathogen sample to be tested; Step 3: Import the channel height of the fluid channel corresponding to the pathogen sample to be tested into the flow rate analysis formula In the process of predicting the flow rate of the pathogen sample to be tested in the fluid channel, , μ is the dynamic viscosity of the fluid corresponding to the pathogen sample to be tested, ΔP is the pressure difference between the two ends of the fluid channel, and h' is the channel height of the fluid channel corresponding to the pathogen sample to be tested; Step 4: Feedback the channel height of the fluid channel corresponding to the pathogen sample to be tested and the flow rate of the pathogen sample to be tested in the fluid channel to the central control unit, and the central control unit adjusts the fluid parameters of the pathogen sample to be tested.
[0021] The above-mentioned dynamic adjustment of the height and flow rate of microfluidic channels through smart materials and microelectromechanical systems (MEMS) has significant advantages. Traditional microfluidic systems usually rely on fixed geometric structures and external pump systems to control fluid flow, which has limitations in flexibility and response speed. By introducing smart materials (such as electroactive polymers and shape memory alloys) and MEMS technology, real-time adjustment of channel geometric parameters can be achieved to optimize fluid flow characteristics; First, the use of smart materials makes the adjustment of channel height and width more precise and controllable. Electroactive polymers (EAPs) can quickly change shape under electric field stimulation, and this property can be used for precise adjustment of microfluidic channels. For example, by applying electric fields of different intensities, the height of the channel can be dynamically adjusted at the micron level. This precise control capability is particularly important for biological detection applications that require high sensitivity and specificity, because it allows rapid optimization of fluid flow under different experimental conditions; Secondly, the ability to dynamically adjust the flow rate significantly improves the reaction efficiency of the system. By changing the channel height, the flow rate can be adjusted without the need for an external pump. This not only reduces the complexity and cost of the system, but also improves the reliability and stability of the system. Dynamic flow rate adjustment can ensure that the sample and reagent are mixed under optimal conditions, maximizing reaction efficiency and signal intensity. In addition, precise control of the flow rate can also reduce the damage to the sample caused by shear stress, especially for fragile biological samples (such as cells or proteins); Traditional microfluidic systems usually require specific channel structures to be designed for different applications, but the combination of smart materials and MEMS technology allows a system to adapt to a variety of application requirements. This adaptability not only reduces the time and cost of developing new systems, but also improves the versatility and market competitiveness of the system. This automation capability not only improves the convenience of operation, but also reduces human errors and improves the accuracy and reliability of test results.
[0022] Acoustic wave manipulation module: by changing the frequency of the acoustic wave through the ultrasonic transducer arranged in the fluid channel, the pathogen type of the pathogen sample to be tested can be identified; According to a preferred embodiment, the specific identification operation steps for identifying the pathogen type of the pathogen sample to be tested are: Step 1: Pre-build a resonant frequency database of different types of pathogens in a storage module in the central control unit; Step 2: Determine the appropriate sound wave frequency range according to the physical properties of the pathogen sample to be tested, and set the initial frequency and frequency step value; Step 3: Starting from the initial frequency, gradually increase the sound wave frequency and scan the entire sound wave frequency range according to the set frequency step value; Step 4: At each frequency, the interaction signal strength between each sound wave frequency and the pathogen sample to be tested is extracted by a high-sensitivity sensor; Step 5: Calculate the resonance frequency of the pathogen sample to be tested based on the interaction signal strength between each sound wave frequency and the pathogen sample to be tested; Step 6: Finally identify the pathogen type of the pathogen sample to be tested.
[0023] According to a preferred embodiment, the operation process of step 2 is as follows: Screening the maximum size and minimum size of the pathogen from the pathogen sample to be tested; wherein the physical properties of the pathogen sample to be tested are the maximum size and minimum size of the pathogen; At the same time, the flow rate υ of the pathogen sample to be tested in the fluid channel is extracted; Thus, the appropriate sound wave frequency range for the pathogen sample to be tested is determined ; are the lower and upper limits of the acoustic frequency of the pathogen sample to be tested; Respectively represent the minimum and maximum sizes of pathogens; The initial frequency of the pathogen sample to be tested is set to ; By analyzing the formula , calculate the frequency step value △f of the pathogen sample to be tested, where N is the preset number of scanning steps; The calculation formula for the preset number of scanning steps N in the above calculation formula is: Retrieve the minimum response time t1, single scan time t2 and total scan limit time T of the ultrasonic transducer; Based on the frequency resolution, calculate the preset number of scan steps at the frequency resolution , The characteristic frequency interval of the pathogen sample to be tested refers to the difference between the characteristic frequencies exhibited by different pathogens in acoustic wave detection. These characteristic frequencies are the significant response or resonance frequencies produced by pathogens at specific acoustic wave frequencies; Based on the time limit, calculate the preset number of scan steps under the time limit ; Based on the signal response time, calculate the preset number of scan steps under the signal response time ; Screen N1, N2 and N3. The specific screening conditions are as follows: Condition 1: Compare N1, N2 and N3 with the number 1 respectively, and eliminate the calculated preset scanning steps that are less than the number 1 and are not positive integers; Condition 2: Import N1, N2, and N3 In the calculation formula, the frequency step values of the pathogen samples to be tested corresponding to N1, N2 and N3 are calculated respectively, and then compared with Compare the difference of the calculation formula and find the value greater than The preset number of scanning steps corresponding to the difference in the calculation formula is eliminated; N must be at least 1, indicating that there is at least one frequency point in the frequency range. If N<1, it means that the step value is too large and an effective frequency scan cannot be performed; The frequency step value Δf must be smaller than the frequency range To ensure that there are multiple frequency points within the scanning range. If Δf> , then there is only one or less than one frequency point, and scanning is meaningless; The above two conditions must be met at the same time, thereby screening out the calculated preset scanning steps that meet the screening conditions; the calculated preset scanning steps that meet the screening conditions are compared with each other again, and the smallest calculated preset scanning step number is used as the preset scanning step number N.
[0024] According to a preferred embodiment, the operation process of step five is as follows: Step A1: Pair each sound wave frequency in sequence according to the numbering order, wherein each sound wave frequency pair contains three sound wave frequencies, thereby obtaining the signal strengths corresponding to the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in each sound wave frequency pair, and marking them in sequence as , r is the number of each sound wave frequency pair, and the value range of r is 1 to R; It should be added that, assuming there are sound wave frequencies g1, g2, g3, g4, g5, g6; Then two pairs are formed: [g1, g2, g3] and [g4, g5, g6]; Step A2, selecting the sound wave frequency with the largest signal strength in each sound wave frequency pair from the signal strengths corresponding to the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in each sound wave frequency pair; If the sound wave frequency with the largest signal strength in each sound wave frequency pair is identified as the second sound wave frequency, the signal strength of the first sound wave frequency in each sound wave frequency pair is subtracted from the signal strength of the third sound wave frequency to obtain the signal strength difference between the two boundary points in each sound wave frequency pair. ; Indicates the overall change trend of signal strength between g1 and g3. If: >0: Description Compare Strong, the signal is stronger near the frequency g1; <0: Description Compare Strong, the signal is stronger near frequency g3.
[0025] =0: Description and The signal strengths are equal.
[0026] This value is used to describe the symmetry or asymmetry of the signal between two boundary points.
[0027] Then through the analysis formula , the curvature of the change of the three sound wave frequencies in each sound wave frequency pair is obtained by analysis ; is a second-order difference component, which is used to describe the curvature of the signal between points g1, g2 and g3, indicating the "degree of curvature" or "degree of peak sharpness" of the signal. It reflects the quadratic change trend of the signal near g2. If: <0: indicates that g2 is a local maximum (i.e., the signal intensity reaches its peak at g2); >0: indicates that g2 is a local minimum (i.e., the signal strength reaches a valley value at g2); =0: indicates that the signal is flat near g2 (may be a platform or linear change); The resonant frequency of the pathogen sample to be tested is calculated from this , R is the total number of sound wave frequency pairs, is the signal strength difference between the two boundary points in the rth acoustic wave frequency pair; are the frequency Hertz values of the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in the rth sound wave frequency pair respectively; When calculating the resonant frequency of the pathogen sample to be tested, and The functions are: : Indicates the strength difference of the signal at two boundary points, which is used to measure the symmetry of the signal; : Indicates the quadratic change trend of the signal, which is used to determine the curvature of the signal and thus find the location of the peak.
[0028] Step A3. If the acoustic wave frequency with the maximum signal intensity among the acoustic wave frequency pairs is identified as the first acoustic wave frequency, then rearrange the first acoustic wave frequency, the second acoustic wave frequency, and the third acoustic wave frequency corresponding to each acoustic wave frequency pair. Take the first acoustic wave frequency as the position of the second acoustic wave frequency midpoint in the above Step A2, and take the second acoustic wave frequency and the third acoustic wave frequency as the new boundary points, and then calculate the resonance frequency of the pathogen sample to be measured. For example: Assume the original frequency points are: g1 = 100 kHz, g2 = 110 kHz, g3 = 120 kHz, The corresponding signal intensities are S(g1) = 1.5, S(g2) = 1.2, S(g3) = 0.9; At this time, the maximum signal intensity appears at g3, not g2. To adapt to the formula, we need to redefine the roles: The original g1 (100 kHz) is used as the new g2; The original g2 (110 kHz) is used as the new g1; The original g3 (120 kHz) remains as g3; Then substitute into the formula for calculation.
[0029] Step A4. If the acoustic wave frequency with the maximum signal intensity among the acoustic wave frequency pairs is identified as the third acoustic wave frequency, then rearrange the first acoustic wave frequency, the second acoustic wave frequency, and the third acoustic wave frequency corresponding to each acoustic wave frequency pair. Take the third acoustic wave frequency as the position of the second acoustic wave frequency midpoint in the above Step A2, and take the second acoustic wave frequency and the first acoustic wave frequency as the new boundary points, and then calculate the resonance frequency of the pathogen sample to be measured.
[0030] According to a preferred embodiment, the operation process of Step Six is as follows: Extract the resonance frequency standard values of different types of pathogens from the storage module; Subtract the resonance frequency standard value of the different types of pathogens from the resonance frequency of the pathogen sample to be measured, and perform an absolute value operation on the difference result, and finally use the operation result as the numerator of the fraction; Take the resonance frequency standard value of the different types of pathogens as the denominator of the fraction; Solve the fraction to obtain the matching degree of the pathogen sample to be measured corresponding to different types of pathogens; And screen out the pathogen of the type with the smallest matching degree from the matching degrees of the pathogen sample to be measured corresponding to different types of pathogens as the pathogen type of the pathogen sample to be measured.
[0031] Central control unit: It includes a high-performance processor and a storage module, which are used to coordinate the collaborative work of the microfluidic integration module, the fluid path adjustment module, and the acoustic wave manipulation module, and feedback the pathogen types of the pathogen samples to be tested to the corresponding display terminal.
[0032] Embodiment 2 A microfluidic integrated multi-pathogen visualization detection platform, including a processor, a memory, and a communication bus; A computer-readable program executable by the processor is stored on the memory; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, it implements a microfluidic integrated multi-pathogen visualization detection system as described in the present invention.
[0033] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
[0034] It should be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.
[0035] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
[0036] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims
1. A microfluidic integrated multiple pathogen visualization detection system, characterized in that: It includes a microfluidic integration module, a fluid path adjustment module, an acoustic wave manipulation module and a central control unit, wherein the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; Microfluidic integrated module: The microfluidic chip includes an adjustable fluid channel for accommodating and transmitting pathogen samples to be tested; the fluid channel integrates smart materials, micro-electromechanical systems and ultrasonic transducers at the same time, wherein the smart materials realize dynamic adjustment of the channel geometry through external electric field stimulation; Fluid path adjustment module: adjusts the fluid parameters of the pathogen sample to be tested so that the flow of the pathogen sample to be tested reaches the optimal reaction condition; Acoustic wave manipulation module: by changing the frequency of the acoustic wave through the ultrasonic transducer arranged in the fluid channel, the pathogen type of the pathogen sample to be tested can be identified; Central control unit: includes a high-performance processor and a storage module, which is used to coordinate the collaborative work of the microfluidic integration module, the fluid path adjustment module and the acoustic wave manipulation module, and feed back the pathogen type of the pathogen sample to be tested to the corresponding display terminal.
2. A microfluidic integrated multiple pathogen visualization detection system according to claim 1, characterized in that: The fluid parameters of the pathogen sample to be tested are adjusted, wherein the fluid parameters are divided into a flow rate in the fluid channel and a channel height of the fluid channel.
3. A microfluidic integrated multiple pathogen visualization detection system according to claim 2, characterized in that: Adjust the fluid parameters of the pathogen sample to be tested, including: Step 1: Obtaining the original size parameters of the fluid channel in the microfluidic chip, wherein the original size parameters include the original length L and the original height h; Obtain the curvature coefficient k of the fluid channel curve, and calculate the original length of the fluid channel in the microfluidic chip: , where a and b are the x-axis coordinates of the starting point and end point of the fluid channel, respectively. is the slope of the fluid channel curve at any point, that is, the rate of change of the fluid channel curve in the x-axis direction, , dx represents a small change in the fluid channel in the x-axis direction; Step 2: Calculate the change in fluid channel height Δh based on the response formula Δh=α×E, where α and E represent the electrostrictive coefficient of the smart material and the applied electric field value, respectively; Then, the original height h of the fluid channel in the microfluidic chip and the change △h of the fluid channel height are added to obtain the channel height of the fluid channel corresponding to the pathogen sample to be tested; Step 3: Import the channel height of the fluid channel corresponding to the pathogen sample to be tested into the flow rate analysis formula In the process of predicting the flow rate of the pathogen sample to be tested in the fluid channel, , μ is the dynamic viscosity of the fluid corresponding to the pathogen sample to be tested, ΔP is the pressure difference between the two ends of the fluid channel, and h' is the channel height of the fluid channel corresponding to the pathogen sample to be tested; Step 4: Feedback the channel height of the fluid channel corresponding to the pathogen sample to be tested and the flow rate of the pathogen sample to be tested in the fluid channel to the central control unit, and the central control unit adjusts the fluid parameters of the pathogen sample to be tested.
4. The microfluidic integrated multiple pathogen visualization detection system according to claim 1, characterized in that: The specific identification operation steps for identifying the pathogen type of the pathogen sample to be tested are: Step 1: Pre-build a resonant frequency database of different types of pathogens in a storage module in the central control unit; Step 2: Determine the appropriate sound wave frequency range according to the physical properties of the pathogen sample to be tested, and set the initial frequency and frequency step value; Step 3: Starting from the initial frequency, gradually increase the sound wave frequency and scan the entire sound wave frequency range according to the set frequency step value; Step 4: At each frequency, the interaction signal strength between each sound wave frequency and the pathogen sample to be tested is extracted by a high-sensitivity sensor; Step 5: Calculate the resonance frequency of the pathogen sample to be tested based on the interaction signal strength between each sound wave frequency and the pathogen sample to be tested; Step 6: Finally identify the pathogen type of the pathogen sample to be tested.
5. A microfluidic integrated multiple pathogens visual detection system according to claim 4, characterized in that: The operation process of step 2 is as follows: Screening the maximum size and minimum size of the pathogen from the pathogen sample to be tested; wherein the physical properties of the pathogen sample to be tested are the maximum size and minimum size of the pathogen; At the same time, the flow rate υ of the pathogen sample to be tested in the fluid channel is extracted; Thus, the appropriate sound wave frequency range for the pathogen sample to be tested is determined ; are the lower and upper limits of the acoustic frequency of the pathogen sample to be tested; Respectively represent the minimum and maximum sizes of pathogens; The initial frequency of the pathogen sample to be tested is set to ; By analyzing the formula , calculate the frequency step value △f of the pathogen sample to be tested, and N is the preset number of scanning steps.
6. A microfluidic integrated multiple pathogen visualization detection system according to claim 5, characterized in that: The calculation formula for the preset scanning step number N is: Retrieve the minimum response time t1, single scan time t2 and total scan limit time T of the ultrasonic transducer; Based on the frequency resolution, calculate the preset number of scan steps at the frequency resolution , is the characteristic frequency interval of the pathogen sample to be tested; Based on the time limit, calculate the preset number of scan steps under the time limit ; Based on the signal response time, calculate the preset number of scan steps under the signal response time ; Screen N1, N2 and N3. The specific screening conditions are as follows: Condition 1: Compare N1, N2 and N3 with the number 1 respectively, and eliminate the calculated preset scanning steps that are less than the number 1 and are not positive integers; Condition 2: Import N1, N2, and N3 In the calculation formula, the frequency step values of the pathogen samples to be tested corresponding to N1, N2 and N3 are calculated respectively, and then compared with Compare the difference of the calculation formula and find the value greater than The preset number of scanning steps corresponding to the difference in the calculation formula is eliminated; The above two conditions must be met at the same time, thereby screening out the calculated preset scanning steps that meet the screening conditions; the calculated preset scanning steps that meet the screening conditions are compared with each other again, and the smallest calculated preset scanning step number is used as the preset scanning step number N.
7. A microfluidic integrated multiple pathogen visualization detection system according to claim 6, characterized in that: The operation process of step 5 is as follows: Step A1: Pair each sound wave frequency in sequence according to the numbering order, thereby obtaining the signal strengths of the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in each sound wave frequency pair, and mark them in sequence as , r is the number of each sound wave frequency pair, and the value range of r is 1 to R; Step A2, selecting the sound wave frequency with the largest signal strength in each sound wave frequency pair from the signal strengths corresponding to the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in each sound wave frequency pair; If the sound wave frequency with the largest signal strength in each sound wave frequency pair is identified as the second sound wave frequency, then the signal strength of the third sound wave frequency is subtracted from the signal strength of the first sound wave frequency in each sound wave frequency pair to obtain the signal strength difference between the two boundary points in each sound wave frequency pair; Then through the analysis formula , the curvature of the change of the three sound wave frequencies in each sound wave frequency pair is obtained by analysis ; The resonant frequency of the pathogen sample to be tested is calculated from this , R is the total number of sound wave frequency pairs, is the signal strength difference between the two boundary points in the rth acoustic wave frequency pair; are the frequency Hertz values of the first sound wave frequency, the second sound wave frequency and the third sound wave frequency in the rth sound wave frequency pair respectively; Step A3: If the sound wave frequency with the largest signal strength among the sound wave frequency pairs is identified as the first sound wave frequency, the first sound wave frequency, the second sound wave frequency and the third sound wave frequency corresponding to the sound wave frequency pairs are rearranged, and the first sound wave frequency is used as the position of the middle point of the second sound wave frequency in the above step A2, and the second sound wave frequency and the third sound wave frequency are used as new boundary points, and then the resonance frequency of the pathogen sample to be tested is calculated; Step A4: If the sound wave frequency with the largest signal intensity among the sound wave frequency pairs is identified as the third sound wave frequency, the first sound wave frequency, the second sound wave frequency and the third sound wave frequency corresponding to the sound wave frequency pairs are rearranged, and the third sound wave frequency is used as the position of the middle point of the second sound wave frequency in the above step A2, and the second sound wave frequency and the first sound wave frequency are used as new boundary points, and then the resonant frequency of the pathogen sample to be tested is calculated.
8. The microfluidic integrated multiple pathogen visualization detection system according to claim 7, characterized in that: The operation process of step six is as follows: Extracting standard values of resonance frequencies of different types of pathogens from the storage module; Subtract the resonant frequency of the pathogen sample to be tested from the standard value of the resonant frequency of different types of pathogens, perform an absolute value calculation on the difference result, and finally use the calculation result as the numerator of the fraction; The standard values of the resonance frequencies of different types of pathogens are used as the denominator of the fraction; Solve the fraction to obtain the matching degree of the pathogen sample to be tested corresponding to different types of pathogens; And from the matching degrees of the pathogen sample to be tested corresponding to different types of pathogens, the pathogen type with the smallest matching degree is screened out as the pathogen type of the pathogen sample to be tested.
9. A microfluidic integrated multiple pathogen visualization detection platform, characterized in that: It is implemented based on a microfluidic integrated multiple pathogen visual detection system as described in any one of claims 1 to 8, comprising a processor, a memory and a communication bus; The memory stores a computer-readable program executable by the processor; The communication bus realizes the connection and communication between the processor and the memory; When the processor executes the computer-readable program, it implements a microfluidic integrated multiple pathogen visualization detection system as described in any one of claims 1-8.